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

PubMed Central

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

2015-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

High accuracy position method based on computer vision and error analysis

NASA Astrophysics Data System (ADS)

Chen, Shihao; Shi, Zhongke

2003-09-01

The study of high accuracy position system is becoming the hotspot in the field of autocontrol. And positioning is one of the most researched tasks in vision system. So we decide to solve the object locating by using the image processing method. This paper describes a new method of high accuracy positioning method through vision system. In the proposed method, an edge-detection filter is designed for a certain running condition. Here, the filter contains two mainly parts: one is image-processing module, this module is to implement edge detection, it contains of multi-level threshold self-adapting segmentation, edge-detection and edge filter; the other one is object-locating module, it is to point out the location of each object in high accurate, and it is made up of medium-filtering and curve-fitting. This paper gives some analysis error for the method to prove the feasibility of vision in position detecting. Finally, to verify the availability of the method, an example of positioning worktable, which is using the proposed method, is given at the end of the paper. Results show that the method can accurately detect the position of measured object and identify object attitude.

Automatic cardiac cycle determination directly from EEG-fMRI data by multi-scale peak detection method.

PubMed

Wong, Chung-Ki; Luo, Qingfei; Zotev, Vadim; Phillips, Raquel; Chan, Kam Wai Clifford; Bodurka, Jerzy

2018-03-31

In simultaneous EEG-fMRI, identification of the period of cardioballistic artifact (BCG) in EEG is required for the artifact removal. Recording the electrocardiogram (ECG) waveform during fMRI is difficult, often causing inaccurate period detection. Since the waveform of the BCG extracted by independent component analysis (ICA) is relatively invariable compared to the ECG waveform, we propose a multiple-scale peak-detection algorithm to determine the BCG cycle directly from the EEG data. The algorithm first extracts the high contrast BCG component from the EEG data by ICA. The BCG cycle is then estimated by band-pass filtering the component around the fundamental frequency identified from its energy spectral density, and the peak of BCG artifact occurrence is selected from each of the estimated cycle. The algorithm is shown to achieve a high accuracy on a large EEG-fMRI dataset. It is also adaptive to various heart rates without the needs of adjusting the threshold parameters. The cycle detection remains accurate with the scan duration reduced to half a minute. Additionally, the algorithm gives a figure of merit to evaluate the reliability of the detection accuracy. The algorithm is shown to give a higher detection accuracy than the commonly used cycle detection algorithm fmrib_qrsdetect implemented in EEGLAB. The achieved high cycle detection accuracy of our algorithm without using the ECG waveforms makes possible to create and automate pipelines for processing large EEG-fMRI datasets, and virtually eliminates the need for ECG recordings for BCG artifact removal. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

The ship edge feature detection based on high and low threshold for remote sensing image

NASA Astrophysics Data System (ADS)

Li, Xuan; Li, Shengyang

2018-05-01

In this paper, a method based on high and low threshold is proposed to detect the ship edge feature due to the low accuracy rate caused by the noise. Analyze the relationship between human vision system and the target features, and to determine the ship target by detecting the edge feature. Firstly, using the second-order differential method to enhance the quality of image; Secondly, to improvement the edge operator, we introduction of high and low threshold contrast to enhancement image edge and non-edge points, and the edge as the foreground image, non-edge as a background image using image segmentation to achieve edge detection, and remove the false edges; Finally, the edge features are described based on the result of edge features detection, and determine the ship target. The experimental results show that the proposed method can effectively reduce the number of false edges in edge detection, and has the high accuracy of remote sensing ship edge detection.

Effect of time discretization of the imaging process on the accuracy of trajectory estimation in fluorescence microscopy

PubMed Central

Wong, Yau; Chao, Jerry; Lin, Zhiping; Ober, Raimund J.

2014-01-01

In fluorescence microscopy, high-speed imaging is often necessary for the proper visualization and analysis of fast subcellular dynamics. Here, we examine how the speed of image acquisition affects the accuracy with which parameters such as the starting position and speed of a microscopic non-stationary fluorescent object can be estimated from the resulting image sequence. Specifically, we use a Fisher information-based performance bound to investigate the detector-dependent effect of frame rate on the accuracy of parameter estimation. We demonstrate that when a charge-coupled device detector is used, the estimation accuracy deteriorates as the frame rate increases beyond a point where the detector’s readout noise begins to overwhelm the low number of photons detected in each frame. In contrast, we show that when an electron-multiplying charge-coupled device (EMCCD) detector is used, the estimation accuracy improves with increasing frame rate. In fact, at high frame rates where the low number of photons detected in each frame renders the fluorescent object difficult to detect visually, imaging with an EMCCD detector represents a natural implementation of the Ultrahigh Accuracy Imaging Modality, and enables estimation with an accuracy approaching that which is attainable only when a hypothetical noiseless detector is used. PMID:25321248

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

EPA Science Inventory

For early detection biomonitoring of aquatic invasive species, sensitivity to rare individuals and accurate, high-resolution taxonomic classification are critical to minimize detection errors. Given the great expense and effort associated with morphological identification of many...

Online detecting system of roller wear based on laser-linear array CCD technology

NASA Astrophysics Data System (ADS)

Guo, Yuan

2010-10-01

Roller is an important metallurgy tool in the rolling mill. And the surface of a roller affects the quantity of the rolling product directly. After using a period of time, roller must be repaired or replaced. Examining the profile of a working roller between the intervals of rolling is called online detecting for roller wear. The study of online detecting roller wear is very important for selecting the grinding time in reason, reducing the exchanging times of rollers, improving the quality of the product and realizing online grinding rollers. By applying the laser-linear array CCD detective technology, a method for online non-touch detecting roller wear was brought forward. The principle, composition and the operation process of the linear array CCD detecting system were expatiated. And an error compensation algorithm is exactly calculated to offset the shift of the roller axis in this measurement system. So the stability and the accuracy were improved remarkably. The experiment proves that the accuracy of the detecting system reaches to the demand of practical production process. It can provide a new method of high speed and high accuracy online detecting for roller wear.

No Special K! A Signal Detection Framework for the Strategic Regulation of Memory Accuracy

ERIC Educational Resources Information Center

Higham, Philip A.

2007-01-01

Two experiments investigated criterion setting and metacognitive processes underlying the strategic regulation of accuracy on the Scholastic Aptitude Test (SAT) using Type-2 signal detection theory (SDT). In Experiment 1, report bias was manipulated by penalizing participants either 0.25 (low incentive) or 4 (high incentive) points for each error.…

Crystal Genetics, Inc.

PubMed

Kermani, Bahram G

2016-07-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Sensitivity and accuracy of high-throughput metabarcoding methods used to describe aquatic communities for early detection of invasve fish species

EPA Science Inventory

For early detection biomonitoring of aquatic invasive species, sensitivity to rare individuals and accurate, high-resolution taxonomic classification are critical to minimize Type I and II detection errors. Given the great expense and effort associated with morphological identifi...

Theoretical study of surface plasmon resonance sensors based on 2D bimetallic alloy grating

NASA Astrophysics Data System (ADS)

Dhibi, Abdelhak; Khemiri, Mehdi; Oumezzine, Mohamed

2016-11-01

A surface plasmon resonance (SPR) sensor based on 2D alloy grating with a high performance is proposed. The grating consists of homogeneous alloys of formula MxAg1-x, where M is gold, copper, platinum and palladium. Compared to the SPR sensors based a pure metal, the sensor based on angular interrogation with silver exhibits a sharper (i.e. larger depth-to-width ratio) reflectivity dip, which provides a big detection accuracy, whereas the sensor based on gold exhibits the broadest dips and the highest sensitivity. The detection accuracy of SPR sensor based a metal alloy is enhanced by the increase of silver composition. In addition, the composition of silver which is around 0.8 improves the sensitivity and the quality of SPR sensor of pure metal. Numerical simulations based on rigorous coupled wave analysis (RCWA) show that the sensor based on a metal alloy not only has a high sensitivity and a high detection accuracy, but also exhibits a good linearity and a good quality.

Statistical algorithms improve accuracy of gene fusion detection

PubMed Central

Hsieh, Gillian; Bierman, Rob; Szabo, Linda; Lee, Alex Gia; Freeman, Donald E.; Watson, Nathaniel; Sweet-Cordero, E. Alejandro

2017-01-01

Abstract Gene fusions are known to play critical roles in tumor pathogenesis. Yet, sensitive and specific algorithms to detect gene fusions in cancer do not currently exist. In this paper, we present a new statistical algorithm, MACHETE (Mismatched Alignment CHimEra Tracking Engine), which achieves highly sensitive and specific detection of gene fusions from RNA-Seq data, including the highest Positive Predictive Value (PPV) compared to the current state-of-the-art, as assessed in simulated data. We show that the best performing published algorithms either find large numbers of fusions in negative control data or suffer from low sensitivity detecting known driving fusions in gold standard settings, such as EWSR1-FLI1. As proof of principle that MACHETE discovers novel gene fusions with high accuracy in vivo, we mined public data to discover and subsequently PCR validate novel gene fusions missed by other algorithms in the ovarian cancer cell line OVCAR3. These results highlight the gains in accuracy achieved by introducing statistical models into fusion detection, and pave the way for unbiased discovery of potentially driving and druggable gene fusions in primary tumors. PMID:28541529

Discontinuity Detection in the Shield Metal Arc Welding Process

PubMed Central

Cocota, José Alberto Naves; Garcia, Gabriel Carvalho; da Costa, Adilson Rodrigues; de Lima, Milton Sérgio Fernandes; Rocha, Filipe Augusto Santos; Freitas, Gustavo Medeiros

2017-01-01

This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors—a microphone and piezoelectric—that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system’s high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries. PMID:28489045

Discontinuity Detection in the Shield Metal Arc Welding Process.

PubMed

Cocota, José Alberto Naves; Garcia, Gabriel Carvalho; da Costa, Adilson Rodrigues; de Lima, Milton Sérgio Fernandes; Rocha, Filipe Augusto Santos; Freitas, Gustavo Medeiros

2017-05-10

This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors-a microphone and piezoelectric-that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system's high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries.

A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images

PubMed Central

Bi, Fukun; Chen, Jing; Zhuang, Yin; Bian, Mingming; Zhang, Qingjun

2017-01-01

With the rapid development of optical remote sensing satellites, ship detection and identification based on large-scale remote sensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance and maritime management. However, because the harbor environment is complex, gray information and texture features between docked ships and their connected dock regions are indistinguishable, most of the popular detection methods are limited by their calculation efficiency and detection accuracy. In this paper, a novel hierarchical method that combines an efficient candidate scanning strategy and an accurate candidate identification mixture model is presented for inshore ship detection in complex harbor areas. First, in the candidate region extraction phase, an omnidirectional intersected two-dimension scanning (OITDS) strategy is designed to rapidly extract candidate regions from the land-water segmented images. In the candidate region identification phase, a decision mixture model (DMM) is proposed to identify real ships from candidate objects. Specifically, to improve the robustness regarding the diversity of ships, a deformable part model (DPM) was employed to train a key part sub-model and a whole ship sub-model. Furthermore, to improve the identification accuracy, a surrounding correlation context sub-model is built. Finally, to increase the accuracy of candidate region identification, these three sub-models are integrated into the proposed DMM. Experiments were performed on numerous large-scale harbor remote sensing images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency. PMID:28640236

A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images.

PubMed

Bi, Fukun; Chen, Jing; Zhuang, Yin; Bian, Mingming; Zhang, Qingjun

2017-06-22

With the rapid development of optical remote sensing satellites, ship detection and identification based on large-scale remote sensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance and maritime management. However, because the harbor environment is complex, gray information and texture features between docked ships and their connected dock regions are indistinguishable, most of the popular detection methods are limited by their calculation efficiency and detection accuracy. In this paper, a novel hierarchical method that combines an efficient candidate scanning strategy and an accurate candidate identification mixture model is presented for inshore ship detection in complex harbor areas. First, in the candidate region extraction phase, an omnidirectional intersected two-dimension scanning (OITDS) strategy is designed to rapidly extract candidate regions from the land-water segmented images. In the candidate region identification phase, a decision mixture model (DMM) is proposed to identify real ships from candidate objects. Specifically, to improve the robustness regarding the diversity of ships, a deformable part model (DPM) was employed to train a key part sub-model and a whole ship sub-model. Furthermore, to improve the identification accuracy, a surrounding correlation context sub-model is built. Finally, to increase the accuracy of candidate region identification, these three sub-models are integrated into the proposed DMM. Experiments were performed on numerous large-scale harbor remote sensing images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency.

Comparison of spike-sorting algorithms for future hardware implementation.

PubMed

Gibson, Sarah; Judy, Jack W; Markovic, Dejan

2008-01-01

Applications such as brain-machine interfaces require hardware spike sorting in order to (1) obtain single-unit activity and (2) perform data reduction for wireless transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.

Recollection is a continuous process: implications for dual-process theories of recognition memory.

PubMed

Mickes, Laura; Wais, Peter E; Wixted, John T

2009-04-01

Dual-process theory, which holds that recognition decisions can be based on recollection or familiarity, has long seemed incompatible with signal detection theory, which holds that recognition decisions are based on a singular, continuous memory-strength variable. Formal dual-process models typically regard familiarity as a continuous process (i.e., familiarity comes in degrees), but they construe recollection as a categorical process (i.e., recollection either occurs or does not occur). A continuous process is characterized by a graded relationship between confidence and accuracy, whereas a categorical process is characterized by a binary relationship such that high confidence is associated with high accuracy but all lower degrees of confidence are associated with chance accuracy. Using a source-memory procedure, we found that the relationship between confidence and source-recollection accuracy was graded. Because recollection, like familiarity, is a continuous process, dual-process theory is more compatible with signal detection theory than previously thought.

Imaging of tumor hypermetabolism with near-infrared fluorescence contrast agents

NASA Astrophysics Data System (ADS)

Chen, Yu; Zheng, Gang; Zhang, Zhihong; Blessington, Dana; Intes, Xavier; Achilefu, Samuel I.; Chance, Britton

2004-08-01

We have developed a high sensitivity near-infrared (NIR) optical imaging system for non-invasive cancer detection through molecular labeled fluorescent contrast agents. Near-infrared (NIR) imaging can probe tissue deeply thus possess the potential for non-invasively detection of breast or lymph node cancer. Recent developments in molecular beacons can selectively label various pre-cancer/cancer signatures and provide high tumor to background contrast. To increase the sensitivity in detecting fluorescent photons and the accuracy of localization, phase cancellation (in- and anti-phase) device is employed. This frequency-domain system utilizes the interference-like pattern of diffuse photon density wave to achieve high detection sensitivity and localization accuracy for the fluorescent heterogeneity embedded inside the scattering media. The opto-electronic system consists of the laser sources, fiber optics, interference filter to select the fluorescent photons and the high sensitivity photon detector (photomultiplier tube). The source-detector pair scans the tissue surface in multiple directions and the two-dimensional localization image can be obtained using goniometric reconstruction. In vivo measurements with tumor-bearing mouse model using the novel Cypate-mono-2-deoxy-glucose (Cypate-2-D-Glucosamide) fluorescent contrast agent, which targets the enhanced tumor glycolysis, demonstrated the feasibility on detection of 2 cm deep subsurface tumor in the tissue-like medium, with a localization accuracy within 2 ~ 3 mm. This instrument has the potential for tumor diagnosis and imaging, and the accuracy of the localization suggests that this system could help to guide the clinical fine-needle biopsy. This portable device would be complementary to X-ray mammogram and provide add-on information on early diagnosis and localization of early breast tumor.

Accuracy of imaging methods for detection of bone tissue invasion in patients with oral squamous cell carcinoma

PubMed Central

Uribe, S; Rojas, LA; Rosas, CF

2013-01-01

The objective of this review is to evaluate the diagnostic accuracy of imaging methods for detection of mandibular bone tissue invasion by squamous cell carcinoma (SCC). A systematic review was carried out of studies in MEDLINE, SciELO and ScienceDirect, published between 1960 and 2012, in English, Spanish or German, which compared detection of mandibular bone tissue invasion via different imaging tests against a histopathology reference standard. Sensitivity and specificity data were extracted from each study. The outcome measure was diagnostic accuracy. We found 338 articles, of which 5 fulfilled the inclusion criteria. Tests included were: CT (four articles), MRI (four articles), panoramic radiography (one article), positron emission tomography (PET)/CT (one article) and cone beam CT (CBCT) (one article). The quality of articles was low to moderate and the evidence showed that all tests have a high diagnostic accuracy for detection of mandibular bone tissue invasion by SCC, with sensitivity values of 94% (MRI), 91% (CBCT), 83% (CT) and 55% (panoramic radiography), and specificity values of 100% (CT, MRI, CBCT), 97% (PET/CT) and 91.7% (panoramic radiography). Available evidence is scarce and of only low to moderate quality. However, it is consistently shown that current imaging methods give a moderate to high diagnostic accuracy for the detection of mandibular bone tissue invasion by SCC. Recommendations are given for improving the quality of future reports, in particular provision of a detailed description of the patients' conditions, the imaging instrument and both imaging and histopathological invasion criteria. PMID:23420854

Analysis of microdialysate monoamines, including noradrenaline, dopamine and serotonin, using capillary ultra-high performance liquid chromatography and electrochemical detection.

PubMed

Ferry, Barbara; Gifu, Elena-Patricia; Sandu, Ioana; Denoroy, Luc; Parrot, Sandrine

2014-03-01

Electrochemical methods are very often used to detect catecholamine and indolamine neurotransmitters separated by conventional reverse-phase high performance liquid chromatography (HPLC). The present paper presents the development of a chromatographic method to detect monoamines present in low-volume brain dialysis samples using a capillary column filled with sub-2μm particles. Several parameters (repeatability, linearity, accuracy, limit of detection) for this new ultrahigh performance liquid chromatography (UHPLC) method with electrochemical detection were examined after optimization of the analytical conditions. Noradrenaline, adrenaline, serotonin, dopamine and its metabolite 3-methoxytyramine were separated in 1μL of injected sample volume; they were detected above concentrations of 0.5-1nmol/L, with 2.1-9.5% accuracy and intra-assay repeatability equal to or less than 6%. The final method was applied to very low volume dialysates from rat brain containing monoamine traces. The study demonstrates that capillary UHPLC with electrochemical detection is suitable for monitoring dialysate monoamines collected at high sampling rate. Copyright © 2014 Elsevier B.V. All rights reserved.

Efficient airport detection using region-based fully convolutional neural networks

NASA Astrophysics Data System (ADS)

Xin, Peng; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Lv, Chao

2018-04-01

This paper presents a model for airport detection using region-based fully convolutional neural networks. To achieve fast detection with high accuracy, we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units (GPUs) to speed up the training and testing time. For lack of labeled data, we transferred the convolutional layers of ZF net pretrained by ImageNet to initialize the shared convolutional layers, then we retrained the model using the alternating optimization training strategy. The proposed model has been tested on an airport dataset consisting of 600 images. Experiments show that the proposed method can distinguish airports in our dataset from similar background scenes almost real-time with high accuracy, which is much better than traditional methods.

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

PubMed

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

2018-01-01

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

Detection of convulsive seizures using surface electromyography.

PubMed

Beniczky, Sándor; Conradsen, Isa; Wolf, Peter

2018-06-01

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

Supervised segmentation of microelectrode recording artifacts using power spectral density.

PubMed

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

2015-08-01

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

Design and implementation of an intelligent belt system using accelerometer.

PubMed

Liu, Botong; Wang, Duo; Li, Sha; Nie, Xuhui; Xu, Shan; Jiao, Bingli; Duan, Xiaohui; Huang, Anpeng

2015-01-01

Activity monitor systems are increasing used recently. They are important for athletes and casual users to manage physical activity during daily exercises. In this paper, we use a triaxial accelerometer to design and implement an intelligent belt system, which can detect the user's step and flapping motion. In our system, a wearable intelligent belt is worn on the user's waist to collect activity acceleration signals. We present a step detection algorithm to detect real-time human step, which has high accuracy and low complexity. In our system, an Android App is developed to manage the intelligent belt. We also propose a protocol, which can guarantee data transmission between smartphones and wearable belt effectively and efficiently. In addition, when users flap the belt in emergency, the smartphone will receive alarm signal sending by the belt, and then notifies the emergency contact person, which can be really helpful for users in danger. Our experiment results show our system can detect physical activities with high accuracy (overall accuracy of our algorithm is above 95%) and has an effective alarm subsystem, which is significant for the practical use.

Road sign recognition with fuzzy adaptive pre-processing models.

PubMed

Lin, Chien-Chuan; Wang, Ming-Shi

2012-01-01

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

Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models

PubMed Central

Lin, Chien-Chuan; Wang, Ming-Shi

2012-01-01

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

Detection of neovascularization based on fractal and texture analysis with interaction effects in diabetic retinopathy.

PubMed

Lee, Jack; Zee, Benny Chung Ying; Li, Qing

2013-01-01

Diabetic retinopathy is a major cause of blindness. Proliferative diabetic retinopathy is a result of severe vascular complication and is visible as neovascularization of the retina. Automatic detection of such new vessels would be useful for the severity grading of diabetic retinopathy, and it is an important part of screening process to identify those who may require immediate treatment for their diabetic retinopathy. We proposed a novel new vessels detection method including statistical texture analysis (STA), high order spectrum analysis (HOS), fractal analysis (FA), and most importantly we have shown that by incorporating their associated interactions the accuracy of new vessels detection can be greatly improved. To assess its performance, the sensitivity, specificity and accuracy (AUC) are obtained. They are 96.3%, 99.1% and 98.5% (99.3%), respectively. It is found that the proposed method can improve the accuracy of new vessels detection significantly over previous methods. The algorithm can be automated and is valuable to detect relatively severe cases of diabetic retinopathy among diabetes patients.

Detecting atrial fibrillation by deep convolutional neural networks.

PubMed

Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

2018-02-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dynamic sample size detection in learning command line sequence for continuous authentication.

PubMed

Traore, Issa; Woungang, Isaac; Nakkabi, Youssef; Obaidat, Mohammad S; Ahmed, Ahmed Awad E; Khalilian, Bijan

2012-10-01

Continuous authentication (CA) consists of authenticating the user repetitively throughout a session with the goal of detecting and protecting against session hijacking attacks. While the accuracy of the detector is central to the success of CA, the detection delay or length of an individual authentication period is important as well since it is a measure of the window of vulnerability of the system. However, high accuracy and small detection delay are conflicting requirements that need to be balanced for optimum detection. In this paper, we propose the use of sequential sampling technique to achieve optimum detection by trading off adequately between detection delay and accuracy in the CA process. We illustrate our approach through CA based on user command line sequence and naïve Bayes classification scheme. Experimental evaluation using the Greenberg data set yields encouraging results consisting of a false acceptance rate (FAR) of 11.78% and a false rejection rate (FRR) of 1.33%, with an average command sequence length (i.e., detection delay) of 37 commands. When using the Schonlau (SEA) data set, we obtain FAR = 4.28% and FRR = 12%.

CRF: detection of CRISPR arrays using random forest.

PubMed

Wang, Kai; Liang, Chun

2017-01-01

CRISPRs (clustered regularly interspaced short palindromic repeats) are particular repeat sequences found in wide range of bacteria and archaea genomes. Several tools are available for detecting CRISPR arrays in the genomes of both domains. Here we developed a new web-based CRISPR detection tool named CRF (CRISPR Finder by Random Forest). Different from other CRISPR detection tools, a random forest classifier was used in CRF to filter out invalid CRISPR arrays from all putative candidates and accordingly enhanced detection accuracy. In CRF, particularly, triplet elements that combine both sequence content and structure information were extracted from CRISPR repeats for classifier training. The classifier achieved high accuracy and sensitivity. Moreover, CRF offers a highly interactive web interface for robust data visualization that is not available among other CRISPR detection tools. After detection, the query sequence, CRISPR array architecture, and the sequences and secondary structures of CRISPR repeats and spacers can be visualized for visual examination and validation. CRF is freely available at http://bioinfolab.miamioh.edu/crf/home.php.

Validated method for quantification of genetically modified organisms in samples of maize flour.

PubMed

Kunert, Renate; Gach, Johannes S; Vorauer-Uhl, Karola; Engel, Edwin; Katinger, Hermann

2006-02-08

Sensitive and accurate testing for trace amounts of biotechnology-derived DNA from plant material is the prerequisite for detection of 1% or 0.5% genetically modified ingredients in food products or raw materials thereof. Compared to ELISA detection of expressed proteins, real-time PCR (RT-PCR) amplification has easier sample preparation and detection limits are lower. Of the different methods of DNA preparation CTAB method with high flexibility in starting material and generation of sufficient DNA with relevant quality was chosen. Previous RT-PCR data generated with the SYBR green detection method showed that the method is highly sensitive to sample matrices and genomic DNA content influencing the interpretation of results. Therefore, this paper describes a real-time DNA quantification based on the TaqMan probe method, indicating high accuracy and sensitivity with detection limits of lower than 18 copies per sample applicable and comparable to highly purified plasmid standards as well as complex matrices of genomic DNA samples. The results were evaluated with ValiData for homology of variance, linearity, accuracy of the standard curve, and standard deviation.

The system of high accuracy UV spectral radiation system

NASA Astrophysics Data System (ADS)

Lin, Guan-yu; Yu, Lei; Xu, Dian; Cao, Dian-sheng; Yu, Yu-Xiang

2016-10-01

UV spectral radiation detecting and visible observation telescope is designed by the coaxial optical. In order to decrease due to the incident light polarization effect, and improve the detection precision, polarizer need to be used in the light path. Four pieces of quartz of high Precision UV radiation depolarizer retarder stack together is placed in front of Seya namioka dispersion unit. The coherent detection principle of modulation of light signal and the reference signal multiplied processing, increase the phase sensitive detector can be adjustment function, ensure the UV spectral radiation detection stability. A lock-in amplifier is used in the electrical system to advance the accuracy of measurement. To ensure the precision measurement detected, the phase-sensitive detector function can be adjustable. the output value is not more than 10mV before each measurement, so it can be ensured that the stability of the measured radiation spectrum is less than 1 percent.

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

PubMed Central

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

2011-01-01

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

High resolution mapping of development in the wildland-urban interface using object based image extraction.

PubMed

Caggiano, Michael D; Tinkham, Wade T; Hoffman, Chad; Cheng, Antony S; Hawbaker, Todd J

2016-10-01

The wildland-urban interface (WUI), the area where human development encroaches on undeveloped land, is expanding throughout the western United States resulting in increased wildfire risk to homes and communities. Although census based mapping efforts have provided insights into the pattern of development and expansion of the WUI at regional and national scales, these approaches do not provide sufficient detail for fine-scale fire and emergency management planning, which requires maps of individual building locations. Although fine-scale maps of the WUI have been developed, they are often limited in their spatial extent, have unknown accuracies and biases, and are costly to update over time. In this paper we assess a semi-automated Object Based Image Analysis (OBIA) approach that utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery for the detection of individual buildings within the WUI. We evaluate this approach by comparing the accuracy and overall quality of extracted buildings to a building footprint control dataset. In addition, we assessed the effects of buffer distance, topographic conditions, and building characteristics on the accuracy and quality of building extraction. The overall accuracy and quality of our approach was positively related to buffer distance, with accuracies ranging from 50 to 95% for buffer distances from 0 to 100 m. Our results also indicate that building detection was sensitive to building size, with smaller outbuildings (footprints less than 75 m 2 ) having detection rates below 80% and larger residential buildings having detection rates above 90%. These findings demonstrate that this approach can successfully identify buildings in the WUI in diverse landscapes while achieving high accuracies at buffer distances appropriate for most fire management applications while overcoming cost and time constraints associated with traditional approaches. This study is unique in that it evaluates the ability of an OBIA approach to extract highly detailed data on building locations in a WUI setting.

High resolution mapping of development in the wildland-urban interface using object based image extraction

USGS Publications Warehouse

Caggiano, Michael D.; Tinkham, Wade T.; Hoffman, Chad; Cheng, Antony S.; Hawbaker, Todd J.

2016-01-01

The wildland-urban interface (WUI), the area where human development encroaches on undeveloped land, is expanding throughout the western United States resulting in increased wildfire risk to homes and communities. Although census based mapping efforts have provided insights into the pattern of development and expansion of the WUI at regional and national scales, these approaches do not provide sufficient detail for fine-scale fire and emergency management planning, which requires maps of individual building locations. Although fine-scale maps of the WUI have been developed, they are often limited in their spatial extent, have unknown accuracies and biases, and are costly to update over time. In this paper we assess a semi-automated Object Based Image Analysis (OBIA) approach that utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery for the detection of individual buildings within the WUI. We evaluate this approach by comparing the accuracy and overall quality of extracted buildings to a building footprint control dataset. In addition, we assessed the effects of buffer distance, topographic conditions, and building characteristics on the accuracy and quality of building extraction. The overall accuracy and quality of our approach was positively related to buffer distance, with accuracies ranging from 50 to 95% for buffer distances from 0 to 100 m. Our results also indicate that building detection was sensitive to building size, with smaller outbuildings (footprints less than 75 m2) having detection rates below 80% and larger residential buildings having detection rates above 90%. These findings demonstrate that this approach can successfully identify buildings in the WUI in diverse landscapes while achieving high accuracies at buffer distances appropriate for most fire management applications while overcoming cost and time constraints associated with traditional approaches. This study is unique in that it evaluates the ability of an OBIA approach to extract highly detailed data on building locations in a WUI setting.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

The design of high precision temperature control system for InGaAs short-wave infrared detector

NASA Astrophysics Data System (ADS)

Wang, Zheng-yun; Hu, Yadong; Ni, Chen; Huang, Lin; Zhang, Aiwen; Sun, Xiao-bing; Hong, Jin

2018-02-01

The InGaAs Short-wave infrared detector is a temperature-sensitive device. Accurate temperature control can effectively reduce the background signal and improve detection accuracy, detection sensitivity, and the SNR of the detection system. Firstly, the relationship between temperature and detection background, NEP is analyzed, the principle of TEC and formula between cooling power, cooling current and hot-cold interface temperature difference are introduced. Then, the high precision constant current drive circuit based on triode voltage control current, and an incremental algorithm model based on deviation tracking compensation and PID control are proposed, which effectively suppresses the temperature overshoot, overcomes the temperature inertia, and has strong robustness. Finally, the detector and temperature control system are tested. Results show that: the lower of detector temperature, the smaller the temperature fluctuation, the higher the detection accuracy and the detection sensitivity. The temperature control system achieves the high temperature control with the temperature control rate is 7 8°C/min and the temperature fluctuation is better than +/-0. 04°C.

When and Why Threats Go Undetected: Impacts of Event Rate and Shift Length on Threat Detection Accuracy During Airport Baggage Screening.

PubMed

Meuter, Renata F I; Lacherez, Philippe F

2016-03-01

We aimed to assess the impact of task demands and individual characteristics on threat detection in baggage screeners. Airport security staff work under time constraints to ensure optimal threat detection. Understanding the impact of individual characteristics and task demands on performance is vital to ensure accurate threat detection. We examined threat detection in baggage screeners as a function of event rate (i.e., number of bags per minute) and time on task across 4 months. We measured performance in terms of the accuracy of detection of Fictitious Threat Items (FTIs) randomly superimposed on X-ray images of real passenger bags. Analyses of the percentage of correct FTI identifications (hits) show that longer shifts with high baggage throughput result in worse threat detection. Importantly, these significant performance decrements emerge within the first 10 min of these busy screening shifts only. Longer shift lengths, especially when combined with high baggage throughput, increase the likelihood that threats go undetected. Shorter shift rotations, although perhaps difficult to implement during busy screening periods, would ensure more consistently high vigilance in baggage screeners and, therefore, optimal threat detection and passenger safety. © 2015, Human Factors and Ergonomics Society.

PCA-HOG symmetrical feature based diseased cell detection

NASA Astrophysics Data System (ADS)

Wan, Min-jie

2016-04-01

A histogram of oriented gradient (HOG) feature is applied to the field of diseased cell detection, which can detect diseased cells in high resolution tissue images rapidly, accurately and efficiently. Firstly, motivated by symmetrical cellular forms, a new HOG symmetrical feature based on the traditional HOG feature is proposed to meet the condition of cell detection. Secondly, considering the high feature dimension of traditional HOG feature leads to plenty of memory resources and long runtime in practical applications, a classical dimension reduction method called principal component analysis (PCA) is used to reduce the dimension of high-dimensional HOG descriptor. Because of that, computational speed is increased greatly, and the accuracy of detection can be controlled in a proper range at the same time. Thirdly, support vector machine (SVM) classifier is trained with PCA-HOG symmetrical features proposed above. At last, practical tissue images is detected and analyzed by SVM classifier. In order to verify the effectiveness of this new algorithm, it is practically applied to conduct diseased cell detection which takes 200 pieces of H&E (hematoxylin & eosin) high resolution staining histopathological images collected from 20 breast cancer patients as a sample. The experiment shows that the average processing rate can be 25 frames per second and the detection accuracy can be 92.1%.

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

NASA Astrophysics Data System (ADS)

Guo, Y.; Wang, Y. T.

2006-10-01

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

Post-processing for improving hyperspectral anomaly detection accuracy

NASA Astrophysics Data System (ADS)

Wu, Jee-Cheng; Jiang, Chi-Ming; Huang, Chen-Liang

2015-10-01

Anomaly detection is an important topic in the exploitation of hyperspectral data. Based on the Reed-Xiaoli (RX) detector and a morphology operator, this research proposes a novel technique for improving the accuracy of hyperspectral anomaly detection. Firstly, the RX-based detector is used to process a given input scene. Then, a post-processing scheme using morphology operator is employed to detect those pixels around high-scoring anomaly pixels. Tests were conducted using two real hyperspectral images with ground truth information and the results based on receiver operating characteristic curves, illustrated that the proposed method reduced the false alarm rates of the RXbased detector.

A face in a (temporal) crowd.

PubMed

Hacker, Catrina M; Meschke, Emily X; Biederman, Irving

2018-03-20

Familiar objects, specified by name, can be identified with high accuracy when embedded in a rapidly presented sequence of images at rates exceeding 10 images/s. Not only can target objects be detected at such brief presentation rates, they can also be detected under high uncertainty, where their classification is defined negatively, e.g., "Not a Tool." The identification of a familiar speaker's voice declines precipitously when uncertainty is increased from one to a mere handful of possible speakers. Is the limitation imposed by uncertainty, i.e., the number of possible individuals, a general characteristic of processes for person individuation such that the identifiability of a familiar face would undergo a similar decline with uncertainty? Specifically, could the presence of an unnamed celebrity, thus any celebrity, be detected when presented in a rapid sequence of unfamiliar faces? If so, could the celebrity be identified? Despite the markedly greater physical similarity of faces compared to objects that are, say, not tools, the presence of a celebrity could be detected with moderately high accuracy (∼75%) at rates exceeding 7 faces/s. False alarms were exceedingly rare as almost all the errors were misses. Detection accuracy by moderate congenital prosopagnosics was lower than controls, but still well above chance. Given the detection of the presence of a celebrity, all subjects were almost always able to identify that celebrity, providing no role for a covert familiarity signal outside of awareness. Copyright © 2018 Elsevier Ltd. All rights reserved.

Identification and delineation of areas flood hazard using high accuracy of DEM data

NASA Astrophysics Data System (ADS)

Riadi, B.; Barus, B.; Widiatmaka; Yanuar, M. J. P.; Pramudya, B.

2018-05-01

Flood incidents that often occur in Karawang regency need to be mitigated. These expectations exist on technologies that can predict, anticipate and reduce disaster risks. Flood modeling techniques using Digital Elevation Model (DEM) data can be applied in mitigation activities. High accuracy DEM data used in modeling, will result in better flooding flood models. The result of high accuracy DEM data processing will yield information about surface morphology which can be used to identify indication of flood hazard area. The purpose of this study was to identify and describe flood hazard areas by identifying wetland areas using DEM data and Landsat-8 images. TerraSAR-X high-resolution data is used to detect wetlands from landscapes, while land cover is identified by Landsat image data. The Topography Wetness Index (TWI) method is used to detect and identify wetland areas with basic DEM data, while for land cover analysis using Tasseled Cap Transformation (TCT) method. The result of TWI modeling yields information about potential land of flood. Overlay TWI map with land cover map that produces information that in Karawang regency the most vulnerable areas occur flooding in rice fields. The spatial accuracy of the flood hazard area in this study was 87%.

Research on intrusion detection based on Kohonen network and support vector machine

NASA Astrophysics Data System (ADS)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

Facial emotion recognition and borderline personality pathology.

PubMed

Meehan, Kevin B; De Panfilis, Chiara; Cain, Nicole M; Antonucci, Camilla; Soliani, Antonio; Clarkin, John F; Sambataro, Fabio

2017-09-01

The impact of borderline personality pathology on facial emotion recognition has been in dispute; with impaired, comparable, and enhanced accuracy found in high borderline personality groups. Discrepancies are likely driven by variations in facial emotion recognition tasks across studies (stimuli type/intensity) and heterogeneity in borderline personality pathology. This study evaluates facial emotion recognition for neutral and negative emotions (fear/sadness/disgust/anger) presented at varying intensities. Effortful control was evaluated as a moderator of facial emotion recognition in borderline personality. Non-clinical multicultural undergraduates (n = 132) completed a morphed facial emotion recognition task of neutral and negative emotional expressions across different intensities (100% Neutral; 25%/50%/75% Emotion) and self-reported borderline personality features and effortful control. Greater borderline personality features related to decreased accuracy in detecting neutral faces, but increased accuracy in detecting negative emotion faces, particularly at low-intensity thresholds. This pattern was moderated by effortful control; for individuals with low but not high effortful control, greater borderline personality features related to misattributions of emotion to neutral expressions, and enhanced detection of low-intensity emotional expressions. Individuals with high borderline personality features may therefore exhibit a bias toward detecting negative emotions that are not or barely present; however, good self-regulatory skills may protect against this potential social-cognitive vulnerability. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

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

PubMed Central

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

2015-01-01

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

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

PubMed Central

Pathak, Neha; Dodds, Julie; Khan, Khalid

2014-01-01

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

Attention Measures of Accuracy, Variability, and Fatigue Detect Early Response to Donepezil in Alzheimer's Disease: A Randomized, Double-blind, Placebo-Controlled Pilot Trial.

PubMed

Vila-Castelar, Clara; Ly, Jenny J; Kaplan, Lillian; Van Dyk, Kathleen; Berger, Jeffrey T; Macina, Lucy O; Stewart, Jennifer L; Foldi, Nancy S

2018-04-09

Donepezil is widely used to treat Alzheimer's disease (AD), but detecting early response remains challenging for clinicians. Acetylcholine is known to directly modulate attention, particularly under high cognitive conditions, but no studies to date test whether measures of attention under high load can detect early effects of donepezil. We hypothesized that load-dependent attention tasks are sensitive to short-term treatment effects of donepezil, while global and other domain-specific cognitive measures are not. This longitudinal, randomized, double-blind, placebo-controlled pilot trial (ClinicalTrials.gov Identifier: NCT03073876) evaluated 23 participants newly diagnosed with AD initiating de novo donepezil treatment (5 mg). After baseline assessment, participants were randomized into Drug (n = 12) or Placebo (n = 11) groups, and retested after approximately 6 weeks. Cognitive assessment included: (a) attention tasks (Foreperiod Effect, Attentional Blink, and Covert Orienting tasks) measuring processing speed, top-down accuracy, orienting, intra-individual variability, and fatigue; (b) global measures (Alzheimer's Disease Assessment Scale-Cognitive Subscale, Mini-Mental Status Examination, Dementia Rating Scale); and (c) domain-specific measures (memory, language, visuospatial, and executive function). The Drug but not the Placebo group showed benefits of treatment at high-load measures by preserving top-down accuracy, improving intra-individual variability, and averting fatigue. In contrast, other global or cognitive domain-specific measures could not detect treatment effects over the same treatment interval. The pilot-study suggests that attention measures targeting accuracy, variability, and fatigue under high-load conditions could be sensitive to short-term cholinergic treatment. Given the central role of acetylcholine in attentional function, load-dependent attentional measures may be valuable cognitive markers of early treatment response.

Construction of a Resting High Fidelity ECG "SuperScore" for Management and Screening of Heart Disease

NASA Technical Reports Server (NTRS)

Schlegel, Todd T.; Delgado, Reynolds; Poulin, Greg; Starc, Vito; Arenare, Brian; Rahman, M. A.

2006-01-01

Resting conventional ECG is notoriously insensitive for detecting coronary artery disease (CAD) and only nominally useful in screening for cardiomyopathy (CM). Similarly, conventional exercise stress test ECG is both time- and labor-consuming and its accuracy in identifying CAD is suboptimal for use in population screening. We retrospectively investigated the accuracy of several advanced resting electrocardiographic (ECG) parameters, both alone and in combination, for detecting CAD and cardiomyopathy (CM).

Alarm characterization for a continuous glucose monitor that replaces traditional blood glucose monitoring.

PubMed

McGarraugh, Geoffrey

2010-01-01

Continuous glucose monitoring (CGM) devices available in the United States are approved for use as adjuncts to self-monitoring of blood glucose (SMBG); all CGM alarms require SMBG confirmation before treatment. In this report, an analysis method is proposed to determine the CGM threshold alarm accuracy required to eliminate SMBG confirmation. The proposed method builds on the Clinical and Laboratory Standards Institute (CLSI) guideline for evaluating CGM threshold alarms using data from an in-clinic study of subjects with type 1 diabetes. The CLSI method proposes a maximum time limit of +/-30 minutes for the detection of hypo- and hyperglycemic events but does not include limits for glucose measurement accuracy. The International Standards Organization (ISO) standard for SMBG glucose measurement accuracy (ISO 15197) is +/-15 mg/dl for glucose <75 mg/dl and +/-20% for glucose > or = 75 mg/dl. This standard was combined with the CLSI method to more completely characterize the accuracy of CGM alarms. Incorporating the ISO 15197 accuracy margins, FreeStyle Navigator CGM system alarms detected 70 mg/dl hypoglycemia within 30 minutes at a rate of 70.3%, with a false alarm rate of 11.4%. The device detected high glucose in the range of 140-300 mg/dl within 30 minutes at an average rate of 99.2%, with a false alarm rate of 2.1%. Self-monitoring of blood glucose confirmation is necessary for detecting and treating hypoglycemia with the FreeStyle Navigator CGM system, but at high glucose levels, SMBG confirmation adds little incremental value to CGM alarms. 2010 Diabetes Technology Society.

Diagnostic accuracy of non-contrast abdominal CT scans performed as follow-up for patients with an established cancer diagnosis: a retrospective study.

PubMed

Semaan, Hassan; Bazerbashi, Mohamad F; Siesel, Geoffrey; Aldinger, Paul; Obri, Tawfik

2018-03-01

To determine the accuracy and non-detection rate of cancer related findings (CRFs) on follow-up non-contrast-enhanced CT (NECT) versus contrast-enhanced CT (CECT) images of the abdomen in patients with a known cancer diagnosis. A retrospective review of 352 consecutive CTs of the abdomen performed with and without IV contrast between March 2010 and October 2014 for follow-up of cancer was included. Two radiologists independently assessed the NECT portions of the studies. The reader was provided the primary cancer diagnosis and access to the most recent prior NECT study. The accuracy and non-detection rates were determined by comparing our results to the archived reports as a gold standard. A total of 383 CRFs were found in the archived reports of the 352 abdominal CTs. The average non-detection rate for the NECTs compared to the CECTs was 3.0% (11.5/383) with an accuracy of 97.0% (371.5/383) in identifying CRFs. The most common findings missed were vascular thrombosis with a non-detection rate of 100%. The accuracy for non-vascular CRFs was 99.1%. Follow-up NECT abdomen studies are highly accurate in the detection of CRFs in patients with an established cancer diagnosis, except in cases where vascular involvement is suspected.

Diagnostic Accuracy of the Work Functioning Impairment Scale (WFun): A Method to Detect Workers Who Have Health Problems Affecting their Work and to Evaluate Fitness for Work.

PubMed

Nagata, Tomohisa; Fujino, Yoshihisa; Saito, Kumi; Uehara, Masamichi; Oyama, Ichiro; Izumi, Hiroyuki; Kubo, Tatsuhiko

2017-06-01

This study evaluated the diagnostic accuracy of the Work Functioning Impairment Scale (WFun), a questionnaire to detect workers with health problems which affect their work, using an assessment by an occupational health nurse as objective standard. The WFun was completed by 294 employees. The nurse interviewed to assess 1) health problems; 2) effects of health on their work; necessity for 3) treatment, 4) health care instruction, and 5) consideration of job accommodation. The odds ratio in the high work functioning impairment group compared with the low was highly statistically significant with 9.05, 10.26, 5.77, 9.37, and 14.70, respectively. The WFun demonstrated the high detectability with an area under the receiver operating characteristic of 0.75, 0.81, 0.72, 0.79, and 0.83, respectively. This study suggests that the WFun is useful in detecting those who have health problems affecting their work.

The effect of simulated field storage conditions on the accuracy of rapid user-friendly blood pathogen detection kits.

PubMed

Bienek, Diane R; Charlton, David G

2012-05-01

Being able to test for the presence of blood pathogens at forward locations could reduce morbidity and mortality in the field. Rapid, user-friendly blood typing kits for detecting Human Immunodeficiency Virus (HIV), Hepatitis C Virus (HCV), and Hepatitis B Virus (HBV) were evaluated to determine their accuracy after storage at various temperatures/humidities. Rates of positive tests of control groups, experimental groups, and industry standards were compared (Fisher's exact chi2, p < or = 0.05). Compared to the control group, 2 of 10 HIV detection devices were adversely affected by exposure to high temperature/high humidity or high temperature/low humidity. With one exception, none of the environmentally exposed HCV or HBV detection devices exhibited significant differences compared to those stored under control conditions. For HIV, HCV, and HBV devices, there were differences compared to the industry standard. Collectively, this evaluation of pathogen detection kits revealed that diagnostic performance varies among products and storage conditions, and that the tested products cannot be considered to be approved for use to screen blood, plasma, cell, or tissue donors.

Brain tumour classification and abnormality detection using neuro-fuzzy technique and Otsu thresholding.

PubMed

Renjith, Arokia; Manjula, P; Mohan Kumar, P

2015-01-01

Brain tumour is one of the main causes for an increase in transience among children and adults. This paper proposes an improved method based on Magnetic Resonance Imaging (MRI) brain image classification and image segmentation approach. Automated classification is encouraged by the need of high accuracy when dealing with a human life. The detection of the brain tumour is a challenging problem, due to high diversity in tumour appearance and ambiguous tumour boundaries. MRI images are chosen for detection of brain tumours, as they are used in soft tissue determinations. First of all, image pre-processing is used to enhance the image quality. Second, dual-tree complex wavelet transform multi-scale decomposition is used to analyse texture of an image. Feature extraction extracts features from an image using gray-level co-occurrence matrix (GLCM). Then, the Neuro-Fuzzy technique is used to classify the stages of brain tumour as benign, malignant or normal based on texture features. Finally, tumour location is detected using Otsu thresholding. The classifier performance is evaluated based on classification accuracies. The simulated results show that the proposed classifier provides better accuracy than previous method.

Fusion and Gaussian mixture based classifiers for SONAR data

NASA Astrophysics Data System (ADS)

Kotari, Vikas; Chang, KC

2011-06-01

Underwater mines are inexpensive and highly effective weapons. They are difficult to detect and classify. Hence detection and classification of underwater mines is essential for the safety of naval vessels. This necessitates a formulation of highly efficient classifiers and detection techniques. Current techniques primarily focus on signals from one source. Data fusion is known to increase the accuracy of detection and classification. In this paper, we formulated a fusion-based classifier and a Gaussian mixture model (GMM) based classifier for classification of underwater mines. The emphasis has been on sound navigation and ranging (SONAR) signals due to their extensive use in current naval operations. The classifiers have been tested on real SONAR data obtained from University of California Irvine (UCI) repository. The performance of both GMM based classifier and fusion based classifier clearly demonstrate their superior classification accuracy over conventional single source cases and validate our approach.

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

PubMed

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

2013-01-01

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

Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation

NASA Astrophysics Data System (ADS)

Jeppesen, Christian; Araya, Samuel Simon; Sahlin, Simon Lennart; Thomas, Sobi; Andreasen, Søren Juhl; Kær, Søren Knudsen

2017-08-01

This study proposes a data-drive impedance-based methodology for fault detection and isolation of low and high cathode stoichiometry, high CO concentration in the anode gas, high methanol vapour concentrations in the anode gas and low anode stoichiometry, for high temperature PEM fuel cells. The fault detection and isolation algorithm is based on an artificial neural network classifier, which uses three extracted features as input. Two of the proposed features are based on angles in the impedance spectrum, and are therefore relative to specific points, and shown to be independent of degradation, contrary to other available feature extraction methods in the literature. The experimental data is based on a 35 day experiment, where 2010 unique electrochemical impedance spectroscopy measurements were recorded. The test of the algorithm resulted in a good detectability of the faults, except for high methanol vapour concentration in the anode gas fault, which was found to be difficult to distinguish from a normal operational data. The achieved accuracy for faults related to CO pollution, anode- and cathode stoichiometry is 100% success rate. Overall global accuracy on the test data is 94.6%.

Real-time detection of bacterial spores using coherent anti-Stokes Raman spectroscopy

NASA Astrophysics Data System (ADS)

Dogariu, A.; Goltsov, A.; Pestov, D.; Sokolov, A. V.; Scully, M. O.

2008-02-01

We demonstrate a realistic method for detection of anthrax-type spores in real time based on their chemical fingerprints using coherent anti-Stokes Raman scattering. Specifically, we demonstrate that coherent Raman scattering can be used to successfully identify spores with high accuracy and high selectivity in less than 50ms.

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

NASA Astrophysics Data System (ADS)

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

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

Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images.

PubMed

Alexandridis, Thomas K; Tamouridou, Afroditi Alexandra; Pantazi, Xanthoula Eirini; Lagopodi, Anastasia L; Kashefi, Javid; Ovakoglou, Georgios; Polychronos, Vassilios; Moshou, Dimitrios

2017-09-01

In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

Error-proneness as a handicap signal.

PubMed

De Jaegher, Kris

2003-09-21

This paper describes two discrete signalling models in which the error-proneness of signals can serve as a handicap signal. In the first model, the direct handicap of sending a high-quality signal is not large enough to assure that a low-quality signaller will not send it. However, if the receiver sometimes mistakes a high-quality signal for a low-quality one, then there is an indirect handicap to sending a high-quality signal. The total handicap of sending such a signal may then still be such that a low-quality signaller would not want to send it. In the second model, there is no direct handicap of sending signals, so that nothing would seem to stop a signaller from always sending a high-quality signal. However, the receiver sometimes fails to detect signals, and this causes an indirect handicap of sending a high-quality signal that still stops the low-quality signaller of sending such a signal. The conditions for honesty are that the probability of an error of detection is higher for a high-quality than for a low-quality signal, and that the signaller who does not detect a signal adopts a response that is bad to the signaller. In both our models, we thus obtain the result that signal accuracy should not lie above a certain level in order for honest signalling to be possible. Moreover, we show that the maximal accuracy that can be achieved is higher the lower the degree of conflict between signaller and receiver. As well, we show that it is the conditions for honest signalling that may be constraining signal accuracy, rather than the signaller trying to make honest signals as effective as possible given receiver psychology, or the signaller adapting the accuracy of honest signals depending on his interests.

Clinical evaluation of endoscopic trimodal imaging for the detection and differentiation of colonic polyps.

PubMed

van den Broek, Frank J C; Fockens, Paul; Van Eeden, Susanne; Kara, Mohammed A; Hardwick, James C H; Reitsma, Johannes B; Dekker, Evelien

2009-03-01

Endoscopic trimodal imaging (ETMI) incorporates high-resolution endoscopy (HRE) and autofluorescence imaging (AFI) for adenoma detection, and narrow-band imaging (NBI) for differentiation of adenomas from nonneoplastic polyps. The aim of this study was to compare AFI with HRE for adenoma detection and to assess the diagnostic accuracy of NBI for differentiation of polyps. This was a randomized trial of tandem colonoscopies. The study was performed at the Academic Medical Center in Amsterdam. One hundred patients underwent colonoscopy with ETMI. Each colonic segment was examined twice for polyps, once with HRE and once with AFI, in random order per patient. All detected polyps were assessed with NBI for pit pattern and with AFI for color, and subsequently removed. Histopathology served as the gold standard for diagnosis. The main outcome measures of this study were adenoma miss-rates of AFI and HRE, and diagnostic accuracy of NBI and AFI for differentiating adenomas from nonneoplastic polyps. Among 50 patients examined with AFI first, 32 adenomas were detected initially. Subsequent inspection with HRE identified 8 additional adenomas. Among 50 patients examined with HRE first, 35 adenomas were detected initially. Successive AFI yielded 14 additional adenomas. The adenoma miss-rates of AFI and HRE therefore were 20% and 29%, respectively (P = .351). The sensitivity, specificity, and overall accuracy of NBI for differentiation were 90%, 70%, and 79%, respectively; corresponding figures for AFI were 99%, 35%, and 63%, respectively. The overall adenoma miss-rate was 25%; AFI did not significantly reduce the adenoma miss-rate compared with HRE. Both NBI and AFI had a disappointing diagnostic accuracy for polyp differentiation, although AFI had a high sensitivity.

System and method for high precision isotope ratio destructive analysis

DOEpatents

Bushaw, Bruce A; Anheier, Norman C; Phillips, Jon R

2013-07-02

A system and process are disclosed that provide high accuracy and high precision destructive analysis measurements for isotope ratio determination of relative isotope abundance distributions in liquids, solids, and particulate samples. The invention utilizes a collinear probe beam to interrogate a laser ablated plume. This invention provides enhanced single-shot detection sensitivity approaching the femtogram range, and isotope ratios that can be determined at approximately 1% or better precision and accuracy (relative standard deviation).

Robust vehicle detection under various environmental conditions using an infrared thermal camera and its application to road traffic flow monitoring.

PubMed

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2013-06-17

We have already proposed a method for detecting vehicle positions and their movements (henceforth referred to as "our previous method") using thermal images taken with an infrared thermal camera. Our experiments have shown that our previous method detects vehicles robustly under four different environmental conditions which involve poor visibility conditions in snow and thick fog. Our previous method uses the windshield and its surroundings as the target of the Viola-Jones detector. Some experiments in winter show that the vehicle detection accuracy decreases because the temperatures of many windshields approximate those of the exterior of the windshields. In this paper, we propose a new vehicle detection method (henceforth referred to as "our new method"). Our new method detects vehicles based on tires' thermal energy reflection. We have done experiments using three series of thermal images for which the vehicle detection accuracies of our previous method are low. Our new method detects 1,417 vehicles (92.8%) out of 1,527 vehicles, and the number of false detection is 52 in total. Therefore, by combining our two methods, high vehicle detection accuracies are maintained under various environmental conditions. Finally, we apply the traffic information obtained by our two methods to traffic flow automatic monitoring, and show the effectiveness of our proposal.

A Novel Arc Fault Detector for Early Detection of Electrical Fires

PubMed Central

Yang, Kai; Zhang, Rencheng; Yang, Jianhong; Liu, Canhua; Chen, Shouhong; Zhang, Fujiang

2016-01-01

Arc faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of arc fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of arc faults make them difficult to detect. To improve the accuracy of arc fault detection, a novel arc fault detector (AFD) is developed in this study. First, an experimental arc fault platform is built to study electrical fires. A high-frequency transducer and a current transducer are used to measure typical load signals of arc faults and normal states. After the common features of these signals are studied, high-frequency energy and current variations are extracted as an input eigenvector for use by an arc fault detection algorithm. Then, the detection algorithm based on a weighted least squares support vector machine is designed and successfully applied in a microprocessor. Finally, an AFD is developed. The test results show that the AFD can detect arc faults in a timely manner and interrupt the circuit power supply before electrical fires can occur. The AFD is not influenced by cross talk or transient processes, and the detection accuracy is very high. Hence, the AFD can be installed in low-voltage circuits to monitor circuit states in real-time to facilitate the early detection of electrical fires. PMID:27070618

An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture.

PubMed

Zhang, Xiaopu; Lin, Jun; Chen, Zubin; Sun, Feng; Zhu, Xi; Fang, Gengfa

2018-06-05

Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil and gas production. To detect events in an accurate and efficient way, there are two major challenges. One challenge is how to achieve high accuracy due to a poor signal-to-noise ratio (SNR). The other one is concerned with real-time data transmission. Taking these challenges into consideration, an edge-computing-based platform, namely Edge-to-Center LearnReduce, is presented in this work. The platform consists of a data center with many edge components. At the data center, a neural network model combined with convolutional neural network (CNN) and long short-term memory (LSTM) is designed and this model is trained by using previously obtained data. Once the model is fully trained, it is sent to edge components for events detection and data reduction. At each edge component, a probabilistic inference is added to the neural network model to improve its accuracy. Finally, the reduced data is delivered to the data center. Based on experiment results, a high detection accuracy (over 96%) with less transmitted data (about 90%) was achieved by using the proposed approach on a microseismic monitoring system. These results show that the platform can simultaneously improve the accuracy and efficiency of microseismic monitoring.

