Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods.

PubMed

Cabrieto, Jedelyn; Tuerlinckx, Francis; Kuppens, Peter; Grassmann, Mariel; Ceulemans, Eva

2017-06-01

Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP. Since these methods use different statistical approaches, two issues need to be tackled. First, applied researchers may find it hard to appraise the differences between the methods. Second, a direct comparison of the relative performance of all these methods for capturing change points signaling correlation changes is still lacking. Therefore, we present the basic principles behind DeCon, E-divisive, Multirank, and KCP and the corresponding algorithms, to make them more accessible to readers. We further compared their performance through extensive simulations using the settings of Bulteel et al. (Biological Psychology, 98 (1), 29-42, 2014) implying changes in mean and in correlation structure and those of Matteson and James (Journal of the American Statistical Association, 109 (505), 334-345, 2014) implying different numbers of (noise) variables. KCP emerged as the best method in almost all settings. However, in case of more than two noise variables, only DeCon performed adequately in detecting correlation changes.

lidar change detection using building models

NASA Astrophysics Data System (ADS)

Kim, Angela M.; Runyon, Scott C.; Jalobeanu, Andre; Esterline, Chelsea H.; Kruse, Fred A.

2014-06-01

Terrestrial LiDAR scans of building models collected with a FARO Focus3D and a RIEGL VZ-400 were used to investigate point-to-point and model-to-model LiDAR change detection. LiDAR data were scaled, decimated, and georegistered to mimic real world airborne collects. Two physical building models were used to explore various aspects of the change detection process. The first model was a 1:250-scale representation of the Naval Postgraduate School campus in Monterey, CA, constructed from Lego blocks and scanned in a laboratory setting using both the FARO and RIEGL. The second model at 1:8-scale consisted of large cardboard boxes placed outdoors and scanned from rooftops of adjacent buildings using the RIEGL. A point-to-point change detection scheme was applied directly to the point-cloud datasets. In the model-to-model change detection scheme, changes were detected by comparing Digital Surface Models (DSMs). The use of physical models allowed analysis of effects of changes in scanner and scanning geometry, and performance of the change detection methods on different types of changes, including building collapse or subsistence, construction, and shifts in location. Results indicate that at low false-alarm rates, the point-to-point method slightly outperforms the model-to-model method. The point-to-point method is less sensitive to misregistration errors in the data. Best results are obtained when the baseline and change datasets are collected using the same LiDAR system and collection geometry.

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.

Evidential analysis of difference images for change detection of multitemporal remote sensing images

NASA Astrophysics Data System (ADS)

Chen, Yin; Peng, Lijuan; Cremers, Armin B.

2018-03-01

In this article, we develop two methods for unsupervised change detection in multitemporal remote sensing images based on Dempster-Shafer's theory of evidence (DST). In most unsupervised change detection methods, the probability of difference image is assumed to be characterized by mixture models, whose parameters are estimated by the expectation maximization (EM) method. However, the main drawback of the EM method is that it does not consider spatial contextual information, which may entail rather noisy detection results with numerous spurious alarms. To remedy this, we firstly develop an evidence theory based EM method (EEM) which incorporates spatial contextual information in EM by iteratively fusing the belief assignments of neighboring pixels to the central pixel. Secondly, an evidential labeling method in the sense of maximizing a posteriori probability (MAP) is proposed in order to further enhance the detection result. It first uses the parameters estimated by EEM to initialize the class labels of a difference image. Then it iteratively fuses class conditional information and spatial contextual information, and updates labels and class parameters. Finally it converges to a fixed state which gives the detection result. A simulated image set and two real remote sensing data sets are used to evaluate the two evidential change detection methods. Experimental results show that the new evidential methods are comparable to other prevalent methods in terms of total error rate.

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

Detection of a sudden change of the field time series based on the Lorenz system

PubMed Central

Li, Fang; Shen, BingLu; Yan, PengCheng; Song, Jian; Ma, DeShan

2017-01-01

We conducted an exploratory study of the detection of a sudden change of the field time series based on the numerical solution of the Lorenz system. First, the time when the Lorenz path jumped between the regions on the left and right of the equilibrium point of the Lorenz system was quantitatively marked and the sudden change time of the Lorenz system was obtained. Second, the numerical solution of the Lorenz system was regarded as a vector; thus, this solution could be considered as a vector time series. We transformed the vector time series into a time series using the vector inner product, considering the geometric and topological features of the Lorenz system path. Third, the sudden change of the resulting time series was detected using the sliding t-test method. Comparing the test results with the quantitatively marked time indicated that the method could detect every sudden change of the Lorenz path, thus the method is effective. Finally, we used the method to detect the sudden change of the pressure field time series and temperature field time series, and obtained good results for both series, which indicates that the method can apply to high-dimension vector time series. Mathematically, there is no essential difference between the field time series and vector time series; thus, we provide a new method for the detection of the sudden change of the field time series. PMID:28141832

Fault detection using a two-model test for changes in the parameters of an autoregressive time series

NASA Technical Reports Server (NTRS)

Scholtz, P.; Smyth, P.

1992-01-01

This article describes an investigation of a statistical hypothesis testing method for detecting changes in the characteristics of an observed time series. The work is motivated by the need for practical automated methods for on-line monitoring of Deep Space Network (DSN) equipment to detect failures and changes in behavior. In particular, on-line monitoring of the motor current in a DSN 34-m beam waveguide (BWG) antenna is used as an example. The algorithm is based on a measure of the information theoretic distance between two autoregressive models: one estimated with data from a dynamic reference window and one estimated with data from a sliding reference window. The Hinkley cumulative sum stopping rule is utilized to detect a change in the mean of this distance measure, corresponding to the detection of a change in the underlying process. The basic theory behind this two-model test is presented, and the problem of practical implementation is addressed, examining windowing methods, model estimation, and detection parameter assignment. Results from the five fault-transition simulations are presented to show the possible limitations of the detection method, and suggestions for future implementation are given.

Convolutional neural network features based change detection in satellite images

NASA Astrophysics Data System (ADS)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

Damage diagnosis algorithm using a sequential change point detection method with an unknown distribution for damage

NASA Astrophysics Data System (ADS)

Noh, Hae Young; Rajagopal, Ram; Kiremidjian, Anne S.

2012-04-01

This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method for the cases where the post-damage feature distribution is unknown a priori. This algorithm extracts features from structural vibration data using time-series analysis and then declares damage using the change point detection method. The change point detection method asymptotically minimizes detection delay for a given false alarm rate. The conventional method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori. Therefore, our algorithm estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using multiple sets of simulated data and a set of experimental data collected from a four-story steel special moment-resisting frame. Our algorithm was able to estimate the post-damage distribution consistently and resulted in detection delays only a few seconds longer than the delays from the conventional method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

Change detection of medical images using dictionary learning techniques and PCA

NASA Astrophysics Data System (ADS)

Nika, Varvara; Babyn, Paul; Zhu, Hongmei

2014-03-01

Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of MRI scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. In this paper we present the Eigen-Block Change Detection algorithm (EigenBlockCD). It performs local registration and identifies the changes between consecutive MR images of the brain. Blocks of pixels from baseline scan are used to train local dictionaries that are then used to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between L1 and L2 norms as two possible similarity measures in the EigenBlockCD. We show the advantages of L2 norm over L1 norm theoretically and numerically. We also demonstrate the performance of the EigenBlockCD algorithm for detecting changes of MR images and compare our results with those provided in recent literature. Experimental results with both simulated and real MRI scans show that the EigenBlockCD outperforms the previous methods. It detects clinical changes while ignoring the changes due to patient's position and other acquisition artifacts.

Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery

NASA Astrophysics Data System (ADS)

Bai, Ting; Sun, Kaimin; Deng, Shiquan; Chen, Yan

2018-03-01

High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.

A comparison of change detection methods using multispectral scanner data

USGS Publications Warehouse

Seevers, Paul M.; Jones, Brenda K.; Qiu, Zhicheng; Liu, Yutong

1994-01-01

Change detection methods were investigated as a cooperative activity between the U.S. Geological Survey and the National Bureau of Surveying and Mapping, People's Republic of China. Subtraction of band 2, band 3, normalized difference vegetation index, and tasseled cap bands 1 and 2 data from two multispectral scanner images were tested using two sites in the United States and one in the People's Republic of China. A new statistical method also was tested. Band 2 subtraction gives the best results for detecting change from vegetative cover to urban development. The statistical method identifies areas that have changed and uses a fast classification algorithm to classify the original data of the changed areas by land cover type present for each image date.

Region-based automatic building and forest change detection on Cartosat-1 stereo imagery

NASA Astrophysics Data System (ADS)

Tian, J.; Reinartz, P.; d'Angelo, P.; Ehlers, M.

2013-05-01

In this paper a novel region-based method is proposed for change detection using space borne panchromatic Cartosat-1 stereo imagery. In the first step, Digital Surface Models (DSMs) from two dates are generated by semi-global matching. The geometric lateral resolution of the DSMs is 5 m × 5 m and the height accuracy is in the range of approximately 3 m (RMSE). In the second step, mean-shift segmentation is applied on the orthorectified images of two dates to obtain initial regions. A region intersection following a merging strategy is proposed to get minimum change regions and multi-level change vectors are extracted for these regions. Finally change detection is achieved by combining these features with weighted change vector analysis. The result evaluations demonstrate that the applied DSM generation method is well suited for Cartosat-1 imagery, and the extracted height values can largely improve the change detection accuracy, moreover it is shown that the proposed change detection method can be used robustly for both forest and industrial areas.

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

Rapid Change Detection Algorithm for Disaster Management

NASA Astrophysics Data System (ADS)

Michel, U.; Thunig, H.; Ehlers, M.; Reinartz, P.

2012-07-01

This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects. Standard methods for automated change detection prove not to be sufficient; therefore a new method was developed and tested. The presented method allows a fast detection and visualization of change in areas of crisis or catastrophes. While often new methods of remote sensing are developed without user oriented aspects, organizations and authorities are not able to use these methods because of absence of remote sensing know how. Therefore a semi-automated procedure was developed. Within a transferable framework, the developed algorithm can be implemented for a set of remote sensing data among different investigation areas. Several case studies are the base for the retrieved results. Within a coarse dividing into statistical parts and the segmentation in meaningful objects, the framework is able to deal with different types of change. By means of an elaborated Temporal Change Index (TCI) only panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas where rebuilding has already started.

A novel data-driven learning method for radar target detection in nonstationary environments

DOE PAGES

Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

2016-04-12

Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detectmore » changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. Furthermore, we use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.« less

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.

Earlier Detection of Tumor Treatment Response Using Magnetic Resonance Diffusion Imaging with Oscillating Gradients

PubMed Central

Colvin, Daniel C.; Loveless, Mary E.; Does, Mark D.; Yue, Zou; Yankeelov, Thomas E.; Gore, John C.

2011-01-01

An improved method for detecting early changes in tumors in response to treatment, based on a modification of diffusion-weighted magnetic resonance imaging, has been demonstrated in an animal model. Early detection of therapeutic response in tumors is important both clinically and in pre-clinical assessments of novel treatments. Non-invasive imaging methods that can detect and assess tumor response early in the course of treatment, and before frank changes in tumor morphology are evident, are of considerable interest as potential biomarkers of treatment efficacy. Diffusion-weighted magnetic resonance imaging is sensitive to changes in water diffusion rates in tissues that result from structural variations in the local cellular environment, but conventional methods mainly reflect changes in tissue cellularity and do not convey information specific to micro-structural variations at sub-cellular scales. We implemented a modified imaging technique using oscillating gradients of the magnetic field for evaluating water diffusion rates over very short spatial scales that are more specific for detecting changes in intracellular structure that may precede changes in cellularity. Results from a study of orthotopic 9L gliomas in rat brains indicate that this method can detect changes as early as 24 hours following treatment with 1,3-bis(2-chloroethyl)-1-nitrosourea (BCNU), when conventional approaches do not find significant effects. These studies suggest that diffusion imaging using oscillating gradients may be used to obtain an earlier indication of treatment efficacy than previous magnetic resonance imaging methods. PMID:21190804

Early Detection of Physical Activity for People With Type 1 Diabetes Mellitus.

PubMed

Dasanayake, Isuru S; Bevier, Wendy C; Castorino, Kristin; Pinsker, Jordan E; Seborg, Dale E; Doyle, Francis J; Dassau, Eyal

2015-06-30

Early detection of exercise in individuals with type 1 diabetes mellitus (T1DM) may allow changes in therapy to prevent hypoglycemia. Currently there is limited experience with automated methods that detect the onset and end of exercise in this population. We sought to develop a novel method to quickly and reliably detect the onset and end of exercise in these individuals before significant changes in blood glucose (BG) occur. Sixteen adults with T1DM were studied as outpatients using a diary, accelerometer, heart rate monitor, and continuous glucose monitor for 2 days. These data were used to develop a principal component analysis based exercise detection method. Subjects also performed 60 and 30 minute exercise sessions at 30% and 50% predicted heart rate reserve (HRR), respectively. The detection method was applied to the exercise sessions to determine how quickly the detection of start and end of exercise occurred relative to change in BG. Mild 30% HRR and moderate 50% HRR exercise onset was identified in 6 ± 3 and 5 ± 2 (mean ± SD) minutes, while completion was detected in 3 ± 8 and 6 ± 5 minutes, respectively. BG change from start of exercise to detection time was 1 ± 6 and -1 ± 3 mg/dL, and, from the end of exercise to detection time was 6 ± 4 and -17 ± 13 mg/dL, respectively, for the 2 exercise sessions. False positive and negative ratios were 4 ± 2% and 21 ± 22%. The novel method for exercise detection identified the onset and end of exercise in approximately 5 minutes, with an average BG change of only -6 mg/dL. © 2015 Diabetes Technology Society.

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.

SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos

NASA Astrophysics Data System (ADS)

Işık, Şahin; Özkan, Kemal; Günal, Serkan; Gerek, Ömer Nezih

2018-03-01

Change detection with background subtraction process remains to be an unresolved issue and attracts research interest due to challenges encountered on static and dynamic scenes. The key challenge is about how to update dynamically changing backgrounds from frames with an adaptive and self-regulated feedback mechanism. In order to achieve this, we present an effective change detection algorithm for pixelwise changes. A sliding window approach combined with dynamic control of update parameters is introduced for updating background frames, which we called sliding window-based change detection. Comprehensive experiments on related test videos show that the integrated algorithm yields good objective and subjective performance by overcoming illumination variations, camera jitters, and intermittent object motions. It is argued that the obtained method makes a fair alternative in most types of foreground extraction scenarios; unlike case-specific methods, which normally fail for their nonconsidered scenarios.

Image Change Detection via Ensemble Learning

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

Martin, Benjamin W; Vatsavai, Raju

2013-01-01

The concept of geographic change detection is relevant in many areas. Changes in geography can reveal much information about a particular location. For example, analysis of changes in geography can identify regions of population growth, change in land use, and potential environmental disturbance. A common way to perform change detection is to use a simple method such as differencing to detect regions of change. Though these techniques are simple, often the application of these techniques is very limited. Recently, use of machine learning methods such as neural networks for change detection has been explored with great success. In this work,more » we explore the use of ensemble learning methodologies for detecting changes in bitemporal synthetic aperture radar (SAR) images. Ensemble learning uses a collection of weak machine learning classifiers to create a stronger classifier which has higher accuracy than the individual classifiers in the ensemble. The strength of the ensemble lies in the fact that the individual classifiers in the ensemble create a mixture of experts in which the final classification made by the ensemble classifier is calculated from the outputs of the individual classifiers. Our methodology leverages this aspect of ensemble learning by training collections of weak decision tree based classifiers to identify regions of change in SAR images collected of a region in the Staten Island, New York area during Hurricane Sandy. Preliminary studies show that the ensemble method has approximately 11.5% higher change detection accuracy than an individual classifier.« less

Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method

PubMed Central

Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

2016-01-01

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis. PMID:28029121

Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method.

PubMed

Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

2016-12-24

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis.

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.

A new method of real-time detection of changes in periodic data stream

NASA Astrophysics Data System (ADS)

Lyu, Chen; Lu, Guoliang; Cheng, Bin; Zheng, Xiangwei

2017-07-01

The change point detection in periodic time series is much desirable in many practical usages. We present a novel algorithm for this task, which includes two phases: 1) anomaly measure- on the basis of a typical regression model, we propose a new computation method to measure anomalies in time series which does not require any reference data from other measurement(s); 2) change detection- we introduce a new martingale test for detection which can be operated in an unsupervised and nonparametric way. We have conducted extensive experiments to systematically test our algorithm. The results make us believe that our algorithm can be directly applicable in many real-world change-point-detection applications.

Experimental and environmental factors affect spurious detection of ecological thresholds

USGS Publications Warehouse

Daily, Jonathan P.; Hitt, Nathaniel P.; Smith, David; Snyder, Craig D.

2012-01-01

Threshold detection methods are increasingly popular for assessing nonlinear responses to environmental change, but their statistical performance remains poorly understood. We simulated linear change in stream benthic macroinvertebrate communities and evaluated the performance of commonly used threshold detection methods based on model fitting (piecewise quantile regression [PQR]), data partitioning (nonparametric change point analysis [NCPA]), and a hybrid approach (significant zero crossings [SiZer]). We demonstrated that false detection of ecological thresholds (type I errors) and inferences on threshold locations are influenced by sample size, rate of linear change, and frequency of observations across the environmental gradient (i.e., sample-environment distribution, SED). However, the relative importance of these factors varied among statistical methods and between inference types. False detection rates were influenced primarily by user-selected parameters for PQR (τ) and SiZer (bandwidth) and secondarily by sample size (for PQR) and SED (for SiZer). In contrast, the location of reported thresholds was influenced primarily by SED. Bootstrapped confidence intervals for NCPA threshold locations revealed strong correspondence to SED. We conclude that the choice of statistical methods for threshold detection should be matched to experimental and environmental constraints to minimize false detection rates and avoid spurious inferences regarding threshold location.

Point pattern match-based change detection in a constellation of previously detected objects

DOEpatents

Paglieroni, David W.

2016-06-07

A method and system is provided that applies attribute- and topology-based change detection to objects that were detected on previous scans of a medium. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, detection strength, size, elongation, orientation, etc. The locations define a three-dimensional network topology forming a constellation of previously detected objects. The change detection system stores attributes of the previously detected objects in a constellation database. The change detection system detects changes by comparing the attributes and topological consistency of newly detected objects encountered during a new scan of the medium to previously detected objects in the constellation database. The change detection system may receive the attributes of the newly detected objects as the objects are detected by an object detection system in real time.

Deep learning on temporal-spectral data for anomaly detection

NASA Astrophysics Data System (ADS)

Ma, King; Leung, Henry; Jalilian, Ehsan; Huang, Daniel

2017-05-01

Detecting anomalies is important for continuous monitoring of sensor systems. One significant challenge is to use sensor data and autonomously detect changes that cause different conditions to occur. Using deep learning methods, we are able to monitor and detect changes as a result of some disturbance in the system. We utilize deep neural networks for sequence analysis of time series. We use a multi-step method for anomaly detection. We train the network to learn spectral and temporal features from the acoustic time series. We test our method using fiber-optic acoustic data from a pipeline.

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.

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.

A likelihood ratio test for evolutionary rate shifts and functional divergence among proteins

PubMed Central

Knudsen, Bjarne; Miyamoto, Michael M.

2001-01-01

Changes in protein function can lead to changes in the selection acting on specific residues. This can often be detected as evolutionary rate changes at the sites in question. A maximum-likelihood method for detecting evolutionary rate shifts at specific protein positions is presented. The method determines significance values of the rate differences to give a sound statistical foundation for the conclusions drawn from the analyses. A statistical test for detecting slowly evolving sites is also described. The methods are applied to a set of Myc proteins for the identification of both conserved sites and those with changing evolutionary rates. Those positions with conserved and changing rates are related to the structures and functions of their proteins. The results are compared with an earlier Bayesian method, thereby highlighting the advantages of the new likelihood ratio tests. PMID:11734650

a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

NASA Astrophysics Data System (ADS)

Li, S.; Zhang, S.; Yang, D.

2017-09-01

Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

Transistor-based particle detection systems and methods

DOEpatents

Jain, Ankit; Nair, Pradeep R.; Alam, Muhammad Ashraful

2015-06-09

Transistor-based particle detection systems and methods may be configured to detect charged and non-charged particles. Such systems may include a supporting structure contacting a gate of a transistor and separating the gate from a dielectric of the transistor, and the transistor may have a near pull-in bias and a sub-threshold region bias to facilitate particle detection. The transistor may be configured to change current flow through the transistor in response to a change in stiffness of the gate caused by securing of a particle to the gate, and the transistor-based particle detection system may configured to detect the non-charged particle at least from the change in current flow.

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

Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detectmore » changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. Furthermore, we use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.« less

Multi-Temporal Classification and Change Detection Using Uav Images

NASA Astrophysics Data System (ADS)

Makuti, S.; Nex, F.; Yang, M. Y.

2018-05-01

In this paper different methodologies for the classification and change detection of UAV image blocks are explored. UAV is not only the cheapest platform for image acquisition but it is also the easiest platform to operate in repeated data collections over a changing area like a building construction site. Two change detection techniques have been evaluated in this study: the pre-classification and the post-classification algorithms. These methods are based on three main steps: feature extraction, classification and change detection. A set of state of the art features have been used in the tests: colour features (HSV), textural features (GLCM) and 3D geometric features. For classification purposes Conditional Random Field (CRF) has been used: the unary potential was determined using the Random Forest algorithm while the pairwise potential was defined by the fully connected CRF. In the performed tests, different feature configurations and settings have been considered to assess the performance of these methods in such challenging task. Experimental results showed that the post-classification approach outperforms the pre-classification change detection method. This was analysed using the overall accuracy, where by post classification have an accuracy of up to 62.6 % and the pre classification change detection have an accuracy of 46.5 %. These results represent a first useful indication for future works and developments.

Land use change detection based on multi-date imagery from different satellite sensor systems

NASA Technical Reports Server (NTRS)

Stow, Douglas A.; Collins, Doretta; Mckinsey, David

1990-01-01

An empirical study is conducted to assess the accuracy of land use change detection using satellite image data acquired ten years apart by sensors with differing spatial resolutions. The primary goals of the investigation were to (1) compare standard change detection methods applied to image data of varying spatial resolution, (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice versa in the registration process, (3) determine if Landsat/Thermatic Mapper or SPOT/High Resolution Visible multispectral data provide more accurate detection of land use changes when registered to historical Landsat/MSS data. It is concluded that image ratioing of multisensor, multidate satellite data produced higher change detection accuracies than did principal components analysis, and that it is useful as a land use change enhancement method.

Detection of urban expansion in an urban-rural landscape with multitemporal QuickBird images

PubMed Central

Lu, Dengsheng; Hetrick, Scott; Moran, Emilio; Li, Guiying

2011-01-01

Accurately detecting urban expansion with remote sensing techniques is a challenge due to the complexity of urban landscapes. This paper explored methods for detecting urban expansion with multitemporal QuickBird images in Lucas do Rio Verde, Mato Grosso, Brazil. Different techniques, including image differencing, principal component analysis (PCA), and comparison of classified impervious surface images with the matched filtering method, were used to examine urbanization detection. An impervious surface image classified with the hybrid method was used to modify the urbanization detection results. As a comparison, the original multispectral image and segmentation-based mean-spectral images were used during the detection of urbanization. This research indicates that the comparison of classified impervious surface images with matched filtering method provides the best change detection performance, followed by the image differencing method based on segmentation-based mean spectral images. The PCA is not a good method for urban change detection in this study. Shadows and high spectral variation within the impervious surfaces represent major challenges to the detection of urban expansion when high spatial resolution images are used. PMID:21799706

Psychophysical Models for Signal Detection with Time Varying Uncertainty. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Gai, E.

1975-01-01

Psychophysical models for the behavior of the human operator in detection tasks which include change in detectability, correlation between observations and deferred decisions are developed. Classical Signal Detection Theory (SDT) is discussed and its emphasis on the sensory processes is contrasted to decision strategies. The analysis of decision strategies utilizes detection tasks with time varying signal strength. The classical theory is modified to include such tasks and several optimal decision strategies are explored. Two methods of classifying strategies are suggested. The first method is similar to the analysis of ROC curves, while the second is based on the relation between the criterion level (CL) and the detectability. Experiments to verify the analysis of tasks with changes of signal strength are designed. The results show that subjects are aware of changes in detectability and tend to use strategies that involve changes in the CL's.

A change detection method for remote sensing image based on LBP and SURF feature

NASA Astrophysics Data System (ADS)

Hu, Lei; Yang, Hao; Li, Jin; Zhang, Yun

2018-04-01

Finding the change in multi-temporal remote sensing image is important in many the image application. Because of the infection of climate and illumination, the texture of the ground object is more stable relative to the gray in high-resolution remote sensing image. And the texture features of Local Binary Patterns (LBP) and Speeded Up Robust Features (SURF) are outstanding in extracting speed and illumination invariance. A method of change detection for matched remote sensing image pair is present, which compares the similarity by LBP and SURF to detect the change and unchanged of the block after blocking the image. And region growing is adopted to process the block edge zone. The experiment results show that the method can endure some illumination change and slight texture change of the ground object.

3D change detection at street level using mobile laser scanning point clouds and terrestrial images

NASA Astrophysics Data System (ADS)

Qin, Rongjun; Gruen, Armin

2014-04-01

Automatic change detection and geo-database updating in the urban environment are difficult tasks. There has been much research on detecting changes with satellite and aerial images, but studies have rarely been performed at the street level, which is complex in its 3D geometry. Contemporary geo-databases include 3D street-level objects, which demand frequent data updating. Terrestrial images provides rich texture information for change detection, but the change detection with terrestrial images from different epochs sometimes faces problems with illumination changes, perspective distortions and unreliable 3D geometry caused by the lack of performance of automatic image matchers, while mobile laser scanning (MLS) data acquired from different epochs provides accurate 3D geometry for change detection, but is very expensive for periodical acquisition. This paper proposes a new method for change detection at street level by using combination of MLS point clouds and terrestrial images: the accurate but expensive MLS data acquired from an early epoch serves as the reference, and terrestrial images or photogrammetric images captured from an image-based mobile mapping system (MMS) at a later epoch are used to detect the geometrical changes between different epochs. The method will automatically mark the possible changes in each view, which provides a cost-efficient method for frequent data updating. The methodology is divided into several steps. In the first step, the point clouds are recorded by the MLS system and processed, with data cleaned and classified by semi-automatic means. In the second step, terrestrial images or mobile mapping images at a later epoch are taken and registered to the point cloud, and then point clouds are projected on each image by a weighted window based z-buffering method for view dependent 2D triangulation. In the next step, stereo pairs of the terrestrial images are rectified and re-projected between each other to check the geometrical consistency between point clouds and stereo images. Finally, an over-segmentation based graph cut optimization is carried out, taking into account the color, depth and class information to compute the changed area in the image space. The proposed method is invariant to light changes, robust to small co-registration errors between images and point clouds, and can be applied straightforwardly to 3D polyhedral models. This method can be used for 3D street data updating, city infrastructure management and damage monitoring in complex urban scenes.

An Investigation of Automatic Change Detection for Topographic Map Updating

NASA Astrophysics Data System (ADS)

Duncan, P.; Smit, J.

2012-08-01

Changes to the landscape are constantly occurring and it is essential for geospatial and mapping organisations that these changes are regularly detected and captured, so that map databases can be updated to reflect the current status of the landscape. The Chief Directorate of National Geospatial Information (CD: NGI), South Africa's national mapping agency, currently relies on manual methods of detecting changes and capturing these changes. These manual methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The aim of this research is to do an investigation into a methodology for automatic or semi-automatic change detection for the purpose of updating topographic databases. The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. The major data input into this study is high resolution aerial imagery and existing topographic vector data. Initial results indicate the traditional pixel-based image classification approaches are unsatisfactory for large scale land-use mapping and that object-orientated approaches hold more promise. Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. A solution may lie with a hybrid approach of pixel and object-oriented techniques.

Method of noncontacting ultrasonic process monitoring

DOEpatents

Garcia, Gabriel V.; Walter, John B.; Telschow, Kenneth L.

1992-01-01

A method of monitoring a material during processing comprising the steps of (a) shining a detection light on the surface of a material; (b) generating ultrasonic waves at the surface of the material to cause a change in frequency of the detection light; (c) detecting a change in the frequency of the detection light at the surface of the material; (d) detecting said ultrasonic waves at the surface point of detection of the material; (e) measuring a change in the time elapsed from generating the ultrasonic waves at the surface of the material and return to the surface point of detection of the material, to determine the transit time; and (f) comparing the transit time to predetermined values to determine properties such as, density and the elastic quality of the material.

Enhanced change detection index for disaster response, recovery assessment and monitoring of buildings and critical facilities-A case study for Muzzaffarabad, Pakistan

NASA Astrophysics Data System (ADS)

de Alwis Pitts, Dilkushi A.; So, Emily

2017-12-01

The availability of Very High Resolution (VHR) optical sensors and a growing image archive that is frequently updated, allows the use of change detection in post-disaster recovery and monitoring for robust and rapid results. The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds existing knowledge into the processing to enhance change detection. It also allows targeting specific types of changes pertaining to similar man-made objects such as buildings and critical facilities. The change detection method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within the object and a set of training data. More emphasis is put on the building edges to capture the structural damage in quantifying change after disaster. Once the change is quantified, based on training data, the method can be used automatically to detect change in order to observe recovery over time in potentially large areas. Analysis over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby giving managers a better understanding of productive management and recovery practices. The recovery and monitoring can be analyzed using the index in zones extending from to epicentre of disaster or administrative boundaries over time.

Change detection of medical images using dictionary learning techniques and principal component analysis.

PubMed

Nika, Varvara; Babyn, Paul; Zhu, Hongmei

2014-07-01

Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of magnetic resonance imaging (MRI) scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are being used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. We present an improved version of the EigenBlockCD algorithm, named the EigenBlockCD-2. The EigenBlockCD-2 algorithm performs an initial global registration and identifies the changes between serial MR images of the brain. Blocks of pixels from a baseline scan are used to train local dictionaries to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between [Formula: see text] and [Formula: see text] norms as two possible similarity measures in the improved EigenBlockCD-2 algorithm. We show the advantages of the [Formula: see text] norm over the [Formula: see text] norm both theoretically and numerically. We also demonstrate the performance of the new EigenBlockCD-2 algorithm for detecting changes of MR images and compare our results with those provided in the recent literature. Experimental results with both simulated and real MRI scans show that our improved EigenBlockCD-2 algorithm outperforms the previous methods. It detects clinical changes while ignoring the changes due to the patient's position and other acquisition artifacts.

Comparison between reflectance confocal microscopy and two-photon microscopy in early detection of cutaneous radiation injury in a mouse model in-vivo.

PubMed

Jang, Won Hyuk; Kwon, Soonjae; Shim, Sehwan; Jang, Won-Suk; Myung, Jae Kyung; Yang, Sejung; Park, Sunhoo; Kim, Ki Hean

2018-05-12

Cutaneous radiation injury (CRI) is a skin injury caused by high dose exposure of ionizing radiation (IR). For proper treatment, early detection of CRI before clinical symptoms is important. Optical microscopic techniques such as reflectance confocal microscopy (RCM) and two-photon microscopy (TPM) have been tested as the early diagnosis method by detecting cellular changes. In this study, RCM and TPM were compared in the detection of cellular changes caused by CRI in an in-vivo mouse model. CRI was induced on the mouse hindlimb skin with various IR doses and the injured skin regions were imaged longitudinally by both modalities until the onset of clinical symptoms. Both RCM and TPM detected the changes of epidermal cells and sebaceous glands before clinical symptoms in different optical contrasts. RCM detected changes of cell morphology and scattering property based on light reflection. TPM detected detail changes of cellular structures based on autofluorescence of cells. Since both RCM and TPM were sensitive to the early-stage CRI by using different contrasts, the optimal method for clinical CRI diagnosis could be either individual methods or their combination. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Comparison of multiple methods for detecting changes in urban areas in TerraSAR-X data

NASA Astrophysics Data System (ADS)

Hammer, Horst; Dubois, Clémence; Boldt, Markus; Kuny, Silvia; Cadario, Erich; Thiele, Antje

2016-10-01

The current generation of SAR satellites such as TerraSAR-X, TanDEM-X and COSMO-SkyMed provide resolutions below one meter, permitting the detailed analysis of urban areas while covering large zones. Furthermore, as they are deployable independently of daylight and weather, such remote sensing SAR data are particularly popular for purposes such as rapid damage assessment at building level after a natural disaster. The purpose of our study is the investigation of techniques for the detection of changes based on one pre-event and one post-event SAR amplitude image. We provide a comparison of several methods for detecting changes in urban areas. Especially, changes at building locations are looked for. We analyzed two areas affected differently in detail. First, a suburban area of Paris, France, was considered due to changes caused by an urbanization project. Here, we have two TanDEM-X acquisitions available, before (November 4, 2012) and after (May 10, 2013) the changes. Second, we investigated changes that happened in Kathmandu, Nepal, after the April 25, 2015 earthquake. For this analysis, we have two TerraSAR-X acquisitions, one before (October 13, 2013) and one immediately after (April 27, 2015) the earthquake. Both areas differ by the building types, the image resolution and the available reference, which makes it an interesting challenge. In this paper, we compare six different methods for change detection. The investigated methods contain both standard criteria such as Log Ratio, Kullback-Leibler and the Difference of Entropies detector, and methods developed by the authors such as a Log Ratio combined with an Alternating Sequential Filter. All change detection results are presented and discussed by considering the available ground truth.

Method for early detection of cooling-loss events

DOEpatents

Bermudez, Sergio A.; Hamann, Hendrik; Marianno, Fernando J.

2015-06-30

A method of detecting cooling-loss event early is provided. The method includes defining a relative humidity limit and change threshold for a given space, measuring relative humidity in the given space, determining, with a processing unit, whether the measured relative humidity is within the defined relative humidity limit, generating a warning in an event the measured relative humidity is outside the defined relative humidity limit and determining whether a change in the measured relative humidity is less than the defined change threshold for the given space and generating an alarm in an event the change is greater than the defined change threshold.

Method for early detection of cooling-loss events

DOEpatents

Bermudez, Sergio A.; Hamann, Hendrik F.; Marianno, Fernando J.

2015-12-22

A method of detecting cooling-loss event early is provided. The method includes defining a relative humidity limit and change threshold for a given space, measuring relative humidity in the given space, determining, with a processing unit, whether the measured relative humidity is within the defined relative humidity limit, generating a warning in an event the measured relative humidity is outside the defined relative humidity limit and determining whether a change in the measured relative humidity is less than the defined change threshold for the given space and generating an alarm in an event the change is greater than the defined change threshold.

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.

Detection and localization of change points in temporal networks with the aid of stochastic block models

NASA Astrophysics Data System (ADS)

De Ridder, Simon; Vandermarliere, Benjamin; Ryckebusch, Jan

2016-11-01

A framework based on generalized hierarchical random graphs (GHRGs) for the detection of change points in the structure of temporal networks has recently been developed by Peel and Clauset (2015 Proc. 29th AAAI Conf. on Artificial Intelligence). We build on this methodology and extend it to also include the versatile stochastic block models (SBMs) as a parametric family for reconstructing the empirical networks. We use five different techniques for change point detection on prototypical temporal networks, including empirical and synthetic ones. We find that none of the considered methods can consistently outperform the others when it comes to detecting and locating the expected change points in empirical temporal networks. With respect to the precision and the recall of the results of the change points, we find that the method based on a degree-corrected SBM has better recall properties than other dedicated methods, especially for sparse networks and smaller sliding time window widths.

A Multi-Index Integrated Change detection method for updating the National Land Cover Database

USGS Publications Warehouse

Jin, Suming; Yang, Limin; Xian, George Z.; Danielson, Patrick; Homer, Collin G.

2010-01-01

Land cover change is typically captured by comparing two or more dates of imagery and associating spectral change with true thematic change. A new change detection method, Multi-Index Integrated Change (MIIC), has been developed to capture a full range of land cover disturbance patterns for updating the National Land Cover Database (NLCD). Specific indices typically specialize in identifying only certain types of disturbances; for example, the Normalized Burn Ratio (NBR) has been widely used for monitoring fire disturbance. Recognizing the potential complementary nature of multiple indices, we integrated four indices into one model to more accurately detect true change between two NLCD time periods. The four indices are NBR, Normalized Difference Vegetation Index (NDVI), Change Vector (CV), and a newly developed index called the Relative Change Vector (RCV). The model is designed to provide both change location and change direction (e.g. biomass increase or biomass decrease). The integrated change model has been tested on five image pairs from different regions exhibiting a variety of disturbance types. Compared with a simple change vector method, MIIC can better capture the desired change without introducing additional commission errors. The model is particularly accurate at detecting forest disturbances, such as forest harvest, forest fire, and forest regeneration. Agreement between the initial change map areas derived from MIIC and the retained final land cover type change areas will be showcased from the pilot test sites.

Caries Detection Methods Based on Changes in Optical Properties between Healthy and Carious Tissue

PubMed Central

Karlsson, Lena

2010-01-01

A conservative, noninvasive or minimally invasive approach to clinical management of dental caries requires diagnostic techniques capable of detecting and quantifying lesions at an early stage, when progression can be arrested or reversed. Objective evidence of initiation of the disease can be detected in the form of distinct changes in the optical properties of the affected tooth structure. Caries detection methods based on changes in a specific optical property are collectively referred to as optically based methods. This paper presents a simple overview of the feasibility of three such technologies for quantitative or semiquantitative assessment of caries lesions. Two of the techniques are well-established: quantitative light-induced fluorescence, which is used primarily in caries research, and laser-induced fluorescence, a commercially available method used in clinical dental practice. The third technique, based on near-infrared transillumination of dental enamel is in the developmental stages. PMID:20454579

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.

Cause-specific mortality time series analysis: a general method to detect and correct for abrupt data production changes

PubMed Central

2011-01-01

Background Monitoring the time course of mortality by cause is a key public health issue. However, several mortality data production changes may affect cause-specific time trends, thus altering the interpretation. This paper proposes a statistical method that detects abrupt changes ("jumps") and estimates correction factors that may be used for further analysis. Methods The method was applied to a subset of the AMIEHS (Avoidable Mortality in the European Union, toward better Indicators for the Effectiveness of Health Systems) project mortality database and considered for six European countries and 13 selected causes of deaths. For each country and cause of death, an automated jump detection method called Polydect was applied to the log mortality rate time series. The plausibility of a data production change associated with each detected jump was evaluated through literature search or feedback obtained from the national data producers. For each plausible jump position, the statistical significance of the between-age and between-gender jump amplitude heterogeneity was evaluated by means of a generalized additive regression model, and correction factors were deduced from the results. Results Forty-nine jumps were detected by the Polydect method from 1970 to 2005. Most of the detected jumps were found to be plausible. The age- and gender-specific amplitudes of the jumps were estimated when they were statistically heterogeneous, and they showed greater by-age heterogeneity than by-gender heterogeneity. Conclusion The method presented in this paper was successfully applied to a large set of causes of death and countries. The method appears to be an alternative to bridge coding methods when the latter are not systematically implemented because they are time- and resource-consuming. PMID:21929756

Using classification and NDVI differencing methods for monitoring sparse vegetation coverage: a case study of saltcedar in Nevada, USA.

USDA-ARS?s Scientific Manuscript database

A change detection experiment for an invasive species, saltcedar, near Lovelock, Nevada, was conducted with multi-date Compact Airborne Spectrographic Imager (CASI) hyperspectral datasets. Classification and NDVI differencing change detection methods were tested, In the classification strategy, a p...

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.

Quantification of Forecasting and Change-Point Detection Methods for Predictive Maintenance

DTIC Science & Technology

2015-08-19

industries to manage the service life of equipment, and also to detect precursors to the failure of components found in nuclear power plants, wind turbines ...detection methods for predictive maintenance 5a. CONTRACT NUMBER FA2386-14-1-4096 5b. GRANT NUMBER Grant 14IOA015 AOARD-144096 5c. PROGRAM ELEMENT...sensitive to changes related to abnormality. 15. SUBJECT TERMS predictive maintenance , predictive maintenance , forecasting 16

Tracing the conformational changes in BSA using FRET with environmentally-sensitive squaraine probes

NASA Astrophysics Data System (ADS)

Govor, Iryna V.; Tatarets, Anatoliy L.; Obukhova, Olena M.; Terpetschnig, Ewald A.; Gellerman, Gary; Patsenker, Leonid D.

2016-06-01

A new potential method of detecting the conformational changes in hydrophobic proteins such as bovine serum albumin (BSA) is introduced. The method is based on the change in the Förster resonance energy transfer (FRET) efficiency between protein-sensitive fluorescent probes. As compared to conventional FRET based methods, in this new approach the donor and acceptor dyes are not covalently linked to protein molecules. Performance of the new method is demonstrated using the protein-sensitive squaraine probes Square-634 (donor) and Square-685 (acceptor) to detect the urea-induced conformational changes of BSA. The FRET efficiency between these probes can be considered a more sensitive parameter to trace protein unfolding as compared to the changes in fluorescence intensity of each of these probes. Addition of urea followed by BSA unfolding causes a noticeable decrease in the emission intensities of these probes (factor of 5.6 for Square-634 and 3.0 for Square-685), and the FRET efficiency changes by a factor of up to 17. Compared to the conventional method the new approach therefore demonstrates to be a more sensitive way to detect the conformational changes in BSA.

Change Detection in High-Resolution Remote Sensing Images Using Levene-Test and Fuzzy Evaluation

NASA Astrophysics Data System (ADS)

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

2018-04-01

High-resolution remote sensing images possess complex spatial structure and rich texture information, according to these, this paper presents a new method of change detection based on Levene-Test and Fuzzy Evaluation. It first got map-spots by segmenting two overlapping images which had been pretreated, extracted features such as spectrum and texture. Then, changed information of all map-spots which had been treated by the Levene-Test were counted to obtain the candidate changed regions, hue information (H component) was extracted through the IHS Transform and conducted change vector analysis combined with the texture information. Eventually, the threshold was confirmed by an iteration method, the subject degrees of candidate changed regions were calculated, and final change regions were determined. In this paper experimental results on multi-temporal ZY-3 high-resolution images of some area in Jiangsu Province show that: Through extracting map-spots of larger difference as the candidate changed regions, Levene-Test decreases the computing load, improves the precision of change detection, and shows better fault-tolerant capacity for those unchanged regions which are of relatively large differences. The combination of Hue-texture features and fuzzy evaluation method can effectively decrease omissions and deficiencies, improve the precision of change detection.

Detection and monitoring of invasive exotic plants: a comparison of four sampling methods

Treesearch

Cynthia D. Huebner

2007-01-01

The ability to detect and monitor exotic invasive plants is likely to vary depending on the sampling method employed. Methods with strong qualitative thoroughness for species detection often lack the intensity necessary to monitor vegetation change. Four sampling methods (systematic plot, stratified-random plot, modified Whittaker, and timed meander) in hemlock and red...

Can fractal methods applied to video tracking detect the effects of deltamethrin pesticide or mercury on the locomotion behavior of shrimps?

PubMed

Tenorio, Bruno Mendes; da Silva Filho, Eurípedes Alves; Neiva, Gentileza Santos Martins; da Silva, Valdemiro Amaro; Tenorio, Fernanda das Chagas Angelo Mendes; da Silva, Themis de Jesus; Silva, Emerson Carlos Soares E; Nogueira, Romildo de Albuquerque

2017-08-01

Shrimps can accumulate environmental toxicants and suffer behavioral changes. However, methods to quantitatively detect changes in the behavior of these shrimps are still needed. The present study aims to verify whether mathematical and fractal methods applied to video tracking can adequately describe changes in the locomotion behavior of shrimps exposed to low concentrations of toxic chemicals, such as 0.15µgL -1 deltamethrin pesticide or 10µgL -1 mercuric chloride. Results showed no change after 1min, 4, 24, and 48h of treatment. However, after 72 and 96h of treatment, both the linear methods describing the track length, mean speed, mean distance from the current to the previous track point, as well as the non-linear methods of fractal dimension (box counting or information entropy) and multifractal analysis were able to detect changes in the locomotion behavior of shrimps exposed to deltamethrin. Analysis of angular parameters of the track points vectors and lacunarity were not sensitive to those changes. None of the methods showed adverse effects to mercury exposure. These mathematical and fractal methods applicable to software represent low cost useful tools in the toxicological analyses of shrimps for quality of food, water and biomonitoring of ecosystems. Copyright © 2017 Elsevier Inc. All rights reserved.

Studying Overt Word Reading and Speech Production with Event-Related fMRI: A Method for Detecting, Assessing, and Correcting Articulation-Induced Signal Changes and for Measuring Onset Time and Duration of Articulation

ERIC Educational Resources Information Center

Huang, Jie; Francis, Andrea P.; Carr, Thomas H.

2008-01-01

A quantitative method is introduced for detecting and correcting artifactual signal changes in BOLD time series data arising from the magnetic field warping caused by motion of the articulatory apparatus when speaking aloud, with extensions to detection of subvocal articulatory activity during silent reading. Whole-head images allow the large,…

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.

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.

Detecting background changes in environments with dynamic foreground by separating probability distribution function mixtures using Pearson's method of moments

NASA Astrophysics Data System (ADS)

Jenkins, Colleen; Jordan, Jay; Carlson, Jeff

2007-02-01

This paper presents parameter estimation techniques useful for detecting background changes in a video sequence with extreme foreground activity. A specific application of interest is automated detection of the covert placement of threats (e.g., a briefcase bomb) inside crowded public facilities. We propose that a histogram of pixel intensity acquired from a fixed mounted camera over time for a series of images will be a mixture of two Gaussian functions: the foreground probability distribution function and background probability distribution function. We will use Pearson's Method of Moments to separate the two probability distribution functions. The background function can then be "remembered" and changes in the background can be detected. Subsequent comparisons of background estimates are used to detect changes. Changes are flagged to alert security forces to the presence and location of potential threats. Results are presented that indicate the significant potential for robust parameter estimation techniques as applied to video surveillance.

Detection and Attribution of Anthropogenic Climate Change Impacts

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia; Neofotis, Peter

2013-01-01

Human-influenced climate change is an observed phenomenon affecting physical and biological systems across the globe. The majority of observed impacts are related to temperature changes and are located in the northern high- and midlatitudes. However, new evidence is emerging that demonstrates that impacts are related to precipitation changes as well as temperature, and that climate change is impacting systems and sectors beyond the Northern Hemisphere. In this paper, we highlight some of this new evidence-focusing on regions and sectors that the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) noted as under-represented-in the context of observed climate change impacts, direct and indirect drivers of change (including carbon dioxide itself), and methods of detection. We also present methods and studies attributing observed impacts to anthropogenic forcing. We argue that the expansion of methods of detection (in terms of a broader array of climate variables and data sources, inclusion of the major modes of climate variability, and incorporation of other drivers of change) is key to discerning the climate sensitivities of sectors and systems in regions where the impacts of climate change currently remain elusive. Attributing such changes to human forcing of the climate system, where possible, is important for development of effective mitigation and adaptation. Current challenges in documenting adaptation and the role of indigenous knowledge in detection and attribution are described.

A statistical method (cross-validation) for bone loss region detection after spaceflight

PubMed Central

Zhao, Qian; Li, Wenjun; Li, Caixia; Chu, Philip W.; Kornak, John; Lang, Thomas F.

2010-01-01

Astronauts experience bone loss after the long spaceflight missions. Identifying specific regions that undergo the greatest losses (e.g. the proximal femur) could reveal information about the processes of bone loss in disuse and disease. Methods for detecting such regions, however, remains an open problem. This paper focuses on statistical methods to detect such regions. We perform statistical parametric mapping to get t-maps of changes in images, and propose a new cross-validation method to select an optimum suprathreshold for forming clusters of pixels. Once these candidate clusters are formed, we use permutation testing of longitudinal labels to derive significant changes. PMID:20632144

A new method of scoring radiographic change in rheumatoid arthritis.

PubMed

Rau, R; Wassenberg, S; Herborn, G; Stucki, G; Gebler, A

1998-11-01

To test the reliability and to define the minimal detectable change of a new radiographic scoring method in rheumatoid arthritis (RA). Following the recommendations of an expert panel a new radiographic scoring method was defined. It scores 38 joints [all proximal interphalangeal (PIP) and metacarpophalangeal joints, 4 sites in the wrists, IP of the great toes, and metatarsophalangeals 2 to 5], regarding only the amount of joint surface destruction on a 0 to 5 scale for each joint. Each grade represents 20% of joint surface destruction. The method was tested by 5 readers on a set of 7 serial radiographs of hands and forefeet of 20 patients with progressive and destructive RA. Analysis of variance was performed, as it provides the best information about the capability of a method to detect real change and to define its sensitivity according to the minimal detectable change. Analysis of variance proved a high probability that the readers found real change with a ratio of intrapatient to intrareader standard deviation of 2.6. It also confirmed that one reader could detect a change of 3.5% of the total score with a probability of 95% and that different readers agreed upon a change of 4.6%. Inexperienced readers performed with comparable results to experienced readers. The time required for the reading averaged less than 10 minutes for the scoring of one set. The new radiographic scoring method proved to be reliable, precise, and easy to learn, with reasonable cost. Compared to published data, it may provide better results than the widely used Larsen score. These features favor our new method for use in clinical trials and in longterm observational studies in RA.

A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images.

PubMed

Liu, Jia; Gong, Maoguo; Qin, Kai; Zhang, Puzhao

2018-03-01

We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.

Detection of cardiac activity changes from human speech

NASA Astrophysics Data System (ADS)

Tovarek, Jaromir; Partila, Pavol; Voznak, Miroslav; Mikulec, Martin; Mehic, Miralem

2015-05-01

Impact of changes in blood pressure and pulse from human speech is disclosed in this article. The symptoms of increased physical activity are pulse, systolic and diastolic pressure. There are many methods of measuring and indicating these parameters. The measurements must be carried out using devices which are not used in everyday life. In most cases, the measurement of blood pressure and pulse following health problems or other adverse feelings. Nowadays, research teams are trying to design and implement modern methods in ordinary human activities. The main objective of the proposal is to reduce the delay between detecting the adverse pressure and to the mentioned warning signs and feelings. Common and frequent activity of man is speaking, while it is known that the function of the vocal tract can be affected by the change in heart activity. Therefore, it can be a useful parameter for detecting physiological changes. A method for detecting human physiological changes by speech processing and artificial neural network classification is described in this article. The pulse and blood pressure changes was induced by physical exercises in this experiment. The set of measured subjects was formed by ten healthy volunteers of both sexes. None of the subjects was a professional athlete. The process of the experiment was divided into phases before, during and after physical training. Pulse, systolic, diastolic pressure was measured and voice activity was recorded after each of them. The results of this experiment describe a method for detecting increased cardiac activity from human speech using artificial neural network.

CNV-TV: a robust method to discover copy number variation from short sequencing reads.

PubMed

Duan, Junbo; Zhang, Ji-Gang; Deng, Hong-Wen; Wang, Yu-Ping

2013-05-02

Copy number variation (CNV) is an important structural variation (SV) in human genome. Various studies have shown that CNVs are associated with complex diseases. Traditional CNV detection methods such as fluorescence in situ hybridization (FISH) and array comparative genomic hybridization (aCGH) suffer from low resolution. The next generation sequencing (NGS) technique promises a higher resolution detection of CNVs and several methods were recently proposed for realizing such a promise. However, the performances of these methods are not robust under some conditions, e.g., some of them may fail to detect CNVs of short sizes. There has been a strong demand for reliable detection of CNVs from high resolution NGS data. A novel and robust method to detect CNV from short sequencing reads is proposed in this study. The detection of CNV is modeled as a change-point detection from the read depth (RD) signal derived from the NGS, which is fitted with a total variation (TV) penalized least squares model. The performance (e.g., sensitivity and specificity) of the proposed approach are evaluated by comparison with several recently published methods on both simulated and real data from the 1000 Genomes Project. The experimental results showed that both the true positive rate and false positive rate of the proposed detection method do not change significantly for CNVs with different copy numbers and lengthes, when compared with several existing methods. Therefore, our proposed approach results in a more reliable detection of CNVs than the existing methods.

SuBSENSE: a universal change detection method with local adaptive sensitivity.

PubMed

St-Charles, Pierre-Luc; Bilodeau, Guillaume-Alexandre; Bergevin, Robert

2015-01-01

Foreground/background segmentation via change detection in video sequences is often used as a stepping stone in high-level analytics and applications. Despite the wide variety of methods that have been proposed for this problem, none has been able to fully address the complex nature of dynamic scenes in real surveillance tasks. In this paper, we present a universal pixel-level segmentation method that relies on spatiotemporal binary features as well as color information to detect changes. This allows camouflaged foreground objects to be detected more easily while most illumination variations are ignored. Besides, instead of using manually set, frame-wide constants to dictate model sensitivity and adaptation speed, we use pixel-level feedback loops to dynamically adjust our method's internal parameters without user intervention. These adjustments are based on the continuous monitoring of model fidelity and local segmentation noise levels. This new approach enables us to outperform all 32 previously tested state-of-the-art methods on the 2012 and 2014 versions of the ChangeDetection.net dataset in terms of overall F-Measure. The use of local binary image descriptors for pixel-level modeling also facilitates high-speed parallel implementations: our own version, which used no low-level or architecture-specific instruction, reached real-time processing speed on a midlevel desktop CPU. A complete C++ implementation based on OpenCV is available online.

Graph-based structural change detection for rotating machinery monitoring

NASA Astrophysics Data System (ADS)

Lu, Guoliang; Liu, Jie; Yan, Peng

2018-01-01

Detection of structural changes is critically important in operational monitoring of a rotating machine. This paper presents a novel framework for this purpose, where a graph model for data modeling is adopted to represent/capture statistical dynamics in machine operations. Meanwhile we develop a numerical method for computing temporal anomalies in the constructed graphs. The martingale-test method is employed for the change detection when making decisions on possible structural changes, where excellent performance is demonstrated outperforming exciting results such as the autoregressive-integrated-moving average (ARIMA) model. Comprehensive experimental results indicate good potentials of the proposed algorithm in various engineering applications. This work is an extension of a recent result (Lu et al., 2017).

Detection of abrupt changes in dynamic systems

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1984-01-01

Some of the basic ideas associated with the detection of abrupt changes in dynamic systems are presented. Multiple filter-based techniques and residual-based method and the multiple model and generalized likelihood ratio methods are considered. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed.

