Detecting long-term growth trends using tree rings: a critical evaluation of methods.

PubMed

Peters, Richard L; Groenendijk, Peter; Vlam, Mart; Zuidema, Pieter A

2015-05-01

Tree-ring analysis is often used to assess long-term trends in tree growth. A variety of growth-trend detection methods (GDMs) exist to disentangle age/size trends in growth from long-term growth changes. However, these detrending methods strongly differ in approach, with possible implications for their output. Here, we critically evaluate the consistency, sensitivity, reliability and accuracy of four most widely used GDMs: conservative detrending (CD) applies mathematical functions to correct for decreasing ring widths with age; basal area correction (BAC) transforms diameter into basal area growth; regional curve standardization (RCS) detrends individual tree-ring series using average age/size trends; and size class isolation (SCI) calculates growth trends within separate size classes. First, we evaluated whether these GDMs produce consistent results applied to an empirical tree-ring data set of Melia azedarach, a tropical tree species from Thailand. Three GDMs yielded similar results - a growth decline over time - but the widely used CD method did not detect any change. Second, we assessed the sensitivity (probability of correct growth-trend detection), reliability (100% minus probability of detecting false trends) and accuracy (whether the strength of imposed trends is correctly detected) of these GDMs, by applying them to simulated growth trajectories with different imposed trends: no trend, strong trends (-6% and +6% change per decade) and weak trends (-2%, +2%). All methods except CD, showed high sensitivity, reliability and accuracy to detect strong imposed trends. However, these were considerably lower in the weak or no-trend scenarios. BAC showed good sensitivity and accuracy, but low reliability, indicating uncertainty of trend detection using this method. Our study reveals that the choice of GDM influences results of growth-trend studies. We recommend applying multiple methods when analysing trends and encourage performing sensitivity and reliability analysis. Finally, we recommend SCI and RCS, as these methods showed highest reliability to detect long-term growth trends. © 2014 John Wiley & Sons Ltd.

Landsat Based Woody Vegetation Loss Detection in Queensland, Australia Using the Google Earth Engine

NASA Astrophysics Data System (ADS)

Johansen, K.; Phinn, S. R.; Taylor, M.

2014-12-01

Land clearing detection and woody Foliage Projective Cover (FPC) monitoring at the state and national level in Australia has mainly been undertaken by state governments and the Terrestrial Ecosystem Research Network (TERN) because of the considerable expense, expertise, sustained duration of activities and staffing levels needed. Only recently have services become available, providing low budget, generalized access to change detection tools suited to this task. The objective of this research was to examine if a globally available service, Google Earth Engine Beta, could be used to predict woody vegetation loss with accuracies approaching the methods used by TERN and the government of the state of Queensland, Australia. Two change detection approaches were investigated using Landsat Thematic Mapper time series and the Google Earth Engine Application Programming Interface: (1) CART and Random Forest classifiers; and (2) a normalized time series of Foliage Projective Cover (FPC) and NDVI combined with a spectral index. The CART and Random Forest classifiers produced high user's and producer's mapping accuracies of clearing (77-92% and 54-77%, respectively) when detecting change within epochs for which training data were available, but extrapolation to epochs without training data reduced the mapping accuracies. The use of FPC and NDVI time series provided a more robust approach for calculation of a clearing probability, as it did not rely on training data but instead on the difference of the normalized FPC / NDVI mean and standard deviation of a single year at the change point in relation to the remaining time series. However, the FPC and NDVI time series approach represented a trade-off between user's and producer's accuracies. Both change detection approaches explored in this research were sensitive to ephemeral greening and drying of the landscape. However, the developed normalized FPC and NDVI time series approach can be tuned to provide automated alerts for large woody vegetation clearing events by selecting suitable thresholds to identify very likely clearing. This research provides a comprehensive foundation to build further capacity to use globally accessible, free, online image datasets and processing tools to accurately detect woody vegetation clearing in an automated and rapid manner.

Improved forest change detection with terrain illumination corrected landsat images

USDA-ARS?s Scientific Manuscript database

An illumination correction algorithm has been developed to improve the accuracy of forest change detection from Landsat reflectance data. This algorithm is based on an empirical rotation model and was tested on the Landsat imagery pair over Cherokee National Forest, Tennessee, Uinta-Wasatch-Cache N...

Examining change detection approaches for tropical mangrove monitoring

USGS Publications Warehouse

Myint, Soe W.; Franklin, Janet; Buenemann, Michaela; Kim, Won; Giri, Chandra

2014-01-01

This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.

Iconic memory requires attention

PubMed Central

Persuh, Marjan; Genzer, Boris; Melara, Robert D.

2012-01-01

Two experiments investigated whether attention plays a role in iconic memory, employing either a change detection paradigm (Experiment 1) or a partial-report paradigm (Experiment 2). In each experiment, attention was taxed during initial display presentation, focusing the manipulation on consolidation of information into iconic memory, prior to transfer into working memory. Observers were able to maintain high levels of performance (accuracy of change detection or categorization) even when concurrently performing an easy visual search task (low load). However, when the concurrent search was made difficult (high load), observers' performance dropped to almost chance levels, while search accuracy held at single-task levels. The effects of attentional load remained the same across paradigms. The results suggest that, without attention, participants consolidate in iconic memory only gross representations of the visual scene, information too impoverished for successful detection of perceptual change or categorization of features. PMID:22586389

Iconic memory requires attention.

PubMed

Persuh, Marjan; Genzer, Boris; Melara, Robert D

2012-01-01

Two experiments investigated whether attention plays a role in iconic memory, employing either a change detection paradigm (Experiment 1) or a partial-report paradigm (Experiment 2). In each experiment, attention was taxed during initial display presentation, focusing the manipulation on consolidation of information into iconic memory, prior to transfer into working memory. Observers were able to maintain high levels of performance (accuracy of change detection or categorization) even when concurrently performing an easy visual search task (low load). However, when the concurrent search was made difficult (high load), observers' performance dropped to almost chance levels, while search accuracy held at single-task levels. The effects of attentional load remained the same across paradigms. The results suggest that, without attention, participants consolidate in iconic memory only gross representations of the visual scene, information too impoverished for successful detection of perceptual change or categorization of features.

ROC evaluation of SPECT myocardial lesion detectability with and without single iteration non-uniform Chang attenuation compensation using an anthropomorphic female phantom

NASA Astrophysics Data System (ADS)

Jang, Sunyoung; Jaszczak, R. J.; Tsui, B. M. W.; Metz, C. E.; Gilland, D. R.; Turkington, T. G.; Coleman, R. E.

1998-08-01

The purpose of this work was to evaluate lesion detectability with and without nonuniform attenuation compensation (AC) in myocardial perfusion SPECT imaging in women using an anthropomorphic phantom and receiver operating characteristics (ROC) methodology. Breast attenuation causes artifacts in reconstructed images and may increase the difficulty of diagnosis of myocardial perfusion imaging in women. The null hypothesis tested using the ROC study was that nonuniform AC does not change the lesion detectability in myocardial perfusion SPECT imaging in women. The authors used a filtered backprojection (FBP) reconstruction algorithm and Chang's (1978) single iteration method for AC. In conclusion, with the authors' proposed myocardial defect model nuclear medicine physicians demonstrated no significant difference for the detection of the anterior wall defect; however, a greater accuracy for the detection of the inferior wall defect was observed without nonuniform AC than with it (P-value=0.0034). Medical physicists did not demonstrate any statistically significant difference in defect detection accuracy with or without nonuniform AC in the female phantom.

Comparing CNV detection methods for SNP arrays.

PubMed

Winchester, Laura; Yau, Christopher; Ragoussis, Jiannis

2009-09-01

Data from whole genome association studies can now be used for dual purposes, genotyping and copy number detection. In this review we discuss some of the methods for using SNP data to detect copy number events. We examine a number of algorithms designed to detect copy number changes through the use of signal-intensity data and consider methods to evaluate the changes found. We describe the use of several statistical models in copy number detection in germline samples. We also present a comparison of data using these methods to assess accuracy of prediction and detection of changes in copy number.

Comparing the utility of image algebra operations for characterizing landscape changes: the case of the Mediterranean coast.

PubMed

Alphan, Hakan

2011-11-01

The aim of this study is to compare various image algebra procedures for their efficiency in locating and identifying different types of landscape changes on the margin of a Mediterranean coastal plain, Cukurova, Turkey. Image differencing and ratioing were applied to the reflective bands of Landsat TM datasets acquired in 1984 and 2006. Normalized Difference Vegetation index (NDVI) and Principal Component Analysis (PCA) differencing were also applied. The resulting images were tested for their capacity to detect nine change phenomena, which were a priori defined in a three-level classification scheme. These change phenomena included agricultural encroachment, sand dune afforestation, coastline changes and removal/expansion of reed beds. The percentage overall accuracies of different algebra products for each phenomenon were calculated and compared. The results showed that some of the changes such as sand dune afforestation and reed bed expansion were detected with accuracies varying between 85 and 97% by the majority of the algebra operations, while some other changes such as logging could only be detected by mid-infrared (MIR) ratioing. For optimizing change detection in similar coastal landscapes, underlying causes of these changes were discussed and the guidelines for selecting band and algebra operations were provided. Copyright © 2011 Elsevier Ltd. All rights reserved.

Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru

USGS Publications Warehouse

Shermeyer, Jacob S.; Haack, Barry N.

2015-01-01

Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.

The Feasibility Evaluation of Land Use Change Detection Using GAOFEN-3 Data

NASA Astrophysics Data System (ADS)

Huang, G.; Sun, Y.; Zhao, Z.

2018-04-01

GaoFen-3 (GF-3) satellite, is the first C band and multi-polarimetric Synthetic Aperture Radar (SAR) satellite in China. In order to explore the feasibility of GF-3 satellite in remote sensing interpretation and land-use remote sensing change detection, taking Guangzhou, China as a study area, the full polarimetric image of GF-3 satellite with 8 m resolution of two temporal as the data source. Firstly, the image is pre-processed by orthorectification, image registration and mosaic, and the land-use remote sensing digital orthophoto map (DOM) in 2017 is made according to the each county. Then the classification analysis and judgment of ground objects on the image are carried out by means of ArcGIS combining with the auxiliary data and using artificial visual interpretation, to determine the area of changes and the category of change objects. According to the unified change information extraction principle to extract change areas. Finally, the change detection results are compared with 3 m resolution TerraSAR-X data and 2 m resolution multi-spectral image, and the accuracy is evaluated. Experimental results show that the accuracy of the GF-3 data is over 75 % in detecting the change of ground objects, and the detection capability of new filling soil is better than that of TerraSAR-X data, verify the detection and monitoring capability of GF-3 data to the change information extraction, also, it shows that GF-3 can provide effective data support for the remote sensing detection of land resources.

Monitoring forest dynamics with multi-scale and time series imagery.

PubMed

Huang, Chunbo; Zhou, Zhixiang; Wang, Di; Dian, Yuanyong

2016-05-01

To learn the forest dynamics and evaluate the ecosystem services of forest effectively, a timely acquisition of spatial and quantitative information of forestland is very necessary. Here, a new method was proposed for mapping forest cover changes by combining multi-scale satellite remote-sensing imagery with time series data. Using time series Normalized Difference Vegetation Index products derived from the Moderate Resolution Imaging Spectroradiometer images (MODIS-NDVI) and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images as data source, a hierarchy stepwise analysis from coarse scale to fine scale was developed for detecting the forest change area. At the coarse scale, MODIS-NDVI data with 1-km resolution were used to detect the changes in land cover types and a land cover change map was constructed using NDVI values at vegetation growing seasons. At the fine scale, based on the results at the coarse scale, Landsat TM/ETM+ data with 30-m resolution were used to precisely detect the forest change location and forest change trend by analyzing time series forest vegetation indices (IFZ). The method was tested using the data for Hubei Province, China. The MODIS-NDVI data from 2001 to 2012 were used to detect the land cover changes, and the overall accuracy was 94.02 % at the coarse scale. At the fine scale, the available TM/ETM+ images at vegetation growing seasons between 2001 and 2012 were used to locate and verify forest changes in the Three Gorges Reservoir Area, and the overall accuracy was 94.53 %. The accuracy of the two layer hierarchical monitoring results indicated that the multi-scale monitoring method is feasible and reliable.

Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data

USGS Publications Warehouse

Myint, S.W.; Yuan, M.; Cerveny, R.S.; Giri, C.P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and objectoriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. ?? 2008 by MDPI.

Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data

PubMed Central

Myint, Soe W.; Yuan, May; Cerveny, Randall S.; Giri, Chandra P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. PMID:27879757

Pitch discrimination accuracy in musicians vs nonmusicians: an event-related potential and behavioral study.

PubMed

Tervaniemi, Mari; Just, Viola; Koelsch, Stefan; Widmann, Andreas; Schröger, Erich

2005-02-01

Previously, professional violin players were found to automatically discriminate tiny pitch changes, not discriminable by nonmusicians. The present study addressed the pitch processing accuracy in musicians with expertise in playing a wide selection of instruments (e.g., piano; wind and string instruments). Of specific interest was whether also musicians with such divergent backgrounds have facilitated accuracy in automatic and/or attentive levels of auditory processing. Thirteen professional musicians and 13 nonmusicians were presented with frequent standard sounds and rare deviant sounds (0.8, 2, or 4% higher in frequency). Auditory event-related potentials evoked by these sounds were recorded while first the subjects read a self-chosen book and second they indicated behaviorally the detection of sounds with deviant frequency. Musicians detected the pitch changes faster and more accurately than nonmusicians. The N2b and P3 responses recorded during attentive listening had larger amplitude in musicians than in nonmusicians. Interestingly, the superiority in pitch discrimination accuracy in musicians over nonmusicians was observed not only with the 0.8% but also with the 2% frequency changes. Moreover, also nonmusicians detected quite reliably the smallest pitch changes of 0.8%. However, the mismatch negativity (MMN) and P3a recorded during a reading condition did not differentiate musicians and nonmusicians. These results suggest that musical expertise may exert its effects merely at attentive levels of processing and not necessarily already at the preattentive levels.

Satisfaction of Search in Chest Radiography 2015.

PubMed

Berbaum, Kevin S; Krupinski, Elizabeth A; Schartz, Kevin M; Caldwell, Robert T; Madsen, Mark T; Hur, Seung; Laroia, Archana T; Thompson, Brad H; Mullan, Brian F; Franken, Edmund A

2015-11-01

Two decades have passed since the publication of laboratory studies of satisfaction of search (SOS) in chest radiography. Those studies were performed using film. The current investigation tests for SOS effects in computed radiography of the chest. Sixty-four chest computed radiographs half demonstrating various "test" abnormalities were read twice by 20 radiologists, once with and once without the addition of a simulated pulmonary nodule. Receiver-operating characteristic detection accuracy and decision thresholds were analyzed to study the effects of adding the nodule on detecting the test abnormalities. Results of previous studies were reanalyzed using similar modern techniques. In the present study, adding nodules did not influence detection accuracy for the other abnormalities (P = .93), but did induce a reluctance to report them (P < .001). Adding nodules did not affect inspection time (P = .58) so the reluctance to report was not associated with reduced search. Reanalysis revealed a similar decision threshold shift that had not been recognized in the early studies of SOS in chest radiography (P < .01) in addition to reduced detection accuracy (P < .01). The nature of SOS in chest radiography has changed, but it is not clear why. SOS may be changing as a function of changes in radiology education and practice. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

An Online 3D Database System for Endangered Architectural and Archaeological Heritage in the South-Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Abate, D.; Avgousti, A.; Faka, M.; Hermon, S.; Bakirtzis, N.; Christofi, P.

2017-10-01

This study compares performance of aerial image based point clouds (IPCs) and light detection and ranging (LiDAR) based point clouds in detection of thinnings and clear cuts in forests. IPCs are an appealing method to update forest resource data, because of their accuracy in forest height estimation and cost-efficiency of aerial image acquisition. We predicted forest changes over a period of three years by creating difference layers that displayed the difference in height or volume between the initial and subsequent time points. Both IPCs and LiDAR data were used in this process. The IPCs were constructed with the Semi-Global Matching (SGM) algorithm. Difference layers were constructed by calculating differences in fitted height or volume models or in canopy height models (CHMs) from both time points. The LiDAR-derived digital terrain model (DTM) was used to scale heights to above ground level. The study area was classified in logistic regression into the categories ClearCut, Thinning or NoChange with the values from the difference layers. We compared the predicted changes with the true changes verified in the field, and obtained at best a classification accuracy for clear cuts 93.1 % with IPCs and 91.7 % with LiDAR data. However, a classification accuracy for thinnings was only 8.0 % with IPCs. With LiDAR data 41.4 % of thinnings were detected. In conclusion, the LiDAR data proved to be more accurate method to predict the minor changes in forests than IPCs, but both methods are useful in detection of major changes.

Improved Snow Mapping Accuracy with Revised MODIS Snow Algorithm

NASA Technical Reports Server (NTRS)

Riggs, George; Hall, Dorothy K.

2012-01-01

The MODIS snow cover products have been used in over 225 published studies. From those reports, and our ongoing analysis, we have learned about the accuracy and errors in the snow products. Revisions have been made in the algorithms to improve the accuracy of snow cover detection in Collection 6 (C6), the next processing/reprocessing of the MODIS data archive planned to start in September 2012. Our objective in the C6 revision of the MODIS snow-cover algorithms and products is to maximize the capability to detect snow cover while minimizing snow detection errors of commission and omission. While the basic snow detection algorithm will not change, new screens will be applied to alleviate snow detection commission and omission errors, and only the fractional snow cover (FSC) will be output (the binary snow cover area (SCA) map will no longer be included).

Supervised segmentation of microelectrode recording artifacts using power spectral density.

PubMed

Bakstein, Eduard; Schneider, Jakub; Sieger, Tomas; Novak, Daniel; Wild, Jiri; Jech, Robert

2015-08-01

Appropriate detection of clean signal segments in extracellular microelectrode recordings (MER) is vital for maintaining high signal-to-noise ratio in MER studies. Existing alternatives to manual signal inspection are based on unsupervised change-point detection. We present a method of supervised MER artifact classification, based on power spectral density (PSD) and evaluate its performance on a database of 95 labelled MER signals. The proposed method yielded test-set accuracy of 90%, which was close to the accuracy of annotation (94%). The unsupervised methods achieved accuracy of about 77% on both training and testing data.

Urban Change Detection of Pingtan City based on Bi-temporal Remote Sensing Images

NASA Astrophysics Data System (ADS)

Degang, JIANG; Jinyan, XU; Yikang, GAO

2017-02-01

In this paper, a pair of SPOT 5-6 images with the resolution of 0.5m is selected. An object-oriented classification method is used to the two images and five classes of ground features were identified as man-made objects, farmland, forest, waterbody and unutilized land. An auxiliary ASTER GDEM was used to improve the classification accuracy. And the change detection based on the classification results was performed. Accuracy assessment was carried out finally. Consequently, satisfactory results were obtained. The results show that great changes of the Pingtan city have been detected as the expansion of the city area and the intensity increase of man-made buildings, roads and other infrastructures with the establishment of Pingtan comprehensive experimental zone. Wide range of open sea area along the island coast zones has been reclaimed for port and CBDs construction.

Object-based land-use/land-cover change detection using Landsat imagery: a case study of Ardabil, Namin, and Nir counties in northwest Iran.

PubMed

Aslami, Farnoosh; Ghorbani, Ardavan

2018-06-03

In this study, land-use/land-cover (LULC) change in the Ardabil, Namin, and Nir counties, in the Ardabil province in the northwest of Iran, was detected using an object-based method. Landsat images including Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM + ), and Operational Land Imager (OLI) were used. Preprocessing methods, including geometric and radiometric correction, and topographic normalization were performed. Image processing was conducted according to object-based image analysis using the nearest neighbor algorithm. An accuracy assessment was conducted using overall accuracy and Kappa statistics. Results show that maps obtained from images for 1987, 2002, and 2013 had an overall accuracy of 91.76, 91.06, and 93.00%, and a Kappa coefficient of 0.90, 0.83, and 0.91, respectively. Change detection between 1987 and 2013 shows that most of the rangelands (97,156.6 ha) have been converted to dry farming; moreover, residential and other urban land uses have also increased. The largest change in land use has occurred for irrigated farming, rangelands, and dry farming, of which approximately 3539.8, 3086.9, and 2271.9 ha, respectively, have given way to urban land use for each of the studied years.

Just-in-time classifiers for recurrent concepts.

PubMed

Alippi, Cesare; Boracchi, Giacomo; Roveri, Manuel

2013-04-01

Just-in-time (JIT) classifiers operate in evolving environments by classifying instances and reacting to concept drift. In stationary conditions, a JIT classifier improves its accuracy over time by exploiting additional supervised information coming from the field. In nonstationary conditions, however, the classifier reacts as soon as concept drift is detected; the current classification setup is discarded and a suitable one activated to keep the accuracy high. We present a novel generation of JIT classifiers able to deal with recurrent concept drift by means of a practical formalization of the concept representation and the definition of a set of operators working on such representations. The concept-drift detection activity, which is crucial in promptly reacting to changes exactly when needed, is advanced by considering change-detection tests monitoring both inputs and classes distributions.

A pdf-Free Change Detection Test Based on Density Difference Estimation.

PubMed

Bu, Li; Alippi, Cesare; Zhao, Dongbin

2018-02-01

The ability to detect online changes in stationarity or time variance in a data stream is a hot research topic with striking implications. In this paper, we propose a novel probability density function-free change detection test, which is based on the least squares density-difference estimation method and operates online on multidimensional inputs. The test does not require any assumption about the underlying data distribution, and is able to operate immediately after having been configured by adopting a reservoir sampling mechanism. Thresholds requested to detect a change are automatically derived once a false positive rate is set by the application designer. Comprehensive experiments validate the effectiveness in detection of the proposed method both in terms of detection promptness and accuracy.

Support Vector Machines for Multitemporal and Multisensor Change Detection in a Mining Area

NASA Astrophysics Data System (ADS)

Hecheltjen, Antje; Waske, Bjorn; Thonfeld, Frank; Braun, Matthias; Menz, Gunter

2010-12-01

Long-term change detection often implies the challenge of incorporating multitemporal data from different sensors. Most of the conventional change detection algorithms are designed for bi-temporal datasets from the same sensors detecting only the existence of changes. The labeling of change areas remains a difficult task. To overcome such drawbacks, much attention has been given lately to algorithms arising from machine learning, such as Support Vector Machines (SVMs). While SVMs have been applied successfully for land cover classifications, the exploitation of this approach for change detection is still in its infancy. Few studies have already proven the applicability of SVMs for bi- and multitemporal change detection using data from one sensor only. In this paper we demonstrate the application of SVM for multitemporal and -sensor change detection. Our study site covers lignite open pit mining areas in the German state North Rhine-Westphalia. The dataset consists of bi-temporal Landsat data and multi-temporal ERS SAR data covering two time slots (2001 and 2009). The SVM is conducted using the IDL program imageSVM. Change is deduced from one time slot to the next resulting in two change maps. In contrast to change detection, which is based on post-classification comparison, change detection is seen here as a specific classification problem. Thus, changes are directly classified from a layer-stack of the two years. To reduce the number of change classes, we created a change mask using the magnitude of Change Vector Analysis (CVA). Training data were selected for different change classes (e.g. forest to mining or mining to agriculture) as well as for the no-change classes (e.g. agriculture). Subsequently, they were divided in two independent sets for training the SVMs and accuracy assessment, respectively. Our study shows the applicability of SVMs to classify changes via SVMs. The proposed method yielded a change map of reclaimed and active mines. The use of ERS SAR data, however, did not add to the accuracy compared to Landsat data only. A great advantage compared to other change detection approaches are the labeled change maps, which are a direct output of the methodology. Our approach also overcomes the drawback of post-classification comparison, namely the propagation of classification inaccuracies.

Object memory and change detection: dissociation as a function of visual and conceptual similarity.

PubMed

Yeh, Yei-Yu; Yang, Cheng-Ta

2008-01-01

People often fail to detect a change between two visual scenes, a phenomenon referred to as change blindness. This study investigates how a post-change object's similarity to the pre-change object influences memory of the pre-change object and affects change detection. The results of Experiment 1 showed that similarity lowered detection sensitivity but did not affect the speed of identifying the pre-change object, suggesting that similarity between the pre- and post-change objects does not degrade the pre-change representation. Identification speed for the pre-change object was faster than naming the new object regardless of detection accuracy. Similarity also decreased detection sensitivity in Experiment 2 but improved the recognition of the pre-change object under both correct detection and detection failure. The similarity effect on recognition was greatly reduced when 20% of each pre-change stimulus was masked by random dots in Experiment 3. Together the results suggest that the level of pre-change representation under detection failure is equivalent to the level under correct detection and that the pre-change representation is almost complete. Similarity lowers detection sensitivity but improves explicit access in recognition. Dissociation arises between recognition and change detection as the two judgments rely on the match-to-mismatch signal and mismatch-to-match signal, respectively.

Remote Sensing Monitoring of Changes in Soil Salinity: A Case Study in Inner Mongolia, China.

PubMed

Wu, Jingwei; Vincent, Bernard; Yang, Jinzhong; Bouarfa, Sami; Vidal, Alain

2008-11-07

This study used archived remote sensing images to depict the history of changes in soil salinity in the Hetao Irrigation District in Inner Mongolia, China, with the purpose of linking these changes with land and water management practices and to draw lessons for salinity control. Most data came from LANDSAT satellite images taken in 1973, 1977, 1988, 1991, 1996, 2001, and 2006. In these years salt-affected areas were detected using a normal supervised classification method. Corresponding cropped areas were detected from NVDI (Normalized Difference Vegetation Index) values using an unsupervised method. Field samples and agricultural statistics were used to estimate the accuracy of the classification. Historical data concerning irrigation/drainage and the groundwater table were used to analyze the relation between changes in soil salinity and land and water management practices. Results showed that: (1) the overall accuracy of remote sensing in detecting soil salinity was 90.2%, and in detecting cropped area, 98%; (2) the installation/innovation of the drainage system did help to control salinity; and (3) a low ratio of cropped land helped control salinity in the Hetao Irrigation District. These findings suggest that remote sensing is a useful tool to detect soil salinity and has potential in evaluating and improving land and water management practices.

The Dependence of Cloud Property Trend Detection on Absolute Calibration Accuracy of Passive Satellite Sensors

NASA Astrophysics Data System (ADS)

Shea, Y.; Wielicki, B. A.; Sun-Mack, S.; Minnis, P.; Zelinka, M. D.

2016-12-01

Detecting trends in climate variables on global, decadal scales requires highly accurate, stable measurements and retrieval algorithms. Trend uncertainty depends on its magnitude, natural variability, and instrument and retrieval algorithm accuracy and stability. We applied a climate accuracy framework to quantify the impact of absolute calibration on cloud property trend uncertainty. The cloud properties studied were cloud fraction, effective temperature, optical thickness, and effective radius retrieved using the Clouds and the Earth's Radiant Energy System (CERES) Cloud Property Retrieval System, which uses Moderate-resolution Imaging Spectroradiometer measurements (MODIS). Modeling experiments from the fifth phase of the Climate Model Intercomparison Project (CMIP5) agree that net cloud feedback is likely positive but disagree regarding its magnitude, mainly due to uncertainty in shortwave cloud feedback. With the climate accuracy framework we determined the time to detect trends for instruments with various calibration accuracies. We estimated a relationship between cloud property trend uncertainty, cloud feedback, and Equilibrium Climate Sensitivity and also between effective radius trend uncertainty and aerosol indirect effect trends. The direct relationship between instrument accuracy requirements and climate model output provides the level of instrument absolute accuracy needed to reduce climate model projection uncertainty. Different cloud types have varied radiative impacts on the climate system depending on several attributes, such as their thermodynamic phase, altitude, and optical thickness. Therefore, we also conducted these studies by cloud types for a clearer understanding of instrument accuracy requirements needed to detect changes in their cloud properties. Combining this information with the radiative impact of different cloud types helps to prioritize among requirements for future satellite sensors and understanding the climate detection capabilities of existing sensors.

Feasibility analysis of EDXRF method to detect heavy metal pollution in ecological environment

NASA Astrophysics Data System (ADS)

Hao, Zhixu; Qin, Xulei

2018-02-01

The change of heavy metal content in water environment, soil and plant can reflect the change of heavy metal pollution in ecological environment, and it is important to monitor the trend of heavy metal pollution in eco-environment by using water environment, soil and heavy metal content in plant. However, the content of heavy metals in nature is very low, the background elements of water environment, soil and plant samples are complex, and there are many interfering factors in the EDXRF system that will affect the spectral analysis results and reduce the detection accuracy. Through the contrastive analysis of several heavy metal elements detection methods, it is concluded that the EDXRF method is superior to other chemical methods in testing accuracy and method feasibility when the heavy metal pollution in soil is tested in ecological environment.

Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms

PubMed Central

Khan, Naveed; McClean, Sally; Zhang, Shuai; Nugent, Chris

2016-01-01

In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient’s vital signs in the medical domain or requesting activity labels for generating real-world labeled activity datasets. Our work focuses on change-point detection to identify a transition from one activity to another. Within this paper, we extend our previous work on multivariate exponentially weighted moving average (MEWMA) algorithm by using a genetic algorithm (GA) to identify the optimal set of parameters for online change-point detection. The proposed technique finds the maximum accuracy and F_measure by optimizing the different parameters of the MEWMA, which subsequently identifies the exact location of the change point from an existing activity to a new one. Optimal parameter selection facilitates an algorithm to detect accurate change points and minimize false alarms. Results have been evaluated based on two real datasets of accelerometer data collected from a set of different activities from two users, with a high degree of accuracy from 99.4% to 99.8% and F_measure of up to 66.7%. PMID:27792177

Spatiotemporal Change Detection Using Landsat Imagery: the Case Study of Karacabey Flooded Forest, Bursa, Turkey

NASA Astrophysics Data System (ADS)

Akay, A. E.; Gencal, B.; Taş, İ.

2017-11-01

This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

Application research on land use remote sensing dynamic monitoring: A case study of Anning district, Lanzhou

NASA Astrophysics Data System (ADS)

Zhu, Yunqiang; Zhu, Huazhong; Lu, Heli; Ni, Jianguang; Zhu, Shaoxia

2005-10-01

Remote sensing dynamic monitoring of land use can detect the change information of land use and update the current land use map, which is important for rational utilization and scientific management of land resources. This paper discusses the technological procedure of remote sensing dynamic monitoring of land use including the process of remote sensing images, the extraction of annual change information of land use, field survey, indoor post processing and accuracy assessment. Especially, we emphasize on comparative research on the choice of remote sensing rectifying models, image fusion algorithms and accuracy assessment methods. Taking Anning district in Lanzhou as an example, we extract the land use change information of the district during 2002-2003, access monitoring accuracy and analyze the reason of land use change.

The spectral changes of deforestation in the Brazilian tropical savanna.

PubMed

Trancoso, Ralph; Sano, Edson E; Meneses, Paulo R

2015-01-01

The Cerrado is a biome in Brazil that is experiencing the most rapid loss in natural vegetation. The objective of this study was to analyze the changes in the spectral response in the red, near infrared (NIR), middle infrared (MIR), and normalized difference vegetation index (NDVI) when native vegetation in the Cerrado is deforested. The test sites were regions of the Cerrado located in the states of Bahia, Minas Gerais, and Mato Grosso. For each region, a pair of Landsat Thematic Mapper (TM) scenes from 2008 (before deforestation) and 2009 (after deforestation) was compared. A set of 1,380 samples of deforested polygons and an equal number of samples of native vegetation have their spectral properties statistically analyzed. The accuracy of deforestation detections was also evaluated using high spatial resolution imagery. Results showed that the spectral data of deforested areas and their corresponding native vegetation were statistically different. The red band showed the highest difference between the reflectance data from deforested areas and native vegetation, while the NIR band showed the lowest difference. A consistent pattern of spectral change when native vegetation in the Cerrado is deforested was identified regardless of the location in the biome. The overall accuracy of deforestation detections was 97.75%. Considering both the marked pattern of spectral changes and the high deforestation detection accuracy, this study suggests that deforestation in Cerrado can be accurately monitored, but a strong seasonal and spatial variability of spectral changes might be expected.

Aircraft MSS data registration and vegetation classification of wetland change detection

USGS Publications Warehouse

Christensen, E.J.; Jensen, J.R.; Ramsey, Elijah W.; Mackey, H.E.

1988-01-01

Portions of the Savannah River floodplain swamp were evaluated for vegetation change using high resolution (5a??6 m) aircraft multispectral scanner (MSS) data. Image distortion from aircraft movement prevented precise image-to-image registration in some areas. However, when small scenes were used (200-250 ha), a first-order linear transformation provided registration accuracies of less than or equal to one pixel. A larger area was registered using a piecewise linear method. Five major wetland classes were identified and evaluated for change. Phenological differences and the variable distribution of vegetation limited wetland type discrimination. Using unsupervised methods and ground-collected vegetation data, overall classification accuracies ranged from 84 per cent to 87 per cent for each scene. Results suggest that high-resolution aircraft MSS data can be precisely registered, if small areas are used, and that wetland vegetation change can be accurately detected and monitored.

Visual long-term memory and change blindness: Different effects of pre- and post-change information on one-shot change detection using meaningless geometric objects.

PubMed

Nishiyama, Megumi; Kawaguchi, Jun

2014-11-01

To clarify the relationship between visual long-term memory (VLTM) and online visual processing, we investigated whether and how VLTM involuntarily affects the performance of a one-shot change detection task using images consisting of six meaningless geometric objects. In the study phase, participants observed pre-change (Experiment 1), post-change (Experiment 2), or both pre- and post-change (Experiment 3) images appearing in the subsequent change detection phase. In the change detection phase, one object always changed between pre- and post-change images and participants reported which object was changed. Results showed that VLTM of pre-change images enhanced the performance of change detection, while that of post-change images decreased accuracy. Prior exposure to both pre- and post-change images did not influence performance. These results indicate that pre-change information plays an important role in change detection, and that information in VLTM related to the current task does not always have a positive effect on performance. Copyright © 2014 Elsevier Inc. All rights reserved.

Change detection in multitemporal synthetic aperture radar images using dual-channel convolutional neural network

NASA Astrophysics Data System (ADS)

Liu, Tao; Li, Ying; Cao, Ying; Shen, Qiang

2017-10-01

This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for change detection in SAR images, in an effort to acquire higher detection accuracy and lower misclassification rate. This network model contains two parallel CNN channels, which can extract deep features from two multitemporal SAR images. For comparison and validation, the proposed method is tested along with other change detection algorithms on both simulated SAR images and real-world SAR images captured by different sensors. The experimental results demonstrate that the presented method outperforms the state-of-the-art techniques by a considerable margin.

Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques

PubMed Central

Kim, Il-Hwan; Bong, Jae-Hwan; Park, Jooyoung; Park, Shinsuk

2017-01-01

Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics. PMID:28604582

Acromegaly determination using discriminant analysis of the three-dimensional facial classification in Taiwanese.

PubMed

Wang, Ming-Hsu; Lin, Jen-Der; Chang, Chen-Nen; Chiou, Wen-Ko

2017-08-01

The aim of this study was to assess the size, angles and positional characteristics of facial anthropometry between "acromegalic" patients and control subjects. We also identify possible facial soft tissue measurements for generating discriminant functions toward acromegaly determination in males and females for acromegaly early self-awareness. This is a cross-sectional study. Subjects participating in this study included 70 patients diagnosed with acromegaly (35 females and 35 males) and 140 gender-matched control individuals. Three-dimensional facial images were collected via a camera system. Thirteen landmarks were selected. Eleven measurements from the three categories were selected and applied, including five frontal widths, three lateral depths and three lateral angular measurements. Descriptive analyses were conducted using means and standard deviations for each measurement. Univariate and multivariate discriminant function analyses were applied in order to calculate the accuracy of acromegaly detection. Patients with acromegaly exhibit soft-tissue facial enlargement and hypertrophy. Frontal widths as well as lateral depth and angle of facial changes were evident. The average accuracies of all functions for female patient detection ranged from 80.0-91.40%. The average accuracies of all functions for male patient detection were from 81.0-94.30%. The greatest anomaly observed was evidenced in the lateral angles, with greater enlargement of "nasofrontal" angles for females and greater "mentolabial" angles for males. Additionally, shapes of the lateral angles showed changes. The majority of the facial measurements proved dynamic for acromegaly patients; however, it is problematic to detect the disease with progressive body anthropometric changes. The discriminant functions of detection developed in this study could help patients, their families, medical practitioners and others to identify and track progressive facial change patterns before the possible patients go to the hospital, especially the lateral "angles" which can be calculated by relative point-to-point changes derived from 2D lateral imagery without the 3D anthropometric measurements. This study tries to provide a novel and easy method to detect acromegaly when the patients start to have awareness of abnormal appearance because of facial measurement changes, and it also suggests that undiagnosed patients be urged to go to the hospital as soon as possible for acromegaly early diagnosis.

Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper

NASA Astrophysics Data System (ADS)

Renza, Diego; Martinez, Estibaliz; Molina, Iñigo; Ballesteros L., Dora M.

2017-04-01

This paper presents a new unsupervised change detection methodology for multispectral images applied to specific land covers. The proposed method involves comparing each image against a reference spectrum, where the reference spectrum is obtained from the spectral signature of the type of coverage you want to detect. In this case the method has been tested using multispectral images (SPOT5) of the community of Madrid (Spain), and multispectral images (Quickbird) of an area over Indonesia that was impacted by the December 26, 2004 tsunami; here, the tests have focused on the detection of changes in vegetation. The image comparison is obtained by applying Spectral Angle Mapper between the reference spectrum and each multitemporal image. Then, a threshold to produce a single image of change is applied, which corresponds to the vegetation zones. The results for each multitemporal image are combined through an exclusive or (XOR) operation that selects vegetation zones that have changed over time. Finally, the derived results were compared against a supervised method based on classification with the Support Vector Machine. Furthermore, the NDVI-differencing and the Spectral Angle Mapper techniques were selected as unsupervised methods for comparison purposes. The main novelty of the method consists in the detection of changes in a specific land cover type (vegetation), therefore, for comparison purposes, the best scenario is to compare it with methods that aim to detect changes in a specific land cover type (vegetation). This is the main reason to select NDVI-based method and the post-classification method (SVM implemented in a standard software tool). To evaluate the improvements using a reference spectrum vector, the results are compared with the basic-SAM method. In SPOT5 image, the overall accuracy was 99.36% and the κ index was 90.11%; in Quickbird image, the overall accuracy was 97.5% and the κ index was 82.16%. Finally, the precision results of the method are comparable to those of a supervised method, supported by low detection of false positives and false negatives, along with a high overall accuracy and a high kappa index. On the other hand, the execution times were comparable to those of unsupervised methods of low computational load.

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

An Adaptive Failure Detector Based on Quality of Service in Peer-to-Peer Networks

PubMed Central

Dong, Jian; Ren, Xiao; Zuo, Decheng; Liu, Hongwei

2014-01-01

The failure detector is one of the fundamental components that maintain high availability of Peer-to-Peer (P2P) networks. Under different network conditions, the adaptive failure detector based on quality of service (QoS) can achieve the detection time and accuracy required by upper applications with lower detection overhead. In P2P systems, complexity of network and high churn lead to high message loss rate. To reduce the impact on detection accuracy, baseline detection strategy based on retransmission mechanism has been employed widely in many P2P applications; however, Chen's classic adaptive model cannot describe this kind of detection strategy. In order to provide an efficient service of failure detection in P2P systems, this paper establishes a novel QoS evaluation model for the baseline detection strategy. The relationship between the detection period and the QoS is discussed and on this basis, an adaptive failure detector (B-AFD) is proposed, which can meet the quantitative QoS metrics under changing network environment. Meanwhile, it is observed from the experimental analysis that B-AFD achieves better detection accuracy and time with lower detection overhead compared to the traditional baseline strategy and the adaptive detectors based on Chen's model. Moreover, B-AFD has better adaptability to P2P network. PMID:25198005

Monitoring change in mountainous dry-heath vegetation at a regional scale using multitemporal Landsat TM data.

PubMed

Nordberg, Maj-Liz; Evertson, Joakim

2003-12-01

Vegetation cover-change analysis requires selection of an appropriate set of variables for measuring and characterizing change. Satellite sensors like Landsat TM offer the advantages of wide spatial coverage while providing land-cover information. This facilitates the monitoring of surface processes. This study discusses change detection in mountainous dry-heath communities in Jämtland County, Sweden, using satellite data. Landsat-5 TM and Landsat-7 ETM+ data from 1984, 1994 and 2000, respectively, were used. Different change detection methods were compared after the images had been radiometrically normalized, georeferenced and corrected for topographic effects. For detection of the classes change--no change the NDVI image differencing method was the most accurate with an overall accuracy of 94% (K = 0.87). Additional change information was extracted from an alternative method called NDVI regression analysis and vegetation change in 3 categories within mountainous dry-heath communities were detected. By applying a fuzzy set thresholding technique the overall accuracy was improved from of 65% (K = 0.45) to 74% (K = 0.59). The methods used generate a change product showing the location of changed areas in sensitive mountainous heath communities, and it also indicates the extent of the change (high, moderate and unchanged vegetation cover decrease). A total of 17% of the dry and extremely dry-heath vegetation within the study area has changed between 1984 and 2000. On average 4% of the studied heath communities have been classified as high change, i.e. have experienced "high vegetation cover decrease" during the period. The results show that the low alpine zone of the southern part of the study area shows the highest amount of "high vegetation cover decrease". The results also show that the main change occurred between 1994 and 2000.

Recurrent neural network based virtual detection line

NASA Astrophysics Data System (ADS)

Kadikis, Roberts

2018-04-01

The paper proposes an efficient method for detection of moving objects in the video. The objects are detected when they cross a virtual detection line. Only the pixels of the detection line are processed, which makes the method computationally efficient. A Recurrent Neural Network processes these pixels. The machine learning approach allows one to train a model that works in different and changing outdoor conditions. Also, the same network can be trained for various detection tasks, which is demonstrated by the tests on vehicle and people counting. In addition, the paper proposes a method for semi-automatic acquisition of labeled training data. The labeling method is used to create training and testing datasets, which in turn are used to train and evaluate the accuracy and efficiency of the detection method. The method shows similar accuracy as the alternative efficient methods but provides greater adaptability and usability for different tasks.

Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

NASA Technical Reports Server (NTRS)

Shiffman, Smadar

2004-01-01

Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

Determination of accuracy of winding deformation method using kNN based classifier used for 3 MVA transformer

NASA Astrophysics Data System (ADS)

Ahmed, Mustafa Wasir; Baishya, Manash Jyoti; Sharma, Sasanka Sekhor; Hazarika, Manash

2018-04-01

This paper presents a detecting system on power transformer in transformer winding, core and on load tap changer (OLTC). Accuracy of winding deformation is determined using kNN based classifier. Winding deformation in power transformer can be measured using sweep frequency response analysis (SFRA), which can enhance the diagnosis accuracy to a large degree. It is suggested that in the results minor deformation faults can be detected at frequency range of 1 mHz to 2 MHz. The values of RCL parameters are changed when faults occur and hence frequency response of the winding will change accordingly. The SFRA data of tested transformer is compared with reference trace. The difference between two graphs indicate faults in the transformer. The deformation between 1 mHz to 1kHz gives winding deformation, 1 kHz to 100 kHz gives core deformation and 100 kHz to 2 MHz gives OLTC deformation.

Monitoring Coral Growth - the Dichotomy Between Underwater Photogrammetry and Geodetic Control Network

NASA Astrophysics Data System (ADS)

Neyer, F.; Nocerino, E.; Gruen, A.

2018-05-01

Creating 3-dimensional (3D) models of underwater scenes has become a common approach for monitoring coral reef changes and its structural complexity. Also in underwater archeology, 3D models are often created using underwater optical imagery. In this paper, we focus on the aspect of detecting small changes in the coral reef using a multi-temporal photogrammetric modelling approach, which requires a high quality control network. We show that the quality of a good geodetic network limits the direct change detection, i.e., without any further registration process. As the photogrammetric accuracy is expected to exceed the geodetic network accuracy by at least one order of magnitude, we suggest to do a fine registration based on a number of signalized points. This work is part of the Moorea Island Digital Ecosystem Avatar (IDEA) project that has been initiated in 2013 by a group of international researchers (https://mooreaidea.ethz.ch/).

Change deafness for real spatialized environmental scenes.

PubMed

Gaston, Jeremy; Dickerson, Kelly; Hipp, Daniel; Gerhardstein, Peter

2017-01-01

The everyday auditory environment is complex and dynamic; often, multiple sounds co-occur and compete for a listener's cognitive resources. 'Change deafness', framed as the auditory analog to the well-documented phenomenon of 'change blindness', describes the finding that changes presented within complex environments are often missed. The present study examines a number of stimulus factors that may influence change deafness under real-world listening conditions. Specifically, an AX (same-different) discrimination task was used to examine the effects of both spatial separation over a loudspeaker array and the type of change (sound source additions and removals) on discrimination of changes embedded in complex backgrounds. Results using signal detection theory and accuracy analyses indicated that, under most conditions, errors were significantly reduced for spatially distributed relative to non-spatial scenes. A second goal of the present study was to evaluate a possible link between memory for scene contents and change discrimination. Memory was evaluated by presenting a cued recall test following each trial of the discrimination task. Results using signal detection theory and accuracy analyses indicated that recall ability was similar in terms of accuracy, but there were reductions in sensitivity compared to previous reports. Finally, the present study used a large and representative sample of outdoor, urban, and environmental sounds, presented in unique combinations of nearly 1000 trials per participant. This enabled the exploration of the relationship between change perception and the perceptual similarity between change targets and background scene sounds. These (post hoc) analyses suggest both a categorical and a stimulus-level relationship between scene similarity and the magnitude of change errors.

Direct Quantification of Post-Stress-Rest Left Ventricular Motion and Thickening Changes for Myocardial Perfusion SPECT

PubMed Central

Karimi-Ashtiani, Shahryar; Arsanjani, Reza; Fish, Mathews; Kavanagh, Paul; Germano, Guido; Berman, Daniel; Slomka, Piotr

2012-01-01

Changes in myocardial wall motion and thickening during myocardial perfusion single-photon emission computed tomography (MPS) are typically assessed separately from gated studies to assess for stress induced functional abnormalities. We sought to develop and validate a novel approach for automatic quantification of post-stress-rest myocardial motion and thickening changes (MTC). Methods Endocardial surfaces at the end-diastolic and end-systolic frames for post-stress and rest studies were registered automatically to each other by matching ventricular surfaces. Myocardial MTCs were computed and normal limits of change were determined as the mean and standard deviation for each polar sample. Normal limits were utilized to quantify the MTCs for each map and the accumulated sample values were used for abnormality assessments in segmental regions. A hybrid method was devised by combining the Total Perfusion Deficit (TPD) and MTC for each vessel territory. Normal limits were obtained from 100 subjects with low likelihood (LLK) of coronary artery disease (CAD). For validation, 623 subjects with correlating invasive angiography were studied. All subjects had a stress/rest 99mTc-sestamibi exercise or adenosine test, and all had coronary angiography within 3 months of MPS. All MTC and TPD measurements were derived automatically. The diagnostic accuracy for detection of coronary artery disease for MTC+TPD was compared to TPD alone. Results Segmental normal values for motion change were between −1.3 and −4.1 mm and between −30.1% and −9.8% for thickening change. MTC combined with TPD achieved 61% sensitivity for 3-vessel disease (3VD), 63% for 2-vessel disease (2VD), and 90% for 1-vessel disease (1VD) detection vs. 32% for 3VD (P <0.0001), 53% for 2VD (P < 0.001), and 90% for 1VD (P = 1.0) detection with TPD alone method. The specificity for the combined method was 71% for 3VD, 72% for 2VD, and 47% for 1 VD detection vs. 90% for 3VD (P < 0.0001), 80% for 2VD (P <0.001), and 50% for 1VD detection (P=0.0625) for TPD alone method. The accuracy of 3VD detection by MTC+TPD was higher (69%) than the accuracy of TPD + change in ejection fraction (63%), (P< 0.004). Conclusion We established normal limits and a novel method for computation of regional functional changes between post-stress and rest. Combination of (TPD) with MTC improved the sensitivity for the detection of 3VD and 2VD as compared to TPD alone. PMID:22872739

The value of vital sign trends for detecting clinical deterioration on the wards

PubMed Central

Churpek, Matthew M; Adhikari, Richa; Edelson, Dana P

2016-01-01

Aim Early detection of clinical deterioration on the wards may improve outcomes, and most early warning scores only utilize a patient’s current vital signs. The added value of vital sign trends over time is poorly characterized. We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends. Methods Patients admitted to five hospitals over a five-year period were included in this observational cohort study, with 60% of the data used for model derivation and 40% for validation. Vital signs were utilized to predict the combined outcome of cardiac arrest, intensive care unit transfer, and death. The accuracy of models utilizing both the current value and different trend methods were compared using the area under the receiver operating characteristic curve (AUC). Results A total of 269,999 patient admissions were included, which resulted in 16,452 outcomes. Overall, trends increased accuracy compared to a model containing only current vital signs (AUC 0.78 vs. 0.74; p<0.001). The methods that resulted in the greatest average increase in accuracy were the vital sign slope (AUC improvement 0.013) and minimum value (AUC improvement 0.012), while the change from the previous value resulted in an average worsening of the AUC (change in AUC −0.002). The AUC increased most for systolic blood pressure when trends were added (AUC improvement 0.05). Conclusion Vital sign trends increased the accuracy of models designed to detect critical illness on the wards. Our findings have important implications for clinicians at the bedside and for the development of early warning scores. PMID:26898412

The value of vital sign trends for detecting clinical deterioration on the wards.

PubMed

Churpek, Matthew M; Adhikari, Richa; Edelson, Dana P

2016-05-01

Early detection of clinical deterioration on the wards may improve outcomes, and most early warning scores only utilize a patient's current vital signs. The added value of vital sign trends over time is poorly characterized. We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends. Patients admitted to five hospitals over a five-year period were included in this observational cohort study, with 60% of the data used for model derivation and 40% for validation. Vital signs were utilized to predict the combined outcome of cardiac arrest, intensive care unit transfer, and death. The accuracy of models utilizing both the current value and different trend methods were compared using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patient admissions were included, which resulted in 16,452 outcomes. Overall, trends increased accuracy compared to a model containing only current vital signs (AUC 0.78 vs. 0.74; p<0.001). The methods that resulted in the greatest average increase in accuracy were the vital sign slope (AUC improvement 0.013) and minimum value (AUC improvement 0.012), while the change from the previous value resulted in an average worsening of the AUC (change in AUC -0.002). The AUC increased most for systolic blood pressure when trends were added (AUC improvement 0.05). Vital sign trends increased the accuracy of models designed to detect critical illness on the wards. Our findings have important implications for clinicians at the bedside and for the development of early warning scores. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Illumination Invariant Change Detection (iicd): from Earth to Mars

NASA Astrophysics Data System (ADS)

Wan, X.; Liu, J.; Qin, M.; Li, S. Y.

2018-04-01

Multi-temporal Earth Observation and Mars orbital imagery data with frequent repeat coverage provide great capability for planetary surface change detection. When comparing two images taken at different times of day or in different seasons for change detection, the variation of topographic shades and shadows caused by the change of sunlight angle can be so significant that it overwhelms the real object and environmental changes, making automatic detection unreliable. An effective change detection algorithm therefore has to be robust to the illumination variation. This paper presents our research on developing and testing an Illumination Invariant Change Detection (IICD) method based on the robustness of phase correlation (PC) to the variation of solar illumination for image matching. The IICD is based on two key functions: i) initial change detection based on a saliency map derived from pixel-wise dense PC matching and ii) change quantization which combines change type identification, motion estimation and precise appearance change identification. Experiment using multi-temporal Landsat 7 ETM+ satellite images, Rapid eye satellite images and Mars HiRiSE images demonstrate that our frequency based image matching method can reach sub-pixel accuracy and thus the proposed IICD method can effectively detect and precisely segment large scale change such as landslide as well as small object change such as Mars rover, under daily and seasonal sunlight changes.

[Ways to improve measurement accuracy of blood glucose sensing by mid-infrared spectroscopy].

PubMed

Wang, Yan; Li, Ning; Xu, Kexin

2006-06-01

Mid-infrared (MIR) spectroscopy is applicable to blood glucose sensing without using any reagent, however, due to a result of inadequate accuracy, till now this method has not been used in clinical detection. The principle and key technologies of blood glucose sensing by MIR spectroscopy are presented in this paper. Along with our experimental results, the paper analyzes ways to enhance measurement accuracy and prediction accuracy by the following four methods: selection of optimized spectral region; application of spectra data processing method; elimination of the interference with other components in the blood, and promotion in system hardware. According to these four improving methods, we designed four experiments, i.e., strict determination of the region where glucose concentration changes most sensitively in MIR, application of genetic algorithm for wavelength selection, normalization of spectra for the purpose of enhancing measuring reproduction, and utilization of CO2 laser as light source. The results show that the measurement accuracy of blood glucose concentration is enhanced almost to a clinical detection level.

Climate Benchmark Missions: CLARREO

NASA Technical Reports Server (NTRS)

Wielicki, Bruce A.; Young, David F.

2010-01-01

CLARREO (Climate Absolute Radiance and Refractivity Observatory) is one of the four Tier 1 missions recommended by the recent NRC decadal survey report on Earth Science and Applications from Space (NRC, 2007). The CLARREO mission addresses the need to rigorously observe climate change on decade time scales and to use decadal change observations as the most critical method to determine the accuracy of climate change projections such as those used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). A rigorously known accuracy of both decadal change observations as well as climate projections is critical in order to enable sound policy decisions. The CLARREO mission accomplishes this critical objective through highly accurate and SI traceable decadal change observations sensitive to many of the key uncertainties in climate radiative forcings, responses, and feedbacks that in turn drive uncertainty in current climate model projections. The same uncertainties also lead to uncertainty in attribution of climate change to anthropogenic forcing. The CLARREO breakthrough in decadal climate change observations is to achieve the required levels of accuracy and traceability to SI standards for a set of observations sensitive to a wide range of key decadal change variables. These accuracy levels are determined both by the projected decadal changes as well as by the background natural variability that such signals must be detected against. The accuracy for decadal change traceability to SI standards includes uncertainties of calibration, sampling, and analysis methods. Unlike most other missions, all of the CLARREO requirements are judged not by instantaneous accuracy, but instead by accuracy in large time/space scale average decadal changes. Given the focus on decadal climate change, the NRC Decadal Survey concluded that the single most critical issue for decadal change observations was their lack of accuracy and low confidence in observing the small but critical climate change signals. CLARREO is the recommended attack on this challenge, and builds on the last decade of climate observation advances in the Earth Observing System as well as metrological advances at NIST (National Institute of Standards and Technology) and other standards laboratories.

Accuracy of remotely sensed data: Sampling and analysis procedures

NASA Technical Reports Server (NTRS)

Congalton, R. G.; Oderwald, R. G.; Mead, R. A.

1982-01-01

A review and update of the discrete multivariate analysis techniques used for accuracy assessment is given. A listing of the computer program written to implement these techniques is given. New work on evaluating accuracy assessment using Monte Carlo simulation with different sampling schemes is given. The results of matrices from the mapping effort of the San Juan National Forest is given. A method for estimating the sample size requirements for implementing the accuracy assessment procedures is given. A proposed method for determining the reliability of change detection between two maps of the same area produced at different times is given.

Laser Truss Sensor for Segmented Telescope Phasing

NASA Technical Reports Server (NTRS)

Liu, Duncan T.; Lay, Oliver P.; Azizi, Alireza; Erlig, Herman; Dorsky, Leonard I.; Asbury, Cheryl G.; Zhao, Feng

2011-01-01

A paper describes the laser truss sensor (LTS) for detecting piston motion between two adjacent telescope segment edges. LTS is formed by two point-to-point laser metrology gauges in a crossed geometry. A high-resolution (<30 nm) LTS can be implemented with existing laser metrology gauges. The distance change between the reference plane and the target plane is measured as a function of the phase change between the reference and target beams. To ease the bandwidth requirements for phase detection electronics (or phase meter), homodyne or heterodyne detection techniques have been used. The phase of the target beam also changes with the refractive index of air, which changes with the air pressure, temperature, and humidity. This error can be minimized by enclosing the metrology beams in baffles. For longer-term (weeks) tracking at the micron level accuracy, the same gauge can be operated in the absolute metrology mode with an accuracy of microns; to implement absolute metrology, two laser frequencies will be used on the same gauge. Absolute metrology using heterodyne laser gauges is a demonstrated technology. Complexity of laser source fiber distribution can be optimized using the range-gated metrology (RGM) approach.

Region-Based Building Rooftop Extraction and Change Detection

NASA Astrophysics Data System (ADS)

Tian, J.; Metzlaff, L.; d'Angelo, P.; Reinartz, P.

2017-09-01

Automatic extraction of building changes is important for many applications like disaster monitoring and city planning. Although a lot of research work is available based on 2D as well as 3D data, an improvement in accuracy and efficiency is still needed. The introducing of digital surface models (DSMs) to building change detection has strongly improved the resulting accuracy. In this paper, a post-classification approach is proposed for building change detection using satellite stereo imagery. Firstly, DSMs are generated from satellite stereo imagery and further refined by using a segmentation result obtained from the Sobel gradients of the panchromatic image. Besides the refined DSMs, the panchromatic image and the pansharpened multispectral image are used as input features for mean-shift segmentation. The DSM is used to calculate the nDSM, out of which the initial building candidate regions are extracted. The candidate mask is further refined by morphological filtering and by excluding shadow regions. Following this, all segments that overlap with a building candidate region are determined. A building oriented segments merging procedure is introduced to generate a final building rooftop mask. As the last step, object based change detection is performed by directly comparing the building rooftops extracted from the pre- and after-event imagery and by fusing the change indicators with the roof-top region map. A quantitative and qualitative assessment of the proposed approach is provided by using WorldView-2 satellite data from Istanbul, Turkey.

Role of interoceptive accuracy in topographical changes in emotion-induced bodily sensations

PubMed Central

Jung, Won-Mo; Ryu, Yeonhee; Lee, Ye-Seul; Wallraven, Christian; Chae, Younbyoung

2017-01-01

The emotion-associated bodily sensation map is composed of a specific topographical distribution of bodily sensations to categorical emotions. The present study investigated whether or not interoceptive accuracy was associated with topographical changes in this map following emotion-induced bodily sensations. This study included 31 participants who observed short video clips containing emotional stimuli and then reported their sensations on the body map. Interoceptive accuracy was evaluated with a heartbeat detection task and the spatial patterns of bodily sensations to specific emotions, including anger, fear, disgust, happiness, sadness, and neutral, were visualized using Statistical Parametric Mapping (SPM) analyses. Distinct patterns of bodily sensations were identified for different emotional states. In addition, positive correlations were found between the magnitude of sensation in emotion-specific regions and interoceptive accuracy across individuals. A greater degree of interoceptive accuracy was associated with more specific topographical changes after emotional stimuli. These results suggest that the awareness of one’s internal bodily states might play a crucial role as a required messenger of sensory information during the affective process. PMID:28877218

Investigation on changes in complex vegetation coverage using multi-temporal landsat data of Western Black Sea region--a case study.

PubMed

Coban, Huseyin Oguz; Koc, Ayhan; Eker, Mehmet

2010-01-01

Previous studies have been able to successfully detect changes in gently-sloping forested areas with low-diversity and homogeneous vegetation cover using medium-resolution satellite data such as landsat. The aim of the present study is to examine the capacity of multi-temporal landsat data to identify changes in forested areas with mixed vegetation and generally located on steep slopes or non-uniform topography landsat thematic mapper (TM) and landsat enhanced thematic mapperplus (ETM+) data for the years 1987-2000 was used to detect changes within a 19,500 ha forested area in the Western Black sea region of Turkey. The data comply with the forest cover type maps previously created for forest management plans of the research area. The methods used to detect changes were: post-classification comparison, image differencing, image rationing and NDVI (Normalized Difference Vegetation Index) differencing methods. Following the supervised classification process, error matrices were used to evaluate the accuracy of classified images obtained. The overall accuracy has been calculated as 87.59% for 1987 image and as 91.81% for 2000 image. General kappa statistics have been calculated as 0.8543 and 0.9038 for 1987 and 2000, respectively. The changes identified via the post-classification comparison method were compared with other change detetion methods. Maximum coherence was found to be 74.95% at 4/3 band rate. The NDVI difference and 3rd band difference methods achieved the same coherence with slight variations. The results suggest that landsat satellite data accurately conveys the temporal changes which occur on steeply-sloping forested areas with a mixed structure, providing a limited amount of detail but with a high level of accuracy. Moreover it has been decided that the post-classification comparison method can meet the needs of forestry activities better than other methods as it provides information about the direction of these changes.

Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery.

PubMed

Chen, Zhong; Zhang, Yifei; Ouyang, Chao; Zhang, Feng; Ma, Jie

2018-03-09

Landslides that take place in mountain cities tend to cause huge casualties and economic losses, and a precise survey of landslide areas is a critical task for disaster emergency. However, because of the complicated appearance of the nature, it is difficult to find a spatial regularity that only relates to landslides, thus landslides detection based on only spatial information or artificial features usually performs poorly. In this paper, an automated landslides detection approach that is aiming at mountain cities has been proposed based on pre- and post-event remote sensing images, it mainly utilizes the knowledge of landslide-related surface covering changes, and makes full use of the temporal and spatial information. A change detection method using Deep Convolution Neural Network (DCNN) was introduced to extract the areas where drastic alterations have taken place; then, focusing on the changed areas, the Spatial Temporal Context Learning (STCL) was conducted to identify the landslides areas; finally, we use slope degree which is derived from digital elevation model (DEM) to make the result more reliable, and the change of DEM is used for making the detected areas more complete. The approach was applied to detecting the landslides in Shenzhen, Zhouqu County and Beichuan County in China, and a quantitative accuracy assessment has been taken. The assessment indicates that this approach can guarantee less commission error of landslide areal extent which is below 17.6% and achieves a quality percentage above 61.1%, and for landslide areas, the detection percentage is also competitive, the experimental results proves the feasibility and accuracy of the proposed approach for the detection landslides in mountain cities.

Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery

PubMed Central

Chen, Zhong; Zhang, Yifei; Ouyang, Chao; Zhang, Feng; Ma, Jie

2018-01-01

Landslides that take place in mountain cities tend to cause huge casualties and economic losses, and a precise survey of landslide areas is a critical task for disaster emergency. However, because of the complicated appearance of the nature, it is difficult to find a spatial regularity that only relates to landslides, thus landslides detection based on only spatial information or artificial features usually performs poorly. In this paper, an automated landslides detection approach that is aiming at mountain cities has been proposed based on pre- and post-event remote sensing images, it mainly utilizes the knowledge of landslide-related surface covering changes, and makes full use of the temporal and spatial information. A change detection method using Deep Convolution Neural Network (DCNN) was introduced to extract the areas where drastic alterations have taken place; then, focusing on the changed areas, the Spatial Temporal Context Learning (STCL) was conducted to identify the landslides areas; finally, we use slope degree which is derived from digital elevation model (DEM) to make the result more reliable, and the change of DEM is used for making the detected areas more complete. The approach was applied to detecting the landslides in Shenzhen, Zhouqu County and Beichuan County in China, and a quantitative accuracy assessment has been taken. The assessment indicates that this approach can guarantee less commission error of landslide areal extent which is below 17.6% and achieves a quality percentage above 61.1%, and for landslide areas, the detection percentage is also competitive, the experimental results proves the feasibility and accuracy of the proposed approach for the detection landslides in mountain cities. PMID:29522424

Analysis of Movement, Orientation and Rotation-Based Sensing for Phone Placement Recognition

PubMed Central

Durmaz Incel, Ozlem

2015-01-01

Phone placement, i.e., where the phone is carried/stored, is an important source of information for context-aware applications. Extracting information from the integrated smart phone sensors, such as motion, light and proximity, is a common technique for phone placement detection. In this paper, the efficiency of an accelerometer-only solution is explored, and it is investigated whether the phone position can be detected with high accuracy by analyzing the movement, orientation and rotation changes. The impact of these changes on the performance is analyzed individually and both in combination to explore which features are more efficient, whether they should be fused and, if yes, how they should be fused. Using three different datasets, collected from 35 people from eight different positions, the performance of different classification algorithms is explored. It is shown that while utilizing only motion information can achieve accuracies around 70%, this ratio increases up to 85% by utilizing information also from orientation and rotation changes. The performance of an accelerometer-only solution is compared to solutions where linear acceleration, gyroscope and magnetic field sensors are used, and it is shown that the accelerometer-only solution performs as well as utilizing other sensing information. Hence, it is not necessary to use extra sensing information where battery power consumption may increase. Additionally, I explore the impact of the performed activities on position recognition and show that the accelerometer-only solution can achieve 80% recognition accuracy with stationary activities where movement data are very limited. Finally, other phone placement problems, such as in-pocket and on-body detections, are also investigated, and higher accuracies, ranging from 88% to 93%, are reported, with an accelerometer-only solution. PMID:26445046

Analysis of Movement, Orientation and Rotation-Based Sensing for Phone Placement Recognition.

PubMed

Incel, Ozlem Durmaz

2015-10-05

Phone placement, i.e., where the phone is carried/stored, is an important source of information for context-aware applications. Extracting information from the integrated smart phone sensors, such as motion, light and proximity, is a common technique for phone placement detection. In this paper, the efficiency of an accelerometer-only solution is explored, and it is investigated whether the phone position can be detected with high accuracy by analyzing the movement, orientation and rotation changes. The impact of these changes on the performance is analyzed individually and both in combination to explore which features are more efficient, whether they should be fused and, if yes, how they should be fused. Using three different datasets, collected from 35 people from eight different positions, the performance of different classification algorithms is explored. It is shown that while utilizing only motion information can achieve accuracies around 70%, this ratio increases up to 85% by utilizing information also from orientation and rotation changes. The performance of an accelerometer-only solution is compared to solutions where linear acceleration, gyroscope and magnetic field sensors are used, and it is shown that the accelerometer-only solution performs as well as utilizing other sensing information. Hence, it is not necessary to use extra sensing information where battery power consumption may increase. Additionally, I explore the impact of the performed activities on position recognition and show that the accelerometer-only solution can achieve 80% recognition accuracy with stationary activities where movement data are very limited. Finally, other phone placement problems, such as in-pocket and on-body detections, are also investigated, and higher accuracies, ranging from 88% to 93%, are reported, with an accelerometer-only solution.

A targeted change-detection procedure by combining change vector analysis and post-classification approach

NASA Astrophysics Data System (ADS)

Ye, Su; Chen, Dongmei; Yu, Jie

2016-04-01

In remote sensing, conventional supervised change-detection methods usually require effective training data for multiple change types. This paper introduces a more flexible and efficient procedure that seeks to identify only the changes that users are interested in, here after referred to as "targeted change detection". Based on a one-class classifier "Support Vector Domain Description (SVDD)", a novel algorithm named "Three-layer SVDD Fusion (TLSF)" is developed specially for targeted change detection. The proposed algorithm combines one-class classification generated from change vector maps, as well as before- and after-change images in order to get a more reliable detecting result. In addition, this paper introduces a detailed workflow for implementing this algorithm. This workflow has been applied to two case studies with different practical monitoring objectives: urban expansion and forest fire assessment. The experiment results of these two case studies show that the overall accuracy of our proposed algorithm is superior (Kappa statistics are 86.3% and 87.8% for Case 1 and 2, respectively), compared to applying SVDD to change vector analysis and post-classification comparison.

Panoramic ECG display versus conventional ECG: ischaemia detection by critical care nurses.

PubMed

Wilson, Nick; Hassani, Aimen; Gibson, Vanessa; Lightfoot, Timothy; Zizzo, Claudio

2012-01-01

To compare accuracy and certainty of diagnosis of cardiac ischaemia using the Panoramic ECG display tool plus conventional 12-lead electrocardiogram (ECG) versus 12-lead ECG alone by UK critical care nurses who were members of the British Association of Critical Care Nurses (BACCN). Critically ill patients are prone to myocardial ischaemia. Symptoms may be masked by sedation or analgesia, and ECG changes may be the only sign. Critical care nurses have an essential role in detecting ECG changes promptly. Despite this, critical care nurses may lack expertise in interpreting ECGs and myocardial ischaemia often goes undetected by critical care staff. British Association of Critical Care Nurses (BACCN) members were invited to complete an online survey to evaluate the analysis of two sets of eight ECGs displayed alone and with the new display device. Data from 82 participants showed diagnostic accuracy improved from 67·1% reading ECG traces alone, to 96·0% reading ECG plus Panoramic ECG display tool (P < 0·01, significance level α = 0·05). Participants' diagnostic certainty score rose from 41·7% reading ECG alone to 66·8% reading ECG plus Panoramic ECG display tool (P < 0·01, α = 0·05). The Panoramic ECG display tool improves both accuracy and certainty of detecting ST segment changes among critical care nurses, when compared to conventional 12-lead ECG alone. This benefit was greatest with early ischaemic changes. Critical care nurses who are least confident in reading conventional ECGs benefit the most from the new display. Critical care nurses have an essential role in the monitoring of critically ill patients. However, nurses do not always have the expertise to detect subtle ischaemic ECG changes promptly. Introduction of the Panoramic ECG display tool into clinical practice could lead to patients receiving treatment for myocardial ischaemia sooner with the potential for reduction in morbidity and mortality. © 2012 The Authors. Nursing in Critical Care © 2012 British Association of Critical Care Nurses.

Is Ultrasound As Useful As Metal Artifact Reduction Sequence Magnetic Resonance Imaging in Longitudinal Surveillance of Metal-on-Metal Hip Arthroplasty Patients?

PubMed

Kwon, Young-Min; Dimitriou, Dimitris; Liow, Ming Han Lincoln; Tsai, Tsung-Yuan; Li, Guoan

2016-08-01

Current guidelines recommend longitudinal monitoring of at-risk metal-on-metal (MoM) arthroplasty patients with cross-sectional imaging such as metal artifact reduction sequence (MARS) magnetic resonance imaging (MRI) or ultrasound. During follow-up evaluations, the clinical focus is on the relative interval changes in symptoms, radiographs, laboratory tests, and cross-sectional imaging modalities. Although MRI has the capacity for the detection of adverse local soft tissue reactions (ALTRs), the potential disadvantages of MARS MRI include the obscuration of periprosthetic tissues by metal artifacts and the cost. The aim of this study was to evaluate the diagnostic accuracy of ultrasound in comparison with MARS MRI in detecting ALTR in MoM patients during consecutive follow-up. Thirty-five MoM patients (42 hips) were recruited prospectively to evaluate the sensitivity and specificity of the ultrasound for detecting ALTR in relation to MARS MRI during 2 longitudinal follow-up scans. The agreement between ultrasound and MARS MRI in ALTR grade, size, and size change was calculated. At the initial evaluation and at the subsequent follow-up, ultrasound had a sensitivity of 81% and 86% and a specificity of 92% and 88%, respectively. At the follow-up evaluations, ultrasound was able to detect the "change" in the lesions size with -0.3 cm(2) average bias from the MARS MRI with higher agreement (k = 0.85) with MARS MRI compared to the initial evaluation in detecting any "change" in ALTR size or grade. Ultrasound detected the interval change in the ALTR size and grade with higher accuracy and higher agreement with MARS MRI compared with the initial evaluation, suggesting ultrasound is a valid and useful. Copyright © 2016 Elsevier Inc. All rights reserved.

Evaluating an Automated Approach for Monitoring Forest Disturbances in the Pacific Northwest from Logging, Fire and Insect Outbreaks with Landsat Time Series Data

NASA Technical Reports Server (NTRS)

R.Neigh, Christopher S.; Bolton, Douglas K.; Williams, Jennifer J.; Diabate, Mouhamad

2014-01-01

Forests are the largest aboveground sink for atmospheric carbon (C), and understanding how they change through time is critical to reduce our C-cycle uncertainties. We investigated a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 1991 in Pacific Northwest forests, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometers (AVHRRs). To understand the causal factors of this decline, we evaluated an automated classification method developed for Landsat time series stacks (LTSS) to map forest change. This method included: (1) multiple disturbance index thresholds; and (2) a spectral trajectory-based image analysis with multiple confidence thresholds. We produced 48 maps and verified their accuracy with air photos, monitoring trends in burn severity data and insect aerial detection survey data. Area-based accuracy estimates for change in forest cover resulted in producer's and user's accuracies of 0.21 +/- 0.06 to 0.38 +/- 0.05 for insect disturbance, 0.23 +/- 0.07 to 1 +/- 0 for burned area and 0.74 +/- 0.03 to 0.76 +/- 0.03 for logging. We believe that accuracy was low for insect disturbance because air photo reference data were temporally sparse, hence missing some outbreaks, and the annual anniversary time step is not dense enough to track defoliation and progressive stand mortality. Producer's and user's accuracy for burned area was low due to the temporally abrupt nature of fire and harvest with a similar response of spectral indices between the disturbance index and normalized burn ratio. We conclude that the spectral trajectory approach also captures multi-year stress that could be caused by climate, acid deposition, pathogens, partial harvest, thinning, etc. Our study focused on understanding the transferability of previously successful methods to new ecosystems and found that this automated method does not perform with the same accuracy in Pacific Northwest forests. Using a robust accuracy assessment, we demonstrate the difficulty of transferring change attribution methods to other ecosystems, which has implications for the development of automated detection/attribution approaches. Widespread disturbance was found within AVHRR-negative anomalies, but identifying causal factors in LTSS with adequate mapping accuracy for fire and insects proved to be elusive. Our results provide a background framework for future studies to improve methods for the accuracy assessment of automated LTSS classifications.

Updating Landsat-derived land-cover maps using change detection and masking techniques

NASA Technical Reports Server (NTRS)

Likens, W.; Maw, K.

1982-01-01

The California Integrated Remote Sensing System's San Bernardino County Project was devised to study the utilization of a data base at a number of jurisdictional levels. The present paper discusses the implementation of change-detection and masking techniques in the updating of Landsat-derived land-cover maps. A baseline landcover classification was first created from a 1976 image, then the adjusted 1976 image was compared with a 1979 scene by the techniques of (1) multidate image classification, (2) difference image-distribution tails thresholding, (3) difference image classification, and (4) multi-dimensional chi-square analysis of a difference image. The union of the results of methods 1, 3 and 4 was used to create a mask of possible change areas between 1976 and 1979, which served to limit analysis of the update image and reduce comparison errors in unchanged areas. The techniques of spatial smoothing of change-detection products, and of combining results of difference change-detection algorithms are also shown to improve Landsat change-detection accuracies.