Accuracy of Binary Black Hole waveforms for Advanced LIGO searches

NASA Astrophysics Data System (ADS)

Kumar, Prayush; Barkett, Kevin; Bhagwat, Swetha; Chu, Tony; Fong, Heather; Brown, Duncan; Pfeiffer, Harald; Scheel, Mark; Szilagyi, Bela

2015-04-01

Coalescing binaries of compact objects are flagship sources for the first direct detection of gravitational waves with LIGO-Virgo observatories. Matched-filtering based detection searches aimed at binaries of black holes will use aligned spin waveforms as filters, and their efficiency hinges on the accuracy of the underlying waveform models. A number of gravitational waveform models are available in literature, e.g. the Effective-One-Body, Phenomenological, and traditional post-Newtonian ones. While Numerical Relativity (NR) simulations provide for the most accurate modeling of gravitational radiation from compact binaries, their computational cost limits their application in large scale searches. In this talk we assess the accuracy of waveform models in two regions of parameter space, which have only been explored cursorily in the past: the high mass-ratio regime as well as the comparable mass-ratio + high spin regime.s Using the SpEC code, six q = 7 simulations with aligned-spins and lasting 60 orbits, and tens of q ∈ [1,3] simulations with high black hole spins were performed. We use them to study the accuracy and intrinsic parameter biases of different waveform families, and assess their viability for Advanced LIGO searches.

Hybrid Optimization of Object-Based Classification in High-Resolution Images Using Continous ANT Colony Algorithm with Emphasis on Building Detection

NASA Astrophysics Data System (ADS)

Tamimi, E.; Ebadi, H.; Kiani, A.

2017-09-01

Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.

Does clinical pretest probability influence image quality and diagnostic accuracy in dual-source coronary CT angiography?

PubMed

Thomas, Christoph; Brodoefel, Harald; Tsiflikas, Ilias; Bruckner, Friederike; Reimann, Anja; Ketelsen, Dominik; Drosch, Tanja; Claussen, Claus D; Kopp, Andreas; Heuschmid, Martin; Burgstahler, Christof

2010-02-01

To prospectively evaluate the influence of the clinical pretest probability assessed by the Morise score onto image quality and diagnostic accuracy in coronary dual-source computed tomography angiography (DSCTA). In 61 patients, DSCTA and invasive coronary angiography were performed. Subjective image quality and accuracy for stenosis detection (>50%) of DSCTA with invasive coronary angiography as gold standard were evaluated. The influence of pretest probability onto image quality and accuracy was assessed by logistic regression and chi-square testing. Correlations of image quality and accuracy with the Morise score were determined using linear regression. Thirty-eight patients were categorized into the high, 21 into the intermediate, and 2 into the low probability group. Accuracies for the detection of significant stenoses were 0.94, 0.97, and 1.00, respectively. Logistic regressions and chi-square tests showed statistically significant correlations between Morise score and image quality (P < .0001 and P < .001) and accuracy (P = .0049 and P = .027). Linear regression revealed a cutoff Morise score for a good image quality of 16 and a cutoff for a barely diagnostic image quality beyond the upper Morise scale. Pretest probability is a weak predictor of image quality and diagnostic accuracy in coronary DSCTA. A sufficient image quality for diagnostic images can be reached with all pretest probabilities. Therefore, coronary DSCTA might be suitable also for patients with a high pretest probability. Copyright 2010 AUR. Published by Elsevier Inc. All rights reserved.

Randomized, controlled trial of standard-definition white-light, high-definition white-light, and narrow-band imaging colonoscopy for the detection of colon polyps and prediction of polyp histology.

PubMed

Rastogi, Amit; Early, Dayna S; Gupta, Neil; Bansal, Ajay; Singh, Vikas; Ansstas, Michael; Jonnalagadda, Sreenivasa S; Hovis, Christine E; Gaddam, Srinivas; Wani, Sachin B; Edmundowicz, Steven A; Sharma, Prateek

2011-09-01

Missing adenomas and the inability to accurately differentiate between polyp histology remain the main limitations of standard-definition white-light (SD-WL) colonoscopy. To compare the adenoma detection rates of SD-WL with those of high-definition white-light (HD-WL) and narrow-band imaging (NBI) as well as the accuracy of predicting polyp histology. Multicenter, prospective, randomized, controlled trial. Two academic medical centers in the United States. Subjects undergoing screening or surveillance colonoscopy. Subjects were randomized to undergo colonoscopy with one of the following: SD-WL, HD-WL, or NBI. The proportion of subjects detected with adenomas, adenomas detected per subject, and the accuracy of predicting polyp histology real time. A total of 630 subjects were included. The proportion of subjects with adenomas was 38.6% with SD-WL compared with 45.7% with HD-WL and 46.2% with NBI (P = .17 and P = .14, respectively). Adenomas detected per subject were 0.69 with SD-WL compared with 1.12 with HD-WL and 1.13 with NBI (P = .016 and P = .014, respectively). HD-WL and NBI detected more subjects with flat and right-sided adenomas compared with SD-WL (all P values <.005). NBI had a superior sensitivity (90%) and accuracy (82%) to predict adenomas compared with SD-WL and HD-WL (all P values <.005). Academic medical centers with experienced endoscopists. There was no difference in the proportion of subjects with adenomas detected with SD-WL, HD-WL, and NBI. However, HD-WL and NBI detected significantly more adenomas per subject (>60%) compared with SD-WL. NBI had the highest accuracy in predicting adenomas in real time during colonoscopy. ( NCT 00614770.). Copyright © 2011 American Society for Gastrointestinal Endoscopy. Published by Mosby, Inc. All rights reserved.

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

NASA Astrophysics Data System (ADS)

Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

2010-02-01

Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG

PubMed Central

Chen, Duo; Wan, Suiren; Xiang, Jing; Bao, Forrest Sheng

2017-01-01

In the past decade, Discrete Wavelet Transform (DWT), a powerful time-frequency tool, has been widely used in computer-aided signal analysis of epileptic electroencephalography (EEG), such as the detection of seizures. One of the important hurdles in the applications of DWT is the settings of DWT, which are chosen empirically or arbitrarily in previous works. The objective of this study aimed to develop a framework for automatically searching the optimal DWT settings to improve accuracy and to reduce computational cost of seizure detection. To address this, we developed a method to decompose EEG data into 7 commonly used wavelet families, to the maximum theoretical level of each mother wavelet. Wavelets and decomposition levels providing the highest accuracy in each wavelet family were then searched in an exhaustive selection of frequency bands, which showed optimal accuracy and low computational cost. The selection of frequency bands and features removed approximately 40% of redundancies. The developed algorithm achieved promising performance on two well-tested EEG datasets (accuracy >90% for both datasets). The experimental results of the developed method have demonstrated that the settings of DWT affect its performance on seizure detection substantially. Compared with existing seizure detection methods based on wavelet, the new approach is more accurate and transferable among datasets. PMID:28278203

ROC analysis of the accuracy of Noncycloplegic retinoscopy, Retinomax Autorefractor, and SureSight Vision Screener for preschool vision screening.

PubMed

Ying, Gui-shuang; Maguire, Maureen; Quinn, Graham; Kulp, Marjean Taylor; Cyert, Lynn

2011-12-28

To evaluate, by receiver operating characteristic (ROC) analysis, the accuracy of three instruments of refractive error in detecting eye conditions among 3- to 5-year-old Head Start preschoolers and to evaluate differences in accuracy between instruments and screeners and by age of the child. Children participating in the Vision In Preschoolers (VIP) Study (n = 4040), had screening tests administered by pediatric eye care providers (phase I) or by both nurse and lay screeners (phase II). Noncycloplegic retinoscopy (NCR), the Retinomax Autorefractor (Nikon, Tokyo, Japan), and the SureSight Vision Screener (SureSight, Alpharetta, GA) were used in phase I, and Retinomax and SureSight were used in phase II. Pediatric eye care providers performed a standardized eye examination to identify amblyopia, strabismus, significant refractive error, and reduced visual acuity. The accuracy of the screening tests was summarized by the area under the ROC curve (AUC) and compared between instruments and screeners and by age group. The three screening tests had a high AUC for all categories of screening personnel. The AUC for detecting any VIP-targeted condition was 0.83 for NCR, 0.83 (phase I) to 0.88 (phase II) for Retinomax, and 0.86 (phase I) to 0.87 (phase II) for SureSight. The AUC was 0.93 to 0.95 for detecting group 1 (most severe) conditions and did not differ between instruments or screeners or by age of the child. NCR, Retinomax, and SureSight had similar and high accuracy in detecting vision disorders in preschoolers across all types of screeners and age of child, consistent with previously reported results at specificity levels of 90% and 94%.

A meta-analysis of use of Prostate Imaging Reporting and Data System Version 2 (PI-RADS V2) with multiparametric MR imaging for the detection of prostate cancer.

PubMed

Zhang, Li; Tang, Min; Chen, Sipan; Lei, Xiaoyan; Zhang, Xiaoling; Huan, Yi

2017-12-01

This meta-analysis was undertaken to review the diagnostic accuracy of PI-RADS V2 for prostate cancer (PCa) detection with multiparametric MR (mp-MR). A comprehensive literature search of electronic databases was performed by two observers independently. Inclusion criteria were original research using the PI-RADS V2 system in reporting prostate MRI. The methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Data necessary to complete 2 × 2 contingency tables were obtained from the included studies. Thirteen studies (2,049 patients) were analysed. This is an initial meta-analysis of PI-RADs V2 and the overall diagnostic accuracy in diagnosing PCa was as follows: pooled sensitivity, 0.85 (0.78-0.91); pooled specificity, 0.71 (0.60-0.80); pooled positive likelihood ratio (LR+), 2.92 (2.09-4.09); pooled negative likelihood ratio (LR-), 0.21 (0.14-0.31); pooled diagnostic odds ratio (DOR), 14.08 (7.93-25.01), respectively. Positive predictive values ranged from 0.54 to 0.97 and negative predictive values ranged from 0.26 to 0.92. Currently available evidence indicates that PI-RADS V2 appears to have good diagnostic accuracy in patients with PCa lesions with high sensitivity and moderate specificity. However, no recommendation regarding the best threshold can be provided because of heterogeneity. • PI-RADS V2 shows good diagnostic accuracy for PCa detection. • Initially pooled specificity of PI-RADS v2 remains moderate. • PCa detection is increased by experienced radiologists. • There is currently a high heterogeneity in prostate diagnostics with MRI.

Advancing the speed, sensitivity and accuracy of biomolecular detection using multi-length-scale engineering

PubMed Central

Kelley, Shana O.; Mirkin, Chad A.; Walt, David R.; Ismagilov, Rustem F.; Toner, Mehmet; Sargent, Edward H.

2015-01-01

Rapid progress in identifying disease biomarkers has increased the importance of creating high-performance detection technologies. Over the last decade, the design of many detection platforms has focused on either the nano or micro length scale. Here, we review recent strategies that combine nano- and microscale materials and devices to produce large improvements in detection sensitivity, speed and accuracy, allowing previously undetectable biomarkers to be identified in clinical samples. Microsensors that incorporate nanoscale features can now rapidly detect disease-related nucleic acids expressed in patient samples. New microdevices that separate large clinical samples into nanocompartments allow precise quantitation of analytes, and microfluidic systems that utilize nanoscale binding events can detect rare cancer cells in the bloodstream more accurately than before. These advances will lead to faster and more reliable clinical diagnostic devices. PMID:25466541

Advancing the speed, sensitivity and accuracy of biomolecular detection using multi-length-scale engineering

NASA Astrophysics Data System (ADS)

Kelley, Shana O.; Mirkin, Chad A.; Walt, David R.; Ismagilov, Rustem F.; Toner, Mehmet; Sargent, Edward H.

2014-12-01

Rapid progress in identifying disease biomarkers has increased the importance of creating high-performance detection technologies. Over the last decade, the design of many detection platforms has focused on either the nano or micro length scale. Here, we review recent strategies that combine nano- and microscale materials and devices to produce large improvements in detection sensitivity, speed and accuracy, allowing previously undetectable biomarkers to be identified in clinical samples. Microsensors that incorporate nanoscale features can now rapidly detect disease-related nucleic acids expressed in patient samples. New microdevices that separate large clinical samples into nanocompartments allow precise quantitation of analytes, and microfluidic systems that utilize nanoscale binding events can detect rare cancer cells in the bloodstream more accurately than before. These advances will lead to faster and more reliable clinical diagnostic devices.

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.

Diagnostic value of DIAGNOdent in detecting caries under composite restorations of primary molars.

PubMed

Sichani, Ava Vali; Javadinejad, Shahrzad; Ghafari, Roshanak

2016-01-01

Direct observation cannot detect caries under restorations; therefore, the aim of this study was to compare the accuracy of radiographs and DIAGNOdent in detecting caries under restorations in primary teeth using histologic evaluation. A total of 74 previously extracted primary molars (37 with occlusal caries and 37 without caries) were used. Class 1 cavity preparations were made on each tooth by a single clinician and then the preparations were filled with composite resin. The accuracy of radiographs and DIAGNOdent in detecting caries was compared using histologic evaluation. The data were analyzed by SPSS version 21 using Chi-square, Mc Namara statistical tests and receiver operating characteristic curve. The significance was set at 0.05. The sensitivity and specificity for DIAGNOdent were 70.97 and 83.72, respectively. Few false negative results were observed, and the positive predictive value was high (+PV = 75.9) and the area under curve was more than 0.70 therefore making DIAGNOdenta great method for detecting caries (P = 0.0001). Two observers evaluated the radiographs and both observers had low sensitivity ( first observer: 48.39) (second observer: 51.61) and high specificity (both observers: 79.07). The +PV was lower than DIAGNOdent and the area under curve for both observers was less than 0.70. However, the difference between the two methods was not significant. DIAGNOdent showed a greater accuracy in detecting secondary caries under primary molar restorations, compared to radiographs. Although DIAGNOdent is an effective method for detecting caries under composite restorations, it is better to be used as an adjunctive method alongside other detecting procedures.

A Micro-Resonant Gas Sensor with Nanometer Clearance between the Pole Plates

PubMed Central

Xu, Lizhong

2018-01-01

In micro-resonant gas sensors, the capacitive detection is widely used because of its simple structure. However, its shortcoming is a weak signal output caused by a small capacitance change. Here, we reduced the initial clearance between the pole plates to the nanometer level, and increased the capacitance between the pole plates and its change during resonator vibration. We propose a fabricating process of the micro-resonant gas sensor by which the initial clearance between the pole plates is reduced to the nanometer level and a micro-resonant gas sensor with 200 nm initial clearance is fabricated. With this sensor, the resonant frequency shifts were measured when they were exposed to several different vapors, and high detection accuracies were obtained. The detection accuracy with respect to ethanol vapor was 0.4 ppm per Hz shift, and the detection accuracy with respect to hydrogen and ammonias vapors was 3 ppm and 0.5 ppm per Hz shift, respectively. PMID:29373546

A Micro-Resonant Gas Sensor with Nanometer Clearance between the Pole Plates.

PubMed

Fu, Xiaorui; Xu, Lizhong

2018-01-26

In micro-resonant gas sensors, the capacitive detection is widely used because of its simple structure. However, its shortcoming is a weak signal output caused by a small capacitance change. Here, we reduced the initial clearance between the pole plates to the nanometer level, and increased the capacitance between the pole plates and its change during resonator vibration. We propose a fabricating process of the micro-resonant gas sensor by which the initial clearance between the pole plates is reduced to the nanometer level and a micro-resonant gas sensor with 200 nm initial clearance is fabricated. With this sensor, the resonant frequency shifts were measured when they were exposed to several different vapors, and high detection accuracies were obtained. The detection accuracy with respect to ethanol vapor was 0.4 ppm per Hz shift, and the detection accuracy with respect to hydrogen and ammonias vapors was 3 ppm and 0.5 ppm per Hz shift, respectively.

Topography-based analysis of Hurricane Katrina inundation of New Orleans: Chapter 3G in Science and the storms-the USGS response to the hurricanes of 2005

USGS Publications Warehouse

Gesch, Dean

2007-01-01

The ready availability of high-resolution, high-accuracy elevation data proved valuable for development of topographybased products to determine rough estimates of the inundation of New Orleans, La., from Hurricane Katrina. Because of its high level of spatial detail and vertical accuracy of elevation measurements, light detection and ranging (lidar) remote sensing is an excellent mapping technology for use in low-relief hurricane-prone coastal areas.

Classification method, spectral diversity, band combination and accuracy assessment evaluation for urban feature detection

NASA Astrophysics Data System (ADS)

Erener, A.

2013-04-01

Automatic extraction of urban features from high resolution satellite images is one of the main applications in remote sensing. It is useful for wide scale applications, namely: urban planning, urban mapping, disaster management, GIS (geographic information systems) updating, and military target detection. One common approach to detecting urban features from high resolution images is to use automatic classification methods. This paper has four main objectives with respect to detecting buildings. The first objective is to compare the performance of the most notable supervised classification algorithms, including the maximum likelihood classifier (MLC) and the support vector machine (SVM). In this experiment the primary consideration is the impact of kernel configuration on the performance of the SVM. The second objective of the study is to explore the suitability of integrating additional bands, namely first principal component (1st PC) and the intensity image, for original data for multi classification approaches. The performance evaluation of classification results is done using two different accuracy assessment methods: pixel based and object based approaches, which reflect the third aim of the study. The objective here is to demonstrate the differences in the evaluation of accuracies of classification methods. Considering consistency, the same set of ground truth data which is produced by labeling the building boundaries in the GIS environment is used for accuracy assessment. Lastly, the fourth aim is to experimentally evaluate variation in the accuracy of classifiers for six different real situations in order to identify the impact of spatial and spectral diversity on results. The method is applied to Quickbird images for various urban complexity levels, extending from simple to complex urban patterns. The simple surface type includes a regular urban area with low density and systematic buildings with brick rooftops. The complex surface type involves almost all kinds of challenges, such as high dense build up areas, regions with bare soil, and small and large buildings with different rooftops, such as concrete, brick, and metal. Using the pixel based accuracy assessment it was shown that the percent building detection (PBD) and quality percent (QP) of the MLC and SVM depend on the complexity and texture variation of the region. Generally, PBD values range between 70% and 90% for the MLC and SVM, respectively. No substantial improvements were observed when the SVM and MLC classifications were developed by the addition of more variables, instead of the use of only four bands. In the evaluation of object based accuracy assessment, it was demonstrated that while MLC and SVM provide higher rates of correct detection, they also provide higher rates of false alarms.

A novel method for accurate needle-tip identification in trans-rectal ultrasound-based high-dose-rate prostate brachytherapy.

PubMed

Zheng, Dandan; Todor, Dorin A

2011-01-01

In real-time trans-rectal ultrasound (TRUS)-based high-dose-rate prostate brachytherapy, the accurate identification of needle-tip position is critical for treatment planning and delivery. Currently, needle-tip identification on ultrasound images can be subject to large uncertainty and errors because of ultrasound image quality and imaging artifacts. To address this problem, we developed a method based on physical measurements with simple and practical implementation to improve the accuracy and robustness of needle-tip identification. Our method uses measurements of the residual needle length and an off-line pre-established coordinate transformation factor, to calculate the needle-tip position on the TRUS images. The transformation factor was established through a one-time systematic set of measurements of the probe and template holder positions, applicable to all patients. To compare the accuracy and robustness of the proposed method and the conventional method (ultrasound detection), based on the gold-standard X-ray fluoroscopy, extensive measurements were conducted in water and gel phantoms. In water phantom, our method showed an average tip-detection accuracy of 0.7 mm compared with 1.6 mm of the conventional method. In gel phantom (more realistic and tissue-like), our method maintained its level of accuracy while the uncertainty of the conventional method was 3.4mm on average with maximum values of over 10mm because of imaging artifacts. A novel method based on simple physical measurements was developed to accurately detect the needle-tip position for TRUS-based high-dose-rate prostate brachytherapy. The method demonstrated much improved accuracy and robustness over the conventional method. Copyright © 2011 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope

PubMed Central

2017-01-01

Rapid automatic detection of the fiducial points—namely, the P wave, QRS complex, and T wave—is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs. PMID:29065613

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope.

PubMed

Park, Jeong-Seon; Lee, Sang-Woong; Park, Unsang

2017-01-01

Rapid automatic detection of the fiducial points-namely, the P wave, QRS complex, and T wave-is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.

Clone tag detection in distributed RFID systems.

PubMed

Kamaludin, Hazalila; Mahdin, Hairulnizam; Abawajy, Jemal H

2018-01-01

Although Radio Frequency Identification (RFID) is poised to displace barcodes, security vulnerabilities pose serious challenges for global adoption of the RFID technology. Specifically, RFID tags are prone to basic cloning and counterfeiting security attacks. A successful cloning of the RFID tags in many commercial applications can lead to many serious problems such as financial losses, brand damage, safety and health of the public. With many industries such as pharmaceutical and businesses deploying RFID technology with a variety of products, it is important to tackle RFID tag cloning problem and improve the resistance of the RFID systems. To this end, we propose an approach for detecting cloned RFID tags in RFID systems with high detection accuracy and minimal overhead thus overcoming practical challenges in existing approaches. The proposed approach is based on consistency of dual hash collisions and modified count-min sketch vector. We evaluated the proposed approach through extensive experiments and compared it with existing baseline approaches in terms of execution time and detection accuracy under varying RFID tag cloning ratio. The results of the experiments show that the proposed approach outperforms the baseline approaches in cloned RFID tag detection accuracy.

Automatic localization of cerebral cortical malformations using fractal analysis.

PubMed

De Luca, A; Arrigoni, F; Romaniello, R; Triulzi, F M; Peruzzo, D; Bertoldo, A

2016-08-21

Malformations of cortical development (MCDs) encompass a variety of brain disorders affecting the normal development and organization of the brain cortex. The relatively low incidence and the extreme heterogeneity of these disorders hamper the application of classical group level approaches for the detection of lesions. Here, we present a geometrical descriptor for a voxel level analysis based on fractal geometry, then define two similarity measures to detect the lesions at single subject level. The pipeline was applied to 15 normal children and nine pediatric patients affected by MCDs following two criteria, maximum accuracy (WACC) and minimization of false positives (FPR), and proved that our lesion detection algorithm is able to detect and locate abnormalities of the brain cortex with high specificity (WACC  =  85%, FPR  =  96%), sensitivity (WACC  =  83%, FPR  =  63%) and accuracy (WACC  =  85%, FPR  =  90%). The combination of global and local features proves to be effective, making the algorithm suitable for the detection of both focal and diffused malformations. Compared to other existing algorithms, this method shows higher accuracy and sensitivity.

Automatic localization of cerebral cortical malformations using fractal analysis

NASA Astrophysics Data System (ADS)

De Luca, A.; Arrigoni, F.; Romaniello, R.; Triulzi, F. M.; Peruzzo, D.; Bertoldo, A.

2016-08-01

Malformations of cortical development (MCDs) encompass a variety of brain disorders affecting the normal development and organization of the brain cortex. The relatively low incidence and the extreme heterogeneity of these disorders hamper the application of classical group level approaches for the detection of lesions. Here, we present a geometrical descriptor for a voxel level analysis based on fractal geometry, then define two similarity measures to detect the lesions at single subject level. The pipeline was applied to 15 normal children and nine pediatric patients affected by MCDs following two criteria, maximum accuracy (WACC) and minimization of false positives (FPR), and proved that our lesion detection algorithm is able to detect and locate abnormalities of the brain cortex with high specificity (WACC  =  85%, FPR  =  96%), sensitivity (WACC  =  83%, FPR  =  63%) and accuracy (WACC  =  85%, FPR  =  90%). The combination of global and local features proves to be effective, making the algorithm suitable for the detection of both focal and diffused malformations. Compared to other existing algorithms, this method shows higher accuracy and sensitivity.

Iconic memory requires attention

PubMed Central

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

2012-01-01

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

Iconic memory requires attention.

PubMed

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

2012-01-01

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

Quantifying the effect of side branches in endothelial shear stress estimates

PubMed Central

Giannopoulos, Andreas A.; Chatzizisis, Yiannis S.; Maurovich-Horvat, Pal; Antoniadis, Antonios P.; Hoffmann, Udo; Steigner, Michael L.; Rybicki, Frank J.; Mitsouras, Dimitrios

2016-01-01

Background and aims Low and high endothelial shear stress (ESS) is associated with coronary atherosclerosis progression and high-risk plaque features. Coronary ESS is currently assessed via computational fluid dynamic (CFD) simulation in the lumen geometry determined from invasive imaging such as intravascular ultrasound and optical coherence tomography. This process typically omits side branches of the target vessel in the CFD model as invasive imaging of those vessels is not clinically-indicated. The purpose of this study was to determine the extent to which this simplification affects the determination of those regions of the coronary endothelium subjected to pathologic ESS. Methods We determined the diagnostic accuracy of ESS profiling without side branches to detect pathologic ESS in the major coronary arteries of 5 hearts imaged ex vivo with CT angiography. ESS of the three major coronary arteries was calculated both without (test model), and with (reference model) inclusion of all side branches >1.5 mm in diameter, using previously-validated CFD approaches. Diagnostic test characteristics (accuracy, sensitivity, specificity and negative and positive predictive value [NPV/PPV]) with respect to the reference model were assessed for both the entire length as well as only the proximal portion of each major coronary artery, where the majority of high-risk plaques occur. Results Using the model without side branches overall accuracy, sensitivity, specificity, NPV and PPV were 83.4%, 54.0%, 96%, 95.9% and 55.1%, respectively to detect low ESS, and 87.0%, 67.7%, 90.7%, 93.7% and 57.5%, respectively to detect high ESS. When considering only the proximal arteries, test characteristics differed for low and high ESS, with low sensitivity (67.7%) and high specificity (90.7%) to detect low ESS, and low sensitivity (44.7%) and high specificity (95.5%) to detect high ESS. Conclusions The exclusion of side branches in ESS vascular profiling studies greatly reduces the ability to detect regions of the major coronary arteries subjected to pathologic ESS. Single-conduit models can in general only be used to rule out pathologic ESS. PMID:27372207

Quantifying the effect of side branches in endothelial shear stress estimates.

PubMed

Giannopoulos, Andreas A; Chatzizisis, Yiannis S; Maurovich-Horvat, Pal; Antoniadis, Antonios P; Hoffmann, Udo; Steigner, Michael L; Rybicki, Frank J; Mitsouras, Dimitrios

2016-08-01

Low and high endothelial shear stress (ESS) is associated with coronary atherosclerosis progression and high-risk plaque features. Coronary ESS is currently assessed via computational fluid dynamic (CFD) simulation of coronary blood flow in the lumen geometry determined from invasive imaging such as intravascular ultrasound and optical coherence tomography. This process typically omits side branches of the target vessel in the CFD model as invasive imaging of those vessels is not usually clinically-indicated. The purpose of this study was to determine the extent to which this simplification affects the determination of those regions of the coronary endothelium subjected to pathologic ESS. We determined the diagnostic accuracy of ESS profiling without side branches to detect pathologic ESS in the major coronary arteries of 5 hearts imaged ex vivo with computed tomography angiography (CTA). ESS of the three major coronary arteries was calculated both without (test model), and with (reference model) inclusion of all side branches >1.5 mm in diameter, using previously-validated CFD approaches. Diagnostic test characteristics (accuracy, sensitivity, specificity and negative and positive predictive value [NPV/PPV]) with respect to the reference model were assessed for both the entire length as well as only the proximal portion of each major coronary artery, where the majority of high-risk plaques occur. Using the model without side branches overall accuracy, sensitivity, specificity, NPV and PPV were 83.4%, 54.0%, 96%, 95.9% and 55.1%, respectively to detect low ESS, and 87.0%, 67.7%, 90.7%, 93.7% and 57.5%, respectively to detect high ESS. When considering only the proximal arteries, test characteristics differed for low and high ESS, with low sensitivity (67.7%) and high specificity (90.7%) to detect low ESS, and low sensitivity (44.7%) and high specificity (95.5%) to detect high ESS. The exclusion of side branches in ESS vascular profiling studies greatly reduces the ability to detect regions of the major coronary arteries subjected to pathologic ESS. Single-conduit models can in general only be used to rule out pathologic ESS. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

High-order optical vortex position detection using a Shack-Hartmann wavefront sensor.

PubMed

Luo, Jia; Huang, Hongxin; Matsui, Yoshinori; Toyoda, Haruyoshi; Inoue, Takashi; Bai, Jian

2015-04-06

Optical vortex (OV) beams have null-intensity singular points, and the intensities in the region surrounding the singular point are quite low. This low intensity region influences the position detection accuracy of phase singular point, especially for high-order OV beam. In this paper, we propose a new method for solving this problem, called the phase-slope-combining correlation matching method. A Shack-Hartmann wavefront sensor (SH-WFS) is used to measure phase slope vectors at lenslet positions of the SH-WFS. Several phase slope vectors are combined into one to reduce the influence of low-intensity regions around the singular point, and the combined phase slope vectors are used to determine the OV position with the aid of correlation matching with a pre-calculated database. Experimental results showed that the proposed method works with high accuracy, even when detecting an OV beam with a topological charge larger than six. The estimated precision was about 0.15 in units of lenslet size when detecting an OV beam with a topological charge of up to 20.

The accuracy of confrontation visual field test in comparison with automated perimetry.

PubMed Central

Johnson, L. N.; Baloh, F. G.

1991-01-01

The accuracy of confrontation visual field testing was determined for 512 visual fields using automated static perimetry as the reference standard. The sensitivity of confrontation testing excluding patchy defects was 40% for detecting anterior visual field defects, 68.3% for posterior defects, and 50% for both anterior and posterior visual field defects combined. The sensitivity within each group varied depending on the type of visual field defect encountered. Confrontation testing had a high sensitivity (75% to 100%) for detecting altitudinal visual loss, central/centrocecal scotoma, and homonymous hemianopsia. Confrontation testing was fairly insensitive (20% to 50% sensitivity) for detecting arcuate scotoma and bitemporal hemianopsia. The specificity of confrontation testing was high at 93.4%. The high positive predictive value (72.6%) and negative predictive value (75.7%) would indicate that visual field defects identified during confrontation testing are often true visual field defects. However, the many limitations of confrontation testing should be remembered, particularly its low sensitivity for detecting visual field loss associated with parasellar tumors, glaucoma, and compressive optic neuropathies. PMID:1800764

Ship detection leveraging deep neural networks in WorldView-2 images

NASA Astrophysics Data System (ADS)

Yamamoto, T.; Kazama, Y.

2017-10-01

Interpretation of high-resolution satellite images has been so difficult that skilled interpreters must have checked the satellite images manually because of the following issues. One is the requirement of the high detection accuracy rate. The other is the variety of the target, taking ships for example, there are many kinds of ships, such as boat, cruise ship, cargo ship, aircraft carrier, and so on. Furthermore, there are similar appearance objects throughout the image; therefore, it is often difficult even for the skilled interpreters to distinguish what object the pixels really compose. In this paper, we explore the feasibility of object extraction leveraging deep learning with high-resolution satellite images, especially focusing on ship detection. We calculated the detection accuracy using the WorldView-2 images. First, we collected the training images labelled as "ship" and "not ship". After preparing the training data, we defined the deep neural network model to judge whether ships are existing or not, and trained them with about 50,000 training images for each label. Subsequently, we scanned the evaluation image with different resolution windows and extracted the "ship" images. Experimental result shows the effectiveness of the deep learning based object detection.

Robust Vehicle Detection under Various Environmental Conditions Using an Infrared Thermal Camera and Its Application to Road Traffic Flow Monitoring

PubMed Central

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2013-01-01

We have already proposed a method for detecting vehicle positions and their movements (henceforth referred to as “our previous method”) using thermal images taken with an infrared thermal camera. Our experiments have shown that our previous method detects vehicles robustly under four different environmental conditions which involve poor visibility conditions in snow and thick fog. Our previous method uses the windshield and its surroundings as the target of the Viola-Jones detector. Some experiments in winter show that the vehicle detection accuracy decreases because the temperatures of many windshields approximate those of the exterior of the windshields. In this paper, we propose a new vehicle detection method (henceforth referred to as “our new method”). Our new method detects vehicles based on tires' thermal energy reflection. We have done experiments using three series of thermal images for which the vehicle detection accuracies of our previous method are low. Our new method detects 1,417 vehicles (92.8%) out of 1,527 vehicles, and the number of false detection is 52 in total. Therefore, by combining our two methods, high vehicle detection accuracies are maintained under various environmental conditions. Finally, we apply the traffic information obtained by our two methods to traffic flow automatic monitoring, and show the effectiveness of our proposal. PMID:23774988

Crowdsourcing for translational research: analysis of biomarker expression using cancer microarrays

PubMed Central

Lawson, Jonathan; Robinson-Vyas, Rupesh J; McQuillan, Janette P; Paterson, Andy; Christie, Sarah; Kidza-Griffiths, Matthew; McDuffus, Leigh-Anne; Moutasim, Karwan A; Shaw, Emily C; Kiltie, Anne E; Howat, William J; Hanby, Andrew M; Thomas, Gareth J; Smittenaar, Peter

2017-01-01

Background: Academic pathology suffers from an acute and growing lack of workforce resource. This especially impacts on translational elements of clinical trials, which can require detailed analysis of thousands of tissue samples. We tested whether crowdsourcing – enlisting help from the public – is a sufficiently accurate method to score such samples. Methods: We developed a novel online interface to train and test lay participants on cancer detection and immunohistochemistry scoring in tissue microarrays. Lay participants initially performed cancer detection on lung cancer images stained for CD8, and we measured how extending a basic tutorial by annotated example images and feedback-based training affected cancer detection accuracy. We then applied this tutorial to additional cancer types and immunohistochemistry markers – bladder/ki67, lung/EGFR, and oesophageal/CD8 – to establish accuracy compared with experts. Using this optimised tutorial, we then tested lay participants' accuracy on immunohistochemistry scoring of lung/EGFR and bladder/p53 samples. Results: We observed that for cancer detection, annotated example images and feedback-based training both improved accuracy compared with a basic tutorial only. Using this optimised tutorial, we demonstrate highly accurate (>0.90 area under curve) detection of cancer in samples stained with nuclear, cytoplasmic and membrane cell markers. We also observed high Spearman correlations between lay participants and experts for immunohistochemistry scoring (0.91 (0.78, 0.96) and 0.97 (0.91, 0.99) for lung/EGFR and bladder/p53 samples, respectively). Conclusions: These results establish crowdsourcing as a promising method to screen large data sets for biomarkers in cancer pathology research across a range of cancers and immunohistochemical stains. PMID:27959886

Locating very high energy gamma-ray sources with arcminute accuracy

NASA Technical Reports Server (NTRS)

Akerlof, C. W.; Cawley, M. F.; Chantell, M.; Harris, K.; Lawrence, M. A.; Fegan, D. J.; Lang, M. J.; Hillas, A. M.; Jennings, D. G.; Lamb, R. C.

1991-01-01

The angular accuracy of gamma-ray detectors is intrinsically limited by the physical processes involved in photon detection. Although a number of pointlike sources were detected by the COS B satellite, only two have been unambiguously identified by time signature with counterparts at longer wavelengths. By taking advantage of the extended longitudinal structure of VHE gamma-ray showers, measurements in the TeV energy range can pinpoint source coordinates to arcminute accuracy. This has now been demonstrated with new data analysis procedures applied to observations of the Crab Nebula using Cherenkov air shower imaging techniques. With two telescopes in coincidence, the individual event circular probable error will be 0.13 deg. The half-cone angle of the field of view is effectively 1 deg.

Diagnostic performance of 18F-FDG PET/CT and whole-body diffusion-weighted imaging with background body suppression (DWIBS) in detection of lymph node and bone metastases from pediatric neuroblastoma.

PubMed

Ishiguchi, Hiroaki; Ito, Shinji; Kato, Katsuhiko; Sakurai, Yusuke; Kawai, Hisashi; Fujita, Naotoshi; Abe, Shinji; Narita, Atsushi; Nishio, Nobuhiro; Muramatsu, Hideki; Takahashi, Yoshiyuki; Naganawa, Shinji

2018-06-01

Recent many studies have shown that whole body "diffusion-weighted imaging with background body signal suppression" (DWIBS) seems a beneficial tool having higher tumor detection sensitivity without ionizing radiation exposure for pediatric tumors. In this study, we evaluated the diagnostic performance of whole body DWIBS and 18 F-FDG PET/CT for detecting lymph node and bone metastases in pediatric patients with neuroblastoma. Subjects in this retrospective study comprised 13 consecutive pediatric patients with neuroblastoma (7 males, 6 females; mean age, 2.9 ± 2.0 years old) who underwent both 18 F-FDG PET/CT and whole-body DWIBS. All patients were diagnosed as neuroblastoma on the basis of pathological findings. Eight regions of lymph nodes and 17 segments of skeletons in all patients were evaluated. The images of 123 I-MIBG scintigraphy/SPECT-CT, bone scintigraphy/SPECT, and CT were used to confirm the presence of lymph node and bone metastases. Two radiologists trained in nuclear medicine evaluated independently the uptake of lesions in 18 F-FDG PET/CT and the signal-intensity of lesions in whole-body DWIBS visually. Interobserver difference was overcome through discussion to reach a consensus. The sensitivities, specificities, and overall accuracies of 18 F-FDG PET/CT and whole-body DWIBS were compared using McNemer's test. Positive predictive values (PPVs) and negative predictive values (NPVs) of both modalities were compared using Fisher's exact test. The total numbers of lymph node regions and bone segments which were confirmed to have metastasis in the total 13 patients were 19 and 75, respectively. The sensitivity, specificity, overall accuracy, PPV, and NPV of 18 F-FDG PET/CT for detecting lymph node metastasis from pediatric neuroblastoma were 100, 98.7, 98.9, 95.0, and 100%, respectively, and those for detecting bone metastasis were 90.7, 73.1, 80.3, 70.1, and 91.9%, respectively. In contrast, the sensitivity, specificity, overall accuracy, PPV, and NPV of whole-body DWIBS for detecting bone metastasis from pediatric neuroblastoma were 94.7, 24.0, 53.0, 46.4 and 86.7%, respectively, whereas those for detecting lymph node metastasis were 94.7, 85.3, 87.2, 62.1, and 98.5%, respectively. The low specificity, overall accuracy, and PPV of whole-body DWIBS for detecting bone metastasis were due to a high incidence of false-positive findings (82/108, 75.9%). The specificity, overall accuracy, and PPV of whole-body DWIBS for detecting lymph node metastasis were also significantly lower than those of 18 F-FDG PET/CT for detecting lymph node metastasis, although the difference between these 2 modalities was less than that for detecting bone metastasis. The specificity, overall accuracy, and PPV of whole-body DWIBS are significantly lower than those of 18 F-FDG PET/CT because of a high incidence of false-positive findings particularly for detecting bone metastasis, whereas whole-body DWIBS shows a similar level of sensitivities for detecting lymph node and bone metastases to those of 18 F-FDG PET/CT. DWIBS should be carefully used for cancer staging in children because of its high incidence of false-positive findings in skeletons.

Accuracy of reading liquid based cytology slides using the ThinPrep Imager compared with conventional cytology: prospective study

PubMed Central

d'Assuncao, Jefferson; Irwig, Les; Macaskill, Petra; Chan, Siew F; Richards, Adele; Farnsworth, Annabelle

2007-01-01

Objective To compare the accuracy of liquid based cytology using the computerised ThinPrep Imager with that of manually read conventional cytology. Design Prospective study. Setting Pathology laboratory in Sydney, Australia. Participants 55 164 split sample pairs (liquid based sample collected after conventional sample from one collection) from consecutive samples of women choosing both types of cytology and whose specimens were examined between August 2004 and June 2005. Main outcome measures Primary outcome was accuracy of slides for detecting squamous lesions. Secondary outcomes were rate of unsatisfactory slides, distribution of squamous cytological classifications, and accuracy of detecting glandular lesions. Results Fewer unsatisfactory slides were found for imager read cytology than for conventional cytology (1.8% v 3.1%; P<0.001). More slides were classified as abnormal by imager read cytology (7.4% v 6.0% overall and 2.8% v 2.2% for cervical intraepithelial neoplasia of grade 1 or higher). Among 550 patients in whom imager read cytology was cervical intraepithelial neoplasia grade 1 or higher and conventional cytology was less severe than grade 1, 133 of 380 biopsy samples taken were high grade histology. Among 294 patients in whom imager read cytology was less severe than cervical intraepithelial neoplasia grade 1 and conventional cytology was grade 1 or higher, 62 of 210 biopsy samples taken were high grade histology. Imager read cytology therefore detected 71 more cases of high grade histology than did conventional cytology, resulting from 170 more biopsies. Similar results were found when one pathologist reread the slides, masked to cytology results. Conclusion The ThinPrep Imager detects 1.29 more cases of histological high grade squamous disease per 1000 women screened than conventional cytology, with cervical intraepithelial neoplasia grade 1 as the threshold for referral to colposcopy. More imager read slides than conventional slides were satisfactory for examination and more contained low grade cytological abnormalities. PMID:17604301

A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data

NASA Astrophysics Data System (ADS)

Sun, Lin; Liu, Xinyan; Yang, Yikun; Chen, TingTing; Wang, Quan; Zhou, Xueying

2018-04-01

Although enhanced over prior Landsat instruments, Landsat 8 OLI can obtain very high cloud detection precisions, but for the detection of cloud shadows, it still faces great challenges. Geometry-based cloud shadow detection methods are considered the most effective and are being improved constantly. The Function of Mask (Fmask) cloud shadow detection method is one of the most representative geometry-based methods that has been used for cloud shadow detection with Landsat 8 OLI. However, the Fmask method estimates cloud height employing fixed temperature rates, which are highly uncertain, and errors of large area cloud shadow detection can be caused by errors in estimations of cloud height. This article improves the geometry-based cloud shadow detection method for Landsat OLI from the following two aspects. (1) Cloud height no longer depends on the brightness temperature of the thermal infrared band but uses a possible dynamic range from 200 m to 12,000 m. In this case, cloud shadow is not a specific location but a possible range. Further analysis was carried out in the possible range based on the spectrum to determine cloud shadow location. This effectively avoids the cloud shadow leakage caused by the error in the height determination of a cloud. (2) Object-based and pixel spectral analyses are combined to detect cloud shadows, which can realize cloud shadow detection from two aspects of target scale and pixel scale. Based on the analysis of the spectral differences between the cloud shadow and typical ground objects, the best cloud shadow detection bands of Landsat 8 OLI were determined. The combined use of spectrum and shape can effectively improve the detection precision of cloud shadows produced by thin clouds. Several cloud shadow detection experiments were carried out, and the results were verified by the results of artificial recognition. The results of these experiments indicated that this method can identify cloud shadows in different regions with correct accuracy exceeding 80%, approximately 5% of the areas were wrongly identified, and approximately 10% of the cloud shadow areas were missing. The accuracy of this method is obviously higher than the recognition accuracy of Fmask, which has correct accuracy lower than 60%, and the missing recognition is approximately 40%.

VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

PubMed

Feng, Lichen; Li, Zunchao; Wang, Yuanfa

2018-02-01

Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consists of a feature extraction (FE) module and an SVM module. The FE module performs the three-level Daubechies discrete wavelet transform to fit the physiological bands of the electroencephalogram (EEG) signal and extracts the time-frequency domain features reflecting the nonstationary signal properties. The SVM module integrates the modified sequential minimal optimization algorithm with the table-driven-based Gaussian kernel to enable efficient on-chip learning. The presented design is verified on an Altera Cyclone II field-programmable gate array and tested using the two publicly available EEG datasets. Experiment results show that the designed VLSI system improves the detection accuracy and training efficiency.

Ultrathin disposable gastroscope for screening and surveillance of gastroesophageal varices in patients with liver cirrhosis: a prospective comparative study.

PubMed

Huynh, Dep K; Toscano, Leanne; Phan, Vinh-An; Ow, Tsai-Wing; Schoeman, Mark; Nguyen, Nam Q

2017-06-01

This study aims to evaluate the role of unsedated, ultrathin disposable gastroscopy (TDG) against conventional gastroscopy (CG) in the screening and surveillance of gastroesophageal varices (GEVs) in patients with liver cirrhosis. Forty-eight patients (56.4 ± 1.3 years; 38 male, 10 female) with liver cirrhosis referred for screening (n = 12) or surveillance (n = 36) of GEVs were prospectively enrolled. Unsedated gastroscopy was initially performed with TDG, followed by CG with conscious sedation. The 2 gastroscopies were performed by different endoscopists blinded to the results of the previous examination. Video recordings of both gastroscopies were validated by an independent investigator in a random, blinded fashion. Endpoints were accuracy and interobserver agreement of detecting GEVs, safety, and potential cost saving. CG identified GEVs in 26 (54%) patients, 10 of whom (21%) had high-risk esophageal varices (HREV). Compared with CG, TDG had an accuracy of 92% for the detection of all GEVs, which increased to 100% for high-risk GEVs. The interobserver agreement for detecting all GEVs on TDG was 88% (κ = 0.74). This increased to 94% (κ = 0.82) for high-risk GEVs. There were no serious adverse events. Unsedated TDG is safe and has high diagnostic accuracy and interobserver reliability for the detection of GEVs. The use of clinic-based TDG would allow immediate determination of a follow-up plan, making it attractive for variceal screening and surveillance programs. (Clinical trial (ANZCTR) registration number: ACTRN12616001103459.). Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

Stormwater plume detection by MODIS imagery in the southern California coastal ocean

USGS Publications Warehouse

Nezlin, N.P.; DiGiacomo, P.M.; Diehl, D.W.; Jones, B.H.; Johnson, S.C.; Mengel, M.J.; Reifel, K.M.; Warrick, J.A.; Wang, M.

2008-01-01

Stormwater plumes in the southern California coastal ocean were detected by MODIS-Aqua satellite imagery and compared to ship-based data on surface salinity and fecal indicator bacterial (FIB) counts collected during the Bight'03 Regional Water Quality Program surveys in February-March of 2004 and 2005. MODIS imagery was processed using a combined near-infrared/shortwave-infrared (NIR-SWIR) atmospheric correction method, which substantially improved normalized water-leaving radiation (nLw) optical spectra in coastal waters with high turbidity. Plumes were detected using a minimum-distance supervised classification method based on nLw spectra averaged within the training areas, defined as circular zones of 1.5-5.0-km radii around field stations with a surface salinity of S 33.0 ('ocean'). The plume optical signatures (i.e., the nLw differences between 'plume' and 'ocean') were most evident during the first 2 days after the rainstorms. To assess the accuracy of plume detection, stations were classified into 'plume' and 'ocean' using two criteria: (1) 'plume' included the stations with salinity below a certain threshold estimated from the maximum accuracy of plume detection; and (2) FIB counts in 'plume' exceeded the California State Water Board standards. The salinity threshold between 'plume' and 'ocean' was estimated as 32.2. The total accuracy of plume detection in terms of surface salinity was not high (68% on average), seemingly because of imperfect correlation between plume salinity and ocean color. The accuracy of plume detection in terms of FIB exceedances was even lower (64% on average), resulting from low correlation between ocean color and bacterial contamination. Nevertheless, satellite imagery was shown to be a useful tool for the estimation of the extent of potentially polluted plumes, which was hardly achievable by direct sampling methods (in particular, because the grids of ship-based stations covered only small parts of the plumes detected via synoptic MODIS imagery). In most southern California coastal areas, the zones of bacterial contamination were much smaller than the areas of turbid plumes; an exception was the plume of the Tijuana River, where the zone of bacterial contamination was comparable with the zone of plume detected by ocean color. ?? 2008 Elsevier Ltd.

Stormwater plume detection by MODIS imagery in the southern California coastal ocean

NASA Astrophysics Data System (ADS)

Nezlin, Nikolay P.; DiGiacomo, Paul M.; Diehl, Dario W.; Jones, Burton H.; Johnson, Scott C.; Mengel, Michael J.; Reifel, Kristen M.; Warrick, Jonathan A.; Wang, Menghua

2008-10-01

Stormwater plumes in the southern California coastal ocean were detected by MODIS-Aqua satellite imagery and compared to ship-based data on surface salinity and fecal indicator bacterial (FIB) counts collected during the Bight'03 Regional Water Quality Program surveys in February-March of 2004 and 2005. MODIS imagery was processed using a combined near-infrared/shortwave-infrared (NIR-SWIR) atmospheric correction method, which substantially improved normalized water-leaving radiation (nLw) optical spectra in coastal waters with high turbidity. Plumes were detected using a minimum-distance supervised classification method based on nLw spectra averaged within the training areas, defined as circular zones of 1.5-5.0-km radii around field stations with a surface salinity of S < 32.0 ("plume") and S > 33.0 ("ocean"). The plume optical signatures (i.e., the nLw differences between "plume" and "ocean") were most evident during the first 2 days after the rainstorms. To assess the accuracy of plume detection, stations were classified into "plume" and "ocean" using two criteria: (1) "plume" included the stations with salinity below a certain threshold estimated from the maximum accuracy of plume detection; and (2) FIB counts in "plume" exceeded the California State Water Board standards. The salinity threshold between "plume" and "ocean" was estimated as 32.2. The total accuracy of plume detection in terms of surface salinity was not high (68% on average), seemingly because of imperfect correlation between plume salinity and ocean color. The accuracy of plume detection in terms of FIB exceedances was even lower (64% on average), resulting from low correlation between ocean color and bacterial contamination. Nevertheless, satellite imagery was shown to be a useful tool for the estimation of the extent of potentially polluted plumes, which was hardly achievable by direct sampling methods (in particular, because the grids of ship-based stations covered only small parts of the plumes detected via synoptic MODIS imagery). In most southern California coastal areas, the zones of bacterial contamination were much smaller than the areas of turbid plumes; an exception was the plume of the Tijuana River, where the zone of bacterial contamination was comparable with the zone of plume detected by ocean color.

Laser Spot Center Detection and Comparison Test

NASA Astrophysics Data System (ADS)

Zhu, Jun; Xu, Zhengjie; Fu, Deli; Hu, Cong

2018-04-01

High efficiency and precision of the pot center detection are the foundations of avionics instrument navigation and optics measurement basis for many applications. It has noticeable impact on overall system performance. Among them, laser spot detection is very important in the optical measurement technology. In order to improve the low accuracy of the spot center position, the algorithm is improved on the basis of the circle fitting. The pretreatment is used by circle fitting, and the improved adaptive denoising filter for TV repair technology can effectively improves the accuracy of the spot center position. At the same time, the pretreatment and de-noising can effectively reduce the influence of Gaussian white noise, which enhances the anti-jamming capability.

When Interference Helps: Increasing Executive Load to Facilitate Deception Detection in the Concealed Information Test

PubMed Central

Visu-Petra, George; Varga, Mihai; Miclea, Mircea; Visu-Petra, Laura

2013-01-01

The possibility to enhance the detection efficiency of the Concealed Information Test (CIT) by increasing executive load was investigated, using an interference design. After learning and executing a mock crime scenario, subjects underwent three deception detection tests: an RT-based CIT, an RT-based CIT plus a concurrent memory task (CITMem), and an RT-based CIT plus a concurrent set-shifting task (CITShift). The concealed information effect, consisting in increased RT and lower response accuracy for probe items compared to irrelevant items, was evidenced across all three conditions. The group analyses indicated a larger difference between RTs to probe and irrelevant items in the dual-task conditions, but this difference was not translated in a significantly increased detection efficiency at an individual level. Signal detection parameters based on the comparison with a simulated innocent group showed accurate discrimination for all conditions. Overall response accuracy on the CITMem was highest and the difference between response accuracy to probes and irrelevants was smallest in this condition. Accuracy on the concurrent tasks (Mem and Shift) was high, and responses on these tasks were significantly influenced by CIT stimulus type (probes vs. irrelevants). The findings are interpreted in relation to the cognitive load/dual-task interference literature, generating important insights for research on the involvement of executive functions in deceptive behavior. PMID:23543918

Automatic Fault Characterization via Abnormality-Enhanced Classification

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

Bronevetsky, G; Laguna, I; de Supinski, B R

Enterprise and high-performance computing systems are growing extremely large and complex, employing hundreds to hundreds of thousands of processors and software/hardware stacks built by many people across many organizations. As the growing scale of these machines increases the frequency of faults, system complexity makes these faults difficult to detect and to diagnose. Current system management techniques, which focus primarily on efficient data access and query mechanisms, require system administrators to examine the behavior of various system services manually. Growing system complexity is making this manual process unmanageable: administrators require more effective management tools that can detect faults and help tomore » identify their root causes. System administrators need timely notification when a fault is manifested that includes the type of fault, the time period in which it occurred and the processor on which it originated. Statistical modeling approaches can accurately characterize system behavior. However, the complex effects of system faults make these tools difficult to apply effectively. This paper investigates the application of classification and clustering algorithms to fault detection and characterization. We show experimentally that naively applying these methods achieves poor accuracy. Further, we design novel techniques that combine classification algorithms with information on the abnormality of application behavior to improve detection and characterization accuracy. Our experiments demonstrate that these techniques can detect and characterize faults with 65% accuracy, compared to just 5% accuracy for naive approaches.« less

High-precision processing and detection of the high-caliber off-axis aspheric mirror

NASA Astrophysics Data System (ADS)

Dai, Chen; Li, Ang; Xu, Lingdi; Zhang, Yingjie

2017-10-01

To achieve the efficient, controllable, digital processing and high-precision detection of the high-caliber off-axis aspheric mirror, meeting the high-level development needs of the modern high-resolution, large field of space optical remote sensing camera, we carried out the research on high precision machining and testing technology of off-axis aspheric mirror. First, we forming the off-axis aspheric sample with diameter of 574mm × 302mm by milling it with milling machine, and then the intelligent robot equipment was used for off-axis aspheric high precision polishing. Surface detection of the sample will be proceed with the off-axis aspheric contact contour detection technology and offaxis non-spherical surface interference detection technology after its fine polishing using ion beam equipment. The final surface accuracy RMS is 12nm.

[Research on partial least squares for determination of impurities in the presence of high concentration of matrix by ICP-AES].

PubMed

Wang, Yan-peng; Gong, Qi; Yu, Sheng-rong; Liu, You-yan

2012-04-01

A method for detecting trace impurities in high concentration matrix by ICP-AES based on partial least squares (PLS) was established. The research showed that PLS could effectively correct the interference caused by high level of matrix concentration error and could withstand higher concentrations of matrix than multicomponent spectral fitting (MSF). When the mass ratios of matrix to impurities were from 1 000 : 1 to 20 000 : 1, the recoveries of standard addition were between 95% and 105% by PLS. For the system in which interference effect has nonlinear correlation with the matrix concentrations, the prediction accuracy of normal PLS method was poor, but it can be improved greatly by using LIN-PPLS, which was based on matrix transformation of sample concentration. The contents of Co, Pb and Ga in stream sediment (GBW07312) were detected by MSF, PLS and LIN-PPLS respectively. The results showed that the prediction accuracy of LIN-PPLS was better than PLS, and the prediction accuracy of PLS was better than MSF.

Diagnostic value of DIAGNOdent in detecting caries under composite restorations of primary molars

PubMed Central

Sichani, Ava Vali; Javadinejad, Shahrzad; Ghafari, Roshanak

2016-01-01

Background: Direct observation cannot detect caries under restorations; therefore, the aim of this study was to compare the accuracy of radiographs and DIAGNOdent in detecting caries under restorations in primary teeth using histologic evaluation. Materials and Methods: A total of 74 previously extracted primary molars (37 with occlusal caries and 37 without caries) were used. Class 1 cavity preparations were made on each tooth by a single clinician and then the preparations were filled with composite resin. The accuracy of radiographs and DIAGNOdent in detecting caries was compared using histologic evaluation. The data were analyzed by SPSS version 21 using Chi-square, Mc Namara statistical tests and receiver operating characteristic curve. The significance was set at 0.05. Results: The sensitivity and specificity for DIAGNOdent were 70.97 and 83.72, respectively. Few false negative results were observed, and the positive predictive value was high (+PV = 75.9) and the area under curve was more than 0.70 therefore making DIAGNOdenta great method for detecting caries (P = 0.0001). Two observers evaluated the radiographs and both observers had low sensitivity ( first observer: 48.39) (second observer: 51.61) and high specificity (both observers: 79.07). The +PV was lower than DIAGNOdent and the area under curve for both observers was less than 0.70. However, the difference between the two methods was not significant. Conclusion: DIAGNOdent showed a greater accuracy in detecting secondary caries under primary molar restorations, compared to radiographs. Although DIAGNOdent is an effective method for detecting caries under composite restorations, it is better to be used as an adjunctive method alongside other detecting procedures. PMID:27605990

Multiscale peak detection in wavelet space.

PubMed

Zhang, Zhi-Min; Tong, Xia; Peng, Ying; Ma, Pan; Zhang, Ming-Jin; Lu, Hong-Mei; Chen, Xiao-Qing; Liang, Yi-Zeng

2015-12-07

Accurate peak detection is essential for analyzing high-throughput datasets generated by analytical instruments. Derivatives with noise reduction and matched filtration are frequently used, but they are sensitive to baseline variations, random noise and deviations in the peak shape. A continuous wavelet transform (CWT)-based method is more practical and popular in this situation, which can increase the accuracy and reliability by identifying peaks across scales in wavelet space and implicitly removing noise as well as the baseline. However, its computational load is relatively high and the estimated features of peaks may not be accurate in the case of peaks that are overlapping, dense or weak. In this study, we present multi-scale peak detection (MSPD) by taking full advantage of additional information in wavelet space including ridges, valleys, and zero-crossings. It can achieve a high accuracy by thresholding each detected peak with the maximum of its ridge. It has been comprehensively evaluated with MALDI-TOF spectra in proteomics, the CAMDA 2006 SELDI dataset as well as the Romanian database of Raman spectra, which is particularly suitable for detecting peaks in high-throughput analytical signals. Receiver operating characteristic (ROC) curves show that MSPD can detect more true peaks while keeping the false discovery rate lower than MassSpecWavelet and MALDIquant methods. Superior results in Raman spectra suggest that MSPD seems to be a more universal method for peak detection. MSPD has been designed and implemented efficiently in Python and Cython. It is available as an open source package at .