Development of new structural health monitoring techniques

NASA Astrophysics Data System (ADS)

Fekrmandi, Hadi

During the past two decades, many researchers have developed methods for the detection of structural defects at the early stages to operate the aerospace vehicles safely and to reduce the operating costs. The Surface Response to Excitation (SuRE) method is one of these approaches developed at FIU to reduce the cost and size of the equipment. The SuRE method excites the surface at a series of frequencies and monitors the propagation characteristics of the generated waves. The amplitude of the waves reaching to any point on the surface varies with frequency; however, it remains consistent as long as the integrity and strain distribution on the part is consistent. These spectral characteristics change when cracks develop or the strain distribution changes. The SHM methods may be used for many applications, from the detection of loose screws to the monitoring of manufacturing operations. A scanning laser vibrometer was used in this study to investigate the characteristics of the spectral changes at different points on the parts. The study started with detecting a load on a plate and estimating its location. The modifications on the part with manufacturing operations were detected and the Part-Based Manufacturing Process Performance Monitoring (PbPPM) method was developed. Hardware was prepared to demonstrate the feasibility of the proposed methods in real time. Using low-cost piezoelectric elements and the non-contact scanning laser vibrometer successfully, the data was collected for the SuRE and PbPPM methods. Locational force, loose bolts and material loss could be easily detected by comparing the spectral characteristics of the arriving waves. On-line methods used fast computational methods for estimating the spectrum and detecting the changing operational conditions from sum of the squares of the variations. Neural networks classified the spectrums when the desktop -- DSP combination was used. The results demonstrated the feasibility of the SuRE and PbPPM methods.

Structural-change localization and monitoring through a perturbation-based inverse problem.

PubMed

Roux, Philippe; Guéguen, Philippe; Baillet, Laurent; Hamze, Alaa

2014-11-01

Structural-change detection and characterization, or structural-health monitoring, is generally based on modal analysis, for detection, localization, and quantification of changes in structure. Classical methods combine both variations in frequencies and mode shapes, which require accurate and spatially distributed measurements. In this study, the detection and localization of a local perturbation are assessed by analysis of frequency changes (in the fundamental mode and overtones) that are combined with a perturbation-based linear inverse method and a deconvolution process. This perturbation method is applied first to a bending beam with the change considered as a local perturbation of the Young's modulus, using a one-dimensional finite-element model for modal analysis. Localization is successful, even for extended and multiple changes. In a second step, the method is numerically tested under ambient-noise vibration from the beam support with local changes that are shifted step by step along the beam. The frequency values are revealed using the random decrement technique that is applied to the time-evolving vibrations recorded by one sensor at the free extremity of the beam. Finally, the inversion method is experimentally demonstrated at the laboratory scale with data recorded at the free end of a Plexiglas beam attached to a metallic support.

Systems and methods for neutron detection using scintillator nano-materials

DOEpatents

Letant, Sonia Edith; Wang, Tzu-Fang

2016-03-08

In one embodiment, a neutron detector includes a three dimensional matrix, having nanocomposite materials and a substantially transparent film material for suspending the nanocomposite materials, a detector coupled to the three dimensional matrix adapted for detecting a change in the nanocomposite materials, and an analyzer coupled to the detector adapted for analyzing the change detected by the detector. In another embodiment, a method for detecting neutrons includes receiving radiation from a source, converting neutrons in the radiation into alpha particles using converter material, converting the alpha particles into photons using quantum dot emitters, detecting the photons, and analyzing the photons to determine neutrons in the radiation.

Signal processing for non-destructive testing of railway tracks

NASA Astrophysics Data System (ADS)

Heckel, Thomas; Casperson, Ralf; Rühe, Sven; Mook, Gerhard

2018-04-01

Increased speed, heavier loads, altered material and modern drive systems result in an increasing number of rail flaws. The appearance of these flaws also changes continually due to the rapid change in damage mechanisms of modern rolling stock. Hence, interpretation has become difficult when evaluating non-destructive rail testing results. Due to the changed interplay between detection methods and flaws, the recorded signals may result in unclassified types of rail flaws. Methods for automatic rail inspection (according to defect detection and classification) undergo continual development. Signal processing is a key technology to master the challenge of classification and maintain resolution and detection quality, independent of operation speed. The basic ideas of signal processing, based on the Glassy-Rail-Diagram for classification purposes, are presented herein. Examples for the detection of damages caused by rolling contact fatigue also are given, and synergetic effects of combined evaluation of diverse inspection methods are shown.

Islanding detection technique using wavelet energy in grid-connected PV system

NASA Astrophysics Data System (ADS)

Kim, Il Song

2016-08-01

This paper proposes a new islanding detection method using wavelet energy in a grid-connected photovoltaic system. The method detects spectral changes in the higher-frequency components of the point of common coupling voltage and obtains wavelet coefficients by multilevel wavelet analysis. The autocorrelation of the wavelet coefficients can clearly identify islanding detection, even in the variations of the grid voltage harmonics during normal operating conditions. The advantage of the proposed method is that it can detect islanding condition the conventional under voltage/over voltage/under frequency/over frequency methods fail to detect. The theoretical method to obtain wavelet energies is evolved and verified by the experimental result.

Which homogenisation method is appropriate for daily time series of relative humidity?

NASA Astrophysics Data System (ADS)

Chimani, Barbara; Nemec, Johanna; Auer, Ingeborg; Venema, Victor

2014-05-01

Data homogenisation is an essential part of reliable climate data analyses. Different tools for detecting and adjusting breaks in daily extreme temperatures (Tmin, Tmax) and daily precipitation sums were developed in the last years. Due to its influence on health, plants and construction relative humidity is another parameter of great importance. On the basis of 6 networks of measured (and homogenized with respect to the monthly means) relative humidity data, which cover different climatic areas in Austria, a synthetic data set for testing and validating homogenisation methods was built. Each network consists of 4 to 6 station time series with a minimum length of 5 years. The so-called surrogate networks resemble the statistical properties (e.g. distribution of parameter, auto- and cross correlation within the network) of the measured time series, but are extended to 100 year long time series, which are in a first step assumed to be homogeneous. For creating the best possible surrogate dataset of relative humidity detailed statistical information on potential inhomogeneities is decisive. Information on the potential breaks was taken from parallel measurements available for some Austrian locations, mostly representing changes in instrumentation and/or station relocation. Beside changes in the distribution of the parameter the analyses includes an estimation of changes in the number of missing data, global and local biases, both on a seasonal and annual basis. An additional break is to be expected in the Austrian time series due to a change in observation time in 1970/1971. Since this change occurred simultaneously at all Austrian climate stations, standard homogenisation methods, which rely on a comparison with reference stations, are not able to detect or correct this shift. Therefore an independent correction method for this type of break, to be applied before homogenisation was developed. This type of change point was not included in the surrogate network. Artificial inhomogenities were introduced to the dataset in three steps: (1) deterministic change points: within one homogeneous sub-period (HSP) a constant perturbation is added to each relative humidity values, (2) deterministic + random changes: random changes do not change the mean of the HSP but can affect the distribution of the parameter, (3) in addition realistic changes in break frequency and missing data. In order to tests the efficiency of homogenisation methods, the procedure was separated in break detection and adjustment of inhomogenities. The methods MASH (Szentimrey, 1999), ACMANT (Domonkos, 2011), PRODIGE (Caussinus and Mestre, 2004), SNHT (Alexandersson, 1986), Vincent (Vincent, 1998), E-P method (Easterling and Peterson, 1995) and Bivariate test (Maronna and Yohai, 1978) were selected for break detection. Break detection is in all methods restricted to monthly, seasonal or annual data. Since we are dealing with daily data, the amount of methods for break correction is reduced and we concentrate on the following methods: MASH, Vincent, SPLIDHOM (Mestre et al., 2011) and the percentile method (Stepanek, 2009). Information on the statistical characteristics of breaks in relative humidity series, the correction method concerning the changed observation times and first results concerning break detection will be presented.

Label-free and pH-sensitive colorimetric materials for the sensing of urea

NASA Astrophysics Data System (ADS)

Li, Lu; Long, Yue; Gao, Jin-Ming; Song, Kai; Yang, Guoqiang

2016-02-01

This communication demonstrates a facile method for naked-eye detection of urea based on the structure color change of pH-sensitive photonic crystals. The insertion of urease provides excellent selectivity over other molecules. The detection of urea in different concentration ranges could be realized by changing the molar ratio between the functional monomer and cross-linker.This communication demonstrates a facile method for naked-eye detection of urea based on the structure color change of pH-sensitive photonic crystals. The insertion of urease provides excellent selectivity over other molecules. The detection of urea in different concentration ranges could be realized by changing the molar ratio between the functional monomer and cross-linker. Electronic supplementary information (ESI) available: Materials and chemicals, characterization, experimental details, and SEM images. See DOI: 10.1039/c5nr07690k

Mutual Comparative Filtering for Change Detection in Videos with Unstable Illumination Conditions

NASA Astrophysics Data System (ADS)

Sidyakin, Sergey V.; Vishnyakov, Boris V.; Vizilter, Yuri V.; Roslov, Nikolay I.

2016-06-01

In this paper we propose a new approach for change detection and moving objects detection in videos with unstable, abrupt illumination changes. This approach is based on mutual comparative filters and background normalization. We give the definitions of mutual comparative filters and outline their strong advantage for change detection purposes. Presented approach allows us to deal with changing illumination conditions in a simple and efficient way and does not have drawbacks, which exist in models that assume different color transformation laws. The proposed procedure can be used to improve a number of background modelling methods, which are not specifically designed to work under illumination changes.

Techniques for automatic large scale change analysis of temporal multispectral imagery

NASA Astrophysics Data System (ADS)

Mercovich, Ryan A.

Change detection in remotely sensed imagery is a multi-faceted problem with a wide variety of desired solutions. Automatic change detection and analysis to assist in the coverage of large areas at high resolution is a popular area of research in the remote sensing community. Beyond basic change detection, the analysis of change is essential to provide results that positively impact an image analyst's job when examining potentially changed areas. Present change detection algorithms are geared toward low resolution imagery, and require analyst input to provide anything more than a simple pixel level map of the magnitude of change that has occurred. One major problem with this approach is that change occurs in such large volume at small spatial scales that a simple change map is no longer useful. This research strives to create an algorithm based on a set of metrics that performs a large area search for change in high resolution multispectral image sequences and utilizes a variety of methods to identify different types of change. Rather than simply mapping the magnitude of any change in the scene, the goal of this research is to create a useful display of the different types of change in the image. The techniques presented in this dissertation are used to interpret large area images and provide useful information to an analyst about small regions that have undergone specific types of change while retaining image context to make further manual interpretation easier. This analyst cueing to reduce information overload in a large area search environment will have an impact in the areas of disaster recovery, search and rescue situations, and land use surveys among others. By utilizing a feature based approach founded on applying existing statistical methods and new and existing topological methods to high resolution temporal multispectral imagery, a novel change detection methodology is produced that can automatically provide useful information about the change occurring in large area and high resolution image sequences. The change detection and analysis algorithm developed could be adapted to many potential image change scenarios to perform automatic large scale analysis of change.

Target detection method by airborne and spaceborne images fusion based on past images

NASA Astrophysics Data System (ADS)

Chen, Shanjing; Kang, Qing; Wang, Zhenggang; Shen, ZhiQiang; Pu, Huan; Han, Hao; Gu, Zhongzheng

2017-11-01

To solve the problem that remote sensing target detection method has low utilization rate of past remote sensing data on target area, and can not recognize camouflage target accurately, a target detection method by airborne and spaceborne images fusion based on past images is proposed in this paper. The target area's past of space remote sensing image is taken as background. The airborne and spaceborne remote sensing data is fused and target feature is extracted by the means of airborne and spaceborne images registration, target change feature extraction, background noise suppression and artificial target feature extraction based on real-time aerial optical remote sensing image. Finally, the support vector machine is used to detect and recognize the target on feature fusion data. The experimental results have established that the proposed method combines the target area change feature of airborne and spaceborne remote sensing images with target detection algorithm, and obtains fine detection and recognition effect on camouflage and non-camouflage targets.

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.

Detection of proteins using a colorimetric bio-barcode assay.

PubMed

Nam, Jwa-Min; Jang, Kyung-Jin; Groves, Jay T

2007-01-01

The colorimetric bio-barcode assay is a red-to-blue color change-based protein detection method with ultrahigh sensitivity. This assay is based on both the bio-barcode amplification method that allows for detecting miniscule amount of targets with attomolar sensitivity and gold nanoparticle-based colorimetric DNA detection method that allows for a simple and straightforward detection of biomolecules of interest (here we detect interleukin-2, an important biomarker (cytokine) for many immunodeficiency-related diseases and cancers). The protocol is composed of the following steps: (i) conjugation of target capture molecules and barcode DNA strands onto silica microparticles, (ii) target capture with probes, (iii) separation and release of barcode DNA strands from the separated probes, (iv) detection of released barcode DNA using DNA-modified gold nanoparticle probes and (v) red-to-blue color change analysis with a graphic software. Actual target detection and quantification steps with premade probes take approximately 3 h (whole protocol including probe preparations takes approximately 3 days).

Investigation of Coherent and Incoherent Change Detection Algorithms

DTIC Science & Technology

2017-12-01

Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE December...Data Management System (SDMS) in order to compare the various change detection techniques. These change detection methods include the following: a...SAR) is presented in this thesis. This investigation utilizes data gathered from the Air Force Research Laboratory (AFRL) Sensor Data Management

Dynamic Network Change Detection

DTIC Science & Technology

2008-12-01

Change Detection 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT...Fisher and Mackenzie, 1922). These methods are used in quality engineering to detect small changes in a process (Montgomery, 1991; Ryan , 2000). Larger...Social Network Modeling and Analysis: Workshop Summary and Papers, Ronald Breiger, Kathleen Carley, and Philippa Pattison, (Eds

Fishing for drifts: detecting buoyancy changes of a top marine predator using a step-wise filtering method

PubMed Central

Gordine, Samantha Alex; Fedak, Michael; Boehme, Lars

2015-01-01

ABSTRACT In southern elephant seals (Mirounga leonina), fasting- and foraging-related fluctuations in body composition are reflected by buoyancy changes. Such buoyancy changes can be monitored by measuring changes in the rate at which a seal drifts passively through the water column, i.e. when all active swimming motion ceases. Here, we present an improved knowledge-based method for detecting buoyancy changes from compressed and abstracted dive profiles received through telemetry. By step-wise filtering of the dive data, the developed algorithm identifies fragments of dives that correspond to times when animals drift. In the dive records of 11 southern elephant seals from South Georgia, this filtering method identified 0.8–2.2% of all dives as drift dives, indicating large individual variation in drift diving behaviour. The obtained drift rate time series exhibit that, at the beginning of each migration, all individuals were strongly negatively buoyant. Over the following 75–150 days, the buoyancy of all individuals peaked close to or at neutral buoyancy, indicative of a seal's foraging success. Independent verification with visually inspected detailed high-resolution dive data confirmed that this method is capable of reliably detecting buoyancy changes in the dive records of drift diving species using abstracted data. This also affirms that abstracted dive profiles convey the geometric shape of drift dives in sufficient detail for them to be identified. Further, it suggests that, using this step-wise filtering method, buoyancy changes could be detected even in old datasets with compressed dive information, for which conventional drift dive classification previously failed. PMID:26486362

System and method for adaptively deskewing parallel data signals relative to a clock

DOEpatents

Jenkins, Philip Nord; Cornett, Frank N.

2006-04-18

A system and method of reducing skew between a plurality of signals transmitted with a transmit clock is described. Skew is detected between the received transmit clock and each of received data signals. Delay is added to the clock or to one or more of the plurality of data signals to compensate for the detected skew. Each of the plurality of delayed signals is compared to a reference signal to detect changes in the skew. The delay added to each of the plurality of delayed signals is updated to adapt to changes in the detected skew.

Change Detection of Remote Sensing Images by Dt-Cwt and Mrf

NASA Astrophysics Data System (ADS)

Ouyang, S.; Fan, K.; Wang, H.; Wang, Z.

2017-05-01

Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.

Machine learning plus optical flow: a simple and sensitive method to detect cardioactive drugs

NASA Astrophysics Data System (ADS)

Lee, Eugene K.; Kurokawa, Yosuke K.; Tu, Robin; George, Steven C.; Khine, Michelle

2015-07-01

Current preclinical screening methods do not adequately detect cardiotoxicity. Using human induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs), more physiologically relevant preclinical or patient-specific screening to detect potential cardiotoxic effects of drug candidates may be possible. However, one of the persistent challenges for developing a high-throughput drug screening platform using iPS-CMs is the need to develop a simple and reliable method to measure key electrophysiological and contractile parameters. To address this need, we have developed a platform that combines machine learning paired with brightfield optical flow as a simple and robust tool that can automate the detection of cardiomyocyte drug effects. Using three cardioactive drugs of different mechanisms, including those with primarily electrophysiological effects, we demonstrate the general applicability of this screening method to detect subtle changes in cardiomyocyte contraction. Requiring only brightfield images of cardiomyocyte contractions, we detect changes in cardiomyocyte contraction comparable to - and even superior to - fluorescence readouts. This automated method serves as a widely applicable screening tool to characterize the effects of drugs on cardiomyocyte function.

Spectral anomaly methods for aerial detection using KUT nuisance rejection

NASA Astrophysics Data System (ADS)

Detwiler, R. S.; Pfund, D. M.; Myjak, M. J.; Kulisek, J. A.; Seifert, C. E.

2015-06-01

This work discusses the application and optimization of a spectral anomaly method for the real-time detection of gamma radiation sources from an aerial helicopter platform. Aerial detection presents several key challenges over ground-based detection. For one, larger and more rapid background fluctuations are typical due to higher speeds, larger field of view, and geographically induced background changes. As well, the possible large altitude or stand-off distance variations cause significant steps in background count rate as well as spectral changes due to increased gamma-ray scatter with detection at higher altitudes. The work here details the adaptation and optimization of the PNNL-developed algorithm Nuisance-Rejecting Spectral Comparison Ratios for Anomaly Detection (NSCRAD), a spectral anomaly method previously developed for ground-based applications, for an aerial platform. The algorithm has been optimized for two multi-detector systems; a NaI(Tl)-detector-based system and a CsI detector array. The optimization here details the adaptation of the spectral windows for a particular set of target sources to aerial detection and the tailoring for the specific detectors. As well, the methodology and results for background rejection methods optimized for the aerial gamma-ray detection using Potassium, Uranium and Thorium (KUT) nuisance rejection are shown. Results indicate that use of a realistic KUT nuisance rejection may eliminate metric rises due to background magnitude and spectral steps encountered in aerial detection due to altitude changes and geographically induced steps such as at land-water interfaces.

Wetware, Hardware, or Software Incapacitation: Observational Methods to Determine When Autonomy Should Assume Control

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.; Gregory, Irene M.

2014-01-01

Control-theoretic modeling of human operator's dynamic behavior in manual control tasks has a long, rich history. There has been significant work on techniques used to identify the pilot model of a given structure. This research attempts to go beyond pilot identification based on experimental data to develop a predictor of pilot behavior. Two methods for pre-dicting pilot stick input during changing aircraft dynamics and deducing changes in pilot behavior are presented This approach may also have the capability to detect a change in a subject due to workload, engagement, etc., or the effects of changes in vehicle dynamics on the pilot. With this ability to detect changes in piloting behavior, the possibility now exists to mediate human adverse behaviors, hardware failures, and software anomalies with autono-my that may ameliorate these undesirable effects. However, appropriate timing of when au-tonomy should assume control is dependent on criticality of actions to safety, sensitivity of methods to accurately detect these adverse changes, and effects of changes in levels of auto-mation of the system as a whole.

Dynamical complexity changes during two forms of meditation

NASA Astrophysics Data System (ADS)

Li, Jin; Hu, Jing; Zhang, Yinhong; Zhang, Xiaofeng

2011-06-01

Detection of dynamical complexity changes in natural and man-made systems has deep scientific and practical meaning. We use the base-scale entropy method to analyze dynamical complexity changes for heart rate variability (HRV) series during specific traditional forms of Chinese Chi and Kundalini Yoga meditation techniques in healthy young adults. The results show that dynamical complexity decreases in meditation states for two forms of meditation. Meanwhile, we detected changes in probability distribution of m-words during meditation and explained this changes using probability distribution of sine function. The base-scale entropy method may be used on a wider range of physiologic signals.

Method and apparatus for remote sensing of molecular species at nanoscale utilizing a reverse photoacoustic effect

DOEpatents

Su, Ming [Oviedo, FL; Thundat, Thomas G [Knoxville, TN; Hedden, David [Lenoir City, TN

2010-02-23

A method and apparatus for identifying a sample, involves illuminating the sample with light of varying wavelengths, transmitting an acoustic signal against the sample from one portion and receiving a resulting acoustic signal on another portion, detecting a change of phase in the acoustic signal corresponding to the light of varying wavelengths, and analyzing the change of phase in the acoustic signal for the varying wavelengths of illumination to identify the sample. The apparatus has a controlled source for illuminating the sample with light of varying wavelengths, a transmitter for transmitting an acoustic wave, a receiver for receiving the acoustic wave and converting the acoustic wave to an electronic signal, and an electronic circuit for detecting a change of phase in the acoustic wave corresponding to respective ones of the varying wavelengths and outputting the change of phase for the varying wavelengths to allow identification of the sample. The method and apparatus can be used to detect chemical composition or visual features. A transmission mode and a reflection mode of operation are disclosed. The method and apparatus can be applied at nanoscale to detect molecules in a biological sample.

Statistical methods for change-point detection in surface temperature records

NASA Astrophysics Data System (ADS)

Pintar, A. L.; Possolo, A.; Zhang, N. F.

2013-09-01

We describe several statistical methods to detect possible change-points in a time series of values of surface temperature measured at a meteorological station, and to assess the statistical significance of such changes, taking into account the natural variability of the measured values, and the autocorrelations between them. These methods serve to determine whether the record may suffer from biases unrelated to the climate signal, hence whether there may be a need for adjustments as considered by M. J. Menne and C. N. Williams (2009) "Homogenization of Temperature Series via Pairwise Comparisons", Journal of Climate 22 (7), 1700-1717. We also review methods to characterize patterns of seasonality (seasonal decomposition using monthly medians or robust local regression), and explain the role they play in the imputation of missing values, and in enabling robust decompositions of the measured values into a seasonal component, a possible climate signal, and a station-specific remainder. The methods for change-point detection that we describe include statistical process control, wavelet multi-resolution analysis, adaptive weights smoothing, and a Bayesian procedure, all of which are applicable to single station records.

Comparative study of performance of neutral axis tracking based damage detection

NASA Astrophysics Data System (ADS)

Soman, R.; Malinowski, P.; Ostachowicz, W.

2015-07-01

This paper presents a comparative study of a novel SHM technique for damage isolation. The performance of the Neutral Axis (NA) tracking based damage detection strategy is compared to other popularly used vibration based damage detection methods viz. ECOMAC, Mode Shape Curvature Method and Strain Flexibility Index Method. The sensitivity of the novel method is compared under changing ambient temperature conditions and in the presence of measurement noise. Finite Element Analysis (FEA) of the DTU 10 MW Wind Turbine was conducted to compare the local damage identification capability of each method and the results are presented. Under the conditions examined, the proposed method was found to be robust to ambient condition changes and measurement noise. The damage identification in some is either at par with the methods mentioned in the literature or better under the investigated damage scenarios.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning.

PubMed

Lim, Kwangyong; Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).

Neutron imaging integrated circuit and method for detecting neutrons

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

Nagarkar, Vivek V.; More, Mitali J.

The present disclosure provides a neutron imaging detector and a method for detecting neutrons. In one example, a method includes providing a neutron imaging detector including plurality of memory cells and a conversion layer on the memory cells, setting one or more of the memory cells to a first charge state, positioning the neutron imaging detector in a neutron environment for a predetermined time period, and reading a state change at one of the memory cells, and measuring a charge state change at one of the plurality of memory cells from the first charge state to a second charge statemore » less than the first charge state, where the charge state change indicates detection of neutrons at said one of the memory cells.« less

Motion detection and compensation in infrared retinal image sequences.

PubMed

Scharcanski, J; Schardosim, L R; Santos, D; Stuchi, A

2013-01-01

Infrared image data captured by non-mydriatic digital retinography systems often are used in the diagnosis and treatment of the diabetic macular edema (DME). Infrared illumination is less aggressive to the patient retina, and retinal studies can be carried out without pupil dilation. However, sequences of infrared eye fundus images of static scenes, tend to present pixel intensity fluctuations in time, and noisy and background illumination changes pose a challenge to most motion detection methods proposed in the literature. In this paper, we present a retinal motion detection method that is adaptive to background noise and illumination changes. Our experimental results indicate that this method is suitable for detecting retinal motion in infrared image sequences, and compensate the detected motion, which is relevant in retinal laser treatment systems for DME. Copyright © 2013 Elsevier Ltd. All rights reserved.

Detecting glaucomatous change in visual fields: Analysis with an optimization framework.

PubMed

Yousefi, Siamak; Goldbaum, Michael H; Varnousfaderani, Ehsan S; Belghith, Akram; Jung, Tzyy-Ping; Medeiros, Felipe A; Zangwill, Linda M; Weinreb, Robert N; Liebmann, Jeffrey M; Girkin, Christopher A; Bowd, Christopher

2015-12-01

Detecting glaucomatous progression is an important aspect of glaucoma management. The assessment of longitudinal series of visual fields, measured using Standard Automated Perimetry (SAP), is considered the reference standard for this effort. We seek efficient techniques for determining progression from longitudinal visual fields by formulating the problem as an optimization framework, learned from a population of glaucoma data. The longitudinal data from each patient's eye were used in a convex optimization framework to find a vector that is representative of the progression direction of the sample population, as a whole. Post-hoc analysis of longitudinal visual fields across the derived vector led to optimal progression (change) detection. The proposed method was compared to recently described progression detection methods and to linear regression of instrument-defined global indices, and showed slightly higher sensitivities at the highest specificities than other methods (a clinically desirable result). The proposed approach is simpler, faster, and more efficient for detecting glaucomatous changes, compared to our previously proposed machine learning-based methods, although it provides somewhat less information. This approach has potential application in glaucoma clinics for patient monitoring and in research centers for classification of study participants. Copyright © 2015 Elsevier Inc. All rights reserved.

Nucleic acid-coupled colorimetric analyte detectors

DOEpatents

Charych, Deborah H.; Jonas, Ulrich

2001-01-01

The present invention relates to methods and compositions for the direct detection of analytes and membrane conformational changes through the detection of color changes in biopolymeric materials. In particular, the present invention provide for the direct colorimetric detection of analytes using nucleic acid ligands at surfaces of polydiacetylene liposomes and related molecular layer systems.

Pattern-histogram-based temporal change detection using personal chest radiographs

NASA Astrophysics Data System (ADS)

Ugurlu, Yucel; Obi, Takashi; Hasegawa, Akira; Yamaguchi, Masahiro; Ohyama, Nagaaki

1999-05-01

An accurate and reliable detection of temporal changes from a pair of images has considerable interest in the medical science. Traditional registration and subtraction techniques can be applied to extract temporal differences when,the object is rigid or corresponding points are obvious. However, in radiological imaging, loss of the depth information, the elasticity of object, the absence of clearly defined landmarks and three-dimensional positioning differences constraint the performance of conventional registration techniques. In this paper, we propose a new method in order to detect interval changes accurately without using an image registration technique. The method is based on construction of so-called pattern histogram and comparison procedure. The pattern histogram is a graphic representation of the frequency counts of all allowable patterns in the multi-dimensional pattern vector space. K-means algorithm is employed to partition pattern vector space successively. Any differences in the pattern histograms imply that different patterns are involved in the scenes. In our experiment, a pair of chest radiographs of pneumoconiosis is employed and the changing histogram bins are visualized on both of the images. We found that the method can be used as an alternative way of temporal change detection, particularly when the precise image registration is not available.

Detecting spatial regimes in ecosystems | Science Inventory ...

EPA Pesticide Factsheets

Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory based method, on both terrestrial and aquatic animal data (US Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps, and multivariate analysis such as nMDS (non-metric Multidimensional Scaling) and cluster analysis. We successfully detect spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change. Use an information theory based method to identify ecological boundaries and compare our results to traditional early warning

An ontology-based annotation of cardiac implantable electronic devices to detect therapy changes in a national registry.

PubMed

Rosier, Arnaud; Mabo, Philippe; Chauvin, Michel; Burgun, Anita

2015-05-01

The patient population benefitting from cardiac implantable electronic devices (CIEDs) is increasing. This study introduces a device annotation method that supports the consistent description of the functional attributes of cardiac devices and evaluates how this method can detect device changes from a CIED registry. We designed the Cardiac Device Ontology, an ontology of CIEDs and device functions. We annotated 146 cardiac devices with this ontology and used it to detect therapy changes with respect to atrioventricular pacing, cardiac resynchronization therapy, and defibrillation capability in a French national registry of patients with implants (STIDEFIX). We then analyzed a set of 6905 device replacements from the STIDEFIX registry. Ontology-based identification of therapy changes (upgraded, downgraded, or similar) was accurate (6905 cases) and performed better than straightforward analysis of the registry codes (F-measure 1.00 versus 0.75 to 0.97). This study demonstrates the feasibility and effectiveness of ontology-based functional annotation of devices in the cardiac domain. Such annotation allowed a better description and in-depth analysis of STIDEFIX. This method was useful for the automatic detection of therapy changes and may be reused for analyzing data from other device registries.

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.

Variable threshold method for ECG R-peak detection.

PubMed

Kew, Hsein-Ping; Jeong, Do-Un

2011-10-01

In this paper, a wearable belt-type ECG electrode worn around the chest by measuring the real-time ECG is produced in order to minimize the inconvenient in wearing. ECG signal is detected using a potential instrument system. The measured ECG signal is transmits via an ultra low power consumption wireless data communications unit to personal computer using Zigbee-compatible wireless sensor node. ECG signals carry a lot of clinical information for a cardiologist especially the R-peak detection in ECG. R-peak detection generally uses the threshold value which is fixed. There will be errors in peak detection when the baseline changes due to motion artifacts and signal size changes. Preprocessing process which includes differentiation process and Hilbert transform is used as signal preprocessing algorithm. Thereafter, variable threshold method is used to detect the R-peak which is more accurate and efficient than fixed threshold value method. R-peak detection using MIT-BIH databases and Long Term Real-Time ECG is performed in this research in order to evaluate the performance analysis.

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

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

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

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE PAGES

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...

2014-10-01

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Planetary-scale surface water detection from space

NASA Astrophysics Data System (ADS)

Donchyts, G.; Baart, F.; Winsemius, H.; Gorelick, N.

2017-12-01

Accurate, efficient and high-resolution methods of surface water detection are needed for a better water management. Datasets on surface water extent and dynamics are crucial for a better understanding of natural and human-made processes, and as an input data for hydrological and hydraulic models. In spite of considerable progress in the harmonization of freely available satellite data, producing accurate and efficient higher-level surface water data products remains very challenging. This presentation will provide an overview of existing methods for surface water extent and change detection from multitemporal and multi-sensor satellite imagery. An algorithm to detect surface water changes from multi-temporal satellite imagery will be demonstrated as well as its open-source implementation (http://aqua-monitor.deltares.nl). This algorithm was used to estimate global surface water changes at high spatial resolution. These changes include climate change, land reclamation, reservoir construction/decommissioning, erosion/accretion, and many other. This presentation will demonstrate how open satellite data and open platforms such as Google Earth Engine have helped with this research.

One new method for road data shape change detection

NASA Astrophysics Data System (ADS)

Tang, Luliang; Li, Qingquan; Xu, Feng; Chang, Xiaomeng

2009-10-01

Similarity is a psychological cognition; this paper defines the Difference Distance and puts forward the Similarity Measuring Model for linear spatial data (SMM-L) based on the integration of the Distance View and the Feature Set View which are the views for similarity cognition. Based on the study of the relationship between the spatial data change and the similarity, a change detection algorithm for linear spatial data is developed, and a test on road data change detection is realized.

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.

VHR satellite multitemporal data to extract cultural landscape changes in the roman site of Grumentum

NASA Astrophysics Data System (ADS)

masini, nicola; Lasaponara, Rosa

2013-04-01

The papers deals with the use of VHR satellite multitemporal data set to extract cultural landscape changes in the roman site of Grumentum Grumentum is an ancient town, 50 km south of Potenza, located near the roman road of Via Herculea which connected the Venusia, in the north est of Basilicata, with Heraclea in the Ionian coast. The first settlement date back to the 6th century BC. It was resettled by the Romans in the 3rd century BC. Its urban fabric which evidences a long history from the Republican age to late Antiquity (III BC-V AD) is composed of the typical urban pattern of cardi and decumani. Its excavated ruins include a large amphitheatre, a theatre, the thermae, the Forum and some temples. There are many techniques nowadays available to capture and record differences in two or more images. In this paper we focus and apply the two main approaches which can be distinguished into : (i) unsupervised and (ii) supervised change detection methods. Unsupervised change detection methods are generally based on the transformation of the two multispectral images in to a single band or multiband image which are further analyzed to identify changes Unsupervised change detection techniques are generally based on three basic steps (i) the preprocessing step, (ii) a pixel-by-pixel comparison is performed, (iii). Identification of changes according to the magnitude an direction (positive /negative). Unsupervised change detection are generally based on the transformation of the two multispectral images into a single band or multiband image which are further analyzed to identify changes. Than the separation between changed and unchanged classes is obtained from the magnitude of the resulting spectral change vectors by means of empirical or theoretical well founded approaches Supervised change detection methods are generally based on supervised classification methods, which require the availability of a suitable training set for the learning process of the classifiers. Unsupervised change detection techniques are generally based on three basic steps (i) the preprocessing step, (ii) supervised classification is performed on the single dates or on the map obtained as the difference of two dates, (iii). Identification of changes according to the magnitude an direction (positive /negative). Supervised change detection are generally based on supervised classification methods, which require the availability of a suitable training set for the learning process of the classifiers, therefore these algorithms require a preliminary knowledge necessary: (i) to generate representative parameters for each class of interest; and (ii) to carry out the training stage Advantages and disadvantages of the supervised and unsupervised approaches are discuss. Finally results from the the satellite multitemporal dataset was also integrated with aerial photos from historical archive in order to expand the time window of the investigation and capture landscape changes occurred from the Agrarian Reform, in the 50s, up today.

A simple and highly sensitive colorimetric detection method for gaseous formaldehyde.

PubMed

Feng, Liang; Musto, Christopher J; Suslick, Kenneth S

2010-03-31

A colorimetric detection method using amine-functionalized polymer films doped with a pH indicator has been developed for the rapid, sensitive, and quantitative detection of gaseous formaldehyde at concentrations well below the immediately dangerous to life or health (IDLH) limit. In 1 min, visible color changes are easily observed, even down to the permissible exposure limit (PEL) at 750 ppb. The limit of detection is below 50 ppb (7% of the PEL) after 10 min of exposure. This sensor is essentially unaffected by changes in humidity or temperature (4 to 50 degrees C) and is not sensitive to common interferents.

Diffraction mode terahertz tomography

DOEpatents

Ferguson, Bradley; Wang, Shaohong; Zhang, Xi-Cheng

2006-10-31

A method of obtaining a series of images of a three-dimensional object. The method includes the steps of transmitting pulsed terahertz (THz) radiation through the entire object from a plurality of angles, optically detecting changes in the transmitted THz radiation using pulsed laser radiation, and constructing a plurality of imaged slices of the three-dimensional object using the detected changes in the transmitted THz radiation. The THz radiation is transmitted through the object as a two-dimensional array of parallel rays. The optical detection is an array of detectors such as a CCD sensor.

Monitoring of changes in areas of conflicts: the example of Darfur

NASA Astrophysics Data System (ADS)

Thunig, H.; Michel, U.

2012-10-01

Rapid change detection is used in cases of natural hazards and disasters. This analysis leads to rapid information on areas of damage. In certain cases the lack of information after catastrophe events is obstructing supporting measures within disaster management. Earthquakes, tsunamis, civil war, volcanic eruption, droughts and floods have much in common: people are directly affected, landscapes and buildings are destroyed. In every case geospatial data is necessary to gain knowledge as basement for decision support. Where to go first? Which infrastructure is usable? How much area is affected? These are essential question which need to be answered before appropriate, eligible help can be established. This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects. Standard methods for automated change detection prove not to be sufficient; therefore a new method was developed and tested. The presented method allows a fast detection and visualization of change in areas of crisis or catastrophes. While often new methods of remote sensing are developed without user oriented aspects, organizations and authorities are not able to use these methods because of lack of remote sensing knowledge. Therefore a semi-automated procedure was developed. Within a transferable framework, the developed algorithm can be implemented for a set of remote sensing data among different investigation areas. Several case studies are the base for the retrieved results. Within a coarse dividing into statistical parts and the segmentation in meaningful objects, the framework is able to deal with different types of change. By means of an elaborated Temporal Change Index (TCI) only panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas where rebuilding has already started.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning

PubMed Central

Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea). PMID:28264011

Applications of Fault Detection in Vibrating Structures

NASA Technical Reports Server (NTRS)

Eure, Kenneth W.; Hogge, Edward; Quach, Cuong C.; Vazquez, Sixto L.; Russell, Andrew; Hill, Boyd L.

2012-01-01

Structural fault detection and identification remains an area of active research. Solutions to fault detection and identification may be based on subtle changes in the time series history of vibration signals originating from various sensor locations throughout the structure. The purpose of this paper is to document the application of vibration based fault detection methods applied to several structures. Overall, this paper demonstrates the utility of vibration based methods for fault detection in a controlled laboratory setting and limitations of applying the same methods to a similar structure during flight on an experimental subscale aircraft.

Detecting the effects of forest harvesting on streamflow using hydrologic model change detection

Treesearch

Nicolas P. Zegre; Nicholas A. Som

2011-01-01

Knowledge of the effects of forest management on hydrology primarily comes from paired-catchment study experiments. This approach has contributed fundamental knowledge of the effects of forest management on hydrology, but results from these studies lack insight into catchment processes. Outlined in this study is an alternative method of change detection that uses a...

Study on thin wideband applicator for detecting blood characteristics in human body

NASA Astrophysics Data System (ADS)

Bamba, Kazuki; Kuki, Takao; Nikawa, Yoshio

2016-11-01

Preventive care as well as early detection method and monitoring technique for diseases are highly attracted attention to increase quality of life. Noninvasive measurement method for blood characteristics in body is expected by patients with kidney dysfunction. Complex permittivity of blood is changed a few present at 6GHz. This change is caused by the change of water and albumin contents in blood. In this study, to detect blood characteristics in human body, experiments with phantom model has been performed using thin wideband applicator for examining microwave transmission up to 6GHz. The thin wideband applicator has advantages for detecting living body information in detail. The thin wideband applicator is designed based on Antipodal Vivaldi Antenna and is not required any balun and is very easy handling. Using developed Antipodal Vivaldi Antenna, transmission coefficient can be obtained as a function of thickness of phantom model with high sensitivity. Using this method, highly sensitive sensor for obtaining characteristics of blood in body can be developed.

Sequential structural damage diagnosis algorithm using a change point detection method

NASA Astrophysics Data System (ADS)

Noh, H.; Rajagopal, R.; Kiremidjian, A. S.

2013-11-01

This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method. The general change point detection method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori, unless we are looking for a known specific type of damage. Therefore, we introduce an additional algorithm that estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using a set of experimental data collected from a four-story steel special moment-resisting frame and multiple sets of simulated data. Various features of different dimensions have been explored, and the algorithm was able to identify damage, particularly when it uses multidimensional damage sensitive features and lower false alarm rates, with a known post-damage feature distribution. For unknown feature distribution cases, the post-damage distribution was consistently estimated and the detection delays were only a few time steps longer than the delays from the general method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

PubMed Central

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-01-01

Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation. PMID:27618903

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images.

PubMed

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-08-27

Characterizations of up to date information of the Earth's surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

Saliency predicts change detection in pictures of natural scenes.

PubMed

Wright, Michael J

2005-01-01

It has been proposed that the visual system encodes the salience of objects in the visual field in an explicit two-dimensional map that guides visual selective attention. Experiments were conducted to determine whether salience measurements applied to regions of pictures of outdoor scenes could predict the detection of changes in those regions. To obtain a quantitative measure of change detection, observers located changes in pairs of colour pictures presented across an interstimulus interval (ISI). Salience measurements were then obtained from different observers for image change regions using three independent methods, and all were positively correlated with change detection. Factor analysis extracted a single saliency factor that accounted for 62% of the variance contained in the four measures. Finally, estimates of the magnitude of the image change in each picture pair were obtained, using nine separate visual filters representing low-level vision features (luminance, colour, spatial frequency, orientation, edge density). None of the feature outputs was significantly associated with change detection or saliency. On the other hand it was shown that high-level (structural) properties of the changed region were related to saliency and to change detection: objects were more salient than shadows and more detectable when changed.

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.

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.

Identifying reprioritization response shift in a stroke caregiver population: a comparison of missing data methods.

PubMed

Sajobi, Tolulope T; Lix, Lisa M; Singh, Gurbakhshash; Lowerison, Mark; Engbers, Jordan; Mayo, Nancy E

2015-03-01

Response shift (RS) is an important phenomenon that influences the assessment of longitudinal changes in health-related quality of life (HRQOL) studies. Given that RS effects are often small, missing data due to attrition or item non-response can contribute to failure to detect RS effects. Since missing data are often encountered in longitudinal HRQOL data, effective strategies to deal with missing data are important to consider. This study aims to compare different imputation methods on the detection of reprioritization RS in the HRQOL of caregivers of stroke survivors. Data were from a Canadian multi-center longitudinal study of caregivers of stroke survivors over a one-year period. The Stroke Impact Scale physical function score at baseline, with a cutoff of 75, was used to measure patient stroke severity for the reprioritization RS analysis. Mean imputation, likelihood-based expectation-maximization imputation, and multiple imputation methods were compared in test procedures based on changes in relative importance weights to detect RS in SF-36 domains over a 6-month period. Monte Carlo simulation methods were used to compare the statistical powers of relative importance test procedures for detecting RS in incomplete longitudinal data under different missing data mechanisms and imputation methods. Of the 409 caregivers, 15.9 and 31.3 % of them had missing data at baseline and 6 months, respectively. There were no statistically significant changes in relative importance weights on any of the domains when complete-case analysis was adopted. But statistical significant changes were detected on physical functioning and/or vitality domains when mean imputation or EM imputation was adopted. There were also statistically significant changes in relative importance weights for physical functioning, mental health, and vitality domains when multiple imputation method was adopted. Our simulations revealed that relative importance test procedures were least powerful under complete-case analysis method and most powerful when a mean imputation or multiple imputation method was adopted for missing data, regardless of the missing data mechanism and proportion of missing data. Test procedures based on relative importance measures are sensitive to the type and amount of missing data and imputation method. Relative importance test procedures based on mean imputation and multiple imputation are recommended for detecting RS in incomplete data.

Sensor and methods of detecting target materials and situations in closed systems

DOEpatents

Mee, David K.; Ripley, Edward B.; Nienstedt, Zachary C.; Nienstedt, Alex W.; Howell, Jr., Layton N.

2018-03-13

Disclosed is a passive, in-situ pressure sensor. The sensor includes a sensing element having a ferromagnetic metal and a tension inducing mechanism coupled to the ferromagnetic metal. The tension inducing mechanism is operable to change a tensile stress upon the ferromagnetic metal based on a change in pressure in the sensing element. Changes in pressure are detected based on changes in the magnetic switching characteristics of the ferromagnetic metal when subjected to an alternating magnetic field caused by the change in the tensile stress. The sensing element is embeddable in a closed system for detecting pressure changes without the need for any penetrations of the system for power or data acquisition by detecting changes in the magnetic switching characteristics of the ferromagnetic metal caused by the tensile stress.

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.

Detection of kinetic change points in piece-wise linear single molecule motion

NASA Astrophysics Data System (ADS)

Hill, Flynn R.; van Oijen, Antoine M.; Duderstadt, Karl E.

2018-03-01

Single-molecule approaches present a powerful way to obtain detailed kinetic information at the molecular level. However, the identification of small rate changes is often hindered by the considerable noise present in such single-molecule kinetic data. We present a general method to detect such kinetic change points in trajectories of motion of processive single molecules having Gaussian noise, with a minimum number of parameters and without the need of an assumed kinetic model beyond piece-wise linearity of motion. Kinetic change points are detected using a likelihood ratio test in which the probability of no change is compared to the probability of a change occurring, given the experimental noise. A predetermined confidence interval minimizes the occurrence of false detections. Applying the method recursively to all sub-regions of a single molecule trajectory ensures that all kinetic change points are located. The algorithm presented allows rigorous and quantitative determination of kinetic change points in noisy single molecule observations without the need for filtering or binning, which reduce temporal resolution and obscure dynamics. The statistical framework for the approach and implementation details are discussed. The detection power of the algorithm is assessed using simulations with both single kinetic changes and multiple kinetic changes that typically arise in observations of single-molecule DNA-replication reactions. Implementations of the algorithm are provided in ImageJ plugin format written in Java and in the Julia language for numeric computing, with accompanying Jupyter Notebooks to allow reproduction of the analysis presented here.

Perspectives of Maine Forest Cover Change from Landsat Imagery and Forest Inventory Analysis (FIA)

Treesearch

Steven Sader; Michael Hoppus; Jacob Metzler; Suming Jin

2005-01-01

A forest change detection map was developed to document forest gains and losses during the decade of the 1990s. The effectiveness of the Landsat imagery and methods for detecting Maine forest cover change are indicated by the good accuracy assessment results: forest-no change, forest loss, and forest gain accuracy were 90, 88, and 92% respectively, and the good...

a Voxel-Based Metadata Structure for Change Detection in Point Clouds of Large-Scale Urban Areas

NASA Astrophysics Data System (ADS)

Gehrung, J.; Hebel, M.; Arens, M.; Stilla, U.

2018-05-01

Mobile laser scanning has not only the potential to create detailed representations of urban environments, but also to determine changes up to a very detailed level. An environment representation for change detection in large scale urban environments based on point clouds has drawbacks in terms of memory scalability. Volumes, however, are a promising building block for memory efficient change detection methods. The challenge of working with 3D occupancy grids is that the usual raycasting-based methods applied for their generation lead to artifacts caused by the traversal of unfavorable discretized space. These artifacts have the potential to distort the state of voxels in close proximity to planar structures. In this work we propose a raycasting approach that utilizes knowledge about planar surfaces to completely prevent this kind of artifacts. To demonstrate the capabilities of our approach, a method for the iterative volumetric approximation of point clouds that allows to speed up the raycasting by 36 percent is proposed.

PWAS EMIS-ECIS Active Carbon Filter Residual Life Estimation Methodology

DTIC Science & Technology

2013-09-23

change in the EMIS spectrum. This method is similar to the full width at half maximum (FWHM) method implemented in the fiber Bragg grating ( FBG ), where...the intensity of the light reflected by the FBG at the half peak frequency is used to detect the strain change in the FBG . 4 W911NF-11-1-0210...grating ( FBG ), where the intensity of the light reflected by the FBG at the half peak frequency is used to detect the strain change in the FBG . A brief

Comparison of Methods for Assessing Body Composition Changes during Weight Loss.

ERIC Educational Resources Information Center

Weyers, Anna M.; Mazzetti, Scott A.; Love, Dawn M.; Gomez, Ana L.; Kraemer, William J.; Volek, Jeff S.

2002-01-01

Investigated whether dual-energy x-ray absorptiometry (DXA) and air displacement plethysmography (ADP) would detect similar changes in body composition after moderate weight loss. Twenty adults had their body composition measured using DXA and ADP before and after an 8-week weight loss program. Overall, both DXA and ADP detected similar changes in…

Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison

NASA Astrophysics Data System (ADS)

Sa, Qila; Wang, Zhihui

2018-03-01

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

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.

Moving human full body and body parts detection, tracking, and applications on human activity estimation, walking pattern and face recognition

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike

2016-05-01

We have developed a new way for detection and tracking of human full-body and body-parts with color (intensity) patch morphological segmentation and adaptive thresholding for security surveillance cameras. An adaptive threshold scheme has been developed for dealing with body size changes, illumination condition changes, and cross camera parameter changes. Tests with the PETS 2009 and 2014 datasets show that we can obtain high probability of detection and low probability of false alarm for full-body. Test results indicate that our human full-body detection method can considerably outperform the current state-of-the-art methods in both detection performance and computational complexity. Furthermore, in this paper, we have developed several methods using color features for detection and tracking of human body-parts (arms, legs, torso, and head, etc.). For example, we have developed a human skin color sub-patch segmentation algorithm by first conducting a RGB to YIQ transformation and then applying a Subtractive I/Q image Fusion with morphological operations. With this method, we can reliably detect and track human skin color related body-parts such as face, neck, arms, and legs. Reliable body-parts (e.g. head) detection allows us to continuously track the individual person even in the case that multiple closely spaced persons are merged. Accordingly, we have developed a new algorithm to split a merged detection blob back to individual detections based on the detected head positions. Detected body-parts also allow us to extract important local constellation features of the body-parts positions and angles related to the full-body. These features are useful for human walking gait pattern recognition and human pose (e.g. standing or falling down) estimation for potential abnormal behavior and accidental event detection, as evidenced with our experimental tests. Furthermore, based on the reliable head (face) tacking, we have applied a super-resolution algorithm to enhance the face resolution for improved human face recognition performance.

Detecting cell death with optical coherence tomography and envelope statistics

NASA Astrophysics Data System (ADS)

Farhat, Golnaz; Yang, Victor X. D.; Czarnota, Gregory J.; Kolios, Michael C.

2011-02-01

Currently no standard clinical or preclinical noninvasive method exists to monitor cell death based on morphological changes at the cellular level. In our past work we have demonstrated that quantitative high frequency ultrasound imaging can detect cell death in vitro and in vivo. In this study we apply quantitative methods previously used with high frequency ultrasound to optical coherence tomography (OCT) to detect cell death. The ultimate goal of this work is to use these methods for optically-based clinical and preclinical cancer treatment monitoring. Optical coherence tomography data were acquired from acute myeloid leukemia cells undergoing three modes of cell death. Significant increases in integrated backscatter were observed for cells undergoing apoptosis and mitotic arrest, while necrotic cells induced a decrease. These changes appear to be linked to structural changes observed in histology obtained from the cell samples. Signal envelope statistics were analyzed from fittings of the generalized gamma distribution to histograms of envelope intensities. The parameters from this distribution demonstrated sensitivities to morphological changes in the cell samples. These results indicate that OCT integrated backscatter and first order envelope statistics can be used to detect and potentially differentiate between modes of cell death in vitro.

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.

Detecting of forest afforestation and deforestation in Hainan Jianfengling Forest Park (China) using yearly Landsat time-series images

NASA Astrophysics Data System (ADS)

Jiao, Quanjun; Zhang, Xiao; Sun, Qi

2018-03-01

The availability of dense time series of Landsat images pro-vides a great chance to reconstruct forest disturbance and change history with high temporal resolution, medium spatial resolution and long period. This proposal aims to apply forest change detection method in Hainan Jianfengling Forest Park using yearly Landsat time-series images. A simple detection method from the dense time series Landsat NDVI images will be used to reconstruct forest change history (afforestation and deforestation). The mapping result showed a large decrease occurred in the extent of closed forest from 1980s to 1990s. From the beginning of the 21st century, we found an increase in forest areas with the implementation of forestry measures such as the prohibition of cutting and sealing in our study area. Our findings provide an effective approach for quickly detecting forest changes in tropical original forest, especially for afforestation and deforestation, and a comprehensive analysis tool for forest resource protection.

Moving target detection method based on improved Gaussian mixture model

NASA Astrophysics Data System (ADS)

Ma, J. Y.; Jie, F. R.; Hu, Y. J.

2017-07-01

Gaussian Mixture Model is often employed to build background model in background difference methods for moving target detection. This paper puts forward an adaptive moving target detection algorithm based on improved Gaussian Mixture Model. According to the graylevel convergence for each pixel, adaptively choose the number of Gaussian distribution to learn and update background model. Morphological reconstruction method is adopted to eliminate the shadow.. Experiment proved that the proposed method not only has good robustness and detection effect, but also has good adaptability. Even for the special cases when the grayscale changes greatly and so on, the proposed method can also make outstanding performance.

Inverse Abbe-method for observing small refractive index changes in liquids.

PubMed

Räty, Jukka; Peiponen, Kai-Erik

2015-05-01

This study concerns an optical method for the detection of minuscule refractive index changes in the liquid phase. The proposed method reverses the operation of the traditional Abbe refractometer and thus utilizes the light dispersion properties of materials, i.e. it involves the dependence of the refractive index on light wavelength. In practice, the method includes the detection of light reflection spectra in the visible spectral range. This inverse Abbe method is suitable for liquid quality studies e.g. for monitoring water purity. Tests have shown that the method reveals less than per mil NaCl or ethanol concentrations in water. Copyright © 2015 Elsevier B.V. All rights reserved.

Estimating site occupancy and detection probability parameters for meso- and large mammals in a coastal eosystem

USGS Publications Warehouse

O'Connell, Allan F.; Talancy, Neil W.; Bailey, Larissa L.; Sauer, John R.; Cook, Robert; Gilbert, Andrew T.

2006-01-01

Large-scale, multispecies monitoring programs are widely used to assess changes in wildlife populations but they often assume constant detectability when documenting species occurrence. This assumption is rarely met in practice because animal populations vary across time and space. As a result, detectability of a species can be influenced by a number of physical, biological, or anthropogenic factors (e.g., weather, seasonality, topography, biological rhythms, sampling methods). To evaluate some of these influences, we estimated site occupancy rates using species-specific detection probabilities for meso- and large terrestrial mammal species on Cape Cod, Massachusetts, USA. We used model selection to assess the influence of different sampling methods and major environmental factors on our ability to detect individual species. Remote cameras detected the most species (9), followed by cubby boxes (7) and hair traps (4) over a 13-month period. Estimated site occupancy rates were similar among sampling methods for most species when detection probabilities exceeded 0.15, but we question estimates obtained from methods with detection probabilities between 0.05 and 0.15, and we consider methods with lower probabilities unacceptable for occupancy estimation and inference. Estimated detection probabilities can be used to accommodate variation in sampling methods, which allows for comparison of monitoring programs using different protocols. Vegetation and seasonality produced species-specific differences in detectability and occupancy, but differences were not consistent within or among species, which suggests that our results should be considered in the context of local habitat features and life history traits for the target species. We believe that site occupancy is a useful state variable and suggest that monitoring programs for mammals using occupancy data consider detectability prior to making inferences about species distributions or population change.