Ultrasound Detection of Soft Tissue Abscesses Performed by Non-Physician U.S. Army Medical Providers Naïve to Diagnostic Sonography.

PubMed

LaDuke, Mike; Monti, Jon; Cronin, Aaron; Gillum, Bart

2017-03-01

Patients commonly present to emergency rooms and primary care clinics with cellulitic skin infections with or without abscess formation. In military operational units, non-physician medical personnel provide most primary and initial emergency medical care. The objective of this study was to determine if, after minimal training, Army physician assistants and medics could use portable ultrasound (US) machines to detect superficial soft tissue abscesses. This was a single-blinded, randomized, prospective observational study conducted over the course of 2 days at a military installation. Active duty military physician assistants and medics with little or no US experience were recruited as participants. They received a short block of training on abscess detection using both clinical examination skills (inspection/palpation) and US examination. The participants were then asked to provide a yes/no answer regarding abscess presence in a chicken tissue model. Results were analyzed to assess the participants' abilities to detect abscesses, compare the diagnostic accuracy of their clinical examinations with their US examinations, and assess how often US results changed treatment plans initially on the basis of clinical examination findings alone. 22 participants performed a total of 220 clinical examinations and 220 US scans on 10 chicken tissue abscess models. Clinical examination for abscess detection yielded a sensitivity of 73.5% (95% confidence interval [CI], 65.3-80.3%) and a specificity of 77.2% (95% CI, 67.4-84.9%), although US examination for abscess detection yielded a sensitivity of 99.2% (95% CI, 95.4-99.9%) and a specificity of 95.5% (95% CI, 88.5-98.6%). Clinical examination yielded a diagnostic accuracy of 75.0% (95% CI, 68.9-80.3) although US examination yielded a diagnostic accuracy of 97.7% (95% CI, 94.6-99.2%), a difference in accuracy of 22.7% favoring US (p < 0.01). US changed the diagnosis in 56 of 220 cases (25.4% of all cases, p = 0.02). Of these 56 cases, US led to the correct diagnosis 53 of 56 times (94.6%). Non-physician military medical providers can be trained in a very brief period to use US to detect superficial soft tissue abscesses with excellent accuracy. Reprint & Copyright © 2017 Association of Military Surgeons of the U.S.

SAR Image Change Detection Based on Fuzzy Markov Random Field Model

NASA Astrophysics Data System (ADS)

Zhao, J.; Huang, G.; Zhao, Z.

2018-04-01

Most existing SAR image change detection algorithms only consider single pixel information of different images, and not consider the spatial dependencies of image pixels. So the change detection results are susceptible to image noise, and the detection effect is not ideal. Markov Random Field (MRF) can make full use of the spatial dependence of image pixels and improve detection accuracy. When segmenting the difference image, different categories of regions have a high degree of similarity at the junction of them. It is difficult to clearly distinguish the labels of the pixels near the boundaries of the judgment area. In the traditional MRF method, each pixel is given a hard label during iteration. So MRF is a hard decision in the process, and it will cause loss of information. This paper applies the combination of fuzzy theory and MRF to the change detection of SAR images. The experimental results show that the proposed method has better detection effect than the traditional MRF method.

Changing behavior and accuracy with time on task in mammography screening

NASA Astrophysics Data System (ADS)

Taylor-Phillips, Sian; Jenkinson, David; Stinton, Chris; Wallis, Matthew G.; Clarke, Aileen

2017-03-01

Background: The vigilance decrement and prevalence effect both describe changes to speed and accuracy with time on task. Whilst there is much laboratory based research on these effects, little is known about whether they occur in real world mammography practice. Methods: The Changing Case Order to Optimise Patterns of Performance in Screening (CO-OPS) trial randomised 37,724 batches containing 1.2 million women attending breast screening to intervention or control (222,208 from the Midlands of England). In the control arm the batch was examined in the same order by both readers, in the intervention arm it was examined in a different order by both readers. Time taken, recall decision by both readers, and cancers detected were recorded for each case, and used to examine patterns of performance with time on task. Results: 49,575 women were recalled and 10,484 had cancer detected. Median time taken to examine each case was 35 seconds (out of cases where time taken was 10 minutes or less). The intervention did not affect overall cancer detection rates or recall rates. A more detailed analysis of the Midlands data indicates cancer detection rate did not change when reading up to 60 cases in a batch, but recall rate reduced. Time taken per case reduced with time on task, from a median 41 seconds when examining the second case in the batch to 28.5 seconds examining the 60th case. Conclusion: Reader behavior and performance systematically changes with time on task in breast screening.

Edge detection and localization with edge pattern analysis and inflection characterization

NASA Astrophysics Data System (ADS)

Jiang, Bo

2012-05-01

In general edges are considered to be abrupt changes or discontinuities in two dimensional image signal intensity distributions. The accuracy of front-end edge detection methods in image processing impacts the eventual success of higher level pattern analysis downstream. To generalize edge detectors designed from a simple ideal step function model to real distortions in natural images, research on one dimensional edge pattern analysis to improve the accuracy of edge detection and localization proposes an edge detection algorithm, which is composed by three basic edge patterns, such as ramp, impulse, and step. After mathematical analysis, general rules for edge representation based upon the classification of edge types into three categories-ramp, impulse, and step (RIS) are developed to reduce detection and localization errors, especially reducing "double edge" effect that is one important drawback to the derivative method. But, when applying one dimensional edge pattern in two dimensional image processing, a new issue is naturally raised that the edge detector should correct marking inflections or junctions of edges. Research on human visual perception of objects and information theory pointed out that a pattern lexicon of "inflection micro-patterns" has larger information than a straight line. Also, research on scene perception gave an idea that contours have larger information are more important factor to determine the success of scene categorization. Therefore, inflections or junctions are extremely useful features, whose accurate description and reconstruction are significant in solving correspondence problems in computer vision. Therefore, aside from adoption of edge pattern analysis, inflection or junction characterization is also utilized to extend traditional derivative edge detection algorithm. Experiments were conducted to test my propositions about edge detection and localization accuracy improvements. The results support the idea that these edge detection method improvements are effective in enhancing the accuracy of edge detection and localization.

Road sign recognition with fuzzy adaptive pre-processing models.

PubMed

Lin, Chien-Chuan; Wang, Ming-Shi

2012-01-01

A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance.

Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models

PubMed Central

Lin, Chien-Chuan; Wang, Ming-Shi

2012-01-01

A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance. PMID:22778650

Optical Fiber On-Line Detection System for Non-Touch Monitoring Roller Shape

NASA Astrophysics Data System (ADS)

Guo, Y.; Wang, Y. T.

2006-10-01

Basing on the principle of reflective displacement fiber-optic sensor, a high accuracy non-touch on-line optical fiber measurement system for roller shape is presented. The principle and composition of the detection system and the operation process are expatiated also. By using a novel probe of three optical fibers in equal transverse space, the effects of fluctuations in the light source, reflective changing of target surface and the intensity losses in the fiber lines are automatically compensated. Meantime, an optical fiber sensor model of correcting static error based on BP artificial neural network (ANN) is set up. Also by using interpolation method and value filtering to process the signals, effectively reduce the influence of random noise and the vibration of the roller bearing. So enhance the accuracy and resolution remarkably. Experiment proves that the accuracy of the system reach to the demand of practical production process, it provides a new method for the high speed, accurate and automatic on line detection of the mill roller shape.

Combined optical coherence tomography and optical coherence elastography for glomerulonephritis classification

NASA Astrophysics Data System (ADS)

Liu, Chih-Hao; Du, Yong; Singh, Manmohan; Wu, Chen; Han, Zhaolong; Li, Jiasong; Mohammadzai, Qais; Raghunathan, Raksha; Hsu, Thomas; Noorani, Shezaan; Chang, Anthony; Mohan, Chandra; Larin, Kirill V.

2016-03-01

Acute Glomerulonephritis caused by anti-glomerular basement membrane disease has a high mortality due to delayed diagnosis. Thus, an accurate and early diagnosis is critical for preserving renal function. Currently, blood, urine, and tissue-based diagnoses can be time consuming, while ultrasound and CT imaging have relatively low spatial resolution. Optical coherence tomography (OCT) is a noninvasive imaging technique that provides superior spatial resolution (micron scale) as compared to ultrasound and CT. Pathological changes in tissue properties can be detected based on the optical metrics analyzed from the OCT signal, such as optical attenuation and speckle variance. Moreover, OCT does not rely on ionizing radiation as with CT imaging. In addition to structural changes, the elasticity of the kidney can significantly change due to nephritis. In this work, we utilized OCT to detect the difference in tissue properties between healthy and nephritic murine kidneys. Although OCT imaging could identify the diseased tissue, classification accuracy using only optical metrics was clinically inadequate. By combining optical metrics with elasticity, the classification accuracy improved from 76% to 95%. These results show that OCT combined with OCE can be potentially useful for nephritis detection.

Improved spatial regression analysis of diffusion tensor imaging for lesion detection during longitudinal progression of multiple sclerosis in individual subjects

NASA Astrophysics Data System (ADS)

Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui

2016-03-01

Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.

Vegetation classification of Coffea on Hawaii Island using WorldView-2 satellite imagery

NASA Astrophysics Data System (ADS)

Gaertner, Julie; Genovese, Vanessa Brooks; Potter, Christopher; Sewake, Kelvin; Manoukis, Nicholas C.

2017-10-01

Coffee is an important crop in tropical regions of the world; about 125 million people depend on coffee agriculture for their livelihoods. Understanding the spatial extent of coffee fields is useful for management and control of coffee pests such as Hypothenemus hampei and other pests that use coffee fruit as a host for immature stages such as the Mediterranean fruit fly, for economic planning, and for following changes in coffee agroecosystems over time. We present two methods for detecting Coffea arabica fields using remote sensing and geospatial technologies on WorldView-2 high-resolution spectral data of the Kona region of Hawaii Island. The first method, a pixel-based method using a maximum likelihood algorithm, attained 72% producer accuracy and 69% user accuracy (68% overall accuracy) based on analysis of 104 ground truth testing polygons. The second method, an object-based image analysis (OBIA) method, considered both spectral and textural information and improved accuracy, resulting in 76% producer accuracy and 94% user accuracy (81% overall accuracy) for the same testing areas. We conclude that the OBIA method is useful for detecting coffee fields grown in the open and use it to estimate the distribution of about 1050 hectares under coffee agriculture in the Kona region in 2012.

Permutation Entropy and Signal Energy Increase the Accuracy of Neuropathic Change Detection in Needle EMG

PubMed Central

2018-01-01

Background and Objective. Needle electromyography can be used to detect the number of changes and morphological changes in motor unit potentials of patients with axonal neuropathy. General mathematical methods of pattern recognition and signal analysis were applied to recognize neuropathic changes. This study validates the possibility of extending and refining turns-amplitude analysis using permutation entropy and signal energy. Methods. In this study, we examined needle electromyography in 40 neuropathic individuals and 40 controls. The number of turns, amplitude between turns, signal energy, and “permutation entropy” were used as features for support vector machine classification. Results. The obtained results proved the superior classification performance of the combinations of all of the above-mentioned features compared to the combinations of fewer features. The lowest accuracy from the tested combinations of features had peak-ratio analysis. Conclusion. Using the combination of permutation entropy with signal energy, number of turns and mean amplitude in SVM classification can be used to refine the diagnosis of polyneuropathies examined by needle electromyography. PMID:29606959

Diagnostic accuracy of point-of-care ultrasound for evaluation of early blood-induced joint changes: Comparison with MRI.

PubMed

Foppen, W; van der Schaaf, I C; Beek, F J A; Mali, W P T M; Fischer, K

2018-05-23

Recurrent joint bleeding is the hallmark of haemophilia. Synovial hypertrophy observed with Magnetic Resonance Imaging (MRI) is associated with an increased risk of future joint bleeding. The aim of this study was to investigate whether point-of-care ultrasound (POC-US) is an accurate alternative for MRI for the detection of early joint changes. In this single centre diagnostic accuracy study, bilateral knees and ankles of haemophilia patients with no or minimal arthropathy on X-rays were scanned using POC-US and 3 Tesla MRI. POC-US was performed by 1 medical doctor, blinded for MRI, according to the "Haemophilia Early Arthropathy Detection with Ultrasound" (HEAD-US) protocol. MRIs were independently scored by 2 radiologists, blinded for clinical data and ultrasound results. Diagnostic accuracy parameters were calculated with 95% confidence intervals (CI). Knees and ankles of 24 haemophilia patients (96 joints), aged 18-34, were studied. Synovial hypertrophy on MRI was observed in 20% of joints. POC-US for synovial tissue was correct (overall accuracy) in 97% (CI: 91-99) with a positive predictive value of 94% (CI: 73-100) and a negative predictive value of 97% (CI: 91-100). The overall accuracy of POC-US for cartilage abnormalities was 91% (CI: 83-96) and for bone surface irregularities 97% (CI: 91-99). POC-US could accurately assess synovial hypertrophy, bone surface irregularities and cartilage abnormalities in haemophilia patients with limited joint disease. As POC-US is an accurate and available alternative for MRI, it can be used for routine evaluation of early joint changes. © 2018 The Authors. Haemophilia published by John Wiley & Sons Ltd.

Eddy current gauge for monitoring displacement using printed circuit coil

DOEpatents

Visioli, Jr., Armando J.

1977-01-01

A proximity detection system for non-contact displacement and proximity measurement of static or dynamic metallic or conductive surfaces is provided wherein the measurement is obtained by monitoring the change in impedance of a flat, generally spiral-wound, printed circuit coil which is excited by a constant current, constant frequency source. The change in impedance, which is detected as a corresponding change in voltage across the coil, is related to the eddy current losses in the distant conductive material target. The arrangement provides for considerable linear displacement range with increased accuracies, stability, and sensitivity over the entire range.

SU-F-J-160: Clinical Evaluation of Targeting Accuracy in Radiosurgery Using Tractography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Juh, R; Han, J; Kim, C

Purpose: Focal radiosurgery is a common treatment modality for trigeminal neuralgia (TN), a neuropathic facial pain condition. Assessment of treatment effectiveness is primarily clinical, given the paucity of investigational tools to assess trigeminal nerve changes. The efficiency of radiosurgery is related to its highly precise targeting. We assessed clinically the targeting accuracy of radiosurgery with Gamma knife. We hypothesized that trigeminal tractography provides more information than 2D-MR imaging, allowing detection of unique, focal changes in the target area after radiosurgery. Methods: Sixteen TN patients (2 females, 4 males, average age 65.3 years) treated with Gamma Knife radiosurgery, 40 Gy/50% isodosemore » line underwent 1.5Tesla MR trigeminal nerve. Target accuracy was assessed from deviation of the coordinates of the target compared with the center of enhancement on post MRI. Radiation dose delivered at the borders of contrast enhancement was evaluated. Results: The median deviation of the coordinates between the intended target and the center of contrast enhancement was within 1mm. The radiation doses fitting within the borders of the contrast enhancement the target ranged from 37.5 to 40 Gy. Trigeminal tractography accurately detected the radiosurgical target. Radiosurgery resulted in 47% drop in FA values at the target with no significant change in FA outside the target, suggesting that radiosurgery primarily affects myelin. Tractography was more sensitive, since FA changes were detected regardless of trigeminal nerve enhancement. Conclusion: The median deviation found in clinical assessment of gamma knife treatment for TN Is low and compatible with its high rate of efficiency. DTI parameters accurately detect the effects of focal radiosurgery on the trigeminal nerve, serving as an in vivo imaging tool to study TN. This study is a proof of principle for further assessment of DTI parameters to understand the pathophysiology of TN and treatment effects.« less

SU-E-J-34: Clinical Evaluation of Targeting Accuracy and Tractogrphy Delineation of Radiosurgery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Juh, R; Suh, T; Kim, Y

2014-06-01

Purpose: Focal radiosurgery is a common treatment modality for trigeminal neuralgia (TN), a neuropathic facial pain condition. Assessment of treatment effectiveness is primarily clinical, given the paucity of investigational tools to assess trigeminal nerve changes. The efficiency of radiosurgery is related to its highly precise targeting. We assessed clinically the targeting accuracy of radiosurgery with Gamma knife. We hypothesized that trigeminal tractography provides more information than 2D-MR imaging, allowing detection of unique, focal changes in the target area after radiosurgery. Methods: Sixteen TN patients (2 females, 4 male, average age 65.3 years) treated with Gamma Knife radiosurgery, 40 Gy/50% isodosemore » line underwent 1.5Tesla MR trigeminal nerve . Target accuracy was assessed from deviation of the coordinates of the target compared with the center of enhancement on post MRI. Radiation dose delivered at the borders of contrast enhancement was evaluated Results: The median deviation of the coordinates between the intended target and the center of contrast enhancement was within 1mm. The radiation doses fitting within the borders of the contrast enhancement the target ranged from 37.5 to 40 Gy. Trigeminal tractography accurately detected the radiosurgical target. Radiosurgery resulted in 47% drop in FA values at the target with no significant change in FA outside the target, suggesting that radiosurgery primarily affects myelin. Tractography was more sensitive, since FA changes were detected regardless of trigeminal nerve enhancement Conclusion: The median deviation found in clinical assessment of gamma knife treatment for TN Is low and compatible with its high rate of efficiency. DTI parameters accurately detect the effects of focal radiosurgery on the trigeminal nerve, serving as an in vivo imaging tool to study TN. This study is a proof of principle for further assessment of DTI parameters to understand the pathophysiology of TN and treatment effects.« less

Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones.

PubMed

Lu, Dang-Nhac; Nguyen, Duc-Nhan; Nguyen, Thi-Hau; Nguyen, Ha-Nam

2018-03-29

In this paper, we present a flexible combined system, namely the Vehicle mode-driving Activity Detection System (VADS), that is capable of detecting either the current vehicle mode or the current driving activity of travelers. Our proposed system is designed to be lightweight in computation and very fast in response to the changes of travelers' vehicle modes or driving events. The vehicle mode detection module is responsible for recognizing both motorized vehicles, such as cars, buses, and motorbikes, and non-motorized ones, for instance, walking, and bikes. It relies only on accelerometer data in order to minimize the energy consumption of smartphones. By contrast, the driving activity detection module uses the data collected from the accelerometer, gyroscope, and magnetometer of a smartphone to detect various driving activities, i.e., stopping, going straight, turning left, and turning right. Furthermore, we propose a method to compute the optimized data window size and the optimized overlapping ratio for each vehicle mode and each driving event from the training datasets. The experimental results show that this strategy significantly increases the overall prediction accuracy. Additionally, numerous experiments are carried out to compare the impact of different feature sets (time domain features, frequency domain features, Hjorth features) as well as the impact of various classification algorithms (Random Forest, Naïve Bayes, Decision tree J48, K Nearest Neighbor, Support Vector Machine) contributing to the prediction accuracy. Our system achieves an average accuracy of 98.33% in detecting the vehicle modes and an average accuracy of 98.95% in recognizing the driving events of motorcyclists when using the Random Forest classifier and a feature set containing time domain features, frequency domain features, and Hjorth features. Moreover, on a public dataset of HTC company in New Taipei, Taiwan, our framework obtains the overall accuracy of 97.33% that is considerably higher than that of the state-of the art.

Environmental monitoring and assessment of landscape dynamics in southern coast of the Caspian Sea through intensity analysis and imprecise land-use data.

PubMed

Hasani, Mohammad; Sakieh, Yousef; Dezhkam, Sadeq; Ardakani, Tahereh; Salmanmahiny, Abdolrassoul

2017-04-01

A hierarchical intensity analysis of land-use change is applied to evaluate the dynamics of a coupled urban coastal system in Rasht County, Iran. Temporal land-use layers of 1987, 1999, and 2011 are employed, while spatial accuracy metrics are only available for 2011 data (overall accuracy of 94%). The errors in 1987 and 1999 layers are unknown, which can influence the accuracy of temporal change information. Such data were employed to examine the size and the type of errors that could justify deviations from uniform change intensities. Accordingly, errors comprising 3.31 and 7.47% of 1999 and 2011 maps, respectively, could explain all differences from uniform gains and errors including 5.21 and 1.81% of 1987 and 1999 maps, respectively, could explain all deviations from uniform losses. Additional historical information is also applied for uncertainty assessment and to separate probable map errors from actual land-use changes. In this regard, historical processes in Rasht County can explain different types of transition that are either consistent or inconsistent to known processes. The intensity analysis assisted in identification of systematic transitions and detection of competitive categories, which cannot be investigated through conventional change detection methods. Based on results, built-up area is the most active gaining category in the area and wetland category with less areal extent is more sensitive to intense land-use change processes. Uncertainty assessment results also indicated that there are no considerable classification errors in temporal land-use data and these imprecise layers can reliably provide implications for informed decision making.

Mesial Temporal Sclerosis: Accuracy of NeuroQuant versus Neuroradiologist.

PubMed

Azab, M; Carone, M; Ying, S H; Yousem, D M

2015-08-01

We sought to compare the accuracy of a volumetric fully automated computer assessment of hippocampal volume asymmetry versus neuroradiologists' interpretations of the temporal lobes for mesial temporal sclerosis. Detecting mesial temporal sclerosis (MTS) is important for the evaluation of patients with temporal lobe epilepsy as it often guides surgical intervention. One feature of MTS is hippocampal volume loss. Electronic medical record and researcher reports of scans of patients with proved mesial temporal sclerosis were compared with volumetric assessment with an FDA-approved software package, NeuroQuant, for detection of mesial temporal sclerosis in 63 patients. The degree of volumetric asymmetry was analyzed to determine the neuroradiologists' threshold for detecting right-left asymmetry in temporal lobe volumes. Thirty-six patients had left-lateralized MTS, 25 had right-lateralized MTS, and 2 had bilateral MTS. The estimated accuracy of the neuroradiologist was 72.6% with a κ statistic of 0.512 (95% CI, 0.315-0.710) [moderate agreement, P < 3 × 10(-6)]), whereas the estimated accuracy of NeuroQuant was 79.4% with a κ statistic of 0.588 (95% CI, 0.388-0.787) [moderate agreement, P < 2 × 10(-6)]). This discrepancy in accuracy was not statistically significant. When at least a 5%-10% volume discrepancy between temporal lobes was present, the neuroradiologists detected it 75%-80% of the time. As a stand-alone fully automated software program that can process temporal lobe volume in 5-10 minutes, NeuroQuant compares favorably with trained neuroradiologists in predicting the side of mesial temporal sclerosis. Neuroradiologists can often detect even small temporal lobe volumetric changes visually. © 2015 by American Journal of Neuroradiology.

A scale-invariant change detection method for land use/cover change research

NASA Astrophysics Data System (ADS)

Xing, Jin; Sieber, Renee; Caelli, Terrence

2018-07-01

Land Use/Cover Change (LUCC) detection relies increasingly on comparing remote sensing images with different spatial and spectral scales. Based on scale-invariant image analysis algorithms in computer vision, we propose a scale-invariant LUCC detection method to identify changes from scale heterogeneous images. This method is composed of an entropy-based spatial decomposition, two scale-invariant feature extraction methods, Maximally Stable Extremal Region (MSER) and Scale-Invariant Feature Transformation (SIFT) algorithms, a spatial regression voting method to integrate MSER and SIFT results, a Markov Random Field-based smoothing method, and a support vector machine classification method to assign LUCC labels. We test the scale invariance of our new method with a LUCC case study in Montreal, Canada, 2005-2012. We found that the scale-invariant LUCC detection method provides similar accuracy compared with the resampling-based approach but this method avoids the LUCC distortion incurred by resampling.

Is countershading camouflage robust to lighting change due to weather?

PubMed

Penacchio, Olivier; Lovell, P George; Harris, Julie M

2018-02-01

Countershading is a pattern of coloration thought to have evolved in order to implement camouflage. By adopting a pattern of coloration that makes the surface facing towards the sun darker and the surface facing away from the sun lighter, the overall amount of light reflected off an animal can be made more uniformly bright. Countershading could hence contribute to visual camouflage by increasing background matching or reducing cues to shape. However, the usefulness of countershading is constrained by a particular pattern delivering 'optimal' camouflage only for very specific lighting conditions. In this study, we test the robustness of countershading camouflage to lighting change due to weather, using human participants as a 'generic' predator. In a simulated three-dimensional environment, we constructed an array of simple leaf-shaped items and a single ellipsoidal target 'prey'. We set these items in two light environments: strongly directional 'sunny' and more diffuse 'cloudy'. The target object was given the optimal pattern of countershading for one of these two environment types or displayed a uniform pattern. By measuring detection time and accuracy, we explored whether and how target detection depended on the match between the pattern of coloration on the target object and scene lighting. Detection times were longest when the countershading was appropriate to the illumination; incorrectly camouflaged targets were detected with a similar pattern of speed and accuracy to uniformly coloured targets. We conclude that structural changes in light environment, such as caused by differences in weather, do change the effectiveness of countershading camouflage.

Accelerometer-based method for correcting signal baseline changes caused by motion artifacts in medical near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Virtanen, Jaakko; Noponen, Tommi; Kotilahti, Kalle; Virtanen, Juha; Ilmoniemi, Risto J.

2011-08-01

In medical near-infrared spectroscopy (NIRS), movements of the subject often cause large step changes in the baselines of the measured light attenuation signals. This prevents comparison of hemoglobin concentration levels before and after movement. We present an accelerometer-based motion artifact removal (ABAMAR) algorithm for correcting such baseline motion artifacts (BMAs). ABAMAR can be easily adapted to various long-term monitoring applications of NIRS. We applied ABAMAR to NIRS data collected in 23 all-night sleep measurements and containing BMAs from involuntary movements during sleep. For reference, three NIRS researchers independently identified BMAs from the data. To determine whether the use of an accelerometer improves BMA detection accuracy, we compared ABAMAR to motion detection based on peaks in the moving standard deviation (SD) of NIRS data. The number of BMAs identified by ABAMAR was similar to the number detected by the humans, and 79% of the artifacts identified by ABAMAR were confirmed by at least two humans. While the moving SD of NIRS data could also be used for motion detection, on average 2 out of the 10 largest SD peaks in NIRS data each night occurred without the presence of movement. Thus, using an accelerometer improves BMA detection accuracy in NIRS.

Diagnostic Accuracy of a New Cardiac Electrical Biomarker for Detection of Electrocardiogram Changes Suggestive of Acute Myocardial Ischemic Injury

PubMed Central

Schreck, David M; Fishberg, Robert D

2014-01-01

Objective A new cardiac “electrical” biomarker (CEB) for detection of 12-lead electrocardiogram (ECG) changes indicative of acute myocardial ischemic injury has been identified. Objective was to test CEB diagnostic accuracy. Methods This is a blinded, observational retrospective case-control, noninferiority study. A total of 508 ECGs obtained from archived digital databases were interpreted by cardiologist and emergency physician (EP) blinded reference standards for presence of acute myocardial ischemic injury. CEB was constructed from three ECG cardiac monitoring leads using nonlinear modeling. Comparative active controls included ST voltage changes (J-point, ST area under curve) and a computerized ECG interpretive algorithm (ECGI). Training set of 141 ECGs identified CEB cutoffs by receiver-operating-characteristic (ROC) analysis. Test set of 367 ECGs was analyzed for validation. Poor-quality ECGs were excluded. Sensitivity, specificity, and negative and positive predictive values were calculated with 95% confidence intervals. Adjudication was performed by consensus. Results CEB demonstrated noninferiority to all active controls by hypothesis testing. CEB adjudication demonstrated 85.3–94.4% sensitivity, 92.5–93.0% specificity, 93.8–98.6% negative predictive value, and 74.6–83.5% positive predictive value. CEB was superior against all active controls in EP analysis, and against ST area under curve and ECGI by cardiologist. Conclusion CEB detects acute myocardial ischemic injury with high diagnostic accuracy. CEB is instantly constructed from three ECG leads on the cardiac monitor and displayed instantly allowing immediate cost-effective identification of patients with acute ischemic injury during cardiac rhythm monitoring. PMID:24118724

Multiscale-Driven approach to detecting change in Synthetic Aperture Radar (SAR) imagery

NASA Astrophysics Data System (ADS)

Gens, R.; Hogenson, K.; Ajadi, O. A.; Meyer, F. J.; Myers, A.; Logan, T. A.; Arnoult, K., Jr.

2017-12-01

Detecting changes between Synthetic Aperture Radar (SAR) images can be a useful but challenging exercise. SAR with its all-weather capabilities can be an important resource in identifying and estimating the expanse of events such as flooding, river ice breakup, earthquake damage, oil spills, and forest growth, as it can overcome shortcomings of optical methods related to cloud cover. However, detecting change in SAR imagery can be impeded by many factors including speckle, complex scattering responses, low temporal sampling, and difficulty delineating boundaries. In this presentation we use a change detection method based on a multiscale-driven approach. By using information at different resolution levels, we attempt to obtain more accurate change detection maps in both heterogeneous and homogeneous regions. Integrated within the processing flow are processes that 1) improve classification performance by combining Expectation-Maximization algorithms with mathematical morphology, 2) achieve high accuracy in preserving boundaries using measurement level fusion techniques, and 3) combine modern non-local filtering and 2D-discrete stationary wavelet transform to provide robustness against noise. This multiscale-driven approach to change detection has recently been incorporated into the Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3) using radiometrically terrain corrected SAR images. Examples primarily from natural hazards are presented to illustrate the capabilities and limitations of the change detection method.

An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3.

PubMed

Liu, Wensong; Yang, Jie; Zhao, Jinqi; Shi, Hongtao; Yang, Le

2018-02-12

The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by R j test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient.

Detection of convulsive seizures using surface electromyography.

PubMed

Beniczky, Sándor; Conradsen, Isa; Wolf, Peter

2018-06-01

Bilateral (generalized) tonic-clonic seizures (TCS) increase the risk of sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended. In sleep, TCS often remain unnoticed, which can result in suboptimal treatment decisions. There is a need for automated detection of these major epileptic seizures, using wearable devices. Quantitative surface electromyography (EMG) changes are specific for TCS and characterized by a dynamic evolution of low- and high-frequency signal components. Algorithms targeting increase in high-frequency EMG signals constitute biomarkers of TCS; they can be used both for seizure detection and for differentiating TCS from convulsive nonepileptic seizures. Two large-scale, blinded, prospective studies demonstrated the accuracy of wearable EMG devices for detecting TCS with high sensitivity (76%-100%). The rate of false alarms (0.7-2.5/24 h) needs further improvement. This article summarizes the pathophysiology of muscle activation during convulsive seizures and reviews the published evidence on the accuracy of EMG-based seizure detection. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

Response time accuracy in Apple Macintosh computers.

PubMed

Neath, Ian; Earle, Avery; Hallett, Darcy; Surprenant, Aimée M

2011-06-01

The accuracy and variability of response times (RTs) collected on stock Apple Macintosh computers using USB keyboards was assessed. A photodiode detected a change in the screen's luminosity and triggered a solenoid that pressed a key on the keyboard. The RTs collected in this way were reliable, but could be as much as 100 ms too long. The standard deviation of the measured RTs varied between 2.5 and 10 ms, and the distributions approximated a normal distribution. Surprisingly, two recent Apple-branded USB keyboards differed in their accuracy by as much as 20 ms. The most accurate RTs were collected when an external CRT was used to display the stimuli and Psychtoolbox was able to synchronize presentation with the screen refresh. We conclude that RTs collected on stock iMacs can detect a difference as small as 5-10 ms under realistic conditions, and this dictates which types of research should or should not use these systems.

Potential of fecal microbiota for early-stage detection of colorectal cancer

PubMed Central

Zeller, Georg; Tap, Julien; Voigt, Anita Y; Sunagawa, Shinichi; Kultima, Jens Roat; Costea, Paul I; Amiot, Aurélien; Böhm, Jürgen; Brunetti, Francesco; Habermann, Nina; Hercog, Rajna; Koch, Moritz; Luciani, Alain; Mende, Daniel R; Schneider, Martin A; Schrotz-King, Petra; Tournigand, Christophe; Tran Van Nhieu, Jeanne; Yamada, Takuji; Zimmermann, Jürgen; Benes, Vladimir; Kloor, Matthias; Ulrich, Cornelia M; von Knebel Doeberitz, Magnus; Sobhani, Iradj; Bork, Peer

2014-01-01

Several bacterial species have been implicated in the development of colorectal carcinoma (CRC), but CRC-associated changes of fecal microbiota and their potential for cancer screening remain to be explored. Here, we used metagenomic sequencing of fecal samples to identify taxonomic markers that distinguished CRC patients from tumor-free controls in a study population of 156 participants. Accuracy of metagenomic CRC detection was similar to the standard fecal occult blood test (FOBT) and when both approaches were combined, sensitivity improved > 45% relative to the FOBT, while maintaining its specificity. Accuracy of metagenomic CRC detection did not differ significantly between early- and late-stage cancer and could be validated in independent patient and control populations (N = 335) from different countries. CRC-associated changes in the fecal microbiome at least partially reflected microbial community composition at the tumor itself, indicating that observed gene pool differences may reveal tumor-related host–microbe interactions. Indeed, we deduced a metabolic shift from fiber degradation in controls to utilization of host carbohydrates and amino acids in CRC patients, accompanied by an increase of lipopolysaccharide metabolism. PMID:25432777

Development of Early Warning System Using ALOS-2/PALSAR-2 Data to Detect and Prevent Deforestation

NASA Astrophysics Data System (ADS)

Hayashi, M.; Nagatani, I.; Watanabe, T.; Tadono, T.; Miyoshi, H.; Watanabe, M.; Koyama, C.; Shimada, M.; Ogawa, T.; Ishii, K.; Higashiuwatoko, T.; Miura, M.; Okonogi, H.; Adachi, K.; Morita, T.

2017-12-01

Satellite observation is an efficient method for monitoring deforestation, and a synthetic aperture radar (SAR) is useful especially in cloudy tropical forest regions. In this context, JICA and JAXA cooperate to operate the deforestation monitoring system acquired data by the Phased Array type L-band SAR-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2), which is named as "JICA-JAXA Forest Early Warning System in the Tropics" (JJ-FAST), and it have been released on November 2016. JJ-FAST detects deforestation areas, and provides their positional information for 77 countries, which is covering almost all tropical forests. It uses PALSAR-2 ScanSAR observation mode (wide-observation swath width) image, which is 50 m spatial resolution acquired at 1.5 months interval. The dark change areas compared with in two acquisitions by PALSAR-2 HV-polarization images are identified as deforestations in the system. We conducted field surveys to validate detection accuracy of the JJ-FAST in Peru (November and December, 2016), Botswana (April, 2017), and Gabon (July, 2017). As the results, 15 of 18 detected areas were correct deforestation areas, therefore user's accuracy could be confirmed as 83.3 % from limited number of the validation data. Erroneous detection areas were caused by seasonal change in agricultural land and open burning in grass land. For improvement of the accuracy, such areas must be excluded from the analysis by additional algorithms e.g. estimation of accurate masking for non-forested areas. Therefore, we are revising the forest map used for pre-processing step in the system. The JJ-FAST can be expected to contribute to monitor and reduce illegal deforestation activities in tropical forests.

Space-based Swath Imaging Laser Altimeter for Cryospheric Topographic and Surface Property Mapping

NASA Technical Reports Server (NTRS)

Abshire, James; Harding, David; Shuman, Chris; Sun, Xiaoli; Dabney, Phil; Krainak, Michael; Scambos, Ted

2005-01-01

Uncertainties in the response of the Greenland and Antarctic polar ice sheets to global climatic change inspired the development of ICESat/GLAS as part of NASA's Earth Observing System. ICESat's primary purpose is the measurement of ice sheet surface elevation profiles with sufficient accuracy, spatial density, and temporal coverage so that elevation changes can be derived with an accuracy of <1.5 cm/year for averages of measurements over the ice sheets with areas of 100 x 100 km. The primary means to achieve this elevation change detection is spatial averaging of elevation differences at cross-overs between ascending and descending profiles in areas of low ice surface slope. Additional information is included in the original extended abstract.

Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly.

PubMed

Baldewijns, Greet; Luca, Stijn; Nagels, William; Vanrumste, Bart; Croonenborghs, Tom

2015-01-01

It has been shown that gait speed and transfer times are good measures of functional ability in elderly. However, data currently acquired by systems that measure either gait speed or transfer times in the homes of elderly people require manual reviewing by healthcare workers. This reviewing process is time-consuming. To alleviate this burden, this paper proposes the use of statistical process control methods to automatically detect both positive and negative changes in transfer times. Three SPC techniques: tabular CUSUM, standardized CUSUM and EWMA, known for their ability to detect small shifts in the data, are evaluated on simulated transfer times. This analysis shows that EWMA is the best-suited method with a detection accuracy of 82% and an average detection time of 9.64 days.

Building change detection via a combination of CNNs using only RGB aerial imageries

NASA Astrophysics Data System (ADS)

Nemoto, Keisuke; Hamaguchi, Ryuhei; Sato, Masakazu; Fujita, Aito; Imaizumi, Tomoyuki; Hikosaka, Shuhei

2017-10-01

Building change information extracted from remote sensing imageries is important for various applications such as urban management and marketing planning. The goal of this work is to develop a methodology for automatically capturing building changes from remote sensing imageries. Recent studies have addressed this goal by exploiting 3-D information as a proxy for building height. In contrast, because in practice it is expensive or impossible to prepare 3-D information, we do not rely on 3-D data but focus on using only RGB aerial imageries. Instead, we employ deep convolutional neural networks (CNNs) to extract effective features, and improve change detection accuracy in RGB remote sensing imageries. We consider two aspects of building change detection, building detection and subsequent change detection. Our proposed methodology was tested on several areas, which has some differences such as dominant building characteristics and varying brightness values. On all over the tested areas, the proposed method provides good results for changed objects, with recall values over 75 % with a strict overlap requirement of over 50% in intersection-over-union (IoU). When the IoU threshold was relaxed to over 10%, resulting recall values were over 81%. We conclude that use of CNNs enables accurate detection of building changes without employing 3-D information.

Mapping Sub-Antarctic Cushion Plants Using Random Forests to Combine Very High Resolution Satellite Imagery and Terrain Modelling

PubMed Central

Bricher, Phillippa K.; Lucieer, Arko; Shaw, Justine; Terauds, Aleks; Bergstrom, Dana M.

2013-01-01

Monitoring changes in the distribution and density of plant species often requires accurate and high-resolution baseline maps of those species. Detecting such change at the landscape scale is often problematic, particularly in remote areas. We examine a new technique to improve accuracy and objectivity in mapping vegetation, combining species distribution modelling and satellite image classification on a remote sub-Antarctic island. In this study, we combine spectral data from very high resolution WorldView-2 satellite imagery and terrain variables from a high resolution digital elevation model to improve mapping accuracy, in both pixel- and object-based classifications. Random forest classification was used to explore the effectiveness of these approaches on mapping the distribution of the critically endangered cushion plant Azorella macquariensis Orchard (Apiaceae) on sub-Antarctic Macquarie Island. Both pixel- and object-based classifications of the distribution of Azorella achieved very high overall validation accuracies (91.6–96.3%, κ = 0.849–0.924). Both two-class and three-class classifications were able to accurately and consistently identify the areas where Azorella was absent, indicating that these maps provide a suitable baseline for monitoring expected change in the distribution of the cushion plants. Detecting such change is critical given the threats this species is currently facing under altering environmental conditions. The method presented here has applications to monitoring a range of species, particularly in remote and isolated environments. PMID:23940805

CLARREO Cornerstone of the Earth Observing System: Measuring Decadal Change Through Accurate Emitted Infrared and Reflected Solar Spectra and Radio Occultation

NASA Technical Reports Server (NTRS)

Sandford, Stephen P.

2010-01-01

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is one of four Tier 1 missions recommended by the recent NRC Decadal Survey report on Earth Science and Applications from Space (NRC, 2007). The CLARREO mission addresses the need to provide accurate, broadly acknowledged climate records that are used to enable validated long-term climate projections that become the foundation for informed decisions on mitigation and adaptation policies that address the effects of climate change on society. The CLARREO mission accomplishes this critical objective through rigorous SI traceable decadal change observations that are sensitive to many of the key uncertainties in climate radiative forcings, responses, and feedbacks that in turn drive uncertainty in current climate model projections. These same uncertainties also lead to uncertainty in attribution of climate change to anthropogenic forcing. For the first time CLARREO will make highly accurate, global, SI-traceable decadal change observations sensitive to the most critical, but least understood, climate forcings, responses, and feedbacks. The CLARREO breakthrough is to achieve the required levels of accuracy and traceability to SI standards for a set of observations sensitive to a wide range of key decadal change variables. The required accuracy levels are determined so that climate trend signals can be detected against a background of naturally occurring variability. Climate system natural variability therefore determines what level of accuracy is overkill, and what level is critical to obtain. In this sense, the CLARREO mission requirements are considered optimal from a science value perspective. The accuracy for decadal change traceability to SI standards includes uncertainties associated with instrument calibration, satellite orbit sampling, and analysis methods. Unlike most space missions, the CLARREO requirements are driven not by the instantaneous accuracy of the measurements, but by accuracy in the large time/space scale averages that are key to understanding decadal changes.

Evaluation of diagnostic value of AgNOR and PAP in early detection of dysplastic changes in leukoplakia and lichen planus - a preliminary case-control study.

PubMed

Rao, Dhanya S; Ali, I M; Annigeri, Rajeshwari G

2017-01-01

Early detection of oral cancer has been the most effective approach to reduce morbidity and mortality of cancer patients. If a lesion is clinically considered suspicious, an easily practicable, non-invasive, painless, safe, and accurate screening method for detection of the dysplastic changes is necessary. In an attempt to procure this, a study was conducted with the aim of determining the diagnostic accuracy of rapid Papanicolaou stain (PAP) and silver-stained nucleolar organizer regions (AgNOR) in brush biopsies of potentially malignant lesions for early detection of oral cancer. Brush biopsies taken from 25 cases of leukoplakia and lichen planus each were stained with rapid PAP and silver nitrate stains. Histopathological correlation was performed and further compared with rapid PAP and AgNOR for its diagnostic validity. Statistically significant increase in the mean AgNOR count was seen from normal epithelium to lichen planus to that of leukoplakia. When compared with rapid PAP, a linear correlation was seen in AgNOR counts and stages of dysplasia in leukoplakia which was also found to be statistically significant. Diagnostic accuracy for AgNOR in leukoplakia was found to be 84%, lichen planus 73%, whereas RAPID PAP showed 72% accuracy. AgNOR analysis may be useful as a quantitative marker of incipient cellular alterations and hence would be helpful in assessing suspicious lesions and thus can be regarded as a valuable adjunct. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Prestimulus EEG Power Predicts Conscious Awareness But Not Objective Visual Performance

PubMed Central

Veniero, Domenica

2017-01-01

Abstract Prestimulus oscillatory neural activity has been linked to perceptual outcomes during performance of psychophysical detection and discrimination tasks. Specifically, the power and phase of low frequency oscillations have been found to predict whether an upcoming weak visual target will be detected or not. However, the mechanisms by which baseline oscillatory activity influences perception remain unclear. Recent studies suggest that the frequently reported negative relationship between α power and stimulus detection may be explained by changes in detection criterion (i.e., increased target present responses regardless of whether the target was present/absent) driven by the state of neural excitability, rather than changes in visual sensitivity (i.e., more veridical percepts). Here, we recorded EEG while human participants performed a luminance discrimination task on perithreshold stimuli in combination with single-trial ratings of perceptual awareness. Our aim was to investigate whether the power and/or phase of prestimulus oscillatory activity predict discrimination accuracy and/or perceptual awareness on a trial-by-trial basis. Prestimulus power (3–28 Hz) was inversely related to perceptual awareness ratings (i.e., higher ratings in states of low prestimulus power/high excitability) but did not predict discrimination accuracy. In contrast, prestimulus oscillatory phase did not predict awareness ratings or accuracy in any frequency band. These results provide evidence that prestimulus α power influences the level of subjective awareness of threshold visual stimuli but does not influence visual sensitivity when a decision has to be made regarding stimulus features. Hence, we find a clear dissociation between the influence of ongoing neural activity on conscious awareness and objective performance. PMID:29255794

Vegetation Monitoring of Mashhad Using AN Object-Oriented POST Classification Comparison Method

NASA Astrophysics Data System (ADS)

Khalili Moghadam, N.; Delavar, M. R.; Forati, A.

2017-09-01

By and large, todays mega cities are confronting considerable urban development in which many new buildings are being constructed in fringe areas of these cities. This remarkable urban development will probably end in vegetation reduction even though each mega city requires adequate areas of vegetation, which is considered to be crucial and helpful for these cities from a wide variety of perspectives such as air pollution reduction, soil erosion prevention, and eco system as well as environmental protection. One of the optimum methods for monitoring this vital component of each city is multi-temporal satellite images acquisition and using change detection techniques. In this research, the vegetation and urban changes of Mashhad, Iran, were monitored using an object-oriented (marker-based watershed algorithm) post classification comparison (PCC) method. A Bi-temporal multi-spectral Landsat satellite image was used from the study area to detect the changes of urban and vegetation areas and to find a relation between these changes. The results of this research demonstrate that during 1987-2017, Mashhad urban area has increased about 22525 hectares and the vegetation area has decreased approximately 4903 hectares. These statistics substantiate the close relationship between urban development and vegetation reduction. Moreover, the overall accuracies of 85.5% and 91.2% were achieved for the first and the second image classification, respectively. In addition, the overall accuracy and kappa coefficient of change detection were assessed 84.1% and 70.3%, respectively.

Floor Identification with Commercial Smartphones in Wifi-Based Indoor Localization System

NASA Astrophysics Data System (ADS)

Ai, H. J.; Liu, M. Y.; Shi, Y. M.; Zhao, J. Q.

2016-06-01

In this paper, we utilize novel sensors built-in commercial smart devices to propose a schema which can identify floors with high accuracy and efficiency. This schema can be divided into two modules: floor identifying and floor change detection. Floor identifying module starts at initial phase of positioning, and responsible for determining which floor the positioning start. We have estimated two methods to identify initial floor based on K-Nearest Neighbors (KNN) and BP Neural Network, respectively. In order to improve performance of KNN algorithm, we proposed a novel method based on weighting signal strength, which can identify floors robust and quickly. Floor change detection module turns on after entering into continues positioning procedure. In this module, sensors (such as accelerometer and barometer) of smart devices are used to determine whether the user is going up and down stairs or taking an elevator. This method has fused different kinds of sensor data and can adapt various motion pattern of users. We conduct our experiment with mobile client on Android Phone (Nexus 5) at a four-floors building with an open area between the second and third floor. The results demonstrate that our scheme can achieve an accuracy of 99% to identify floor and 97% to detecting floor changes as a whole.

The characteristics and interpretability of land surface change and implications for project design

USGS Publications Warehouse

Sohl, Terry L.; Gallant, Alisa L.; Loveland, Thomas R.

2004-01-01

The need for comprehensive, accurate information on land-cover change has never been greater. While remotely sensed imagery affords the opportunity to provide information on land-cover change over large geographic expanses at a relatively low cost, the characteristics of land-surface change bring into question the suitability of many commonly used methodologies. Algorithm-based methodologies to detect change generally cannot provide the same level of accuracy as the analyses done by human interpreters. Results from the Land Cover Trends project, a cooperative venture that includes the U.S. Geological Survey, Environmental Protection Agency, and National Aeronautics and Space Administration, have shown that land-cover conversion is a relatively rare event, occurs locally in small patches, varies geographically and temporally, and is spectrally ambiguous. Based on these characteristics of change and the type of information required, manual interpretation was selected as the primary means of detecting change in the Land Cover Trends project. Mixtures of algorithm-based detection and manual interpretation may often prove to be the most feasible and appropriate design for change-detection applications. Serious examination of the expected characteristics and measurability of change must be considered during the design and implementation phase of any change analysis project.

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

NASA Technical Reports Server (NTRS)

Shiffman, Smadar; Nemani, Ramakrishna

2005-01-01

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

Land cover mapping after the tsunami event over Nanggroe Aceh Darussalam (NAD) province, Indonesia

NASA Astrophysics Data System (ADS)

Lim, H. S.; MatJafri, M. Z.; Abdullah, K.; Alias, A. N.; Mohd. Saleh, N.; Wong, C. J.; Surbakti, M. S.

2008-03-01

Remote sensing offers an important means of detecting and analyzing temporal changes occurring in our landscape. This research used remote sensing to quantify land use/land cover changes at the Nanggroe Aceh Darussalam (Nad) province, Indonesia on a regional scale. The objective of this paper is to assess the changed produced from the analysis of Landsat TM data. A Landsat TM image was used to develop land cover classification map for the 27 March 2005. Four supervised classifications techniques (Maximum Likelihood, Minimum Distance-to- Mean, Parallelepiped and Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier) were performed to the satellite image. Training sites and accuracy assessment were needed for supervised classification techniques. The training sites were established using polygons based on the colour image. High detection accuracy (>80%) and overall Kappa (>0.80) were achieved by the Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier in this study. This preliminary study has produced a promising result. This indicates that land cover mapping can be carried out using remote sensing classification method of the satellite digital imagery.

Research on High Accuracy Detection of Red Tide Hyperspecrral Based on Deep Learning Cnn

NASA Astrophysics Data System (ADS)

Hu, Y.; Ma, Y.; An, J.

2018-04-01

Increasing frequency in red tide outbreaks has been reported around the world. It is of great concern due to not only their adverse effects on human health and marine organisms, but also their impacts on the economy of the affected areas. this paper put forward a high accuracy detection method based on a fully-connected deep CNN detection model with 8-layers to monitor red tide in hyperspectral remote sensing images, then make a discussion of the glint suppression method for improving the accuracy of red tide detection. The results show that the proposed CNN hyperspectral detection model can detect red tide accurately and effectively. The red tide detection accuracy of the proposed CNN model based on original image and filter-image is 95.58 % and 97.45 %, respectively, and compared with the SVM method, the CNN detection accuracy is increased by 7.52 % and 2.25 %. Compared with SVM method base on original image, the red tide CNN detection accuracy based on filter-image increased by 8.62 % and 6.37 %. It also indicates that the image glint affects the accuracy of red tide detection seriously.

Forest fuel treatment detection using multi-temporal airborne Lidar data and high resolution aerial imagery ---- A case study at Sierra Nevada, California

NASA Astrophysics Data System (ADS)

Su, Y.; Guo, Q.; Collins, B.; Fry, D.; Kelly, M.

2014-12-01

Forest fuel treatments (FFT) are often employed in Sierra Nevada forest (located in California, US) to enhance forest health, regulate stand density, and reduce wildfire risk. However, there have been concerns that FFTs may have negative impacts on certain protected wildlife species. Due to the constraints and protection of resources (e.g., perennial streams, cultural resources, wildlife habitat, etc.), the actual FFT extents are usually different from planned extents. Identifying the actual extent of treated areas is of primary importance to understand the environmental influence of FFTs. Light detection and ranging (Lidar) is a powerful remote sensing technique that can provide accurate forest structure measurements, which provides great potential to monitor forest changes. This study used canopy height model (CHM) and canopy cover (CC) products derived from multi-temporal airborne Lidar data to detect FFTs by an approach combining a pixel-wise thresholding method and a object-of-interest segmentation method. We also investigated forest change following the implementation of landscape-scale FFT projects through the use of normalized difference vegetation index (NDVI) and standardized principle component analysis (PCA) from multi-temporal high resolution aerial imagery. The same FFT detection routine was applied on the Lidar data and aerial imagery for the purpose of comparing the capability of Lidar data and aerial imagery on FFT detection. Our results demonstrated that the FFT detection using Lidar derived CC products produced both the highest total accuracy and kappa coefficient, and was more robust at identifying areas with light FFTs. The accuracy using Lidar derived CHM products was significantly lower than that of the result using Lidar derived CC, but was still slightly higher than using aerial imagery. FFT detection results using NDVI and standardized PCA using multi-temporal aerial imagery produced almost identical total accuracy and kappa coefficient. Both methods showed relatively limited capacity to detect light FFT areas, and had higher false detection rate (recognized untreated areas as treated areas) compared to the methods using Lidar derived parameters.

Is countershading camouflage robust to lighting change due to weather?

PubMed Central

2018-01-01

Countershading is a pattern of coloration thought to have evolved in order to implement camouflage. By adopting a pattern of coloration that makes the surface facing towards the sun darker and the surface facing away from the sun lighter, the overall amount of light reflected off an animal can be made more uniformly bright. Countershading could hence contribute to visual camouflage by increasing background matching or reducing cues to shape. However, the usefulness of countershading is constrained by a particular pattern delivering ‘optimal’ camouflage only for very specific lighting conditions. In this study, we test the robustness of countershading camouflage to lighting change due to weather, using human participants as a ‘generic’ predator. In a simulated three-dimensional environment, we constructed an array of simple leaf-shaped items and a single ellipsoidal target ‘prey’. We set these items in two light environments: strongly directional ‘sunny’ and more diffuse ‘cloudy’. The target object was given the optimal pattern of countershading for one of these two environment types or displayed a uniform pattern. By measuring detection time and accuracy, we explored whether and how target detection depended on the match between the pattern of coloration on the target object and scene lighting. Detection times were longest when the countershading was appropriate to the illumination; incorrectly camouflaged targets were detected with a similar pattern of speed and accuracy to uniformly coloured targets. We conclude that structural changes in light environment, such as caused by differences in weather, do change the effectiveness of countershading camouflage. PMID:29515822

Influence of Computer-Aided Detection on Performance of Screening Mammography

PubMed Central

Fenton, Joshua J.; Taplin, Stephen H.; Carney, Patricia A.; Abraham, Linn; Sickles, Edward A.; D'Orsi, Carl; Berns, Eric A.; Cutter, Gary; Hendrick, R. Edward; Barlow, William E.; Elmore, Joann G.

2011-01-01

Background Computer-aided detection identifies suspicious findings on mammograms to assist radiologists. Since the Food and Drug Administration approved the technology in 1998, it has been disseminated into practice, but its effect on the accuracy of interpretation is unclear. Methods We determined the association between the use of computer-aided detection at mammography facilities and the performance of screening mammography from 1998 through 2002 at 43 facilities in three states. We had complete data for 222,135 women (a total of 429,345 mammograms), including 2351 women who received a diagnosis of breast cancer within 1 year after screening. We calculated the specificity, sensitivity, and positive predictive value of screening mammography with and without computer-aided detection, as well as the rates of biopsy and breast-cancer detection and the overall accuracy, measured as the area under the receiver-operating-characteristic (ROC) curve. Results Seven facilities (16%) implemented computer-aided detection during the study period. Diagnostic specificity decreased from 90.2% before implementation to 87.2% after implementation (P<0.001), the positive predictive value decreased from 4.1% to 3.2% (P = 0.01), and the rate of biopsy increased by 19.7% (P<0.001). The increase in sensitivity from 80.4% before implementation of computer-aided detection to 84.0% after implementation was not significant (P = 0.32). The change in the cancer-detection rate (including invasive breast cancers and ductal carcinomas in situ) was not significant (4.15 cases per 1000 screening mammograms before implementation and 4.20 cases after implementation, P = 0.90). Analyses of data from all 43 facilities showed that the use of computer-aided detection was associated with significantly lower overall accuracy than was nonuse (area under the ROC curve, 0.871 vs. 0.919; P = 0.005). Conclusions The use of computer-aided detection is associated with reduced accuracy of interpretation of screening mammograms. The increased rate of biopsy with the use of computer-aided detection is not clearly associated with improved detection of invasive breast cancer. PMID:17409321

a Landsat Time-Series Stacks Model for Detection of Cropland Change

NASA Astrophysics Data System (ADS)

Chen, J.; Chen, J.; Zhang, J.

2017-09-01

Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the "true change" without overestimating the "false" one, while CVA pointed out "true change" pixels with a large number of "false changes". The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

Robust vehicle detection under various environments to realize road traffic flow surveillance using an infrared thermal camera.

PubMed

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2015-01-01

To realize road traffic flow surveillance under various environments which contain poor visibility conditions, we have already proposed two vehicle detection methods using thermal images taken with an infrared thermal camera. The first method uses pattern recognition for the windshields and their surroundings to detect vehicles. However, the first method decreases the vehicle detection accuracy in winter season. To maintain high vehicle detection accuracy in all seasons, we developed the second method. The second method uses tires' thermal energy reflection areas on a road as the detection targets. The second method did not achieve high detection accuracy for vehicles on left-hand and right-hand lanes except for two center-lanes. Therefore, we have developed a new method based on the second method to increase the vehicle detection accuracy. This paper proposes the new method and shows that the detection accuracy for vehicles on all lanes is 92.1%. Therefore, by combining the first method and the new method, high vehicle detection accuracies are maintained under various environments, and road traffic flow surveillance can be realized.

Robust Vehicle Detection under Various Environments to Realize Road Traffic Flow Surveillance Using an Infrared Thermal Camera

PubMed Central

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2015-01-01

To realize road traffic flow surveillance under various environments which contain poor visibility conditions, we have already proposed two vehicle detection methods using thermal images taken with an infrared thermal camera. The first method uses pattern recognition for the windshields and their surroundings to detect vehicles. However, the first method decreases the vehicle detection accuracy in winter season. To maintain high vehicle detection accuracy in all seasons, we developed the second method. The second method uses tires' thermal energy reflection areas on a road as the detection targets. The second method did not achieve high detection accuracy for vehicles on left-hand and right-hand lanes except for two center-lanes. Therefore, we have developed a new method based on the second method to increase the vehicle detection accuracy. This paper proposes the new method and shows that the detection accuracy for vehicles on all lanes is 92.1%. Therefore, by combining the first method and the new method, high vehicle detection accuracies are maintained under various environments, and road traffic flow surveillance can be realized. PMID:25763384

Unique volatolomic signatures of TP53 and KRAS in lung cells

PubMed Central

Davies, M P A; Barash, O; Jeries, R; Peled, N; Ilouze, M; Hyde, R; Marcus, M W; Field, J K; Haick, H

2014-01-01

Background: Volatile organic compounds (VOCs) are potential biomarkers for cancer detection in breath, but it is unclear if they reflect specific mutations. To test this, we have compared human bronchial epithelial cell (HBEC) cell lines carrying the KRASV12 mutation, knockdown of TP53 or both with parental HBEC cells. Methods: VOC from headspace above cultured cells were collected by passive sampling and analysed by thermal desorption gas chromatography mass spectrometry (TD-GC–MS) or sensor array with discriminant factor analysis (DFA). Results: In TD-GC–MS analysis, individual compounds had limited ability to discriminate between cell lines, but by applying DFA analysis combinations of 20 VOCs successfully discriminated between all cell types (accuracies 80–100%, with leave-one-out cross validation). Sensor array detection DFA demonstrated the ability to discriminate samples based on their cell type for all comparisons with accuracies varying between 77% and 93%. Conclusions: Our results demonstrate that minimal genetic changes in bronchial airway cells lead to detectable differences in levels of specific VOCs identified by TD-GC–MS or of patterns of VOCs identified by sensor array output. From the clinical aspect, these results suggest the possibility of breath analysis for detection of minimal genetic changes for earlier diagnosis or for genetic typing of lung cancers. PMID:25051409

Applying signal-detection theory to the study of observer accuracy and bias in behavioral assessment.

PubMed

Lerman, Dorothea C; Tetreault, Allison; Hovanetz, Alyson; Bellaci, Emily; Miller, Jonathan; Karp, Hilary; Mahmood, Angela; Strobel, Maggie; Mullen, Shelley; Keyl, Alice; Toupard, Alexis

2010-01-01

We evaluated the feasibility and utility of a laboratory model for examining observer accuracy within the framework of signal-detection theory (SDT). Sixty-one individuals collected data on aggression while viewing videotaped segments of simulated teacher-child interactions. The purpose of Experiment 1 was to determine if brief feedback and contingencies for scoring accurately would bias responding reliably. Experiment 2 focused on one variable (specificity of the operational definition) that we hypothesized might decrease the likelihood of bias. The effects of social consequences and information about expected behavior change were examined in Experiment 3. Results indicated that feedback and contingencies reliably biased responding and that the clarity of the definition only moderately affected this outcome.

Simultaneous Detection of Displacement, Rotation Angle, and Contact Pressure Using Sandpaper Molded Elastomer Based Triple Electrode Sensor

PubMed Central

Sul, Onejae; Lee, Seung-Beck

2017-01-01

In this article, we report on a flexible sensor based on a sandpaper molded elastomer that simultaneously detects planar displacement, rotation angle, and vertical contact pressure. When displacement, rotation, and contact pressure are applied, the contact area between the translating top elastomer electrode and the stationary three bottom electrodes change characteristically depending on the movement, making it possible to distinguish between them. The sandpaper molded undulating surface of the elastomer reduces friction at the contact allowing the sensor not to affect the movement during measurement. The sensor showed a 0.25 mm−1 displacement sensitivity with a ±33 μm accuracy, a 0.027 degree−1 of rotation sensitivity with ~0.95 degree accuracy, and a 4.96 kP−1 of pressure sensitivity. For possible application to joint movement detection, we demonstrated that our sensor effectively detected the up-and-down motion of a human forefinger and the bending and straightening motion of a human arm. PMID:28878166

Simultaneous Detection of Displacement, Rotation Angle, and Contact Pressure Using Sandpaper Molded Elastomer Based Triple Electrode Sensor.

PubMed

Choi, Eunsuk; Sul, Onejae; Lee, Seung-Beck

2017-09-06

In this article, we report on a flexible sensor based on a sandpaper molded elastomer that simultaneously detects planar displacement, rotation angle, and vertical contact pressure. When displacement, rotation, and contact pressure are applied, the contact area between the translating top elastomer electrode and the stationary three bottom electrodes change characteristically depending on the movement, making it possible to distinguish between them. The sandpaper molded undulating surface of the elastomer reduces friction at the contact allowing the sensor not to affect the movement during measurement. The sensor showed a 0.25 mm −1 displacement sensitivity with a ±33 μm accuracy, a 0.027 degree −1 of rotation sensitivity with ~0.95 degree accuracy, and a 4.96 kP −1 of pressure sensitivity. For possible application to joint movement detection, we demonstrated that our sensor effectively detected the up-and-down motion of a human forefinger and the bending and straightening motion of a human arm.

A travel time forecasting model based on change-point detection method

NASA Astrophysics Data System (ADS)

LI, Shupeng; GUANG, Xiaoping; QIAN, Yongsheng; ZENG, Junwei

2017-06-01

Travel time parameters obtained from road traffic sensors data play an important role in traffic management practice. A travel time forecasting model is proposed for urban road traffic sensors data based on the method of change-point detection in this paper. The first-order differential operation is used for preprocessing over the actual loop data; a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns; then a travel time forecasting model is established based on autoregressive integrated moving average (ARIMA) model. By computer simulation, different control parameters are chosen for adaptive change point search for travel time series, which is divided into several sections of similar state.Then linear weight function is used to fit travel time sequence and to forecast travel time. The results show that the model has high accuracy in travel time forecasting.

Measurement methods and accuracy analysis of Chang'E-5 Panoramic Camera installation parameters

NASA Astrophysics Data System (ADS)

Yan, Wei; Ren, Xin; Liu, Jianjun; Tan, Xu; Wang, Wenrui; Chen, Wangli; Zhang, Xiaoxia; Li, Chunlai

2016-04-01

Chang'E-5 (CE-5) is a lunar probe for the third phase of China Lunar Exploration Project (CLEP), whose main scientific objectives are to implement lunar surface sampling and to return the samples back to the Earth. To achieve these goals, investigation of lunar surface topography and geological structure within sampling area seems to be extremely important. The Panoramic Camera (PCAM) is one of the payloads mounted on CE-5 lander. It consists of two optical systems which installed on a camera rotating platform. Optical images of sampling area can be obtained by PCAM in the form of a two-dimensional image and a stereo images pair can be formed by left and right PCAM images. Then lunar terrain can be reconstructed based on photogrammetry. Installation parameters of PCAM with respect to CE-5 lander are critical for the calculation of exterior orientation elements (EO) of PCAM images, which is used for lunar terrain reconstruction. In this paper, types of PCAM installation parameters and coordinate systems involved are defined. Measurement methods combining camera images and optical coordinate observations are studied for this work. Then research contents such as observation program and specific solution methods of installation parameters are introduced. Parametric solution accuracy is analyzed according to observations obtained by PCAM scientifically validated experiment, which is used to test the authenticity of PCAM detection process, ground data processing methods, product quality and so on. Analysis results show that the accuracy of the installation parameters affects the positional accuracy of corresponding image points of PCAM stereo images within 1 pixel. So the measurement methods and parameter accuracy studied in this paper meet the needs of engineering and scientific applications. Keywords: Chang'E-5 Mission; Panoramic Camera; Installation Parameters; Total Station; Coordinate Conversion

Radiologic methods of evaluating generalized osteopenia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schneider, R.

1984-10-01

Noninvasive methods of evaluating generalized osteopenia include radiography, radionuclide studies, and various quantitative studies. These methods differ in availability, cost, accuracy, precision, radiation dose, and information supplied about bony change. A combination of methods is necessary to detect and follow the course and treatment of osteopenia.

Cumulative detection probabilities and range accuracy of a pulsed Geiger-mode avalanche photodiode laser ranging system

NASA Astrophysics Data System (ADS)

Luo, Hanjun; Ouyang, Zhengbiao; Liu, Qiang; Chen, Zhiliang; Lu, Hualan

2017-10-01

Cumulative pulses detection with appropriate cumulative pulses number and threshold has the ability to improve the detection performance of the pulsed laser ranging system with GM-APD. In this paper, based on Poisson statistics and multi-pulses cumulative process, the cumulative detection probabilities and their influence factors are investigated. With the normalized probability distribution of each time bin, the theoretical model of the range accuracy and precision is established, and the factors limiting the range accuracy and precision are discussed. The results show that the cumulative pulses detection can produce higher target detection probability and lower false alarm probability. However, for a heavy noise level and extremely weak echo intensity, the false alarm suppression performance of the cumulative pulses detection deteriorates quickly. The range accuracy and precision is another important parameter evaluating the detection performance, the echo intensity and pulse width are main influence factors on the range accuracy and precision, and higher range accuracy and precision is acquired with stronger echo intensity and narrower echo pulse width, for 5-ns echo pulse width, when the echo intensity is larger than 10, the range accuracy and precision lower than 7.5 cm can be achieved.

Real-time monitoring for detection of retained surgical sponges and team motion in the surgical operation room using radio-frequency-identification (RFID) technology: a preclinical evaluation.

PubMed

Kranzfelder, Michael; Zywitza, Dorit; Jell, Thomas; Schneider, Armin; Gillen, Sonja; Friess, Helmut; Feussner, Hubertus

2012-06-15

Technical progress in the surgical operating room (OR) increases constantly, facilitating the development of intelligent OR systems functioning as "safety backup" in the background of surgery. Precondition is comprehensive data retrieval to identify imminent risky situations and inaugurate adequate security mechanisms. Radio-frequency-identification (RFID) technology may have the potential to meet these demands. We set up a pilot study investigating feasibility and appliance reliability of a stationary RFID system for real-time surgical sponge monitoring (passive tagged sponges, position monitoring: mayo-stand/abdominal situs/waste bucket) and OR team tracking (active transponders, position monitoring: right/left side of OR table). In vitro: 20/20 sponges (100%) were detected on the mayo-stand and within the OR-phantom, however, real-time detection accuracy declined to 7/20 (33%) when the tags were moved simultaneously. All retained sponges were detected correctly. In vivo (animal): 7-10/10 sterilized sponges (70%-100%) were detected correctly within the abdominal cavity. OR-team: detection accuracy within the OR (surveillance antenna) and on both sides of the OR table (sector antenna) was 100%. Mean detection time for position change (left to right side and contrariwise) was 30-60 s. No transponder failure was noted. This is the first combined RFID system that has been developed for stationary use in the surgical OR. Preclinical evaluation revealed a reliable sponge tracking and correct detection of retained textiles (passive RFID) but also demonstrated feasibility of comprehensive data acquisition of team motion (active RFID). However, detection accuracy needs to be further improved before implementation into the surgical OR. Copyright © 2012 Elsevier Inc. All rights reserved.

A Practical and Automated Approach to Large Area Forest Disturbance Mapping with Remote Sensing

PubMed Central

Ozdogan, Mutlu

2014-01-01

In this paper, I describe a set of procedures that automate forest disturbance mapping using a pair of Landsat images. The approach is built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data. The steps in this procedure include: i) creating masks for water, non-forested areas, clouds, and cloud shadows; ii) identifying training pixels whose value is above or below a threshold defined by the number of standard deviations from the mean value of the histograms generated from local windows in the short-wave infrared (SWIR) difference image; iii) filtering the original training data through a number of classification algorithms using an n-fold cross validation to eliminate mislabeled training samples; and finally, iv) mapping forest disturbance using a supervised classification algorithm. When applied to 17 Landsat footprints across the U.S. at five-year intervals between 1985 and 2010, the proposed approach produced forest disturbance maps with 80 to 95% overall accuracy, comparable to those obtained from traditional approaches to forest change detection. The primary sources of mis-classification errors included inaccurate identification of forests (errors of commission), issues related to the land/water mask, and clouds and cloud shadows missed during image screening. The approach requires images from the peak growing season, at least for the deciduous forest sites, and cannot readily distinguish forest harvest from natural disturbances or other types of land cover change. The accuracy of detecting forest disturbance diminishes with the number of years between the images that make up the image pair. Nevertheless, the relatively high accuracies, little or no user input needed for processing, speed of map production, and simplicity of the approach make the new method especially practical for forest cover change analysis over very large regions. PMID:24717283

Weight status and the perception of body image in men

PubMed Central

Gardner, Rick M

2014-01-01

Understanding the role of body size in relation to the accuracy of body image perception in men is an important topic because of the implications for avoiding and treating obesity, and it may serve as a potential diagnostic criterion for eating disorders. The early research on this topic produced mixed findings. About one-half of the early studies showed that obese men overestimated their body size, with the remaining half providing accurate estimates. Later, improvements in research technology and methodology provided a clearer indication of the role of weight status in body image perception. Research in our laboratory has also produced diverse findings, including that obese subjects sometimes overestimate their body size. However, when examining our findings across several studies, obese subjects had about the same level of accuracy in estimating their body size as normal-weight subjects. Studies in our laboratory also permitted the separation of sensory and nonsensory factors in body image perception. In all but one instance, no differences were found overall between the ability of obese and normal-weight subjects to detect overall changes in body size. Importantly, however, obese subjects are better at detecting changes in their body size when the image is distorted to be too thin as compared to too wide. Both obese and normal-weight men require about a 3%–7% change in the width of their body size in order to detect the change reliably. Correlations between a range of body mass index values and body size estimation accuracy indicated no relationship between these variables. Numerous studies in other laboratories asked men to place their body size into discrete categorizes, ranging from thin to obese. Researchers found that overweight and obese men underestimate their weight status, and that men are less accurate in their categorizations than are women. Cultural influences have been found to be important, with body size underestimations occurring in cultures where a larger body is found to be desirable. Methodological issues are reviewed with recommendations for future studies. PMID:25114606

A practical and automated approach to large area forest disturbance mapping with remote sensing.

PubMed

Ozdogan, Mutlu

2014-01-01

In this paper, I describe a set of procedures that automate forest disturbance mapping using a pair of Landsat images. The approach is built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data. The steps in this procedure include: i) creating masks for water, non-forested areas, clouds, and cloud shadows; ii) identifying training pixels whose value is above or below a threshold defined by the number of standard deviations from the mean value of the histograms generated from local windows in the short-wave infrared (SWIR) difference image; iii) filtering the original training data through a number of classification algorithms using an n-fold cross validation to eliminate mislabeled training samples; and finally, iv) mapping forest disturbance using a supervised classification algorithm. When applied to 17 Landsat footprints across the U.S. at five-year intervals between 1985 and 2010, the proposed approach produced forest disturbance maps with 80 to 95% overall accuracy, comparable to those obtained from traditional approaches to forest change detection. The primary sources of mis-classification errors included inaccurate identification of forests (errors of commission), issues related to the land/water mask, and clouds and cloud shadows missed during image screening. The approach requires images from the peak growing season, at least for the deciduous forest sites, and cannot readily distinguish forest harvest from natural disturbances or other types of land cover change. The accuracy of detecting forest disturbance diminishes with the number of years between the images that make up the image pair. Nevertheless, the relatively high accuracies, little or no user input needed for processing, speed of map production, and simplicity of the approach make the new method especially practical for forest cover change analysis over very large regions.

In vivo detection of hemoglobin oxygen saturation and carboxyhemoglobin saturation with multiwavelength photoacoustic microscopy.

PubMed

Chen, Zhongjiang; Yang, Sihua; Xing, Da

2012-08-15

A method for noninvasively detecting hemoglobin oxygen saturation (SO2) and carboxyhemoglobin saturation (SCO) in subcutaneous microvasculature with multiwavelength photoacoustic microscopy is presented. Blood samples mixed with different concentrations of carboxyhemoglobin were used to test the feasibility and accuracy of photoacoustic microscopy compared with the blood-gas analyzer. Moreover, fixed-point detection of SO2 and SCO in mouse ear was obtained, and the changes from normoxia to carbon monoxide hypoxia were dynamically monitored in vivo. Experimental results demonstrate that multiwavelength photoacoustic microscopy can detect SO2 and SCO, which has future potential clinical applications.

Camera system considerations for geomorphic applications of SfM photogrammetry

USGS Publications Warehouse

Mosbrucker, Adam; Major, Jon J.; Spicer, Kurt R.; Pitlick, John

2017-01-01

The availability of high-resolution, multi-temporal, remotely sensed topographic data is revolutionizing geomorphic analysis. Three-dimensional topographic point measurements acquired from structure-from-motion (SfM) photogrammetry have been shown to be highly accurate and cost-effective compared to laser-based alternatives in some environments. Use of consumer-grade digital cameras to generate terrain models and derivatives is becoming prevalent within the geomorphic community despite the details of these instruments being largely overlooked in current SfM literature. This article is protected by copyright. All rights reserved.A practical discussion of camera system selection, configuration, and image acquisition is presented. The hypothesis that optimizing source imagery can increase digital terrain model (DTM) accuracy is tested by evaluating accuracies of four SfM datasets conducted over multiple years of a gravel bed river floodplain using independent ground check points with the purpose of comparing morphological sediment budgets computed from SfM- and lidar-derived DTMs. Case study results are compared to existing SfM validation studies in an attempt to deconstruct the principle components of an SfM error budget. This article is protected by copyright. All rights reserved.Greater information capacity of source imagery was found to increase pixel matching quality, which produced 8 times greater point density and 6 times greater accuracy. When propagated through volumetric change analysis, individual DTM accuracy (6–37 cm) was sufficient to detect moderate geomorphic change (order 100,000 m3) on an unvegetated fluvial surface; change detection determined from repeat lidar and SfM surveys differed by about 10%. Simple camera selection criteria increased accuracy by 64%; configuration settings or image post-processing techniques increased point density by 5–25% and decreased processing time by 10–30%. This article is protected by copyright. All rights reserved.Regression analysis of 67 reviewed datasets revealed that the best explanatory variable to predict accuracy of SfM data is photographic scale. Despite the prevalent use of object distance ratios to describe scale, nominal ground sample distance is shown to be a superior metric, explaining 68% of the variability in mean absolute vertical error.