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

High order filtering methods for approximating hyperbolic systems of conservation laws

NASA Technical Reports Server (NTRS)

Lafon, F.; Osher, S.

1991-01-01

The essentially nonoscillatory (ENO) schemes, while potentially useful in the computation of discontinuous solutions of hyperbolic conservation-law systems, are computationally costly relative to simple central-difference methods. A filtering technique is presented which employs central differencing of arbitrarily high-order accuracy except where a local test detects the presence of spurious oscillations and calls upon the full ENO apparatus to remove them. A factor-of-three speedup is thus obtained over the full-ENO method for a wide range of problems, with high-order accuracy in regions of smooth flow.

Training sample selection based on self-training for liver cirrhosis classification using ultrasound images

NASA Astrophysics Data System (ADS)

Fujita, Yusuke; Mitani, Yoshihiro; Hamamoto, Yoshihiko; Segawa, Makoto; Terai, Shuji; Sakaida, Isao

2017-03-01

Ultrasound imaging is a popular and non-invasive tool used in the diagnoses of liver disease. Cirrhosis is a chronic liver disease and it can advance to liver cancer. Early detection and appropriate treatment are crucial to prevent liver cancer. However, ultrasound image analysis is very challenging, because of the low signal-to-noise ratio of ultrasound images. To achieve the higher classification performance, selection of training regions of interest (ROIs) is very important that effect to classification accuracy. The purpose of our study is cirrhosis detection with high accuracy using liver ultrasound images. In our previous works, training ROI selection by MILBoost and multiple-ROI classification based on the product rule had been proposed, to achieve high classification performance. In this article, we propose self-training method to select training ROIs effectively. Evaluation experiments were performed to evaluate effect of self-training, using manually selected ROIs and also automatically selected ROIs. Experimental results show that self-training for manually selected ROIs achieved higher classification performance than other approaches, including our conventional methods. The manually ROI definition and sample selection are important to improve classification accuracy in cirrhosis detection using ultrasound images.

The performance of magnetic resonance imaging in the detection of triangular fibrocartilage complex injury: a meta-analysis.

PubMed

Wang, Z X; Chen, S L; Wang, Q Q; Liu, B; Zhu, J; Shen, J

2015-06-01

The aim of this study was to evaluate the accuracy of magnetic resonance imaging in the detection of triangular fibrocartilage complex injury through a meta-analysis. A comprehensive literature search was conducted before 1 April 2014. All studies comparing magnetic resonance imaging results with arthroscopy or open surgery findings were reviewed, and 25 studies that satisfied the eligibility criteria were included. Data were pooled to yield pooled sensitivity and specificity, which were respectively 0.83 and 0.82. In detection of central and peripheral tears, magnetic resonance imaging had respectively a pooled sensitivity of 0.90 and 0.88 and a pooled specificity of 0.97 and 0.97. Six high-quality studies using Ringler's recommended magnetic resonance imaging parameters were selected for analysis to determine whether optimal imaging protocols yielded better results. The pooled sensitivity and specificity of these six studies were 0.92 and 0.82, respectively. The overall accuracy of magnetic resonance imaging was acceptable. For peripheral tears, the pooled data showed a relatively high accuracy. Magnetic resonance imaging with appropriate parameters are an ideal method for diagnosing different types of triangular fibrocartilage complex tears. © The Author(s) 2015.

Robust object tracking techniques for vision-based 3D motion analysis applications

NASA Astrophysics Data System (ADS)

Knyaz, Vladimir A.; Zheltov, Sergey Y.; Vishnyakov, Boris V.

2016-04-01

Automated and accurate spatial motion capturing of an object is necessary for a wide variety of applications including industry and science, virtual reality and movie, medicine and sports. For the most part of applications a reliability and an accuracy of the data obtained as well as convenience for a user are the main characteristics defining the quality of the motion capture system. Among the existing systems for 3D data acquisition, based on different physical principles (accelerometry, magnetometry, time-of-flight, vision-based), optical motion capture systems have a set of advantages such as high speed of acquisition, potential for high accuracy and automation based on advanced image processing algorithms. For vision-based motion capture accurate and robust object features detecting and tracking through the video sequence are the key elements along with a level of automation of capturing process. So for providing high accuracy of obtained spatial data the developed vision-based motion capture system "Mosca" is based on photogrammetric principles of 3D measurements and supports high speed image acquisition in synchronized mode. It includes from 2 to 4 technical vision cameras for capturing video sequences of object motion. The original camera calibration and external orientation procedures provide the basis for high accuracy of 3D measurements. A set of algorithms as for detecting, identifying and tracking of similar targets, so for marker-less object motion capture is developed and tested. The results of algorithms' evaluation show high robustness and high reliability for various motion analysis tasks in technical and biomechanics applications.

Automatic detection of swallowing events by acoustical means for applications of monitoring of ingestive behavior.

PubMed

Sazonov, Edward S; Makeyev, Oleksandr; Schuckers, Stephanie; Lopez-Meyer, Paulo; Melanson, Edward L; Neuman, Michael R

2010-03-01

Our understanding of etiology of obesity and overweight is incomplete due to lack of objective and accurate methods for monitoring of ingestive behavior (MIB) in the free-living population. Our research has shown that frequency of swallowing may serve as a predictor for detecting food intake, differentiating liquids and solids, and estimating ingested mass. This paper proposes and compares two methods of acoustical swallowing detection from sounds contaminated by motion artifacts, speech, and external noise. Methods based on mel-scale Fourier spectrum, wavelet packets, and support vector machines are studied considering the effects of epoch size, level of decomposition, and lagging on classification accuracy. The methodology was tested on a large dataset (64.5 h with a total of 9966 swallows) collected from 20 human subjects with various degrees of adiposity. Average weighted epoch-recognition accuracy for intravisit individual models was 96.8%, which resulted in 84.7% average weighted accuracy in detection of swallowing events. These results suggest high efficiency of the proposed methodology in separation of swallowing sounds from artifacts that originate from respiration, intrinsic speech, head movements, food ingestion, and ambient noise. The recognition accuracy was not related to body mass index, suggesting that the methodology is suitable for obese individuals.

Automatic Detection of Swallowing Events by Acoustical Means for Applications of Monitoring of Ingestive Behavior

PubMed Central

Sazonov, Edward S.; Makeyev, Oleksandr; Schuckers, Stephanie; Lopez-Meyer, Paulo; Melanson, Edward L.; Neuman, Michael R.

2010-01-01

Our understanding of etiology of obesity and overweight is incomplete due to lack of objective and accurate methods for Monitoring of Ingestive Behavior (MIB) in the free living population. Our research has shown that frequency of swallowing may serve as a predictor for detecting food intake, differentiating liquids and solids, and estimating ingested mass. This paper proposes and compares two methods of acoustical swallowing detection from sounds contaminated by motion artifacts, speech and external noise. Methods based on mel-scale Fourier spectrum, wavelet packets, and support vector machines are studied considering the effects of epoch size, level of decomposition and lagging on classification accuracy. The methodology was tested on a large dataset (64.5 hours with a total of 9,966 swallows) collected from 20 human subjects with various degrees of adiposity. Average weighted epoch recognition accuracy for intra-visit individual models was 96.8% which resulted in 84.7% average weighted accuracy in detection of swallowing events. These results suggest high efficiency of the proposed methodology in separation of swallowing sounds from artifacts that originate from respiration, intrinsic speech, head movements, food ingestion, and ambient noise. The recognition accuracy was not related to body mass index, suggesting that the methodology is suitable for obese individuals. PMID:19789095

Ultrasound detection of simulated intra-ocular foreign bodies by minimally trained personnel.

PubMed

Sargsyan, Ashot E; Dulchavsky, Alexandria G; Adams, James; Melton, Shannon; Hamilton, Douglas R; Dulchavsky, Scott A

2008-01-01

To test the ability of non-expert ultrasound operators of divergent backgrounds to detect the presence, size, location, and composition of foreign bodies in an ocular model. High school students (N = 10) and NASA astronauts (N = 4) completed a brief ultrasound training session which focused on basic ultrasound principles and the detection of foreign bodies. The operators used portable ultrasound devices to detect foreign objects of varying location, size (0.5-2 mm), and material (glass, plastic, metal) in a gelatinous ocular model. Operator findings were compared to known foreign object parameters and ultrasound experts (N = 2) to determine accuracy across and between groups. Ultrasound had high sensitivity (astronauts 85%, students 87%, and experts 100%) and specificity (astronauts 81%, students 83%, and experts 95%) for the detection of foreign bodies. All user groups were able to accurately detect the presence of foreign bodies in this model (astronauts 84%, students 81%, and experts 97%). Astronaut and student sensitivity results for material (64% vs. 48%), size (60% vs. 46%), and position (77% vs. 64%) were not statistically different. Experts' results for material (85%), size (90%), and position (98%) were higher; however, the small sample size precluded statistical conclusions. Ultrasound can be used by operators with varying training to detect the presence, location, and composition of intraocular foreign bodies with high sensitivity, specificity, and accuracy.

OM300 Direction Drilling Module

DOE Data Explorer

MacGugan, Doug

2013-08-22

OM300 – Geothermal Direction Drilling Navigation Tool: Design and produce a prototype directional drilling navigation tool capable of high temperature operation in geothermal drilling Accuracies of 0.1° Inclination and Tool Face, 0.5° Azimuth Environmental Ruggedness typical of existing oil/gas drilling Multiple Selectable Sensor Ranges High accuracy for navigation, low bandwidth High G-range & bandwidth for Stick-Slip and Chirp detection Selectable serial data communications Reduce cost of drilling in high temperature Geothermal reservoirs Innovative aspects of project Honeywell MEMS* Vibrating Beam Accelerometers (VBA) APS Flux-gate Magnetometers Honeywell Silicon-On-Insulator (SOI) High-temperature electronics Rugged High-temperature capable package and assembly process

Influence of Waveform Characteristics on LiDAR Ranging Accuracy and Precision

PubMed Central

Yang, Bingwei; Xie, Xinhao; Li, Duan

2018-01-01

Time of flight (TOF) based light detection and ranging (LiDAR) is a technology for calculating distance between start/stop signals of time of flight. In lab-built LiDAR, two ranging systems for measuring flying time between start/stop signals include time-to-digital converter (TDC) that counts time between trigger signals and analog-to-digital converter (ADC) that processes the sampled start/stop pulses waveform for time estimation. We study the influence of waveform characteristics on range accuracy and precision of two kinds of ranging system. Comparing waveform based ranging (WR) with analog discrete return system based ranging (AR), a peak detection method (WR-PK) shows the best ranging performance because of less execution time, high ranging accuracy, and stable precision. Based on a novel statistic mathematical method maximal information coefficient (MIC), WR-PK precision has a high linear relationship with the received pulse width standard deviation. Thus keeping the received pulse width of measuring a constant distance as stable as possible can improve ranging precision. PMID:29642639

Human ear detection in the thermal infrared spectrum

NASA Astrophysics Data System (ADS)

Abaza, Ayman; Bourlai, Thirimachos

2012-06-01

In this paper the problem of human ear detection in the thermal infrared (IR) spectrum is studied in order to illustrate the advantages and limitations of the most important steps of ear-based biometrics that can operate in day and night time environments. The main contributions of this work are two-fold: First, a dual-band database is assembled that consists of visible and thermal profile face images. The thermal data was collected using a high definition middle-wave infrared (3-5 microns) camera that is capable of acquiring thermal imprints of human skin. Second, a fully automated, thermal imaging based ear detection method is developed for real-time segmentation of human ears in either day or night time environments. The proposed method is based on Haar features forming a cascaded AdaBoost classifier (our modified version of the original Viola-Jones approach1 that was designed to be applied mainly in visible band images). The main advantage of the proposed method, applied on our profile face image data set collected in the thermal-band, is that it is designed to reduce the learning time required by the original Viola-Jones method from several weeks to several hours. Unlike other approaches reported in the literature, which have been tested but not designed to operate in the thermal band, our method yields a high detection accuracy that reaches ~ 91.5%. Further analysis on our data set yielded that: (a) photometric normalization techniques do not directly improve ear detection performance. However, when using a certain photometric normalization technique (CLAHE) on falsely detected images, the detection rate improved by ~ 4%; (b) the high detection accuracy of our method did not degrade when we lowered down the original spatial resolution of thermal ear images. For example, even after using one third of the original spatial resolution (i.e. ~ 20% of the original computational time) of the thermal profile face images, the high ear detection accuracy of our method remained unaffected. This resulted also in speeding up the detection time of an ear image from 265 to 17 milliseconds per image. To the best of our knowledge this is the first time that the problem of human ear detection in the thermal band is being investigated in the open literature.

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

PubMed Central

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-01-01

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies. PMID:29695113

An incremental anomaly detection model for virtual machines.

PubMed

Zhang, Hancui; Chen, Shuyu; Liu, Jun; Zhou, Zhen; Wu, Tianshu

2017-01-01

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.

An incremental anomaly detection model for virtual machines

PubMed Central

Zhang, Hancui; Chen, Shuyu; Liu, Jun; Zhou, Zhen; Wu, Tianshu

2017-01-01

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform. PMID:29117245

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor.

PubMed

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-04-24

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies.

Parkinson's disease detection based on dysphonia measurements

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2017-04-01

Assessing dysphonic symptoms is a noninvasive and effective approach to detect Parkinson's disease (PD) in patients. The main purpose of this study is to investigate the effect of different dysphonia measurements on PD detection by support vector machine (SVM). Seven categories of dysphonia measurements are considered. Experimental results from ten-fold cross-validation technique demonstrate that vocal fundamental frequency statistics yield the highest accuracy of 88 % ± 0.04. When all dysphonia measurements are employed, the SVM classifier achieves 94 % ± 0.03 accuracy. A refinement of the original patterns space by removing dysphonia measurements with similar variation across healthy and PD subjects allows achieving 97.03 % ± 0.03 accuracy. The latter performance is larger than what is reported in the literature on the same dataset with ten-fold cross-validation technique. Finally, it was found that measures of ratio of noise to tonal components in the voice are the most suitable dysphonic symptoms to detect PD subjects as they achieve 99.64 % ± 0.01 specificity. This finding is highly promising for understanding PD symptoms.

No special K! A signal detection framework for the strategic regulation of memory accuracy.

PubMed

Higham, Philip A

2007-02-01

Two experiments investigated criterion setting and metacognitive processes underlying the strategic regulation of accuracy on the Scholastic Aptitude Test (SAT) using Type-2 signal detection theory (SDT). In Experiment 1, report bias was manipulated by penalizing participants either 0.25 (low incentive) or 4 (high incentive) points for each error. Best guesses to unanswered items were obtained so that Type-2 signal detection indices of discrimination and bias could be calculated. The same incentive manipulation was used in Experiment 2, only the test was computerized, confidence ratings were taken so that receiver operating characteristic (ROC) curves could be generated, and feedback was manipulated. The results of both experiments demonstrated that SDT provides a viable alternative to A. Koriat and M. Goldsmith's (1996c) framework of monitoring and control and reveals information about the regulation of accuracy that their framework does not. For example, ROC analysis indicated that the threshold model implied by formula scoring is inadequate. Instead, performance on the SAT should be modeled with an equal-variance Gaussian, Type-2 signal detection model. ((c) 2007 APA, all rights reserved).

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Clone tag detection in distributed RFID systems

PubMed Central

Kamaludin, Hazalila; Mahdin, Hairulnizam

2018-01-01

Although Radio Frequency Identification (RFID) is poised to displace barcodes, security vulnerabilities pose serious challenges for global adoption of the RFID technology. Specifically, RFID tags are prone to basic cloning and counterfeiting security attacks. A successful cloning of the RFID tags in many commercial applications can lead to many serious problems such as financial losses, brand damage, safety and health of the public. With many industries such as pharmaceutical and businesses deploying RFID technology with a variety of products, it is important to tackle RFID tag cloning problem and improve the resistance of the RFID systems. To this end, we propose an approach for detecting cloned RFID tags in RFID systems with high detection accuracy and minimal overhead thus overcoming practical challenges in existing approaches. The proposed approach is based on consistency of dual hash collisions and modified count-min sketch vector. We evaluated the proposed approach through extensive experiments and compared it with existing baseline approaches in terms of execution time and detection accuracy under varying RFID tag cloning ratio. The results of the experiments show that the proposed approach outperforms the baseline approaches in cloned RFID tag detection accuracy. PMID:29565982

Detection of Clinically Significant Retinopathy of Prematurity Using Wide-angle Digital Retinal Photography

PubMed Central

Chiang, Michael F.; Melia, Michele; Buffenn, Angela N.; Lambert, Scott R.; Recchia, Franco M.; Simpson, Jennifer L.; Yang, Michael B.

2013-01-01

Objective To evaluate the accuracy of detecting clinically significant retinopathy of prematurity (ROP) using wide-angle digital retinal photography. Methods Literature searches of PubMed and the Cochrane Library databases were conducted last on December 7, 2010, and yielded 414 unique citations. The authors assessed these 414 citations and marked 82 that potentially met the inclusion criteria. These 82 studies were reviewed in full text; 28 studies met inclusion criteria. The authors extracted from these studies information about study design, interventions, outcomes, and study quality. After data abstraction, 18 were excluded for study deficiencies or because they were superseded by a more recent publication. The methodologist reviewed the remaining 10 studies and assigned ratings of evidence quality; 7 studies were rated level I evidence and 3 studies were rated level III evidence. Results There is level I evidence from ≥5 studies demonstrating that digital retinal photography has high accuracy for detection of clinically significant ROP. Level III studies have reported high accuracy, without any detectable complications, from real-world operational programs intended to detect clinically significant ROP through remote site interpretation of wide-angle retinal photographs. Conclusions Wide-angle digital retinal photography has the potential to complement standard ROP care. It may provide advantages through objective documentation of clinical examination findings, improved recognition of disease progression by comparing previous photographs, and the creation of image libraries for education and research. Financial Disclosure(s) Proprietary or commercial disclosure may be found after the references. PMID:22541632

68Ga-PSMA PET/CT in Patients with Rising Prostatic-Specific Antigen After Definitive Treatment of Prostate Cancer: Detection Efficacy and Diagnostic accuracy.

PubMed

Hamed, Maged Abdel Galil; Basha, Mohammad Abd Alkhalik; Ahmed, Hussien; Obaya, Ahmed Ali; Afifi, Amira Hamed Mohamed; Abdelbary, Eman H

2018-06-20

68 Ga-prostate-specific membrane antigen-11 ( 68 Ga-PSMA-11) is a recently developed positron emission tomography (PET) tracer that can detect prostate cancer (PC) relapses and metastases with high contrast resolution. The aim of this study was to assess the detection efficacy and diagnostic accuracy of 68 Ga-PSMA PET/CT image in patients with rising prostatic-specific antigen (PSA) after treatment of PC. The present prospective study included 188 patients who exhibited rising of PSA level on a routine follow-up examination after definitive treatment of PC. All patients underwent a 68 Ga-PSMA PET/CT examination. For each patient, we determined the disease stage, the Gleason score, and the maximum standardized uptake value of the local recurrence and extraprostatic metastases. The detection efficacy and diagnostic accuracy of 68 Ga-PSMA PET/CT were established by histopathology and clinical and imaging follow-up as the reference standards. 68 Ga-PSMA PET/CT detected tumour relapse in 165 patients (35 patients had local recurrence, 106 patients had extraprostatic metastases, and 24 patients had combined lesions). The sensitivity, specificity, and accuracy values of 68 Ga-PSMA PET/CT examination in the detection of PC recurrence were 98.8%, 100%, and 98.8%, respectively. 68 Ga-PSMA PET/CT revealed an overall detection rate of 87.8% (165/188) in patients with rising PSA (median of 2.2 ng/mL, and range of 0.01-70 ng/mL). 68 Ga-PSMA PET/CT is a valuable tool for the detection of PC local recurrence or extraprostatic metastases following rising PSA levels after primary definitive therapy and should be incorporated during routine work-up. Copyright © 2018. Published by Elsevier Inc.

Analysis of Sources of Large Positioning Errors in Deterministic Fingerprinting

PubMed Central

2017-01-01

Wi-Fi fingerprinting is widely used for indoor positioning and indoor navigation due to the ubiquity of wireless networks, high proliferation of Wi-Fi-enabled mobile devices, and its reasonable positioning accuracy. The assumption is that the position can be estimated based on the received signal strength intensity from multiple wireless access points at a given point. The positioning accuracy, within a few meters, enables the use of Wi-Fi fingerprinting in many different applications. However, it has been detected that the positioning error might be very large in a few cases, which might prevent its use in applications with high accuracy positioning requirements. Hybrid methods are the new trend in indoor positioning since they benefit from multiple diverse technologies (Wi-Fi, Bluetooth, and Inertial Sensors, among many others) and, therefore, they can provide a more robust positioning accuracy. In order to have an optimal combination of technologies, it is crucial to identify when large errors occur and prevent the use of extremely bad positioning estimations in hybrid algorithms. This paper investigates why large positioning errors occur in Wi-Fi fingerprinting and how to detect them by using the received signal strength intensities. PMID:29186921

Study of comparison between Ultra-high Frequency (UHF) method and ultrasonic method on PD detection for GIS

NASA Astrophysics Data System (ADS)

Li, Yanran; Chen, Duo; Li, Li; Zhang, Jiwei; Li, Guang; Liu, Hongxia

2017-11-01

GIS (gas insulated switchgear), is an important equipment in power system. Partial discharge plays an important role in detecting the insulation performance of GIS. UHF method and ultrasonic method frequently used in partial discharge (PD) detection for GIS. However, few studies have been conducted on comparison of this two methods. From the view point of safety, it is necessary to investigate UHF method and ultrasonic method for partial discharge in GIS. This paper presents study aimed at clarifying the effect of UHF method and ultrasonic method for partial discharge caused by free metal particles in GIS. Partial discharge tests were performed in laboratory simulated environment. Obtained results show the ability of anti-interference of signal detection and the accuracy of fault localization for UHF method and ultrasonic method. A new method based on UHF method and ultrasonic method of PD detection for GIS is proposed in order to greatly enhance the ability of anti-interference of signal detection and the accuracy of detection localization.

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.

Underwater single beam circumferentially scanning detection system using range-gated receiver and adaptive filter

NASA Astrophysics Data System (ADS)

Tan, Yayun; Zhang, He; Zha, Bingting

2017-09-01

Underwater target detection and ranging in seawater are of interest in unmanned underwater vehicles. This study presents an underwater detection system that synchronously scans a collimated laser beam and a narrow field of view to circumferentially detect an underwater target. Hybrid methods of range-gated and variable step-size least mean squares (VSS-LMS) adaptive filter are proposed to suppress water backscattering. The range-gated receiver eliminates the backscattering of near-field water. The VSS-LMS filter extracts the target echo in the remaining backscattering and the constant fraction discriminator timing method is used to improve ranging accuracy. The optimal constant fraction is selected by analysing the jitter noise and slope of the target echo. The prototype of the underwater detection system is constructed and tested in coastal seawater, then the effectiveness of backscattering suppression and high-ranging accuracy is verified through experimental results and analysis discussed in this paper.

Cascade Classification with Adaptive Feature Extraction for Arrhythmia Detection.

PubMed

Park, Juyoung; Kang, Mingon; Gao, Jean; Kim, Younghoon; Kang, Kyungtae

2017-01-01

Detecting arrhythmia from ECG data is now feasible on mobile devices, but in this environment it is necessary to trade computational efficiency against accuracy. We propose an adaptive strategy for feature extraction that only considers normalized beat morphology features when running in a resource-constrained environment; but in a high-performance environment it takes account of a wider range of ECG features. This process is augmented by a cascaded random forest classifier. Experiments on data from the MIT-BIH Arrhythmia Database showed classification accuracies from 96.59% to 98.51%, which are comparable to state-of-the art methods.

Accuracy of microRNAs as markers for the detection of neck lymph node metastases in patients with head and neck squamous cell carcinoma.

PubMed

de Carvalho, Ana Carolina; Scapulatempo-Neto, Cristovam; Maia, Danielle Calheiros Campelo; Evangelista, Adriane Feijó; Morini, Mariana Andozia; Carvalho, André Lopes; Vettore, André Luiz

2015-05-09

The presence of metastatic disease in cervical lymph nodes of head and neck squamous cell carcinoma (HNSCC) patients is a very important determinant in therapy choice and prognosis, with great impact in overall survival. Frequently, routine lymph node staging cannot detect occult metastases and the post-surgical histologic evaluation of resected lymph nodes is not sensitive in detecting small metastatic deposits. Molecular markers based on tissue-specific microRNA expression are alternative accurate diagnostic markers. Herein, we evaluated the feasibility of using the expression of microRNAs to detect metastatic cells in formalin-fixed paraffin-embedded (FFPE) lymph nodes and in fine-needle aspiration (FNA) biopsies of HNSCC patients. An initial screening compared the expression of 667 microRNAs in a discovery set comprised by metastatic and non-metastatic lymph nodes from HNSCC patients. The most differentially expressed microRNAs were validated by qRT-PCR in two independent cohorts: i) 48 FFPE lymph node samples, and ii) 113 FNA lymph node biopsies. The accuracy of the markers in identifying metastatic samples was assessed through the analysis of sensitivity, specificity, accuracy, negative predictive value, positive predictive value, and area under the curve values. Seven microRNAs highly expressed in metastatic lymph nodes from the discovery set were validated in FFPE lymph node samples. MiR-203 and miR-205 identified all metastatic samples, regardless of the size of the metastatic deposit. Additionally, these markers also showed high accuracy when FNA samples were examined. The high accuracy of miR-203 and miR-205 warrant these microRNAs as diagnostic markers of neck metastases in HNSCC. These can be evaluated in entire lymph nodes and in FNA biopsies collected at different time-points such as pre-treatment samples, intraoperative sentinel node biopsy, and during patient follow-up. These markers can be useful in a clinical setting in the management of HNSCC patients from initial disease staging and therapy planning to patient surveillance.

Accuracy evaluation of 3D lidar data from small UAV

NASA Astrophysics Data System (ADS)

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

2015-10-01

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

The design method and research status of vehicle detection system based on geomagnetic detection principle

NASA Astrophysics Data System (ADS)

Lin, Y. H.; Bai, R.; Qian, Z. H.

2018-03-01

Vehicle detection systems are applied to obtain real-time information of vehicles, realize traffic control and reduce traffic pressure. This paper reviews geomagnetic sensors as well as the research status of the vehicle detection system. Presented in the paper are also our work on the vehicle detection system, including detection algorithms and experimental results. It is found that the GMR based vehicle detection system has a detection accuracy up to 98% with a high potential for application in the road traffic control area.

Diagnostic Performance of DNA Hypermethylation Markers in Peripheral Blood for the Detection of Colorectal Cancer: A Meta-Analysis and Systematic Review

PubMed Central

Li, Bingsheng; Gan, Aihua; Chen, Xiaolong; Wang, Xinying; He, Weifeng; Zhang, Xiaohui; Huang, Renxiang; Zhou, Shuzhu; Song, Xiaoxiao; Xu, Angao

2016-01-01

DNA hypermethylation in blood is becoming an attractive candidate marker for colorectal cancer (CRC) detection. To assess the diagnostic accuracy of blood hypermethylation markers for CRC in different clinical settings, we conducted a meta-analysis of published reports. Of 485 publications obtained in the initial literature search, 39 studies were included in the meta-analysis. Hypermethylation markers in peripheral blood showed a high degree of accuracy for the detection of CRC. The summary sensitivity was 0.62 [95% confidence interval (CI), 0.56–0.67] and specificity was 0.91 (95% CI, 0.89–0.93). Subgroup analysis showed significantly greater sensitivity for the methylated Septin 9 gene (SEPT9) subgroup (0.75; 95% CI, 0.67–0.81) than for the non-methylated SEPT9 subgroup (0.58; 95% CI, 0.52–0.64). Sensitivity and specificity were not affected significantly by target gene number, CRC staging, study region, or methylation analysis method. These findings show that hypermethylation markers in blood are highly sensitive and specific for CRC detection, with methylated SEPT9 being particularly robust. The diagnostic performance of hypermethylation markers, which have varied across different studies, can be improved by marker optimization. Future research should examine variation in diagnostic accuracy according to non-neoplastic factors. PMID:27158984

Automatic threshold optimization in nonlinear energy operator based spike detection.

PubMed

Malik, Muhammad H; Saeed, Maryam; Kamboh, Awais M

2016-08-01

In neural spike sorting systems, the performance of the spike detector has to be maximized because it affects the performance of all subsequent blocks. Non-linear energy operator (NEO), is a popular spike detector due to its detection accuracy and its hardware friendly architecture. However, it involves a thresholding stage, whose value is usually approximated and is thus not optimal. This approximation deteriorates the performance in real-time systems where signal to noise ratio (SNR) estimation is a challenge, especially at lower SNRs. In this paper, we propose an automatic and robust threshold calculation method using an empirical gradient technique. The method is tested on two different datasets. The results show that our optimized threshold improves the detection accuracy in both high SNR and low SNR signals. Boxplots are presented that provide a statistical analysis of improvements in accuracy, for instance, the 75th percentile was at 98.7% and 93.5% for the optimized NEO threshold and traditional NEO threshold, respectively.

Single photon detection and timing in the Lunar Laser Ranging Experiment.

NASA Technical Reports Server (NTRS)

Poultney, S. K.

1972-01-01

The goals of the Lunar Laser Ranging Experiment lead to the need for the measurement of a 2.5 sec time interval to an accuracy of a nanosecond or better. The systems analysis which included practical retroreflector arrays, available laser systems, and large telescopes led to the necessity of single photon detection. Operation under all background illumination conditions required auxiliary range gates and extremely narrow spectral and spatial filters in addition to the effective gate provided by the time resolution. Nanosecond timing precision at relatively high detection efficiency was obtained using the RCA C31000F photomultiplier and Ortec 270 constant fraction of pulse-height timing discriminator. The timing accuracy over the 2.5 sec interval was obtained using a digital interval with analog vernier ends. Both precision and accuracy are currently checked internally using a triggerable, nanosecond light pulser. Future measurements using sub-nanosecond laser pulses will be limited by the time resolution of single photon detectors.

Mixture of learners for cancer stem cell detection using CD13 and H and E stained images

NASA Astrophysics Data System (ADS)

Oǧuz, Oǧuzhan; Akbaş, Cem Emre; Mallah, Maen; Taşdemir, Kasım.; Akhan Güzelcan, Ece; Muenzenmayer, Christian; Wittenberg, Thomas; Üner, Ayşegül; Cetin, A. E.; ćetin Atalay, Rengül

2016-03-01

In this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis systems (CAD) aims to help pathologists in this tedious and repetitive work. The first algorithm locates CSCs in CD13 stained liver tissue images. The method has also an online learning algorithm to improve the accuracy of detection. The second family of algorithms classify the cancer tissues stained with H and E which is clinically routine and cost effective than immunohistochemistry (IHC) procedure. The algorithms utilize 1D-SIFT and Eigen-analysis based feature sets as descriptors. Normal and cancerous tissues can be classified with 92.1% accuracy in H and E stained images. Classification accuracy of low and high-grade cancerous tissue images is 70.4%. Therefore, this study paves the way for diagnosing the cancerous tissue and grading the level of it using H and E stained microscopic tissue images.

Accuracy of recommended sampling and assay methods for the determination of plasma-free and urinary fractionated metanephrines in the diagnosis of pheochromocytoma and paraganglioma: a systematic review.

PubMed

Därr, Roland; Kuhn, Matthias; Bode, Christoph; Bornstein, Stefan R; Pacak, Karel; Lenders, Jacques W M; Eisenhofer, Graeme

2017-06-01

To determine the accuracy of biochemical tests for the diagnosis of pheochromocytoma and paraganglioma. A search of the PubMed database was conducted for English-language articles published between October 1958 and December 2016 on the biochemical diagnosis of pheochromocytoma and paraganglioma using immunoassay methods or high-performance liquid chromatography with coulometric/electrochemical or tandem mass spectrometric detection for measurement of fractionated metanephrines in 24-h urine collections or plasma-free metanephrines obtained under seated or supine blood sampling conditions. Application of the Standards for Reporting of Diagnostic Studies Accuracy Group criteria yielded 23 suitable articles. Summary receiver operating characteristic analysis revealed sensitivities/specificities of 94/93% and 91/93% for measurement of plasma-free metanephrines and urinary fractionated metanephrines using high-performance liquid chromatography or immunoassay methods, respectively. Partial areas under the curve were 0.947 vs. 0.911. Irrespective of the analytical method, sensitivity was significantly higher for supine compared with seated sampling, 95 vs. 89% (p < 0.02), while specificity was significantly higher for supine sampling compared with 24-h urine, 95 vs. 90% (p < 0.03). Partial areas under the curve were 0.942, 0.913, and 0.932 for supine sampling, seated sampling, and urine. Test accuracy increased linearly from 90 to 93% for 24-h urine at prevalence rates of 0.0-1.0, decreased linearly from 94 to 89% for seated sampling and was constant at 95% for supine conditions. Current tests for the biochemical diagnosis of pheochromocytoma and paraganglioma show excellent diagnostic accuracy. Supine sampling conditions and measurement of plasma-free metanephrines using high-performance liquid chromatography with coulometric/electrochemical or tandem mass spectrometric detection provides the highest accuracy at all prevalence rates.

Variability of Diabetes Alert Dog Accuracy in a Real-World Setting

PubMed Central

Gonder-Frederick, Linda A.; Grabman, Jesse H.; Shepard, Jaclyn A.; Tripathi, Anand V.; Ducar, Dallas M.; McElgunn, Zachary R.

2017-01-01

Background: Diabetes alert dogs (DADs) are growing in popularity as an alternative method of glucose monitoring for individuals with type 1 diabetes (T1D). Only a few empirical studies have assessed DAD accuracy, with inconsistent results. The present study examined DAD accuracy and variability in performance in real-world conditions using a convenience sample of owner-report diaries. Method: Eighteen DAD owners (44.4% female; 77.8% youth) with T1D completed diaries of DAD alerts during the first year after placement. Diary entries included daily BG readings and DAD alerts. For each DAD, percentage hits (alert with BG ≤ 5.0 or ≥ 11.1 mmol/L; ≤90 or ≥200 mg/dl), percentage misses (no alert with BG out of range), and percentage false alarms (alert with BG in range) were computed. Sensitivity, specificity, positive likelihood ratio (PLR), and true positive rates were also calculated. Results: Overall comparison of DAD Hits to Misses yielded significantly more Hits for both low and high BG. Total sensitivity was 57.0%, with increased sensitivity to low BG (59.2%) compared to high BG (56.1%). Total specificity was 49.3% and PLR = 1.12. However, high variability in accuracy was observed across DADs, with low BG sensitivity ranging from 33% to 100%. Number of DADs achieving ≥ 60%, 65% and 70% true positive rates was 71%, 50% and 44%, respectively. Conclusions: DADs may be able to detect out-of-range BG, but variability across DADs is evident. Larger trials are needed to further assess DAD accuracy and to identify factors influencing the complexity of DAD accuracy in BG detection. PMID:28627305

Effect of varying displays and room illuminance on caries diagnostic accuracy in digital dental radiographs.

PubMed

Pakkala, T; Kuusela, L; Ekholm, M; Wenzel, A; Haiter-Neto, F; Kortesniemi, M

2012-01-01

In clinical practice, digital radiographs taken for caries diagnostics are viewed on varying types of displays and usually in relatively high ambient lighting (room illuminance) conditions. Our purpose was to assess the effect of room illuminance and varying display types on caries diagnostic accuracy in digital dental radiographs. Previous studies have shown that the diagnostic accuracy of caries detection is significantly better in reduced lighting conditions. Our hypothesis was that higher display luminance could compensate for this in higher ambient lighting conditions. Extracted human teeth with approximal surfaces clinically ranging from sound to demineralized were radiographed and evaluated by 3 observers who detected carious lesions on 3 different types of displays in 3 different room illuminance settings ranging from low illumination, i.e. what is recommended for diagnostic viewing, to higher illumination levels corresponding to those found in an average dental office. Sectioning and microscopy of the teeth validated the presence or absence of a carious lesion. Sensitivity, specificity and accuracy were calculated for each modality and observer. Differences were estimated by analyzing the binary data assuming the added effects of observer and modality in a generalized linear model. The observers obtained higher sensitivities in lower illuminance settings than in higher illuminance settings. However, this was related to a reduction in specificity, which meant that there was no significant difference in overall accuracy. Contrary to our hypothesis, there were no significant differences between the accuracy of different display types. Therefore, different displays and room illuminance levels did not affect the overall accuracy of radiographic caries detection. Copyright © 2012 S. Karger AG, Basel.

Is High Resolution Melting Analysis (HRMA) Accurate for Detection of Human Disease-Associated Mutations? A Meta Analysis

PubMed Central

Ma, Feng-Li; Jiang, Bo; Song, Xiao-Xiao; Xu, An-Gao

2011-01-01

Background High Resolution Melting Analysis (HRMA) is becoming the preferred method for mutation detection. However, its accuracy in the individual clinical diagnostic setting is variable. To assess the diagnostic accuracy of HRMA for human mutations in comparison to DNA sequencing in different routine clinical settings, we have conducted a meta-analysis of published reports. Methodology/Principal Findings Out of 195 publications obtained from the initial search criteria, thirty-four studies assessing the accuracy of HRMA were included in the meta-analysis. We found that HRMA was a highly sensitive test for detecting disease-associated mutations in humans. Overall, the summary sensitivity was 97.5% (95% confidence interval (CI): 96.8–98.5; I2 = 27.0%). Subgroup analysis showed even higher sensitivity for non-HR-1 instruments (sensitivity 98.7% (95%CI: 97.7–99.3; I2 = 0.0%)) and an eligible sample size subgroup (sensitivity 99.3% (95%CI: 98.1–99.8; I2 = 0.0%)). HRMA specificity showed considerable heterogeneity between studies. Sensitivity of the techniques was influenced by sample size and instrument type but by not sample source or dye type. Conclusions/Significance These findings show that HRMA is a highly sensitive, simple and low-cost test to detect human disease-associated mutations, especially for samples with mutations of low incidence. The burden on DNA sequencing could be significantly reduced by the implementation of HRMA, but it should be recognized that its sensitivity varies according to the number of samples with/without mutations, and positive results require DNA sequencing for confirmation. PMID:22194806

Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review

PubMed Central

van Mourik, Maaike S M; van Duijn, Pleun Joppe; Moons, Karel G M; Bonten, Marc J M; Lee, Grace M

2015-01-01

Objective Measuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI. Methods Systematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995–2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics. Results 57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances. Conclusions Administrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative. PMID:26316651

Automatic fall detection using wearable biomedical signal measurement terminal.

PubMed

Nguyen, Thuy-Trang; Cho, Myeong-Chan; Lee, Tae-Soo

2009-01-01

In our study, we developed a mobile waist-mounted device which can monitor the subject's acceleration signal and detect the fall events in real-time with high accuracy and automatically send an emergency message to a remote server via CDMA module. When fall event happens, the system also generates an alarm sound at 50Hz to alarm other people until a subject can sit up or stand up. A Kionix KXM52-1050 tri-axial accelerometer and a Bellwave BSM856 CDMA standalone modem were used to detect and manage fall events. We used not only a simple threshold algorithm but also some supporting methods to increase an accuracy of our system (nearly 100% in laboratory environment). Timely fall detection can prevent regrettable death due to long-lie effect; therefore increase the independence of elderly people in an unsupervised living environment.

Confabulation Based Real-time Anomaly Detection for Wide-area Surveillance Using Heterogeneous High Performance Computing Architecture

DTIC Science & Technology

2015-06-01

system accuracy. The AnRAD system was also generalized for the additional application of network intrusion detection . A self-structuring technique...to Host- based Intrusion Detection Systems using Contiguous and Discontiguous System Call Patterns,” IEEE Transactions on Computer, 63(4), pp. 807...square kilometer areas. The anomaly recognition and detection (AnRAD) system was built as a cogent confabulation network . It represented road

Simultaneous inter-arm and inter-leg systolic blood pressure differences to diagnose peripheral artery disease: a diagnostic accuracy study.

PubMed

Herráiz-Adillo, Ángel; Soriano-Cano, Alba; Martínez-Hortelano, José Alberto; Garrido-Miguel, Miriam; Mariana-Herráiz, Julián Ángel; Martínez-Vizcaíno, Vicente; Notario-Pacheco, Blanca

2018-04-01

Inter-arm systolic blood pressure differences (IASBPD) and inter-leg systolic blood pressure differences (ILSBPD) have arisen as potential tools to detect peripheral artery disease (PAD) and individuals at high cardiovascular risk. This study aims to evaluate the diagnostic accuracy of IASBPD and ILSBPD to detect PAD, and whether IASBPD or ILSBPD improves diagnostic accuracy of the oscillometric ankle-brachial index (ABI). In this prospective study, eligible for inclusion were consecutive adults, with at least one of the following cardiovascular risk factors: diabetes, dyslipidemia, hypertension, smoking habit or age ≥65. IASBPD, ILSBPD and ankle-brachial index (ABI) were measured in all participants through four-limb simultaneous oscillometric measurements and compared with Doppler ABI (reference test, positive cut-off: ≤ 0.9). Of 171 subjects included, PAD was confirmed in 23 and excluded in 148. Thirteen and 38 subjects had IASBPD and ILSBPD ≥10 mmHg, respectively. Pearson correlation with Doppler ABI of IASBPD and ILSBPD was 0.073 (P = .343) and -0.628 (P < .001), respectively. Diagnostic accuracy of an ILSBPD ≥10 mmHg to detect PAD was: sensitivity = 69.6% (95%CI = 48.6-90.5), specificity = 85.1% (79.1-91.2), diagnostic odds ratio (dOR) = 13.1 (4.8-35.5) and area under ROC curve (AUC) = 0.765 (0.616-0.915). IASBPD had an AUC = 0.532 (0.394-0.669), and oscillometric ABI had an AUC = 0.977 (0.950-1.000). The addition of ILSBPD to oscillometric ABI reduced dOR from 174.0 (38.3-789.9) to 34.4 (9.5-125.1). Similarly, the addition of IASBPD reduced dOR to 49.3 (14.6-167.0). In a Primary Care population with ≥1 cardiovascular risk factors, ILSBPD showed acceptable diagnostic accuracy for PAD, whilst IASBPD accuracy was negligible. However, the combination of ILSBPD (or IASBPD) with oscillometric ABI did not improve the ability to detect PAD. Thus, oscillometer ABI seems to be preferable to detect PAD and individuals at high cardiovascular risk. ILSBPD could be uniquely recommended for the diagnosis of PAD when blood pressure measurements in upper limbs are not possible.

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Automated detection of feeding strikes by larval fish using continuous high-speed digital video: a novel method to extract quantitative data from fast, sparse kinematic events.

PubMed

Shamur, Eyal; Zilka, Miri; Hassner, Tal; China, Victor; Liberzon, Alex; Holzman, Roi

2016-06-01

Using videography to extract quantitative data on animal movement and kinematics constitutes a major tool in biomechanics and behavioral ecology. Advanced recording technologies now enable acquisition of long video sequences encompassing sparse and unpredictable events. Although such events may be ecologically important, analysis of sparse data can be extremely time-consuming and potentially biased; data quality is often strongly dependent on the training level of the observer and subject to contamination by observer-dependent biases. These constraints often limit our ability to study animal performance and fitness. Using long videos of foraging fish larvae, we provide a framework for the automated detection of prey acquisition strikes, a behavior that is infrequent yet critical for larval survival. We compared the performance of four video descriptors and their combinations against manually identified feeding events. For our data, the best single descriptor provided a classification accuracy of 77-95% and detection accuracy of 88-98%, depending on fish species and size. Using a combination of descriptors improved the accuracy of classification by ∼2%, but did not improve detection accuracy. Our results indicate that the effort required by an expert to manually label videos can be greatly reduced to examining only the potential feeding detections in order to filter false detections. Thus, using automated descriptors reduces the amount of manual work needed to identify events of interest from weeks to hours, enabling the assembly of an unbiased large dataset of ecologically relevant behaviors. © 2016. Published by The Company of Biologists Ltd.

Diagnostic accuracy of Xpert MTB/RIF assay for musculoskeletal tuberculosis: a meta-analysis.

PubMed

Wen, Hai; Li, Pengzhi; Ma, Hong; Lv, Guohua

2017-01-01

Xpert MTB/RIF assay, a rapid and automated real-time nucleic acid amplification test, has been reported for the diagnosis of musculoskeletal tuberculosis (TB) in current years. This meta-analysis aims to determine the diagnostic accuracy of Xpert for the detection of musculoskeletal TB and rifampicin (RIF) resistance. We searched PubMed, Embase, China National Knowledge Infrastructure, and Wanfang for original articles published up to 1st June 2017 to identify studies in which the Xpert assay was applied to diagnose musculoskeletal TB. Pooled estimates were calculated using a random-effects model or a fixed-effects model according to heterogeneity. Summary receiver operating characteristic curves and the area under the curve (AUC) were used to summarize overall diagnostic performance. Deeks' test was performed to evaluate potential publication bias. Twelve studies were identified with a pooled sensitivity and specificity of respectively 0.81 (95% confidence interval [CI] 0.78-0.83) and 0.83 (95% CI 0.80-0.86) of Xpert for the diagnosis of musculoskeletal TB. Xpert was highly sensitive (0.89, 95% CI 0.79-0.95) and highly specific (0.96, 95% CI 0.92-0.98) in detecting RIF resistance. AUC (over 0.9) suggested a relatively high level of overall diagnostic accuracy of Xpert for detecting musculoskeletal TB and RIF resistance. Prevalence and reference standard were indicated to be sources of heterogeneity between studies. No publication bias was found. This study provides available evidence of the rapid and effective role of Xpert in diagnosing musculoskeletal TB and detecting RIF resistance.

CoLiTec software - detection of the near-zero apparent motion

NASA Astrophysics Data System (ADS)

Khlamov, Sergii V.; Savanevych, Vadym E.; Briukhovetskyi, Olexandr B.; Pohorelov, Artem V.

2017-06-01

In this article we described CoLiTec software for full automated frames processing. CoLiTec software allows processing the Big Data of observation results as well as processing of data that is continuously formed during observation. The scope of solving tasks includes frames brightness equalization, moving objects detection, astrometry, photometry, etc. Along with the high efficiency of Big Data processing CoLiTec software also ensures high accuracy of data measurements. A comparative analysis of the functional characteristics and positional accuracy was performed between CoLiTec and Astrometrica software. The benefits of CoLiTec used with wide field and low quality frames were observed. The efficiency of the CoLiTec software was proved by about 700.000 observations and over 1.500 preliminary discoveries.

Trimodal endoscopic imaging for the detection and differentiation of colorectal adenomas: a prospective single-centre clinical evaluation.

PubMed

Rotondano, Gianluca; Bianco, Maria Antonia; Sansone, Stefano; Prisco, Antonio; Meucci, Costantino; Garofano, Maria Lucia; Cipolletta, Livio

2012-03-01

The purpose of this study is to evaluate an endoscopic trimodal imaging (ETMI) system (high resolution, autofluorescence, and NBI) in the detection and differentiation of colorectal adenomas. A prospective randomised trial of tandem colonoscopies was carried out using the Olympus XCF-FH260AZI system. Each colonic segment was examined twice for lesions, once with HRE and once with AFI, in random order per patient. All detected lesions were assessed with NBI for pit pattern and with AFI for colour. All lesions were removed and sent for histology. Any lesion identified on the second examination was considered as missed by the first examination. Outcome measures are adenoma miss rates of AFI and HRE, and diagnostic accuracy of NBI and AFI for differentiating neoplastic from non-neoplastic lesions. Ninety-four patients underwent colonoscopy with ETMI (47 in each group). Among 47 patients examined with AFI first, 31 adenomas in 15 patients were detected initially [detection rate 0.66 (0.52-0.75)]. Subsequent HRE inspection identified six additional adenomas. Among 47 patients examined with HRE first, 29 adenomas in 14 patients were detected initially [detection rate 0.62 (0.53-0.79)]. Successive AFI yielded seven additional adenomas. Adenoma miss rates of AFI and HRE were 14% and 16.2%, respectively (p = 0.29). Accuracy of AFI alone for differentiation was lower than NBI (63% vs. 80%, p < 0.001). Combined use of AFI and NBI achieved improved accuracy for differentiation (84%), showing a trend for superiority compared with NBI alone (p = 0.064). AFI did not significantly reduce the adenoma miss rate compared with HRE. AFI alone had a disappointing accuracy for adenoma differentiation, which could be improved by combination of AFI and NBI.

Study of New Method Combined Ultra-High Frequency (UHF) Method and Ultrasonic Method on PD Detection for GIS

NASA Astrophysics Data System (ADS)

Li, Yanran; Chen, Duo; Zhang, Jiwei; Chen, Ning; Li, Xiaoqi; Gong, Xiaojing

2017-09-01

GIS (gas insulated switchgear), is an important equipment in power system. Partial discharge plays an important role in detecting the insulation performance of GIS. UHF method and ultrasonic method frequently used in partial discharge (PD) detection for GIS. It is necessary to investigate UHF method and ultrasonic method for partial discharge in GIS. However, very few studies have been conducted on the method combined this two methods. From the view point of safety, a new method based on UHF method and ultrasonic method of PD detection for GIS is proposed in order to greatly enhance the ability of anti-interference of signal detection and the accuracy of fault localization. This paper presents study aimed at clarifying the effect of the new method combined UHF method and ultrasonic method. Partial discharge tests were performed in laboratory simulated environment. Obtained results show the ability of anti-interference of signal detection and the accuracy of fault localization for this new method combined UHF method and ultrasonic method.

A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm.

PubMed

Pandit, Diptangshu; Zhang, Li; Liu, Chengyu; Chattopadhyay, Samiran; Aslam, Nauman; Lim, Chee Peng

2017-06-01

Detection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals. A lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals. The proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation. In comparison with other related studies, the proposed algorithm offers a lightweight adaptive alternative for R-peak detection with good computational efficiency. The empirical results indicate that it not only yields a high accuracy rate in QRS detection, but also exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal. Copyright © 2017 Elsevier B.V. All rights reserved.

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

PubMed

Uddin, M B; Chow, C M; Su, S W

2018-03-26

Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

Novel CE-MS technique for detection of high explosives using perfluorooctanoic acid as a MEKC and mass spectrometric complexation reagent.

PubMed

Brensinger, Karen; Rollman, Christopher; Copper, Christine; Genzman, Ashton; Rine, Jacqueline; Lurie, Ira; Moini, Mehdi

2016-01-01

To address the need for the forensic analysis of high explosives, a novel capillary electrophoresis mass spectrometry (CE-MS) technique has been developed for high resolution, sensitivity, and mass accuracy detection of these compounds. The technique uses perfluorooctanoic acid (PFOA) as both a micellar electrokinetic chromatography (MEKC) reagent for separation of neutral explosives and as the complexation reagent for mass spectrometric detection of PFOA-explosive complexes in the negative ion mode. High explosives that formed complexes with PFOA included RDX, HMX, tetryl, and PETN. Some nitroaromatics were detected as molecular ions. Detection limits in the high parts per billion range and linear calibration responses over two orders of magnitude were obtained. For proof of concept, the technique was applied to the quantitative analysis of high explosives in sand samples. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Assessing risk of reoffending in adolescents who have committed a sexual offense: the accuracy of clinical judgments after completion of risk assessment instruments.

PubMed

Elkovitch, Natasha; Viljoen, Jodi L; Scalora, Mario J; Ullman, Daniel

2008-01-01

As courts often rely on clinicians when differentiating between sexually abusive youth at a low versus high risk of reoffense, understanding factors that contribute to accuracy in assessment of risk is imperative. The present study built on existing research by examining (1) the accuracy of clinical judgments of risk made after completing risk assessment instruments, (2) whether instrument-informed clinical judgments made with a high degree of confidence are associated with greater accuracy, and (3) the risk assessment instruments and subscales most predictive of clinical judgments. Raters assessed each youth's (n = 166) risk of reoffending after completing the SAVRY and J-SOAP-II. Raters were not able to predict detected cases of either sexual recidivism or nonsexual violent recidivism above chance, and a high degree of rater confidence was not associated with higher levels of accuracy. Total scores on the J-SOAP-II were predictive of instrument-informed clinical judgments of sexual risk, and total scores on the SAVRY of nonsexual risk.

Coefficient of Friction Patterns Can Identify Damage in Native and Engineered Cartilage Subjected to Frictional-Shear Stress

PubMed Central

Whitney, G. A.; Mansour, J. M.; Dennis, J. E.

2015-01-01

The mechanical loading environment encountered by articular cartilage in situ makes frictional-shear testing an invaluable technique for assessing engineered cartilage. Despite the important information that is gained from this testing, it remains under-utilized, especially for determining damage behavior. Currently, extensive visual inspection is required to assess damage; this is cumbersome and subjective. Tools to simplify, automate, and remove subjectivity from the analysis may increase the accessibility and usefulness of frictional-shear testing as an evaluation method. The objective of this study was to determine if the friction signal could be used to detect damage that occurred during the testing. This study proceeded in two phases: first, a simplified model of biphasic lubrication that does not require knowledge of interstitial fluid pressure was developed. In the second phase, frictional-shear tests were performed on 74 cartilage samples, and the simplified model was used to extract characteristic features from the friction signals. Using support vector machine classifiers, the extracted features were able to detect damage with a median accuracy of approximately 90%. The accuracy remained high even in samples with minimal damage. In conclusion, the friction signal acquired during frictional-shear testing can be used to detect resultant damage to a high level of accuracy. PMID:25691395

Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning.

PubMed

Treder, Maximilian; Lauermann, Jost Lennart; Eter, Nicole

2018-02-01

Our purpose was to use deep learning for the automated detection of age-related macular degeneration (AMD) in spectral domain optical coherence tomography (SD-OCT). A total of 1112 cross-section SD-OCT images of patients with exudative AMD and a healthy control group were used for this study. In the first step, an open-source multi-layer deep convolutional neural network (DCNN), which was pretrained with 1.2 million images from ImageNet, was trained and validated with 1012 cross-section SD-OCT scans (AMD: 701; healthy: 311). During this procedure training accuracy, validation accuracy and cross-entropy were computed. The open-source deep learning framework TensorFlow™ (Google Inc., Mountain View, CA, USA) was used to accelerate the deep learning process. In the last step, a created DCNN classifier, using the information of the above mentioned deep learning process, was tested in detecting 100 untrained cross-section SD-OCT images (AMD: 50; healthy: 50). Therefore, an AMD testing score was computed: 0.98 or higher was presumed for AMD. After an iteration of 500 training steps, the training accuracy and validation accuracies were 100%, and the cross-entropy was 0.005. The average AMD scores were 0.997 ± 0.003 in the AMD testing group and 0.9203 ± 0.085 in the healthy comparison group. The difference between the two groups was highly significant (p < 0.001). With a deep learning-based approach using TensorFlow™, it is possible to detect AMD in SD-OCT with high sensitivity and specificity. With more image data, an expansion of this classifier for other macular diseases or further details in AMD is possible, suggesting an application for this model as a support in clinical decisions. Another possible future application would involve the individual prediction of the progress and success of therapy for different diseases by automatically detecting hidden image information.

Effects of night work, sleep loss and time on task on simulated threat detection performance.

PubMed

Basner, Mathias; Rubinstein, Joshua; Fomberstein, Kenneth M; Coble, Matthew C; Ecker, Adrian; Avinash, Deepa; Dinges, David F

2008-09-01

To investigate the effects of night work and sleep loss on a simulated luggage screening task (SLST) that mimicked the x-ray system used by airport luggage screeners. We developed more than 5,800 unique simulated x-ray images of luggage organized into 31 stimulus sets of 200 bags each. 25% of each set contained either a gun or a knife with low or high target difficulty. The 200-bag stimuli sets were then run on software that simulates an x-ray screening system (SLST). Signal detection analysis was used to obtain measures of hit rate (HR), false alarm rate (FAR), threat detection accuracy (A'), and response bias (B"(D)). Experimental laboratory study 24 healthy nonprofessional volunteers (13 women, mean age +/- SD = 29.9 +/- 6.5 years). Subjects performed the SLST every 2 h during a 5-day period that included a 35 h period of wakefulness that extended to night work and then another day work period after the night without sleep. Threat detection accuracy A' decreased significantly (P < 0.001) while FAR increased significantly (P < 0.001) during night work, while both A' (P = 0.001) and HR decreased (P = 0.008) during day work following sleep loss. There were prominent time-on-task effects on response bias B"(D) (P= 0.002) and response latency (P = 0.004), but accuracy A' was unaffected. Both HR and FAR increased significantly with increasing study duration (both P < 0.001), while response latency decreased significantly (P <0.001). This study provides the first systematic evidence that night work and sleep loss adversely affect the accuracy of detecting complex real world objects among high levels of background clutter. If the results can be replicated in professional screeners and real work environments, fatigue in luggage screening personnel may pose a threat for air traffic safety unless countermeasures for fatigue are deployed.