Comprehensive methods for earlier detection and monitoring of forest decline

Treesearch

Jennifer Pontius; Richard Hallett

2014-01-01

Forested ecosystems are threatened by invasive pests, pathogens, and unusual climatic events brought about by climate change. Earlier detection of incipient forest health problems and a quantitatively rigorous assessment method is increasingly important. Here, we describe a method that is adaptable across tree species and stress agents and practical for use in the...

Detecting spatial regimes in ecosystems

USGS Publications Warehouse

Sundstrom, Shana M.; Eason, Tarsha; Nelson, R. John; Angeler, David G.; Barichievy, Chris; Garmestani, Ahjond S.; Graham, Nicholas A.J.; Granholm, Dean; Gunderson, Lance; Knutson, Melinda; Nash, Kirsty L.; Spanbauer, Trisha; Stow, Craig A.; Allen, Craig R.

2017-01-01

Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory-based method, on both terrestrial and aquatic animal data (U.S. Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps and multivariate analyses such as nMDS and cluster analysis. We successfully detected spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change.

A Hopfield neural network for image change detection.

PubMed

Pajares, Gonzalo

2006-09-01

This paper outlines an optimization relaxation approach based on the analog Hopfield neural network (HNN) for solving the image change detection problem between two images. A difference image is obtained by subtracting pixel by pixel both images. The network topology is built so that each pixel in the difference image is a node in the network. Each node is characterized by its state, which determines if a pixel has changed. An energy function is derived, so that the network converges to stable states. The analog Hopfield's model allows each node to take on analog state values. Unlike most widely used approaches, where binary labels (changed/unchanged) are assigned to each pixel, the analog property provides the strength of the change. The main contribution of this paper is reflected in the customization of the analog Hopfield neural network to derive an automatic image change detection approach. When a pixel is being processed, some existing image change detection procedures consider only interpixel relations on its neighborhood. The main drawback of such approaches is the labeling of this pixel as changed or unchanged according to the information supplied by its neighbors, where its own information is ignored. The Hopfield model overcomes this drawback and for each pixel allows a tradeoff between the influence of its neighborhood and its own criterion. This is mapped under the energy function to be minimized. The performance of the proposed method is illustrated by comparative analysis against some existing image change detection methods.

Structural Damage Detection Using Changes in Natural Frequencies: Theory and Applications

NASA Astrophysics Data System (ADS)

He, K.; Zhu, W. D.

2011-07-01

A vibration-based method that uses changes in natural frequencies of a structure to detect damage has advantages over conventional nondestructive tests in detecting various types of damage, including loosening of bolted joints, using minimum measurement data. Two major challenges associated with applications of the vibration-based damage detection method to engineering structures are addressed: accurate modeling of structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems, respectively. To resolve the forward problem, new physics-based finite element modeling techniques are developed for fillets in thin-walled beams and for bolted joints, so that complex structures can be accurately modeled with a reasonable model size. To resolve the inverse problem, a logistical function transformation is introduced to convert the constrained optimization problem to an unconstrained one, and a robust iterative algorithm using a trust-region method, called the Levenberg-Marquardt method, is developed to accurately detect the locations and extent of damage. The new methodology can ensure global convergence of the iterative algorithm in solving under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. The vibration-based damage detection method is applied to various structures including lightning masts, a space frame structure and one of its components, and a pipeline. The exact locations and extent of damage can be detected in the numerical simulation where there is no modeling error and measurement noise. The locations and extent of damage can be successfully detected in experimental damage detection.

Multiplexed highly sensitive detections of cancer biomarkers in thermal space using encapsulated phase change nanoparticles

NASA Astrophysics Data System (ADS)

Ma, Liyuan; Hong, Yan; Ma, Zeyu; Kaittanis, Charalambos; Perez, J. Manuel; Su, Ming

2009-07-01

We describe a multiplexed highly sensitive method to detect cancer biomarkers using silica encapsulated phase change nanoparticles as thermal barcodes. During phase changes, nanoparticles absorb heat energy without much temperature rise and show sharp melting peaks (0.6 °C). A series of phase change nanoparticles of metals or alloys can be synthesized in such a way that they melt between 100 and 700 °C, thus the multiplicity could reach 1000. The method has high sensitivity (8 nM) that can be enhanced using materials with large latent heat, nanoparticles with large diameter, or reducing the grafting density of biomolecules on nanoparticles.

Change-point detection of induced and natural seismicity

NASA Astrophysics Data System (ADS)

Fiedler, B.; Holschneider, M.; Zoeller, G.; Hainzl, S.

2016-12-01

Earthquake rates are influenced by tectonic stress buildup, earthquake-induced stress changes, and transient aseismic sources. While the first two sources can be well modeled due to the fact that the source is known, transient aseismic processes are more difficult to detect. However, the detection of the associated changes of the earthquake activity is of great interest, because it might help to identify natural aseismic deformation patterns (such as slow slip events) and the occurrence of induced seismicity related to human activities. We develop a Bayesian approach to detect change-points in seismicity data which are modeled by Poisson processes. By means of a Likelihood-Ratio-Test, we proof the significance of the change of the intensity. The model is also extended to spatiotemporal data to detect the area of the transient changes. The method is firstly tested for synthetic data and then applied to observational data from central US and the Bardarbunga volcano in Iceland.

Adaptive 4d Psi-Based Change Detection

NASA Astrophysics Data System (ADS)

Yang, Chia-Hsiang; Soergel, Uwe

2018-04-01

In a previous work, we proposed a PSI-based 4D change detection to detect disappearing and emerging PS points (3D) along with their occurrence dates (1D). Such change points are usually caused by anthropic events, e.g., building constructions in cities. This method first divides an entire SAR image stack into several subsets by a set of break dates. The PS points, which are selected based on their temporal coherences before or after a break date, are regarded as change candidates. Change points are then extracted from these candidates according to their change indices, which are modelled from their temporal coherences of divided image subsets. Finally, we check the evolution of the change indices for each change point to detect the break date that this change occurred. The experiment validated both feasibility and applicability of our method. However, two questions still remain. First, selection of temporal coherence threshold associates with a trade-off between quality and quantity of PS points. This selection is also crucial for the amount of change points in a more complex way. Second, heuristic selection of change index thresholds brings vulnerability and causes loss of change points. In this study, we adapt our approach to identify change points based on statistical characteristics of change indices rather than thresholding. The experiment validates this adaptive approach and shows increase of change points compared with the old version. In addition, we also explore and discuss optimal selection of temporal coherence threshold.

SQUID-detected FMR: Resonance in single crystalline and polycrystalline yttrium iron garnet

NASA Astrophysics Data System (ADS)

O'Reilly, J. M.; Stamenov, P.

2018-04-01

Here two new techniques for the detection of broadband (100 MHz-20 GHz) ferromagnetic resonance (FMR)/ferrimagnetic resonance in single and poly-crystalline materials, which rely on SQUID-based gradiometry detection of small changes in the magnetisation, are developed. In the first method, small changes in the along-the-applied-field projection of the coupled magnetic moment (Δmz) are detected as the material is driven into resonance. Absolute measurement of the longitudinal component of the magnetisation and the resonance induced lowering of this moment makes estimation of the precession cone angle accessible, which is typically difficult to extract using conventional cavity or stripline based detection methods. The second method invokes the change in Δmz with the resonance-induced thermal heating (d/mz d T ). Magnetisation dynamics in bulk Y3Fe5O12 are observed over a broad range of experimental temperatures (4 K-400 K) and fields (10-500 mT). The inhomogeneous microwave excitation allows for the observation of higher magnetostatic modes and the convenient tracking of very broad resonances. The two SQUID-detection techniques when combined with conventional broadband vector network analyser-FMR, low-frequency magnetic susceptibility, and DC magnetometry, all easily realised, essentially concurrently, using the same module, greatly expand the amount of static and dynamic information accessible.

Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery

NASA Astrophysics Data System (ADS)

Qin, Rongjun

2014-10-01

Due to the fast development of the urban environment, the need for efficient maintenance and updating of 3D building models is ever increasing. Change detection is an essential step to spot the changed area for data (map/3D models) updating and urban monitoring. Traditional methods based on 2D images are no longer suitable for change detection in building scale, owing to the increased spectral variability of the building roofs and larger perspective distortion of the very high resolution (VHR) imagery. Change detection in 3D is increasingly being investigated using airborne laser scanning data or matched Digital Surface Models (DSM), but rare study has been conducted regarding to change detection on 3D city models with VHR images, which is more informative but meanwhile more complicated. This is due to the fact that the 3D models are abstracted geometric representation of the urban reality, while the VHR images record everything. In this paper, a novel method is proposed to detect changes directly on LOD (Level of Detail) 2 building models with VHR spaceborne stereo images from a different date, with particular focus on addressing the special characteristics of the 3D models. In the first step, the 3D building models are projected onto a raster grid, encoded with building object, terrain object, and planar faces. The DSM is extracted from the stereo imagery by hierarchical semi-global matching (SGM). In the second step, a multi-channel change indicator is extracted between the 3D models and stereo images, considering the inherent geometric consistency (IGC), height difference, and texture similarity for each planar face. Each channel of the indicator is then clustered with the Self-organizing Map (SOM), with "change", "non-change" and "uncertain change" status labeled through a voting strategy. The "uncertain changes" are then determined with a Markov Random Field (MRF) analysis considering the geometric relationship between faces. In the third step, buildings are extracted combining the multispectral images and the DSM by morphological operators, and the new buildings are determined by excluding the verified unchanged buildings from the second step. Both the synthetic experiment with Worldview-2 stereo imagery and the real experiment with IKONOS stereo imagery are carried out to demonstrate the effectiveness of the proposed method. It is shown that the proposed method can be applied as an effective way to monitoring the building changes, as well as updating 3D models from one epoch to the other.

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.

Development of a database and processing method for detecting hematotoxicity adverse drug events.

PubMed

Shimai, Yoshie; Takeda, Toshihiro; Manabe, Shirou; Teramoto, Kei; Mihara, Naoki; Matsumura, Yasushi

2015-01-01

Adverse events are detected by monitoring the patient's status, including blood test results. However, it is difficult to identify all adverse events based on recognition by individual doctors. We developed a system that can be used to detect hematotoxicity adverse events according to blood test results recorded in an electronic medical record system. The blood test results were graded based on Common Terminology Criteria for Adverse Events (CTCAE) and changes in the blood test results (Up, Down, Flat) were assessed according to the variation in the grade. The changes in the blood test and injection data were stored in a database. By comparing the date of injection and start and end dates of the change in the blood test results, adverse events related to a designated drug were detected. Using this method, we searched for the occurrence of serious adverse events (CTCAE Grades 3 or 4) concerning WBC, ALT and creatinine related to paclitaxel at Osaka University Hospital. The rate of occurrence of a decreased WBC count, increased ALT level and increased creatinine level was 36.0%, 0.6% and 0.4%, respectively. This method is useful for detecting and estimating the rate of occurrence of hematotoxicity adverse drug events.

Change detection from remotely sensed images: From pixel-based to object-based approaches

NASA Astrophysics Data System (ADS)

Hussain, Masroor; Chen, Dongmei; Cheng, Angela; Wei, Hui; Stanley, David

2013-06-01

The appetite for up-to-date information about earth's surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.

Method Apparatus And System For Detecting Seismic Waves In A Borehole

DOEpatents

West, Phillip B.; Sumstine, Roger L.

2006-03-14

A method, apparatus and system for detecting seismic waves. A sensing apparatus is deployed within a bore hole and may include a source magnet for inducing a magnetic field within a casing of the borehole. An electrical coil is disposed within the magnetic field to sense a change in the magnetic field due to a displacement of the casing. The electrical coil is configured to remain substantially stationary relative to the well bore and its casing along a specified axis such that displacement of the casing induces a change within the magnetic field which may then be sensed by the electrical coil. Additional electrical coils may be similarly utilized to detect changes in the same or other associated magnetic fields along other specified axes. The additional sensor coils may be oriented substantially orthogonally relative to one another so as to detect seismic waves along multiple orthogonal axes in three dimensional space.

Invasive species change detection using artificial neural networks and CASI hyperspectral imagery

USDA-ARS?s Scientific Manuscript database

For monitoring and controlling the extent and intensity of an invasive species, a direct multi-date image classification method was applied in invasive species (saltcedar) change detection in the study area of Lovelock, Nevada. With multi-date Compact Airborne Spectrographic Imager (CASI) hyperspec...

Smart materials: strain sensing and stress determination by means of nanotube sensing systems, composites, and devices

NASA Technical Reports Server (NTRS)

Kim, Jong Dae (Inventor); Nagarajaiah, Satish (Inventor); Barrera, Enrique V. (Inventor); Dharap, Prasad (Inventor); Zhiling, Li (Inventor)

2010-01-01

The present invention is directed toward devices comprising carbon nanotubes that are capable of detecting displacement, impact, stress, and/or strain in materials, methods of making such devices, methods for sensing/detecting/monitoring displacement, impact, stress, and/or strain via carbon nanotubes, and various applications for such methods and devices. The devices and methods of the present invention all rely on mechanically-induced electronic perturbations within the carbon nanotubes to detect and quantify such stress/strain. Such detection and quantification can rely on techniques which include, but are not limited to, electrical conductivity/conductance and/or resistivity/resistance detection/measurements, thermal conductivity detection/measurements, electroluminescence detection/measurements, photoluminescence detection/measurements, and combinations thereof. All such techniques rely on an understanding of how such properties change in response to mechanical stress and/or strain.

Evaluation of a Region-of-Interest Approach for Detecting Progressive Glaucomatous Macular Damage on Optical Coherence Tomography

PubMed Central

Weng, Denis S. D.; Thenappan, Abinaya; Ritch, Robert; Hood, Donald C.

2018-01-01

Purpose To evaluate a manual region-of-interest (ROI) approach for detecting progressive macular ganglion cell complex (GCC) changes on optical coherence tomography (OCT) imaging. Methods One hundred forty-six eyes with a clinical diagnosis of glaucoma or suspected glaucoma with macular OCT scans obtained at least 1 year apart were evaluated. Changes in the GCC thickness were identified using a manual ROI approach (ROIM), whereby region(s) of observed or suspected glaucomatous damage were manually identified when using key features from the macular OCT scan on the second visit. Progression was also evaluated using the global GCC thickness and an automatic ROI approach (ROIA), where contiguous region(s) that fell below the 1% lower normative limit and exceeded 288 μm2 in size were evaluated. Longitudinal signal-to-noise ratios (SNRs) were calculated for progressive changes detected by each of these methods using individualized estimates of test–retest variability and age-related changes, obtained from 303 glaucoma and 394 healthy eyes, respectively. Results On average, the longitudinal SNR for the global thickness, ROIA and ROIM methods were −0.90 y−1, −0.91 y−1, and −1.03 y−1, respectively, and was significantly more negative for the ROIM compared with the global thickness (P = 0.003) and ROIA methods (P = 0.021). Conclusions Progressive glaucomatous macular GCC changes were optimally detected with a manual ROI approach. Translational Relevance These findings suggests that an approach based on a qualitative evaluation of OCT imaging information and consideration of known patterns of damage can improve the detection of progressive glaucomatous macular damage. PMID:29616153

Quantifying spatial and temporal trends in beach-dune volumetric changes using spatial statistics

NASA Astrophysics Data System (ADS)

Eamer, Jordan B. R.; Walker, Ian J.

2013-06-01

Spatial statistics are generally underutilized in coastal geomorphology, despite offering great potential for identifying and quantifying spatial-temporal trends in landscape morphodynamics. In particular, local Moran's Ii provides a statistical framework for detecting clusters of significant change in an attribute (e.g., surface erosion or deposition) and quantifying how this changes over space and time. This study analyzes and interprets spatial-temporal patterns in sediment volume changes in a beach-foredune-transgressive dune complex following removal of invasive marram grass (Ammophila spp.). Results are derived by detecting significant changes in post-removal repeat DEMs derived from topographic surveys and airborne LiDAR. The study site was separated into discrete, linked geomorphic units (beach, foredune, transgressive dune complex) to facilitate sub-landscape scale analysis of volumetric change and sediment budget responses. Difference surfaces derived from a pixel-subtraction algorithm between interval DEMs and the LiDAR baseline DEM were filtered using the local Moran's Ii method and two different spatial weights (1.5 and 5 m) to detect statistically significant change. Moran's Ii results were compared with those derived from a more spatially uniform statistical method that uses a simpler student's t distribution threshold for change detection. Morphodynamic patterns and volumetric estimates were similar between the uniform geostatistical method and Moran's Ii at a spatial weight of 5 m while the smaller spatial weight (1.5 m) consistently indicated volumetric changes of less magnitude. The larger 5 m spatial weight was most representative of broader site morphodynamics and spatial patterns while the smaller spatial weight provided volumetric changes consistent with field observations. All methods showed foredune deflation immediately following removal with increased sediment volumes into the spring via deposition at the crest and on lobes in the lee, despite erosion on the stoss slope and dune toe. Generally, the foredune became wider by landward extension and the seaward slope recovered from erosion to a similar height and form to that of pre-restoration despite remaining essentially free of vegetation.

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.

Detection of microbial contamination in platelets

NASA Astrophysics Data System (ADS)

Berg, Tracy L.; Leparc, German; Huffman, Debra E.; Gennaccaro, Angela L.; Garcia-Lopez, Alicia; Klungness, Greta; Stephans, Christie; Garcia-Rubio, Luis H.

2005-03-01

In the United States, approximately 100 patients develop fatal sepsis associated with platelet transfusions every year. Current culture methods take 24-48 hours to acquire results, which in turn decrease the shelf life of platelets. Many of the microorganisms that contaminate platelets can replicate easily at room temperature, which is the necessary storage temperature to keep platelets functional. Therefore, there is a need for in-situ quality control assessment of the platelet quality. For this purpose, a real time spectrophotometric technique has been developed. The Spectral Acquisition Processing Detection (SAPD) method, comprised of a UV-vis spectrophotometer and modeling algorithms, is a rapid method that can be performed prior to platelet transfusion to decrease the risk of bacterial infection to patients. The SAPD method has been used to determine changes in cell suspensions, based on size, shape, chemical composition and internal structure. Changes in these cell characteristics can in turn be used to determine microbial contamination, platelet aging and other physiologic changes. Detection limits of this method for platelet suspensions seeded with bacterial contaminants were identified to be less than 100 cfu/ml of sample. Bacterial counts below 1000 cfu/ml are not considered clinically significant. The SAPD method can provide real-time identification of bacterial contamination of platelets affording patients an increased level of safety without causing undue strain on laboratory budgets or personnel while increasing the time frame that platelets can be used by dramatically shortening contaminant detection time.

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.

Unconstrained Respiration Measurement and Respiratory Arrest Detection Method by Dynamic Threshold in Transferring Patients by Stretchers

NASA Astrophysics Data System (ADS)

Kurihara, Yosuke; Watanabe, Kajiro; Kobayashi, Kazuyuki; Tanaka, Hiroshi

General anesthesia used for surgical operations may cause unstable conditions of the patients after the operations, which could lead to respiratory arrests. Under such circumstances, nurses could fail in finding the change of the conditions, and other malpractices could also occur. It is highly possible that such malpractices may occur while transferring a patient from ICU to the room using a stretcher. Monitoring the change in the blood oxygen saturation concentration and other vital signs to detect a respiratory arrest is not easy when transferring a patient on a stretcher. Here we present several noise reduction system and algorithm to detect respiratory arrests in transferring a patient, based on the unconstrained air pressure method that the authors presented previously. As the result, when the acceleration level of the stretcher noise was 0.5G, the respiratory arrest detection ratio using this novel method was 65%, while that with the conventional method was 0%.

Detecting and isolating abrupt changes in linear switching systems

NASA Astrophysics Data System (ADS)

Nazari, Sohail; Zhao, Qing; Huang, Biao

2015-04-01

In this paper, a novel fault detection and isolation (FDI) method for switching linear systems is developed. All input and output signals are assumed to be corrupted with measurement noises. In the proposed method, a 'lifted' linear model named as stochastic hybrid decoupling polynomial (SHDP) is introduced. The SHDP model governs the dynamics of the switching linear system with all different modes, and is independent of the switching sequence. The error-in-variable (EIV) representation of SHDP is derived, and is used for the fault residual generation and isolation following the well-adopted local approach. The proposed FDI method can detect and isolate the fault-induced abrupt changes in switching models' parameters without estimating the switching modes. Furthermore, in this paper, the analytical expressions of the gradient vector and Hessian matrix are obtained based on the EIV SHDP formulation, so that they can be used to implement the online fault detection scheme. The performance of the proposed method is then illustrated by simulation examples.

BMI and BMI SDS in childhood: annual increments and conditional change.

PubMed

Brannsether, Bente; Eide, Geir Egil; Roelants, Mathieu; Bjerknes, Robert; Júlíusson, Pétur Benedikt

2017-02-01

Background Early detection of abnormal weight gain in childhood may be important for preventive purposes. It is still debated which annual changes in BMI should warrant attention. Aim To analyse 1-year increments of Body Mass Index (BMI) and standardised BMI (BMI SDS) in childhood and explore conditional change in BMI SDS as an alternative method to evaluate 1-year changes in BMI. Subjects and methods The distributions of 1-year increments of BMI (kg/m 2 ) and BMI SDS are summarised by percentiles. Differences according to sex, age, height, weight, initial BMI and weight status on the BMI and BMI SDS increments were assessed with multiple linear regression. Conditional change in BMI SDS was based on the correlation between annual BMI measurements converted to SDS. Results BMI increments depended significantly on sex, height, weight and initial BMI. Changes in BMI SDS depended significantly only on the initial BMI SDS. The distribution of conditional change in BMI SDS using a two-correlation model was close to normal (mean = 0.11, SD = 1.02, n = 1167), with 3.2% (2.3-4.4%) of the observations below -2 SD and 2.8% (2.0-4.0%) above +2 SD. Conclusion Conditional change in BMI SDS can be used to detect unexpected large changes in BMI SDS. Although this method requires the use of a computer, it may be clinically useful to detect aberrant weight development.

Osmotic fragility changes in preserved blood: measurements by coil planet centrifuge and parpart methods.

PubMed

Sasakawa, S; Tokunaga, E; Hasegawa, G; Nakagawa, S

1977-09-01

The coil planet centrifuge (CPC) can be used to measure the osmotic fragility of erythrocytes. Fragility measured by this method alters when different salts are used. The CPC and Parpart methods were used to measure the changes during storage in red cell osmotic fragility in ACD or CPD blood with or without adenine. More marked changes were detected by the CPC method, especially in old cells. The changes of fragility of erythrocytes during storage seem to occur mainly in old cells. Adenine is effective in preventing such changes.

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.

Sol-Gel Matrices For Direct Colorimetric Detection Of Analytes

DOEpatents

Charych, Deborah H.; Sasaki, Darryl; Yamanaka, Stacey

2002-11-26

The present invention relates to methods and compositions for the direct detection of analytes using color changes that occur in immobilized biopolymeric material in response to selective binding of analytes to their surface. In particular, the present invention provides methods and compositions related to the encapsulation of biopolymeric material into metal oxide glass using the sol-gel method.

Sol-gel matrices for direct colorimetric detection of analytes

DOEpatents

Charych, Deborah H.; Sasaki, Darryl; Yamanaka, Stacey

2000-01-01

The present invention relates to methods and compositions for the direct detection of analytes using color changes that occur in immobilized biopolymeric material in response to selective binding of analytes to their surface. In particular, the present invention provides methods and compositions related to the encapsulation of biopolymeric material into metal oxide glass using the sol-gel method.

Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images.

PubMed

Pang, Shiyan; Hu, Xiangyun; Cai, Zhongliang; Gong, Jinqi; Zhang, Mi

2018-03-24

In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic classification of changed objects into buildings or non-buildings. For the acquisition of changed objects above ground, the change detection problem is converted into a binary classification, in which the changed area above ground is regarded as the foreground and the other area as the background. For the gridded points of each period, the graph cuts algorithm is adopted to classify the points into foreground and background, followed by the region-growing algorithm to form candidate changed building objects. A novel structural feature that was extracted from aerial images is constructed to classify the candidate changed building objects into buildings and non-buildings. The changed building objects are further classified as "newly built", "taller", "demolished", and "lower" by combining the classification and the digital surface models of two periods. Finally, three typical areas from a large dataset are used to validate the proposed method. Numerous experiments demonstrate the effectiveness of the proposed algorithm.

Highly sensitive detection of DNA methylation levels by using a quantum dot-based FRET method

NASA Astrophysics Data System (ADS)

Ma, Yunfei; Zhang, Honglian; Liu, Fangming; Wu, Zhenhua; Lu, Shaohua; Jin, Qinghui; Zhao, Jianlong; Zhong, Xinhua; Mao, Hongju

2015-10-01

DNA methylation is the most frequently studied epigenetic modification that is strongly involved in genomic stability and cellular plasticity. Aberrant changes in DNA methylation status are ubiquitous in human cancer and the detection of these changes can be informative for cancer diagnosis. Herein, we reported a facile quantum dot-based (QD-based) fluorescence resonance energy transfer (FRET) technique for the detection of DNA methylation. The method relies on methylation-sensitive restriction enzymes for the differential digestion of genomic DNA based on its methylation status. Digested DNA is then subjected to PCR amplification for the incorporation of Alexa Fluor-647 (A647) fluorophores. DNA methylation levels can be detected qualitatively through gel analysis and quantitatively by the signal amplification from QDs to A647 during FRET. Furthermore, the methylation levels of three tumor suppressor genes, PCDHGB6, HOXA9 and RASSF1A, in 20 lung adenocarcinoma and 20 corresponding adjacent nontumorous tissue (NT) samples were measured to verify the feasibility of the QD-based FRET method and a high sensitivity for cancer detection (up to 90%) was achieved. Our QD-based FRET method is a convenient, continuous and high-throughput method, and is expected to be an alternative for detecting DNA methylation as a biomarker for certain human cancers.DNA methylation is the most frequently studied epigenetic modification that is strongly involved in genomic stability and cellular plasticity. Aberrant changes in DNA methylation status are ubiquitous in human cancer and the detection of these changes can be informative for cancer diagnosis. Herein, we reported a facile quantum dot-based (QD-based) fluorescence resonance energy transfer (FRET) technique for the detection of DNA methylation. The method relies on methylation-sensitive restriction enzymes for the differential digestion of genomic DNA based on its methylation status. Digested DNA is then subjected to PCR amplification for the incorporation of Alexa Fluor-647 (A647) fluorophores. DNA methylation levels can be detected qualitatively through gel analysis and quantitatively by the signal amplification from QDs to A647 during FRET. Furthermore, the methylation levels of three tumor suppressor genes, PCDHGB6, HOXA9 and RASSF1A, in 20 lung adenocarcinoma and 20 corresponding adjacent nontumorous tissue (NT) samples were measured to verify the feasibility of the QD-based FRET method and a high sensitivity for cancer detection (up to 90%) was achieved. Our QD-based FRET method is a convenient, continuous and high-throughput method, and is expected to be an alternative for detecting DNA methylation as a biomarker for certain human cancers. Electronic supplementary information (ESI) available: Synthesis of CdSe/CdS/ZnS core/shell/shell QDs. Sequences of primers used for amplifying the promoter regions in bisulfate-modified DNA. Comparison of detected methylation levels in different gene promoters using the QD-based FRET method versus bisulfite pyrosequencing. Methylation levels of the RASSF1A gene in one pair of NT and cancer samples as indicated by pyrosequencing. Theoretical calculation of the Förster distance R0. See DOI: 10.1039/c5nr04956c

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.

Iris features-based heart disease diagnosis by computer vision

NASA Astrophysics Data System (ADS)

Nguchu, Benedictor A.; Li, Li

2017-07-01

The study takes advantage of several new breakthroughs in computer vision technology to develop a new mid-irisbiomedical platform that processes iris image for early detection of heart-disease. Guaranteeing early detection of heart disease provides a possibility of having non-surgical treatment as suggested by biomedical researchers and associated institutions. However, our observation discovered that, a clinical practicable solution which could be both sensible and specific for early detection is still lacking. Due to this, the rate of majority vulnerable to death is highly increasing. The delayed diagnostic procedures, inefficiency, and complications of available methods are the other reasons for this catastrophe. Therefore, this research proposes the novel IFB (Iris Features Based) method for diagnosis of premature, and early stage heart disease. The method incorporates computer vision and iridology to obtain a robust, non-contact, nonradioactive, and cost-effective diagnostic tool. The method analyzes abnormal inherent weakness in tissues, change in color and patterns, of a specific region of iris that responds to impulses of heart organ as per Bernard Jensen-iris Chart. The changes in iris infer the presence of degenerative abnormalities in heart organ. These changes are precisely detected and analyzed by IFB method that includes, tensor-based-gradient(TBG), multi orientations gabor filters(GF), textural oriented features(TOF), and speed-up robust features(SURF). Kernel and Multi class oriented support vector machines classifiers are used for classifying normal and pathological iris features. Experimental results demonstrated that the proposed method, not only has better diagnostic performance, but also provides an insight for early detection of other diseases.

Measurement of Postmortem Pupil Size: A New Method with Excellent Reliability and Its Application to Pupil Changes in the Early Postmortem Period.

PubMed

Fleischer, Luise; Sehner, Susanne; Gehl, Axel; Riemer, Martin; Raupach, Tobias; Anders, Sven

2017-05-01

Measurement of postmortem pupil width is a potential component of death time estimation. However, no standardized measurement method has been described. We analyzed a total of 71 digital images for pupil-iris ratio using the software ImageJ. Images were analyzed three times by four different examiners. In addition, serial images from 10 cases were taken between 2 and 50 h postmortem to detect spontaneous pupil changes. Intra- and inter-rater reliability of the method was excellent (ICC > 0.95). The method is observer independent and yields consistent results, and images can be digitally stored and re-evaluated. The method seems highly eligible for forensic and scientific purposes. While statistical analysis of spontaneous pupil changes revealed a significant polynomial of quartic degree for postmortem time (p = 0.001), an obvious pattern was not detected. These results do not indicate suitability of spontaneous pupil changes for forensic death time estimation, as formerly suggested. © 2016 American Academy of Forensic Sciences.

A simple and effective radiometric correction method to improve landscape change detection across sensors and across time

USGS Publications Warehouse

Chen, X.; Vierling, Lee; Deering, D.

2005-01-01

Satellite data offer unrivaled utility in monitoring and quantifying large scale land cover change over time. Radiometric consistency among collocated multi-temporal imagery is difficult to maintain, however, due to variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle that can obscure surface change detection. To detect accurate landscape change using multi-temporal images, we developed a variation of the pseudoinvariant feature (PIF) normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on June 9, 1990 (Landsat 4), June 20, 2000 (Landsat 7), and August 26, 2001 (Landsat 7) to analyze boreal forests near the Siberian city of Krasnoyarsk using the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and reduced simple ratio (RSR). The temporally invariant cluster (TIC) centers were identified via a point density map of collocated pixel VIs from the base image and the target image, and a normalization regression line was created to intersect all TIC centers. Target image VI values were then recalculated using the regression function so that these two images could be compared using the resulting common radiometric scale. We found that EVI was very indicative of vegetation structure because of its sensitivity to shadowing effects and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. Conversely, because NDVI reduced the radiometric influence of shadow, it did not allow for distinctions among these vegetation types. After normalization, correlations of NDVI and EVI with forest leaf area index (LAI) field measurements combined for 2000 and 2001 were significantly improved; the r 2 values in these regressions rose from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI "cancellation effect" where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a post fire chronosequence with normalized data. These findings indicate that the TIC method provides a simple, effective and repeatable method to create radiometrically comparable data sets for remote detection of landscape change. Compared to some previous relative radiometric normalization methods, this new method does not require high level programming and statistical skills, yet remains sensitive to landscape changes occurring over seasonal and inter-annual time scales. In addition, the TIC method maintains sensitivity to subtle changes in vegetation phenology and enables normalization even when invariant features are rare. While this normalization method allowed detection of a range of land use, land cover, and phenological/biophysical changes in the Siberian boreal forest region studied here, it is necessary to further examine images representing a wide variety of ecoregions to thoroughly evaluate the TIC method against other normalization schemes. ?? 2005 Elsevier Inc. All rights reserved.

Temperature-Sensitive Coating Sensor Based on Hematite

NASA Technical Reports Server (NTRS)

Bencic, Timothy J.

2011-01-01

A temperature-sensitive coating, based on hematite (iron III oxide), has been developed to measure surface temperature using spectral techniques. The hematite powder is added to a binder that allows the mixture to be painted on the surface of a test specimen. The coating dynamically changes its relative spectral makeup or color with changes in temperature. The color changes from a reddish-brown appearance at room temperature (25 C) to a black-gray appearance at temperatures around 600 C. The color change is reversible and repeatable with temperature cycling from low to high and back to low temperatures. Detection of the spectral changes can be recorded by different sensors, including spectrometers, photodiodes, and cameras. Using a-priori information obtained through calibration experiments in known thermal environments, the color change can then be calibrated to yield accurate quantitative temperature information. Temperature information can be obtained at a point, or over an entire surface, depending on the type of equipment used for data acquisition. Because this innovation uses spectrophotometry principles of operation, rather than the current methods, which use photoluminescence principles, white light can be used for illumination rather than high-intensity short wavelength excitation. The generation of high-intensity white (or potentially filtered long wavelength light) is much easier, and is used more prevalently for photography and video technologies. In outdoor tests, the Sun can be used for short durations as an illumination source as long as the amplitude remains relatively constant. The reflected light is also much higher in intensity than the emitted light from the inefficient current methods. Having a much brighter surface allows a wider array of detection schemes and devices. Because color change is the principle of operation, the development of high-quality, lower-cost digital cameras can be used for detection, as opposed to the high-cost imagers needed for intensity measurements with the current methods. Alternative methods of detection are possible to increase the measurement sensitivity. For example, a monochrome camera can be used with an appropriate filter and a radiometric measurement of normalized intensity change that is proportional to the change coating temperature. Using different spectral regions yields different sensitivities and calibration curves for converting intensity change to temperature units. Alternatively, using a color camera, a ratio of the standard red, green, and blue outputs can be used as a self-referenced change. The blue region (less than 500 nm) does not change nearly as much as the red region (greater than 575 nm), so a ratio of color intensities will yield a calibrated temperature image. The new temperature sensor coating is easy to apply, is inexpensive, can contour complex shape surfaces, and can be a global surface measurement system based on spectrophotometry. The color change, or relative intensity change, at different colors makes the optical detection under white light illumination, and associated interpretation, much easier to measure and interpret than in the detection systems of the current methods.

A comparison of moving object detection methods for real-time moving object detection

NASA Astrophysics Data System (ADS)

Roshan, Aditya; Zhang, Yun

2014-06-01

Moving object detection has a wide variety of applications from traffic monitoring, site monitoring, automatic theft identification, face detection to military surveillance. Many methods have been developed across the globe for moving object detection, but it is very difficult to find one which can work globally in all situations and with different types of videos. The purpose of this paper is to evaluate existing moving object detection methods which can be implemented in software on a desktop or laptop, for real time object detection. There are several moving object detection methods noted in the literature, but few of them are suitable for real time moving object detection. Most of the methods which provide for real time movement are further limited by the number of objects and the scene complexity. This paper evaluates the four most commonly used moving object detection methods as background subtraction technique, Gaussian mixture model, wavelet based and optical flow based methods. The work is based on evaluation of these four moving object detection methods using two (2) different sets of cameras and two (2) different scenes. The moving object detection methods have been implemented using MatLab and results are compared based on completeness of detected objects, noise, light change sensitivity, processing time etc. After comparison, it is observed that optical flow based method took least processing time and successfully detected boundary of moving objects which also implies that it can be implemented for real-time moving object detection.

COMPARISON OF METHODS FOR DETECTION AND ENUMERATION OF AIRBORNE MICROORGANISMS COLLECTED BY LIQUID IMPINGEMENT

EPA Science Inventory

Bacterial agents and cell components can be spread as bioaerosols, producing infections and asthmatic problems. This study compares four methods for the detection and enumeration of aerosolized bacteria collected in an AGI-30 impinger. Changes in the total and viable concentratio...

A colorimetric detection of acrylamide in potato chips based on nucleophile-initiated thiol-ene Michael addition.

PubMed

Hu, Qinqin; Fu, Yingchun; Xu, Xiahong; Qiao, Zhaohui; Wang, Ronghui; Zhang, Ying; Li, Yanbin

2016-02-07

Acrylamide (AA), a neurotoxin and a potential carcinogen, has been found in various thermally processed foods such as potato chips, biscuits, and coffee. Simple, cost-effective, and sensitive methods for the rapid detection of AA are needed to ensure food safety. Herein, a novel colorimetric method was proposed for the visual detection of AA based on a nucleophile-initiated thiol-ene Michael addition reaction. Gold nanoparticles (AuNPs) were aggregated by glutathione (GSH) because of a ligand-replacement, accompanied by a color change from red to purple. In the presence of AA, after the thiol-ene Michael addition reaction between GSH and AA with the catalysis of a nucleophile, the sulfhydryl group of GSH was consumed by AA, which hindered the subsequent ligand-replacement and the aggregation of AuNPs. Therefore, the concentration of AA could be determined by the visible color change caused by dispersion/aggregation of AuNPs. This new method showed high sensitivity with a linear range from 0.1 μmol L(-1) to 80 μmol L(-1) and a detection limit of 28.6 nmol L(-1), and especially revealed better selectivity than the fluorescence sensing method reported previously. Moreover, this new method was used to detect AA in potato chips with a satisfactory result in comparison with the standard methods based on chromatography, which indicated that the colorimetric method can be expanded for the rapid detection of AA in thermally processed foods.

Microbial metabolism and dynamic changes in the electrical conductivity of soil solutions - A method for detecting extraterrestrial life

NASA Technical Reports Server (NTRS)

Silverman, M. P.; Munoz, E. F.

1974-01-01

Experiments are reported which show that measuring metabolic activity in soil solutions by means of dynamic changes in electrical conductivity, water-soluble Ca, or water-soluble Mg is a feasible life detection method. The addition of 0.5% glucose solutions to 12 different air-dried soils always resulted in increases in all three of these parameters. The kinetics and magnitude of these changes for at least two and usually all three of the parameters over a 14-day period were clearly distinguishable from the changes in heat-sterilized controls or unsterilized controls without added glucose. In general, maximal values were achieved more rapidly under aerobic than under anaerobic incubation.

Detecting dryland degradation through the use of Time Series Segmentation and Residual Trend analysis (TSS-RESTREND)

NASA Astrophysics Data System (ADS)

Burrell, A. L.; Evans, J. P.; Liu, Y.

2017-12-01

Dryland degradation is an issue of international significance as dryland regions play a substantial role in global food production. Remotely sensed data provide the only long term, large scale record of changes within dryland ecosystems. The Residual Trend, or RESTREND, method is applied to satellite observations to detect dryland degradation. Whilst effective in most cases, it has been shown that the RESTREND method can fail to identify degraded pixels if the relationship between vegetation and precipitation has broken-down as a result of severe or rapid degradation. This study presents an extended version of the RESTREND methodology that incorporates the Breaks For Additive Seasonal and Trend method to identify step changes in the time series that are related to significant structural changes in the ecosystem, e.g. land use changes. When applied to Australia, this new methodology, termed Time Series Segmentation and Residual Trend analysis (TSS-RESTREND), was able to detect degradation in 5.25% of pixels compared to only 2.0% for RESTREND alone. This modified methodology was then assessed in two regions with known histories of degradation where it was found to accurately capture both the timing and directionality of ecosystem change.

In-situ fault detection apparatus and method for an encased energy storing device

DOEpatents

Hagen, Ronald A.; Comte, Christophe; Knudson, Orlin B.; Rosenthal, Brian; Rouillard, Jean

2000-01-01

An apparatus and method for detecting a breach in an electrically insulating surface of an electrically conductive power system enclosure within which a number of series connected energy storing devices are disposed. The energy storing devices disposed in the enclosure are connected to a series power connection. A detector is coupled to the series connection and detects a change of state in a test signal derived from the series connected energy storing devices. The detector detects a breach in the insulating layer of the enclosure by detecting a state change in the test signal from a nominal state to a non-nominal state. A voltage detector detects a state change of the test signals from a nominal state, represented by a voltage of a selected end energy storing device, to a non-nominal state, represented by a voltage that substantially exceeds the voltage of the selected opposing end energy storing device. Alternatively, the detector may comprise a signal generator that produces the test signal as a time-varying or modulated test signal and injects the test signal into the series connection. The detector detects the state change of the time-varying or modulated test signal from a nominal state, represented by a signal substantially equivalent to the test signal, to a non-nominal state, representative by an absence of the test signal.

Detecting a trend change in cross-border epidemic transmission

NASA Astrophysics Data System (ADS)

Maeno, Yoshiharu

2016-09-01

A method for a system of Langevin equations is developed for detecting a trend change in cross-border epidemic transmission. The equations represent a standard epidemiological SIR compartment model and a meta-population network model. The method analyzes a time series of the number of new cases reported in multiple geographical regions. The method is applicable to investigating the efficacy of the implemented public health intervention in managing infectious travelers across borders. It is found that the change point of the probability of travel movements was one week after the WHO worldwide alert on the SARS outbreak in 2003. The alert was effective in managing infectious travelers. On the other hand, it is found that the probability of travel movements did not change at all for the flu pandemic in 2009. The pandemic did not affect potential travelers despite the WHO alert.

Using adversary text to detect adversary phase changes.

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

Speed, Ann Elizabeth; Doser, Adele Beatrice; Warrender, Christina E.

2009-05-01

The purpose of this work was to help develop a research roadmap and small proof ofconcept for addressing key problems and gaps from the perspective of using text analysis methods as a primary tool for detecting when a group is undergoing a phase change. Self- rganizing map (SOM) techniques were used to analyze text data obtained from the tworld-wide web. Statistical studies indicate that it may be possible to predict phase changes, as well as detect whether or not an example of writing can be attributed to a group of interest.

Evaluation of a Region-of-Interest Approach for Detecting Progressive Glaucomatous Macular Damage on Optical Coherence Tomography.

PubMed

Wu, Zhichao; Weng, Denis S D; Thenappan, Abinaya; Ritch, Robert; Hood, Donald C

2018-04-01

To evaluate a manual region-of-interest (ROI) approach for detecting progressive macular ganglion cell complex (GCC) changes on optical coherence tomography (OCT) imaging. One hundred forty-six eyes with a clinical diagnosis of glaucoma or suspected glaucoma with macular OCT scans obtained at least 1 year apart were evaluated. Changes in the GCC thickness were identified using a manual ROI approach (ROI M ), whereby region(s) of observed or suspected glaucomatous damage were manually identified when using key features from the macular OCT scan on the second visit. Progression was also evaluated using the global GCC thickness and an automatic ROI approach (ROI A ), where contiguous region(s) that fell below the 1% lower normative limit and exceeded 288 μm 2 in size were evaluated. Longitudinal signal-to-noise ratios (SNRs) were calculated for progressive changes detected by each of these methods using individualized estimates of test-retest variability and age-related changes, obtained from 303 glaucoma and 394 healthy eyes, respectively. On average, the longitudinal SNR for the global thickness, ROI A and ROI M methods were -0.90 y -1 , -0.91 y -1 , and -1.03 y -1 , respectively, and was significantly more negative for the ROI M compared with the global thickness ( P = 0.003) and ROI A methods ( P = 0.021). Progressive glaucomatous macular GCC changes were optimally detected with a manual ROI approach. These findings suggests that an approach based on a qualitative evaluation of OCT imaging information and consideration of known patterns of damage can improve the detection of progressive glaucomatous macular damage.

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.

MPAI (mass probes aided ionization) method for total analysis of biomolecules by mass spectrometry.

PubMed

Honda, Aki; Hayashi, Shinichiro; Hifumi, Hiroki; Honma, Yuya; Tanji, Noriyuki; Iwasawa, Naoko; Suzuki, Yoshio; Suzuki, Koji

2007-01-01

We have designed and synthesized various mass probes, which enable us to effectively ionize various molecules to be detected with mass spectrometry. We call the ionization method using mass probes the "MPAI (mass probes aided ionization)" method. We aim at the sensitive detection of various biological molecules, and also the detection of bio-molecules by a single mass spectrometry serially without changing the mechanical settings. Here, we review mass probes for small molecules with various functional groups and mass probes for proteins. Further, we introduce newly developed mass probes for proteins for highly sensitive detection.

On the Power of Multivariate Latent Growth Curve Models to Detect Correlated Change

ERIC Educational Resources Information Center

Hertzog, Christopher; Lindenberger, Ulman; Ghisletta, Paolo; Oertzen, Timo von

2006-01-01

We evaluated the statistical power of single-indicator latent growth curve models (LGCMs) to detect correlated change between two variables (covariance of slopes) as a function of sample size, number of longitudinal measurement occasions, and reliability (measurement error variance). Power approximations following the method of Satorra and Saris…

Towards a Framework for Change Detection in Data Sets

NASA Astrophysics Data System (ADS)

Böttcher, Mirko; Nauck, Detlef; Ruta, Dymitr; Spott, Martin

Since the world with its markets, innovations and customers is changing faster than ever before, the key to survival for businesses is the ability to detect, assess and respond to changing conditions rapidly and intelligently. Discovering changes and reacting to or acting upon them before others do has therefore become a strategical issue for many companies. However, existing data analysis techniques are insufflent for this task since they typically assume that the domain under consideration is stable over time. This paper presents a framework that detects changes within a data set at virtually any level of granularity. The underlying idea is to derive a rule-based description of the data set at different points in time and to subsequently analyse how these rules change. Nevertheless, further techniques are required to assist the data analyst in interpreting and assessing their changes. Therefore the framework also contains methods to discard rules that are non-drivers for change and to assess the interestingness of detected changes.

Evaluation of two outlier-detection-based methods for detecting tissue-selective genes from microarray data.

PubMed

Kadota, Koji; Konishi, Tomokazu; Shimizu, Kentaro

2007-05-01

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent's non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent's method is not suitable for ROKU.

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.

Low angle light scattering analysis: a novel quantitative method for functional characterization of human and murine platelet receptors.

PubMed

Mindukshev, Igor; Gambaryan, Stepan; Kehrer, Linda; Schuetz, Claudia; Kobsar, Anna; Rukoyatkina, Natalia; Nikolaev, Viacheslav O; Krivchenko, Alexander; Watson, Steve P; Walter, Ulrich; Geiger, Joerg

2012-07-01

Determinations of platelet receptor functions are indispensable diagnostic indicators of cardiovascular and hemostatic diseases including hereditary and acquired receptor defects and receptor responses to drugs. However, presently available techniques for assessing platelet function have some disadvantages, such as low sensitivity and the requirement of large sample sizes and unphysiologically high agonist concentrations. Our goal was to develop and initially characterize a new technique designed to quantitatively analyze platelet receptor activation and platelet function on the basis of measuring changes in low angle light scattering. We developed a novel technique based on low angle light scattering registering changes in light scattering at a range of different angles in platelet suspensions during activation. The method proved to be highly sensitive for simultaneous real time detection of changes in size and shape of platelets during activation. Unlike commonly-used methods, the light scattering method could detect platelet shape change and aggregation in response to nanomolar concentrations of extracellular nucleotides. Furthermore, our results demonstrate that the advantages of the light scattering method make it a choice method for platelet receptor monitoring and for investigation of both murine and human platelets in disease models. Our data demonstrate the suitability and superiority of this new low angle light scattering method for comprehensive analyses of platelet receptors and functions. This highly sensitive, quantitative, and online detection of essential physiological, pathophysiological and pharmacological-response properties of human and mouse platelets is a significant improvement over conventional techniques.

Reliability, minimal detectable change and responsiveness to change: Indicators to select the best method to measure sedentary behaviour in older adults in different study designs.

PubMed

Dontje, Manon L; Dall, Philippa M; Skelton, Dawn A; Gill, Jason M R; Chastin, Sebastien F M

2018-01-01

Prolonged sedentary behaviour (SB) is associated with poor health. It is unclear which SB measure is most appropriate for interventions and population surveillance to measure and interpret change in behaviour in older adults. The aims of this study: to examine the relative and absolute reliability, Minimal Detectable Change (MDC) and responsiveness to change of subjective and objective methods of measuring SB in older adults and give recommendations of use for different study designs. SB of 18 older adults (aged 71 (IQR 7) years) was assessed using a systematic set of six subjective tools, derived from the TAxonomy of Self report Sedentary behaviour Tools (TASST), and one objective tool (activPAL3c), over 14 days. Relative reliability (Intra Class Correlation coefficients-ICC), absolute reliability (SEM), MDC, and the relative responsiveness (Cohen's d effect size (ES) and Guyatt's Responsiveness coefficient (GR)) were calculated for each of the different tools and ranked for different study designs. ICC ranged from 0.414 to 0.946, SEM from 36.03 to 137.01 min, MDC from 1.66 to 8.42 hours, ES from 0.017 to 0.259 and GR from 0.024 to 0.485. Objective average day per week measurement ranked as most responsive in a clinical practice setting, whereas a one day measurement ranked highest in quasi-experimental, longitudinal and controlled trial study designs. TV viewing-Previous Week Recall (PWR) ranked as most responsive subjective measure in all study designs. The reliability, Minimal Detectable Change and responsiveness to change of subjective and objective methods of measuring SB is context dependent. Although TV viewing-PWR is the more reliable and responsive subjective method in most situations, it may have limitations as a reliable measure of total SB. Results of this study can be used to guide choice of tools for detecting change in sedentary behaviour in older adults in the contexts of population surveillance, intervention evaluation and individual care.

Automatic detection of Floating Ice at Antarctic Continental Margin from Remotely Sensed Image with Object-oriented Matching

NASA Astrophysics Data System (ADS)

Zhao, Z.

2011-12-01

Changes in ice sheet and floating ices around that have great significance for global change research. In the context of global warming, rapidly changing of Antarctic continental margin, caving of ice shelves, movement of iceberg are all closely related to climate change and ocean circulation. Using automatic change detection technology to rapid positioning the melting Region of Polar ice sheet and the location of ice drift would not only strong support for Global Change Research but also lay the foundation for establishing early warning mechanism for melting of the polar ice and Ice displacement. This paper proposed an automatic change detection method using object-based segmentation technology. The process includes three parts: ice extraction using image segmentation, object-baed ice tracking, change detection based on similarity matching. An approach based on similarity matching of eigenvector is proposed in this paper, which used area, perimeter, Hausdorff distance, contour, shape and other information of each ice-object. Different time of LANDSAT ETM+ data, Chinese environment disaster satellite HJ1B date, MODIS 1B date are used to detect changes of Floating ice at Antarctic continental margin respectively. We select different time of ETM+ data(January 7, 2003 and January 16, 2003) with the area around Antarctic continental margin near the Lazarev Bay, which is from 70.27454853 degrees south latitude, longitude 12.38573410 degrees to 71.44474167 degrees south latitude, longitude 10.39252222 degrees,included 11628 sq km of Antarctic continental margin area, as a sample. Then we can obtain the area of floating ices reduced 371km2, and the number of them reduced 402 during the time. In addition, the changes of all the floating ices around the margin region of Antarctic within 1200 km are detected using MODIS 1B data. During the time from January 1, 2008 to January 7, 2008, the floating ice area decreased by 21644732 km2, and the number of them reduced by 83080. The results show that the object-based information extraction algorithm can obtain more precise details of a single object, while the change detection method based on similarity matching can effectively tracking the change of floating ice.

Predicting Boat-Generated Wave Heights: A Quantitative Analysis through Video Observations of Vessel Wakes

DTIC Science & Technology

2012-05-18

by the AWAC. It is a surface- penetrating device that measures continuous changes in the water elevations over time at much higher sampling rates of...background subtraction, a technique based on detecting change from a background scene. Their study highlights the difficulty in object detection and tracking...movements (Zhang et al. 2009) Alternatively, another common object detection method , known as Optical Flow Analysis , may be utilized for vessel

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.

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.

Image change detection systems, methods, and articles of manufacture

DOEpatents

Jones, James L.; Lassahn, Gordon D.; Lancaster, Gregory D.

2010-01-05

Aspects of the invention relate to image change detection systems, methods, and articles of manufacture. According to one aspect, a method of identifying differences between a plurality of images is described. The method includes loading a source image and a target image into memory of a computer, constructing source and target edge images from the source and target images to enable processing of multiband images, displaying the source and target images on a display device of the computer, aligning the source and target edge images, switching displaying of the source image and the target image on the display device, to enable identification of differences between the source image and the target image.

Monitoring gypsy moth defoliation by applying change detection techniques to Landsat imagery

NASA Technical Reports Server (NTRS)

Williams, D. L.; Stauffer, M. L.

1978-01-01

The overall objective of a research effort at NASA's Goddard Space Flight Center is to develop and evaluate digital image processing techniques that will facilitate the assessment of the intensity and spatial distribution of forest insect damage in Northeastern U.S. forests using remotely sensed data from Landsats 1, 2 and C. Automated change detection techniques are presently being investigated as a method of isolating the areas of change in the forest canopy resulting from pest outbreaks. In order to follow the change detection approach, Landsat scene correction and overlay capabilities are utilized to provide multispectral/multitemporal image files of 'defoliation' and 'nondefoliation' forest stand conditions.

Detecting Abrupt Changes in a Piecewise Locally Stationary Time Series

PubMed Central

Last, Michael; Shumway, Robert

2007-01-01

Non-stationary time series arise in many settings, such as seismology, speech-processing, and finance. In many of these settings we are interested in points where a model of local stationarity is violated. We consider the problem of how to detect these change-points, which we identify by finding sharp changes in the time-varying power spectrum. Several different methods are considered, and we find that the symmetrized Kullback-Leibler information discrimination performs best in simulation studies. We derive asymptotic normality of our test statistic, and consistency of estimated change-point locations. We then demonstrate the technique on the problem of detecting arrival phases in earthquakes. PMID:19190715

Change Detection Processing Chain Dedicated to Sentinel Data Time Series. Application to Forest and Water Bodies Monitoring

NASA Astrophysics Data System (ADS)

Perez Saavedra, L.-M.; Mercier, G.; Yesou, H.; Liege, F.; Pasero, G.