Can (18)F-FDG PET/CT scan change treatment planning and be prognostic in recurrent colorectal carcinoma? A prospective and follow-up study.

PubMed

Artiko, Vera; Odalovic, Strahinja; Sobic-Saranovic, Dragana; Petrovic, Milorad; Stojiljkovic, Milica; Petrovic, Nebojsa; Kozarevic, Nebojsa; Grozdic-Milojevic, Isidora; Obradovic, Vladimir

2015-01-01

To prospectively study whether in patients with resected primary colorectal cancer fluorine- 18-fluorodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) examination could diagnose the stage, specify treatment procedure and be prognostic. This prospective study included 75 patients with resected primary colorectal adenocarcinoma referred for (18)F-FDG PET/CT to the National PET Center, at the Clinical Center of Serbia, Belgrade, from January 2010 to May 2013. Findings of (18)F-FDG PET/CT were compared to findings of subsequent histopathological examinations or with results of clinical and imaging follow-up. Patients were followed after PET/CT examination for a mean follow-up time of 16.7±5.9 months. In the detection of recurrent disease (18)F-FDG PET/CT showed overall sensitivity, specificity, PPV, NPV and accuracy of 96.6%, 82.4%, 94.9%, 87.5% and 93.3%, respectively. In the detection of stages I and II sensitivity, specificity and accuracy of (18)F-FDG PET/CT were: 88%, 96.6% and 94.7%, respectively, and in the detection of stages III and IV sensitivity, specificity and accuracy were 94.9%, 87.5% and 93.3%, respectively. These findings prevented or changed intended surgical treatment in 12/32 cases. Univariate and multivariate Cox proportional regression analyses revealed that metastatic recurrence (stages III and IV) was the only and independent prognostic factor of disease progression during follow-up (P=0.012 and P=0.023, respectively). Although, survival seemed better in patients with local recurrence compared to metastatic recurrent disease, this difference did not reach significance (Log-rank test; P=0.324). In addition, progression-free survival time was significantly longer in patients in whom (18)F-FDG PET/CT scan led to treatment changes (Log-rank test; P=0.037). (18)F-FDG PET/CT was sensitive and accurate for the detection and staging of local and metastatic recurrent colorectal carcinoma, with higher specificity in the detection of local recurrences. The (18)F-FDG PET/CT scan induced treatment changes in 30/75 patients, including 12/32 patients in which surgical treatment was previously planned, and progression free survival time was significantly longer in these patients.

Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models.

PubMed

AlDahoul, Nouar; Md Sabri, Aznul Qalid; Mansoor, Ali Mohammed

2018-01-01

Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine) for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running). Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM). H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU), H-ELM's training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU).

Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones †

PubMed Central

Guo, Hansong; Huang, He; Huang, Liusheng; Sun, Yu-E

2016-01-01

As the size of smartphone touchscreens has become larger and larger in recent years, operability with a single hand is getting worse, especially for female users. We envision that user experience can be significantly improved if smartphones are able to recognize the current operating hand, detect the hand-changing process and then adjust the user interfaces subsequently. In this paper, we proposed, implemented and evaluated two novel systems. The first one leverages the user-generated touchscreen traces to recognize the current operating hand, and the second one utilizes the accelerometer and gyroscope data of all kinds of activities in the user’s daily life to detect the hand-changing process. These two systems are based on two supervised classifiers constructed from a series of refined touchscreen trace, accelerometer and gyroscope features. As opposed to existing solutions that all require users to select the current operating hand or confirm the hand-changing process manually, our systems follow much more convenient and practical methods and allow users to change the operating hand frequently without any harm to the user experience. We conduct extensive experiments on Samsung Galaxy S4 smartphones, and the evaluation results demonstrate that our proposed systems can recognize the current operating hand and detect the hand-changing process with 94.1% and 93.9% precision and 94.1% and 93.7% True Positive Rates (TPR) respectively, when deciding with a single touchscreen trace or accelerometer-gyroscope data segment, and the False Positive Rates (FPR) are as low as 2.6% and 0.7% accordingly. These two systems can either work completely independently and achieve pretty high accuracies or work jointly to further improve the recognition accuracy. PMID:27556461

Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones.

PubMed

Guo, Hansong; Huang, He; Huang, Liusheng; Sun, Yu-E

2016-08-20

As the size of smartphone touchscreens has become larger and larger in recent years, operability with a single hand is getting worse, especially for female users. We envision that user experience can be significantly improved if smartphones are able to recognize the current operating hand, detect the hand-changing process and then adjust the user interfaces subsequently. In this paper, we proposed, implemented and evaluated two novel systems. The first one leverages the user-generated touchscreen traces to recognize the current operating hand, and the second one utilizes the accelerometer and gyroscope data of all kinds of activities in the user's daily life to detect the hand-changing process. These two systems are based on two supervised classifiers constructed from a series of refined touchscreen trace, accelerometer and gyroscope features. As opposed to existing solutions that all require users to select the current operating hand or confirm the hand-changing process manually, our systems follow much more convenient and practical methods and allow users to change the operating hand frequently without any harm to the user experience. We conduct extensive experiments on Samsung Galaxy S4 smartphones, and the evaluation results demonstrate that our proposed systems can recognize the current operating hand and detect the hand-changing process with 94.1% and 93.9% precision and 94.1% and 93.7% True Positive Rates (TPR) respectively, when deciding with a single touchscreen trace or accelerometer-gyroscope data segment, and the False Positive Rates (FPR) are as low as 2.6% and 0.7% accordingly. These two systems can either work completely independently and achieve pretty high accuracies or work jointly to further improve the recognition accuracy.

Chondromalacia patellae: fat-suppressed MR imaging.

PubMed

Rose, P M; Demlow, T A; Szumowski, J; Quinn, S F

1994-11-01

To evaluate the accuracy of fat-suppressed magnetic resonance (MR) imaging in diagnosing chondromalacia patellae. Seventy-one patients underwent fat-suppressed MR imaging and arthroscopy of the patellofemoral compartment. Findings were classified as early or advanced chondromalacia or as normal and were correlated with arthroscopic findings. Early and advanced stages of chondromalacia patellae were reliably detected, with positive predictive values of 85% and 92%, respectively. Specificity in early stages was 94% and in late stages was 98%. However, the overall accuracies did not differ substantially from those reported in studies that did not use fat-suppressed imaging. Axial, fat-suppressed MR imaging accurately depicts changes caused by chondromalacia patellae. Early stages can be seen as intrasubstance changes of increased signal intensity. Results of this study suggest a high degree of specificity in excluding both early and advanced changes.

High-performance combination method of electric network frequency and phase for audio forgery detection in battery-powered devices.

PubMed

Savari, Maryam; Abdul Wahab, Ainuddin Wahid; Anuar, Nor Badrul

2016-09-01

Audio forgery is any act of tampering, illegal copy and fake quality in the audio in a criminal way. In the last decade, there has been increasing attention to the audio forgery detection due to a significant increase in the number of forge in different type of audio. There are a number of methods for forgery detection, which electric network frequency (ENF) is one of the powerful methods in this area for forgery detection in terms of accuracy. In spite of suitable accuracy of ENF in a majority of plug-in powered devices, the weak accuracy of ENF in audio forgery detection for battery-powered devices, especially in laptop and mobile phone, can be consider as one of the main obstacles of the ENF. To solve the ENF problem in terms of accuracy in battery-powered devices, a combination method of ENF and phase feature is proposed. From experiment conducted, ENF alone give 50% and 60% accuracy for forgery detection in mobile phone and laptop respectively, while the proposed method shows 88% and 92% accuracy respectively, for forgery detection in battery-powered devices. The results lead to higher accuracy for forgery detection with the combination of ENF and phase feature. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Diagnosis of hydronephrosis: comparison of radionuclide scanning and sonography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Malave, S.R.; Neiman, H.L.; Spies, S.M.

1980-12-01

Diagnostic sonographic and radioisotope scanning techniques have been shown to be useful in the diagnosis of obstructive uropathy. The accuracy of both methods was compared and sonography was found to provide the more accurate data (sensitivity, 90%, specificity, 98%; accuracy, 97%). Sonography provides excellent anatomic information and enables one to grade the degree of dilatation. Renal radionuclide studies were less sensitive in detecting obstruction, particularly in the presence of chronic renal disease, but offered additional information regarding relative renal blood flow, total effective renal plasma flow, and interval change in renal parenchymal function.

Paper-based Platform for Urinary Creatinine Detection.

PubMed

Sittiwong, Jarinya; Unob, Fuangfa

2016-01-01

A new paper platform was developed for the colorimetric detection of creatinine. The filter paper was coated with 3-propylsulfonic acid trimethoxysilane and used as the platform. Creatinine in a cationic form was extracted onto the paper via an ion-exchange mechanism and detected through the Jaffé reaction, resulting in a yellow-orange color complex. The color change on the paper could be observed visually, and the quantitative detection of creatinine was achieved through monitoring the color intensity change. The color intensity of creatinine complexes on the paper platform as a function of the creatinine concentration provided a linear range for creatinine detection in the range of 10 - 60 mg L(-1) and a detection limit of 4.2 mg L(-1). The accuracy of the proposed paper-based method was comparable to the conventional standard Jaffé method. This paper platform could be applied for simple and rapid detection of creatinine in human urine samples with a low consumption of reagent.

Satellite Instrument Calibration for Measuring Global Climate Change. Report of a Workshop at the University of Maryland Inn and Conference Center, College Park, MD. , November 12-14, 2002

NASA Technical Reports Server (NTRS)

Ohring, G.; Wielicki, B.; Spencer, R.; Emery, B.; Datla, R.

2004-01-01

Measuring the small changes associated with long-term global climate change from space is a daunting task. To address these problems and recommend directions for improvements in satellite instrument calibration some 75 scientists, including researchers who develop and analyze long-term data sets from satellites, experts in the field of satellite instrument calibration, and physicists working on state of the art calibration sources and standards met November 12 - 14, 2002 and discussed the issues. The workshop defined the absolute accuracies and long-term stabilities of global climate data sets that are needed to detect expected trends, translated these data set accuracies and stabilities to required satellite instrument accuracies and stabilities, and evaluated the ability of current observing systems to meet these requirements. The workshop's recommendations include a set of basic axioms or overarching principles that must guide high quality climate observations in general, and a roadmap for improving satellite instrument characterization, calibration, inter-calibration, and associated activities to meet the challenge of measuring global climate change. It is also recommended that a follow-up workshop be conducted to discuss implementation of the roadmap developed at this workshop.

Quantifying Surface Water Dynamics at 30 Meter Spatial Resolution in the North American High Northern Latitudes 1991-2011

NASA Technical Reports Server (NTRS)

Carroll, Mark; Wooten, Margaret; DiMiceli, Charlene; Sohlberg, Robert; Kelly, Maureen

2016-01-01

The availability of a dense time series of satellite observations at moderate (30 m) spatial resolution is enabling unprecedented opportunities for understanding ecosystems around the world. A time series of data from Landsat was used to generate a series of three maps at decadal time step to show how surface water has changed from 1991 to 2011 in the high northern latitudes of North America. Previous attempts to characterize the change in surface water in this region have been limited in either spatial or temporal resolution, or both. This series of maps was generated for the NASA Arctic and Boreal Vulnerability Experiment (ABoVE), which began in fall 2015. These maps show a nominal extent of surface water by using multiple observations to make a single map for each time step. This increases the confidence that any detected changes are related to climate or ecosystem changes not simply caused by short duration weather events such as flood or drought. The methods and comparison to other contemporary maps of the region are presented here. Initial verification results indicate 96% producer accuracy and 54% user accuracy when compared to 2-m resolution World View-2 data. All water bodies that were omitted were one Landsat pixel or smaller, hence below detection limits of the instrument.

Three-dimensional ultrasound-based texture analysis of the effect of atorvastatin on carotid atherosclerosis

NASA Astrophysics Data System (ADS)

Awad, Joseph; Krasinski, Adam; Spence, David; Parraga, Grace; Fenster, Aaron

2010-03-01

Carotid atherosclerosis is the major cause of ischemic stroke, a leading cause of death and disability. This is driving the development of image analysis methods to quantitatively evaluate local arterial effects of potential treatments of carotid disease. Here we investigate the use of novel texture analysis tools to detect potential changes in the carotid arteries after statin therapy. Three-dimensional (3D) carotid ultrasound images were acquired from the left and right carotid arteries of 35 subjects (16 treated with 80 mg atorvastatin and 19 treated with placebo) at baseline and after 3 months of treatment. Two-hundred and seventy texture features were extracted from 3D ultrasound carotid artery images. These images previously had their vessel walls (VW) manually segmented. Highly ranked individual texture features were selected and compared to the VW volume (VWV) change using 3 measures: distance between classes, Wilcoxon rank sum test, and accuracy of the classifiers. Six classifiers were used. Using texture feature (L7R7) increases the average accuracy and area under the ROC curve to 74.4% and 0.72 respectively compared to 57.2% and 0.61 using VWV change. Thus, the results demonstrate that texture features are more sensitive in detecting drug effects on the carotid vessel wall than VWV change.

Obtaining changes in calibration-coil to seismometer output constants using sine waves

USGS Publications Warehouse

Ringler, Adam T.; Hutt, Charles R.; Gee, Lind S.; Sandoval, Leo D.; Wilson, David C.

2013-01-01

The midband sensitivity of a broadband seismometer is one of the most commonly used parameters from station metadata. Thus, it is critical for station operators to robustly estimate this quantity with a high degree of accuracy. We develop an in situ method for estimating changes in sensitivity using sine‐wave calibrations, assuming the calibration coil and its drive are stable over time and temperature. This approach has been used in the past for passive instruments (e.g., geophones) but has not been applied, to our knowledge, to derive sensitivities of modern force‐feedback broadband seismometers. We are able to detect changes in sensitivity to well within 1%, and our method is capable of detecting these sensitivity changes using any frequency of sine calibration within the passband of the instrument.

Hydrography change detection: the usefulness of surface channels derived From LiDAR DEMs for updating mapped hydrography

USGS Publications Warehouse

Poppenga, Sandra K.; Gesch, Dean B.; Worstell, Bruce B.

2013-01-01

The 1:24,000-scale high-resolution National Hydrography Dataset (NHD) mapped hydrography flow lines require regular updating because land surface conditions that affect surface channel drainage change over time. Historically, NHD flow lines were created by digitizing surface water information from aerial photography and paper maps. Using these same methods to update nationwide NHD flow lines is costly and inefficient; furthermore, these methods result in hydrography that lacks the horizontal and vertical accuracy needed for fully integrated datasets useful for mapping and scientific investigations. Effective methods for improving mapped hydrography employ change detection analysis of surface channels derived from light detection and ranging (LiDAR) digital elevation models (DEMs) and NHD flow lines. In this article, we describe the usefulness of surface channels derived from LiDAR DEMs for hydrography change detection to derive spatially accurate and time-relevant mapped hydrography. The methods employ analyses of horizontal and vertical differences between LiDAR-derived surface channels and NHD flow lines to define candidate locations of hydrography change. These methods alleviate the need to analyze and update the nationwide NHD for time relevant hydrography, and provide an avenue for updating the dataset where change has occurred.

Detection of degenerative change in lateral projection cervical spine x-ray images

NASA Astrophysics Data System (ADS)

Jebri, Beyrem; Phillips, Michael; Knapp, Karen; Appelboam, Andy; Reuben, Adam; Slabaugh, Greg

2015-03-01

Degenerative changes to the cervical spine can be accompanied by neck pain, which can result from narrowing of the intervertebral disc space and growth of osteophytes. In a lateral x-ray image of the cervical spine, degenerative changes are characterized by vertebral bodies that have indistinct boundaries and limited spacing between vertebrae. In this paper, we present a machine learning approach to detect and localize degenerative changes in lateral x-ray images of the cervical spine. Starting from a user-supplied set of points in the center of each vertebral body, we fit a central spline, from which a region of interest is extracted and image features are computed. A Random Forest classifier labels regions as degenerative change or normal. Leave-one-out cross-validation studies performed on a dataset of 103 patients demonstrates performance of above 95% accuracy.

Ice sheet topography by satellite altimetry

USGS Publications Warehouse

Brooks, R.L.; Campbell, W.J.; Ramseier, R.O.; Stanley, H.R.; Zwally, H.J.

1978-01-01

The surface elevation of the southern Greenland ice sheet and surface features of the ice flow are obtained from the radar altimeter on the GEOS 3 satellite. The achieved accuracy in surface elevation is ???2 m. As changes in surface elevation are indicative of changes in ice volume, the mass balance of the present ice sheets could be determined by repetitive mapping of the surface elevation and the surface could be monitored to detect surging or significant changes in ice flow. ?? 1978 Nature Publishing Group.

Detecting event-related changes in organizational networks using optimized neural network models.

PubMed

Li, Ze; Sun, Duoyong; Zhu, Renqi; Lin, Zihan

2017-01-01

Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification. Detecting event-related changes could be effectively useful in providing early warnings and faster responses to both positive and negative organizational activities. In this study, event-related change in an organizational network was defined, and artificial neural network models were used to quantitatively determine whether and when a change occurred. To achieve a higher accuracy, Back Propagation Neural Networks (BPNNs) were optimized using Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). We showed the feasibility of the proposed method by comparing its performance with that of other methods using two cases. The results suggested that the proposed method could identify organizational events based on a correlation between the organizational networks and events. The results also suggested that the proposed method not only has a higher precision but also has a better robustness than the previously used techniques.

Detecting event-related changes in organizational networks using optimized neural network models

PubMed Central

Sun, Duoyong; Zhu, Renqi; Lin, Zihan

2017-01-01

Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification. Detecting event-related changes could be effectively useful in providing early warnings and faster responses to both positive and negative organizational activities. In this study, event-related change in an organizational network was defined, and artificial neural network models were used to quantitatively determine whether and when a change occurred. To achieve a higher accuracy, Back Propagation Neural Networks (BPNNs) were optimized using Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). We showed the feasibility of the proposed method by comparing its performance with that of other methods using two cases. The results suggested that the proposed method could identify organizational events based on a correlation between the organizational networks and events. The results also suggested that the proposed method not only has a higher precision but also has a better robustness than the previously used techniques. PMID:29190799

Computerized evaluation of holographic interferograms for fatigue crack detection in riveted lap joints

NASA Astrophysics Data System (ADS)

Zhou, Xiang

Using an innovative portable holographic inspection and testing system (PHITS) developed at the Australian Defence Force Academy, fatigue cracks in riveted lap joints can be detected by visually inspecting the abnormal fringe changes recorded on holographic interferograms. In this thesis, for automatic crack detection, some modern digital image processing techniques are investigated and applied to holographic interferogram evaluation. Fringe analysis algorithms are developed for identification of the crack-induced fringe changes. Theoretical analysis of PHITS and riveted lap joints and two typical experiments demonstrate that the fatigue cracks in lightly-clamped joints induce two characteristic fringe changes: local fringe discontinuities at the cracking sites; and the global crescent fringe distribution near to the edge of the rivet hole. Both of the fringe features are used for crack detection in this thesis. As a basis of the fringe feature extraction, an algorithm for local fringe orientation calculation is proposed. For high orientation accuracy and computational efficiency, Gaussian gradient filtering and neighboring direction averaging are used to minimize the effects of image background variations and random noise. The neighboring direction averaging is also used to approximate the fringe directions in centerlines of bright and dark fringes. Experimental results indicate that for high orientation accuracy the scales of the Gaussian filter and neighboring direction averaging should be chosen according to the local fringe spacings. The orientation histogram technique is applied to detect the local fringe discontinuity due to the fatigue cracks. The Fourier descriptor technique is used to characterize the global fringe distribution change from a circular to a crescent distribution with the fatigue crack growth. Experiments and computer simulations are conducted to analyze the detectability and reliability of crack detection using the two techniques. Results demonstrate that the Fourier descriptor technique is more promising in the detection of the short cracks near the edge of the rivet head. However, it is not as reliable as the fringe orientation technique for detection of the long through cracks. For reliability, both techniques should be used in practical crack detection. Neither the Fourier descriptor technique nor the orientation histogram technique have been previously applied to holographic interferometry. While this work related primarily to interferograms of cracked rivets, the techniques would be readily applied to other areas of fringe pattern analysis.

Enhancing the performance of cooperative face detector by NFGS

NASA Astrophysics Data System (ADS)

Yesugade, Snehal; Dave, Palak; Srivastava, Srinkhala; Das, Apurba

2015-07-01

Computerized human face detection is an important task of deformable pattern recognition in today's world. Especially in cooperative authentication scenarios like ATM fraud detection, attendance recording, video tracking and video surveillance, the accuracy of the face detection engine in terms of accuracy, memory utilization and speed have been active areas of research for the last decade. The Haar based face detection or SIFT and EBGM based face recognition systems are fairly reliable in this regard. But, there the features are extracted in terms of gray textures. When the input is a high resolution online video with a fairly large viewing area, Haar needs to search for face everywhere (say 352×250 pixels) and every time (e.g., 30 FPS capture all the time). In the current paper we have proposed to address both the aforementioned scenarios by a neuro-visually inspired method of figure-ground segregation (NFGS) [5] to result in a two-dimensional binary array from gray face image. The NFGS would identify the reference video frame in a low sampling rate and updates the same with significant change of environment like illumination. The proposed algorithm would trigger the face detector only when appearance of a new entity is encountered into the viewing area. To address the detection accuracy, classical face detector would be enabled only in a narrowed down region of interest (RoI) as fed by the NFGS. The act of updating the RoI would be done in each frame online with respect to the moving entity which in turn would improve both FR (False Rejection) and FA (False Acceptance) of the face detection system.

Traffic Sign Recognition with Invariance to Lighting in Dual-Focal Active Camera System

NASA Astrophysics Data System (ADS)

Gu, Yanlei; Panahpour Tehrani, Mehrdad; Yendo, Tomohiro; Fujii, Toshiaki; Tanimoto, Masayuki

In this paper, we present an automatic vision-based traffic sign recognition system, which can detect and classify traffic signs at long distance under different lighting conditions. To realize this purpose, the traffic sign recognition is developed in an originally proposed dual-focal active camera system. In this system, a telephoto camera is equipped as an assistant of a wide angle camera. The telephoto camera can capture a high accuracy image for an object of interest in the view field of the wide angle camera. The image from the telephoto camera provides enough information for recognition when the accuracy of traffic sign is low from the wide angle camera. In the proposed system, the traffic sign detection and classification are processed separately for different images from the wide angle camera and telephoto camera. Besides, in order to detect traffic sign from complex background in different lighting conditions, we propose a type of color transformation which is invariant to light changing. This color transformation is conducted to highlight the pattern of traffic signs by reducing the complexity of background. Based on the color transformation, a multi-resolution detector with cascade mode is trained and used to locate traffic signs at low resolution in the image from the wide angle camera. After detection, the system actively captures a high accuracy image of each detected traffic sign by controlling the direction and exposure time of the telephoto camera based on the information from the wide angle camera. Moreover, in classification, a hierarchical classifier is constructed and used to recognize the detected traffic signs in the high accuracy image from the telephoto camera. Finally, based on the proposed system, a set of experiments in the domain of traffic sign recognition is presented. The experimental results demonstrate that the proposed system can effectively recognize traffic signs at low resolution in different lighting conditions.

Detection of epileptic seizure in EEG signals using linear least squares preprocessing.

PubMed

Roshan Zamir, Z

2016-09-01

An epileptic seizure is a transient event of abnormal excessive neuronal discharge in the brain. This unwanted event can be obstructed by detection of electrical changes in the brain that happen before the seizure takes place. The automatic detection of seizures is necessary since the visual screening of EEG recordings is a time consuming task and requires experts to improve the diagnosis. Much of the prior research in detection of seizures has been developed based on artificial neural network, genetic programming, and wavelet transforms. Although the highest achieved accuracy for classification is 100%, there are drawbacks, such as the existence of unbalanced datasets and the lack of investigations in performances consistency. To address these, four linear least squares-based preprocessing models are proposed to extract key features of an EEG signal in order to detect seizures. The first two models are newly developed. The original signal (EEG) is approximated by a sinusoidal curve. Its amplitude is formed by a polynomial function and compared with the predeveloped spline function. Different statistical measures, namely classification accuracy, true positive and negative rates, false positive and negative rates and precision, are utilised to assess the performance of the proposed models. These metrics are derived from confusion matrices obtained from classifiers. Different classifiers are used over the original dataset and the set of extracted features. The proposed models significantly reduce the dimension of the classification problem and the computational time while the classification accuracy is improved in most cases. The first and third models are promising feature extraction methods with the classification accuracy of 100%. Logistic, LazyIB1, LazyIB5, and J48 are the best classifiers. Their true positive and negative rates are 1 while false positive and negative rates are 0 and the corresponding precision values are 1. Numerical results suggest that these models are robust and efficient for detecting epileptic seizure. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Quantifying Structural and Compositional Changes in Forest Cover in NW Yunnan, China

NASA Astrophysics Data System (ADS)

Hakkenberg, C.

2012-12-01

NW Yunnan, China is a region renowned for high levels of biodiversity, endemism and genetically distinct refugial plant populations. It is also a focal area for China's national reforestation efforts like the Natural Forest Protection Program (NFPP), intended to control erosion in the Upper Yangtze watershed. As part of a larger project to investigate the role of reforestation programs in facilitating the emergence of increasingly species-rich forest communities on a previously degraded and depauperate land mosaic in montane SW China, this study uses a series of Landsat TM images to quantify the spatial pattern and rate of structural and compositional change in forests recovering from medium to large-scale disturbances in the area over the past 25 years. Beyond the fundamental need to assess the outcomes of one of the world's largest reforestation programs, this research offers approaches to confronting two critical methodological issues: (1) techniques for characterizing subtle changes in the nature of vegetation cover, and (2) reducing change detection uncertainty due to persistent cloud cover and shadow. To address difficulties in accurately assessing the structure and composition of vegetative regrowth, a biophysical model was parameterized with over 300 ground-truthed canopy cover assessment points to determine pattern and rate of long-term vegetation changes. To combat pervasive shadow and cloud cover, an interactive generalized additive model (GAM) model based on topographic and spatial predictors was used to overcome some of the constraints of satellite image analysis in Himalayan regions characterized by extreme topography and extensive cloud cover during the summer monsoon. The change detection is assessed for accuracy using ground-truthed observations in a variety of forest cover types and topographic positions. Results indicate effectiveness in reducing the areal extent of unclassified regions and increasing total change detection accuracy. In addition to quantifying forest cover change in this section of NW Yunnan, the analysis attempts to qualify that change - distinguishing among distinct disturbance histories and post-recovery successional pathways.

Analysis of ICESat Data Using Kalman Filter and Kriging to Study Height Changes in East Antarctica

NASA Technical Reports Server (NTRS)

Herring, Thomas A.

2005-01-01

We analyze ICESat derived heights collected between Feb. 03-Nov. 04 using a kriging/Kalman filtering approach to investigate height changes in East Antarctica. The model's parameters are height change to an a priori static digital height model, seasonal signal expressed as an amplitude Beta and phase Theta, and height-change rate dh/dt for each (100 km)(exp 2) block. From the Kalman filter results, dh/dt has a mean of -0.06 m/yr in the flat interior of East Antarctica. Spatially correlated pointing errors in the current data releases give uncertainties in the range 0.06 m/yr, making height change detection unreliable at this time. Our test shows that when using all available data with pointing knowledge equivalent to that of Laser 2a, height change detection with an accuracy level 0.02 m/yr can be achieved over flat terrains in East Antarctica.

Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative

NASA Astrophysics Data System (ADS)

Zhu, Zhe; Gallant, Alisa L.; Woodcock, Curtis E.; Pengra, Bruce; Olofsson, Pontus; Loveland, Thomas R.; Jin, Suming; Dahal, Devendra; Yang, Limin; Auch, Roger F.

2016-12-01

The U.S. Geological Survey's Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a new end-to-end capability to continuously track and characterize changes in land cover, use, and condition to better support research and applications relevant to resource management and environmental change. Among the LCMAP product suite are annual land cover maps that will be available to the public. This paper describes an approach to optimize the selection of training and auxiliary data for deriving the thematic land cover maps based on all available clear observations from Landsats 4-8. Training data were selected from map products of the U.S. Geological Survey's Land Cover Trends project. The Random Forest classifier was applied for different classification scenarios based on the Continuous Change Detection and Classification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence of land cover classes was superior to an equal distribution of training data per class, and suggest using a total of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced training data was alleviated by extracting a minimum of 600 training pixels and a maximum of 8000 training pixels per class. We additionally explored removing outliers contained within the training data based on their spectral and spatial criteria, but observed no significant improvement in classification results. We also tested the importance of different types of auxiliary data that were available for the conterminous United States, including: (a) five variables used by the National Land Cover Database, (b) three variables from the cloud screening "Function of mask" (Fmask) statistics, and (c) two variables from the change detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and its derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability, and cloud probability improved the accuracy of land cover classification. Compared to the original strategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification accuracies substantially (15-percentage point increase in overall accuracy and 4-percentage point increase in minimum accuracy).

A Deep Learning Approach to Examine Ischemic ST Changes in Ambulatory ECG Recordings.

PubMed

Xiao, Ran; Xu, Yuan; Pelter, Michele M; Mortara, David W; Hu, Xiao

2018-01-01

Patients with suspected acute coronary syndrome (ACS) are at risk of transient myocardial ischemia (TMI), which could lead to serious morbidity or even mortality. Early detection of myocardial ischemia can reduce damage to heart tissues and improve patient condition. Significant ST change in the electrocardiogram (ECG) is an important marker for detecting myocardial ischemia during the rule-out phase of potential ACS. However, current ECG monitoring software is vastly underused due to excessive false alarms. The present study aims to tackle this problem by combining a novel image-based approach with deep learning techniques to improve the detection accuracy of significant ST depression change. The obtained convolutional neural network (CNN) model yields an average area under the curve (AUC) at 89.6% from an independent testing set. At selected optimal cutoff thresholds, the proposed model yields a mean sensitivity at 84.4% while maintaining specificity at 84.9%.

Action change detection in video using a bilateral spatial-temporal constraint

NASA Astrophysics Data System (ADS)

Tian, Jing; Chen, Li

2016-08-01

Action change detection in video aims to detect action discontinuity in video. The silhouettes-based features are desirable for action change detection. This paper studies the problem of silhouette-quality assessment. For that, a non-reference approach without the need for ground truth is proposed in this paper to evaluate the quality of silhouettes, by exploiting both the boundary contrast of the silhouettes in the spatial domain and the consistency of the silhouettes in the temporal domain. This is in contrast to that either only spatial information or only temporal information of silhouettes is exploited in conventional approaches. Experiments are conducted using artificially generated degraded silhouettes to show that the proposed approach outperforms conventional approaches to achieve more accurate quality assessment. Furthermore, experiments are performed to show that the proposed approach is able to improve the accuracy performance of conventional action change approaches in two human action video data-sets. The average runtime of the proposed approach for Weizmann action video data-set is 0.08 second for one frame using Matlab programming language. It is computationally efficient and potential to real-time implementations.

SU-G-JeP3-01: A Method to Quantify Lung SBRT Target Localization Accuracy Based On Digitally Reconstructed Fluoroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lafata, K; Ren, L; Cai, J

2016-06-15

Purpose: To develop a methodology based on digitally-reconstructed-fluoroscopy (DRF) to quantitatively assess target localization accuracy of lung SBRT, and to evaluate using both a dynamic digital phantom and a patient dataset. Methods: For each treatment field, a 10-phase DRF is generated based on the planning 4DCT. Each frame is pre-processed with a morphological top-hat filter, and corresponding beam apertures are projected to each detector plane. A template-matching algorithm based on cross-correlation is used to detect the tumor location in each frame. Tumor motion relative beam aperture is extracted in the superior-inferior direction based on each frame’s impulse response to themore » template, and the mean tumor position (MTP) is calculated as the average tumor displacement. The DRF template coordinates are then transferred to the corresponding MV-cine dataset, which is retrospectively filtered as above. The treatment MTP is calculated within each field’s projection space, relative to the DRF-defined template. The field’s localization error is defined as the difference between the DRF-derived-MTP (planning) and the MV-cine-derived-MTP (delivery). A dynamic digital phantom was used to assess the algorithm’s ability to detect intra-fractional changes in patient alignment, by simulating different spatial variations in the MV-cine and calculating the corresponding change in MTP. Inter-and-intra-fractional variation, IGRT accuracy, and filtering effects were investigated on a patient dataset. Results: Phantom results demonstrated a high accuracy in detecting both translational and rotational variation. The lowest localization error of the patient dataset was achieved at each fraction’s first field (mean=0.38mm), with Fx3 demonstrating a particularly strong correlation between intra-fractional motion-caused localization error and treatment progress. Filtering significantly improved tracking visibility in both the DRF and MV-cine images. Conclusion: We have developed and evaluated a methodology to quantify lung SBRT target localization accuracy based on digitally-reconstructed-fluoroscopy. Our approach may be useful in potentially reducing treatment margins to optimize lung SBRT outcomes. R01-184173.« less

Accuracy evaluation of 3D lidar data from small UAV

NASA Astrophysics Data System (ADS)

Tulldahl, H. M.; Bissmarck, Fredrik; Larsson, Hâkan; Grönwall, Christina; Tolt, Gustav

2015-10-01

A UAV (Unmanned Aerial Vehicle) with an integrated lidar can be an efficient system for collection of high-resolution and accurate three-dimensional (3D) data. In this paper we evaluate the accuracy of a system consisting of a lidar sensor on a small UAV. High geometric accuracy in the produced point cloud is a fundamental qualification for detection and recognition of objects in a single-flight dataset as well as for change detection using two or several data collections over the same scene. Our work presented here has two purposes: first to relate the point cloud accuracy to data processing parameters and second, to examine the influence on accuracy from the UAV platform parameters. In our work, the accuracy is numerically quantified as local surface smoothness on planar surfaces, and as distance and relative height accuracy using data from a terrestrial laser scanner as reference. The UAV lidar system used is the Velodyne HDL-32E lidar on a multirotor UAV with a total weight of 7 kg. For processing of data into a geographically referenced point cloud, positioning and orientation of the lidar sensor is based on inertial navigation system (INS) data combined with lidar data. The combination of INS and lidar data is achieved in a dynamic calibration process that minimizes the navigation errors in six degrees of freedom, namely the errors of the absolute position (x, y, z) and the orientation (pitch, roll, yaw) measured by GPS/INS. Our results show that low-cost and light-weight MEMS based (microelectromechanical systems) INS equipment with a dynamic calibration process can obtain significantly improved accuracy compared to processing based solely on INS data.

Social Eavesdropping: Can You Hear the Emotionality in a "Hello" That Is Not Meant for You?

PubMed

Karthikeyan, Sethu; Ramachandra, Vijayachandra

2017-01-01

The study examined third-party listeners' ability to detect the Hellos spoken to prevalidated happy, neutral, and sad facial expressions. The average detection accuracies from the happy and sad (HS), happy and neutral (HN), and sad and neutral (SN) listening tests followed the average vocal pitch differences between the two sets of Hellos in each of the tests; HS and HN detection accuracies were above chance reflecting the significant pitch differences between the respective Hellos. The SN detection accuracy was at chance reflecting the lack of pitch difference between sad and neutral Hellos. As expected, the SN detection accuracy positively correlated with theory of mind; participating in these tests has been likened to the act of eavesdropping, which has been discussed from an evolutionary perspective. An unexpected negative correlation between the HS detection accuracy and the empathy quotient has been discussed with respect to autism research on empathy and pitch discrimination.

Accuracy requirements. [for monitoring of climate changes

NASA Technical Reports Server (NTRS)

Delgenio, Anthony

1993-01-01

Satellite and surface measurements, if they are to serve as a climate monitoring system, must be accurate enough to permit detection of changes of climate parameters on decadal time scales. The accuracy requirements are difficult to define a priori since they depend on unknown future changes of climate forcings and feedbacks. As a framework for evaluation of candidate Climsat instruments and orbits, we estimate the accuracies that would be needed to measure changes expected over two decades based on theoretical considerations including GCM simulations and on observational evidence in cases where data are available for rates of change. One major climate forcing known with reasonable accuracy is that caused by the anthropogenic homogeneously mixed greenhouse gases (CO2, CFC's, CH4 and N2O). Their net forcing since the industrial revolution began is about 2 W/sq m and it is presently increasing at a rate of about 1 W/sq m per 20 years. Thus for a competing forcing or feedback to be important, it needs to be of the order of 0.25 W/sq m or larger on this time scale. The significance of most climate feedbacks depends on their sensitivity to temperature change. Therefore we begin with an estimate of decadal temperature change. Presented are the transient temperature trends simulated by the GISS GCM when subjected to various scenarios of trace gas concentration increases. Scenario B, which represents the most plausible near-term emission rates and includes intermittent forcing by volcanic aerosols, yields a global mean surface air temperature increase Delta Ts = 0.7 degrees C over the time period 1995-2015. This is consistent with the IPCC projection of about 0.3 degrees C/decade global warming (IPCC, 1990). Several of our estimates below are based on this assumed rate of warming.

Effects of changes in size, speed and distance on the perception of curved 3D trajectories

PubMed Central

Zhang, Junjun; Braunstein, Myron L.; Andersen, George J.

2012-01-01

Previous research on the perception of 3D object motion has considered time to collision, time to passage, collision detection and judgments of speed and direction of motion, but has not directly studied the perception of the overall shape of the motion path. We examined the perception of the magnitude of curvature and sign of curvature of the motion path for objects moving at eye level in a horizontal plane parallel to the line of sight. We considered two sources of information for the perception of motion trajectories: changes in angular size and changes in angular speed. Three experiments examined judgments of relative curvature for objects moving at different distances. At the closest distance studied, accuracy was high with size information alone but near chance with speed information alone. At the greatest distance, accuracy with size information alone decreased sharply but accuracy for displays with both size and speed information remained high. We found similar results in two experiments with judgments of sign of curvature. Accuracy was higher for displays with both size and speed information than with size information alone, even when the speed information was based on parallel projections and was not informative about sign of curvature. For both magnitude of curvature and sign of curvature judgments, information indicating that the trajectory was curved increased accuracy, even when this information was not directly relevant to the required judgment. PMID:23007204

The effect of transponder motion on the accuracy of the Calypso Electromagnetic localization system.

PubMed

Murphy, Martin J; Eidens, Richard; Vertatschitsch, Edward; Wright, J Nelson

2008-09-01

To determine position and velocity-dependent effects in the overall accuracy of the Calypso Electromagnetic localization system, under conditions that emulate transponder motion during normal free breathing. Three localization transponders were mounted on a remote-controlled turntable that could move the transponders along a circular trajectory at speeds up to 3 cm/s. A stationary calibration established the coordinates of multiple points on each transponder's circular path. Position measurements taken while the transponders were in motion at a constant speed were then compared with the stationary coordinates. No statistically significant changes in the transponder positions in (x,y,z) were detected when the transponders were in motion. The accuracy of the localization system is unaffected by transponder motion.

Accuracy requirements of optical linear algebra processors in adaptive optics imaging systems

NASA Technical Reports Server (NTRS)

Downie, John D.

1990-01-01

A ground-based adaptive optics imaging telescope system attempts to improve image quality by detecting and correcting for atmospherically induced wavefront aberrations. The required control computations during each cycle will take a finite amount of time. Longer time delays result in larger values of residual wavefront error variance since the atmosphere continues to change during that time. Thus an optical processor may be well-suited for this task. This paper presents a study of the accuracy requirements in a general optical processor that will make it competitive with, or superior to, a conventional digital computer for the adaptive optics application. An optimization of the adaptive optics correction algorithm with respect to an optical processor's degree of accuracy is also briefly discussed.

Novel images extraction model using improved delay vector variance feature extraction and multi-kernel neural network for EEG detection and prediction.

PubMed

Ge, Jing; Zhang, Guoping

2015-01-01

Advanced intelligent methodologies could help detect and predict diseases from the EEG signals in cases the manual analysis is inefficient available, for instance, the epileptic seizures detection and prediction. This is because the diversity and the evolution of the epileptic seizures make it very difficult in detecting and identifying the undergoing disease. Fortunately, the determinism and nonlinearity in a time series could characterize the state changes. Literature review indicates that the Delay Vector Variance (DVV) could examine the nonlinearity to gain insight into the EEG signals but very limited work has been done to address the quantitative DVV approach. Hence, the outcomes of the quantitative DVV should be evaluated to detect the epileptic seizures. To develop a new epileptic seizure detection method based on quantitative DVV. This new epileptic seizure detection method employed an improved delay vector variance (IDVV) to extract the nonlinearity value as a distinct feature. Then a multi-kernel functions strategy was proposed in the extreme learning machine (ELM) network to provide precise disease detection and prediction. The nonlinearity is more sensitive than the energy and entropy. 87.5% overall accuracy of recognition and 75.0% overall accuracy of forecasting were achieved. The proposed IDVV and multi-kernel ELM based method was feasible and effective for epileptic EEG detection. Hence, the newly proposed method has importance for practical applications.

Applicability Assessment of Uavsar Data in Wetland Monitoring: a Case Study of Louisiana Wetland

NASA Astrophysics Data System (ADS)

Zhao, J.; Niu, Y.; Lu, Z.; Yang, J.; Li, P.; Liu, W.

2018-04-01

Wetlands are highly productive and support a wide variety of ecosystem goods and services. Monitoring wetland is essential and potential. Because of the repeat-pass nature of satellite orbit and airborne, time-series of remote sensing data can be obtained to monitor wetland. UAVSAR is a NASA L-band synthetic aperture radar (SAR) sensor compact pod-mounted polarimetric instrument for interferometric repeat-track observations. Moreover, UAVSAR images can accurately map crustal deformations associated with natural hazards, such as volcanoes and earthquakes. And its polarization agility facilitates terrain and land-use classification and change detection. In this paper, the multi-temporal UAVSAR data are applied for monitoring the wetland change. Using the multi-temporal polarimetric SAR (PolSAR) data, the change detection maps are obtained by unsupervised and supervised method. And the coherence is extracted from the interfometric SAR (InSAR) data to verify the accuracy of change detection map. The experimental results show that the multi-temporal UAVSAR data is fit for wetland monitor.

Nanoporous Anodic Alumina Surface Modification by Electrostatic, Covalent, and Immune Complexation Binding Investigated by Capillary Filling.

PubMed

Eckstein, Chris; Acosta, Laura K; Pol, Laura; Xifré-Pérez, Elisabet; Pallares, Josep; Ferré-Borrull, Josep; Marsal, Lluis F

2018-03-28

The fluid imbibition-coupled laser interferometry (FICLI) technique has been applied to detect and quantify surface changes and pore dimension variations in nanoporous anodic alumina (NAA) structures. FICLI is a noninvasive optical technique that permits the determination of the NAA average pore radius with high accuracy. In this work, the technique is applied after each step of different surface modification paths of the NAA pores: (i) electrostatic immobilization of bovine serum albumin (BSA), (ii) covalent attachment of streptavidin via (3-aminipropyl)-triethoxysilane and glutaraldehyde grafting, and (iii) immune complexation. Results show that BSA attachment can be detected as a reduction in estimated radius from FICLI with high accuracy and reproducibility. In the case of the covalent attachment of streptavidin, FICLI is able to recognize a multilayer formation of the silane and the protein. For immune complexation, the technique is able to detect different antibody-antigen bindings and distinguish different dynamics among different immune species.

Perceiving goals and actions in individuals with autism spectrum disorders.

PubMed

Zalla, Tiziana; Labruyère, Nelly; Georgieff, Nicolas

2013-10-01

In the present study, we investigated the ability to parse familiar sequences of action into meaningful events in young individuals with autism spectrum disorders (ASDs), as compared to young individuals with typical development (TD) and young individuals with moderate mental retardation or learning disabilities (MLDs). While viewing two videotaped movies, participants were requested to detect the boundary transitions between component events at both fine and coarse levels of the action hierarchical structure. Overall, reduced accuracy for event detection was found in participants with ASDs, relative to participants with TD, at both levels of action segmentation. The performance was, however, equally diminished in participants with ASDs and MLDs under the course-grained segmentation suggesting that difficulties to detect fine-grained events in ASDs cannot be explained by a general intellectual dysfunction. Reduced accuracy for event detection was related to diminished event recall, memory for event sequence and Theory of Mind abilities. We hypothesized that difficulties with event detection result from a deficit disrupting the on-line processing of kinematic features and physical changes of dynamic human actions. An impairment at the earlier stages of the event encoding process might contribute to deficits in episodic memory and social functioning in individuals with ASDs.

Single-trial lie detection using a combined fNIRS-polygraph system

PubMed Central

Bhutta, M. Raheel; Hong, Melissa J.; Kim, Yun-Hee; Hong, Keum-Shik

2015-01-01

Deception is a human behavior that many people experience in daily life. It involves complex neuronal activities in addition to several physiological changes in the body. A polygraph, which can measure some of the physiological responses from the body, has been widely employed in lie-detection. Many researchers, however, believe that lie detection can become more precise if the neuronal changes that occur in the process of deception can be isolated and measured. In this study, we combine both measures (i.e., physiological and neuronal changes) for enhanced lie-detection. Specifically, to investigate the deception-related hemodynamic response, functional near-infrared spectroscopy (fNIRS) is applied at the prefrontal cortex besides a commercially available polygraph system. A mock crime scenario with a single-trial stimulus is set up as a deception protocol. The acquired data are classified into “true” and “lie” classes based on the fNIRS-based hemoglobin-concentration changes and polygraph-based physiological signal changes. Linear discriminant analysis is utilized as a classifier. The results indicate that the combined fNIRS-polygraph system delivers much higher classification accuracy than that of a singular system. This study demonstrates a plausible solution toward single-trial lie-detection by combining fNIRS and the polygraph. PMID:26082733

Rapid systematic assessment of the detection and attribution of regional anthropogenic climate change

NASA Astrophysics Data System (ADS)

Stone, Dáithí A.; Hansen, Gerrit

2016-09-01

Despite being a well-established research field, the detection and attribution of observed climate change to anthropogenic forcing is not yet provided as a climate service. One reason for this is the lack of a methodology for performing tailored detection and attribution assessments on a rapid time scale. Here we develop such an approach, based on the translation of quantitative analysis into the "confidence" language employed in recent Assessment Reports of the Intergovernmental Panel on Climate Change. While its systematic nature necessarily ignores some nuances examined in detailed expert assessments, the approach nevertheless goes beyond most detection and attribution studies in considering contributors to building confidence such as errors in observational data products arising from sparse monitoring networks. When compared against recent expert assessments, the results of this approach closely match those of the existing assessments. Where there are small discrepancies, these variously reflect ambiguities in the details of what is being assessed, reveal nuances or limitations of the expert assessments, or indicate limitations of the accuracy of the sort of systematic approach employed here. Deployment of the method on 116 regional assessments of recent temperature and precipitation changes indicates that existing rules of thumb concerning the detectability of climate change ignore the full range of sources of uncertainty, most particularly the importance of adequate observational monitoring.

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

PubMed

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

Evaluating the performance of the quick CSF method in detecting contrast sensitivity function changes

PubMed Central

Hou, Fang; Lesmes, Luis Andres; Kim, Woojae; Gu, Hairong; Pitt, Mark A.; Myung, Jay I.; Lu, Zhong-Lin

2016-01-01

The contrast sensitivity function (CSF) has shown promise as a functional vision endpoint for monitoring the changes in functional vision that accompany eye disease or its treatment. However, detecting CSF changes with precision and efficiency at both the individual and group levels is very challenging. By exploiting the Bayesian foundation of the quick CSF method (Lesmes, Lu, Baek, & Albright, 2010), we developed and evaluated metrics for detecting CSF changes at both the individual and group levels. A 10-letter identification task was used to assess the systematic changes in the CSF measured in three luminance conditions in 112 naïve normal observers. The data from the large sample allowed us to estimate the test–retest reliability of the quick CSF procedure and evaluate its performance in detecting CSF changes at both the individual and group levels. The test–retest reliability reached 0.974 with 50 trials. In 50 trials, the quick CSF method can detect a medium 0.30 log unit area under log CSF change with 94.0% accuracy at the individual observer level. At the group level, a power analysis based on the empirical distribution of CSF changes from the large sample showed that a very small area under log CSF change (0.025 log unit) could be detected by the quick CSF method with 112 observers and 50 trials. These results make it plausible to apply the method to monitor the progression of visual diseases or treatment effects on individual patients and greatly reduce the time, sample size, and costs in clinical trials at the group level. PMID:27120074

Assessment of cerebral perfusion in childhood strokes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gates, G.F.; Fishman, L.S.; Segall, H.D.

1982-11-01

Thirty-three children who had strokes were studied by dynamic and static scintigraphy, 29 by CT scanning, and 10 by cerebral angiography. The accuracy of dynamic scintigraphy in stroke detection during the first week of clinical symptoms was 94% while CT scanning was 60% accurate and static scintigraphy 11% accurate. During the second week the accuracy of CT scanning increased to 100%, but static scintigraphy improved to only 50%. Fifty percent of scintiangiograms performed during the first week showed either luxuriant perfusion or flip-flop patterns. In some patients these two flow patterns changed to that of cerebral hemispheric ischemia after goingmore » through a phase during which perfusion appeared to be equal in the two hemispheres. Dynamic scintigraphy is believed to be the test of choice for stroke detection in children during the first week.« less

A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic

PubMed Central

Qi, Jin-Peng; Qi, Jie; Zhang, Qing

2016-01-01

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals. PMID:27413364

A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic.

PubMed

Qi, Jin-Peng; Qi, Jie; Zhang, Qing

2016-01-01

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals.

Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data

USGS Publications Warehouse

Yang, Limin; Xian, George Z.; Klaver, Jacqueline M.; Deal, Brian

2003-01-01

We developed a Sub-pixel Imperviousness Change Detection (SICD) approach to detect urban land-cover changes using Landsat and high-resolution imagery. The sub-pixel percent imperviousness was mapped for two dates (09 March 1993 and 11 March 2001) over western Georgia using a regression tree algorithm. The accuracy of the predicted imperviousness was reasonable based on a comparison using independent reference data. The average absolute error between predicted and reference data was 16.4 percent for 1993 and 15.3 percent for 2001. The correlation coefficient (r) was 0.73 for 1993 and 0.78 for 2001, respectively. Areas with a significant increase (greater than 20 percent) in impervious surface from 1993 to 2001 were mostly related to known land-cover/land-use changes that occurred in this area, suggesting that the spatial change of an impervious surface is a useful indicator for identifying spatial extent, intensity, and, potentially, type of urban land-cover/land-use changes. Compared to other pixel-based change-detection methods (band differencing, rationing, change vector, post-classification), information on changes in sub-pixel percent imperviousness allow users to quantify and interpret urban land-cover/land-use changes based on their own definition. Such information is considered complementary to products generated using other change-detection methods. In addition, the procedure for mapping imperviousness is objective and repeatable, hence, can be used for monitoring urban land-cover/land-use change over a large geographic area. Potential applications and limitations of the products developed through this study in urban environmental studies are also discussed.

Pole Photogrammetry with AN Action Camera for Fast and Accurate Surface Mapping

NASA Astrophysics Data System (ADS)

Gonçalves, J. A.; Moutinho, O. F.; Rodrigues, A. C.

2016-06-01

High resolution and high accuracy terrain mapping can provide height change detection for studies of erosion, subsidence or land slip. A UAV flying at a low altitude above the ground, with a compact camera, acquires images with resolution appropriate for these change detections. However, there may be situations where different approaches may be needed, either because higher resolution is required or the operation of a drone is not possible. Pole photogrammetry, where a camera is mounted on a pole, pointing to the ground, is an alternative. This paper describes a very simple system of this kind, created for topographic change detection, based on an action camera. These cameras have high quality and very flexible image capture. Although radial distortion is normally high, it can be treated in an auto-calibration process. The system is composed by a light aluminium pole, 4 meters long, with a 12 megapixel GoPro camera. Average ground sampling distance at the image centre is 2.3 mm. The user moves along a path, taking successive photos, with a time lapse of 0.5 or 1 second, and adjusting the speed in order to have an appropriate overlap, with enough redundancy for 3D coordinate extraction. Marked ground control points are surveyed with GNSS for precise georeferencing of the DSM and orthoimage that are created by structure from motion processing software. An average vertical accuracy of 1 cm could be achieved, which is enough for many applications, for example for soil erosion. The GNSS survey in RTK mode with permanent stations is now very fast (5 seconds per point), which results, together with the image collection, in a very fast field work. If an improved accuracy is needed, since image resolution is 1/4 cm, it can be achieved using a total station for the control point survey, although the field work time increases.

External and internal facial features modulate processing of vertical but not horizontal spatial relations.

PubMed

Meinhardt, Günter; Kurbel, David; Meinhardt-Injac, Bozana; Persike, Malte

2018-03-22

Some years ago an asymmetry was reported for the inversion effect for horizontal (H) and vertical (V) relational face manipulations (Goffaux & Rossion, 2007). Subsequent research examined whether a specific disruption of long-range relations underlies the H/V inversion asymmetry (Sekunova & Barton, 2008). Here, we tested how detection of changes in interocular distance (H) and eye height (V) depends on cardinal internal features and external feature surround. Results replicated the H/V inversion asymmetry. Moreover, we found very different face cue dependencies for both change types. Performance and inversion effects did not depend on the presence of other face cues for detecting H changes. In contrast, accuracy for detecting V changes strongly depended on internal and external features, showing cumulative improvement when more cues were added. Inversion effects were generally large, and larger with external feature surround. The cue independence in detecting H relational changes indicates specialized local processing tightly tuned to the eyes region, while the strong cue dependency in detecting V relational changes indicates a global mechanism of cue integration across different face regions. These findings suggest that the H/V asymmetry of the inversion effect rests on an H/V anisotropy of face cue dependency, since only the global V mechanism suffers from disruption of cue integration as the major effect of face inversion. Copyright © 2018. Published by Elsevier Ltd.

A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011

USGS Publications Warehouse

Jin, Suming; Yang, Limin; Zhu, Zhe; Homer, Collin G.

2017-01-01

Monitoring and mapping land cover changes are important ways to support evaluation of the status and transition of ecosystems. The Alaska National Land Cover Database (NLCD) 2001 was the first 30-m resolution baseline land cover product of the entire state derived from circa 2001 Landsat imagery and geospatial ancillary data. We developed a comprehensive approach named AKUP11 to update Alaska NLCD from 2001 to 2011 and provide a 10-year cyclical update of the state's land cover and land cover changes. Our method is designed to characterize the main land cover changes associated with different drivers, including the conversion of forests to shrub and grassland primarily as a result of wildland fire and forest harvest, the vegetation successional processes after disturbance, and changes of surface water extent and glacier ice/snow associated with weather and climate changes. For natural vegetated areas, a component named AKUP11-VEG was developed for updating the land cover that involves four major steps: 1) identify the disturbed and successional areas using Landsat images and ancillary datasets; 2) update the land cover status for these areas using a SKILL model (System of Knowledge-based Integrated-trajectory Land cover Labeling); 3) perform decision tree classification; and 4) develop a final land cover and land cover change product through the postprocessing modeling. For water and ice/snow areas, another component named AKUP11-WIS was developed for initial land cover change detection, removal of the terrain shadow effects, and exclusion of ephemeral snow changes using a 3-year MODIS snow extent dataset from 2010 to 2012. The overall approach was tested in three pilot study areas in Alaska, with each area consisting of four Landsat image footprints. The results from the pilot study show that the overall accuracy in detecting change and no-change is 90% and the overall accuracy of the updated land cover label for 2011 is 86%. The method provided a robust, consistent, and efficient means for capturing major disturbance events and updating land cover for Alaska. The method has subsequently been applied to generate the land cover and land cover change products for the entire state of Alaska.

Detection of increased vasa vasorum in artery walls: improving CT number accuracy using image deconvolution

NASA Astrophysics Data System (ADS)

Rajendran, Kishore; Leng, Shuai; Jorgensen, Steven M.; Abdurakhimova, Dilbar; Ritman, Erik L.; McCollough, Cynthia H.

2017-03-01

Changes in arterial wall perfusion are an indicator of early atherosclerosis. This is characterized by an increased spatial density of vasa vasorum (VV), the micro-vessels that supply oxygen and nutrients to the arterial wall. Detection of increased VV during contrast-enhanced computed tomography (CT) imaging is limited due to contamination from blooming effect from the contrast-enhanced lumen. We report the application of an image deconvolution technique using a measured system point-spread function, on CT data obtained from a photon-counting CT system to reduce blooming and to improve the CT number accuracy of arterial wall, which enhances detection of increased VV. A phantom study was performed to assess the accuracy of the deconvolution technique. A porcine model was created with enhanced VV in one carotid artery; the other carotid artery served as a control. CT images at an energy range of 25-120 keV were reconstructed. CT numbers were measured for multiple locations in the carotid walls and for multiple time points, pre and post contrast injection. The mean CT number in the carotid wall was compared between the left (increased VV) and right (control) carotid arteries. Prior to deconvolution, results showed similar mean CT numbers in the left and right carotid wall due to the contamination from blooming effect, limiting the detection of increased VV in the left carotid artery. After deconvolution, the mean CT number difference between the left and right carotid arteries was substantially increased at all the time points, enabling detection of the increased VV in the artery wall.

Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application.

PubMed

Chen, Pengyun; Zhang, Yichen; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

2017-06-06

Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information's relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT) for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images. After that, the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model. Then, using the Fuzzy Local Information C-means (FLICM) algorithm, the difference image is divided into the change area and unchanged area. The proposed algorithm is applied to a real remote sensing data set. The application results show that the proposed algorithm can effectively suppress clutter noise, and retain more detailed information from the original images. The proposed algorithm has higher detection accuracy than the Markov Random Field-Fuzzy C-means (MRF-FCM), the non-subsampled contourlet transform-Fuzzy C-means clustering (NSCT-FCM), the pointwise approach and graph theory (PA-GT), and the Principal Component Analysis-Nonlocal Means (PCA-NLM) denosing algorithm. Finally, the five algorithms are used to detect the southern boundary of the Gurbantunggut Desert in Xinjiang Uygur Autonomous Region of China, and the results show that the proposed algorithm has the best effect on real remote sensing image change detection.

Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application

PubMed Central

Chen, Pengyun; Zhang, Yichen; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

2017-01-01

Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information’s relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT) for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images. After that, the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model. Then, using the Fuzzy Local Information C-means (FLICM) algorithm, the difference image is divided into the change area and unchanged area. The proposed algorithm is applied to a real remote sensing data set. The application results show that the proposed algorithm can effectively suppress clutter noise, and retain more detailed information from the original images. The proposed algorithm has higher detection accuracy than the Markov Random Field-Fuzzy C-means (MRF-FCM), the non-subsampled contourlet transform-Fuzzy C-means clustering (NSCT-FCM), the pointwise approach and graph theory (PA-GT), and the Principal Component Analysis-Nonlocal Means (PCA-NLM) denosing algorithm. Finally, the five algorithms are used to detect the southern boundary of the Gurbantunggut Desert in Xinjiang Uygur Autonomous Region of China, and the results show that the proposed algorithm has the best effect on real remote sensing image change detection. PMID:28587299

[Application of optical flow dynamic texture in land use/cover change detection].

PubMed

Yan, Li; Gong, Yi-Long; Zhang, Yi; Duan, Wei

2014-11-01

In the present study, a novel change detection approach for high resolution remote sensing images is proposed based on the optical flow dynamic texture (OFDT), which could achieve the land use & land cover change information automatically with a dynamic description of ground-object changes. This paper describes the ground-object gradual change process from the principle using optical flow theory, which breaks the ground-object sudden change hypothesis in remote sensing change detection methods in the past. As the steps of this method are simple, it could be integrated in the systems and software such as Land Resource Management and Urban Planning software that needs to find ground-object changes. This method takes into account the temporal dimension feature between remote sensing images, which provides a richer set of information for remote sensing change detection, thereby improving the status that most of the change detection methods are mainly dependent on the spatial dimension information. In this article, optical flow dynamic texture is the basic reflection of changes, and it is used in high resolution remote sensing image support vector machine post-classification change detection, combined with spectral information. The texture in the temporal dimension which is considered in this article has a smaller amount of data than most of the textures in the spatial dimensions. The highly automated texture computing has only one parameter to set, which could relax the onerous manual evaluation present status. The effectiveness of the proposed approach is evaluated with the 2011 and 2012 QuickBird datasets covering Duerbert Mongolian Autonomous County of Daqing City, China. Then, the effects of different optical flow smooth coefficient and the impact on the description of the ground-object changes in the method are deeply analyzed: The experiment result is satisfactory, with an 87.29% overall accuracy and an 0.850 7 Kappa index, and the method achieves better performance than the post-classification change detection methods using spectral information only.

Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters

NASA Astrophysics Data System (ADS)

Chitsobhuk, Orachat; Kasemsiri, Watjanapong; Glomglome, Sorayut; Lapamonpinyo, Pipatphon

2018-04-01

In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user's defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.

Detecting Montane Meadows in the Tahoe National Forest Using LiDAR and ASTER Imagery

NASA Astrophysics Data System (ADS)

Lorenz, A.; Blesius, L.; Davis, J. D.

2016-12-01

In the Sierra Nevada mountains, meadows provide numerous hydraulic and ecosystem functions such as flood attenuation, groundwater storage, and wildlife habitat. However, many meadows have been degraded from historical land use such as water diversion, grazing, and logging. Land managers have altered management strategies for restoration purposes, but there is a lack of comprehensive data on meadow locations. Previous attempts to inventory Sierra Nevada meadows have included several remote sensing techniques including heads up digitizing and pixel based image analysis, but this has been challenging due to geographic variability, seasonal changes, and meadow health. I present a remote sensing method using multiple return LiDAR (Light Detection and Ranging) and ASTER imagery to detect montane meadows in a subset of the Tahoe National Forest. The project used LiDAR data to create a digital terrain model and digital surface model. From these models, I derived canopy height, surface slope, and watercourse for the entire study area. Literature queries returned known values for canopy height and surface slope characteristic of montane meadows. These values were used to select for possible meadows within the study area. To filter out noise, only contiguous areas greater than one acre that satisfied the queries were used. Finally, 15-meter ASTER imagery was used to de-select for areas such as dirt patches or gravel bars that might have satisfied the previous queries and meadow criteria. When using high resolution aerial imagery to assess model accuracy, preliminary results show user accuracy of greater than 80%. Further validation is still needed to improve the accuracy of modeled meadow delineation. This method allows for meadows to be inventoried without discriminating based on geographic variability, seasonal changes, or meadow health.

Change Detection Based on Persistent Scatterer Interferometry - a New Method of Monitoring Building Changes

NASA Astrophysics Data System (ADS)

Yang, C. H.; Kenduiywo, B. K.; Soergel, U.

2016-06-01

Persistent Scatterer Interferometry (PSI) is a technique to detect a network of extracted persistent scatterer (PS) points which feature temporal phase stability and strong radar signal throughout time-series of SAR images. The small surface deformations on such PS points are estimated. PSI particularly works well in monitoring human settlements because regular substructures of man-made objects give rise to large number of PS points. If such structures and/or substructures substantially alter or even vanish due to big change like construction, their PS points are discarded without additional explorations during standard PSI procedure. Such rejected points are called big change (BC) points. On the other hand, incoherent change detection (ICD) relies on local comparison of multi-temporal images (e.g. image difference, image ratio) to highlight scene modifications of larger size rather than detail level. However, image noise inevitably degrades ICD accuracy. We propose a change detection approach based on PSI to synergize benefits of PSI and ICD. PS points are extracted by PSI procedure. A local change index is introduced to quantify probability of a big change for each point. We propose an automatic thresholding method adopting change index to extract BC points along with a clue of the period they emerge. In the end, PS ad BC points are integrated into a change detection image. Our method is tested at a site located around north of Berlin main station where steady, demolished, and erected building substructures are successfully detected. The results are consistent with ground truth derived from time-series of aerial images provided by Google Earth. In addition, we apply our technique for traffic infrastructure, business district, and sports playground monitoring.

Accuracy comparison among different machine learning techniques for detecting malicious codes

NASA Astrophysics Data System (ADS)

Narang, Komal

2016-03-01

In this paper, a machine learning based model for malware detection is proposed. It can detect newly released malware i.e. zero day attack by analyzing operation codes on Android operating system. The accuracy of Naïve Bayes, Support Vector Machine (SVM) and Neural Network for detecting malicious code has been compared for the proposed model. In the experiment 400 benign files, 100 system files and 500 malicious files have been used to construct the model. The model yields the best accuracy 88.9% when neural network is used as classifier and achieved 95% and 82.8% accuracy for sensitivity and specificity respectively.

Progress in the detection of neoplastic progress and cancer by Raman spectroscopy

NASA Astrophysics Data System (ADS)

Bakker Schut, Tom C.; Stone, Nicholas; Kendall, Catherine A.; Barr, Hugh; Bruining, Hajo A.; Puppels, Gerwin J.

2000-05-01

Early detection of cancer is important because of the improved survival rates when the cancer is treated early. We study the application of NIR Raman spectroscopy for detection of dysplasia because this technique is sensitive to the small changes in molecular invasive in vivo detection using fiber-optic probes. The result of an in vitro study to detect neoplastic progress of esophageal Barrett's esophageal tissue will be presented. Using multivariate statistics, we developed three different linear discriminant analysis classification models to predict tissue type on the basis of the measured spectrum. Spectra of normal, metaplastic and dysplasia tissue could be discriminated with an accuracy of up to 88 percent. Therefore Raman spectroscopy seems to be a very suitable technique to detect dysplasia in Barrett's esophageal tissue.

Diagnostic accuracy of fused positron emission tomography/magnetic resonance mammography: initial results.

PubMed

Heusner, T A; Hahn, S; Jonkmanns, C; Kuemmel, S; Otterbach, F; Hamami, M E; Stahl, A R; Bockisch, A; Forsting, M; Antoch, G

2011-02-01

The aim of this study was to evaluate the diagnostic accuracy of fused fluoro-deoxy-D-glucose positron emission tomography/magnetic resonance mammography (FDG-PET/MRM) in breast cancer patients and to compare FDG-PET/MRM with MRM. 27 breast cancer patients (mean age 58.9±9.9 years) underwent MRM and prone FDG-PET. Images were fused software-based to FDG-PET/MRM images. Histopathology served as the reference standard to define the following parameters for both MRM and FDG-PET/MRM: sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy for the detection of breast cancer lesions. Furthermore, the number of patients with correctly determined lesion focality was assessed. Differences between both modalities were assessed by McNemaŕs test (p<0.05). The number of patients in whom FDG-PET/MRM would have changed the surgical approach was determined. 58 breast lesions were evaluated. The sensitivity, specificity, PPV, NPV and accuracy were 93%, 60%, 87%, 75% and 85% for MRM, respectively. For FDG-PET/MRM they were 88%, 73%, 90%, 69% and 92%, respectively. FDG-PET/MRM was as accurate for lesion detection (p = 1) and determination of the lesions' focality (p = 0.7722) as MRM. In only 1 patient FDG-PET/MRM would have changed the surgical treatment. FDG-PET/MRM is as accurate as MRM for the evaluation of local breast cancer. FDG-PET/MRM defines the tumours' focality as accurately as MRM and may have an impact on the surgical treatment in only a small portion of patients. Based on these results, FDG-PET/MRM cannot be recommended as an adjunct or alternative to MRM.

Limits of Spatial Attention in Three-Dimensional Space and Dual-task Driving Performance

PubMed Central

Andersen, George J.; Ni, Rui; Bian, Zheng; Kang, Julie

2010-01-01

The present study examined the limits of spatial attention while performing two driving relevant tasks that varied in depth. The first task was to maintain a fixed headway distance behind a lead vehicle that varied speed. The second task was to detect a light-change target in an array of lights located above the roadway. In Experiment 1 the light detection task required drivers to encode color and location. The results indicated that reaction time to detect a light-change target increased and accuracy decreased as a function of the horizontal location of the light-change target and as a function of the distance from the driver. In a second experiment the light change task was changed to a singleton search (detect the onset of a yellow light) and the workload of the car following task was systematically varied. The results of Experiment 2 indicated that RT increased as a function of task workload, the 2D position of the light-change target and the distance of the light-change target. A multiple regression analysis indicated that the effect of distance on light detection performance was not due to changes in the projected size of the light target. In Experiment 3 we found that the distance effect in detecting a light change could not be explained by the location of eye fixations. The results demonstrate that when drivers attend to a roadway scene attention is limited in three-dimensional space. These results have important implications for developing tests for assessing crash risk among drivers as well as the design of in vehicle technologies such as head-up displays. PMID:21094336

Feasibility of Multispectral Airborne Laser Scanning for Land Cover Classification, Road Mapping and Map Updating

NASA Astrophysics Data System (ADS)

Matikainen, L.; Karila, K.; Hyyppä, J.; Puttonen, E.; Litkey, P.; Ahokas, E.

2017-10-01

This article summarises our first results and experiences on the use of multispectral airborne laser scanner (ALS) data. Optech Titan multispectral ALS data over a large suburban area in Finland were acquired on three different dates in 2015-2016. We investigated the feasibility of the data from the first date for land cover classification and road mapping. Object-based analyses with segmentation and random forests classification were used. The potential of the data for change detection of buildings and roads was also demonstrated. The overall accuracy of land cover classification results with six classes was 96 % compared with validation points. The data also showed high potential for road detection, road surface classification and change detection. The multispectral intensity information appeared to be very important for automated classifications. Compared to passive aerial images, the intensity images have interesting advantages, such as the lack of shadows. Currently, we focus on analyses and applications with the multitemporal multispectral data. Important questions include, for example, the potential and challenges of the multitemporal data for change detection.

Accuracy of mini peak flow meters in indicating changes in lung function in children with asthma.

PubMed Central

Sly, P. D.; Cahill, P.; Willet, K.; Burton, P.

1994-01-01

OBJECTIVE--To assess whether mini flow meters used to measure peak expiratory flow can track changes in lung function and indicate clinically important changes. DESIGN--Comparison of measurements with a spirometer and different brands of mini flow meter; the meters were allocated to subjects haphazardly. SUBJECTS--12 boys with asthma aged 11 to 17 attending boarding school. MAIN OUTCOME MEASURES--Peak expiratory flow measured twice daily for three months with a spirometer and at least one of four brands of mini flow meter. RESULTS--The relation between changes in lung function measured with the spirometer and those measured with the mini flow meters was generally poor. In all, 26 episodes (range 1-3 in an individual child) of clinically important deterioration in lung function were detected from the records obtained with the spirometer. One mini flow meter detected six of 19 episodes, one detected six of 15, one detected six of 18, and one detected three of 21. CONCLUSIONS--Not only are the absolute values of peak expiratory flow obtained with mini flow meters inaccurate but the clinical message may also be incorrect. These findings do not imply that home monitoring of peak expiratory flow has no place in the management of childhood asthma but that the values obtained should be interpreted cautiously. PMID:8148680

Family History of Alzheimer's Disease is Associated with Impaired Perceptual Discrimination of Novel Objects.

PubMed

Mason, Emily J; Hussey, Erin P; Molitor, Robert J; Ko, Philip C; Donahue, Manus J; Ally, Brandon A

2017-01-01

Early detection may be the key to developing therapies that will combat Alzheimer's disease (AD). It has been consistently demonstrated that one of the main pathologies of AD, tau, is present in the brain decades before a clinical diagnosis. Tau pathology follows a stereotypical route through the medial temporal lobe beginning in the entorhinal and perirhinal cortices. If early pathology leads to very subtle changes in behavior, it may be possible to detect these changes in subjects years before a clinical diagnosis can currently be made. We aimed to discover if cognitively normal middle-aged adults (40-60 years old) at increased risk for AD due to family history would have impaired performance on a cognitive task known to challenge the perirhinal cortex. Using an oddity detection task, we found that subjects with a family history of AD had lowered accuracy without demonstrating differences in rate of acquisition. There were no differences between subjects' medial temporal lobe volume or cortical thickness, indicating that the changes in behavior were not due to significant atrophy. These results demonstrate that subtle changes in perceptual processing are detectable years before a typical diagnosis even when there are no differences detectable in structural imaging data. Anatomically-targeted cognitive testing may be useful in identifying subjects in the earliest stages of AD.

Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds.

PubMed

Tran, Thi Huong Giang; Ressl, Camillo; Pfeifer, Norbert

2018-02-03

This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning, and features combining the point clouds of both epochs to identify the change. All these features are merged in the points and then training samples are acquired to create the model for supervised classification, which is then applied to the whole study area. The final results reach an overall accuracy of over 90% for both epochs of eight classes: lost tree, new tree, lost building, new building, changed ground, unchanged building, unchanged tree, and unchanged ground.

Monitoring Farmland Loss Caused by Urbanization in Beijing from Modis Time Series Using Hierarchical Hidden Markov Model

NASA Astrophysics Data System (ADS)

Yuan, Y.; Meng, Y.; Chen, Y. X.; Jiang, C.; Yue, A. Z.

2018-04-01

In this study, we proposed a method to map urban encroachment onto farmland using satellite image time series (SITS) based on the hierarchical hidden Markov model (HHMM). In this method, the farmland change process is decomposed into three hierarchical levels, i.e., the land cover level, the vegetation phenology level, and the SITS level. Then a three-level HHMM is constructed to model the multi-level semantic structure of farmland change process. Once the HHMM is established, a change from farmland to built-up could be detected by inferring the underlying state sequence that is most likely to generate the input time series. The performance of the method is evaluated on MODIS time series in Beijing. Results on both simulated and real datasets demonstrate that our method improves the change detection accuracy compared with the HMM-based method.

Cest Analysis: Automated Change Detection from Very-High Remote Sensing Images

NASA Astrophysics Data System (ADS)

Ehlers, M.; Klonus, S.; Jarmer, T.; Sofina, N.; Michel, U.; Reinartz, P.; Sirmacek, B.