Ultra-deep mutant spectrum profiling: improving sequencing accuracy using overlapping read pairs.

PubMed

Chen-Harris, Haiyin; Borucki, Monica K; Torres, Clinton; Slezak, Tom R; Allen, Jonathan E

2013-02-12

High throughput sequencing is beginning to make a transformative impact in the area of viral evolution. Deep sequencing has the potential to reveal the mutant spectrum within a viral sample at high resolution, thus enabling the close examination of viral mutational dynamics both within- and between-hosts. The challenge however, is to accurately model the errors in the sequencing data and differentiate real viral mutations, particularly those that exist at low frequencies, from sequencing errors. We demonstrate that overlapping read pairs (ORP) -- generated by combining short fragment sequencing libraries and longer sequencing reads -- significantly reduce sequencing error rates and improve rare variant detection accuracy. Using this sequencing protocol and an error model optimized for variant detection, we are able to capture a large number of genetic mutations present within a viral population at ultra-low frequency levels (<0.05%). Our rare variant detection strategies have important implications beyond viral evolution and can be applied to any basic and clinical research area that requires the identification of rare mutations.

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

PubMed

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

2018-03-28

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

Influence of left ventricular hypertrophy and geometry on diagnostic accuracy of wall motion and perfusion magnetic resonance during dobutamine stress.

PubMed

Gebker, Rolf; Mirelis, Jesus G; Jahnke, Cosima; Hucko, Thomas; Manka, Robert; Hamdan, Ashraf; Schnackenburg, Bernhard; Fleck, Eckart; Paetsch, Ingo

2010-09-01

The purpose of this study was to determine the influence of left ventricular (LV) hypertrophy and geometry on the diagnostic accuracy of wall motion and additional perfusion imaging during high-dose dobutamine/atropine stress magnetic resonance for the detection of coronary artery disease. Combined dobutamine stress magnetic resonance (DSMR)-wall motion and DSMR-perfusion imaging was performed in a single session in 187 patients scheduled for invasive coronary angiography. Patients were classified into 4 categories on the basis of LV mass (normal, ≤ 81 g/m(2) in men and ≤ 62 g/m(2) in women) and relative wall thickness (RWT) (normal, <0.45) as follows: normal geometry (normal mass, normal RWT), concentric remodeling (normal mass, increased RWT), concentric hypertrophy (increased mass, increased RWT), and eccentric hypertrophy (increased mass, normal RWT). Wall motion and perfusion images were interpreted sequentially, with observers blinded to other data. Significant coronary artery disease was defined as ≥ 70% stenosis. In patients with increased LV concentricity (defined by an RWT ≥ 0.45), sensitivity and accuracy of DSMR-wall motion were significantly reduced (63% and 73%, respectively; P<0.05) compared with patients without increased LV concentricity (90% and 88%, respectively; P<0.05). Although accuracy of DSMR-perfusion was higher than that of DSMR-wall motion in patients with concentric hypertrophy (82% versus 71%; P < 0.05), accuracy of DSMR-wall motion was superior to DSMR-perfusion (90% versus 85%; P < 0.05) in patients with eccentric hypertrophy. The accuracy of DSMR-wall motion is influenced by LV geometry. In patients with concentric remodeling and concentric hypertrophy, additional first-pass perfusion imaging during high-dose dobutamine stress improves the diagnostic accuracy for the detection of coronary artery disease.

Research on IPv6 intrusion detection system Snort-based

NASA Astrophysics Data System (ADS)

Shen, Zihao; Wang, Hui

2010-07-01

This paper introduces the common intrusion detection technologies, discusses the work flow of Snort intrusion detection system, and analyzes IPv6 data packet encapsulation and protocol decoding technology. We propose the expanding Snort architecture to support IPv6 intrusion detection in accordance with CIDF standard combined with protocol analysis technology and pattern matching technology, and present its composition. The research indicates that the expanding Snort system can effectively detect various intrusion attacks; it is high in detection efficiency and detection accuracy and reduces false alarm and omission report, which effectively solves the problem of IPv6 intrusion detection.

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.

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

PubMed

Karthikeyan, Sethu; Ramachandra, Vijayachandra

2017-01-01

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

Label propagation algorithm for community detection based on node importance and label influence

NASA Astrophysics Data System (ADS)

Zhang, Xian-Kun; Ren, Jing; Song, Chen; Jia, Jia; Zhang, Qian

2017-09-01

Recently, the detection of high-quality community has become a hot spot in the research of social network. Label propagation algorithm (LPA) has been widely concerned since it has the advantages of linear time complexity and is unnecessary to define objective function and the number of community in advance. However, LPA has the shortcomings of uncertainty and randomness in the label propagation process, which affects the accuracy and stability of the community. For large-scale social network, this paper proposes a novel label propagation algorithm for community detection based on node importance and label influence (LPA_NI). The experiments with comparative algorithms on real-world networks and synthetic networks have shown that LPA_NI can significantly improve the quality of community detection and shorten the iteration period. Also, it has better accuracy and stability in the case of similar complexity.

Just-in-time classifiers for recurrent concepts.

PubMed

Alippi, Cesare; Boracchi, Giacomo; Roveri, Manuel

2013-04-01

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

Toward On-Demand Deep Brain Stimulation Using Online Parkinson's Disease Prediction Driven by Dynamic Detection.

PubMed

Mohammed, Ameer; Zamani, Majid; Bayford, Richard; Demosthenous, Andreas

2017-12-01

In Parkinson's disease (PD), on-demand deep brain stimulation is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation, and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction, and classification algorithms that have been used in brain-machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction, and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves a classification accuracy of 99.29%, an F1-score of 97.90%, and a choice probability of 99.86%.

A Novel Energy-Efficient Approach for Human Activity Recognition.

PubMed

Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Peng, Ao; Tang, Biyu; Lu, Hai; Shi, Haibin; Zheng, Huiru

2017-09-08

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.

Combination of Autoantibody Signature with PSA Level Enables a Highly Accurate Blood-Based Differentiation of Prostate Cancer Patients from Patients with Benign Prostatic Hyperplasia.

PubMed

Leidinger, Petra; Keller, Andreas; Milchram, Lisa; Harz, Christian; Hart, Martin; Werth, Angelika; Lenhof, Hans-Peter; Weinhäusel, Andreas; Keck, Bastian; Wullich, Bernd; Ludwig, Nicole; Meese, Eckart

2015-01-01

Although an increased level of the prostate-specific antigen can be an indication for prostate cancer, other reasons often lead to a high rate of false positive results. Therefore, an additional serological screening of autoantibodies in patients' sera could improve the detection of prostate cancer. We performed protein macroarray screening with sera from 49 prostate cancer patients, 70 patients with benign prostatic hyperplasia and 28 healthy controls and compared the autoimmune response in those groups. We were able to distinguish prostate cancer patients from normal controls with an accuracy of 83.2%, patients with benign prostatic hyperplasia from normal controls with an accuracy of 86.0% and prostate cancer patients from patients with benign prostatic hyperplasia with an accuracy of 70.3%. Combining seroreactivity pattern with a PSA level of higher than 4.0 ng/ml this classification could be improved to an accuracy of 84.1%. For selected proteins we were able to confirm the differential expression by using luminex on 84 samples. We provide a minimally invasive serological method to reduce false positive results in detection of prostate cancer and according to PSA screening to distinguish men with prostate cancer from men with benign prostatic hyperplasia.

Exploiting semantics for sensor re-calibration in event detection systems

NASA Astrophysics Data System (ADS)

Vaisenberg, Ronen; Ji, Shengyue; Hore, Bijit; Mehrotra, Sharad; Venkatasubramanian, Nalini

2008-01-01

Event detection from a video stream is becoming an important and challenging task in surveillance and sentient systems. While computer vision has been extensively studied to solve different kinds of detection problems over time, it is still a hard problem and even in a controlled environment only simple events can be detected with a high degree of accuracy. Instead of struggling to improve event detection using image processing only, we bring in semantics to direct traditional image processing. Semantics are the underlying facts that hide beneath video frames, which can not be "seen" directly by image processing. In this work we demonstrate that time sequence semantics can be exploited to guide unsupervised re-calibration of the event detection system. We present an instantiation of our ideas by using an appliance as an example--Coffee Pot level detection based on video data--to show that semantics can guide the re-calibration of the detection model. This work exploits time sequence semantics to detect when re-calibration is required to automatically relearn a new detection model for the newly evolved system state and to resume monitoring with a higher rate of accuracy.

Field programmable gate array based fuzzy neural signal processing system for differential diagnosis of QRS complex tachycardia and tachyarrhythmia in noisy ECG signals.

PubMed

Chowdhury, Shubhajit Roy

2012-04-01

The paper reports of a Field Programmable Gate Array (FPGA) based embedded system for detection of QRS complex in a noisy electrocardiogram (ECG) signal and thereafter differential diagnosis of tachycardia and tachyarrhythmia. The QRS complex has been detected after application of entropy measure of fuzziness to build a detection function of ECG signal, which has been previously filtered to remove power line interference and base line wander. Using the detected QRS complexes, differential diagnosis of tachycardia and tachyarrhythmia has been performed. The entire algorithm has been realized in hardware on an FPGA. Using the standard CSE ECG database, the algorithm performed highly effectively. The performance of the algorithm in respect of QRS detection with sensitivity (Se) of 99.74% and accuracy of 99.5% is achieved when tested using single channel ECG with entropy criteria. The performance of the QRS detection system has been compared and found to be better than most of the QRS detection systems available in literature. Using the system, 200 patients have been diagnosed with an accuracy of 98.5%.

Rule-driven defect detection in CT images of hardwood logs

Treesearch

Erol Sarigul; A. Lynn Abbott; Daniel L. Schmoldt

2000-01-01

This paper deals with automated detection and identification of internal defects in hardwood logs using computed tomography (CT) images. We have developed a system that employs artificial neural networks to perform tentative classification of logs on a pixel-by-pixel basis. This approach achieves a high level of classification accuracy for several hardwood species (...

Real-time myocardial perfusion imaging for pharmacologic stress testing: added value to single photon emission computed tomography.

PubMed

Korosoglou, Grigorios; Dubart, Alain-Eric; DaSilva, K Gaspar C; Labadze, Nino; Hardt, Stefan; Hansen, Alexander; Bekeredjian, Raffi; Zugck, Christian; Zehelein, Joerg; Katus, Hugo A; Kuecherer, Helmut

2006-01-01

Little is known about the incremental value of real-time myocardial contrast echocardiography (MCE) as an adjunct to pharmacologic stress testing. This study was performed to evaluate the diagnostic value of MCE to detect abnormal myocardial perfusion by technetium Tc 99m sestamibi-single photon emission computed tomography (SPECT) and anatomically significant coronary artery disease (CAD) by angiography. Myocardial contrast echocardiography was performed at rest and during vasodilator stress in consecutive patients (N = 120) undergoing SPECT imaging for known or suspected CAD. Myocardial opacification, wall motion, and tracer uptake were visually analyzed in 12 myocardial segments by 2 pairs of blinded observers. Concordance between the 2 methods was assessed using the kappa statistic. Of 1356 segments, 1025 (76%) were interpretable by MCE, wall motion, and SPECT. Sensitivity of wall motion was 75%, specificity 83%, and accuracy 81% for detecting abnormal myocardial perfusion by SPECT (kappa = 0.53). Myocardial contrast echocardiography and wall motion together yielded significantly higher sensitivity (85% vs 74%, P < .05), specificity of 83%, and accuracy of 85% (kappa = 0.64) for the detection of abnormal myocardial perfusion. In 89 patients who underwent coronary angiography, MCE and wall motion together yielded higher sensitivity (83% vs 64%, P < .05) and accuracy (77% vs 68%, P < .05) but similar specificity (72%) compared with SPECT for the detection of high-grade, stenotic (> or = 75%) coronary lesions. Assessment of myocardial perfusion adds value to conventional stress echocardiography by increasing its sensitivity for the detection of functionally abnormal myocardial perfusion. Myocardial contrast echocardiography and wall motion together provide higher sensitivity and accuracy for detection of CAD compared with SPECT.

panelcn.MOPS: Copy-number detection in targeted NGS panel data for clinical diagnostics.

PubMed

Povysil, Gundula; Tzika, Antigoni; Vogt, Julia; Haunschmid, Verena; Messiaen, Ludwine; Zschocke, Johannes; Klambauer, Günter; Hochreiter, Sepp; Wimmer, Katharina

2017-07-01

Targeted next-generation-sequencing (NGS) panels have largely replaced Sanger sequencing in clinical diagnostics. They allow for the detection of copy-number variations (CNVs) in addition to single-nucleotide variants and small insertions/deletions. However, existing computational CNV detection methods have shortcomings regarding accuracy, quality control (QC), incidental findings, and user-friendliness. We developed panelcn.MOPS, a novel pipeline for detecting CNVs in targeted NGS panel data. Using data from 180 samples, we compared panelcn.MOPS with five state-of-the-art methods. With panelcn.MOPS leading the field, most methods achieved comparably high accuracy. panelcn.MOPS reliably detected CNVs ranging in size from part of a region of interest (ROI), to whole genes, which may comprise all ROIs investigated in a given sample. The latter is enabled by analyzing reads from all ROIs of the panel, but presenting results exclusively for user-selected genes, thus avoiding incidental findings. Additionally, panelcn.MOPS offers QC criteria not only for samples, but also for individual ROIs within a sample, which increases the confidence in called CNVs. panelcn.MOPS is freely available both as R package and standalone software with graphical user interface that is easy to use for clinical geneticists without any programming experience. panelcn.MOPS combines high sensitivity and specificity with user-friendliness rendering it highly suitable for routine clinical diagnostics. © 2017 The Authors. Human Mutation published by Wiley Periodicals, Inc.

panelcn.MOPS: Copy‐number detection in targeted NGS panel data for clinical diagnostics

PubMed Central

Povysil, Gundula; Tzika, Antigoni; Vogt, Julia; Haunschmid, Verena; Messiaen, Ludwine; Zschocke, Johannes; Klambauer, Günter; Wimmer, Katharina

2017-01-01

Abstract Targeted next‐generation‐sequencing (NGS) panels have largely replaced Sanger sequencing in clinical diagnostics. They allow for the detection of copy‐number variations (CNVs) in addition to single‐nucleotide variants and small insertions/deletions. However, existing computational CNV detection methods have shortcomings regarding accuracy, quality control (QC), incidental findings, and user‐friendliness. We developed panelcn.MOPS, a novel pipeline for detecting CNVs in targeted NGS panel data. Using data from 180 samples, we compared panelcn.MOPS with five state‐of‐the‐art methods. With panelcn.MOPS leading the field, most methods achieved comparably high accuracy. panelcn.MOPS reliably detected CNVs ranging in size from part of a region of interest (ROI), to whole genes, which may comprise all ROIs investigated in a given sample. The latter is enabled by analyzing reads from all ROIs of the panel, but presenting results exclusively for user‐selected genes, thus avoiding incidental findings. Additionally, panelcn.MOPS offers QC criteria not only for samples, but also for individual ROIs within a sample, which increases the confidence in called CNVs. panelcn.MOPS is freely available both as R package and standalone software with graphical user interface that is easy to use for clinical geneticists without any programming experience. panelcn.MOPS combines high sensitivity and specificity with user‐friendliness rendering it highly suitable for routine clinical diagnostics. PMID:28449315

Automated high-grade prostate cancer detection and ranking on whole slide images

NASA Astrophysics Data System (ADS)

Huang, Chao-Hui; Racoceanu, Daniel

2017-03-01

Recently, digital pathology (DP) has been largely improved due to the development of computer vision and machine learning. Automated detection of high-grade prostate carcinoma (HG-PCa) is an impactful medical use-case showing the paradigm of collaboration between DP and computer science: given a field of view (FOV) from a whole slide image (WSI), the computer-aided system is able to determine the grade by classifying the FOV. Various approaches have been reported based on this approach. However, there are two reasons supporting us to conduct this work: first, there is still room for improvement in terms of detection accuracy of HG-PCa; second, a clinical practice is more complex than the operation of simple image classification. FOV ranking is also an essential step. E.g., in clinical practice, a pathologist usually evaluates a case based on a few FOVs from the given WSI. Then, makes decision based on the most severe FOV. This important ranking scenario is not yet being well discussed. In this work, we introduce an automated detection and ranking system for PCa based on Gleason pattern discrimination. Our experiments suggested that the proposed system is able to perform high-accuracy detection ( 95:57% +/- 2:1%) and excellent performance of ranking. Hence, the proposed system has a great potential to support the daily tasks in the medical routine of clinical pathology.

Border-oriented post-processing refinement on detected vehicle bounding box for ADAS

NASA Astrophysics Data System (ADS)

Chen, Xinyuan; Zhang, Zhaoning; Li, Minne; Li, Dongsheng

2018-04-01

We investigate a new approach for improving localization accuracy of detected vehicles for object detection in advanced driver assistance systems(ADAS). Specifically, we implement a bounding box refinement as a post-processing of the state-of-the-art object detectors (Faster R-CNN, YOLOv2, etc.). The bounding box refinement is achieved by individually adjusting each border of the detected bounding box to its target location using a regression method. We use HOG features which perform well on the edge detection of vehicles to train the regressor and the regressor is independent of the CNN-based object detectors. Experiment results on the KITTI 2012 benchmark show that we can achieve up to 6% improvements over YOLOv2 and Faster R-CNN object detectors on the IoU threshold of 0.8. Also, the proposed refinement framework is computationally light, allowing for processing one bounding box within a few milliseconds on CPU. Further, this refinement method can be added to any object detectors, especially those with high speed but less accuracy.

Not all ultrasounds are created equal: general sonography versus musculoskeletal sonography in the detection of rotator cuff tears

PubMed Central

Cole, Brandi; Twibill, Kristen; Lam, Patrick; Hackett, Lisa

2016-01-01

Background This cross-sectional analytic diagnostic accuracy study was designed to compare the accuracy of ultrasound performed by general sonographers in local radiology practices with ultrasound performed by an experienced musculoskeletal sonographer for the detection of rotator cuff tears. Methods In total, 238 patients undergoing arthroscopy who had previously had an ultrasound performed by both a general sonographer and a specialist musculoskeletal sonographer made up the study cohort. Accuracy of diagnosis was compared with the findings at arthroscopy. Results When analyzed as all tears versus no tears, musculoskeletal sonography had an accuracy of 97%, a sensitivity of 97% and a specificity of 95%, whereas general sonography had an accuracy of 91%, a sensitivity of 91% and a specificity of 86%. When the partial tears were split with those ≥ 50% thickness in the tear group and those < 50% thickness in the no-tear group, musculoskeletal sonography had an accuracy of 97%, a sensitivity of 97% and a specificity of 100% and general sonography had an accuracy of 85%, a sensitivity of 84% and a specificity of 87%. Conclusions Ultrasound in the hands of an experienced musculoskeletal sonographer is highly accurate for the diagnosis of rotator cuff tears. General sonography has improved subsequent to earlier studies but remains inferior to an ultrasound performed by a musculoskeletal sonographer. PMID:27660657

Quantification of confocal fluorescence microscopy for the detection of cervical intraepithelial neoplasia.

PubMed

Sheikhzadeh, Fahime; Ward, Rabab K; Carraro, Anita; Chen, Zhao Yang; van Niekerk, Dirk; Miller, Dianne; Ehlen, Tom; MacAulay, Calum E; Follen, Michele; Lane, Pierre M; Guillaud, Martial

2015-10-24

Cervical cancer remains a major health problem, especially in developing countries. Colposcopic examination is used to detect high-grade lesions in patients with a history of abnormal pap smears. New technologies are needed to improve the sensitivity and specificity of this technique. We propose to test the potential of fluorescence confocal microscopy to identify high-grade lesions. We examined the quantification of ex vivo confocal fluorescence microscopy to differentiate among normal cervical tissue, low-grade Cervical Intraepithelial Neoplasia (CIN), and high-grade CIN. We sought to (1) quantify nuclear morphology and tissue architecture features by analyzing images of cervical biopsies; and (2) determine the accuracy of high-grade CIN detection via confocal microscopy relative to the accuracy of detection by colposcopic impression. Forty-six biopsies obtained from colposcopically normal and abnormal cervical sites were evaluated. Confocal images were acquired at different depths from the epithelial surface and histological images were analyzed using in-house software. The features calculated from the confocal images compared well with those features obtained from the histological images and histopathological reviews of the specimens (obtained by a gynecologic pathologist). The correlations between two of these features (the nuclear-cytoplasmic ratio and the average of three nearest Delaunay-neighbors distance) and the grade of dysplasia were higher than that of colposcopic impression. The sensitivity of detecting high-grade dysplasia by analysing images collected at the surface of the epithelium, and at 15 and 30 μm below the epithelial surface were respectively 100, 100, and 92 %. Quantitative analysis of confocal fluorescence images showed its capacity for discriminating high-grade CIN lesions vs. low-grade CIN lesions and normal tissues, at different depth of imaging. This approach could be used to help clinicians identify high-grade CIN in clinical settings.

Precisely detecting atomic position of atomic intensity images.

PubMed

Wang, Zhijun; Guo, Yaolin; Tang, Sai; Li, Junjie; Wang, Jincheng; Zhou, Yaohe

2015-03-01

We proposed a quantitative method to detect atomic position in atomic intensity images from experiments such as high-resolution transmission electron microscopy, atomic force microscopy, and simulation such as phase field crystal modeling. The evaluation of detection accuracy proves the excellent performance of the method. This method provides a chance to precisely determine atomic interactions based on the detected atomic positions from the atomic intensity image, and hence to investigate the related physical, chemical and electrical properties. Copyright © 2014 Elsevier B.V. All rights reserved.

F-18 FDG PET, CT, and MRI for detecting the malignant potential in patients with gastrointestinal stromal tumors: A protocol for a network meta-analysis of diagnostic test accuracy.

PubMed

Wei, Kongyuan; Pan, Bei; Yang, Huan; Lu, Cuncun; Ge, Long; Cao, Nong

2018-04-01

Gastrointestinal stromal tumor (GIST) is a rare cancer in gastrointestinal carcinomas and has been widely known as a curable disease among all the digestive tumors. However, early detection of malignant potential in patients with GIST has still been a huge challenge all around the world. CT, MRI, and F-18 FDG PET are all considered as good tests for diagnosing malignant GIST efficiently, but no recommended suggestions presents which test among the 3 is the prior one in detecting the malignant potential of GIST. We perform this study to assess the accuracy between CT, MRI, and F-18 FDG PET through network meta-analysis method, and to rank these tests. PubMed, EMBASE.com, CNKI, and CBM databases will be searched without search date and language restrictions. We will include diagnostic tests which assessed the accuracy of CT, MRI, and F-18 FDG PET in detecting the malignant potential of GIST. The risk of bias in each study will be independently assessed as low, moderate, or high using criteria adapted from Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis will be performed using STATA 12.0 and R 3.4.1 software. The competing diagnostic tests will be ranked by a superiority index. This study is ongoing, and will be submitted to a peer-reviewed journal for publication. This study will provide a comprehensive evidence summary of CT, MRI, and F-18 FDG PET in detecting the malignant potential of GIST.

Optical nano-biosensing interface via nucleic acid amplification strategy: construction and application.

PubMed

Zhou, Hong; Liu, Jing; Xu, Jing-Juan; Zhang, Shu-Sheng; Chen, Hong-Yuan

2018-03-21

Modern optical detection technology plays a critical role in current clinical detection due to its high sensitivity and accuracy. However, higher requirements such as extremely high detection sensitivity have been put forward due to the clinical needs for the early finding and diagnosing of malignant tumors which are significant for tumor therapy. The technology of isothermal amplification with nucleic acids opens up avenues for meeting this requirement. Recent reports have shown that a nucleic acid amplification-assisted modern optical sensing interface has achieved satisfactory sensitivity and accuracy, high speed and specificity. Compared with isothermal amplification technology designed to work completely in a solution system, solid biosensing interfaces demonstrated better performances in stability and sensitivity due to their ease of separation from the reaction mixture and the better signal transduction on these optical nano-biosensing interfaces. Also the flexibility and designability during the construction of these nano-biosensing interfaces provided a promising research topic for the ultrasensitive detection of cancer diseases. In this review, we describe the construction of the burgeoning number of optical nano-biosensing interfaces assisted by a nucleic acid amplification strategy, and provide insightful views on: (1) approaches to the smart fabrication of an optical nano-biosensing interface, (2) biosensing mechanisms via the nucleic acid amplification method, (3) the newest strategies and future perspectives.

A Hybrid Approach for CpG Island Detection in the Human Genome.

PubMed

Yang, Cheng-Hong; Lin, Yu-Da; Chiang, Yi-Cheng; Chuang, Li-Yeh

2016-01-01

CpG islands have been demonstrated to influence local chromatin structures and simplify the regulation of gene activity. However, the accurate and rapid determination of CpG islands for whole DNA sequences remains experimentally and computationally challenging. A novel procedure is proposed to detect CpG islands by combining clustering technology with the sliding-window method (PSO-based). Clustering technology is used to detect the locations of all possible CpG islands and process the data, thus effectively obviating the need for the extensive and unnecessary processing of DNA fragments, and thus improving the efficiency of sliding-window based particle swarm optimization (PSO) search. This proposed approach, named ClusterPSO, provides versatile and highly-sensitive detection of CpG islands in the human genome. In addition, the detection efficiency of ClusterPSO is compared with eight CpG island detection methods in the human genome. Comparison of the detection efficiency for the CpG islands in human genome, including sensitivity, specificity, accuracy, performance coefficient (PC), and correlation coefficient (CC), ClusterPSO revealed superior detection ability among all of the test methods. Moreover, the combination of clustering technology and PSO method can successfully overcome their respective drawbacks while maintaining their advantages. Thus, clustering technology could be hybridized with the optimization algorithm method to optimize CpG island detection. The prediction accuracy of ClusterPSO was quite high, indicating the combination of CpGcluster and PSO has several advantages over CpGcluster and PSO alone. In addition, ClusterPSO significantly reduced implementation time.

Sound localization in noise in hearing-impaired listeners.

PubMed

Lorenzi, C; Gatehouse, S; Lever, C

1999-06-01

The present study assesses the ability of four listeners with high-frequency, bilateral symmetrical sensorineural hearing loss to localize and detect a broadband click train in the frontal-horizontal plane, in quiet and in the presence of a white noise. The speaker array and stimuli are identical to those described by Lorenzi et al. (in press). The results show that: (1) localization performance is only slightly poorer in hearing-impaired listeners than in normal-hearing listeners when noise is at 0 deg azimuth, (2) localization performance begins to decrease at higher signal-to-noise ratios for hearing-impaired listeners than for normal-hearing listeners when noise is at +/- 90 deg azimuth, and (3) the performance of hearing-impaired listeners is less consistent when noise is at +/- 90 deg azimuth than at 0 deg azimuth. The effects of a high-frequency hearing loss were also studied by measuring the ability of normal-hearing listeners to localize the low-pass filtered version of the clicks. The data reproduce the effects of noise on three out of the four hearing-impaired listeners when noise is at 0 deg azimuth. They reproduce the effects of noise on only two out of the four hearing-impaired listeners when noise is at +/- 90 deg azimuth. The additional effects of a low-frequency hearing loss were investigated by attenuating the low-pass filtered clicks and the noise by 20 dB. The results show that attenuation does not strongly affect localization accuracy for normal-hearing listeners. Measurements of the clicks' detectability indicate that the hearing-impaired listeners who show the poorest localization accuracy also show the poorest ability to detect the clicks. The inaudibility of high frequencies, "distortions," and reduced detectability of the signal are assumed to have caused the poorer-than-normal localization accuracy for hearing-impaired listeners.

Detection of proximal caries using digital radiographic systems with different resolutions.

PubMed

Nikneshan, Sima; Abbas, Fatemeh Mashhadi; Sabbagh, Sedigheh

2015-01-01

Dental radiography is an important tool for detection of caries and digital radiography is the latest advancement in this regard. Spatial resolution is a characteristic of digital receptors used for describing the quality of images. This study was aimed to compare the diagnostic accuracy of two digital radiographic systems with three different resolutions for detection of noncavitated proximal caries. Diagnostic accuracy. Seventy premolar teeth were mounted in 14 gypsum blocks. Digora; Optime and RVG Access were used for obtaining digital radiographs. Six observers evaluated the proximal surfaces in radiographs for each resolution in order to determine the depth of caries based on a 4-point scale. The teeth were then histologically sectioned, and the results of histologic analysis were considered as the gold standard. Data were entered using SPSS version 18 software and the Kruskal-Wallis test was used for data analysis. P <0.05 was considered as statistically significant. No significant difference was found between different resolutions for detection of proximal caries (P > 0.05). RVG access system had the highest specificity (87.7%) and Digora; Optime at high resolution had the lowest specificity (84.2%). Furthermore, Digora; Optime had higher sensitivity for detection of caries exceeding outer half of enamel. Judgment of oral radiologists for detection of the depth of caries had higher reliability than that of restorative dentistry specialists. The three resolutions of Digora; Optime and RVG access had similar accuracy in detection of noncavitated proximal caries.

Comparison of direct 13C and indirect 1H-[13C] MR detection methods for the study of dynamic metabolic turnover in the human brain

NASA Astrophysics Data System (ADS)

Chen, Hao; De Feyter, Henk M.; Brown, Peter B.; Rothman, Douglas L.; Cai, Shuhui; de Graaf, Robin A.

2017-10-01

A wide range of direct 13C and indirect 1H-[13C] MR detection methods exist to probe dynamic metabolic pathways in the human brain. Choosing an optimal detection method is difficult as sequence-specific features regarding spatial localization, broadband decoupling, spectral resolution, power requirements and sensitivity complicate a straightforward comparison. Here we combine density matrix simulations with experimentally determined values for intrinsic 1H and 13C sensitivity, T1 and T2 relaxation and transmit efficiency to allow selection of an optimal 13C MR detection method for a given application and magnetic field. The indirect proton-observed, carbon-edited (POCE) detection method provides the highest accuracy at reasonable RF power deposition both at 4 T and 7 T. The various polarization transfer methods all have comparable performances, but may become infeasible at 7 T due to the high RF power deposition. 2D MR methods have limited value for the metabolites considered (primarily glutamate, glutamine and γ-amino butyric acid (GABA)), but may prove valuable when additional information can be extracted, such as isotopomers or lipid composition. While providing the lowest accuracy, the detection of non-protonated carbons is the simplest to implement with the lowest RF power deposition. The magnetic field homogeneity is one of the most important parameters affecting the detection accuracy for all metabolites and all acquisition methods.

Effects of Night Work, Sleep Loss and Time on Task on Simulated Threat Detection Performance

PubMed Central

Basner, Mathias; Rubinstein, Joshua; Fomberstein, Kenneth M.; Coble, Matthew C.; Ecker, Adrian; Avinash, Deepa; Dinges, David F.

2008-01-01

Study Objectives: To investigate the effects of night work and sleep loss on a simulated luggage screening task (SLST) that mimicked the x-ray system used by airport luggage screeners. Design: We developed more than 5,800 unique simulated x-ray images of luggage organized into 31 stimulus sets of 200 bags each. 25% of each set contained either a gun or a knife with low or high target difficulty. The 200-bag stimuli sets were then run on software that simulates an x-ray screening system (SLST). Signal detection analysis was used to obtain measures of hit rate (HR), false alarm rate (FAR), threat detection accuracy (A′), and response bias (B″D). Setting: Experimental laboratory study Participants: 24 healthy nonprofessional volunteers (13 women, mean age ± SD = 29.9 ± 6.5 years). Interventions: Subjects performed the SLST every 2 h during a 5-day period that included a 35 h period of wakefulness that extended to night work and then another day work period after the night without sleep. Results: Threat detection accuracy A′ decreased significantly (P < 0.001) while FAR increased significantly (P < 0.001) during night work, while both A′ (P = 0.001) and HR decreased (P = 0.008) during day work following sleep loss. There were prominent time-on-task effects on response bias B″D (P = 0.002) and response latency (P = 0.004), but accuracy A′ was unaffected. Both HR and FAR increased significantly with increasing study duration (both P < 0.001), while response latency decreased significantly (P < 0.001). Conclusions: This study provides the first systematic evidence that night work and sleep loss adversely affect the accuracy of detecting complex real world objects among high levels of background clutter. If the results can be replicated in professional screeners and real work environments, fatigue in luggage screening personnel may pose a threat for air traffic safety unless countermeasures for fatigue are deployed. Citation: Basner M; Rubinstein J; Fomberstein KM; Coble MC; Avinash D; Dinges DF. Effects of Night Work, Sleep Loss and Time on Task on Simulated Threat Detection Performance. SLEEP 2008;31(9):1251-1259. PMID:18788650

Amplicon-based next-generation sequencing of plasma cell-free DNA for detection of driver and resistance mutations in advanced non-small cell lung cancer.

PubMed

Guibert, N; Hu, Y; Feeney, N; Kuang, Y; Plagnol, V; Jones, G; Howarth, K; Beeler, J F; Paweletz, C P; Oxnard, G R

2018-04-01

Genomic analysis of plasma cell-free DNA is transforming lung cancer care; however, available assays are limited by cost, turnaround time, and imperfect accuracy. Here, we study amplicon-based plasma next-generation sequencing (NGS), rather than hybrid-capture-based plasma NGS, hypothesizing this would allow sensitive detection and monitoring of driver and resistance mutations in advanced non-small cell lung cancer (NSCLC). Plasma samples from patients with NSCLC and a known targetable genotype (EGFR, ALK/ROS1, and other rare genotypes) were collected while on therapy and analyzed blinded to tumor genotype. Plasma NGS was carried out using enhanced tagged amplicon sequencing of hotspots and coding regions from 36 genes, as well as intronic coverage for detection of ALK/ROS1 fusions. Diagnostic accuracy was compared with plasma droplet digital PCR (ddPCR) and tumor genotype. A total of 168 specimens from 46 patients were studied. Matched plasma NGS and ddPCR across 120 variants from 80 samples revealed high concordance of allelic fraction (R2 = 0.95). Pretreatment, sensitivity of plasma NGS for the detection of EGFR driver mutations was 100% (30/30), compared with 87% for ddPCR (26/30). A full spectrum of rare driver oncogenic mutations could be detected including sensitive detection of ALK/ROS1 fusions (8/9 detected, 89%). Studying 25 patients positive for EGFR T790M that developed resistance to osimertinib, 15 resistance mechanisms could be detected including tertiary EGFR mutations (C797S, Q791P) and mutations or amplifications of non-EGFR genes, some of which could be detected pretreatment or months before progression. This blinded analysis demonstrates the ability of amplicon-based plasma NGS to detect a full range of targetable genotypes in NSCLC, including fusion genes, with high accuracy. The ability of plasma NGS to detect a range of preexisting and acquired resistance mechanisms highlights its potential value as an alternative to single mutation digital PCR-based plasma assays for personalizing treatment of TKI resistance in lung cancer.

"Dilute-and-inject" multi-target screening assay for highly polar doping agents using hydrophilic interaction liquid chromatography high resolution/high accuracy mass spectrometry for sports drug testing.

PubMed

Görgens, Christian; Guddat, Sven; Orlovius, Anne-Katrin; Sigmund, Gerd; Thomas, Andreas; Thevis, Mario; Schänzer, Wilhelm

2015-07-01

In the field of LC-MS, reversed phase liquid chromatography is the predominant method of choice for the separation of prohibited substances from various classes in sports drug testing. However, highly polar and charged compounds still represent a challenging task in liquid chromatography due to their difficult chromatographic behavior using reversed phase materials. A very promising approach for the separation of hydrophilic compounds is hydrophilic interaction liquid chromatography (HILIC). Despite its great potential and versatile advantages for the separation of highly polar compounds, HILIC is up to now not very common in doping analysis, although most manufacturers offer a variety of HILIC columns in their portfolio. In this study, a novel multi-target approach based on HILIC high resolution/high accuracy mass spectrometry is presented to screen for various polar stimulants, stimulant sulfo-conjugates, glycerol, AICAR, ethyl glucuronide, morphine-3-glucuronide, and myo-inositol trispyrophosphate after direct injection of diluted urine specimens. The usage of an effective online sample cleanup and a zwitterionic HILIC analytical column in combination with a new generation Hybrid Quadrupol-Orbitrap® mass spectrometer enabled the detection of highly polar analytes without any time-consuming hydrolysis or further purification steps, far below the required detection limits. The methodology was fully validated for qualitative and quantitative (AICAR, glycerol) purposes considering the parameters specificity; robustness (rRT < 2.0%); linearity (R > 0.99); intra- and inter-day precision at low, medium, and high concentration levels (CV < 20%); limit of detection (stimulants and stimulant sulfo-conjugates < 10 ng/mL; norfenefrine; octopamine < 30 ng/mL; AICAR < 10 ng/mL; glycerol 100 μg/mL; ETG < 100 ng/mL); accuracy (AICAR 103.8-105.5%, glycerol 85.1-98.3% at three concentration levels) and ion suppression/enhancement effects.

Myocardial perfusion imaging with PET

PubMed Central

Nakazato, Ryo; Berman, Daniel S; Alexanderson, Erick; Slomka, Piotr

2013-01-01

PET-myocardial perfusion imaging (MPI) allows accurate measurement of myocardial perfusion, absolute myocardial blood flow and function at stress and rest in a single study session performed in approximately 30 min. Various PET tracers are available for MPI, and rubidium-82 or nitrogen-13-ammonia is most commonly used. In addition, a new fluorine-18-based PET-MPI tracer is currently being evaluated. Relative quantification of PET perfusion images shows very high diagnostic accuracy for detection of obstructive coronary artery disease. Dynamic myocardial blood flow analysis has demonstrated additional prognostic value beyond relative perfusion imaging. Patient radiation dose can be reduced and image quality can be improved with latest advances in PET/CT equipment. Simultaneous assessment of both anatomy and perfusion by hybrid PET/CT can result in improved diagnostic accuracy. Compared with SPECT-MPI, PET-MPI provides higher diagnostic accuracy, using lower radiation doses during a shorter examination time period for the detection of coronary artery disease. PMID:23671459

Development of an embedded instrument for autofocus and polarization alignment of polarization maintaining fiber

NASA Astrophysics Data System (ADS)

Feng, Di; Fang, Qimeng; Huang, Huaibo; Zhao, Zhengqi; Song, Ningfang

2017-12-01

The development and implementation of a practical instrument based on an embedded technique for autofocus and polarization alignment of polarization maintaining fiber is presented. For focusing efficiency and stability, an image-based focusing algorithm fully considering the image definition evaluation and the focusing search strategy was used to accomplish autofocus. For improving the alignment accuracy, various image-based algorithms of alignment detection were developed with high calculation speed and strong robustness. The instrument can be operated as a standalone device with real-time processing and convenience operations. The hardware construction, software interface, and image-based algorithms of main modules are described. Additionally, several image simulation experiments were also carried out to analyze the accuracy of the above alignment detection algorithms. Both the simulation results and experiment results indicate that the instrument can achieve the accuracy of polarization alignment <±0.1 deg.

Identification of the Species of Origin for Meat Products by Rapid Evaporative Ionization Mass Spectrometry.

PubMed

Balog, Julia; Perenyi, Dora; Guallar-Hoyas, Cristina; Egri, Attila; Pringle, Steven D; Stead, Sara; Chevallier, Olivier P; Elliott, Chris T; Takats, Zoltan

2016-06-15

Increasingly abundant food fraud cases have brought food authenticity and safety into major focus. This study presents a fast and effective way to identify meat products using rapid evaporative ionization mass spectrometry (REIMS). The experimental setup was demonstrated to be able to record a mass spectrometric profile of meat specimens in a time frame of <5 s. A multivariate statistical algorithm was developed and successfully tested for the identification of animal tissue with different anatomical origin, breed, and species with 100% accuracy at species and 97% accuracy at breed level. Detection of the presence of meat originating from a different species (horse, cattle, and venison) has also been demonstrated with high accuracy using mixed patties with a 5% detection limit. REIMS technology was found to be a promising tool in food safety applications providing a reliable and simple method for the rapid characterization of food products.

Accuracy of a radiofrequency identification (RFID) badge system to monitor hand hygiene behavior during routine clinical activities.

PubMed

Pineles, Lisa L; Morgan, Daniel J; Limper, Heather M; Weber, Stephen G; Thom, Kerri A; Perencevich, Eli N; Harris, Anthony D; Landon, Emily

2014-02-01

Hand hygiene (HH) is a critical part of infection prevention in health care settings. Hospitals around the world continuously struggle to improve health care personnel (HCP) HH compliance. The current gold standard for monitoring compliance is direct observation; however, this method is time-consuming and costly. One emerging area of interest involves automated systems for monitoring HH behavior such as radiofrequency identification (RFID) tracking systems. To assess the accuracy of a commercially available RFID system in detecting HCP HH behavior, we compared direct observation with data collected by the RFID system in a simulated validation setting and to a real-life clinical setting over 2 hospitals. A total of 1,554 HH events was observed. Accuracy for identifying HH events was high in the simulated validation setting (88.5%) but relatively low in the real-life clinical setting (52.4%). This difference was significant (P < .01). Accuracy for detecting HCP movement into and out of patient rooms was also high in the simulated setting but not in the real-life clinical setting (100% on entry and exit in simulated setting vs 54.3% entry and 49.5% exit in real-life clinical setting, P < .01). In this validation study of an RFID system, almost half of the HH events were missed. More research is necessary to further develop these systems and improve accuracy prior to widespread adoption. Copyright © 2014 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

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

PubMed Central

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

2015-01-01

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

Computerized detection of leukocytes in microscopic leukorrhea images.

PubMed

Zhang, Jing; Zhong, Ya; Wang, Xiangzhou; Ni, Guangming; Du, Xiaohui; Liu, Juanxiu; Liu, Lin; Liu, Yong

2017-09-01

Detection of leukocytes is critical for the routine leukorrhea exam, which is widely used in gynecological examinations. An elevated vaginal leukocyte count in women with bacterial vaginosis is a strong predictor of vaginal or cervical infections. In the routine leukorrhea exam, the counting of leukocytes is primarily performed by manual techniques. However, the viewing and counting of leukocytes from multiple high-power viewing fields on a glass slide under a microscope leads to subjectivity, low efficiency, and low accuracy. To date, many biological cells in stool, blood, and breast cancer have been studied to realize computerized detection; however, the detection of leukocytes in microscopic leukorrhea images has not been studied. Thus, there is an increasing need for computerized detection of leukocytes. There are two key processes in the computerized detection of leukocytes in digital image processing. One is segmentation; the other is intelligent classification. In this paper, we propose a combined ensemble to detect leukocytes in the microscopic leukorrhea image. After image segmentation and selecting likely leukocyte subimages, we obtain the leukocyte candidates. Then, for intelligent classification, we adopt two methods: feature extraction and classification by a support vector machine (SVM); applying a modified convolutional neural network (CNN) to the larger subimages. If different methods classify a candidate in the same category, the process is finished. If not, the outputs of the methods are provided to a classifier to further classify the candidate. After acquiring leukocyte candidates, we attempted three methods to perform classification. The first approach using features and SVM achieved 88% sensitivity, 97% specificity, and 92.5% accuracy. The second method using CNN achieved 95% sensitivity, 84% specificity, and 89.5% accuracy. Then, in the combination approach, we achieved 92% sensitivity, 95% specificity, and 93.5% accuracy. Finally, the images with marked and counted leukocytes were obtained. A novel computerized detection system was developed for automated detection of leukocytes in microscopic images. Different methods resulted in comparable overall qualities by enabling computerized detection of leukocytes. The proposed approach further improved the performance. This preliminary study proves the feasibility of computerized detection of leukocytes in clinical use. © 2017 American Association of Physicists in Medicine.

Design on wireless auto-measurement system for lead rail straightness measurement based on PSD

NASA Astrophysics Data System (ADS)

Yan, Xiugang; Zhang, Shuqin; Dong, Dengfeng; Cheng, Zhi; Wu, Guanghua; Wang, Jie; Zhou, Weihu

2016-10-01

Straightness detection is not only one of the key technologies for the product quality and installation accuracy of all types of lead rail, but also an important dimensional measurement technology. The straightness measuring devices now available have disadvantages of low automation level, limiting by measuring environment, and low measurement efficiency. In this paper, a wireless measurement system for straightness detection based on position sensitive detector (PSD) is proposed. The system has some advantage of high automation-level, convenient, high measurement efficiency, easy to transplanting and expanding, and can detect straightness of lead rail in real-time.

A design of optical modulation system with pixel-level modulation accuracy

NASA Astrophysics Data System (ADS)

Zheng, Shiwei; Qu, Xinghua; Feng, Wei; Liang, Baoqiu

2018-01-01

Vision measurement has been widely used in the field of dimensional measurement and surface metrology. However, traditional methods of vision measurement have many limits such as low dynamic range and poor reconfigurability. The optical modulation system before image formation has the advantage of high dynamic range, high accuracy and more flexibility, and the modulation accuracy is the key parameter which determines the accuracy and effectiveness of optical modulation system. In this paper, an optical modulation system with pixel level accuracy is designed and built based on multi-points reflective imaging theory and digital micromirror device (DMD). The system consisted of digital micromirror device, CCD camera and lens. Firstly we achieved accurate pixel-to-pixel correspondence between the DMD mirrors and the CCD pixels by moire fringe and an image processing of sampling and interpolation. Then we built three coordinate systems and calculated the mathematic relationship between the coordinate of digital micro-mirror and CCD pixels using a checkerboard pattern. A verification experiment proves that the correspondence error is less than 0.5 pixel. The results show that the modulation accuracy of system meets the requirements of modulation. Furthermore, the high reflecting edge of a metal circular piece can be detected using the system, which proves the effectiveness of the optical modulation system.

Gleason Score Determination with Transrectal Ultrasound-Magnetic Resonance Imaging Fusion Guided Prostate Biopsies--Are We Gaining in Accuracy?

PubMed

Lanz, Camille; Cornud, François; Beuvon, Frédéric; Lefèvre, Arnaud; Legmann, Paul; Zerbib, Marc; Delongchamps, Nicolas Barry

2016-01-01

We evaluated the accuracy of prostate magnetic resonance imaging- transrectal ultrasound targeted biopsy for Gleason score determination. We selected 125 consecutive patients treated with radical prostatectomy for a clinically localized prostate cancer diagnosed on magnetic resonance imaging-transrectal ultrasound targeted biopsy and/or systematic biopsy. On multiparametric magnetic resonance imaging each suspicious area was graded according to PI-RADS™ score. A correlation analysis between multiparametric magnetic resonance imaging and pathological findings was performed. Factors associated with determining the accuracy of Gleason score on targeted biopsy were statistically assessed. Pathological analysis of radical prostatectomy specimens detected 230 tumor foci. Multiparametric magnetic resonance imaging detected 151 suspicious areas. Of these areas targeted biopsy showed 126 cancer foci in 115 patients, and detected the index lesion in all of them. The primary Gleason grade, secondary Gleason grade and Gleason score of the 126 individual tumors were determined accurately in 114 (90%), 75 (59%) and 85 (67%) cases, respectively. Maximal Gleason score was determined accurately in 80 (70%) patients. Gleason score determination accuracy on targeted biopsy was significantly higher for low Gleason and high PI-RADS score tumors. Magnetic resonance imaging-transrectal ultrasound targeted biopsy allowed for an accurate estimation of Gleason score in more than two-thirds of patients. Gleason score misclassification was mostly due to a lack of accuracy in the determination of the secondary Gleason grade. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Study on Classification Accuracy Inspection of Land Cover Data Aided by Automatic Image Change Detection Technology

NASA Astrophysics Data System (ADS)

Xie, W.-J.; Zhang, L.; Chen, H.-P.; Zhou, J.; Mao, W.-J.

2018-04-01

The purpose of carrying out national geographic conditions monitoring is to obtain information of surface changes caused by human social and economic activities, so that the geographic information can be used to offer better services for the government, enterprise and public. Land cover data contains detailed geographic conditions information, thus has been listed as one of the important achievements in the national geographic conditions monitoring project. At present, the main issue of the production of the land cover data is about how to improve the classification accuracy. For the land cover data quality inspection and acceptance, classification accuracy is also an important check point. So far, the classification accuracy inspection is mainly based on human-computer interaction or manual inspection in the project, which are time consuming and laborious. By harnessing the automatic high-resolution remote sensing image change detection technology based on the ERDAS IMAGINE platform, this paper carried out the classification accuracy inspection test of land cover data in the project, and presented a corresponding technical route, which includes data pre-processing, change detection, result output and information extraction. The result of the quality inspection test shows the effectiveness of the technical route, which can meet the inspection needs for the two typical errors, that is, missing and incorrect update error, and effectively reduces the work intensity of human-computer interaction inspection for quality inspectors, and also provides a technical reference for the data production and quality control of the land cover data.

Detection of the spatial accuracy of an O-arm in the region of surgical interest

NASA Astrophysics Data System (ADS)

Koivukangas, Tapani; Katisko, Jani P. A.; Koivukangsa, John P.

2013-03-01

Medical imaging is an essential component of a wide range of surgical procedures1. For image guided surgical (IGS) procedures, medical images are the main source of information2. The IGS procedures rely largely on obtained image data, so the data needs to provide differentiation between normal and abnormal tissues, especially when other surgical guidance devices are used in the procedures. The image data also needs to provide accurate spatial representation of the patient3. This research has concentrated on the concept of accuracy assessment of IGS devices to meet the needs of quality assurance in the hospital environment. For this purpose, two precision engineered accuracy assessment phantoms have been developed as advanced materials and methods for the community. The phantoms were designed to mimic the volume of a human head as the common region of surgical interest (ROSI). This paper introduces the utilization of the phantoms in spatial accuracy assessment of a commercial surgical 3D CT scanner, the O-Arm. The study presents methods and results of image quality detection of possible geometrical distortions in the region of surgical interest. The results show that in the pre-determined ROSI there are clear image distortion and artefacts using too high imaging parameters when scanning the objects. On the other hand, when using optimal parameters, the O-Arm causes minimal error in IGS accuracy. The detected spatial inaccuracy of the O-Arm with used parameters was in the range of less than 1.00 mm.

High-Reproducibility and High-Accuracy Method for Automated Topic Classification

NASA Astrophysics Data System (ADS)

Lancichinetti, Andrea; Sirer, M. Irmak; Wang, Jane X.; Acuna, Daniel; Körding, Konrad; Amaral, Luís A. Nunes

2015-01-01

Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent searching, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state of the art in topic modeling. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results that are not accurate in inferring the most suitable model parameters. Adapting approaches from community detection in networks, we propose a new algorithm that displays high reproducibility and high accuracy and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure.

Android Malware Classification Using K-Means Clustering Algorithm

NASA Astrophysics Data System (ADS)

Hamid, Isredza Rahmi A.; Syafiqah Khalid, Nur; Azma Abdullah, Nurul; Rahman, Nurul Hidayah Ab; Chai Wen, Chuah

2017-08-01

Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.

Method for oil pipeline leak detection based on distributed fiber optic technology

NASA Astrophysics Data System (ADS)

Chen, Huabo; Tu, Yaqing; Luo, Ting

1998-08-01

Pipeline leak detection is a difficult problem to solve up to now. Some traditional leak detection methods have such problems as high rate of false alarm or missing detection, low location estimate capability. For the problems given above, a method for oil pipeline leak detection based on distributed optical fiber sensor with special coating is presented. The fiber's coating interacts with hydrocarbon molecules in oil, which alters the refractive indexed of the coating. Therefore the light-guiding properties of the fiber are modified. Thus pipeline leak location can be determined by OTDR. Oil pipeline lead detection system is designed based on the principle. The system has some features like real time, multi-point detection at the same time and high location accuracy. In the end, some factors that probably influence detection are analyzed and primary improving actions are given.

Examining the Pilot and Controller Performance Data When in a Free Flight with Weather Phenomenon

NASA Technical Reports Server (NTRS)

Nituen, Celestine A.; Lozito, Sandra C. (Technical Monitor)

2002-01-01

The present study investigated effects of weather related factors on the performance of pilots under free flight. A weather scenario was defined by a combination of precipitation factors (light rain, moderate rain, and heavy rain or snow), visibility (1,4,8 miles), wind conditions (light, medium, or heavy), cloud ceiling (800ft. below, 1800ft above, and 4000ft horizontal). The performance of the aircraft self-separation was evaluated in terms of detection accuracy and detection times for student- and commercial (expert) pilots. Overall, the results obtained from a behavioral analysis showed that in general, the ability to recognize intruder aircraft conflict incidents, followed by the ability to acquire the spatial location of the intruder aircraft relative to ownership aircraft were judged to be the major cognitive tasks as perceived by the participants during self-separation. Further, the participants rarely used cockpit display of traffic information (CDTI) during conflict management related to aircraft separation, but used CDTI highly during decision-making tasks. In all weather scenarios, there were remarkable differences between expert and student pilots in detection times. In summary, weather scenarios were observed to affect intruder aircraft detection performance accuracies. There was interaction effects between weather Scenario-1 and Scenario-2 for climbing task data generated by both expert- and student- pilots at high traffic density. Scenario-3 weather condition provided an opportunity for poor detection accuracy as well as detection time increase. This may be attributed to low visibility. The intruder aircraft detection times were not affected by the weather conditions during climbing and descending tasks. The decision of pilots to fly into certain weather condition was dependent in part on the warning distance to the location of the weather. When pilots were warned of the weather conditions, they were more likely to fly their aircraft into it, but mostly when the warning was not close to the weather location.

Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells.

PubMed

Park, Han Sang; Rinehart, Matthew T; Walzer, Katelyn A; Chi, Jen-Tsan Ashley; Wax, Adam

2016-01-01

Malaria detection through microscopic examination of stained blood smears is a diagnostic challenge that heavily relies on the expertise of trained microscopists. This paper presents an automated analysis method for detection and staging of red blood cells infected by the malaria parasite Plasmodium falciparum at trophozoite or schizont stage. Unlike previous efforts in this area, this study uses quantitative phase images of unstained cells. Erythrocytes are automatically segmented using thresholds of optical phase and refocused to enable quantitative comparison of phase images. Refocused images are analyzed to extract 23 morphological descriptors based on the phase information. While all individual descriptors are highly statistically different between infected and uninfected cells, each descriptor does not enable separation of populations at a level satisfactory for clinical utility. To improve the diagnostic capacity, we applied various machine learning techniques, including linear discriminant classification (LDC), logistic regression (LR), and k-nearest neighbor classification (NNC), to formulate algorithms that combine all of the calculated physical parameters to distinguish cells more effectively. Results show that LDC provides the highest accuracy of up to 99.7% in detecting schizont stage infected cells compared to uninfected RBCs. NNC showed slightly better accuracy (99.5%) than either LDC (99.0%) or LR (99.1%) for discriminating late trophozoites from uninfected RBCs. However, for early trophozoites, LDC produced the best accuracy of 98%. Discrimination of infection stage was less accurate, producing high specificity (99.8%) but only 45.0%-66.8% sensitivity with early trophozoites most often mistaken for late trophozoite or schizont stage and late trophozoite and schizont stage most often confused for each other. Overall, this methodology points to a significant clinical potential of using quantitative phase imaging to detect and stage malaria infection without staining or expert analysis.

Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells

PubMed Central

Park, Han Sang; Rinehart, Matthew T.; Walzer, Katelyn A.; Chi, Jen-Tsan Ashley; Wax, Adam

2016-01-01

Malaria detection through microscopic examination of stained blood smears is a diagnostic challenge that heavily relies on the expertise of trained microscopists. This paper presents an automated analysis method for detection and staging of red blood cells infected by the malaria parasite Plasmodium falciparum at trophozoite or schizont stage. Unlike previous efforts in this area, this study uses quantitative phase images of unstained cells. Erythrocytes are automatically segmented using thresholds of optical phase and refocused to enable quantitative comparison of phase images. Refocused images are analyzed to extract 23 morphological descriptors based on the phase information. While all individual descriptors are highly statistically different between infected and uninfected cells, each descriptor does not enable separation of populations at a level satisfactory for clinical utility. To improve the diagnostic capacity, we applied various machine learning techniques, including linear discriminant classification (LDC), logistic regression (LR), and k-nearest neighbor classification (NNC), to formulate algorithms that combine all of the calculated physical parameters to distinguish cells more effectively. Results show that LDC provides the highest accuracy of up to 99.7% in detecting schizont stage infected cells compared to uninfected RBCs. NNC showed slightly better accuracy (99.5%) than either LDC (99.0%) or LR (99.1%) for discriminating late trophozoites from uninfected RBCs. However, for early trophozoites, LDC produced the best accuracy of 98%. Discrimination of infection stage was less accurate, producing high specificity (99.8%) but only 45.0%-66.8% sensitivity with early trophozoites most often mistaken for late trophozoite or schizont stage and late trophozoite and schizont stage most often confused for each other. Overall, this methodology points to a significant clinical potential of using quantitative phase imaging to detect and stage malaria infection without staining or expert analysis. PMID:27636719

Rear-end vision-based collision detection system for motorcyclists

NASA Astrophysics Data System (ADS)

Muzammel, Muhammad; Yusoff, Mohd Zuki; Meriaudeau, Fabrice

2017-05-01

In many countries, the motorcyclist fatality rate is much higher than that of other vehicle drivers. Among many other factors, motorcycle rear-end collisions are also contributing to these biker fatalities. To increase the safety of motorcyclists and minimize their road fatalities, this paper introduces a vision-based rear-end collision detection system. The binary road detection scheme contributes significantly to reduce the negative false detections and helps to achieve reliable results even though shadows and different lane markers are present on the road. The methodology is based on Harris corner detection and Hough transform. To validate this methodology, two types of dataset are used: (1) self-recorded datasets (obtained by placing a camera at the rear end of a motorcycle) and (2) online datasets (recorded by placing a camera at the front of a car). This method achieved 95.1% accuracy for the self-recorded dataset and gives reliable results for the rear-end vehicle detections under different road scenarios. This technique also performs better for the online car datasets. The proposed technique's high detection accuracy using a monocular vision camera coupled with its low computational complexity makes it a suitable candidate for a motorbike rear-end collision detection system.