2016-08-01

The Copernicus program of ESA and European commission (6 Sentinels Missions, among them Sentinel-1 with Synthetic Aperture Radar sensor and Sentinel-2 with 13-band 10 to 60 meter resolution optical sensors), offers a new opportunity to Earth Observation with high temporal acquisition capability ( 12 days repetitiveness and 5 days in some geographic areas of the world) with high spatial resolution.Due to these high temporal and spatial resolutions, it opens new challenges in several fields such as image processing, new algorithms for Time Series and big data analysis. In addition, these missions will be able to analyze several topics of earth temporal evolution such as crop vegetation, water bodies, Land use and Land Cover (LULC), sea and ice information, etc. This is particularly useful for end users and policy makers to detect early signs of damages, vegetation illness, flooding areas, etc.From the state of the art, one can find algorithms and methods that use a bi-date comparison for change detection [1-3] or time series analysis. Actually, these methods are essentially used for target detection or for abrupt change detection that requires 2 observations only.A Hölder means-based change detection technique has been proposed in [2,3] for high resolution radar images. This so-called MIMOSA technique has been mainly dedicated to man-made change detection in urban areas and CARABAS - II project by using a couple of SAR images. An extension to multitemporal change detection technique has been investigated but its application to land use and cover changes still has to be validated.The Hölder Hp is a Time Series pixel by pixel feature extraction and is defined by:H𝑝[X]=[1/n∑ⁿᵢ₌1 Xᴾᵢ]1/p p∈R Hp[X] : N images * S Bandes * t datesn is the number of images in the time series. N > 2Hp (X) is continuous and monotonic increasing in p for - ∞ < p < ∞

Gold Nanoparticle-Based Facile Detection of Human Serum Albumin and Its Application as an INHIBIT Logic Gate.

PubMed

Huang, Zhenzhen; Wang, Haonan; Yang, Wensheng

2015-05-06

In this work, a facile colorimetric method is developed for quantitative detection of human serum albumin (HSA) based on the antiaggregation effect of gold nanoparticles (Au NPs) in the presence of HSA. The citrate-capped Au NPs undergo a color change from red to blue when melamine is added as a cross-linker to induce the aggregation of the NPs. Such an aggregation is efficiently suppressed upon the adsorption of HSA on the particle surface. This method provides the advantages of simplicity and cost-efficiency for quantitative detection of HSA with a detection limit of ∼1.4 nM by monitoring the colorimetric changes of the Au NPs with UV-vis spectroscopy. In addition, this approach shows good selectivity for HSA over various amino acids, peptides, and proteins and is qualified for detection of HSA in a biological sample. Such an antiaggregation effect can be further extended to fabricate an INHIBIT logic gate by using HSA and melamine as inputs and the color changes of Au NPs as outputs, which may have application potentials in point-of-care medical diagnosis.

Detection of Road Markings Recorded in In-Vehicle Camera Images by Using Position-Dependent Classifiers

NASA Astrophysics Data System (ADS)

Noda, Masafumi; Takahashi, Tomokazu; Deguchi, Daisuke; Ide, Ichiro; Murase, Hiroshi; Kojima, Yoshiko; Naito, Takashi

In this study, we propose a method for detecting road markings recorded in an image captured by an in-vehicle camera by using a position-dependent classifier. Road markings are symbols painted on the road surface that help in preventing traffic accidents and in ensuring traffic smooth. Therefore, driver support systems for detecting road markings, such as a system that provides warning in the case when traffic signs are overlooked, and supporting the stopping of a vehicle are required. It is difficult to detect road markings because their appearance changes with the actual traffic conditions, e. g. the shape and resolution change. The variation in these appearances depend on the positional relation between the vehicle and the road markings, and on the vehicle posture. Although these variations are quite large in an entire image, they are relatively small in a local area of the image. Therefore, we try to improve the detection performance by taking into account the local variations in these appearances. We propose a method in which a position-dependent classifier is used to detect road markings recorded in images captured by an in-vehicle camera. Further, to train the classifier efficiently, we propose a generative learning method that takes into consideration the positional relation between the vehicle and road markings, and also the vehicle posture. Experimental results showed that the detection performance when the proposed method was used was better than when a method involving a single classifier was used.

Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning

PubMed Central

Xu, Yuan; Ding, Kun; Huo, Chunlei; Zhong, Zisha; Li, Haichang; Pan, Chunhong

2015-01-01

Very high resolution (VHR) image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information. Most change feature extraction techniques put emphasis on the change degree description (i.e., in what degree the changes have happened), while they ignore the change pattern description (i.e., how the changes changed), which is of equal importance in characterizing the change signatures. Moreover, the simultaneous consideration of the classification robust to the registration noise and the multiscale region-consistent fusion is often neglected in change decision. To overcome such drawbacks, in this paper, a novel VHR image change detection method is proposed based on sparse change descriptor and robust discriminative dictionary learning. Sparse change descriptor combines the change degree component and the change pattern component, which are encoded by the sparse representation error and the morphological profile feature, respectively. Robust change decision is conducted by multiscale region-consistent fusion, which is implemented by the superpixel-level cosparse representation with robust discriminative dictionary and the conditional random field model. Experimental results confirm the effectiveness of the proposed change detection technique. PMID:25918748

Method and apparatus for real time weld monitoring

DOEpatents

Leong, Keng H.; Hunter, Boyd V.

1997-01-01

An improved method and apparatus are provided for real time weld monitoring. An infrared signature emitted by a hot weld surface during welding is detected and this signature is compared with an infrared signature emitted by the weld surface during steady state conditions. The result is correlated with weld penetration. The signal processing is simpler than for either UV or acoustic techniques. Changes in the weld process, such as changes in the transmitted laser beam power, quality or positioning of the laser beam, change the resulting weld surface features and temperature of the weld surface, thereby resulting in a change in the direction and amount of infrared emissions. This change in emissions is monitored by an IR sensitive detecting apparatus that is sensitive to the appropriate wavelength region for the hot weld surface.

3D change detection in staggered voxels model for robotic sensing and navigation

NASA Astrophysics Data System (ADS)

Liu, Ruixu; Hampshire, Brandon; Asari, Vijayan K.

2016-05-01

3D scene change detection is a challenging problem in robotic sensing and navigation. There are several unpredictable aspects in performing scene change detection. A change detection method which can support various applications in varying environmental conditions is proposed. Point cloud models are acquired from a RGB-D sensor, which provides the required color and depth information. Change detection is performed on robot view point cloud model. A bilateral filter smooths the surface and fills the holes as well as keeps the edge details on depth image. Registration of the point cloud model is implemented by using Random Sample Consensus (RANSAC) algorithm. It uses surface normal as the previous stage for the ground and wall estimate. After preprocessing the data, we create a point voxel model which defines voxel as surface or free space. Then we create a color model which defines each voxel that has a color by the mean of all points' color value in this voxel. The preliminary change detection is detected by XOR subtract on the point voxel model. Next, the eight neighbors for this center voxel are defined. If they are neither all `changed' voxels nor all `no changed' voxels, a histogram of location and hue channel color is estimated. The experimental evaluations performed to evaluate the capability of our algorithm show promising results for novel change detection that indicate all the changing objects with very limited false alarm rate.

Detection of urinary creatinine using gold nanoparticles after solid phase extraction

NASA Astrophysics Data System (ADS)

Sittiwong, Jarinya; Unob, Fuangfa

2015-03-01

Label-free gold nanoparticles (AuNPs) were utilized in the detection of creatinine in human urine after a sample preparation by extraction of creatinine on sulfonic acid functionalized silica gel. With the proposed sample preparation method, the interfering effects of the urine matrix on creatinine detection by AuNPs were eliminated. Parameters affecting creatinine extraction were investigated. The aggregation of AuNPs induced by creatinine resulted in a change in the surface plasmon resonance signal with a concomitant color change that could be observed by the naked eye and quantified spectrometrically. The effect of AuNP concentration and reaction time on AuNP aggregation was investigated. The method described herein provides a determination of creatinine in a range of 15-40 mg L-1 with a detection limit of 13.7 mg L-1 and it was successfully used in the detection of creatinine in human urine samples.

Method and apparatus for detecting and determining event characteristics with reduced data collection

NASA Technical Reports Server (NTRS)

Totman, Peter D. (Inventor); Everton, Randy L. (Inventor); Egget, Mark R. (Inventor); Macon, David J. (Inventor)

2007-01-01

A method and apparatus for detecting and determining event characteristics such as, for example, the material failure of a component, in a manner which significantly reduces the amount of data collected. A sensor array, including a plurality of individual sensor elements, is coupled to a programmable logic device (PLD) configured to operate in a passive state and an active state. A triggering event is established such that the PLD records information only upon detection of the occurrence of the triggering event which causes a change in state within one or more of the plurality of sensor elements. Upon the occurrence of the triggering event, the change in state of the one or more sensor elements causes the PLD to record in memory which sensor element detected the event and at what time the event was detected. The PLD may be coupled with a computer for subsequent downloading and analysis of the acquired data.

TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas Sensors

PubMed Central

Pashami, Sepideh; Lilienthal, Achim J.; Schaffernicht, Erik; Trincavelli, Marco

2013-01-01

Many applications of metal oxide gas sensors can benefit from reliable algorithms to detect significant changes in the sensor response. Significant changes indicate a change in the emission modality of a distant gas source and occur due to a sudden change of concentration or exposure to a different compound. As a consequence of turbulent gas transport and the relatively slow response and recovery times of metal oxide sensors, their response in open sampling configuration exhibits strong fluctuations that interfere with the changes of interest. In this paper we introduce TREFEX, a novel change point detection algorithm, especially designed for metal oxide gas sensors in an open sampling system. TREFEX models the response of MOX sensors as a piecewise exponential signal and considers the junctions between consecutive exponentials as change points. We formulate non-linear trend filtering and change point detection as a parameter-free convex optimization problem for single sensors and sensor arrays. We evaluate the performance of the TREFEX algorithm experimentally for different metal oxide sensors and several gas emission profiles. A comparison with the previously proposed GLR method shows a clearly superior performance of the TREFEX algorithm both in detection performance and in estimating the change time. PMID:23736853

Algorithms for Autonomous Plume Detection on Outer Planet Satellites

NASA Astrophysics Data System (ADS)

Lin, Y.; Bunte, M. K.; Saripalli, S.; Greeley, R.

2011-12-01

We investigate techniques for automated detection of geophysical events (i.e., volcanic plumes) from spacecraft images. The algorithms presented here have not been previously applied to detection of transient events on outer planet satellites. We apply Scale Invariant Feature Transform (SIFT) to raw images of Io and Enceladus from the Voyager, Galileo, Cassini, and New Horizons missions. SIFT produces distinct interest points in every image; feature descriptors are reasonably invariant to changes in illumination, image noise, rotation, scaling, and small changes in viewpoint. We classified these descriptors as plumes using the k-nearest neighbor (KNN) algorithm. In KNN, an object is classified by its similarity to examples in a training set of images based on user defined thresholds. Using the complete database of Io images and a selection of Enceladus images where 1-3 plumes were manually detected in each image, we successfully detected 74% of plumes in Galileo and New Horizons images, 95% in Voyager images, and 93% in Cassini images. Preliminary tests yielded some false positive detections; further iterations will improve performance. In images where detections fail, plumes are less than 9 pixels in size or are lost in image glare. We compared the appearance of plumes and illuminated mountain slopes to determine the potential for feature classification. We successfully differentiated features. An advantage over other methods is the ability to detect plumes in non-limb views where they appear in the shadowed part of the surface; improvements will enable detection against the illuminated background surface where gradient changes would otherwise preclude detection. This detection method has potential applications to future outer planet missions for sustained plume monitoring campaigns and onboard automated prioritization of all spacecraft data. The complementary nature of this method is such that it could be used in conjunction with edge detection algorithms to increase effectiveness. We have demonstrated an ability to detect transient events above the planetary limb and on the surface and to distinguish feature classes in spacecraft images.

New Optical Methods for Liveness Detection on Fingers

PubMed Central

Dolezel, Michal; Vana, Jan; Brezinova, Eva; Yim, Jaegeol; Shim, Kyubark

2013-01-01

This paper is devoted to new optical methods, which are supposed to be used for liveness detection on fingers. First we describe the basics about fake finger use in fingerprint recognition process and the possibilities of liveness detection. Then we continue with introducing three new liveness detection methods, which we developed and tested in the scope of our research activities—the first one is based on measurement of the pulse, the second one on variations of optical characteristics caused by pressure change, and the last one is based on reaction of skin to illumination with different wavelengths. The last part deals with the influence of skin diseases on fingerprint recognition, especially on liveness detection. PMID:24151584

Method for measuring liquid viscosity and ultrasonic viscometer

DOEpatents

Sheen, Shuh-Haw; Lawrence, William P.; Chien, Hual-Te; Raptis, Apostolos C.

1994-01-01

An ultrasonic viscometer and method for measuring fluid viscosity are provided. Ultrasonic shear and longitudinal waves are generated and coupled to the fluid. Reflections from the generated ultrasonic shear and longitudinal waves are detected. Phase velocity of the fluid is determined responsive to the detected ultrasonic longitudinal waves reflections. Viscosity of the fluid is determined responsive to the detected ultrasonic shear waves reflections. Unique features of the ultrasonic viscometer include the use of a two-interface fluid and air transducer wedge to measure relative signal change and to enable self calibration and the use of a ratio of reflection coefficients for two different frequencies to compensate for environmental changes, such as temperature.

Use of Simpson's method of disc to detect early echocardiographic changes in Doberman Pinschers with dilated cardiomyopathy.

PubMed

Wess, G; Mäurer, J; Simak, J; Hartmann, K

2010-01-01

M-mode is the echocardiographic gold standard to diagnose dilated cardiomyopathy (DCM) in dogs, whereas Simpson's method of discs (SMOD) is the preferred method to detect echocardiographic evidence of disease in humans. To establish reference values for SMOD and to compare those with M-mode measurements. Nine hundred and sixty-nine examinations of 471 Doberman Pinschers. Using a prospective longitudinal study design. Reference values for SMOD were established using 75 healthy Doberman Pinschers >8 years old with <50 ventricular premature contractions (VPCs) in 24 hours. The ability of the new SMOD cut-off values, normalized to body surface area (BSA), for left ventricular end-diastolic volume (LVEDV/BSA >95mL/m(2) ) and end-systolic volume (LVESV/BSA > 55mL/m(2) ) to detect echocardiographic changes in Doberman Pinschers with DCM was compared with currently recommended M-mode values. Dogs with elevated SMOD values but normal M-mode measurements were followed-up using a prospective longitudinal study design. At the final examination 175 dogs were diagnosed with DCM according to both methods (M-mode and SMOD). At previous examinations, M-mode values were abnormal in 142 examinations only, whereas all 175 SMOD already had detected changes. Additionally, 19 of 154 dogs with >100 VPCs/24 hours and normal M-mode values had abnormal SMOD measurement. Six dogs with increased SMOD measurements remained healthy at several follow-up examinations (classified as false positive); in 24 dogs with increased SMOD measurements, no follow-up examinations were available (classified as unclear). SMOD measurements are superior to M-mode to detect early echocardiographic changes in Dobermans with occult DCM. Copyright © 2010 by the American College of Veterinary Internal Medicine.

Novel method for edge detection of retinal vessels based on the model of the retinal vascular network and mathematical morphology

NASA Astrophysics Data System (ADS)

Xu, Lei; Zheng, Xiaoxiang; Zhang, Hengyi; Yu, Yajun

1998-09-01

Accurate edge detection of retinal vessels is a prerequisite for quantitative analysis of subtle morphological changes of retinal vessels under different pathological conditions. A novel method for edge detection of retinal vessels is presented in this paper. Methods: (1) Wavelet-based image preprocessing. (2) The signed edge detection algorithm and mathematical morphological operation are applied to get the approximate regions that contain retinal vessels. (3) By convolving the preprocessed image with a LoG operator only on the detected approximate regions of retinal vessels, followed by edges refining, clear edge maps of the retinal vessels are fast obtained. Results: A detailed performance evaluation together with the existing techniques is given to demonstrate the strong features of our method. Conclusions: True edge locations of retinal vessels can be fast detected with continuous structures of retinal vessels, less non- vessel segments left and insensitivity to noise. The method is also suitable for other application fields such as road edge detection.

An integration time adaptive control method for atmospheric composition detection of occultation

NASA Astrophysics Data System (ADS)

Ding, Lin; Hou, Shuai; Yu, Fei; Liu, Cheng; Li, Chao; Zhe, Lin

2018-01-01

When sun is used as the light source for atmospheric composition detection, it is necessary to image sun for accurate identification and stable tracking. In the course of 180 second of the occultation, the magnitude of sun light intensity through the atmosphere changes greatly. It is nearly 1100 times illumination change between the maximum atmospheric and the minimum atmospheric. And the process of light change is so severe that 2.9 times per second of light change can be reached. Therefore, it is difficult to control the integration time of sun image camera. In this paper, a novel adaptive integration time control method for occultation is presented. In this method, with the distribution of gray value in the image as the reference variable, and the concepts of speed integral PID control, the integration time adaptive control problem of high frequency imaging. The large dynamic range integration time automatic control in the occultation can be achieved.

Method and apparatus for detecting an analyte

DOEpatents

Allendorf, Mark D [Pleasanton, CA; Hesketh, Peter J [Atlanta, GA

2011-11-29

We describe the use of coordination polymers (CP) as coatings on microcantilevers for the detection of chemical analytes. CP exhibit changes in unit cell parameters upon adsorption of analytes, which will induce a stress in a static microcantilever upon which a CP layer is deposited. We also describe fabrication methods for depositing CP layers on surfaces.

Proactive Interference Does Not Meaningfully Distort Visual Working Memory Capacity Estimates in the Canonical Change Detection Task

PubMed Central

Lin, Po-Han; Luck, Steven J.

2012-01-01

The change detection task has become a standard method for estimating the storage capacity of visual working memory. Most researchers assume that this task isolates the properties of an active short-term storage system that can be dissociated from long-term memory systems. However, long-term memory storage may influence performance on this task. In particular, memory traces from previous trials may create proactive interference that sometimes leads to errors, thereby reducing estimated capacity. Consequently, the capacity of visual working memory may be higher than is usually thought, and correlations between capacity and other measures of cognition may reflect individual differences in proactive interference rather than individual differences in the capacity of the short-term storage system. Indeed, previous research has shown that change detection performance can be influenced by proactive interference under some conditions. The purpose of the present study was to determine whether the canonical version of the change detection task – in which the to-be-remembered information consists of simple, briefly presented features – is influenced by proactive interference. Two experiments were conducted using methods that ordinarily produce substantial evidence of proactive interference, but no proactive interference was observed. Thus, the canonical version of the change detection task can be used to assess visual working memory capacity with no meaningful influence of proactive interference. PMID:22403556

Proactive interference does not meaningfully distort visual working memory capacity estimates in the canonical change detection task.

PubMed

Lin, Po-Han; Luck, Steven J

2012-01-01

The change detection task has become a standard method for estimating the storage capacity of visual working memory. Most researchers assume that this task isolates the properties of an active short-term storage system that can be dissociated from long-term memory systems. However, long-term memory storage may influence performance on this task. In particular, memory traces from previous trials may create proactive interference that sometimes leads to errors, thereby reducing estimated capacity. Consequently, the capacity of visual working memory may be higher than is usually thought, and correlations between capacity and other measures of cognition may reflect individual differences in proactive interference rather than individual differences in the capacity of the short-term storage system. Indeed, previous research has shown that change detection performance can be influenced by proactive interference under some conditions. The purpose of the present study was to determine whether the canonical version of the change detection task - in which the to-be-remembered information consists of simple, briefly presented features - is influenced by proactive interference. Two experiments were conducted using methods that ordinarily produce substantial evidence of proactive interference, but no proactive interference was observed. Thus, the canonical version of the change detection task can be used to assess visual working memory capacity with no meaningful influence of proactive interference.

Simultaneous detection of changes in protein expression and oxidative modification as function of age and APOE genotype in human APOE-targeted replacement mice

EPA Science Inventory

Background The purpose of this study was to improve the current method for detecting differentially-oxidized (carbonyl-modified) proteins by 2D-DIGE, while at the same time determining changes in total protein expression. Protein oxidation is a widely accepted model of aging and...

Change detection using NALC MSS triplicates to set forest planning context

Treesearch

D. R. Grey; P. E. Gessler; M. Hoppus; S. L. Boudreau

2000-01-01

The USDA Forest Service has purchased the North American Landscape Characterization (NALC) Landsat Multispectral Scanner (MSS) triplicates (70's, 80's, and 90's) for every national forest in the United States. To encourage analysis and use of these data for forest planning, a change-detection training course was developed. The course covers basic methods...

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.

Lamb wave-based damage quantification and probability of detection modeling for fatigue life assessment of riveted lap joint

NASA Astrophysics Data System (ADS)

He, Jingjing; Wang, Dengjiang; Zhang, Weifang

2015-03-01

This study presents an experimental and modeling study for damage detection and quantification in riveted lap joints. Embedded lead zirconate titanate piezoelectric (PZT) ceramic wafer-type sensors are employed to perform in-situ non-destructive testing during fatigue cyclical loading. A multi-feature integration method is developed to quantify the crack size using signal features of correlation coefficient, amplitude change, and phase change. In addition, probability of detection (POD) model is constructed to quantify the reliability of the developed sizing method. Using the developed crack size quantification method and the resulting POD curve, probabilistic fatigue life prediction can be performed to provide comprehensive information for decision-making. The effectiveness of the overall methodology is demonstrated and validated using several aircraft lap joint specimens from different manufactures and under different loading conditions.

Oil Spill Detection and Tracking Using Lipschitz Regularity and Multiscale Techniques in Synthetic Aperture Radar Imagery

NASA Astrophysics Data System (ADS)

Ajadi, O. A.; Meyer, F. J.

2014-12-01

Automatic oil spill detection and tracking from Synthetic Aperture Radar (SAR) images is a difficult task, due in large part to the inhomogeneous properties of the sea surface, the high level of speckle inherent in SAR data, the complexity and the highly non-Gaussian nature of amplitude information, and the low temporal sampling that is often achieved with SAR systems. This research presents a promising new oil spill detection and tracking method that is based on time series of SAR images. Through the combination of a number of advanced image processing techniques, the develop approach is able to mitigate some of these previously mentioned limitations of SAR-based oil-spill detection and enables fully automatic spill detection and tracking across a wide range of spatial scales. The method combines an initial automatic texture analysis with a consecutive change detection approach based on multi-scale image decomposition. The first step of the approach, a texture transformation of the original SAR images, is performed in order to normalize the ocean background and enhance the contrast between oil-covered and oil-free ocean surfaces. The Lipschitz regularity (LR), a local texture parameter, is used here due to its proven ability to normalize the reflectivity properties of ocean water and maximize the visibly of oil in water. To calculate LR, the images are decomposed using two-dimensional continuous wavelet transform (2D-CWT), and transformed into Holder space to measure LR. After texture transformation, the now normalized images are inserted into our multi-temporal change detection algorithm. The multi-temporal change detection approach is a two-step procedure including (1) data enhancement and filtering and (2) multi-scale automatic change detection. The performance of the developed approach is demonstrated by an application to oil spill areas in the Gulf of Mexico. In this example, areas affected by oil spills were identified from a series of ALOS PALSAR images acquired in 2010. The comparison showed exceptional performance of our method. This method can be applied to emergency management and decision support systems with a need for real-time data, and it shows great potential for rapid data analysis in other areas, including volcano detection, flood boundaries, forest health, and wildfires.

Clustering approaches to feature change detection

NASA Astrophysics Data System (ADS)

G-Michael, Tesfaye; Gunzburger, Max; Peterson, Janet

2018-05-01

The automated detection of changes occurring between multi-temporal images is of significant importance in a wide range of medical, environmental, safety, as well as many other settings. The usage of k-means clustering is explored as a means for detecting objects added to a scene. The silhouette score for the clustering is used to define the optimal number of clusters that should be used. For simple images having a limited number of colors, new objects can be detected by examining the change between the optimal number of clusters for the original and modified images. For more complex images, new objects may need to be identified by examining the relative areas covered by corresponding clusters in the original and modified images. Which method is preferable depends on the composition and range of colors present in the images. In addition to describing the clustering and change detection methodology of our proposed approach, we provide some simple illustrations of its application.

SAR image change detection using watershed and spectral clustering

NASA Astrophysics Data System (ADS)

Niu, Ruican; Jiao, L. C.; Wang, Guiting; Feng, Jie

2011-12-01

A new method of change detection in SAR images based on spectral clustering is presented in this paper. Spectral clustering is employed to extract change information from a pair images acquired on the same geographical area at different time. Watershed transform is applied to initially segment the big image into non-overlapped local regions, leading to reduce the complexity. Experiments results and system analysis confirm the effectiveness of the proposed algorithm.

Method for detecting pathogens attached to specific antibodies

DOEpatents

Miles, Robin R.; Venkateswaran, Kodumudi S.; Fuller, Christopher K.

2005-01-25

The use of impedance measurements to detect the presence of pathogens attached to antibody-coated beads. In a fluidic device antibodies are immobilized on a surface of a patterned interdigitated electrode. Pathogens in a sample fluid streaming past the electrode attach to the immobilized antibodies, which produces a change in impedance between two adjacent electrodes, which impedance change is measured and used to detect the presence of a pathogen. To amplify the signal, beads coated with antibodies are introduced and the beads would stick to the pathogen causing a greater change in impedance between the two adjacent electrodes.

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.

Do dam constructions in a Vietnamese river basin result in change points in hydrologic regime and how reliable are different methods?

NASA Astrophysics Data System (ADS)

Vu, Tinh Thi; Kiesel, Jens; Guse, Bjoern; Fohrer, Nicola

2017-04-01

The damming of rivers causes one of the most considerable impacts of our society on the riverine environment. More than 50% of the world's streams and rivers are currently impounded by dams before reaching the oceans. The construction of dams is of high importance in developing and emerging countries, i.e. for power generation and water storage. In the Vietnamese Vu Gia - Thu Bon Catchment (10,350 km2), about 23 dams were built during the last decades and store approximately 2,156 billion m3 of water. The water impoundment in 10 dams in upstream regions amounts to 17 % of the annual discharge volume. It is expected that impacts from these dams have altered the natural flow regime. However, up to now it is unclear how the flow regime was altered. For this, it needs to be investigated at what point in time these changes became significant and detectable. Many approaches exist to detect changes in stationary or consistency of hydrological records using statistical analysis of time series for the pre- and post-dam period. The objective of this study is to reliably detect and assess hydrologic shifts occurring in the discharge regime of an anthropogenically influenced river basin, mainly affected by the construction of dams. To achieve this, we applied nine available change-point tests to detect change in mean, variance and median on the daily and annual discharge records at two main gauges of the basin. The tests yield conflicting results: The majority of tests found abrupt changes that coincide with the damming-period, while others did not. To interpret how significant the changes in discharge regime are, and to which different properties of the time series each test responded, we calculated Indicators of Hydrologic Alteration (IHAs) for the time period before and after the detected change points. From the results, we can deduce, that the change point tests are influenced in different levels by different indicator groups (magnitude, duration, frequency, etc) and that within the indicator groups, some indicators are more sensitive than others. For instance, extreme low-flow, especially 7- and, 30-day minima and mean minimum low flow, as well as the variability of monthly flow are highly-sensitive to most detected change points. Our study clearly shows that, the detected change points depend on which test is chosen. For an objective assessment of change points, it is therefore necessary to explain the change points by calculating differences in IHAs. This analysis can be used to assess which change point method reacts to which type of hydrological change and, more importantly, it can be used to rank the change points according to their overall impact on the discharge regime. This leads to an improved evaluation of hydrologic change-points caused by anthropogenic impacts. Our study clearly shows that, the detected change points depend on which test is chosen. For an objective assessment of change points, it is therefore necessary to explain the change points by calculating differences in IHAs. This analysis can be used to assess which change point method reacts to which type of hydrological change and, more importantly, it can be used to rank the change points according to their overall impact on the discharge regime. This leads to an improved evaluation of hydrologic change-points caused by anthropogenic impacts.

Diffusion tensor imaging detects age related white matter change over a 2 year follow-up which is associated with working memory decline.

PubMed

Charlton, R A; Schiavone, F; Barrick, T R; Morris, R G; Markus, H S

2010-01-01

Diffusion tensor imaging (DTI) is a sensitive method for detecting white matter damage, and in cross sectional studies DTI measures correlate with age related cognitive decline. However, there are few data on whether DTI can detect age related changes over short time periods and whether such change correlates with cognitive function. In a community sample of 84 middle-aged and elderly adults, MRI and cognitive testing were performed at baseline and after 2 years. Changes in DTI white matter histograms, white matter hyperintensity (WMH) volume and brain volume were determined. Change over time in performance on tests of executive function, working memory and information processing speed were also assessed. Significant change in all MRI measures was detected. For cognition, change was detected for working memory and this correlated with change in DTI only. In a stepwise regression, with change in working memory as the dependent variable, a DTI histogram measure explained 10.8% of the variance in working memory. Change in WMH or brain volume did not contribute to the model. DTI is sensitive to age related change in white matter ultrastructure and appears useful for monitoring age related white matter change even over short time periods.

The Changing Role of the Clinical Microbiology Laboratory in Defining Resistance in Gram-negatives.

PubMed

Endimiani, Andrea; Jacobs, Michael R

2016-06-01

The evolution of resistance in Gram-negatives has challenged the clinical microbiology laboratory to implement new methods for their detection. Multidrug-resistant strains present major challenges to conventional and new detection methods. More rapid pathogen identification and antimicrobial susceptibility testing have been developed for use directly on specimens, including fluorescence in situ hybridization tests, automated polymerase chain reaction systems, microarrays, mass spectroscopy, next-generation sequencing, and microfluidics. Review of these methods shows the advances that have been made in rapid detection of resistance in cultures, but limited progress in direct detection from specimens. Copyright © 2016 Elsevier Inc. All rights reserved.

Mass detection, localization and estimation for wind turbine blades based on statistical pattern recognition

NASA Astrophysics Data System (ADS)

Colone, L.; Hovgaard, M. K.; Glavind, L.; Brincker, R.

2018-07-01

A method for mass change detection on wind turbine blades using natural frequencies is presented. The approach is based on two statistical tests. The first test decides if there is a significant mass change and the second test is a statistical group classification based on Linear Discriminant Analysis. The frequencies are identified by means of Operational Modal Analysis using natural excitation. Based on the assumption of Gaussianity of the frequencies, a multi-class statistical model is developed by combining finite element model sensitivities in 10 classes of change location on the blade, the smallest area being 1/5 of the span. The method is experimentally validated for a full scale wind turbine blade in a test setup and loaded by natural wind. Mass change from natural causes was imitated with sand bags and the algorithm was observed to perform well with an experimental detection rate of 1, localization rate of 0.88 and mass estimation rate of 0.72.

Early detection of aging cartilage and osteoarthritis in mice and patient samples using atomic force microscopy

NASA Astrophysics Data System (ADS)

Stolz, Martin; Gottardi, Riccardo; Raiteri, Roberto; Miot, Sylvie; Martin, Ivan; Imer, Raphaël; Staufer, Urs; Raducanu, Aurelia; Düggelin, Marcel; Baschong, Werner; Daniels, A. U.; Friederich, Niklaus F.; Aszodi, Attila; Aebi, Ueli

2009-03-01

The pathological changes in osteoarthritis-a degenerative joint disease prevalent among older people-start at the molecular scale and spread to the higher levels of the architecture of articular cartilage to cause progressive and irreversible structural and functional damage. At present, there are no treatments to cure or attenuate the degradation of cartilage. Early detection and the ability to monitor the progression of osteoarthritis are therefore important for developing effective therapies. Here, we show that indentation-type atomic force microscopy can monitor age-related morphological and biomechanical changes in the hips of normal and osteoarthritic mice. Early damage in the cartilage of osteoarthritic patients undergoing hip or knee replacements could similarly be detected using this method. Changes due to aging and osteoarthritis are clearly depicted at the nanometre scale well before morphological changes can be observed using current diagnostic methods. Indentation-type atomic force microscopy may potentially be developed into a minimally invasive arthroscopic tool to diagnose the early onset of osteoarthritis in situ.

Modeling Patterns of Activities using Activity Curves

PubMed Central

Dawadi, Prafulla N.; Cook, Diane J.; Schmitter-Edgecombe, Maureen

2016-01-01

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve, which represents an abstraction of an individual’s normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics. PMID:27346990

Modeling Patterns of Activities using Activity Curves.

PubMed

Dawadi, Prafulla N; Cook, Diane J; Schmitter-Edgecombe, Maureen

2016-06-01

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve , which represents an abstraction of an individual's normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics.

Research on a Denial of Service (DoS) Detection System Based on Global Interdependent Behaviors in a Sensor Network Environment

PubMed Central

Song, Jae-gu; Jung, Sungmo; Kim, Jong Hyun; Seo, Dong Il; Kim, Seoksoo

2010-01-01

This research suggests a Denial of Service (DoS) detection method based on the collection of interdependent behavior data in a sensor network environment. In order to collect the interdependent behavior data, we use a base station to analyze traffic and behaviors among nodes and introduce methods of detecting changes in the environment with precursor symptoms. The study presents a DoS Detection System based on Global Interdependent Behaviors and shows the result of detecting a sensor carrying out DoS attacks through the test-bed. PMID:22163475

Automatic background updating for video-based vehicle detection

NASA Astrophysics Data System (ADS)

Hu, Chunhai; Li, Dongmei; Liu, Jichuan

2008-03-01

Video-based vehicle detection is one of the most valuable techniques for the Intelligent Transportation System (ITS). The widely used video-based vehicle detection technique is the background subtraction method. The key problem of this method is how to subtract and update the background effectively. In this paper an efficient background updating scheme based on Zone-Distribution for vehicle detection is proposed to resolve the problems caused by sudden camera perturbation, sudden or gradual illumination change and the sleeping person problem. The proposed scheme is robust and fast enough to satisfy the real-time constraints of vehicle detection.

Assessment of landscape change associated with tropical cyclone phenomena in Baja California Sur, Mexico, using satellite remote sensing

NASA Astrophysics Data System (ADS)

Martinez-Gutierrez, Genaro

Baja California Sur (Mexico), as well as mainland Mexico, is affected by tropical cyclone storms, which originate in the eastern north Pacific. Historical records show that Baja has been damaged by intense summer storms. An arid to semiarid climate characterizes the study area, where precipitation mainly occurs during the summer and winter seasons. Natural and anthropogenic changes have impacted the landscape of southern Baja. The present research documents the effects of tropical storms over the southern region of Baja California for a period of approximately twenty-six years. The goal of the research is to demonstrate how remote sensing can be used to detect the important effects of tropical storms including: (a) evaluation of change detection algorithms, and (b) delineating changes to the landscape including coastal modification, fluvial erosion and deposition, vegetation change, river avulsion using change detection algorithms. Digital image processing methods with temporal Landsat satellite remotely sensed data from the North America Landscape Characterization archive (NALC), Thematic Mapper (TM), and Enhanced Thematic Mapper (ETM) images were used to document the landscape change. Two image processing methods were tested including Image differencing (ID), and Principal Component Analysis (PCA). Landscape changes identified with the NALC archive and TM images showed that the major changes included a rapid change of land use in the towns of San Jose del Cabo and Cabo San Lucas between 1973 and 1986. The features detected using the algorithms included flood deposits within the channels of active streams, erosion banks, and new channels caused by channel avulsion. Despite the 19 year period covered by the NALC data and approximately 10 year intervals between acquisition dates, there were changed features that could be identified in the images. The TM images showed that flooding from Hurricane Isis (1998) produced new large deposits within the stream channels. This research has shown that remote sensing based change detection can delineate the effects of flooding on the landscape at scales down to the nominal resolution of the sensor. These findings indicate that many other applications for change detection are both viable and important. These include disaster response, flood hazard planning, geomorphic studies, water supply management in deserts.

A detection method in living plant cells for rapidly monitoring the response of plants to exogenous lanthanum.

PubMed

Cheng, Mengzhu; Wang, Lihong; Yang, Qing; Huang, Xiaohua

2018-08-30

The pollution of rare earth elements (REEs) in ecosystem is becoming more and more serious, so it is urgent to establish methods for monitoring the pollution of REEs. Monitoring environmental pollution via the response of plants to pollutants has become the most stable and accurate method compared with traditional methods, but scientists still need to find the primary response of plants to pollutants to improve the sensitivity and speed of this method. Based on the facts that the initiation of endocytosis is the primary cellular response of the plant leaf cells to REEs and the detection of endocytosis is complex and expensive, we constructed a detection method in living plant cells for rapidly monitoring the response of plants to exogenous lanthanum [La(III), a representative of REEs] by designing a new immuno-electrochemical method for detecting the content change in extracellular vitronectin-like protein (VN) that are closely related to endocytosis. Results showed that when 30 μM La(III) initiated a small amount of endocytosis, the content of extracellular VN increased by 5.46 times, but the structure and function of plasma membrane were not interfered by La(III); when 80 μM La(III) strongly initiated a large amount of endocytosis, the content of extracellular VN increased by 119 times, meanwhile, the structure and function of plasma membrane were damaged. In summary, the detection method can reflect the response of plants to La(III) via detecting the content change in extracellular VN, which provides an effective and convenient way to monitor the response of plants to exogenous REEs. Copyright © 2018. Published by Elsevier Inc.

Detecting and Attributing Health Burdens to Climate Change.

PubMed

Ebi, Kristie L; Ogden, Nicholas H; Semenza, Jan C; Woodward, Alistair

2017-08-07

Detection and attribution of health impacts caused by climate change uses formal methods to determine a ) whether the occurrence of adverse health outcomes has changed, and b ) the extent to which that change could be attributed to climate change. There have been limited efforts to undertake detection and attribution analyses in health. Our goal was to show a range of approaches for conducting detection and attribution analyses. Case studies for heatwaves, Lyme disease in Canada, and Vibrio emergence in northern Europe highlight evidence that climate change is adversely affecting human health. Changes in rates and geographic distribution of adverse health outcomes were detected, and, in each instance, a proportion of the observed changes could, in our judgment, be attributed to changes in weather patterns associated with climate change. The results of detection and attribution studies can inform evidence-based risk management to reduce current, and plan for future, changes in health risks associated with climate change. Gaining a better understanding of the size, timing, and distribution of the climate change burden of disease and injury requires reliable long-term data sets, more knowledge about the factors that confound and modify the effects of climate on health, and refinement of analytic techniques for detection and attribution. At the same time, significant advances are possible in the absence of complete data and statistical certainty: there is a place for well-informed judgments, based on understanding of underlying processes and matching of patterns of health, climate, and other determinants of human well-being. https://doi.org/10.1289/EHP1509.

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.

Finding Direction in the Search for Selection.

PubMed

Thiltgen, Grant; Dos Reis, Mario; Goldstein, Richard A

2017-01-01

Tests for positive selection have mostly been developed to look for diversifying selection where change away from the current amino acid is often favorable. However, in many cases we are interested in directional selection where there is a shift toward specific amino acids, resulting in increased fitness in the species. Recently, a few methods have been developed to detect and characterize directional selection on a molecular level. Using the results of evolutionary simulations as well as HIV drug resistance data as models of directional selection, we compare two such methods with each other, as well as against a standard method for detecting diversifying selection. We find that the method to detect diversifying selection also detects directional selection under certain conditions. One method developed for detecting directional selection is powerful and accurate for a wide range of conditions, while the other can generate an excessive number of false positives.

A novel method to detect shadows on multispectral images

NASA Astrophysics Data System (ADS)

Daǧlayan Sevim, Hazan; Yardımcı ćetin, Yasemin; Özışık Başkurt, Didem

2016-10-01

Shadowing occurs when the direct light coming from a light source is obstructed by high human made structures, mountains or clouds. Since shadow regions are illuminated only by scattered light, true spectral properties of the objects are not observed in such regions. Therefore, many object classification and change detection problems utilize shadow detection as a preprocessing step. Besides, shadows are useful for obtaining 3D information of the objects such as estimating the height of buildings. With pervasiveness of remote sensing images, shadow detection is ever more important. This study aims to develop a shadow detection method on multispectral images based on the transformation of C1C2C3 space and contribution of NIR bands. The proposed method is tested on Worldview-2 images covering Ankara, Turkey at different times. The new index is used on these 8-band multispectral images with two NIR bands. The method is compared with methods in the literature.

Attribution of hydrological change using the Method of Multiple Working Hypotheses

NASA Astrophysics Data System (ADS)

Harrigan, Shaun

2017-04-01

The methods we have developed for managing our long-term water supply and protection from extreme hydrological events such as droughts and floods have been founded on the assumption that the hydrological cycle operates under natural conditions. However, it increasingly recognised that humans have the potential to induce significant change in almost every component of the hydrological cycle, for example, climate change, land-use change, and river engineering. Statistical detection of change in streamflow, outside that of natural variability, is an important scientific endeavour, but it does not tell us anything about the drivers of change. Attribution is the process of establishing the most likely cause(s) of a detected change - the why. Attribution is complex due to the integrated nature of streamflow and the proliferation of multiple possible drivers. It is perhaps this complexity, combined with few proven theoretical approaches to this problem in hydrology that has led to others to call for "more efforts and scientific rigour" (Merz et al., 2012). It is easier to limit the cause of a detected change to a single driver, or use simple correlation analysis alone as evidence of causation. It is convenient when the direction of a change in streamflow is consistent with what is expected from a well-known driver such as climate change. Over a century ago, Thomas Chamberlin argued these types of issues were common in many disciplines given how the scientific method is approached in general. His 1890 article introduces the Method of Multiple Working Hypotheses (MMWH) in an attempt to limit our confirmation bias and strives for increased objectivity. This presentation will argue that the MMWH offers an attractive theoretical approach to the attribution of hydrological change in modern hydrology as demonstrated through a case study of a well-documented change point in streamflow within the Boyne Catchment in Ireland. Further Reading Chamberlin, T. C.: The Method of Multiple Working Hypotheses, Science (old series), 15(366), 92-96, doi:10.1126/science.ns-15.366.92, 1890. Harrigan, S., Murphy, C., Hall, J., Wilby, R. L. and Sweeney, J.: Attribution of detected changes in streamflow using multiple working hypotheses, Hydrol. Earth Syst. Sci., 18(5), 1935-1952, doi:10.5194/hess-18-1935-2014, 2014. Merz, B., Vorogushyn, S., Uhlemann, S., Delgado, J. and Hundecha, Y.: HESS Opinions "More efforts and scientific rigour are needed to attribute trends in flood time series," Hydrol. Earth Syst. Sci., 16(5), 1379-1387, doi:10.5194/hess-16-1379-2012, 2012.

Landslide Inventory Mapping from Bitemporal 10 m SENTINEL-2 Images Using Change Detection Based Markov Random Field

NASA Astrophysics Data System (ADS)

Qin, Y.; Lu, P.; Li, Z.

2018-04-01

Landslide inventory mapping is essential for hazard assessment and mitigation. In most previous studies, landslide mapping was achieved by visual interpretation of aerial photos and remote sensing images. However, such method is labor-intensive and time-consuming, especially over large areas. Although a number of semi-automatic landslide mapping methods have been proposed over the past few years, limitations remain in terms of their applicability over different study areas and data, and there is large room for improvement in terms of the accuracy and automation degree. For these reasons, we developed a change detection-based Markov Random Field (CDMRF) method for landslide inventory mapping. The proposed method mainly includes two steps: 1) change detection-based multi-threshold for training samples generation and 2) MRF for landslide inventory mapping. Compared with the previous methods, the proposed method in this study has three advantages: 1) it combines multiple image difference techniques with multi-threshold method to generate reliable training samples; 2) it takes the spectral characteristics of landslides into account; and 3) it is highly automatic with little parameter tuning. The proposed method was applied for regional landslides mapping from 10 m Sentinel-2 images in Western China. Results corroborated the effectiveness and applicability of the proposed method especially the capability of rapid landslide mapping. Some directions for future research are offered. This study to our knowledge is the first attempt to map landslides from free and medium resolution satellite (i.e., Sentinel-2) images in China.

Pine Beetle Detection

NASA Technical Reports Server (NTRS)

1995-01-01

Earth Systems Science Office scientists worked with officials in St. Tammany Parish, La., to detect and battle pine beetle infestation in Fontainebleu State Park. The scientists used a new method of detecting plant stress by using special lenses and modified sensors to detect a change in light levels given off by the plant before the stress is visible to the naked eye.

Evaluation of Two Outlier-Detection-Based Methods for Detecting Tissue-Selective Genes from Microarray Data

PubMed Central

Kadota, Koji; Konishi, Tomokazu; Shimizu, Kentaro

2007-01-01

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent’s non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent’s method is not suitable for ROKU. PMID:19936074

A study on detection of glucose concentration using changes in color coordinates.

PubMed

Kim, Ji-Sun; Oh, Han-Byeol; Kim, A-Hee; Kim, Jun-Sik; Lee, Eun-Suk; Baek, Jin-Young; Lee, Ki Sung; Chung, Soon-Cheol; Jun, Jae-Hoon

2017-01-02

Glucose concentration is closely related to the metabolic activity of cells and it is the most important substance as the energy source of a living body which plays an important role in the human body. This paper proposes an optical method that can measure the concentration of glucose. The change in glucose concentration was observed by using CIE diagram, and wavelength and purity values were detected. Also, even small changes in glucose concentration can be evaluated through mathematical modeling. This system is simple, economical, and capable of quantifying optical signals with numerical values for glucose sensing. This method can be applicable to the clinical field that examines diabetes mellitus or metabolic syndrome.

Mutation Analysis of SLC26A4 for Pendred Syndrome and Nonsyndromic Hearing Loss by High-Resolution Melting

PubMed Central

Chen, Neng; Tranebjærg, Lisbeth; Rendtorff, Nanna Dahl; Schrijver, Iris

2011-01-01

Pendred syndrome and DFNB4 (autosomal recessive nonsyndromic congenital deafness, locus 4) are associated with autosomal recessive congenital sensorineural hearing loss and mutations in the SLC26A4 gene. Extensive allelic heterogeneity, however, necessitates analysis of all exons and splice sites to identify mutations for individual patients. Although Sanger sequencing is the gold standard for mutation detection, screening methods supplemented with targeted sequencing can provide a cost-effective alternative. One such method, denaturing high-performance liquid chromatography, was developed for clinical mutation detection in SLC26A4. However, this method inherently cannot distinguish homozygous changes from wild-type sequences. High-resolution melting (HRM), on the other hand, can detect heterozygous and homozygous changes cost-effectively, without any post-PCR modifications. We developed a closed-tube HRM mutation detection method specific for SLC26A4 that can be used in the clinical diagnostic setting. Twenty-eight primer pairs were designed to cover all 21 SLC26A4 exons and splice junction sequences. Using the resulting amplicons, initial HRM analysis detected all 45 variants previously identified by sequencing. Subsequently, a 384-well plate format was designed for up to three patient samples per run. Blinded HRM testing on these plates of patient samples collected over 1 year in a clinical diagnostic laboratory accurately detected all variants identified by sequencing. In conclusion, HRM with targeted sequencing is a reliable, simple, and cost-effective method for SLC26A4 mutation screening and detection. PMID:21704276

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.

A Generalized Machine Fault Detection Method Using Unified Change Detection

DTIC Science & Technology

2014-10-02

SOCIETY 2014 11 of the extension shaft. It can be induced by a lack of tightening torque of the end-nut and consequently causes a load...Test Facility (HTTF). The objective of the study was to provide HUMS systems with the capability to detect the loss of tightening torque of the end...from pinion SSA (at Ring-Front sensor & cruise power) change signal with cross-over at 75th shaft order Ten end-nut tightening torques were used in

Colorimetric detection of cholesterol based on enzyme modified gold nanoparticles

NASA Astrophysics Data System (ADS)

Nirala, Narsingh R.; Saxena, Preeti S.; Srivastava, Anchal

2018-02-01

We develop a simple colorimetric method for determination of free cholesterol in aqueous solution based on functionalized gold nanoparticles with cholesterol oxidase. Functionalized gold nanoparticles interact with free cholesterol to produce H2O2 in proportion to the level of cholesterol visually is being detected. The quenching in optical properties and agglomeration of functionalized gold nanoparticles play a key role in cholesterol sensing due to the electron accepting property of H2O2. While the lower ranges of cholesterol (lower detection limit i.e. 0.2 mg/dL) can be effectively detected using fluorescence study, the absorption study attests evident visual color change which becomes effective for detection of higher ranges of cholesterol (lower detection limit i.e. 19 mg/dL). The shades of red gradually change to blue/purple as the level of cholesterol detected (as evident at 100 mg/dL) using unaided eye without the use of expensive instruments. The potential of the proposed method to be applied in the field is shown by the proposed cholesterol measuring color wheel.

Method for detection of nuclear-plasma interactions in a 134Xe-doped exploding pusher at the National Ignition Facility

DOE PAGES

Bleuel, Daniel L.; Bernstein, Lee A.; Brand, Christopher A.; ...

2016-06-10

Angular momentum changes due to nuclear-plasma interactions on highly-excited nuclei in high energy density plasmas created at the National Ignition Facility can be measured through a change in isomer feeding following gamma emission. Here, we propose an experiment to detect these effects in 133Xe* in exploding pusher capsules.

Method for detection of nuclear-plasma interactions in a 134Xe-doped exploding pusher at the National Ignition Facility

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

Bleuel, Daniel L.; Bernstein, Lee A.; Brand, Christopher A.

Angular momentum changes due to nuclear-plasma interactions on highly-excited nuclei in high energy density plasmas created at the National Ignition Facility can be measured through a change in isomer feeding following gamma emission. Here, we propose an experiment to detect these effects in 133Xe* in exploding pusher capsules.

Tree-species range shifts in a changing climate: detecting, modeling, assisting

Treesearch

Louis R. Iverson; Donald McKenzie

2013-01-01

In these times of rapidly changing climate, the science of detecting and modeling shifts in the ranges of tree species is advancing of necessity. We briefly review the current state of the science on several fronts. First, we review current and historical evidence for shifting ranges and migration. Next, we review two broad categories of methods, focused on the spatial...

Development of ultrasound-assisted fluorescence imaging of indocyanine green.

PubMed

Morikawa, Hiroyasu; Toyota, Shin; Wada, Kenji; Uchida-Kobayashi, Sawako; Kawada, Norifumi; Horinaka, Hiromichi

2017-01-01

Indocyanine green (ICG) accumulation in hepatocellular carcinoma means tumors can be located by fluorescence. However, because of light scattering, it is difficult to detect ICG fluorescence from outside the body. We propose a new fluorescence imaging method that detects changes in the intensity of ICG fluorescence by ultrasound-induced temperature changes. ICG fluorescence intensity decreases as the temperature rises. Therefore, it should theoretically be possible to detect tissue distribution of ICG using ultrasound to heat tissue, moving the point of ultrasound transmission, and monitoring changes in fluorescence intensity. A new probe was adapted for clinical application. It consisted of excitation light from a laser, fluorescence sensing through a light pipe, and heating by ultrasound. We applied the probe to bovine liver to image the accumulation of ICG. ICG emits fluorescence (820 nm) upon light irradiation (783 nm). With a rise in temperature, the fluorescence intensity of ICG decreased by 0.85 %/°C. The distribution of fluorescent ICG was detected using an ultrasonic warming method in a new integrated probe. Modulating fluorescence by changing the temperature using ultrasound can determine where ICG accumulates at a depth, highlighting its potential as a means to locate hepatocellular carcinoma.

Manmade target extraction based on multistage decision and its application for change detection in polarimetric synthetic aperture radar image

NASA Astrophysics Data System (ADS)

Cong, Runmin; Han, Ping; Li, Chongyi; He, Jiaji; Zhang, Zaiji

2016-09-01

Targets of interest are different in various applications in which manmade targets, such as aircraft, ships, and buildings, are given more attention. Manmade target extraction methods using synthetic aperture radar (SAR) images are designed in response to various demands, which include civil uses, business purposes, and military industries. This plays an increasingly vital role in monitoring, military reconnaissance, and precision strikes. Achieving accurate and complete results through traditional methods is becoming more challenging because of the scattered complexity of polarization in polarimetric synthetic aperture radar (PolSAR) image. A multistage decision-based method is proposed composed of power decision, dominant scattering mechanism decision, and reflection symmetry decision. In addition, the theories of polarimetric contrast enhancement, generalized Y decomposition, and maximum eigenvalue ratio are applied to assist the decision. Fully PolSAR data are adopted to evaluate and verify the approach. Experimental results show that the method can achieve an effective result with a lower false alarm rate and clear contours. Finally, on this basis, a universal framework of change detection for manmade targets is presented as an application of our method. Two sets of measured data are also used to evaluate and verify the effectiveness of the change-detection algorithm.

Quantification of susceptibility change at high-concentrated SPIO-labeled target by characteristic phase gradient recognition.

PubMed

Zhu, Haitao; Nie, Binbin; Liu, Hua; Guo, Hua; Demachi, Kazuyuki; Sekino, Masaki; Shan, Baoci

2016-05-01

Phase map cross-correlation detection and quantification may produce highlighted signal at superparamagnetic iron oxide nanoparticles, and distinguish them from other hypointensities. The method may quantify susceptibility change by performing least squares analysis between a theoretically generated magnetic field template and an experimentally scanned phase image. Because characteristic phase recognition requires the removal of phase wrap and phase background, additional steps of phase unwrapping and filtering may increase the chance of computing error and enlarge the inconsistence among algorithms. To solve problem, phase gradient cross-correlation and quantification method is developed by recognizing characteristic phase gradient pattern instead of phase image because phase gradient operation inherently includes unwrapping and filtering functions. However, few studies have mentioned the detectable limit of currently used phase gradient calculation algorithms. The limit may lead to an underestimation of large magnetic susceptibility change caused by high-concentrated iron accumulation. In this study, mathematical derivation points out the value of maximum detectable phase gradient calculated by differential chain algorithm in both spatial and Fourier domain. To break through the limit, a modified quantification method is proposed by using unwrapped forward differentiation for phase gradient generation. The method enlarges the detectable range of phase gradient measurement and avoids the underestimation of magnetic susceptibility. Simulation and phantom experiments were used to quantitatively compare different methods. In vivo application performs MRI scanning on nude mice implanted by iron-labeled human cancer cells. Results validate the limit of detectable phase gradient and the consequent susceptibility underestimation. Results also demonstrate the advantage of unwrapped forward differentiation compared with differential chain algorithms for susceptibility quantification at high-concentrated iron accumulation. Copyright © 2015 Elsevier Inc. All rights reserved.