2012-08-01

A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye) new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST) analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT) and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment) with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST) of the change algorithms is applied to calculate the probability of change for a particular location. CEST was tested with high-resolution satellite images of the crisis areas of Darfur (Sudan). CEST results are compared with a number of standard algorithms for automated change detection such as image difference, image ratioe, principal component analysis, delta cue technique and post classification change detection. The new combined method shows superior results averaging between 45% and 15% improvement in accuracy.

Mapping and improving frequency, accuracy, and interpretation of land cover change: Classifying coastal Louisiana with 1990, 1993, 1996, and 1999 Landsat Thematic Mapper image data

USGS Publications Warehouse

Nelson, G.; Ramsey, Elijah W.; Rangoonwala, A.

2005-01-01

Landsat Thematic Mapper images and collateral data sources were used to classify the land cover of the Mermentau River Basin within the chenier coastal plain and the adjacent uplands of Louisiana, USA. Landcover classes followed that of the National Oceanic and Atmospheric Administration's Coastal Change Analysis Program; however, classification methods needed to be developed to meet these national standards. Our first classification was limited to the Mermentau River Basin (MRB) in southcentral Louisiana, and the years of 1990, 1993, and 1996. To overcome problems due to class spectral inseparable, spatial and spectra continuums, mixed landcovers, and abnormal transitions, we separated the coastal area into regions of commonality and applying masks to specific land mixtures. Over the three years and 14 landcover classes (aggregating the cultivated land and grassland, and water and floating vegetation classes), overall accuracies ranged from 82% to 90%. To enhance landcover change interpretation, three indicators were introduced as Location Stability, Residence stability, and Turnover. Implementing methods substantiated in the multiple date MRB classification, we spatially extended the classification to the entire Louisiana coast and temporally extended the original 1990, 1993, 1996 classifications to 1999 (Figure 1). We also advanced the operational functionality of the classification and increased the credibility of change detection results. Increased operational functionality that resulted in diminished user input was for the most part gained by implementing a classification logic based on forbidden transitions. The logic detected and corrected misclassifications and mostly alleviated the necessity of subregion separation prior to the classification. The new methods provided an improved ability for more timely detection and response to landcover impact. ?? 2005 IEEE.

An information-theoretic approach to designing the plane spacing for multifocal plane microscopy

PubMed Central

Tahmasbi, Amir; Ram, Sripad; Chao, Jerry; Abraham, Anish V.; Ward, E. Sally; Ober, Raimund J.

2015-01-01

Multifocal plane microscopy (MUM) is a 3D imaging modality which enables the localization and tracking of single molecules at high spatial and temporal resolution by simultaneously imaging distinct focal planes within the sample. MUM overcomes the depth discrimination problem of conventional microscopy and allows high accuracy localization of a single molecule in 3D along the z-axis. An important question in the design of MUM experiments concerns the appropriate number of focal planes and their spacings to achieve the best possible 3D localization accuracy along the z-axis. Ideally, it is desired to obtain a 3D localization accuracy that is uniform over a large depth and has small numerical values, which guarantee that the single molecule is continuously detectable. Here, we address this concern by developing a plane spacing design strategy based on the Fisher information. In particular, we analyze the Fisher information matrix for the 3D localization problem along the z-axis and propose spacing scenarios termed the strong coupling and the weak coupling spacings, which provide appropriate 3D localization accuracies. Using these spacing scenarios, we investigate the detectability of the single molecule along the z-axis and study the effect of changing the number of focal planes on the 3D localization accuracy. We further review a software module we recently introduced, the MUMDesignTool, that helps to design the plane spacings for a MUM setup. PMID:26113764

SU-E-T-144: Effective Analysis of VMAT QA Generated Trajectory Log Files for Medical Accelerator Predictive Maintenance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Able, CM; Baydush, AH; Nguyen, C

Purpose: To determine the effectiveness of SPC analysis for a model predictive maintenance process that uses accelerator generated parameter and performance data contained in trajectory log files. Methods: Each trajectory file is decoded and a total of 131 axes positions are recorded (collimator jaw position, gantry angle, each MLC, etc.). This raw data is processed and either axis positions are extracted at critical points during the delivery or positional change over time is used to determine axis velocity. The focus of our analysis is the accuracy, reproducibility and fidelity of each axis. A reference positional trace of the gantry andmore » each MLC is used as a motion baseline for cross correlation (CC) analysis. A total of 494 parameters (482 MLC related) were analyzed using Individual and Moving Range (I/MR) charts. The chart limits were calculated using a hybrid technique that included the use of the standard 3σ limits and parameter/system specifications. Synthetic errors/changes were introduced to determine the initial effectiveness of I/MR charts in detecting relevant changes in operating parameters. The magnitude of the synthetic errors/changes was based on: TG-142 and published analysis of VMAT delivery accuracy. Results: All errors introduced were detected. Synthetic positional errors of 2mm for collimator jaw and MLC carriage exceeded the chart limits. Gantry speed and each MLC speed are analyzed at two different points in the delivery. Simulated Gantry speed error (0.2 deg/sec) and MLC speed error (0.1 cm/sec) exceeded the speed chart limits. Gantry position error of 0.2 deg was detected by the CC maximum value charts. The MLC position error of 0.1 cm was detected by the CC maximum value location charts for every MLC. Conclusion: SPC I/MR evaluation of trajectory log file parameters may be effective in providing an early warning of performance degradation or component failure for medical accelerator systems.« less

Land use mapping and modelling for the Phoenix Quadrangle

NASA Technical Reports Server (NTRS)

Place, J. L. (Principal Investigator)

1973-01-01

The author has identified the following significant results. The land use of the Phoenix Quadrangle in Arizona had been mapped previously from aerial photographs and recorded in a computer data bank. During the ERTS-1 experiment, changes in land use were detected using only the ERTS-1 images. The I2S color additive viewer was used as the principal image enhancement tool, operated in a multispectral mode. Hard copy color composite images of the best multiband combinations from ERTS-1 were made by photographic and diazo processes. The I2S viewer was also used to enhance changes between successive images by quick flip techniques or by registering with different color filters. More recently, a Bausch and Lomb zoom transferscope has been used for the same purpose. Improved interpretation of land use change resulted, and a map of changes within the Phoenix Quadrangle was compiled. The first level of a proposed standard land use classification system was sucessfully used. ERTS-1 underflight photography was used to check the accuracy of the ERTS-1 image interpretation. It was found that the total areas of change detected in the photos were comparable with the total areas of change detected in the ERTS-1 images.

Snow cover monitoring by machine processing of multitemporal LANDSAT MSS data

NASA Technical Reports Server (NTRS)

Luther, S. G.; Bartolucci, L. A.; Hoffer, R. M.

1975-01-01

LANDSAT frames were geometrically corrected and data sets from six different dates were overlaid to produce a 24 channel (six dates and four wavelength bands) data tape. Changes in the extent of the snowpack could be accurately and easily determined using a change detection technique on data which had previously been classified by the LARSYS software system. A second phase of the analysis involved determination of the relationship between spatial resolution or data sampling frequency and accuracy of measuring the area of the snowpack.

Rapid systematic assessment of the detection and attribution of regional anthropogenic climate change

DOE PAGES

Stone, Daithi A.; Hansen, Gerrit

2015-11-21

Despite being a well-established research field, the detection and attribution of observed climate change to anthropogenic forcing is not yet provided as a climate service. One reason for this is the lack of a methodology for performing tailored detection and attribution assessments on a rapid time scale. Here we develop such an approach, based on the translation of quantitative analysis into the “confidence” language employed in recent Assessment Reports of the Intergovernmental Panel on Climate Change. While its systematic nature necessarily ignores some nuances examined in detailed expert assessments, the approach nevertheless goes beyond most detection and attribution studies inmore » considering contributors to building confidence such as errors in observational data products arising from sparse monitoring networks. When compared against recent expert assessments, the results of this approach closely match those of the existing assessments. Where there are small discrepancies, these variously reflect ambiguities in the details of what is being assessed, reveal nuances or limitations of the expert assessments, or indicate limitations of the accuracy of the sort of systematic approach employed here. Deployment of the method on 116 regional assessments of recent temperature and precipitation changes indicates that existing rules of thumb concerning the detectability of climate change ignore the full range of sources of uncertainty, most particularly the importance of adequate observational monitoring.« less

Fourier Transform Mass Spectrometry: The Transformation of Modern Environmental Analyses

PubMed Central

Lim, Lucy; Yan, Fangzhi; Bach, Stephen; Pihakari, Katianna; Klein, David

2016-01-01

Unknown compounds in environmental samples are difficult to identify using standard mass spectrometric methods. Fourier transform mass spectrometry (FTMS) has revolutionized how environmental analyses are performed. With its unsurpassed mass accuracy, high resolution and sensitivity, researchers now have a tool for difficult and complex environmental analyses. Two features of FTMS are responsible for changing the face of how complex analyses are accomplished. First is the ability to quickly and with high mass accuracy determine the presence of unknown chemical residues in samples. For years, the field has been limited by mass spectrometric methods that were based on knowing what compounds of interest were. Secondly, by utilizing the high resolution capabilities coupled with the low detection limits of FTMS, analysts also could dilute the sample sufficiently to minimize the ionization changes from varied matrices. PMID:26784175

Increasing Deception Detection Accuracy with Strategic Questioning

ERIC Educational Resources Information Center

Levine, Timothy R.; Shaw, Allison; Shulman, Hillary C.

2010-01-01

One explanation for the finding of slightly above-chance accuracy in detecting deception experiments is limited variance in sender transparency. The current study sought to increase accuracy by increasing variance in sender transparency with strategic interrogative questioning. Participants (total N = 128) observed cheaters and noncheaters who…

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

PubMed Central

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

Laser-induced photo emission detection: data acquisition based on light intensity counting

NASA Astrophysics Data System (ADS)

Yulianto, N.; Yudasari, N.; Putri, K. Y.

2017-04-01

Laser Induced Breakdown Detection (LIBD) is one of the quantification techniques for colloids. There are two ways of detection in LIBD: optical detection and acoustic detection. LIBD is based on the detection of plasma emission due to the interaction between particle and laser beam. In this research, the changing of light intensity during plasma formations was detected by a photodiode sensor. A photo emission data acquisition system was built to collect and transform them into digital counts. The real-time system used data acquisition device National Instrument DAQ 6009 and LABVIEW software. The system has been tested on distilled water and tap water samples. The result showed 99.8% accuracy by using counting technique in comparison to the acoustic detection with sample rate of 10 Hz, thus the acquisition system can be applied as an alternative method to the existing LIBD acquisition system.

Video change detection for fixed wing UAVs

NASA Astrophysics Data System (ADS)

Bartelsen, Jan; Müller, Thomas; Ring, Jochen; Mück, Klaus; Brüstle, Stefan; Erdnüß, Bastian; Lutz, Bastian; Herbst, Theresa

2017-10-01

In this paper we proceed the work of Bartelsen et al.1 We present the draft of a process chain for an image based change detection which is designed for videos acquired by fixed wing unmanned aerial vehicles (UAVs). From our point of view, automatic video change detection for aerial images can be useful to recognize functional activities which are typically caused by the deployment of improvised explosive devices (IEDs), e.g. excavations, skid marks, footprints, left-behind tooling equipment, and marker stones. Furthermore, in case of natural disasters, like flooding, imminent danger can be recognized quickly. Due to the necessary flight range, we concentrate on fixed wing UAVs. Automatic change detection can be reduced to a comparatively simple photogrammetric problem when the perspective change between the "before" and "after" image sets is kept as small as possible. Therefore, the aerial image acquisition demands a mission planning with a clear purpose including flight path and sensor configuration. While the latter can be enabled simply by a fixed and meaningful adjustment of the camera, ensuring a small perspective change for "before" and "after" videos acquired by fixed wing UAVs is a challenging problem. Concerning this matter, we have performed tests with an advanced commercial off the shelf (COTS) system which comprises a differential GPS and autopilot system estimating the repetition accuracy of its trajectory. Although several similar approaches have been presented,23 as far as we are able to judge, the limits for this important issue are not estimated so far. Furthermore, we design a process chain to enable the practical utilization of video change detection. It consists of a front-end of a database to handle large amounts of video data, an image processing and change detection implementation, and the visualization of the results. We apply our process chain on the real video data acquired by the advanced COTS fixed wing UAV and synthetic data. For the image processing and change detection, we use the approach of Muller.4 Although it was developed for unmanned ground vehicles (UGVs), it enables a near real time video change detection for aerial videos. Concluding, we discuss the demands on sensor systems in the matter of change detection.

Plasmonic SERS nanochips and nanoprobes for medical diagnostics and bio-energy applications

NASA Astrophysics Data System (ADS)

Ngo, Hoan T.; Wang, Hsin-Neng; Crawford, Bridget M.; Fales, Andrew M.; Vo-Dinh, Tuan

2017-02-01

The development of rapid, easy-to-use, cost-effective, high accuracy, and high sensitive DNA detection methods for molecular diagnostics has been receiving increasing interest. Over the last five years, our laboratory has developed several chip-based DNA detection techniques including the molecular sentinel-on-chip (MSC), the multiplex MSC, and the inverse molecular sentinel-on-chip (iMS-on-Chip). In these techniques, plasmonic surface-enhanced Raman scattering (SERS) Nanowave chips were functionalized with DNA probes for single-step DNA detection. Sensing mechanisms were based on hybridization of target sequences and DNA probes, resulting in a distance change between SERS reporters and the Nanowave chip's gold surface. This distance change resulted in change in SERS intensity, thus indicating the presence and capture of the target sequences. Our techniques were single-step DNA detection techniques. Target sequences were detected by simple delivery of sample solutions onto DNA probe-functionalized Nanowave chips and SERS signals were measured after 1h - 2h incubation. Target sequence labeling or washing to remove unreacted components was not required, making the techniques simple, easy-to-use, and cost effective. The usefulness of the techniques for medical diagnostics was illustrated by the detection of genetic biomarkers for respiratory viral infection and of dengue virus 4 DNA.

Accounting for speed-accuracy tradeoff in perceptual learning

PubMed Central

Liu, Charles C.; Watanabe, Takeo

2011-01-01

In the perceptual learning (PL) literature, researchers typically focus on improvements in accuracy, such as d’. In contrast, researchers who investigate the practice of cognitive skills focus on improvements in response times (RT). Here, we argue for the importance of accounting for both accuracy and RT in PL experiments, due to the phenomenon of speed-accuracy tradeoff (SAT): at a given level of discriminability, faster responses tend to produce more errors. A formal model of the decision process, such as the diffusion model, can explain the SAT. In this model, a parameter known as the drift rate represents the perceptual strength of the stimulus, where higher drift rates lead to more accurate and faster responses. We applied the diffusion model to analyze responses from a yes-no coherent motion detection task. The results indicate that observers do not use a fixed threshold for evidence accumulation, so changes in the observed accuracy may not provide the most appropriate estimate of learning. Instead, our results suggest that SAT can be accounted for by a modeling approach, and that drift rates offer a promising index of PL. PMID:21958757

Change blindness, aging, and cognition

PubMed Central

Rizzo, Matthew; Sparks, JonDavid; McEvoy, Sean; Viamonte, Sarah; Kellison, Ida; Vecera, Shaun P.

2011-01-01

Change blindness (CB), the inability to detect changes in visual scenes, may increase with age and early Alzheimer’s disease (AD). To test this hypothesis, participants were asked to localize changes in natural scenes. Dependent measures were response time (RT), hit rate, false positives (FP), and true sensitivity (d′). Increased age correlated with increased sensitivity and RT; AD predicted even slower RT. Accuracy and RT were negatively correlated. Differences in FP were nonsignificant. CB correlated with impaired attention, working memory, and executive function. Advanced age and AD were associated with increased CB, perhaps due to declining memory and attention. CB could affect real-world tasks, like automobile driving. PMID:19051127

Change blindness, aging, and cognition.

PubMed

Rizzo, Matthew; Sparks, Jondavid; McEvoy, Sean; Viamonte, Sarah; Kellison, Ida; Vecera, Shaun P

2009-02-01

Change blindness (CB), the inability to detect changes in visual scenes, may increase with age and early Alzheimer's disease (AD). To test this hypothesis, participants were asked to localize changes in natural scenes. Dependent measures were response time (RT), hit rate, false positives (FP), and true sensitivity (d'). Increased age correlated with increased sensitivity and RT; AD predicted even slower RT. Accuracy and RT were negatively correlated. Differences in FP were nonsignificant. CB correlated with impaired attention, working memory, and executive function. Advanced age and AD were associated with increased CB, perhaps due to declining memory and attention. CB could affect real-world tasks, like automobile driving.

Measuring changes in Plasmodium falciparum transmission: Precision, accuracy and costs of metrics

PubMed Central

Tusting, Lucy S.; Bousema, Teun; Smith, David L.; Drakeley, Chris

2016-01-01

As malaria declines in parts of Africa and elsewhere, and as more countries move towards elimination, it is necessary to robustly evaluate the effect of interventions and control programmes on malaria transmission. To help guide the appropriate design of trials to evaluate transmission-reducing interventions, we review eleven metrics of malaria transmission, discussing their accuracy, precision, collection methods and costs, and presenting an overall critique. We also review the non-linear scaling relationships between five metrics of malaria transmission; the entomological inoculation rate, force of infection, sporozoite rate, parasite rate and the basic reproductive number, R0. Our review highlights that while the entomological inoculation rate is widely considered the gold standard metric of malaria transmission and may be necessary for measuring changes in transmission in highly endemic areas, it has limited precision and accuracy and more standardised methods for its collection are required. In areas of low transmission, parasite rate, sero-conversion rates and molecular metrics including MOI and mFOI may be most appropriate. When assessing a specific intervention, the most relevant effects will be detected by examining the metrics most directly affected by that intervention. Future work should aim to better quantify the precision and accuracy of malaria metrics and to improve methods for their collection. PMID:24480314

MO-FG-CAMPUS-TeP1-03: Pre-Treatment Surface Imaging Based Collision Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wiant, D; Maurer, J; Liu, H

2016-06-15

Purpose: Modern radiotherapy increasingly employs large immobilization devices, gantry attachments, and couch rotations for treatments. All of which raise the risk of collisions between the patient and the gantry / couch. Collision detection is often achieved by manually checking each couch position in the treatment room and sometimes results in extraneous imaging if collisions are detected after image based setup has begun. In the interest of improving efficiency and avoiding extra imaging, we explore the use of a surface imaging based collision detection model. Methods: Surfaces acquired from AlignRT (VisionRT, London, UK) were transferred in wavefront format to a custommore » Matlab (Mathworks, Natick, MA) software package (CCHECK). Computed tomography (CT) scans acquired at the same time were sent to CCHECK in DICOM format. In CCHECK, binary maps of the surfaces were created and overlaid on the CT images based on the fixed relationship of the AlignRT and CT coordinate systems. Isocenters were added through a graphical user interface (GUI). CCHECK then compares the inputted surfaces to a model of the linear accelerator (linac) to check for collisions at defined gantry and couch positions. Note, CCHECK may be used with or without a CT. Results: The nominal surface image field of view is 650 mm × 900 mm, with variance based on patient position and size. The accuracy of collision detections is primarily based on the linac model and the surface mapping process. The current linac model and mapping process yield detection accuracies on the order of 5 mm, assuming no change in patient posture between surface acquisition and treatment. Conclusions: CCHECK provides a non-ionizing method to check for collisions without the patient in the treatment room. Collision detection accuracy may be improved with more robust linac modeling. Additional gantry attachments (e.g. conical collimators) can be easily added to the model.« less

Adaptive classifier for steel strip surface defects

NASA Astrophysics Data System (ADS)

Jiang, Mingming; Li, Guangyao; Xie, Li; Xiao, Mang; Yi, Li

2017-01-01

Surface defects detection system has been receiving increased attention as its precision, speed and less cost. One of the most challenges is reacting to accuracy deterioration with time as aged equipment and changed processes. These variables will make a tiny change to the real world model but a big impact on the classification result. In this paper, we propose a new adaptive classifier with a Bayes kernel (BYEC) which update the model with small sample to it adaptive for accuracy deterioration. Firstly, abundant features were introduced to cover lots of information about the defects. Secondly, we constructed a series of SVMs with the random subspace of the features. Then, a Bayes classifier was trained as an evolutionary kernel to fuse the results from base SVMs. Finally, we proposed the method to update the Bayes evolutionary kernel. The proposed algorithm is experimentally compared with different algorithms, experimental results demonstrate that the proposed method can be updated with small sample and fit the changed model well. Robustness, low requirement for samples and adaptive is presented in the experiment.

Piezoresistive position microsensors with ppm-accuracy

NASA Astrophysics Data System (ADS)

Stavrov, Vladimir; Shulev, Assen; Stavreva, Galina; Todorov, Vencislav

2015-05-01

In this article, the relation between position accuracy and the number of simultaneously measured values, such as coordinates, has been analyzed. Based on this, a conceptual layout of MEMS devices (microsensors) for multidimensional position monitoring comprising a single anchored and a single actuated part has been developed. Both parts are connected with a plurality of micromechanical flexures, and each flexure includes position detecting cantilevers. Microsensors having detecting cantilevers oriented in X and Y direction have been designed and prototyped. Experimentally measured results at characterization of 1D, 2D and 3D position microsensors are reported as well. Exploiting different flexure layouts, a travel range between 50μm and 1.8mm and sensors' sensitivity in the range between 30μV/μm and 5mV/μm@ 1V DC supply voltage have been demonstrated. A method for accurate calculation of all three Cartesian coordinates, based on measurement of at least three microsensors' signals has also been described. The analyses of experimental results prove the capability of position monitoring with ppm-(part per million) accuracy. The technology for fabrication of MEMS devices with sidewall embedded piezoresistors removes restrictions in strong improvement of their usability for position sensing with a high accuracy. The present study is, also a part of a common strategy for developing a novel MEMS-based platform for simultaneous accurate measurement of various physical values when they are transduced to a change of position.

Object-based change detection method using refined Markov random field

NASA Astrophysics Data System (ADS)

Peng, Daifeng; Zhang, Yongjun

2017-01-01

In order to fully consider the local spatial constraints between neighboring objects in object-based change detection (OBCD), an OBCD approach is presented by introducing a refined Markov random field (MRF). First, two periods of images are stacked and segmented to produce image objects. Second, object spectral and textual histogram features are extracted and G-statistic is implemented to measure the distance among different histogram distributions. Meanwhile, object heterogeneity is calculated by combining spectral and textual histogram distance using adaptive weight. Third, an expectation-maximization algorithm is applied for determining the change category of each object and the initial change map is then generated. Finally, a refined change map is produced by employing the proposed refined object-based MRF method. Three experiments were conducted and compared with some state-of-the-art unsupervised OBCD methods to evaluate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method obtains the highest accuracy among the methods used in this paper, which confirms its validness and effectiveness in OBCD.

Validation of distal limb mounted inertial measurement unit sensors for stride detection in Warmblood horses at walk and trot.

PubMed

Bragança, F M; Bosch, S; Voskamp, J P; Marin-Perianu, M; Van der Zwaag, B J; Vernooij, J C M; van Weeren, P R; Back, W

2017-07-01

Inertial measurement unit (IMU) sensor-based techniques are becoming more popular in horses as a tool for objective locomotor assessment. To describe, evaluate and validate a method of stride detection and quantification at walk and trot using distal limb mounted IMU sensors. Prospective validation study comparing IMU sensors and motion capture with force plate data. A total of seven Warmblood horses equipped with metacarpal/metatarsal IMU sensors and reflective markers for motion capture were hand walked and trotted over a force plate. Using four custom built algorithms hoof-on/hoof-off timing over the force plate were calculated for each trial from the IMU data. Accuracy of the computed parameters was calculated as the mean difference in milliseconds between the IMU or motion capture generated data and the data from the force plate, precision as the s.d. of these differences and percentage of error with accuracy of the calculated parameter as a percentage of the force plate stance duration. Accuracy, precision and percentage of error of the best performing IMU algorithm for stance duration at walk were 28.5, 31.6 ms and 3.7% for the forelimbs and -5.5, 20.1 ms and -0.8% for the hindlimbs, respectively. At trot the best performing algorithm achieved accuracy, precision and percentage of error of -27.6/8.8 ms/-8.4% for the forelimbs and 6.3/33.5 ms/9.1% for the hindlimbs. The described algorithms have not been assessed on different surfaces. Inertial measurement unit technology can be used to determine temporal kinematic stride variables at walk and trot justifying its use in gait and performance analysis. However, precision of the method may not be sufficient to detect all possible lameness-related changes. These data seem promising enough to warrant further research to evaluate whether this approach will be useful for appraising the majority of clinically relevant gait changes encountered in practice. © 2016 The Authors. Equine Veterinary Journal published by John Wiley & Sons Ltd on behalf of EVJ Ltd.

A Smartphone Client-Server Teleradiology System for Primary Diagnosis of Acute Stroke

PubMed Central

2011-01-01

Background Recent advances in the treatment of acute ischemic stroke have made rapid acquisition, visualization, and interpretation of images a key factor for positive patient outcomes. We have developed a new teleradiology system based on a client-server architecture that enables rapid access to interactive advanced 2-D and 3-D visualization on a current generation smartphone device (Apple iPhone or iPod Touch, or an Android phone) without requiring patient image data to be stored on the device. Instead, a server loads and renders the patient images, then transmits a rendered frame to the remote device. Objective Our objective was to determine if a new smartphone client-server teleradiology system is capable of providing accuracies and interpretation times sufficient for diagnosis of acute stroke. Methods This was a retrospective study. We obtained 120 recent consecutive noncontrast computed tomography (NCCT) brain scans and 70 computed tomography angiogram (CTA) head scans from the Calgary Stroke Program database. Scans were read by two neuroradiologists, one on a medical diagnostic workstation and an iPod or iPhone (hereafter referred to as an iOS device) and the other only on an iOS device. NCCT brain scans were evaluated for early signs of infarction, which includes early parenchymal ischemic changes and dense vessel sign, and to exclude acute intraparenchymal hemorrhage and stroke mimics. CTA brain scans were evaluated for any intracranial vessel occlusion. The interpretations made on an iOS device were compared with those made at a workstation. The total interpretation times were recorded for both platforms. Interrater agreement was assessed. True positives, true negatives, false positives, and false negatives were obtained, and sensitivity, specificity, and accuracy of detecting the abnormalities on the iOS device were computed. Results The sensitivity, specificity, and accuracy of detecting intraparenchymal hemorrhage were 100% using the iOS device with a perfect interrater agreement (kappa = 1). The sensitivity, specificity, and accuracy of detecting acute parenchymal ischemic change were 94.1%, 100%, and 98.09% respectively for reader 1 and 97.05%, 100%, and 99.04% for reader 2 with nearly perfect interrater agreement (kappa = .8). The sensitivity, specificity, and accuracy of detecting dense vessel sign were 100%, 95.4%, and 96.19% respectively for reader 1 and 72.2%, 100%, and 95.23% for reader 2 using the iOS device with a good interrater agreement (kappa = .69). The sensitivity, specificity, and accuracy of detecting vessel occlusion on CT angiography scans were 94.4%, 100%, and 98.46% respectively for both readers using the iOS device, with perfect interrater agreement (kappa = 1). No significant difference (P < .05) was noted in the interpretation time between the workstation and iOS device. Conclusion The smartphone client-server teleradiology system appears promising and may have the potential to allow urgent management decisions in acute stroke. However, this study was retrospective, involved relatively few patient studies, and only two readers. Generalizing conclusions about its clinical utility, especially in other diagnostic use cases, should not be made until additional studies are performed. PMID:21550961

A smartphone client-server teleradiology system for primary diagnosis of acute stroke.

PubMed

Mitchell, J Ross; Sharma, Pranshu; Modi, Jayesh; Simpson, Mark; Thomas, Monroe; Hill, Michael D; Goyal, Mayank

2011-05-06

Recent advances in the treatment of acute ischemic stroke have made rapid acquisition, visualization, and interpretation of images a key factor for positive patient outcomes. We have developed a new teleradiology system based on a client-server architecture that enables rapid access to interactive advanced 2-D and 3-D visualization on a current generation smartphone device (Apple iPhone or iPod Touch, or an Android phone) without requiring patient image data to be stored on the device. Instead, a server loads and renders the patient images, then transmits a rendered frame to the remote device. Our objective was to determine if a new smartphone client-server teleradiology system is capable of providing accuracies and interpretation times sufficient for diagnosis of acute stroke. This was a retrospective study. We obtained 120 recent consecutive noncontrast computed tomography (NCCT) brain scans and 70 computed tomography angiogram (CTA) head scans from the Calgary Stroke Program database. Scans were read by two neuroradiologists, one on a medical diagnostic workstation and an iPod or iPhone (hereafter referred to as an iOS device) and the other only on an iOS device. NCCT brain scans were evaluated for early signs of infarction, which includes early parenchymal ischemic changes and dense vessel sign, and to exclude acute intraparenchymal hemorrhage and stroke mimics. CTA brain scans were evaluated for any intracranial vessel occlusion. The interpretations made on an iOS device were compared with those made at a workstation. The total interpretation times were recorded for both platforms. Interrater agreement was assessed. True positives, true negatives, false positives, and false negatives were obtained, and sensitivity, specificity, and accuracy of detecting the abnormalities on the iOS device were computed. The sensitivity, specificity, and accuracy of detecting intraparenchymal hemorrhage were 100% using the iOS device with a perfect interrater agreement (kappa=1). The sensitivity, specificity, and accuracy of detecting acute parenchymal ischemic change were 94.1%, 100%, and 98.09% respectively for reader 1 and 97.05%, 100%, and 99.04% for reader 2 with nearly perfect interrater agreement (kappa=.8). The sensitivity, specificity, and accuracy of detecting dense vessel sign were 100%, 95.4%, and 96.19% respectively for reader 1 and 72.2%, 100%, and 95.23% for reader 2 using the iOS device with a good interrater agreement (kappa=.69). The sensitivity, specificity, and accuracy of detecting vessel occlusion on CT angiography scans were 94.4%, 100%, and 98.46% respectively for both readers using the iOS device, with perfect interrater agreement (kappa=1). No significant difference (P<.05) was noted in the interpretation time between the workstation and iOS device. The smartphone client-server teleradiology system appears promising and may have the potential to allow urgent management decisions in acute stroke. However, this study was retrospective, involved relatively few patient studies, and only two readers. Generalizing conclusions about its clinical utility, especially in other diagnostic use cases, should not be made until additional studies are performed. ©J Ross Mitchell, Pranshu Sharma, Jayesh Modi, Mark Simpson, Monroe Thomas, Michael D. Hill, Mayank Goyal.

Predicting Future Morphological Changes of Lesions from Radiotracer Uptake in 18F-FDG-PET Images

PubMed Central

Bagci, Ulas; Yao, Jianhua; Miller-Jaster, Kirsten; Chen, Xinjian; Mollura, Daniel J.

2013-01-01

We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on 18F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 18F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75±1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUVmax (p<0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUVmax. We also found that integrating textural features with SUV measurements significantly improves the prediction accuracy of morphological changes (Spearman correlation coefficient = 0.8715, p<2e-16). PMID:23431398

An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection

NASA Astrophysics Data System (ADS)

Cook, Kristen L.

2017-02-01

The measurement of topography and of topographic change is essential for the study of many geomorphic processes. In recent years, structure from motion (SfM) techniques applied to photographs taken by camera-equipped unmanned aerial vehicles (UAVs) has become a powerful new tool for the generation of high resolution topography. The variety of available UAV systems continues to increase rapidly, but it is not clear whether increased UAV sophistication translates into improved quality of the calculated topography. To evaluate the lower end of the UAV spectrum, a simple low cost UAV was deployed to calculate high resolution topography in the Daan River gorge in western Taiwan, a site with a complicated 3D morphology and a wide range of surface types, making it a challenging site for topographic measurement. Terrestrial lidar surveys were conducted in parallel with UAV surveys in both June and November 2014, enabling an assessment of the reliability of the UAV survey to detect geomorphic changes in the range of 30 cm to several meters. A further UAV survey was conducted in June 2015 in order to quantify changes resulting from the 2015 spring monsoon. To evaluate the accuracy of the UAV derived topography, it was compared to terrestrial lidar data collected during the same survey period using the cloud-to-cloud comparison algorithm M3C2. The UAV-generated point clouds match the lidar point clouds well, with RMS errors of 30-40 cm; however, the accuracy of the SfM point clouds depends strongly on the characteristics of the surface being considered, with vegetation, water, and small scale texture causing inaccuracies. The lidar and SfM data yield similar maps of change from June to November 2014, with the same areas of geomorphic change detected by both methods. The SfM-generated change map for November 2014 to June 2015 indicates that the 2015 spring monsoon caused erosion throughout the gorge and highlights the importance of event-driven erosion in the Daan River. The results suggest that even very basic UAVs can yield data suitable for measuring geomorphic change on the scale of a channel reach.

Additive value of 3T cardiovascular magnetic resonance coronary angiography for detecting coronary artery disease.

PubMed

Zhang, Lijun; Song, Xiantao; Dong, Li; Li, Jianan; Dou, Ruiyu; Fan, Zhanming; An, Jing; Li, Debiao

2018-04-30

The purpose of the work was to evaluate the incremental diagnostic value of free-breathing, contrast-enhanced, whole-heart, 3 T cardiovascular magnetic resonance coronary angiography (CE-MRCA) to stress/rest myocardial perfusion imaging (MPI) and late gadolinium enhancement (LGE) imaging for detecting coronary artery disease (CAD). Fifty-one patients with suspected CAD underwent a comprehensive cardiovascular magnetic resonance (CMR) examination (CE-MRCA, MPI, and LGE). The additive diagnostic value of MRCA to MPI and LGE was evaluated using invasive x-ray coronary angiography (XA) as the standard for defining functionally significant CAD (≥ 50% stenosis in vessels > 2 mm in diameter). 90.2% (46/51) patients (54.0 ± 11.5 years; 71.7% men) completed CE-MRCA successfully. On per-patient basis, compared to MPI/LGE alone or MPI alone, the addition of MRCA resulted in higher sensitivity (100% vs. 76.5%, p < 0.01), no change in specificity (58.3% vs. 66.7%, p = 0.6), and higher accuracy (89.1% vs 73.9%, p < 0.01) for CAD detection (prevalence = 73.9%). Compared to LGE alone, the addition of CE-MRCA resulted in higher sensitivity (97.1% vs. 41.2%, p < 0.01), inferior specificity (83.3% vs. 91.7%, p = 0.02), and higher diagnostic accuracy (93.5% vs. 54.3%, p < 0.01). The inclusion of successful free-breathing, whole-heart, 3 T CE-MRCA significantly improved the sensitivity and diagnostic accuracy as compared to MPI and LGE alone for CAD detection.

Detection of tissue coagulation by decorrelation of ultrasonic echo signals in cavitation-enhanced high-intensity focused ultrasound treatment.

PubMed

Yoshizawa, Shin; Matsuura, Keiko; Takagi, Ryo; Yamamoto, Mariko; Umemura, Shin-Ichiro

2016-01-01

A noninvasive technique to monitor thermal lesion formation is necessary to ensure the accuracy and safety of high-intensity focused ultrasound (HIFU) treatment. The purpose of this study is to ultrasonically detect the tissue change due to thermal coagulation in the HIFU treatment enhanced by cavitation microbubbles. An ultrasound imaging probe transmitted plane waves at a center frequency of 4.5 MHz. Ultrasonic radio-frequency (RF) echo signals during HIFU exposure at a frequency of 1.2 MHz were acquired. Cross-correlation coefficients were calculated between in-phase and quadrature (IQ) data of two B-mode images with an interval time of 50 and 500 ms for the estimation of the region of cavitation and coagulation, respectively. Pathological examination of the coagulated tissue was also performed to compare with the corresponding ultrasonically detected coagulation region. The distribution of minimum hold cross-correlation coefficient between two sets of IQ data with 50-ms intervals was compared with a pulse inversion (PI) image. The regions with low cross-correlation coefficients approximately corresponded to those with high brightness in the PI image. The regions with low cross-correlation coefficients in 500-ms intervals showed a good agreement with those with significant change in histology. The results show that the regions of coagulation and cavitation could be ultrasonically detected as those with low cross-correlation coefficients between RF frames with certain intervals. This method will contribute to improve the safety and accuracy of the HIFU treatment enhanced by cavitation microbubbles.

A Technique for Real-Time Ionospheric Ranging Error Correction Based On Radar Dual-Frequency Detection

NASA Astrophysics Data System (ADS)

Lyu, Jiang-Tao; Zhou, Chen

2017-12-01

Ionospheric refraction is one of the principal error sources for limiting the accuracy of radar systems for space target detection. High-accuracy measurement of the ionospheric electron density along the propagation path of radar wave is the most important procedure for the ionospheric refraction correction. Traditionally, the ionospheric model and the ionospheric detection instruments, like ionosonde or GPS receivers, are employed for obtaining the electron density. However, both methods are not capable of satisfying the requirements of correction accuracy for the advanced space target radar system. In this study, we propose a novel technique for ionospheric refraction correction based on radar dual-frequency detection. Radar target range measurements at two adjacent frequencies are utilized for calculating the electron density integral exactly along the propagation path of the radar wave, which can generate accurate ionospheric range correction. The implementation of radar dual-frequency detection is validated by a P band radar located in midlatitude China. The experimental results present that the accuracy of this novel technique is more accurate than the traditional ionospheric model correction. The technique proposed in this study is very promising for the high-accuracy radar detection and tracking of objects in geospace.

Factors affecting the palpability of breast lesion by self-examination.

PubMed

Lam, W W M; Chan, C P; Chan, C F; Mak, C C C; Chan, C F; Chong, K W H; Leung, M H J; Tang, M H

2008-03-01

This study aims to assess the accuracy of detection of breast lesion by breast self-examination and to assess different factors affecting the accuracy. All consecutive Chinese female patients, who attended our breast imaging unit in 2001, completed our questionnaire, had retrievable hard copy films, and had more than three years clinical follow-up, were recruited for this study. Different factors, such as age, menopausal status, previous experience of breastfeeding, family history of breast cancer, previous history of mastectomy or lumpectomy, hormonal therapy, oral contraceptive pills and previous history of mammography, were correlated with accuracy in self-detection of breast lesions retrospectively. The nature, size and location of the lesion, and breast size based on imaging, were also correlated with the accuracy in self-detection of breast lesions. A total of 163 questionnaires were analysed. 111 patients detected a breast lesion themselves and 24 of these lesions were false-positives. A total of 173 lesions (27 cancerous, 146 benign lesions) were documented by either ultrasonography and/or mammography, and confirmed by either histology or three-year clinical follow-up. The overall sensitivity in detecting both benign and malignant breast lesions was 71% when number of breast lesions was used as the denominator, and up to 78% sensitivity was achieved when number of patients was used as the denominator. History of mastectomy, and size and nature of the lesions were found to affect the accuracy of self-detection of breast lesions. Overall, breast self-examinations were effective in the detection of breast lesions and factors such as size of lesion, nature of the lesion and history of mastectomy affect the accuracy of the detections. Breast self-examination should be promoted for early detection of breast cancer.

Optimal acquisition and modeling parameters for accurate assessment of low Ktrans blood-brain barrier permeability using dynamic contrast-enhanced MRI.

PubMed

Barnes, Samuel R; Ng, Thomas S C; Montagne, Axel; Law, Meng; Zlokovic, Berislav V; Jacobs, Russell E

2016-05-01

To determine optimal parameters for acquisition and processing of dynamic contrast-enhanced MRI (DCE-MRI) to detect small changes in near normal low blood-brain barrier (BBB) permeability. Using a contrast-to-noise ratio metric (K-CNR) for Ktrans precision and accuracy, the effects of kinetic model selection, scan duration, temporal resolution, signal drift, and length of baseline on the estimation of low permeability values was evaluated with simulations. The Patlak model was shown to give the highest K-CNR at low Ktrans . The Ktrans transition point, above which other models yielded superior results, was highly dependent on scan duration and tissue extravascular extracellular volume fraction (ve ). The highest K-CNR for low Ktrans was obtained when Patlak model analysis was combined with long scan times (10-30 min), modest temporal resolution (<60 s/image), and long baseline scans (1-4 min). Signal drift as low as 3% was shown to affect the accuracy of Ktrans estimation with Patlak analysis. DCE acquisition and modeling parameters are interdependent and should be optimized together for the tissue being imaged. Appropriately optimized protocols can detect even the subtlest changes in BBB integrity and may be used to probe the earliest changes in neurodegenerative diseases such as Alzheimer's disease and multiple sclerosis. © 2015 Wiley Periodicals, Inc.

Deconvolution improves the accuracy and depth sensitivity of time-resolved measurements

NASA Astrophysics Data System (ADS)

Diop, Mamadou; St. Lawrence, Keith

2013-03-01

Time-resolved (TR) techniques have the potential to distinguish early- from late-arriving photons. Since light travelling through superficial tissue is detected earlier than photons that penetrate the deeper layers, time-windowing can in principle be used to improve the depth sensitivity of TR measurements. However, TR measurements also contain instrument contributions - referred to as the instrument-response-function (IRF) - which cause temporal broadening of the measured temporal-point-spread-function (TPSF). In this report, we investigate the influence of the IRF on pathlength-resolved absorption changes (Δμa) retrieved from TR measurements using the microscopic Beer-Lambert law (MBLL). TPSFs were acquired on homogeneous and two-layer tissue-mimicking phantoms with varying optical properties. The measured IRF and TPSFs were deconvolved to recover the distribution of time-of-flights (DTOFs) of the detected photons. The microscopic Beer-Lambert law was applied to early and late time-windows of the TPSFs and DTOFs to access the effects of the IRF on pathlength-resolved Δμa. The analysis showed that the late part of the TPSFs contains substantial contributions from early-arriving photons, due to the smearing effects of the IRF, which reduced its sensitivity to absorption changes occurring in deep layers. We also demonstrated that the effects of the IRF can be efficiently eliminated by applying a robust deconvolution technique, thereby improving the accuracy and sensitivity of TR measurements to deep-tissue absorption changes.

Population variability complicates the accurate detection of climate change responses.

PubMed

McCain, Christy; Szewczyk, Tim; Bracy Knight, Kevin

2016-06-01

The rush to assess species' responses to anthropogenic climate change (CC) has underestimated the importance of interannual population variability (PV). Researchers assume sampling rigor alone will lead to an accurate detection of response regardless of the underlying population fluctuations of the species under consideration. Using population simulations across a realistic, empirically based gradient in PV, we show that moderate to high PV can lead to opposite and biased conclusions about CC responses. Between pre- and post-CC sampling bouts of modeled populations as in resurvey studies, there is: (i) A 50% probability of erroneously detecting the opposite trend in population abundance change and nearly zero probability of detecting no change. (ii) Across multiple years of sampling, it is nearly impossible to accurately detect any directional shift in population sizes with even moderate PV. (iii) There is up to 50% probability of detecting a population extirpation when the species is present, but in very low natural abundances. (iv) Under scenarios of moderate to high PV across a species' range or at the range edges, there is a bias toward erroneous detection of range shifts or contractions. Essentially, the frequency and magnitude of population peaks and troughs greatly impact the accuracy of our CC response measurements. Species with moderate to high PV (many small vertebrates, invertebrates, and annual plants) may be inaccurate 'canaries in the coal mine' for CC without pertinent demographic analyses and additional repeat sampling. Variation in PV may explain some idiosyncrasies in CC responses detected so far and urgently needs more careful consideration in design and analysis of CC responses. © 2016 John Wiley & Sons Ltd.

Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images.

PubMed

Møllersen, Kajsa; Zortea, Maciel; Schopf, Thomas R; Kirchesch, Herbert; Godtliebsen, Fred

2017-01-01

Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure.

Enhancement of snow cover change detection with sparse representation and dictionary learning

NASA Astrophysics Data System (ADS)

Varade, D.; Dikshit, O.

2014-11-01

Sparse representation and decoding is often used for denoising images and compression of images with respect to inherent features. In this paper, we adopt a methodology incorporating sparse representation of a snow cover change map using the K-SVD trained dictionary and sparse decoding to enhance the change map. The pixels often falsely characterized as "changes" are eliminated using this approach. The preliminary change map was generated using differenced NDSI or S3 maps in case of Resourcesat-2 and Landsat 8 OLI imagery respectively. These maps are extracted into patches for compressed sensing using Discrete Cosine Transform (DCT) to generate an initial dictionary which is trained by the K-SVD approach. The trained dictionary is used for sparse coding of the change map using the Orthogonal Matching Pursuit (OMP) algorithm. The reconstructed change map incorporates a greater degree of smoothing and represents the features (snow cover changes) with better accuracy. The enhanced change map is segmented using kmeans to discriminate between the changed and non-changed pixels. The segmented enhanced change map is compared, firstly with the difference of Support Vector Machine (SVM) classified NDSI maps and secondly with a reference data generated as a mask by visual interpretation of the two input images. The methodology is evaluated using multi-spectral datasets from Resourcesat-2 and Landsat-8. The k-hat statistic is computed to determine the accuracy of the proposed approach.

Deep Learning Method for Denial of Service Attack Detection Based on Restricted Boltzmann Machine.

PubMed

Imamverdiyev, Yadigar; Abdullayeva, Fargana

2018-06-01

In this article, the application of the deep learning method based on Gaussian-Bernoulli type restricted Boltzmann machine (RBM) to the detection of denial of service (DoS) attacks is considered. To increase the DoS attack detection accuracy, seven additional layers are added between the visible and the hidden layers of the RBM. Accurate results in DoS attack detection are obtained by optimization of the hyperparameters of the proposed deep RBM model. The form of the RBM that allows application of the continuous data is used. In this type of RBM, the probability distribution of the visible layer is replaced by a Gaussian distribution. Comparative analysis of the accuracy of the proposed method with Bernoulli-Bernoulli RBM, Gaussian-Bernoulli RBM, deep belief network type deep learning methods on DoS attack detection is provided. Detection accuracy of the methods is verified on the NSL-KDD data set. Higher accuracy from the proposed multilayer deep Gaussian-Bernoulli type RBM is obtained.

Effects of Listening Conditions, Error Types, and Ensemble Textures on Error Detection Skills

ERIC Educational Resources Information Center

Waggoner, Dori T.

2011-01-01

This study was designed with three main purposes: (a) to investigate the effects of two listening conditions on error detection accuracy, (b) to compare error detection responses for rhythm errors and pitch errors, and (c) to examine the influences of texture on error detection accuracy. Undergraduate music education students (N = 18) listened to…

Awareness of Memory Ability and Change: (In)Accuracy of Memory Self-Assessments in Relation to Performance.

PubMed

Rickenbach, Elizabeth Hahn; Agrigoroaei, Stefan; Lachman, Margie E

2015-03-01

Little is known about subjective assessments of memory abilities and decline among middle-aged adults or their association with objective memory performance in the general population. In this study we examined self-ratings of memory ability and change in relation to episodic memory performance in two national samples of middle-aged and older adults from the Midlife in the United States study (MIDUS II in 2005-06) and the Health and Retirement Study (HRS; every two years from 2002 to 2012). MIDUS (Study 1) participants (N=3,581) rated their memory compared to others their age and to themselves five years ago; HRS (Study 2) participants (N=14,821) rated their current memory and their memory compared to two years ago, with up to six occasions of longitudinal data over ten years. In both studies, episodic memory performance was the total number of words recalled in immediate and delayed conditions. When controlling for demographic and health correlates, self-ratings of memory abilities, but not subjective change, were related to performance. We examined accuracy by comparing subjective and objective memory ability and change. More than one third of the participants across the studies had self-assessments that were inaccurate relative to their actual level of performance and change, and accuracy differed as a function of demographic and health factors. Further understanding of self-awareness of memory abilities and change beginning in midlife may be useful for identifying early warning signs of decline, with implications regarding policies and practice for early detection and treatment of cognitive impairment.

Complex Dynamic Scene Perception: Effects of Attentional Set on Perceiving Single and Multiple Event Types

ERIC Educational Resources Information Center

Sanocki, Thomas; Sulman, Noah

2013-01-01

Three experiments measured the efficiency of monitoring complex scenes composed of changing objects, or events. All events lasted about 4 s, but in a given block of trials, could be of a single type (single task) or of multiple types (multitask, with a total of four event types). Overall accuracy of detecting target events amid distractors was…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stone, Daithi A.; Hansen, Gerrit

Despite being a well-established research field, the detection and attribution of observed climate change to anthropogenic forcing is not yet provided as a climate service. One reason for this is the lack of a methodology for performing tailored detection and attribution assessments on a rapid time scale. Here we develop such an approach, based on the translation of quantitative analysis into the “confidence” language employed in recent Assessment Reports of the Intergovernmental Panel on Climate Change. While its systematic nature necessarily ignores some nuances examined in detailed expert assessments, the approach nevertheless goes beyond most detection and attribution studies inmore » considering contributors to building confidence such as errors in observational data products arising from sparse monitoring networks. When compared against recent expert assessments, the results of this approach closely match those of the existing assessments. Where there are small discrepancies, these variously reflect ambiguities in the details of what is being assessed, reveal nuances or limitations of the expert assessments, or indicate limitations of the accuracy of the sort of systematic approach employed here. Deployment of the method on 116 regional assessments of recent temperature and precipitation changes indicates that existing rules of thumb concerning the detectability of climate change ignore the full range of sources of uncertainty, most particularly the importance of adequate observational monitoring.« less

Monitoring strip mining and reclamation with LANDSAT data in Belmont County, Ohio

NASA Technical Reports Server (NTRS)

Witt, R. G.; Schaal, G. M.; Bly, B. G.

1983-01-01

The utility of LANDSAT digital data for mapping and monitoring surface mines in Belmont County, Ohio was investigated. Two data sets from 1976 and 1979 were processed to classify level 1 land covers and three strip mine categories in order to examine change over time and assess reclamation efforts. The two classifications were compared with aerial photographs. Results of the accuracy assessment show that both classifications are approximately 86 per cent correct, and that surface mine change detection (date-to-date comparison) is facilitated by the digital format of LANDSAT data.

Investigations on vertical crustal movements in the Venezuelan Andes by gravimetric methods

NASA Technical Reports Server (NTRS)

Drewes, H.

1978-01-01

A precise gravimetric network has been installed in the Venezuelan Andes to study eventual gravity changes due to vertical tectonic movements. The design and the measurements of the network are described and the accuracy is estimated. In the center of the region a local gravity network has been reobserved three times. The detected variations are discussed. In order to obtain a genuine statement as far as possible about the significance of observed gravity changes, requirements for the procedure of monitoring precise gravity networks are pointed out.

Long-range, noncoherent laser Doppler velocimeter.

PubMed

Bloom, S H; Kremer, R; Searcy, P A; Rivers, M; Menders, J; Korevaar, E

1991-11-15

An experimental demonstration of a long-range, noncoherent laser Doppler velocimeter (LDV) is presented. The LDV detects incoming Doppler-shifted signal photons by using the sharp spectral absorption features in atomic or molecular vapors. The edge of the absorption feature is used to convert changes in frequency to large changes in transmission. Preliminary measurements of wind velocity using seeded aerosols showed that the LDV results agreed with mechanical anemometer measurements to within the accuracy of the LDV measurements. With optimization the LDV will provide accurate range-resolved and vibration-tolerant wind-speed measurements at large distances.

Loss of heterozygosity assay for molecular detection of cancer using energy-transfer primers and capillary array electrophoresis.

PubMed

Medintz, I L; Lee, C C; Wong, W W; Pirkola, K; Sidransky, D; Mathies, R A

2000-08-01

Microsatellite DNA loci are useful markers for the detection of loss of heterozygosity (LOH) and microsatellite instability (MI) associated with primary cancers. To carry out large-scale studies of LOH and MI in cancer progression, high-throughput instrumentation and assays with high accuracy and sensitivity need to be validated. DNA was extracted from 26 renal tumor and paired lymphocyte samples and amplified with two-color energy-transfer (ET) fluorescent primers specific for loci associated with cancer-induced chromosomal changes. PCR amplicons were separated on the MegaBACE-1000 96 capillary array electrophoresis (CAE) instrument and analyzed with MegaBACE Genetic Profiler v.1.0 software. Ninety-six separations were achieved in parallel in 75 minutes. Loss of heterozygosity was easily detected in tumor samples as was the gain/loss of microsatellite core repeats. Allelic ratios were determined with a precision of +/- 10% or better. Prior analysis of these samples with slab gel electrophoresis and radioisotope labeling had not detected these changes with as much sensitivity or precision. This study establishes the validity of this assay and the MegaBACE instrument for large-scale, high-throughput studies of the molecular genetic changes associated with cancer.

Tumor margin detection using optical biopsy techniques

NASA Astrophysics Data System (ADS)

Zhou, Yan; Liu, Cheng-hui; Li, Jiyou; Li, Zhongwu; Zhou, Lixin; Chen, Ke; Pu, Yang; He, Yong; Zhu, Ke; Li, Qingbo; Alfano, Robert R.

2014-03-01

The aim of this study is to use the Resonance Raman (RR) and fluorescence spectroscopic technique for tumor margin detection with high accuracy based on native molecular fingerprints of breast and gastrointestinal (GI) tissues. This tumor margins detection method utilizes advantages of RR spectroscopic technique in situ and in real-time to diagnose tumor changes providing powerful tools for clinical guiding intraoperative margin assessments and postoperative treatments. The tumor margin detection procedures by RR spectroscopy were taken by scanning lesion from center or around tumor region in ex-vivo to find the changes in cancerous tissues with the rim of normal tissues using the native molecular fingerprints. The specimens used to analyze tumor margins include breast and GI carcinoma and normal tissues. The sharp margin of the tumor was found by the changes of RR spectral peaks within 2 mm distance. The result was verified using fluorescence spectra with 300 nm, 320 nm and 340 nm excitation, in a typical specimen of gastric cancerous tissue within a positive margin in comparison with normal gastric tissues. This study demonstrates the potential of RR and fluorescence spectroscopy as new approaches with labeling free to determine the intraoperative margin assessment.

Evaluation of diagnostic accuracy in detecting ordered symptom statuses without a gold standard

PubMed Central

Wang, Zheyu; Zhou, Xiao-Hua; Wang, Miqu

2011-01-01

Our research is motivated by 2 methodological problems in assessing diagnostic accuracy of traditional Chinese medicine (TCM) doctors in detecting a particular symptom whose true status has an ordinal scale and is unknown—imperfect gold standard bias and ordinal scale symptom status. In this paper, we proposed a nonparametric maximum likelihood method for estimating and comparing the accuracy of different doctors in detecting a particular symptom without a gold standard when the true symptom status had an ordered multiple class. In addition, we extended the concept of the area under the receiver operating characteristic curve to a hyper-dimensional overall accuracy for diagnostic accuracy and alternative graphs for displaying a visual result. The simulation studies showed that the proposed method had good performance in terms of bias and mean squared error. Finally, we applied our method to our motivating example on assessing the diagnostic abilities of 5 TCM doctors in detecting symptoms related to Chills disease. PMID:21209155

Detection of Deforestation and Land Conversion in Rondonia, Brazil Using Change Detection Techniques

NASA Technical Reports Server (NTRS)

Guild, Liane S.; Cohen, Warren B,; Kauffman, J. Boone; Peterson, David L. (Technical Monitor)

2001-01-01

Fires associated with tropical deforestation, land conversion, and land use greatly contribute to emissions as well as the depletion of carbon and nutrient pools. The objective of this research was to compare change detection techniques for identifying deforestation and cattle pasture formation during a period of early colonization and agricultural expansion in the vicinity of Jamari, Rond6nia. Multi-date Landsat Thematic Mapper (TM) data between 1984 and 1992 was examined in a 94 370-ha area of active deforestation to map land cover change. The Tasseled Cap (TC) transformation was used to enhance the contrast between forest, cleared areas, and regrowth. TC images were stacked into a composite multi-date TC and used in a principal components (PC) transformation to identify change components. In addition, consecutive TC image pairs were differenced and stacked into a composite multi-date differenced image. A maximum likelihood classification of each image composite was compared for identification of land cover change. The multi-date TC composite classification had the best accuracy of 78.1% (kappa). By 1984, only 5% of the study area had been cleared, but by 1992, 11% of the area had been deforested, primarily for pasture and 7% lost due to hydroelectric dam flooding. Finally, discrimination of pasture versus cultivation was improved due to the ability to detect land under sustained clearing opened to land exhibiting regrowth with infrequent clearing.

Accuracy and impact of Xpert MTB/RIF for the diagnosis of smear-negative or sputum-scarce tuberculosis using bronchoalveolar lavage fluid.

PubMed

Theron, Grant; Peter, Jonny; Meldau, Richard; Khalfey, Hoosain; Gina, Phindile; Matinyena, Brian; Lenders, Laura; Calligaro, Gregory; Allwood, Brian; Symons, Gregory; Govender, Ureshnie; Setshedi, Mashiko; Dheda, Keertan

2013-11-01

The accuracy and impact of new tuberculosis (TB) tests, such as Xpert MTB/RIF, when performed on bronchoalveolar lavage fluid (BALF) obtained from patients with sputum-scarce or smear-negative TB is unclear. South African patients with suspected pulmonary TB (n=160) who were sputum-scarce or smear-negative underwent bronchoscopy. MTB/RIF was performed on uncentrifuged BALF (1 ml) and/or a resuspended pellet of centrifuged BALF (∼10 ml). Time to TB detection and anti-TB treatment initiation were compared between phase one, when MTB/RIF was performed as a research tool, and phase two, when it was used for patient management. 27 of 154 patients with complete data had culture-confirmed TB. Of these, a significantly lower proportion were detected by smear microscopy compared with MTB/RIF (58%, 95% CI 39% to 75% versus 93%, 77% to 98%; p<0.001). Of the 127 patients who were culture negative, 96% (91% to 98%) were MTB/RIF negative. When phase two was compared with phase one, MTB/RIF reduced the median days to TB detection (29 (18-41) to 0 (0-0); p<0.001). However, more patients initiated empirical therapy (absence of a positive test in those commencing treatment) in phase one versus phase two (79% (11/14) versus 28% (10/25); p=0.026). Consequently, there was no detectable difference in the overall proportion of patients initiating treatment (26% (17/67; 17% to 37%) versus 36% (26/73; 26% to 47%); p=0.196) or the days to treatment initiation (10 (1-49) versus 7 (0-21); p=0.330). BALF centrifugation, HIV coinfection and a second MTB/RIF did not result in detectable changes in accuracy. MTB/RIF detected TB cases more accurately and more rapidly than smear microscopy and significantly reduced the rate of empirical treatment.

Accuracy and impact of Xpert MTB/RIF for the diagnosis of smear-negative or sputum-scarce tuberculosis using bronchoalveolar lavage fluid

PubMed Central

Theron, Grant; Peter, Jonny; Meldau, Richard; Khalfey, Hoosain; Gina, Phindile; Matinyena, Brian; Lenders, Laura; Calligaro, Gregory; Allwood, Brian; Symons, Gregory; Govender, Ureshnie; Setshedi, Mashiko; Dheda, Keertan

2017-01-01

Rationale The accuracy and impact of new tuberculosis (TB) tests, such as Xpert MTB/RIF, when performed on bronchoalveolar lavage fluid (BALF) obtained from patients with sputum-scarce or smear-negative TB is unclear. Methods South African patients with suspected pulmonary TB (n=160) who were sputum-scarce or smear-negative underwent bronchoscopy. MTB/RIF was performed on uncentrifuged BALF (1 ml) and/or a resuspended pellet of centrifuged BALF (~10 ml). Time to TB detection and anti-TB treatment initiation were compared between phase one, when MTB/RIF was performed as a research tool, and phase two, when it was used for patient management. Results 27 of 154 patients with complete data had culture-confirmed TB. Of these, a significantly lower proportion were detected by smear microscopy compared with MTB/RIF (58%, 95% CI 39% to 75% versus 93%, 77% to 98%; p<0.001). Of the 127 patients who were culture negative, 96% (91% to 98%) were MTB/RIF negative. When phase two was compared with phase one, MTB/RIF reduced the median days to TB detection (29 (18–41) to 0 (0–0); p<0.001). However, more patients initiated empirical therapy (absence of a positive test in those commencing treatment) in phase one versus phase two (79% (11/14) versus 28% (10/25); p=0.026). Consequently, there was no detectable difference in the overall proportion of patients initiating treatment (26% (17/67; 17% to 37%) versus 36% (26/73; 26% to 47%); p=0.196) or the days to treatment initiation (10 (1–49) versus 7 (0–21); p=0.330). BALF centrifugation, HIV coinfection and a second MTB/RIF did not result in detectable changes in accuracy. Conclusions MTB/RIF detected TB cases more accurately and more rapidly than smear microscopy and significantly reduced the rate of empirical treatment. PMID:23811536

On-board error correction improves IR earth sensor accuracy

NASA Astrophysics Data System (ADS)

Alex, T. K.; Kasturirangan, K.; Shrivastava, S. K.

1989-10-01

Infra-red earth sensors are used in satellites for attitude sensing. Their accuracy is limited by systematic and random errors. The sources of errors in a scanning infra-red earth sensor are analyzed in this paper. The systematic errors arising from seasonal variation of infra-red radiation, oblate shape of the earth, ambient temperature of sensor, changes in scan/spin rates have been analyzed. Simple relations are derived using least square curve fitting for on-board correction of these errors. Random errors arising out of noise from detector and amplifiers, instability of alignment and localized radiance anomalies are analyzed and possible correction methods are suggested. Sun and Moon interference on earth sensor performance has seriously affected a number of missions. The on-board processor detects Sun/Moon interference and corrects the errors on-board. It is possible to obtain eight times improvement in sensing accuracy, which will be comparable with ground based post facto attitude refinement.

Design and numerical analysis of highly sensitive Au-MoS2-graphene based hybrid surface plasmon resonance biosensor

NASA Astrophysics Data System (ADS)

Rahman, M. Saifur; Anower, Md. Shamim; Hasan, Md. Rabiul; Hossain, Md. Biplob; Haque, Md. Ismail

2017-08-01

We demonstrate a highly sensitive Au-MoS2-Graphene based hybrid surface plasmon resonance (SPR) biosensor for the detection of DNA hybridization. The performance parameters of the proposed sensor are investigated in terms of sensitivity, detection accuracy and quality factor at operating wavelength of 633 nm. We observed in the numerical study that sensitivity can be greatly increased by adding MoS2 layer in the middle of a Graphene-on-Au layer. It is shown that by using single layer of MoS2 in between gold and graphene layer, the proposed biosensor exhibits simultaneously high sensitivity of 87.8 deg/RIU, high detection accuracy of 1.28 and quality factor of 17.56 with gold layer thickness of 50 nm. This increased performance is due to the absorption ability and optical characteristics of graphene biomolecules and high fluorescence quenching ability of MoS2. On the basis of changing in SPR angle and minimum reflectance, the proposed sensor can sense nucleotides bonding happened between double-stranded DNA (dsDNA) helix structures. Therefore, this sensor can successfully detect the hybridization of target DNAs to the probe DNAs pre-immobilized on the Au-MoS2-Graphene hybrid with capability of distinguishing single-base mismatch.

Experimental study of digital image processing techniques for LANDSAT data

NASA Technical Reports Server (NTRS)

Rifman, S. S. (Principal Investigator); Allendoerfer, W. B.; Caron, R. H.; Pemberton, L. J.; Mckinnon, D. M.; Polanski, G.; Simon, K. W.

1976-01-01

The author has identified the following significant results. Results are reported for: (1) subscene registration, (2) full scene rectification and registration, (3) resampling techniques, (4) and ground control point (GCP) extraction. Subscenes (354 pixels x 234 lines) were registered to approximately 1/4 pixel accuracy and evaluated by change detection imagery for three cases: (1) bulk data registration, (2) precision correction of a reference subscene using GCP data, and (3) independently precision processed subscenes. Full scene rectification and registration results were evaluated by using a correlation technique to measure registration errors of 0.3 pixel rms thoughout the full scene. Resampling evaluations of nearest neighbor and TRW cubic convolution processed data included change detection imagery and feature classification. Resampled data were also evaluated for an MSS scene containing specular solar reflections.

Assessment of changes in formations of non-forest woody vegetation in southern Denmark based on airborne LiDAR.

PubMed

Angelidis, Ioannis; Levin, Gregor; Díaz-Varela, Ramón Alberto; Malinowski, Radek

2017-09-01

LiDAR (Light Detection and Ranging) is a remote sensing technology that uses light in the form of pulses to measure the range between a sensor and the Earth's surface. Recent increase in availability of airborne LiDAR scanning (ALS) data providing national coverage with high point densities has opened a wide range of possibilities for monitoring landscape elements and their changes at broad geographical extent. We assessed the dynamics of the spatial extent of non-forest woody vegetation (NFW) in a study area of approx. 2500 km 2 in southern Jutland, Denmark, based on two acquisitions of ALS data for 2006 and 2014 in combination with other spatial data. Our results show a net-increase (4.8%) in the total area of NFW. Furthermore, this net change comprises of both areas with a decrease and areas with an increase of NFW. An accuracy assessment based on visual interpretation of aerial photos indicates high accuracy (>95%) in the delineation of NFW without changes during the study period. For NFW that changed between 2006 and 2014, accuracies were lower (90 and 82% in removed and new features, respectively), which is probably due to lower point densities of the 2006 ALS data (0.5 pts./m 2 ) compared to the 2014 data (4-5 pts./m 2 ). We conclude that ALS data, if combined with other spatial data, in principle are highly suitable for detailed assessment of changes in landscape features, such as formations of NFW at broad geographical extent. However, in change assessment based on multi-temporal ALS data with different point densities errors occur, particularly when examining small or narrow NFW objects.

A simulation study of scene confusion factors in sensing soil moisture from orbital radar

NASA Technical Reports Server (NTRS)

Ulaby, F. T. (Principal Investigator); Dobson, M. C.; Moezzi, S.; Roth, F. T.

1983-01-01

Simulated C-band radar imagery for a 124-km by 108-km test site in eastern Kansas is used to classify soil moisture. Simulated radar resolutions are 100 m by 100 m, 1 km by 1km, and 3 km by 3 km. Distributions of actual near-surface soil moisture are established daily for a 23-day accounting period using a water budget model. Within the 23-day period, three orbital radar overpasses are simulated roughly corresponding to generally moist, wet, and dry soil moisture conditions. The radar simulations are performed by a target/sensor interaction model dependent upon a terrain model, land-use classification, and near-surface soil moisture distribution. The accuracy of soil-moisture classification is evaluated for each single-date radar observation and also for multi-date detection of relative soil moisture change. In general, the results for single-date moisture detection show that 70% to 90% of cropland can be correctly classified to within +/- 20% of the true percent of field capacity. For a given radar resolution, the expected classification accuracy is shown to be dependent upon both the general soil moisture condition and also the geographical distribution of land-use and topographic relief. An analysis of cropland, urban, pasture/rangeland, and woodland subregions within the test site indicates that multi-temporal detection of relative soil moisture change is least sensitive to classification error resulting from scene complexity and topographic effects.

Prostate cancer localization with endorectal MR imaging and MR spectroscopic imaging: effect of clinical data on reader accuracy.

PubMed

Dhingsa, Rajpal; Qayyum, Aliya; Coakley, Fergus V; Lu, Ying; Jones, Kirk D; Swanson, Mark G; Carroll, Peter R; Hricak, Hedvig; Kurhanewicz, John

2004-01-01

To determine the effect of digital rectal examination findings, sextant biopsy results, and prostate-specific antigen (PSA) levels on reader accuracy in the localization of prostate cancer with endorectal magnetic resonance (MR) imaging and MR spectroscopic imaging. This was a retrospective study of 37 patients (mean age, 57 years) with biopsy-proved prostate cancer. Transverse T1-weighted, transverse high-spatial-resolution, and coronal T2-weighted MR images and MR spectroscopic images were obtained. Two independent readers, unaware of clinical data, recorded the size and location of suspicious peripheral zone tumor nodules on a standardized diagram of the prostate. Readers also recorded their degree of diagnostic confidence for each nodule on a five-point scale. Both readers repeated this interpretation with knowledge of rectal examination findings, sextant biopsy results, and PSA level. Step-section histopathologic findings were the reference standard. Logistic regression analysis with generalized estimating equations was used to correlate tumor detection with clinical data, and alternative free-response receiver operating characteristic (AFROC) curve analysis was used to examine the overall effect of clinical data on all positive results. Fifty-one peripheral zone tumor nodules were identified at histopathologic evaluation. Logistic regression analysis showed awareness of clinical data significantly improved tumor detection rate (P <.02) from 15 to 19 nodules for reader 1 and from 13 to 19 nodules for reader 2 (27%-37% overall) by using both size and location criteria. AFROC analysis showed no significant change in overall reader performance because there was an associated increase in the number of false-positive findings with awareness of clinical data, from 11 to 21 for reader 1 and from 16 to 25 for reader 2. Awareness of clinical data significantly improves reader detection of prostate cancer nodules with endorectal MR imaging and MR spectroscopic imaging, but there is no overall change in reader accuracy, because of an associated increase in false-positive findings. A stricter definition of a true-positive result is associated with reduced sensitivity for prostate cancer nodule detection. Copyright RSNA, 2004

The validity and accuracy of MRI arthrogram in the assessment of painful articular disorders of the hip.

PubMed

Rajeev, Aysha; Tuinebreijer, Wim; Mohamed, Abdalla; Newby, Mike

2018-01-01

The assessment of a patient with chronic hip pain can be challenging. The differential diagnosis of intra-articular pathology causing hip pain can be diverse. These includes conditions such as osteoarthritis, fracture, and avascular necrosis, synovitis, loose bodies, labral tears, articular pathology and, femoro-acetabular impingement. Magnetic resonance imaging (MRI) arthrography of the hip has been widely used now for diagnosis of articular pathology of the hip. A retrospective analysis of 113 patients who had MRI arthrogram and who underwent hip arthroscopy was included in the study. The MRI arthrogram was performed using gadolinium injection and reported by a single radiologist. The findings were then compared to that found on arthroscopy. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and 95% confidence interval were calculated for each pathology. Labral tear-sensitivity 84% (74.3-90.5), specificity 64% (40.7-82.8), PPV 91% (82.1-95.8), NPV 48% (29.5-67.5), accuracy 80%. Delamination -sensitivity 7% (0.8-22.1), specificity 98% (91.6-99.7), PPV 50% (6.8-93.2), NPV 74% (65.1-82.2) and accuracy 39%. Chondral changes-sensitivity 25% (13.3-38.9), specificity 83% (71.3-91.1), PPV 52% (30.6-73.2), NPV 59% (48.0-69.2) and accuracy 58%. Femoro-acetabular impingement (CAM deformity)-sensitivity 34% (19.6-51.4), specificity 83% (72.2-90.4), PPV 50% (29.9-70.1), NPV 71% (60.6-80.5) and accuracy 66%. Synovitis-sensitivity 11% (2.3-28.2), specificity 99% (93.6-100), PPV 75% (19.4-99.4), NPV 77% (68.1-84.6) and accuracy 77%. Our study conclusions are MRI arthrogram is a useful investigation tool in detecting labral tears, it is also helpful in the diagnosis of femoro-acetabular impingement. However, when it comes to the diagnosis of chondral changes, defects and cartilage delamination, the sensitivity and accuracy are low.

A fiber optic sensor for on-line non-touch monitoring of roll shape

NASA Astrophysics Data System (ADS)

Guo, Yuan; Qu, Weijian; Yuan, Qi

2009-07-01

Basing on the principle of reflective displacement fibre-optic sensor, a high accuracy non-touch on-line optical fibre sensor for detecting roll shape is presented. The principle and composition of the detection system and the operation process are expatiated also. By using a novel probe of three optical fibres in equal transverse space, the effects of fluctuations in the light source, reflective changing of target surface and the intensity losses in the fibre lines are automatically compensated. Meantime, an optical fibre sensor model of correcting static error based on BP artificial neural network (ANN) is set up. Also by using interpolation method and value filtering to process the signals, effectively reduce the influence of random noise and the vibration of the roll bearing. So the accuracy and resolution were enhanced remarkably. Experiment proves that the resolution is 1μm and the precision can reach to 0.1%. So the system reaches to the demand of practical production process.

Summit-to-sea mapping and change detection using satellite imagery: tools for conservation and management of coral reefs.

PubMed

Shapiro, A C; Rohmann, S O

2005-05-01

Continuous summit-to-sea maps showing both land features and shallow-water coral reefs have been completed in Puerto Rico and the U.S. Virgin Islands, using circa 2000 Landsat 7 Enhanced Thematic Mapper (ETM+) Imagery. Continuous land/sea terrain was mapped by merging Digital Elevation Models (DEM) with satellite-derived bathymetry. Benthic habitat characterizations were created by unsupervised classifications of Landsat imagery clustered using field data, and produced maps with an estimated overall accuracy of>75% (Tau coefficient >0.65). These were merged with Geocover-LC (land use/land cover) data to create continuous land/ sea cover maps. Image pairs from different dates were analyzed using Principle Components Analysis (PCA) in order to detect areas of change in the marine environment over two different time intervals: 2000 to 2001, and 1991 to 2003. This activity demonstrates the capabilities of Landsat imagery to produce continuous summit-to-sea maps, as well as detect certain changes in the shallow-water marine environment, providing a valuable tool for efficient coastal zone monitoring and effective management and conservation.

Visual-Spatial Attention Aids the Maintenance of Object Representations in Visual Working Memory

PubMed Central

Williams, Melonie; Pouget, Pierre; Boucher, Leanne; Woodman, Geoffrey F.

2013-01-01

Theories have proposed that the maintenance of object representations in visual working memory is aided by a spatial rehearsal mechanism. In this study, we used two different approaches to test the hypothesis that overt and covert visual-spatial attention mechanisms contribute to the maintenance of object representations in visual working memory. First, we tracked observers’ eye movements while remembering a variable number of objects during change-detection tasks. We observed that during the blank retention interval, participants spontaneously shifted gaze to the locations that the objects had occupied in the memory array. Next, we hypothesized that if attention mechanisms contribute to the maintenance of object representations, then drawing attention away from the object locations during the retention interval would impair object memory during these change-detection tasks. Supporting this prediction, we found that attending to the fixation point in anticipation of a brief probe stimulus during the retention interval reduced change-detection accuracy even on the trials in which no probe occurred. These findings support models of working memory in which visual-spatial selection mechanisms contribute to the maintenance of object representations. PMID:23371773

Detection of motion and posture change using an IR-UWB radar.