Diagnostic accuracy of the MMSE in detecting probable and possible Alzheimer's disease in ethnically diverse highly educated individuals: an analysis of the NACC database.

PubMed

Spering, Cynthia C; Hobson, Valerie; Lucas, John A; Menon, Chloe V; Hall, James R; O'Bryant, Sid E

2012-08-01

To validate and extend the findings of a raised cut score of O'Bryant and colleagues (O'Bryant SE, Humphreys JD, Smith GE, et al. Detecting dementia with the mini-mental state examination in highly educated individuals. Arch Neurol. 2008;65(7):963-967.) for the Mini-Mental State Examination in detecting cognitive dysfunction in a bilingual sample of highly educated ethnically diverse individuals. Archival data were reviewed from participants enrolled in the National Alzheimer's Coordinating Center minimum data set. Data on 7,093 individuals with 16 or more years of education were analyzed, including 2,337 cases with probable and possible Alzheimer's disease, 1,418 mild cognitive impairment patients, and 3,088 nondemented controls. Ethnic composition was characterized as follows: 6,296 Caucasians, 581 African Americans, 4 American Indians or Alaska natives, 2 native Hawaiians or Pacific Islanders, 149 Asians, 43 "Other," and 18 of unknown origin. Diagnostic accuracy estimates (sensitivity, specificity, and likelihood ratio) of Mini-Mental State Examination cut scores in detecting probable and possible Alzheimer's disease were examined. A standard Mini-Mental State Examination cut score of 24 (≤23) yielded a sensitivity of 0.58 and a specificity of 0.98 in detecting probable and possible Alzheimer's disease across ethnicities. A cut score of 27 (≤26) resulted in an improved balance of sensitivity and specificity (0.79 and 0.90, respectively). In the cognitively impaired group (mild cognitive impairment and probable and possible Alzheimer's disease), the standard cut score yielded a sensitivity of 0.38 and a specificity of 1.00 while raising the cut score to 27 resulted in an improved balance of 0.59 and 0.96 of sensitivity and specificity, respectively. These findings cross-validate our previous work and extend them to an ethnically diverse cohort. A higher cut score is needed to maximize diagnostic accuracy of the Mini-Mental State Examination in individuals with college degrees.

Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Ha, Jin Gwan; Moon, Hyeonjoon; Kwak, Jin Tae; Hassan, Syed Ibrahim; Dang, Minh; Lee, O. New; Park, Han Yong

2017-10-01

Recently, unmanned aerial vehicles (UAVs) have gained much attention. In particular, there is a growing interest in utilizing UAVs for agricultural applications such as crop monitoring and management. We propose a computerized system that is capable of detecting Fusarium wilt of radish with high accuracy. The system adopts computer vision and machine learning techniques, including deep learning, to process the images captured by UAVs at low altitudes and to identify the infected radish. The whole radish field is first segmented into three distinctive regions (radish, bare ground, and mulching film) via a softmax classifier and K-means clustering. Then, the identified radish regions are further classified into healthy radish and Fusarium wilt of radish using a deep convolutional neural network (CNN). In identifying radish, bare ground, and mulching film from a radish field, we achieved an accuracy of ≥97.4%. In detecting Fusarium wilt of radish, the CNN obtained an accuracy of 93.3%. It also outperformed the standard machine learning algorithm, obtaining 82.9% accuracy. Therefore, UAVs equipped with computational techniques are promising tools for improving the quality and efficiency of agriculture today.

Molecular Approaches for High Throughput Detection and Quantification of Genetically Modified Crops: A Review

PubMed Central

Salisu, Ibrahim B.; Shahid, Ahmad A.; Yaqoob, Amina; Ali, Qurban; Bajwa, Kamran S.; Rao, Abdul Q.; Husnain, Tayyab

2017-01-01

As long as the genetically modified crops are gaining attention globally, their proper approval and commercialization need accurate and reliable diagnostic methods for the transgenic content. These diagnostic techniques are mainly divided into two major groups, i.e., identification of transgenic (1) DNA and (2) proteins from GMOs and their products. Conventional methods such as PCR (polymerase chain reaction) and enzyme-linked immunosorbent assay (ELISA) were routinely employed for DNA and protein based quantification respectively. Although, these Techniques (PCR and ELISA) are considered as significantly convenient and productive, but there is need for more advance technologies that allow for high throughput detection and the quantification of GM event as the production of more complex GMO is increasing day by day. Therefore, recent approaches like microarray, capillary gel electrophoresis, digital PCR and next generation sequencing are more promising due to their accuracy and precise detection of transgenic contents. The present article is a brief comparative study of all such detection techniques on the basis of their advent, feasibility, accuracy, and cost effectiveness. However, these emerging technologies have a lot to do with detection of a specific event, contamination of different events and determination of fusion as well as stacked gene protein are the critical issues to be addressed in future. PMID:29085378

Accuracy comparison among different machine learning techniques for detecting malicious codes

NASA Astrophysics Data System (ADS)

Narang, Komal

2016-03-01

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

Monitoring microearthquakes with the San Andreas fault observatory at depth

USGS Publications Warehouse

Oye, V.; Ellsworth, W.L.

2007-01-01

In 2005, the San Andreas Fault Observatory at Depth (SAFOD) was drilled through the San Andreas Fault zone at a depth of about 3.1 km. The borehole has subsequently been instrumented with high-frequency geophones in order to better constrain locations and source processes of nearby microearthquakes that will be targeted in the upcoming phase of SAFOD. The microseismic monitoring software MIMO, developed by NORSAR, has been installed at SAFOD to provide near-real time locations and magnitude estimates using the high sampling rate (4000 Hz) waveform data. To improve the detection and location accuracy, we incorporate data from the nearby, shallow borehole (???250 m) seismometers of the High Resolution Seismic Network (HRSN). The event association algorithm of the MIMO software incorporates HRSN detections provided by the USGS real time earthworm software. The concept of the new event association is based on the generalized beam forming, primarily used in array seismology. The method requires the pre-computation of theoretical travel times in a 3D grid of potential microearthquake locations to the seismometers of the current station network. By minimizing the differences between theoretical and observed detection times an event is associated and the location accuracy is significantly improved.

Breast Cancer Detection by B7-H3-Targeted Ultrasound Molecular Imaging.

PubMed

Bachawal, Sunitha V; Jensen, Kristin C; Wilson, Katheryne E; Tian, Lu; Lutz, Amelie M; Willmann, Jürgen K

2015-06-15

Ultrasound complements mammography as an imaging modality for breast cancer detection, especially in patients with dense breast tissue, but its utility is limited by low diagnostic accuracy. One emerging molecular tool to address this limitation involves contrast-enhanced ultrasound using microbubbles targeted to molecular signatures on tumor neovasculature. In this study, we illustrate how tumor vascular expression of B7-H3 (CD276), a member of the B7 family of ligands for T-cell coregulatory receptors, can be incorporated into an ultrasound method that can distinguish normal, benign, precursor, and malignant breast pathologies for diagnostic purposes. Through an IHC analysis of 248 human breast specimens, we found that vascular expression of B7-H3 was selectively and significantly higher in breast cancer tissues. B7-H3 immunostaining on blood vessels distinguished benign/precursors from malignant lesions with high diagnostic accuracy in human specimens. In a transgenic mouse model of cancer, the B7-H3-targeted ultrasound imaging signal was increased significantly in breast cancer tissues and highly correlated with ex vivo expression levels of B7-H3 on quantitative immunofluorescence. Our findings offer a preclinical proof of concept for the use of B7-H3-targeted ultrasound molecular imaging as a tool to improve the diagnostic accuracy of breast cancer detection in patients. ©2015 American Association for Cancer Research.

Detecting the Water-soluble Chloride Distribution of Cement Paste in a High-precision Way.

PubMed

Chang, Honglei; Mu, Song

2017-11-21

To improve the accuracy of the chloride distribution along the depth of cement paste under cyclic wet-dry conditions, a new method is proposed to obtain a high-precision chloride profile. Firstly, paste specimens are molded, cured, and exposed to cyclic wet-dry conditions. Then, powder samples at different specimen depths are grinded when the exposure age is reached. Finally, the water-soluble chloride content is detected using a silver nitrate titration method, and chloride profiles are plotted. The key to improving the accuracy of the chloride distribution along the depth is to exclude the error in the powderization, which is the most critical step for testing the distribution of chloride. Based on the above concept, the grinding method in this protocol can be used to grind powder samples automatically layer by layer from the surface inward, and it should be noted that a very thin grinding thickness (less than 0.5 mm) with a minimum error less than 0.04 mm can be obtained. The chloride profile obtained by this method better reflects the chloride distribution in specimens, which helps researchers to capture the distribution features that are often overlooked. Furthermore, this method can be applied to studies in the field of cement-based materials, which require high chloride distribution accuracy.

High-resolution myocardial stress perfusion at 3 T in patients with suspected coronary artery disease.

PubMed

Meyer, Carsten; Strach, Katharina; Thomas, Daniel; Litt, Harold; Nähle, Claas P; Tiemann, Klaus; Schwenger, Ulrich; Schild, Hans H; Sommer, Torsten

2008-02-01

To implement a high-resolution first-pass myocardial perfusion imaging protocol (HRPI) at 3 T, and to evaluate the feasibility, image quality and accuracy of this approach prospectively in patients with suspected CAD. We hypothesized that utilizing the gain in SNR at 3 T to increase spatial resolution would reduce partial volume effects and subendocardial dark rim artifacts in comparison to 1.5 T. HRPI studies were performed on 60 patients using a segmented k-space gradient echo sequence (in plane resolution 1.97 x 1.94 mm(2)). Semiquantitative assessment of dark rim artifacts was performed for the stress studies on a slice-by-slice basis. Qualitative visual analysis was compared to quantitative coronary angiography (QCA) results; hemodynamically significant CAD was defined as stenosis >or=70% at QCA. Dark rim artifacts appeared in 108 of 180 slices (average extent 1.3 +/- 1.2 mm representing 11.8 +/- 10.8% of the transmural myocardial thickness). Sensitivity, specifity, and test accuracy for the detection of significant CAD were 89%,79%, and 85%. HRPI studies at 3 T are feasible in a clinical setting, providing good image quality and high accuracy for detection of significant CAD. The presence of dark rim artifacts does not appear to represent a diagnostic problem when using a HRPI approach.

High-accurate optical fiber liquid level sensor

NASA Astrophysics Data System (ADS)

Sun, Dexing; Chen, Shouliu; Pan, Chao; Jin, Henghuan

1991-08-01

A highly accurate optical fiber liquid level sensor is presented. The single-chip microcomputer is used to process and control the signal. This kind of sensor is characterized by self-security and is explosion-proof, so it can be applied in any liquid level detecting areas, especially in the oil and chemical industries. The theories and experiments about how to improve the measurement accuracy are described. The relative error for detecting the measurement range 10 m is up to 0.01%.

Diagnostic accuracy of tablet-based software for the detection of concussion.

PubMed

Yang, Suosuo; Flores, Benjamin; Magal, Rotem; Harris, Kyrsti; Gross, Jonathan; Ewbank, Amy; Davenport, Sasha; Ormachea, Pablo; Nasser, Waleed; Le, Weidong; Peacock, W Frank; Katz, Yael; Eagleman, David M

2017-01-01

Despite the high prevalence of traumatic brain injuries (TBI), there are few rapid and straightforward tests to improve its assessment. To this end, we developed a tablet-based software battery ("BrainCheck") for concussion detection that is well suited to sports, emergency department, and clinical settings. This article is a study of the diagnostic accuracy of BrainCheck. We administered BrainCheck to 30 TBI patients and 30 pain-matched controls at a hospital Emergency Department (ED), and 538 healthy individuals at 10 control test sites. We compared the results of the tablet-based assessment against physician diagnoses derived from brain scans, clinical examination, and the SCAT3 test, a traditional measure of TBI. We found consistent distributions of normative data and high test-retest reliability. Based on these assessments, we defined a composite score that distinguishes TBI from non-TBI individuals with high sensitivity (83%) and specificity (87%). We conclude that our testing application provides a rapid, portable testing method for TBI.

Microarcsecond Astrometry As A Probe Of Circumstellar Structure

NASA Astrophysics Data System (ADS)

Velusamy, T.; Turyshev, S. G.

1999-12-01

The Space Interferometry Mission (SIM) is a space-based long-baseline optical interferometer for precision astrometry. This mission will open up many areas of astrophysics, via astrometry with unprecedented accuracy. Wide-angle measurements, which include annual parallax, will reach a design accuracy of 4 μ as. Over a narrow field of view the relative accuracy is better, and SIM is expected to achieve an accuracy of 1 μ as. In this mode, SIM will search for planetary companions to nearby stars, by detecting the astrometric `wobble' relative to a nearby (<= 1o) reference star. The expected proper motion accuracy is 2 μ as yr-1, corresponding to a transverse velocity of 10 m s-1 at a distance of 1 kpc. Such an accuracy of the future SIM instrument provides a very useful astrometric tool for probing the circumstellar structure. The motion of the photo center as detected by SIM is not necessarily that of the center of mass. It is expected that unmodelled dynamics of the stellar systems may be a potential source for systematic astrometric errors. In this paper we discuss the possibility of using SIM's precision astrometry not only to detect Keplerian signatures due to the planetary motion around nearby stars, but also to characterize the structure of the planetary and proto-planetary orbits, accretions disks, debris disks, circumstellar material, jets and other types of the mass transfer mechanisms. We evaluate possible astrometric signatures due to different types of dynamical processes (both gravitational, non-gravitational) and characterize the magnitude of the corresponding astrometric signal. We attempt to address the most natural scenario of non-Keplerian motion, caused by an extended structure and complex dynamics of the stellar systems that may produce a detectable wobble in the motion of the optical center of a target star. We examine the use of μ as astrometry, as complementary to high resolution imaging, to detect some of the structures present around stars. This work was performed by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

The advantages of SMRT sequencing.

PubMed

Roberts, Richard J; Carneiro, Mauricio O; Schatz, Michael C

2013-07-03

Of the current next-generation sequencing technologies, SMRT sequencing is sometimes overlooked. However, attributes such as long reads, modified base detection and high accuracy make SMRT a useful technology and an ideal approach to the complete sequencing of small genomes.

Arrester Resistive Current Measuring System Based on Heterogeneous Network

NASA Astrophysics Data System (ADS)

Zhang, Yun Hua; Li, Zai Lin; Yuan, Feng; Hou Pan, Feng; Guo, Zhan Nan; Han, Yue

2018-03-01

Metal Oxide Arrester (MOA) suffers from aging and poor insulation due to long-term impulse voltage and environmental impact, and the value and variation tendency of resistive current can reflect the health conditions of MOA. The common wired MOA detection need to use long cables, which is complicated to operate, and that wireless measurement methods are facing the problems of poor data synchronization and instability. Therefore a novel synchronous measurement system of arrester current resistive based on heterogeneous network is proposed, which simplifies the calculation process and improves synchronization, accuracy and stability and of the measuring system. This system combines LoRa wireless network, high speed wireless personal area network and the process layer communication, and realizes the detection of arrester working condition. Field test data shows that the system has the characteristics of high accuracy, strong anti-interference ability and good synchronization, which plays an important role in ensuring the stable operation of the power grid.

Research on detecting spot selection and signal pretreatment of four-quadrant detector

NASA Astrophysics Data System (ADS)

Liu, Wenli; Han, Shaokun

2018-01-01

The four-quadrant detector is a photoelectric position sensor based on the photovoltaic effect. It is widely used in many fields such as target azimuth measurement, end-guided weapon and so on. The selection of the spot and the calculation of the center position are one of the main factors that affect the accuracy of the position measurement of the fourquadrant detector. In order to improve the positioning accuracy of the four-quadrant detector, the method of determining the best spot size is obtained from the theoretical research. The output signal of the four-quadrant detector is a weak narrow pulse signal, which needs to be magnified and widened at high magnitudes. The signal preprocessing method is simulated and experimentally studied. Detecting the spot and the signal processing is realized by the four-quadrant detector, which is important for the use of quadrant detectors for high-precision position measurements.

Fast and accurate metrology of multi-layered ceramic materials by an automated boundary detection algorithm developed for optical coherence tomography data

PubMed Central

Ekberg, Peter; Su, Rong; Chang, Ernest W.; Yun, Seok Hyun; Mattsson, Lars

2014-01-01

Optical coherence tomography (OCT) is useful for materials defect analysis and inspection with the additional possibility of quantitative dimensional metrology. Here, we present an automated image-processing algorithm for OCT analysis of roll-to-roll multilayers in 3D manufacturing of advanced ceramics. It has the advantage of avoiding filtering and preset modeling, and will, thus, introduce a simplification. The algorithm is validated for its capability of measuring the thickness of ceramic layers, extracting the boundaries of embedded features with irregular shapes, and detecting the geometric deformations. The accuracy of the algorithm is very high, and the reliability is better than 1 µm when evaluating with the OCT images using the same gauge block step height reference. The method may be suitable for industrial applications to the rapid inspection of manufactured samples with high accuracy and robustness. PMID:24562018

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 low noise photoelectric signal acquisition system applying in nuclear magnetic resonance gyroscope

NASA Astrophysics Data System (ADS)

Lu, Qilin; Zhang, Xian; Zhao, Xinghua; Yang, Dan; Zhou, Binquan; Hu, Zhaohui

2017-10-01

The nuclear magnetic resonance gyroscope serves as a new generation of strong support for the development of high-tech weapons, it solves the core problem that limits the development of the long-playing seamless navigation and positioning. In the NMR gyroscope, the output signal with atomic precession frequency is detected by the probe light, the final crucial photoelectric signal of the probe light directly decides the quality of the gyro signal. But the output signal has high sensitivity, resolution and measurement accuracy for the photoelectric detection system. In order to detect the measured signal better, this paper proposed a weak photoelectric signal rapid acquisition system, which has high SNR and the frequency of responded signal is up to 100 KHz to let the weak output signal with high frequency of the NMR gyroscope can be detected better.

Erythrocyte membrane analysis for type II diabetes detection using Raman spectroscopy in high-wavenumber region

NASA Astrophysics Data System (ADS)

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

2014-03-01

Raman spectroscopy was employed to detect lipid variation occurring in type II diabetic erythrocyte membrane (EM) without using exogenous reagents. In high-wavenumber (HW) region, significant Raman spectral differences between diabetic and normal EM are observed at 2850, 2873, 2885, 2935, and 2965 cm-1, which are mainly related to lipid in EM. Based on principal component analysis, the diagnostic accuracy of HW region for diabetes detection is 98.8%, which is much higher than that of low-wavenumber region (82.9%). The results suggest that EM HW Raman region has great promise for the reagent-free and non-invasive detection of type II diabetes.

LLNL Location and Detection Research

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

Myers, S C; Harris, D B; Anderson, M L

2003-07-16

We present two LLNL research projects in the topical areas of location and detection. The first project assesses epicenter accuracy using a multiple-event location algorithm, and the second project employs waveform subspace Correlation to detect and identify events at Fennoscandian mines. Accurately located seismic events are the bases of location calibration. A well-characterized set of calibration events enables new Earth model development, empirical calibration, and validation of models. In a recent study, Bondar et al. (2003) develop network coverage criteria for assessing the accuracy of event locations that are determined using single-event, linearized inversion methods. These criteria are conservative andmore » are meant for application to large bulletins where emphasis is on catalog completeness and any given event location may be improved through detailed analysis or application of advanced algorithms. Relative event location techniques are touted as advancements that may improve absolute location accuracy by (1) ensuring an internally consistent dataset, (2) constraining a subset of events to known locations, and (3) taking advantage of station and event correlation structure. Here we present the preliminary phase of this work in which we use Nevada Test Site (NTS) nuclear explosions, with known locations, to test the effect of travel-time model accuracy on relative location accuracy. Like previous studies, we find that the reference velocity-model and relative-location accuracy are highly correlated. We also find that metrics based on travel-time residual of relocated events are not a reliable for assessing either velocity-model or relative-location accuracy. In the topical area of detection, we develop specialized correlation (subspace) detectors for the principal mines surrounding the ARCES station located in the European Arctic. Our objective is to provide efficient screens for explosions occurring in the mines of the Kola Peninsula (Kovdor, Zapolyarny, Olenogorsk, Khibiny) and the major iron mines of northern Sweden (Malmberget, Kiruna). In excess of 90% of the events detected by the ARCES station are mining explosions, and a significant fraction are from these northern mining groups. The primary challenge in developing waveform correlation detectors is the degree of variation in the source time histories of the shots, which can result in poor correlation among events even in close proximity. Our approach to solving this problem is to use lagged subspace correlation detectors, which offer some prospect of compensating for variation and uncertainty in source time functions.« less

Trace element analysis by EPMA in geosciences: detection limit, precision and accuracy

NASA Astrophysics Data System (ADS)

Batanova, V. G.; Sobolev, A. V.; Magnin, V.

2018-01-01

Use of the electron probe microanalyser (EPMA) for trace element analysis has increased over the last decade, mainly because of improved stability of spectrometers and the electron column when operated at high probe current; development of new large-area crystal monochromators and ultra-high count rate spectrometers; full integration of energy-dispersive / wavelength-dispersive X-ray spectrometry (EDS/WDS) signals; and the development of powerful software packages. For phases that are stable under a dense electron beam, the detection limit and precision can be decreased to the ppm level by using high acceleration voltage and beam current combined with long counting time. Data on 10 elements (Na, Al, P, Ca, Ti, Cr, Mn, Co, Ni, Zn) in olivine obtained on a JEOL JXA-8230 microprobe with tungsten filament show that the detection limit decreases proportionally to the square root of counting time and probe current. For all elements equal or heavier than phosphorus (Z = 15), the detection limit decreases with increasing accelerating voltage. The analytical precision for minor and trace elements analysed in olivine at 25 kV accelerating voltage and 900 nA beam current is 4 - 18 ppm (2 standard deviations of repeated measurements of the olivine reference sample) and is similar to the detection limit of corresponding elements. To analyse trace elements accurately requires careful estimation of background, and consideration of sample damage under the beam and secondary fluorescence from phase boundaries. The development and use of matrix reference samples with well-characterised trace elements of interest is important for monitoring and improving of the accuracy. An evaluation of the accuracy of trace element analyses in olivine has been made by comparing EPMA data for new reference samples with data obtained by different in-situ and bulk analytical methods in six different laboratories worldwide. For all elements, the measured concentrations in the olivine reference sample were found to be identical (within internal precision) to reference values, suggesting that achieved precision and accuracy are similar. The spatial resolution of EPMA in a silicate matrix, even at very extreme conditions (accelerating voltage 25 kV), does not exceed 7 - 8 μm and thus is still better than laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) or secondary ion mass spectrometry (SIMS) of similar precision. These make the electron microprobe an indispensable method with applications in experimental petrology, geochemistry and cosmochemistry.

Electrocardiographic recognition of right ventricular hypertrophy.

PubMed

Nikus, Kjell; Pérez-Riera, Andrés Ricardo; Konttila, Kaari; Barbosa-Barros, Raimundo

The electrocardiogram (ECG) is a relatively insensitive tool for the detection of right ventricular hypertrophy (RVH), but some criteria have high specificity. The recommended ECG screening criteria for RVH are not sufficiently sensitive or specific for screening for mild RVH in adults without clinical cardiovascular disease. The greatest accuracy of the ECG is in congenital heart disease, with intermediate accuracy in acquired heart disease and primary pulmonary hypertension in adults. Copyright © 2017 Elsevier Inc. All rights reserved.

Object-Based Arctic Sea Ice Feature Extraction through High Spatial Resolution Aerial photos

NASA Astrophysics Data System (ADS)

Miao, X.; Xie, H.

2015-12-01

High resolution aerial photographs used to detect and classify sea ice features can provide accurate physical parameters to refine, validate, and improve climate models. However, manually delineating sea ice features, such as melt ponds, submerged ice, water, ice/snow, and pressure ridges, is time-consuming and labor-intensive. An object-based classification algorithm is developed to automatically extract sea ice features efficiently from aerial photographs taken during the Chinese National Arctic Research Expedition in summer 2010 (CHINARE 2010) in the MIZ near the Alaska coast. The algorithm includes four steps: (1) the image segmentation groups the neighboring pixels into objects based on the similarity of spectral and textural information; (2) the random forest classifier distinguishes four general classes: water, general submerged ice (GSI, including melt ponds and submerged ice), shadow, and ice/snow; (3) the polygon neighbor analysis separates melt ponds and submerged ice based on spatial relationship; and (4) pressure ridge features are extracted from shadow based on local illumination geometry. The producer's accuracy of 90.8% and user's accuracy of 91.8% are achieved for melt pond detection, and shadow shows a user's accuracy of 88.9% and producer's accuracies of 91.4%. Finally, pond density, pond fraction, ice floes, mean ice concentration, average ridge height, ridge profile, and ridge frequency are extracted from batch processing of aerial photos, and their uncertainties are estimated.

Comparison of the accuracy of Hybrid Capture II and polymerase chain reaction in detecting clinically important cervical dysplasia: a systematic review and meta-analysis

PubMed Central

Luu, Hung N; Dahlstrom, Kristina R; Mullen, Patricia Dolan; VonVille, Helena M; Scheurer, Michael E

2013-01-01

The effectiveness of screening programs for cervical cancer has benefited from the inclusion of Human papillomavirus (HPV) DNA assays; which assay to choose, however, is not clear based on previous reviews. Our review addressed test accuracy of Hybrid Capture II (HCII) and polymerase chain reaction (PCR) assays based on studies with stronger designs and with more clinically relevant outcomes. We searched OvidMedline, PubMed, and the Cochrane Library for English language studies comparing both tests, published 1985–2012, with cervical dysplasia defined by the Bethesda classification. Meta-analysis provided pooled sensitivity, specificity, and 95% confidence intervals (CIs); meta-regression identified sources of heterogeneity. From 29 reports, we found that the pooled sensitivity and specificity to detect high-grade squamous intraepithelial lesion (HSIL) was higher for HCII than PCR (0.89 [CI: 0.89–0.90] and 0.85 [CI: 0.84–0.86] vs. 0.73 [CI: 0.73–0.74] and 0.62 [CI: 0.62–0.64]). Both assays had higher accuracy to detect cervical dysplasia in Europe than in Asia-Pacific or North America (diagnostic odd ratio – dOR = 4.08 [CI: 1.39–11.91] and 4.56 [CI: 1.86–11.17] for HCII vs. 2.66 [CI: 1.16–6.53] and 3.78 [CI: 1.50–9.51] for PCR) and accuracy to detect HSIL than atypical squamous cells of undetermined significance (ASCUS)/ low-grade squamous intraepithelial lesion (LSIL) (HCII-dOR = 9.04 [CI: 4.12–19.86] and PCR-dOR = 5.60 [CI: 2.87–10.94]). For HCII, using histology as a gold standard results in higher accuracy than using cytology (dOR = 2.87 [CI: 1.31–6.29]). Based on higher test accuracy, our results support the use of HCII in cervical cancer screening programs. The role of HPV type distribution should be explored to determine the worldwide comparability of HPV test accuracy. PMID:23930214

Parametric boundary reconstruction algorithm for industrial CT metrology application.

PubMed

Yin, Zhye; Khare, Kedar; De Man, Bruno

2009-01-01

High-energy X-ray computed tomography (CT) systems have been recently used to produce high-resolution images in various nondestructive testing and evaluation (NDT/NDE) applications. The accuracy of the dimensional information extracted from CT images is rapidly approaching the accuracy achieved with a coordinate measuring machine (CMM), the conventional approach to acquire the metrology information directly. On the other hand, CT systems generate the sinogram which is transformed mathematically to the pixel-based images. The dimensional information of the scanned object is extracted later by performing edge detection on reconstructed CT images. The dimensional accuracy of this approach is limited by the grid size of the pixel-based representation of CT images since the edge detection is performed on the pixel grid. Moreover, reconstructed CT images usually display various artifacts due to the underlying physical process and resulting object boundaries from the edge detection fail to represent the true boundaries of the scanned object. In this paper, a novel algorithm to reconstruct the boundaries of an object with uniform material composition and uniform density is presented. There are three major benefits in the proposed approach. First, since the boundary parameters are reconstructed instead of image pixels, the complexity of the reconstruction algorithm is significantly reduced. The iterative approach, which can be computationally intensive, will be practical with the parametric boundary reconstruction. Second, the object of interest in metrology can be represented more directly and accurately by the boundary parameters instead of the image pixels. By eliminating the extra edge detection step, the overall dimensional accuracy and process time can be improved. Third, since the parametric reconstruction approach shares the boundary representation with other conventional metrology modalities such as CMM, boundary information from other modalities can be directly incorporated as prior knowledge to improve the convergence of an iterative approach. In this paper, the feasibility of parametric boundary reconstruction algorithm is demonstrated with both simple and complex simulated objects. Finally, the proposed algorithm is applied to the experimental industrial CT system data.

Evaluation of 18F-FDG PET/CT Parameters for Detection of Lymph Node Metastasis in Cutaneous Melanoma.

PubMed

Cha, Jongtae; Kim, Soyoung; Wang, Jiyoung; Yun, Mijin; Cho, Arthur

2018-02-01

The purpose of this study was to investigate the value of 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) parameters in the detection of regional lymph node (LN) metastasis in patients with cutaneous melanoma. We evaluated patients with cutaneous melanoma who underwent FDG PET/CT for initial staging or recurrence evaluation. A total of 103 patients were enrolled, and 165 LNs were evaluated. LNs that were confirmed pathologically or by follow-up imaging were included in this study. PET parameters, including maximum standardized uptake value (SUVmax), total lesion glycolysis and tumour-to-liver ratio, were used to determine the presence of metastases, and the results were compared with CT-determined LN metastasis. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off values of the FDG PET parameters. A total of 93 LNs were malignant, and 84 LNs were smaller than 10 mm. In all 165 LNs, an SUVmax of >2.51 showed a sensitivity of 73.1%, a specificity of 88.9%, and an accuracy of 80.0% in detecting metastatic LNs. CT showed a higher specificity (87.3%) and lower accuracy (65.5%). For non-enlarged regional LNs (<10 mm), an SUVmax cut-off value of 1.4 showed the highest negative predictive value (81.3%). For enlarged LNs (≥10 mm), an SUVmax cut-off value of 2.4 showed the highest sensitivity (90.7%) and accuracy (88.9%) in detecting metastatic LNs. In patients with cutaneous melanoma, an SUVmax of >2.4 showed a high sensitivity (91%) and accuracy (89%) in detecting metastasis in LNs ≥1 cm, and LNs <1 cm with an SUVmax <1.4 were likely to be benign.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

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

PubMed Central

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

2013-01-01

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

A Novel Energy-Efficient Approach for Human Activity Recognition

PubMed Central

Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Tang, Biyu; Lu, Hai; Shi, Haibin

2017-01-01

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper. PMID:28885560

An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier.

PubMed

He, Jian; Bai, Shuang; Wang, Xiaoyi

2017-06-16

Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based fall detection technologies have focused on the accuracy of detection while neglecting quantization noise caused by inertial sensor. In this paper, an activity model based on tri-axial acceleration and gyroscope is proposed, and the difference between activities of daily living (ADLs) and falls is analyzed. Meanwhile, a Kalman filter is proposed to preprocess the raw data so as to reduce noise. A sliding window and Bayes network classifier are introduced to develop a wearable fall detection system, which is composed of a wearable motion sensor and a smart phone. The experiment shows that the proposed system distinguishes simulated falls from ADLs with a high accuracy of 95.67%, while sensitivity and specificity are 99.0% and 95.0%, respectively. Furthermore, the smart phone can issue an alarm to caregivers so as to provide timely and accurate help for the elderly, as soon as the system detects a fall.

Multiple sensor integration for seizure onset detection in human patients comparing conventional disc versus novel tripolar concentric ring electrodes.

PubMed

Makeyev, Oleksandr; Ding, Quan; Martínez-Juárez, Iris E; Gaitanis, John; Kay, Steven M; Besio, Walter G

2013-01-01

As epilepsy affects approximately one percent of the world population, electrical stimulation of the brain has recently shown potential for additive seizure control therapy. Closed-loop systems that apply electrical stimulation when seizure onset is automatically detected require high accuracy of automatic seizure detection based on electrographic brain activity. To improve this accuracy we propose to use noninvasive tripolar concentric ring electrodes that have been shown to have significantly better signal-to-noise ratio, spatial selectivity, and mutual information compared to conventional disc electrodes. The proposed detection methodology is based on integration of multiple sensors using exponentially embedded family (EEF). In this preliminary study it is validated on over 26.3 hours of data collected using both tripolar concentric ring and conventional disc electrodes concurrently each from 7 human patients with epilepsy including five seizures. For a cross-validation based group model EEF correctly detected 100% and 80% of seizures respectively with <0.76 and <1.56 false positive detections per hour respectively for the two electrode modalities. These results clearly suggest the potential of seizure onset detection based on data from tripolar concentric ring electrodes.

Using Multi-Temporal Imaging Spectroscopy Data to Detect Drought and Bark Beetle Related Conifer Mortality across the Central Sierra Nevada, California, USA

NASA Astrophysics Data System (ADS)

Tane, Z.; Ramirez, C.; Roberts, D. A.; Koltunov, A.; Sweeney, S.

2016-12-01

There is considerable scientific and public interest in the ongoing drought and bark beetle driven conifer mortality in the Central and Southern Sierra Nevada, the scale of which has not been seen previously in California's recorded history. Just before and during this mortality event (2013-2016), Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) data were acquired seasonally over part of the affected area as part of the HyspIRI Preparatory Mission. In this study, we used 11 AVIRIS flight lines from 8 seasonal flights (from spring 2013 to summer 2015) to detect conifer mortality. In addition to the standard pre-processing completed by NASA's Jet Propulsion Lab, AVIRIS images were co-registered and georeferenced between time steps and images were resampled to the spatial resolution and signal-to-noise ratio expected from the proposed HyspIRI satellite. We used summer 2015 high-spatial resolution WorldView-2 and WorldView-3 images from across the study area to collect training data from five scenes, and independent validation data from five additional scenes. A cover class map developed with a machine-learning algorithm, separated pixels into green conifer, red-attack conifer, and non-conifer dominant cover, yielding a high accuracy (above 85% accuracy on the independent validation data) in the tree mortality final map. Discussion will include the effects of temporal information and input dimensionality on classification accuracy, comparison with multi-spectral classification accuracy, the ecological and forest management implications of this work, incorporating 2016 AVIRS images to detect 2016 mortality, and future work in understanding the spatial patterns underlying the mortality.

The clinical value of MRA at 3.0 T for the diagnosis and therapeutic planning of patients with subarachnoid haemorrhage.

PubMed

Chen, Yuan-Chang; Sun, Zhen-Kui; Li, Ming-Hua; Li, Yong-Dong; Wang, Wu; Tan, Hua-Qiao; Gu, Bin-Xian; Chen, Shi-Wen

2012-07-01

To evaluate the clinical value of unenhanced magnetic resonance angiography (MRA) at 3.0 T for the diagnosis and therapeutic planning of patients with subarachnoid haemorrhage (SAH). A total of 165 patients with SAH were referred for three-dimensional time-of-flight MRA (3D-TOF-MRA) before digital subtraction angiography (DSA). For each aneurysm, 3D-TOF-MRA was used to determine whether the aneurysm was suitable for coil placement with or without balloon/stent-assisted coiling, surgical clipping or conservative treatment. Treatment planning with 3D-TOF-MRA was compared with actual treatment decisions or treatment that had been carried out in each aneurysm decided using DSA. The aneurysm-based evaluation yielded accuracy of 96.9%, sensitivity of 97.6%, specificity of 93.1%, positive predictive value (PPV) of 98.8% and negative predictive value (NPV) of 87.1%, in the detection of intracranial aneurysms. Treatment planning could be correctly made on the basis of aneurysm anatomy and working view by volume rendering (VR) 3D-TOF-MRA with accuracy, sensitivity, specificity, PPV and NPV of 94.9%, 94.0%, 100%, 100% and 74.4%, respectively, on a per aneurysm-based evaluation. VR 3D-TOF-MRA offers high diagnostic accuracy in the detection of ruptured intracranial aneurysms, and appears to be an effective treatment planning tool for most patients with SAH. VR 3D-TOF-MRA offers high diagnostic accuracy for detecting ruptured intracranial aneurysms. • VR 3D-TOF-MRA helps treatment planning for patients with subarachnoid haemorrhage. • 3D-TOF-MRA is non-invasive and avoids using ionising radiation or contrast agents.

Framework for Detection and Localization of Extreme Climate Event with Pixel Recursive Super Resolution

NASA Astrophysics Data System (ADS)

Kim, S. K.; Lee, J.; Zhang, C.; Ames, S.; Williams, D. N.

2017-12-01

Deep learning techniques have been successfully applied to solve many problems in climate and geoscience using massive-scaled observed and modeled data. For extreme climate event detections, several models based on deep neural networks have been recently proposed and attend superior performance that overshadows all previous handcrafted expert based method. The issue arising, though, is that accurate localization of events requires high quality of climate data. In this work, we propose framework capable of detecting and localizing extreme climate events in very coarse climate data. Our framework is based on two models using deep neural networks, (1) Convolutional Neural Networks (CNNs) to detect and localize extreme climate events, and (2) Pixel recursive recursive super resolution model to reconstruct high resolution climate data from low resolution climate data. Based on our preliminary work, we have presented two CNNs in our framework for different purposes, detection and localization. Our results using CNNs for extreme climate events detection shows that simple neural nets can capture the pattern of extreme climate events with high accuracy from very coarse reanalysis data. However, localization accuracy is relatively low due to the coarse resolution. To resolve this issue, the pixel recursive super resolution model reconstructs the resolution of input of localization CNNs. We present a best networks using pixel recursive super resolution model that synthesizes details of tropical cyclone in ground truth data while enhancing their resolution. Therefore, this approach not only dramat- ically reduces the human effort, but also suggests possibility to reduce computing cost required for downscaling process to increase resolution of data.

Highly Portable, Sensor-Based System for Human Fall Monitoring.

PubMed

Mao, Aihua; Ma, Xuedong; He, Yinan; Luo, Jie

2017-09-13

Falls are a very dangerous situation especially among elderly people, because they may lead to fractures, concussion, and other injuries. Without timely rescue, falls may even endanger their lives. The existing optical sensor-based fall monitoring systems have some disadvantages, such as limited monitoring range and inconvenience to carry for users. Furthermore, the fall detection system based only on an accelerometer often mistakenly determines some activities of daily living (ADL) as falls, leading to low accuracy in fall detection. We propose a human fall monitoring system consisting of a highly portable sensor unit including a triaxis accelerometer, a triaxis gyroscope, and a triaxis magnetometer, and a mobile phone. With the data from these sensors, we obtain the acceleration and Euler angle (yaw, pitch, and roll), which represents the orientation of the user's body. Then, a proposed fall detection algorithm was used to detect falls based on the acceleration and Euler angle. With this monitoring system, we design a series of simulated falls and ADL and conduct the experiment by placing the sensors on the shoulder, waist, and foot of the subjects. Through the experiment, we re-identify the threshold of acceleration for accurate fall detection and verify the best body location to place the sensors by comparing the detection performance on different body segments. We also compared this monitoring system with other similar works and found that better fall detection accuracy and portability can be achieved by our system.

Highly Portable, Sensor-Based System for Human Fall Monitoring

PubMed Central

Mao, Aihua; Ma, Xuedong; He, Yinan; Luo, Jie

2017-01-01

Falls are a very dangerous situation especially among elderly people, because they may lead to fractures, concussion, and other injuries. Without timely rescue, falls may even endanger their lives. The existing optical sensor-based fall monitoring systems have some disadvantages, such as limited monitoring range and inconvenience to carry for users. Furthermore, the fall detection system based only on an accelerometer often mistakenly determines some activities of daily living (ADL) as falls, leading to low accuracy in fall detection. We propose a human fall monitoring system consisting of a highly portable sensor unit including a triaxis accelerometer, a triaxis gyroscope, and a triaxis magnetometer, and a mobile phone. With the data from these sensors, we obtain the acceleration and Euler angle (yaw, pitch, and roll), which represents the orientation of the user’s body. Then, a proposed fall detection algorithm was used to detect falls based on the acceleration and Euler angle. With this monitoring system, we design a series of simulated falls and ADL and conduct the experiment by placing the sensors on the shoulder, waist, and foot of the subjects. Through the experiment, we re-identify the threshold of acceleration for accurate fall detection and verify the best body location to place the sensors by comparing the detection performance on different body segments. We also compared this monitoring system with other similar works and found that better fall detection accuracy and portability can be achieved by our system. PMID:28902149

Effect of CTA Tube Current on Spot Sign Detection and Accuracy for Prediction of Intracerebral Hemorrhage Expansion.

PubMed

Morotti, A; Romero, J M; Jessel, M J; Brouwers, H B; Gupta, R; Schwab, K; Vashkevich, A; Ayres, A; Anderson, C D; Gurol, M E; Viswanathan, A; Greenberg, S M; Rosand, J; Goldstein, J N

2016-05-19

Reduction of CT tube current is an effective strategy to minimize radiation load. However, tube current is also a major determinant of image quality. We investigated the impact of CTA tube current on spot sign detection and diagnostic performance for intracerebral hemorrhage expansion. We retrospectively analyzed a prospectively collected cohort of consecutive patients with primary intracerebral hemorrhage from January 2001 to April 2015 who underwent CTA. The study population was divided into 2 groups according to the median CTA tube current level: low current (<350 mA) and high current (≥350 mA). CTA first-pass readings for spot sign presence were independently analyzed by 2 readers. Baseline and follow-up hematoma volumes were assessed by semiautomated computer-assisted volumetric analysis. Sensitivity, specificity, positive and negative predictive values, and accuracy of spot sign in predicting hematoma expansion were calculated. This study included 709 patients (288 and 421 in the low- and high-current groups, respectively). A higher proportion of low-current scans identified at least 1 spot sign (20.8% versus 14.7%, P = .034), but hematoma expansion frequency was similar in the 2 groups (18.4% versus 16.2%, P = .434). Sensitivity and positive and negative predictive values were not significantly different between the 2 groups. Conversely, high-current scans showed superior specificity (91% versus 84%, P = .015) and overall accuracy (84% versus 77%, P = .038). CTA obtained at high levels of tube current showed better diagnostic accuracy for prediction of hematoma expansion by using spot sign. These findings may have implications for future studies using the CTA spot sign to predict hematoma expansion for clinical trials. © 2016 American Society of Neuroradiology.

New method of 2-dimensional metrology using mask contouring

NASA Astrophysics Data System (ADS)

Matsuoka, Ryoichi; Yamagata, Yoshikazu; Sugiyama, Akiyuki; Toyoda, Yasutaka

2008-10-01

We have developed a new method of accurately profiling and measuring of a mask shape by utilizing a Mask CD-SEM. The method is intended to realize high accuracy, stability and reproducibility of the Mask CD-SEM adopting an edge detection algorithm as the key technology used in CD-SEM for high accuracy CD measurement. In comparison with a conventional image processing method for contour profiling, this edge detection method is possible to create the profiles with much higher accuracy which is comparable with CD-SEM for semiconductor device CD measurement. This method realizes two-dimensional metrology for refined pattern that had been difficult to measure conventionally by utilizing high precision contour profile. In this report, we will introduce the algorithm in general, the experimental results and the application in practice. As shrinkage of design rule for semiconductor device has further advanced, an aggressive OPC (Optical Proximity Correction) is indispensable in RET (Resolution Enhancement Technology). From the view point of DFM (Design for Manufacturability), a dramatic increase of data processing cost for advanced MDP (Mask Data Preparation) for instance and surge of mask making cost have become a big concern to the device manufacturers. This is to say, demands for quality is becoming strenuous because of enormous quantity of data growth with increasing of refined pattern on photo mask manufacture. In the result, massive amount of simulated error occurs on mask inspection that causes lengthening of mask production and inspection period, cost increasing, and long delivery time. In a sense, it is a trade-off between the high accuracy RET and the mask production cost, while it gives a significant impact on the semiconductor market centered around the mask business. To cope with the problem, we propose the best method of a DFM solution using two-dimensional metrology for refined pattern.

A hierarchical approach for online temporal lobe seizure detection in long-term intracranial EEG recordings

NASA Astrophysics Data System (ADS)

Liang, Sheng-Fu; Chen, Yi-Chun; Wang, Yu-Lin; Chen, Pin-Tzu; Yang, Chia-Hsiang; Chiueh, Herming

2013-08-01

Objective. Around 1% of the world's population is affected by epilepsy, and nearly 25% of patients cannot be treated effectively by available therapies. The presence of closed-loop seizure-triggered stimulation provides a promising solution for these patients. Realization of fast, accurate, and energy-efficient seizure detection is the key to such implants. In this study, we propose a two-stage on-line seizure detection algorithm with low-energy consumption for temporal lobe epilepsy (TLE). Approach. Multi-channel signals are processed through independent component analysis and the most representative independent component (IC) is automatically selected to eliminate artifacts. Seizure-like intracranial electroencephalogram (iEEG) segments are fast detected in the first stage of the proposed method and these seizures are confirmed in the second stage. The conditional activation of the second-stage signal processing reduces the computational effort, and hence energy, since most of the non-seizure events are filtered out in the first stage. Main results. Long-term iEEG recordings of 11 patients who suffered from TLE were analyzed via leave-one-out cross validation. The proposed method has a detection accuracy of 95.24%, a false alarm rate of 0.09/h, and an average detection delay time of 9.2 s. For the six patients with mesial TLE, a detection accuracy of 100.0%, a false alarm rate of 0.06/h, and an average detection delay time of 4.8 s can be achieved. The hierarchical approach provides a 90% energy reduction, yielding effective and energy-efficient implementation for real-time epileptic seizure detection. Significance. An on-line seizure detection method that can be applied to monitor continuous iEEG signals of patients who suffered from TLE was developed. An IC selection strategy to automatically determine the most seizure-related IC for seizure detection was also proposed. The system has advantages of (1) high detection accuracy, (2) low false alarm, (3) short detection latency, and (4) energy-efficient design for hardware implementation.

Increasing Deception Detection Accuracy with Strategic Questioning

ERIC Educational Resources Information Center

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

2010-01-01

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

An assessment of ground-based techniques for detecting other planetary systems. Volume 1: An overview. [workshop conclusions

NASA Technical Reports Server (NTRS)

Black, D. C. (Editor); Brunk, W. E. (Editor)

1980-01-01

The feasibility and limitations of ground-based techniques for detecting other planetary systems are discussed as well as the level of accuracy at which these limitations would occur and the extent to which they can be overcome by new technology and instrumenation. Workshop conclusions and recommendations are summarized and a proposed high priority program is considered.

Hippocampus and Basal Forebrain Volumetry for Dementia and Mild Cognitive Impairment Diagnosis: Could It Be Useful in Primary Care?

PubMed

Teipel, Stefan J; Keller, Felix; Thyrian, Jochen R; Strohmaier, Urs; Altiner, Attila; Hoffmann, Wolfgang; Kilimann, Ingo

2017-01-01

Once a patient or a knowledgeable informant has noticed decline in memory or other cognitive functions, initiation of early dementia assessment is recommended. Hippocampus and cholinergic basal forebrain (BF) volumetry supports the detection of prodromal and early stages of Alzheimer's disease (AD) dementia in highly selected patient populations. To compare effect size and diagnostic accuracy of hippocampus and BF volumetry between patients recruited in highly specialized versus primary care and to assess the effect of white matter lesions as a proxy for cerebrovascular comorbidity on diagnostic accuracy. We determined hippocampus and BF volumes and white matter lesion load from MRI scans of 71 participants included in a primary care intervention trial (clinicaltrials.gov identifier: NCT01401582) and matched 71 participants stemming from a memory clinic. Samples included healthy controls and people with mild cognitive impairment (MCI), AD dementia, mixed dementia, and non-AD related dementias. Volumetric measures reached similar effect sizes and cross-validated levels of accuracy in the primary care and the memory clinic samples for the discrimination of AD and mixed dementia cases from healthy controls. In the primary care MCI cases, volumetric measures reached only random guessing levels of accuracy. White matter lesions had only a modest effect on effect size and diagnostic accuracy. Hippocampus and BF volumetry may usefully be employed for the identification of AD and mixed dementia, but the detection of MCI does not benefit from the use of these volumetric markers in a primary care setting.

Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics.

PubMed

Abdolali, Fatemeh; Zoroofi, Reza Aghaeizadeh; Otake, Yoshito; Sato, Yoshinobu

2017-02-01

Accurate detection of maxillofacial cysts is an essential step for diagnosis, monitoring and planning therapeutic intervention. Cysts can be of various sizes and shapes and existing detection methods lead to poor results. Customizing automatic detection systems to gain sufficient accuracy in clinical practice is highly challenging. For this purpose, integrating the engineering knowledge in efficient feature extraction is essential. This paper presents a novel framework for maxillofacial cysts detection. A hybrid methodology based on surface and texture information is introduced. The proposed approach consists of three main steps as follows: At first, each cystic lesion is segmented with high accuracy. Then, in the second and third steps, feature extraction and classification are performed. Contourlet and SPHARM coefficients are utilized as texture and shape features which are fed into the classifier. Two different classifiers are used in this study, i.e. support vector machine and sparse discriminant analysis. Generally SPHARM coefficients are estimated by the iterative residual fitting (IRF) algorithm which is based on stepwise regression method. In order to improve the accuracy of IRF estimation, a method based on extra orthogonalization is employed to reduce linear dependency. We have utilized a ground-truth dataset consisting of cone beam CT images of 96 patients, belonging to three maxillofacial cyst categories: radicular cyst, dentigerous cyst and keratocystic odontogenic tumor. Using orthogonalized SPHARM, residual sum of squares is decreased which leads to a more accurate estimation. Analysis of the results based on statistical measures such as specificity, sensitivity, positive predictive value and negative predictive value is reported. The classification rate of 96.48% is achieved using sparse discriminant analysis and orthogonalized SPHARM features. Classification accuracy at least improved by 8.94% with respect to conventional features. This study demonstrated that our proposed methodology can improve the computer assisted diagnosis (CAD) performance by incorporating more discriminative features. Using orthogonalized SPHARM is promising in computerized cyst detection and may have a significant impact in future CAD systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Direct mass spectrometric screening of antibiotics from bacterial surfaces using liquid extraction surface analysis.

PubMed

Kai, Marco; González, Ignacio; Genilloud, Olga; Singh, Sheo B; Svatoš, Aleš

2012-10-30

There is a need to find new antibiotic agents to fight resistant pathogenic bacteria. To search successfully for novel antibiotics from bacteria cultivated under diverse conditions, we need a fast and cost-effective screening method. A combination of Liquid Extraction Surface Analysis (LESA), automated chip-based nanoelectrospray ionization, and high-resolution mass or tandem mass spectrometry using an Orbitrap XL was tested as the screening platform. Actinobacteria, known to produce well-recognized thiazolyl peptide antibiotics, were cultivated on a plate of solid medium and the antibiotics were extracted by organic solvent mixtures from the surface of colonies grown on the plate and analyzed using mass spectrometry (MS). LESA combined with high-resolution MS is a powerful tool with which to extract and detect thiazolyl peptide antibiotics from different Actinobacteria. Known antibiotics were correctly detected with high mass accuracy (<4 ppm) and structurally characterized using tandem mass spectra. Our method is the first step toward the development of a novel high-throughput extraction and identification tool for antibiotics in particular and natural products in general. The method described in this paper is suitable for (1) screening the natural products produced by bacterial colonies on cultivation plates within the first 2 min following extraction and (2) detecting antibiotics at high mass accuracy; the cost is around 2 Euro per sample. Copyright © 2012 John Wiley & Sons, Ltd.

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

NASA Technical Reports Server (NTRS)

Shiffman, Smadar

2004-01-01

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

Accuracy of optical spectroscopy for the detection of cervical intraepithelial neoplasia without colposcopic tissue information; a step toward automation for low resource settings

PubMed Central

Zewdie, Getie A.; Cox, Dennis D.; Neely Atkinson, E.; Cantor, Scott B.; MacAulay, Calum; Davies, Kalatu; Adewole, Isaac; Buys, Timon P. H.; Follen, Michele

2012-01-01

Abstract. Optical spectroscopy has been proposed as an accurate and low-cost alternative for detection of cervical intraepithelial neoplasia. We previously published an algorithm using optical spectroscopy as an adjunct to colposcopy and found good accuracy (sensitivity=1.00 [95% confidence interval (CI)=0.92 to 1.00], specificity=0.71 [95% CI=0.62 to 0.79]). Those results used measurements taken by expert colposcopists as well as the colposcopy diagnosis. In this study, we trained and tested an algorithm for the detection of cervical intraepithelial neoplasia (i.e., identifying those patients who had histology reading CIN 2 or worse) that did not include the colposcopic diagnosis. Furthermore, we explored the interaction between spectroscopy and colposcopy, examining the importance of probe placement expertise. The colposcopic diagnosis-independent spectroscopy algorithm had a sensitivity of 0.98 (95% CI=0.89 to 1.00) and a specificity of 0.62 (95% CI=0.52 to 0.71). The difference in the partial area under the ROC curves between spectroscopy with and without the colposcopic diagnosis was statistically significant at the patient level (p=0.05) but not the site level (p=0.13). The results suggest that the device has high accuracy over a wide range of provider accuracy and hence could plausibly be implemented by providers with limited training. PMID:22559693

Rapid determination of letrozole, citalopram and their metabolites by high performance liquid chromatography-fluorescence detection in urine: Method validation and application to real samples.

PubMed

Rodríguez, J; Castañeda, G; Muñoz, L

2013-01-15

This work reports the validation of a high precision and accuracy method for the simultaneous determination of letrozole, citalopram and their metabolites in urine by high performance liquid chromatography with fluorescence detection. Dilution (urine:mobile phase, 1:2, v/v) was the only sample preparation step. The separation was carried out in a Kromasil C(18) (150mm×4.6mm) column, and the mobile phase was phosphate buffer 80mM (pH 3.0) and acetonitrile (65:35, v/v) at a flow rate of 1.0mL/min. The analytes were detected at 295nm after excitation at 230nm. Linearity was observed in the range of 1.0-1000ng/mL for letrozole and its metabolite and 2.5-1000ng/mL for citalopram and their metabolites, with limits of detection and quantification between 0.09-1.0 and 0.27-1.65ng/mL, respectively. The precisions were satisfactory with RSDs between 0.17 and 5.71%. The accuracy was studied by spiking three urines from healthy female volunteers, and the recoveries were from 85 to 103%. The method was applied to urine samples from women under treatment for breast cancer and depression diseases. Copyright © 2012 Elsevier B.V. All rights reserved.

Ultrasensitive, self-calibrated cavity ring-down spectrometer for quantitative trace gas analysis.

PubMed

Chen, Bing; Sun, Yu R; Zhou, Ze-Yi; Chen, Jian; Liu, An-Wen; Hu, Shui-Ming

2014-11-10

A cavity ring-down spectrometer is built for trace gas detection using telecom distributed feedback (DFB) diode lasers. The longitudinal modes of the ring-down cavity are used as frequency markers without active-locking either the laser or the high-finesse cavity. A control scheme is applied to scan the DFB laser frequency, matching the cavity modes one by one in sequence and resulting in a correct index at each recorded spectral data point, which allows us to calibrate the spectrum with a relative frequency precision of 0.06 MHz. Besides the frequency precision of the spectrometer, a sensitivity (noise-equivalent absorption) of 4×10^-11  cm^-1  Hz^-1/2 has also been demonstrated. A minimum detectable absorption coefficient of 5×10^-12  cm^-1 has been obtained by averaging about 100 spectra recorded in 2  h. The quantitative accuracy is tested by measuring the CO₂ concentrations in N₂ samples prepared by the gravimetric method, and the relative deviation is less than 0.3%. The trace detection capability is demonstrated by detecting CO₂ of ppbv-level concentrations in a high-purity nitrogen gas sample. Simple structure, high sensitivity, and good accuracy make the instrument very suitable for quantitative trace gas analysis.

Automated Detection of Stereotypical Motor Movements in Autism Spectrum Disorder Using Recurrence Quantification Analysis

PubMed Central

Großekathöfer, Ulf; Manyakov, Nikolay V.; Mihajlović, Vojkan; Pandina, Gahan; Skalkin, Andrew; Ness, Seth; Bangerter, Abigail; Goodwin, Matthew S.

2017-01-01

A number of recent studies using accelerometer features as input to machine learning classifiers show promising results for automatically detecting stereotypical motor movements (SMM) in individuals with Autism Spectrum Disorder (ASD). However, replicating these results across different types of accelerometers and their position on the body still remains a challenge. We introduce a new set of features in this domain based on recurrence plot and quantification analyses that are orientation invariant and able to capture non-linear dynamics of SMM. Applying these features to an existing published data set containing acceleration data, we achieve up to 9% average increase in accuracy compared to current state-of-the-art published results. Furthermore, we provide evidence that a single torso sensor can automatically detect multiple types of SMM in ASD, and that our approach allows recognition of SMM with high accuracy in individuals when using a person-independent classifier. PMID:28261082

Automated Detection of Stereotypical Motor Movements in Autism Spectrum Disorder Using Recurrence Quantification Analysis.