Detection of Fatigue Crack in Basalt FRP Laminate Composite Pipe using Electrical Potential Change Method

NASA Astrophysics Data System (ADS)

Altabey, Wael A.; Noori, Mohammed

2017-05-01

Novel modulation electrical potential change (EPC) method for fatigue crack detection in a basalt fibre reinforced polymer (FRP) laminate composite pipe is carried out in this paper. The technique is applied to a laminate pipe with an embedded crack in three layers [0º/90º/0º]s. EPC is applied for evaluating the dielectric properties of basalt FRP pipe by using an electrical capacitance sensor (ECS) to discern damages in the pipe. Twelve electrodes are mounted on the outer surface of the pipe and the changes in the modulation dielectric properties of the piping system are analyzed to detect damages in the pipe. An embedded crack is created by a fatigue internal pressure test. The capacitance values, capacitance change and node potential distribution of ECS electrodes are calculated before and after crack initiates using a finite element method (FEM) by ANSYS and MATLAB, which are combined to simulate sensor characteristics and fatigue behaviour. The crack lengths of the basalt FRP are investigated for various number of cycles to failure for determining crack growth rate. Response surfaces are adopted as a tool for solving inverse problems to estimate crack lengths from the measured electric potential differences of all segments between electrodes to validate the FEM results. The results show that, the good convergence between the FEM and estimated results. Also the results of this study show that the electrical potential difference of the basalt FRP laminate increases during cyclic loading, caused by matrix cracking. The results indicate that the proposed method successfully provides fatigue crack detection for basalt FRP laminate composite pipes.

Measurement of internal defects in aluminum using a nano-granular in-gap magnetic sensor

NASA Astrophysics Data System (ADS)

Ozawa, T.; Yabukami, S.; Totsuka, J.; Koyama, S.; Hayasaka, J.; Wako, N.; Arai, K. I.

2015-05-01

Techniques for identifying defects in metals are very important in a wide variety of manufacturing areas. The present paper reports an eddy current testing method that employs a nano-granular in-gap magnetic sensor (GIGS) to detect internal defects in aluminum boards. The GIGS consists of a tunnel magnetoresistive film with nanometer sized grains and two yokes. In the presence of an external magnetic field, the nano-granular film exhibits only a small change in resistance due to the tunnel magnetoresistive effect. However, by placing it between two yokes, the magnetic flux can be greatly concentrated, thus increasing the change in resistance. The GIGS is a magnetic-field sensor that exploits this principle to achieve enhanced sensitivity. Moreover, because it has a cross-sectional yolk area of just 80 μm × 0.5 μm, it achieves outstanding spatial resolution. In the present study, it is used in combination with an eddy-current method in order to detect internal defects in aluminum. In this method, an excitation coil is used to apply an AC magnetic field perpendicular to the aluminum surface. This induces eddy currents in the metal, which in turn give rise to an AC magnetic field, which is then measured by the GIGS. The presence of defects in the aluminum distorts the eddy current flow, causing a change in the magnitude and distribution of the magnetic field. Such changes can be detected using the GIGS. In the present study, the proposed method was used to successfully detect indentations with diameters of 5 mm on the rear surface of an aluminum plate.

Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

NASA Astrophysics Data System (ADS)

Gong, Maoguo; Yang, Hailun; Zhang, Puzhao

2017-07-01

Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework.

Cross-linking gold nanoparticles aggregation method based on localised surface plasmon resonance for quantitative detection of miR-155.

PubMed

Esmaeili-Bandboni, Aghil; Amini, Seyed Mohammad; Faridi-Majidi, Reza; Bagheri, Jamshid; Mohammadnejad, Javad; Sadroddiny, Esmaeil

2018-06-01

MiR-155 plays a critical role in the formation of cancers and other diseases. In this study, the authors aimed to design and fabricate a biosensor based on cross-linking gold nanoparticles (AuNPs) aggregation for the detection and quantification of miR-155. Also, they intended to compare this method with SYBR Green real-time polymerase chain reaction (PCR). Primers for real-time PCR, and two thiolated capture probes for biosensor, complementary with miR-155, were designed. Citrate capped AuNPs (18.7 ± 3.6 nm) were synthesised and thiolated capture probes immobilised to AuNPs. The various concentrations of synthetic miR-155 were measured by this biosensor and real-time PCR method. Colorimetric changes were studied, and the calibration curves were plotted. Results showed the detection limit of 10 nM for the fabricated biosensor and real-time PCR. Also, eye detection using colour showed the weaker detection limit (1 µM), for this biosensor. MiR-133b as the non-complementary target could not cause a change in both colour and UV-visible spectrum. The increase in hydrodynamic diameter and negative zeta potential of AuNPs after the addition of probes verified the biosensor accurately fabricated. This fabricated biosensor could detect miR-155 simpler and faster than previous methods.

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.

76 FR 31369 - Biweekly Notice; Applications and Amendments to Facility Operating Licenses Involving No...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-31

... detection instrumentation to operable status; establish alternate methods of monitoring RCS leakage when one... the RCS leakage detection instrumentation. These changes are consistent with NRC-approved Revision 3... requirements for the RCS leakage detection instrumentation and reduces the time allowed for the plant to...

30 CFR 250.1629 - Additional production and fuel gas system requirements.

Code of Federal Regulations, 2014 CFR

2014-07-01

... structure. (4) Fire- and gas-detection system. (i) Fire (flame, heat, or smoke) sensors shall be installed... explosive limit. One approved method of providing adequate ventilation is a change of air volume each 5... detection systems shall be capable of continuous monitoring. Fire-detection systems and portions of...

30 CFR 250.1629 - Additional production and fuel gas system requirements.

Code of Federal Regulations, 2013 CFR

2013-07-01

... structure. (4) Fire- and gas-detection system. (i) Fire (flame, heat, or smoke) sensors shall be installed... explosive limit. One approved method of providing adequate ventilation is a change of air volume each 5... detection systems shall be capable of continuous monitoring. Fire-detection systems and portions of...

A Data Stream Model For Runoff Simulation In A Changing Environment

NASA Astrophysics Data System (ADS)

Yang, Q.; Shao, J.; Zhang, H.; Wang, G.

2017-12-01

Runoff simulation is of great significance for water engineering design, water disaster control, water resources planning and management in a catchment or region. A large number of methods including concept-based process-driven models and statistic-based data-driven models, have been proposed and widely used in worldwide during past decades. Most existing models assume that the relationship among runoff and its impacting factors is stationary. However, in the changing environment (e.g., climate change, human disturbance), their relationship usually evolves over time. In this study, we propose a data stream model for runoff simulation in a changing environment. Specifically, the proposed model works in three steps: learning a rule set, expansion of a rule, and simulation. The first step is to initialize a rule set. When a new observation arrives, the model will check which rule covers it and then use the rule for simulation. Meanwhile, Page-Hinckley (PH) change detection test is used to monitor the online simulation error of each rule. If a change is detected, the corresponding rule is removed from the rule set. In the second step, for each rule, if it covers more than a given number of instance, the rule is expected to expand. In the third step, a simulation model of each leaf node is learnt with a perceptron without activation function, and is updated with adding a newly incoming observation. Taking Fuxi River catchment as a case study, we applied the model to simulate the monthly runoff in the catchment. Results show that abrupt change is detected in the year of 1997 by using the Page-Hinckley change detection test method, which is consistent with the historic record of flooding. In addition, the model achieves good simulation results with the RMSE of 13.326, and outperforms many established methods. The findings demonstrated that the proposed data stream model provides a promising way to simulate runoff in a changing environment.

System and Method for Determining Fluence of a Substance

NASA Technical Reports Server (NTRS)

Banks, Bruce A. (Inventor)

2016-01-01

A system and method for measuring a fluence of gas are disclosed. The system has a first light detector capable of outputting an electrical signal based on an amount of light received. A barrier is positionable adjacent the first light detector and is susceptible to a change in dimension from the fluence of the gas. The barrier permits a portion of light from being received by the first light detector. The change in the dimension of the barrier changes the electrical signal output from the first light detector. A second light detector is positionable to receive light representative of the first light detector without the barrier. The system and method have broad application to detect fluence of gas that may cause erosion chemical reaction causing erosive deterioration. One application is in low orbit Earth for detecting the fluence of atomic oxygen.

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.

Localized surface plasmon resonance-based abscisic acid biosensor using aptamer-functionalized gold nanoparticles

PubMed Central

Wang, Shun; Li, Wei; Chang, Keke; Liu, Juan; Guo, Qingqian; Sun, Haifeng; Jiang, Min; Zhang, Hao; Chen, Jing

2017-01-01

Abscisic acid (ABA) plays an important role in abiotic stress response and physiological signal transduction resisting to the adverse environment. Therefore, it is very essential for the quantitative detection of abscisic acid (ABA) due to its indispensable role in plant physiological activities. Herein, a new detection method based on localized surface plasmon resonance (LSPR) using aptamer-functionalized gold nanoparticles (AuNPs) is developed without using expensive instrument and antibody. In the presence of ABA, ABA specifically bind with their aptamers to form the ABA-aptamer complexes with G-quadruplex-like structure and lose the ability to stabilize AuNPs against NaCl-induced aggregation. Meanwhile, the changes of the LSPR spectra of AuNP solution occur and therefore the detection of ABA achieved. Under optimized conditions, this method showed a good linear range covering from 5×10−7 M to 5×10−5 M with a detection limit of 0.33 μM. In practice, the usage of this novel method has been demonstrated by its application to detect ABA from fresh leaves of rice with the relative error of 6.59%-7.93% compared with ELISA bioassay. The experimental results confirmed that this LSPR-based biosensor is simple, selective and sensitive for the detection of ABA. The proposed LSPR method could offer a new analytical platform for the detection of other plant hormones by changing the corresponding aptamer. PMID:28953934

Detection and manipulation of phosphoinositides.

PubMed

Idevall-Hagren, Olof; De Camilli, Pietro

2015-06-01

Phosphoinositides (PIs) are minor components of cell membranes, but play key roles in cell function. Recent refinements in techniques for their detection, together with imaging methods to study their distribution and changes, have greatly facilitated the study of these lipids. Such methods have been complemented by the parallel development of techniques for the acute manipulation of their levels, which in turn allow bypassing the long-term adaptive changes implicit in genetic perturbations. Collectively, these advancements have helped elucidate the role of PIs in physiology and the impact of the dysfunction of their metabolism in disease. Combining methods for detection and manipulation enables the identification of specific roles played by each of the PIs and may eventually lead to the complete deconstruction of the PI signaling network. Here, we review current techniques used for the study and manipulation of cellular PIs and also discuss advantages and disadvantages associated with the various methods. This article is part of a Special Issue entitled Phosphoinositides. Copyright © 2014 Elsevier B.V. All rights reserved.

Detection and manipulation of phosphoinositides☆

PubMed Central

Idevall-Hagren, Olof; Camilli, Pietro De

2016-01-01

Phosphoinositides (PIs) are minor components of cell membranes, but play key roles in cell function. Recent refinements in techniques for their detection, together with imaging methods to study their distribution and changes, have greatly facilitated the study of these lipids. Such methods have been complemented by the parallel development of techniques for the acute manipulation of their levels, which in turn allow bypassing the long-term adaptive changes implicit in genetic perturbations. Collectively, these advancements have helped elucidate the role of PIs in physiology and the impact of the dysfunction of their metabolism in disease. Combining methods for detection and manipulation enables the identification of specific roles played by each of the PIs and may eventually lead to the complete deconstruction of the PI signaling network. Here, we review current techniques used for the study and manipulation of cellular PIs and also discuss advantages and disadvantages associated with the various methods. This article is part of a Special Issue entitled Phosphoinositides. PMID:25514766

Colorimetric Detection of Mercury(II) Ion in Aqueous Solution Using Silver Nanoparticles.

PubMed

Firdaus, M Lutfi; Fitriani, Ikka; Wyantuti, Santhy; Hartati, Yeni W; Khaydarov, Renat; McAlister, Jason A; Obata, Hajime; Gamo, Toshitaka

2017-01-01

Novel green-chemistry synthesis of silver nanoparticles (AgNPs) is introduced as a low-cost, rapid and easy-to-use analytical method for mercury ion detection. Aqueous fruit extract of water apple (Syzygium aqueum) was used for the first time as bioreductant to synthesize stable AgNPs. The prepared AgNPs have a yellowish-brown color with a surface plasmon resonance peak at 420 nm. The addition of Hg(II) ions then changes the AgNPs color to colorless. The color change was in proportion to the concentration of Hg(II) ions. The presence of other metal ions in the system was also evaluated. The proposed method shows good selectivity and sensitivity towards Hg(II) ions. Using UV-visible spectrophotometry, the detection limit of the developed method was 8.5 × 10 -7 M. The proposed method has been successfully applied for determination of Hg(II) ions in tap and lake water samples with precision better than 5%.

Modelling spatiotemporal change using multidimensional arrays Meng

NASA Astrophysics Data System (ADS)

Lu, Meng; Appel, Marius; Pebesma, Edzer

2017-04-01

The large variety of remote sensors, model simulations, and in-situ records provide great opportunities to model environmental change. The massive amount of high-dimensional data calls for methods to integrate data from various sources and to analyse spatiotemporal and thematic information jointly. An array is a collection of elements ordered and indexed in arbitrary dimensions, which naturally represent spatiotemporal phenomena that are identified by their geographic locations and recording time. In addition, array regridding (e.g., resampling, down-/up-scaling), dimension reduction, and spatiotemporal statistical algorithms are readily applicable to arrays. However, the role of arrays in big geoscientific data analysis has not been systematically studied: How can arrays discretise continuous spatiotemporal phenomena? How can arrays facilitate the extraction of multidimensional information? How can arrays provide a clean, scalable and reproducible change modelling process that is communicable between mathematicians, computer scientist, Earth system scientist and stakeholders? This study emphasises on detecting spatiotemporal change using satellite image time series. Current change detection methods using satellite image time series commonly analyse data in separate steps: 1) forming a vegetation index, 2) conducting time series analysis on each pixel, and 3) post-processing and mapping time series analysis results, which does not consider spatiotemporal correlations and ignores much of the spectral information. Multidimensional information can be better extracted by jointly considering spatial, spectral, and temporal information. To approach this goal, we use principal component analysis to extract multispectral information and spatial autoregressive models to account for spatial correlation in residual based time series structural change modelling. We also discuss the potential of multivariate non-parametric time series structural change methods, hierarchical modelling, and extreme event detection methods to model spatiotemporal change. We show how array operations can facilitate expressing these methods, and how the open-source array data management and analytics software SciDB and R can be used to scale the process and make it easily reproducible.

Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

NASA Technical Reports Server (NTRS)

Forkel, Matthias; Carvalhais, Nuno; Verbesselt, Jan; Mahecha, Miguel D.; Neigh, Christopher S.R.; Reichstein, Markus

2013-01-01

Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy.

The aftermath of memory retrieval for recycling visual working memory representations.

PubMed

Park, Hyung-Bum; Zhang, Weiwei; Hyun, Joo-Seok

2017-07-01

We examined the aftermath of accessing and retrieving a subset of information stored in visual working memory (VWM)-namely, whether detection of a mismatch between memory and perception can impair the original memory of an item while triggering recognition-induced forgetting for the remaining, untested items. For this purpose, we devised a consecutive-change detection task wherein two successive testing probes were displayed after a single set of memory items. Across two experiments utilizing different memory-testing methods (whole vs. single probe), we observed a reliable pattern of poor performance in change detection for the second test when the first test had exhibited a color change. The impairment after a color change was evident even when the same memory item was repeatedly probed; this suggests that an attention-driven, salient visual change made it difficult to reinstate the previously remembered item. The second change detection, for memory items untested during the first change detection, was also found to be inaccurate, indicating that recognition-induced forgetting had occurred for the unprobed items in VWM. In a third experiment, we conducted a task that involved change detection plus continuous recall, wherein a memory recall task was presented after the change detection task. The analyses of the distributions of recall errors with a probabilistic mixture model revealed that the memory impairments from both visual changes and recognition-induced forgetting are explained better by the stochastic loss of memory items than by their degraded resolution. These results indicate that attention-driven visual change and recognition-induced forgetting jointly influence the "recycling" of VWM representations.

Change-Based Satellite Monitoring Using Broad Coverage and Targetable Sensing

NASA Technical Reports Server (NTRS)

Chien, Steve A.; Tran, Daniel Q.; Doubleday, Joshua R.; Doggett, Thomas

2013-01-01

A generic software framework analyzes data from broad coverage sweeps or general larger areas of interest. Change detection methods are used to extract subsets of directed swath areas that intersect areas of change. These areas are prioritized and allocated to targetable assets. This method is deployed in an automatic fashion, and has operated without human monitoring or intervention for sustained periods of time (months).

Modified screening and ranking algorithm for copy number variation detection.

PubMed

Xiao, Feifei; Min, Xiaoyi; Zhang, Heping

2015-05-01

Copy number variation (CNV) is a type of structural variation, usually defined as genomic segments that are 1 kb or larger, which present variable copy numbers when compared with a reference genome. The screening and ranking algorithm (SaRa) was recently proposed as an efficient approach for multiple change-points detection, which can be applied to CNV detection. However, some practical issues arise from application of SaRa to single nucleotide polymorphism data. In this study, we propose a modified SaRa on CNV detection to address these issues. First, we use the quantile normalization on the original intensities to guarantee that the normal mean model-based SaRa is a robust method. Second, a novel normal mixture model coupled with a modified Bayesian information criterion is proposed for candidate change-point selection and further clustering the potential CNV segments to copy number states. Simulations revealed that the modified SaRa became a robust method for identifying change-points and achieved better performance than the circular binary segmentation (CBS) method. By applying the modified SaRa to real data from the HapMap project, we illustrated its performance on detecting CNV segments. In conclusion, our modified SaRa method improves SaRa theoretically and numerically, for identifying CNVs with high-throughput genotyping data. The modSaRa package is implemented in R program and freely available at http://c2s2.yale.edu/software/modSaRa. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Review of methodology and technology available for the detection of extrasolar planetary systems

NASA Technical Reports Server (NTRS)

Tarter, J. C.; Black, D. C.; Billingham, J.

1985-01-01

Four approaches exist for the detection of extrasolar planets. According to the only direct method, the planet is imaged at some wavelength in a manner which makes it possible to differentiate its own feeble luminosity (internal energy source plus reflected starlight) from that of the nearby host star. The three indirect methods involve the detection of a planetary mass companion on the basis of the observable effects it has on the host star. A search is conducted regarding the occurrence of regular, periodic changes in the stellar spatial motion (astrometric method) or the velocity of stellar emission line spectra (spectroscopic method) or in the apparent total stellar luminosity (photometric method). Details regarding the approaches employed for implementing the considered methods are discussed.

A Framework for Detecting Glaucomatous Progression in the Optic Nerve Head of an Eye using Proper Orthogonal Decomposition

PubMed Central

Balasubramanian, Madhusudhanan; Žabić, Stanislav; Bowd, Christopher; Thompson, Hilary W.; Wolenski, Peter; Iyengar, S. Sitharama; Karki, Bijaya B.; Zangwill, Linda M.

2009-01-01

Glaucoma is the second leading cause of blindness worldwide. Often the optic nerve head (ONH) glaucomatous damage and ONH changes occur prior to visual field loss and are observable in vivo. Thus, digital image analysis is a promising choice for detecting the onset and/or progression of glaucoma. In this work, we present a new framework for detecting glaucomatous changes in the ONH of an eye using the method of proper orthogonal decomposition (POD). A baseline topograph subspace was constructed for each eye to describe the structure of the ONH of the eye at a reference/baseline condition using POD. Any glaucomatous changes in the ONH of the eye present during a follow-up exam were estimated by comparing the follow-up ONH topography with its baseline topograph subspace representation. Image correspondence measures of L1 and L2 norms, correlation, and image Euclidean distance (IMED) were used to quantify the ONH changes. An ONH topographic library built from the Louisiana State University Experimental Glaucoma study was used to evaluate the performance of the proposed method. The area under the receiver operating characteristic curves (AUC) were used to compare the diagnostic performance of the POD induced parameters with the parameters of Topographic Change Analysis (TCA) method. The IMED and L2 norm parameters in the POD framework provided the highest AUC of 0.94 at 10° field of imaging and 0.91 at 15° field of imaging compared to the TCA parameters with an AUC of 0.86 and 0.88 respectively. The proposed POD framework captures the instrument measurement variability and inherent structure variability and shows promise for improving our ability to detect glaucomatous change over time in glaucoma management. PMID:19369163

Implication of relationship between natural impacts and land use/land cover (LULC) changes of urban area in Mongolia

NASA Astrophysics Data System (ADS)

Gantumur, Byambakhuu; Wu, Falin; Zhao, Yan; Vandansambuu, Battsengel; Dalaibaatar, Enkhjargal; Itiritiphan, Fareda; Shaimurat, Dauryenbyek

2017-10-01

Urban growth can profoundly alter the urban landscape structure, ecosystem processes, and local climates. Timely and accurate information on the status and trends of urban ecosystems is critical to develop strategies for sustainable development and to improve the urban residential environment and living quality. Ulaanbaatar city was urbanized very rapidly caused by herders and farmers, many of them migrating from rural places, have played a big role in this urban expansion (sprawl). Today, 1.3 million residents for about 40% of total population are living in the Ulaanbaatar region. Those human activities influenced stronger to green environments. Therefore, the aim of this study is determined to change detection of land use/land cover (LULC) and estimating their areas for the trend of future by remote sensing and statistical methods. The implications of analysis were provided by change detection methods of LULC, remote sensing spectral indices including normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI). In addition, it can relate to urban heat island (UHI) provided by Land surface temperature (LST) with local climate issues. Statistical methods for image processing used to define relations between those spectral indices and change detection images and regression analysis for time series trend in future. Remote sensing data are used by Landsat (TM/ETM+/OLI) satellite images over the period between 1990 and 2016 by 5 years. The advantages of this study are very useful remote sensing approaches with statistical analysis and important to detecting changes of LULC. The experimental results show that the LULC changes can image on the present and after few years and determined relations between impacts of environmental conditions.

Chemical Fingerprinting of Materials Developed Due to Environmental Issues

NASA Technical Reports Server (NTRS)

Smith, Doris A.; McCool, A. (Technical Monitor)

2000-01-01

Instrumental chemical analysis methods are developed and used to chemically fingerprint new and modified External Tank materials made necessary by changing environmental requirements. Chemical fingerprinting can detect and diagnose variations in material composition. To chemically characterize each material, fingerprint methods are selected from an extensive toolbox based on the material's chemistry and the ability of the specific methods to detect the material's critical ingredients. Fingerprint methods have been developed for a variety of materials including Thermal Protection System foams, adhesives, primers, and composites.

Persistent Genital Tract HIV-1 RNA Shedding After Change in Treatment Regimens in Antiretroviral-Experienced Women with Detectable Plasma Viral Load

PubMed Central

DeLong, Allison K.; Kantor, Rami; Chapman, Stacey; Ingersoll, Jessica; Kurpewski, Jaclynn; De Pasquale, Maria Pia; D'Aquila, Richard; Caliendo, Angela M.; Cu-Uvin, Susan

2013-01-01

Abstract Objective To longitudinally assess the association between plasma viral load (PVL) and genital tract human immunodeficiency virus (GT HIV) RNA among HIV-1 infected women changing highly active antiretroviral therapy (HAART) because of detectable PVL on current treatment. Methods Women were eligible for the study if they had detectable PVL (defined as two consecutive samples with PVL>1000 copies/mL) and intended to change their current HAART regimen at the time of enrollment. Paired plasma and GT HIV-1 RNA were measured prospectively over 3 years. Longitudinal analyses examined rates of GT HIV-1 RNA shedding and the association with PVL. Results Sixteen women were followed for a median of 11 visits contributing a total of 205 study visits. At study enrollment, all had detectable PVL and 69% had detectable GT HIV-1 RNA. Half of the women changed to a new HAART regimen with ≥3 active antiretroviral drugs. The probability of having detectable PVL ≥30 days after changing HAART was 0.56 (95% CI: 0.37 to 0.74). Fourteen women (88%) had detectable PVL on a follow-up visit ≥30 or 60 days after changing HAART; and 12 women (75%) had detectable GT HIV-1 RNA on a follow-up visit ≥30 or 60 days after changing HAART. When PVL was undetectable, GT shedding occurred at 11% of visits, and when PVL was detectable, GT shedding occurred at 47% of visits. Conclusions Some treatment-experienced HIV-infected women continue to have detectable virus in both the plasma and GT following a change in HAART, highlighting the difficulty of viral suppression in this patient population. PMID:23531097

Uav-Based 3d Urban Environment Monitoring

NASA Astrophysics Data System (ADS)

Boonpook, Wuttichai; Tan, Yumin; Liu, Huaqing; Zhao, Binbin; He, Lingfeng

2018-04-01

Unmanned Aerial Vehicle (UAV) based remote sensing can be used to make three-dimensions (3D) mapping with great flexibility, besides the ability to provide high resolution images. In this paper we propose a quick-change detection method on UAV images by combining altitude from Digital Surface Model (DSM) and texture analysis from images. Cases of UAV images with and without georeferencing are both considered. Research results show that the accuracy of change detection can be enhanced with georeferencing procedure, and the accuracy and precision of change detection on UAV images which are collected both vertically and obliquely but without georeferencing also have a good performance.

Incremental Principal Component Analysis Based Outlier Detection Methods for Spatiotemporal Data Streams

NASA Astrophysics Data System (ADS)

Bhushan, A.; Sharker, M. H.; Karimi, H. A.

2015-07-01

In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

Dosimeter and method for using the same

DOEpatents

Warner, Benjamin P.; Johns, Deidre M.

2003-06-24

A very sensitive dosimeter that detects ionizing radiation is described. The dosimeter includes a breakable sealed container. A solution of a reducing agent is inside the container. The dosimeter has an air-tight dosimeter body with a transparent portion and an opaque portion. The transparent portion includes a transparent chamber that holds the breakable container with the reducing agent. The opaque portion includes an opaque chamber that holds an emulsion of silver salt (AgX) selected from silver chloride, silver bromide, silver iodide, and combinations of them. A passageway in the dosimeter provides fluid communication between the transparent chamber and the opaque chamber. The dosimeter may also include a chemical pH indicator in the breakable container that provides a detectable color change to the solution for a pH of about 3-10. The invention also includes a method of detecting ionizing radiation that involves producing the dosimeter, breaking the breakable container, allowing the solution to flow through the passageway and contact the emulsion, detecting any color change in the solution and using the color change to determine a radiation dosage.

The affection of boreal forest changes on imbalance of Nature (Invited)

NASA Astrophysics Data System (ADS)

Tana, G.; Tateishi, R.

2013-12-01

Abstract: The balance of nature does not exist, and, perhaps, never has existed [1]. In other words, the Mother Nature is imbalanced at all. The Mother Nature is changing every moment and never returns to previous condition. Because of the imbalance of nature, global climate has been changing gradually. To reveal the imbalance of nature, there is a need to monitor the dynamic changes of the Earth surface. Forest cover and forest cover change have been grown in importance as basic variables for modelling of global biogeochemical cycles as well as climate [2]. The boreal area contains 1/3 of the earth's trees. These trees play a large part in limiting harmful greenhouse gases by aborbing much of the earth's carbon dioxide (CO2) [3]. The boreal area mainly consists of needleleaf evergreen forest and needleleaf deciduous forest. Both of the needleleaf evergreen forest and needleleaf deciduous forest play the important roles on the uptake of CO2. However, because of the dormant period of needleleaf evergreen forest are shorter than that of needleleaf deciduous forest, needleleaf evergreen forest makes a greater contribution to the absorbtion of CO2. Satellite sensor because of its ability to observe the Earth continuously, can provide the opportunity to monitor the dynamic changes of the Earth. In this study, we used the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data to monitor the dynamic change of boreal forest area which are mainly consist from needleleaf evergreen forest and needleleaf deciduous forest during 2003-2012. Three years MODIS data from the year 2003, 2008 and 2012 were used to detect the forest changed area. A hybrid change detection method which combines the threshold method and unsupervised classification method was used to detect the changes of forest area. In the first step, the difference of Normalized Difference Vegetation Index (NDVI) of the three years were calculated and were used to extract the changed areas by the threshold method. In the second step, the unsupervised classification method was used to classify and analyze detected change areas derived from the first step. Finally, the changed area were validated using the traning data collected for the three years. The validation result revealed that the forest in the study area has undergone the area and type changes during 2003-2012. The detailed procedure will be presented in the meeting. References: [1] Elton, C.S. (1930). Animal Ecology and Evolution. New York, Oxford University Press. [2] Potapov, P., Hansen, M. C., Stehman, S. V., Loveland, T. R., Pittman, K. (2008). Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss, Remote Sensing of Environment, 112, 3708-3719. [3] Houghton, R. A. (2003). Why are estimates of the terrestrial carbon balance so different? Global Change Biology, 9, 500-509.

Bacteria detection instrument and method

NASA Technical Reports Server (NTRS)

Renner, W.; Fealey, R. D. (Inventor)

1972-01-01

A method and apparatus for screening a sample fluid for bacterial presence are disclosed wherein the fluid sample is mixed with culture media of sufficient quantity to permit bacterial growth in order to obtain a test solution. The concentration of oxygen dissolved in the test solution is then monitored using the potential difference between a reference electrode and a noble metal electrode which are in contact with the test solution. The change in oxygen concentration which occurs during a period of time as indicated by the electrode potential difference is compared with a detection criterion which exceeds the change which would occur absent bacteria.

Redundancy analysis allows improved detection of methylation changes in large genomic regions.

PubMed

Ruiz-Arenas, Carlos; González, Juan R

2017-12-14

DNA methylation is an epigenetic process that regulates gene expression. Methylation can be modified by environmental exposures and changes in the methylation patterns have been associated with diseases. Methylation microarrays measure methylation levels at more than 450,000 CpGs in a single experiment, and the most common analysis strategy is to perform a single probe analysis to find methylation probes associated with the outcome of interest. However, methylation changes usually occur at the regional level: for example, genomic structural variants can affect methylation patterns in regions up to several megabases in length. Existing DMR methods provide lists of Differentially Methylated Regions (DMRs) of up to only few kilobases in length, and cannot check if a target region is differentially methylated. Therefore, these methods are not suitable to evaluate methylation changes in large regions. To address these limitations, we developed a new DMR approach based on redundancy analysis (RDA) that assesses whether a target region is differentially methylated. Using simulated and real datasets, we compared our approach to three common DMR detection methods (Bumphunter, blockFinder, and DMRcate). We found that Bumphunter underestimated methylation changes and blockFinder showed poor performance. DMRcate showed poor power in the simulated datasets and low specificity in the real data analysis. Our method showed very high performance in all simulation settings, even with small sample sizes and subtle methylation changes, while controlling type I error. Other advantages of our method are: 1) it estimates the degree of association between the DMR and the outcome; 2) it can analyze a targeted or region of interest; and 3) it can evaluate the simultaneous effects of different variables. The proposed methodology is implemented in MEAL, a Bioconductor package designed to facilitate the analysis of methylation data. We propose a multivariate approach to decipher whether an outcome of interest alters the methylation pattern of a region of interest. The method is designed to analyze large target genomic regions and outperforms the three most popular methods for detecting DMRs. Our method can evaluate factors with more than two levels or the simultaneous effect of more than one continuous variable, which is not possible with the state-of-the-art methods.

System and method for adaptively deskewing parallel data signals relative to a clock

DOEpatents

Jenkins, Philip Nord [Eau Claire, WI; Cornett, Frank N [Chippewa Falls, WI

2008-10-07

A system and method of reducing skew between a plurality of signals transmitted with a transmit clock is described. Skew is detected between the received transmit clock and each of received data signals. Delay is added to the clock or to one or more of the plurality of data signals to compensate for the detected skew. The delay added to each of the plurality of delayed signals is updated to adapt to changes in detected skew.

System and method for adaptively deskewing parallel data signals relative to a clock

DOEpatents

Jenkins, Philip Nord [Redwood Shores, CA; Cornett, Frank N [Chippewa Falls, WI

2011-10-04

A system and method of reducing skew between a plurality of signals transmitted with a transmit clock is described. Skew is detected between the received transmit clock and each of received data signals. Delay is added to the clock or to one or more of the plurality of data signals to compensate for the detected skew. The delay added to each of the plurality of delayed signals is updated to adapt to changes in detected skew.

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.

Bacteriological incidence in pneumonia patients with pulmonary emphysema: a bacterial floral analysis using the 16S ribosomal RNA gene in bronchoalveolar lavage fluid.

PubMed

Naito, Keisuke; Yamasaki, Kei; Yatera, Kazuhiro; Akata, Kentaro; Noguchi, Shingo; Kawanami, Toshinori; Fukuda, Kazumasa; Kido, Takashi; Ishimoto, Hiroshi; Mukae, Hiroshi

2017-01-01

Pulmonary emphysema is an important radiological finding in chronic obstructive pulmonary disease patients, but bacteriological differences in pneumonia patients according to the severity of emphysematous changes have not been reported. Therefore, we evaluated the bacteriological incidence in the bronchoalveolar lavage fluid (BALF) of pneumonia patients using cultivation and a culture-independent molecular method. Japanese patients with community-acquired pneumonia (83) and healthcare-associated pneumonia (94) between April 2010 and February 2014 were evaluated. The BALF obtained from pneumonia lesions was evaluated by both cultivation and a molecular method. In the molecular method, ~600 base pairs of bacterial 16S ribosomal RNA genes in the BALF were amplified by polymerase chain reaction, and clone libraries were constructed. The nucleotide sequences of 96 randomly selected colonies were determined, and a homology search was performed to identify the bacterial species. A qualitative radiological evaluation of pulmonary emphysema based on chest computed tomography (CT) images was performed using the Goddard classification. The severity of pulmonary emphysema based on the Goddard classification was none in 47.4% (84/177), mild in 36.2% (64/177), moderate in 10.2% (18/177), and severe in 6.2% (11/177). Using the culture-independent molecular method, Moraxella catarrhalis was significantly more frequently detected in moderate or severe emphysema patients than in patients with no or mild emphysematous changes. The detection rates of Haemophilus influenzae and Pseudomonas aeruginosa were unrelated to the severity of pulmonary emphysematous changes, and Streptococcus species - except for the S. anginosus group and S. pneumoniae - were detected more frequently using the molecular method we used for the BALF of patients with pneumonia than using culture methods. Our findings suggest that M. catarrhalis is more frequently detected in pneumonia patients with moderate or severe emphysema than in those with no or mild emphysematous changes on chest CT. M. catarrhalis may play a major role in patients with pneumonia complicating severe pulmonary emphysema.

Change detection of bitemporal multispectral images based on FCM and D-S theory

NASA Astrophysics Data System (ADS)

Shi, Aiye; Gao, Guirong; Shen, Shaohong

2016-12-01

In this paper, we propose a change detection method of bitemporal multispectral images based on the D-S theory and fuzzy c-means (FCM) algorithm. Firstly, the uncertainty and certainty regions are determined by thresholding method applied to the magnitudes of difference image (MDI) and spectral angle information (SAI) of bitemporal images. Secondly, the FCM algorithm is applied to the MDI and SAI in the uncertainty region, respectively. Then, the basic probability assignment (BPA) functions of changed and unchanged classes are obtained by the fuzzy membership values from the FCM algorithm. In addition, the optimal value of fuzzy exponent of FCM is adaptively determined by conflict degree between the MDI and SAI in uncertainty region. Finally, the D-S theory is applied to obtain the new fuzzy partition matrix for uncertainty region and further the change map is obtained. Experiments on bitemporal Landsat TM images and bitemporal SPOT images validate that the proposed method is effective.

Improvement of automatic hemorrhage detection methods using brightness correction on fundus images

NASA Astrophysics Data System (ADS)

Hatanaka, Yuji; Nakagawa, Toshiaki; Hayashi, Yoshinori; Kakogawa, Masakatsu; Sawada, Akira; Kawase, Kazuhide; Hara, Takeshi; Fujita, Hiroshi

2008-03-01

We have been developing several automated methods for detecting abnormalities in fundus images. The purpose of this study is to improve our automated hemorrhage detection method to help diagnose diabetic retinopathy. We propose a new method for preprocessing and false positive elimination in the present study. The brightness of the fundus image was changed by the nonlinear curve with brightness values of the hue saturation value (HSV) space. In order to emphasize brown regions, gamma correction was performed on each red, green, and blue-bit image. Subsequently, the histograms of each red, blue, and blue-bit image were extended. After that, the hemorrhage candidates were detected. The brown regions indicated hemorrhages and blood vessels and their candidates were detected using density analysis. We removed the large candidates such as blood vessels. Finally, false positives were removed by using a 45-feature analysis. To evaluate the new method for the detection of hemorrhages, we examined 125 fundus images, including 35 images with hemorrhages and 90 normal images. The sensitivity and specificity for the detection of abnormal cases was were 80% and 88%, respectively. These results indicate that the new method may effectively improve the performance of our computer-aided diagnosis system for hemorrhages.

Automatic Building Damage Detection Method Using High-Resolution Remote Sensing Images and 3d GIS Model

NASA Astrophysics Data System (ADS)

Tu, Jihui; Sui, Haigang; Feng, Wenqing; Song, Zhina

2016-06-01

In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.

A power analysis for multivariate tests of temporal trend in species composition.

PubMed

Irvine, Kathryn M; Dinger, Eric C; Sarr, Daniel

2011-10-01

Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.

Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

Detection method of visible and invisible nipples on digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Chae, Seung-Hoon; Jeong, Ji-Wook; Lee, Sooyeul; Chae, Eun Young; Kim, Hak Hee; Choi, Young-Wook

2015-03-01

Digital Breast Tomosynthesis(DBT) with 3D breast image can improve detection sensitivity of breast cancer more than 2D mammogram on dense breast. The nipple location information is needed to analyze DBT. The nipple location is invaluable information in registration and as a reference point for classifying mass or micro-calcification clusters. Since there are visible nipple and invisible nipple in 2D mammogram or DBT, the nipple detection of breast must be possible to detect visible and invisible nipple of breast. The detection method of visible nipple using shape information of nipple is simple and highly efficient. However, it is difficult to detect invisible nipple because it doesn't have prominent shape. Mammary glands in breast connect nipple, anatomically. The nipple location is detected through analyzing location of mammary glands in breast. In this paper, therefore, we propose a method to detect the nipple on a breast, which has a visible or invisible nipple using changes of breast area and mammary glands, respectively. The result shows that our proposed method has average error of 2.54+/-1.47mm.

Development of a spatial analysis method using ground-based repeat photography to detect changes in the alpine treeline ecotone, Glacier National Park, Montana, U.S.A.

USGS Publications Warehouse

Roush, W.; Munroe, Jeffrey S.; Fagre, D.B.

2007-01-01

Repeat photography is a powerful tool for detection of landscape change over decadal timescales. Here a novel method is presented that applies spatial analysis software to digital photo-pairs, allowing vegetation change to be categorized and quantified. This method is applied to 12 sites within the alpine treeline ecotone of Glacier National Park, Montana, and is used to examine vegetation changes over timescales ranging from 71 to 93 years. Tree cover at the treeline ecotone increased in 10 out of the 12 photo-pairs (mean increase of 60%). Establishment occurred at all sites, infilling occurred at 11 sites. To demonstrate the utility of this method, patterns of tree establishment at treeline are described and the possible causes of changes within the treeline ecotone are discussed. Local factors undoubtedly affect the magnitude and type of the observed changes, however the ubiquity of the increase in tree cover implies a common forcing mechanism. Mean minimum summer temperatures have increased by 1.5??C over the past century and, coupled with variations in the amount of early spring snow water equivalent, likely account for much of the increase in tree cover at the treeline ecotone. Lastly, shortcomings of this method are presented along with possible solutions and areas for future research. ?? 2007 Regents of the University of Colorado.

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.

Rapid detection of coliforms in drinking water of Arak city using multiplex PCR method in comparison with the standard method of culture (Most Probably Number)

PubMed Central

Fatemeh, Dehghan; Reza, Zolfaghari Mohammad; Mohammad, Arjomandzadegan; Salomeh, Kalantari; Reza, Ahmari Gholam; Hossein, Sarmadian; Maryam, Sadrnia; Azam, Ahmadi; Mana, Shojapoor; Negin, Najarian; Reza, Kasravi Alii; Saeed, Falahat

2014-01-01

Objective To analyse molecular detection of coliforms and shorten the time of PCR. Methods Rapid detection of coliforms by amplification of lacZ and uidA genes in a multiplex PCR reaction was designed and performed in comparison with most probably number (MPN) method for 16 artificial and 101 field samples. The molecular method was also conducted on isolated coliforms from positive MPN samples; standard sample for verification of microbial method certificated reference material; isolated strains from certificated reference material and standard bacteria. The PCR and electrophoresis parameters were changed for reducing the operation time. Results Results of PCR for lacZ and uidA genes were similar in all of standard, operational and artificial samples and showed the 876 bp and 147 bp bands of lacZ and uidA genes by multiplex PCR. PCR results were confirmed by MPN culture method by sensitivity 86% (95% CI: 0.71-0.93). Also the total execution time, with a successful change of factors, was reduced to less than two and a half hour. Conclusions Multiplex PCR method with shortened operation time was used for the simultaneous detection of total coliforms and Escherichia coli in distribution system of Arak city. It's recommended to be used at least as an initial screening test, and then the positive samples could be randomly tested by MPN. PMID:25182727

Rapid surface defect detection based on singular value decomposition using steel strips as an example

NASA Astrophysics Data System (ADS)

Sun, Qianlai; Wang, Yin; Sun, Zhiyi

2018-05-01

For most surface defect detection methods based on image processing, image segmentation is a prerequisite for determining and locating the defect. In our previous work, a method based on singular value decomposition (SVD) was used to determine and approximately locate surface defects on steel strips without image segmentation. For the SVD-based method, the image to be inspected was projected onto its first left and right singular vectors respectively. If there were defects in the image, there would be sharp changes in the projections. Then the defects may be determined and located according sharp changes in the projections of each image to be inspected. This method was simple and practical but the SVD should be performed for each image to be inspected. Owing to the high time complexity of SVD itself, it did not have a significant advantage in terms of time consumption over image segmentation-based methods. Here, we present an improved SVD-based method. In the improved method, a defect-free image is considered as the reference image which is acquired under the same environment as the image to be inspected. The singular vectors of each image to be inspected are replaced by the singular vectors of the reference image, and SVD is performed only once for the reference image off-line before detecting of the defects, thus greatly reducing the time required. The improved method is more conducive to real-time defect detection. Experimental results confirm its validity.

Automated health alerts from Kinect-based in-home gait measurements.

PubMed

Stone, Erik E; Skubic, Marjorie; Back, Jessica

2014-01-01

A method for automatically generating alerts to clinicians in response to changes in in-home gait parameters is investigated. Kinect-based gait measurement systems were installed in apartments in a senior living facility. The systems continuously monitored the walking speed, stride time, and stride length of apartment residents. A framework for modeling uncertainty in the residents' gait parameter estimates, which is critical for robust change detection, is developed; along with an algorithm for detecting changes that may be clinically relevant. Three retrospective case studies, of individuals who had their gait monitored for periods ranging from 12 to 29 months, are presented to illustrate use of the alert method. Evidence suggests that clinicians could be alerted to health changes at an early stage, while they are still small and interventions may be most successful. Additional potential uses are also discussed.

Bayesian Model Averaging with Change Points to Assess the Impact of Vaccination and Public Health Interventions

PubMed Central

Warren, Joshua L.; Schuck-Paim, Cynthia; Lustig, Roger; Lewnard, Joseph A.; Fuentes, Rodrigo; Bruhn, Christian A. W.; Taylor, Robert J.; Simonsen, Lone; Weinberger, Daniel M.

2017-01-01

Background: Pneumococcal conjugate vaccines (PCVs) prevent invasive pneumococcal disease and pneumonia. However, some low-and middle-income countries have yet to introduce PCV into their immunization programs due, in part, to lack of certainty about the potential impact. Assessing PCV benefits is challenging because specific data on pneumococcal disease are often lacking, and it can be difficult to separate the effects of factors other than the vaccine that could also affect pneumococcal disease rates. Methods: We assess PCV impact by combining Bayesian model averaging with change-point models to estimate the timing and magnitude of vaccine-associated changes, while controlling for seasonality and other covariates. We applied our approach to monthly time series of age-stratified hospitalizations related to pneumococcal infection in children younger 5 years of age in the United States, Brazil, and Chile. Results: Our method accurately detected changes in data in which we knew true and noteworthy changes occurred, i.e., in simulated data and for invasive pneumococcal disease. Moreover, 24 months after the vaccine introduction, we detected reductions of 14%, 9%, and 9% in the United States, Brazil, and Chile, respectively, in all-cause pneumonia (ACP) hospitalizations for age group 0 to <1 years of age. Conclusions: Our approach provides a flexible and sensitive method to detect changes in disease incidence that occur after the introduction of a vaccine or other intervention, while avoiding biases that exist in current approaches to time-trend analyses. PMID:28767518

Feature-based registration of historical aerial images by Area Minimization

NASA Astrophysics Data System (ADS)

Nagarajan, Sudhagar; Schenk, Toni

2016-06-01

The registration of historical images plays a significant role in assessing changes in land topography over time. By comparing historical aerial images with recent data, geometric changes that have taken place over the years can be quantified. However, the lack of ground control information and precise camera parameters has limited scientists' ability to reliably incorporate historical images into change detection studies. Other limitations include the methods of determining identical points between recent and historical images, which has proven to be a cumbersome task due to continuous land cover changes. Our research demonstrates a method of registering historical images using Time Invariant Line (TIL) features. TIL features are different representations of the same line features in multi-temporal data without explicit point-to-point or straight line-to-straight line correspondence. We successfully determined the exterior orientation of historical images by minimizing the area formed between corresponding TIL features in recent and historical images. We then tested the feasibility of the approach with synthetic and real data and analyzed the results. Based on our analysis, this method shows promise for long-term 3D change detection studies.

A comparison between the clinical significance and growth mixture modelling early change methods at predicting negative outcomes.

PubMed

Flood, Nicola; Page, Andrew; Hooke, Geoff

2018-05-03

Routine outcome monitoring benefits treatment by identifying potential no change and deterioration. The present study compared two methods of identifying early change and their ability to predict negative outcomes on self-report symptom and wellbeing measures. 1467 voluntary day patients participated in a 10-day group Cognitive Behaviour Therapy (CBT) program and completed the symptom and wellbeing measures daily. Early change, as defined by (a) the clinical significance method and (b) longitudinal modelling, was compared on each measure. Early change, as defined by the simpler clinical significance method, was superior at predicting negative outcomes than longitudinal modelling. The longitudinal modelling method failed to detect a group of deteriorated patients, and agreement between the early change methods and the final unchanged outcome was higher for the clinical significance method. Therapists could use the clinical significance early change method during treatment to alert them of patients at risk for negative outcomes, which in turn could allow therapists to prevent those negative outcomes from occurring.

Label-free detection of specific DNA sequence-telomere using unmodified gold nanoparticles as colorimetric probes

NASA Astrophysics Data System (ADS)

Qi, Yingying; Li, Li; Li, Baoxin

2009-09-01

A simple and sensitive label-free colorimetric detection of telomere DNA has been developed. It was based on the color change of gold nanoparticles (AuNPs) due to DNA hybridization. UV-vis spectra and transmission electron microscopy (TEM) were used to investigate the change of AuNPs. Under the optimized conditions, the linear range for determination of telomere DNA was 5.7 × 10 -13 to 4.5 × 10 -6 mol/L. The detection limit (3 σ) of this method has decreased to pico-molar level.

A survey of design methods for failure detection in dynamic systems

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1975-01-01

A number of methods for detecting abrupt changes (such as failures) in stochastic dynamical systems are surveyed. The class of linear systems is concentrated on but the basic concepts, if not the detailed analyses, carry over to other classes of systems. The methods surveyed range from the design of specific failure-sensitive filters, to the use of statistical tests on filter innovations, to the development of jump process formulations. Tradeoffs in complexity versus performance are discussed.

Development of the dichlorvos-ammonia (DV-AM) method for the visual detection of aflatoxigenic fungi.

PubMed

Yabe, Kimiko; Hatabayashi, Hidemi; Ikehata, Akifumi; Zheng, Yazhi; Kushiro, Masayo

2015-12-01

Aflatoxins (AFs) are carcinogenic and toxic secondary metabolites produced mainly by Aspergillus flavus and Aspergillus parasiticus. To monitor and regulate the AF contamination of crops, a sensitive and precise detection method for these toxigenic fungi in environments is necessary. We herein developed a novel visual detection method, the dichlorvos-ammonia (DV-AM) method, for identifying AF-producing fungi using DV and AM vapor on agar culture plates, in which DV inhibits the esterase in AF biosynthesis, causing the accumulation of anthraquinone precursors (versiconal hemiacetal acetate and versiconol acetate) of AFs in mycelia on the agar plate, followed by a change in the color of the colonies from light yellow to brilliant purple-red by the AM vapor treatment. We also investigated the appropriate culture conditions to increase the color intensity. It should be noted that other species producing the same precursors of AFs such as Aspergillus nidulans and Aspergillus versicolor could be discriminated from the Aspergillus section Flavi based on the differences of their phenotypes. The DV-AM method was also useful for the isolation of nonaflatoxigenic fungi showing no color change, for screening microorganisms that inhibit the AF production by fungi, and for the characterization of the fungi infecting corn kernels. Thus, the DV-AM method can provide a highly sensitive and visible indicator for the detection of aflatoxigenic fungi.

Simplified Identification of mRNA or DNA in Whole Cells

NASA Technical Reports Server (NTRS)

Almeida, Eduardo; Kadambi, Geeta

2007-01-01

A recently invented method of detecting a selected messenger ribonucleic acid (mRNA) or deoxyribonucleic acid (DNA) sequence offers two important advantages over prior such methods: it is simpler and can be implemented by means of compact equipment. The simplification and miniaturization achieved by this invention are such that this method is suitable for use outside laboratories, in field settings in which space and power supplies may be limited. The present method is based partly on hybridization of nucleic acid, which is a powerful technique for detection of specific complementary nucleic acid sequences and is increasingly being used for detection of changes in gene expression in microarrays containing thousands of gene probes.

A biomolecular detection method based on charge pumping in a nanogap embedded field-effect-transistor biosensor

NASA Astrophysics Data System (ADS)

Kim, Sungho; Ahn, Jae-Hyuk; Park, Tae Jung; Lee, Sang Yup; Choi, Yang-Kyu

2009-06-01

A unique direct electrical detection method of biomolecules, charge pumping, was demonstrated using a nanogap embedded field-effect-transistor (FET). With aid of a charge pumping method, sensitivity can fall below the 1 ng/ml concentration regime in antigen-antibody binding of an avian influenza case. Biomolecules immobilized in the nanogap are mainly responsible for the acute changes of the interface trap density due to modulation of the energy level of the trap. This finding is supported by a numerical simulation. The proposed detection method for biomolecules using a nanogap embedded FET represents a foundation for a chip-based biosensor capable of high sensitivity.

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions.

PubMed

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-06-20

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

PubMed Central

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-01-01

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions. PMID:28773035

New optical method for enhanced detection of colon cancer by capsule endoscopy

NASA Astrophysics Data System (ADS)

AnkriEqually Contributed, Rinat; Peretz, Dolev; Motiei, Menachem; Sella-Tavor, Osnat; Popovtzer, Rachela

2013-09-01

PillCam®COLON capsule endoscopy (CE), a non-invasive diagnostic tool of the digestive tract, has dramatically changed the diagnostic approach and has become an attractive alternative to the conventional colonoscopy for early detection of colorectal cancer. However, despite the significant progress and non-invasive detection capability, studies have shown that its sensitivity and specificity is lower than that of conventional colonoscopy. This work presents a new optical detection method, specifically tailored to colon cancer detection and based on the well-known optical properties of immune-conjugated gold nanorods (GNRs). We show, on a colon cancer model implanted in a chick chorioallantoic membrane (CAM), that this detection method enables conclusive differentiation between cancerous and normal tissues, where neither the distance between the light source and the intestinal wall, nor the background signal, affects the monitored signal. This optical method, which can easily be integrated in CE, is expected to reduce false positive and false negative results and improve identification of tumors and micro metastases.

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.

Subsurface event detection and classification using Wireless Signal Networks.

PubMed

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

2012-11-05

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

Subsurface Event Detection and Classification Using Wireless Signal Networks

PubMed Central

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T.

2012-01-01

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

Expanding Cancer Detection Using Molecular Imprinting for a Novel Point-of-Care Diagnostic Device

NASA Astrophysics Data System (ADS)

Yu, Yingjie; Rafailovich, Miriam; Wang, Yantian; Ranjbaran, Alina; Wang, Tom; Nam, David

2012-02-01

We propose the use of a potentiometric biosensor that incorporates the efficient and specific molecular imprinting (MI) method with a self-assembled monolayer (SAM). We first tested the biosensor using carcinoembryonic antigen, CEA, a biomarker associated with pancreatic cancer. No change in detection efficiency was observed when detection was performed in the presence of 100% serum albumin, indicating that the sensor is able to discriminate for the template analyte even in concentrated solution of similar substances. Computer simulations of the protein structure were performed in order to estimate the changes in morphology and determine the sensitivity of the biosensor to conformational changes in the proteins. We found that even small changes in PH can generate rotation of the surface functional groups, without significant change in the morphology. Yet, the results show that only when the detection and imprinting conditions are similar, robust signals occurs. Hence we concluded that both morphology and surface chemistry play a role in the recognition.

Laser paint stripping

NASA Astrophysics Data System (ADS)

Head, J. D.; Niedzielski, J. Peter

1991-06-01

A study to assess the utility of high powered CO2 pulsed laser depainting methods was conducted on aluminum and graphite epoxy composites. The various tests were designed to detect potential forms of damage or loss of properties of various aircraft structural materials during removal of paint with pulsed laser energy. Tests for changes in physical properties, paint adhesion and corrosion protection of repainted materials showed no detectable adverse changes in any of the samples studied.