PubMed

Van Nguyen; Javaid, Abdul Q; Weitnauer, Mary A

2016-08-01

Impulse radio ultra-wide band (IR-UWB) radar has recently emerged as a promising candidate for non-contact monitoring of respiration and heart rate. Different studies have reported various radar based algorithms for estimation of these physiological parameters. The radar can be placed under a subject's mattress as he lays stationary on his back or it can be attached to the ceiling directly above the subject's bed. However, advertent or inadvertent movement on part of the subject and different postures can affect the radar returned signal and also the accuracy of the estimated parameters from it. The detection and analysis of these postural changes can not only lead to improvement in estimation algorithms but also towards prevention of bed sores and ulcers in patients who require periodic posture changes. In this paper, we present an algorithm that detects and quantifies different types of motion events using an under-the-mattress IR-UWB radar. The algorithm also indicates a change in posture after a macro-movement event. Based on the findings of this paper, we anticipate that IR-UWB radar can be used for extracting posture related information in non-clinical enviroments for patients who are bed-ridden.

Chromatography related performance of the Monitor for AeRosols and GAses in ambient air (MARGA): laboratory and field-based evaluation

NASA Astrophysics Data System (ADS)

Chen, Xi; Walker, John T.; Geron, Chris

2017-10-01

Evaluation of the semi-continuous Monitor for AeRosols and GAses in ambient air (MARGA, Metrohm Applikon B.V.) was conducted with an emphasis on examination of accuracy and precision associated with processing of chromatograms. Using laboratory standards and atmospheric measurements, analytical accuracy, precision and method detection limits derived using the commercial MARGA software were compared to an alternative chromatography procedure consisting of a custom Java script to reformat raw MARGA conductivity data and Chromeleon (Thermo Scientific Dionex) software for peak integration. Our analysis revealed issues with accuracy and precision resulting from misidentification and misintegration of chromatograph peaks by the MARGA automated software as well as a systematic bias at low concentrations for anions. Reprocessing and calibration of raw MARGA data using the alternative chromatography method lowered method detection limits and reduced variability (precision) between parallel sampler boxes. Instrument performance was further evaluated during a 1-month intensive field campaign in the fall of 2014, including analysis of diurnal patterns of gaseous and particulate water-soluble species (NH3, SO2, HNO3, NH4+, SO42- and NO3-), gas-to-particle partitioning and particle neutralization state. At ambient concentrations below ˜ 1 µg m-3, concentrations determined using the MARGA software are biased +30 and +10 % for NO3- and SO42-, respectively, compared to concentrations determined using the alternative chromatography procedure. Differences between the two methods increase at lower concentrations. We demonstrate that positively biased NO3- and SO42- measurements result in overestimation of aerosol acidity and introduce nontrivial errors to ion balances of inorganic aerosol. Though the source of the bias is uncertain, it is not corrected by the MARGA online single-point internal LiBr standard. Our results show that calibration and verification of instrument accuracy by multilevel external standards is required to adequately control analytical accuracy. During the field intensive, the MARGA was able to capture rapid compositional changes in PM2.5 due to changes in meteorology and air mass history relative to known source regions of PM precursors, including a fine NO3- aerosol event associated with intrusion of Arctic air into the southeastern US.

Exponentially Weighted Moving Average Change Detection Around the Country (and the World)

NASA Astrophysics Data System (ADS)

Brooks, E.; Wynne, R. H.; Thomas, V. A.; Blinn, C. E.; Coulston, J.

2014-12-01

With continuous, freely available moderate-resolution imagery of the Earth's surface available, and with the promise of more imagery to come, change detection based on continuous process models continues to be a major area of research. One such method, exponentially weighted moving average change detection (EWMACD), is based on a mixture of harmonic regression (HR) and statistical quality control, a branch of statistics commonly used to detect aberrations in industrial and medical processes. By using HR to approximate per-pixel seasonal curves, the resulting residuals characterize information about the pixels which stands outside of the periodic structure imposed by HR. Under stable pixels, these residuals behave as might be expected, but in the presence of changes (growth, stress, removal), the residuals clearly show these changes when they are used as inputs into an EWMA chart. In prior work in Alabama, USA, EWMACD yielded an overall accuracy of 85% on a random sample of known thinned stands, in some cases detecting thinnings as sparse as 25% removal. It was also shown to correctly identify the timing of the thinning activity, typically within a single image date of the change. The net result of the algorithm was to produce date-by-date maps of afforestation and deforestation on a variable scale of severity. In other research, EWMACD has also been applied to detect land use and land cover changes in central Java, Indonesia, despite the heavy incidence of clouds and a monsoonal climate. Preliminary results show that EWMACD accurately identifies land use conversion (agricultural to residential, for example) and also identifies neighborhoods where the building density has increased, removing neighborhood vegetation. In both cases, initial results indicate the potential utility of EWMACD to detect both gross and subtle ecosystem disturbance, but further testing across a range of ecosystems and disturbances is clearly warranted.

Urban Land Cover/use Change Detection Using High Resolution SPOT 5 and SPOT 6 Images and Urban Atlas Nomenclature

NASA Astrophysics Data System (ADS)

Akay, S. S.; Sertel, E.

2016-06-01

Urban land cover/use changes like urbanization and urban sprawl have been impacting the urban ecosystems significantly therefore determination of urban land cover/use changes is an important task to understand trends and status of urban ecosystems, to support urban planning and to aid decision-making for urban-based projects. High resolution satellite images could be used to accurately, periodically and quickly map urban land cover/use and their changes by time. This paper aims to determine urban land cover/use changes in Gaziantep city centre between 2010 and 2105 using object based images analysis and high resolution SPOT 5 and SPOT 6 images. 2.5 m SPOT 5 image obtained in 5th of June 2010 and 1.5 m SPOT 6 image obtained in 7th of July 2015 were used in this research to precisely determine land changes in five-year period. In addition to satellite images, various ancillary data namely Normalized Difference Vegetation Index (NDVI), Difference Water Index (NDWI) maps, cadastral maps, OpenStreetMaps, road maps and Land Cover maps, were integrated into the classification process to produce high accuracy urban land cover/use maps for these two years. Both images were geometrically corrected to fulfil the 1/10,000 scale geometric accuracy. Decision tree based object oriented classification was applied to identify twenty different urban land cover/use classes defined in European Urban Atlas project. Not only satellite images and satellite image-derived indices but also different thematic maps were integrated into decision tree analysis to create rule sets for accurate mapping of each class. Rule sets of each satellite image for the object based classification involves spectral, spatial and geometric parameter to automatically produce urban map of the city centre region. Total area of each class per related year and their changes in five-year period were determined and change trend in terms of class transformation were presented. Classification accuracy assessment was conducted by creating a confusion matrix to illustrate the thematic accuracy of each class.

PPP Sliding Window Algorithm and Its Application in Deformation Monitoring.

PubMed

Song, Weiwei; Zhang, Rui; Yao, Yibin; Liu, Yanyan; Hu, Yuming

2016-05-31

Compared with the double-difference relative positioning method, the precise point positioning (PPP) algorithm can avoid the selection of a static reference station and directly measure the three-dimensional position changes at the observation site and exhibit superiority in a variety of deformation monitoring applications. However, because of the influence of various observing errors, the accuracy of PPP is generally at the cm-dm level, which cannot meet the requirements needed for high precision deformation monitoring. For most of the monitoring applications, the observation stations maintain stationary, which can be provided as a priori constraint information. In this paper, a new PPP algorithm based on a sliding window was proposed to improve the positioning accuracy. Firstly, data from IGS tracking station was processed using both traditional and new PPP algorithm; the results showed that the new algorithm can effectively improve positioning accuracy, especially for the elevation direction. Then, an earthquake simulation platform was used to simulate an earthquake event; the results illustrated that the new algorithm can effectively detect the vibrations change of a reference station during an earthquake. At last, the observed Wenchuan earthquake experimental results showed that the new algorithm was feasible to monitor the real earthquakes and provide early-warning alerts.

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

NASA Technical Reports Server (NTRS)

Fagan, Matthew E.; Defries, Ruth S.; Sesnie, Steven E.; Arroyo-Mora, J. Pablo; Soto, Carlomagno; Singh, Aditya; Townsend, Philip A.; Chazdon, Robin L.

2015-01-01

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p less than 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes.

Accounting for speed-accuracy tradeoff in perceptual learning.

PubMed

Liu, Charles C; Watanabe, Takeo

2012-05-15

In the perceptual learning (PL) literature, researchers typically focus on improvements in accuracy, such as d'. In contrast, researchers who investigate the practice of cognitive skills focus on improvements in response times (RT). Here, we argue for the importance of accounting for both accuracy and RT in PL experiments, due to the phenomenon of speed-accuracy tradeoff (SAT): at a given level of discriminability, faster responses tend to produce more errors. A formal model of the decision process, such as the diffusion model, can explain the SAT. In this model, a parameter known as the drift rate represents the perceptual strength of the stimulus, where higher drift rates lead to more accurate and faster responses. We applied the diffusion model to analyze responses from a yes-no coherent motion detection task. The results indicate that observers do not use a fixed threshold for evidence accumulation, so changes in the observed accuracy may not provide the most appropriate estimate of learning. Instead, our results suggest that SAT can be accounted for by a modeling approach, and that drift rates offer a promising index of PL. Copyright © 2011 Elsevier Ltd. All rights reserved.

Direct Detection Doppler Lidar for Spaceborne Wind Measurement

NASA Technical Reports Server (NTRS)

Korb, C. Laurence; Flesia, Cristina

1999-01-01

The theory of double edge lidar techniques for measuring the atmospheric wind using aerosol and molecular backscatter is described. Two high spectral resolution filters with opposite slopes are located about the laser frequency for the aerosol based measurement or in the wings of the Rayleigh - Brillouin profile for the molecular measurement. This doubles the signal change per unit Doppler shift and improves the measurement accuracy by nearly a factor of 2 relative to the single edge technique. For the aerosol based measurement, the use of two high resolution edge filters reduces the effects of background, Rayleigh scattering, by as much as an order of magnitude and substantially improves the measurement accuracy. Also, we describe a method that allows the Rayleigh and aerosol components of the signal to be independently determined. A measurement accuracy of 1.2 m/s can be obtained for a signal level of 1000 detected photons which corresponds to signal levels in the boundary layer. For the molecular based measurement, we describe the use of a crossover region where the sensitivity of a molecular and aerosol-based measurement are equal. This desensitizes the molecular measurement to the effects of aerosol scattering and greatly simplifies the measurement. Simulations using a conical scanning spaceborne lidar at 355 nm give an accuracy of 2-3 m/s for altitudes of 2-15 km for a 1 km vertical resolution, a satellite altitude of 400 km, and a 200 km x 200 km spatial.

Detection of tamarisk defoliation by the northern tamarisk beetle based on multitemporal Landsat 5 thematic mapper imagery

USGS Publications Warehouse

Meng, Ran; Dennison, Philip E.; Jamison, Levi R.; van Riper, Charles; Nager, Pamela; Hultine, Kevin R.; Bean, Dan W.; Dudley, Tom

2012-01-01

The spread of tamarisk (Tamarix spp., also known as saltcedar) is a significant ecological disturbance in western North America and has long been targeted for control, leading to the importation of the northern tamarisk beetle (Diorhabda carinulata) as a biological control agent. Following its initial release along the Colorado River near Moab, Utah in 2004, the beetle has successfully established and defoliated tamarisk across much of the upper Colorado River Basin. However, the spatial distribution and seasonal timing of defoliation are complex and difficult to quantify over large areas. To address this challenge, we tested and compared two remote sensing approaches to mapping tamarisk defoliation: Disturbance Index (DI) and a decision tree method called Random Forest (RF). Based on multitemporal Landsat 5 TM imagery for 2006-2010, changes in DI and defoliation probability from RF were calculated to detect tamarisk defoliation along the banks of Green, Colorado, Dolores and San Juan rivers within the Colorado Plateau area. Defoliation mapping accuracy was assessed based on field surveys partitioned into 10 km sections of river and on regions of interest created for continuous riparian vegetation. The DI method detected 3711 ha of defoliated area in 2007, 7350 ha in 2008, 10,457 ha in 2009 and 5898 ha in 2010. The RF method detected much smaller areas of defoliation but proved to have higher accuracy, as demonstrated by accuracy assessment and sensitivity analysis, with 784 ha in 2007, 960 ha in 2008, 934 ha in 2009, and 1008 ha in 2010. Results indicate that remote sensing approaches are likely to be useful for studying spatiotemporal patterns of tamarisk defoliation as the tamarisk leaf beetle spreads throughout the western United States.

Development and clinical application of a computer-aided real-time feedback system for detecting in-bed physical activities.

PubMed

Lu, Liang-Hsuan; Chiang, Shang-Lin; Wei, Shun-Hwa; Lin, Chueh-Ho; Sung, Wen-Hsu

2017-08-01

Being bedridden long-term can cause deterioration in patients' physiological function and performance, limiting daily activities and increasing the incidence of falls and other accidental injuries. Little research has been carried out in designing effective detecting systems to monitor the posture and status of bedridden patients and to provide accurate real-time feedback on posture. The purposes of this research were to develop a computer-aided system for real-time detection of physical activities in bed and to validate the system's validity and test-retest reliability in determining eight postures: motion leftward/rightward, turning over leftward/rightward, getting up leftward/rightward, and getting off the bed leftward/rightward. The in-bed physical activity detecting system consists mainly of a clinical sickbed, signal amplifier, a data acquisition (DAQ) system, and operating software for computing and determining postural changes associated with four load cell sensing components. Thirty healthy subjects (15 males and 15 females, mean age = 27.8 ± 5.3 years) participated in the study. All subjects were asked to execute eight in-bed activities in a random order and to participate in an evaluation of the test-retest reliability of the results 14 days later. Spearman's rank correlation coefficient was used to compare the system's determinations of postural states with researchers' recordings of postural changes. The test-retest reliability of the system's ability to determine postures was analyzed using the interclass correlation coefficient ICC(3,1). The system was found to exhibit high validity and accuracy (r = 0.928, p < 0.001; accuracy rate: 87.9%) in determining in-bed displacement, turning over, sitting up, and getting off the bed. The system was particularly accurate in detecting motion rightward (90%), turning over leftward (83%), sitting up leftward or rightward (87-93%), and getting off the bed (100%). The test-retest reliability ICC(3,1) value was 0.968 (p < 0.001). The system developed in this study exhibits satisfactory validity and reliability in detecting changes in-bed body postures and can be beneficial in assisting caregivers and clinical nursing staff in detecting the in-bed physical activities of bedridden patients and in developing fall prevention warning systems. Copyright © 2017 Elsevier B.V. All rights reserved.

Detection of prostate cancer with multiparametric MRI (mpMRI): effect of dedicated reader education on accuracy and confidence of index and anterior cancer diagnosis

PubMed Central

Garcia-Reyes, Kirema; Passoni, Niccolò M.; Palmeri, Mark L.; Kauffman, Christopher R.; Choudhury, Kingshuk Roy; Polascik, Thomas J.; Gupta, Rajan T.

2015-01-01

Purpose To evaluate the impact of dedicated reader education on accuracy/confidence of peripheral zone index cancer and anterior prostate cancer (PCa) diagnosis with mpMRI; secondary aim was to assess the ability of readers to differentiate low-grade cancer (Gleason 6 or below) from high-grade cancer (Gleason 7+). Materials and methods Five blinded radiology fellows evaluated 31 total prostate mpMRIs in this IRB-approved, HIPAA-compliant, retrospective study for index lesion detection, confidence in lesion diagnosis (1–5 scale), and Gleason grade (Gleason 6 or lower vs. Gleason 7+). Following a dedicated education program, readers reinterpreted cases after a memory extinction period, blinded to initial reads. Reference standard was established combining whole mount histopathology with mpMRI findings by a board-certified radiologist with 5 years of prostate mpMRI experience. Results Index cancer detection: pre-education accuracy 74.2%; post-education accuracy 87.7% (p = 0.003). Confidence in index lesion diagnosis: pre-education 4.22 ± 1.04; post-education 3.75 ± 1.41 (p = 0.0004). Anterior PCa detection: pre-education accuracy 54.3%; post-education accuracy 94.3% (p = 0.001). Confidence in anterior PCa diagnosis: pre-education 3.22 ± 1.54; post-education 4.29 ± 0.83 (p = 0.0003). Gleason score accuracy: pre-education 54.8%; post-education 73.5% (p = 0.0005). Conclusions A dedicated reader education program on PCa detection with mpMRI was associated with a statistically significant increase in diagnostic accuracy of index cancer and anterior cancer detection as well as Gleason grade identification as compared to pre-education values. This was also associated with a significant increase in reader diagnostic confidence. This suggests that substantial interobserver variability in mpMRI interpretation can potentially be reduced with a focus on education and that this can occur over a fellowship training year. PMID:25034558

Towards Investigating Global Warming Impact on Human Health Using Derivatives of Photoplethysmogram Signals

PubMed Central

Elgendi, Mohamed; Norton, Ian; Brearley, Matt; Fletcher, Richard R.; Abbott, Derek; Lovell, Nigel H.; Schuurmans, Dale

2015-01-01

Recent clinical studies show that the contour of the photoplethysmogram (PPG) wave contains valuable information for characterizing cardiovascular activity. However, analyzing the PPG wave contour is difficult; therefore, researchers have applied first or higher order derivatives to emphasize and conveniently quantify subtle changes in the filtered PPG contour. Our hypothesis is that analyzing the whole PPG recording rather than each PPG wave contour or on a beat-by-beat basis can detect heat-stressed subjects and that, consequently, we will be able to investigate the impact of global warming on human health. Here, we explore the most suitable derivative order for heat stress assessment based on the energy and entropy of the whole PPG recording. The results of our study indicate that the use of the entropy of the seventh derivative of the filtered PPG signal shows promising results in detecting heat stress using 20-second recordings, with an overall accuracy of 71.6%. Moreover, the combination of the entropy of the seventh derivative of the filtered PPG signal with the root mean square of successive differences, or RMSSD (a traditional heart rate variability index of heat stress), improved the detection of heat stress to 88.9% accuracy. PMID:26473907

Dual-color plasmonic enzyme-linked immunosorbent assay based on enzyme-mediated etching of Au nanoparticles

NASA Astrophysics Data System (ADS)

Guo, Longhua; Xu, Shaohua; Ma, Xiaoming; Qiu, Bin; Lin, Zhenyu; Chen, Guonan

2016-09-01

Colorimetric enzyme-linked immunosorbent assay utilizing 3‧-3-5‧-5-tetramethylbenzidine(TMB) as the chromogenic substrate has been widely used in the hospital for the detection of all kinds of disease biomarkers. Herein, we demonstrate a strategy to change this single-color display into dual-color responses to improve the accuracy of visual inspection. Our investigation firstly reveals that oxidation state of 3‧-3-5‧-5-tetramethylbenzidine (TMB2+) can quantitatively etch gold nanoparticles. Therefore, the incorporation of gold nanoparticles into a commercial TMB-based ELISA kit could generate dual-color responses: the solution color varied gradually from wine red (absorption peak located at ~530 nm) to colorless, and then from colorless to yellow (absorption peak located at ~450 nm) with the increase amount of targets. These dual-color responses effectively improved the sensitivity as well as the accuracy of visual inspection. For example, the proposed dual-color plasmonic ELISA is demonstrated for the detection of prostate-specific antigen (PSA) in human serum with a visual limit of detection (LOD) as low as 0.0093 ng/mL.

Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine

NASA Astrophysics Data System (ADS)

Adhi, H. A.; Wijaya, S. K.; Prawito; Badri, C.; Rezal, M.

2017-03-01

Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. The signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. The performance of detection was observed by the value of accuracy, sensitivity and specificity. The result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM.

The objects of visuospatial short-term memory: Perceptual organization and change detection.

PubMed

Nikolova, Atanaska; Macken, Bill

2016-01-01

We used a colour change-detection paradigm where participants were required to remember colours of six equally spaced circles. Items were superimposed on a background so as to perceptually group them within (a) an intact ring-shaped object, (b) a physically segmented but perceptually completed ring-shaped object, or (c) a corresponding background segmented into three arc-shaped objects. A nonpredictive cue at the location of one of the circles was followed by the memory items, which in turn were followed by a test display containing a probe indicating the circle to be judged same/different. Reaction times for correct responses revealed a same-object advantage; correct responses were faster to probes on the same object as the cue than to equidistant probes on a segmented object. This same-object advantage was identical for physically and perceptually completed objects, but was only evident in reaction times, and not in accuracy measures. Not only, therefore, is it important to consider object-level perceptual organization of stimulus elements when assessing the influence of a range of factors (e.g., number and complexity of elements) in visuospatial short-term memory, but a more detailed picture of the structure of information in memory may be revealed by measuring speed as well as accuracy.

Role of New Functional MRI Techniques in the Diagnosis, Staging, and Followup of Gynecological Cancer: Comparison with PET-CT

PubMed Central

Alvarez Moreno, Elena; Jimenez de la Peña, Mar; Cano Alonso, Raquel

2012-01-01

Recent developments in diagnostic imaging techniques have magnified the role and potential of both MRI and PET-CT in female pelvic imaging. This article reviews the techniques and clinical applications of new functional MRI (fMRI) including diffusion-weighted MRI (DWI), dynamic contrast-enhanced (DCE)-MRI, comparing with PET-CT. These new emerging provide not only anatomic but also functional imaging, allowing detection of small volumes of active tumor at diagnosis and early disease relapse, which may not result in detectable morphological changes at conventional imaging. This information is useful in distinguishing between recurrent/residual tumor and post-treatment changes and assessing treatment response, with a clear impact on patient management. Both PET-CT and now fMRI have proved to be very valuable tools for evaluation of gynecologic tumors. Most papers try to compare these techniques, but in our experience both are complementary in management of these patients. Meanwhile PET-CT is superior in diagnosis of ganglionar disease; fMRI presents higher accuracy in local preoperative staging. Both techniques can be used as biomarkers of tumor response and present high accuracy in diagnosis of local recurrence and peritoneal dissemination, with complementary roles depending on histological type, anatomic location and tumoral volume. PMID:22315683

The objects of visuospatial short-term memory: Perceptual organization and change detection

PubMed Central

Nikolova, Atanaska; Macken, Bill

2016-01-01

We used a colour change-detection paradigm where participants were required to remember colours of six equally spaced circles. Items were superimposed on a background so as to perceptually group them within (a) an intact ring-shaped object, (b) a physically segmented but perceptually completed ring-shaped object, or (c) a corresponding background segmented into three arc-shaped objects. A nonpredictive cue at the location of one of the circles was followed by the memory items, which in turn were followed by a test display containing a probe indicating the circle to be judged same/different. Reaction times for correct responses revealed a same-object advantage; correct responses were faster to probes on the same object as the cue than to equidistant probes on a segmented object. This same-object advantage was identical for physically and perceptually completed objects, but was only evident in reaction times, and not in accuracy measures. Not only, therefore, is it important to consider object-level perceptual organization of stimulus elements when assessing the influence of a range of factors (e.g., number and complexity of elements) in visuospatial short-term memory, but a more detailed picture of the structure of information in memory may be revealed by measuring speed as well as accuracy. PMID:26286369

1-D grating based SPR biosensor for the detection of lung cancer biomarkers using Vroman effect

NASA Astrophysics Data System (ADS)

Teotia, Pradeep Kumar; Kaler, R. S.

2018-01-01

Grating based surface plasmon resonance waveguide biosensor have been reported for the detection of lung cancer biomarkers using Vroman effect. The proposed grating based multilayered biosensor is designed with high detection accuracy for Epidermal growth factor receptor (EGFR) and also analysed to show high detection accuracy with acceptable sensitivity for both cancer biomarkers. The introduction of periodic grating with multilayer metals generates a good resonance that make it possible for early detection of cancerous cells. Using finite difference time domain method, it is observed wavelength of biosensor get red-shifted on variations of the refractive index due to the presence of both the cancerous bio-markers. The reported detection accuracy and sensitivity of proposed biosensor is quite acceptable for both lung cancer biomarkers i.e. Carcinoembryonic antigen (CEA) and Epidermal growth factor receptor (EGFR) which further offer us label free early detection of lung cancer using these biomarkers.

Determination of Land Use/ Land Cover Changes in Igneada Alluvial (Longos) Forest Ecosystem, Turkey

NASA Astrophysics Data System (ADS)

Bektas Balcik, F.

2012-12-01

Alluvial (Longos) forests are one of the most fragile and threatened ecosystems in the world. Typically, these types of ecosystems have high biological diversity, high productivity, and high habitat dynamism. In this study, Igneada, Kirklareli was selected as study area. The region, lies between latitudes 41° 46' N and 41° 59' N and stretches between longitudes 27° 50' E and 28° 02' E and it covers approximately 24000 (ha). Igneada Longos ecosystems include mixed forests, streams, flooded (alluvial) forests, marshes, wetlands, lakes and coastal sand dunes with different types of flora and fauna. Igneada was classified by Conservation International as one of the world's top 122 Important Plant Areas, and 185 Important Bird Areas. These types of wild forest in other parts of Turkey and in Europe have been damaged due to anthropogenic effects. Remote sensing is very effective tool to monitor these types of sensitive regions for sustainable management. In this study, 1984 and 2011 dated Landsat 5 TM data were used to determine land cover/land use change detection of the selected region by using six vegetation indices such as Tasseled Cap index of greenness (TCG), brightness (TCB), and wetness (TCW), ratios of near-infrared to red image (RVI), normalized difference vegetation index (NDVI), and soil-adjusted vegetation index (SAVI). Geometric and radiometric corrections were applied in image pre-processing step. Selective Principle Component Analysis (PCA) change detection method was applied to the selected vegetation index imagery to generate change imagery for extracting the changed features between the year of 1984 and 2011. Accuracy assessment was applied based on error matrix by calculating overall accuracy and Kappa statistics.

The effects of changes in object location on object identity detection: A simultaneous EEG-fMRI study.

PubMed

Yang, Ping; Fan, Chenggui; Wang, Min; Fogelson, Noa; Li, Ling

2017-08-15

Object identity and location are bound together to form a unique integration that is maintained and processed in visual working memory (VWM). Changes in task-irrelevant object location have been shown to impair the retrieval of memorial representations and the detection of object identity changes. However, the neural correlates of this cognitive process remain largely unknown. In the present study, we aim to investigate the underlying brain activation during object color change detection and the modulatory effects of changes in object location and VWM load. To this end we used simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings, which can reveal the neural activity with both high temporal and high spatial resolution. Subjects responded faster and with greater accuracy in the repeated compared to the changed object location condition, when a higher VWM load was utilized. These results support the spatial congruency advantage theory and suggest that it is more pronounced with higher VWM load. Furthermore, the spatial congruency effect was associated with larger posterior N1 activity, greater activation of the right inferior frontal gyrus (IFG) and less suppression of the right supramarginal gyrus (SMG), when object location was repeated compared to when it was changed. The ERP-fMRI integrative analysis demonstrated that the object location discrimination-related N1 component is generated in the right SMG. Copyright © 2017 Elsevier Inc. All rights reserved.

Inverse Transient Analysis for Classification of Wall Thickness Variations in Pipelines

PubMed Central

Tuck, Jeffrey; Lee, Pedro

2013-01-01

Analysis of transient fluid pressure signals has been investigated as an alternative method of fault detection in pipeline systems and has shown promise in both laboratory and field trials. The advantage of the method is that it can potentially provide a fast and cost effective means of locating faults such as leaks, blockages and pipeline wall degradation within a pipeline while the system remains fully operational. The only requirement is that high speed pressure sensors are placed in contact with the fluid. Further development of the method requires detailed numerical models and enhanced understanding of transient flow within a pipeline where variations in pipeline condition and geometry occur. One such variation commonly encountered is the degradation or thinning of pipe walls, which can increase the susceptible of a pipeline to leak development. This paper aims to improve transient-based fault detection methods by investigating how changes in pipe wall thickness will affect the transient behaviour of a system; this is done through the analysis of laboratory experiments. The laboratory experiments are carried out on a stainless steel pipeline of constant outside diameter, into which a pipe section of variable wall thickness is inserted. In order to detect the location and severity of these changes in wall conditions within the laboratory system an inverse transient analysis procedure is employed which considers independent variations in wavespeed and diameter. Inverse transient analyses are carried out using a genetic algorithm optimisation routine to match the response from a one-dimensional method of characteristics transient model to the experimental time domain pressure responses. The accuracy of the detection technique is evaluated and benefits associated with various simplifying assumptions and simulation run times are investigated. It is found that for the case investigated, changes in the wavespeed and nominal diameter of the pipeline are both important to the accuracy of the inverse analysis procedure and can be used to differentiate the observed transient behaviour caused by changes in wall thickness from that caused by other known faults such as leaks. Further application of the method to real pipelines is discussed.

2D Fast Vessel Visualization Using a Vessel Wall Mask Guiding Fine Vessel Detection

PubMed Central

Raptis, Sotirios; Koutsouris, Dimitris

2010-01-01

The paper addresses the fine retinal-vessel's detection issue that is faced in diagnostic applications and aims at assisting in better recognizing fine vessel anomalies in 2D. Our innovation relies in separating key visual features vessels exhibit in order to make the diagnosis of eventual retinopathologies easier to detect. This allows focusing on vessel segments which present fine changes detectable at different sampling scales. We advocate that these changes can be addressed as subsequent stages of the same vessel detection procedure. We first carry out an initial estimate of the basic vessel-wall's network, define the main wall-body, and then try to approach the ridges and branches of the vasculature's using fine detection. Fine vessel screening looks into local structural inconsistencies in vessels properties, into noise, or into not expected intensity variations observed inside pre-known vessel-body areas. The vessels are first modelled sufficiently but not precisely by their walls with a tubular model-structure that is the result of an initial segmentation. This provides a chart of likely Vessel Wall Pixels (VWPs) yielding a form of a likelihood vessel map mainly based on gradient filter's intensity and spatial arrangement parameters (e.g., linear consistency). Specific vessel parameters (centerline, width, location, fall-away rate, main orientation) are post-computed by convolving the image with a set of pre-tuned spatial filters called Matched Filters (MFs). These are easily computed as Gaussian-like 2D forms that use a limited range sub-optimal parameters adjusted to the dominant vessel characteristics obtained by Spatial Grey Level Difference statistics limiting the range of search into vessel widths of 16, 32, and 64 pixels. Sparse pixels are effectively eliminated by applying a limited range Hough Transform (HT) or region growing. Major benefits are limiting the range of parameters, reducing the search-space for post-convolution to only masked regions, representing almost 2% of the 2D volume, good speed versus accuracy/time trade-off. Results show the potentials of our approach in terms of time for detection ROC analysis and accuracy of vessel pixel (VP) detection. PMID:20706682

Spinal arteriovenous shunts: accuracy of shunt detection, localization, and subtype discrimination using spinal magnetic resonance angiography and manual contrast injection using a syringe.

PubMed

Unsrisong, Kittisak; Taphey, Siriporn; Oranratanachai, Kanokporn

2016-04-01

The object of this study was to evaluate the accuracy of fast 3D contrast-enhanced spinal MR angiography (MRA) using a manual syringe contrast injection technique for detecting and evaluating spinal arteriovenous shunts (AVSs). This was a retrospective study of 15 patients and 20 spinal MRA and catheter angiography studies. The accuracy of using spinal MRA to detect spinal AVS, localize shunts, and discriminate the subtype and dominant arterial feeder of the AVS were studied. There were 14 pretherapeutic and 6 posttherapeutic follow-up spinal MRA and catheter spinal angiography studies. The spinal AVS was demonstrated in 17 of 20 studies. Spinal MRA demonstrated 100% sensitivity for detecting spinal AVS with no false-negative results. A 97% accuracy rate for AVS subtype discrimination and shunt level localization was achieved using this study's diagnostic criteria. The detection of the dominant arterial feeder was limited to 9 of these 17 cases (53%). The fast 3D contrast-enhanced MRA technique performed using manual syringe contrast injection can detect the presence of a spinal AVS, locate the shunt level, and discriminate AVS subtype in most cases, but is limited when detecting small arterial feeders.

Small-size pedestrian detection in large scene based on fast R-CNN

NASA Astrophysics Data System (ADS)

Wang, Shengke; Yang, Na; Duan, Lianghua; Liu, Lu; Dong, Junyu

2018-04-01

Pedestrian detection is a canonical sub-problem of object detection with high demand during recent years. Although recent deep learning object detectors such as Fast/Faster R-CNN have shown excellent performance for general object detection, they have limited success for small size pedestrian detection in large-view scene. We study that the insufficient resolution of feature maps lead to the unsatisfactory accuracy when handling small instances. In this paper, we investigate issues involving Fast R-CNN for pedestrian detection. Driven by the observations, we propose a very simple but effective baseline for pedestrian detection based on Fast R-CNN, employing the DPM detector to generate proposals for accuracy, and training a fast R-CNN style network to jointly optimize small size pedestrian detection with skip connection concatenating feature from different layers to solving coarseness of feature maps. And the accuracy is improved in our research for small size pedestrian detection in the real large scene.

Accuracy of urinary human papillomavirus testing for presence of cervical HPV: systematic review and meta-analysis

PubMed Central

Pathak, Neha; Dodds, Julie; Khan, Khalid

2014-01-01

Objective To determine the accuracy of testing for human papillomavirus (HPV) DNA in urine in detecting cervical HPV in sexually active women. Design Systematic review and meta-analysis. Data sources Searches of electronic databases from inception until December 2013, checks of reference lists, manual searches of recent issues of relevant journals, and contact with experts. Eligibility criteria Test accuracy studies in sexually active women that compared detection of urine HPV DNA with detection of cervical HPV DNA. Data extraction and synthesis Data relating to patient characteristics, study context, risk of bias, and test accuracy. 2×2 tables were constructed and synthesised by bivariate mixed effects meta-analysis. Results 16 articles reporting on 14 studies (1443 women) were eligible for meta-analysis. Most used commercial polymerase chain reaction methods on first void urine samples. Urine detection of any HPV had a pooled sensitivity of 87% (95% confidence interval 78% to 92%) and specificity of 94% (95% confidence interval 82% to 98%). Urine detection of high risk HPV had a pooled sensitivity of 77% (68% to 84%) and specificity of 88% (58% to 97%). Urine detection of HPV 16 and 18 had a pooled sensitivity of 73% (56% to 86%) and specificity of 98% (91% to 100%). Metaregression revealed an increase in sensitivity when urine samples were collected as first void compared with random or midstream (P=0.004). Limitations The major limitations of this review are the lack of a strictly uniform method for the detection of HPV in urine and the variation in accuracy between individual studies. Conclusions Testing urine for HPV seems to have good accuracy for the detection of cervical HPV, and testing first void urine samples is more accurate than random or midstream sampling. When cervical HPV detection is considered difficult in particular subgroups, urine testing should be regarded as an acceptable alternative. PMID:25232064

A Complete and Accurate Ab Initio Repeat Finding Algorithm.

PubMed

Lian, Shuaibin; Chen, Xinwu; Wang, Peng; Zhang, Xiaoli; Dai, Xianhua

2016-03-01

It has become clear that repetitive sequences have played multiple roles in eukaryotic genome evolution including increasing genetic diversity through mutation, changes in gene expression and facilitating generation of novel genes. However, identification of repetitive elements can be difficult in the ab initio manner. Currently, some classical ab initio tools of finding repeats have already presented and compared. The completeness and accuracy of detecting repeats of them are little pool. To this end, we proposed a new ab initio repeat finding tool, named HashRepeatFinder, which is based on hash index and word counting. Furthermore, we assessed the performances of HashRepeatFinder with other two famous tools, such as RepeatScout and Repeatfinder, in human genome data hg19. The results indicated the following three conclusions: (1) The completeness of HashRepeatFinder is the best one among these three compared tools in almost all chromosomes, especially in chr9 (8 times of RepeatScout, 10 times of Repeatfinder); (2) in terms of detecting large repeats, HashRepeatFinder also performed best in all chromosomes, especially in chr3 (24 times of RepeatScout and 250 times of Repeatfinder) and chr19 (12 times of RepeatScout and 60 times of Repeatfinder); (3) in terms of accuracy, HashRepeatFinder can merge the abundant repeats with high accuracy.

Diagnostic accuracy of noncontrast MRI for detection of glenohumeral cartilage lesions: a prospective comparison to arthroscopy.

PubMed

VanBeek, Corinne; Loeffler, Bryan J; Narzikul, Alexa; Gordon, Victoria; Rasiej, Michael J; Kazam, Jonathan K; Abboud, Joseph A

2014-07-01

The purpose of this study was to determine the prevalence of glenohumeral articular cartilage lesions in patients with rotator cuff tendinopathy and to assess the accuracy of noncontrast magnetic resonance imaging (MRI) in detecting these defects compared with the "gold standard" of arthroscopy. Noncontrast MRI studies obtained in 84 consecutive patients undergoing shoulder arthroscopy for rotator cuff tendinopathy (mean age, 54.8 years; range, 17-82 years) were prospectively evaluated for glenohumeral cartilage lesions. Two fellowship-trained, experienced musculoskeletal radiologists were blinded from the arthroscopic findings and independently evaluated the glenoid and humeral head cartilage on 2 separate occasions. At arthroscopy, cartilage lesions of the humeral head were detected in 23 patients (frequency, 27.4%), and glenoid cartilage lesions were found in 20 patients (frequency, 23.8%). For detection of a humeral lesion on MRI, the radiologists' combined accuracy was 78%, sensitivity was 43%, and specificity was 91%. The combined accuracy for detection of glenoid lesions on MRI was 84%, sensitivity was 53%, and specificity was 93%. Combining the readers, low-grade lesions (International Cartilage Repair Society grades 1 and 2) of the glenoid and humerus were read as negative on MRI in 63% and 86% of cases, respectively. Overall accuracy of noncontrast MRI for detection of glenohumeral articular cartilage lesions is good; however, interpretation is reader dependent, and accuracy is significantly reduced for detection of low-grade lesions. On the basis of these findings, we recommend that patients with rotator cuff tendinopathy undergoing arthroscopy be informed that the presence and severity of cartilage lesions may be underestimated on MRI. Copyright © 2014 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Mosby, Inc. All rights reserved.

Sensitivity of a computer adaptive assessment for measuring functional mobility changes in children enrolled in a community fitness programme.

PubMed

Haley, Stephen M; Fragala-Pinkham, Maria; Ni, Pengsheng

2006-07-01

To examine the relative sensitivity to detect functional mobility changes with a full-length parent questionnaire compared with a computerized adaptive testing version of the questionnaire after a 16-week group fitness programme. Prospective, pre- and posttest study with a 16-week group fitness intervention. Three community-based fitness centres. Convenience sample of children (n = 28) with physical or developmental disabilities. A 16-week group exercise programme held twice a week in a community setting. A full-length (161 items) paper version of a mobility parent questionnaire based on the Pediatric Evaluation of Disability Inventory, but expanded to include expected skills of children up to 15 years old was compared with a 15-item computer adaptive testing version. Both measures were administered at pre- and posttest intervals. Both the full-length Pediatric Evaluation of Disability Inventory and the 15-item computer adaptive testing version detected significant changes between pre- and posttest scores, had large effect sizes, and standardized response means, with a modest decrease in the computer adaptive test as compared with the 161-item paper version. Correlations between the computer adaptive and paper formats across pre- and posttest scores ranged from r = 0.76 to 0.86. Both functional mobility test versions were able to detect positive functional changes at the end of the intervention period. Greater variability in score estimates was generated by the computerized adaptive testing version, which led to a relative reduction in sensitivity as defined by the standardized response mean. Extreme scores were generally more difficult for the computer adaptive format to estimate with as much accuracy as scores in the mid-range of the scale. However, the reduction in accuracy and sensitivity, which did not influence the group effect results in this study, is counterbalanced by the large reduction in testing burden.

Lack of Accuracy of Body Temperature for Detecting Serious Bacterial Infection in Febrile Episodes.

PubMed

De, Sukanya; Williams, Gabrielle J; Teixeira-Pinto, Armando; Macaskill, Petra; McCaskill, Mary; Isaacs, David; Craig, Jonathan C

2015-09-01

Body temperature is a time-honored marker of serious bacterial infection, but there are few studies of its test performance. The aim of our study was to determine the accuracy of temperature measured on presentation to medical care for detecting serious bacterial infection. Febrile children 0-5 years of age presenting to the emergency department of a tertiary care pediatric hospital were sampled consecutively. The accuracy of the axillary temperature measured at presentation was evaluated using logistic regression models to generate receiver operating characteristic curves. Reference standard tests for serious bacterial infection were standard microbiologic/radiologic tests and clinical follow-up. Age, clinicians' impression of appearance of the child (well versus unwell) and duration of illness were assessed as possible effect modifiers. Of 15,781 illness episodes 1120 (7.1%) had serious bacterial infection. The area under the receiver operating characteristic curve for temperature was 0.60 [95% confidence intervals (CI): 0.58-0.62]. A threshold of ≥ 38°C had a sensitivity of 0.67 (95% CI: 0.64-0.70), specificity of 0.45 (95% CI: 0.44-0.46), positive likelihood ratio of 1.2 (95% CI: 1.2-1.3) and negative likelihood ratio of 0.7 (95% CI: 0.7-0.8). Age and illness duration had a small but significant effect on the accuracy of temperature increasing its "rule-in" potential. Measured temperature at presentation to hospital is not an accurate marker of serious bacterial infection in febrile children. Younger age and longer duration of illness increase the rule-in potential of temperature but without substantial overall change in its test accuracy.

Camera sensor arrangement for crop/weed detection accuracy in agronomic images.

PubMed

Romeo, Juan; Guerrero, José Miguel; Montalvo, Martín; Emmi, Luis; Guijarro, María; Gonzalez-de-Santos, Pablo; Pajares, Gonzalo

2013-04-02

In Precision Agriculture, images coming from camera-based sensors are commonly used for weed identification and crop line detection, either to apply specific treatments or for vehicle guidance purposes. Accuracy of identification and detection is an important issue to be addressed in image processing. There are two main types of parameters affecting the accuracy of the images, namely: (a) extrinsic, related to the sensor's positioning in the tractor; (b) intrinsic, related to the sensor specifications, such as CCD resolution, focal length or iris aperture, among others. Moreover, in agricultural applications, the uncontrolled illumination, existing in outdoor environments, is also an important factor affecting the image accuracy. This paper is exclusively focused on two main issues, always with the goal to achieve the highest image accuracy in Precision Agriculture applications, making the following two main contributions: (a) camera sensor arrangement, to adjust extrinsic parameters and (b) design of strategies for controlling the adverse illumination effects.

Management applications of lidar-derived mean high water shorelines in North Carolina

USGS Publications Warehouse

Limber, Patrick W.; List, Jeffrey H.; Warren, Jeffrey D.

2007-01-01

The accuracy of shoreline change analysis is dependent on how the shoreline is defined and the consistency of the techniques(s) used to define it. Using the concurrent lidar (light detection and ranging) and orthophotography dataset from August and September of 2004 covering North Carolina's 516 kilometers of barrier island oceanfront, Limber et al. (2007) examined the spatial relationship between two common shoreline definitions used in shoreline change analysis, mean high water [MHW] derived from lidar data and the wet/dry line digitized from orthophotography. Here, we summarize this work and extend the analysis with a comparison between two different methods of MHW shoreline extraction from liar data: a profile-based method (Stockdon et al., 2002) and a method based on correction of the lidar data to a MHW datum (Hess et al., 2005). Potential bias generated by using these different shoreline types together can affect not only the accuracy of shoreline change analysis, but also the coastal management policies and decision that rely on it. Therefore, the implications of this study potential extend far beyond North Carolina and Atlantic Coast of the United States.

An adaptive confidence limit for periodic non-steady conditions fault detection

NASA Astrophysics Data System (ADS)

Wang, Tianzhen; Wu, Hao; Ni, Mengqi; Zhang, Milu; Dong, Jingjing; Benbouzid, Mohamed El Hachemi; Hu, Xiong

2016-05-01

System monitoring has become a major concern in batch process due to the fact that failure rate in non-steady conditions is much higher than in steady ones. A series of approaches based on PCA have already solved problems such as data dimensionality reduction, multivariable decorrelation, and processing non-changing signal. However, if the data follows non-Gaussian distribution or the variables contain some signal changes, the above approaches are not applicable. To deal with these concerns and to enhance performance in multiperiod data processing, this paper proposes a fault detection method using adaptive confidence limit (ACL) in periodic non-steady conditions. The proposed ACL method achieves four main enhancements: Longitudinal-Standardization could convert non-Gaussian sampling data to Gaussian ones; the multiperiod PCA algorithm could reduce dimensionality, remove correlation, and improve the monitoring accuracy; the adaptive confidence limit could detect faults under non-steady conditions; the fault sections determination procedure could select the appropriate parameter of the adaptive confidence limit. The achieved result analysis clearly shows that the proposed ACL method is superior to other fault detection approaches under periodic non-steady conditions.

Research on Horizontal Accuracy Method of High Spatial Resolution Remotely Sensed Orthophoto Image

NASA Astrophysics Data System (ADS)

Xu, Y. M.; Zhang, J. X.; Yu, F.; Dong, S.

2018-04-01

At present, in the inspection and acceptance of high spatial resolution remotly sensed orthophoto image, the horizontal accuracy detection is testing and evaluating the accuracy of images, which mostly based on a set of testing points with the same accuracy and reliability. However, it is difficult to get a set of testing points with the same accuracy and reliability in the areas where the field measurement is difficult and the reference data with high accuracy is not enough. So it is difficult to test and evaluate the horizontal accuracy of the orthophoto image. The uncertainty of the horizontal accuracy has become a bottleneck for the application of satellite borne high-resolution remote sensing image and the scope of service expansion. Therefore, this paper proposes a new method to test the horizontal accuracy of orthophoto image. This method using the testing points with different accuracy and reliability. These points' source is high accuracy reference data and field measurement. The new method solves the horizontal accuracy detection of the orthophoto image in the difficult areas and provides the basis for providing reliable orthophoto images to the users.

EEG channels reduction using PCA to increase XGBoost's accuracy for stroke detection

NASA Astrophysics Data System (ADS)

Fitriah, N.; Wijaya, S. K.; Fanany, M. I.; Badri, C.; Rezal, M.

2017-07-01

In Indonesia, based on the result of Basic Health Research 2013, the number of stroke patients had increased from 8.3 ‰ (2007) to 12.1 ‰ (2013). These days, some researchers are using electroencephalography (EEG) result as another option to detect the stroke disease besides CT Scan image as the gold standard. A previous study on the data of stroke and healthy patients in National Brain Center Hospital (RS PON) used Brain Symmetry Index (BSI), Delta-Alpha Ratio (DAR), and Delta-Theta-Alpha-Beta Ratio (DTABR) as the features for classification by an Extreme Learning Machine (ELM). The study got 85% accuracy with sensitivity above 86 % for acute ischemic stroke detection. Using EEG data means dealing with many data dimensions, and it can reduce the accuracy of classifier (the curse of dimensionality). Principal Component Analysis (PCA) could reduce dimensionality and computation cost without decreasing classification accuracy. XGBoost, as the scalable tree boosting classifier, can solve real-world scale problems (Higgs Boson and Allstate dataset) with using a minimal amount of resources. This paper reuses the same data from RS PON and features from previous research, preprocessed with PCA and classified with XGBoost, to increase the accuracy with fewer electrodes. The specific fewer electrodes improved the accuracy of stroke detection. Our future work will examine the other algorithm besides PCA to get higher accuracy with less number of channels.

Epidemiology of prostate cancer in Asian countries.

PubMed

Kimura, Takahiro; Egawa, Shin

2018-06-01

The incidence of prostate cancer has been increasing worldwide in recent years. The GLOBOCAN project showed that prostate cancer was the second most frequently diagnosed cancer and the fifth leading cause of cancer mortality among men worldwide in 2012. This trend has been growing even in Asian countries, where the incidence had previously been low. However, the accuracy of data about incidence and mortality as a result of prostate cancer in some Asian countries is limited. The cause of this increasing trend is multifactorial. One possible explanation is changes in lifestyles due to more Westernized diets. The incidence is also statistically biased by the wide implementation of early detection systems and the accuracy of national cancer registration systems, which are still immature in most Asian countries. Mortality rate decreases in Australia, New Zealand and Japan since the 1990s are possibly due to the improvements in treatment and/or early detection efforts employed. However, this rate is increasing in the majority of other Asian countries. Studies of latent and incidental prostate cancer provide less biased information. The prevalence of latent and incidental prostate cancer in contemporary Japan and Korea is similar to those in Western countries, suggesting the influence of lifestyle changes on carcinogenesis. Many studies reported evidence of both congenital and acquired risk factors for carcinogenesis of prostate cancer. Recent changes in the acquired risk factors might be associated with the increasing occurrence of prostate cancer in Asian countries. This trend could continue, especially in developing Asian countries. © 2018 The Japanese Urological Association.

A new metric for detecting change in slowly evolving brain tumors: validation in meningioma patients.

PubMed

Pohl, Kilian M; Konukoglu, Ender; Novellas, Sebastian; Ayache, Nicholas; Fedorov, Andriy; Talos, Ion-Florin; Golby, Alexandra; Wells, William M; Kikinis, Ron; Black, Peter M

2011-03-01

Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. We describe a semiautomatic procedure targeted toward identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts' findings. We also perform benchmark testing with synthetic data. Our experiments indicated that experts' visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts' manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts' results. However, our approach required far less user input and generated more consistent measurements. The sensitivity of experts' visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts' segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.

Utilization of ALOS PALSAR-2 Data for Mangrove Detection Using OBIA Method Approach

NASA Astrophysics Data System (ADS)

Anggraini, N.; Julzarika, A.

2017-12-01

Mangroves have an important role for climate change mitigation. This is because mangroves have high carbon stock potential. The ability of mangroves to absorb carbon is very high and it is estimated that the mangrove carbon stock reaches 1023 Mg C. The current problem is the area of mangrove forest is decreasing due to land conversion. One technology that can be used to detect changes in the area of mangrove forest is by utilizing ALOS PALSAR-2 satellite imagery. The purpose of this research is to detect mangrove forest area from ALOS PALSAR-2 data by using object-based image analysis (OBIA) method. The location of the study is Taman Nasional Sembilang in Banyuasin Regency of South Sumatra. The data used are ALOS PALSAR-2 dualpolarization (HH and HV), recording year 2015. The calculation of mangrove forest area in Sembilang National Park has ∼ 82% accuracy. The results of this study can be used for various applications and mapping activities.

Improved assessment of accuracy and performance using a rotational paper-based device for multiplexed detection of heavy metals.

PubMed

Sun, Xiange; Li, Bowei; Qi, Anjin; Tian, Chongguo; Han, Jinglong; Shi, Yajun; Lin, Bingcheng; Chen, Lingxin

2018-02-01

In this work, a novel rotational microfluidic paper-based device was developed to improve the accuracy and performance of the multiplexed colorimetric detection by effectively avoiding the diffusion of colorimetric reagent on the detection zone. The integrated paper-based rotational valves were used to control the connection or disconnection between detection zones and fluid channels. Based on the manipulation of the rotational valves, this rotational paper-based device could prevent the random diffusion of colorimetric reagent and reduce the error of quantitative analysis considerably. The multiplexed colorimetric detection of heavy metals Ni(II), Cu(II) and Cr(VI) were implemented on the rotational device and the detection limits could be found to be 4.8, 1.6, and 0.18mg/L, respectively. The developed rotational device showed the great advantage in improving the detection accuracy and was expected to be a low-cost, portable analytical platform for the on-site detection. Copyright © 2017 Elsevier B.V. All rights reserved.

Accuracy assessment of NLCD 2006 land cover and impervious surface

USGS Publications Warehouse

Wickham, James D.; Stehman, Stephen V.; Gass, Leila; Dewitz, Jon; Fry, Joyce A.; Wade, Timothy G.

2013-01-01

Release of NLCD 2006 provides the first wall-to-wall land-cover change database for the conterminous United States from Landsat Thematic Mapper (TM) data. Accuracy assessment of NLCD 2006 focused on four primary products: 2001 land cover, 2006 land cover, land-cover change between 2001 and 2006, and impervious surface change between 2001 and 2006. The accuracy assessment was conducted by selecting a stratified random sample of pixels with the reference classification interpreted from multi-temporal high resolution digital imagery. The NLCD Level II (16 classes) overall accuracies for the 2001 and 2006 land cover were 79% and 78%, respectively, with Level II user's accuracies exceeding 80% for water, high density urban, all upland forest classes, shrubland, and cropland for both dates. Level I (8 classes) accuracies were 85% for NLCD 2001 and 84% for NLCD 2006. The high overall and user's accuracies for the individual dates translated into high user's accuracies for the 2001–2006 change reporting themes water gain and loss, forest loss, urban gain, and the no-change reporting themes for water, urban, forest, and agriculture. The main factor limiting higher accuracies for the change reporting themes appeared to be difficulty in distinguishing the context of grass. We discuss the need for more research on land-cover change accuracy assessment.

Flood Mapping in the Lower Mekong River Basin Using Daily MODIS Observations

NASA Technical Reports Server (NTRS)

Fayne, Jessica V.; Bolten, John D.; Doyle, Colin S.; Fuhrmann, Sven; Rice, Matthew T.; Houser, Paul R.; Lakshmi, Venkat

2017-01-01

In flat homogenous terrain such as in Cambodia and Vietnam, the monsoon season brings significant and consistent flooding between May and November. To monitor flooding in the Lower Mekong region, the near real-time NASA Flood Extent Product (NASA-FEP) was developed using seasonal normalized difference vegetation index (NDVI) differences from the 250 m resolution Moderate Resolution Imaging Spectroradiometer (MODIS) sensor compared to daily observations. The use of a percentage change interval classification relating to various stages of flooding reduces might be confusing to viewers or potential users, and therefore reducing the product usage. To increase the product usability through simplification, the classification intervals were compared with other commonly used change detection schemes to identify the change classification scheme that best delineates flooded areas. The percentage change method used in the NASA-FEP proved to be helpful in delineating flood boundaries compared to other change detection methods. The results of the accuracy assessments indicate that the -75% NDVI change interval can be reclassified to a descriptive 'flood' classification. A binary system was used to simplify the interpretation of the NASA-FEP by removing extraneous information from lower interval change classes.

Does aging impair first impression accuracy? Differentiating emotion recognition from complex social inferences.

PubMed

Krendl, Anne C; Rule, Nicholas O; Ambady, Nalini

2014-09-01

Young adults can be surprisingly accurate at making inferences about people from their faces. Although these first impressions have important consequences for both the perceiver and the target, it remains an open question whether first impression accuracy is preserved with age. Specifically, could age differences in impressions toward others stem from age-related deficits in accurately detecting complex social cues? Research on aging and impression formation suggests that young and older adults show relative consensus in their first impressions, but it is unknown whether they differ in accuracy. It has been widely shown that aging disrupts emotion recognition accuracy, and that these impairments may predict deficits in other social judgments, such as detecting deceit. However, it is unclear whether general impression formation accuracy (e.g., emotion recognition accuracy, detecting complex social cues) relies on similar or distinct mechanisms. It is important to examine this question to evaluate how, if at all, aging might affect overall accuracy. Here, we examined whether aging impaired first impression accuracy in predicting real-world outcomes and categorizing social group membership. Specifically, we studied whether emotion recognition accuracy and age-related cognitive decline (which has been implicated in exacerbating deficits in emotion recognition) predict first impression accuracy. Our results revealed that emotion recognition accuracy did not predict first impression accuracy, nor did age-related cognitive decline impair it. These findings suggest that domains of social perception outside of emotion recognition may rely on mechanisms that are relatively unimpaired by aging. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Studies of the cosmic ray spectrum and large scale anisotropies with the KASCADE-Grande experiment

NASA Astrophysics Data System (ADS)

Chiavassa, A.; Apel, W. D.; Arteaga-Velázquez, J. C.; Bekk, K.; Bertaina, M.; Blümer, J.; Bozdog, H.; Brancus, I. M.; Cantoni, E.; Cossavella, F.; Curcio, C.; Daumiller, K.; de Souza, V.; Di Pierro, F.; Doll, P.; Engel, R.; Engler, J.; Fuchs, B.; Fuhrmann, D.; Gils, H. J.; Glasstetter, R.; Grupen, C.; Haungs, A.; Heck, D.; Hörandel, J. R.; Huber, D.; Huege, T.; Kampert, K.-H.; Kang, D.; Klages, H. O.; Link, K.; Łuczak, P.; Ludwig, M.; Mathes, H. J.; Mayer, H. J.; Melissas, M.; Milke, J.; Mitrica, B.; Morello, C.; Oehlschläger, J.; Ostapchenko, S.; Palmieri, N.; Petcu, M.; Pierog, T.; Rebel, H.; Roth, M.; Schieler, H.; Schoo, S.; Schröder, F. G.; Sima, O.; Toma, G.; Trinchero, G. C.; Ulrich, H.; Weindl, A.; Wochele, J.; Zabierowski, J.

2014-08-01

KASCADE-Grande is an air shower observatory devoted to the detection of cosmic rays in the 1016 - 1018eV energy range. For each event the arrival direction, the total number of charged particles (Nch) and the total number of muons (Nμ), at detection level (i.e. 110 m a.s.l.), are measured. The detection of these observarbles, with high accuracy, allows the study of the primary spectrum, chemical composition and large scale anisotropies, that are the relevant informations to investigate the astrophysics of cosmic rays in this energy range. These studies are of main importance to deeply investigate the change of slope of the primary spectrum detected at ~ 4 × 1015 eV, also known as the knee, and to search for the transition from galactic to extra-galactic cosmic rays.

Producing good font attribute determination using error-prone information

NASA Astrophysics Data System (ADS)

Cooperman, Robert

1997-04-01

A method to provide estimates of font attributes in an OCR system, using detectors of individual attributes that are error-prone. For an OCR system to preserve the appearance of a scanned document, it needs accurate detection of font attributes. However, OCR environments have noise and other sources of errors, tending to make font attribute detection unreliable. Certain assumptions about font use can greatly enhance accuracy. Attributes such as boldness and italics are more likely to change between neighboring words, while attributes such as serifness are less likely to change within the same paragraph. Furthermore, the document as a whole, tends to have a limited number of sets of font attributes. These assumptions allow a better use of context than the raw data, or what would be achieved by simpler methods that would oversmooth the data.

Retrieving improved multi-temporal CryoSat elevations over ice caps and glaciers - a case study of Barnes ice cap

NASA Astrophysics Data System (ADS)

Nilsson, Johan; Burgess, David

2014-05-01

The CryoSat mission was launched in 2010 to observe the Earth's cryosphere. In contrast to previous satellite radar altimeters, this mission is expected to monitor the elevation of small ice caps and glaciers, which according to the IPCC will be the largest contributor to 21st century sea level rise. To date the ESA CryoSat SARiN level-2 (L2) elevation product is not yet fully optimized for use over these types of glaciated regions, as its processed with a more universal algorithm. Thus the aim of this study is to demonstrate that with the use of improved processing CryoSat SARiN data can be used for more accurate topography mapping and elevation change detection for ice caps and glaciers. To demonstrate this, elevations and elevation changes over Barnes ice cap, located on Baffin Island in the Canadian Arctic, have been estimated from available data from the years 2010-2013. ESA's CryoSat level-1b (L1b) SARiN baseline "B" data product was used and processed in-house to estimate surface elevations. The resulting product is referred to as DTU-L2. The processing focused on improving the retracker, reducing phase noise and correcting phase ambiguities. The accuracy of the DTU-L2 and the ESA-L2 product was determined by comparing the measured elevations against NASA's IceBridge Airborne Topographic Mapper (ATM) elevations from May 2011. The resulting difference in accuracy was determined by comparing their associated errors. From the multi-temporal measurements spanning the period 2010-2013, elevation changes where estimated and compared to ICESat derived changes from 2003-2009. The result of the study shows good agreement between the NASA measured ATM elevations and the DTU-L2 data. It also shows that the pattern of elevation change is similar to that derived from ICESat data. The accuracy of the DTU-L2 estimated elevations is on average several factors higher compared to the ESA-L2 elevation product. These preliminary results demonstrates that CryoSat elevation data, using improved processing, can be used for accurate topographic mapping and elevation change detection on ice caps and glaciers. Future work would entail extending this processing to other regions of this type to support these results.

A fuzzy pattern matching method based on graph kernel for lithography hotspot detection

NASA Astrophysics Data System (ADS)

Nitta, Izumi; Kanazawa, Yuzi; Ishida, Tsutomu; Banno, Koji

2017-03-01

In advanced technology nodes, lithography hotspot detection has become one of the most significant issues in design for manufacturability. Recently, machine learning based lithography hotspot detection has been widely investigated, but it has trade-off between detection accuracy and false alarm. To apply machine learning based technique to the physical verification phase, designers require minimizing undetected hotspots to avoid yield degradation. They also need a ranking of similar known patterns with a detected hotspot to prioritize layout pattern to be corrected. To achieve high detection accuracy and to prioritize detected hotspots, we propose a novel lithography hotspot detection method using Delaunay triangulation and graph kernel based machine learning. Delaunay triangulation extracts features of hotspot patterns where polygons locate irregularly and closely one another, and graph kernel expresses inner structure of graphs. Additionally, our method provides similarity between two patterns and creates a list of similar training patterns with a detected hotspot. Experiments results on ICCAD 2012 benchmarks show that our method achieves high accuracy with allowable range of false alarm. We also show the ranking of the similar known patterns with a detected hotspot.

Effect of absorbing impurities on the accuracy of the optical method for the detection of the iodine-containing substances resulting from the processing of waste nuclear fuel

NASA Astrophysics Data System (ADS)

Kireev, S. V.; Simanovsky, I. G.; Shnyrev, S. L.

2010-12-01

The study is aimed at an increase in the accuracy of the optical method for the detection of the iodine-containing substances in technological liquids resulting form the processing of the waste nuclear fuel. It is demonstrated that the accuracy can be increased owing to the measurements at various combinations of wavelengths depending on the concentrations of impurities that are contained in the sample under study and absorb in the spectral range used for the detection of the iodine-containing substances.

Effects of divided attention and operating room noise on perception of pulse oximeter pitch changes: a laboratory study.

PubMed

Stevenson, Ryan A; Schlesinger, Joseph J; Wallace, Mark T

2013-02-01

Anesthesiology requires performing visually oriented procedures while monitoring auditory information about a patient's vital signs. A concern in operating room environments is the amount of competing information and the effects that divided attention has on patient monitoring, such as detecting auditory changes in arterial oxygen saturation via pulse oximetry. The authors measured the impact of visual attentional load and auditory background noise on the ability of anesthesia residents to monitor the pulse oximeter auditory display in a laboratory setting. Accuracies and response times were recorded reflecting anesthesiologists' abilities to detect changes in oxygen saturation across three levels of visual attention in quiet and with noise. Results show that visual attentional load substantially affects the ability to detect changes in oxygen saturation concentrations conveyed by auditory cues signaling 99 and 98% saturation. These effects are compounded by auditory noise, up to a 17% decline in performance. These deficits are seen in the ability to accurately detect a change in oxygen saturation and in speed of response. Most anesthesia accidents are initiated by small errors that cascade into serious events. Lack of monitor vigilance and inattention are two of the more commonly cited factors. Reducing such errors is thus a priority for improving patient safety. Specifically, efforts to reduce distractors and decrease background noise should be considered during induction and emergence, periods of especially high risk, when anesthesiologists has to attend to many tasks and are thus susceptible to error.

The predictive value of magnetic resonance imaging of retinoblastoma for the likelihood of high-risk pathologic features.

PubMed

Hiasat, Jamila G; Saleh, Alaa; Al-Hussaini, Maysa; Al Nawaiseh, Ibrahim; Mehyar, Mustafa; Qandeel, Monther; Mohammad, Mona; Deebajah, Rasha; Sultan, Iyad; Jaradat, Imad; Mansour, Asem; Yousef, Yacoub A

2018-06-01

To evaluate the predictive value of magnetic resonance imaging in retinoblastoma for the likelihood of high-risk pathologic features. A retrospective study of 64 eyes enucleated from 60 retinoblastoma patients. Contrast-enhanced magnetic resonance imaging was performed before enucleation. Main outcome measures included demographics, laterality, accuracy, sensitivity, and specificity of magnetic resonance imaging in detecting high-risk pathologic features. Optic nerve invasion and choroidal invasion were seen microscopically in 34 (53%) and 28 (44%) eyes, respectively, while they were detected in magnetic resonance imaging in 22 (34%) and 15 (23%) eyes, respectively. The accuracy of magnetic resonance imaging in detecting prelaminar invasion was 77% (sensitivity 89%, specificity 98%), 56% for laminar invasion (sensitivity 27%, specificity 94%), 84% for postlaminar invasion (sensitivity 42%, specificity 98%), and 100% for optic cut edge invasion (sensitivity100%, specificity 100%). The accuracy of magnetic resonance imaging in detecting focal choroidal invasion was 48% (sensitivity 33%, specificity 97%), and 84% for massive choroidal invasion (sensitivity 53%, specificity 98%), and the accuracy in detecting extrascleral extension was 96% (sensitivity 67%, specificity 98%). Magnetic resonance imaging should not be the only method to stratify patients at high risk from those who are not, eventhough it can predict with high accuracy extensive postlaminar optic nerve invasion, massive choroidal invasion, and extrascleral tumor extension.

A Flexible Analysis Tool for the Quantitative Acoustic Assessment of Infant Cry

PubMed Central

Reggiannini, Brian; Sheinkopf, Stephen J.; Silverman, Harvey F.; Li, Xiaoxue; Lester, Barry M.

2015-01-01

Purpose In this article, the authors describe and validate the performance of a modern acoustic analyzer specifically designed for infant cry analysis. Method Utilizing known algorithms, the authors developed a method to extract acoustic parameters describing infant cries from standard digital audio files. They used a frame rate of 25 ms with a frame advance of 12.5 ms. Cepstral-based acoustic analysis proceeded in 2 phases, computing frame-level data and then organizing and summarizing this information within cry utterances. Using signal detection methods, the authors evaluated the accuracy of the automated system to determine voicing and to detect fundamental frequency (F0) as compared to voiced segments and pitch periods manually coded from spectrogram displays. Results The system detected F0 with 88% to 95% accuracy, depending on tolerances set at 10 to 20 Hz. Receiver operating characteristic analyses demonstrated very high accuracy at detecting voicing characteristics in the cry samples. Conclusions This article describes an automated infant cry analyzer with high accuracy to detect important acoustic features of cry. A unique and important aspect of this work is the rigorous testing of the system’s accuracy as compared to ground-truth manual coding. The resulting system has implications for basic and applied research on infant cry development. PMID:23785178

Long fiber Bragg grating sensor interrogation using discrete-time microwave photonic filtering techniques.

PubMed

Ricchiuti, Amelia Lavinia; Barrera, David; Sales, Salvador; Thevenaz, Luc; Capmany, José

2013-11-18

A novel technique for interrogating photonic sensors based on long fiber Bragg gratings (FBGs) is presented and experimentally demonstrated, dedicated to detect the presence and the precise location of several spot events. The principle of operation is based on a technique used to analyze microwave photonics (MWP) filters. The long FBGs are used as quasi-distributed sensors. Several hot-spots can be detected along the FBG with a spatial accuracy under 0.5 mm using a modulator and a photo-detector (PD) with a modest bandwidth of less than 1 GHz. The proposed interrogation system is intrinsically robust against environmental changes.

Determination of mangrove change in Matang Mangrove Forest using multi temporal satellite imageries

NASA Astrophysics Data System (ADS)

Ibrahim, N. A.; Mustapha, M. A.; Lihan, T.; Ghaffar, M. A.

2013-11-01

Mangrove protects shorelines from damaging storm and hurricane winds, waves, and floods. Mangroves also help prevent erosion by stabilizing sediments with their tangled root systems. They maintain water quality and clarity, filtering pollutants and trapping sediments originating from land. However, mangrove has been reported to be threatened by land conversion for other activities. In this study, land use and land cover changes in Matang Mangrove Forest during the past 18 years (1993 to 2011) were determined using multi-temporal satellite imageries by Landsat TM and RapidEye. In this study, classification of land use and land cover approach was performed using the maximum likelihood classifier (MCL) method along with vegetation index differencing (NDVI) technique. Data obtained was evaluated through Kappa coefficient calculation for accuracy and results revealed that the classification accuracy was 81.25% with Kappa Statistics of 0.78. The results indicated changes in mangrove forest area to water body with 2,490.6 ha, aquaculture with 890.7 ha, horticulture with 1,646.1 ha, palm oil areas with 1,959.2 ha, dry land forest with 2,906.7 ha and urban settlement area with 224.1 ha. Combinations of these approaches were useful for change detection and for indication of the nature of these changes.

Use of diagnostic accuracy as a metric for evaluating laboratory proficiency with microarray assays using mixed-tissue RNA reference samples.

PubMed

Pine, P S; Boedigheimer, M; Rosenzweig, B A; Turpaz, Y; He, Y D; Delenstarr, G; Ganter, B; Jarnagin, K; Jones, W D; Reid, L H; Thompson, K L

2008-11-01

Effective use of microarray technology in clinical and regulatory settings is contingent on the adoption of standard methods for assessing performance. The MicroArray Quality Control project evaluated the repeatability and comparability of microarray data on the major commercial platforms and laid the groundwork for the application of microarray technology to regulatory assessments. However, methods for assessing performance that are commonly applied to diagnostic assays used in laboratory medicine remain to be developed for microarray assays. A reference system for microarray performance evaluation and process improvement was developed that includes reference samples, metrics and reference datasets. The reference material is composed of two mixes of four different rat tissue RNAs that allow defined target ratios to be assayed using a set of tissue-selective analytes that are distributed along the dynamic range of measurement. The diagnostic accuracy of detected changes in expression ratios, measured as the area under the curve from receiver operating characteristic plots, provides a single commutable value for comparing assay specificity and sensitivity. The utility of this system for assessing overall performance was evaluated for relevant applications like multi-laboratory proficiency testing programs and single-laboratory process drift monitoring. The diagnostic accuracy of detection of a 1.5-fold change in signal level was found to be a sensitive metric for comparing overall performance. This test approaches the technical limit for reliable discrimination of differences between two samples using this technology. We describe a reference system that provides a mechanism for internal and external assessment of laboratory proficiency with microarray technology and is translatable to performance assessments on other whole-genome expression arrays used for basic and clinical research.

Sensitivity and accuracy of high-throughput metabarcoding methods for early detection of invasive fish species

NASA Astrophysics Data System (ADS)

Hatzenbuhler, Chelsea; Kelly, John R.; Martinson, John; Okum, Sara; Pilgrim, Erik

2017-04-01

High-throughput DNA metabarcoding has gained recognition as a potentially powerful tool for biomonitoring, including early detection of aquatic invasive species (AIS). DNA based techniques are advancing, but our understanding of the limits to detection for metabarcoding complex samples is inadequate. For detecting AIS at an early stage of invasion when the species is rare, accuracy at low detection limits is key. To evaluate the utility of metabarcoding in future fish community monitoring programs, we conducted several experiments to determine the sensitivity and accuracy of routine metabarcoding methods. Experimental mixes used larval fish tissue from multiple “common” species spiked with varying proportions of tissue from an additional “rare” species. Pyrosequencing of genetic marker, COI (cytochrome c oxidase subunit I) and subsequent sequence data analysis provided experimental evidence of low-level detection of the target “rare” species at biomass percentages as low as 0.02% of total sample biomass. Limits to detection varied interspecifically and were susceptible to amplification bias. Moreover, results showed some data processing methods can skew sequence-based biodiversity measurements from corresponding relative biomass abundances and increase false absences. We suggest caution in interpreting presence/absence and relative abundance in larval fish assemblages until metabarcoding methods are optimized for accuracy and precision.

Accuracy of contrast-enhanced ultrasound in the detection of bladder cancer

PubMed Central

Nicolau, C; Bunesch, L; Peri, L; Salvador, R; Corral, J M; Mallofre, C; Sebastia, C

2011-01-01

Objective To assess the accuracy contrast-enhanced ultrasound (CEUS) in bladder cancer detection using transurethral biopsy in conventional cystoscopy as the reference standard and to determine whether CEUS improves the bladder cancer detection rate of baseline ultrasound. Methods 43 patients with suspected bladder cancer underwent conventional cystoscopy with transurethral biopsy of the suspicious lesions. 64 bladder cancers were confirmed in 33 out of 43 patients. Baseline ultrasound and CEUS were performed the day before surgery and the accuracy of both techniques for bladder cancer detection and number of detected tumours were analysed and compared with the final diagnosis. Results CEUS was significantly more accurate than ultrasound in determining presence or absence of bladder cancer: 88.37% vs 72.09%. Seven of eight uncertain baseline ultrasound results were correctly diagnosed using CEUS. CEUS sensitivity was also better than that of baseline ultrasound per number of tumours: 65.62% vs 60.93%. CEUS sensitivity for bladder cancer detection was very high for tumours larger than 5 mm (94.7%) but very low for tumours <5 mm (20%) and also had a very low negative predictive value (28.57%) in tumours <5 mm. Conclusion CEUS provided higher accuracy than baseline ultrasound for bladder cancer detection, being especially useful in non-conclusive baseline ultrasound studies. PMID:21123306

Developing Best Practices for Detecting Change at Marine Renewable Energy Sites

NASA Astrophysics Data System (ADS)

Linder, H. L.; Horne, J. K.

2016-02-01

In compliance with the National Environmental Policy Act (NEPA), an evaluation of environmental effects is mandatory for obtaining permits for any Marine Renewable Energy (MRE) project in the US. Evaluation includes an assessment of baseline conditions and on-going monitoring during operation to determine if biological conditions change relative to the baseline. Currently, there are no best practices for the analysis of MRE monitoring data. We have developed an approach to evaluate and recommend analytic models used to characterize and detect change in biological monitoring data. The approach includes six steps: review current MRE monitoring practices, identify candidate models to analyze data, fit models to a baseline dataset, develop simulated scenarios of change, evaluate model fit to simulated data, and produce recommendations on the choice of analytic model for monitoring data. An empirical data set from a proposed tidal turbine site at Admiralty Inlet, Puget Sound, Washington was used to conduct the model evaluation. Candidate models that were evaluated included: linear regression, time series, and nonparametric models. Model fit diagnostics Root-Mean-Square-Error and Mean-Absolute-Scaled-Error were used to measure accuracy of predicted values from each model. A power analysis was used to evaluate the ability of each model to measure and detect change from baseline conditions. As many of these models have yet to be applied in MRE monitoring studies, results of this evaluation will generate comprehensive guidelines on choice of model to detect change in environmental monitoring data from MRE sites. The creation of standardized guidelines for model selection enables accurate comparison of change between life stages of a MRE project, within life stages to meet real time regulatory requirements, and comparison of environmental changes among MRE sites.

Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD)

USGS Publications Warehouse

Wickham, James; Stehman, Stephen V.; Gass, Leila; Dewitz, Jon; Sorenson, Daniel G.; Granneman, Brian J.; Poss, Richard V.; Baer, Lori Anne

2017-01-01

Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land cover for ca. 2001, ca. 2006, and ca. 2011. The two main objectives were assessment of agreement between map and reference labels for the three, single-date NLCD land cover products at Level II and Level I of the classification hierarchy, and agreement for 17 land cover change reporting themes based on Level I classes (e.g., forest loss; forest gain; forest, no change) for three change periods (2001–2006, 2006–2011, and 2001–2011). The single-date overall accuracies were 82%, 83%, and 83% at Level II and 88%, 89%, and 89% at Level I for 2011, 2006, and 2001, respectively. Many class-specific user's accuracies met or exceeded a previously established nominal accuracy benchmark of 85%. Overall accuracies for 2006 and 2001 land cover components of NLCD 2011 were approximately 4% higher (at Level II and Level I) than the overall accuracies for the same components of NLCD 2006. The high Level I overall, user's, and producer's accuracies for the single-date eras in NLCD 2011 did not translate into high class-specific user's and producer's accuracies for many of the 17 change reporting themes. User's accuracies were high for the no change reporting themes, commonly exceeding 85%, but were typically much lower for the reporting themes that represented change. Only forest loss, forest gain, and urban gain had user's accuracies that exceeded 70%. Lower user's accuracies for the other change reporting themes may be attributable to the difficulty in determining the context of grass (e.g., open urban, grassland, agriculture) and between the components of the forest-shrubland-grassland gradient at either the mapping phase, reference label assignment phase, or both. NLCD 2011 user's accuracies for forest loss, forest gain, and urban gain compare favorably with results from other land cover change accuracy assessments.

Foundations for Measuring Volume Rendering Quality

NASA Technical Reports Server (NTRS)

Williams, Peter L.; Uselton, Samuel P.; Chancellor, Marisa K. (Technical Monitor)

1997-01-01

The goal of this paper is to provide a foundation for objectively comparing volume rendered images. The key elements of the foundation are: (1) a rigorous specification of all the parameters that need to be specified to define the conditions under which a volume rendered image is generated; (2) a methodology for difference classification, including a suite of functions or metrics to quantify and classify the difference between two volume rendered images that will support an analysis of the relative importance of particular differences. The results of this method can be used to study the changes caused by modifying particular parameter values, to compare and quantify changes between images of similar data sets rendered in the same way, and even to detect errors in the design, implementation or modification of a volume rendering system. If one has a benchmark image, for example one created by a high accuracy volume rendering system, the method can be used to evaluate the accuracy of a given image.

Colorimetric and Fluorescent Bimodal Ratiometric Probes for pH Sensing of Living Cells.

PubMed

Liu, Yuan-Yuan; Wu, Ming; Zhu, Li-Na; Feng, Xi-Zeng; Kong, De-Ming

2015-06-01

pH measurement is widely used in many fields. Ratiometric pH sensing is an important way to improve the detection accuracy. Herein, five water-soluble cationic porphyrin derivatives were synthesized and their optical property changes with pH value were investigated. Their pH-dependent assembly/disassembly behaviors caused significant changes in both absorption and fluorescence spectra, thus making them promising bimodal ratiometric probes for both colorimetric and fluorescent pH sensing. Different substituent identity and position confer these probes with different sensitive pH-sensing ranges, and the substituent position gives a larger effect. By selecting different porphyrins, different signal intensity ratios and different fluorescence excitation wavelengths, sensitive pH sensing can be achieved in the range of 2.1-8.0. Having demonstrated the excellent reversibility, good accuracy and low cytotoxicity of the probes, they were successfully applied in pH sensing inside living cells. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Crystal Genetics, Inc.

PubMed

Kermani, Bahram G

2016-07-01

Crystal Genetics, Inc. is an early-stage genetic test company, focused on achieving the highest possible clinical-grade accuracy and comprehensiveness for detecting germline (e.g., in hereditary cancer) and somatic (e.g., in early cancer detection) mutations. Crystal's mission is to significantly improve the health status of the population, by providing high accuracy, comprehensive, flexible and affordable genetic tests, primarily in cancer. Crystal's philosophy is that when it comes to detecting mutations that are strongly correlated with life-threatening diseases, the detection accuracy of every single mutation counts: a single false-positive error could cause severe anxiety for the patient. And, more importantly, a single false-negative error could potentially cost the patient's life. Crystal's objective is to eliminate both of these error types.

Impact of various color LED flashlights and different lighting source to skin distances on the manual and the computer-aided detection of basal cell carcinoma borders.

PubMed

Bakht, Mohamadreza K; Pouladian, Majid; Mofrad, Farshid B; Honarpisheh, Hamid

2014-02-01

Quantitative analysis based on digital skin image has been proven to be helpful in dermatology. Moreover, the borders of the basal cell carcinoma (BCC) lesions have been challenging borders for the automatic detection methods. In this work, a computer-aided dermatoscopy system was proposed to enhance the clinical detection of BCC lesion borders. Fifty cases of BCC were selected and 2000 pictures were taken. The lesion images data were obtained with eight colors of flashlights and in five different lighting source to skin distances (SSDs). Then, the image-processing techniques were used for automatic detection of lesion borders. Further, the dermatologists marked the lesions on the obtained photos. Considerable differences between the obtained values referring to the photographs that were taken at super blue and aqua green color lighting were observed for most of the BCC borders. It was observed that by changing the SSD, an optimum distance could be found where that the accuracy of the detection reaches to a maximum value. This study clearly indicates that by changing SSD and lighting color, manual and automatic detection of BCC lesions borders can be enhanced. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A fast complex domain-matching pursuit algorithm and its application to deep-water gas reservoir detection

NASA Astrophysics Data System (ADS)

Zeng, Jing; Huang, Handong; Li, Huijie; Miao, Yuxin; Wen, Junxiang; Zhou, Fei

2017-12-01

The main emphasis of exploration and development is shifting from simple structural reservoirs to complex reservoirs, which all have the characteristics of complex structure, thin reservoir thickness and large buried depth. Faced with these complex geological features, hydrocarbon detection technology is a direct indication of changes in hydrocarbon reservoirs and a good approach for delimiting the distribution of underground reservoirs. It is common to utilize the time-frequency (TF) features of seismic data in detecting hydrocarbon reservoirs. Therefore, we research the complex domain-matching pursuit (CDMP) method and propose some improvements. First is the introduction of a scale parameter, which corrects the defect that atomic waveforms only change with the frequency parameter. Its introduction not only decomposes seismic signal with high accuracy and high efficiency but also reduces iterations. We also integrate jumping search with ergodic search to improve computational efficiency while maintaining the reasonable accuracy. Then we combine the improved CDMP with the Wigner-Ville distribution to obtain a high-resolution TF spectrum. A one-dimensional modeling experiment has proved the validity of our method. Basing on the low-frequency domain reflection coefficient in fluid-saturated porous media, we finally get an approximation formula for the mobility attributes of reservoir fluid. This approximation formula is used as a hydrocarbon identification factor to predict deep-water gas-bearing sand of the M oil field in the South China Sea. The results are consistent with the actual well test results and our method can help inform the future exploration of deep-water gas reservoirs.

Diagnostic accuracy of 3D-transvaginal ultrasound in detecting uterine cavity abnormalities in infertile patients as compared with hysteroscopy.

PubMed

Apirakviriya, Chayanis; Rungruxsirivorn, Tassawan; Phupong, Vorapong; Wisawasukmongchol, Wirach

2016-05-01

To assess diagnostic accuracy of 3D transvaginal ultrasound (3D-TVS) compared with hysteroscopy in detecting uterine cavity abnormalities in infertile women. This prospective observational cross-sectional study was conducted during the July 2013 to December 2013 study period. Sixty-nine women with infertility were enrolled. In the mid to late follicular phase of each subject's menstrual cycle, 3D transvaginal ultrasound and hysteroscopy were performed on the same day in each patient. Hysteroscopy is widely considered to be the gold standard method for investigation of the uterine cavity. Uterine cavity characteristics and abnormalities were recorded. Diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratios were evaluated. Hysteroscopy was successfully performed in all subjects. Hysteroscopy diagnosed pathological findings in 22 of 69 cases (31.8%). There were 18 endometrial polyps, 3 submucous myomas, and 1 septate uterus. Three-dimensional transvaginal ultrasound in comparison with hysteroscopy had 84.1% diagnostic accuracy, 68.2% sensitivity, 91.5% specificity, 79% positive predictive value, and 86% negative predictive value. The positive and negative likelihood ratios were 8.01 and 0.3, respectively. 3D-TVS successfully detected every case of submucous myoma and uterine anomaly. For detection of endometrial polyps, 3D-TVS had 61.1% sensitivity, 91.5% specificity, and 83.1% diagnostic accuracy. 3D-TVS demonstrated 84.1% diagnostic accuracy for detecting uterine cavity abnormalities in infertile women. A significant percentage of infertile patients had evidence of uterine cavity pathology. Hysteroscopy is, therefore, recommended for accurate detection and diagnosis of uterine cavity lesion. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Fluorescence-based methods for detecting caries lesions: systematic review, meta-analysis and sources of heterogeneity.

PubMed

Gimenez, Thais; Braga, Mariana Minatel; Raggio, Daniela Procida; Deery, Chris; Ricketts, David N; Mendes, Fausto Medeiros

2013-01-01

Fluorescence-based methods have been proposed to aid caries lesion detection. Summarizing and analysing findings of studies about fluorescence-based methods could clarify their real benefits. We aimed to perform a comprehensive systematic review and meta-analysis to evaluate the accuracy of fluorescence-based methods in detecting caries lesions. Two independent reviewers searched PubMed, Embase and Scopus through June 2012 to identify papers/articles published. Other sources were checked to identify non-published literature. STUDY ELIGIBILITY CRITERIA, PARTICIPANTS AND DIAGNOSTIC METHODS: The eligibility criteria were studies that: (1) have assessed the accuracy of fluorescence-based methods of detecting caries lesions on occlusal, approximal or smooth surfaces, in both primary or permanent human teeth, in the laboratory or clinical setting; (2) have used a reference standard; and (3) have reported sufficient data relating to the sample size and the accuracy of methods. A diagnostic 2×2 table was extracted from included studies to calculate the pooled sensitivity, specificity and overall accuracy parameters (Diagnostic Odds Ratio and Summary Receiver-Operating curve). The analyses were performed separately for each method and different characteristics of the studies. The quality of the studies and heterogeneity were also evaluated. Seventy five studies met the inclusion criteria from the 434 articles initially identified. The search of the grey or non-published literature did not identify any further studies. In general, the analysis demonstrated that the fluorescence-based method tend to have similar accuracy for all types of teeth, dental surfaces or settings. There was a trend of better performance of fluorescence methods in detecting more advanced caries lesions. We also observed moderate to high heterogeneity and evidenced publication bias. Fluorescence-based devices have similar overall performance; however, better accuracy in detecting more advanced caries lesions has been observed.

Accuracy of ultrasound versus computed tomography urogram in detecting urinary tract calculi.

PubMed

Salinawati, B; Hing, E Y; Fam, X I; Zulfiqar, M A

2015-08-01

To determine the (i) sensitivity and specificity of ultrasound (USG) in the detection of urinary tract calculi, (ii) size of renal calculi detected on USG, and (iii) size of renal calculi not seen on USG but detected on computed tomography urogram (CTU). A total of 201 patients' USG and CTU were compared retrospectively for the presence of calculi. Sensitivity, specificity, accuracy, positive predictive value and negative predictive value of USG were calculated with CTU as the gold standard. From the 201 sets of data collected, 59 calculi were detected on both USG and CTU. The sensitivity and specificity of renal calculi detection on USG were 53% and 85% respectively. The mean size of the renal calculus detected on USG was 7.6 mm ± 4.1 mm and the mean size of the renal calculus not visualised on USG but detected on CTU was 4 mm ± 2.4 mm. The sensitivity and specificity of ureteric calculi detection on USG were 12% and 97% respectively. The sensitivity and specificity of urinary bladder calculi detection on USG were 20% and 100% respectively. This study showed that the accuracy of US in detecting renal, ureteric and urinary bladder calculi were 67%, 80% and 98% respectively.

Linkage disequilibrium among commonly genotyped SNP and variants detected from bull sequence

USDA-ARS?s Scientific Manuscript database

Genomic prediction utilizing causal variants could increase selection accuracy above that achieved with SNP genotyped by commercial assays. A number of variants detected from sequencing influential sires are likely to be causal, but noticable improvements in prediction accuracy using imputed sequen...

SVM classifier on chip for melanoma detection.

PubMed

Afifi, Shereen; GholamHosseini, Hamid; Sinha, Roopak

2017-07-01

Support Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM-based diagnosis system for use in primary care for early detection of melanoma. In this paper, an optimized SVM classifier is implemented onto a recent FPGA platform using the latest design methodology to be embedded into the proposed device for realizing online efficient melanoma detection on a single system on chip/device. The hardware implementation results demonstrate a high classification accuracy of 97.9% and a significant acceleration factor of 26 from equivalent software implementation on an embedded processor, with 34% of resources utilization and 2 watts for power consumption. Consequently, the implemented system meets crucial embedded systems constraints of high performance and low cost, resources utilization and power consumption, while achieving high classification accuracy.

[Study on high accuracy detection of multi-component gas in oil-immerse power transformer].

PubMed

Fan, Jie; Chen, Xiao; Huang, Qi-Feng; Zhou, Yu; Chen, Gang

2013-12-01

In order to solve the problem of low accuracy and mutual interference in multi-component gas detection, a kind of multi-component gas detection network with high accuracy was designed. A semiconductor laser with narrow bandwidth was utilized as light source and a novel long-path gas cell was also used in this system. By taking the single sine signal to modulate the spectrum of laser and using space division multiplexing (SDM) and time division multiplexing (TDM) technique, the detection of multi-component gas was achieved. The experiments indicate that the linearity relevance coefficient is 0. 99 and the measurement relative error is less than 4%. The system dynamic response time is less than 15 s, by filling a volume of multi-component gas into the gas cell gradually. The system has advantages of high accuracy and quick response, which can be used in the fault gas on-line monitoring for power transformers in real time.

Meal Detection in Patients With Type 1 Diabetes: A New Module for the Multivariable Adaptive Artificial Pancreas Control System.

PubMed

Turksoy, Kamuran; Samadi, Sediqeh; Feng, Jianyuan; Littlejohn, Elizabeth; Quinn, Laurie; Cinar, Ali

2016-01-01

A novel meal-detection algorithm is developed based on continuous glucose measurements. Bergman's minimal model is modified and used in an unscented Kalman filter for state estimations. The estimated rate of appearance of glucose is used for meal detection. Data from nine subjects are used to assess the performance of the algorithm. The results indicate that the proposed algorithm works successfully with high accuracy. The average change in glucose levels between the meals and the detection points is 16(±9.42) [mg/dl] for 61 successfully detected meals and snacks. The algorithm is developed as a new module of an integrated multivariable adaptive artificial pancreas control system. Meal detection with the proposed method is used to administer insulin boluses and prevent most of postprandial hyperglycemia without any manual meal announcements. A novel meal bolus calculation method is proposed and tested with the UVA/Padova simulator. The results indicate significant reduction in hyperglycemia.

Accuracy of giant African pouched rats for diagnosing tuberculosis: comparison with culture and Xpert® MTB/RIF.

PubMed

Mulder, C; Mgode, G F; Ellis, H; Valverde, E; Beyene, N; Cox, C; Reid, S E; Van't Hoog, A H; Edwards, T L

2017-11-01

Enhanced tuberculosis (TB) case finding using detection rats in Tanzania. To assess the diagnostic accuracy of detection rats compared with culture and Xpert® MTB/RIF, and to compare enhanced case-finding algorithms using rats in smear-negative presumptive TB patients. A fully paired diagnostic accuracy study in which sputum of new adult presumptive TB patients in Tanzania was tested using smear microscopy, 11 detection rats, culture and Xpert. Of 771 eligible participants, 345 (45%) were culture-positive for Mycobacterium tuberculosis, and 264 (34%) were human immunodeficiency virus (HIV) positive. The sensitivity of the detection rats was up to 75.1% (95%CI 70.1-79.5) when compared with culture, and up to 81.8% (95%CI 76.0-86.5) when compared with Xpert, which was statistically significantly higher than the sensitivity of smear microscopy. Corresponding specificity was 40.6% (95%CI 35.9-45.5) compared with culture. The accuracy of rat detection was independent of HIV status. Using rats for triage, followed by Xpert, would result in a statistically higher yield than rats followed by light-emitting diode fluorescence microscopy, whereas the number of false-positives would be significantly lower than when using Xpert alone. Although detection rats did not meet the accuracy criteria as standalone diagnostic or triage testing for presumptive TB, they have additive value as a triage test for enhanced case finding among smear-negative TB patients if more advanced diagnostics are not available.

A reference standard-based quality assurance program for radiology.

PubMed

Liu, Patrick T; Johnson, C Daniel; Miranda, Rafael; Patel, Maitray D; Phillips, Carrie J

2010-01-01

The authors have developed a comprehensive radiology quality assurance (QA) program that evaluates radiology interpretations and procedures by comparing them with reference standards. Performance metrics are calculated and then compared with benchmarks or goals on the basis of published multicenter data and meta-analyses. Additional workload for physicians is kept to a minimum by having trained allied health staff members perform the comparisons of radiology reports with the reference standards. The performance metrics tracked by the QA program include the accuracy of CT colonography for detecting polyps, the false-negative rate for mammographic detection of breast cancer, the accuracy of CT angiography detection of coronary artery stenosis, the accuracy of meniscal tear detection on MRI, the accuracy of carotid artery stenosis detection on MR angiography, the accuracy of parathyroid adenoma detection by parathyroid scintigraphy, the success rate for obtaining cortical tissue on ultrasound-guided core biopsies of pelvic renal transplants, and the technical success rate for peripheral arterial angioplasty procedures. In contrast with peer-review programs, this reference standard-based QA program minimizes the possibilities of reviewer bias and erroneous second reviewer interpretations. The more objective assessment of performance afforded by the QA program will provide data that can easily be used for education and management conferences, research projects, and multicenter evaluations. Additionally, such performance data could be used by radiology departments to demonstrate their value over nonradiology competitors to referring clinicians, hospitals, patients, and third-party payers. Copyright 2010 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas.

PubMed

Karan, Shivesh Kishore; Samadder, Sukha Ranjan

2016-08-01

One objective of the present study was to evaluate the performance of support vector machine (SVM)-based image classification technique with the maximum likelihood classification (MLC) technique for a rapidly changing landscape of an open-cast mine. The other objective was to assess the change in land use pattern due to coal mining from 2006 to 2016. Assessing the change in land use pattern accurately is important for the development and monitoring of coalfields in conjunction with sustainable development. For the present study, Landsat 5 Thematic Mapper (TM) data of 2006 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data of 2016 of a part of Jharia Coalfield, Dhanbad, India, were used. The SVM classification technique provided greater overall classification accuracy when compared to the MLC technique in classifying heterogeneous landscape with limited training dataset. SVM exceeded MLC in handling a difficult challenge of classifying features having near similar reflectance on the mean signature plot, an improvement of over 11 % was observed in classification of built-up area, and an improvement of 24 % was observed in classification of surface water using SVM; similarly, the SVM technique improved the overall land use classification accuracy by almost 6 and 3 % for Landsat 5 and Landsat 8 images, respectively. Results indicated that land degradation increased significantly from 2006 to 2016 in the study area. This study will help in quantifying the changes and can also serve as a basis for further decision support system studies aiding a variety of purposes such as planning and management of mines and environmental impact assessment.

High frequency QRS ECG predicts ischemic defects during myocardial perfusion imaging

NASA Technical Reports Server (NTRS)

2004-01-01

Changes in high frequency QRS components of the electrocardiogram (HF QRS ECG) (150-250 Hz) are more sensitive than changes in conventional ST segments for detecting myocardial ischemia. We investigated the accuracy of 12-lead HF QRS ECG in detecting ischemia during adenosine tetrofosmin myocardial perfusion imaging (MPI). 12-lead HF QRS ECG recordings were obtained from 45 patients before and during adenosine technetium-99 tetrofosmin MPI tests. Before the adenosine infusions, recordings of HF QRS were analyzed according to a morphological score that incorporated the number, type and location of reduced amplitude zones (RAZs) present in the 12 leads. During the adenosine infusions, recordings of HF QRS were analyzed according to the maximum percentage changes (in both the positive and negative directions) that occurred in root mean square (RMS) voltage amplitudes within the 12 leads. The best set of prospective HF QRS criteria had a sensitivity of 94% and a specificity of 83% for correctly identifying the MPI result. The sensitivity of simultaneous ST segment changes (18%) was significantly lower than that of any individual HF QRS criterion (P less than 0.00l). Analysis of 12-lead HF QRS ECG is highly sensitive and specific for detecting ischemic perfusion defects during adenosine MPI stress tests and significantly more sensitive than analysis of conventional ST segments.

High frequency QRS ECG predicts ischemic defects during myocardial perfusion imaging

NASA Technical Reports Server (NTRS)

Rahman, Atiar

2006-01-01

Background: Changes in high frequency QRS components of the electrocardiogram (HF QRS ECG) (150-250 Hz) are more sensitive than changes in conventional ST segments for detecting myocardial ischemia. We investigated the accuracy of 12-lead HF QRS ECG in detecting ischemia during adenosine tetrofosmin myocardial perfusion imaging (MPI). Methods and Results: 12-lead HF QRS ECG recordings were obtained from 45 patients before and during adenosine technetium-99 tetrofosmin MPI tests. Before the adenosine infusions, recordings of HF QRS were analyzed according to a morphological score that incorporated the number, type and location of reduced amplitude zones (RAZs) present in the 12 leads. During the adenosine infusions, recordings of HF QRS were analyzed according to the maximum percentage changes (in both the positive and negative directions) that occurred in root mean square (RMS) voltage amplitudes within the 12 leads. The best set of prospective HF QRS criteria had a sensitivity of 94% and a specificity of 83% for correctly identifying the MPI result. The sensitivity of simultaneous ST segment changes (18%) was significantly lower than that of any individual HF QRS criterion (P<0.001). Conclusions: Analysis of 12-lead HF QRS ECG is highly sensitive and specific for detecting ischemic perfusion defects during adenosine MPI stress tests and significantly more sensitive than analysis of conventional ST segments.

Characterization of lipid films by an angle-interrogation surface plasmon resonance imaging device.

PubMed

Liu, Linlin; Wang, Qiong; Yang, Zhong; Wang, Wangang; Hu, Ning; Luo, Hongyan; Liao, Yanjian; Zheng, Xiaolin; Yang, Jun

2015-04-01

Surface topographies of lipid films have an important significance in the analysis of the preparation of giant unilamellar vesicles (GUVs). In order to achieve accurately high-throughput and rapidly analysis of surface topographies of lipid films, a homemade SPR imaging device is constructed based on the classical Kretschmann configuration and an angle interrogation manner. A mathematical model is developed to accurately describe the shift including the light path in different conditions and the change of the illumination point on the CCD camera, and thus a SPR curve for each sampling point can also be achieved, based on this calculation method. The experiment results show that the topographies of lipid films formed in distinct experimental conditions can be accurately characterized, and the measuring resolution of the thickness lipid film may reach 0.05 nm. Compared with existing SPRi devices, which realize detection by monitoring the change of the reflective-light intensity, this new SPRi system can achieve the change of the resonance angle on the entire sensing surface. Thus, it has higher detection accuracy as the traditional angle-interrogation SPR sensor, with much wider detectable range of refractive index. Copyright © 2015 Elsevier B.V. All rights reserved.

Spatial and temporal task characteristics as stress: a test of the dynamic adaptability theory of stress, workload, and performance.

PubMed

Szalma, James L; Teo, Grace W L

2012-03-01

The goal for this study was to test assertions of the dynamic adaptability theory of stress, which proposes two fundamental task dimensions, information rate (temporal properties of a task) and information structure (spatial properties of a task). The theory predicts adaptive stability across stress magnitudes, with progressive and precipitous changes in adaptive response manifesting first as increases in perceived workload and stress and then as performance failure. Information structure was manipulated by varying the number of displays to be monitored (1, 2, 4 or 8 displays). Information rate was manipulated by varying stimulus presentation rate (8, 12, 16, or 20 events/min). A signal detection task was used in which critical signals were pairs of digits that differed by 0 or 1. Performance accuracy declined and workload and stress increased as a function of increased task demand, with a precipitous decline in accuracy at the highest demand levels. However, the form of performance change as well as the pattern of relationships between speed and accuracy and between performance and workload/stress indicates that some aspects of the theory need revision. Implications of the results for the theory and for future research are discussed. Copyright © 2011 Elsevier B.V. All rights reserved.

Dynamic sample size detection in learning command line sequence for continuous authentication.

PubMed

Traore, Issa; Woungang, Isaac; Nakkabi, Youssef; Obaidat, Mohammad S; Ahmed, Ahmed Awad E; Khalilian, Bijan

2012-10-01

Continuous authentication (CA) consists of authenticating the user repetitively throughout a session with the goal of detecting and protecting against session hijacking attacks. While the accuracy of the detector is central to the success of CA, the detection delay or length of an individual authentication period is important as well since it is a measure of the window of vulnerability of the system. However, high accuracy and small detection delay are conflicting requirements that need to be balanced for optimum detection. In this paper, we propose the use of sequential sampling technique to achieve optimum detection by trading off adequately between detection delay and accuracy in the CA process. We illustrate our approach through CA based on user command line sequence and naïve Bayes classification scheme. Experimental evaluation using the Greenberg data set yields encouraging results consisting of a false acceptance rate (FAR) of 11.78% and a false rejection rate (FRR) of 1.33%, with an average command sequence length (i.e., detection delay) of 37 commands. When using the Schonlau (SEA) data set, we obtain FAR = 4.28% and FRR = 12%.

Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA

USGS Publications Warehouse

Miller, J.D.; Knapp, E.E.; Key, C.H.; Skinner, C.N.; Isbell, C.J.; Creasy, R.M.; Sherlock, J.W.

2009-01-01

Multispectral satellite data have become a common tool used in the mapping of wildland fire effects. Fire severity, defined as the degree to which a site has been altered, is often the variable mapped. The Normalized Burn Ratio (NBR) used in an absolute difference change detection protocol (dNBR), has become the remote sensing method of choice for US Federal land management agencies to map fire severity due to wildland fire. However, absolute differenced vegetation indices are correlated to the pre-fire chlorophyll content of the vegetation occurring within the fire perimeter. Normalizing dNBR to produce a relativized dNBR (RdNBR) removes the biasing effect of the pre-fire condition. Employing RdNBR hypothetically allows creating categorical classifications using the same thresholds for fires occurring in similar vegetation types without acquiring additional calibration field data on each fire. In this paper we tested this hypothesis by developing thresholds on random training datasets, and then comparing accuracies for (1) fires that occurred within the same geographic region as the training dataset and in similar vegetation, and (2) fires from a different geographic region that is climatically and floristically similar to the training dataset region but supports more complex vegetation structure. We additionally compared map accuracies for three measures of fire severity: the composite burn index (CBI), percent change in tree canopy cover, and percent change in tree basal area. User's and producer's accuracies were highest for the most severe categories, ranging from 70.7% to 89.1%. Accuracies of the moderate fire severity category for measures describing effects only to trees (percent change in canopy cover and basal area) indicated that the classifications were generally not much better than random. Accuracies of the moderate category for the CBI classifications were somewhat better, averaging in the 50%-60% range. These results underscore the difficulty in isolating fire effects to individual vegetation strata when fire effects are mixed. We conclude that the models presented here and in Miller and Thode ([Miller, J.D. & Thode, A.E., (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 109, 66-80.]) can produce fire severity classifications (using either CBI, or percent change in canopy cover or basal area) that are of similar accuracy in fires not used in the original calibration process, at least in conifer dominated vegetation types in Mediterranean-climate California.

Unobtrusive Estimation of Cardiac Contractility and Stroke Volume Changes Using Ballistocardiogram Measurements on a High Bandwidth Force Plate

PubMed Central

Ashouri, Hazar; Orlandic, Lara; Inan, Omer T.

2016-01-01

Unobtrusive and inexpensive technologies for monitoring the cardiovascular health of heart failure (HF) patients outside the clinic can potentially improve their continuity of care by enabling therapies to be adjusted dynamically based on the changing needs of the patients. Specifically, cardiac contractility and stroke volume (SV) are two key aspects of cardiovascular health that change significantly for HF patients as their condition worsens, yet these parameters are typically measured only in hospital/clinical settings, or with implantable sensors. In this work, we demonstrate accurate measurement of cardiac contractility (based on pre-ejection period, PEP, timings) and SV changes in subjects using ballistocardiogram (BCG) signals detected via a high bandwidth force plate. The measurement is unobtrusive, as it simply requires the subject to stand still on the force plate while holding electrodes in the hands for simultaneous electrocardiogram (ECG) detection. Specifically, we aimed to assess whether the high bandwidth force plate can provide accuracy beyond what is achieved using modified weighing scales we have developed in prior studies, based on timing intervals, as well as signal-to-noise ratio (SNR) estimates. Our results indicate that the force plate BCG measurement provides more accurate timing information and allows for better estimation of PEP than the scale BCG (r2 = 0.85 vs. r2 = 0.81) during resting conditions. This correlation is stronger during recovery after exercise due to more significant changes in PEP (r2 = 0.92). The improvement in accuracy can be attributed to the wider bandwidth of the force plate. ∆SV (i.e., changes in stroke volume) estimations from the force plate BCG resulted in an average error percentage of 5.3% with a standard deviation of ±4.2% across all subjects. Finally, SNR calculations showed slightly better SNR in the force plate measurements among all subjects but the small difference confirmed that SNR is limited by motion artifacts rather than instrumentation. PMID:27240380

Diagnostic accuracy of non-contrast abdominal CT scans performed as follow-up for patients with an established cancer diagnosis: a retrospective study.

PubMed

Semaan, Hassan; Bazerbashi, Mohamad F; Siesel, Geoffrey; Aldinger, Paul; Obri, Tawfik

2018-03-01

To determine the accuracy and non-detection rate of cancer related findings (CRFs) on follow-up non-contrast-enhanced CT (NECT) versus contrast-enhanced CT (CECT) images of the abdomen in patients with a known cancer diagnosis. A retrospective review of 352 consecutive CTs of the abdomen performed with and without IV contrast between March 2010 and October 2014 for follow-up of cancer was included. Two radiologists independently assessed the NECT portions of the studies. The reader was provided the primary cancer diagnosis and access to the most recent prior NECT study. The accuracy and non-detection rates were determined by comparing our results to the archived reports as a gold standard. A total of 383 CRFs were found in the archived reports of the 352 abdominal CTs. The average non-detection rate for the NECTs compared to the CECTs was 3.0% (11.5/383) with an accuracy of 97.0% (371.5/383) in identifying CRFs. The most common findings missed were vascular thrombosis with a non-detection rate of 100%. The accuracy for non-vascular CRFs was 99.1%. Follow-up NECT abdomen studies are highly accurate in the detection of CRFs in patients with an established cancer diagnosis, except in cases where vascular involvement is suspected.

Application of remotely sensed land-use information to improve estimates of streamflow characteristics, volume 8. [Maryland, Virginia, and Delaware

NASA Technical Reports Server (NTRS)

Pluhowski, E. J. (Principal Investigator)

1977-01-01

The author has identified the following significant results. Land use data derived from high altitude photography and satellite imagery were studied for 49 basins in Delaware, and eastern Maryland and Virginia. Applying multiple regression techniques to a network of gaging stations monitoring runoff from 39 of the basins, demonstrated that land use data from high altitude photography provided an effective means of significantly improving estimates of stream flow. Forty stream flow characteristic equations for incorporating remotely sensed land use information, were compared with a control set of equations using map derived land cover. Significant improvement was detected in six equations where level 1 data was added and in five equations where level 2 information was utilized. Only four equations were improved significantly using land use data derived from LANDSAT imagery. Significant losses in accuracy due to the use of remotely sensed land use information were detected only in estimates of flood peaks. Losses in accuracy for flood peaks were probably due to land cover changes associated with temporal differences among the primary land use data sources.

Modeling Systematic Change in Stopover Duration Does Not Improve Bias in Trends Estimated from Migration Counts.

PubMed

Crewe, Tara L; Taylor, Philip D; Lepage, Denis

2015-01-01

The use of counts of unmarked migrating animals to monitor long term population trends assumes independence of daily counts and a constant rate of detection. However, migratory stopovers often last days or weeks, violating the assumption of count independence. Further, a systematic change in stopover duration will result in a change in the probability of detecting individuals once, but also in the probability of detecting individuals on more than one sampling occasion. We tested how variation in stopover duration influenced accuracy and precision of population trends by simulating migration count data with known constant rate of population change and by allowing daily probability of survival (an index of stopover duration) to remain constant, or to vary randomly, cyclically, or increase linearly over time by various levels. Using simulated datasets with a systematic increase in stopover duration, we also tested whether any resulting bias in population trend could be reduced by modeling the underlying source of variation in detection, or by subsampling data to every three or five days to reduce the incidence of recounting. Mean bias in population trend did not differ significantly from zero when stopover duration remained constant or varied randomly over time, but bias and the detection of false trends increased significantly with a systematic increase in stopover duration. Importantly, an increase in stopover duration over time resulted in a compounding effect on counts due to the increased probability of detection and of recounting on subsequent sampling occasions. Under this scenario, bias in population trend could not be modeled using a covariate for stopover duration alone. Rather, to improve inference drawn about long term population change using counts of unmarked migrants, analyses must include a covariate for stopover duration, as well as incorporate sampling modifications (e.g., subsampling) to reduce the probability that individuals will be detected on more than one occasion.

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery.

PubMed

Tan, Kok Chooi; Lim, Hwee San; Matjafri, Mohd Zubir; Abdullah, Khiruddin

2012-06-01

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.

Towards Investigating Global Warming Impact on Human Health Using Derivatives of Photoplethysmogram Signals.

PubMed

Elgendi, Mohamed; Norton, Ian; Brearley, Matt; Fletcher, Richard R; Abbott, Derek; Lovell, Nigel H; Schuurmans, Dale

2015-10-14

Recent clinical studies show that the contour of the photoplethysmogram (PPG) wave contains valuable information for characterizing cardiovascular activity. However, analyzing the PPG wave contour is difficult; therefore, researchers have applied first or higher order derivatives to emphasize and conveniently quantify subtle changes in the filtered PPG contour. Our hypothesis is that analyzing the whole PPG recording rather than each PPG wave contour or on a beat-by-beat basis can detect heat-stressed subjects and that, consequently, we will be able to investigate the impact of global warming on human health. Here, we explore the most suitable derivative order for heat stress assessment based on the energy and entropy of the whole PPG recording. The results of our study indicate that the use Int. J. Environ. Res. Public Health 2015, 7 12777 of the entropy of the seventh derivative of the filtered PPG signal shows promising results in detecting heat stress using 20-second recordings, with an overall accuracy of 71.6%. Moreover, the combination of the entropy of the seventh derivative of the filtered PPG signal with the root mean square of successive differences, or RMSSD (a traditional heart rate variability index of heat stress), improved the detection of heat stress to 88.9% accuracy.

Capture-based next-generation sequencing reveals multiple actionable mutations in cancer patients failed in traditional testing.

PubMed

Xie, Jing; Lu, Xiongxiong; Wu, Xue; Lin, Xiaoyi; Zhang, Chao; Huang, Xiaofang; Chang, Zhili; Wang, Xinjing; Wen, Chenlei; Tang, Xiaomei; Shi, Minmin; Zhan, Qian; Chen, Hao; Deng, Xiaxing; Peng, Chenghong; Li, Hongwei; Fang, Yuan; Shao, Yang; Shen, Baiyong

2016-05-01

Targeted therapies including monoclonal antibodies and small molecule inhibitors have dramatically changed the treatment of cancer over past 10 years. Their therapeutic advantages are more tumor specific and with less side effects. For precisely tailoring available targeted therapies to each individual or a subset of cancer patients, next-generation sequencing (NGS) has been utilized as a promising diagnosis tool with its advantages of accuracy, sensitivity, and high throughput. We developed and validated a NGS-based cancer genomic diagnosis targeting 115 prognosis and therapeutics relevant genes on multiple specimen including blood, tumor tissue, and body fluid from 10 patients with different cancer types. The sequencing data was then analyzed by the clinical-applicable analytical pipelines developed in house. We have assessed analytical sensitivity, specificity, and accuracy of the NGS-based molecular diagnosis. Also, our developed analytical pipelines were capable of detecting base substitutions, indels, and gene copy number variations (CNVs). For instance, several actionable mutations of EGFR,PIK3CA,TP53, and KRAS have been detected for indicating drug susceptibility and resistance in the cases of lung cancer. Our study has shown that NGS-based molecular diagnosis is more sensitive and comprehensive to detect genomic alterations in cancer, and supports a direct clinical use for guiding targeted therapy.

Plant pathogen nanodiagnostic techniques: forthcoming changes?

PubMed Central

Khiyami, Mohammad A.; Almoammar, Hassan; Awad, Yasser M.; Alghuthaymi, Mousa A.; Abd-Elsalam, Kamel A.

2014-01-01

Plant diseases are among the major factors limiting crop productivity. A first step towards managing a plant disease under greenhouse and field conditions is to correctly identify the pathogen. Current technologies, such as quantitative polymerase chain reaction (Q-PCR), require a relatively large amount of target tissue and rely on multiple assays to accurately identify distinct plant pathogens. The common disadvantage of the traditional diagnostic methods is that they are time consuming and lack high sensitivity. Consequently, developing low-cost methods to improve the accuracy and rapidity of plant pathogens diagnosis is needed. Nanotechnology, nano particles and quantum dots (QDs) have emerged as essential tools for fast detection of a particular biological marker with extreme accuracy. Biosensor, QDs, nanostructured platforms, nanoimaging and nanopore DNA sequencing tools have the potential to raise sensitivity, specificity and speed of the pathogen detection, facilitate high-throughput analysis, and to be used for high-quality monitoring and crop protection. Furthermore, nanodiagnostic kit equipment can easily and quickly detect potential serious plant pathogens, allowing experts to help farmers in the prevention of epidemic diseases. The current review deals with the application of nanotechnology for quicker, more cost-effective and precise diagnostic procedures of plant diseases. Such an accurate technology may help to design a proper integrated disease management system which may modify crop environments to adversely affect crop pathogens. PMID:26740775

A hybrid intelligence approach to artifact recognition in digital publishing

NASA Astrophysics Data System (ADS)

Vega-Riveros, J. Fernando; Santos Villalobos, Hector J.

2006-02-01

The system presented integrates rule-based and case-based reasoning for artifact recognition in Digital Publishing. In Variable Data Printing (VDP) human proofing could result prohibitive since a job could contain millions of different instances that may contain two types of artifacts: 1) evident defects, like a text overflow or overlapping 2) style-dependent artifacts, subtle defects that show as inconsistencies with regard to the original job design. We designed a Knowledge-Based Artifact Recognition tool for document segmentation, layout understanding, artifact detection, and document design quality assessment. Document evaluation is constrained by reference to one instance of the VDP job proofed by a human expert against the remaining instances. Fundamental rules of document design are used in the rule-based component for document segmentation and layout understanding. Ambiguities in the design principles not covered by the rule-based system are analyzed by case-based reasoning, using the Nearest Neighbor Algorithm, where features from previous jobs are used to detect artifacts and inconsistencies within the document layout. We used a subset of XSL-FO and assembled a set of 44 document samples. The system detected all the job layout changes, while obtaining an overall average accuracy of 84.56%, with the highest accuracy of 92.82%, for overlapping and the lowest, 66.7%, for the lack-of-white-space.

Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks

NASA Astrophysics Data System (ADS)

Ghasemian, Amir; Zhang, Pan; Clauset, Aaron; Moore, Cristopher; Peel, Leto

2016-07-01

The detection of communities within a dynamic network is a common means for obtaining a coarse-grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information.

DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

PubMed

Lawhern, Vernon; Hairston, W David; Robbins, Kay

2013-01-01

Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration.

DETECT: A MATLAB Toolbox for Event Detection and Identification in Time Series, with Applications to Artifact Detection in EEG Signals

PubMed Central

Lawhern, Vernon; Hairston, W. David; Robbins, Kay

2013-01-01

Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration. PMID:23638169

Effect of time discretization of the imaging process on the accuracy of trajectory estimation in fluorescence microscopy

PubMed Central

Wong, Yau; Chao, Jerry; Lin, Zhiping; Ober, Raimund J.

2014-01-01

In fluorescence microscopy, high-speed imaging is often necessary for the proper visualization and analysis of fast subcellular dynamics. Here, we examine how the speed of image acquisition affects the accuracy with which parameters such as the starting position and speed of a microscopic non-stationary fluorescent object can be estimated from the resulting image sequence. Specifically, we use a Fisher information-based performance bound to investigate the detector-dependent effect of frame rate on the accuracy of parameter estimation. We demonstrate that when a charge-coupled device detector is used, the estimation accuracy deteriorates as the frame rate increases beyond a point where the detector’s readout noise begins to overwhelm the low number of photons detected in each frame. In contrast, we show that when an electron-multiplying charge-coupled device (EMCCD) detector is used, the estimation accuracy improves with increasing frame rate. In fact, at high frame rates where the low number of photons detected in each frame renders the fluorescent object difficult to detect visually, imaging with an EMCCD detector represents a natural implementation of the Ultrahigh Accuracy Imaging Modality, and enables estimation with an accuracy approaching that which is attainable only when a hypothetical noiseless detector is used. PMID:25321248

Remembering Complex Objects in Visual Working Memory: Do Capacity Limits Restrict Objects or Features?

PubMed Central

Hardman, Kyle; Cowan, Nelson

2014-01-01

Visual working memory stores stimuli from our environment as representations that can be accessed by high-level control processes. This study addresses a longstanding debate in the literature about whether storage limits in visual working memory include a limit to the complexity of discrete items. We examined the issue with a number of change-detection experiments that used complex stimuli which possessed multiple features per stimulus item. We manipulated the number of relevant features of the stimulus objects in order to vary feature load. In all of our experiments, we found that increased feature load led to a reduction in change-detection accuracy. However, we found that feature load alone could not account for the results, but that a consideration of the number of relevant objects was also required. This study supports capacity limits for both feature and object storage in visual working memory. PMID:25089739

Evaluation of the patient with an exposure-related disease: the occupational and environmental history.

PubMed

Papali, Alfred; Hines, Stella E

2015-03-01

Although the process of taking an occupational and environmental history has remained largely the same, the context in which it is done has changed dramatically over recent years. This review examines the role of the occupational and environmental history in the context of the changing nature of medical practice and discusses methods for evaluating patients with contemporary exposure-related respiratory illnesses. Surveillance for occupational lung disease using mnemonic devices, screening questions and the use of structured questionnaires can significantly increase the likelihood and accuracy of detection. Electronic health records likewise can be adapted to include the most important elements of the occupational and environmental history. The emergence of new technologies and industries will lead to respiratory diseases in novel occupational and environmental contexts. Using the methods described herein can make detecting these diseases easier and less time-consuming.

Evaluation of electrical impedance ratio measurements in accuracy of electronic apex locators.

PubMed

Kim, Pil-Jong; Kim, Hong-Gee; Cho, Byeong-Hoon

2015-05-01

The aim of this paper was evaluating the ratios of electrical impedance measurements reported in previous studies through a correlation analysis in order to explicit it as the contributing factor to the accuracy of electronic apex locator (EAL). The literature regarding electrical property measurements of EALs was screened using Medline and Embase. All data acquired were plotted to identify correlations between impedance and log-scaled frequency. The accuracy of the impedance ratio method used to detect the apical constriction (APC) in most EALs was evaluated using linear ramp function fitting. Changes of impedance ratios for various frequencies were evaluated for a variety of file positions. Among the ten papers selected in the search process, the first-order equations between log-scaled frequency and impedance were in the negative direction. When the model for the ratios was assumed to be a linear ramp function, the ratio values decreased if the file went deeper and the average ratio values of the left and right horizontal zones were significantly different in 8 out of 9 studies. The APC was located within the interval of linear relation between the left and right horizontal zones of the linear ramp model. Using the ratio method, the APC was located within a linear interval. Therefore, using the impedance ratio between electrical impedance measurements at different frequencies was a robust method for detection of the APC.

Diagnosis of Tempromandibular Disorders Using Local Binary Patterns.

PubMed

Haghnegahdar, A A; Kolahi, S; Khojastepour, L; Tajeripour, F

2018-03-01

Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment. CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis. K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity. We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages.

Unsupervised Multi-Scale Change Detection from SAR Imagery for Monitoring Natural and Anthropogenic Disasters

NASA Astrophysics Data System (ADS)

Ajadi, Olaniyi A.

Radar remote sensing can play a critical role in operational monitoring of natural and anthropogenic disasters. Despite its all-weather capabilities, and its high performance in mapping, and monitoring of change, the application of radar remote sensing in operational monitoring activities has been limited. This has largely been due to: (1) the historically high costs associated with obtaining radar data; (2) slow data processing, and delivery procedures; and (3) the limited temporal sampling that was provided by spaceborne radar-based satellites. Recent advances in the capabilities of spaceborne Synthetic Aperture Radar (SAR) sensors have developed an environment that now allows for SAR to make significant contributions to disaster monitoring. New SAR processing strategies that can take full advantage of these new sensor capabilities are currently being developed. Hence, with this PhD dissertation, I aim to: (i) investigate unsupervised change detection techniques that can reliably extract signatures from time series of SAR images, and provide the necessary flexibility for application to a variety of natural, and anthropogenic hazard situations; (ii) investigate effective methods to reduce the effects of speckle and other noise on change detection performance; (iii) automate change detection algorithms using probabilistic Bayesian inferencing; and (iv) ensure that the developed technology is applicable to current, and future SAR sensors to maximize temporal sampling of a hazardous event. This is achieved by developing new algorithms that rely on image amplitude information only, the sole image parameter that is available for every single SAR acquisition.. The motivation and implementation of the change detection concept are described in detail in Chapter 3. In the same chapter, I demonstrated the technique's performance using synthetic data as well as a real-data application to map wildfire progression. I applied Radiometric Terrain Correction (RTC) to the data to increase the sampling frequency, while the developed multiscale-driven approach reliably identified changes embedded in largely stationary background scenes. With this technique, I was able to identify the extent of burn scars with high accuracy. I further applied the application of the change detection technology to oil spill mapping. The analysis highlights that the approach described in Chapter 3 can be applied to this drastically different change detection problem with only little modification. While the core of the change detection technique remained unchanged, I made modifications to the pre-processing step to enable change detection from scenes of continuously varying background. I introduced the Lipschitz regularity (LR) transformation as a technique to normalize the typically dynamic ocean surface, facilitating high performance oil spill detection independent of environmental conditions during image acquisition. For instance, I showed that LR processing reduces the sensitivity of change detection performance to variations in surface winds, which is a known limitation in oil spill detection from SAR. Finally, I applied the change detection technique to aufeis flood mapping along the Sagavanirktok River. Due to the complex nature of aufeis flooded areas, I substituted the resolution-preserving speckle filter used in Chapter 3 with curvelet filters. In addition to validating the performance of the change detection results, I also provide evidence of the wealth of information that can be extracted about aufeis flooding events once a time series of change detection information was extracted from SAR imagery. A summary of the developed change detection techniques is conducted and suggested future work is presented in Chapter 6.

Photogrammetric Accuracy and Modeling of Rolling Shutter Cameras

NASA Astrophysics Data System (ADS)

Ye, W.; Qiao, G.; Kong, F.; Guo, S.; Ma, X.; Tong, X.; Li, R.

2016-06-01

Global climate change is one of the major challenges that all nations are commonly facing. Long-term observations of the Antarctic ice sheet have been playing a critical role in quantitatively estimating and predicting effects resulting from the global changes. The film-based ARGON reconnaissance imagery provides a remarkable data source for studying the Antarctic ice-sheet in 1960s, thus greatly extending the time period of Antarctica surface observations. To deal with the low-quality images and the unavailability of camera poses, a systematic photogrammetric approach is proposed to reconstruct the interior and exterior orientation information for further glacial mapping applications, including ice flow velocity mapping and mass balance estimation. Some noteworthy details while performing geometric modelling using the ARGON images were introduced, including methods and results for handling specific effects of film deformation, damaged or missing fiducial marks and calibration report, automatic fiducial mark detection, control point selection through Antarctic shadow and ice surface terrain analysis, and others. Several sites in East Antarctica were tested. As an example, four images in the Byrd glacier region were used to assess the accuracy of the geometric modelling. A digital elevation model (DEM) and an orthophoto map of Byrd glacier were generated. The accuracy of the ground positions estimated by using independent check points is within one nominal pixel of 140 m of ARGON imagery. Furthermore, a number of significant features, such as ice flow velocity and regional change patterns, will be extracted and analysed.

Estimating factors influencing the detection probability of semiaquatic freshwater snails using quadrat survey methods

USGS Publications Warehouse

Roesler, Elizabeth L.; Grabowski, Timothy B.

2018-01-01

Developing effective monitoring methods for elusive, rare, or patchily distributed species requires extra considerations, such as imperfect detection. Although detection is frequently modeled, the opportunity to assess it empirically is rare, particularly for imperiled species. We used Pecos assiminea (Assiminea pecos), an endangered semiaquatic snail, as a case study to test detection and accuracy issues surrounding quadrat searches. Quadrats (9 × 20 cm; n = 12) were placed in suitable Pecos assiminea habitat and randomly assigned a treatment, defined as the number of empty snail shells (0, 3, 6, or 9). Ten observers rotated through each quadrat, conducting 5-min visual searches for shells. The probability of detecting a shell when present was 67.4 ± 3.0%, but it decreased with the increasing litter depth and fewer number of shells present. The mean (± SE) observer accuracy was 25.5 ± 4.3%. Accuracy was positively correlated to the number of shells in the quadrat and negatively correlated to the number of times a quadrat was searched. The results indicate quadrat surveys likely underrepresent true abundance, but accurately determine the presence or absence. Understanding detection and accuracy of elusive, rare, or imperiled species improves density estimates and aids in monitoring and conservation efforts.

d'plus: A program to calculate accuracy and bias measures from detection and discrimination data.

PubMed

Macmillan, N A; Creelman, C D

1997-01-01

The program d'plus calculates accuracy (sensitivity) and response-bias parameters using Signal Detection Theory. Choice Theory, and 'nonparametric' models. is is appropriate for data from one-interval, two- and three-interval forced-choice, same different, ABX, and oddity experimental paradigms.

Accuracy of a prey-specific DNA assay and a generic prey-immunomarking assay for detecting predation

USDA-ARS?s Scientific Manuscript database

1. Predator gut examinations are useful for detecting arthropod predation events. Here, the accuracy and reproducibility of two different types of gut assays are tested on various predator species that consumed an immature lacewing, Chrysoperla carnea (Stephens), that was externally labelled with ra...

Comparison of digital tomosynthesis and chest radiography for the detection of pulmonary nodules: systematic review and meta-analysis.

PubMed

Kim, Jun H; Lee, Kyung H; Kim, Kyoung-Tae; Kim, Hyun J; Ahn, Hyeong S; Kim, Yeo J; Lee, Ha Y; Jeon, Yong S

2016-12-01

To compare the diagnostic accuracy of digital tomosynthesis (DTS) with that of chest radiography for the detection of pulmonary nodules by meta-analysis. A systematic literature search was performed to identify relevant original studies from 1 January 1 1976 to 31 August 31 2016. The quality of included studies was assessed by quality assessment of diagnostic accuracy studies-2. Per-patient data were used to calculate the sensitivity and specificity and per-lesion data were used to calculate the detection rate. Summary receiver-operating characteristic curves were drawn for pulmonary nodule detection. 16 studies met the inclusion criteria. 1017 patients on a per-patient basis and 2159 lesions on a per-lesion basis from 16 eligible studies were evaluated. The pooled patient-based sensitivity of DTS was 0.85 [95% confidence interval (CI) 0.83-0.88] and the specificity was 0.95 (0.93-0.96). The pooled sensitivity and specificity of chest radiography were 0.47 (0.44-0.51) and 0.37 (0.34-0.40), respectively. The per-lesion detection rate was 2.90 (95% CI 2.63-3.19). DTS has higher diagnostic accuracy than chest radiography for detection of pulmonary nodules. Chest radiography has low sensitivity but similar specificity, comparable with that of DTS. Advances in knowledge: DTS has higher diagnostic accuracy than chest radiography for the detection of pulmonary nodules.

Behavior Analysis Based on Coordinates of Body Tags

NASA Astrophysics Data System (ADS)

Luštrek, Mitja; Kaluža, Boštjan; Dovgan, Erik; Pogorelc, Bogdan; Gams, Matjaž

This paper describes fall detection, activity recognition and the detection of anomalous gait in the Confidence project. The project aims to prolong the independence of the elderly by detecting falls and other types of behavior indicating a health problem. The behavior will be analyzed based on the coordinates of tags worn on the body. The coordinates will be detected with radio sensors. We describe two Confidence modules. The first one classifies the user's activity into one of six classes, including falling. The second one detects walking anomalies, such as limping, dizziness and hemiplegia. The walking analysis can automatically adapt to each person by using only the examples of normal walking of that person. Both modules employ machine learning: the paper focuses on the features they use and the effect of tag placement and sensor noise on the classification accuracy. Four tags were enough for activity recognition accuracy of over 93% at moderate sensor noise, while six were needed to detect walking anomalies with the accuracy of over 90%.

Detection of neovascularization based on fractal and texture analysis with interaction effects in diabetic retinopathy.

PubMed

Lee, Jack; Zee, Benny Chung Ying; Li, Qing

2013-01-01

Diabetic retinopathy is a major cause of blindness. Proliferative diabetic retinopathy is a result of severe vascular complication and is visible as neovascularization of the retina. Automatic detection of such new vessels would be useful for the severity grading of diabetic retinopathy, and it is an important part of screening process to identify those who may require immediate treatment for their diabetic retinopathy. We proposed a novel new vessels detection method including statistical texture analysis (STA), high order spectrum analysis (HOS), fractal analysis (FA), and most importantly we have shown that by incorporating their associated interactions the accuracy of new vessels detection can be greatly improved. To assess its performance, the sensitivity, specificity and accuracy (AUC) are obtained. They are 96.3%, 99.1% and 98.5% (99.3%), respectively. It is found that the proposed method can improve the accuracy of new vessels detection significantly over previous methods. The algorithm can be automated and is valuable to detect relatively severe cases of diabetic retinopathy among diabetes patients.

Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection

NASA Astrophysics Data System (ADS)

Yang, Huijuan; Guan, Cuntai; Sui Geok Chua, Karen; San Chok, See; Wang, Chuan Chu; Kok Soon, Phua; Tang, Christina Ka Yin; Keng Ang, Kai

2014-06-01

Objective. Detection of motor imagery of hand/arm has been extensively studied for stroke rehabilitation. This paper firstly investigates the detection of motor imagery of swallow (MI-SW) and motor imagery of tongue protrusion (MI-Ton) in an attempt to find a novel solution for post-stroke dysphagia rehabilitation. Detection of MI-SW from a simple yet relevant modality such as MI-Ton is then investigated, motivated by the similarity in activation patterns between tongue movements and swallowing and there being fewer movement artifacts in performing tongue movements compared to swallowing. Approach. Novel features were extracted based on the coefficients of the dual-tree complex wavelet transform to build multiple training models for detecting MI-SW. The session-to-session classification accuracy was boosted by adaptively selecting the training model to maximize the ratio of between-classes distances versus within-class distances, using features of training and evaluation data. Main results. Our proposed method yielded averaged cross-validation (CV) classification accuracies of 70.89% and 73.79% for MI-SW and MI-Ton for ten healthy subjects, which are significantly better than the results from existing methods. In addition, averaged CV accuracies of 66.40% and 70.24% for MI-SW and MI-Ton were obtained for one stroke patient, demonstrating the detectability of MI-SW and MI-Ton from the idle state. Furthermore, averaged session-to-session classification accuracies of 72.08% and 70% were achieved for ten healthy subjects and one stroke patient using the MI-Ton model. Significance. These results and the subjectwise strong correlations in classification accuracies between MI-SW and MI-Ton demonstrated the feasibility of detecting MI-SW from MI-Ton models.

Operational monitoring of land-cover change using multitemporal remote sensing data

NASA Astrophysics Data System (ADS)

Rogan, John

2005-11-01

Land-cover change, manifested as either land-cover modification and/or conversion, can occur at all spatial scales, and changes at local scales can have profound, cumulative impacts at broader scales. The implication of operational land-cover monitoring is that researchers have access to a continuous stream of remote sensing data, with the long term goal of providing for consistent and repetitive mapping. Effective large area monitoring of land-cover (i.e., >1000 km2) can only be accomplished by using remotely sensed images as an indirect data source in land-cover change mapping and as a source for land-cover change model projections. Large area monitoring programs face several challenges: (1) choice of appropriate classification scheme/map legend over large, topographically and phenologically diverse areas; (2) issues concerning data consistency and map accuracy (i.e., calibration and validation); (3) very large data volumes; (4) time consuming data processing and interpretation. Therefore, this dissertation research broadly addresses these challenges in the context of examining state-of-the-art image pre-processing, spectral enhancement, classification, and accuracy assessment techniques to assist the California Land-cover Mapping and Monitoring Program (LCMMP). The results of this dissertation revealed that spatially varying haze can be effectively corrected from Landsat data for the purposes of change detection. The Multitemporal Spectral Mixture Analysis (MSMA) spectral enhancement technique produced more accurate land-cover maps than those derived from the Multitemporal Kauth Thomas (MKT) transformation in northern and southern California study areas. A comparison of machine learning classifiers showed that Fuzzy ARTMAP outperformed two classification tree algorithms, based on map accuracy and algorithm robustness. Variation in spatial data error (positional and thematic) was explored in relation to environmental variables using geostatistical interpolation techniques. Finally, the land-cover modification maps generated for three time intervals (1985--1990--1996--2000), with nine change-classes revealed important variations in land-cover gain and loss between northern and southern California study areas.

Fast and Robust Segmentation and Classification for Change Detection in Urban Point Clouds

NASA Astrophysics Data System (ADS)

Roynard, X.; Deschaud, J.-E.; Goulette, F.

2016-06-01

Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

Validation of cardiac accelerometer sensor measurements.

PubMed

Remme, Espen W; Hoff, Lars; Halvorsen, Per Steinar; Naerum, Edvard; Skulstad, Helge; Fleischer, Lars A; Elle, Ole Jakob; Fosse, Erik

2009-12-01

In this study we have investigated the accuracy of an accelerometer sensor designed for the measurement of cardiac motion and automatic detection of motion abnormalities caused by myocardial ischaemia. The accelerometer, attached to the left ventricular wall, changed its orientation relative to the direction of gravity during the cardiac cycle. This caused a varying gravity component in the measured acceleration signal that introduced an error in the calculation of myocardial motion. Circumferential displacement, velocity and rotation of the left ventricular apical region were calculated from the measured acceleration signal. We developed a mathematical method to separate translational and gravitational acceleration components based on a priori assumptions of myocardial motion. The accuracy of the measured motion was investigated by comparison with known motion of a robot arm programmed to move like the heart wall. The accuracy was also investigated in an animal study. The sensor measurements were compared with simultaneously recorded motion from a robot arm attached next to the sensor on the heart and with measured motion by echocardiography and a video camera. The developed compensation method for the varying gravity component improved the accuracy of the calculated velocity and displacement traces, giving very good agreement with the reference methods.

Airborne and Ground-Based Measurements Using a High-Performance Raman Lidar. Part 2; Ground Based

NASA Technical Reports Server (NTRS)

Whiteman, David N.; Cadirola, Martin; Venable, Demetrius; Connell, Rasheen; Rush, Kurt; Leblanc, Thierry; McDermid, Stuart

2009-01-01

The same RASL hardware as described in part I was installed in a ground-based mobile trailer and used in a water vapor lidar intercomparison campaign, hosted at Table Mountain, CA, under the auspices of the Network for the Detection of Atmospheric Composition Change (NDACC). The converted RASL hardware demonstrated high sensitivity to lower stratospheric water vapor indicating that profiling water vapor at those altitudes with sufficient accuracy to monitor climate change is possible. The measurements from Table Mountain also were used to explain the reason, and correct , for sub-optimal airborne aerosol extinction performance during the flight campaign.

Design of a specialized computer for on-line monitoring of cardiac stroke volume

NASA Technical Reports Server (NTRS)

Webb, J. A., Jr.; Gebben, V. D.

1972-01-01

The design of a specialized analog computer for on-line determination of cardiac stroke volume by means of a modified version of the pressure pulse contour method is presented. The design consists of an analog circuit for computation and a timing circuit for detecting necessary events on the pressure waveform. Readouts of arterial pressures, systolic duration, heart rate, percent change in stroke volume, and percent change in cardiac output are provided for monitoring cardiac patients. Laboratory results showed that computational accuracy was within 3 percent, while animal experiments verified the operational capability of the computer. Patient safety considerations are also discussed.

Applications of space technology to developing nations

NASA Technical Reports Server (NTRS)

Freden, S. C.

1976-01-01

The use of imagery from the Landsat spacecraft for the monitoring and management of natural resources in developing countries is discussed. The Landsat imagery can be used to make cartographic maps at scales of 1:250,000 which meet the US National Map Accuracy Standards, providing a means of map updating to correct for river meanders or changing shorelines. The Landsat data can also be used in defining and measuring agricultural areas, identifying pest breeding areas, and monitoring irrigation practices and crop performance. Total volume estimates can be obtained in many cases for surface bodies of water, and subsurface water supplies can be detected from changes in vegetation in some instances.

Measuring the uncertainty of coupling

NASA Astrophysics Data System (ADS)

Zhao, Xiaojun; Shang, Pengjian

2015-06-01

A new information-theoretic measure, called coupling entropy, is proposed here to detect the causal links in complex systems by taking into account the inner composition alignment of temporal structure. It is a permutation-based asymmetric association measure to infer the uncertainty of coupling between two time series. The coupling entropy is found to be effective in the analysis of Hénon maps, where different noises are added to test its accuracy and sensitivity. The coupling entropy is also applied to analyze the relationship between unemployment rate and CPI change in the U.S., where the CPI change turns out to be the driving variable while the unemployment rate is the responding one.

Functional DNA quantification guides accurate next-generation sequencing mutation detection in formalin-fixed, paraffin-embedded tumor biopsies

PubMed Central

2013-01-01

The formalin-fixed, paraffin-embedded (FFPE) biopsy is a challenging sample for molecular assays such as targeted next-generation sequencing (NGS). We compared three methods for FFPE DNA quantification, including a novel PCR assay (‘QFI-PCR’) that measures the absolute copy number of amplifiable DNA, across 165 residual clinical specimens. The results reveal the limitations of commonly used approaches, and demonstrate the value of an integrated workflow using QFI-PCR to improve the accuracy of NGS mutation detection and guide changes in input that can rescue low quality FFPE DNA. These findings address a growing need for improved quality measures in NGS-based patient testing. PMID:24001039

Identification of irrigated crop types from ERTS-1 density contour maps and color infrared aerial photography. [Wyoming

NASA Technical Reports Server (NTRS)

Marrs, R. W.; Evans, M. A.

1974-01-01

The author has identified the following significant results. The crop types of a Great Plains study area were mapped from color infrared aerial photography. Each field was positively identified from field checks in the area. Enlarged (50x) density contour maps were constructed from three ERTS-1 images taken in the summer of 1973. The map interpreted from the aerial photography was compared to the density contour maps and the accuracy of the ERTS-1 density contour map interpretations were determined. Changes in the vegetation during the growing season and harvest periods were detectable on the ERTS-1 imagery. Density contouring aids in the detection of such charges.

Repeat Absolute and Relative Gravity Measurements for Geothermal Reservoir Monitoring in the Ogiri Geothermal Field, Southern Kyushu, Japan

NASA Astrophysics Data System (ADS)

Nishijima, J.; Umeda, C.; Fujimitsu, Y.; Takayama, J.; Hiraga, N.; Higuchi, S.

2016-09-01

Repeat hybrid microgravity measurements were conducted around the Ogiri Geothermal Field on the western slope of Kirishima volcano, southern Kyushu, Japan. This study was undertaken to detect the short-term gravity change caused by the temporary shutdown of production and reinjection wells for regular maintenance in 2011 and 2013. Repeat microgravity measurements were taken using an A-10 absolute gravimeter (Micro-g LaCoste) and CG-5 gravimeter (Scintrex) before and after regular maintenance. Both instruments had an accuracy of 10 μgal. The gravity stations were established at 27 stations (two stations for absolute measurements and 25 stations for relative measurements). After removal of noise effects (e.g., tidal movement, precipitation, shallow groundwater level changes), the residual gravity changes were subdivided into five types of response. We detected a gravity decrease (up to 20 μgal) in the reinjection area and a gravity increase (up to 30 μgal) in the production area 1 month after the temporary shutdown. Most of the gravity stations recovered after the maintenance. The temporal density changes in the geothermal reservoir were estimated based on these gravity changes.

Development of Matched (migratory Analytical Time Change Easy Detection) Method for Satellite-Tracked Migratory Birds

NASA Astrophysics Data System (ADS)

Doko, Tomoko; Chen, Wenbo; Higuchi, Hiroyoshi

2016-06-01

Satellite tracking technology has been used to reveal the migration patterns and flyways of migratory birds. In general, bird migration can be classified according to migration status. These statuses include the wintering period, spring migration, breeding period, and autumn migration. To determine the migration status, periods of these statuses should be individually determined, but there is no objective method to define 'a threshold date' for when an individual bird changes its status. The research objective is to develop an effective and objective method to determine threshold dates of migration status based on satellite-tracked data. The developed method was named the "MATCHED (Migratory Analytical Time Change Easy Detection) method". In order to demonstrate the method, data acquired from satellite-tracked Tundra Swans were used. MATCHED method is composed by six steps: 1) dataset preparation, 2) time frame creation, 3) automatic identification, 4) visualization of change points, 5) interpretation, and 6) manual correction. Accuracy was tested. In general, MATCHED method was proved powerful to identify the change points between migration status as well as stopovers. Nevertheless, identifying "exact" threshold dates is still challenging. Limitation and application of this method was discussed.

Improved wavelet de-noising method of rail vibration signal for wheel tread detection

NASA Astrophysics Data System (ADS)

Zhao, Quan-ke; Zhao, Quanke; Gao, Xiao-rong; Luo, Lin

2011-12-01

The irregularities of wheel tread can be detected by processing acceleration vibration signal of railway. Various kinds of noise from different sources such as wheel-rail resonance, bad weather and artificial reasons are the key factors influencing detection accuracy. A method which uses wavelet threshold de-noising is investigated to reduce noise in the detection signal, and an improved signal processing algorithm based on it has been established. The results of simulations and field experiments show that the proposed method can increase signal-to-noise ratio (SNR) of the rail vibration signal effectively, and improve the detection accuracy.

A comprehensive change detection method for updating the National Land Cover Database to circa 2011

USGS Publications Warehouse

Jin, Suming; Yang, Limin; Danielson, Patrick; Homer, Collin G.; Fry, Joyce; Xian, George

2013-01-01

The importance of characterizing, quantifying, and monitoring land cover, land use, and their changes has been widely recognized by global and environmental change studies. Since the early 1990s, three U.S. National Land Cover Database (NLCD) products (circa 1992, 2001, and 2006) have been released as free downloads for users. The NLCD 2006 also provides land cover change products between 2001 and 2006. To continue providing updated national land cover and change datasets, a new initiative in developing NLCD 2011 is currently underway. We present a new Comprehensive Change Detection Method (CCDM) designed as a key component for the development of NLCD 2011 and the research results from two exemplar studies. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (CV, RCVMAX, dNBR, and dNDVI) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. For NLCD 2011, the improved and enhanced change products obtained from the CCDM provide critical information on location, magnitude, and direction of potential change areas and serve as a basis for further characterizing land cover changes for the nation. An accuracy assessment from the two study areas show 100% agreement between CCDM mapped no-change class with reference dataset, and 18% and 82% disagreement for the change class for WRS path/row p22r39 and p33r33, respectively. The strength of the CCDM is that the method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and anthropogenic disturbances potentially associated with land cover changes on different landscapes.

A robust multi-kernel change detection framework for detecting leaf beetle defoliation using Landsat 7 ETM+ data

NASA Astrophysics Data System (ADS)

Anees, Asim; Aryal, Jagannath; O'Reilly, Małgorzata M.; Gale, Timothy J.; Wardlaw, Tim

2016-12-01

A robust non-parametric framework, based on multiple Radial Basic Function (RBF) kernels, is proposed in this study, for detecting land/forest cover changes using Landsat 7 ETM+ images. One of the widely used frameworks is to find change vectors (difference image) and use a supervised classifier to differentiate between change and no-change. The Bayesian Classifiers e.g. Maximum Likelihood Classifier (MLC), Naive Bayes (NB), are widely used probabilistic classifiers which assume parametric models, e.g. Gaussian function, for the class conditional distributions. However, their performance can be limited if the data set deviates from the assumed model. The proposed framework exploits the useful properties of Least Squares Probabilistic Classifier (LSPC) formulation i.e. non-parametric and probabilistic nature, to model class posterior probabilities of the difference image using a linear combination of a large number of Gaussian kernels. To this end, a simple technique, based on 10-fold cross-validation is also proposed for tuning model parameters automatically instead of selecting a (possibly) suboptimal combination from pre-specified lists of values. The proposed framework has been tested and compared with Support Vector Machine (SVM) and NB for detection of defoliation, caused by leaf beetles (Paropsisterna spp.) in Eucalyptus nitens and Eucalyptus globulus plantations of two test areas, in Tasmania, Australia, using raw bands and band combination indices of Landsat 7 ETM+. It was observed that due to multi-kernel non-parametric formulation and probabilistic nature, the LSPC outperforms parametric NB with Gaussian assumption in change detection framework, with Overall Accuracy (OA) ranging from 93.6% (κ = 0.87) to 97.4% (κ = 0.94) against 85.3% (κ = 0.69) to 93.4% (κ = 0.85), and is more robust to changing data distributions. Its performance was comparable to SVM, with added advantages of being probabilistic and capable of handling multi-class problems naturally with its original formulation.

Research on intrusion detection based on Kohonen network and support vector machine

NASA Astrophysics Data System (ADS)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

Improvement of a picking algorithm real-time P-wave detection by kurtosis

NASA Astrophysics Data System (ADS)

Ishida, H.; Yamada, M.

2016-12-01

Earthquake early warning (EEW) requires fast and accurate P-wave detection. The current EEW system in Japan uses the STA/LTAalgorithm (Allen, 1978) to detect P-wave arrival.However, some stations did not trigger during the 2011 Great Tohoku Earthquake due to the emergent onset. In addition, accuracy of the P-wave detection is very important: on August 1, 2016, the EEW issued a false alarm with M9 in Tokyo region due to a thunder noise.To solve these problems, we use a P-wave detection method using kurtosis statistics. It detects the change of statistic distribution of the waveform amplitude. This method was recently developed (Saragiotis et al., 2002) and used for off-line analysis such as making seismic catalogs. To apply this method for EEW, we need to remove an acausal calculation and enable a real-time processing. Here, we propose a real-time P-wave detection method using kurtosis statistics with a noise filter.To avoid false triggering by a noise, we incorporated a simple filter to classify seismic signal and noise. Following Kong et al. (2016), we used the interquartilerange and zero cross rate for the classification. The interquartile range is an amplitude measure that is equal to the middle 50% of amplitude in a certain time window. The zero cross rate is a simple frequency measure that counts the number of times that the signal crosses baseline zero. A discriminant function including these measures was constructed by the linear discriminant analysis.To test this kurtosis method, we used strong motion records for 62 earthquakes between April, 2005 and July, 2015, which recorded the seismic intensity greater equal to 6 lower in the JMA intensity scale. The records with hypocentral distance < 200km were used for the analysis. An attached figure shows the error of P-wave detection speed for STA/LTA and kurtosis methods against manual picks. It shows that the median error is 0.13 sec and 0.035 sec for STA/LTA and kurtosis method. The kurtosis method tends to be more sensitive to small changes in amplitude.Our approach will contribute to improve the accuracy of source location determination of earthquakes and improve the shaking intensity estimation for an earthquake early warning.

Detecting declines in the abundance of a bull trout (Salvelinus confluentus) population: Understanding the accuracy, precision, and costs of our efforts

USGS Publications Warehouse

Al-Chokhachy, R.; Budy, P.; Conner, M.

2009-01-01

Using empirical field data for bull trout (Salvelinus confluentus), we evaluated the trade-off between power and sampling effort-cost using Monte Carlo simulations of commonly collected mark-recapture-resight and count data, and we estimated the power to detect changes in abundance across different time intervals. We also evaluated the effects of monitoring different components of a population and stratification methods on the precision of each method. Our results illustrate substantial variability in the relative precision, cost, and information gained from each approach. While grouping estimates by age or stage class substantially increased the precision of estimates, spatial stratification of sampling units resulted in limited increases in precision. Although mark-resight methods allowed for estimates of abundance versus indices of abundance, our results suggest snorkel surveys may be a more affordable monitoring approach across large spatial scales. Detecting a 25% decline in abundance after 5 years was not possible, regardless of technique (power = 0.80), without high sampling effort (48% of study site). Detecting a 25% decline was possible after 15 years, but still required high sampling efforts. Our results suggest detecting moderate changes in abundance of freshwater salmonids requires considerable resource and temporal commitments and highlight the difficulties of using abundance measures for monitoring bull trout populations.

Technologic developments in the field of photonics for the detection of urinary bladder cancer.