PubMed

Großekathöfer, Ulf; Manyakov, Nikolay V; Mihajlović, Vojkan; Pandina, Gahan; Skalkin, Andrew; Ness, Seth; Bangerter, Abigail; Goodwin, Matthew S

2017-01-01

A number of recent studies using accelerometer features as input to machine learning classifiers show promising results for automatically detecting stereotypical motor movements (SMM) in individuals with Autism Spectrum Disorder (ASD). However, replicating these results across different types of accelerometers and their position on the body still remains a challenge. We introduce a new set of features in this domain based on recurrence plot and quantification analyses that are orientation invariant and able to capture non-linear dynamics of SMM. Applying these features to an existing published data set containing acceleration data, we achieve up to 9% average increase in accuracy compared to current state-of-the-art published results. Furthermore, we provide evidence that a single torso sensor can automatically detect multiple types of SMM in ASD, and that our approach allows recognition of SMM with high accuracy in individuals when using a person-independent classifier.

Detection of Foreign Matter in Transfusion Solution Based on Gaussian Background Modeling and an Optimized BP Neural Network

PubMed Central

Zhou, Fuqiang; Su, Zhen; Chai, Xinghua; Chen, Lipeng

2014-01-01

This paper proposes a new method to detect and identify foreign matter mixed in a plastic bottle filled with transfusion solution. A spin-stop mechanism and mixed illumination style are applied to obtain high contrast images between moving foreign matter and a static transfusion background. The Gaussian mixture model is used to model the complex background of the transfusion image and to extract moving objects. A set of features of moving objects are extracted and selected by the ReliefF algorithm, and optimal feature vectors are fed into the back propagation (BP) neural network to distinguish between foreign matter and bubbles. The mind evolutionary algorithm (MEA) is applied to optimize the connection weights and thresholds of the BP neural network to obtain a higher classification accuracy and faster convergence rate. Experimental results show that the proposed method can effectively detect visible foreign matter in 250-mL transfusion bottles. The misdetection rate and false alarm rate are low, and the detection accuracy and detection speed are satisfactory. PMID:25347581

Multifunctional devices based on SnO2@rGO-coated fibers for human motion monitoring, ethanol detection, and photo response

NASA Astrophysics Data System (ADS)

Mi, Qing; Wang, Qi; Zang, Siyao; Chai, Zhaoer; Zhang, Jinnan; Ren, Xiaomin

2018-05-01

In this study, we developed a multifunctional device based on SnO2@rGO-coated fibers utilizing plasma treatment, dip coating, and microwave irradiation in sequence, and finally realized highly sensitive human motion monitoring, relatively good ethanol detection, and an obvious photo response. Moreover, the high level of comfort and compactness derived from highly elastic and comfortable fabrics contributes to the long-term availability and test accuracy. As an attempt at multifunctional integration of smart clothing, this work provides an attractive and relatively practical research direction.

Multifunctional devices based on SnO2@rGO-coated fibers for human motion monitoring, ethanol detection, and photo response.

PubMed

Mi, Qing; Wang, Qi; Zang, Siyao; Chai, Zhaoer; Zhang, Jinnan; Ren, Xiaomin

2018-05-11

In this study, we developed a multifunctional device based on SnO 2 @rGO-coated fibers utilizing plasma treatment, dip coating, and microwave irradiation in sequence, and finally realized highly sensitive human motion monitoring, relatively good ethanol detection, and an obvious photo response. Moreover, the high level of comfort and compactness derived from highly elastic and comfortable fabrics contributes to the long-term availability and test accuracy. As an attempt at multifunctional integration of smart clothing, this work provides an attractive and relatively practical research direction.

Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor

PubMed Central

He, Yong; Nie, Pengcheng; Dong, Tao; Qu, Fangfang; Lin, Lei

2017-01-01

Nitrogen is one of the important indexes to evaluate the physiological and biochemical properties of soil. The level of soil nitrogen content influences the nutrient levels of crops directly. The near infrared sensor can be used to detect the soil nitrogen content rapidly, nondestructively, and conveniently. In order to investigate the effect of the different soil water content on soil nitrogen detection by near infrared sensor, the soil samples were dealt with different drying times and the corresponding water content was measured. The drying time was set from 1 h to 8 h, and every 1 h 90 samples (each nitrogen concentration of 10 samples) were detected. The spectral information of samples was obtained by near infrared sensor, meanwhile, the soil water content was calculated every 1 h. The prediction model of soil nitrogen content was established by two linear modeling methods, including partial least squares (PLS) and uninformative variable elimination (UVE). The experiment shows that the soil has the highest detection accuracy when the drying time is 3 h and the corresponding soil water content is 1.03%. The correlation coefficients of the calibration set are 0.9721 and 0.9656, and the correlation coefficients of the prediction set are 0.9712 and 0.9682, respectively. The prediction accuracy of both models is high, while the prediction effect of PLS model is better and more stable. The results indicate that the soil water content at 1.03% has the minimum influence on the detection of soil nitrogen content using a near infrared sensor while the detection accuracy is the highest and the time cost is the lowest, which is of great significance to develop a portable apparatus detecting nitrogen in the field accurately and rapidly. PMID:28880202

Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor.

PubMed

He, Yong; Xiao, Shupei; Nie, Pengcheng; Dong, Tao; Qu, Fangfang; Lin, Lei

2017-09-07

Nitrogen is one of the important indexes to evaluate the physiological and biochemical properties of soil. The level of soil nitrogen content influences the nutrient levels of crops directly. The near infrared sensor can be used to detect the soil nitrogen content rapidly, nondestructively, and conveniently. In order to investigate the effect of the different soil water content on soil nitrogen detection by near infrared sensor, the soil samples were dealt with different drying times and the corresponding water content was measured. The drying time was set from 1 h to 8 h, and every 1 h 90 samples (each nitrogen concentration of 10 samples) were detected. The spectral information of samples was obtained by near infrared sensor, meanwhile, the soil water content was calculated every 1 h. The prediction model of soil nitrogen content was established by two linear modeling methods, including partial least squares (PLS) and uninformative variable elimination (UVE). The experiment shows that the soil has the highest detection accuracy when the drying time is 3 h and the corresponding soil water content is 1.03%. The correlation coefficients of the calibration set are 0.9721 and 0.9656, and the correlation coefficients of the prediction set are 0.9712 and 0.9682, respectively. The prediction accuracy of both models is high, while the prediction effect of PLS model is better and more stable. The results indicate that the soil water content at 1.03% has the minimum influence on the detection of soil nitrogen content using a near infrared sensor while the detection accuracy is the highest and the time cost is the lowest, which is of great significance to develop a portable apparatus detecting nitrogen in the field accurately and rapidly.

Accuracy of magnetic resonance imaging, magnetic resonance arthrography and computed tomography for the detection of chondral lesions of the knee.

PubMed

Smith, Toby O; Drew, Benjamin T; Toms, Andoni P; Donell, Simon T; Hing, Caroline B

2012-12-01

To assess the diagnostic test accuracy of magnetic resonance imaging (MRI), magnetic resonance arthrography (MRA) and computed tomography arthrography (CTA) for the detection of chondral lesions of the patellofemoral and tibiofemoral joints. A review of published and unpublished literature sources was conducted on 22nd September 2011. All studies assessing the diagnostic test accuracy (sensitivity/specificity) of MRI or MRA or CTA for the assessment of adults with chondral (cartilage) lesions of the knee (tibiofemoral/patellofemoral joints) with surgical comparison (arthroscopic or open) as the reference test were included. Data were analysed through meta-analysis. Twenty-seven studies assessing 2,592 knees from 2,509 patients were included. The findings indicated that whilst presenting a high specificity (0.95-0.99), the sensitivity of MRA, MRI and CTA ranged from 0.70 to 0.80. MRA was superior to MRI and CTA for the detection of patellofemoral joint chondral lesions and that higher field-strength MRI scanner and grade four lesions were more accurately detected compared with lower field-strength and grade one lesions. There appeared no substantial difference in diagnostic accuracy between the interpretation from musculoskeletal and general radiologists when undertaking an MRI review of tibiofemoral and patellofemoral chondral lesions. Specialist radiological imaging is specific for cartilage disease in the knee but has poorer sensitivity to determine the therapeutic options in this population. Due to this limitation, there remains little indication to replace the 'gold-standard' arthroscopic investigation with MRI, MRA or CTA for the assessment of adults with chondral lesions of the knee. II.

Aerial Images and Convolutional Neural Network for Cotton Bloom Detection.

PubMed

Xu, Rui; Li, Changying; Paterson, Andrew H; Jiang, Yu; Sun, Shangpeng; Robertson, Jon S

2017-01-01

Monitoring flower development can provide useful information for production management, estimating yield and selecting specific genotypes of crops. The main goal of this study was to develop a methodology to detect and count cotton flowers, or blooms, using color images acquired by an unmanned aerial system. The aerial images were collected from two test fields in 4 days. A convolutional neural network (CNN) was designed and trained to detect cotton blooms in raw images, and their 3D locations were calculated using the dense point cloud constructed from the aerial images with the structure from motion method. The quality of the dense point cloud was analyzed and plots with poor quality were excluded from data analysis. A constrained clustering algorithm was developed to register the same bloom detected from different images based on the 3D location of the bloom. The accuracy and incompleteness of the dense point cloud were analyzed because they affected the accuracy of the 3D location of the blooms and thus the accuracy of the bloom registration result. The constrained clustering algorithm was validated using simulated data, showing good efficiency and accuracy. The bloom count from the proposed method was comparable with the number counted manually with an error of -4 to 3 blooms for the field with a single plant per plot. However, more plots were underestimated in the field with multiple plants per plot due to hidden blooms that were not captured by the aerial images. The proposed methodology provides a high-throughput method to continuously monitor the flowering progress of cotton.

Reliability of chemotherapy preparation processes: Evaluating independent double-checking and computer-assisted gravimetric control.

PubMed

Carrez, Laurent; Bouchoud, Lucie; Fleury-Souverain, Sandrine; Combescure, Christophe; Falaschi, Ludivine; Sadeghipour, Farshid; Bonnabry, Pascal

2017-03-01

Background and objectives Centralized chemotherapy preparation units have established systematic strategies to avoid errors. Our work aimed to evaluate the accuracy of manual preparations associated with different control methods. Method A simulation study in an operational setting used phenylephrine and lidocaine as markers. Each operator prepared syringes that were controlled using a different method during each of three sessions (no control, visual double-checking, and gravimetric control). Eight reconstitutions and dilutions were prepared in each session, with variable doses and volumes, using different concentrations of stock solutions. Results were analyzed according to qualitative (choice of stock solution) and quantitative criteria (accurate, <5% deviation from the target concentration; weakly accurate, 5%-10%; inaccurate, 10%-30%; wrong, >30% deviation). Results Eleven operators carried out 19 sessions. No final preparation (n = 438) contained a wrong drug. The protocol involving no control failed to detect 1 of 3 dose errors made and double-checking failed to detect 3 of 7 dose errors. The gravimetric control method detected all 5 out of 5 dose errors. The accuracy of the doses measured was equivalent across the control methods ( p = 0.63 Kruskal-Wallis). The final preparations ranged from 58% to 60% accurate, 25% to 27% weakly accurate, 14% to 17% inaccurate and 0.9% wrong. A high variability was observed between operators. Discussion Gravimetric control was the only method able to detect all dose errors, but it did not improve dose accuracy. A dose accuracy with <5% deviation cannot always be guaranteed using manual production. Automation should be considered in the future.

Optical coherence tomography in the diagnosis of dysplasia and adenocarcinoma in Barret's esophagus

NASA Astrophysics Data System (ADS)

Gladkova, N. D.; Zagaynova, E. V.; Zuccaro, G.; Kareta, M. V.; Feldchtein, F. I.; Balalaeva, I. V.; Balandina, E. B.

2007-02-01

Statistical analysis of endoscopic optical coherence tomography (EOCT) surveillance of 78 patients with Barrett's esophagus (BE) is presented in this study. The sensitivity of OCT device in retrospective open detection of early malignancy (including high grade dysplasia and intramucosal adenocarcinoma (IMAC)) was 75%, specificity 82%, diagnostic accuracy - 80%, positive predictive value- 60%, negative predictive value- 87%. In the open recognition of IMAC sensitivity was 81% and specificity were 85% each. Results of a blind recognition with the same material were similar: sensitivity - 77%, specificity 85%, diagnostic accuracy - 82%, positive predictive value- 70%, negative predictive value- 87%. As the endoscopic detection of early malignancy is problematic, OCT holds great promise in enhancing the diagnostic capability of clinical GI endoscopy.

Towards SSVEP-based, portable, responsive Brain-Computer Interface.

PubMed

Kaczmarek, Piotr; Salomon, Pawel

2015-08-01

A Brain-Computer Interface in motion control application requires high system responsiveness and accuracy. SSVEP interface consisted of 2-8 stimuli and 2 channel EEG amplifier was presented in this paper. The observed stimulus is recognized based on a canonical correlation calculated in 1 second window, ensuring high interface responsiveness. A threshold classifier with hysteresis (T-H) was proposed for recognition purposes. Obtained results suggest that T-H classifier enables to significantly increase classifier performance (resulting in accuracy of 76%, while maintaining average false positive detection rate of stimulus different then observed one between 2-13%, depending on stimulus frequency). It was shown that the parameters of T-H classifier, maximizing true positive rate, can be estimated by gradient-based search since the single maximum was observed. Moreover the preliminary results, performed on a test group (N=4), suggest that for T-H classifier exists a certain set of parameters for which the system accuracy is similar to accuracy obtained for user-trained classifier.

Accuracy Assessment of Coastal Topography Derived from Uav Images

NASA Astrophysics Data System (ADS)

Long, N.; Millescamps, B.; Pouget, F.; Dumon, A.; Lachaussée, N.; Bertin, X.

2016-06-01

To monitor coastal environments, Unmanned Aerial Vehicle (UAV) is a low-cost and easy to use solution to enable data acquisition with high temporal frequency and spatial resolution. Compared to Light Detection And Ranging (LiDAR) or Terrestrial Laser Scanning (TLS), this solution produces Digital Surface Model (DSM) with a similar accuracy. To evaluate the DSM accuracy on a coastal environment, a campaign was carried out with a flying wing (eBee) combined with a digital camera. Using the Photoscan software and the photogrammetry process (Structure From Motion algorithm), a DSM and an orthomosaic were produced. Compared to GNSS surveys, the DSM accuracy is estimated. Two parameters are tested: the influence of the methodology (number and distribution of Ground Control Points, GCPs) and the influence of spatial image resolution (4.6 cm vs 2 cm). The results show that this solution is able to reproduce the topography of a coastal area with a high vertical accuracy (< 10 cm). The georeferencing of the DSM require a homogeneous distribution and a large number of GCPs. The accuracy is correlated with the number of GCPs (use 19 GCPs instead of 10 allows to reduce the difference of 4 cm); the required accuracy should be dependant of the research problematic. Last, in this particular environment, the presence of very small water surfaces on the sand bank does not allow to improve the accuracy when the spatial resolution of images is decreased.

Recollection can be Weak and Familiarity can be Strong

PubMed Central

Ingram, Katherine M.; Mickes, Laura; Wixted, John T.

2012-01-01

The Remember/Know procedure is widely used to investigate recollection and familiarity in recognition memory, but almost all of the results obtained using that procedure can be readily accommodated by a unidimensional model based on signal-detection theory. The unidimensional model holds that Remember judgments reflect strong memories (associated with high confidence, high accuracy, and fast reaction times), whereas Know judgments reflect weaker memories (associated with lower confidence, lower accuracy, and slower reaction times). Although this is invariably true on average, a new two-dimensional account (the Continuous Dual-Process model) suggests that Remember judgments made with low confidence should be associated with lower old/new accuracy, but higher source accuracy, than Know judgments made with high confidence. We tested this prediction – and found evidence to support it – using a modified Remember/Know procedure in which participants were first asked to indicate a degree of recollection-based or familiarity-based confidence for each word presented on a recognition test and were then asked to recollect the color (red or blue) and screen location (top or bottom) associated with the word at study. For familiarity-based decisions, old/new accuracy increased with old/new confidence, but source accuracy did not (suggesting that stronger old/new memory was supported by higher degrees of familiarity). For recollection-based decisions, both old/new accuracy and source accuracy increased with old/new confidence (suggesting that stronger old/new memory was supported by higher degrees of recollection). These findings suggest that recollection and familiarity are continuous processes and that participants can indicate which process mainly contributed to their recognition decisions. PMID:21967320

Automated sub-5 nm image registration in integrated correlative fluorescence and electron microscopy using cathodoluminescence pointers

NASA Astrophysics Data System (ADS)

Haring, Martijn T.; Liv, Nalan; Zonnevylle, A. Christiaan; Narvaez, Angela C.; Voortman, Lenard M.; Kruit, Pieter; Hoogenboom, Jacob P.

2017-03-01

In the biological sciences, data from fluorescence and electron microscopy is correlated to allow fluorescence biomolecule identification within the cellular ultrastructure and/or ultrastructural analysis following live-cell imaging. High-accuracy (sub-100 nm) image overlay requires the addition of fiducial markers, which makes overlay accuracy dependent on the number of fiducials present in the region of interest. Here, we report an automated method for light-electron image overlay at high accuracy, i.e. below 5 nm. Our method relies on direct visualization of the electron beam position in the fluorescence detection channel using cathodoluminescence pointers. We show that image overlay using cathodoluminescence pointers corrects for image distortions, is independent of user interpretation, and does not require fiducials, allowing image correlation with molecular precision anywhere on a sample.

Automated sub-5 nm image registration in integrated correlative fluorescence and electron microscopy using cathodoluminescence pointers.

PubMed

Haring, Martijn T; Liv, Nalan; Zonnevylle, A Christiaan; Narvaez, Angela C; Voortman, Lenard M; Kruit, Pieter; Hoogenboom, Jacob P

2017-03-02

In the biological sciences, data from fluorescence and electron microscopy is correlated to allow fluorescence biomolecule identification within the cellular ultrastructure and/or ultrastructural analysis following live-cell imaging. High-accuracy (sub-100 nm) image overlay requires the addition of fiducial markers, which makes overlay accuracy dependent on the number of fiducials present in the region of interest. Here, we report an automated method for light-electron image overlay at high accuracy, i.e. below 5 nm. Our method relies on direct visualization of the electron beam position in the fluorescence detection channel using cathodoluminescence pointers. We show that image overlay using cathodoluminescence pointers corrects for image distortions, is independent of user interpretation, and does not require fiducials, allowing image correlation with molecular precision anywhere on a sample.

Automated sub-5 nm image registration in integrated correlative fluorescence and electron microscopy using cathodoluminescence pointers

PubMed Central

Haring, Martijn T.; Liv, Nalan; Zonnevylle, A. Christiaan; Narvaez, Angela C.; Voortman, Lenard M.; Kruit, Pieter; Hoogenboom, Jacob P.

2017-01-01

In the biological sciences, data from fluorescence and electron microscopy is correlated to allow fluorescence biomolecule identification within the cellular ultrastructure and/or ultrastructural analysis following live-cell imaging. High-accuracy (sub-100 nm) image overlay requires the addition of fiducial markers, which makes overlay accuracy dependent on the number of fiducials present in the region of interest. Here, we report an automated method for light-electron image overlay at high accuracy, i.e. below 5 nm. Our method relies on direct visualization of the electron beam position in the fluorescence detection channel using cathodoluminescence pointers. We show that image overlay using cathodoluminescence pointers corrects for image distortions, is independent of user interpretation, and does not require fiducials, allowing image correlation with molecular precision anywhere on a sample. PMID:28252673

Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors.

PubMed

Xi, Xugang; Tang, Minyan; Miran, Seyed M; Luo, Zhizeng

2017-05-27

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short calculation time (65.586 ms), making it a possible choice for pre-impact fall detection. The thorough quantitative comparison of the features and classifiers in this study supports the feasibility of a wireless, wearable sEMG sensor system for automatic activity monitoring and fall detection.

Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors

PubMed Central

Xi, Xugang; Tang, Minyan; Miran, Seyed M.; Luo, Zhizeng

2017-01-01

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short calculation time (65.586 ms), making it a possible choice for pre-impact fall detection. The thorough quantitative comparison of the features and classifiers in this study supports the feasibility of a wireless, wearable sEMG sensor system for automatic activity monitoring and fall detection. PMID:28555016

Wireless LAN security management with location detection capability in hospitals.

PubMed

Tanaka, K; Atarashi, H; Yamaguchi, I; Watanabe, H; Yamamoto, R; Ohe, K

2012-01-01

In medical institutions, unauthorized access points and terminals obstruct the stable operation of a large-scale wireless local area network (LAN) system. By establishing a real-time monitoring method to detect such unauthorized wireless devices, we can improve the efficiency of security management. We detected unauthorized wireless devices by using a centralized wireless LAN system and a location detection system at 370 access points at the University of Tokyo Hospital. By storing the detected radio signal strength and location information in a database, we evaluated the risk level from the detection history. We also evaluated the location detection performance in our hospital ward using Wi-Fi tags. The presence of electric waves outside the hospital and those emitted from portable game machines with wireless communication capability was confirmed from the detection result. The location detection performance showed an error margin of approximately 4 m in detection accuracy and approximately 5% in false detection. Therefore, it was effective to consider the radio signal strength as both an index of likelihood at the detection location and an index for the level of risk. We determined the location of wireless devices with high accuracy by filtering the detection results on the basis of radio signal strength and detection history. Results of this study showed that it would be effective to use the developed location database containing radio signal strength and detection history for security management of wireless LAN systems and more general-purpose location detection applications.

Imaging evaluation of non-alcoholic fatty liver disease: focused on quantification.

PubMed

Lee, Dong Ho

2017-12-01

Non-alcoholic fatty liver disease (NAFLD) has been an emerging major health problem, and the most common cause of chronic liver disease in Western countries. Traditionally, liver biopsy has been gold standard method for quantification of hepatic steatosis. However, its invasive nature with potential complication as well as measurement variability are major problem. Thus, various imaging studies have been used for evaluation of hepatic steatosis. Ultrasonography provides fairly good accuracy to detect moderate-to-severe degree hepatic steatosis, but limited accuracy for mild steatosis. Operator-dependency and subjective/qualitative nature of examination are another major drawbacks of ultrasonography. Computed tomography can be considered as an unsuitable imaging modality for evaluation of NAFLD due to potential risk of radiation exposure and limited accuracy in detecting mild steatosis. Both magnetic resonance spectroscopy and magnetic resonance imaging using chemical shift technique provide highly accurate and reproducible diagnostic performance for evaluating NAFLD, and therefore, have been used in many clinical trials as a non-invasive reference of standard method.

Imaging evaluation of non-alcoholic fatty liver disease: focused on quantification

PubMed Central

2017-01-01

Non-alcoholic fatty liver disease (NAFLD) has been an emerging major health problem, and the most common cause of chronic liver disease in Western countries. Traditionally, liver biopsy has been gold standard method for quantification of hepatic steatosis. However, its invasive nature with potential complication as well as measurement variability are major problem. Thus, various imaging studies have been used for evaluation of hepatic steatosis. Ultrasonography provides fairly good accuracy to detect moderate-to-severe degree hepatic steatosis, but limited accuracy for mild steatosis. Operator-dependency and subjective/qualitative nature of examination are another major drawbacks of ultrasonography. Computed tomography can be considered as an unsuitable imaging modality for evaluation of NAFLD due to potential risk of radiation exposure and limited accuracy in detecting mild steatosis. Both magnetic resonance spectroscopy and magnetic resonance imaging using chemical shift technique provide highly accurate and reproducible diagnostic performance for evaluating NAFLD, and therefore, have been used in many clinical trials as a non-invasive reference of standard method. PMID:28994271

Implementation of a close range photogrammetric system for 3D reconstruction of a scoliotic torso

NASA Astrophysics Data System (ADS)

Detchev, Ivan Denislavov

Scoliosis is a deformity of the human spine most commonly encountered with children. After being detected, periodic examinations via x-rays are traditionally used to measure its progression. However, due to the increased risk of cancer, a non-invasive and radiation-free scoliosis detection and progression monitoring methodology is needed. Quantifying the scoliotic deformity through the torso surface is a valid alternative, because of its high correlation with the internal spine curvature. This work proposes a low-cost multi-camera photogrammetric system for semi-automated 3D reconstruction of a torso surface with sub-millimetre level accuracy. The thesis describes the system design and calibration for optimal accuracy. It also covers the methodology behind the reconstruction and registration procedures. The experimental results include the complete reconstruction of a scoliotic torso mannequin. The final accuracy is evaluated through the goodness of fit between the reconstructed surface and a more accurate set of points measured by a coordinate measuring machine.

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks.

PubMed

Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-11-24

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed.

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks

PubMed Central

Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-01-01

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed. PMID:29186756

Intelligent agent-based intrusion detection system using enhanced multiclass SVM.

PubMed

Ganapathy, S; Yogesh, P; Kannan, A

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

PubMed Central

Ganapathy, S.; Yogesh, P.; Kannan, A.

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

Using a high spatial resolution tactile sensor for intention detection.

PubMed

Castellini, Claudio; Koiva, Risto

2013-06-01

Intention detection is the interpretation of biological signals with the aim of automatically, reliably and naturally understanding what a human subject desires to do. Although intention detection is not restricted to disabled people, such methods can be crucial in improving a patient's life, e.g., aiding control of a robotic wheelchair or of a self-powered prosthesis. Traditionally, intention detection is done using, e.g., gaze tracking, surface electromyography and electroencephalography. In this paper we present exciting initial results of an experiment aimed at intention detection using a high-spatial-resolution, high-dynamic-range tactile sensor. The tactile image of the ventral side of the forearm of 9 able-bodied participants was recorded during a variable-force task stimulated at the fingertip. Both the forces at the fingertip and at the forearm were synchronously recorded. We show that a standard dimensionality reduction technique (Principal Component Analysis) plus a Support Vector Machine attain almost perfect detection accuracy of the direction and the intensity of the intended force. This paves the way for high spatial resolution tactile sensors to be used as a means for intention detection.

Individual Patient Diagnosis of AD and FTD via High-Dimensional Pattern Classification of MRI

PubMed Central

Davatzikos, C.; Resnick, S. M.; Wu, X.; Parmpi, P.; Clark, C. M.

2008-01-01

The purpose of this study is to determine the diagnostic accuracy of MRI-based high-dimensional pattern classification in differentiating between patients with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and healthy controls, on an individual patient basis. MRI scans of 37 patients with AD and 37 age-matched cognitively normal elderly individuals, as well as 12 patients with FTD and 12 age-matched cognitively normal elderly individuals, were analyzed using voxel-based analysis and high-dimensional pattern classification. Diagnostic sensitivity and specificity of spatial patterns of regional brain atrophy found to be characteristic of AD and FTD were determined via cross-validation and via split-sample methods. Complex spatial patterns of relatively reduced brain volumes were identified, including temporal, orbitofrontal, parietal and cingulate regions, which were predominantly characteristic of either AD or FTD. These patterns provided 100% diagnostic accuracy, when used to separate AD or FTD from healthy controls. The ability to correctly distinguish AD from FTD averaged 84.3%. All estimates of diagnostic accuracy were determined via cross-validation. In conclusion, AD- and FTD-specific patterns of brain atrophy can be detected with high accuracy using high-dimensional pattern classification of MRI scans obtained in a typical clinical setting. PMID:18474436

Self-Nulling Lock-in Detection Electronics for Capacitance Probe Electrometer

NASA Technical Reports Server (NTRS)

Blaes, Brent R.; Schaefer, Rembrandt T.

2012-01-01

A multi-channel electrometer voltmeter that employs self-nulling lock-in detection electronics in conjunction with a mechanical resonator with noncontact voltage sensing electrodes has been developed for space-based measurement of an Internal Electrostatic Discharge Monitor (IESDM). The IESDM is new sensor technology targeted for integration into a Space Environmental Monitor (SEM) subsystem used for the characterization and monitoring of deep dielectric charging on spacecraft. Use of an AC-coupled lock-in amplifier with closed-loop sense-signal nulling via generation of an active guard-driving feedback voltage provides the resolution, accuracy, linearity and stability needed for long-term space-based measurement of the IESDM. This implementation relies on adjusting the feedback voltage to drive the sense current received from the resonator s variable-capacitance-probe voltage transducer to approximately zero, as limited by the signal-to-noise performance of the loop electronics. The magnitude of the sense current is proportional to the difference between the input voltage being measured and the feedback voltage, which matches the input voltage when the sense current is zero. High signal-to-noise-ratio (SNR) is achieved by synchronous detection of the sense signal using the correlated reference signal derived from the oscillator circuit that drives the mechanical resonator. The magnitude of the feedback voltage, while the loop is in a settled state with essentially zero sense current, is an accurate estimate of the input voltage being measured. This technique has many beneficial attributes including immunity to drift, high linearity, high SNR from synchronous detection of a single-frequency carrier selected to avoid potentially noisy 1/f low-frequency spectrum of the signal-chain electronics, and high accuracy provided through the benefits of a driven shield encasing the capacitance- probe transducer and guarded input triaxial lead-in. Measurements obtained from a 2- channel prototype electrometer have demonstrated good accuracy (|error| < 0.2 V) and high stability. Twenty-four-hour tests have been performed with virtually no drift. Additionally, 5,500 repeated one-second measurements of 100 V input were shown to be approximately normally distributed with a standard deviation of 140 mV.

Fluorescent microplate assay method for high-throughput detection of lipase transesterification activity.

PubMed

Zheng, Jianyong; Wei, Wei; Lan, Xing; Zhang, Yinjun; Wang, Zhao

2018-05-15

This study describes a sensitive and fluorescent microplate assay method to detect lipase transesterification activity. Lipase-catalyzed transesterification between butyryl 4-methyl umbelliferone (Bu-4-Mu) and methanol in tert-butanol was selected as the model reaction. The release of 4-methylumbelliferone (4-Mu) in the reaction was determined by detecting the fluorescence intensity at λ ex 330 nm and λ em 390 nm. Several lipases were used to investigate the accuracy and efficiency of the proposed method. Apparent Michaelis constant (Km) was calculated for transesterification between Bu-4-Mu and methanol by the lipases. The main advantages of the assay method include high sensitivity, inexpensive reagents, and simple detection process. Copyright © 2018 Elsevier Inc. All rights reserved.

Objective-lens-free Fiber-based Position Detection with Nanometer Resolution in a Fiber Optical Trapping System.

PubMed

Ti, Chaoyang; Ho-Thanh, Minh-Tri; Wen, Qi; Liu, Yuxiang

2017-10-13

Position detection with high accuracy is crucial for force calibration of optical trapping systems. Most existing position detection methods require high-numerical-aperture objective lenses, which are bulky, expensive, and difficult to miniaturize. Here, we report an affordable objective-lens-free, fiber-based position detection scheme with 2 nm spatial resolution and 150 MHz bandwidth. This fiber based detection mechanism enables simultaneous trapping and force measurements in a compact fiber optical tweezers system. In addition, we achieved more reliable signal acquisition with less distortion compared with objective based position detection methods, thanks to the light guiding in optical fibers and small distance between the fiber tips and trapped particle. As a demonstration of the fiber based detection, we used the fiber optical tweezers to apply a force on a cell membrane and simultaneously measure the cellular response.

High-speed cell recognition algorithm for ultrafast flow cytometer imaging system.

PubMed

Zhao, Wanyue; Wang, Chao; Chen, Hongwei; Chen, Minghua; Yang, Sigang

2018-04-01

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

High-speed cell recognition algorithm for ultrafast flow cytometer imaging system

NASA Astrophysics Data System (ADS)

Zhao, Wanyue; Wang, Chao; Chen, Hongwei; Chen, Minghua; Yang, Sigang

2018-04-01

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform.

Target-induced proximity ligation triggers recombinase polymerase amplification and transcription-mediated amplification to detect tumor-derived exosomes in nasopharyngeal carcinoma with high sensitivity.

PubMed

Liu, Wanli; Li, Jianpei; Wu, Yixian; Xing, Shan; Lai, Yanzhen; Zhang, Ge

2018-04-15

Tumor-derived exosomes (TEXs) are extracellular vesicles that are continuously released into the blood by tumor cells and carry specific surface markers of the original tumor cells. Substantial evidence has implicated TEXs as attractive diagnostic markers for cancer. However, the detection of TEXs in blood at an early tumor stage is challenging due to their very low concentration. Here, we established a method called PLA-RPA-TMA assay that allows TEXs to be detected with high sensitivity and specificity. Based on two proximity ligation assay (PLA) probes that recognize a biomarker on a TEX, we generated a unique surrogate DNA signal for the specific biomarker, which was synchronously amplified twice by recombinase polymerase amplification (RPA) coupled with transcription-mediated amplification (TMA), and then the products of the RPA-TMA reaction were quantitatively detected using a gold nanoparticle-based colorimetric assay. We established proof-of-concept evidence for this approach using TEXs from nasopharyngeal carcinoma (NPC) cells, with a detection limit of 10 2 particles/mL, and reported the measurement of plasma Epstein-Barr virus latent membrane protein 1 (LPM1)-positive (LMP1 + , accuracy: 0.956) and epidermal growth factor receptor (EGFR)-positive (EGFR + , accuracy: 0.906) TEXs as potent early diagnostic biomarkers for NPC. Copyright © 2017 Elsevier B.V. All rights reserved.

Geologic Carbon Sequestration Leakage Detection: A Physics-Guided Machine Learning Approach

NASA Astrophysics Data System (ADS)

Lin, Y.; Harp, D. R.; Chen, B.; Pawar, R.

2017-12-01

One of the risks of large-scale geologic carbon sequestration is the potential migration of fluids out of the storage formations. Accurate and fast detection of this fluids migration is not only important but also challenging, due to the large subsurface uncertainty and complex governing physics. Traditional leakage detection and monitoring techniques rely on geophysical observations including pressure. However, the resulting accuracy of these methods is limited because of indirect information they provide requiring expert interpretation, therefore yielding in-accurate estimates of leakage rates and locations. In this work, we develop a novel machine-learning technique based on support vector regression to effectively and efficiently predict the leakage locations and leakage rates based on limited number of pressure observations. Compared to the conventional data-driven approaches, which can be usually seem as a "black box" procedure, we develop a physics-guided machine learning method to incorporate the governing physics into the learning procedure. To validate the performance of our proposed leakage detection method, we employ our method to both 2D and 3D synthetic subsurface models. Our novel CO2 leakage detection method has shown high detection accuracy in the example problems.

Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface.

PubMed

Tu, Yiheng; Huang, Gan; Hung, Yeung Sam; Hu, Li; Hu, Yong; Zhang, Zhiguo

2013-01-01

Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.

[The history of hepatitis B virus-related determination tests and inspection and the measurements of problems in Japan].

PubMed

Shibata, Hiroshi

2013-09-01

Since Hepatitis B virus was detected as the cause of hepatitis, many high-sensitive measurement methods have been developed. In the development history, there are many problems in accuracy, sensitivity and health insurance regulations among different types of kits with different measurement principles. Advanced medical treatments cause problems of gene mutation or reactivation of HBV, leading to the necessity for high sensitive and sophisticated determination. The history of clinical analysis for the detection of HBV was reviewed from the viewpoint of our experiences.

The spectral changes of deforestation in the Brazilian tropical savanna.

PubMed

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

2015-01-01

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

Accuracy and effectiveness of ultrasound-guided core-needle biopsy in the diagnosis of focal lesions in the salivary glands.

PubMed

Del Cura, Jose Luis; Coronado, Gloria; Zabala, Rosa; Korta, Igone; López, Ignacio

2018-01-31

To review the diagnostic accuracy of ultrasound-guided core-needle biopsy (CNB) in the diagnosis of salivary gland tumours (SGT). Retrospective, institutional review board approved, analysis of the CNB of SGT performed at our centre in 8 years. We used an automatic 18-G spring-loaded device. The final diagnosis was based on surgery in the cases that were operated on, and on clinical evolution and biopsy findings in the rest. Four hundred and nine biopsies were performed in 381 patients (ages, 2-97 years; mean, 55.9). There were two minor complications. Biopsy was diagnostic in 98.3%. There were eight false negatives. The diagnostic values for malignancy were: sensitivity 89.6%, specificity 100%, positive predictive value (PPV) 100% and negative predictive value (NPV) 98%. For the detection of neoplasms were: sensitivity 98.7%, specificity 99%, PPV 99.7% and VPN 96.1%. Accuracy of CNB in SGT is very high, with a very high sensitivity and an absolutely reliable diagnosis of malignancy. Complication rate is very low. It should be considered the technique of choice when a STG is detected. Normal tissue results warrant repeating biopsy. • Ultrasound-guided core-biopsy is the technique of choice in salivary glands nodules • Sensitivity, specificity for detecting neoplasms (which should be resected) are around 99% • Diagnosis of malignancy in core-biopsy is absolutely reliable • A CNB result of "normal tissue", however, warrants repeating the biopsy • Complication rate is very low.

Detection of artifacts from high energy bursts in neonatal EEG.

PubMed

Bhattacharyya, Sourya; Biswas, Arunava; Mukherjee, Jayanta; Majumdar, Arun Kumar; Majumdar, Bandana; Mukherjee, Suchandra; Singh, Arun Kumar

2013-11-01

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well. © 2013 Elsevier Ltd. All rights reserved.

Method and apparatus for current-output peak detection

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

De Geronimo, Gianluigi

2017-01-24

A method and apparatus for a current-output peak detector. A current-output peak detector circuit is disclosed and works in two phases. The peak detector circuit includes switches to switch the peak detector circuit from the first phase to the second phase upon detection of the peak voltage of an input voltage signal. The peak detector generates a current output with a high degree of accuracy in the second phase.

Influence of instrument conditions on the evaporation behavior of uranium dioxide with UV laser-assisted atom probe tomography

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

Valderrama, B.; Henderson, H.B.; Gan, J.

2015-04-01

Atom probe tomography (APT) provides the ability to detect subnanometer chemical variations spatially, with high accuracy. However, it is known that compositional accuracy can be affected by experimental conditions. A study of the effect of laser energy, specimen base temperature, and detection rate is performed on the evaporation behavior of uranium dioxide (UO 2). In laser-assisted mode, tip geometry and standing voltage also contribute to the evaporation behavior. In this investigation, it was determined that modifying the detection rate and temperature did not affect the evaporation behavior as significantly as laser energy. It was also determined that three laser evaporationmore » regimes are present in UO 2. Very low laser energy produces a behavior similar to DC-field evaporation, moderate laser energy produces the desired laser-assisted field evaporation characteristic and high laser energy induces thermal effects, negatively altering the evaporation behavior. The need for UO 2 to be analyzed under moderate laser energies to produce accurate stoichiometry distinguishes it from other oxides. The following experimental conditions providing the best combination of mass resolving power, accurate stoichiometry, and uniform evaporation behavior: 50 K, 10 pJ laser energy, a detection rate of 0.003 atoms per pulse, and a 100 kHz repetition rate.« less

Detection of food intake from swallowing sequences by supervised and unsupervised methods.

PubMed

Lopez-Meyer, Paulo; Makeyev, Oleksandr; Schuckers, Stephanie; Melanson, Edward L; Neuman, Michael R; Sazonov, Edward

2010-08-01

Studies of food intake and ingestive behavior in free-living conditions most often rely on self-reporting-based methods that can be highly inaccurate. Methods of Monitoring of Ingestive Behavior (MIB) rely on objective measures derived from chewing and swallowing sequences and thus can be used for unbiased study of food intake with free-living conditions. Our previous study demonstrated accurate detection of food intake in simple models relying on observation of both chewing and swallowing. This article investigates methods that achieve comparable accuracy of food intake detection using only the time series of swallows and thus eliminating the need for the chewing sensor. The classification is performed for each individual swallow rather than for previously used time slices and thus will lead to higher accuracy in mass prediction models relying on counts of swallows. Performance of a group model based on a supervised method (SVM) is compared to performance of individual models based on an unsupervised method (K-means) with results indicating better performance of the unsupervised, self-adapting method. Overall, the results demonstrate that highly accurate detection of intake of foods with substantially different physical properties is possible by an unsupervised system that relies on the information provided by the swallowing alone.

Detection of Food Intake from Swallowing Sequences by Supervised and Unsupervised Methods

PubMed Central

Lopez-Meyer, Paulo; Makeyev, Oleksandr; Schuckers, Stephanie; Melanson, Edward L.; Neuman, Michael R.; Sazonov, Edward

2010-01-01

Studies of food intake and ingestive behavior in free-living conditions most often rely on self-reporting-based methods that can be highly inaccurate. Methods of Monitoring of Ingestive Behavior (MIB) rely on objective measures derived from chewing and swallowing sequences and thus can be used for unbiased study of food intake with free-living conditions. Our previous study demonstrated accurate detection of food intake in simple models relying on observation of both chewing and swallowing. This article investigates methods that achieve comparable accuracy of food intake detection using only the time series of swallows and thus eliminating the need for the chewing sensor. The classification is performed for each individual swallow rather than for previously used time slices and thus will lead to higher accuracy in mass prediction models relying on counts of swallows. Performance of a group model based on a supervised method (SVM) is compared to performance of individual models based on an unsupervised method (K-means) with results indicating better performance of the unsupervised, self-adapting method. Overall, the results demonstrate that highly accurate detection of intake of foods with substantially different physical properties is possible by an unsupervised system that relies on the information provided by the swallowing alone. PMID:20352335

A tactile sensing element based on a hetero-core optical fiber for force measurement and texture detection

NASA Astrophysics Data System (ADS)

Yamazaki, Hiroshi; Koyama, Yuya; Watanabe, Kazuhiro

2014-05-01

Tactile sensing technology can measure a given property of an object through physical contact between a sensing element and the object. Various tactile sensing techniques have been developed for several applications such as intelligent robots, tactile interface, medical support and nursing care support. A desirable tactile sensing element for supporting human daily life can be embedded in the soft material with high sensitivity and accuracy in order to prevent from damaging to human or object physically. This report describes a new tactile sensing element. Hetero-core optical fibers have high sensitivity of macro-bending at local sensor portion and temperature independency, including advantages of optical fiber itself; thin size, light weight, flexible transmission line, and immunity to electro-magnetic interference. The proposed tactile sensing element could detect textures of touched objects through the optical loss caused by the force applied to the sensing element. The characteristics of the sensing element have been evaluated, in which the sensing element has the monotonic and non-linear sensitivity against the normal force ranged from 0 to 5 N with lower accuracy than 0.25 dB. Additionally, texture detection have been successfully demonstrated in which small surface figures of 0.1 mm in height were detected with spatial resolution of 0.4 mm.

Analytical method for lipoperoxidation relevant reactive aldehydes in human sera by high-performance liquid chromatography-fluorescence detection.

PubMed

El-Maghrabey, Mahmoud H; Kishikawa, Naoya; Ohyama, Kaname; Kuroda, Naotaka

2014-11-01

A validated, simple and sensitive HPLC method was developed for the simultaneous determination of lipoperoxidation relevant reactive aldehydes: glyoxal (GO), acrolein (ACR), malondialdehyde (MDA), and 4-hydroxy-2-nonenal (HNE) in human serum. The studied aldehydes were reacted with 2,2'-furil to form fluorescent difurylimidazole derivatives that were separated on a C18 column using gradient elution and fluorescence detection at excitation and emission wavelengths of 250 and 355nm, respectively. The method showed good linearity over the concentration ranges of 0.100-5.00, 0.200-10.0, 0.200-40.0, and 0.400-10.0nmol/mL for GO, ACR, HNE, and MDA, respectively, with detection limits ranging from 0.030 to 0.11nmol/mL. The percentage RSD of intraday and interday precision did not exceed 5.0 and 6.2%, respectively, and the accuracy (%found) ranged from 95.5 to 103%. The proposed method was applied for monitoring the four aldehydes in sera of healthy, diabetic, and rheumatic human subjects with simple pretreatment steps and without interference from endogenous components. By virtue of its high sensitivity and accuracy, our method enabled detection of differences between analytes concentrations in sera of human subjects under different clinical conditions. Copyright © 2014 Elsevier Inc. All rights reserved.

Enhancing the performance of cooperative face detector by NFGS

NASA Astrophysics Data System (ADS)

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

2015-07-01

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

Underwater electric field detection system based on weakly electric fish

NASA Astrophysics Data System (ADS)

Xue, Wei; Wang, Tianyu; Wang, Qi

2018-04-01

Weakly electric fish sense their surroundings in complete darkness by their active electric field detection system. However, due to the insufficient detection capacity of the electric field, the detection distance is not enough, and the detection accuracy is not high. In this paper, a method of underwater detection based on rotating current field theory is proposed to improve the performance of underwater electric field detection system. First of all, we built underwater detection system based on the theory of the spin current field mathematical model with the help of the results of previous researchers. Then we completed the principle prototype and finished the metal objects in the water environment detection experiments, laid the foundation for the further experiments.

Semantic Segmentation of Forest Stands of Pure Species as a Global Optimization Problem

NASA Astrophysics Data System (ADS)

Dechesne, C.; Mallet, C.; Le Bris, A.; Gouet-Brunet, V.

2017-05-01

Forest stand delineation is a fundamental task for forest management purposes, that is still mainly manually performed through visual inspection of geospatial (very) high spatial resolution images. Stand detection has been barely addressed in the literature which has mainly focused, in forested environments, on individual tree extraction and tree species classification. From a methodological point of view, stand detection can be considered as a semantic segmentation problem. It offers two advantages. First, one can retrieve the dominant tree species per segment. Secondly, one can benefit from existing low-level tree species label maps from the literature as a basis for high-level object extraction. Thus, the semantic segmentation issue becomes a regularization issue in a weakly structured environment and can be formulated in an energetical framework. This papers aims at investigating which regularization strategies of the literature are the most adapted to delineate and classify forest stands of pure species. Both airborne lidar point clouds and multispectral very high spatial resolution images are integrated for that purpose. The local methods (such as filtering and probabilistic relaxation) are not adapted for such problem since the increase of the classification accuracy is below 5%. The global methods, based on an energy model, tend to be more efficient with an accuracy gain up to 15%. The segmentation results using such models have an accuracy ranging from 96% to 99%.

Forward collision warning based on kernelized correlation filters

NASA Astrophysics Data System (ADS)

Pu, Jinchuan; Liu, Jun; Zhao, Yong

2017-07-01

A vehicle detection and tracking system is one of the indispensable methods to reduce the occurrence of traffic accidents. The nearest vehicle is the most likely to cause harm to us. So, this paper will do more research on about the nearest vehicle in the region of interest (ROI). For this system, high accuracy, real-time and intelligence are the basic requirement. In this paper, we set up a system that combines the advanced KCF tracking algorithm with the HaarAdaBoost detection algorithm. The KCF algorithm reduces computation time and increase the speed through the cyclic shift and diagonalization. This algorithm satisfies the real-time requirement. At the same time, Haar features also have the same advantage of simple operation and high speed for detection. The combination of this two algorithm contribute to an obvious improvement of the system running rate comparing with previous works. The detection result of the HaarAdaBoost classifier provides the initial value for the KCF algorithm. This fact optimizes KCF algorithm flaws that manual car marking in the initial phase, which is more scientific and more intelligent. Haar detection and KCF tracking with Histogram of Oriented Gradient (HOG) ensures the accuracy of the system. We evaluate the performance of framework on dataset that were self-collected. The experimental results demonstrate that the proposed method is robust and real-time. The algorithm can effectively adapt to illumination variation, even in the night it can meet the detection and tracking requirements, which is an improvement compared with the previous work.

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

PubMed

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

2008-03-01

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

Plant pathogen nanodiagnostic techniques: forthcoming changes?

PubMed Central

Khiyami, Mohammad A.; Almoammar, Hassan; Awad, Yasser M.; Alghuthaymi, Mousa A.; Abd-Elsalam, Kamel A.

2014-01-01

Plant diseases are among the major factors limiting crop productivity. A first step towards managing a plant disease under greenhouse and field conditions is to correctly identify the pathogen. Current technologies, such as quantitative polymerase chain reaction (Q-PCR), require a relatively large amount of target tissue and rely on multiple assays to accurately identify distinct plant pathogens. The common disadvantage of the traditional diagnostic methods is that they are time consuming and lack high sensitivity. Consequently, developing low-cost methods to improve the accuracy and rapidity of plant pathogens diagnosis is needed. Nanotechnology, nano particles and quantum dots (QDs) have emerged as essential tools for fast detection of a particular biological marker with extreme accuracy. Biosensor, QDs, nanostructured platforms, nanoimaging and nanopore DNA sequencing tools have the potential to raise sensitivity, specificity and speed of the pathogen detection, facilitate high-throughput analysis, and to be used for high-quality monitoring and crop protection. Furthermore, nanodiagnostic kit equipment can easily and quickly detect potential serious plant pathogens, allowing experts to help farmers in the prevention of epidemic diseases. The current review deals with the application of nanotechnology for quicker, more cost-effective and precise diagnostic procedures of plant diseases. Such an accurate technology may help to design a proper integrated disease management system which may modify crop environments to adversely affect crop pathogens. PMID:26740775

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

PubMed

Imamverdiyev, Yadigar; Abdullayeva, Fargana

2018-06-01

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

Integrated Kidney Exosome Analysis for the Detection of Kidney Transplant Rejection.

PubMed

Park, Jongmin; Lin, Hsing-Ying; Assaker, Jean Pierre; Jeong, Sangmoo; Huang, Chen-Han; Kurdi, A; Lee, Kyungheon; Fraser, Kyle; Min, Changwook; Eskandari, Siawosh; Routray, Sujit; Tannous, Bakhos; Abdi, Reza; Riella, Leonardo; Chandraker, Anil; Castro, Cesar M; Weissleder, Ralph; Lee, Hakho; Azzi, Jamil R

2017-11-28

Kidney transplant patients require life-long surveillance to detect allograft rejection. Repeated biopsy, albeit the clinical gold standard, is an invasive procedure with the risk of complications and comparatively high cost. Conversely, serum creatinine or urinary proteins are noninvasive alternatives but are late markers with low specificity. We report a urine-based platform to detect kidney transplant rejection. Termed iKEA (integrated kidney exosome analysis), the approach detects extracellular vesicles (EVs) released by immune cells into urine; we reasoned that T cells, attacking kidney allografts, would shed EVs, which in turn can be used as a surrogate marker for inflammation. We optimized iKEA to detect T-cell-derived EVs and implemented a portable sensing system. When applied to clinical urine samples, iKEA revealed high level of CD3-positive EVs in kidney rejection patients and achieved high detection accuracy (91.1%). Fast, noninvasive, and cost-effective, iKEA could offer new opportunities in managing transplant recipients, perhaps even in a home setting.

Accuracy and reliability of tablet computer as an imaging console for detection of radiological signs of acute appendicitis using PACS workstation as reference standard.

PubMed

Awais, Muhammad; Khan, Dawar Burhan; Barakzai, Muhammad Danish; Rehman, Abdul; Baloch, Noor Ul-Ain; Nadeem, Naila

2018-05-01

To ascertain the accuracy and reliability of tablet as an imaging console for detection of radiological signs of acute appendicitis [on focused appendiceal computed tomography (FACT)] using Picture Archiving and Communication System (PACS) workstation as reference standard. From January, 2014 to June, 2015, 225 patients underwent FACT at our institution. These scans were blindly re-interpreted by an independent consultant radiologist, first on PACS workstation and, two weeks later, on tablet. Scans were interpreted for the presence of radiological signs of acute appendicitis. Accuracy of tablet was calculated using PACS as reference standard. Kappa (κ) statistics were calculated as a measure of reliability. Of 225 patients, 99 had radiological evidence of acute appendicitis on PACS workstation. Tablet was 100% accurate in detecting radiological signs of acute appendicitis. Appendicoliths, free fluid, lymphadenopathy, phlegmon/abscess, and perforation were identified on PACS in 90, 43, 39, 10, and 12 scans, respectively. There was excellent agreement between tablet and PACS for detection of appendicolith (к = 0.924), phlegmon/abscess (к = 0.904), free fluid (к = 0.863), lymphadenopathy (к = 0.879), and perforation (к = 0.904). Tablet computer, as an imaging console, was highly reliable and was as accurate as PACS workstation for the radiological diagnosis of acute appendicitis.

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

ERIC Educational Resources Information Center

Waggoner, Dori T.

2011-01-01

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

Direct Detection Doppler Lidar for Spaceborne Wind Measurement

NASA Technical Reports Server (NTRS)

Korb, C. Laurence; Flesia, Cristina

1999-01-01

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

Using Combination of Planar and Height Features for Detecting Built-Up Areas from High-Resolution Stereo Imagery

NASA Astrophysics Data System (ADS)

Peng, F.; Cai, X.; Tan, W.

2017-09-01

Within-class spectral variation and between-class spectral confusion in remotely sensed imagery degrades the performance of built-up area detection when using planar texture, shape, and spectral features. Terrain slope and building height are often used to optimize the results, but extracted from auxiliary data (e.g. LIDAR data, DSM). Moreover, the auxiliary data must be acquired around the same time as image acquisition. Otherwise, built-up area detection accuracy is affected. Stereo imagery incorporates both planar and height information unlike single remotely sensed images. Stereo imagery acquired by many satellites (e.g. Worldview-4, Pleiades-HR, ALOS-PRISM, and ZY-3) can be used as data source of identifying built-up areas. A new method of identifying high-accuracy built-up areas from stereo imagery is achieved by using a combination of planar and height features. The digital surface model (DSM) and digital orthophoto map (DOM) are first generated from stereo images. Then, height values of above-ground objects (e.g. buildings) are calculated from the DSM, and used to obtain raw built-up areas. Other raw built-up areas are obtained from the DOM using Pantex and Gabor, respectively. Final high-accuracy built-up area results are achieved from these raw built-up areas using the decision level fusion. Experimental results show that accurate built-up areas can be achieved from stereo imagery. The height information used in the proposed method is derived from stereo imagery itself, with no need to require auxiliary height data (e.g. LIDAR data). The proposed method is suitable for spaceborne and airborne stereo pairs and triplets.

The influence of Stochastic perturbation of Geotechnical media On Electromagnetic tomography

NASA Astrophysics Data System (ADS)

Song, Lei; Yang, Weihao; Huangsonglei, Jiahui; Li, HaiPeng

2015-04-01

Electromagnetic tomography (CT) are commonly utilized in Civil engineering to detect the structure defects or geological anomalies. CT are generally recognized as a high precision geophysical method and the accuracy of CT are expected to be several centimeters and even to be several millimeters. Then, high frequency antenna with short wavelength are utilized commonly in Civil Engineering. As to the geotechnical media, stochastic perturbation of the EM parameters are inevitably exist in geological scales, in structure scales and in local scales, et al. In those cases, the geometric dimensionings of the target body, the EM wavelength and the accuracy expected might be of the same order. When the high frequency EM wave propagated in the stochastic geotechnical media, the GPR signal would be reflected not only from the target bodies but also from the stochastic perturbation of the background media. To detect the karst caves in dissolution fracture rock, one need to assess the influence of the stochastic distributed dissolution holes and fractures; to detect the void in a concrete structure, one should master the influence of the stochastic distributed stones, et al. In this paper, on the base of stochastic media discrete realizations, the authors try to evaluate quantificationally the influence of the stochastic perturbation of Geotechnical media by Radon/Iradon Transfer through full-combined Monte Carlo numerical simulation. It is found the stochastic noise is related with transfer angle, perturbing strength, angle interval, autocorrelation length, et al. And the quantitative formula of the accuracy of the electromagnetic tomography is also established, which could help on the precision estimation of GPR tomography in stochastic perturbation Geotechnical media. Key words: Stochastic Geotechnical Media; Electromagnetic Tomography; Radon/Iradon Transfer.

Energy-dispersive X-ray fluorescence systems as analytical tool for assessment of contaminated soils.

PubMed

Vanhoof, Chris; Corthouts, Valère; Tirez, Kristof

2004-04-01

To determine the heavy metal content in soil samples at contaminated locations, a static and time consuming procedure is used in most cases. Soil samples are collected and analyzed in the laboratory at high quality and high analytical costs. The demand by government and consultants for a more dynamic approach and by customers requiring performances in which analyses are performed in the field with immediate feedback of the analytical results, is growing. Especially during the follow-up of remediation projects or during the determination of the sampling strategy, field analyses are advisable. For this purpose four types of ED-XRF systems, ranging from portable up to high performance laboratory systems, have been evaluated. The evaluation criteria are based on the performance characteristics for all the ED-XRF systems such as limit of detection, accuracy and the measurement uncertainty on one hand, and also the influence of the sample pretreatment on the obtained results on the other hand. The study proved that the field portable system and the bench top system, placed in a mobile van, can be applied as field techniques, resulting in semi-quantitative analytical results. A limited homogenization of the analyzed sample significantly increases the representativeness of the soil sample. The ED-XRF systems can be differentiated by their limits of detection which are a factor of 10 to 20 higher for the portable system. The accuracy of the results and the measurement uncertainty also improved using the bench top system. Therefore, the selection criteria for applicability of both field systems are based on the required detection level and also the required accuracy of the results.

Lidar detection of underwater objects using a neuro-SVM-based architecture.