Change detection of polarimetric SAR images based on the KummerU Distribution

NASA Astrophysics Data System (ADS)

Chen, Quan; Zou, Pengfei; Li, Zhen; Zhang, Ping

2014-11-01

In the society of PolSAR image segmentation, change detection and classification, the classical Wishart distribution has been used for a long time, but it especially suit to low-resolution SAR image, because in traditional sensors, only a small number of scatterers are present in each resolution cell. With the improving of SAR systems these years, the classical statistical models can therefore be reconsidered for high resolution and polarimetric information contained in the images acquired by these advanced systems. In this study, SAR image segmentation algorithm based on level-set method, added with distance regularized level-set evolution (DRLSE) is performed using Envisat/ASAR single-polarization data and Radarsat-2 polarimetric images, respectively. KummerU heterogeneous clutter model is used in the later to overcome the homogeneous hypothesis at high resolution cell. An enhanced distance regularized level-set evolution (DRLSE-E) is also applied in the later, to ensure accurate computation and stable level-set evolution. Finally, change detection based on four polarimetric Radarsat-2 time series images is carried out at Genhe area of Inner Mongolia Autonomous Region, NorthEastern of China, where a heavy flood disaster occurred during the summer of 2013, result shows the recommend segmentation method can detect the change of watershed effectively.

A chemosensor for micro- to nano-molar detection of Ag+ and Hg2+ ions in pure aqueous media and its applications in cell imaging.

PubMed

Nandre, Jitendra P; Patil, Samadhan R; Sahoo, Suban K; Pradeep, Chullikkattil P; Churakov, Andrei; Yu, Fabiao; Chen, Lingxin; Redshaw, Carl; Patil, Ashok A; Patil, Umesh D

2017-10-24

The pyridine substituted thiourea derivative PTB-1 was synthesized and characterized by spectroscopic techniques as well as by single crystal X-ray crystallography. The metal ion sensing ability of PTB-1 was explored by various experimental (naked-eye, UV-Vis, fluorescence, mass spectrometry and 1 H NMR spectroscopy) and theoretical (B3LYP/6-31G**/LANL2DZ) methods. PTB-1 exhibited a highly selective naked-eye detectable color change from colorless to dark brown and UV-Vis spectral changes for the detection of Ag + with a detection limit of 3.67 μM in aqueous medium. The detection of Ag + ions was achieved by test paper strip and supported silica methods. In contrast, PTB-1 exhibited a 23-fold enhanced emission at 420 nm in the presence of Hg 2+ ions with a nano-molar detection limit of 0.69 nM. Finally, the sensor PTB-1 was applied successfully for the intracellular detection of Hg 2+ ions in a HepG2 liver cell line, which was monitored by the use of confocal imaging techniques.

Detection method of financial crisis in Indonesia using MSGARCH models based on banking condition indicators

NASA Astrophysics Data System (ADS)

Sugiyanto; Zukhronah, E.; Sari, S. P.

2018-05-01

Financial crisis has hit Indonesia for several times resulting the needs for an early detection system to minimize the impact. One of many methods that can be used to detect the crisis is to model the crisis indicators using combination of volatility and Markov switching models [5]. There are some indicators that can be used to detect financial crisis. Three of them are the difference between interest rate on deposit and lending, the real interest rate on deposit, and the difference between real BI rate and real Fed rate which can be referred as banking condition indicators. Volatility model used to overcome the conditional variance that change over time. Combination of volatility and Markov switching models used to detect condition change on the data. The smoothed probability from the combined models can be used to detect the crisis. This research resulted that the best combined volatility and Markov switching models for the three indicators are MS-GARCH(3,1,1) models with three states assumption. Crises in mid of 1997 until 1998 has successfully detected with a certain range of smoothed probability value for the three indicators.

Evaluation of shrinkage and cracking in concrete of ring test by acoustic emission method

NASA Astrophysics Data System (ADS)

Watanabe, Takeshi; Hashimoto, Chikanori

2015-03-01

Drying shrinkage of concrete is one of the typical problems related to reduce durability and defilation of concrete structures. Lime stone, expansive additive and low-heat Portland cement are used to reduce drying shrinkage in Japan. Drying shrinkage is commonly evaluated by methods of measurement for length change of mortar and concrete. In these methods, there is detected strain due to drying shrinkage of free body, although visible cracking does not occur. In this study, the ring test was employed to detect strain and age cracking of concrete. The acoustic emission (AE) method was adopted to detect micro cracking due to shrinkage. It was recognized that in concrete using lime stone, expansive additive and low-heat Portland cement are effective to decrease drying shrinkage and visible cracking. Micro cracking due to shrinkage of this concrete was detected and evaluated by the AE method.

Smart accelerometer. [vibration damage detection

NASA Technical Reports Server (NTRS)

Bozeman, Richard J., Jr. (Inventor)

1994-01-01

The invention discloses methods and apparatus for detecting vibrations from machines which indicate an impending malfunction for the purpose of preventing additional damage and allowing for an orderly shutdown or a change in mode of operation. The method and apparatus is especially suited for reliable operation in providing thruster control data concerning unstable vibration in an electrical environment which is typically noisy and in which unrecognized ground loops may exist.

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.

Curvature methods of damage detection using digital image correlation

NASA Astrophysics Data System (ADS)

Helfrick, Mark N.; Niezrecki, Christopher; Avitabile, Peter

2009-03-01

Analytical models have shown that local damage in a structure can be detected by studying changes in the curvature of the structure's displaced shape while under an applied load. In order for damage to be detected, located, and quantified using curvature methods, a spatially dense set of measurement points is required on the structure of interest and the change in curvature must be measurable. Experimental testing done to validate the theory is often plagued by sparse data sets and experimental noise. Furthermore, the type of load, the location and severity of the damage, and the mechanical properties (material and geometry) of the structure have a significant effect on how much the curvature will change. Within this paper, three-dimensional (3D) Digital Image Correlation (DIC) as one possible method for detecting damage through curvature methods is investigated. 3D DIC is a non-contacting full-field measurement technique which uses a stereo pair of digital cameras to capture surface shape. This approach allows for an extremely dense data set across the entire visible surface of an object. A test is performed to validate the approach on an aluminum cantilever beam. A dynamic load is applied to the beam which allows for measurements to be made of the beam's response at each of its first three resonant frequencies, corresponding to the first three bending modes of the structure. DIC measurements are used with damage detection algorithms to predict damage location with varying levels of damage inflicted in the form of a crack with a prescribed depth. The testing demonstrated that this technique will likely only work with structures where a large displaced shape is easily achieved and in cases where the damage is relatively severe. Practical applications and limitations of the technique are discussed.

Preliminary study of detection of buried landmines using a programmable hyperspectral imager

NASA Astrophysics Data System (ADS)

McFee, John E.; Ripley, Herb T.; Buxton, Roger; Thriscutt, Andrew M.

1996-05-01

Experiments were conducted to determine if buried mines could be detected by measuring the change in reflectance spectra of vegetation above mine burial sites. Mines were laid using hand methods and simulated mechanical methods and spectral images were obtained over a three month period using a casi hyperspectral imager scanned from a personnel lift. Mines were not detectable by measurement of the shift of the red edge of vegetative spectra. By calculating the linear correlation coefficient image, some mines in light vegetative cover (grass, grass/blueberries) were apparently detected, but mines buried in heavy vegetation cover (deep ferns) were not detectable. Due to problems with ground truthing, accurate probabilities of detection and false alarm rates were not obtained.

Spatial early warning signals in a lake manipulation

USGS Publications Warehouse

Butitta, Vince L.; Carpenter, Stephen R.; Loken, Luke; Pace, Michael L.; Stanley, Emily H.

2017-01-01

Rapid changes in state have been documented for many of Earth's ecosystems. Despite a growing toolbox of methods for detecting declining resilience or early warning indicators (EWIs) of ecosystem transitions, these methods have rarely been evaluated in whole-ecosystem trials using reference ecosystems. In this study, we experimentally tested EWIs of cyanobacteria blooms based on changes in the spatial structure of a lake. We induced a cyanobacteria bloom by adding nutrients to an experimental lake and mapped fine-resolution spatial patterning of cyanobacteria using a mobile sensor platform. Prior to the bloom, we detected theoretically predicted spatial EWIs based on variance and spatial autocorrelation, as well as a new index based on the extreme values. Changes in EWIs were not discernible in an unenriched reference lake. Despite the fluid environment of a lake where spatial heterogeneity driven by biological processes may be overwhelmed by physical mixing, spatial EWIs detected an approaching bloom suggesting the utility of spatial metrics for signaling ecological thresholds.

Detecting sulphate aerosol geoengineering with different methods

DOE PAGES

Lo, Y. T. Eunice; Charlton-Perez, Andrew J.; Lott, Fraser C.; ...

2016-12-15

Sulphate aerosol injection has been widely discussed as a possible way to engineer future climate. Monitoring it would require detecting its effects amidst internal variability and in the presence of other external forcings. Here, we investigate how the use of different detection methods and filtering techniques affects the detectability of sulphate aerosol geoengineering in annual-mean global-mean near-surface air temperature. This is done by assuming a future scenario that injects 5 Tg yr -1 of sulphur dioxide into the stratosphere and cross-comparing simulations from 5 climate models. 64% of the studied comparisons would require 25 years or more for detection whenmore » no filter and the multi-variate method that has been extensively used for attributing climate change are used, while 66% of the same comparisons would require fewer than 10 years for detection using a trend-based filter. This then highlights the high sensitivity of sulphate aerosol geoengineering detectability to the choice of filter. With the same trend-based filter but a non-stationary method, 80% of the comparisons would require fewer than 10 years for detection. This does not imply sulphate aerosol geoengineering should be deployed, but suggests that both detection methods could be used for monitoring geoengineering in global, annual mean temperature should it be needed.« less

Change detection in the dynamics of an intracellular protein synthesis model using nonlinear Kalman filtering.

PubMed

Rigatos, Gerasimos G; Rigatou, Efthymia G; Djida, Jean Daniel

2015-10-01

A method for early diagnosis of parametric changes in intracellular protein synthesis models (e.g. the p53 protein - mdm2 inhibitor model) is developed with the use of a nonlinear Kalman Filtering approach (Derivative-free nonlinear Kalman Filter) and of statistical change detection methods. The intracellular protein synthesis dynamic model is described by a set of coupled nonlinear differential equations. It is shown that such a dynamical system satisfies differential flatness properties and this allows to transform it, through a change of variables (diffeomorphism), to the so-called linear canonical form. For the linearized equivalent of the dynamical system, state estimation can be performed using the Kalman Filter recursion. Moreover, by applying an inverse transformation based on the previous diffeomorphism it becomes also possible to obtain estimates of the state variables of the initial nonlinear model. By comparing the output of the Kalman Filter (which is assumed to correspond to the undistorted dynamical model) with measurements obtained from the monitored protein synthesis system, a sequence of differences (residuals) is obtained. The statistical processing of the residuals with the use of x2 change detection tests, can provide indication within specific confidence intervals about parametric changes in the considered biological system and consequently indications about the appearance of specific diseases (e.g. malignancies).

Eye gazing direction inspection based on image processing technique

NASA Astrophysics Data System (ADS)

Hao, Qun; Song, Yong

2005-02-01

According to the research result in neural biology, human eyes can obtain high resolution only at the center of view of field. In the research of Virtual Reality helmet, we design to detect the gazing direction of human eyes in real time and feed it back to the control system to improve the resolution of the graph at the center of field of view. In the case of current display instruments, this method can both give attention to the view field of virtual scene and resolution, and improve the immersion of virtual system greatly. Therefore, detecting the gazing direction of human eyes rapidly and exactly is the basis of realizing the design scheme of this novel VR helmet. In this paper, the conventional method of gazing direction detection that based on Purklinje spot is introduced firstly. In order to overcome the disadvantage of the method based on Purklinje spot, this paper proposed a method based on image processing to realize the detection and determination of the gazing direction. The locations of pupils and shapes of eye sockets change with the gazing directions. With the aid of these changes, analyzing the images of eyes captured by the cameras, gazing direction of human eyes can be determined finally. In this paper, experiments have been done to validate the efficiency of this method by analyzing the images. The algorithm can carry out the detection of gazing direction base on normal eye image directly, and it eliminates the need of special hardware. Experiment results show that the method is easy to implement and have high precision.

Real-time biscuit tile image segmentation method based on edge detection.

PubMed

Matić, Tomislav; Aleksi, Ivan; Hocenski, Željko; Kraus, Dieter

2018-05-01

In this paper we propose a novel real-time Biscuit Tile Segmentation (BTS) method for images from ceramic tile production line. BTS method is based on signal change detection and contour tracing with a main goal of separating tile pixels from background in images captured on the production line. Usually, human operators are visually inspecting and classifying produced ceramic tiles. Computer vision and image processing techniques can automate visual inspection process if they fulfill real-time requirements. Important step in this process is a real-time tile pixels segmentation. BTS method is implemented for parallel execution on a GPU device to satisfy the real-time constraints of tile production line. BTS method outperforms 2D threshold-based methods, 1D edge detection methods and contour-based methods. Proposed BTS method is in use in the biscuit tile production line. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Understanding the impacts of climate change and human activities on streamflow: a case study of the Soan River basin, Pakistan

NASA Astrophysics Data System (ADS)

Shahid, Muhammad; Cong, Zhentao; Zhang, Danwu

2017-09-01

Climate change and land use change are the two main factors that can alter the catchment hydrological process. The objective of this study is to evaluate the relative contribution of climate change and land use change to runoff change of the Soan River basin. The Mann-Kendal and the Pettit tests are used to find out the trends and change point in hydroclimatic variables during the period 1983-2012. Two different approaches including the abcd hydrological model and the Budyko framework are then used to quantify the impact of climate change and land use change on streamflow. The results from both methods are consistent and show that annual runoff has significantly decreased with a change point around 1997. The decrease in precipitation and increases in potential evapotranspiration contribute 68% of the detected change while the rest of the detected change is due to land use change. The land use change acquired from Landsat shows that during post-change period, the agriculture has increased in the Soan basin, which is in line with the positive contribution of land use change to runoff decrease. This study concludes that aforementioned methods performed well in quantifying the relative contribution of land use change and climate change to runoff change.

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.

Object-Based Change Detection Using High-Resolution Remotely Sensed Data and GIS

NASA Astrophysics Data System (ADS)

Sofina, N.; Ehlers, M.

2012-08-01

High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same sensor which should be acquired at the same time of season, at the same time of a day, and - for electro-optical sensors - in cloudless conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. A promising alternative is the use of vector-based maps containing information about the original urban layout which can be related to a single image obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' (DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show the high reliability of the DPC feature as an indicator for change.

TU-G-BRD-08: In-Vivo EPID Dosimetry: Quantifying the Detectability of Four Classes of Errors

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

Ford, E; Phillips, M; Bojechko, C

Purpose: EPID dosimetry is an emerging method for treatment verification and QA. Given that the in-vivo EPID technique is in clinical use at some centers, we investigate the sensitivity and specificity for detecting different classes of errors. We assess the impact of these errors using dose volume histogram endpoints. Though data exist for EPID dosimetry performed pre-treatment, this is the first study quantifying its effectiveness when used during patient treatment (in-vivo). Methods: We analyzed 17 patients; EPID images of the exit dose were acquired and used to reconstruct the planar dose at isocenter. This dose was compared to the TPSmore » dose using a 3%/3mm gamma criteria. To simulate errors, modifications were made to treatment plans using four possible classes of error: 1) patient misalignment, 2) changes in patient body habitus, 3) machine output changes and 4) MLC misalignments. Each error was applied with varying magnitudes. To assess the detectability of the error, the area under a ROC curve (AUC) was analyzed. The AUC was compared to changes in D99 of the PTV introduced by the simulated error. Results: For systematic changes in the MLC leaves, changes in the machine output and patient habitus, the AUC varied from 0.78–0.97 scaling with the magnitude of the error. The optimal gamma threshold as determined by the ROC curve varied between 84–92%. There was little diagnostic power in detecting random MLC leaf errors and patient shifts (AUC 0.52–0.74). Some errors with weak detectability had large changes in D99. Conclusion: These data demonstrate the ability of EPID-based in-vivo dosimetry in detecting variations in patient habitus and errors related to machine parameters such as systematic MLC misalignments and machine output changes. There was no correlation found between the detectability of the error using the gamma pass rate, ROC analysis and the impact on the dose volume histogram. Funded by grant R18HS022244 from AHRQ.« less

Investigation of recrystallization of amorphous trehalose through hot-humidity stage X-ray powder diffraction.

PubMed

Jójárt-Laczkovich, Orsolya; Katona, Gábor; Aigner, Zoltán; Szabó-Révész, Piroska

2016-12-01

The aim of this work was an investigation of the physical changes of the amorphous model material spray-dried trehalose through the use of various analytical techniques and to identify a suitable, rapid method able to quantify the changes. The crystallinity changes and recrystallization process of amorphous samples were investigated by hot-humidity stage X-ray powder diffractometry (HH-XRPD) with fresh samples, conventional X-ray powder diffractometry (XRPD) used stored samples and by differential scanning calorimetry (DSC). The data from the three methods were compared and the various forms of trehalose were analysed. HH-XRPD demonstrated that the recrystallization began at 40 and 60°C up to 45% RH and at 70°C up to 30% RH into dihydrate form. At 70°C up to 60% RH the anhydrous form of trehalose appeared too. Conventional XRPD results showed, that in the 28days stored samples the dihydrate form was detected at 40°C, 50% RH. Storage at 60°C, 40% RH resulted in the appearance of the anhydrous form and at 60°C, 50% RH both polymorphic forms were detected. By carrying out the DSC measurements at different temperatures the fraction of recrystallized trehalose dihydrate was detected. The recrystallization investigated by HH-XRPD and DSC followed Avrami kinetics, the calculated rate constants of isothermal crystallization (K) were same. Both HH-XRPD and conventional XRPD was suitable for the detection of the physical changes of the amorphous model material. DSC measurements showed similar results as HH-XRPD. Primarily HH-XRPD could be suggested for prediction, because the method is fast and every changes could be studied on one sample. Copyright © 2016 Elsevier B.V. All rights reserved.

Measuring Treatment Outcomes in Comorbid Insomnia and Fibromyalgia: Concordance of Subjective and Objective Assessments.

PubMed

Mundt, Jennifer M; Crew, Earl C; Krietsch, Kendra; Roth, Alicia J; Vatthauer, Karlyn; Robinson, Michael E; Staud, Roland; Berry, Richard B; McCrae, Christina S

2016-02-01

In insomnia, actigraphy tends to underestimate wake time compared to diaries and PSG. When chronic pain co-occurs with insomnia, sleep may be more fragmented, including more movement and arousals. However, individuals may not be consciously aware of these arousals. We examined the baseline concordance of diaries, actigraphy, and PSG as well as the ability of each assessment method to detect changes in sleep following cognitive behavioral therapy for insomnia (CBT-I). Adults with insomnia and fibromyalgia (n = 113) were randomized to CBT-I, CBT for pain, or waitlist control. At baseline and posttreatment, participants completed one night of PSG and two weeks of diaries/actigraphy. At baseline, objective measures estimated lower SOL, higher TST, and higher SE than diaries (ps < 0.05). Compared to PSG, actigraphic estimates were higher for SOL and lower for WASO (ps < 0.05). Repeated measures ANOVAs were conducted for the CBT-I group (n = 15), and significant method by time interactions indicated that the assessment methods differed in their sensitivity to detect treatment-related changes. PSG values did not change significantly for any sleep parameters. However, diaries showed improvements in SOL, WASO, and SE, and actigraphy also detected the WASO and SE improvements (ps < 0.05). Actigraphy was generally more concordant with PSG than with diaries, which are the recommended assessment for diagnosing insomnia. However, actigraphy showed greater sensitivity to treatment-related changes than PSG; PSG failed to detect any improvements, but actigraphy demonstrated changes in WASO and SE, which were also found with diaries. In comorbid insomnia/fibromyalgia, actigraphy may therefore have utility in measuring treatment outcomes. © 2015 American Academy of Sleep Medicine.

Red-breasted nuthatches detect early increases in spruce budworm populations

Treesearch

Hewlette S. Crawford; Daniel T. Jennings; Timothy L. Stone

1990-01-01

Early suppression .of increasing spruce budworm populations is essential to prevent epidemics; however, early changes in budworm numbers are difficult to detect. An effective and inexpensive method to detect early increases is needed. Red-breasted nuthatches eat more spruce budworm larvae and pupae as the insect increases in number. We estimated the number of large...

Modified Method for Detection of Benzoylecgonine in Human Urine by GC-MS: Derivatization Using Pentafluoropropanol/Acetic Anhydride.

PubMed

Serafin, Michelle C; Paulemon, Kasandra M; Fuller, Zachary J; Bronner, William E

2017-05-01

An existing GC-MS method for detecting benzoylecgonine (BZE) in urine was modified by changing derivatizing reagents. This method modification presents a cost-effective alternative derivatization procedure for the detection of BZE in urine by GC-MS. The combination of pentafluoropropanol and acetic anhydride was found to produce the same reaction product for BZE as pentafluoropropanol with pentafluoropropionic anhydride, while reducing reagent cost. With no anhydride present, derivatization of BZE by pentafluoropropanol did not occur. Published by Oxford University Press 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US.

[Comparison of projection radiography and computed tomography for the detection of pulmonary nodules in the dog and cat].

PubMed

Niesterok, C; Köhler, C; Ludewig, E; Alef, M; Oechtering, G; Kiefer, I

2013-01-01

The aim of our study was to evaluate the value of projection radiography as a standard screening method for the detection of lung nodules compared to computed tomography (CT). Furthermore, we attempted to describe the reasons that might lead to a failed detection of pulmonary nodules in radiography. From dogs and cats which were diagnosed in CT (multislice CT) with nodular changes in the lung pattern we selected radiographs (projection radiography with soft copy reading) in at least two projection planes produced in the same timeframe as the CT images. Exclusion criteria were nodules > 3 cm and homogenously calcified nodules (osteomata). A total of 70 animals (50 dogs and 20 cats) met the inclusion criteria. In 43 animals (61%), nodular changes had already been detected using radiography and were then confirmed by the results of the computed tomography. In detail, 32 of 50 dogs (64%) and 11 of 20 cats (55%) showed nodular lesions in the radiographs. In cats, undetected nodules were often accompanied by highly changed lung opacities, resulting in a poor contrast of the lung. In dogs the reasons for a failed detection of lung nodules were relatively equally distributed to several causes. Interestingly, small nodule size itself was not the predominant reason for missing the nodules in radiographs. In general, radiography still plays an important role as a screening method for the detection of nodular lung lesions. However, one needs to be aware, that a quite high percentage of nodular lung changes can be missed in radiographs. The overall detection rate in this study was 61%. Furthermore, we showed that plane radiographs are of poor diagnostic value when concurrent problems exist which lead to increased lung opacity.

A survey of design methods for failure detection in dynamic systems

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1975-01-01

A number of methods for the detection of abrupt changes (such as failures) in stochastic dynamical systems were surveyed. The class of linear systems were emphasized, but the basic concepts, if not the detailed analyses, carry over to other classes of systems. The methods surveyed range from the design of specific failure-sensitive filters, to the use of statistical tests on filter innovations, to the development of jump process formulations. Tradeoffs in complexity versus performance are discussed.

Hydatid detection using the near-infrared transmission angular spectra of porous silicon microcavity biosensors

NASA Astrophysics Data System (ADS)

Li, Peng; Jia, Zhenhong; Lü, Guodong

2017-03-01

Hydatid, which is a parasitic disease, occurs today in many regions worldwide. Because it can present a serious threat to people’s health, finding a fast, convenient, and economical means of detection is important. This paper proposes a label- and spectrophotometer-free apparatus that uses optical biological detection based on porous silicon microcavities. In this approach, the refractive index change induced by the biological reactions of a sample in a porous silicon microcavity is detected by measuring the change in the incidence angle corresponding to the maximum transmitted intensity of a near-infrared probe laser. This paper reports that the proposed method can achieve the label-free detection of 43 kDa molecular weight hydatid disease antigens with high sensitivity.

Finger wear detection for production line battery tester

DOEpatents

Depiante, Eduardo V.

1997-01-01

A method for detecting wear in a battery tester probe. The method includes providing a battery tester unit having at least one tester finger, generating a tester signal using the tester fingers and battery tester unit with the signal characteristic of the electrochemical condition of the battery and the tester finger, applying wavelet transformation to the tester signal including computing a mother wavelet to produce finger wear indicator signals, analyzing the signals to create a finger wear index, comparing the wear index for the tester finger with the index for a new tester finger and generating a tester finger signal change signal to indicate achieving a threshold wear change.

Living microorganisms change the information (Shannon) content of a geophysical system.

PubMed

Tang, Fiona H M; Maggi, Federico

2017-06-12

The detection of microbial colonization in geophysical systems is becoming of interest in various disciplines of Earth and planetary sciences, including microbial ecology, biogeochemistry, geomicrobiology, and astrobiology. Microorganisms are often observed to colonize mineral surfaces, modify the reactivity of minerals either through the attachment of their own biomass or the glueing of mineral particles with their mucilaginous metabolites, and alter both the physical and chemical components of a geophysical system. Here, we hypothesise that microorganisms engineer their habitat, causing a substantial change to the information content embedded in geophysical measures (e.g., particle size and space-filling capacity). After proving this hypothesis, we introduce and test a systematic method that exploits this change in information content to detect microbial colonization in geophysical systems. Effectiveness and robustness of this method are tested using a mineral sediment suspension as a model geophysical system; tests are carried out against 105 experiments conducted with different suspension types (i.e., pure mineral and microbially-colonized) subject to different abiotic conditions, including various nutrient and mineral concentrations, and different background entropy production rates. Results reveal that this method can systematically detect microbial colonization with less than 10% error in geophysical systems with low-entropy background production rate.

A New Intrusion Detection Method Based on Antibody Concentration

NASA Astrophysics Data System (ADS)

Zeng, Jie; Li, Tao; Li, Guiyang; Li, Haibo

Antibody is one kind of protein that fights against the harmful antigen in human immune system. In modern medical examination, the health status of a human body can be diagnosed by detecting the intrusion intensity of a specific antigen and the concentration indicator of corresponding antibody from human body’s serum. In this paper, inspired by the principle of antigen-antibody reactions, we present a New Intrusion Detection Method Based on Antibody Concentration (NIDMBAC) to reduce false alarm rate without affecting detection rate. In our proposed method, the basic definitions of self, nonself, antigen and detector in the intrusion detection domain are given. Then, according to the antigen intrusion intensity, the change of antibody number is recorded from the process of clone proliferation for detectors based on the antigen classified recognition. Finally, building upon the above works, a probabilistic calculation method for the intrusion alarm production, which is based on the correlation between the antigen intrusion intensity and the antibody concen-tration, is proposed. Our theoretical analysis and experimental results show that our proposed method has a better performance than traditional methods.

Measurement of bone marrow lesions by MR imaging in knee osteoarthritis using quantitative segmentation methods--a reliability and sensitivity to change analysis.

PubMed

Nielsen, Flemming K; Egund, Niels; Peters, David; Jurik, Anne Grethe

2014-12-20

Longitudinal assessment of bone marrow lesions (BMLs) in knee osteoarthritis (KOA) by MRI is usually performed using semi-quantitative grading methods. Quantitative segmentation methods may be more sensitive to detect change over time. The purpose of this study was to evaluate and compare the validity and sensitivity to detect changes of two quantitative MR segmentation methods for measuring BMLs in KOA, one computer assisted (CAS) and one manual (MS) method. Twenty-two patients with KOA confined to the medial femoro-tibial compartment obtained MRI at baseline and follow-up (median 334 days in between). STIR, T1 and fat saturated T1 post-contrast sequences were obtained using a 1.5 T system. The 44 sagittal STIR sequences were assessed independently by two readers for quantification of BML. The signal intensities (SIs) of the normal bone marrow in the lateral femoral condyles and tibial plateaus were used as threshold values. The volume of bone marrow with SIs exceeding the threshold values (BML) was measured in the medial femoral condyle and tibial plateau and related to the total volume of the condyles/plateaus.The 95% limits of agreement at baseline were used to determine the sensitivity to change. The mean threshold values of CAS and MS were almost identical but the absolute and relative BML volumes differed being 1319 mm3/10% and 1828 mm3/15% in the femur and 941 mm3/7% and 2097 mm3/18% in the tibia using CAS and MS, respectively. The BML volumes obtained by CAS and MS were significantly correlated but the tissue changes measured were different. The volume of voxels exceeding the threshold values was measured by CAS whereas MS included intervening voxels with normal SI.The 95% limits of agreement were narrower by CAS than by MS; a significant change of relative BML by CAS was outside the limits of -2.0%-4.7% whereas the limits by MS were -6.9%-8.2%. The BML changed significantly in 13 knees using CAS and in 10 knees by MS. CAS was a reliable method for measuring BML and more sensitive to detect changes over time than MS. The BML volumes measured by the two methods differed but were significantly correlated.

Abnormal Circulation Changes in the Winter Stratosphere, Detected Through Variations of D Region Ionospheric Absorption

NASA Technical Reports Server (NTRS)

Delamorena, B. A.

1984-01-01

A method to detect stratospheric warmings using ionospheric absorption records obtained by an Absorption Meter (method A3) is introduced. The activity of the stratospheric circulation and the D region ionospheric absorption as well as other atmospheric parameters during the winter anomaly experience an abnormal variation. A simultaneity was found in the beginning of abnormal variation in the mentioned parameters, using the absorption records for detecting the initiation of the stratospheric warming. Results of this scientific experience of forecasting in the El Arenosillo Range, are presented.

Detection of Early Ischemic Changes in Noncontrast CT Head Improved with "Stroke Windows".

PubMed

Mainali, Shraddha; Wahba, Mervat; Elijovich, Lucas

2014-01-01

Introduction. Noncontrast head CT (NCCT) is the standard radiologic test for patients presenting with acute stroke. Early ischemic changes (EIC) are often overlooked on initial NCCT. We determine the sensitivity and specificity of improved EIC detection by a standardized method of image evaluation (Stroke Windows). Methods. We performed a retrospective chart review to identify patients with acute ischemic stroke who had NCCT at presentation. EIC was defined by the presence of hyperdense MCA/basilar artery sign; sulcal effacement; basal ganglia/subcortical hypodensity; and loss of cortical gray-white differentiation. NCCT was reviewed with standard window settings and with specialized Stroke Windows. Results. Fifty patients (42% females, 58% males) with a mean NIHSS of 13.4 were identified. EIC was detected in 9 patients with standard windows, while EIC was detected using Stroke Windows in 35 patients (18% versus 70%; P < 0.0001). Hyperdense MCA sign was the most commonly reported EIC; it was better detected with Stroke Windows (14% and 36%; P < 0.0198). Detection of the remaining EIC also improved with Stroke Windows (6% and 46%; P < 0.0001). Conclusions. Detection of EIC has important implications in diagnosis and treatment of acute ischemic stroke. Utilization of Stroke Windows significantly improved detection of EIC.

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.

A joint estimation detection of Glaucoma progression in 3D spectral domain optical coherence tomography optic nerve head images

NASA Astrophysics Data System (ADS)

Belghith, Akram; Bowd, Christopher; Weinreb, Robert N.; Zangwill, Linda M.

2014-03-01

Glaucoma is an ocular disease characterized by distinctive changes in the optic nerve head (ONH) and visual field. Glaucoma can strike without symptoms and causes blindness if it remains without treatment. Therefore, early disease detection is important so that treatment can be initiated and blindness prevented. In this context, important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, an essential element for glaucoma detection and monitoring. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new framework for detection of glaucoma progression using 3D SD-OCT images. In contrast to previous works that the retinal nerve fiber layer (RNFL) thickness measurement provided by commercially available spectral-domain optical coherence tomograph, we consider the whole 3D volume for change detection. To integrate a priori knowledge and in particular the spatial voxel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To accommodate the presence of false positive detection, the estimated change detection map is then used to classify a 3D SDOCT image into the "non-progressing" and "progressing" glaucoma classes, based on a fuzzy logic classifier. We compared the diagnostic performance of the proposed framework to existing methods of progression detection.

A joint estimation detection of Glaucoma progression in 3D spectral domain optical coherence tomography optic nerve head images.

PubMed

Belghith, Akram; Bowd, Christopher; Weinreb, Robert N; Zangwill, Linda M

2014-03-18

Glaucoma is an ocular disease characterized by distinctive changes in the optic nerve head (ONH) and visual field. Glaucoma can strike without symptoms and causes blindness if it remains without treatment. Therefore, early disease detection is important so that treatment can be initiated and blindness prevented. In this context, important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, an essential element for glaucoma detection and monitoring. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new framework for detection of glaucoma progression using 3D SD-OCT images. In contrast to previous works that the retinal nerve fiber layer (RNFL) thickness measurement provided by commercially available spectral-domain optical coherence tomograph, we consider the whole 3D volume for change detection. To integrate a priori knowledge and in particular the spatial voxel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To accommodate the presence of false positive detection, the estimated change detection map is then used to classify a 3D SDOCT image into the "non-progressing" and "progressing" glaucoma classes, based on a fuzzy logic classifier. We compared the diagnostic performance of the proposed framework to existing methods of progression detection.

Nano-particle enhanced impedimetric biosensor for detedtion of foodborne pathogens

NASA Astrophysics Data System (ADS)

Kim, G.; Om, A. S.; Mun, J. H.

2007-03-01

Recent outbreaks of foodborne illness have been increased the need for rapid and sensitive methods for detection of these pathogens. Conventional methods for pathogens detection and identification involve prolonged multiple enrichment steps. Even though some immunological rapid assays are available, these assays still need enrichment steps result in delayed detection. Biosensors have shown great potential for rapid detection of foodborne pathogens. They are capable of direct monitoring the antigen-antibody reactions in real time. Among the biosensors, impedimetric biosensors have been widely adapted as an analysis tool for the study of various biological binding reactions because of their high sensitivity and reagentless operation. In this study a nanoparticle-enhanced impedimetric biosensor for Salmonella enteritidis detection was developed which detected impedance changes caused by the attachment of the cells to the anti-Salmonella antibodies immobilized on interdigitated gold electrodes. Successive immobilization of neutravidin followed by anti-Salmonella antibodies was performed to the sensing area to create a biological detection surface. To enhance the impedance responses generated by antigen-antibody reactions, anti-Salmonella antibody conjugated nanoparticles were introduced on the sensing area. Using a portable impedance analyzer, the impedance across the interdigital electrodes was measured after the series of antigen-antibody bindings. Bacteria cells present in solution attached to capture antibodies and became tethered to the sensor surface. Attached bacteria cells changed the dielectric constant of the media between the electrodes thereby causing a change in measured impedance. Optimum input frequency was determined by analyzing frequency characteristics of the biosensor over ranges of applied frequencies from 10 Hz to 400 Hz. At 100 Hz of input frequency, the biosensor was most sensitive to the changes of the bacteria concentration and this frequency was used for the detection experiments. The biosensor was able to detect 106 CFU/mL in phosphate buffered saline (PBS) with a detection time of 3 minutes. Additional use of nanoparticles significantly enhanced the detection performance. By using the nanoparticles the biosensor could detect 104 CFU/mL of Salmonella enteritidis in PBS and 105 CFU/mL of cells in milk.

Rapid visual detection of quaternary ammonium surfactants using citrate-capped silver nanoparticles (Ag NPs) based on hydrophobic effect.

PubMed

Zheng, Li-Qing; Yu, Xiao-Dong; Xu, Jing-Juan; Chen, Hong-Yuan

2014-01-01

In this work, a rapid, sensitive and low-cost colorimetric method for detection of quaternary ammonium surfactants using citrate-capped silver nanoparticles (Ag NPs) was developed. The quaternary ammonium surfactants induce the aggregation of Ag NPs through the hydrophobic effect, which is a novel aggregation mechanism of Ag NPs. The addition of cationic surfactant results in color change of Ag NPs solution from yellow to red and finally to colorless, which is due to the broadening of the surface plasmon band. The color change was monitored using a UV-vis spectrophotometer. The LOD of different cationic surfactants was in the range of 0.5-5 µM. More importantly, this detection method was successfully utilized to the disinfectant residual sample. Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved.

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

NASA Astrophysics Data System (ADS)

Tatar, Nurollah; Saadatseresht, Mohammad; Arefi, Hossein; Hadavand, Ahmad

2018-06-01

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C4 index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.

Toward unsupervised outbreak detection through visual perception of new patterns

PubMed Central

Lévy, Pierre P; Valleron, Alain-Jacques

2009-01-01

Background Statistical algorithms are routinely used to detect outbreaks of well-defined syndromes, such as influenza-like illness. These methods cannot be applied to the detection of emerging diseases for which no preexisting information is available. This paper presents a method aimed at facilitating the detection of outbreaks, when there is no a priori knowledge of the clinical presentation of cases. Methods The method uses a visual representation of the symptoms and diseases coded during a patient consultation according to the International Classification of Primary Care 2nd version (ICPC-2). The surveillance data are transformed into color-coded cells, ranging from white to red, reflecting the increasing frequency of observed signs. They are placed in a graphic reference frame mimicking body anatomy. Simple visual observation of color-change patterns over time, concerning a single code or a combination of codes, enables detection in the setting of interest. Results The method is demonstrated through retrospective analyses of two data sets: description of the patients referred to the hospital by their general practitioners (GPs) participating in the French Sentinel Network and description of patients directly consulting at a hospital emergency department (HED). Informative image color-change alert patterns emerged in both cases: the health consequences of the August 2003 heat wave were visualized with GPs' data (but passed unnoticed with conventional surveillance systems), and the flu epidemics, which are routinely detected by standard statistical techniques, were recognized visually with HED data. Conclusion Using human visual pattern-recognition capacities to detect the onset of unexpected health events implies a convenient image representation of epidemiological surveillance and well-trained "epidemiology watchers". Once these two conditions are met, one could imagine that the epidemiology watchers could signal epidemiological alerts, based on "image walls" presenting the local, regional and/or national surveillance patterns, with specialized field epidemiologists assigned to validate the signals detected. PMID:19515246

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.

Optics-Only Calibration of a Neural-Net Based Optical NDE Method for Structural Health Monitoring

NASA Technical Reports Server (NTRS)

Decker, Arthur J.

2004-01-01

A calibration process is presented that uses optical measurements alone to calibrate a neural-net based NDE method. The method itself detects small changes in the vibration mode shapes of structures. The optics-only calibration process confirms previous work that the sensitivity to vibration-amplitude changes can be as small as 10 nanometers. A more practical value in an NDE service laboratory is shown to be 50 nanometers. Both model-generated and experimental calibrations are demonstrated using two implementations of the calibration technique. The implementations are based on previously published demonstrations of the NDE method and an alternative calibration procedure that depends on comparing neural-net and point sensor measurements. The optics-only calibration method, unlike the alternative method, does not require modifications of the structure being tested or the creation of calibration objects. The calibration process can be used to test improvements in the NDE process and to develop a vibration-mode-independence of damagedetection sensitivity. The calibration effort was intended to support NASA s objective to promote safety in the operations of ground test facilities or aviation safety, in general, by allowing the detection of the gradual onset of structural changes and damage.

Quantification of the methylation status of the PWS/AS imprinted region: comparison of two approaches based on bisulfite sequencing and methylation-sensitive MLPA.

PubMed

Dikow, Nicola; Nygren, Anders Oh; Schouten, Jan P; Hartmann, Carolin; Krämer, Nikola; Janssen, Bart; Zschocke, Johannes

2007-06-01

Standard methods used for genomic methylation analysis allow the detection of complete absence of either methylated or non-methylated alleles but are usually unable to detect changes in the proportion of methylated and unmethylated alleles. We compare two methods for quantitative methylation analysis, using the chromosome 15q11-q13 imprinted region as model. Absence of the non-methylated paternal allele in this region leads to Prader-Willi syndrome (PWS) whilst absence of the methylated maternal allele results in Angelman syndrome (AS). A proportion of AS is caused by mosaic imprinting defects which may be missed with standard methods and require quantitative analysis for their detection. Sequence-based quantitative methylation analysis (SeQMA) involves quantitative comparison of peaks generated through sequencing reactions after bisulfite treatment. It is simple, cost-effective and can be easily established for a large number of genes. However, our results support previous suggestions that methods based on bisulfite treatment may be problematic for exact quantification of methylation status. Methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) avoids bisulfite treatment. It detects changes in both CpG methylation as well as copy number of up to 40 chromosomal sequences in one simple reaction. Once established in a laboratory setting, the method is more accurate, reliable and less time consuming.

Damage detection and locating using tone burst and continuous excitation modulation method

NASA Astrophysics Data System (ADS)

Li, Zheng; Wang, Zhi; Xiao, Li; Qu, Wenzhong

2014-03-01

Among structural health monitoring techniques, nonlinear ultrasonic spectroscopy methods are found to be effective diagnostic approach to detecting nonlinear damage such as fatigue crack, due to their sensitivity to incipient structural changes. In this paper, a nonlinear ultrasonic modulation method was developed to detect and locate a fatigue crack on an aluminum plate. The method is different with nonlinear wave modulation method which recognizes the modulation of low-frequency vibration and high-frequency ultrasonic wave; it recognizes the modulation of tone burst and high-frequency ultrasonic wave. In the experiment, a Hanning window modulated sinusoidal tone burst and a continuous sinusoidal excitation were simultaneously imposed on the PZT array which was bonded on the surface of an aluminum plate. The modulations of tone burst and continuous sinusoidal excitation was observed in different actuator-sensor paths, indicating the presence and location of fatigue crack. The results of experiments show that the proposed method is capable of detecting and locating the fatigue crack successfully.

Quantum-state anomaly detection for arbitrary errors using a machine-learning technique

NASA Astrophysics Data System (ADS)

Hara, Satoshi; Ono, Takafumi; Okamoto, Ryo; Washio, Takashi; Takeuchi, Shigeki

2016-10-01

The accurate detection of small deviations in given density matrice is important for quantum information processing, which is a difficult task because of the intrinsic fluctuation in density matrices reconstructed using a limited number of experiments. We previously proposed a method for decoherence error detection using a machine-learning technique [S. Hara, T. Ono, R. Okamoto, T. Washio, and S. Takeuchi, Phys. Rev. A 89, 022104 (2014), 10.1103/PhysRevA.89.022104]. However, the previous method is not valid when the errors are just changes in phase. Here, we propose a method that is valid for arbitrary errors in density matrices. The performance of the proposed method is verified using both numerical simulation data and real experimental data.

Automatic Temporal Tracking of Supra-Glacial Lakes

NASA Astrophysics Data System (ADS)

Liang, Y.; Lv, Q.; Gallaher, D. W.; Fanning, D.

2010-12-01

During the recent years, supra-glacial lakes in Greenland have attracted extensive global attention as they potentially play an important role in glacier movement, sea level rise, and climate change. Previous works focused on classification methods and individual cloud-free satellite images, which have limited capabilities in terms of tracking changes of lakes over time. The challenges of tracking supra-glacial lakes automatically include (1) massive amount of satellite images with diverse qualities and frequent cloud coverage, and (2) diversity and dynamics of large number of supra-glacial lakes on the Greenland ice sheet. In this study, we develop an innovative method to automatically track supra-glacial lakes temporally using the Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data. The method works for both cloudy and cloud-free data and is unsupervised, i.e., no manual identification is required. After selecting the highest-quality image within each time interval, our method automatically detects supra-glacial lakes in individual images, using adaptive thresholding to handle diverse image qualities. We then track lakes across time series of images as lakes appear, change in size, and disappear. Using multi-year MODIS data during melting season, we demonstrate that this new method can detect and track supra-glacial lakes in both space and time with 95% accuracy. Attached figure shows an example of the current result. Detailed analysis of the temporal variation of detected lakes will be presented. (a) One of our experimental data. The Investigated region is centered at Jakobshavn Isbrae glacier in west Greenland. (b) Enlarged view of part of ice sheet. It is partially cloudy and with supra-glacial lakes on it. Lakes are shown as dark spots. (c) Current result. Red spots are detected lakes.

Drawing for Traffic Marking Using Bidirectional Gradient-Based Detection with MMS LIDAR Intensity

NASA Astrophysics Data System (ADS)

Takahashi, G.; Takeda, H.; Nakamura, K.

2016-06-01

Recently, the development of autonomous cars is accelerating on the integration of highly advanced artificial intelligence, which increases demand for a digital map with high accuracy. In particular, traffic markings are required to be precisely digitized since automatic driving utilizes them for position detection. To draw traffic markings, we benefit from Mobile Mapping Systems (MMS) equipped with high-density Laser imaging Detection and Ranging (LiDAR) scanners, which produces large amount of data efficiently with XYZ coordination along with reflectance intensity. Digitizing this data, on the other hand, conventionally has been dependent on human operation, which thus suffers from human errors, subjectivity errors, and low reproductivity. We have tackled this problem by means of automatic extraction of traffic marking, which partially accomplished to draw several traffic markings (G. Takahashi et al., 2014). The key idea of the method was extracting lines using the Hough transform strategically focused on changes in local reflection intensity along scan lines. However, it failed to extract traffic markings properly in a densely marked area, especially when local changing points are close each other. In this paper, we propose a bidirectional gradient-based detection method where local changing points are labelled with plus or minus group. Given that each label corresponds to the boundary between traffic markings and background, we can identify traffic markings explicitly, meaning traffic lines are differentiated correctly by the proposed method. As such, our automated method, a highly accurate and non-human-operator-dependent method using bidirectional gradient-based algorithm, can successfully extract traffic lines composed of complex shapes such as a cross walk, resulting in minimizing cost and obtaining highly accurate results.

(Quickly) Testing the Tester via Path Coverage

NASA Technical Reports Server (NTRS)

Groce, Alex

2009-01-01

The configuration complexity and code size of an automated testing framework may grow to a point that the tester itself becomes a significant software artifact, prone to poor configuration and implementation errors. Unfortunately, testing the tester by using old versions of the software under test (SUT) may be impractical or impossible: test framework changes may have been motivated by interface changes in the tested system, or fault detection may become too expensive in terms of computing time to justify running until errors are detected on older versions of the software. We propose the use of path coverage measures as a "quick and dirty" method for detecting many faults in complex test frameworks. We also note the possibility of using techniques developed to diversify state-space searches in model checking to diversify test focus, and an associated classification of tester changes into focus-changing and non-focus-changing modifications.

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.

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.

Multi-scale occupancy estimation and modelling using multiple detection methods

USGS Publications Warehouse

Nichols, James D.; Bailey, Larissa L.; O'Connell, Allan F.; Talancy, Neil W.; Grant, Evan H. Campbell; Gilbert, Andrew T.; Annand, Elizabeth M.; Husband, Thomas P.; Hines, James E.

2008-01-01

Occupancy estimation and modelling based on detection–nondetection data provide an effective way of exploring change in a species’ distribution across time and space in cases where the species is not always detected with certainty. Today, many monitoring programmes target multiple species, or life stages within a species, requiring the use of multiple detection methods. When multiple methods or devices are used at the same sample sites, animals can be detected by more than one method.We develop occupancy models for multiple detection methods that permit simultaneous use of data from all methods for inference about method-specific detection probabilities. Moreover, the approach permits estimation of occupancy at two spatial scales: the larger scale corresponds to species’ use of a sample unit, whereas the smaller scale corresponds to presence of the species at the local sample station or site.We apply the models to data collected on two different vertebrate species: striped skunks Mephitis mephitis and red salamanders Pseudotriton ruber. For striped skunks, large-scale occupancy estimates were consistent between two sampling seasons. Small-scale occupancy probabilities were slightly lower in the late winter/spring when skunks tend to conserve energy, and movements are limited to males in search of females for breeding. There was strong evidence of method-specific detection probabilities for skunks. As anticipated, large- and small-scale occupancy areas completely overlapped for red salamanders. The analyses provided weak evidence of method-specific detection probabilities for this species.Synthesis and applications. Increasingly, many studies are utilizing multiple detection methods at sampling locations. The modelling approach presented here makes efficient use of detections from multiple methods to estimate occupancy probabilities at two spatial scales and to compare detection probabilities associated with different detection methods. The models can be viewed as another variation of Pollock's robust design and may be applicable to a wide variety of scenarios where species occur in an area but are not always near the sampled locations. The estimation approach is likely to be especially useful in multispecies conservation programmes by providing efficient estimates using multiple detection devices and by providing device-specific detection probability estimates for use in survey design.

Spatially-Aware Temporal Anomaly Mapping of Gamma Spectra

NASA Astrophysics Data System (ADS)

Reinhart, Alex; Athey, Alex; Biegalski, Steven

2014-06-01

For security, environmental, and regulatory purposes it is useful to continuously monitor wide areas for unexpected changes in radioactivity. We report on a temporal anomaly detection algorithm which uses mobile detectors to build a spatial map of background spectra, allowing sensitive detection of any anomalies through many days or months of monitoring. We adapt previously-developed anomaly detection methods, which compare spectral shape rather than count rate, to function with limited background data, allowing sensitive detection of small changes in spectral shape from day to day. To demonstrate this technique we collected daily observations over the period of six weeks on a 0.33 square mile research campus and performed source injection simulations.

Serum albumin and globulin analysis for hepatocellular carcinoma detection avoiding false-negative results from alpha-fetoprotein test negative subjects

NASA Astrophysics Data System (ADS)

Wang, Jing; Feng, Shangyuan; Lin, Juqiang; Zeng, Yongyi; Li, Ling; Huang, Zufang; Li, Buhong; Zeng, Haishan; Chen, Rong

2013-11-01

Surface-enhanced Raman spectroscopy (SERS) of serum albumin and globulin were employed to detect hepatocellular carcinoma (HCC). Tentative assignments of SERS bands show specific biomolecular changes associated with cancer development. These changes include a decrease in relative amounts of tryptophan, glutamine, glycine, and serine, indicating excessive consumption of amino acids for protein duplication. Principal component analysis was also introduced to analyze the obtained spectra, resulting in both diagnostic sensitivity and specificity of 100%. More importantly, it reveals that this method can detect HCC patients with alpha-fetoprotein negative test results, suggesting its great potential as a new alternative to detect HCC.

Electrical detection of microwave assisted magnetization reversal by spin pumping

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

Rao, Siddharth; Subhra Mukherjee, Sankha; Elyasi, Mehrdad

2014-03-24

Microwave assisted magnetization reversal has been investigated in a bilayer system of Pt/ferromagnet by detecting a change in the polarity of the spin pumping signal. The reversal process is studied in two material systems, Pt/CoFeB and Pt/NiFe, for different aspect ratios. The onset of the switching behavior is indicated by a sharp transition in the spin pumping voltage. At a threshold value of the external field, the switching process changes from partial to full reversal with increasing microwave power. The proposed method provides a simple way to detect microwave assisted magnetization reversal.

Evaluate ERTS imagery for mapping and detection of changes of snowcover on land and on glaciers

NASA Technical Reports Server (NTRS)

Meier, M. F. (Principal Investigator)

1973-01-01

The author has identified the following significant results. A possibly more accurate method to determine snowcover area change has been tried; snowcover area change over periods of an ERTS-1 cycle are very useful in determining energy balances over regional areas and to determine snow depth as a function of altitude. Also since shadow and cloud cover areas are highlighted this method may be a step toward more complete machine processing.

Hands-on resonance-enhanced photoacoustic detection

NASA Astrophysics Data System (ADS)

Euler, Manfred

2001-10-01

The design of an improved photoacoustic converter cell using kitchen equipment is described. It operates by changing manually the Helmholtz resonance frequency of bottles by adjusting the distance between the bottleneck and the outer ear. The experiment helps to gain insights in ear performance, in photoacoustic detection methods, in resonance phenomena and their role for detecting small periodic signals in the presence of noise.

Longitudinal Detection of Optic Nerve Head Changes by Spectral Domain Optical Coherence Tomography in Early Experimental Glaucoma

PubMed Central

He, Lin; Yang, Hongli; Gardiner, Stuart K.; Williams, Galen; Hardin, Christy; Strouthidis, Nicholas G.; Fortune, Brad; Burgoyne, Claude F.

2014-01-01

Purpose. We determined if the detection of spectral-domain optical coherence tomography (SDOCT) optic nerve head (ONH) change precedes the detection of confocal scanning laser tomography (CSLT) ONH surface, SDOCT retinal nerve fiber layer (RNFL), scanning laser perimetry (SLP), and multifocal electroretinography (mfERG) change in eight experimental glaucoma (EG) eyes. Methods. Both eyes from eight monkeys were tested at least three times at baseline, and then every 2 weeks following laser-induced chronic unilateral IOP elevation. Event and trend-based definitions of onset in the control and EG eyes for 11 SDOCT neural and connective tissue, CSLT surface, SDOCT RNFL, SLP, and mfERG parameters were explored. The frequency and timing of onset for each parameter were compared using a logrank test. Results. Maximum post-laser IOP was 18 to 42 mm Hg in the EG eyes and 12 to 20 mm Hg in the control eyes. For event- and trend-based analyses, onsets were achieved earliest and most frequently within the ONH neural and connective tissues using SDOCT, and at the ONH surface using CSLT. SDOCT ONH neural and connective tissue parameter change preceded or coincided with CSLT ONH surface change in most EG eyes. The SDOCT and SLP measures of RNFL thickness, and mfERG measures of visual function demonstrated similar onset rates, but occurred later than SDOCT ONH and CSLT surface change, and in fewer eyes. Conclusions. SDOCT ONH change detection commonly precedes or coincides with CSLT ONH surface change detection, and consistently precedes RNFLT, SLP, and mfERG change detection in monkey experimental glaucoma. PMID:24255047

Method for detection of a few pathogenic bacteria and determination of live versus dead cells

NASA Astrophysics Data System (ADS)

Horikawa, Shin; Chen, I.-Hsuan; Du, Songtao; Liu, Yuzhe; Wikle, Howard C.; Suh, Sang-Jin; Barbaree, James M.; Chin, Bryan A.