PubMed

Palmer, Scott; Sokolovski, Sergei G; Rafailov, Edik; Nabi, Ghulam

2013-12-01

Bladder cancer is a common cause of morbidity and mortality worldwide in an aging population. Each year, thousands of people, mostly men, are diagnosed with this disease, but many of them present too late to receive optimal treatment. As with all cancers, early diagnosis of bladder cancer significantly improves the efficacy of therapy and increases survival and recurrence-free survival rates. Ongoing research has identified many limitations about the sensitivity of standard diagnostic procedures in detecting early-stage tumors and precancerous changes. The consequences of this are often tumor progression and increased tumor burden, leading to a decrease in patient quality of life and a vast increase in treatment costs. The necessity for improved early detection of bladder cancer has spurred on research into novel methods that use a wide range of biological and photonic phenomena. This review will broadly discuss standard detection methodologies and their major limitations before covering novel photonic techniques for early tumor detection and staging, assessing their diagnostic accuracy for flat and precancerous changes. We will do so in the context of both cystoscopic examination and the screening of voided urine and will also touch on the concept of using photonic technology as a surgical tool for tumor ablation. Copyright © 2013 Elsevier Inc. All rights reserved.

Effect of dental technician disparities on the 3-dimensional accuracy of definitive casts.

PubMed

Emir, Faruk; Piskin, Bulent; Sipahi, Cumhur

2017-03-01

Studies that evaluated the effect of dental technician disparities on the accuracy of presectioned and postsectioned definitive casts are lacking. The purpose of this in vitro study was to evaluate the accuracy of presectioned and postsectioned definitive casts fabricated by different dental technicians by using a 3-dimensional computer-aided measurement method. An arch-shaped metal master model consisting of 5 abutments resembling prepared mandibular incisors, canines, and first molars and with a 6-degree total angle of convergence was designed and fabricated by computer-aided design and computer-aided manufacturing (CAD-CAM) technology. Complete arch impressions were made (N=110) from the master model, using polyvinyl siloxane (PVS) and delivered to 11 dental technicians. Each technician fabricated 10 definitive casts with dental stone, and the obtained casts were numbered. All casts were sectioned, and removable dies were obtained. The master model and the presectioned and postsectioned definitive casts were digitized with an extraoral scanner, and the virtual master model and virtual presectioned and postsectioned definitive casts were obtained. All definitive casts were compared with the master model by using computer-aided measurements, and the 3-dimensional accuracy of the definitive casts was determined with best fit alignment and represented in color-coded maps. Differences were analyzed using univariate analyses of variance, and the Tukey honest significant differences post hoc tests were used for multiple comparisons (α=.05). The accuracy of presectioned and postsectioned definitive casts was significantly affected by dental technician disparities (P<.001). The largest dimensional changes were detected in the anterior abutments of both of the definitive casts. The changes mostly occurred in the mesiodistal dimension (P<.001). Within the limitations of this in vitro study, the accuracy of presectioned and postsectioned definitive casts is susceptible to dental technician differences. Copyright © 2016 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

[Detection of Heart Rate of Fetal ECG Based on STFT and BSS].

PubMed

Wang, Xu; Cai, Kun

2016-01-01

Changes in heart rate of fetal is function regulating performance of the circulatory system and the central nervous system, it is significant to detect heart rate of fetus in perinatal fetal. This paper puts forward the fetal heart rate detection method based on short time Fourier transform and blind source separation. First of all, the mixed ECG signal was preprocessed, and then the wavelet transform technique was used to separate the fetal ECG signal with noise from mixed ECG signal, after that, the short-time Fourier transform and the blind separation were carried on it, and then calculated the correlation coefficient of it, Finally, An independent component that it has strongest correlation with the original signal was selected to make FECG peak detection and calculated the fetal instantaneous heart rate. The experimental results show that the method can improve the detection rate of the FECG peak (R), and it has high accuracy in fixing peak(R) location in the case of low signal-noise ratio.

Remote sensing of changes in morphology and physiology of trees under stress. [for detecting Fomes annosus

NASA Technical Reports Server (NTRS)

Olson, C. F., Jr.

1972-01-01

Previsual detection of Fomes annosus in pine plantations was studied. Detailed analyses of photographic imagery obtained over the Ann Arbor Test Site during 1969 and 1970 reveal that the Ektachrome Infrared film was superior to Ektachrome Aerographic, Infrared Aerographic, or Plus-X Aerographic films for detecting Fomes annosus damage. Of far more significance in controlling the accuracy of damage detection, however, was the experience of the photo interpreter. Ratio-processing of multispectral scanner data was investigated with data collected in June of 1970 and in June of 1972. Ratioing of the 1.5-1.8 and 1.0-1.4 micrometer channels gave good results at detecting openings in the crown canopy and adjacent infected trees. Combined level slicing of the 1.5-1.8 micrometer channel and the 1.5-1.8 to 1.0-1.4 micrometer ratio permitted separation and recognition of forest litter in the openings and stressed trees adjacent ot the openings.

Eye pupil detection system using an ensemble of regression forest and fast radial symmetry transform with a near infrared camera

NASA Astrophysics Data System (ADS)

Jeong, Mira; Nam, Jae-Yeal; Ko, Byoung Chul

2017-09-01

In this paper, we focus on pupil center detection in various video sequences that include head poses and changes in illumination. To detect the pupil center, we first find four eye landmarks in each eye by using cascade local regression based on a regression forest. Based on the rough location of the pupil, a fast radial symmetric transform is applied using the previously found pupil location to rearrange the fine pupil center. As the final step, the pupil displacement is estimated between the previous frame and the current frame to maintain the level of accuracy against a false locating result occurring in a particular frame. We generated a new face dataset, called Keimyung University pupil detection (KMUPD), with infrared camera. The proposed method was successfully applied to the KMUPD dataset, and the results indicate that its pupil center detection capability is better than that of other methods and with a shorter processing time.

Automatic spatiotemporal matching of detected pleural thickenings

NASA Astrophysics Data System (ADS)

Chaisaowong, Kraisorn; Keller, Simon Kai; Kraus, Thomas

2014-01-01

Pleural thickenings can be found in asbestos exposed patient's lung. Non-invasive diagnosis including CT imaging can detect aggressive malignant pleural mesothelioma in its early stage. In order to create a quantitative documentation of automatic detected pleural thickenings over time, the differences in volume and thickness of the detected thickenings have to be calculated. Physicians usually estimate the change of each thickening via visual comparison which provides neither quantitative nor qualitative measures. In this work, automatic spatiotemporal matching techniques of the detected pleural thickenings at two points of time based on the semi-automatic registration have been developed, implemented, and tested so that the same thickening can be compared fully automatically. As result, the application of the mapping technique using the principal components analysis turns out to be advantageous than the feature-based mapping using centroid and mean Hounsfield Units of each thickening, since the resulting sensitivity was improved to 98.46% from 42.19%, while the accuracy of feature-based mapping is only slightly higher (84.38% to 76.19%).

Study and Experiment on Non-Contact Voltage Sensor Suitable for Three-Phase Transmission Line

PubMed Central

Zhou, Qiang; He, Wei; Xiao, Dongping; Li, Songnong; Zhou, Kongjun

2015-01-01

A voltage transformer, as voltage signal detection equipment, plays an important role in a power system. Presently, more and more electric power systems are adopting potential transformer and capacitance voltage transformers. Transformers are often large in volume and heavyweight, their insulation design is difficult, and an iron core or multi-grade capacitance voltage division structure is generally adopted. As a result, the detection accuracy of transformer is reduced, a huge phase difference exists between detection signal and voltage signal to be measured, and the detection signal cannot accurately and timely reflect the change of conductor voltage signal to be measured. By aiming at the current problems of electric transformation, based on electrostatic induction principle, this paper designed a non-contact voltage sensor and gained detection signal of the sensor through electrostatic coupling for the electric field generated by electric charges of the conductor to be measured. The insulation structure design of the sensor is simple and its volume is small; phase difference of sensor measurement is effectively reduced through optimization design of the electrode; and voltage division ratio and measurement accuracy are increased. The voltage sensor was tested on the experimental platform of simulating three-phase transmission line. According to the result, the designed non-contact voltage sensor can realize accurate and real-time measurement for the conductor voltage. It can be applied to online monitoring for the voltage of three-phase transmission line or three-phase distribution network line, which is in accordance with the development direction of the smart grid. PMID:26729119

Study and Experiment on Non-Contact Voltage Sensor Suitable for Three-Phase Transmission Line.

PubMed

Zhou, Qiang; He, Wei; Xiao, Dongping; Li, Songnong; Zhou, Kongjun

2015-12-30

A voltage transformer, as voltage signal detection equipment, plays an important role in a power system. Presently, more and more electric power systems are adopting potential transformer and capacitance voltage transformers. Transformers are often large in volume and heavyweight, their insulation design is difficult, and an iron core or multi-grade capacitance voltage division structure is generally adopted. As a result, the detection accuracy of transformer is reduced, a huge phase difference exists between detection signal and voltage signal to be measured, and the detection signal cannot accurately and timely reflect the change of conductor voltage signal to be measured. By aiming at the current problems of electric transformation, based on electrostatic induction principle, this paper designed a non-contact voltage sensor and gained detection signal of the sensor through electrostatic coupling for the electric field generated by electric charges of the conductor to be measured. The insulation structure design of the sensor is simple and its volume is small; phase difference of sensor measurement is effectively reduced through optimization design of the electrode; and voltage division ratio and measurement accuracy are increased. The voltage sensor was tested on the experimental platform of simulating three-phase transmission line. According to the result, the designed non-contact voltage sensor can realize accurate and real-time measurement for the conductor voltage. It can be applied to online monitoring for the voltage of three-phase transmission line or three-phase distribution network line, which is in accordance with the development direction of the smart grid.

Verification of OpenSSL version via hardware performance counters

NASA Astrophysics Data System (ADS)

Bruska, James; Blasingame, Zander; Liu, Chen

2017-05-01

Many forms of malware and security breaches exist today. One type of breach downgrades a cryptographic program by employing a man-in-the-middle attack. In this work, we explore the utilization of hardware events in conjunction with machine learning algorithms to detect which version of OpenSSL is being run during the encryption process. This allows for the immediate detection of any unknown downgrade attacks in real time. Our experimental results indicated this detection method is both feasible and practical. When trained with normal TLS and SSL data, our classifier was able to detect which protocol was being used with 99.995% accuracy. After the scope of the hardware event recording was enlarged, the accuracy diminished greatly, but to 53.244%. Upon removal of TLS 1.1 from the data set, the accuracy returned to 99.905%.

Accuracy of software-assisted detection of tumour feeders in transcatheter hepatic chemoembolization using three target definition protocols.

PubMed

Iwazawa, J; Ohue, S; Hashimoto, N; Mitani, T

2014-02-01

To compare the accuracy of computer software analysis using three different target-definition protocols to detect tumour feeder vessels for transarterial chemoembolization of hepatocellular carcinoma. C-arm computed tomography (CT) data were analysed for 81 tumours from 57 patients who had undergone chemoembolization using software-assisted detection of tumour feeders. Small, medium, and large-sized targets were manually defined for each tumour. The tumour feeder was verified when the target tumour was enhanced on selective C-arm CT of the investigated vessel during chemoembolization. The sensitivity, specificity, and accuracy of the three protocols were evaluated and compared. One hundred and eight feeder vessels supplying 81 lesions were detected. The sensitivity of the small, medium, and large target protocols was 79.8%, 91.7%, and 96.3%, respectively; specificity was 95%, 88%, and 50%, respectively; and accuracy was 87.5%, 89.9%, and 74%, respectively. The sensitivity was significantly higher for the medium (p = 0.003) and large (p < 0.001) target protocols than for the small target protocol. The specificity and accuracy were higher for the small (p < 0.001 and p < 0.001, respectively) and medium (p < 0.001 and p < 0.001, respectively) target protocols than for the large target protocol. The overall accuracy of software-assisted automated feeder analysis in transarterial chemoembolization for hepatocellular carcinoma is affected by the target definition size. A large target definition increases sensitivity and decreases specificity in detecting tumour feeders. A target size equivalent to the tumour size most accurately predicts tumour feeders. Copyright © 2013 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Thematic Accuracy Assessment of the 2011 National Land ...

EPA Pesticide Factsheets

Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land cover for ca. 2001, ca. 2006, and ca. 2011. The two main objectives were assessment of agreement between map and reference labels for the three, single-date NLCD land cover products at Level II and Level I of the classification hierarchy, and agreement for 17 land cover change reporting themes based on Level I classes (e.g., forest loss; forest gain; forest, no change) for three change periods (2001–2006, 2006–2011, and 2001–2011). The single-date overall accuracies were 82%, 83%, and 83% at Level II and 88%, 89%, and 89% at Level I for 2011, 2006, and 2001, respectively. Many class-specific user's accuracies met or exceeded a previously established nominal accuracy benchmark of 85%. Overall accuracies for 2006 and 2001 land cover components of NLCD 2011 were approximately 4% higher (at Level II and Level I) than the overall accuracies for the same components of NLCD 2006. The high Level I overall, user's, and producer's accuracies for the single-date eras in NLCD 2011 did not translate into high class-specific user's and producer's accuracies for many of the 17 change reporting themes. User's accuracies were high for the no change reporting themes, commonly exceeding 85%, but were typically much lower for the reporting themes that represented change. Only forest l

Diagnostic value of ST-segment deviations during cardiac exercise stress testing: Systematic comparison of different ECG leads and time-points.

PubMed

Puelacher, Christian; Wagener, Max; Abächerli, Roger; Honegger, Ursina; Lhasam, Nundsin; Schaerli, Nicolas; Prêtre, Gil; Strebel, Ivo; Twerenbold, Raphael; Boeddinghaus, Jasper; Nestelberger, Thomas; Rubini Giménez, Maria; Hillinger, Petra; Wildi, Karin; Sabti, Zaid; Badertscher, Patrick; Cupa, Janosch; Kozhuharov, Nikola; du Fay de Lavallaz, Jeanne; Freese, Michael; Roux, Isabelle; Lohrmann, Jens; Leber, Remo; Osswald, Stefan; Wild, Damian; Zellweger, Michael J; Mueller, Christian; Reichlin, Tobias

2017-07-01

Exercise ECG stress testing is the most widely available method for evaluation of patients with suspected myocardial ischemia. Its major limitation is the relatively poor accuracy of ST-segment changes regarding ischemia detection. Little is known about the optimal method to assess ST-deviations. A total of 1558 consecutive patients undergoing bicycle exercise stress myocardial perfusion imaging (MPI) were enrolled. Presence of inducible myocardial ischemia was adjudicated using MPI results. The diagnostic value of ST-deviations for detection of exercise-induced myocardial ischemia was systematically analyzed 1) for each individual lead, 2) at three different intervals after the J-point (J+40ms, J+60ms, J+80ms), and 3) at different time points during the test (baseline, maximal workload, 2min into recovery). Exercise-induced ischemia was detected in 481 (31%) patients. The diagnostic accuracy of ST-deviations was highest at +80ms after the J-point, and at 2min into recovery. At this point, ST-amplitude showed an AUC of 0.63 (95% CI 0.59-0.66) for the best-performing lead I. The combination of ST-amplitude and ST-slope in lead I did not increase the AUC. Lead I reached a sensitivity of 37% and a specificity of 83%, with similar sensitivity to manual ECG analysis (34%, p=0.31) but lower specificity (90%, p<0.001). When using ECG stress testing for evaluation of patients with suspected myocardial ischemia, the diagnostic accuracy of ST-deviations is highest when evaluated at +80ms after the J-point, and at 2min into recovery. Copyright © 2017 Elsevier B.V. All rights reserved.

Subclinical keratoconus detection by pattern analysis of corneal and epithelial thickness maps with optical coherence tomography

PubMed Central

Li, Yan; Chamberlain, Winston; Tan, Ou; Brass, Robert; Weiss, Jack L.; Huang, David

2016-01-01

PURPOSE To screen for subclinical keratoconus by analyzing corneal, epithelial, and stromal thickness map patterns with Fourier-domain optical coherence tomography (OCT). SETTING Four centers in the United States. DESIGN Cross-sectional observational study. METHODS Eyes of normal subjects, subclinical keratoconus eyes, and the topographically normal eye of a unilateral keratoconus patient were studied. Corneas were scanned using a 26 000 Hz Fourier-domain OCT system (RTVue). Normal subjects were divided into training and evaluation groups. Corneal, epithelial, and stromal thickness maps and derived diagnostic indices, including pattern standard deviation (PSD) variables and pachymetric map–based keratoconus risk scores were calculated from the OCT data. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the diagnostic accuracy of the indices. RESULTS The study comprised 150 eyes of 83 normal subjects, 50 subclinical keratoconus eyes of 32 patients, and 1 topographically normal eye of a unilateral keratoconus patient. Subclinical keratoconus was characterized by inferotemporal thinning of the cornea, epithelium, and stroma. The PSD values for corneal (P < .001), epithelial (P < .001), and stromal (P = .049) thickness maps were all significantly higher in subclinical keratoconic eyes than in the normal group. The diagnostic accuracy was significantly higher for PSD variables (pachymetric PSD, AUC = 0.941; epithelial PSD, AUC = 0.985; stromal PSD, AUC = 0.924) than for the pachymetric map–based keratoconus risk score (AUC = 0.735). CONCLUSIONS High-resolution Fourier-domain OCT could map corneal, epithelial, and stromal thicknesses. Corneal and sublayer thickness changes in subclinical keratoconus could be detected with high accuracy using PSD variables. These new diagnostic variables might be useful in the detection of early keratoconus. PMID:27026454

Cluster signal-to-noise analysis for evaluation of the information content in an image.

PubMed

Weerawanich, Warangkana; Shimizu, Mayumi; Takeshita, Yohei; Okamura, Kazutoshi; Yoshida, Shoko; Yoshiura, Kazunori

2018-01-01

(1) To develop an observer-free method of analysing image quality related to the observer performance in the detection task and (2) to analyse observer behaviour patterns in the detection of small mass changes in cone-beam CT images. 13 observers detected holes in a Teflon phantom in cone-beam CT images. Using the same images, we developed a new method, cluster signal-to-noise analysis, to detect the holes by applying various cut-off values using ImageJ and reconstructing cluster signal-to-noise curves. We then evaluated the correlation between cluster signal-to-noise analysis and the observer performance test. We measured the background noise in each image to evaluate the relationship with false positive rates (FPRs) of the observers. Correlations between mean FPRs and intra- and interobserver variations were also evaluated. Moreover, we calculated true positive rates (TPRs) and accuracies from background noise and evaluated their correlations with TPRs from observers. Cluster signal-to-noise curves were derived in cluster signal-to-noise analysis. They yield the detection of signals (true holes) related to noise (false holes). This method correlated highly with the observer performance test (R 2 = 0.9296). In noisy images, increasing background noise resulted in higher FPRs and larger intra- and interobserver variations. TPRs and accuracies calculated from background noise had high correlation with actual TPRs from observers; R 2 was 0.9244 and 0.9338, respectively. Cluster signal-to-noise analysis can simulate the detection performance of observers and thus replace the observer performance test in the evaluation of image quality. Erroneous decision-making increased with increasing background noise.

Early Diagnosis of Respiratory Abnormalities in Asbestos-Exposed Workers by the Forced Oscillation Technique.

PubMed

de Sá, Paula Morisco; Castro, Hermano Albuquerque; Lopes, Agnaldo José; Melo, Pedro Lopes de

2016-01-01

The current reference test for the detection of respiratory abnormalities in asbestos-exposed workers is spirometry. However, spirometry has several shortcomings that greatly affect the efficacy of current asbestos control programs. The forced oscillation technique (FOT) represents the current state-of-the-art technique in the assessment of lung function. This method provides a detailed analysis of respiratory resistance and reactance at different oscillatory frequencies during tidal breathing. Here, we evaluate the FOT as an alternative method to standard spirometry for the early detection and quantification of respiratory abnormalities in asbestos-exposed workers. Seventy-two subjects were analyzed. The control group was composed of 33 subjects with a normal spirometric exam who had no history of smoking or pulmonary disease. Thirty-nine subjects exposed to asbestos were also studied, including 32 volunteers in radiological category 0/0 and 7 volunteers with radiological categories of 0/1 or 1/1. FOT data were interpreted using classical parameters as well as integer (InOr) and fractional-order (FrOr) modeling. The diagnostic accuracy was evaluated by investigating the area under the receiver operating characteristic curve (AUC). Exposed workers presented increased obstruction (resistance p<0.001) and a reduced compliance (p<0.001), with a predominance of obstructive changes. The FOT parameter changes were correlated with the standard pulmonary function analysis methods (R = -0.52, p<0.001). Early respiratory abnormalities were identified with a high diagnostic accuracy (AUC = 0.987) using parameters obtained from the FrOr modeling. This accuracy was significantly better than those obtained with classical (p<0.001) and InOr (p<0.001) model parameters. The FOT improved our knowledge about the biomechanical abnormalities in workers exposed to asbestos. Additionally, a high diagnostic accuracy in the diagnosis of early respiratory abnormalities in asbestos-exposed workers was obtained. This makes the FOT particularly useful as a screening tool in the context of asbestos control and elimination. Moreover, it can facilitate epidemiological research and the longitudinal follow-up of asbestos exposure and asbestos-related diseases.

Measurement of tissue optical properties with optical coherence tomography: Implication for noninvasive blood glucose concentration monitoring

NASA Astrophysics Data System (ADS)

Larin, Kirill V.

Approximately 14 million people in the USA and more than 140 million people worldwide suffer from diabetes mellitus. The current glucose sensing technique involves a finger puncture several times a day to obtain a droplet of blood for analysis. There have been enormous efforts by many scientific groups and companies to quantify glucose concentration noninvasively using different optical techniques. However, these techniques face limitations associated with low sensitivity, accuracy, and insufficient specificity of glucose concentrations over a physiological range. Optical coherence tomography (OCT), a new technology, is being applied for noninvasive imaging in tissues with high resolution. OCT utilizes sensitive detection of photons coherently scattered from tissue. The high resolution of this technique allows for exceptionally accurate measurement of tissue scattering from a specific layer of skin compared with other optical techniques and, therefore, may provide noninvasive and continuous monitoring of blood glucose concentration with high accuracy. In this dissertation work I experimentally and theoretically investigate feasibility of noninvasive, real-time, sensitive, and specific monitoring of blood glucose concentration using an OCT-based biosensor. The studies were performed in scattering media with stable optical properties (aqueous suspensions of polystyrene microspheres and milk), animals (New Zealand white rabbits and Yucatan micropigs), and normal subjects (during oral glucose tolerance tests). The results of these studies demonstrated: (1) capability of the OCT technique to detect changes in scattering coefficient with the accuracy of about 1.5%; (2) a sharp and linear decrease of the OCT signal slope in the dermis with the increase of blood glucose concentration; (3) the change in the OCT signal slope measured during bolus glucose injection experiments (characterized by a sharp increase of blood glucose concentration) is higher than that measured in the glucose clamping experiments (characterized by slow, controlled increase of the blood glucose concentration); and (4) the accuracy of glucose concentration monitoring may substantially be improved if optimal dimensions of the probed skin area are used. The results suggest that high-resolution OCT technique has a potential for noninvasive, accurate, and continuous glucose monitoring with high sensitivity.

The Accuracy of Integrated [18F] Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography in Detection of Pelvic and Para-aortic Nodal Metastasis in Patients with High Risk Endometrial Cancer

PubMed Central

Gholkar, Nikhil Shirish; Saha, Subhas Chandra; Prasad, GRV; Bhattacharya, Anish; Srinivasan, Radhika; Suri, Vanita

2014-01-01

Lymph nodal (LN) metastasis is the most important prognostic factor in high-risk endometrial cancer. However, the benefit of routine lymphadenectomy in endometrial cancer is controversial. This study was conducted to assess the accuracy of [18F] fluorodeoxyglucose-positron emission tomography/computed tomography ([18F] FDG-PET/CT) in detection of pelvic and para-aortic nodal metastases in high-risk endometrial cancer. 20 patients with high-risk endometrial carcinoma underwent [18F] FDG-PET/CT followed by total abdominal hysterectomy, bilateral salpingo-oophorectomy and systematic pelvic lymphadenectomy with or without para-aortic lymphadenectomy. The findings on histopathology were compared with [18F] FDG-PET/CT findings to calculate the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of [18F] FDG-PET/CT. The pelvic nodal findings were analyzed on a patient and nodal chain based criteria. The para-aortic nodal findings were reported separately. Histopathology documented nodal involvement in two patients (10%). For detection of pelvic nodes, on a patient based analysis, [18F] FDG-PET/CT had a sensitivity of 100%, specificity of 61.11%, PPV of 22.22%, NPV of 100% and accuracy of 65% and on a nodal chain based analysis, [18F] FDG-PET/CT had a sensitivity of 100%, specificity of 80%, PPV of 20%, NPV of 100%, and accuracy of 80.95%. For detection of para-aortic nodes, [18F] FDG-PET/CT had sensitivity of 100%, specificity of 66.67%, PPV of 20%, NPV of 100%, and accuracy of 69.23%. Although [18F] FDG-PET/CT has high sensitivity for detection of LN metastasis in endometrial carcinoma, it had moderate accuracy and high false positivity. However, the high NPV is important in selecting patients in whom lymphadenectomy may be omitted. PMID:25538488

Telephony-based voice pathology assessment using automated speech analysis.

PubMed

Moran, Rosalyn J; Reilly, Richard B; de Chazal, Philip; Lacy, Peter D

2006-03-01

A system for remotely detecting vocal fold pathologies using telephone-quality speech is presented. The system uses a linear classifier, processing measurements of pitch perturbation, amplitude perturbation and harmonic-to-noise ratio derived from digitized speech recordings. Voice recordings from the Disordered Voice Database Model 4337 system were used to develop and validate the system. Results show that while a sustained phonation, recorded in a controlled environment, can be classified as normal or pathologic with accuracy of 89.1%, telephone-quality speech can be classified as normal or pathologic with an accuracy of 74.2%, using the same scheme. Amplitude perturbation features prove most robust for telephone-quality speech. The pathologic recordings were then subcategorized into four groups, comprising normal, neuromuscular pathologic, physical pathologic and mixed (neuromuscular with physical) pathologic. A separate classifier was developed for classifying the normal group from each pathologic subcategory. Results show that neuromuscular disorders could be detected remotely with an accuracy of 87%, physical abnormalities with an accuracy of 78% and mixed pathology voice with an accuracy of 61%. This study highlights the real possibility for remote detection and diagnosis of voice pathology.

MRI EVALUATION OF KNEE CARTILAGE

PubMed Central

Rodrigues, Marcelo Bordalo; Camanho, Gilberto Luís

2015-01-01

Through the ability of magnetic resonance imaging (MRI) to characterize soft tissue noninvasively, it has become an excellent method for evaluating cartilage. The development of new and faster methods allowed increased resolution and contrast in evaluating chondral structure, with greater diagnostic accuracy. In addition, physiological techniques for cartilage assessment that can detect early changes before the appearance of cracks and erosion have been developed. In this updating article, the various techniques for chondral assessment using knee MRI will be discussed and demonstrated. PMID:27022562

Biomechanical and morphological multi-parameter photoacoustic endoscope for identification of early esophageal disease

NASA Astrophysics Data System (ADS)

Jin, Dayang; Yang, Fen; Chen, Zhongjiang; Yang, Sihua; Xing, Da

2017-09-01

The combination of phase-sensitive photoacoustic (PA) imaging of tissue viscoelasticity with the esophagus-adaptive PA endoscope (PAE) technique allows the characterization of the biomechanical and morphological changes in the early stage of esophageal disease with high accuracy. In this system, the tissue biomechanics and morphology are obtained by detecting the PA phase and PA amplitude information, respectively. The PAE has a transverse resolution of approximately 37 μm and an outer diameter of 1.2 mm, which is suitable for detecting rabbit esophagus. Here, an in-situ biomechanical and morphological study of normal and diseased rabbit esophagus (tumors of esophagus and reflux esophagitis) was performed. The in-situ findings were highly consistent with those observed by histology. In summary, we demonstrated the potential application of PAE for early clinical detection of esophageal diseases.

Robust vehicle detection under various environmental conditions using an infrared thermal camera and its application to road traffic flow monitoring.

PubMed

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2013-06-17

We have already proposed a method for detecting vehicle positions and their movements (henceforth referred to as "our previous method") using thermal images taken with an infrared thermal camera. Our experiments have shown that our previous method detects vehicles robustly under four different environmental conditions which involve poor visibility conditions in snow and thick fog. Our previous method uses the windshield and its surroundings as the target of the Viola-Jones detector. Some experiments in winter show that the vehicle detection accuracy decreases because the temperatures of many windshields approximate those of the exterior of the windshields. In this paper, we propose a new vehicle detection method (henceforth referred to as "our new method"). Our new method detects vehicles based on tires' thermal energy reflection. We have done experiments using three series of thermal images for which the vehicle detection accuracies of our previous method are low. Our new method detects 1,417 vehicles (92.8%) out of 1,527 vehicles, and the number of false detection is 52 in total. Therefore, by combining our two methods, high vehicle detection accuracies are maintained under various environmental conditions. Finally, we apply the traffic information obtained by our two methods to traffic flow automatic monitoring, and show the effectiveness of our proposal.

Online detecting system of roller wear based on laser-linear array CCD technology

NASA Astrophysics Data System (ADS)

Guo, Yuan

2010-10-01

Roller is an important metallurgy tool in the rolling mill. And the surface of a roller affects the quantity of the rolling product directly. After using a period of time, roller must be repaired or replaced. Examining the profile of a working roller between the intervals of rolling is called online detecting for roller wear. The study of online detecting roller wear is very important for selecting the grinding time in reason, reducing the exchanging times of rollers, improving the quality of the product and realizing online grinding rollers. By applying the laser-linear array CCD detective technology, a method for online non-touch detecting roller wear was brought forward. The principle, composition and the operation process of the linear array CCD detecting system were expatiated. And an error compensation algorithm is exactly calculated to offset the shift of the roller axis in this measurement system. So the stability and the accuracy were improved remarkably. The experiment proves that the accuracy of the detecting system reaches to the demand of practical production process. It can provide a new method of high speed and high accuracy online detecting for roller wear.

Vocal Accuracy and Neural Plasticity Following Micromelody-Discrimination Training

PubMed Central

Zarate, Jean Mary; Delhommeau, Karine; Wood, Sean; Zatorre, Robert J.

2010-01-01

Background Recent behavioral studies report correlational evidence to suggest that non-musicians with good pitch discrimination sing more accurately than those with poorer auditory skills. However, other studies have reported a dissociation between perceptual and vocal production skills. In order to elucidate the relationship between auditory discrimination skills and vocal accuracy, we administered an auditory-discrimination training paradigm to a group of non-musicians to determine whether training-enhanced auditory discrimination would specifically result in improved vocal accuracy. Methodology/Principal Findings We utilized micromelodies (i.e., melodies with seven different interval scales, each smaller than a semitone) as the main stimuli for auditory discrimination training and testing, and we used single-note and melodic singing tasks to assess vocal accuracy in two groups of non-musicians (experimental and control). To determine if any training-induced improvements in vocal accuracy would be accompanied by related modulations in cortical activity during singing, the experimental group of non-musicians also performed the singing tasks while undergoing functional magnetic resonance imaging (fMRI). Following training, the experimental group exhibited significant enhancements in micromelody discrimination compared to controls. However, we did not observe a correlated improvement in vocal accuracy during single-note or melodic singing, nor did we detect any training-induced changes in activity within brain regions associated with singing. Conclusions/Significance Given the observations from our auditory training regimen, we therefore conclude that perceptual discrimination training alone is not sufficient to improve vocal accuracy in non-musicians, supporting the suggested dissociation between auditory perception and vocal production. PMID:20567521

Using UAV data for soil surface change detection at a loess field plot

NASA Astrophysics Data System (ADS)

Eltner, Anette; Baumgart, Philipp

2014-05-01

Application of unmanned aerial vehicles (UAV) denotes an increasing interest in geosciences due to major developments within the last years. Today, UAV are economical, reliable and flexible in usage. They provide a non-invasive method to measure the soil surface and its changes - e.g. due to erosion - with high resolution. Advances in digital photogrammetry and computer vision allow for fast and dense digital surface reconstruction from overlapping images. The study site is located in the Saxonian loess (Germany). The area is fragile due to erodible soils and intense agricultural utilisation. Hence, detectable soil surface changes are expected. The size of the field plot is 20 x 30 meters and the period of investigation lasts from October 2012 till July 2013 at which four surveys were performed. The UAV deployed in this study is equipped with a compact camera which is attached to an active stabilising camera mount. In addition, the micro drone integrates GPS and IMU that enables autonomous surveys with programmed flight patterns. About 100 photos are needed to cover the study site at a minimal flying height of eight metres and 65%/80% image overlap. For multi-temporal comparison a stable local reference system is established. Total station control of the signalised ground control points confirms two mm accuracy for the study period. To estimate the accuracy of the digital surface models (DSM) derived from the UAV images a comparison to DSM from terrestrial laser scanning (TLS) is conducted. The standard deviation of differences amounts five millimetres. To analyse surface changes methods from image processing are applied to the DSM. Erosion rills could be extracted for quantitative and qualitative consideration. Furthermore, volumetric changes are measured. First results indicate levelling processes during the winter season and reveal rill and inter-rill erosion during spring and summer season.

ICan: An Optimized Ion-Current-Based Quantification Procedure with Enhanced Quantitative Accuracy and Sensitivity in Biomarker Discovery

PubMed Central

2015-01-01

The rapidly expanding availability of high-resolution mass spectrometry has substantially enhanced the ion-current-based relative quantification techniques. Despite the increasing interest in ion-current-based methods, quantitative sensitivity, accuracy, and false discovery rate remain the major concerns; consequently, comprehensive evaluation and development in these regards are urgently needed. Here we describe an integrated, new procedure for data normalization and protein ratio estimation, termed ICan, for improved ion-current-based analysis of data generated by high-resolution mass spectrometry (MS). ICan achieved significantly better accuracy and precision, and lower false-positive rate for discovering altered proteins, over current popular pipelines. A spiked-in experiment was used to evaluate the performance of ICan to detect small changes. In this study E. coli extracts were spiked with moderate-abundance proteins from human plasma (MAP, enriched by IgY14-SuperMix procedure) at two different levels to set a small change of 1.5-fold. Forty-five (92%, with an average ratio of 1.71 ± 0.13) of 49 identified MAP protein (i.e., the true positives) and none of the reference proteins (1.0-fold) were determined as significantly altered proteins, with cutoff thresholds of ≥1.3-fold change and p ≤ 0.05. This is the first study to evaluate and prove competitive performance of the ion-current-based approach for assigning significance to proteins with small changes. By comparison, other methods showed remarkably inferior performance. ICan can be broadly applicable to reliable and sensitive proteomic survey of multiple biological samples with the use of high-resolution MS. Moreover, many key features evaluated and optimized here such as normalization, protein ratio determination, and statistical analyses are also valuable for data analysis by isotope-labeling methods. PMID:25285707

Development and validation of a dual sensing scheme to improve accuracy of bradycardia and pause detection in an insertable cardiac monitor.

PubMed

Passman, Rod S; Rogers, John D; Sarkar, Shantanu; Reiland, Jerry; Reisfeld, Erin; Koehler, Jodi; Mittal, Suneet

2017-07-01

Undersensing of premature ventricular beats and low-amplitude R waves are primary causes for inappropriate bradycardia and pause detections in insertable cardiac monitors (ICMs). The purpose of this study was to develop and validate an enhanced algorithm to reduce inappropriately detected bradycardia and pause episodes. Independent data sets to develop and validate the enhanced algorithm were derived from a database of ICM-detected bradycardia and pause episodes in de-identified patients monitored for unexplained syncope. The original algorithm uses an auto-adjusting sensitivity threshold for R-wave sensing to detect tachycardia and avoid T-wave oversensing. In the enhanced algorithm, a second sensing threshold is used with a long blanking and fixed lower sensitivity threshold, looking for evidence of undersensed signals. Data reported includes percent change in appropriate and inappropriate bradycardia and pause detections as well as changes in episode detection sensitivity and positive predictive value with the enhanced algorithm. The validation data set, from 663 consecutive patients, consisted of 4904 (161 patients) bradycardia and 2582 (133 patients) pause episodes, of which 2976 (61%) and 996 (39%) were appropriately detected bradycardia and pause episodes. The enhanced algorithm reduced inappropriate bradycardia and pause episodes by 95% and 47%, respectively, with 1.7% and 0.6% reduction in appropriate episodes, respectively. The average episode positive predictive value improved by 62% (P < .001) for bradycardia detection and by 26% (P < .001) for pause detection, with an average relative sensitivity of 95% (P < .001) and 99% (P = .5), respectively. The enhanced dual sense algorithm for bradycardia and pause detection in ICMs substantially reduced inappropriate episode detection with a minimal reduction in true episode detection. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Remote sensing of a dynamic sub-arctic peatland reservoir using optical and synthetic aperture radar data

NASA Astrophysics Data System (ADS)

Larter, Jarod Lee

Stephens Lake, Manitoba is an example of a peatland reservoir that has undergone physical changes related to mineral erosion and peatland disintegration processes since its initial impoundment. In this thesis I focused on the processes of peatland upheaval, transport, and disintegration as the primary drivers of dynamic change within the reservoir. The changes related to these processes are most frequent after initial reservoir impoundment and decline over time. They continue to occur over 35 years after initial flooding. I developed a remote sensing approach that employs both optical and microwave sensors for discriminating land (Le. floating peatlands, forested land, and barren land) from open water within the reservoir. High spatial resolution visible and near-infrared (VNIR) optical data obtained from the QuickBird satellite, and synthetic aperture radar (SAR) microwave data obtained from the RADARSAT-1 satellite were implemented. The approach was facilitated with a Geographic Information System (GIS) based validation map for the extraction of optical and SAR pixel data. Each sensor's extracted data set was first analyzed separately using univariate and multivariate statistical methods to determine the discriminant ability of each sensor. The initial analyses were followed by an integrated sensor approach; the development of an image classification model; and a change detection analysis. Results showed excellent (> 95%) classification accuracy using QuickBird satellite image data. Discrimination and classification of studied land cover classes using SAR image texture data resulted in lower overall classification accuracies (˜ 60%). SAR data classification accuracy improved to > 90% when classifying only land and water, demonstrating SAR's utility as a land and water mapping tool. An integrated sensor data approach showed no considerable improvement over the use of optical satellite image data alone. An image classification model was developed that could be used to map both detailed land cover classes and the land and water interface within the reservoir. Change detection analysis over a seven year period indicated that physical changes related to mineral erosion, peatland upheaval, transport, and disintegration, and operational water level variation continue to take place in the reservoir some 35 years after initial flooding. This thesis demonstrates the ability of optical and SAR satellite image remote sensing data sets to be used in an operational context for the routine discrimination of the land and water boundaries within a dynamic peatland reservoir. Future monitoring programs would benefit most from a complementary image acquisition program in which SAR images, known for their acquisition reliability under cloud cover, are acquired along with optical images given their ability to discriminate land cover classes in greater detail.

Integrating Radarsat-2, Lidar, and Worldview-3 Imagery to maximize detection of forested inundation extent in the Delmarva Peninsula, USA

USGS Publications Warehouse

Vanderhoof, Melanie; Distler, Hayley; Mendiola, Di Ana; Lang, Megan

2017-01-01

Natural variability in surface-water extent and associated characteristics presents a challenge to gathering timely, accurate information, particularly in environments that are dominated by small and/or forested wetlands. This study mapped inundation extent across the Upper Choptank River Watershed on the Delmarva Peninsula, occurring within both Maryland and Delaware. We integrated six quad-polarized Radarsat-2 images, Worldview-3 imagery, and an enhanced topographic wetness index in a random forest model. Output maps were filtered using light detection and ranging (lidar)-derived depressions to maximize the accuracy of forested inundation extent. Overall accuracy within the integrated and filtered model was 94.3%, with 5.5% and 6.0% errors of omission and commission for inundation, respectively. Accuracy of inundation maps obtained using Radarsat-2 alone were likely detrimentally affected by less than ideal angles of incidence and recent precipitation, but were likely improved by targeting the period between snowmelt and leaf-out for imagery collection. Across the six Radarsat-2 dates, filtering inundation outputs by lidar-derived depressions slightly elevated errors of omission for water (+1.0%), but decreased errors of commission (−7.8%), resulting in an average increase of 5.4% in overall accuracy. Depressions were derived from lidar datasets collected under both dry and average wetness conditions. Although antecedent wetness conditions influenced the abundance and total area mapped as depression, the two versions of the depression datasets showed a similar ability to reduce error in the inundation maps. Accurate mapping of surface water is critical to predicting and monitoring the effect of human-induced change and interannual variability on water quantity and quality.

A Systematic Review and Meta-analysis of the Diagnostic Accuracy of Prostate Health Index and 4-Kallikrein Panel Score in Predicting Overall and High-grade Prostate Cancer.

PubMed

Russo, Giorgio Ivan; Regis, Federica; Castelli, Tommaso; Favilla, Vincenzo; Privitera, Salvatore; Giardina, Raimondo; Cimino, Sebastiano; Morgia, Giuseppe

2017-08-01

Markers for prostate cancer (PCa) have progressed over recent years. In particular, the prostate health index (PHI) and the 4-kallikrein (4K) panel have been demonstrated to improve the diagnosis of PCa. We aimed to review the diagnostic accuracy of PHI and the 4K panel for PCa detection. We performed a systematic literature search of PubMed, EMBASE, Cochrane, and Academic One File databases until July 2016. We included diagnostic accuracy studies that used PHI or 4K panel for the diagnosis of PCa or high-grade PCa. The methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Twenty-eight studies including 16,762 patients have been included for the analysis. The pooled data showed a sensitivity of 0.89 and 0.74 for PHI and 4K panel, respectively, for PCa detection and a pooled specificity of 0.34 and 0.60 for PHI and 4K panel, respectively. The derived area under the curve (AUC) from the hierarchical summary receiver operating characteristic (HSROC) showed an accuracy of 0.76 and 0.72 for PHI and 4K panel respectively. For high-grade PCa detection, the pooled sensitivity was 0.93 and 0.87 for PHI and 4K panel, respectively, whereas the pooled specificity was 0.34 and 0.61 for PHI and 4K panel, respectively. The derived AUC from the HSROC showed an accuracy of 0.82 and 0.81 for PHI and 4K panel, respectively. Both PHI and the 4K panel provided good diagnostic accuracy in detecting overall and high-grade PCa. Copyright © 2016 Elsevier Inc. All rights reserved.

The Effect of Study Design Biases on the Diagnostic Accuracy of Magnetic Resonance Imaging to Detect Silicone Breast Implant Ruptures: A Meta-Analysis

PubMed Central

Song, Jae W.; Kim, Hyungjin Myra; Bellfi, Lillian T.; Chung, Kevin C.

2010-01-01

Background All silicone breast implant recipients are recommended by the US Food and Drug Administration to undergo serial screening to detect implant rupture with magnetic resonance imaging (MRI). We performed a systematic review of the literature to assess the quality of diagnostic accuracy studies utilizing MRI or ultrasound to detect silicone breast implant rupture and conducted a meta-analysis to examine the effect of study design biases on the estimation of MRI diagnostic accuracy measures. Method Studies investigating the diagnostic accuracy of MRI and ultrasound in evaluating ruptured silicone breast implants were identified using MEDLINE, EMBASE, ISI Web of Science, and Cochrane library databases. Two reviewers independently screened potential studies for inclusion and extracted data. Study design biases were assessed using the QUADAS tool and the STARDS checklist. Meta-analyses estimated the influence of biases on diagnostic odds ratios. Results Among 1175 identified articles, 21 met the inclusion criteria. Most studies using MRI (n= 10 of 16) and ultrasound (n=10 of 13) examined symptomatic subjects. Meta-analyses revealed that MRI studies evaluating symptomatic subjects had 14-fold higher diagnostic accuracy estimates compared to studies using an asymptomatic sample (RDOR 13.8; 95% CI 1.83–104.6) and 2-fold higher diagnostic accuracy estimates compared to studies using a screening sample (RDOR 1.89; 95% CI 0.05–75.7). Conclusion Many of the published studies utilizing MRI or ultrasound to detect silicone breast implant rupture are flawed with methodological biases. These methodological shortcomings may result in overestimated MRI diagnostic accuracy measures and should be interpreted with caution when applying the data to a screening population. PMID:21364405

Comparison of digital tomosynthesis and chest radiography for the detection of pulmonary nodules: systematic review and meta-analysis

PubMed Central

Kim, Jun H; Lee, Kyung H; Kim, Kyoung-Tae; Ahn, Hyeong S; Kim, Yeo J; Lee, Ha Y; Jeon, Yong S

2016-01-01

Objective: To compare the diagnostic accuracy of digital tomosynthesis (DTS) with that of chest radiography for the detection of pulmonary nodules by meta-analysis. Methods: A systematic literature search was performed to identify relevant original studies from 1 January 1 1976 to 31 August 31 2016. The quality of included studies was assessed by quality assessment of diagnostic accuracy studies-2. Per-patient data were used to calculate the sensitivity and specificity and per-lesion data were used to calculate the detection rate. Summary receiver-operating characteristic curves were drawn for pulmonary nodule detection. Results: 16 studies met the inclusion criteria. 1017 patients on a per-patient basis and 2159 lesions on a per-lesion basis from 16 eligible studies were evaluated. The pooled patient-based sensitivity of DTS was 0.85 [95% confidence interval (CI) 0.83–0.88] and the specificity was 0.95 (0.93–0.96). The pooled sensitivity and specificity of chest radiography were 0.47 (0.44–0.51) and 0.37 (0.34–0.40), respectively. The per-lesion detection rate was 2.90 (95% CI 2.63–3.19). Conclusion: DTS has higher diagnostic accuracy than chest radiography for detection of pulmonary nodules. Chest radiography has low sensitivity but similar specificity, comparable with that of DTS. Advances in knowledge: DTS has higher diagnostic accuracy than chest radiography for the detection of pulmonary nodules. PMID:27759428

Unexploded Ordnance Characterization And Detection in Muddy Estuarine Environments

NASA Astrophysics Data System (ADS)

Trembanis, A. C.; DuVal, C.

2017-12-01

There is recognized need for better quantitative understanding of the impact of coastal environments on UXO mobility, burial, and detection. Current efforts are underway to address aspects of UXO mobility and detection in sandy coastal areas. However, a significant data gap has been identified regarding UXO in shallow, muddy environments; 139 Formally Used Defense Sites (FUDS), in U.S. tidal waters alone, have been identified as containing muddy sediments. This study works to address this data gap. Using a shallow estuarine site in the Delaware Bay, this study 1) monitors the mobility and behavior of sensor-integrated surrogate munitions in muddy environments using a high-accuracy acoustic positioning system, 2) directly observes surrogate munition response to hydrodynamic forcing through instrumented bottom frame time-lapse hydrodynamic data and sonar imagery, and 3) monitors site changes through repetitive site surveying autonomous underwater vehicle (AUV) using both sonar and magnetometry. Surrogate UXO, modified with acoustic tracking devices and inertial motion units (IMU), are being deployed at a previously characterized muddy estuarine site. The surrogates are being monitored for changes in mobility and burial using the VEMCO positioning system, an off-the-shelf acoustic positioning system that is capable of tracking the position of multiple acoustic tags with accuracies down to 10 cm. Concurrently, time-series acoustic imagery and hydrodynamic sensors are being deployed to characterize UXO response to varied hydrodynamic conditions and compared to site-wide surrogate behavior. A series of repetitive surveys are being conducted using a magnetometer specifically designed for UXO detection on an autonomous underwater vehicle (AUV). Survey results will be compared to long-term acoustic positioning of the surrogate UXO to determine the effectiveness of the magnetometer for efficiently and effectively locating UXO in shallow, muddy environments. Additionally, this study will help inform parameters for UXO mobility and behavior in storms and muddy environments for integration into existing expert system models of UXO burial and mobility.

Individual differences in functional connectivity during naturalistic viewing conditions.

PubMed

Vanderwal, Tamara; Eilbott, Jeffrey; Finn, Emily S; Craddock, R Cameron; Turnbull, Adam; Castellanos, F Xavier

2017-08-15

Naturalistic viewing paradigms such as movies have been shown to reduce participant head motion and improve arousal during fMRI scanning relative to task-free rest, and have been used to study both functional connectivity and stimulus-evoked BOLD-signal changes. These task-based hemodynamic changes are synchronized across subjects and involve large areas of the cortex, and it is unclear whether individual differences in functional connectivity are enhanced or diminished under such naturalistic conditions. This work first aims to characterize variability in BOLD-signal based functional connectivity (FC) across 2 distinct movie conditions and eyes-open rest (n=31 healthy adults, 2 scan sessions each). We found that movies have higher within- and between-subject correlations in cluster-wise FC relative to rest. The anatomical distribution of inter-individual variability was similar across conditions, with higher variability occurring at the lateral prefrontal lobes and temporoparietal junctions. Second, we used an unsupervised test-retest matching algorithm that identifies individual subjects from within a group based on FC patterns, quantifying the accuracy of the algorithm across the three conditions. The movies and resting state all enabled identification of individual subjects based on FC matrices, with accuracies between 61% and 100%. Overall, pairings involving movies outperformed rest, and the social, faster-paced movie attained 100% accuracy. When the parcellation resolution, scan duration, and number of edges used were increased, accuracies improved across conditions, and the pattern of movies>rest was preserved. These results suggest that using dynamic stimuli such as movies enhances the detection of FC patterns that are unique at the individual level. Copyright © 2017 Elsevier Inc. All rights reserved.

Automatic cardiac cycle determination directly from EEG-fMRI data by multi-scale peak detection method.

PubMed

Wong, Chung-Ki; Luo, Qingfei; Zotev, Vadim; Phillips, Raquel; Chan, Kam Wai Clifford; Bodurka, Jerzy

2018-03-31

In simultaneous EEG-fMRI, identification of the period of cardioballistic artifact (BCG) in EEG is required for the artifact removal. Recording the electrocardiogram (ECG) waveform during fMRI is difficult, often causing inaccurate period detection. Since the waveform of the BCG extracted by independent component analysis (ICA) is relatively invariable compared to the ECG waveform, we propose a multiple-scale peak-detection algorithm to determine the BCG cycle directly from the EEG data. The algorithm first extracts the high contrast BCG component from the EEG data by ICA. The BCG cycle is then estimated by band-pass filtering the component around the fundamental frequency identified from its energy spectral density, and the peak of BCG artifact occurrence is selected from each of the estimated cycle. The algorithm is shown to achieve a high accuracy on a large EEG-fMRI dataset. It is also adaptive to various heart rates without the needs of adjusting the threshold parameters. The cycle detection remains accurate with the scan duration reduced to half a minute. Additionally, the algorithm gives a figure of merit to evaluate the reliability of the detection accuracy. The algorithm is shown to give a higher detection accuracy than the commonly used cycle detection algorithm fmrib_qrsdetect implemented in EEGLAB. The achieved high cycle detection accuracy of our algorithm without using the ECG waveforms makes possible to create and automate pipelines for processing large EEG-fMRI datasets, and virtually eliminates the need for ECG recordings for BCG artifact removal. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images

PubMed Central

Bi, Fukun; Chen, Jing; Zhuang, Yin; Bian, Mingming; Zhang, Qingjun

2017-01-01

With the rapid development of optical remote sensing satellites, ship detection and identification based on large-scale remote sensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance and maritime management. However, because the harbor environment is complex, gray information and texture features between docked ships and their connected dock regions are indistinguishable, most of the popular detection methods are limited by their calculation efficiency and detection accuracy. In this paper, a novel hierarchical method that combines an efficient candidate scanning strategy and an accurate candidate identification mixture model is presented for inshore ship detection in complex harbor areas. First, in the candidate region extraction phase, an omnidirectional intersected two-dimension scanning (OITDS) strategy is designed to rapidly extract candidate regions from the land-water segmented images. In the candidate region identification phase, a decision mixture model (DMM) is proposed to identify real ships from candidate objects. Specifically, to improve the robustness regarding the diversity of ships, a deformable part model (DPM) was employed to train a key part sub-model and a whole ship sub-model. Furthermore, to improve the identification accuracy, a surrounding correlation context sub-model is built. Finally, to increase the accuracy of candidate region identification, these three sub-models are integrated into the proposed DMM. Experiments were performed on numerous large-scale harbor remote sensing images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency. PMID:28640236

A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images.

PubMed

Bi, Fukun; Chen, Jing; Zhuang, Yin; Bian, Mingming; Zhang, Qingjun

2017-06-22

With the rapid development of optical remote sensing satellites, ship detection and identification based on large-scale remote sensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance and maritime management. However, because the harbor environment is complex, gray information and texture features between docked ships and their connected dock regions are indistinguishable, most of the popular detection methods are limited by their calculation efficiency and detection accuracy. In this paper, a novel hierarchical method that combines an efficient candidate scanning strategy and an accurate candidate identification mixture model is presented for inshore ship detection in complex harbor areas. First, in the candidate region extraction phase, an omnidirectional intersected two-dimension scanning (OITDS) strategy is designed to rapidly extract candidate regions from the land-water segmented images. In the candidate region identification phase, a decision mixture model (DMM) is proposed to identify real ships from candidate objects. Specifically, to improve the robustness regarding the diversity of ships, a deformable part model (DPM) was employed to train a key part sub-model and a whole ship sub-model. Furthermore, to improve the identification accuracy, a surrounding correlation context sub-model is built. Finally, to increase the accuracy of candidate region identification, these three sub-models are integrated into the proposed DMM. Experiments were performed on numerous large-scale harbor remote sensing images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency.

Monitoring endemic livestock diseases using laboratory diagnostic data: A simulation study to evaluate the performance of univariate process monitoring control algorithms.

PubMed

Lopes Antunes, Ana Carolina; Dórea, Fernanda; Halasa, Tariq; Toft, Nils

2016-05-01

Surveillance systems are critical for accurate, timely monitoring and effective disease control. In this study, we investigated the performance of univariate process monitoring control algorithms in detecting changes in seroprevalence for endemic diseases. We also assessed the effect of sample size (number of sentinel herds tested in the surveillance system) on the performance of the algorithms. Three univariate process monitoring control algorithms were compared: Shewart p Chart(1) (PSHEW), Cumulative Sum(2) (CUSUM) and Exponentially Weighted Moving Average(3) (EWMA). Increases in seroprevalence were simulated from 0.10 to 0.15 and 0.20 over 4, 8, 24, 52 and 104 weeks. Each epidemic scenario was run with 2000 iterations. The cumulative sensitivity(4) (CumSe) and timeliness were used to evaluate the algorithms' performance with a 1% false alarm rate. Using these performance evaluation criteria, it was possible to assess the accuracy and timeliness of the surveillance system working in real-time. The results showed that EWMA and PSHEW had higher CumSe (when compared with the CUSUM) from week 1 until the end of the period for all simulated scenarios. Changes in seroprevalence from 0.10 to 0.20 were more easily detected (higher CumSe) than changes from 0.10 to 0.15 for all three algorithms. Similar results were found with EWMA and PSHEW, based on the median time to detection. Changes in the seroprevalence were detected later with CUSUM, compared to EWMA and PSHEW for the different scenarios. Increasing the sample size 10 fold halved the time to detection (CumSe=1), whereas increasing the sample size 100 fold reduced the time to detection by a factor of 6. This study investigated the performance of three univariate process monitoring control algorithms in monitoring endemic diseases. It was shown that automated systems based on these detection methods identified changes in seroprevalence at different times. Increasing the number of tested herds would lead to faster detection. However, the practical implications of increasing the sample size (such as the costs associated with the disease) should also be taken into account. Copyright © 2016 Elsevier B.V. All rights reserved.

Classification Accuracy of MMPI-2 Validity Scales in the Detection of Pain-Related Malingering: A Known-Groups Study

ERIC Educational Resources Information Center

Bianchini, Kevin J.; Etherton, Joseph L.; Greve, Kevin W.; Heinly, Matthew T.; Meyers, John E.

2008-01-01

The purpose of this study was to determine the accuracy of "Minnesota Multiphasic Personality Inventory" 2nd edition (MMPI-2; Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) validity indicators in the detection of malingering in clinical patients with chronic pain using a hybrid clinical-known groups/simulator design. The…

Applying Signal-Detection Theory to the Study of Observer Accuracy and Bias in Behavioral Assessment

ERIC Educational Resources Information Center

Lerman, Dorothea C.; Tetreault, Allison; Hovanetz, Alyson; Bellaci, Emily; Miller, Jonathan; Karp, Hilary; Mahmood, Angela; Strobel, Maggie; Mullen, Shelley; Keyl, Alice; Toupard, Alexis

2010-01-01

We evaluated the feasibility and utility of a laboratory model for examining observer accuracy within the framework of signal-detection theory (SDT). Sixty-one individuals collected data on aggression while viewing videotaped segments of simulated teacher-child interactions. The purpose of Experiment 1 was to determine if brief feedback and…

No Special K! A Signal Detection Framework for the Strategic Regulation of Memory Accuracy

ERIC Educational Resources Information Center

Higham, Philip A.

2007-01-01

Two experiments investigated criterion setting and metacognitive processes underlying the strategic regulation of accuracy on the Scholastic Aptitude Test (SAT) using Type-2 signal detection theory (SDT). In Experiment 1, report bias was manipulated by penalizing participants either 0.25 (low incentive) or 4 (high incentive) points for each error.…

Bi-temporal analysis of landscape changes in the easternmost mediterranean deltas using binary and classified change information.

PubMed

Alphan, Hakan

2013-03-01

The aim of this study is (1) to quantify landscape changes in the easternmost Mediterranean deltas using bi-temporal binary change detection approach and (2) to analyze relationships between conservation/management designations and various categories of change that indicate type, degree and severity of human impact. For this purpose, image differencing and ratioing were applied to Landsat TM images of 1984 and 2006. A total of 136 candidate change images including normalized difference vegetation index (NDVI) and principal component analysis (PCA) difference images were tested to understand performance of bi-temporal pre-classification analysis procedures in the Mediterranean delta ecosystems. Results showed that visible image algebra provided high accuracies than did NDVI and PCA differencing. On the other hand, Band 5 differencing had one of the lowest change detection performances. Seven superclasses of change were identified using from/to change categories between the earlier and later dates. These classes were used to understand spatial character of anthropogenic impacts in the study area and derive qualitative and quantitative change information within and outside of the conservation/management areas. Change analysis indicated that natural site and wildlife reserve designations fell short of protecting sand dunes from agricultural expansion in the west. East of the study area, however, was exposed to least human impact owing to the fact that nature conservation status kept human interference at a minimum. Implications of these changes were discussed and solutions were proposed to deal with management problems leading to environmental change.

An affordable and easy-to-use diagnostic method for keratoconus detection using a smartphone

NASA Astrophysics Data System (ADS)

Askarian, Behnam; Tabei, Fatemehsadat; Askarian, Amin; Chong, Jo Woon

2018-02-01

Recently, smartphones are used for disease diagnosis and healthcare. In this paper, we propose a novel affordable diagnostic method of detecting keratoconus using a smartphone. Keratoconus is usually detected in clinics with ophthalmic devices, which are large, expensive and not portable, and need to be operated by trained technicians. However, our proposed smartphone-based eye disease detection method is small, affordable, portable, and it can be operated by patients in a convenient way. The results show that the proposed keratoconus detection method detects severe, advanced, and moderate keratoconus with accuracies of 93%, 86%, 67%, respectively. Due to its convenience with these accuracies, the proposed keratoconus detection method is expected to be applied in detecting keratoconus at an earlier stage in an affordable way.

A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG

PubMed Central

Chen, Duo; Wan, Suiren; Xiang, Jing; Bao, Forrest Sheng

2017-01-01

In the past decade, Discrete Wavelet Transform (DWT), a powerful time-frequency tool, has been widely used in computer-aided signal analysis of epileptic electroencephalography (EEG), such as the detection of seizures. One of the important hurdles in the applications of DWT is the settings of DWT, which are chosen empirically or arbitrarily in previous works. The objective of this study aimed to develop a framework for automatically searching the optimal DWT settings to improve accuracy and to reduce computational cost of seizure detection. To address this, we developed a method to decompose EEG data into 7 commonly used wavelet families, to the maximum theoretical level of each mother wavelet. Wavelets and decomposition levels providing the highest accuracy in each wavelet family were then searched in an exhaustive selection of frequency bands, which showed optimal accuracy and low computational cost. The selection of frequency bands and features removed approximately 40% of redundancies. The developed algorithm achieved promising performance on two well-tested EEG datasets (accuracy >90% for both datasets). The experimental results of the developed method have demonstrated that the settings of DWT affect its performance on seizure detection substantially. Compared with existing seizure detection methods based on wavelet, the new approach is more accurate and transferable among datasets. PMID:28278203

An incremental knowledge assimilation system (IKAS) for mine detection

NASA Astrophysics Data System (ADS)

Porway, Jake; Raju, Chaitanya; Varadarajan, Karthik Mahesh; Nguyen, Hieu; Yadegar, Joseph

2010-04-01

In this paper we present an adaptive incremental learning system for underwater mine detection and classification that utilizes statistical models of seabed texture and an adaptive nearest-neighbor classifier to identify varied underwater targets in many different environments. The first stage of processing uses our Background Adaptive ANomaly detector (BAAN), which identifies statistically likely target regions using Gabor filter responses over the image. Using this information, BAAN classifies the background type and updates its detection using background-specific parameters. To perform classification, a Fully Adaptive Nearest Neighbor (FAAN) determines the best label for each detection. FAAN uses an extremely fast version of Nearest Neighbor to find the most likely label for the target. The classifier perpetually assimilates new and relevant information into its existing knowledge database in an incremental fashion, allowing improved classification accuracy and capturing concept drift in the target classes. Experiments show that the system achieves >90% classification accuracy on underwater mine detection tasks performed on synthesized datasets provided by the Office of Naval Research. We have also demonstrated that the system can incrementally improve its detection accuracy by constantly learning from new samples.

Gear Tooth Wear Detection Algorithm

NASA Technical Reports Server (NTRS)

Delgado, Irebert R.

2015-01-01

Vibration-based condition indicators continue to be developed for Health Usage Monitoring of rotorcraft gearboxes. Testing performed at NASA Glenn Research Center have shown correlations between specific condition indicators and specific types of gear wear. To speed up the detection and analysis of gear teeth, an image detection program based on the Viola-Jones algorithm was trained to automatically detect spiral bevel gear wear pitting. The detector was tested using a training set of gear wear pictures and a blind set of gear wear pictures. The detector accuracy for the training set was 75 percent while the accuracy for the blind set was 15 percent. Further improvements on the accuracy of the detector are required but preliminary results have shown its ability to automatically detect gear tooth wear. The trained detector would be used to quickly evaluate a set of gear or pinion pictures for pits, spalls, or abrasive wear. The results could then be used to correlate with vibration or oil debris data. In general, the program could be retrained to detect features of interest from pictures of a component taken over a period of time.

Surface regions of illusory images are detected with a slower processing speed than those of luminance-defined images.

PubMed

Mihaylova, Milena; Manahilov, Velitchko

2010-11-24

Research has shown that the processing time for discriminating illusory contours is longer than for real contours. We know, however, little whether the visual processes, associated with detecting regions of illusory surfaces, are also slower as those responsible for detecting luminance-defined images. Using a speed-accuracy trade-off (SAT) procedure, we measured accuracy as a function of processing time for detecting illusory Kanizsa-type and luminance-defined squares embedded in 2D static luminance noise. The data revealed that the illusory images were detected at slower processing speed than the real images, while the points in time, when accuracy departed from chance, were not significantly different for both stimuli. The classification images for detecting illusory and real squares showed that observers employed similar detection strategies using surface regions of the real and illusory squares. The lack of significant differences between the x-intercepts of the SAT functions for illusory and luminance-modulated stimuli suggests that the detection of surface regions of both images could be based on activation of a single mechanism (the dorsal magnocellular visual pathway). The slower speed for detecting illusory images as compared to luminance-defined images could be attributed to slower processes of filling-in of regions of illusory images within the dorsal pathway.

Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection.

PubMed

Li, Baopu; Meng, Max Q-H

2012-05-01

Tumor in digestive tract is a common disease and wireless capsule endoscopy (WCE) is a relatively new technology to examine diseases for digestive tract especially for small intestine. This paper addresses the problem of automatic recognition of tumor for WCE images. Candidate color texture feature that integrates uniform local binary pattern and wavelet is proposed to characterize WCE images. The proposed features are invariant to illumination change and describe multiresolution characteristics of WCE images. Two feature selection approaches based on support vector machine, sequential forward floating selection and recursive feature elimination, are further employed to refine the proposed features for improving the detection accuracy. Extensive experiments validate that the proposed computer-aided diagnosis system achieves a promising tumor recognition accuracy of 92.4% in WCE images on our collected data.

The Shuttle Orbital Maneuvering System P-V-T Propellant Quantity Gaging Accuracy and Leak Detection Allowance for Four Instrumentation Conditions

NASA Technical Reports Server (NTRS)

Duhon, D. D.

1975-01-01

The shuttle orbital maneuvering system (OMS) pressure-volume-temperature (P-V-T) propellant gaging module computes the quantity of usable OMS propellant remaining based on the real gas P-V-T relationship for the propellant tank pressurant, helium. The OMS P-V-T propellant quantity gaging error was determined for four sets of instrumentation configurations and accuracies with the propellant tank operating in the normal constant pressure mode and in the blowdown mode. The instrumentation inaccuracy allowance for propellant leak detection was also computed for these same four sets of instrumentation. These gaging errors and leak detection allowances are presented in tables designed to permit a direct comparison of the effectiveness of the four instrumentation sets. The results show the magnitudes of the improvements in propellant quantity gaging accuracies and propellant leak detection allowances which can be achieved by employing more accurate pressure and temperature instrumentation.

Monitoring gully change: A comparison of airborne and terrestrial laser scanning using a case study from Aratula, Queensland

NASA Astrophysics Data System (ADS)

Goodwin, Nicholas R.; Armston, John D.; Muir, Jasmine; Stiller, Issac

2017-04-01

Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) technologies capture spatially detailed estimates of surface topography and when collected multi-temporally can be used to assess geomorphic change. The sensitivity and repeatability of ALS measurements to characterise geomorphic change in topographically complex environments such as gullies; however, remains an area lacking quantitative research. In this study, we captured coincident ALS and TLS datasets to assess their ability and synergies to detect geomorphic change for a gully located in Aratula, southeast Queensland, Australia. We initially used the higher spatial density and ranging accuracy of TLS to provide an assessment of the Digital Elevation Models (DEM) derived from ALS within a gully environment. Results indicated mean residual errors of 0.13 and 0.09 m along with standard deviation (SD) of residual errors of 0.20 and 0.16 m using pixel sizes of 0.5 and 1.0 m, respectively. The positive mean residual errors confirm that TLS data consistently detected deeper sections of the gully than ALS. We also compared the repeatability of ALS and TLS for characterising gully morphology. This indicated that the sensitivity to detect change using ALS is substantially lower than TLS, as expected, and that the ALS survey characteristics influence the ability to detect change. Notably, we found that using one ALS transect (mean density of 5 points / m2) as opposed to three transects increased the SD of residual error by approximately 30%. The supplied classification of ALS ground points was also demonstrated to misclassify gully features as non-ground, with minimum elevation filtering found to provide a more accurate DEM of the gully. The number and placement of terrestrial laser scans were also found to influence the derived DEMs. Furthermore, we applied change detection using two ALS data captures over a four year period and four TLS field surveys over an eight month period. This demonstrated that ALS can detect large scale erosional changes with head cutting of gully branches migrating approximately 10 m upslope. In comparison, TLS captured smaller scale intra-annual erosional patterns largely undetectable by the ALS dataset with a large rainfall event coinciding with the highest volumetric change (net change > 46 m3). Overall, these findings reaffirm the importance of quantifying DEM errors and demonstrate that ALS is unlikely to detect subtle geomorphic changes (< 0.45 m) potentially missing significant sediment change. TLS was able to detect more subtle intra-annual changes but was limited in its spatial coverage. This suggests TLS and ALS surveys are complementary technologies and when used together can provide a more detailed understanding of gully processes at different temporal and spatial scales, provided the inherent errors are taken into account.

Gravitational waveforms for neutron star binaries from binary black hole simulations

NASA Astrophysics Data System (ADS)

Barkett, Kevin; Scheel, Mark; Haas, Roland; Ott, Christian; Bernuzzi, Sebastiano; Brown, Duncan; Szilagyi, Bela; Kaplan, Jeffrey; Lippuner, Jonas; Muhlberger, Curran; Foucart, Francois; Duez, Matthew

2016-03-01

Gravitational waves from binary neutron star (BNS) and black-hole/neutron star (BHNS) inspirals are primary sources for detection by the Advanced Laser Interferometer Gravitational-Wave Observatory. The tidal forces acting on the neutron stars induce changes in the phase evolution of the gravitational waveform, and these changes can be used to constrain the nuclear equation of state. Current methods of generating BNS and BHNS waveforms rely on either computationally challenging full 3D hydrodynamical simulations or approximate analytic solutions. We introduce a new method for computing inspiral waveforms for BNS/BHNS systems by adding the post-Newtonian (PN) tidal effects to full numerical simulations of binary black holes (BBHs), effectively replacing the non-tidal terms in the PN expansion with BBH results. Comparing a waveform generated with this method against a full hydrodynamical simulation of a BNS inspiral yields a phase difference of < 1 radian over ~ 15 orbits. The numerical phase accuracy required of BNS simulations to measure the accuracy of the method we present here is estimated as a function of the tidal deformability parameter λ.

Gravitational waveforms for neutron star binaries from binary black hole simulations

NASA Astrophysics Data System (ADS)

Barkett, Kevin; Scheel, Mark A.; Haas, Roland; Ott, Christian D.; Bernuzzi, Sebastiano; Brown, Duncan A.; Szilágyi, Béla; Kaplan, Jeffrey D.; Lippuner, Jonas; Muhlberger, Curran D.; Foucart, Francois; Duez, Matthew D.

2016-02-01

Gravitational waves from binary neutron star (BNS) and black hole/neutron star (BHNS) inspirals are primary sources for detection by the Advanced Laser Interferometer Gravitational-Wave Observatory. The tidal forces acting on the neutron stars induce changes in the phase evolution of the gravitational waveform, and these changes can be used to constrain the nuclear equation of state. Current methods of generating BNS and BHNS waveforms rely on either computationally challenging full 3D hydrodynamical simulations or approximate analytic solutions. We introduce a new method for computing inspiral waveforms for BNS/BHNS systems by adding the post-Newtonian (PN) tidal effects to full numerical simulations of binary black holes (BBHs), effectively replacing the nontidal terms in the PN expansion with BBH results. Comparing a waveform generated with this method against a full hydrodynamical simulation of a BNS inspiral yields a phase difference of <1 radian over ˜15 orbits. The numerical phase accuracy required of BNS simulations to measure the accuracy of the method we present here is estimated as a function of the tidal deformability parameter λ .

Simple apparatus for polarization sensing of analytes

NASA Astrophysics Data System (ADS)

Gryczynski, Zygmunt; Gryczynski, Ignacy; Lakowicz, Joseph R.

2000-09-01

We describe a simple device for fluorescence sensing based on an unexpansive light source, a dual photocell and a Watson bridge. The emission is detected from two fluorescent samples, one of which changes intensity in response to the analyte. The emission from these two samples is observed through two orthogonally oriented polarizers and an analyzer polarizer. The latter polarizer is rotated to yield equal intensities from both sides of the dual photocell, as determined by a zero voltage from the Watson bridge. Using this device, we are able to measure fluorescein concentration to an accuracy near 2% at 1 (mu) M fluorescein, and pH values accurate to +/- 0.02 pH units. We also use this approach with a UV hand lamp and a glucose-sensitive protein to measure glucose concentrations near 2 (mu) M to an accuracy of +/- 0.1 (mu) M. This approach requires only simple electronics, which can be battery powered. Additionally, the method is generic, and can be applied with any fluorescent sample that displays a change in intensity. One can imagine this approach being used to develop portable point-of-care clinical devices.

Heart energy signature spectrogram for cardiovascular diagnosis

PubMed Central

Kudriavtsev, Vladimir; Polyshchuk, Vladimir; Roy, Douglas L

2007-01-01

A new method and application is proposed to characterize intensity and pitch of human heart sounds and murmurs. Using recorded heart sounds from the library of one of the authors, a visual map of heart sound energy was established. Both normal and abnormal heart sound recordings were studied. Representation is based on Wigner-Ville joint time-frequency transformations. The proposed methodology separates acoustic contributions of cardiac events simultaneously in pitch, time and energy. The resolution accuracy is superior to any other existing spectrogram method. The characteristic energy signature of the innocent heart murmur in a child with the S3 sound is presented. It allows clear detection of S1, S2 and S3 sounds, S2 split, systolic murmur, and intensity of these components. The original signal, heart sound power change with time, time-averaged frequency, energy density spectra and instantaneous variations of power and frequency/pitch with time, are presented. These data allow full quantitative characterization of heart sounds and murmurs. High accuracy in both time and pitch resolution is demonstrated. Resulting visual images have self-referencing quality, whereby individual features and their changes become immediately obvious. PMID:17480232

Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging

NASA Astrophysics Data System (ADS)

Litkey, P.; Nurminen, K.; Honkavaara, E.

2013-05-01

The risks of storms that cause damage in forests are increasing due to climate change. Quickly detecting fallen trees, assessing the amount of fallen trees and efficiently collecting them are of great importance for economic and environmental reasons. Visually detecting and delineating storm damage is a laborious and error-prone process; thus, it is important to develop cost-efficient and highly automated methods. Objective of our research project is to investigate and develop a reliable and efficient method for automatic storm damage detection, which is based on airborne imagery that is collected after a storm. The requirements for the method are the before-storm and after-storm surface models. A difference surface is calculated using two DSMs and the locations where significant changes have appeared are automatically detected. In our previous research we used four-year old airborne laser scanning surface model as the before-storm surface. The after-storm DSM was provided from the photogrammetric images using the Next Generation Automatic Terrain Extraction (NGATE) algorithm of Socet Set software. We obtained 100% accuracy in detection of major storm damages. In this investigation we will further evaluate the sensitivity of the storm-damage detection process. We will investigate the potential of national airborne photography, that is collected at no-leaf season, to automatically produce a before-storm DSM using image matching. We will also compare impact of the terrain extraction algorithm to the results. Our results will also promote the potential of national open source data sets in the management of natural disasters.

A Kalman filtering framework for physiological detection of anxiety-related arousal in children with autism spectrum disorder.