PubMed

Mitra, Vikramjit; Wang, Chia-Jiu; Banerjee, Satarupa

2006-05-01

This paper presents a neural network architecture using a support vector machine (SVM) as an inference engine (IE) for classification of light detection and ranging (Lidar) data. Lidar data gives a sequence of laser backscatter intensities obtained from laser shots generated from an airborne object at various altitudes above the earth surface. Lidar data is pre-filtered to remove high frequency noise. As the Lidar shots are taken from above the earth surface, it has some air backscatter information, which is of no importance for detecting underwater objects. Because of these, the air backscatter information is eliminated from the data and a segment of this data is subsequently selected to extract features for classification. This is then encoded using linear predictive coding (LPC) and polynomial approximation. The coefficients thus generated are used as inputs to the two branches of a parallel neural architecture. The decisions obtained from the two branches are vector multiplied and the result is fed to an SVM-based IE that presents the final inference. Two parallel neural architectures using multilayer perception (MLP) and hybrid radial basis function (HRBF) are considered in this paper. The proposed structure fits the Lidar data classification task well due to the inherent classification efficiency of neural networks and accurate decision-making capability of SVM. A Bayesian classifier and a quadratic classifier were considered for the Lidar data classification task but they failed to offer high prediction accuracy. Furthermore, a single-layered artificial neural network (ANN) classifier was also considered and it failed to offer good accuracy. The parallel ANN architecture proposed in this paper offers high prediction accuracy (98.9%) and is found to be the most suitable architecture for the proposed task of Lidar data classification.

Preliminary clinical results: an analyzing tool for 2D optical imaging in detection of active inflammation in rheumatoid arthritis

NASA Astrophysics Data System (ADS)

Adi Aizudin Bin Radin Nasirudin, Radin; Meier, Reinhard; Ahari, Carmen; Sievert, Matti; Fiebich, Martin; Rummeny, Ernst J.; No"l, Peter B.

2011-03-01

Optical imaging (OI) is a relatively new method in detecting active inflammation of hand joints of patients suffering from rheumatoid arthritis (RA). With the high number of people affected by this disease especially in western countries, the availability of OI as an early diagnostic imaging method is clinically highly relevant. In this paper, we present a newly in-house developed OI analyzing tool and a clinical evaluation study. Our analyzing tool extends the capability of existing OI tools. We include many features in the tool, such as region-based image analysis, hyper perfusion curve analysis, and multi-modality image fusion to aid clinicians in localizing and determining the intensity of inflammation in joints. Additionally, image data management options, such as the full integration of PACS/RIS, are included. In our clinical study we demonstrate how OI facilitates the detection of active inflammation in rheumatoid arthritis. The preliminary clinical results indicate a sensitivity of 43.5%, a specificity of 80.3%, an accuracy of 65.7%, a positive predictive value of 76.6%, and a negative predictive value of 64.9% in relation to clinical results from MRI. The accuracy of inflammation detection serves as evidence to the potential of OI as a useful imaging modality for early detection of active inflammation in patients with rheumatoid arthritis. With our in-house developed tool we extend the usefulness of OI imaging in the clinical arena. Overall, we show that OI is a fast, inexpensive, non-invasive and nonionizing yet highly sensitive and accurate imaging modality.-

Automatic and Robust Delineation of the Fiducial Points of the Seismocardiogram Signal for Non-invasive Estimation of Cardiac Time Intervals.

PubMed

Khosrow-Khavar, Farzad; Tavakolian, Kouhyar; Blaber, Andrew; Menon, Carlo

2016-10-12

The purpose of this research was to design a delineation algorithm that could detect specific fiducial points of the seismocardiogram (SCG) signal with or without using the electrocardiogram (ECG) R-wave as the reference point. The detected fiducial points were used to estimate cardiac time intervals. Due to complexity and sensitivity of the SCG signal, the algorithm was designed to robustly discard the low-quality cardiac cycles, which are the ones that contain unrecognizable fiducial points. The algorithm was trained on a dataset containing 48,318 manually annotated cardiac cycles. It was then applied to three test datasets: 65 young healthy individuals (dataset 1), 15 individuals above 44 years old (dataset 2), and 25 patients with previous heart conditions (dataset 3). The algorithm accomplished high prediction accuracy with the rootmean- square-error of less than 5 ms for all the test datasets. The algorithm overall mean detection rate per individual recordings (DRI) were 74, 68, and 42 percent for the three test datasets when concurrent ECG and SCG were used. For the standalone SCG case, the mean DRI was 32, 14 and 21 percent. When the proposed algorithm applied to concurrent ECG and SCG signals, the desired fiducial points of the SCG signal were successfully estimated with a high detection rate. For the standalone case, however, the algorithm achieved high prediction accuracy and detection rate for only the young individual dataset. The presented algorithm could be used for accurate and non-invasive estimation of cardiac time intervals.

Comparison of the benzoyl-DL-arginine-naphthylamide (BANA) test, DNA probes, and immunological reagents for ability to detect anaerobic periodontal infections due to Porphyromonas gingivalis, Treponema denticola, and Bacteroides forsythus.

PubMed Central

Loesche, W J; Lopatin, D E; Giordano, J; Alcoforado, G; Hujoel, P

1992-01-01

Most forms of periodontal disease are associated with the presence or overgrowth of anaerobic species that could include Treponema denticola, Porphyromonas gingivalis, and Bacteroides forsythus among others. These three organisms are among the few cultivable plaque species that can hydrolyze the synthetic trypsin substrate benzoyl-DL-arginine-naphthylamide (BANA). In turn, BANA hydrolysis by the plaque can be associated with periodontal morbidity and with the presence of these three BANA-positive organisms in the plaque. In this investigation, the results of the BANA test, which simultaneously detects one or more of these organisms, were compared with the detection of these organisms by (i) highly specific antibodies to P. gingivalis, T. denticola, and B. forsythus; (ii) whole genomic DNA probes to P. gingivalis and T. denticola; and (iii) culturing or microscopic procedures. The BANA test, the DNA probes, and an enzyme-linked immunosorbent assay or an indirect immunofluorescence assay procedure exhibited high sensitivities, i.e., 90 ot 96%, and high accuracies, i.e., 83 to 92%, in their ability to detect combinations of these organisms in over 200 subgingival plaque samples taken from the most periodontally diseased sites in 67 patients. This indicated that if P. gingivalis, T. denticola, and B. forsythus are appropriate marker organisms for an anaerobic periodontal infection, then the three detection methods are equally accurate in their ability to diagnose this infection. The same statement could not be made for the culturing approach, where accuracies of 50 to 62% were observed. PMID:1311335

Fast obstacle detection based on multi-sensor information fusion

NASA Astrophysics Data System (ADS)

Lu, Linli; Ying, Jie

2014-11-01

Obstacle detection is one of the key problems in areas such as driving assistance and mobile robot navigation, which cannot meet the actual demand by using a single sensor. A method is proposed to realize the real-time access to the information of the obstacle in front of the robot and calculating the real size of the obstacle area according to the mechanism of the triangle similarity in process of imaging by fusing datum from a camera and an ultrasonic sensor, which supports the local path planning decision. In the part of image analyzing, the obstacle detection region is limited according to complementary principle. We chose ultrasonic detection range as the region for obstacle detection when the obstacle is relatively near the robot, and the travelling road area in front of the robot is the region for a relatively-long-distance detection. The obstacle detection algorithm is adapted from a powerful background subtraction algorithm ViBe: Visual Background Extractor. We extracted an obstacle free region in front of the robot in the initial frame, this region provided a reference sample set of gray scale value for obstacle detection. Experiments of detecting different obstacles at different distances respectively, give the accuracy of the obstacle detection and the error percentage between the calculated size and the actual size of the detected obstacle. Experimental results show that the detection scheme can effectively detect obstacles in front of the robot and provide size of the obstacle with relatively high dimensional accuracy.

Multiresidue analysis of 22 sulfonamides and their metabolites in animal tissues using quick, easy, cheap, effective, rugged, and safe extraction and high resolution mass spectrometry (hybrid linear ion trap-Orbitrap).

PubMed

Abdallah, H; Arnaudguilhem, C; Jaber, F; Lobinski, R

2014-08-15

A new high performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) method was developed for a simultaneous multi-residue analysis of 22 sulfonamides (SAs) and their metabolites in edible animal (pig, beef, sheep and chicken) tissues. Sample preparation was optimized on the basis of the "QuEChERS" protocol. The analytes were identified using their LC retention times and accurate mass; the identification was further confirmed by multi-stage high mass accuracy (<5ppm) mass spectrometry. The performance of the method was evaluated according to the EU guidelines for the validation of screening methods for the analysis of veterinary drugs residues. Acceptable values were obtained for: linearity (R(2)<0.99), limit of detection (LOD, 3-26μg/kg), limit of quantification (LOQ, 11-88μg/kg), accuracy (recovery 88-112%), intra- and inter-day precision 1-14 and 1-17%, respectively, decision limit (CCα) and detection capability (CCβ) around the maximum residue limits (MRL) of SAs (100μg/kg). The method was validated by analysis of a reference material FAPAS-02188 "Pig kidney" with ǀ Z-scoreǀ<0.63. The method was applied to various matrices (kidney, liver, muscle) originated from pig, beef, sheep, and chicken) allowing the simultaneous quantification of target sulfonamides at concentration levels above the MRL/2 and the identification of untargeted compounds such as N(4)-acetyl metabolites using multi-stage high mass accuracy mass spectrometry. Copyright © 2014 Elsevier B.V. All rights reserved.

Diagnostic accuracy of 64-slice multidetector CT angiography for detection of in-stent restenosis of vertebral artery ostium stents: comparison with conventional angiography.

PubMed

Lee, Youn Joo; Lim, Yeon Soo; Lim, Hyun Wook; Yoo, Won Jong; Choi, Byung Gil; Kim, Bum Soo

2014-10-01

There are very few reports assessing in-stent restenosis (ISR) after vertebral artery ostium (VAO) stents using multidetector computed tomography (MDCT). To compare the diagnostic accuracy of computed tomography angiography (CTA) using 64-slice MDCT with digital subtraction angiography (DSA) for detection of significant ISR after VAO stenting. The study evaluated 57 VAO stents in 57 patients (39 men, 18 women; mean age 64 years [range, 48-90 years]). All stents were scanned with a 64-slice MDCT scanner. Three sets of images were reconstructed with three different convolution kernels. Two observers who were blinded to the results of DSA assessed the diagnostic accuracy of CTA for detecting significant ISR (≥50% diameter narrowing) of VAO stents in comparison with DSA as the reference standard. The sensitivity, specificity, positive and negative predictive values, and accuracy were calculated. Of the 57 stents, 46 (81%) were assessable using CTA, while 11 (19%) were not. No stents with diameters ≤2.75 mm were assessable. DSA revealed 13 cases of significant ISR in all stents. The respective sensitivity, specificity, positive and negative predictive values, and accuracy were 92%, 82%, 60%, 97%, and 84% for all stents. On excluding the 11 non-assessable stents, the respective values were 88%, 95%, 78%, 97%, and 93%. Of the 46 CTA assessable stents, eight significant ISRs were diagnosed on DSA. Seven of eight patients with significant ISR by DSA were diagnosed correctly with CTA. The area under the receiver-operating characteristic curve (AUC) was 0.87 for all stents and 0.91 for assessable stents, indicating good to excellent agreement between CTA and DSA for detecting significant ISR after VAO stenting. Sixty-four-slice MDCT is a promising non-invasive method of assessing stent patency and can exclude significant ISR with high diagnostic values after VAO stenting. © The Foundation Acta Radiologica 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Detection of Anomalies in Citrus Leaves Using Laser-Induced Breakdown Spectroscopy (LIBS).

PubMed

Sankaran, Sindhuja; Ehsani, Reza; Morgan, Kelly T

2015-08-01

Nutrient assessment and management are important to maintain productivity in citrus orchards. In this study, laser-induced breakdown spectroscopy (LIBS) was applied for rapid and real-time detection of citrus anomalies. Laser-induced breakdown spectroscopy spectra were collected from citrus leaves with anomalies such as diseases (Huanglongbing, citrus canker) and nutrient deficiencies (iron, manganese, magnesium, zinc), and compared with those of healthy leaves. Baseline correction, wavelet multivariate denoising, and normalization techniques were applied to the LIBS spectra before analysis. After spectral pre-processing, features were extracted using principal component analysis and classified using two models, quadratic discriminant analysis and support vector machine (SVM). The SVM resulted in a high average classification accuracy of 97.5%, with high average canker classification accuracy (96.5%). LIBS peak analysis indicated that high intensities at 229.7, 247.9, 280.3, 393.5, 397.0, and 769.8 nm were observed of 11 peaks found in all the samples. Future studies using controlled experiments with variable nutrient applications are required for quantification of foliar nutrients by using LIBS-based sensing.

Cross-calibrating interferon-γ detection by using eletrochemical impedance spectroscopy and paraboloidal mirror enabled surface plasmon resonance interferometer

NASA Astrophysics Data System (ADS)

Liu, Meng-Wei; Chang, Hao-Jung; Lee, Shu-sheng; Lee, Chih-Kung

2016-03-01

Tuberculosis is a highly contagious disease such that global latent patient can be as high as one third of the world population. Currently, latent tuberculosis was diagnosed by stimulating the T cells to produce the biomarker of tuberculosis, i.e., interferon-γ. In this paper, we developed a paraboloidal mirror enabled surface plasmon resonance (SPR) interferometer that has the potential to also integrate ellipsometry to analyze the antibody and antigen reactions. To examine the feasibility of developing a platform for cross calibrating the performance and detection limit of various bio-detection techniques, electrochemical impedance spectroscopy (EIS) method was also implemented onto a biochip that can be incorporated into this newly developed platform. The microfluidic channel of the biochip was functionalized by coating the interferon-γ antibody so as to enhance the detection specificity. To facilitate the processing steps needed for using the biochip to detect various antigen of vastly different concentrations, a kinetic mount was also developed to guarantee the biochip re-positioning accuracy whenever the biochip was removed and placed back for another round of detection. With EIS being utilized, SPR was also adopted to observe the real-time signals on the computer in order to analyze the success of each biochip processing steps such as functionalization, wash, etc. Finally, the EIS results and the optical signals obtained from the newly developed optical detection platform was cross-calibrated. Preliminary experimental results demonstrate the accuracy and performance of SPR and EIS measurement done at the newly integrated platform.

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure and increase the robustness of the proposed algorithm. The proposed algorithm is validated with a publicly available 10-class object detection dataset.

High-performance Chinese multiclass traffic sign detection via coarse-to-fine cascade and parallel support vector machine detectors

NASA Astrophysics Data System (ADS)

Chang, Faliang; Liu, Chunsheng

2017-09-01

The high variability of sign colors and shapes in uncontrolled environments has made the detection of traffic signs a challenging problem in computer vision. We propose a traffic sign detection (TSD) method based on coarse-to-fine cascade and parallel support vector machine (SVM) detectors to detect Chinese warning and danger traffic signs. First, a region of interest (ROI) extraction method is proposed to extract ROIs using color contrast features in local regions. The ROI extraction can reduce scanning regions and save detection time. For multiclass TSD, we propose a structure that combines a coarse-to-fine cascaded tree with a parallel structure of histogram of oriented gradients (HOG) + SVM detectors. The cascaded tree is designed to detect different types of traffic signs in a coarse-to-fine process. The parallel HOG + SVM detectors are designed to do fine detection of different types of traffic signs. The experiments demonstrate the proposed TSD method can rapidly detect multiclass traffic signs with different colors and shapes in high accuracy.

Social Power Increases Interoceptive Accuracy

PubMed Central

Moeini-Jazani, Mehrad; Knoeferle, Klemens; de Molière, Laura; Gatti, Elia; Warlop, Luk

2017-01-01

Building on recent psychological research showing that power increases self-focused attention, we propose that having power increases accuracy in perception of bodily signals, a phenomenon known as interoceptive accuracy. Consistent with our proposition, participants in a high-power experimental condition outperformed those in the control and low-power conditions in the Schandry heartbeat-detection task. We demonstrate that the effect of power on interoceptive accuracy is not explained by participants’ physiological arousal, affective state, or general intention for accuracy. Rather, consistent with our reasoning that experiencing power shifts attentional resources inward, we show that the effect of power on interoceptive accuracy is dependent on individuals’ chronic tendency to focus on their internal sensations. Moreover, we demonstrate that individuals’ chronic sense of power also predicts interoceptive accuracy similar to, and independent of, how their situationally induced feeling of power does. We therefore provide further support on the relation between power and enhanced perception of bodily signals. Our findings offer a novel perspective–a psychophysiological account–on how power might affect judgments and behavior. We highlight and discuss some of these intriguing possibilities for future research. PMID:28824501

Accuracy of saline contrast sonohysterography in detection of endometrial polyps and submucosal leiomyomas in women of reproductive age with abnormal uterine bleeding: systematic review and meta-analysis.

PubMed

Bittencourt, C A; Dos Santos Simões, R; Bernardo, W M; Fuchs, L F P; Soares Júnior, J M; Pastore, A R; Baracat, E C

2017-07-01

To analyze the diagnostic accuracy of two- (2D) and three- (3D) dimensional saline contrast sonohysterography (SCSH) in the detection of endometrial polyps and submucosal uterine leiomyomas in women of reproductive age with abnormal uterine bleeding compared with gold standard hysteroscopy. A systematic review of diagnostic studies that compared 2D- and/or 3D-SCSH with hysteroscopy and anatomopathology was conducted according to PRISMA and SEDATE recommendations. The databases MEDLINE, EMBASE and The Cochrane Library were searched electronically using specific terms with no restriction on language or publication year. Quality assessment of included studies was performed using the QUADAS-2 tool. Meta-analysis was performed with the Meta-DiSk program and data presented as forest plots and summary receiver-operating characteristics (SROC) curves. Pooled sensitivity, specificity and positive (LR+) and negative (LR-) likelihood ratios of SCSH in the detection of uterine cavity abnormalities were calculated. A total of 1398 citations were identified and five studies were included in the systematic review and meta-analysis. Pooled sensitivity and specificity of 2D-SCSH in detecting endometrial polyps were 93% (95% CI, 89-96%) and 81% (95% CI, 76-86%), respectively, with pooled LR+ of 5.41 (95% CI, 2.60-11.28) and LR- of 0.10 (95% CI, 0.06-0.17). In the detection of submucosal uterine leiomyomas, pooled sensitivity and specificity were 94% (95% CI, 89-97%) and 81% (95% CI, 76-86%), respectively, with pooled LR+ of 4.25 (95% CI, 2.20-8.21) and LR- of 0.11 (95% CI, 0.05-0.22). 2D-SCSH had good accuracy in detecting endometrial polyps and submucosal uterine leiomyomas, with areas under the SROC curves of 0.97 ± 0.02 and 0.97 ± 0.03, respectively. Studies that analyzed the diagnostic accuracy of 3D-SCSH could not be compared due to high heterogeneity related to menopausal status, type of technique used and primary outcome being investigation of infertility. 2D-SCSH proved to be a highly sensitive method for detection of endometrial polyps and submucosal uterine leiomyomas, making it a potential first-line diagnostic method in the work-up for women with abnormal uterine bleeding. More studies are needed on 3D-SCSH in women of reproductive age. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

Evaluation of microplate immunocapture method for detection of Vibrio cholerae, Salmonella Typhi and Shigella flexneri from food.

PubMed

Fakruddin, Md; Hossain, Md Nur; Ahmed, Monzur Morshed

2017-08-29

Improved methods with better separation and concentration ability for detection of foodborne pathogens are in constant need. The aim of this study was to evaluate microplate immunocapture (IC) method for detection of Salmonella Typhi, Shigella flexneri and Vibrio cholerae from food samples to provide a better alternative to conventional culture based methods. The IC method was optimized for incubation time, bacterial concentration, and capture efficiency. 6 h incubation and log 6 CFU/ml cell concentration provided optimal results. The method was shown to be highly specific for the pathogens concerned. Capture efficiency (CE) was around 100% of the target pathogens, whereas CE was either zero or very low for non-target pathogens. The IC method also showed better pathogen detection ability at different concentrations of cells from artificially contaminated food samples in comparison with culture based methods. Performance parameter of the method was also comparable (Detection limit- 25 CFU/25 g; sensitivity 100%; specificity-96.8%; Accuracy-96.7%), even better than culture based methods (Detection limit- 125 CFU/25 g; sensitivity 95.9%; specificity-97%; Accuracy-96.2%). The IC method poses to be the potential to be used as a method of choice for detection of foodborne pathogens in routine laboratory practice after proper validation.

A novel method based on learning automata for automatic lesion detection in breast magnetic resonance imaging.

PubMed

Salehi, Leila; Azmi, Reza

2014-07-01

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. In this way, magnetic resonance imaging (MRI) is emerging as a powerful tool for the detection of breast cancer. Breast MRI presently has two major challenges. First, its specificity is relatively poor, and it detects many false positives (FPs). Second, the method involves acquiring several high-resolution image volumes before, during, and after the injection of a contrast agent. The large volume of data makes the task of interpretation by the radiologist both complex and time-consuming. These challenges have led to the development of the computer-aided detection systems to improve the efficiency and accuracy of the interpretation process. Detection of suspicious regions of interests (ROIs) is a critical preprocessing step in dynamic contrast-enhanced (DCE)-MRI data evaluation. In this regard, this paper introduces a new automatic method to detect the suspicious ROIs for breast DCE-MRI based on region growing. The results indicate that the proposed method is thoroughly able to identify suspicious regions (accuracy of 75.39 ± 3.37 on PIDER breast MRI dataset). Furthermore, the FP per image in this method is averagely 7.92, which shows considerable improvement comparing to other methods like ROI hunter.

A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer.

PubMed

Wu, Jiang; Ji, Yanju; Zhao, Ling; Ji, Mengying; Ye, Zhuang; Li, Suyi

2016-01-01

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

The association between reduced knee joint proprioception and medial meniscal abnormalities using MRI in knee osteoarthritis: results from the Amsterdam osteoarthritis cohort.

PubMed

van der Esch, M; Knoop, J; Hunter, D J; Klein, J-P; van der Leeden, M; Knol, D L; Reiding, D; Voorneman, R E; Gerritsen, M; Roorda, L D; Lems, W F; Dekker, J

2013-05-01

Osteoarthritis (OA) of the knee is characterized by pain and activity limitations. In knee OA, proprioceptive accuracy is reduced and might be associated with pain and activity limitations. Although causes of reduced proprioceptive accuracy are divergent, medial meniscal abnormalities, which are highly prevalent in knee OA, have been suggested to play an important role. No study has focussed on the association between proprioceptive accuracy and meniscal abnormalities in knee OA. To explore the association between reduced proprioceptive accuracy and medial meniscal abnormalities in a clinical sample of knee OA subjects. Cross-sectional study in 105 subjects with knee OA. Knee proprioceptive accuracy was assessed by determining the joint motion detection threshold in the knee extension direction. The knee was imaged with a 3.0 T magnetic resonance (MR) scanner. Number of regions with medial meniscal abnormalities and the extent of abnormality in the anterior and posterior horn and body were scored according to the Boston-Leeds Osteoarthritis Knee Score (BLOKS) method. Multiple regression analyzes were used to examine whether reduced proprioceptive accuracy was associated with medial meniscal abnormalities in knee OA subjects. Mean proprioceptive accuracy was 2.9° ± 1.9°. Magnetic resonance imaging (MRI)-detected medial meniscal abnormalities were found in the anterior horn (78%), body (80%) and posterior horn (90%). Reduced proprioceptive accuracy was associated with both the number of regions with meniscal abnormalities (P < 0.01) and the extent of abnormality (P = 0.02). These associations were not confounded by muscle strength, joint laxity, pain, age, gender, body mass index (BMI) and duration of knee complaints. This is the first study showing that reduced proprioceptive accuracy is associated with medial meniscal abnormalities in knee OA. The study highlights the importance of meniscal abnormalities in understanding reduced proprioceptive accuracy in persons with knee OA. Copyright © 2013 Osteoarthritis Research Society International. All rights reserved.

Method for targetless tracking subpixel in-plane movements.

PubMed

Espinosa, Julian; Perez, Jorge; Ferrer, Belen; Mas, David

2015-09-01

We present a targetless motion tracking method for detecting planar movements with subpixel accuracy. This method is based on the computation and tracking of the intersection of two nonparallel straight-line segments in the image of a moving object in a scene. The method is simple and easy to implement because no complex structures have to be detected. It has been tested and validated using a lab experiment consisting of a vibrating object that was recorded with a high-speed camera working at 1000 fps. We managed to track displacements with an accuracy of hundredths of pixel or even of thousandths of pixel in the case of tracking harmonic vibrations. The method is widely applicable because it can be used for distance measuring amplitude and frequency of vibrations with a vision system.

Characterization of ANFO explosive by high accuracy ESI(±)-FTMS with forensic identification on real samples by EASI(-)-MS.

PubMed

Hernandes, Vinicius Veri; Franco, Marcos Fernado; Santos, Jandyson Machado; Melendez-Perez, Jose J; de Morais, Damila Rodrigues; Rocha, Werickson Fortunato de Carvalho; Borges, Rodrigo; de Souza, Wanderley; Zacca, Jorge Jardim; Logrado, Lucio Paulo Lima; Eberlin, Marcos Nogueira; Correa, Deleon Nascimento

2015-04-01

Ammonium nitrate fuel oil (ANFO) is an explosive used in many civil applications. In Brazil, ANFO has unfortunately also been used in criminal attacks, mainly in automated teller machine (ATM) explosions. In this paper, we describe a detailed characterization of the ANFO composition and its two main constituents (diesel and a nitrate explosive) using high resolution and accuracy mass spectrometry performed on an FT-ICR-mass spectrometer with electrospray ionization (ESI(±)-FTMS) in both the positive and negative ion modes. Via ESI(-)-MS, an ion marker for ANFO was characterized. Using a direct and simple ambient desorption/ionization technique, i.e., easy ambient sonic-spray ionization mass spectrometry (EASI-MS), in a simpler, lower accuracy but robust single quadrupole mass spectrometer, the ANFO ion marker was directly detected from the surface of banknotes collected from ATM explosion theft. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A Comparison of Approaches to Detect Deception

DTIC Science & Technology

2010-12-30

blood pressure has been demonstrated for this instrument (Fortin et al., 2006; Sackl- Pietsch , 2010). A sampling rate of 62.5 Hz is considerably...Psychophysiology, 15, 344-359. Sackl- Pietsch , E. (2010). Continuous non-invasive arterial pressure shows high accuracy in comparison to invasive intra-arterial

Diagnostic Accuracy of the MMSE in Detecting Probable and Possible Alzheimer's Disease in Ethnically Diverse Highly Educated Individuals: An Analysis of the NACC Database

PubMed Central

Spering, Cynthia C.; Hobson, Valerie; Lucas, John A.; Menon, Chloe V.; Hall, James R.

2012-01-01

Background. To validate and extend the findings of a raised cut score of O’Bryant and colleagues (O’Bryant SE, Humphreys JD, Smith GE, et al. Detecting dementia with the mini-mental state examination in highly educated individuals. Arch Neurol. 2008;65(7):963–967.) for the Mini-Mental State Examination in detecting cognitive dysfunction in a bilingual sample of highly educated ethnically diverse individuals. Methods. Archival data were reviewed from participants enrolled in the National Alzheimer's Coordinating Center minimum data set. Data on 7,093 individuals with 16 or more years of education were analyzed, including 2,337 cases with probable and possible Alzheimer's disease, 1,418 mild cognitive impairment patients, and 3,088 nondemented controls. Ethnic composition was characterized as follows: 6,296 Caucasians, 581 African Americans, 4 American Indians or Alaska natives, 2 native Hawaiians or Pacific Islanders, 149 Asians, 43 “Other,” and 18 of unknown origin. Results. Diagnostic accuracy estimates (sensitivity, specificity, and likelihood ratio) of Mini-Mental State Examination cut scores in detecting probable and possible Alzheimer's disease were examined. A standard Mini-Mental State Examination cut score of 24 (≤23) yielded a sensitivity of 0.58 and a specificity of 0.98 in detecting probable and possible Alzheimer's disease across ethnicities. A cut score of 27 (≤26) resulted in an improved balance of sensitivity and specificity (0.79 and 0.90, respectively). In the cognitively impaired group (mild cognitive impairment and probable and possible Alzheimer's disease), the standard cut score yielded a sensitivity of 0.38 and a specificity of 1.00 while raising the cut score to 27 resulted in an improved balance of 0.59 and 0.96 of sensitivity and specificity, respectively. Conclusions. These findings cross-validate our previous work and extend them to an ethnically diverse cohort. A higher cut score is needed to maximize diagnostic accuracy of the Mini-Mental State Examination in individuals with college degrees. PMID:22396476

A blood tumor marker combination assay produces high sensitivity and specificity for cancer according to the natural history.

PubMed

Kobayashi, Tsuneo

2018-03-01

Diagnosis using a specific tumor marker is difficult because the sensitivity of this detection method is under 20%. Herein, a tumor marker combination assay, combining growth-related tumor marker and associated tumor marker (Cancer, 73(7), 1994), was employed. This double-blind tumor marker combination assay (TMCA) showed 87.5% sensitivity as the results, but a low specificity, ranging from 30 to 76%. To overcome this low specificity, we exploited complex markers, a multivariate analysis and serum fractionation by biochemical biopsy. Thus, in this study, a combination of new techniques was used to re-evaluate these serum samples. Three serum panels, containing 90, 120, and 97 samples were obtained from the Mayo Clinic. The final results showed 80-90% sensitivity, 84-85% specificity, and 83-88% accuracy. We demonstrated a notable tumor marker combination assay with high accuracy. This TMCA should be applicable for primary cancer detection and recurrence prevention. © 2018 The Author. Cancer Medicine published by John Wiley & Sons Ltd.

Multi-sensor information fusion method for vibration fault diagnosis of rolling bearing

NASA Astrophysics Data System (ADS)

Jiao, Jing; Yue, Jianhai; Pei, Di

2017-10-01

Bearing is a key element in high-speed electric multiple unit (EMU) and any defect of it can cause huge malfunctioning of EMU under high operation speed. This paper presents a new method for bearing fault diagnosis based on least square support vector machine (LS-SVM) in feature-level fusion and Dempster-Shafer (D-S) evidence theory in decision-level fusion which were used to solve the problems about low detection accuracy, difficulty in extracting sensitive characteristics and unstable diagnosis system of single-sensor in rolling bearing fault diagnosis. Wavelet de-nosing technique was used for removing the signal noises. LS-SVM was used to make pattern recognition of the bearing vibration signal, and then fusion process was made according to the D-S evidence theory, so as to realize recognition of bearing fault. The results indicated that the data fusion method improved the performance of the intelligent approach in rolling bearing fault detection significantly. Moreover, the results showed that this method can efficiently improve the accuracy of fault diagnosis.

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

PubMed Central

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

2011-01-01

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

A deep learning and novelty detection framework for rapid phenotyping in high-content screening

PubMed Central

Sommer, Christoph; Hoefler, Rudolf; Samwer, Matthias; Gerlich, Daniel W.

2017-01-01

Supervised machine learning is a powerful and widely used method for analyzing high-content screening data. Despite its accuracy, efficiency, and versatility, supervised machine learning has drawbacks, most notably its dependence on a priori knowledge of expected phenotypes and time-consuming classifier training. We provide a solution to these limitations with CellCognition Explorer, a generic novelty detection and deep learning framework. Application to several large-scale screening data sets on nuclear and mitotic cell morphologies demonstrates that CellCognition Explorer enables discovery of rare phenotypes without user training, which has broad implications for improved assay development in high-content screening. PMID:28954863

Detection of Fractured Endodontic Instruments in Root Canals: Comparison between Different Digital Radiography Systems and Cone-beam Computed Tomography.

PubMed

Ramos Brito, Ana Caroline; Verner, Francielle Silvestre; Junqueira, Rafael Binato; Yamasaki, Mayra Cristina; Queiroz, Polyane Mazucato; Freitas, Deborah Queiroz; Oliveira-Santos, Christiano

2017-04-01

This study compared the detection of fractured instruments in root canals with and without filling by periapical radiographs from 3 digital systems and cone-beam computed tomographic (CBCT) images with different resolutions. Thirty-one human molars (80 canals) were used. Root canals were divided into the following groups: the control group, without fillings; the fracture group, without fillings and with fractured files; the fill group, filled; and the fill/fracture group, filled and with fractured files. Digital radiographs in ortho-, mesio-, and distoradial directions were performed in 2 semidirect systems (VistaScan [Dürr Dental, Beitigheim-Bissinger, Germany] and Express [Instrumentarium Imaging, Tuusula, Finland]) and a direct system (SnapShot [Instrumentarium Imaging]). CBCT images were acquired with 0.085-mm and 0.2-mm voxel sizes. All images were assessed and reassessed by 4 observers for the presence or absence of fractured files on a 5-point scale. The sensitivity, specificity, and accuracy were calculated. In the absence of filling, accuracy values were high, and there were no statistical differences among the radiographic techniques, different digital systems, or the different CBCT voxels sizes. In the presence of filling, the accuracy of periapical radiographs was significantly higher than CBCT images. In general, SnapShot showed higher accuracy than VistaScan and Express. Periapical radiographs in 1 incidence were accurate for the detection of fractured endodontic instruments inside the root canal in the absence or presence of filling, suggesting that this technique should be the first choice as well as the direct digital radiographic system. In the presence of filling, the decision to perform a CBCT examination must take into consideration its low accuracy. Copyright © 2016 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Micro-computed Tomography Provides High Accuracy Congenital Heart Disease Diagnosis in Neonatal and Fetal Mice

PubMed Central

Kim, Andrew J.; Francis, Richard; Liu, Xiaoqin; Devine, William A.; Ramirez, Ricardo; Anderton, Shane J.; Wong, Li Yin; Faruque, Fahim; Gabriel, George C.; Leatherbury, Linda; Tobita, Kimimasa; Lo, Cecilia W.

2013-01-01

Background Mice are well suited for modeling human congenital heart defects (CHD), given their four-chamber cardiac anatomy. However, mice with CHD invariably die prenatally/neonatally, causing CHD phenotypes to be missed. Therefore, we investigated the efficacy of noninvasive micro-computed tomography (micro-CT) to screen for CHD in stillborn/fetal mice. These studies were carried out using chemically mutagenized mice expected to be enriched for birth defects including CHD. Methods and Results Stillborn/fetal mice obtained from the breeding of N-ethyl-N-nitrosourea (ENU) mutagenized mice were formalin-fixed and stained with iodine, then micro-CT scanned. Those diagnosed with CHD and some CHD-negative pups were necropsied. A subset of these were further analyzed by histopathology to confirm the CHD/no-CHD diagnosis. Micro-CT scanning of 2105 fetal/newborn mice revealed an abundance of ventricular septal defects (VSD) (n=307). Overall, we observed an accuracy of 89.8% for VSD diagnosis. Outflow tract anomalies identified by micro-CT included double outlet right ventricle (n=36), transposition of the great arteries (n=14), and persistent truncus arteriosus (n=3). These were diagnosed with a 97.4% accuracy. Aortic arch anomalies also were readily detected with an overall 99.6% accuracy. This included right aortic arch (n=28) and coarctation/interrupted aortic arch (n=12). Also detected by micro-CT were atrioventricular septal defects (n=22), tricuspid hypoplasia/atresia (n=13), and coronary artery fistulas (n=16). They yielded accuracies of 98.9%, 100%, and 97.8% respectively. Conclusions Contrast enhanced micro-CT imaging in neonatal/fetal mice can reliably detect a wide spectrum of CHD. We conclude micro-CT imaging can be used for routine rapid assessments of structural heart defects in fetal/newborn mice. PMID:23759365

Detecting Forests Damaged by Pine Wilt Disease at the Individual Tree Level Using Airborne Laser Data and WORLDVIEW-2/3 Images Over Two Seasons

NASA Astrophysics Data System (ADS)

Takenaka, Y.; Katoh, M.; Deng, S.; Cheung, K.

2017-10-01

Pine wilt disease is caused by the pine wood nematode (Bursaphelenchus xylophilus) and Japanese pine sawyer (Monochamus alternatus). This study attempted to detect damaged pine trees at different levels using a combination of airborne laser scanning (ALS) data and high-resolution space-borne images. A canopy height model with a resolution of 50 cm derived from the ALS data was used for the delineation of tree crowns using the Individual Tree Detection method. Two pan-sharpened images were established using the ortho-rectified images. Next, we analyzed two kinds of intensity-hue-saturation (IHS) images and 18 remote sensing indices (RSI) derived from the pan-sharpened images. The mean and standard deviation of the 2 IHS images, 18 RSI, and 8 bands of the WV-2 and WV-3 images were extracted for each tree crown and were used to classify tree crowns using a support vector machine classifier. Individual tree crowns were assigned to one of nine classes: bare ground, Larix kaempferi, Cryptomeria japonica, Chamaecyparis obtusa, broadleaved trees, healthy pines, and damaged pines at slight, moderate, and heavy levels. The accuracy of the classifications using the WV-2 images ranged from 76.5 to 99.6 %, with an overall accuracy of 98.5 %. However, the accuracy of the classifications using the WV-3 images ranged from 40.4 to 95.4 %, with an overall accuracy of 72 %, which suggests poorer accuracy compared to those classes derived from the WV-2 images. This is because the WV-3 images were acquired in October 2016 from an area with low sun, at a low altitude.

Preoperative N Staging of Gastric Cancer by Stomach Protocol Computed Tomography

PubMed Central

Kim, Se Hoon; Kim, Jeong Jae; Lee, Jeong Sub; Kim, Seung Hyoung; Kim, Bong Soo; Maeng, Young Hee; Hyun, Chang Lim; Kim, Min Jeong

2013-01-01

Purpose Clinical stage of gastric cancer is currently assessed by computed tomography. Accurate clinical staging is important for the tailoring of therapy. This study evaluated the accuracy of clinical N staging using stomach protocol computed tomography. Materials and Methods Between March 2004 and November 2012, 171 patients with gastric cancer underwent preoperative stomach protocol computed tomography (Jeju National University Hospital; Jeju, Korea). Their demographic and clinical characteristics were reviewed retrospectively. Two radiologists evaluated cN staging using axial and coronal computed tomography images, and cN stage was matched with pathologic results. The diagnostic accuracy of stomach protocol computed tomography for clinical N staging and clinical characteristics associated with diagnostic accuracy were evaluated. Results The overall accuracy of stomach protocol computed tomography for cN staging was 63.2%. Computed tomography images of slice thickness 3.0 mm had a sensitivity of 60.0%; a specificity of 89.6%; an accuracy of 78.4%; and a positive predictive value of 78.0% in detecting lymph node metastases. Underestimation of cN stage was associated with larger tumor size (P<0.001), undifferentiated type (P=0.003), diffuse type (P=0.020), more advanced pathologic stage (P<0.001), and larger numbers of harvested and metastatic lymph nodes (P<0.001 each). Tumor differentiation was an independent factor affecting underestimation by computed tomography (P=0.045). Conclusions Computed tomography with a size criterion of 8 mm is highly specific but relatively insensitive in detecting nodal metastases. Physicians should keep in mind that computed tomography may not be an appropriate tool to detect nodal metastases for choosing appropriate treatment. PMID:24156034

Diagnostic Accuracy of Central Venous Catheter Confirmation by Bedside Ultrasound Versus Chest Radiography in Critically Ill Patients: A Systematic Review and Meta-Analysis.

PubMed

Ablordeppey, Enyo A; Drewry, Anne M; Beyer, Alexander B; Theodoro, Daniel L; Fowler, Susan A; Fuller, Brian M; Carpenter, Christopher R

2017-04-01

We performed a systematic review and meta-analysis to examine the accuracy of bedside ultrasound for confirmation of central venous catheter position and exclusion of pneumothorax compared with chest radiography. PubMed, Embase, Cochrane Central Register of Controlled Trials, reference lists, conference proceedings and ClinicalTrials.gov. Articles and abstracts describing the diagnostic accuracy of bedside ultrasound compared with chest radiography for confirmation of central venous catheters in sufficient detail to reconstruct 2 × 2 contingency tables were reviewed. Primary outcomes included the accuracy of confirming catheter positioning and detecting a pneumothorax. Secondary outcomes included feasibility, interrater reliability, and efficiency to complete bedside ultrasound confirmation of central venous catheter position. Investigators abstracted study details including research design and sonographic imaging technique to detect catheter malposition and procedure-related pneumothorax. Diagnostic accuracy measures included pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio. Fifteen studies with 1,553 central venous catheter placements were identified with a pooled sensitivity and specificity of catheter malposition by ultrasound of 0.82 (0.77-0.86) and 0.98 (0.97-0.99), respectively. The pooled positive and negative likelihood ratios of catheter malposition by ultrasound were 31.12 (14.72-65.78) and 0.25 (0.13-0.47). The sensitivity and specificity of ultrasound for pneumothorax detection was nearly 100% in the participating studies. Bedside ultrasound reduced mean central venous catheter confirmation time by 58.3 minutes. Risk of bias and clinical heterogeneity in the studies were high. Bedside ultrasound is faster than radiography at identifying pneumothorax after central venous catheter insertion. When a central venous catheter malposition exists, bedside ultrasound will identify four out of every five earlier than chest radiography.

Accuracy of the high-throughput amplicon sequencing to identify species within the genus Aspergillus.

PubMed

Lee, Seungeun; Yamamoto, Naomichi

2015-12-01

This study characterized the accuracy of high-throughput amplicon sequencing to identify species within the genus Aspergillus. To this end, we sequenced the internal transcribed spacer 1 (ITS1), β-tubulin (BenA), and calmodulin (CaM) gene encoding sequences as DNA markers from eight reference Aspergillus strains with known identities using 300-bp sequencing on the Illumina MiSeq platform, and compared them with the BLASTn outputs. The identifications with the sequences longer than 250 bp were accurate at the section rank, with some ambiguities observed at the species rank due to mostly cross detection of sibling species. Additionally, in silico analysis was performed to predict the identification accuracy for all species in the genus Aspergillus, where 107, 210, and 187 species were predicted to be identifiable down to the species rank based on ITS1, BenA, and CaM, respectively. Finally, air filter samples were analysed to quantify the relative abundances of Aspergillus species in outdoor air. The results were reproducible across biological duplicates both at the species and section ranks, but not strongly correlated between ITS1 and BenA, suggesting the Aspergillus detection can be taxonomically biased depending on the selection of the DNA markers and/or primers. Copyright © 2015 The British Mycological Society. Published by Elsevier Ltd. All rights reserved.

[Comparison and Discussion of National/Military Standards Related to Flow Measurement of Medical Injection Pump].

PubMed

Zhang, Nan; Zhou, Juan; Yu, Jinlai; Hua, Ziyu; Li, Yongxue; Wu, Jiangang

2018-05-30

Medical injection pump is a commonly used clinical equipment with high risk. Accurate detection of flow is an important aspect to ensure its reliable operation. In this paper, we carefully studied and analyzed the flow detection methods of three standards being used in medical injection pump detection in our country. The three standards were compared from the aspects of standard device, flow test point selection, length of test time and accuracy judgment. The advantages and disadvantages of these standards were analyzed and suggestions for improvement were put forward.

Accuracy of Estimating Highly Eccentric Binary Black Hole Parameters with Gravitational-wave Detections

NASA Astrophysics Data System (ADS)

Gondán, László; Kocsis, Bence; Raffai, Péter; Frei, Zsolt

2018-03-01

Mergers of stellar-mass black holes on highly eccentric orbits are among the targets for ground-based gravitational-wave detectors, including LIGO, VIRGO, and KAGRA. These sources may commonly form through gravitational-wave emission in high-velocity dispersion systems or through the secular Kozai–Lidov mechanism in triple systems. Gravitational waves carry information about the binaries’ orbital parameters and source location. Using the Fisher matrix technique, we determine the measurement accuracy with which the LIGO–VIRGO–KAGRA network could measure the source parameters of eccentric binaries using a matched filtering search of the repeated burst and eccentric inspiral phases of the waveform. We account for general relativistic precession and the evolution of the orbital eccentricity and frequency during the inspiral. We find that the signal-to-noise ratio and the parameter measurement accuracy may be significantly higher for eccentric sources than for circular sources. This increase is sensitive to the initial pericenter distance, the initial eccentricity, and the component masses. For instance, compared to a 30 {M}ȯ –30 {M}ȯ non-spinning circular binary, the chirp mass and sky-localization accuracy can improve by a factor of ∼129 (38) and ∼2 (11) for an initially highly eccentric binary assuming an initial pericenter distance of 20 M tot (10 M tot).

One-laser-based generation/detection of Brillouin dynamic grating and its application to distributed discrimination of strain and temperature.

PubMed

Zou, Weiwen; He, Zuyuan; Hotate, Kazuo

2011-01-31

This paper presents a novel scheme to generate and detect Brillouin dynamic grating in a polarization-maintaining optical fiber based on one laser source. Precise measurement of Brillouin dynamic grating spectrum is achieved benefiting from that the pump, probe and readout waves are coherently originated from the same laser source. Distributed discrimination of strain and temperature is also achieved with high accuracy.

What Makes a Message Stick? The Role of Content and Context in Social Media Epidemics

DTIC Science & Technology

2013-09-23

First, we propose visual memes , or frequently re-posted short video segments, for detecting and monitoring latent video interactions at scale. Content...interactions (such as quoting, or remixing, parts of a video). Visual memes are extracted by scalable detection algorithms that we develop, with...high accuracy. We further augment visual memes with text, via a statistical model of latent topics. We model content interactions on YouTube with

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

[Evaluation of the Performance of Two Kinds of Anti-TP Enzyme-Linked Immunosorbent Assay].

PubMed

Gao, Nan; Huang, Li-Qin; Wang, Rui; Jia, Jun-Jie; Wu, Shuo; Zhang, Jing; Ge, Hong-Wei

2018-06-01

To evaluate the accuracy and precision of 2 kinds of anti-treponema pallidum (anti-TP) ELISA reagents in our laboratory for detecting the anti-TP in voluntary blood donors, so as to provide the data support for use of ELISA reagents after introduction of chemiluminescene immunoassay (CLIA). The route detection of anti-TP was performed by using 2 kinds of ELISA reagents, then 546 responsive positive samples detected by anti-TP ELISA were collected, and the infections status of samples confirmed by treponema pallidum particle agglutination (TPPA) test was identified. The confirmed results of responsive samples detected by 2 kinds of anti-TP ELISA reagents were compared, the accuracy of 2 kinds of anti-TP ELISA reagents was analyzed by drawing ROC and comparing area under curve (AUC), and precision of 2 kinds of anti-TP ELISA reagents was compared by statistical analysis of quality control data from 7.1 2016 to 6.30 2017. There were no statistical difference in confirmed positive rate of responsive samples and weak positive samples between 2 kinds of anti-TP ELISA reagents. The responsive samples detected by 2 kinds of anti-TP ELISA reagents accounted for 85.53%(467/546) of all responsive samples, the positive rate confirmed by TPPA test was 82.87%. 44 responsive samples detected by anti-TP ELISA reagent A and 35 responsive samples detected by anti-TP ELISA reagent B were confirmed to be negative by TPPA test. Comparison of AUC showed that the accuracy of 2 kinds of anti-TP ELISA reagents was more high, the difference between 2 reagents was not statistically significant. The coefficient of variation (CV) of anti-TP ELISA reagent A and B was 14.98% and 18.04% respectively, which met the precision requirement of ELISA test. The accuracy and precision of 2 kinds of anti-TP ELISA reagents used in our laboratory are similar, and using any one of anti-TP ELISA reagents all can satisfy the requirements of blood screening.

Free-breathing diffusion-weighted single-shot echo-planar MR imaging using parallel imaging (GRAPPA 2) and high b value for the detection of primary rectal adenocarcinoma.

PubMed

Soyer, Philippe; Lagadec, Matthieu; Sirol, Marc; Dray, Xavier; Duchat, Florent; Vignaud, Alexandre; Fargeaudou, Yann; Placé, Vinciane; Gault, Valérie; Hamzi, Lounis; Pocard, Marc; Boudiaf, Mourad

2010-02-11

Our objective was to determine the diagnostic accuracy of a free-breathing diffusion-weighted single-shot echo-planar magnetic resonance imaging (FBDW-SSEPI) technique with parallel imaging and high diffusion factor value (b = 1000 s/mm2) in the detection of primary rectal adenocarcinomas. Thirty-one patients (14M and 17F; mean age 67 years) with histopathologically proven primary rectal adenocarcinomas and 31 patients without rectal malignancies (14M and 17F; mean age 63.6 years) were examined with FBDW-SSEPI (repetition time (TR/echo time (TE) 3900/91 ms, gradient strength 45 mT/m, acquisition time 2 min) at 1.5 T using generalized autocalibrating partially parallel acquisitions (GRAPPA, acceleration factor 2) and a b value of 1000 s/mm2. Apparent diffusion coefficients (ADCs) of rectal adenocarcinomas and normal rectal wall were measured. FBDW-SSEPI images were evaluated for tumour detection by 2 readers. Sensitivity, specificity, accuracy and Youden score for rectal adenocarcinoma detection were calculated with their 95% confidence intervals (CI) for ADC value measurement and visual image analysis. Rectal adenocarcinomas had significantly lower ADCs (mean 1.036 x 10(-3)+/- 0.107 x 10(-3) mm2/s; median 1.015 x 10(-3) mm2/s; range (0.827-1.239) x 10(-3) mm2/s) compared with the rectal wall of control subjects (mean 1.387 x 10(-3)+/- 0.106 x 10(-3) mm2/s; median 1.385 x 10(-3) mm2/s; range (1.176-1.612) x 10(-3) mm2/s) (p < 0.0001). Using a threshold value < or = 1.240 x 10(-3) mm2/s, all rectal adenocarcinomas were correctly categorized and 100% sensitivity (31/31; 95% CI 95-100%), 94% specificity (31/33; 95% CI 88-100%), 97% accuracy (60/62; 95% CI 92-100%) and Youden index 0.94 were obtained for the diagnosis of rectal adenocarcinoma. FBDW-SSEPI image analysis allowed depiction of all rectal adenocarcinomas but resulted in 2 false-positive findings, yielding 100% sensitivity (31/31; 95% CI 95-100%), 94% specificity (31/33; 95% CI 88-100%), 97% accuracy (60/62; 95% CI 92-100%) and Youden index 0.94 for the diagnosis of primary rectal adenocarcinoma. We can conclude that FBDW-SSEPI using parallel imaging and high b value may be helpful in the detection of primary rectal adenocarcinomas.

How personal standards perfectionism and evaluative concerns perfectionism affect the error positivity and post-error behavior with varying stimulus visibility.

PubMed

Drizinsky, Jessica; Zülch, Joachim; Gibbons, Henning; Stahl, Jutta

2016-10-01

Error detection is required in order to correct or avoid imperfect behavior. Although error detection is beneficial for some people, for others it might be disturbing. We investigated Gaudreau and Thompson's (Personality and Individual Differences, 48, 532-537, 2010) model, which combines personal standards perfectionism (PSP) and evaluative concerns perfectionism (ECP). In our electrophysiological study, 43 participants performed a combination of a modified Simon task, an error awareness paradigm, and a masking task with a variation of stimulus onset asynchrony (SOA; 33, 67, and 100 ms). Interestingly, relative to low-ECP participants, high-ECP participants showed a better post-error accuracy (despite a worse classification accuracy) in the high-visibility SOA 100 condition than in the two low-visibility conditions (SOA 33 and SOA 67). Regarding the electrophysiological results, first, we found a positive correlation between ECP and the amplitude of the error positivity (Pe) under conditions of low stimulus visibility. Second, under the condition of high stimulus visibility, we observed a higher Pe amplitude for high-ECP-low-PSP participants than for high-ECP-high-PSP participants. These findings are discussed within the framework of the error-processing avoidance hypothesis of perfectionism (Stahl, Acharki, Kresimon, Völler, & Gibbons, International Journal of Psychophysiology, 97, 153-162, 2015).

Development of a high sensitivity pinhole type gamma camera using semiconductors for low dose rate fields

NASA Astrophysics Data System (ADS)

Ueno, Yuichiro; Takahashi, Isao; Ishitsu, Takafumi; Tadokoro, Takahiro; Okada, Koichi; Nagumo, Yasushi; Fujishima, Yasutake; Yoshida, Akira; Umegaki, Kikuo

2018-06-01

We developed a pinhole type gamma camera, using a compact detector module of a pixelated CdTe semiconductor, which has suitable sensitivity and quantitative accuracy for low dose rate fields. In order to improve the sensitivity of the pinhole type semiconductor gamma camera, we adopted three methods: a signal processing method to set the discriminating level lower, a high sensitivity pinhole collimator and a smoothing image filter that improves the efficiency of the source identification. We tested basic performances of the developed gamma camera and carefully examined effects of the three methods. From the sensitivity test, we found that the effective sensitivity was about 21 times higher than that of the gamma camera for high dose rate fields which we had previously developed. We confirmed that the gamma camera had sufficient sensitivity and high quantitative accuracy; for example, a weak hot spot (0.9 μSv/h) around a tree root could be detected within 45 min in a low dose rate field test, and errors of measured dose rates with point sources were less than 7% in a dose rate accuracy test.

Detection of protein-small molecule binding using a self-referencing external cavity laser biosensor.

PubMed

Meng Zhang; Peh, Jessie; Hergenrother, Paul J; Cunningham, Brian T

2014-01-01

High throughput screening of protein-small molecule binding interactions using label-free optical biosensors is challenging, as the detected signals are often similar in magnitude to experimental noise. Here, we describe a novel self-referencing external cavity laser (ECL) biosensor approach that achieves high resolution and high sensitivity, while eliminating thermal noise with sub-picometer wavelength accuracy. Using the self-referencing ECL biosensor, we demonstrate detection of binding between small molecules and a variety of immobilized protein targets with binding affinities or inhibition constants in the sub-nanomolar to low micromolar range. The demonstrated ability to perform detection in the presence of several interfering compounds opens the potential for increasing the throughput of the approach. As an example application, we performed a "needle-in-the-haystack" screen for inhibitors against carbonic anhydrase isozyme II (CA II), in which known inhibitors are clearly differentiated from inactive molecules within a compound library.

Waveguide integrated low noise NbTiN nanowire single-photon detectors with milli-Hz dark count rate

PubMed Central

Schuck, Carsten; Pernice, Wolfram H. P.; Tang, Hong X.

2013-01-01

Superconducting nanowire single-photon detectors are an ideal match for integrated quantum photonic circuits due to their high detection efficiency for telecom wavelength photons. Quantum optical technology also requires single-photon detection with low dark count rate and high timing accuracy. Here we present very low noise superconducting nanowire single-photon detectors based on NbTiN thin films patterned directly on top of Si3N4 waveguides. We systematically investigate a large variety of detector designs and characterize their detection noise performance. Milli-Hz dark count rates are demonstrated over the entire operating range of the nanowire detectors which also feature low timing jitter. The ultra-low dark count rate, in combination with the high detection efficiency inherent to our travelling wave detector geometry, gives rise to a measured noise equivalent power at the 10−20 W/Hz1/2 level. PMID:23714696

Hollow fiber surface plasmon resonance sensor for the detection of liquid with high refractive index.

PubMed

Liu, Bing-Hong; Jiang, Yong-Xiang; Zhu, Xiao-Song; Tang, Xiao-Li; Shi, Yi-Wei

2013-12-30

A new kind of surface plasmon resonance (SPR) sensor based on silver-coated hollow fiber (HF) structure for the detection of liquids with high refractive index (RI) is presented. Liquid sensed medium with high RI is filled in the hollow core of the HF and its RI can be detected by measuring the transmission spectra of the HF SPR sensor. The designed sensors with different silver thicknesses are fabricated and the transmission spectra for filled liquids with different RI are measured to investigate the performances of the sensors. Theoretical analysis is also carried out to evaluate the performance. The simulation results agree well with the experimental results. Factors that might affect sensitivity and detection accuracy of the sensor are discussed. The highest sensitivity achieved is 6,607 nm/RIU, which is comparable to the sensitivities of the other reported fiber SPR sensors.

Hierarchical Leak Detection and Localization Method in Natural Gas Pipeline Monitoring Sensor Networks

PubMed Central

Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

2012-01-01

In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point’s position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate. PMID:22368464

Hierarchical leak detection and localization method in natural gas pipeline monitoring sensor networks.

PubMed

Wan, Jiangwen; Yu, Yang; Wu, Yinfeng; Feng, Renjian; Yu, Ning

2012-01-01

In light of the problems of low recognition efficiency, high false rates and poor localization accuracy in traditional pipeline security detection technology, this paper proposes a type of hierarchical leak detection and localization method for use in natural gas pipeline monitoring sensor networks. In the signal preprocessing phase, original monitoring signals are dealt with by wavelet transform technology to extract the single mode signals as well as characteristic parameters. In the initial recognition phase, a multi-classifier model based on SVM is constructed and characteristic parameters are sent as input vectors to the multi-classifier for initial recognition. In the final decision phase, an improved evidence combination rule is designed to integrate initial recognition results for final decisions. Furthermore, a weighted average localization algorithm based on time difference of arrival is introduced for determining the leak point's position. Experimental results illustrate that this hierarchical pipeline leak detection and localization method could effectively improve the accuracy of the leak point localization and reduce the undetected rate as well as false alarm rate.

Head-To-Head Comparison Between High- and Standard-b-Value DWI for Detecting Prostate Cancer: A Systematic Review and Meta-Analysis.