2016-05-01

This paper presents a method for detection of a few pathogenic bacteria and determination of live versus dead cells. The method combines wireless phage-coated magnetoelastic (ME) biosensors and a surface-scanning dectector, enabling real-time monitoring of the growth of specific bacteria in a nutrient broth. The ME biosensor used in this investigation is composed of a strip-shaped ME resonator upon which an engineered bacteriophage is coated to capture a pathogen of interest. E2 phage with high binding affinity for Salmonella Typhimurium was used as a model study. The specificity of E2 phage has been reported to be 1 in 105 background bacteria. The phage-coated ME biosensors were first exposed to a low-concentration Salmonella suspension to capture roughly 300 cells on the sensor surface. When the growth of Salmonella in the broth occurs, the mass of the biosensor increases, which results in a decrease in the biosensor's resonant frequency. Monitoring of this mass- induced resonant frequency change allows for real-time detection of the presence of Salmonella. Detection of a few bacteria is also possible by growing them to a sufficient number. The surface-scanning detector was used to measure resonant frequency changes of 25 biosensors sequentially in an automated manner as a function of time. This methodology offers direct, real-time detection, quantification, and viability determination of specific bacteria. The rate of the sensor's resonant frequency change was found to be largely dependent on the number of initially bound cells and the efficiency of cell growth.

Cell biomechanics and its applications in human disease diagnosis

NASA Astrophysics Data System (ADS)

Nematbakhsh, Yasaman; Lim, Chwee Teck

2015-04-01

Certain diseases are known to cause changes in the physical and biomechanical properties of cells. These include cancer, malaria, and sickle cell anemia among others. Typically, such physical property changes can result in several fold increases or decreases in cell stiffness, which are significant and can result in severe pathology and eventual catastrophic breakdown of the bodily functions. While there are developed biochemical and biological assays to detect the onset or presence of diseases, there is always a need to develop more rapid, precise, and sensitive methods to detect and diagnose diseases. Biomechanical property changes can play a significant role in this regard. As such, research into disease biomechanics can not only give us an in-depth knowledge of the mechanisms underlying disease progression, but can also serve as a powerful tool for detection and diagnosis. This article provides some insights into opportunities for how significant changes in cellular mechanical properties during onset or progression of a disease can be utilized as useful means for detection and diagnosis. We will also showcase several technologies that have already been developed to perform such detection and diagnosis.

Colorimetric Method of Loop-Mediated Isothermal Amplification with the Pre-Addition of Calcein for Detecting Flavobacterium columnare and its Assessment in Tilapia Farms.

PubMed

Suebsing, Rungkarn; Kampeera, Jantana; Sirithammajak, Sarawut; Withyachumnarnkul, Boonsirm; Turner, Warren; Kiatpathomchai, Wansika

2015-03-01

Flavobacterium columnare, the causative agent of columnaris disease in fish, affects many economically important freshwater fish species. A colorimetric method of loop-mediated isothermal amplification with the pre-addition of calcein (LAMP-calcein) was developed and used to detect the presence of F. columnare in farmed tilapia (Nile Tilapia Oreochromis niloticus and red tilapia [Nile Tilapia × Mozambique Tilapia O. mossambicus]) and rearing water. The detection method, based on a change in color from orange to green, could be performed within 45 min at 63°C. The method was highly specific, as it had no cross-detections with 14 other bacterial species, including other fish pathogens and two Flavobacterium species. The method has a minimum detection limit of 2.2 × 10(2) F. columnare CFU; thus, it is about 10 times more sensitive than conventional PCR. With this method, F. columnare was detected in gonad, gill, and blood samples from apparently healthy tilapia broodstock as well as in samples of fertilized eggs, newly hatched fry, and rearing water. The bacteria isolated from the blood were further characterized biochemically and found to be phenotypically identical to F. columnare. The amplified products from the LAMP-calcein method had 97% homology with the DNA sequence of F. columnare.

A Colorimetric and Fluorescent Probe for the Detection of Cu2+ in a Complete Aqueous Solution.

PubMed

Xu, Jing; Wang, Zuokai; Liu, Caiyun; Xu, Zhenghe; Zhu, Baocun; Wang, Ning; Wang, Kun; Wang, Jiangting

2018-01-01

The fluorescent probe has become an important method for the detection of heavy metal ions. In the present work, a new and simple fluorescent probe, Cu-P, for detecting copper ion (Cu 2+ ) was designed and synthesized. The probe has shown high sensitivity and selectivity toward Cu 2+ . The detection limit was 13 nM (based on the 3σ/slope). A significant color change from yellow to pink was observed; thus, the probe Cu-P could serve as a "naked-eye" indicator for Cu 2+ . Furthermore, the proposed probe was used to detect Cu 2+ in real water and soil extract samples, with the result being satisfactory. Therefore, our proposed probe would provide a promising method for the detection of Cu 2+ in the environment.

Capillary electrophoresis with laser-induced fluorescence detection: a sensitive method for monitoring extracellular concentrations of amino acids in the periaqueductal grey matter.

PubMed

Bergquist, J; Vona, M J; Stiller, C O; O'Connor, W T; Falkenberg, T; Ekman, R

1996-03-01

The use of capillary electrophoresis with laser-induced fluorescence detection (CE-LIF) for the analysis of microdialysate samples from the periaqueductal grey matter (PAG) of freely moving rats is described. By employing 3-(4-carboxybenzoyl)-2-quinoline-carboxaldehyde (CBQCA) as a derivatization agent, we simultaneously monitored the concentrations of 8 amino acids (arginine, glutamine, valine, gamma-amino-n-butyric acid (GABA), alanine, glycine, glutamate, and aspartate), with nanomolar and subnanomolar detection limits. Two of the amino acids (GABA and glutamate) were analysed in parallel by conventional high-performance liquid chromatography (HPLC) in order to directly compare the two analytical methods. Other CE methods for analysis of microdialysate have been previously described, and this improved method offers greater sensitivity, ease of use, and the possibility to monitor several amino acids simultaneously. By using this technique together with an optimised form of microdialysis technique, the tiny sample consumption and the improved detection limits permit the detection of fast and transient transmitter changes.

Boundary-Layer Detection at Cryogenic Conditions Using Temperature Sensitive Paint Coupled with a Carbon Nanotube Heating Layer

NASA Technical Reports Server (NTRS)

Goodman, Kyle Z.; Lipford, William E.; Watkins, Anthony Neal

2016-01-01

Detection of flow transition on aircraft surfaces and models can be vital to the development of future vehicles and computational methods for evaluating vehicle concepts. In testing at ambient conditions, IR thermography is ideal for this measurement. However, for higher Reynolds number testing, cryogenic facilities are often used, in which IR thermography is difficult to employ. In these facilities, temperature sensitive paint is an alternative with a temperature step introduced to enhance the natural temperature change from transition. Traditional methods for inducing the temperature step by changing the liquid nitrogen injection rate often change the tunnel conditions. Recent work has shown that adding a layer consisting of carbon nanotubes to the surface can be used to impart a temperature step on the model surface with little change in the operating conditions. Unfortunately, this system physically degraded at 130 K and lost heating capability. This paper describes a modification of this technique enabling operation down to at least 77 K, well below the temperature reached in cryogenic facilities. This is possible because the CNT layer is in a polyurethane binder. This was tested on a Natural Laminar Flow model in a cryogenic facility and transition detection was successfully visualized at conditions from 200 K to 110 K. Results were also compared with the traditional temperature step method.

Boundary-Layer Detection at Cryogenic Conditions Using Temperature Sensitive Paint Coupled with a Carbon Nanotube Heating Layer.

PubMed

Goodman, Kyle Z; Lipford, William E; Watkins, Anthony Neal

2016-12-03

Detection of flow transition on aircraft surfaces and models can be vital to the development of future vehicles and computational methods for evaluating vehicle concepts. In testing at ambient conditions, IR thermography is ideal for this measurement. However, for higher Reynolds number testing, cryogenic facilities are often used, in which IR thermography is difficult to employ. In these facilities, temperature sensitive paint is an alternative with a temperature step introduced to enhance the natural temperature change from transition. Traditional methods for inducing the temperature step by changing the liquid nitrogen injection rate often change the tunnel conditions. Recent work has shown that adding a layer consisting of carbon nanotubes to the surface can be used to impart a temperature step on the model surface with little change in the operating conditions. Unfortunately, this system physically degraded at 130 K and lost heating capability. This paper describes a modification of this technique enabling operation down to at least 77 K, well below the temperature reached in cryogenic facilities. This is possible because the CNT layer is in a polyurethane binder. This was tested on a Natural Laminar Flow model in a cryogenic facility and transition detection was successfully visualized at conditions from 200 K to 110 K. Results were also compared with the traditional temperature step method.

Boundary-Layer Detection at Cryogenic Conditions Using Temperature Sensitive Paint Coupled with a Carbon Nanotube Heating Layer

PubMed Central

Goodman, Kyle Z.; Lipford, William E.; Watkins, Anthony Neal

2016-01-01

Detection of flow transition on aircraft surfaces and models can be vital to the development of future vehicles and computational methods for evaluating vehicle concepts. In testing at ambient conditions, IR thermography is ideal for this measurement. However, for higher Reynolds number testing, cryogenic facilities are often used, in which IR thermography is difficult to employ. In these facilities, temperature sensitive paint is an alternative with a temperature step introduced to enhance the natural temperature change from transition. Traditional methods for inducing the temperature step by changing the liquid nitrogen injection rate often change the tunnel conditions. Recent work has shown that adding a layer consisting of carbon nanotubes to the surface can be used to impart a temperature step on the model surface with little change in the operating conditions. Unfortunately, this system physically degraded at 130 K and lost heating capability. This paper describes a modification of this technique enabling operation down to at least 77 K, well below the temperature reached in cryogenic facilities. This is possible because the CNT layer is in a polyurethane binder. This was tested on a Natural Laminar Flow model in a cryogenic facility and transition detection was successfully visualized at conditions from 200 K to 110 K. Results were also compared with the traditional temperature step method. PMID:27918493

21st Century Changes in Precipitation Extremes Based on Resolved Atmospheric Patterns

NASA Astrophysics Data System (ADS)

Gao, X.; Schlosser, C. A.; O'Gorman, P. A.; Monier, E.

2014-12-01

Global warming is expected to alter the frequency and/or magnitude of extreme precipitation events. Such changes could have substantial ecological, economic, and sociological consequences. However, climate models in general do not correctly reproduce the frequency distribution of precipitation, especially at the regional scale. In this study, a validated analogue method is employed to diagnose the potential future shifts in the probability of extreme precipitation over the United States under global warming. The method is based on the use of the resolved large-scale meteorological conditions (i.e. flow features, moisture supply) to detect the occurrence of extreme precipitation. The CMIP5 multi-model projections have been compiled for two radiative forcing scenarios (Representative Concentration Pathways 4.5 and 8.5). We further analyze the accompanying circulation features and their changes that may be responsible for shifts in extreme precipitation in response to changed climate. The application of such analogue method to detect other types of hazard events, i.e. landslides is also explored. The results from this study may guide hazardous weather watches and help society develop adaptive strategies for preventing catastrophic losses.

The reliability and internal consistency of one-shot and flicker change detection for measuring individual differences in visual working memory capacity.

PubMed

Pailian, Hrag; Halberda, Justin

2015-04-01

We investigated the psychometric properties of the one-shot change detection task for estimating visual working memory (VWM) storage capacity-and also introduced and tested an alternative flicker change detection task for estimating these limits. In three experiments, we found that the one-shot whole-display task returns estimates of VWM storage capacity (K) that are unreliable across set sizes-suggesting that the whole-display task is measuring different things at different set sizes. In two additional experiments, we found that the one-shot single-probe variant shows improvements in the reliability and consistency of K estimates. In another additional experiment, we found that a one-shot whole-display-with-click task (requiring target localization) also showed improvements in reliability and consistency. The latter results suggest that the one-shot task can return reliable and consistent estimates of VWM storage capacity (K), and they highlight the possibility that the requirement to localize the changed target is what engenders this enhancement. Through a final series of four experiments, we introduced and tested an alternative flicker change detection method that also requires the observer to localize the changing target and that generates, from response times, an estimate of VWM storage capacity (K). We found that estimates of K from the flicker task correlated with estimates from the traditional one-shot task and also had high reliability and consistency. We highlight the flicker method's ability to estimate executive functions as well as VWM storage capacity, and discuss the potential for measuring multiple abilities with the one-shot and flicker tasks.

Method to detect the end-point for PCR DNA amplification using an ionically labeled probe and measuring impedance change

DOEpatents

Miles, Robin R [Danville, CA; Belgrader, Phillip [Severna Park, MD; Fuller, Christopher D [Oakland, CA

2007-01-02

Impedance measurements are used to detect the end-point for PCR DNA amplification. A pair of spaced electrodes are located on a surface of a microfluidic channel and an AC or DC voltage is applied across the electrodes to produce an electric field. An ionically labeled probe will attach to a complementary DNA segment, and a polymerase enzyme will release the ionic label. This causes the conductivity of the solution in the area of the electrode to change. This change in conductivity is measured as a change in the impedance been the two electrodes.

The method of pulsed x-ray detection with a diode laser.

PubMed

Liu, Jun; Ouyang, Xiaoping; Zhang, Zhongbing; Sheng, Liang; Chen, Liang; Tan, Xinjian; Weng, Xiufeng

2016-12-01

A new class of pulsed X-ray detection methods by sensing carrier changes in a diode laser cavity has been presented and demonstrated. The proof-of-principle experiments on detecting pulsed X-ray temporal profile have been done through the diode laser with a multiple quantum well active layer. The result shows that our method can achieve the aim of detecting the temporal profile of a pulsed X-ray source. We predict that there is a minimum value for the pre-bias current of the diode laser by analyzing the carrier rate equation, which exists near the threshold current of the diode laser chip in experiments. This behaviour generally agrees with the characterizations of theoretical analysis. The relative sensitivity is estimated at about 3.3 × 10 -17 C ⋅ cm 2 . We have analyzed the time scale of about 10 ps response with both rate equation and Monte Carlo methods.

Boosting instance prototypes to detect local dermoscopic features.

PubMed

Situ, Ning; Yuan, Xiaojing; Zouridakis, George

2010-01-01

Local dermoscopic features are useful in many dermoscopic criteria for skin cancer detection. We address the problem of detecting local dermoscopic features from epiluminescence (ELM) microscopy skin lesion images. We formulate the recognition of local dermoscopic features as a multi-instance learning (MIL) problem. We employ the method of diverse density (DD) and evidence confidence (EC) function to convert MIL to a single-instance learning (SIL) problem. We apply Adaboost to improve the classification performance with support vector machines (SVMs) as the base classifier. We also propose to boost the selection of instance prototypes through changing the data weights in the DD function. We validate the methods on detecting ten local dermoscopic features from a dataset with 360 images. We compare the performance of the MIL approach, its boosting version, and a baseline method without using MIL. Our results show that boosting can provide performance improvement compared to the other two methods.

Satellite-based assessment of grassland yields

NASA Astrophysics Data System (ADS)

Grant, K.; Siegmund, R.; Wagner, M.; Hartmann, S.

2015-04-01

Cutting date and frequency are important parameters determining grassland yields in addition to the effects of weather, soil conditions, plant composition and fertilisation. Because accurate and area-wide data of grassland yields are currently not available, cutting frequency can be used to estimate yields. In this project, a method to detect cutting dates via surface changes in radar images is developed. The combination of this method with a grassland yield model will result in more reliable and regional-wide numbers of grassland yields. For the test-phase of the monitoring project, a study area situated southeast of Munich, Germany, was chosen due to its high density of managed grassland. For determining grassland cutting robust amplitude change detection techniques are used evaluating radar amplitude or backscatter statistics before and after the cutting event. CosmoSkyMed and Sentinel-1A data were analysed. All detected cuts were verified according to in-situ measurements recorded in a GIS database. Although the SAR systems had various acquisition geometries, the amount of detected grassland cut was quite similar. Of 154 tested grassland plots, covering in total 436 ha, 116 and 111 cuts were detected using CosmoSkyMed and Sentinel-1A radar data, respectively. Further improvement of radar data processes as well as additional analyses with higher sample number and wider land surface coverage will follow for optimisation of the method and for validation and generalisation of the results of this feasibility study. The automation of this method will than allow for an area-wide and cost efficient cutting date detection service improving grassland yield models.

The importance of aerodynamics for prosthetic limb design used by competitive cyclists with an amputation: An introduction.

PubMed

Dyer, Bryce

2015-06-01

This study introduces the importance of the aerodynamics to prosthetic limb design for athletes with either a lower-limb or upper-limb amputation. The study comprises two elements: 1) An initial experiment investigating the stability of outdoor velodrome-based field tests, and 2) An experiment evaluating the application of outdoor velodrome aerodynamic field tests to detect small-scale changes in aerodynamic drag respective of prosthetic limb componentry changes. An outdoor field-testing method is used to detect small and repeatable changes in the aerodynamic drag of an able-bodied cyclist. These changes were made at levels typical of alterations in prosthetic componentry. The field-based test method of assessment is used at a smaller level of resolution than previously reported. With a carefully applied protocol, the field test method proved to be statistically stable. The results of the field test experiments demonstrate a noticeable change in overall athlete performance. Aerodynamic refinement of artificial limbs is worthwhile for athletes looking to maximise their competitive performance. A field-testing method illustrates the importance of the aerodynamic optimisation of prosthetic limb components. The field-testing protocol undertaken in this study gives an accessible and affordable means of doing so by prosthetists and sports engineers. Using simple and accessible field-testing methods, this exploratory experiment demonstrates how small changes to riders' equipment, consummate of the scale of a small change in prosthetics componentry, can affect the performance of an athlete. Prosthetists should consider such opportunities for performance enhancement when possible. © The International Society for Prosthetics and Orthotics 2014.

Stochastic sensing through covalent interactions

DOEpatents

Bayley, Hagan; Shin, Seong-Ho; Luchian, Tudor; Cheley, Stephen

2013-03-26

A system and method for stochastic sensing in which the analyte covalently bonds to the sensor element or an adaptor element. If such bonding is irreversible, the bond may be broken by a chemical reagent. The sensor element may be a protein, such as the engineered P.sub.SH type or .alpha.HL protein pore. The analyte may be any reactive analyte, including chemical weapons, environmental toxins and pharmaceuticals. The analyte covalently bonds to the sensor element to produce a detectable signal. Possible signals include change in electrical current, change in force, and change in fluorescence. Detection of the signal allows identification of the analyte and determination of its concentration in a sample solution. Multiple analytes present in the same solution may be detected.

Design and analysis of surface plasmon resonance (SPR) sensor to check the quality of food from adulteration

NASA Astrophysics Data System (ADS)

Kumar, Manish; Raghuwanshi, Sanjeev Kumar

2018-02-01

In recent years, food safety issues caused by contamination of chemical substances or microbial species have raised a major area of concern to mankind. The conventional chromatography-based methods for detection of chemical are based on human-observation and slow for real-time monitoring. The surface plasmon resonance (SPR) sensors offers the capability of detection of very low concentrations of adulterated chemical and biological agents for real-time by monitoring. Thus, adulterant agent in food gives change in refractive index of pure food result in corresponding phase change. These changes can be detected at the output and can be related to the concentration of the chemical species present at the point.

Isolation and identification of Duck tembusu virus strain lH and development of latex-agglutination diagnostic method for rapid detection of antibodies.

PubMed

Wang, Quanxi; Wen, Yaping; Yifan Huang; Wu, Yijian; Cai, Yilong; Xu, Lihui; Wang, Changkang; Li, Ang; Wu, Baocheng; Chen, Jilong

2014-12-01

SUMMARY. An outbreak of egg-drop syndrome occurred on a Sheldrake duck farm in Longhai in Fujian Province, China, in 2012. The main clinical symptoms were sharply reduced egg production, crooked necks, and death. We isolated the virus from the sick ducks, identified it, and observed the histopathologic changes after viral infection. We detected viral RNA in the blood and feces of the infected ducks and developed a latex-agglutination diagnostic method to detect anti-Tembusu-virus antibodies. Our results show that the pathogenic virus is a Tembusu virus. The histopathologic changes included follicular cell degeneration and necrosis, follicular cavity filled with blood cells, massive necrosis in the brain, and degeneration and necrosis of the nerve and glial cells. When the transmission of the virus in the infected ducks was studied, the duck blood was positive for viral nucleic acid for up to 29 days, and the feces were positive for viral nucleic acid for up to 13 days. We successfully established a simple, rapid, and easy- to-use latex-agglutination diagnostic method for the detection of antibodies against duck Tembusu virus.

Recent Efforts to Improve the Near Real Time Forest Disturbance Monitoring Capabilities of the ForWarn System

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Hargrove, William; Gasser, Gerald

2013-01-01

This presentation discusses the development of anew method for computing NDVI temporal composites from near real time eMODIS data This research is being conducted to improve forest change products used in the ForWarn system for monitoring regional forest disturbances in the United States. ForWarn provides nation-wide NDVI-based forest disturbance detection products that are refreshed every 8 days. Current eMODIS and historical MOD13 24 day NDVI data are used to compute the disturbance detection products. The eMODIS 24 day NDVI data re-aggregated from 7 day NDVI products. The 24 day eMODIS NDVIs are generally cloud free, but do not necessarily use the freshest quality data. To shorten the disturbance detection time, a method has been developed that performs adaptive length/maximum value compositing of eMODIS NDVI, along with cloud and shadow "noise" mitigation. Tests indicate that this method can reduce detection rates by 8-16 days for known recent disturbance events, depending on the cloud frequencies and disturbance type. The noise mitigation in these tests, though imperfect, helped to improve quality of the resulting NDVI and forest change products.

Detection of the Dinozoans Pfiesteria piscicida and P. shumwayae: a review of detection methods and geographic distribution.

PubMed

Rublee, Parke A; Remington, David L; Schaefer, Eric F; Marshall, Michael M

2005-01-01

Molecular methods, including conventional PCR, real-time PCR, denaturing gradient gel electrophoresis, fluorescent fragment detection PCR, and fluorescent in situ hybridization, have all been developed for use in identifying and studying the distribution of the toxic dinoflagellates Pfiesteria piscicida and P. shumwayae. Application of the methods has demonstrated a worldwide distribution of both species and provided insight into their environmental tolerance range and temporal changes in distribution. Genetic variability among geographic locations generally appears low in rDNA genes, and detection of the organisms in ballast water is consistent with rapid dispersal or high gene flow among populations, but additional sequence data are needed to verify this hypothesis. The rapid development and application of these tools serves as a model for study of other microbial taxa and provides a basis for future development of tools that can simultaneously detect multiple targets.

The sequentially discounting autoregressive (SDAR) method for on-line automatic seismic event detecting on long term observation

NASA Astrophysics Data System (ADS)

Wang, L.; Toshioka, T.; Nakajima, T.; Narita, A.; Xue, Z.

2017-12-01

In recent years, more and more Carbon Capture and Storage (CCS) studies focus on seismicity monitoring. For the safety management of geological CO2 storage at Tomakomai, Hokkaido, Japan, an Advanced Traffic Light System (ATLS) combined different seismic messages (magnitudes, phases, distributions et al.) is proposed for injection controlling. The primary task for ATLS is the seismic events detection in a long-term sustained time series record. Considering the time-varying characteristics of Signal to Noise Ratio (SNR) of a long-term record and the uneven energy distributions of seismic event waveforms will increase the difficulty in automatic seismic detecting, in this work, an improved probability autoregressive (AR) method for automatic seismic event detecting is applied. This algorithm, called sequentially discounting AR learning (SDAR), can identify the effective seismic event in the time series through the Change Point detection (CPD) of the seismic record. In this method, an anomaly signal (seismic event) can be designed as a change point on the time series (seismic record). The statistical model of the signal in the neighborhood of event point will change, because of the seismic event occurrence. This means the SDAR aims to find the statistical irregularities of the record thought CPD. There are 3 advantages of SDAR. 1. Anti-noise ability. The SDAR does not use waveform messages (such as amplitude, energy, polarization) for signal detecting. Therefore, it is an appropriate technique for low SNR data. 2. Real-time estimation. When new data appears in the record, the probability distribution models can be automatic updated by SDAR for on-line processing. 3. Discounting property. the SDAR introduces a discounting parameter to decrease the influence of present statistic value on future data. It makes SDAR as a robust algorithm for non-stationary signal processing. Within these 3 advantages, the SDAR method can handle the non-stationary time-varying long-term series and achieve real-time monitoring. Finally, we employ the SDAR on a synthetic model and Tomakomai Ocean Bottom Cable (OBC) baseline data to prove the feasibility and advantage of our method.

Dry-Enzyme Test For Gaseous Chemicals

NASA Technical Reports Server (NTRS)

Barzana, Eduardo; Karel, Marcus; Klibanov, Alexander

1990-01-01

Simple, dry-chemical test detects ethanol in human breath. Method of test also adapted to detection of such toxic chemicals as formaldehyde in airstreams. Used qualitatively to detect chemical compounds above present level; for example, ethanol above legal level for driving. Also used to indicate quantitatively concentrations of compounds. Involves dry enzyme and color indicator. Adapted to detect any gaseous compound transformed by enzymes to produce change evident to human eye or to instrument.

Saliency-Guided Change Detection of Remotely Sensed Images Using Random Forest

NASA Astrophysics Data System (ADS)

Feng, W.; Sui, H.; Chen, X.

2018-04-01

Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.

Investigation of the cross-ship comparison monitoring method of failure detection in the HIMAT RPRV. [digital control techniques using airborne microprocessors

NASA Technical Reports Server (NTRS)

Wolf, J. A.

1978-01-01

The Highly maneuverable aircraft technology (HIMAT) remotely piloted research vehicle (RPRV) uses cross-ship comparison monitoring of the actuator RAM positions to detect a failure in the aileron, canard, and elevator control surface servosystems. Some possible sources of nuisance trips for this failure detection technique are analyzed. A FORTRAN model of the simplex servosystems and the failure detection technique were utilized to provide a convenient means of changing parameters and introducing system noise. The sensitivity of the technique to differences between servosystems and operating conditions was determined. The cross-ship comparison monitoring method presently appears to be marginal in its capability to detect an actual failure and to withstand nuisance trips.

Making great leaps forward: Accounting for detectability in herpetological field studies

USGS Publications Warehouse

Mazerolle, Marc J.; Bailey, Larissa L.; Kendall, William L.; Royle, J. Andrew; Converse, Sarah J.; Nichols, James D.

2007-01-01

Detecting individuals of amphibian and reptile species can be a daunting task. Detection can be hindered by various factors such as cryptic behavior, color patterns, or observer experience. These factors complicate the estimation of state variables of interest (e.g., abundance, occupancy, species richness) as well as the vital rates that induce changes in these state variables (e.g., survival probabilities for abundance; extinction probabilities for occupancy). Although ad hoc methods (e.g., counts uncorrected for detection, return rates) typically perform poorly in the face of no detection, they continue to be used extensively in various fields, including herpetology. However, formal approaches that estimate and account for the probability of detection, such as capture-mark-recapture (CMR) methods and distance sampling, are available. In this paper, we present classical approaches and recent advances in methods accounting for detectability that are particularly pertinent for herpetological data sets. Through examples, we illustrate the use of several methods, discuss their performance compared to that of ad hoc methods, and we suggest available software to perform these analyses. The methods we discuss control for imperfect detection and reduce bias in estimates of demographic parameters such as population size, survival, or, at other levels of biological organization, species occurrence. Among these methods, recently developed approaches that no longer require marked or resighted individuals should be particularly of interest to field herpetologists. We hope that our effort will encourage practitioners to implement some of the estimation methods presented herein instead of relying on ad hoc methods that make more limiting assumptions.

Expanding Cancer Detection Using Molecular Imprinting for a Novel Point-of-Care Diagnostic Device

NASA Astrophysics Data System (ADS)

Yu, Yingjie; Rafailovich, Miriam; Wang, Yantian; Kang, Yeona; Zhang, Lingxi; Rigas, Basil; Division of Gastroenterology, School of Medicine Team

2013-03-01

We propose the use of a potentiometric biosensor that incorporates the efficient and specific molecular imprinting (MI) method with a self-assembled monolayer (SAM). We first tested the biosensor using carcinoembryonic antigen, CEA, a biomarker associated with pancreatic cancer. No change in detection efficiency was observed, indicating that the sensor is able to discriminate for the template analyte even in concentrated solution of similar substances. In addition, we use biosensor to discriminate normal fibrinogen and damaged fibrinogen, which is critical for the detection of bleeding disorder. Computer simulations of the protein structure were performed in order to estimate the changes in morphology and determine the sensitivity of the biosensor to conformational changes in the proteins. We found that even small changes in PH can generate rotation of the surface functional groups. Yet, the results show that only when the detection and imprinting conditions are similar, robust signals occurs. Hence we concluded that both morphology and surface chemistry play a role in the recognition.

A symmetry measure for damage detection with mode shapes

NASA Astrophysics Data System (ADS)

Chen, Justin G.; Büyüköztürk, Oral

2017-11-01

This paper introduces a feature for detecting damage or changes in structures, the continuous symmetry measure, which can quantify the amount of a particular rotational, mirror, or translational symmetry in a mode shape of a structure. Many structures in the built environment have geometries that are either symmetric or almost symmetric, however damage typically occurs in a local manner causing asymmetric changes in the structure's geometry or material properties, and alters its mode shapes. The continuous symmetry measure can quantify these changes in symmetry as a novel indicator of damage for data-based structural health monitoring approaches. This paper describes the concept as a basis for detecting changes in mode shapes and detecting structural damage. Application of the method is demonstrated in various structures with different symmetrical properties: a pipe cross-section with a finite element model and experimental study, the NASA 8-bay truss model, and the simulated IASC-ASCE structural health monitoring benchmark structure. The applicability and limitations of the feature in applying it to structures of varying geometries is discussed.

Using occupancy modeling to compare traditional versus DNA metabarcoding methods for characterizing zooplankton biodiversity

EPA Science Inventory

DNA metabarcoding tools could increase our ability to detect changes in zooplankton communities and to detect invasive zooplankton taxa while they are still rare. Nonetheless, the use of DNA-metabarcoding for characterizing zooplankton biodiversity in the Great Lakes has not bee...

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.

Enhanced data validation strategy of air quality monitoring network.

PubMed

Harkat, Mohamed-Faouzi; Mansouri, Majdi; Nounou, Mohamed; Nounou, Hazem

2018-01-01

Quick validation and detection of faults in measured air quality data is a crucial step towards achieving the objectives of air quality networks. Therefore, the objectives of this paper are threefold: (i) to develop a modeling technique that can be used to predict the normal behavior of air quality variables and help provide accurate reference for monitoring purposes; (ii) to develop fault detection method that can effectively and quickly detect any anomalies in measured air quality data. For this purpose, a new fault detection method that is based on the combination of generalized likelihood ratio test (GLRT) and exponentially weighted moving average (EWMA) will be developed. GLRT is a well-known statistical fault detection method that relies on maximizing the detection probability for a given false alarm rate. In this paper, we propose to develop GLRT-based EWMA fault detection method that will be able to detect the changes in the values of certain air quality variables; (iii) to develop fault isolation and identification method that allows defining the fault source(s) in order to properly apply appropriate corrective actions. In this paper, reconstruction approach that is based on Midpoint-Radii Principal Component Analysis (MRPCA) model will be developed to handle the types of data and models associated with air quality monitoring networks. All air quality modeling, fault detection, fault isolation and reconstruction methods developed in this paper will be validated using real air quality data (such as particulate matter, ozone, nitrogen and carbon oxides measurement). Copyright © 2017 Elsevier Inc. All rights reserved.

Two-stage Keypoint Detection Scheme for Region Duplication Forgery Detection in Digital Images.

PubMed

Emam, Mahmoud; Han, Qi; Zhang, Hongli

2018-01-01

In digital image forensics, copy-move or region duplication forgery detection became a vital research topic recently. Most of the existing keypoint-based forgery detection methods fail to detect the forgery in the smooth regions, rather than its sensitivity to geometric changes. To solve these problems and detect points which cover all the regions, we proposed two steps for keypoint detection. First, we employed the scale-invariant feature operator to detect the spatially distributed keypoints from the textured regions. Second, the keypoints from the missing regions are detected using Harris corner detector with nonmaximal suppression to evenly distribute the detected keypoints. To improve the matching performance, local feature points are described using Multi-support Region Order-based Gradient Histogram descriptor. Based on precision-recall rates and commonly tested dataset, comprehensive performance evaluation is performed. The results demonstrated that the proposed scheme has better detection and robustness against some geometric transformation attacks compared with state-of-the-art methods. © 2017 American Academy of Forensic Sciences.

Evaluation of ground-penetrating radar to detect free-phase hydrocarbons in fractured rocks - Results of numerical modeling and physical experiments

USGS Publications Warehouse

Lane, J.W.; Buursink, M.L.; Haeni, F.P.; Versteeg, R.J.

2000-01-01

The suitability of common-offset ground-penetrating radar (GPR) to detect free-phase hydrocarbons in bedrock fractures was evaluated using numerical modeling and physical experiments. The results of one- and two-dimensional numerical modeling at 100 megahertz indicate that GPR reflection amplitudes are relatively insensitive to fracture apertures ranging from 1 to 4 mm. The numerical modeling and physical experiments indicate that differences in the fluids that fill fractures significantly affect the amplitude and the polarity of electromagnetic waves reflected by subhorizontal fractures. Air-filled and hydrocarbon-filled fractures generate low-amplitude reflections that are in-phase with the transmitted pulse. Water-filled fractures create reflections with greater amplitude and opposite polarity than those reflections created by air-filled or hydrocarbon-filled fractures. The results from the numerical modeling and physical experiments demonstrate it is possible to distinguish water-filled fracture reflections from air- or hydrocarbon-filled fracture reflections, nevertheless subsurface heterogeneity, antenna coupling changes, and other sources of noise will likely make it difficult to observe these changes in GPR field data. This indicates that the routine application of common-offset GPR reflection methods for detection of hydrocarbon-filled fractures will be problematic. Ideal cases will require appropriately processed, high-quality GPR data, ground-truth information, and detailed knowledge of subsurface physical properties. Conversely, the sensitivity of GPR methods to changes in subsurface physical properties as demonstrated by the numerical and experimental results suggests the potential of using GPR methods as a monitoring tool. GPR methods may be suited for monitoring pumping and tracer tests, changes in site hydrologic conditions, and remediation activities.The suitability of common-offset ground-penetrating radar (GPR) to detect free-phase hydrocarbons in bedrock fractures was evaluated using numerical modeling and physical experiments. The results of one- and two-dimensional numerical modeling at 100 megahertz indicate that GPR reflection amplitudes are relatively insensitive to fracture apertures ranging from 1 to 4 mm. The numerical modeling and physical experiments indicate that differences in the fluids that fill fractures significantly affect the amplitude and the polarity of electromagnetic waves reflected by subhorizontal fractures. Air-filled and hydrocarbon-filled fractures generate low-amplitude reflections that are in-phase with the transmitted pulse. Water-filled fractures create reflections with greater amplitude and opposite polarity than those reflections created by air-filled or hydrocarbon-filled fractures. The results from the numerical modeling and physical experiments demonstrate it is possible to distinguish water-filled fracture reflections from air- or hydrocarbon-filled fracture reflections, nevertheless subsurface heterogeneity, antenna coupling changes, and other sources of noise will likely make it difficult to observe these changes in GPR field data. This indicates that the routine application of common-offset GPR reflection methods for detection of hydrocarbon-filled fractures will be problematic. Ideal cases will require appropriately processed, high-quality GPR data, ground-truth information, and detailed knowledge of subsurface physical properties. Conversely, the sensitivity of GPR methods to changes in subsurface physical properties as demonstrated by the numerical and experimental results suggests the potential of using GPR methods as a monitoring tool. GPR methods may be suited for monitoring pumping and tracer tests, changes in site hydrologic conditions, and remediation activities.

Nanostructure-initiator mass spectrometry biometrics

DOEpatents

Leclerc, Marion; Bowen, Benjamin; Northen, Trent

2015-09-08

Several embodiments described herein are drawn to methods of identifying an analyte on a subject's skin, methods of generating a fingerprint, methods of determining a physiological change in a subject, methods of diagnosing health status of a subject, and assay systems for detecting an analyte and generating a fingerprint, by nanostructure-initiator mass spectrometry (NIMS).

Simultaneous Ionic Current and Potential Detection of Nanoparticles by a Multifunctional Nanopipette.

PubMed

Panday, Namuna; Qian, Gongming; Wang, Xuewen; Chang, Shuai; Pandey, Popular; He, Jin

2016-12-27

Nanopore sensing-based technologies have made significant progress for single molecule and single nanoparticle detection and analysis. In recent years, multimode sensing by multifunctional nanopores shows the potential to greatly improve the sensitivity and selectivity of traditional resistive-pulse sensing methods. In this paper, we showed that two label-free electric sensing modes could work cooperatively to detect the motion of 40 nm diameter spherical gold nanoparticles (GNPs) in solution by a multifunctional nanopipette. The multifunctional nanopipettes containing both nanopore and nanoelectrode (pyrolytic carbon) at the tip were fabricated quickly and cheaply. We demonstrated that the ionic current and local electrical potential changes could be detected simultaneously during the translocation of individual GNPs. We also showed that the nanopore/CNE tip geometry enabled the CNE not only to detect the translocation of single GNP but also to collectively detect several GNPs outside the nanopore entrance. The dynamic accumulation of GNPs near the nanopore entrance resulted in no detectable current changes, but was detected by the potential changes at the CNE. We revealed the motions of GNPs both outside and inside the nanopore, individually and collectively, with the combination of ionic current and potential measurements.

New reporting procedures based on long-term method detection levels and some considerations for interpretations of water-quality data provided by the U.S. Geological Survey National Water Quality Laboratory

USGS Publications Warehouse

Childress, Carolyn J. Oblinger; Foreman, William T.; Connor, Brooke F.; Maloney, Thomas J.

1999-01-01

This report describes the U.S. Geological Survey National Water Quality Laboratory?s approach for determining long-term method detection levels and establishing reporting levels, details relevant new reporting conventions, and provides preliminary guidance on interpreting data reported with the new conventions. At the long-term method detection level concentration, the risk of a false positive detection (analyte reported present at the long-term method detection level when not in sample) is no more than 1 percent. However, at the long-term method detection level, the risk of a false negative occurrence (analyte reported not present when present at the long-term method detection level concentration) is up to 50 percent. Because this false negative rate is too high for use as a default 'less than' reporting level, a more reliable laboratory reporting level is set at twice the determined long-term method detection level. For all methods, concentrations measured between the laboratory reporting level and the long-term method detection level will be reported as estimated concentrations. Non-detections will be censored to the laboratory reporting level. Adoption of the new reporting conventions requires a full understanding of how low-concentration data can be used and interpreted and places responsibility for using and presenting final data with the user rather than with the laboratory. Users must consider that (1) new laboratory reporting levels may differ from previously established minimum reporting levels, (2) long-term method detection levels and laboratory reporting levels may change over time, and (3) estimated concentrations are less certain than concentrations reported above the laboratory reporting level. The availability of uncensored but qualified low-concentration data for interpretation and statistical analysis is a substantial benefit to the user. A decision to censor data after they are reported from the laboratory may still be made by the user, if merited, on the basis of the intended use of the data.

Complementary use of optical coherence tomography and 5-aminolevulinic acid induced fluorescent spectroscopy for diagnosis of neoplastic processes in cervix and vulva

NASA Astrophysics Data System (ADS)

Sapozhnikova, Veronika V.; Shakhova, Natalia M.; Kamensky, Vladislav A.; Kuranov, Roman V.; Loshenov, Victor B.; Petrova, Svetlana A.

2003-07-01

A new approach to improving the diagnostic value of optical methods is suggested, which is based on a complementary investigation of different optical parameters of biotissues. The aim of this paper is comparative study of the feasibility of two optical methods - fluorescence spectroscopy and optical coherence tomography - for visualization of borders of neoplastic processes in the uterine cervix and vulva. Fluorescence spectroscopy is based on the detection of biochemical and optical coherence tomography on backscattering properties in norm and pathological changes of tissues. By means of these optical methods changes in biochemical and morphological properties of tissues were investigated. A parallel analysis of these two optical methods and histology from the center of tumors and their optical borders was made. Thirteen female patients with neoplastic changes in uterine cervix and vulva were enrolled in this study. The borders of the tumor determined by optical methods (fluorescence spectroscopy and optical coherence tomography) are coinciding with the biopsy proved ones. In addition, OCT and fluorescence borders of tumor in the uterine cervix and vulva exceeds colposcopically detectable borders, the averaging difference 2 mm. In future optical methods would considerably enhance diagnostic accuracy of conventional methods used in oncogynecology.

Bacteriological incidence in pneumonia patients with pulmonary emphysema: a bacterial floral analysis using the 16S ribosomal RNA gene in bronchoalveolar lavage fluid

PubMed Central

Naito, Keisuke; Yamasaki, Kei; Yatera, Kazuhiro; Akata, Kentaro; Noguchi, Shingo; Kawanami, Toshinori; Fukuda, Kazumasa; Kido, Takashi; Ishimoto, Hiroshi; Mukae, Hiroshi

2017-01-01

Pulmonary emphysema is an important radiological finding in chronic obstructive pulmonary disease patients, but bacteriological differences in pneumonia patients according to the severity of emphysematous changes have not been reported. Therefore, we evaluated the bacteriological incidence in the bronchoalveolar lavage fluid (BALF) of pneumonia patients using cultivation and a culture-independent molecular method. Japanese patients with community-acquired pneumonia (83) and healthcare-associated pneumonia (94) between April 2010 and February 2014 were evaluated. The BALF obtained from pneumonia lesions was evaluated by both cultivation and a molecular method. In the molecular method, ~600 base pairs of bacterial 16S ribosomal RNA genes in the BALF were amplified by polymerase chain reaction, and clone libraries were constructed. The nucleotide sequences of 96 randomly selected colonies were determined, and a homology search was performed to identify the bacterial species. A qualitative radiological evaluation of pulmonary emphysema based on chest computed tomography (CT) images was performed using the Goddard classification. The severity of pulmonary emphysema based on the Goddard classification was none in 47.4% (84/177), mild in 36.2% (64/177), moderate in 10.2% (18/177), and severe in 6.2% (11/177). Using the culture-independent molecular method, Moraxella catarrhalis was significantly more frequently detected in moderate or severe emphysema patients than in patients with no or mild emphysematous changes. The detection rates of Haemophilus influenzae and Pseudomonas aeruginosa were unrelated to the severity of pulmonary emphysematous changes, and Streptococcus species – except for the S. anginosus group and S. pneumoniae – were detected more frequently using the molecular method we used for the BALF of patients with pneumonia than using culture methods. Our findings suggest that M. catarrhalis is more frequently detected in pneumonia patients with moderate or severe emphysema than in those with no or mild emphysematous changes on chest CT. M. catarrhalis may play a major role in patients with pneumonia complicating severe pulmonary emphysema. PMID:28790814

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.

Self-sensing of dielectric elastomer actuator enhanced by artificial neural network

NASA Astrophysics Data System (ADS)

Ye, Zhihang; Chen, Zheng

2017-09-01

Dielectric elastomer (DE) is a type of soft actuating material, the shape of which can be changed under electrical voltage stimuli. DE materials have promising usage in future’s soft actuators and sensors, such as soft robotics, energy harvesters, and wearable sensors. In this paper, a stripe DE actuator with integrated sensing capability is designed, fabricated, and characterized. Since the strip actuator can be approximated as a compliant capacitor, it is possible to detect the actuator’s displacement by analyzing the actuator’s impedance change. An integrated sensing scheme that adds a high frequency probing signal into actuation signal is developed. Electrical impedance changes in the probing signal are extracted by fast Fourier transform algorithm, and nonlinear data fitting methods involving artificial neural network are implemented to detect the actuator’s displacement. A series of experiments show that by improving data processing and analyzing methods, the integrated sensing method can achieve error level of lower than 1%.

Finger wear detection for production line battery tester

DOEpatents

Depiante, E.V.

1997-11-18

A method is described for detecting wear in a battery tester probe. The method includes providing a battery tester unit having at least one tester finger, generating a tester signal using the tester fingers and battery tester unit with the signal characteristic of the electrochemical condition of the battery and the tester finger, applying wavelet transformation to the tester signal including computing a mother wavelet to produce finger wear indicator signals, analyzing the signals to create a finger wear index, comparing the wear index for the tester finger with the index for a new tester finger and generating a tester finger signal change signal to indicate achieving a threshold wear change. 9 figs.

Assessing the response of runoff to climate change and human activities for a typical basin in the Northern Taihang Mountain, China

NASA Astrophysics Data System (ADS)

Wang, Jinfeng; Gao, Yanchuan; Wang, Sheng

2018-04-01

Climate change and human activities are the two main factors on runoff change. Quantifying the contribution of climate change and human activities on runoff change is important for water resources planning and management. In this study, the variation trend and abrupt change point of hydro-meteorological factors during 1960-2012 were detected by using the Mann-Kendall test and Pettitt change-point statistics. Then the runoff was simulated by SWAT model. The contribution of climate change and human activities on runoff change was calculated based on the SWAT model and the elasticity coefficient method. The results showed that in contrast to the increasing trend for annual temperature, the significant decreasing trends were detected for annual runoff and precipitation, with an abrupt change point in 1982. The simulated results of SWAT had good consistency with observed ones, and the values of R2 and E_{NS} all exceeded 0.75. The two methods used for assessing the contribution of climate change and human activities on runoff reduction yielded consistent results. The contribution of climate change (precipitation reduction and temperature rise) was {˜ }37.5%, while the contribution of human activities (the increase of economic forest and built-up land, hydrologic projects) was {˜ }62.5%.

Use of uterine electromyography to diagnose term and preterm labor

PubMed Central

LUCOVNIK, MIHA; KUON, RUBEN J.; CHAMBLISS, LINDA R.; MANER, WILLIAM L.; SHI, SHAO-QING; SHI, LEILI; BALDUCCI, JAMES; GARFIELD, ROBERT E.

2011-01-01

Current methodologies to assess the process of labor, such as tocodynamometry or intrauterine pressure catheters, fetal fibronectin, cervical length measurement and digital cervical examination, have several major drawbacks. They only measure the onset of labor indirectly and do not detect cellular changes characteristic of true labor. Consequently, their predictive values for term or preterm delivery are poor. Uterine contractions are a result of the electrical activity within the myometrium. Measurement of uterine electromyography (EMG) has been shown to detect contractions as accurately as the currently used methods. In addition, changes in cell excitability and coupling required for effective contractions that lead to delivery are reflected in changes of several EMG parameters. Use of uterine EMG can help to identify patients in true labor better than any other method presently employed in the clinic. PMID:21241260

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.

Detection of target-probe oligonucleotide hybridization using synthetic nanopore resistive pulse sensing.

PubMed

Booth, Marsilea Adela; Vogel, Robert; Curran, James M; Harbison, SallyAnn; Travas-Sejdic, Jadranka

2013-07-15

Despite the plethora of DNA sensor platforms available, a portable, sensitive, selective and economic sensor able to rival current fluorescence-based techniques would find use in many applications. In this research, probe oligonucleotide-grafted particles are used to detect target DNA in solution through a resistive pulse nanopore detection technique. Using carbodiimide chemistry, functionalized probe DNA strands are attached to carboxylated dextran-based magnetic particles. Subsequent incubation with complementary target DNA yields a change in surface properties as the two DNA strands hybridize. Particle-by-particle analysis with resistive pulse sensing is performed to detect these changes. A variable pressure method allows identification of changes in the surface charge of particles. As proof-of-principle, we demonstrate that target hybridization is selectively detected at micromolar concentrations (nanomoles of target) using resistive pulse sensing, confirmed by fluorescence and phase analysis light scattering as complementary techniques. The advantages, feasibility and limitations of using resistive pulse sensing for sample analysis are discussed. Copyright © 2013 Elsevier B.V. All rights reserved.

Development and Application of Quantitative Detection Method for Viral Hemorrhagic Septicemia Virus (VHSV) Genogroup IVa

PubMed Central

Kim, Jong-Oh; Kim, Wi-Sik; Kim, Si-Woo; Han, Hyun-Ja; Kim, Jin Woo; Park, Myoung Ae; Oh, Myung-Joo

2014-01-01

Viral hemorrhagic septicemia virus (VHSV) is a problematic pathogen in olive flounder (Paralichthys olivaceus) aquaculture farms in Korea. Thus, it is necessary to develop a rapid and accurate diagnostic method to detect this virus. We developed a quantitative RT-PCR (qRT-PCR) method based on the nucleocapsid (N) gene sequence of Korean VHSV isolate (Genogroup IVa). The slope and R2 values of the primer set developed in this study were −0.2928 (96% efficiency) and 0.9979, respectively. Its comparison with viral infectivity calculated by traditional quantifying method (TCID50) showed a similar pattern of kinetic changes in vitro and in vivo. The qRT-PCR method reduced detection time compared to that of TCID50, making it a very useful tool for VHSV diagnosis. PMID:24859343

Compact Surface Plasmon Resonance Biosensor for Fieldwork Environmental Detection

NASA Astrophysics Data System (ADS)

Boyd, Margrethe; Drake, Madison; Stipe, Kristian; Serban, Monica; Turner, Ivana; Thomas, Aaron; Macaluso, David

2017-04-01

The ability to accurately and reliably detect biomolecular targets is important in innumerable applications, including the identification of food-borne parasites, viral pathogens in human tissue, and environmental pollutants. While detection methods do exist, they are typically slow, expensive, and restricted to laboratory use. The method of surface plasmon resonance based biosensing offers a unique opportunity to characterize molecular targets while avoiding these constraints. By incorporating a plasmon-supporting gold film within a prism/laser optical system, it is possible to reliably detect and quantify the presence of specific biomolecules of interest in real time. This detection is accomplished by observing shifts in plasmon formation energies corresponding to optical absorption due to changes in index of refraction near the gold-prism interface caused by the binding of target molecules. A compact, inexpensive, battery-powered surface plasmon resonance biosensor based on this method is being developed at the University of Montana to detect waterborne pollutants in field-based environmental research.

[Shock shape representation of sinus heart rate based on cloud model].

PubMed

Yin, Wenfeng; Zhao, Jie; Chen, Tiantian; Zhang, Junjian; Zhang, Chunyou; Li, Dapeng; An, Baijing

2014-04-01

The present paper is to analyze the trend of sinus heart rate RR interphase sequence after a single ventricular premature beat and to compare it with the two parameters, turbulence onset (TO) and turbulence slope (TS). Based on the acquisition of sinus rhythm concussion sample, we in this paper use a piecewise linearization method to extract its linear characteristics, following which we describe shock form with natural language through cloud model. In the process of acquisition, we use the exponential smoothing method to forecast the position where QRS wave may appear to assist QRS wave detection, and use template to judge whether current cardiac is sinus rhythm. And we choose some signals from MIT-BIH Arrhythmia Database to detect whether the algorithm is effective in Matlab. The results show that our method can correctly detect the changing trend of sinus heart rate. The proposed method can achieve real-time detection of sinus rhythm shocks, which is simple and easily implemented, so that it is effective as a supplementary method.

Estimating forestland area change from inventory data

Treesearch

Paul Van Deusen; Francis Roesch; Thomas Wigley

2013-01-01

Simple methods for estimating the proportion of land changing from forest to nonforest are developed. Variance estimators are derived to facilitate significance tests. A power analysis indicates that 400 inventory plots are required to reliably detect small changes in net or gross forest loss. This is an important result because forest certification programs may...

Detection of Historical and Future Precipitation Variations and Extremes Over the Continental United States

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

Anderson, Bruce T.

2015-12-11

Problem: The overall goal of this proposal is to detect observed seasonal-mean precipitation variations and extreme event occurrences over the United States. Detection, e.g. the process of demonstrating that an observed change in climate is unusual, first requires some means of estimating the range of internal variability absent any external drivers. Ideally, the internal variability would be derived from the observations themselves, however generally the observed variability is a confluence of both internal variability and variability in response to external drivers. Further, numerical climate models—the standard tool for detection studies—have their own estimates of intrinsic variability, which may differ substantiallymore » from that found in the observed system as well as other model systems. These problems are further compounded for weather and climate extremes, which as singular events are particularly ill-suited for detection studies because of their infrequent occurrence, limited spatial range, and underestimation within global and even regional numerical models. Rationale: As a basis for this research we will show how stochastic daily-precipitation models—models in which the simulated interannual-to-multidecadal precipitation variance is purely the result of the random evolution of daily precipitation events within a given time period—can be used to address many of these issues simultaneously. Through the novel application of these well-established models, we can first estimate the changes/trends in various means and extremes that can occur even with fixed daily-precipitation characteristics, e.g. that can occur simply as a result of the stochastic evolution of daily weather events within a given climate. Detection of a change in the observed climate—either naturally or anthropogenically forced—can then be defined as any change relative to this stochastic variability, e.g. as changes/trends in the means and extremes that could only have occurred through a change in the underlying climate. As such, this method is capable of detecting “hot spot” regions—as well as “flare ups” within the hot spot regions—that have experienced interannual to multi-decadal scale variations and trends in seasonal-mean precipitation and extreme events. Further by applying the same methods to numerical climate models we can discern the fidelity of the current-generation climate models in representing detectability within the observed climate system. In this way, we can objectively determine the utility of these model systems for performing detection studies of historical and future climate change.« less

Finding trap stiffness of optical tweezers using digital filters.

PubMed

Almendarez-Rangel, Pedro; Morales-Cruzado, Beatriz; Sarmiento-Gómez, Erick; Pérez-Gutiérrez, Francisco G

2018-02-01

Obtaining trap stiffness and calibration of the position detection system is the basis of a force measurement using optical tweezers. Both calibration quantities can be calculated using several experimental methods available in the literature. In most cases, stiffness determination and detection system calibration are performed separately, often requiring procedures in very different conditions, and thus confidence of calibration methods is not assured due to possible changes in the environment. In this work, a new method to simultaneously obtain both the detection system calibration and trap stiffness is presented. The method is based on the calculation of the power spectral density of positions through digital filters to obtain the harmonic contributions of the position signal. This method has the advantage of calculating both trap stiffness and photodetector calibration factor from the same dataset in situ. It also provides a direct method to avoid unwanted frequencies that could greatly affect calibration procedure, such as electric noise, for example.

Application of NIR Raman spectroscopy for detecting and characterizing early dental caries

NASA Astrophysics Data System (ADS)

Ko, A. C.; Choo-Smith, L.-P.; Zhu, R.; Hewko, M.; Dong, C.; Cleghorn, B.; Sowa, M. G.