PubMed

Kushki, Azadeh; Khan, Ajmal; Brian, Jessica; Anagnostou, Evdokia

2015-03-01

Anxiety is associated with physiological changes that can be noninvasively measured using inexpensive and wearable sensors. These changes provide an objective and language-free measure of arousal associated with anxiety, which can complement treatment programs for clinical populations who have difficulty with introspection, communication, and emotion recognition. This motivates the development of automatic methods for detection of anxiety-related arousal using physiology signals. While several supervised learning methods have been proposed for this purpose, these methods require regular collection and updating of training data and are, therefore, not suitable for clinical populations, where obtaining labelled data may be challenging due to impairments in communication and introspection. In this context, the objective of this paper is to develop an unsupervised and real-time arousal detection algorithm. We propose a learning framework based on the Kalman filtering theory for detection of physiological arousal based on cardiac activity. The performance of the system was evaluated on data obtained from a sample of children with autism spectrum disorder. The results indicate that the system can detect anxiety-related arousal in these children with sensitivity and specificity of 99% and 92%, respectively. Our results show that the proposed method can detect physiological arousal associated with anxiety with high accuracy, providing support for technical feasibility of augmenting anxiety treatments with automatic detection techniques. This approach can ultimately lead to more effective anxiety treatment for a larger and more diverse population.

High-Accuracy Ultrasound Contrast Agent Detection Method for Diagnostic Ultrasound Imaging Systems.

PubMed

Ito, Koichi; Noro, Kazumasa; Yanagisawa, Yukari; Sakamoto, Maya; Mori, Shiro; Shiga, Kiyoto; Kodama, Tetsuya; Aoki, Takafumi

2015-12-01

An accurate method for detecting contrast agents using diagnostic ultrasound imaging systems is proposed. Contrast agents, such as microbubbles, passing through a blood vessel during ultrasound imaging are detected as blinking signals in the temporal axis, because their intensity value is constantly in motion. Ultrasound contrast agents are detected by evaluating the intensity variation of a pixel in the temporal axis. Conventional methods are based on simple subtraction of ultrasound images to detect ultrasound contrast agents. Even if the subject moves only slightly, a conventional detection method will introduce significant error. In contrast, the proposed technique employs spatiotemporal analysis of the pixel intensity variation over several frames. Experiments visualizing blood vessels in the mouse tail illustrated that the proposed method performs efficiently compared with conventional approaches. We also report that the new technique is useful for observing temporal changes in microvessel density in subiliac lymph nodes containing tumors. The results are compared with those of contrast-enhanced computed tomography. Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Detecting potential anomalies in projections of rainfall trends and patterns using human observations

NASA Astrophysics Data System (ADS)

Kohfeld, K. E.; Savo, V.; Sillmann, J.; Morton, C.; Lepofsky, D.

2016-12-01

Shifting precipitation patterns are a well-documented consequence of climate change, but their spatial variability is particularly difficult to assess. While the accuracy of global models has increased, specific regional changes in precipitation regimes are not well captured by these models. Typically, researchers who wish to detect trends and patterns in climatic variables, such as precipitation, use instrumental observations. In our study, we combined observations of rainfall by subsistence-oriented communities with several metrics of rainfall estimated from global instrumental records for comparable time periods (1955 - 2005). This comparison was aimed at identifying: 1) which rainfall metrics best match human observations of changes in precipitation; 2) areas where local communities observe changes not detected by global models. The collated observations ( 3800) made by subsistence-oriented communities covered 129 countries ( 1830 localities). For comparable time periods, we saw a substantial correspondence between instrumental records and human observations (66-77%) at the same locations, regardless of whether we considered trends in general rainfall, drought, or extreme rainfall. We observed a clustering of mismatches in two specific regions, possibly indicating some climatic phenomena not completely captured by the currently available global models. Many human observations also indicated an increased unpredictability in the start, end, duration, and continuity of the rainy seasons, all of which may hamper the performance of subsistence activities. We suggest that future instrumental metrics should capture this unpredictability of rainfall. This information would be important for thousands of subsistence-oriented communities in planning, coping, and adapting to climate change.

Exploring Raman spectroscopy for the evaluation of glaucomatous retinal changes

NASA Astrophysics Data System (ADS)

Wang, Qi; Grozdanic, Sinisa D.; Harper, Matthew M.; Hamouche, Nicolas; Kecova, Helga; Lazic, Tatjana; Yu, Chenxu

2011-10-01

Glaucoma is a chronic neurodegenerative disease characterized by apoptosis of retinal ganglion cells and subsequent loss of visual function. Early detection of glaucoma is critical for the prevention of permanent structural damage and irreversible vision loss. Raman spectroscopy is a technique that provides rapid biochemical characterization of tissues in a nondestructive and noninvasive fashion. In this study, we explored the potential of using Raman spectroscopy for detection of glaucomatous changes in vitro. Raman spectroscopic imaging was conducted on retinal tissues of dogs with hereditary glaucoma and healthy control dogs. The Raman spectra were subjected to multivariate discriminant analysis with a support vector machine algorithm, and a classification model was developed to differentiate disease tissues versus healthy tissues. Spectroscopic analysis of 105 retinal ganglion cells (RGCs) from glaucomatous dogs and 267 RGCs from healthy dogs revealed spectroscopic markers that differentiated glaucomatous specimens from healthy controls. Furthermore, the multivariate discriminant model differentiated healthy samples and glaucomatous samples with good accuracy [healthy 89.5% and glaucomatous 97.6% for the same breed (Basset Hounds); and healthy 85.0% and glaucomatous 85.5% for different breeds (Beagles versus Basset Hounds)]. Raman spectroscopic screening can be used for in vitro detection of glaucomatous changes in retinal tissue with a high specificity.

Exploring Raman spectroscopy for the evaluation of glaucomatous retinal changes.

PubMed

Wang, Qi; Grozdanic, Sinisa D; Harper, Matthew M; Hamouche, Nicolas; Kecova, Helga; Lazic, Tatjana; Yu, Chenxu

2011-10-01

Glaucoma is a chronic neurodegenerative disease characterized by apoptosis of retinal ganglion cells and subsequent loss of visual function. Early detection of glaucoma is critical for the prevention of permanent structural damage and irreversible vision loss. Raman spectroscopy is a technique that provides rapid biochemical characterization of tissues in a nondestructive and noninvasive fashion. In this study, we explored the potential of using Raman spectroscopy for detection of glaucomatous changes in vitro. Raman spectroscopic imaging was conducted on retinal tissues of dogs with hereditary glaucoma and healthy control dogs. The Raman spectra were subjected to multivariate discriminant analysis with a support vector machine algorithm, and a classification model was developed to differentiate disease tissues versus healthy tissues. Spectroscopic analysis of 105 retinal ganglion cells (RGCs) from glaucomatous dogs and 267 RGCs from healthy dogs revealed spectroscopic markers that differentiated glaucomatous specimens from healthy controls. Furthermore, the multivariate discriminant model differentiated healthy samples and glaucomatous samples with good accuracy [healthy 89.5% and glaucomatous 97.6% for the same breed (Basset Hounds); and healthy 85.0% and glaucomatous 85.5% for different breeds (Beagles versus Basset Hounds)]. Raman spectroscopic screening can be used for in vitro detection of glaucomatous changes in retinal tissue with a high specificity.

Integrated change detection and temporal trajectory analysis of coastal wetlands using high spatial resolution Korean Multi-Purpose Satellite series imagery

NASA Astrophysics Data System (ADS)

Nguyen, Hoang Hai; Tran, Hien; Sunwoo, Wooyeon; Yi, Jong-hyuk; Kim, Dongkyun; Choi, Minha

2017-04-01

A series of multispectral high-resolution Korean Multi-Purpose Satellite (KOMPSAT) images was used to detect the geographical changes in four different tidal flats between the Yellow Sea and the west coast of South Korea. The method of unsupervised classification was used to generate a series of land use/land cover (LULC) maps from satellite images, which were then used as input for temporal trajectory analysis to detect the temporal change of coastal wetlands and its association with natural and anthropogenic activities. The accurately classified LULC maps of KOMPSAT images, with overall accuracy ranging from 83.34% to 95.43%, indicate that these multispectral high-resolution satellite data are highly applicable to the generation of high-quality thematic maps for extracting wetlands. The result of the trajectory analysis showed that, while the variation of the tidal flats in the Gyeonggi and Jeollabuk provinces was well correlated with the regular tidal regimes, the reductive trajectory of the wetland areas belonging to the Saemangeum province was caused by a high degree of human-induced activities including large reclamation and urbanization. The conservation of the Jeungdo Wetland Protected Area in the Jeollanam province revealed that effective social and environmental policies could help in protecting coastal wetlands from degradation.

Sferic propagation perturbations caused by energetic particle events as seen in global lightning data

NASA Astrophysics Data System (ADS)

Anderson, T.; Holzworth, R. H., II; Brundell, J. B.

2017-12-01

Energetic particle precipitation associated with solar events have been known to cause changes in the Earth-ionosphere waveguide. Previous studies of solar proton events (SPEs) have shown that high-energy protons can ionize lower-altitude layers of the ionosphere, leading to changes in Schumann resonance parameters (Schlegel and Fullekrug, 1999) and absorption of radio waves over the polar cap (Kundu and Haddock, 1960). We use the World-Wide Lightning Location Network (WWLLN) to study propagation of VLF waves during SPEs. WWLLN detects lightning-generated sferics in the VLF band using 80 stations distributed around the world. By comparing received power at individual stations from specific lightning source regions during SPEs, we can infer changes in the lower ionosphere conductivity profile caused by high-energy proton precipitation. In particular, we find that some WWLLN stations see different distributions of sferic power and range during SPEs. We also use the power/propagation analysis to improve WWLLN's lightning detection accuracy, by developing a better model for ionosphere parameters and speed of light in the waveguide than we have previously used.

Change detection technique for muscle tone during static stretching by continuous muscle viscoelasticity monitoring using wearable indentation tester.

PubMed

Okamura, Naomi; Kobayashi, Yo; Sugano, Shigeki; Fujie, Masakatsu G

2017-07-01

Static stretching is widely performed to decrease muscle tone as a part of rehabilitation protocols. Finding out the optimal duration of static stretching is important to minimize the time required for rehabilitation therapy and it would be helpful for maintaining the patient's motivation towards daily rehabilitation tasks. Several studies have been conducted for the evaluation of static stretching; however, the recommended duration of static stretching varies widely between 15-30 s in general, because the traditional methods for the assessment of muscle tone do not monitor the continuous change in the target muscle's state. We have developed a method to monitor the viscoelasticity of one muscle continuously during static stretching, using a wearable indentation tester. In this study, we investigated a suitable signal processing method to detect the time required to change the muscle tone, utilizing the data collected using a wearable indentation tester. By calculating a viscoelastic index with a certain time window, we confirmed that the stretching duration required to bring about a decrease in muscle tone could be obtained with an accuracy in the order of 1 s.

Estimation of color modification in digital images by CFA pattern change.

PubMed

Choi, Chang-Hee; Lee, Hae-Yeoun; Lee, Heung-Kyu

2013-03-10

Extensive studies have been carried out for detecting image forgery such as copy-move, re-sampling, blurring, and contrast enhancement. Although color modification is a common forgery technique, there is no reported forensic method for detecting this type of manipulation. In this paper, we propose a novel algorithm for estimating color modification in images acquired from digital cameras when the images are modified. Most commercial digital cameras are equipped with a color filter array (CFA) for acquiring the color information of each pixel. As a result, the images acquired from such digital cameras include a trace from the CFA pattern. This pattern is composed of the basic red green blue (RGB) colors, and it is changed when color modification is carried out on the image. We designed an advanced intermediate value counting method for measuring the change in the CFA pattern and estimating the extent of color modification. The proposed method is verified experimentally by using 10,366 test images. The results confirmed the ability of the proposed method to estimate color modification with high accuracy. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Forensic individual age estimation with DNA: From initial approaches to methylation tests.

PubMed

Freire-Aradas, A; Phillips, C; Lareu, M V

2017-07-01

Individual age estimation is a key factor in forensic science analysis that can provide very useful information applicable to criminal, legal, and anthropological investigations. Forensic age inference was initially based on morphological inspection or radiography and only later began to adopt molecular approaches. However, a lack of accuracy or technical problems hampered the introduction of these DNA-based methodologies in casework analysis. A turning point occurred when the epigenetic signature of DNA methylation was observed to gradually change during an individual´s lifespan. In the last four years, the number of publications reporting DNA methylation age-correlated changes has gradually risen and the forensic community now has a range of age methylation tests applicable to forensic casework. Most forensic age predictor models have been developed based on blood DNA samples, but additional tissues are now also being explored. This review assesses the most widely adopted genes harboring methylation sites, detection technologies, statistical age-predictive analyses, and potential causes of variation in age estimates. Despite the need for further work to improve predictive accuracy and establishing a broader range of tissues for which tests can analyze the most appropriate methylation sites, several forensic age predictors have now been reported that provide consistency in their prediction accuracies (predictive error of ±4 years); this makes them compelling tools with the potential to contribute key information to help guide criminal investigations. Copyright © 2017 Central Police University.

Investigations on the Possibilities of Monitoring Coastal Changes Including Shallow Under Water Areas with Uas Photo Bathmetry

NASA Astrophysics Data System (ADS)

Grenzdörffer, G. J.; Naumann, M.

2016-06-01

UAS become a very valuable tool for coastal morphology. Not only for mapping but also for change detection and a better understanding of processes along and across the shore. This contribution investigates the possibilities of UAS to determine the water depth in clear shallow waters by means of the so called "photo bathymetry". From the results of several test flights it became clear that three factors influence the ability and the accuracy of bathymetric sea floor measurements. Firstly, weather conditions. Sunny weather is not always good. Due to the high image resolution the sunlight gets focussed even in very small waves causing moving patterns on shallow grounds with high reflection properties, such as sand. This effect invisible under overcast weather conditions. Waves, may also introduce problems and mismatches. Secondly the quality and the accuracy of the georeferencing with SFM algorithms. As multi image key point matching will not work over water, the proposed approach will only work for projects closely to the coastline with enough control on the land. Thirdly the software used and the intensity of post processing and filtering. Refraction correction and the final interpolation of the point cloud into a DTM are the last steps. If everything is done appropriately, accuracies in the bathymetry in the range of 10 - 50 cm, depending on the water depth are possible.

Accurate Radiometry from Space: An Essential Tool for Climate Studies

NASA Technical Reports Server (NTRS)

Fox, Nigel; Kaiser-Weiss, Andrea; Schmutz, Werner; Thome, Kurtis; Young, Dave; Wielicki, Bruce; Winkler, Rainer; Woolliams, Emma

2011-01-01

The Earth s climate is undoubtedly changing; however, the time scale, consequences and causal attribution remain the subject of significant debate and uncertainty. Detection of subtle indicators from a background of natural variability requires measurements over a time base of decades. This places severe demands on the instrumentation used, requiring measurements of sufficient accuracy and sensitivity that can allow reliable judgements to be made decades apart. The International System of Units (SI) and the network of National Metrology Institutes were developed to address such requirements. However, ensuring and maintaining SI traceability of sufficient accuracy in instruments orbiting the Earth presents a significant new challenge to the metrology community. This paper highlights some key measurands and applications driving the uncertainty demand of the climate community in the solar reflective domain, e.g. solar irradiances and reflectances/radiances of the Earth. It discusses how meeting these uncertainties facilitate significant improvement in the forecasting abilities of climate models. After discussing the current state of the art, it describes a new satellite mission, called TRUTHS, which enables, for the first time, high-accuracy SI traceability to be established in orbit. The direct use of a primary standard and replication of the terrestrial traceability chain extends the SI into space, in effect realizing a metrology laboratory in space . Keywords: climate change; Earth observation; satellites; radiometry; solar irradiance

Phenology-based Spartina alterniflora mapping in coastal wetland of the Yangtze Estuary using time series of GaoFen satellite no. 1 wide field of view imagery

NASA Astrophysics Data System (ADS)

Ai, Jinquan; Gao, Wei; Gao, Zhiqiang; Shi, Runhe; Zhang, Chao

2017-04-01

Spartina alterniflora is an aggressive invasive plant species that replaces native species, changes the structure and function of the ecosystem across coastal wetlands in China, and is thus a major conservation concern. Mapping the spread of its invasion is a necessary first step for the implementation of effective ecological management strategies. The performance of a phenology-based approach for S. alterniflora mapping is explored in the coastal wetland of the Yangtze Estuary using a time series of GaoFen satellite no. 1 wide field of view camera (GF-1 WFV) imagery. First, a time series of the normalized difference vegetation index (NDVI) was constructed to evaluate the phenology of S. alterniflora. Two phenological stages (the senescence stage from November to mid-December and the green-up stage from late April to May) were determined as important for S. alterniflora detection in the study area based on NDVI temporal profiles, spectral reflectance curves of S. alterniflora and its coexistent species, and field surveys. Three phenology feature sets representing three major phenology-based detection strategies were then compared to map S. alterniflora: (1) the single-date imagery acquired within the optimal phenological window, (2) the multitemporal imagery, including four images from the two important phenological windows, and (3) the monthly NDVI time series imagery. Support vector machines and maximum likelihood classifiers were applied on each phenology feature set at different training sample sizes. For all phenology feature sets, the overall results were produced consistently with high mapping accuracies under sufficient training samples sizes, although significantly improved classification accuracies (10%) were obtained when the monthly NDVI time series imagery was employed. The optimal single-date imagery had the lowest accuracies of all detection strategies. The multitemporal analysis demonstrated little reduction in the overall accuracy compared with the use of monthly NDVI time series imagery. These results show the importance of considering the phenological stage for image selection for mapping S. alterniflora using GF-1 WFV imagery. Furthermore, in light of the better tradeoff between the number of images and classification accuracy when using multitemporal GF-1 WFV imagery, we suggest using multitemporal imagery acquired at appropriate phenological windows for S. alterniflora mapping at regional scales.

Automatic food intake detection based on swallowing sounds.

PubMed

Makeyev, Oleksandr; Lopez-Meyer, Paulo; Schuckers, Stephanie; Besio, Walter; Sazonov, Edward

2012-11-01

This paper presents a novel fully automatic food intake detection methodology, an important step toward objective monitoring of ingestive behavior. The aim of such monitoring is to improve our understanding of eating behaviors associated with obesity and eating disorders. The proposed methodology consists of two stages. First, acoustic detection of swallowing instances based on mel-scale Fourier spectrum features and classification using support vector machines is performed. Principal component analysis and a smoothing algorithm are used to improve swallowing detection accuracy. Second, the frequency of swallowing is used as a predictor for detection of food intake episodes. The proposed methodology was tested on data collected from 12 subjects with various degrees of adiposity. Average accuracies of >80% and >75% were obtained for intra-subject and inter-subject models correspondingly with a temporal resolution of 30s. Results obtained on 44.1 hours of data with a total of 7305 swallows show that detection accuracies are comparable for obese and lean subjects. They also suggest feasibility of food intake detection based on swallowing sounds and potential of the proposed methodology for automatic monitoring of ingestive behavior. Based on a wearable non-invasive acoustic sensor the proposed methodology may potentially be used in free-living conditions.

Automatic food intake detection based on swallowing sounds

PubMed Central

Makeyev, Oleksandr; Lopez-Meyer, Paulo; Schuckers, Stephanie; Besio, Walter; Sazonov, Edward

2012-01-01

This paper presents a novel fully automatic food intake detection methodology, an important step toward objective monitoring of ingestive behavior. The aim of such monitoring is to improve our understanding of eating behaviors associated with obesity and eating disorders. The proposed methodology consists of two stages. First, acoustic detection of swallowing instances based on mel-scale Fourier spectrum features and classification using support vector machines is performed. Principal component analysis and a smoothing algorithm are used to improve swallowing detection accuracy. Second, the frequency of swallowing is used as a predictor for detection of food intake episodes. The proposed methodology was tested on data collected from 12 subjects with various degrees of adiposity. Average accuracies of >80% and >75% were obtained for intra-subject and inter-subject models correspondingly with a temporal resolution of 30s. Results obtained on 44.1 hours of data with a total of 7305 swallows show that detection accuracies are comparable for obese and lean subjects. They also suggest feasibility of food intake detection based on swallowing sounds and potential of the proposed methodology for automatic monitoring of ingestive behavior. Based on a wearable non-invasive acoustic sensor the proposed methodology may potentially be used in free-living conditions. PMID:23125873

Lung imaging in rodents using dual energy micro-CT

NASA Astrophysics Data System (ADS)

Badea, C. T.; Guo, X.; Clark, D.; Johnston, S. M.; Marshall, C.; Piantadosi, C.

2012-03-01

Dual energy CT imaging is expected to play a major role in the diagnostic arena as it provides material decomposition on an elemental basis. The purpose of this work is to investigate the use of dual energy micro-CT for the estimation of vascular, tissue, and air fractions in rodent lungs using a post-reconstruction three-material decomposition method. We have tested our method using both simulations and experimental work. Using simulations, we have estimated the accuracy limits of the decomposition for realistic micro-CT noise levels. Next, we performed experiments involving ex vivo lung imaging in which intact lungs were carefully removed from the thorax, were injected with an iodine-based contrast agent and inflated with air at different volume levels. Finally, we performed in vivo imaging studies in (n=5) C57BL/6 mice using fast prospective respiratory gating in endinspiration and end-expiration for three different levels of positive end-expiratory pressure (PEEP). Prior to imaging, mice were injected with a liposomal blood pool contrast agent. The mean accuracy values were for Air (95.5%), Blood (96%), and Tissue (92.4%). The absolute accuracy in determining all fraction materials was 94.6%. The minimum difference that we could detect in material fractions was 15%. As expected, an increase in PEEP levels for the living mouse resulted in statistically significant increases in air fractions at end-expiration, but no significant changes in end-inspiration. Our method has applicability in preclinical pulmonary studies where various physiological changes can occur as a result of genetic changes, lung disease, or drug effects.

Diagnosis of Tempromandibular Disorders Using Local Binary Patterns

PubMed Central

Haghnegahdar, A.A.; Kolahi, S.; Khojastepour, L.; Tajeripour, F.

2018-01-01

Background: Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment. Material and Methods: CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis. Results: K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity. Conclusion: We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages. PMID:29732343

Accuracy of implementing principles of fusion imaging in the follow up and surveillance of complex aneurysm repair.

PubMed

Martin-Gonzalez, Teresa; Penney, Graeme; Chong, Debra; Davis, Meryl; Mastracci, Tara M

2018-05-01

Fusion imaging is standard for the endovascular treatment of complex aortic aneurysms, but its role in follow up has not been explored. A critical issue is renal function deterioration over time. Renal volume has been used as a marker of renal impairment; however, it is not reproducible and remains a complex and resource-intensive procedure. The aim of this study is to determine the accuracy of a fusion-based software to automatically calculate the renal volume changes during follow up. In this study, computerized tomography (CT) scans of 16 patients who underwent complex aortic endovascular repair were analysed. Preoperative, 1-month and 1-year follow-up CT scans have been analysed using a conventional approach of semi-automatic segmentation, and a second approach with automatic segmentation. For each kidney and at each time point the percentage of change in renal volume was calculated using both techniques. After review, volume assessment was feasible for all CT scans. For the left kidney, the intraclass correlation coefficient (ICC) was 0.794 and 0.877 at 1 month and 1 year, respectively. For the right side, the ICC was 0.817 at 1 month and 0.966 at 1 year. The automated technique reliably detected a decrease in renal volume for the eight patients with occluded renal arteries during follow up. This is the first report of a fusion-based algorithm to detect changes in renal volume during postoperative surveillance using an automated process. Using this technique, the standardized assessment of renal volume could be implemented with greater ease and reproducibility and serve as a warning of potential renal impairment.

Satellite inventory of Minnesota forest resources

NASA Technical Reports Server (NTRS)

Bauer, Marvin E.; Burk, Thomas E.; Ek, Alan R.; Coppin, Pol R.; Lime, Stephen D.; Walsh, Terese A.; Walters, David K.; Befort, William; Heinzen, David F.

1993-01-01

The methods and results of using Landsat Thematic Mapper (TM) data to classify and estimate the acreage of forest covertypes in northeastern Minnesota are described. Portions of six TM scenes covering five counties with a total area of 14,679 square miles were classified into six forest and five nonforest classes. The approach involved the integration of cluster sampling, image processing, and estimation. Using cluster sampling, 343 plots, each 88 acres in size, were photo interpreted and field mapped as a source of reference data for classifier training and calibration of the TM data classifications. Classification accuracies of up to 75 percent were achieved; most misclassification was between similar or related classes. An inverse method of calibration, based on the error rates obtained from the classifications of the cluster plots, was used to adjust the classification class proportions for classification errors. The resulting area estimates for total forest land in the five-county area were within 3 percent of the estimate made independently by the USDA Forest Service. Area estimates for conifer and hardwood forest types were within 0.8 and 6.0 percent respectively, of the Forest Service estimates. A trial of a second method of estimating the same classes as the Forest Service resulted in standard errors of 0.002 to 0.015. A study of the use of multidate TM data for change detection showed that forest canopy depletion, canopy increment, and no change could be identified with greater than 90 percent accuracy. The project results have been the basis for the Minnesota Department of Natural Resources and the Forest Service to define and begin to implement an annual system of forest inventory which utilizes Landsat TM data to detect changes in forest cover.

Long-term Satellite NDVI Data Sets: Evaluating Their Ability to Detect Ecosystem Functional Changes in South America.

PubMed

Baldi, Germán; Nosetto, Marcelo D; Aragón, Roxana; Aversa, Fernando; Paruelo, José M; Jobbágy, Esteban G

2008-09-03

In the last decades, South American ecosystems underwent important functional modifications due to climate alterations and direct human intervention on land use and land cover. Among remotely sensed data sets, NOAA-AVHRR "Normalized Difference Vegetation Index" (NDVI) represents one of the most powerful tools to evaluate these changes thanks to their extended temporal coverage. In this paper we explored the possibilities and limitations of three commonly used NOAA-AVHRR NDVI series (PAL, GIMMS and FASIR) to detect ecosystem functional changes in the South American continent. We performed pixel-based linear regressions for four NDVI variables (average annual, maximum annual, minimum annual and intra-annual coefficient of variation) for the 1982-1999 period and (1) analyzed the convergences and divergences of significant multi-annual trends identified across all series, (2) explored the degree of aggregation of the trends using the O-ring statistic, and (3) evaluated observed trends using independent information on ecosystem functional changes in five focal regions. Several differences arose in terms of the patterns of change (the sign, localization and total number of pixels with changes). FASIR presented the highest proportion of changing pixels (32.7%) and GIMMS the lowest (16.2%). PAL and FASIR data sets showed the highest agreement, with a convergence of detected trends on 71.2% of the pixels. Even though positive and negative changes showed substantial spatial aggregation, important differences in the scale of aggregation emerged among the series, with GIMMS showing the smaller scale (≤11 pixels). The independent evaluations suggest higher accuracy in the detection of ecosystem changes among PAL and FASIR series than with GIMMS, as they detected trends that match expected shifts. In fact, this last series eliminated most of the long term patterns over the continent. For example, in the "Eastern Paraguay" and "Uruguay River margins" focal regions, the extensive changes due to land use and land cover change expansion were detected by PAL and FASIR, but completely ignored by GIMMS. Although the technical explanation of the differences remains unclear and needs further exploration, we found that the evaluation of this type of remote sensing tools should not only be focused at the level of assumptions (i.e. physical or mathematical aspects of image processing), but also at the level of results (i.e. contrasting observed patterns with independent proofs of change). We finally present the online collaborative initiative "Land ecosystem change utility for South America", which facilitates this type of evaluations and helps to identify the most important functional changes of the continent.

Long-term Satellite NDVI Data Sets: Evaluating Their Ability to Detect Ecosystem Functional Changes in South America

PubMed Central

Baldi, Germán; Nosetto, Marcelo D.; Aragón, Roxana; Aversa, Fernando; Paruelo, José M.; Jobbágy, Esteban G.

2008-01-01

In the last decades, South American ecosystems underwent important functional modifications due to climate alterations and direct human intervention on land use and land cover. Among remotely sensed data sets, NOAA-AVHRR “Normalized Difference Vegetation Index” (NDVI) represents one of the most powerful tools to evaluate these changes thanks to their extended temporal coverage. In this paper we explored the possibilities and limitations of three commonly used NOAA-AVHRR NDVI series (PAL, GIMMS and FASIR) to detect ecosystem functional changes in the South American continent. We performed pixel-based linear regressions for four NDVI variables (average annual, maximum annual, minimum annual and intra-annual coefficient of variation) for the 1982-1999 period and (1) analyzed the convergences and divergences of significant multi-annual trends identified across all series, (2) explored the degree of aggregation of the trends using the O-ring statistic, and (3) evaluated observed trends using independent information on ecosystem functional changes in five focal regions. Several differences arose in terms of the patterns of change (the sign, localization and total number of pixels with changes). FASIR presented the highest proportion of changing pixels (32.7%) and GIMMS the lowest (16.2%). PAL and FASIR data sets showed the highest agreement, with a convergence of detected trends on 71.2% of the pixels. Even though positive and negative changes showed substantial spatial aggregation, important differences in the scale of aggregation emerged among the series, with GIMMS showing the smaller scale (≤11 pixels). The independent evaluations suggest higher accuracy in the detection of ecosystem changes among PAL and FASIR series than with GIMMS, as they detected trends that match expected shifts. In fact, this last series eliminated most of the long term patterns over the continent. For example, in the “Eastern Paraguay” and “Uruguay River margins” focal regions, the extensive changes due to land use and land cover change expansion were detected by PAL and FASIR, but completely ignored by GIMMS. Although the technical explanation of the differences remains unclear and needs further exploration, we found that the evaluation of this type of remote sensing tools should not only be focused at the level of assumptions (i.e. physical or mathematical aspects of image processing), but also at the level of results (i.e. contrasting observed patterns with independent proofs of change). We finally present the online collaborative initiative “Land ecosystem change utility for South America”, which facilitates this type of evaluations and helps to identify the most important functional changes of the continent. PMID:27873821

Knowing where is different from knowing what: Distinct response time profiles and accuracy effects for target location, orientation, and color probability.

PubMed

Jabar, Syaheed B; Filipowicz, Alex; Anderson, Britt

2017-11-01

When a location is cued, targets appearing at that location are detected more quickly. When a target feature is cued, targets bearing that feature are detected more quickly. These attentional cueing effects are only superficially similar. More detailed analyses find distinct temporal and accuracy profiles for the two different types of cues. This pattern parallels work with probability manipulations, where both feature and spatial probability are known to affect detection accuracy and reaction times. However, little has been done by way of comparing these effects. Are probability manipulations on space and features distinct? In a series of five experiments, we systematically varied spatial probability and feature probability along two dimensions (orientation or color). In addition, we decomposed response times into initiation and movement components. Targets appearing at the probable location were reported more quickly and more accurately regardless of whether the report was based on orientation or color. On the other hand, when either color probability or orientation probability was manipulated, response time and accuracy improvements were specific for that probable feature dimension. Decomposition of the response time benefits demonstrated that spatial probability only affected initiation times, whereas manipulations of feature probability affected both initiation and movement times. As detection was made more difficult, the two effects further diverged, with spatial probability disproportionally affecting initiation times and feature probability disproportionately affecting accuracy. In conclusion, all manipulations of probability, whether spatial or featural, affect detection. However, only feature probability affects perceptual precision, and precision effects are specific to the probable attribute.

Accuracy of imaging methods for detection of bone tissue invasion in patients with oral squamous cell carcinoma

PubMed Central

Uribe, S; Rojas, LA; Rosas, CF

2013-01-01

The objective of this review is to evaluate the diagnostic accuracy of imaging methods for detection of mandibular bone tissue invasion by squamous cell carcinoma (SCC). A systematic review was carried out of studies in MEDLINE, SciELO and ScienceDirect, published between 1960 and 2012, in English, Spanish or German, which compared detection of mandibular bone tissue invasion via different imaging tests against a histopathology reference standard. Sensitivity and specificity data were extracted from each study. The outcome measure was diagnostic accuracy. We found 338 articles, of which 5 fulfilled the inclusion criteria. Tests included were: CT (four articles), MRI (four articles), panoramic radiography (one article), positron emission tomography (PET)/CT (one article) and cone beam CT (CBCT) (one article). The quality of articles was low to moderate and the evidence showed that all tests have a high diagnostic accuracy for detection of mandibular bone tissue invasion by SCC, with sensitivity values of 94% (MRI), 91% (CBCT), 83% (CT) and 55% (panoramic radiography), and specificity values of 100% (CT, MRI, CBCT), 97% (PET/CT) and 91.7% (panoramic radiography). Available evidence is scarce and of only low to moderate quality. However, it is consistently shown that current imaging methods give a moderate to high diagnostic accuracy for the detection of mandibular bone tissue invasion by SCC. Recommendations are given for improving the quality of future reports, in particular provision of a detailed description of the patients' conditions, the imaging instrument and both imaging and histopathological invasion criteria. PMID:23420854

Utility of Endoanal Ultrasonography in Assessment of Primary and Recurrent Anal Fistulas and for Detection of Associated Anal Sphincter Defects.

PubMed

Emile, Sameh Hany; Magdy, Alaa; Youssef, Mohamed; Thabet, Waleed; Abdelnaby, Mahmoud; Omar, Waleed; Khafagy, Wael

2017-11-01

Tridimensional endoanal ultrasonography (3D-EAUS) has been used for the assessment of various anorectal lesions. Previous studies have reported good accuracy of 3D-EAUS in preoperative assessment of fistula-in-ano (FIA). This study aimed to assess the diagnostic utility of 3D-EAUS in preoperative evaluation of primary and recurrent FIA and its role in detection of associated anal sphincter (AS) defects. Prospectively collected data of patients with FIA who were investigated with 3D-EAUS were reviewed. The findings of EAUS were compared with the intraoperative findings, the reference standard, to find the degree of agreement regarding the position of the internal opening (IO) and primary tract (PT), and presence of secondary tracts using kappa (k) coefficient test. A subgroup analysis was performed to compare the accuracy and sensitivity of EAUS for primary and recurrent FIA. Of the patients, 131 were included to the study. EAUS had an overall accuracy of 87, 88.5, and 89.5% in detection of IO, PT, and AS defects, respectively. There was very good concordance between the findings of EAUS and intraoperative findings for the investigated parameters (kappa = 0.748, 0.83, 0.935), respectively. Accuracy and sensitivity of EAUS in recurrent FIA were insignificantly lower than primary cases. EAUS detected occult AS defects in 5.3% of the patients studied. The diagnostic utility of 3D-EAUS was comparable in primary and recurrent FIA. 3D-EAUS was able to detect symptomatic and occult AS defects with higher accuracy than clinical examination.

Circular magnetoplasmonic modes in gold nanoparticles.

PubMed

Pineider, Francesco; Campo, Giulio; Bonanni, Valentina; Fernández, César de Julián; Mattei, Giovanni; Caneschi, Andrea; Gatteschi, Dante; Sangregorio, Claudio

2013-10-09

The quest for efficient ways of modulating localized surface plasmon resonance is one of the frontiers in current research in plasmonics; the use of a magnetic field as a source of modulation is among the most promising candidates for active plasmonics. Here we report the observation of magnetoplasmonic modes on colloidal gold nanoparticles detected by means of magnetic circular dichroism (MCD) spectroscopy and provide a model that is able to rationalize and reproduce the experiment with unprecedented qualitative and quantitative accuracy. We believe that the steep slope observed at the plasmon resonance in the MCD spectrum can be very efficient in detecting changes in the refractive index of the surrounding medium, and we give a simple proof of principle of its possible implementation for magnetoplasmonic refractometric sensing.

Smart textile plasmonic fiber dew sensors.

PubMed

Esmaeilzadeh, Hamid; Rivard, Maxime; Arzi, Ezatollah; Légaré, François; Hassani, Alireza

2015-06-01

We propose a novel Surface Plasmon Resonance (SPR)-based sensor that detects dew formation in optical fiber-based smart textiles. The proposed SPR sensor facilitates the observation of two phenomena: condensation of moisture and evaporation of water molecules in air. This sensor detects dew formation in less than 0.25 s, and determines dew point temperature with an accuracy of 4%. It can be used to monitor water layer depth changes during dew formation and evaporation in the range of a plasmon depth probe, i.e., 250 nm, with a resolution of 7 nm. Further, it facilitates estimation of the relative humidity of a medium over a dynamic range of 30% to 70% by measuring the evaporation time via the plasmon depth probe.

Accuracy of Perceptual and Acoustic Methods for the Detection of Inspiratory Loci in Spontaneous Speech

PubMed Central

Wang, Yu-Tsai; Nip, Ignatius S. B.; Green, Jordan R.; Kent, Ray D.; Kent, Jane Finley; Ullman, Cara

2012-01-01

The current study investigates the accuracy of perceptually and acoustically determined inspiratory loci in spontaneous speech for the purpose of identifying breath groups. Sixteen participants were asked to talk about simple topics in daily life at a comfortable speaking rate and loudness while connected to a pneumotach and audio microphone. The locations of inspiratory loci were determined based on the aerodynamic signal, which served as a reference for loci identified perceptually and acoustically. Signal detection theory was used to evaluate the accuracy of the methods. The results showed that the greatest accuracy in pause detection was achieved (1) perceptually based on the agreement between at least 2 of the 3 judges; (2) acoustically using a pause duration threshold of 300 ms. In general, the perceptually-based method was more accurate than was the acoustically-based method. Inconsistencies among perceptually-determined, acoustically-determined, and aerodynamically-determined inspiratory loci for spontaneous speech should be weighed in selecting a method of breath-group determination. PMID:22362007

Automated thematic mapping and change detection of ERTS-A images. [digital interpretation of Arizona imagery

NASA Technical Reports Server (NTRS)

Gramenopoulos, N. (Principal Investigator)

1973-01-01

The author has identified the following significant results. For the recognition of terrain types, spatial signatures are developed from the diffraction patterns of small areas of ERTS-1 images. This knowledge is exploited for the measurements of a small number of meaningful spatial features from the digital Fourier transforms of ERTS-1 image cells containing 32 x 32 picture elements. Using these spatial features and a heuristic algorithm, the terrain types in the vicinity of Phoenix, Arizona were recognized by the computer with a high accuracy. Then, the spatial features were combined with spectral features and using the maximum likelihood criterion the recognition accuracy of terrain types increased substantially. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. Nonlinear transformations of the feature vectors are required so that the terrain class statistics become approximately Gaussian. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month but vary substantially between seasons.

Integrated multi-ISE arrays with improved sensitivity, accuracy and precision

NASA Astrophysics Data System (ADS)

Wang, Chunling; Yuan, Hongyan; Duan, Zhijuan; Xiao, Dan

2017-03-01

Increasing use of ion-selective electrodes (ISEs) in the biological and environmental fields has generated demand for high-sensitivity ISEs. However, improving the sensitivities of ISEs remains a challenge because of the limit of the Nernstian slope (59.2/n mV). Here, we present a universal ion detection method using an electronic integrated multi-electrode system (EIMES) that bypasses the Nernstian slope limit of 59.2/n mV, thereby enabling substantial enhancement of the sensitivity of ISEs. The results reveal that the response slope is greatly increased from 57.2 to 1711.3 mV, 57.3 to 564.7 mV and 57.7 to 576.2 mV by electronic integrated 30 Cl- electrodes, 10 F- electrodes and 10 glass pH electrodes, respectively. Thus, a tiny change in the ion concentration can be monitored, and correspondingly, the accuracy and precision are substantially improved. The EIMES is suited for all types of potentiometric sensors and may pave the way for monitoring of various ions with high accuracy and precision because of its high sensitivity.

Oil Spill Detection: Past and Future Trends

NASA Astrophysics Data System (ADS)

Topouzelis, Konstantinos; Singha, Suman

2016-08-01

In the last 15 years, the detection of oil spills by satellite means has been moved from experimental to operational. Actually, what is really changed is the satellite image availability. From the late 1990's, in the age of "no data" we have moved forward 15 years to the age of "Sentinels" with an abundance of data. Either large accident related to offshore oil exploration and production activity or illegal discharges from tankers, oil on the sea surface is or can be now regularly monitored, over European Waters. National and transnational organizations (i.e. European Maritime Safety Agency's 'CleanSeaNet' Service) are routinely using SAR imagery to detect oil due to it's all weather, day and night imaging capability. However, all these years the scientific methodology on the detection remains relatively constant. From manual analysis to fully automatic detection methodologies, no significant contribution has been published in the last years and certainly none has dramatically changed the rules of the detection. On the contrary, although the overall accuracy of the methodology is questioned, the four main classification steps (dark area detection, features extraction, statistic database creation, and classification) are continuously improving. In recent years, researchers came up with the use of polarimetric SAR data for oil spill detection and characterizations, although utilization of Pol-SAR data for this purpose still remains questionable due to lack of verified dataset and low spatial coverage of Pol-SAR data. The present paper is trying to point out the drawbacks of the oil spill detection in the last years and focus on the bottlenecks of the oil spill detection methodologies. Also, solutions on the basis of data availability, management and analysis are proposed. Moreover, an ideal detection system is discussed regarding satellite image and in situ observations using different scales and sensors.

AATSR: global-change and surface-temperature measurements from Envisat

NASA Astrophysics Data System (ADS)

Llewellyn-Jones, D.; Edwards, M. C.; Mutlow, C. T.; Birks, A. R.; Barton, I. J.; Tait, H.

2001-02-01

The Advanced Along-Track Scanning Radiometer (AATSR) onboard ESA's Envisat spacecraft is designed to meet the challenging task of monitoring and detecting climate change. It builds on the success of its predecessor instruments on the ERS-1 and ERS-2 satellites, and will lead to a 15+ year record of precise and accurate global Sea-Surface Temperature (SST) measurements, thereby making a valuable contribution to the long-term climate record. With its high-accuracy, high-quality imagery and channels in the visible, near-infrared and thermal wavelengths, AATSR data will support many applications in addition to oceanographic and climate research, including a wide range of land-surface, cryosphere and atmospheric studies.

Robust Vehicle Detection under Various Environmental Conditions Using an Infrared Thermal Camera and Its Application to Road Traffic Flow Monitoring

PubMed Central

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2013-01-01

We have already proposed a method for detecting vehicle positions and their movements (henceforth referred to as “our previous method”) using thermal images taken with an infrared thermal camera. Our experiments have shown that our previous method detects vehicles robustly under four different environmental conditions which involve poor visibility conditions in snow and thick fog. Our previous method uses the windshield and its surroundings as the target of the Viola-Jones detector. Some experiments in winter show that the vehicle detection accuracy decreases because the temperatures of many windshields approximate those of the exterior of the windshields. In this paper, we propose a new vehicle detection method (henceforth referred to as “our new method”). Our new method detects vehicles based on tires' thermal energy reflection. We have done experiments using three series of thermal images for which the vehicle detection accuracies of our previous method are low. Our new method detects 1,417 vehicles (92.8%) out of 1,527 vehicles, and the number of false detection is 52 in total. Therefore, by combining our two methods, high vehicle detection accuracies are maintained under various environmental conditions. Finally, we apply the traffic information obtained by our two methods to traffic flow automatic monitoring, and show the effectiveness of our proposal. PMID:23774988

Accuracy of Person-Fit Statistics: A Monte Carlo Study of the Influence of Aberrance Rates

ERIC Educational Resources Information Center

St-Onge, Christina; Valois, Pierre; Abdous, Belkacem; Germain, Stephane

2011-01-01

Using a Monte Carlo experimental design, this research examined the relationship between answer patterns' aberrance rates and person-fit statistics (PFS) accuracy. It was observed that as the aberrance rate increased, the detection rates of PFS also increased until, in some situations, a peak was reached and then the detection rates of PFS…

Accuracy of "Modified Checklist for Autism in Toddlers" ("M-CHAT") in Detecting Autism and Other Developmental Disorders in Community Clinics

ERIC Educational Resources Information Center

Toh, Teck-Hock; Tan, Vivian Wee-Yen; Lau, Peter Sie-Teck; Kiyu, Andrew

2018-01-01

This study determined the accuracy of "Modified Checklist for Autism in Toddlers" ("M-CHAT") in detecting toddlers with autism spectrum disorder (ASD) and other developmental disorders (DD) in community mother and child health clinics. We analysed 19,297 eligible toddlers (15-36 months) who had "M-CHAT" performed in…

Quantitative Assessment of Spatio-Temporal Desertification Rates in Azerbaijan during Using Timeseries Landsat-8 Satellite Images

NASA Astrophysics Data System (ADS)

Bayramov, Emil; Mammadov, Ramiz

2016-07-01

The main goals of this research are the object-based landcover classification of LANDSAT-8 multi-spectral satellite images in 2014 and 2015, quantification of Normalized Difference Vegetation Indices (NDVI) rates within the land-cover classes, change detection analysis between the NDVIs derived from multi-temporal LANDSAT-8 satellite images and the quantification of those changes within the land-cover classes and detection of changes between land-cover classes. The object-based classification accuracy of the land-cover classes was validated through the standard confusion matrix which revealed 80 % of land-cover classification accuracy for both years. The analysis revealed that the area of agricultural lands increased from 30911 sq. km. in 2014 to 31999 sq. km. in 2015. The area of barelands increased from 3933 sq. km. in 2014 to 4187 sq. km. in 2015. The area of forests increased from 8211 sq. km. in 2014 to 9175 sq. km. in 2015. The area of grasslands decreased from 27176 sq. km. in 2014 to 23294 sq. km. in 2015. The area of urban areas increased from 12479 sq. km. in 2014 to 12956 sq. km. in 2015. The decrease in the area of grasslands was mainly explained by the landuse shifts of grasslands to agricultural and urban lands. The quantification of low and medium NDVI rates revealed the increase within the agricultural, urban and forest land-cover classes in 2015. However, the high NDVI rates within agricultural, urban and forest land-cover classes in 2015 revealed to be lower relative to 2014. The change detection analysis between landscover types of 2014 and 2015 allowed to determine that 7740 sq. km. of grasslands shifted to agricultural landcover type whereas 5442sq. km. of agricultural lands shifted to rangelands. This means that the spatio-temporal patters of agricultural activities occurred in Azerbaijan because some of the areas reduced agricultural activities whereas some of them changed their landuse type to agricultural. Based on the achieved results, it is possible to conclude that the area of agricultural lands in Azerbaijan increased from 2014 to 2015. The crop productivity also increased in the croplands, however some of the areas showed lower productivity in 2015 relative to 2014.

An immunity-based anomaly detection system with sensor agents.

PubMed

Okamoto, Takeshi; Ishida, Yoshiteru

2009-01-01

This paper proposes an immunity-based anomaly detection system with sensor agents based on the specificity and diversity of the immune system. Each agent is specialized to react to the behavior of a specific user. Multiple diverse agents decide whether the behavior is normal or abnormal. Conventional systems have used only a single sensor to detect anomalies, while the immunity-based system makes use of multiple sensors, which leads to improvements in detection accuracy. In addition, we propose an evaluation framework for the anomaly detection system, which is capable of evaluating the differences in detection accuracy between internal and external anomalies. This paper focuses on anomaly detection in user's command sequences on UNIX-like systems. In experiments, the immunity-based system outperformed some of the best conventional systems.

Unsupervised building detection from irregularly spaced LiDAR and aerial imagery

NASA Astrophysics Data System (ADS)

Shorter, Nicholas Sven

As more data sources containing 3-D information are becoming available, an increased interest in 3-D imaging has emerged. Among these is the 3-D reconstruction of buildings and other man-made structures. A necessary preprocessing step is the detection and isolation of individual buildings that subsequently can be reconstructed in 3-D using various methodologies. Applications for both building detection and reconstruction have commercial use for urban planning, network planning for mobile communication (cell phone tower placement), spatial analysis of air pollution and noise nuisances, microclimate investigations, geographical information systems, security services and change detection from areas affected by natural disasters. Building detection and reconstruction are also used in the military for automatic target recognition and in entertainment for virtual tourism. Previously proposed building detection and reconstruction algorithms solely utilized aerial imagery. With the advent of Light Detection and Ranging (LiDAR) systems providing elevation data, current algorithms explore using captured LiDAR data as an additional feasible source of information. Additional sources of information can lead to automating techniques (alleviating their need for manual user intervention) as well as increasing their capabilities and accuracy. Several building detection approaches surveyed in the open literature have fundamental weaknesses that hinder their use; such as requiring multiple data sets from different sensors, mandating certain operations to be carried out manually, and limited functionality to only being able to detect certain types of buildings. In this work, a building detection system is proposed and implemented which strives to overcome the limitations seen in existing techniques. The developed framework is flexible in that it can perform building detection from just LiDAR data (first or last return), or just nadir, color aerial imagery. If data from both LiDAR and aerial imagery are available, then the algorithm will use them both for improved accuracy. Additionally, the proposed approach does not employ severely limiting assumptions thus enabling the end user to apply the approach to a wider variety of different building types. The proposed approach is extensively tested using real data sets and it is also compared with other existing techniques. Experimental results are presented.

Analytical and experimental studies on detection of longitudinal, L and inverted T cracks in isotropic and bi-material beams based on changes in natural frequencies

NASA Astrophysics Data System (ADS)

Ravi, J. T.; Nidhan, S.; Muthu, N.; Maiti, S. K.

2018-02-01

An analytical method for determination of dimensions of longitudinal crack in monolithic beams, based on frequency measurements, has been extended to model L and inverted T cracks. Such cracks including longitudinal crack arise in beams made of layered isotropic or composite materials. A new formulation for modelling cracks in bi-material beams is presented. Longitudinal crack segment sizes, for L and inverted T cracks, varying from 2.7% to 13.6% of length of Euler-Bernoulli beams are considered. Both forward and inverse problems have been examined. In the forward problems, the analytical results are compared with finite element (FE) solutions. In the inverse problems, the accuracy of prediction of crack dimensions is verified using FE results as input for virtual testing. The analytical results show good agreement with the actual crack dimensions. Further, experimental studies have been done to verify the accuracy of the analytical method for prediction of dimensions of three types of crack in isotropic and bi-material beams. The results show that the proposed formulation is reliable and can be employed for crack detection in slender beam like structures in practice.

Using remote sensing and GIS to detect and monitor land use and land cover change in Dhaka Metropolitan of Bangladesh during 1960-2005.

PubMed

Dewan, Ashraf M; Yamaguchi, Yasushi

2009-03-01

This paper illustrates the result of land use/cover change in Dhaka Metropolitan of Bangladesh using topographic maps and multi-temporal remotely sensed data from 1960 to 2005. The Maximum likelihood supervised classification technique was used to extract information from satellite data, and post-classification change detection method was employed to detect and monitor land use/cover change. Derived land use/cover maps were further validated by using high resolution images such as SPOT, IRS, IKONOS and field data. The overall accuracy of land cover change maps, generated from Landsat and IRS-1D data, ranged from 85% to 90%. The analysis indicated that the urban expansion of Dhaka Metropolitan resulted in the considerable reduction of wetlands, cultivated land, vegetation and water bodies. The maps showed that between 1960 and 2005 built-up areas increased approximately 15,924 ha, while agricultural land decreased 7,614 ha, vegetation decreased 2,336 ha, wetland/lowland decreased 6,385 ha, and water bodies decreased about 864 ha. The amount of urban land increased from 11% (in 1960) to 344% in 2005. Similarly, the growth of landfill/bare soils category was about 256% in the same period. Much of the city's rapid growth in population has been accommodated in informal settlements with little attempt being made to limit the risk of environmental impairments. The study quantified the patterns of land use/cover change for the last 45 years for Dhaka Metropolitan that forms valuable resources for urban planners and decision makers to devise sustainable land use and environmental planning.

Coarse-to-fine deep neural network for fast pedestrian detection

NASA Astrophysics Data System (ADS)

Li, Yaobin; Yang, Xinmei; Cao, Lijun

2017-11-01

Pedestrian detection belongs to a category of object detection is a key issue in the field of video surveillance and automatic driving. Although recent object detection methods, such as Fast/Faster RCNN, have achieved excellent performance, it is difficult to meet real-time requirements and limits the application in real scenarios. A coarse-to-fine deep neural network for fast pedestrian detection is proposed in this paper. Two-stage approach is presented to realize fine trade-off between accuracy and speed. In the coarse stage, we train a fast deep convolution neural network to generate most pedestrian candidates at the cost of a number of false positives. The detector can cover the majority of scales, sizes, and occlusions of pedestrians. After that, a classification network is introduced to refine the pedestrian candidates generated from the previous stage. Refining through classification network, most of false detections will be excluded easily and the final pedestrian predictions with bounding box and confidence score are produced. Competitive results have been achieved on INRIA dataset in terms of accuracy, especially the method can achieve real-time detection that is faster than the previous leading methods. The effectiveness of coarse-to-fine approach to detect pedestrians is verified, and the accuracy and stability are also improved.

Design of stepwise screening for prediabetes and type 2 diabetes based on costs and cases detected.

PubMed

de Graaf, Gimon; Postmus, Douwe; Bakker, Stephan J L; Buskens, Erik

2015-09-01

To provide insight into the trade-off between cost per case detected (CPCD) and the detection rate in questionnaire-based stepwise screening for impaired fasting glucose and undiagnosed type 2 diabetes. We considered a stepwise screening in which individuals whose risk score exceeds a predetermined cutoff value are invited for further blood glucose testing. Using individual patient data to determine questionnaire sensitivity and specificity and external sources to determine screening costs and patient response rates, we rolled back a decision tree to estimate the CPCD and the detection rate for all possible cutoffs on the questionnaire. We found a U-shaped relation between CPCD and detection rate, with high costs per case detected at very low and very high detection rates. Changes in patient response rates had a large impact on both the detection rate and the CPCD, whereas screening costs and questionnaire accuracy mainly impacted the CPCD. Our applied method makes it possible to identify a range of efficient cutoffs where higher detection rates can be achieved at an additional cost per detected patient. This enables decision makers to choose an optimal cutoff based on their willingness to pay for additional detected patients. Copyright © 2015 Elsevier Inc. All rights reserved.

Kidney volume measurement methods for clinical studies on autosomal dominant polycystic kidney disease

PubMed Central

Sharma, Kanishka; Caroli, Anna; Quach, Le Van; Petzold, Katja; Bozzetto, Michela; Serra, Andreas L.; Remuzzi, Giuseppe; Remuzzi, Andrea

2017-01-01

Background In autosomal dominant polycystic kidney disease (ADPKD), total kidney volume (TKV) is regarded as an important biomarker of disease progression and different methods are available to assess kidney volume. The purpose of this study was to identify the most efficient kidney volume computation method to be used in clinical studies evaluating the effectiveness of treatments on ADPKD progression. Methods and findings We measured single kidney volume (SKV) on two series of MR and CT images from clinical studies on ADPKD (experimental dataset) by two independent operators (expert and beginner), twice, using all of the available methods: polyline manual tracing (reference method), free-hand manual tracing, semi-automatic tracing, Stereology, Mid-slice and Ellipsoid method. Additionally, the expert operator also measured the kidney length. We compared different methods for reproducibility, accuracy, precision, and time required. In addition, we performed a validation study to evaluate the sensitivity of these methods to detect the between-treatment group difference in TKV change over one year, using MR images from a previous clinical study. Reproducibility was higher on CT than MR for all methods, being highest for manual and semiautomatic contouring methods (planimetry). On MR, planimetry showed highest accuracy and precision, while on CT accuracy and precision of both planimetry and Stereology methods were comparable. Mid-slice and Ellipsoid method, as well as kidney length were fast but provided only a rough estimate of kidney volume. The results of the validation study indicated that planimetry and Stereology allow using an importantly lower number of patients to detect changes in kidney volume induced by drug treatment as compared to other methods. Conclusions Planimetry should be preferred over fast and simplified methods for accurately monitoring ADPKD progression and assessing drug treatment effects. Expert operators, especially on MR images, are required for performing reliable estimation of kidney volume. The use of efficient TKV quantification methods considerably reduces the number of patients to enrol in clinical investigations, making them more feasible and significant. PMID:28558028

68Ga-PSMA PET/CT in Patients with Rising Prostatic-Specific Antigen After Definitive Treatment of Prostate Cancer: Detection Efficacy and Diagnostic accuracy.

PubMed

Hamed, Maged Abdel Galil; Basha, Mohammad Abd Alkhalik; Ahmed, Hussien; Obaya, Ahmed Ali; Afifi, Amira Hamed Mohamed; Abdelbary, Eman H

2018-06-20

68 Ga-prostate-specific membrane antigen-11 ( 68 Ga-PSMA-11) is a recently developed positron emission tomography (PET) tracer that can detect prostate cancer (PC) relapses and metastases with high contrast resolution. The aim of this study was to assess the detection efficacy and diagnostic accuracy of 68 Ga-PSMA PET/CT image in patients with rising prostatic-specific antigen (PSA) after treatment of PC. The present prospective study included 188 patients who exhibited rising of PSA level on a routine follow-up examination after definitive treatment of PC. All patients underwent a 68 Ga-PSMA PET/CT examination. For each patient, we determined the disease stage, the Gleason score, and the maximum standardized uptake value of the local recurrence and extraprostatic metastases. The detection efficacy and diagnostic accuracy of 68 Ga-PSMA PET/CT were established by histopathology and clinical and imaging follow-up as the reference standards. 68 Ga-PSMA PET/CT detected tumour relapse in 165 patients (35 patients had local recurrence, 106 patients had extraprostatic metastases, and 24 patients had combined lesions). The sensitivity, specificity, and accuracy values of 68 Ga-PSMA PET/CT examination in the detection of PC recurrence were 98.8%, 100%, and 98.8%, respectively. 68 Ga-PSMA PET/CT revealed an overall detection rate of 87.8% (165/188) in patients with rising PSA (median of 2.2 ng/mL, and range of 0.01-70 ng/mL). 68 Ga-PSMA PET/CT is a valuable tool for the detection of PC local recurrence or extraprostatic metastases following rising PSA levels after primary definitive therapy and should be incorporated during routine work-up. Copyright © 2018. Published by Elsevier Inc.

Direct Detection Doppler Lidar for Spaceborne Wind Measurement

NASA Technical Reports Server (NTRS)

Korb, C. Laurence; Flesia, Cristina

1999-01-01

Aerosol and molecular based versions of the double-edge technique can be used for direct detection Doppler lidar spaceborne wind measurement. The edge technique utilizes the edge of a high spectral resolution filter for high accuracy wind measurement using direct detection lidar. The signal is split between an edge filter channel and a broadband energy monitor channel. The energy monitor channel is used for signal normalization. The edge measurement is made as a differential frequency measurement between the outgoing laser signal and the atmospheric backscattered return for each pulse. As a result the measurement is insensitive to laser and edge filter frequency jitter and drift at a level less than a few parts in 10(exp 10). We have developed double edge versions of the edge technique for aerosol and molecular-based lidar measurement of the wind. Aerosol-based wind measurements have been made at Goddard Space Flight Center and molecular-based wind measurements at the University of Geneva. We have demonstrated atmospheric measurements using these techniques for altitudes from 1 to more than 10 km. Measurement accuracies of better than 1.25 m/s have been obtained with integration times from 5 to 30 seconds. The measurements can be scaled to space and agree, within a factor of two, with satellite-based simulations of performance based on Poisson statistics. The theory of the double edge aerosol technique is described by a generalized formulation which substantially extends the capabilities of the edge technique. It uses two edges with opposite slopes located about the laser frequency at approximately the half-width of each edge filter. This doubles the signal change for a given Doppler shift and yields a factor of 1.6 improvement in the measurement accuracy compared to the single edge technique. The use of two high resolution edge filters substantially reduces the effects of Rayleigh scattering on the measurement, as much as order of magnitude, and allows the signal to noise ratio to be substantially improved in areas of low aerosol backscatter. We describe a method that allows the Rayleigh and aerosol components of the signal to be independently determined using the two edge channels and an energy monitor channel. The effects of Rayleigh scattering may then subtracted from the measurement and we show that the correction process does not significantly increase the measurement noise for Rayleigh to aerosol ratios up to 10. We show that for small Doppler shifts a measurement accuracy of 0.4 m/s can be obtained for 5000 detected photon, 1.2 m/s for 1000 detected photons, and 3.7 m/s for 50 detected photons for a Rayleigh to aerosol ratio of 5. Methods for increasing the dynamic range of the aerosol-based system to more than +/- 100 m/s are given.

Post-Disaster Damage Assessment Through Coherent Change Detection on SAR Imagery

NASA Astrophysics Data System (ADS)

Guida, L.; Boccardo, P.; Donevski, I.; Lo Schiavo, L.; Molinari, M. E.; Monti-Guarnieri, A.; Oxoli, D.; Brovelli, M. A.

2018-04-01

Damage assessment is a fundamental step to support emergency response and recovery activities in a post-earthquake scenario. In recent years, UAVs and satellite optical imagery was applied to assess major structural damages before technicians could reach the areas affected by the earthquake. However, bad weather conditions may harm the quality of these optical assessments, thus limiting the practical applicability of these techniques. In this paper, the application of Synthetic Aperture Radar (SAR) imagery is investigated and a novel approach to SAR-based damage assessment is presented. Coherent Change Detection (CCD) algorithms on multiple interferometrically pre-processed SAR images of the area affected by the seismic event are exploited to automatically detect potential damages to buildings and other physical structures. As a case study, the 2016 Central Italy earthquake involving the cities of Amatrice and Accumoli was selected. The main contribution of the research outlined above is the integration of a complex process, requiring the coordination of a variety of methods and tools, into a unitary framework, which allows end-to-end application of the approach from SAR data pre-processing to result visualization in a Geographic Information System (GIS). A prototype of this pipeline was implemented, and the outcomes of this methodology were validated through an extended comparison with traditional damage assessment maps, created through photo-interpretation of high resolution aerial imagery. The results indicate that the proposed methodology is able to perform damage detection with a good level of accuracy, as most of the detected points of change are concentrated around highly damaged buildings.

Validation of bovine oestrous-specific synthetic molecules with trained scent dogs; similarities between natural and synthetic oestrous smell.

PubMed

Fischer-Tenhagen, C; Johnen, D; Le Danvic, C; Gatien, J; Salvetti, P; Tenhagen, B A; Heuwieser, W

2015-02-01

Oestrous detection is crucial for successful dairy cow reproduction. Bulls identify cows in oestrus by oestrous-specific odours especially in urine and vaginal fluid. These have been used to train dogs to detect cows in heat. To improve and simplify the dog training, a spray containing synthetic oestrous molecules was developed. The objective of this study was to test the spray on similarities to the natural substance thus to assess its suitability as a training substance for heat detection dogs. Ten privately owned dogs of various breeds were trained. Dogs should be trained either to differentiate natural vaginal fluid from cows in oestrus and dioestrus (n = 5), or spray with or without synthetic oestrous molecules (n = 5). Dogs trained on natural fluid and on spray could detect the oestrous odour they had been trained on with an overall accuracy of 69.0% and 82.4%, respectively (p = 0.019). To validate the synthetic molecules, dogs trained with synthetic molecules had to detect oestrous odour in natural fluid without further training (accuracy 37.6%). Dogs trained on natural fluid detected the synthetic molecules with an accuracy of 50.0% (50% vs 37.4%, p < 0.05). Dogs can recognize natural vaginal fluid from cows in oestrus after they have been trained with synthetic oestrous molecules, but accuracy needs to be improved. © 2014 Blackwell Verlag GmbH.

Intelligent detection of cracks in metallic surfaces using a waveguide sensor loaded with metamaterial elements.

PubMed

Ali, Abdulbaset; Hu, Bing; Ramahi, Omar

2015-05-15

This work presents a real life experiment of implementing an artificial intelligence model for detecting sub-millimeter cracks in metallic surfaces on a dataset obtained from a waveguide sensor loaded with metamaterial elements. Crack detection using microwave sensors is typically based on human observation of change in the sensor's signal (pattern) depicted on a high-resolution screen of the test equipment. However, as demonstrated in this work, implementing artificial intelligence to classify cracked from non-cracked surfaces has appreciable impact in terms of sensing sensitivity, cost, and automation. Furthermore, applying artificial intelligence for post-processing data collected from microwave sensors is a cornerstone for handheld test equipment that can outperform rack equipment with large screens and sophisticated plotting features. The proposed method was tested on a metallic plate with different cracks and the obtained experimental results showed good crack classification accuracy rates.

A common-path optical coherence tomography based electrode for structural imaging of nerves and recording of action potentials

NASA Astrophysics Data System (ADS)

Islam, M. Shahidul; Haque, Md. Rezuanul; Oh, Christian M.; Wang, Yan; Park, B. Hyle

2013-03-01

Current technologies for monitoring neural activity either use different variety of electrodes (electrical recording) or require contrast agents introduced exogenously or through genetic modification (optical imaging). Here we demonstrate an optical method for non-contact and contrast agent free detection of nerve activity using phase-resolved optical coherence tomography (pr-OCT). A common-path variation of the pr-OCT is recently implemented and the developed system demonstrated the capability to detect rapid transient structural changes that accompany neural spike propagation. No averaging over multiple trials was required, indicating its capability of single-shot detection of individual impulses from functionally stimulated Limulus optic nerve. The strength of this OCT-based optical electrode is that it is a contactless method and does not require any exogenous contrast agent. With further improvements in accuracy and sensitivity, this optical electrode will play a complementary role to the existing recording technologies in future.

Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors.

PubMed

Godino-Llorente, J I; Gómez-Vilda, P

2004-02-01

It is well known that vocal and voice diseases do not necessarily cause perceptible changes in the acoustic voice signal. Acoustic analysis is a useful tool to diagnose voice diseases being a complementary technique to other methods based on direct observation of the vocal folds by laryngoscopy. Through the present paper two neural-network based classification approaches applied to the automatic detection of voice disorders will be studied. Structures studied are multilayer perceptron and learning vector quantization fed using short-term vectors calculated accordingly to the well-known Mel Frequency Coefficient cepstral parameterization. The paper shows that these architectures allow the detection of voice disorders--including glottic cancer--under highly reliable conditions. Within this context, the Learning Vector quantization methodology demonstrated to be more reliable than the multilayer perceptron architecture yielding 96% frame accuracy under similar working conditions.

Feasibility evaluation of a motion detection system with face images for stereotactic radiosurgery.

PubMed

Yamakawa, Takuya; Ogawa, Koichi; Iyatomi, Hitoshi; Kunieda, Etsuo

2011-01-01

In stereotactic radiosurgery we can irradiate a targeted volume precisely with a narrow high-energy x-ray beam, and thus the motion of a targeted area may cause side effects to normal organs. This paper describes our motion detection system with three USB cameras. To reduce the effect of change in illuminance in a tracking area we used an infrared light and USB cameras that were sensitive to the infrared light. The motion detection of a patient was performed by tracking his/her ears and nose with three USB cameras, where pattern matching between a predefined template image for each view and acquired images was done by an exhaustive search method with a general-purpose computing on a graphics processing unit (GPGPU). The results of the experiments showed that the measurement accuracy of our system was less than 0.7 mm, amounting to less than half of that of our previous system.

Intelligent Detection of Cracks in Metallic Surfaces Using a Waveguide Sensor Loaded with Metamaterial Elements

PubMed Central

Ali, Abdulbaset; Hu, Bing; Ramahi, Omar M.

2015-01-01

This work presents a real-life experiment implementing an artificial intelligence model for detecting sub-millimeter cracks in metallic surfaces on a dataset obtained from a waveguide sensor loaded with metamaterial elements. Crack detection using microwave sensors is typically based on human observation of change in the sensor's signal (pattern) depicted on a high-resolution screen of the test equipment. However, as demonstrated in this work, implementing artificial intelligence to classify cracked from non-cracked surfaces has appreciable impacts in terms of sensing sensitivity, cost, and automation. Furthermore, applying artificial intelligence for post-processing the data collected from microwave sensors is a cornerstone for handheld test equipment that can outperform rack equipment with large screens and sophisticated plotting features. The proposed method was tested on a metallic plate with different cracks, and the experimental results showed good crack classification accuracy rates. PMID:25988871

Measuring Blood Glucose Concentrations in Photometric Glucometers Requiring Very Small Sample Volumes.

PubMed

Demitri, Nevine; Zoubir, Abdelhak M

2017-01-01

Glucometers present an important self-monitoring tool for diabetes patients and, therefore, must exhibit high accuracy as well as good usability features. Based on an invasive photometric measurement principle that drastically reduces the volume of the blood sample needed from the patient, we present a framework that is capable of dealing with small blood samples, while maintaining the required accuracy. The framework consists of two major parts: 1) image segmentation; and 2) convergence detection. Step 1 is based on iterative mode-seeking methods to estimate the intensity value of the region of interest. We present several variations of these methods and give theoretical proofs of their convergence. Our approach is able to deal with changes in the number and position of clusters without any prior knowledge. Furthermore, we propose a method based on sparse approximation to decrease the computational load, while maintaining accuracy. Step 2 is achieved by employing temporal tracking and prediction, herewith decreasing the measurement time, and, thus, improving usability. Our framework is tested on several real datasets with different characteristics. We show that we are able to estimate the underlying glucose concentration from much smaller blood samples than is currently state of the art with sufficient accuracy according to the most recent ISO standards and reduce measurement time significantly compared to state-of-the-art methods.

Comparison of point counts and territory mapping for detecting effects of forest management on songbirds

USGS Publications Warehouse

Newell, Felicity L.; Sheehan, James; Wood, Petra Bohall; Rodewald, Amanda D.; Buehler, David A.; Keyser, Patrick D.; Larkin, Jeffrey L.; Beachy, Tiffany A.; Bakermans, Marja H.; Boves, Than J.; Evans, Andrea; George, Gregory A.; McDermott, Molly E.; Perkins, Kelly A.; White, Matthew; Wigley, T. Bently

2013-01-01

Point counts are commonly used to assess changes in bird abundance, including analytical approaches such as distance sampling that estimate density. Point-count methods have come under increasing scrutiny because effects of detection probability and field error are difficult to quantify. For seven forest songbirds, we compared fixed-radii counts (50 m and 100 m) and density estimates obtained from distance sampling to known numbers of birds determined by territory mapping. We applied point-count analytic approaches to a typical forest management question and compared results to those obtained by territory mapping. We used a before–after control impact (BACI) analysis with a data set collected across seven study areas in the central Appalachians from 2006 to 2010. Using a 50-m fixed radius, variance in error was at least 1.5 times that of the other methods, whereas a 100-m fixed radius underestimated actual density by >3 territories per 10 ha for the most abundant species. Distance sampling improved accuracy and precision compared to fixed-radius counts, although estimates were affected by birds counted outside 10-ha units. In the BACI analysis, territory mapping detected an overall treatment effect for five of the seven species, and effects were generally consistent each year. In contrast, all point-count methods failed to detect two treatment effects due to variance and error in annual estimates. Overall, our results highlight the need for adequate sample sizes to reduce variance, and skilled observers to reduce the level of error in point-count data. Ultimately, the advantages and disadvantages of different survey methods should be considered in the context of overall study design and objectives, allowing for trade-offs among effort, accuracy, and power to detect treatment effects.

26 CFR 1.6662-7 - Omnibus Budget Reconciliation Act of 1993 changes to the accuracy-related penalty.

Code of Federal Regulations, 2010 CFR

2010-04-01

... changes to the accuracy-related penalty. 1.6662-7 Section 1.6662-7 Internal Revenue INTERNAL REVENUE SERVICE, DEPARTMENT OF THE TREASURY (CONTINUED) INCOME TAX (CONTINUED) INCOME TAXES Additions to the Tax... changes to the accuracy-related penalty in section 6662. This section provides rules reflecting those...

Measuring and modeling high-resolution topographic change at archaeological sites in Grand Canyon National Park, Arizona, U.S.A.

NASA Astrophysics Data System (ADS)

Collins, B. D.; Corbett, S. C.; Fairley, H. C.

2012-04-01

Erosion of archaeological sites within Grand Canyon National Park (GCNP) Arizona, located in the southwestern United States is a subject of continuing interest to land and resource managers. This is partly fueled by an ongoing debate about whether and to what degree controlled releases from Glen Canyon Dam, located immediately upstream of GCNP, are affecting the physical integrity of archaeological sites. Long-term topographic change due to natural sources is typical in the desert southwest region. However, continuing erosion, which may be related in-part to anthropogenic factors, threatens both the preservation of archaeological sites as well as our ability to study evidence of past human habitation in GCNP that dates back at least 8,000 years before present. To quantitatively identify changes to archaeological sites in this region, and with the broader intention of developing numerical models to predict how and under what circumstances dam-controlled flows influence archaeological sites, we undertook a detailed terrestrial-lidar based monitoring program at thirteen sites between 2006 and 2010. Our studies looked specifically at sites located along the Colorado River that are potentially subject to changes related to dam operations. This could occur, for example, by limited sediment supply to sand bars which in turn contribute aeolian sediment to archaeologic sites. Each site was several hundred to several thousand square meters in size and was surveyed multiple times during the 5-year period. Our monitoring program shows how various data registration and georeferencing techniques result in varying degrees of topographic surface model accuracy. For example, surveys performed between 2006 and 2007 used point cloud registration methods and resulted in estimated change detection thresholds of 8 cm between repeat surveys. In 2010, surveys at the same sites used control point registration methods and resulted in estimated change detection thresholds of 3 cm. Error thresholds were determined using two types of change detection error analyses. The first used the absolute errors inherent in each step of the lidar data collection process (i.e., directly combining laser, survey, and registration errors) and provides a conservative estimate of potential errors. The second used an empirical metric based on the closest point-to-point match between known fixed objects (e.g., large boulders) and results in a more realistic error bound. Our data indicate that some sites changed significantly during the monitored time period. These measurements provide much of the essential data required for developing an in-house, physically-based, numerical sediment transport model that can provide estimates on the likelihood for future archaeological site change in GCNP. Thus far, we are finding that the data provided by typical terrestrial lidar surveys is likely overly-dense for numerical model requirements with respect to computational efficiency. Despite this, we also find that high-resolution data is necessary to perform change detection at the accuracy required for model calibration and to document changes before they have progressed beyond the point when site integrity is compromised. The results of the study will provide land and resource managers with the pertinent information needed to oversee these archaeological resources in the best way possible.

Quick probabilistic binary image matching: changing the rules of the game

NASA Astrophysics Data System (ADS)

Mustafa, Adnan A. Y.

2016-09-01

A Probabilistic Matching Model for Binary Images (PMMBI) is presented that predicts the probability of matching binary images with any level of similarity. The model relates the number of mappings, the amount of similarity between the images and the detection confidence. We show the advantage of using a probabilistic approach to matching in similarity space as opposed to a linear search in size space. With PMMBI a complete model is available to predict the quick detection of dissimilar binary images. Furthermore, the similarity between the images can be measured to a good degree if the images are highly similar. PMMBI shows that only a few pixels need to be compared to detect dissimilarity between images, as low as two pixels in some cases. PMMBI is image size invariant; images of any size can be matched at the same quick speed. Near-duplicate images can also be detected without much difficulty. We present tests on real images that show the prediction accuracy of the model.