2006-02-01

Early dental caries detection facilitates implementation of non-surgical methods for arresting caries progression and promoting tooth remineralization. We present a method based on Raman spectroscopy with near-IR laser excitation to provide biochemical contrast for detecting and characterizing incipient carious lesions found in extracted human teeth. Changes in Raman spectra are observed in PO 4 3- vibrations arising from hydroxyapatite of mineralized tooth tissue. Examination of various intensities of the PO 4 3- ν2, ν3, ν4 vibrations showed consistent increased intensities in spectra of carious lesions compared to sound enamel. The spectral changes are attributed to demineralization-induced alterations of enamel crystallite morphology and/or orientation. This hypothesis is supported by reduced Raman polarization anisotropy derived from polarized Raman spectra of carious lesions. Polarized Raman spectral imaging of carious lesions found on whole (i.e. un-sectioned) tooth samples will also be presented.

Electromagnetic and nuclear radiation detector using micromechanical sensors

DOEpatents

Thundat, Thomas G.; Warmack, Robert J.; Wachter, Eric A.

2000-01-01

Electromagnetic and nuclear radiation is detected by micromechanical sensors that can be coated with various interactive materials. As the micromechanical sensors absorb radiation, the sensors bend and/or undergo a shift in resonance characteristics. The bending and resonance changes are detected with high sensitivity by any of several detection methods including optical, capacitive, and piezoresistive methods. Wide bands of the electromagnetic spectrum can be imaged with picoJoule sensitivity, and specific absorptive coatings can be used for selective sensitivity in specific wavelength bands. Microcantilevers coated with optical cross-linking polymers are useful as integrating optical radiation dosimeters. Nuclear radiation dosimetry is possible by fabricating cantilevers from materials that are sensitive to various nuclear particles or radiation. Upon exposure to radiation, the cantilever bends due to stress and its resonance frequency shifts due to changes in elastic properties, based on cantilever shape and properties of the coating.

[Research on early fire detection with CO-CO2 FTIR-spectroscopy].

PubMed

Du, Jian-hua; Zhang, Ren-cheng; Huang, Xiang-ying; Gong, Xue; Zhang, Xiao-hua

2007-05-01

A new fire detection method is put forward based on the theory of FTIR spectroscopy through analyzing all kinds of detection methods, in which CO and CO2 are chosen as early fire detection objects, and an early fire experiment system has been set up. The concentration characters of CO and CO2 were obtained through early fire experiments including real alarm sources and nuisance alarm sources. In real alarm sources there are abundant CO and CO2 which change regularly. In nuisance alarm sources there is almost no CO. So it's feasible to reduce the false alarms and increase the sensitivity of early fire detectors through analyzing the concentration characters of CO and CO2.

Land use change monitoring in Maryland using a probabilistic sample and rapid photointerpretation

Treesearch

Tonya Lister; Andrew Lister; Eunice Alexander

2014-01-01

The U.S. state of Maryland needs to monitor land use change in order to address land management objectives. This paper presents a change detection method that, through automation and standard geographic information system (GIS) techniques, facilitates the estimation of landscape change via photointerpretation. Using the protocols developed, we show a net loss of forest...

Quantifying ecological thresholds from response surfaces

Treesearch

Heather E. Lintz; Bruce McCune; Andrew N. Gray; Katherine A. McCulloh

2011-01-01

Ecological thresholds are abrupt changes of ecological state. While an ecological threshold is a widely accepted concept, most empirical methods detect them in time or across geographic space. Although useful, these approaches do not quantify the direct drivers of threshold response. Causal understanding of thresholds detected empirically requires their investigation...

Development of a label-free immunosensor system for detecting plasma cortisol levels in fish.

PubMed

Wu, Haiyun; Ohnuki, Hitoshi; Hibi, Kyoko; Ren, Huifeng; Endo, Hideaki

2016-02-01

Fishes display a wide variation in their physiological responses to stress, which is clearly evident in the plasma corticosteroid changes, chiefly cortisol levels in fish. In the present study, we describe a novel label-free immunosensor for detecting plasma cortisol levels. The method is based on immunologic reactions and amperometric measurement using cyclic voltammetry. For the immobilization of the antibody on the surface of sensing electrode, we used a self-assembled monolayer of thiol-containing compounds. Using this electrode, we detect the CV signal change caused by the generation of antigen-antibody complex. The immunosensor showed a response to cortisol levels, and the anodic peak value linearly decreased with a correlation coefficient of 0.990 in diluted plasma. The specificity of the label-free immunosensor system was investigated using other steroid hormones, such as 17α, 20β-dihydroxy-4-pregnen-3-one, progesterone, estriol, estradiol, and testosterone. The specific detection of cortisol was suggested by a minimal change from -0.32 to 0.51 μA in the anodic peak value of the other steroid hormones. The sensor system was used to determine the plasma cortisol levels in Nile tilapia (Oreochromis niloticus), and the results were compared with those of the same samples determined using the conventional method (ELISA). A good correlation was obtained between values determined using both methods (correlation coefficient 0.993). These findings suggest that the proposed label-free immunosensor could be useful for rapid and convenient analysis of cortisol levels in fish plasma samples.

Sunlight Induced Preparation of Functionalized Gold Nanoparticles as Recyclable Colorimetric Dual Sensor for Aluminum and Fluoride in Water.

PubMed

Kumar, Anshu; Bhatt, Madhuri; Vyas, Gaurav; Bhatt, Shreya; Paul, Parimal

2017-05-24

A sunlight induced simple green route has been developed for the synthesis of polyacrylate functionalized gold nanoparticles (PAA-AuNPs), in which poly(acrylic acid) functions as a reducing as well as stabilizing agent. This material has been characterized on the basis of spectroscopic and microscopic studies; it exhibited selective colorimetric detection of Al 3+ in aqueous media, and the Al 3+ induced aggregated PAA-AuNPs exhibited detection of F - with sharp color change and high selectivity and sensitivity out of a large number of metal ions and anions tested. The mechanistic study revealed that, for Al 3+ , the color change is due to a shift of the SPR band because of the Al 3+ induced aggregation of PAA-AuNPs, whereas for F - , the reverse color change (blue to red) with return of the SPR band to its original position is due to dispersion of aggregated PAA-AuNPs, as F - removes Al 3+ from the aggregated species by complex formation. Only concentration-dependent fluoride ion can prevent Al 3+ from aggregating PAA-AuNPs. The method is successfully used for the detection of F - in water collected from various sources by the spiking method, in toothpastes of different brands by the direct method. The solid Al 3+ -PAA-AuNPs were isolated, adsorbed on ZIF@8 (zeolitic imidazolate framework) and on a cotton strip, and applied as solid sensing material for detection of F - in aqueous media.

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.

DELTACON: A Principled Massive-Graph Similarity Function with Attribution

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

Koutra, Danai; Shah, Neil; Vogelstein, Joshua T.

How much did a network change since yesterday? How different is the wiring between Bob's brain (a left-handed male) and Alice's brain (a right-handed female)? Graph similarity with known node correspondence, i.e. the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-of-the-art methods fail to detect crucial connectivity changes in graphs. We propose DeltaCon, a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g. employees of a company, customersmore » of a mobile carrier). In our experiments on various synthetic and real graphs we showcase the advantages of our method over existing similarity measures. We also employ DeltaCon to real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, and (b) do temporal anomaly detection in the who-emails-whom Enron graph.« less

DELTACON: A Principled Massive-Graph Similarity Function with Attribution

DOE PAGES

Koutra, Danai; Shah, Neil; Vogelstein, Joshua T.; ...

2014-05-22

How much did a network change since yesterday? How different is the wiring between Bob's brain (a left-handed male) and Alice's brain (a right-handed female)? Graph similarity with known node correspondence, i.e. the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-of-the-art methods fail to detect crucial connectivity changes in graphs. We propose DeltaCon, a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g. employees of a company, customersmore » of a mobile carrier). In our experiments on various synthetic and real graphs we showcase the advantages of our method over existing similarity measures. We also employ DeltaCon to real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, and (b) do temporal anomaly detection in the who-emails-whom Enron graph.« less

Detecting vapour bubbles in simulations of metastable water

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

González, Miguel A.; Abascal, Jose L. F.; Valeriani, Chantal, E-mail: christoph.dellago@univie.ac.at, E-mail: cvaleriani@quim.ucm.es

2014-11-14

The investigation of cavitation in metastable liquids with molecular simulations requires an appropriate definition of the volume of the vapour bubble forming within the metastable liquid phase. Commonly used approaches for bubble detection exhibit two significant flaws: first, when applied to water they often identify the voids within the hydrogen bond network as bubbles thus masking the signature of emerging bubbles and, second, they lack thermodynamic consistency. Here, we present two grid-based methods, the M-method and the V-method, to detect bubbles in metastable water specifically designed to address these shortcomings. The M-method incorporates information about neighbouring grid cells to distinguishmore » between liquid- and vapour-like cells, which allows for a very sensitive detection of small bubbles and high spatial resolution of the detected bubbles. The V-method is calibrated such that its estimates for the bubble volume correspond to the average change in system volume and are thus thermodynamically consistent. Both methods are computationally inexpensive such that they can be used in molecular dynamics and Monte Carlo simulations of cavitation. We illustrate them by computing the free energy barrier and the size of the critical bubble for cavitation in water at negative pressure.« less

Optical sensor for rapid microbial detection

NASA Astrophysics Data System (ADS)

Al-Adhami, Mustafa; Tilahun, Dagmawi; Rao, Govind; Kostov, Yordan

2016-05-01

In biotechnology, the ability to instantly detect contaminants is key to running a reliable bioprocess. Bioprocesses are prone to be contaminated by cells that are abundant in our environment; detection and quantification of these cells would aid in the preservation of the bioprocess product. This paper discusses the design and development of a portable kinetics fluorometer which acts as a single-excitation, single-emission photometer that continuously measures fluorescence intensity of an indicator dye, and plots it. Resazurin is used as an indicator dye since the viable contaminant cells reduce Resazurin toResorufin, the latter being strongly fluorescent. A photodiode detects fluorescence change by generating current proportional to the intensity of the light that reached it, and a trans-impedance differential op-amp ensures amplification of the photodiodes' signal. A microfluidic chip was designed specifically for the device. It acts as a fully enclosed cuvette, which enhances the Resazurin reduction rate. E. coli in LB media, along with Resazurin were injected into the microfluidic chip. The optical sensor detected the presence of E. coli in the media based on the fluorescence change that occurred in the indicator dye in concentrations as low as 10 CFU/ml. A method was devised to detect and determine an approximate amount of contamination with this device. This paper discusses application of this method to detect and estimate sample contamination. This device provides fast, accurate, and inexpensive means to optically detect the presence of viable cells.

Scoping review of response shift methods: current reporting practices and recommendations.

PubMed

Sajobi, Tolulope T; Brahmbatt, Ronak; Lix, Lisa M; Zumbo, Bruno D; Sawatzky, Richard

2018-05-01

Response shift (RS) has been defined as a change in the meaning of an individual's self-evaluation of his/her health status and quality of life. Several statistical model- and design-based methods have been developed to test for RS in longitudinal data. We reviewed the uptake of these methods in patient-reported outcomes (PRO) literature. CINHAHL, EMBASE, Medline, ProQuest, PsycINFO, and Web of Science were searched to identify English-language articles about RS published until 2016. Data on year and country of publication, PRO measure adopted, RS detection method, type of RS detected, and testing of underlying model assumptions were extracted from the included articles. Of the 1032 articles identified, 101 (9.8%) articles were included in the study. While 54.5 of the articles reported on the Then-test, 30.7% of the articles reported on Oort's or Schmitt's structural equation modeling (SEM) procedure. Newer RS detection methods, such as relative importance analysis and random forest regression, have been used less frequently. Less than 25% reported on testing the assumptions underlying the adopted RS detection method(s). Despite rapid methodological advancements in RS research, this review highlights the need for further research about RS detection methods for complex longitudinal data and standardized reporting guidelines.

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods.

PubMed

Xu, Lina; Tetteh, Giles; Lipkova, Jana; Zhao, Yu; Li, Hongwei; Christ, Patrick; Piraud, Marie; Buck, Andreas; Shi, Kuangyu; Menze, Bjoern H

2018-01-01

The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68 Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68 Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68 Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k -Nearest Neighbors ( k -NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.

Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods

PubMed Central

Tetteh, Giles; Lipkova, Jana; Zhao, Yu; Li, Hongwei; Christ, Patrick; Buck, Andreas; Menze, Bjoern H.

2018-01-01

The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k-Nearest Neighbors (k-NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study. PMID:29531504

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.

Image change detection using paradoxical theory for patient follow-up quantitation and therapy assessment.

PubMed

David, Simon; Visvikis, Dimitris; Quellec, Gwénolé; Le Rest, Catherine Cheze; Fernandez, Philippe; Allard, Michèle; Roux, Christian; Hatt, Mathieu

2012-09-01

In clinical oncology, positron emission tomography (PET) imaging can be used to assess therapeutic response by quantifying the evolution of semi-quantitative values such as standardized uptake value, early during treatment or after treatment. Current guidelines do not include metabolically active tumor volume (MATV) measurements and derived parameters such as total lesion glycolysis (TLG) to characterize the response to the treatment. To achieve automatic MATV variation estimation during treatment, we propose an approach based on the change detection principle using the recent paradoxical theory, which models imprecision, uncertainty, and conflict between sources. It was applied here simultaneously to pre- and post-treatment PET scans. The proposed method was applied to both simulated and clinical datasets, and its performance was compared to adaptive thresholding applied separately on pre- and post-treatment PET scans. On simulated datasets, the adaptive threshold was associated with significantly higher classification errors than the developed approach. On clinical datasets, the proposed method led to results more consistent with the known partial responder status of these patients. The method requires accurate rigid registration of both scans which can be obtained only in specific body regions and does not explicitly model uptake heterogeneity. In further investigations, the change detection of intra-MATV tracer uptake heterogeneity will be developed by incorporating textural features into the proposed approach.

[Online endpoint detection algorithm for blending process of Chinese materia medica].

PubMed

Lin, Zhao-Zhou; Yang, Chan; Xu, Bing; Shi, Xin-Yuan; Zhang, Zhi-Qiang; Fu, Jing; Qiao, Yan-Jiang

2017-03-01

Blending process, which is an essential part of the pharmaceutical preparation, has a direct influence on the homogeneity and stability of solid dosage forms. With the official release of Guidance for Industry PAT, online process analysis techniques have been more and more reported in the applications in blending process, but the research on endpoint detection algorithm is still in the initial stage. By progressively increasing the window size of moving block standard deviation (MBSD), a novel endpoint detection algorithm was proposed to extend the plain MBSD from off-line scenario to online scenario and used to determine the endpoint in the blending process of Chinese medicine dispensing granules. By online learning of window size tuning, the status changes of the materials in blending process were reflected in the calculation of standard deviation in a real-time manner. The proposed method was separately tested in the blending processes of dextrin and three other extracts of traditional Chinese medicine. All of the results have shown that as compared with traditional MBSD method, the window size changes according to the proposed MBSD method (progressively increasing the window size) could more clearly reflect the status changes of the materials in blending process, so it is suitable for online application. Copyright© by the Chinese Pharmaceutical Association.

Active doublet method for measuring small changes in physical properties

DOEpatents

Roberts, Peter M.; Fehler, Michael C.; Johnson, Paul A.; Phillips, W. Scott

1994-01-01

Small changes in material properties of a work piece are detected by measuring small changes in elastic wave velocity and attenuation within a work piece. Active, repeatable source generate coda wave responses from a work piece, where the coda wave responses are temporally displaced. By analyzing progressive relative phase and amplitude changes between the coda wave responses as a function of elapsed time, accurate determinations of velocity and attenuation changes are made. Thus, a small change in velocity occurring within a sample region during the time periods between excitation origin times (herein called "doublets") will produce a relative delay that changes with elapsed time over some portion of the scattered waves. This trend of changing delay is easier to detect than an isolated delay based on a single arrival and provides a direct measure of elastic wave velocity changes arising from changed material properties of the work piece.

Urban Growth Detection Using Filtered Landsat Dense Time Trajectory in an Arid City

NASA Astrophysics Data System (ADS)

Ye, Z.; Schneider, A.

2014-12-01

Among all remote sensing environment monitoring techniques, time series analysis of biophysical index is drawing increasing attention. Although many of them studied forest disturbance and land cover change detection, few focused on urban growth mapping at medium spatial resolution. As Landsat archive becomes open accessible, methods using Landsat time-series imagery to detect urban growth is possible. It is found that a time trajectory from a newly developed urban area shows a dramatic drop of vegetation index. This enable the utilization of time trajectory analysis to distinguish impervious surface and crop land that has a different temporal biophysical pattern. Also, the time of change can be estimated, yet many challenges remain. Landsat data has lower temporal resolution, which may be worse when cloud-contaminated pixels and SLC-off effect exist. It is difficult to tease apart intra-annual, inter-annual, and land cover difference in a time series. Here, several methods of time trajectory analysis are utilized and compared to find a computationally efficient and accurate way on urban growth detection. A case study city, Ankara, Turkey is chosen for its arid climate and various landscape distributions. For preliminary research, Landsat TM and ETM+ scenes from 1998 to 2002 are chosen. NDVI, EVI, and SAVI are selected as research biophysical indices. The procedure starts with a seasonality filtering. Only areas with seasonality need to be filtered so as to decompose seasonality and extract overall trend. Harmonic transform, wavelet transform, and a pre-defined bell shape filter are used to estimate the overall trend in the time trajectory for each pixel. The point with significant drop in the trajectory is tagged as change point. After an urban change is detected, forward and backward checking is undertaken to make sure it is really new urban expansion other than short time crop fallow or forest disturbance. The method proposed here can capture most of the urban growth during research time period, although the accuracy of time point determination is a bit lower than this. Results from several biophysical indices and filtering methods are similar. Some fallows and bare lands in arid area are easily confused with urban impervious surface.

Improving power to detect changes in blood miRNA expression by accounting for sources of variability in experimental designs.

PubMed

Daniels, Sarah I; Sillé, Fenna C M; Goldbaum, Audrey; Yee, Brenda; Key, Ellen F; Zhang, Luoping; Smith, Martyn T; Thomas, Reuben

2014-12-01

Blood miRNAs are a new promising area of disease research, but variability in miRNA measurements may limit detection of true-positive findings. Here, we measured sources of miRNA variability and determine whether repeated measures can improve power to detect fold-change differences between comparison groups. Blood from healthy volunteers (N = 12) was collected at three time points. The miRNAs were extracted by a method predetermined to give the highest miRNA yield. Nine different miRNAs were quantified using different qPCR assays and analyzed using mixed models to identify sources of variability. A larger number of miRNAs from a publicly available blood miRNA microarray dataset with repeated measures were used for a bootstrapping procedure to investigate effects of repeated measures on power to detect fold changes in miRNA expression for a theoretical case-control study. Technical variability in qPCR replicates was identified as a significant source of variability (P < 0.05) for all nine miRNAs tested. Variability was larger in the TaqMan qPCR assays (SD = 0.15-0.61) versus the qScript qPCR assays (SD = 0.08-0.14). Inter- and intraindividual and extraction variability also contributed significantly for two miRNAs. The bootstrapping procedure demonstrated that repeated measures (20%-50% of N) increased detection of a 2-fold change for approximately 10% to 45% more miRNAs. Statistical power to detect small fold changes in blood miRNAs can be improved by accounting for sources of variability using repeated measures and choosing appropriate methods to minimize variability in miRNA quantification. This study demonstrates the importance of including repeated measures in experimental designs for blood miRNA research. See all the articles in this CEBP Focus section, "Biomarkers, Biospecimens, and New Technologies in Molecular Epidemiology." ©2014 American Association for Cancer Research.

Antibiotic resistance and biofilm formation among coagulase-negative staphylococci isolated from clinical samples at a tertiary care hospital of eastern Nepal.

PubMed

Shrestha, Lok Bahadur; Bhattarai, Narayan Raj; Khanal, Basudha

2017-01-01

Coagulase negative staphylococci were long regarded non-pathogenic as they are the commensals of human skin and mucosa but the recent changes in the medical practice and changes in underlying host populations, they are being considered significant pathogens associated with number of nosocomial infections. The objective of the study was to determine the species, antimicrobial susceptibility pattern, biofilm forming ability of the clinically significant CoNS isolates and to compare the different methods for the detection of biofilm formation. A total of 52 clinically significant CoNS isolates obtained from different units during a year period were studied. Characterization was done using standard microbiological guidelines and antimicrobial susceptibility was done following CLSI guidelines. Biofilm formation was detected by using three methods i.e. tissue culture plate method, congo red agar method and tube adherence method. Among 52 isolates , S. epidermidis (52%) was the most common species which was followed by S. saprophyticus (18%) and S. haemolyticus (14%). Antimicrobial susceptibility pattern of CoNS documented resistance of 80% to ampicillin. Resistance to cefoxitin and ceftriaxone was observed in 58% of the isolates. Biofilm formation was observed in 65.38% of the isolates. The accuracy of Congo red agar and tube adherence method for the detection of biofilm formation was 82% and 76% respectively. CoNS isolates obtained from clinical samples should be processed routinely and antimicrobial susceptibility testing should be performed. Multidrug-resistant CoNS are prevalent. All the three methods i.e. tissue culture plate, Congo red agar and tube adherence method can be used in detecting biofilm formation.

Developing new automated alternation flicker using optic disc photography for the detection of glaucoma progression

PubMed Central

Ahn, J; Yun, I S; Yoo, H G; Choi, J-J; Lee, M

2017-01-01

Purpose To evaluate a progression-detecting algorithm for a new automated matched alternation flicker (AMAF) in glaucoma patients. Methods Open-angle glaucoma patients with a baseline mean deviation of visual field (VF) test>−6 dB were included in this longitudinal and retrospective study. Functional progression was detected by two VF progression criteria and structural progression by both AMAF and conventional comparison methods using optic disc and retinal nerve fiber layer (RNFL) photography. Progression-detecting performances of AMAF and the conventional method were evaluated by an agreement between functional and structural progression criteria. RNFL thickness changes measured by optical coherence tomography (OCT) were compared between progressing and stable eyes determined by each method. Results Among 103 eyes, 47 (45.6%), 21 (20.4%), and 32 (31.1%) eyes were evaluated as glaucoma progression using AMAF, the conventional method, and guided progression analysis (GPA) of the VF test, respectively. The AMAF showed better agreement than the conventional method, using GPA of the VF test (κ=0.337; P<0.001 and κ=0.124; P=0.191, respectively). The rates of RNFL thickness decay using OCT were significantly different between the progressing and stable eyes when progression was determined by AMAF (−3.49±2.86 μm per year vs −1.83±3.22 μm per year; P=0.007) but not by the conventional method (−3.24±2.42 μm per year vs −2.42±3.33 μm per year; P=0.290). Conclusions The AMAF was better than the conventional comparison method in discriminating structural changes during glaucoma progression, and showed a moderate agreement with functional progression criteria. PMID:27662466

Erosion and Deposition Monitoring Using High-Density Aerial Lidar and Geomorphic Change Detection Software Analysis at Los Alamos National Laboratory, Los Alamos New Mexico, LA-UR-17-26743

NASA Astrophysics Data System (ADS)

Walker, T.; Kostrubala, T. L.; Muggleton, S. R.; Veenis, S.; Reid, K. D.; White, A. B.

2017-12-01

The Los Alamos National Laboratory storm water program installed sediment transport mitigation structures to reduce the migration of contaminants within the Los Alamos and Pueblo (LA/P) watershed in Los Alamos, NM. The goals of these structures are to minimize storm water runoff and erosion, enhance deposition, and reduce mobility of contaminated sediments. Previous geomorphological monitoring used GPS surveyed cross-sections on a reach scale to interpolate annual geomorphic change in sediment volumes. While monitoring has confirmed the LA/P watershed structures are performing as designed, the cross-section method proved difficult to estimate uncertainty and the coverage area was limited. A new method, using the Geomorphic Change Detection (GCD) plugin for ESRI ArcGIS developed by Wheaton et al. (2010), with high-density aerial lidar data, has been used to provide high confidence uncertainty estimates and greater areal coverage. Following the 2014 monsoon season, airborne lidar data has been collected annually and the resulting DEMs processed using the GCD method. Additionally, a more accurate characterization of low-amplitude geomorphic changes, typical of low-flow/low-rainfall monsoon years, has been documented by applying a spatially variable error to volume change calculations using the GCD based fuzzy inference system (FIS). The FIS method allows for the calculation of uncertainty based on data set quality and density e.g. point cloud density, ground slope, and degree of surface roughness. At the 95% confidence level, propagated uncertainty estimates of the 2015 and 2016 lidar DEM comparisons yielded detectable changes greater than 0.3 m - 0.46 m. Geomorphic processes identified and verified in the field are typified by low-amplitude, within-channel aggradation and incision and out of channel bank collapse that over the course of a monsoon season result in localized and dectetable change. While the resulting reach scale volume change from 2015 - 2016 was often nonsignificant, it is estimated with a higher degree of confidence than the previous cross-section/interpolation method. Results from comparisons of the recent low-intensity rainfalls/storm peak discharges monsoon season DEMs have established the expected amount of geomorphic change to be minor and localized, yet demonstrable.

Spatial heterogeneity in statistical power to detect changes in lake area in Alaskan National Wildlife Refuges

USGS Publications Warehouse

Nicol, Samuel; Roach, Jennifer K.; Griffith, Brad

2013-01-01

Over the past 50 years, the number and size of high-latitude lakes have decreased throughout many regions; however, individual lake trends have been variable in direction and magnitude. This spatial heterogeneity in lake change makes statistical detection of temporal trends challenging, particularly in small analysis areas where weak trends are difficult to separate from inter- and intra-annual variability. Factors affecting trend detection include inherent variability, trend magnitude, and sample size. In this paper, we investigated how the statistical power to detect average linear trends in lake size of 0.5, 1.0 and 2.0 %/year was affected by the size of the analysis area and the number of years of monitoring in National Wildlife Refuges in Alaska. We estimated power for large (930–4,560 sq km) study areas within refuges and for 2.6, 12.9, and 25.9 sq km cells nested within study areas over temporal extents of 4–50 years. We found that: (1) trends in study areas could be detected within 5–15 years, (2) trends smaller than 2.0 %/year would take >50 years to detect in cells within study areas, and (3) there was substantial spatial variation in the time required to detect change among cells. Power was particularly low in the smallest cells which typically had the fewest lakes. Because small but ecologically meaningful trends may take decades to detect, early establishment of long-term monitoring will enhance power to detect change. Our results have broad applicability and our method is useful for any study involving change detection among variable spatial and temporal extents.

Piezoelectrically tunable resonance frequency beam utilizing a stress-sensitive film

DOEpatents

Thundat, Thomas G.; Wachter, Eric A.

2002-01-01

Methods and apparatus for detecting particular frequencies of acoustic vibration utilize a piezoelectrically-tunable beam element having a piezoelectric layer and a stress sensitive layer and means for providing an electrical potential across the piezoelectric layer to controllably change the beam's stiffness and thereby change its resonance frequency. It is then determined from the response of the piezoelectrically-tunable beam element to the acoustical vibration to which the beam element is exposed whether or not a particular frequency or frequencies of acoustic vibration are detected.

Adaptive Automation for Human Supervision of Multiple Uninhabited Vehicles: Effects on Change Detection, Situation Awareness, and Mental Workload

DTIC Science & Technology

2009-01-01

transient was present. BASELINE EXPERIMENT Methods Participants Sixteen young adults (9 women , 7 men ) aged 18–26 years (mean = 20.5) partic- ipated...Sixteen young adults (8 women , 8 men ) aged 18–28 years (mean = 21.9) partici- pated. The experiment lasted approximately 2 hours and participants were...based on the operator’s change detection performance. Mis- sion scenarios involved supervision of multiple UVs and required multitasking . Effects of

Color-Changing Sensors for Detecting the Presence of Hypergolic Fuels

NASA Technical Reports Server (NTRS)

Roberson, Luke; Captain, Janine; Santiago-Maldonado, Edgardo; Starr, Stanley; DeVor, Robert

2013-01-01

Hypergolic fuel sensors were designed to incorporate novel chemochromic pigments into substrates for use in various methods of leak detection. There are several embodiments to this invention that would provide specific visual indication of hypergols used during and after transfer. The ability to incorporate these pigments into various polymer matrices provides a unique opportunity to manufacture nearly any type of sensor shape that is required. The vibrant color change from yellow to black instantaneously shows the worker the presence of hypergols in the area.

Characterization of ceramic matrix composite degradation using Fourier transform infrared spectroscopy

NASA Astrophysics Data System (ADS)

Henry, Christine; Criner, Amanda Keck; Imel, Megan; King, Derek

2018-04-01

Data collected with a handheld Fourier Transform Infrared (FTIR) device is analyzed and considered as a useful method for detecting and quantifying oxidation on the surface of ceramic matrix composite (CMC) materials. Experiments examine silicon carbide (SiC) coupons, looking for changes in chemical composition before and after thermal exposure. Using mathematical, physical and statistical models for FTIR reflectance data, this research seeks to quantify any detected spectral changes as an indicator of surface oxidation on the CMC coupon.

Combined fluorimetric caspase 3/7 assay and bradford protein determination for assessment of polycation-mediated cytotoxicity.

PubMed

Larsen, Anna K; Hall, Arnaldur; Lundsgart, Henrik; Moghimi, S Moein

2013-01-01

Cationic polyplexes and lipoplexes are widely used as artificial systems for nucleic acid delivery into the cells, but they can also induce cell death. Mechanistic understanding of cell toxicity and biological side effects of these cationic entities is essential for optimization strategies and design of safe and efficient nucleic acid delivery systems. Numerous methods are presently available to detect and delineate cytotoxicity and cell death-mediated signals in cell cultures. Activation of caspases is part of the classical apoptosis program and increased caspase activity is therefore a well-established hallmark of programmed cell death. Additional methods to monitor cell death-related signals must, however, also be carried out to fully define the type of cell toxicity in play. These may include methods that detect plasma membrane damage, loss of mitochondrial membrane potential, phosphatidylserine exposure, and cell morphological changes (e.g., membrane blebbing, nuclear changes, cytoplasmic swelling, cell rounding). Here we describe a 96-well format protocol for detection of capsase-3/7 activity in cell lysates, based on a fluorescent caspase-3 assay, combined with a method to simultaneously determine relative protein contents in the individual wells.

Detection and monitoring of anaerobic rumen fungi using an ARISA method.

PubMed

Denman, S E; Nicholson, M J; Brookman, J L; Theodorou, M K; McSweeney, C S

2008-12-01

To develop an automated ribosomal intergenic spacer region analysis (ARISA) method for the detection of anaerobic rumen fungi and also to demonstrate utility of the technique to monitor colonization and persistence of fungi, and diet-induced changes in community structure. The method could discriminate between three genera of anaerobic rumen fungal isolates, representing Orpinomyces, Piromyces and Neocallimastix species. Changes in anaerobic fungal composition were observed between animals fed a high-fibre diet compared with a grain-based diet. ARISA analysis of rumen samples from animals on grain showed a decrease in fungal diversity with a dominance of Orpinomyces and Piromyces spp. Clustering analysis of ARISA profile patterns grouped animals based on diet. A single strain of Orpinomyces was dosed into a cow and was detectable within the rumen fungal population for several weeks afterwards. The ARISA technique was capable of discriminating between pure cultures at the genus level. Diet composition has a significant influence on the diversity of anaerobic fungi in the rumen and the method can be used to monitor introduced strains. Through the use of ARISA analysis, a better understanding of the effect of diets on rumen anaerobic fungi populations is provided.

Arterial endothelial function measurement method and apparatus

DOEpatents

Maltz, Jonathan S; Budinger, Thomas F

2014-03-04

A "relaxoscope" (100) detects the degree of arterial endothelial function. Impairment of arterial endothelial function is an early event in atherosclerosis and correlates with the major risk factors for cardiovascular disease. An artery (115), such as the brachial artery (BA) is measured for diameter before and after several minutes of either vasoconstriction or vasorelaxation. The change in arterial diameter is a measure of flow-mediated vasomodification (FMVM). The relaxoscope induces an artificial pulse (128) at a superficial radial artery (115) via a linear actuator (120). An ultrasonic Doppler stethoscope (130) detects this pulse 10-20 cm proximal to the point of pulse induction (125). The delay between pulse application and detection provides the pulse transit time (PTT). By measuring PTT before (160) and after arterial diameter change (170), FMVM may be measured based on the changes in PTT caused by changes in vessel caliber, smooth muscle tone and wall thickness.

Monitoring the Vertical Distribution of Rainfall-Induced Strain Changes in a Landslide Measured by Distributed Fiber Optic Sensing With Rayleigh Backscattering

NASA Astrophysics Data System (ADS)

Kogure, Tetsuya; Okuda, Yudai

2018-05-01

Distributed fiber optic sensing with Rayleigh backscattering, which has been recognized as a novel technique for measuring differences in temperature or strain, was adopted in a borehole to a depth of 16 m in an actual landslide to detect a vertical profile of strain changes. Strain changes were measured every 6 hr from 19 June 2017 to 18 October 2017 with a spatial resolution of 10 cm and strain resolution of 1.87 μɛ. The measurements provided a clear-cut vertical profile of the strain changes caused by rainfalls that cannot be detected by conventional methods. The results show that there are two types of deformation in the landslide mass: (1) sliding at the boundary between tuff and mudstone and (2) creep in mudstone layers. Activation of deeper sections of the landslide by heavy rainfalls has also been detected.

Method and apparatus for inspecting reflection masks for defects

DOEpatents

Bokor, Jeffrey; Lin, Yun

2003-04-29

An at-wavelength system for extreme ultraviolet lithography mask blank defect detection is provided. When a focused beam of wavelength 13 nm is incident on a defective region of a mask blank, three possible phenomena can occur. The defect will induce an intensity reduction in the specularly reflected beam, scatter incoming photons into an off-specular direction, and change the amplitude and phase of the electric field at the surface which can be monitored through the change in the photoemission current. The magnitude of these changes will depend on the incident beam size, and the nature, extent and size of the defect. Inspection of the mask blank is performed by scanning the mask blank with 13 nm light focused to a spot a few .mu.m in diameter, while measuring the reflected beam intensity (bright field detection), the scattered beam intensity (dark-field detection) and/or the change in the photoemission current.

Assessment and improvement of statistical tools for comparative proteomics analysis of sparse data sets with few experimental replicates.

PubMed

Schwämmle, Veit; León, Ileana Rodríguez; Jensen, Ole Nørregaard

2013-09-06

Large-scale quantitative analyses of biological systems are often performed with few replicate experiments, leading to multiple nonidentical data sets due to missing values. For example, mass spectrometry driven proteomics experiments are frequently performed with few biological or technical replicates due to sample-scarcity or due to duty-cycle or sensitivity constraints, or limited capacity of the available instrumentation, leading to incomplete results where detection of significant feature changes becomes a challenge. This problem is further exacerbated for the detection of significant changes on the peptide level, for example, in phospho-proteomics experiments. In order to assess the extent of this problem and the implications for large-scale proteome analysis, we investigated and optimized the performance of three statistical approaches by using simulated and experimental data sets with varying numbers of missing values. We applied three tools, including standard t test, moderated t test, also known as limma, and rank products for the detection of significantly changing features in simulated and experimental proteomics data sets with missing values. The rank product method was improved to work with data sets containing missing values. Extensive analysis of simulated and experimental data sets revealed that the performance of the statistical analysis tools depended on simple properties of the data sets. High-confidence results were obtained by using the limma and rank products methods for analyses of triplicate data sets that exhibited more than 1000 features and more than 50% missing values. The maximum number of differentially represented features was identified by using limma and rank products methods in a complementary manner. We therefore recommend combined usage of these methods as a novel and optimal way to detect significantly changing features in these data sets. This approach is suitable for large quantitative data sets from stable isotope labeling and mass spectrometry experiments and should be applicable to large data sets of any type. An R script that implements the improved rank products algorithm and the combined analysis is available.

Cost-Effectiveness Analysis of Three Leprosy Case Detection Methods in Northern Nigeria

PubMed Central

Ezenduka, Charles; Post, Erik; John, Steven; Suraj, Abdulkarim; Namadi, Abdulahi; Onwujekwe, Obinna

2012-01-01

Background Despite several leprosy control measures in Nigeria, child proportion and disability grade 2 cases remain high while new cases have not significantly reduced, suggesting continuous spread of the disease. Hence, there is the need to review detection methods to enhance identification of early cases for effective control and prevention of permanent disability. This study evaluated the cost-effectiveness of three leprosy case detection methods in Northern Nigeria to identify the most cost-effective approach for detection of leprosy. Methods A cross-sectional study was carried out to evaluate the additional benefits of using several case detection methods in addition to routine practice in two north-eastern states of Nigeria. Primary and secondary data were collected from routine practice records and the Nigerian Tuberculosis and Leprosy Control Programme of 2009. The methods evaluated were Rapid Village Survey (RVS), Household Contact Examination (HCE) and Traditional Healers incentive method (TH). Effectiveness was measured as number of new leprosy cases detected and cost-effectiveness was expressed as cost per case detected. Costs were measured from both providers' and patients' perspectives. Additional costs and effects of each method were estimated by comparing each method against routine practise and expressed as incremental cost-effectiveness ratio (ICER). All costs were converted to the U.S. dollar at the 2010 exchange rate. Univariate sensitivity analysis was used to evaluate uncertainties around the ICER. Results The ICER for HCE was $142 per additional case detected at all contact levels and it was the most cost-effective method. At ICER of $194 per additional case detected, THs method detected more cases at a lower cost than the RVS, which was not cost-effective at $313 per additional case detected. Sensitivity analysis showed that varying the proportion of shared costs and subsistent wage for valuing unpaid time did not significantly change the results. Conclusion Complementing routine practice with household contact examination is the most cost-effective approach to identify new leprosy cases and we recommend that, depending on acceptability and feasibility, this intervention is introduced for improved case detection in Northern Nigeria. PMID:23029580

Improvement of statistical methods for detecting anomalies in climate and environmental monitoring systems

NASA Astrophysics Data System (ADS)

Yakunin, A. G.; Hussein, H. M.

2018-01-01

The article shows how the known statistical methods, which are widely used in solving financial problems and a number of other fields of science and technology, can be effectively applied after minor modification for solving such problems in climate and environment monitoring systems, as the detection of anomalies in the form of abrupt changes in signal levels, the occurrence of positive and negative outliers and the violation of the cycle form in periodic processes.

Development of a method for detecting trace metals in aqueous solutions based on the coordination chemistry of hexahydrotriazines.

PubMed

Wojtecki, Rudy J; Yuen, Alexander Y; Zimmerman, Thomas G; Jones, Gavin O; Horn, Hans W; Boday, Dylan J; Hedrick, James L; García, Jeannette M

2015-08-07

The detection of trace amounts (<10 ppb) of heavy metals in aqueous solutions is described using 1,3,5-hexahydro-1,3,5-triazines (HTs) as chemical indicators and a low cost fluorimeter-based detection system. This method takes advantage of the inherent properties of HTs to coordinate strongly with metal ions in solution, a fundamental property that was studied using a combination of analytical tools (UV-Vis titrations, (1)H-NMR titrations and computational modeling). Based on these fundamental studies that show significant changes in the HT UV signature when a metal ion is present, HT compounds were used to prepare indicator strips that resulted in significant fluorescence changes when a metal was present. A portable and economical approach was adopted to test the concept of utilizing HTs to detect heavy metals using a fluorimeter system that consisted of a low-pressure mercury lamp, a photo-detector, a monolithic photodiode and an amplifier, which produces a voltage proportional to the magnitude of the visible fluorescence emission. Readings of the prepared HT test strips were evaluated by exposure to two different heavy metals at the safe threshold concentration described by the U.S. Environmental Protection Agency (EPA) for Cr(3+) and Ag(2+) (100 μg L(-1) and 6.25, respectively). This method of detection could be used to the presence of either metal at these threshold concentrations.

Geomorphological change detection of fluvial processes of lower Siret channel using LIDAR data

NASA Astrophysics Data System (ADS)

Niculita, Mihai; Obreja, Florin; Boca, Bogdan

2015-04-01

Geomorphological change detection is a relatively new method risen from the availability of high resolution multitemporal DEMs (James et. al., 2011; Brodu & Lague, 2012; Barnhart & Crosby, 2013). The main issue in regard with this method is the identification of real change, given by geomorphologic processes, and not by the noise, method artefacts, vegetation or various other errors (Wheaton et. al., 2009). We present the results of geomorphological change detection applied to a part of the lower Siret river channel (from 60 to 140 km above the Siret-Dunăre confluence, between Adjud and Namoloasa). The data sources used were LIDAR DEMs provided by the Siret and Prut-Barlad Water Administrations, one version for 2008, at 2 m resolution, and the other at 0.5 m resolution for 2012. The geomorphological change detection was performed at a resolution of 2 m using the methodology of Wheaton et. al., 2009, on 4 sites with a cumulated length of 47 km, with 41.6 km covering meandering channels and 5.4 km Movileni anthropic lake shore. In the studied period (2008-2012), two major flood events were registered, one in 2008 and the other in 2010 (Olariu et. al., 2009, Serbu et. al., 2009, Nedelcu et. al., 2011). The geomorphological change detection approach managed to outline the presence and the rate of process (expressed as volumetric change) for: channel erosion, channel aggradation, lateral migration of river bank, meander migration, lake bank erosion, alluvial fan deposition and anthropic excavation of channel and river bank. Barnhart T.B., Crosby B.T., 2013. Comparing Two Methods of Surface Change Detection on an Evolving Thermokarst Using High-Temporal-Frequency Terrestrial Laser Scanning, Selawik River, Alaska. Remote Sensing, 5:2813-23937. Brodu N, Lague D. 2012. 3D Terrestrial LiDAR data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology, ISPRS journal of Photogrammmetry and Remote Sensing, 68:121-134. Lague D., Brodu N., Leroux J., 2013. Accurate 3D comparison of complex topography with terrestrial laser scanner: application to the Rangitikei canyon (N-Z), ISPRS journal of Photogrammmetry and Remote Sensing, 80:10-26. James L.A., Hodgson M.E., Ghoshal S., Latiolais M.M., 2012. Geomorphic change detection using historic maps and DEM differencing: the temporal dimension of geospatial analysis. Geomorphology, 137:181-198. Nedelcu G., Borcan M., Branescu E., Petre C., Teleanu B., Preda A., Murafa R., 2011. Exceptional floods from the years 2008 and 2010 in Siret river basin, Proceedings of the Annual Scientific Conference of National Romanian Institute of Hydrology and Water Administration, 1-3 November 2011. (in Romanian) Olariu P., Obreja F., Obreja I., 2009. Some aspects regarding the sediment transit from Trotus catchment and lower sector of Siret river during the exceptional floods from 1991 and 2005, Annals of Stefan cel Mare University of Suceava, XVIII:93-104.(in Romanian) Serbu M., Obreja F., Olariu P., 2009. The 2008 floods from upper Siret catchment. Causes, effects, evaluation, Hidrotechnics, 54(12):1-38. (in Romanian) Wheaton J.M., Brasington J., Darby S., Sear D., 2009. Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets. Earth Surface Processes and Landforms, 35(2):136-156.

CHARACTERIZATION OF PRECURSOR 165 RRNA FOR AEROMONAS HYDROPHILA

EPA Science Inventory

Current strategies for monitoring drinking water quality involve culture-based methods to detect the presence of microbial indicators. However, these methods are insensitive when the organisms have undergone physiological changes such as injury and starvation that can occur in h...

Object-based change detection: dimension of damage in residential areas of Abu Suruj, Sudan

NASA Astrophysics Data System (ADS)

Demharter, Timo; Michel, Ulrich; Ehlers, Manfred; Reinartz, Peter

2011-11-01

Given the importance of Change Detection, especially in the field of crisis management, this paper discusses the advantage of object-based Change Detection. This project and the used methods give an opportunity to coordinate relief actions strategically. The principal objective of this project was to develop an algorithm which allows to detect rapidly damaged and destroyed buildings in the area of Abu Suruj. This Sudanese village is located in West-Darfur and has become the victim of civil war. The software eCognition Developer was used to per-form an object-based Change Detection on two panchromatic Quickbird 2 images from two different time slots. The first image shows the area before, the second image shows the area after the massacres in this region. Seeking a classification for the huts of the Sudanese town Abu Suruj was reached by first segmenting the huts and then classifying them on the basis of geo-metrical and brightness-related values. The huts were classified as "new", "destroyed" and "preserved" with the help of a automated algorithm. Finally the results were presented in the form of a map which displays the different conditions of the huts. The accuracy of the project is validated by an accuracy assessment resulting in an Overall Classification Accuracy of 90.50 percent. These change detection results allow aid organizations to provide quick and efficient help where it is needed the most.

Fetal heart rate deceleration detection using a discrete cosine transform implementation of singular spectrum analysis.

PubMed

Warrick, P A; Precup, D; Hamilton, E F; Kearney, R E

2007-01-01

To develop a singular-spectrum analysis (SSA) based change-point detection algorithm applicable to fetal heart rate (FHR) monitoring to improve the detection of deceleration events. We present a method for decomposing a signal into near-orthogonal components via the discrete cosine transform (DCT) and apply this in a novel online manner to change-point detection based on SSA. The SSA technique forms models of the underlying signal that can be compared over time; models that are sufficiently different indicate signal change points. To adapt the algorithm to deceleration detection where many successive similar change events can occur, we modify the standard SSA algorithm to hold the reference model constant under such conditions, an approach that we term "base-hold SSA". The algorithm is applied to a database of 15 FHR tracings that have been preprocessed to locate candidate decelerations and is compared to the markings of an expert obstetrician. Of the 528 true and 1285 false decelerations presented to the algorithm, the base-hold approach improved on standard SSA, reducing the number of missed decelerations from 64 to 49 (21.9%) while maintaining the same reduction in false-positives (278). The standard SSA assumption that changes are infrequent does not apply to FHR analysis where decelerations can occur successively and in close proximity; our base-hold SSA modification improves detection of these types of event series.

Online Conditional Outlier Detection in Nonstationary Time Series

PubMed Central

Liu, Siqi; Wright, Adam; Hauskrecht, Milos

2017-01-01

The objective of this work is to develop methods for detecting outliers in time series data. Such methods can become the key component of various monitoring and alerting systems, where an outlier may be equal to some adverse condition that needs human attention. However, real-world time series are often affected by various sources of variability present in the environment that may influence the quality of detection; they may (1) explain some of the changes in the signal that would otherwise lead to false positive detections, as well as, (2) reduce the sensitivity of the detection algorithm leading to increase in false negatives. To alleviate these problems, we propose a new two-layer outlier detection approach that first tries to model and account for the nonstationarity and periodic variation in the time series, and then tries to use other observable variables in the environment to explain any additional signal variation. Our experiments on several data sets in different domains show that our method provides more accurate modeling of the time series, and that it is able to significantly improve outlier detection performance. PMID:29644345

Online Conditional Outlier Detection in Nonstationary Time Series.

PubMed

Liu, Siqi; Wright, Adam; Hauskrecht, Milos

2017-05-01

The objective of this work is to develop methods for detecting outliers in time series data. Such methods can become the key component of various monitoring and alerting systems, where an outlier may be equal to some adverse condition that needs human attention. However, real-world time series are often affected by various sources of variability present in the environment that may influence the quality of detection; they may (1) explain some of the changes in the signal that would otherwise lead to false positive detections, as well as, (2) reduce the sensitivity of the detection algorithm leading to increase in false negatives. To alleviate these problems, we propose a new two-layer outlier detection approach that first tries to model and account for the nonstationarity and periodic variation in the time series, and then tries to use other observable variables in the environment to explain any additional signal variation. Our experiments on several data sets in different domains show that our method provides more accurate modeling of the time series, and that it is able to significantly improve outlier detection performance.

PSO-based methods for medical image registration and change assessment of pigmented skin

NASA Astrophysics Data System (ADS)

Kacenjar, Steve; Zook, Matthew; Balint, Michael

2011-03-01

There are various scientific and technological areas in which it is imperative to rapidly detect and quantify changes in imagery over time. In fields such as earth remote sensing, aerospace systems, and medical imaging, searching for timedependent, regional changes across deformable topographies is complicated by varying camera acquisition geometries, lighting environments, background clutter conditions, and occlusion. Under these constantly-fluctuating conditions, the use of standard, rigid-body registration approaches often fail to provide sufficient fidelity to overlay image scenes together. This is problematic because incorrect assessments of the underlying changes of high-level topography can result in systematic errors in the quantification and classification of interested areas. For example, in the current naked-eye detection strategies of melanoma, a dermatologist often uses static morphological attributes to identify suspicious skin lesions for biopsy. This approach does not incorporate temporal changes which suggest malignant degeneration. By performing the co-registration of time-separated skin imagery, a dermatologist may more effectively detect and identify early morphological changes in pigmented lesions; enabling the physician to detect cancers at an earlier stage resulting in decreased morbidity and mortality. This paper describes an image processing system which will be used to detect changes in the characteristics of skin lesions over time. The proposed system consists of three main functional elements: 1.) coarse alignment of timesequenced imagery, 2.) refined alignment of local skin topographies, and 3.) assessment of local changes in lesion size. During the coarse alignment process, various approaches can be used to obtain a rough alignment, including: 1.) a manual landmark/intensity-based registration method1, and 2.) several flavors of autonomous optical matched filter methods2. These procedures result in the rough alignment of a patient's back topography. Since the skin is a deformable membrane, this process only provides an initial condition for subsequent refinements in aligning the localized topography of the skin. To achieve a refined enhancement, a Particle Swarm Optimizer (PSO) is used to optimally determine the local camera models associated with a generalized geometric transform. Here the optimization process is driven using the minimization of entropy between the multiple time-separated images. Once the camera models are corrected for local skin deformations, the images are compared using both pixel-based and regional-based methods. Limits on the detectability of change are established by the fidelity to which the algorithm corrects for local skin deformation and background alterations. These limits provide essential information in establishing early-warning thresholds for Melanoma detection. Key to this work is the development of a PSO alignment algorithm to perform the refined alignment in local skin topography between the time sequenced imagery (TSI). Test and validation of this alignment process is achieved using a forward model producing known geometric artifacts in the images and afterwards using a PSO algorithm to demonstrate the ability to identify and correct for these artifacts. Specifically, the forward model introduces local translational, rotational, and magnification changes within the image. These geometric modifiers are expected during TSI acquisition because of logistical issues to precisely align the patient to the image recording geometry and is therefore of paramount importance to any viable image registration system. This paper shows that the PSO alignment algorithm is effective in autonomously determining and mitigating these geometric modifiers. The degree of efficacy is measured by several statistically and morphologically based pre-image filtering operations applied to the TSI imagery before applying the PSO alignment algorithm. These trade studies show that global image threshold binarization provides rapid and superior convergence characteristics relative to that of morphologically based methods.

Longitudinal Change Detected by Spectral Domain Optical Coherence Tomography in the Optic Nerve Head and Peripapillary Retina in Experimental Glaucoma

PubMed Central

Strouthidis, Nicholas G.; Fortune, Brad; Yang, Hongli; Sigal, Ian A.

2011-01-01

Purpose. To investigate whether longitudinal changes deep within the optic nerve head (ONH) are detectable by spectral domain optical coherence tomography (SDOCT) in experimental glaucoma (EG) and whether these changes are detectable at the onset of Heidelberg Retina Tomography (HRT; Heidelberg Engineering, Heidelberg, Germany)–defined surface topography depression. Methods. Longitudinal SDOCT imaging (Spectralis; Heidelberg Engineering) was performed in both eyes of nine rhesus macaques every 1 to 3 weeks. One eye of each underwent trabecular laser-induced IOP elevation. Four masked operators delineated internal limiting membrane (ILM), retinal nerve fiber layer (RNFL), Bruch's membrane/retinal pigment epithelium (BM/RPE), neural canal opening (NCO), and anterior lamina cribrosa surface (ALCS) by using custom software. Longitudinal changes were assessed and compared between the EG and control (nonlasered) eyes at the onset of HRT-detected surface depression (follow-up 1; [FU1]) and at the most recent image (follow-up 2; [FU2]). Results. Mean IOP in EG eyes was 7.1 to 24.6 mm Hg at FU1 and 13.5 to 31.9 mm Hg at FU2. In control eyes, the mean IOP was 7.2 to 12.6 mm Hg (FU1) and 8.9 to 16.0 mm Hg (FU2). At FU1, neuroretinal rim decreased and ALCS depth increased significantly (paired t-test, P < 0.01); no change in RNFL thickness was detected. At FU2, however, significant prelaminar tissue thinning, posterior displacement of NCO, and RNFL thinning were observed. Conclusions. Longitudinal SDOCT imaging can detect deep ONH changes in EG eyes, the earliest of which are present at the onset of HRT-detected ONH surface height depression. These parameters represent realistic targets for SDOCT detection of glaucomatous progression in human subjects. PMID:21217108

VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls.

PubMed

Kim, Byoungjip; Kang, Seungwoo; Ha, Jin-Young; Song, Junehwa

2015-07-16

In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user's place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense.

Low-temperature infiltration identified using infrared thermography in patients with subcutaneous edema revealed ultrasonographically: A case report.

PubMed

Oya, Maiko; Takahashi, Toshiaki; Tanabe, Hidenori; Oe, Makoto; Murayama, Ryoko; Yabunaka, Koichi; Matsui, Yuko; Sanada, Hiromi

Infiltration is a frequent complication of infusion therapy. We previously demonstrated the usefulness of infrared thermography as an objective method of detecting infiltration in healthy people. However, whether thermography can detect infiltration in clinical settings remains unknown. Therefore, we report two cases where thermography was useful in detecting infiltration at puncture sites. In both cases, tissue changes were verified ultrasonographically. The patients were a 56-year-old male with cholangitis and a 76-year-old female with hepatoma. In both cases, infiltration symptoms such as swelling and erythema occurred one day after the insertion of a peripheral intravenous catheter. Thermographic images from both patients revealed low-temperature areas spreading from the puncture sites; however, these changes were not observed in other patients. The temperature difference between the low-temperature areas and their surrounding skin surface exceeded 1.0°C. Concurrently, ultrasound images revealed that tissues surrounding the vein had a cobblestone appearance, indicating edema. In both patients, subcutaneous tissue changes suggested infiltration and both had low-temperature areas spreading from the puncture sites. Thus, subcutaneous edema may indicate infusion leakage, resulting in a decrease in the temperature of the associated skin surface. These cases suggest that infrared thermography is an effective method of objectively and noninvasively detecting infiltration.

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