PubMed

Woo, Sungmin; Suh, Chong Hyun; Kim, Sang Youn; Cho, Jeong Yeon; Kim, Seung Hyup

2018-01-01

The purpose of this study was to perform a head-to-head comparison between high-b-value (> 1000 s/mm 2 ) and standard-b-value (800-1000 s/mm 2 ) DWI regarding diagnostic performance in the detection of prostate cancer. The MEDLINE and EMBASE databases were searched up to April 1, 2017. The analysis included diagnostic accuracy studies in which high- and standard-b-value DWI were used for prostate cancer detection with histopathologic examination as the reference standard. Methodologic quality was assessed with the revised Quality Assessment of Diagnostic Accuracy Studies tool. Sensitivity and specificity of all studies were calculated and were pooled and plotted in a hierarchic summary ROC plot. Meta-regression and multiple-subgroup analyses were performed to compare the diagnostic performances of high- and standard-b-value DWI. Eleven studies (789 patients) were included. High-b-value DWI had greater pooled sensitivity (0.80 [95% CI, 0.70-0.87]) (p = 0.03) and specificity (0.92 [95% CI, 0.87-0.95]) (p = 0.01) than standard-b-value DWI (sensitivity, 0.78 [95% CI, 0.66-0.86]); specificity, 0.87 [95% CI, 0.77-0.93] (p < 0.01). Multiple-subgroup analyses showed that specificity was consistently higher for high- than for standard-b-value DWI (p ≤ 0.05). Sensitivity was significantly higher for high- than for standard-b-value DWI only in the following subgroups: peripheral zone only, transition zone only, multiparametric protocol (DWI and T2-weighted imaging), visual assessment of DW images, and per-lesion analysis (p ≤ 0.04). In a head-to-head comparison, high-b-value DWI had significantly better sensitivity and specificity for detection of prostate cancer than did standard-b-value DWI. Multiple-subgroup analyses showed that specificity was consistently superior for high-b-value DWI.

Diagnostic accuracy of functional, imaging and biochemical tests for patients presenting with chest pain to the emergency department: A systematic review and meta-analysis.

PubMed

Iannaccone, Mario; Gili, Sebastiano; De Filippo, Ovidio; D'Amico, Salvatore; Gagliardi, Marco; Bertaina, Maurizio; Mazzilli, Silvia; Rettegno, Sara; Bongiovanni, Federica; Gatti, Paolo; Ugo, Fabrizio; Boccuzzi, Giacomo G; Colangelo, Salvatore; Prato, Silvia; Moretti, Claudio; D'Amico, Maurizio; Noussan, Patrizia; Garbo, Roberto; Hildick-Smith, David; Gaita, Fiorenzo; D'Ascenzo, Fabrizio

2018-01-01

Non-invasive ischaemia tests and biomarkers are widely adopted to rule out acute coronary syndrome in the emergency department. Their diagnostic accuracy has yet to be precisely defined. Medline, Cochrane Library CENTRAL, EMBASE and Biomed Central were systematically screened (start date 1 September 2016, end date 1 December 2016). Prospective studies (observational or randomised controlled trial) comparing functional/imaging or biochemical tests for patients presenting with chest pain to the emergency department were included. Overall, 77 studies were included, for a total of 49,541 patients (mean age 59.9 years). Fast and six-hour highly sensitive troponin T protocols did not show significant differences in their ability to detect acute coronary syndromes, as they reported a sensitivity and specificity of 0.89 (95% confidence interval 0.79-0.94) and 0.84 (0.74-0.9) vs 0.89 (0.78-0.94) and 0.83 (0.70-0.92), respectively. The addition of copeptin to troponin increased sensitivity and reduced specificity, without improving diagnostic accuracy. The diagnostic value of non-invasive tests for patients without troponin increase was tested. Coronary computed tomography showed the highest level of diagnostic accuracy (sensitivity 0.93 (0.81-0.98) and specificity 0.90 (0.93-0.94)), along with myocardial perfusion scintigraphy (sensitivity 0.85 (0.77-0.91) and specificity 0.92 (0.83-0.96)). Stress echography was inferior to coronary computed tomography but non-inferior to myocardial perfusion scintigraphy, while exercise testing showed the lower level of diagnostic accuracy. Fast and six-hour highly sensitive troponin T protocols provide an overall similar level of diagnostic accuracy to detect acute coronary syndrome. Among the non-invasive ischaemia tests for patients without troponin increase, coronary computed tomography and myocardial perfusion scintigraphy showed the highest sensitivity and specificity.

Accuracy of body fat percent and adiposity indicators cut off values to detect metabolic risk factors in a sample of Mexican adults

PubMed Central

2014-01-01

Background Although body fat percent (BF%) may be used for screening metabolic risk factors, its accuracy compared to BMI and waist circumference is unknown in a Mexican population. We compared the classification accuracy of BF%, BMI and WC for the detection of metabolic risk factors in a sample of Mexican adults; optimized cutoffs as well as sensitivity and specificity at commonly used BF% and BMI international cutoffs were estimated. We also estimated conditional BF% means at BMI international cutoffs. Methods We performed a cross-sectional analysis of data on body composition, anthropometry and metabolic risk factors(high glucose, high triglycerides, low HDL cholesterol and hypertension) from 5,100 Mexican men and women. The association between BMI, WC and BF%was evaluated with linear regression models. The BF%, BMI and WC optimal cutoffs for the detection of metabolic risk factors were selected at the point where sensitivity was closest to specificity. Areas under the ROC Curve (AUC) were compared among classifiers using a non-parametric method. Results After adjustment for WC, a 1% increase in BMI was associated with a BF% rise of 0.05 percentage points (p.p.) in men (P < 0.05) and 0.25 p.p. in women (P < 0.001). At BMI = 25.0 predicted BF% was 27.6 ± 0.16 (mean ± SE) in men and 41.2 ± 0.07 in women. Estimated BF% cutoffs for detection of metabolic risk factors were close to 30.0 in men and close to 44.0 in women. In men WC had higher AUC than BF% for the classification of all conditions whereas BMI had higher AUC than BF% for the classification of high triglycerides and hypertension. In womenBMI and WC had higher AUC than BF% for the classification of all metabolic risk factors. Conclusions BMI and WC were more accurate than BF% for classifying the studied metabolic disorders. International BF% cutoffs had very low specificity and thus produced a high rate of false positives in both sexes. PMID:24721260

Accuracy and high-speed technique for autoprocessing of Young's fringes

NASA Astrophysics Data System (ADS)

Chen, Wenyi; Tan, Yushan

1991-12-01

In this paper, an accurate and high-speed method for auto-processing of Young's fringes is proposed. A group of 1-D sampled intensity values along three or more different directions are taken from Young's fringes, and the fringe spacings of each direction are obtained by 1-D FFT respectively. Two directions that have smaller fringe spacing are selected from all directions. The accurate fringe spacings along these two directions are obtained by using orthogonal coherent phase detection technique (OCPD). The actual spacing and angle of Young's fringes, therefore, can be calculated. In this paper, the principle of OCPD is introduced in detail. The accuracy of the method is evaluated theoretically and experimentally.

Accuracy of CT Colonography for Detection of Polypoid and Nonpolypoid Neoplasia by Gastroenterologists and Radiologists: A Nationwide Multicenter Study in Japan.

PubMed

Nagata, Koichi; Endo, Shungo; Honda, Tetsuro; Yasuda, Takaaki; Hirayama, Michiaki; Takahashi, Sho; Kato, Takashi; Horita, Shoichi; Furuya, Ken; Kasai, Kenji; Matsumoto, Hiroshi; Kimura, Yoshiki; Utano, Kenichi; Sugimoto, Hideharu; Kato, Hiroyuki; Yamada, Rieko; Yamamichi, Junta; Shimamoto, Takeshi; Ryu, Yasuji; Matsui, Osamu; Kondo, Hitoshi; Doi, Ayako; Abe, Taro; Yamano, Hiro-O; Takeuchi, Ken; Hanai, Hiroyuki; Saida, Yukihisa; Fukuda, Katsuyuki; Näppi, Janne; Yoshida, Hiroyuki

2017-01-01

The objective of this study was to assess prospectively the diagnostic accuracy of computer-assisted computed tomographic colonography (CTC) in the detection of polypoid (pedunculated or sessile) and nonpolypoid neoplasms and compare the accuracy between gastroenterologists and radiologists. This nationwide multicenter prospective controlled trial recruited 1,257 participants with average or high risk of colorectal cancer at 14 Japanese institutions. Participants had CTC and colonoscopy on the same day. CTC images were interpreted independently by trained gastroenterologists and radiologists. The main outcome was the accuracy of CTC in the detection of neoplasms ≥6 mm in diameter, with colonoscopy results as the reference standard. Detection sensitivities of polypoid vs. nonpolypoid lesions were also evaluated. Of the 1,257 participants, 1,177 were included in the final analysis: 42 (3.6%) were at average risk of colorectal cancer, 456 (38.7%) were at elevated risk, and 679 (57.7%) had recent positive immunochemical fecal occult blood tests. The overall per-participant sensitivity, specificity, and positive and negative predictive values for neoplasms ≥6 mm in diameter were 0.90, 0.93, 0.83, and 0.96, respectively, among gastroenterologists and 0.86, 0.90, 0.76, and 0.95 among radiologists (P<0.05 for gastroenterologists vs. radiologists). The sensitivity and specificity for neoplasms ≥10 mm in diameter were 0.93 and 0.99 among gastroenterologists and 0.91 and 0.98 among radiologists (not significant for gastroenterologists vs. radiologists). The CTC interpretation time by radiologists was shorter than that by gastroenterologists (9.97 vs. 15.8 min, P<0.05). Sensitivities for pedunculated and sessile lesions exceeded those for flat elevated lesions ≥10 mm in diameter in both groups (gastroenterologists 0.95, 0.92, and 0.68; radiologists: 0.94, 0.87, and 0.61; P<0.05 for polypoid vs. nonpolypoid), although not significant (P>0.05) for gastroenterologists vs. radiologists. CTC interpretation by gastroenterologists and radiologists was accurate for detection of polypoid neoplasms, but less so for nonpolypoid neoplasms. Gastroenterologists had a higher accuracy in the detection of neoplasms ≥6 mm than did radiologists, although their interpretation time was longer than that of radiologists.

Detection of Unknown LEO Satellite Using Radar Measurements

NASA Astrophysics Data System (ADS)

Kamensky, S.; Samotokhin, A.; Khutorovsky, Z.; Alfriend, T.

While processing of the radar information aimed at satellite catalog maintenance some measurements do not correlate with cataloged and tracked satellites. These non-correlated measurements participate in the detection (primary orbit determination) of new (not cataloged) satellites. The satellite is considered newly detected when it is missing in the catalog and the primary orbit determination on the basis of the non-correlated measurements provides the accuracy sufficient for reliable correlation of future measurements. We will call this the detection condition. One non-correlated measurement in real conditions does not have enough accuracy and thus does not satisfy the detection condition. Two measurements separated by a revolution or more normally provides orbit determination with accuracy sufficient for selection of other measurements. However, it is not always possible to say with high probability (close to 1) that two measurements belong to one satellite. Three measurements for different revolutions, which are included into one orbit, have significantly higher chances to belong to one satellite. Thus the suggested detection (primary orbit determination) algorithm looks for three uncorrelated measurements in different revolutions for which we can determine the orbit inscribing them. The detection procedure based on search for the triplets is rather laborious. Thus only relatively high efficiency can be the reason for its practical implementation. The work presents the detailed description of the suggested detection procedure based on the search for triplets of uncorrelated measurements (for radar measurements). The break-ups of the tracked satellites provide the most difficult conditions for the operation of the detection algorithm and reveal explicitly its characteristics. The characteristics of time efficiency and reliability of the detected orbits are of maximum interest. Within this work we suggest to determine these characteristics using simulation of break-ups with further acquisition of measurements generated by the fragments. In particular, using simulation we can not only evaluate the characteristics of the algorithm but adjust its parameters for certain conditions: the orbit of the fragmented satellite, the features of the break-up, capabilities of detection radars etc. We describe the algorithm performing the simulation of radar measurements produced by the fragments of the parent satellite. This algorithm accounts of the basic factors affecting the characteristics of time efficiency and reliability of the detection. The catalog maintenance algorithm includes two major components detection and tracking. These are two processes permanently interacting with each other. This is actually in place for the processing of real radar data. The simulation must take this into account since one cannot obtain reliable characteristics of detection procedure simulating only this process. Thus we simulated both processes in their interaction. The work presents the results of simulation for the simplest case of a break-up in near-circular orbit with insignificant atmospheric drag. The simulations show rather high efficiency. We demonstrate as well that the characteristics of time efficiency and reliability of determined orbits essentially depend on the density of the observed break-up fragments.

Direct Detection Doppler Lidar for Spaceborne Wind Measurement

NASA Technical Reports Server (NTRS)

Korb, C. Laurence; Flesia, Cristina

1999-01-01

Aerosol and molecular based versions of the double-edge technique can be used for direct detection Doppler lidar spaceborne wind measurement. The edge technique utilizes the edge of a high spectral resolution filter for high accuracy wind measurement using direct detection lidar. The signal is split between an edge filter channel and a broadband energy monitor channel. The energy monitor channel is used for signal normalization. The edge measurement is made as a differential frequency measurement between the outgoing laser signal and the atmospheric backscattered return for each pulse. As a result the measurement is insensitive to laser and edge filter frequency jitter and drift at a level less than a few parts in 10(exp 10). We have developed double edge versions of the edge technique for aerosol and molecular-based lidar measurement of the wind. Aerosol-based wind measurements have been made at Goddard Space Flight Center and molecular-based wind measurements at the University of Geneva. We have demonstrated atmospheric measurements using these techniques for altitudes from 1 to more than 10 km. Measurement accuracies of better than 1.25 m/s have been obtained with integration times from 5 to 30 seconds. The measurements can be scaled to space and agree, within a factor of two, with satellite-based simulations of performance based on Poisson statistics. The theory of the double edge aerosol technique is described by a generalized formulation which substantially extends the capabilities of the edge technique. It uses two edges with opposite slopes located about the laser frequency at approximately the half-width of each edge filter. This doubles the signal change for a given Doppler shift and yields a factor of 1.6 improvement in the measurement accuracy compared to the single edge technique. The use of two high resolution edge filters substantially reduces the effects of Rayleigh scattering on the measurement, as much as order of magnitude, and allows the signal to noise ratio to be substantially improved in areas of low aerosol backscatter. We describe a method that allows the Rayleigh and aerosol components of the signal to be independently determined using the two edge channels and an energy monitor channel. The effects of Rayleigh scattering may then subtracted from the measurement and we show that the correction process does not significantly increase the measurement noise for Rayleigh to aerosol ratios up to 10. We show that for small Doppler shifts a measurement accuracy of 0.4 m/s can be obtained for 5000 detected photon, 1.2 m/s for 1000 detected photons, and 3.7 m/s for 50 detected photons for a Rayleigh to aerosol ratio of 5. Methods for increasing the dynamic range of the aerosol-based system to more than +/- 100 m/s are given.

Accuracy and generalizability of using automated methods for identifying adverse events from electronic health record data: a validation study protocol.

PubMed

Rochefort, Christian M; Buckeridge, David L; Tanguay, Andréanne; Biron, Alain; D'Aragon, Frédérick; Wang, Shengrui; Gallix, Benoit; Valiquette, Louis; Audet, Li-Anne; Lee, Todd C; Jayaraman, Dev; Petrucci, Bruno; Lefebvre, Patricia

2017-02-16

Adverse events (AEs) in acute care hospitals are frequent and associated with significant morbidity, mortality, and costs. Measuring AEs is necessary for quality improvement and benchmarking purposes, but current detection methods lack in accuracy, efficiency, and generalizability. The growing availability of electronic health records (EHR) and the development of natural language processing techniques for encoding narrative data offer an opportunity to develop potentially better methods. The purpose of this study is to determine the accuracy and generalizability of using automated methods for detecting three high-incidence and high-impact AEs from EHR data: a) hospital-acquired pneumonia, b) ventilator-associated event and, c) central line-associated bloodstream infection. This validation study will be conducted among medical, surgical and ICU patients admitted between 2013 and 2016 to the Centre hospitalier universitaire de Sherbrooke (CHUS) and the McGill University Health Centre (MUHC), which has both French and English sites. A random 60% sample of CHUS patients will be used for model development purposes (cohort 1, development set). Using a random sample of these patients, a reference standard assessment of their medical chart will be performed. Multivariate logistic regression and the area under the curve (AUC) will be employed to iteratively develop and optimize three automated AE detection models (i.e., one per AE of interest) using EHR data from the CHUS. These models will then be validated on a random sample of the remaining 40% of CHUS patients (cohort 1, internal validation set) using chart review to assess accuracy. The most accurate models developed and validated at the CHUS will then be applied to EHR data from a random sample of patients admitted to the MUHC French site (cohort 2) and English site (cohort 3)-a critical requirement given the use of narrative data -, and accuracy will be assessed using chart review. Generalizability will be determined by comparing AUCs from cohorts 2 and 3 to those from cohort 1. This study will likely produce more accurate and efficient measures of AEs. These measures could be used to assess the incidence rates of AEs, evaluate the success of preventive interventions, or benchmark performance across hospitals.

Rapid Target Detection in High Resolution Remote Sensing Images Using Yolo Model

NASA Astrophysics Data System (ADS)

Wu, Z.; Chen, X.; Gao, Y.; Li, Y.

2018-04-01

Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.

Testing the discrimination and detection limits of WorldView-2 imagery on a challenging invasive plant target

NASA Astrophysics Data System (ADS)

Robinson, T. P.; Wardell-Johnson, G. W.; Pracilio, G.; Brown, C.; Corner, R.; van Klinken, R. D.

2016-02-01

Invasive plants pose significant threats to biodiversity and ecosystem function globally, leading to costly monitoring and management effort. While remote sensing promises cost-effective, robust and repeatable monitoring tools to support intervention, it has been largely restricted to airborne platforms that have higher spatial and spectral resolutions, but which lack the coverage and versatility of satellite-based platforms. This study tests the ability of the WorldView-2 (WV2) eight-band satellite sensor for detecting the invasive shrub mesquite (Prosopis spp.) in the north-west Pilbara region of Australia. Detectability was challenged by the target taxa being largely defoliated by a leaf-tying biological control agent (Gelechiidae: Evippe sp. #1) and the presence of other shrubs and trees. Variable importance in the projection (VIP) scores identified bands offering greatest capacity for discrimination were those covering the near-infrared, red, and red-edge wavelengths. Wavelengths between 400 nm and 630 nm (coastal blue, blue, green, yellow) were not useful for species level discrimination in this case. Classification accuracy was tested on three band sets (simulated standard multispectral, all bands, and bands with VIP scores ≥1). Overall accuracies were comparable amongst all band-sets (Kappa = 0.71-0.77). However, mesquite omission rates were unacceptably high (21.3%) when using all eight bands relative to the simulated standard multispectral band-set (9.5%) and the band-set informed by VIP scores (11.9%). An incremental cover evaluation on the latter identified most omissions to be for objects <16 m2. Mesquite omissions reduced to 2.6% and overall accuracy significantly improved (Kappa = 0.88) when these objects were left out of the confusion matrix calculations. Very high mapping accuracy of objects >16 m2 allows application for mapping mesquite shrubs and coalesced stands, the former not previously possible, even with 3 m resolution hyperspectral imagery. WV2 imagery offers excellent portability potential for detecting other species where spectral/spatial resolution or coverage has been an impediment. New generation satellite sensors are removing barriers previously preventing widespread adoption of remote sensing technologies in natural resource management.

Technical Advances in Endoscopic Ultrasound (EUS)-Guided Tissue Acquisition for Pancreatic Cancers: How Can We Get the Best Results with EUS-Guided Fine Needle Aspiration?

PubMed Central

Kedia, Prashant; Gaidhane, Monica

2013-01-01

Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) is one of the least invasive and most effective modality in diagnosing pancreatic adenocarcinoma in solid pancreatic lesions, with a higher diagnostic accuracy than cystic tumors. EUS-FNA has been shown to detect tumors less than 3 mm, due to high spatial resolution allowing the detection of very small lesions and vascular invasion, particularly in the pancreatic head and neck, which may not be detected on transverse computed tomography. Furthermore, this minimally invasive procedure is often ideal in the endoscopic procurement of tissue in patients with unresectable tumors. While EUS-FNA has been increasingly used as a diagnostic tool, most studies have collectively looked at all primary pancreatic solid lesions, including lymphomas and pancreatic neuroendocrine neoplasms, whereas very few studies have examined the diagnostic utility of EUS-FNA of pancreatic ductal carcinoma only. As with any novel and advanced endoscopic procedure that may incorporate several practices and approaches, endoscopists have adopted diverse techniques to improve the tissue procurement practice and increase diagnostic accuracy. In this article, we present a review of literature to date and discuss currently practiced EUS-FNA technique, including indications, technical details, equipment, patient selection, and diagnostic accuracy. PMID:24143320

A Hybrid Remote Sensing Approach for Detecting the Florida Red Tide

NASA Astrophysics Data System (ADS)

Carvalho, G. A.; Minnett, P. J.; Banzon, V.; Baringer, W.

2008-12-01

Harmful algal blooms (HABs) have caused major worldwide economic losses commonly linked with health problems for humans and wildlife. In the Eastern Gulf of Mexico the toxic marine dinoflagellate Karenia brevis is responsible for nearly annual, massive red tides causing fish kills, shellfish poisoning, and acute respiratory irritation in humans: the so-called Florida Red Tide. Near real-time satellite measurements could be an effective method for identifying HABs. The use of space-borne data would be a highly desired, low-cost technique offering the remote and accurate detection of K. brevis blooms over the West Florida Shelf, bringing tremendous societal benefits to the general public, scientific community, resource managers and medical health practitioners. An extensive in situ database provided by the Florida Fish and Wildlife Conservation Commission's Research Institute was used to examine the long-term accuracy of two satellite- based algorithms at detecting the Florida Red Tide. Using MODIS data from 2002 to 2006, the two algorithms are optimized and their accuracy assessed. It has been found that the sequential application of the algorithms results in improved predictability characteristics, correctly identifying ~80% of the cases (for both sensitivity and specificity, as well as overall accuracy), and exhibiting strong positive (70%) and negative (86%) predictive values.

Thz and Long Path Fourier Transform Spectroscopy of Methanol; Torsionally Coupled High-K Levels

NASA Astrophysics Data System (ADS)

Pearson, John C.; Yu, Shanshan; Drouin, Brian J.; Lees, Ronald M.; Xu, Li-Hong; Billinghurst, Brant E.

2012-06-01

Methanol is nearly ubiquitous in the interstellar gas. The presence of both a-type and b-type dipole moments, asymmetry, and internal rotation assure that any small astronomical observation window will contain multiple methanol transitions. This often allows a great deal about the local physical conditions to be deduced, but only insofar as the spectra are characterized. The Herschel Space Observatory has detected numerous, clearly beam diluted, methanol transitions with quanta surpassing J = 35 in many regions. Unfortunately, observations of methanol often display strong non-thermal behavior whose modeling requires many additional levels to be included in a radiative transfer analysis. Additionally, the intensities of many more highly excited transitions are strongly dependent on the accuracy of the wave functions used in the calculation. We report a combined Fourier Transform Infrared and THz study targeting the high J and K transitions in the ground torsional manifold. Microwave accuracy energy levels have been derived to J > 40 and K as high as 20. These levels illuminate a number of strongly resonant torsional interactions that dominate the high K spectrum of the molecule. Comparison with levels calculated from the rho-axis method Hamiltonian suggest that the rho-axis method should be able to model v_t = 0, 1 and probably v_t = 2 to experimental accuracy. The challenges in determining methanol wave functions to experimental accuracy will be discussed.

The added value of 68Ga-DOTA-TATE-PET to contrast-enhanced CT for primary site detection in CUP of neuroendocrine origin.

PubMed

Kazmierczak, Philipp M; Rominger, Axel; Wenter, Vera; Spitzweg, Christine; Auernhammer, Christoph; Angele, Martin K; Rist, Carsten; Cyran, Clemens C

2017-04-01

To quantify the additional value of 68 Ga-DOTA-TATE PET/CT in comparison with contrast-enhanced CT alone for primary tumour detection in neuroendocrine cancer of unknown primary (CUP-NET). In total, 38 consecutive patients (27 men, 11 women; mean age 62 years) with histologically proven CUP-NET who underwent a contrast-enhanced 68 Ga-DOTA-TATE PET/CT scan for primary tumour detection and staging between 2010 and 2014 were included in this IRB-approved retrospective study. Two blinded readers independently analysed the contrast-enhanced CT and 68 Ga-DOTA-TATE PET datasets separately and noted from which modality they suspected a primary tumour. Consensus was reached if the results were divergent. Postoperative histopathology (24 patients) and follow-up 68 Ga-DOTA-TATE PET/CT imaging (14 patients) served as the reference standards and statistical measures of diagnostic accuracy were calculated accordingly. The majority of confirmed primary tumours were located in the abdomen (ileum in 19 patients, pancreas in 12, lung in 2, small pelvis in 1). High interobserver agreement was noted regarding the suspected primary tumour site (Cohen's k 0.90, p < 0.001). 68 Ga-DOTA-TATE PET demonstrated a significantly higher sensitivity (94 % vs. 63 %, p = 0.005) and a significantly higher accuracy (87 % vs. 68 %, p = 0.003) than contrast-enhanced CT. Ga-DOTA-TATE PET/CT compared with contrast-enhanced CT alone provides an improvement in sensitivity of 50 % and an improvement in accuracy of 30 % in primary tumour detection in CUP-NET. • 68 Ga-DOTA-TATE PET augments the sensitivity of contrast-enhanced CT by 50 % • 68 Ga-DOTA-TATE PET augments the accuracy of contrast-enhanced CT by 30 % • Somatostatin receptor-targeted hybrid imaging optimizes primary tumour detection in CUP-NET.

A Fast Alignment-Free Approach for De Novo Detection of Protein Conserved Regions

PubMed Central

Abnousi, Armen; Broschat, Shira L.; Kalyanaraman, Ananth

2016-01-01

Background Identifying conserved regions in protein sequences is a fundamental operation, occurring in numerous sequence-driven analysis pipelines. It is used as a way to decode domain-rich regions within proteins, to compute protein clusters, to annotate sequence function, and to compute evolutionary relationships among protein sequences. A number of approaches exist for identifying and characterizing protein families based on their domains, and because domains represent conserved portions of a protein sequence, the primary computation involved in protein family characterization is identification of such conserved regions. However, identifying conserved regions from large collections (millions) of protein sequences presents significant challenges. Methods In this paper we present a new, alignment-free method for detecting conserved regions in protein sequences called NADDA (No-Alignment Domain Detection Algorithm). Our method exploits the abundance of exact matching short subsequences (k-mers) to quickly detect conserved regions, and the power of machine learning is used to improve the prediction accuracy of detection. We present a parallel implementation of NADDA using the MapReduce framework and show that our method is highly scalable. Results We have compared NADDA with Pfam and InterPro databases. For known domains annotated by Pfam, accuracy is 83%, sensitivity 96%, and specificity 44%. For sequences with new domains not present in the training set an average accuracy of 63% is achieved when compared to Pfam. A boost in results in comparison with InterPro demonstrates the ability of NADDA to capture conserved regions beyond those present in Pfam. We have also compared NADDA with ADDA and MKDOM2, assuming Pfam as ground-truth. On average NADDA shows comparable accuracy, more balanced sensitivity and specificity, and being alignment-free, is significantly faster. Excluding the one-time cost of training, runtimes on a single processor were 49s, 10,566s, and 456s for NADDA, ADDA, and MKDOM2, respectively, for a data set comprised of approximately 2500 sequences. PMID:27552220

Detection of Bacteriuria by Canine Olfaction

PubMed Central

Maurer, Maureen; McCulloch, Michael; Willey, Angel M.; Hirsch, Wendi; Dewey, Danielle

2016-01-01

Background. Urinary tract infections (UTIs) are a significant medical problem , particularly for patients with neurological conditions and the elderly. Detection is often difficult in these patients, resulting in delayed diagnoses and more serious infections such as pyelonephritis and life-threatening sepsis. Many patients have a higher risk of UTIs because of impaired bladder function, catheterization, and lack of symptoms. Urinary tract infections are the most common nosocomial infection; however, better strategies are needed to improve early detection of the disease. Methods. In this double-blinded, case-control, validation study, we obtained fresh urine samples daily in a consecutive case series over a period of 16 weeks. Dogs were trained to distinguish urine samples that were culture-positive for bacteriuria from those of culture-negative controls, using reward-based clicker and treat methods. Results. Samples were obtained from 687 individuals (from 3 months to 92 years of age; 86% female and 14% male; 34% culture-positive and 66% culture-negative controls). Dogs detected urine samples positive for 100 000 colony-forming units/mL Escherichia coli (N = 250 trials; sensitivity 99.6%, specificity 91.5%). Dilution of E coli urine with distilled water did not affect accuracy at 1% (sensitivity 100%, specificity 91.1%) or 0.1% (sensitivity 100%, specificity 93.6%) concentration. Diagnostic accuracy was similar to Enterococcus (n = 50; sensitivity 100%, specificity 93.9%), Klebsiella (n = 50; sensitivity 100%, specificity 95.1%), and Staphylococcus aureus (n = 50; sensitivity 100%, specificity 96.3%). All dogs performed with similarly high accuracy: overall sensitivity was at or near 100%, and specificity was above 90%. Conclusions. Canine scent detection is an accurate and feasible method for detection of bacteriuria. PMID:27186578

ScanIndel: a hybrid framework for indel detection via gapped alignment, split reads and de novo assembly.

PubMed

Yang, Rendong; Nelson, Andrew C; Henzler, Christine; Thyagarajan, Bharat; Silverstein, Kevin A T

2015-12-07

Comprehensive identification of insertions/deletions (indels) across the full size spectrum from second generation sequencing is challenging due to the relatively short read length inherent in the technology. Different indel calling methods exist but are limited in detection to specific sizes with varying accuracy and resolution. We present ScanIndel, an integrated framework for detecting indels with multiple heuristics including gapped alignment, split reads and de novo assembly. Using simulation data, we demonstrate ScanIndel's superior sensitivity and specificity relative to several state-of-the-art indel callers across various coverage levels and indel sizes. ScanIndel yields higher predictive accuracy with lower computational cost compared with existing tools for both targeted resequencing data from tumor specimens and high coverage whole-genome sequencing data from the human NIST standard NA12878. Thus, we anticipate ScanIndel will improve indel analysis in both clinical and research settings. ScanIndel is implemented in Python, and is freely available for academic use at https://github.com/cauyrd/ScanIndel.

Brain MRI Tumor Detection using Active Contour Model and Local Image Fitting Energy

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel

2014-03-01

Automatic abnormality detection in Magnetic Resonance Imaging (MRI) is an important issue in many diagnostic and therapeutic applications. Here an automatic brain tumor detection method is introduced that uses T1-weighted images and K. Zhang et. al.'s active contour model driven by local image fitting (LIF) energy. Local image fitting energy obtains the local image information, which enables the algorithm to segment images with intensity inhomogeneities. Advantage of this method is that the LIF energy functional has less computational complexity than the local binary fitting (LBF) energy functional; moreover, it maintains the sub-pixel accuracy and boundary regularization properties. In Zhang's algorithm, a new level set method based on Gaussian filtering is used to implement the variational formulation, which is not only vigorous to prevent the energy functional from being trapped into local minimum, but also effective in keeping the level set function regular. Experiments show that the proposed method achieves high accuracy brain tumor segmentation results.

Simulation study and experimental results for detection and classification of the transient capacitor inrush current using discrete wavelet transform and artificial intelligence

NASA Astrophysics Data System (ADS)

Patcharoen, Theerasak; Yoomak, Suntiti; Ngaopitakkul, Atthapol; Pothisarn, Chaichan

2018-04-01

This paper describes the combination of discrete wavelet transforms (DWT) and artificial intelligence (AI), which are efficient techniques to identify the type of inrush current, analyze the origin and possible cause on the capacitor bank switching. The experiment setup used to verify the proposed techniques can be detected and classified the transient inrush current from normal capacitor rated current. The discrete wavelet transforms are used to detect and classify the inrush current. Then, output from wavelet is acted as input of fuzzy inference system for discriminating the type of switching transient inrush current. The proposed technique shows enhanced performance with a discrimination accuracy of 90.57%. Both simulation study and experimental results are quite satisfactory with providing the high accuracy and reliability which can be developed and implemented into a numerical overcurrent (50/51) and unbalanced current (60C) protection relay for an application of shunt capacitor bank protection in the future.

Development of a computer aided diagnosis model for prostate cancer classification on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Alfano, R.; Soetemans, D.; Bauman, G. S.; Gibson, E.; Gaed, M.; Moussa, M.; Gomez, J. A.; Chin, J. L.; Pautler, S.; Ward, A. D.

2018-02-01

Multi-parametric MRI (mp-MRI) is becoming a standard in contemporary prostate cancer screening and diagnosis, and has shown to aid physicians in cancer detection. It offers many advantages over traditional systematic biopsy, which has shown to have very high clinical false-negative rates of up to 23% at all stages of the disease. However beneficial, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a solution as they have the power to perform deterministic quantitative image analysis. We measured the accuracy of such a system validated using accurately co-registered whole-mount digitized histology. We trained a logistic linear classifier (LOGLC), support vector machine (SVC), k-nearest neighbour (KNN) and random forest classifier (RFC) in a four part ROI based experiment against: 1) cancer vs. non-cancer, 2) high-grade (Gleason score ≥4+3) vs. low-grade cancer (Gleason score <4+3), 3) high-grade vs. other tissue components and 4) high-grade vs. benign tissue by selecting the classifier with the highest AUC using 1-10 features from forward feature selection. The CAD model was able to classify malignant vs. benign tissue and detect high-grade cancer with high accuracy. Once fully validated, this work will form the basis for a tool that enhances the radiologist's ability to detect malignancies, potentially improving biopsy guidance, treatment selection, and focal therapy for prostate cancer patients, maximizing the potential for cure and increasing quality of life.

Nocturnal continuous glucose monitoring: accuracy and reliability of hypoglycemia detection in patients with type 1 diabetes at high risk of severe hypoglycemia.

PubMed

Bay, Christiane; Kristensen, Peter Lommer; Pedersen-Bjergaard, Ulrik; Tarnow, Lise; Thorsteinsson, Birger

2013-05-01

A reliable method to detect biochemical nocturnal hypoglycemia is highly needed, especially in patients with recurrent severe hypoglycemia. We evaluated reliability of nocturnal continuous glucose monitoring (CGM) in patients with type 1 diabetes at high risk of severe hypoglycemia. Seventy-two type 1 diabetes patients with recurrent severe hypoglycemia (two or more events within the last year) participated for 4 nights in blinded CGM recordings (Guardian(®) REAL-Time CGMS and Sof-Sensor(®); Medtronic MiniMed, Northridge, CA). Blood was drawn hourly from 23:00 to 07:00 h for plasma glucose (PG) measurements (gold standard). Valid data were obtained in 217 nights. The sensitivity of CGM was 65% (95% confidence interval, 53-77%) below 4 mmol/L, 40% (24-56%) below 3 mmol/L, and 17% (0-47%) below 2.2 mmol/L. PG and CGM readings correlated in the total measurement range (Spearman's ρ=0.82; P<0.001). In the normo- and hyperglycemic ranges CGM underestimated PG by 1.1 mmol/L (0.9-1.2 mmol/L) (P<0.001); in contrast, in the hypoglycemic range (PG<4 mmol/L) CGM overestimated PG levels by 1.0 mmol/L (P<0.001). The mean absolute relative differences in the hypo- (≤3.9 mmol/L), normo- (4-9.9 mmol/L), and hyperglycemic (≥10 mmol/L) ranges were 45% (37-53%), 23% (22-25%), and 20% (19-21%), respectively. Continuous glucose error grid analysis indicated a clinical accuracy of 56%, 99%, and 93% in the hypo-, normo-, and hyperglycemic ranges, respectively. The accuracy in the hypoglycemic range of nocturnal CGM data using Sof-Sensor is suboptimal in type 1 diabetes patients at high risk of severe hypoglycemia. To ensure clinical useful sensitivity in detection of nocturnal hypoglycemic episodes, an alarm threshold should not be lower than 4 mmol/L.

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

High quality 3D shape reconstruction via digital refocusing and pupil apodization in multi-wavelength holographic interferometry

NASA Astrophysics Data System (ADS)

Xu, Li

Multi-wavelength holographic interferometry (MWHI) has good potential for evolving into a high quality 3D shape reconstruction technique. There are several remaining challenges, including I) depth-of-field limitation, leading to axial dimension inaccuracy of out-of-focus objects; and 2) smearing from shiny smooth objects to their dark dull neighbors, generating fake measurements within the dark area. This research is motivated by the goal of developing an advanced optical metrology system that provides accurate 3D profiles for target object or objects of axial dimension larger than the depth-of-field, and for objects with dramatically different surface conditions. The idea of employing digital refocusing in MWHI has been proposed as a solution to the depth-of-field limitation. One the one hand, traditional single wavelength refocusing formula is revised to reduce sensitivity to wavelength error. Investigation over real example demonstrates promising accuracy and repeatability of reconstructed 3D profiles. On the other hand, a phase contrast based focus detection criterion is developed especially for MWHI, which overcomes the problem of phase unwrapping. The combination for these two innovations gives birth to a systematic strategy of acquiring high quality 3D profiles. Following the first phase contrast based focus detection step, interferometric distance measurement by MWHI is implemented as a next step to conduct relative focus detection with high accuracy. This strategy results in +/-100mm 3D profile with micron level axial accuracy, which is not available in traditional extended focus image (EFI) solutions. Pupil apodization has been implemented to address the second challenge of smearing. The process of reflective rough surface inspection has been mathematically modeled, which explains the origin of stray light and the necessity of replacing hard-edged pupil with one of gradually attenuating transmission (apodization). Metrics to optimize pupil types and parameters have been chosen especially for MWHI. A Gaussian apodized pupil has been installed and tested. A reduction of smearing in measurement result has been experimentally demonstrated.

Is whole-body fluorine-18 fluorodeoxyglucose PET/CT plus additional pelvic images (oral hydration-voiding-refilling) useful for detecting recurrent bladder cancer?

PubMed

Yang, Zhongyi; Cheng, Jingyi; Pan, Lingling; Hu, Silong; Xu, Junyan; Zhang, Yongping; Wang, Mingwei; Zhang, Jianping; Ye, Dingwei; Zhang, Yingjian

2012-08-01

Because of the urinary excretion of fluorine-18 fluorodeoxyglucose ((18)F-FDG), FDG-PET or PET/CT is thought of little value in patients with bladder cancer. The purpose of our study was to investigate the value of (18)F-FDG PET/CT with additional pelvic images in detection of recurrent bladder cancers. From December 2006 to August 2010, 35 bladder cancer patients (median age 56 years old, ranging from 35 to 96) underwent routine (18)F-FDG PET/CT. To better detect bladder lesions, a new method called as oral hydration-voiding-refilling was introduced, which included that all the patients firstly received oral hydration, then were required to void frequently and finally were demanded to hold back urine when the additional pelvic images were scanned. Lesions were confirmed by either histopathology or clinical follow-up for at least 6 months. Finally, 12 recurrent cases of 35 patients were confirmed by cystoscope. PET/CT correctly detected 11 of them. Among these 11 true positive patients, 5 patients (45.5 %) were detected only after additional pelvic images. Lichenoid lesions on the bladder wall were missed, which caused 1 false negative result. All three false positive cases were testified to be inflammatory tissues by cystoscope. Therefore, the sensitivity, specificity and accuracy of PET/CT were 91.7 % (11/12), 87.0 % (20/23) and 88.6 % (31/35), respectively. PET/CT with additional pelvic images can highly detect recurrent lesions in residual bladder tissues. Our method with high accuracy and better endurance could be potentially applied.

Automatic Building Detection based on Supervised Classification using High Resolution Google Earth Images

NASA Astrophysics Data System (ADS)

Ghaffarian, S.; Ghaffarian, S.

2014-08-01

This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervised classification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions. Therefore, the methodology begins with the detection and masking out the shadow areas using luminance component of the LAB color space, which indicates the lightness of the image, and a novel double thresholding technique. Further, the training areas for supervised classification are selected by automatically determining a buffer zone on each building whose shadow is detected by using the shadow shape and the sun illumination direction. Thereafter, by calculating the statistic values of each buffer zone which is collected from the building areas the Improved Parallelepiped Supervised Classification is executed to detect the buildings. Standard deviation thresholding applied to the Parallelepiped classification method to improve its accuracy. Finally, simple morphological operations conducted for releasing the noises and increasing the accuracy of the results. The experiments were performed on set of high resolution Google Earth images. The performance of the proposed approach was assessed by comparing the results of the proposed approach with the reference data by using well-known quality measurements (Precision, Recall and F1-score) to evaluate the pixel-based and object-based performances of the proposed approach. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.4 % and 853 % overall pixel-based and object-based precision performances, respectively.

A novel new tsunami detection network using GNSS on commercial ships

NASA Astrophysics Data System (ADS)

Foster, J. H.; Ericksen, T.; Avery, J.

2015-12-01

Accurate and rapid detection and assessment of tsunamis in the open ocean is critical for predicting how they will impact distant coastlines, enabling appropriate mitigation efforts. The unexpectedly huge fault slip for the 2011 Tohoku, Japan earthquake, and the unanticipated type of slip for the 2012 event at Queen Charlotte Islands, Canada highlighted weaknesses in our understanding of earthquake and tsunami hazards, and emphasized the need for more densely-spaced observing capabilities. Crucially, when each sensor is extremely expensive to build, deploy, and maintain, only a limited network of them can be installed. Gaps in the coverage of the network as well as routine outages of instruments, limit the ability of the detection system to accurately characterize events. Ship-based geodetic GNSS has been demonstrated to be able to detect and measure the properties of tsunamis in the open ocean. Based on this approach, we have used commercial ships operating in the North Pacific to construct a pilot network of low-cost, tsunami sensors to augment the existing detection systems. Partnering with NOAA, Maersk and Matson Navigation, we have equipped 10 ships with high-accuracy GNSS systems running the Trimble RTX high-accuracy real-time positioning service. Satellite communications transmit the position data streams to our shore-side server for processing and analysis. We present preliminary analyses of this novel network, assessing the robustness of the system, the quality of the time-series and the effectiveness of various processing and filtering strategies for retrieving accurate estimates of sea surface height variations for triggering detection and characterization of tsunami in the open ocean.

Cubesat-Derived Detection of Seagrasses Using Planet Imagery Following Unmixing-Based Denoising: is Small the Next Big?

NASA Astrophysics Data System (ADS)

Traganos, D.; Cerra, D.; Reinartz, P.

2017-05-01

Seagrasses are one of the most productive and widespread yet threatened coastal ecosystems on Earth. Despite their importance, they are declining due to various threats, which are mainly anthropogenic. Lack of data on their distribution hinders any effort to rectify this decline through effective detection, mapping and monitoring. Remote sensing can mitigate this data gap by allowing retrospective quantitative assessment of seagrass beds over large and remote areas. In this paper, we evaluate the quantitative application of Planet high resolution imagery for the detection of seagrasses in the Thermaikos Gulf, NW Aegean Sea, Greece. The low Signal-to-noise Ratio (SNR), which characterizes spectral bands at shorter wavelengths, prompts the application of the Unmixing-based denoising (UBD) as a pre-processing step for seagrass detection. A total of 15 spectral-temporal patterns is extracted from a Planet image time series to restore the corrupted blue and green band in the processed Planet image. Subsequently, we implement Lyzenga's empirical water column correction and Support Vector Machines (SVM) to evaluate quantitative benefits of denoising. Denoising aids detection of Posidonia oceanica seagrass species by increasing its producer and user accuracy by 31.7 % and 10.4 %, correspondingly, with a respective increase in its Kappa value from 0.3 to 0.48. In the near future, our objective is to improve accuracies in seagrass detection by applying more sophisticated, analytical water column correction algorithms to Planet imagery, developing time- and cost-effective monitoring of seagrass distribution that will enable in turn the effective management and conservation of these highly valuable and productive ecosystems.

An AdaBoost Based Approach to Automatic Classification and Detection of Buildings Footprints, Vegetation Areas and Roads from Satellite Images

NASA Astrophysics Data System (ADS)

Gonulalan, Cansu

In recent years, there has been an increasing demand for applications to monitor the targets related to land-use, using remote sensing images. Advances in remote sensing satellites give rise to the research in this area. Many applications ranging from urban growth planning to homeland security have already used the algorithms for automated object recognition from remote sensing imagery. However, they have still problems such as low accuracy on detection of targets, specific algorithms for a specific area etc. In this thesis, we focus on an automatic approach to classify and detect building foot-prints, road networks and vegetation areas. The automatic interpretation of visual data is a comprehensive task in computer vision field. The machine learning approaches improve the capability of classification in an intelligent way. We propose a method, which has high accuracy on detection and classification. The multi class classification is developed for detecting multiple objects. We present an AdaBoost-based approach along with the supervised learning algorithm. The combi- nation of AdaBoost with "Attentional Cascade" is adopted from Viola and Jones [1]. This combination decreases the computation time and gives opportunity to real time applications. For the feature extraction step, our contribution is to combine Haar-like features that include corner, rectangle and Gabor. Among all features, AdaBoost selects only critical features and generates in extremely efficient cascade structured classifier. Finally, we present and evaluate our experimental results. The overall system is tested and high performance of detection is achieved. The precision rate of the final multi-class classifier is over 98%.

Semi-automated quantitative Drosophila wings measurements.

PubMed

Loh, Sheng Yang Michael; Ogawa, Yoshitaka; Kawana, Sara; Tamura, Koichiro; Lee, Hwee Kuan

2017-06-28

Drosophila melanogaster is an important organism used in many fields of biological research such as genetics and developmental biology. Drosophila wings have been widely used to study the genetics of development, morphometrics and evolution. Therefore there is much interest in quantifying wing structures of Drosophila. Advancement in technology has increased the ease in which images of Drosophila can be acquired. However such studies have been limited by the slow and tedious process of acquiring phenotypic data. We have developed a system that automatically detects and measures key points and vein segments on a Drosophila wing. Key points are detected by performing image transformations and template matching on Drosophila wing images while vein segments are detected using an Active Contour algorithm. The accuracy of our key point detection was compared against key point annotations of users. We also performed key point detection using different training data sets of Drosophila wing images. We compared our software with an existing automated image analysis system for Drosophila wings and showed that our system performs better than the state of the art. Vein segments were manually measured and compared against the measurements obtained from our system. Our system was able to detect specific key points and vein segments from Drosophila wing images with high accuracy.

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

PubMed

Unsrisong, Kittisak; Taphey, Siriporn; Oranratanachai, Kanokporn

2016-04-01

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

Quantitative and Sensitive Detection of Chloramphenicol by Surface-Enhanced Raman Scattering

PubMed Central

Ding, Yufeng; Yin, Hongjun; Meng, Qingyun; Zhao, Yongmei; Liu, Luo; Wu, Zhenglong; Xu, Haijun

2017-01-01

We used surface-enhanced Raman scattering (SERS) for the quantitative and sensitive detection of chloramphenicol (CAP). Using 30 nm colloidal Au nanoparticles (NPs), a low detection limit for CAP of 10−8 M was obtained. The characteristic Raman peak of CAP centered at 1344 cm−1 was used for the rapid quantitative detection of CAP in three different types of CAP eye drops, and the accuracy of the measurement result was verified by high-performance liquid chromatography (HPLC). The experimental results reveal that the SERS technique based on colloidal Au NPs is accurate and sensitive, and can be used for the rapid detection of various antibiotics. PMID:29261161

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

DOE PAGES

Bilbao, Aivett; Gibbons, Bryson C.; Slysz, Gordon W.; ...

2017-11-06

We present that the mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can yield erroneous results if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flightmore » MS. Here, in this method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases for highly saturated species and dynamic range increased by 1–2 orders of magnitude for peptides in a blood serum sample.« less

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

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

Bilbao, Aivett; Gibbons, Bryson C.; Slysz, Gordon W.

The mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can easily cause problems if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flight MS. In thismore » method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases with highly saturated species and dynamic range increased by 1-2 orders of magnitude for peptides in a blood serum sample.« less

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

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

Bilbao, Aivett; Gibbons, Bryson C.; Slysz, Gordon W.

We present that the mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can yield erroneous results if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flightmore » MS. Here, in this method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases for highly saturated species and dynamic range increased by 1–2 orders of magnitude for peptides in a blood serum sample.« less

Research of building information extraction and evaluation based on high-resolution remote-sensing imagery

NASA Astrophysics Data System (ADS)

Cao, Qiong; Gu, Lingjia; Ren, Ruizhi; Wang, Lang

2016-09-01

Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection

Sensitive and selective determination of methylenedioxylated amphetamines by high-performance liquid chromatography with fluorimetric detection.

PubMed

Sadeghipour, F; Veuthey, J L

1997-11-07

A rapid, sensitive and selective liquid chromatographic method with fluorimetric detection was developed for the separation and quantification of four methylenedioxylated amphetamines without interference of other drugs of abuse and common substances found in illicit tablets. The method was validated by examining linearity, precision and accuracy as well as detection and quantification limits. Methylenedioxylated amphetamines were quantified in eight tablets from illicit drug seizures and results were quantitatively compared to HPLC-UV analyses. To demonstrate the better sensitivity of the fluorimetric detection, methylenedioxylated amphetamines were analyzed in serum after a liquid-liquid extraction procedure and results were also compared to HPLC-UV analyses.

Detecting and treating occlusal caries lesions: a cost-effectiveness analysis.

PubMed

Schwendicke, F; Stolpe, M; Meyer-Lueckel, H; Paris, S

2015-02-01

The health gains and costs resulting from using different caries detection strategies might not only depend on the accuracy of the used method but also the treatment emanating from its use in different populations. We compared combinations of visual-tactile, radiographic, or laser-fluorescence-based detection methods with 1 of 3 treatments (non-, micro-, and invasive treatment) initiated at different cutoffs (treating all or only dentinal lesions) in populations with low or high caries prevalence. A Markov model was constructed to follow an occlusal surface in a permanent molar in an initially 12-y-old male German patient over his lifetime. Prevalence data and transition probabilities were extracted from the literature, while validity parameters of different methods were synthesized or obtained from systematic reviews. Microsimulations were performed to analyze the model, assuming a German health care setting and a mixed public-private payer perspective. Radiographic and fluorescence-based methods led to more overtreatments, especially in populations with low prevalence. For the latter, combining visual-tactile or radiographic detection with microinvasive treatment retained teeth longest (mean 66 y) at lowest costs (329 and 332 Euro, respectively), while combining radiographic or fluorescence-based detections with invasive treatment was the least cost-effective (<60 y, >700 Euro). In populations with high prevalence, combining radiographic detection with microinvasive treatment was most cost-effective (63 y, 528 Euro), while sensitive detection methods combined with invasive treatments were again the least cost-effective (<59 y, >690 Euro). The suitability of detection methods differed significantly between populations, and the cost-effectiveness was greatly influenced by the treatment initiated after lesion detection. The accuracy of a detection method relative to a "gold standard" did not automatically convey into better health or reduced costs. Detection methods should be evaluated not only against their criterion validity but also the long-term effects resulting from their use in different populations. © International & American Associations for Dental Research 2014.

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

PubMed

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

2014-07-01

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

Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification

PubMed Central

Xi, Jinxiang; Zhao, Weizhong; Yuan, Jiayao Eddie; Kim, JongWon; Si, Xiuhua; Xu, Xiaowei

2015-01-01

Background Each lung structure exhales a unique pattern of aerosols, which can be used to detect and monitor lung diseases non-invasively. The challenges are accurately interpreting the exhaled aerosol fingerprints and quantitatively correlating them to the lung diseases. Objective and Methods In this study, we presented a paradigm of an exhaled aerosol test that addresses the above two challenges and is promising to detect the site and severity of lung diseases. This paradigm consists of two steps: image feature extraction using sub-regional fractal analysis and data classification using a support vector machine (SVM). Numerical experiments were conducted to evaluate the feasibility of the breath test in four asthmatic lung models. A high-fidelity image-CFD approach was employed to compute the exhaled aerosol patterns under different disease conditions. Findings By employing the 10-fold cross-validation method, we achieved 100% classification accuracy among four asthmatic models using an ideal 108-sample dataset and 99.1% accuracy using a more realistic 324-sample dataset. The fractal-SVM classifier has been shown to be robust, highly sensitive to structural variations, and inherently suitable for investigating aerosol-disease correlations. Conclusion For the first time, this study quantitatively linked the exhaled aerosol patterns with their underlying diseases and set the stage for the development of a computer-aided diagnostic system for non-invasive detection of obstructive respiratory diseases. PMID:26422016

Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils.

PubMed

Brandes, Susanne; Mokhtari, Zeinab; Essig, Fabian; Hünniger, Kerstin; Kurzai, Oliver; Figge, Marc Thilo

2015-02-01

Time-lapse microscopy is an important technique to study the dynamics of various biological processes. The labor-intensive manual analysis of microscopy videos is increasingly replaced by automated segmentation and tracking methods. These methods are often limited to certain cell morphologies and/or cell stainings. In this paper, we present an automated segmentation and tracking framework that does not have these restrictions. In particular, our framework handles highly variable cell shapes and does not rely on any cell stainings. Our segmentation approach is based on a combination of spatial and temporal image variations to detect moving cells in microscopy videos. This method yields a sensitivity of 99% and a precision of 95% in object detection. The tracking of cells consists of different steps, starting from single-cell tracking based on a nearest-neighbor-approach, detection of cell-cell interactions and splitting of cell clusters, and finally combining tracklets using methods from graph theory. The segmentation and tracking framework was applied to synthetic as well as experimental datasets with varying cell densities implying different numbers of cell-cell interactions. We established a validation framework to measure the performance of our tracking technique. The cell tracking accuracy was found to be >99% for all datasets indicating a high accuracy for connecting the detected cells between different time points. Copyright © 2014 Elsevier B.V. All rights reserved.

Accuracy of a hexapod parallel robot kinematics based external fixator.

PubMed

Faschingbauer, Maximilian; Heuer, Hinrich J D; Seide, Klaus; Wendlandt, Robert; Münch, Matthias; Jürgens, Christian; Kirchner, Rainer

2015-12-01

Different hexapod-based external fixators are increasingly used to treat bone deformities and fractures. Accuracy has not been measured sufficiently for all models. An infrared tracking system was applied to measure positioning maneuvers with a motorized Precision Hexapod® fixator, detecting three-dimensional positions of reflective balls mounted in an L-arrangement on the fixator, simulating bone directions. By omitting one dimension of the coordinates, projections were simulated as if measured on standard radiographs. Accuracy was calculated as the absolute difference between targeted and measured positioning values. In 149 positioning maneuvers, the median values for positioning accuracy of translations and rotations (torsions/angulations) were below 0.3 mm and 0.2° with quartiles ranging from -0.5 mm to 0.5 mm and -1.0° to 0.9°, respectively. The experimental setup was found to be precise and reliable. It can be applied to compare different hexapod-based fixators. Accuracy of the investigated hexapod system was high. Copyright © 2014 John Wiley & Sons, Ltd.

Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar.

PubMed

Long, Teng; Zhang, Honggang; Zeng, Tao; Chen, Xinliang; Liu, Quanhua; Zheng, Le

2016-09-09

Distributed array radar can improve radar detection capability and measurement accuracy. However, it will suffer cyclic ambiguity in its angle estimates according to the spatial Nyquist sampling theorem since the large sparse array is undersampling. Consequently, the state estimation accuracy and track validity probability degrades when the ambiguous angles are directly used for target tracking. This paper proposes a second probability data association filter (SePDAF)-based tracking method for distributed array radar. Firstly, the target motion model and radar measurement model is built. Secondly, the fusion result of each radar's estimation is employed to the extended Kalman filter (EKF) to finish the first filtering. Thirdly, taking this result as prior knowledge, and associating with the array-processed ambiguous angles, the SePDAF is applied to accomplish the second filtering, and then achieving a high accuracy and stable trajectory with relatively low computational complexity. Moreover, the azimuth filtering accuracy will be promoted dramatically and the position filtering accuracy will also improve. Finally, simulations illustrate the effectiveness of the proposed method.

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

PubMed Central

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

2017-01-01

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

Bioassays for TSH Receptor Autoantibodies, from FRTL-5 Cells to TSH Receptor-LH/CG Receptor Chimeras: The Contribution of Leonard D. Kohn.

PubMed

Giuliani, Cesidio; Saji, Motoyasu; Bucci, Ines; Napolitano, Giorgio

2016-01-01

Since the discovery 60 years ago of the "long-acting thyroid stimulator" by Adams and Purves, great progress has been made in the detection of thyroid-stimulating hormone (TSH) receptor (TSHR) autoantibodies (TRAbs) in Graves' disease. Today, commercial assays are available that can detect TRAbs with high accuracy and provide diagnostic and prognostic evaluation of patients with Graves' disease. The present review focuses on the development of TRAbs bioassays, and particularly on the role that Leonard D. Kohn had in this. Indeed, 30 years ago, the Kohn group developed a bioassay based on the use of FRTL-5 cells that was characterized by high reproducibility, feasibility, and diagnostic accuracy. Using this FRTL-5 bioassay, Kohn and his colleagues were the first to develop monoclonal antibodies (moAbs) against the TSHR. Furthermore, they demonstrated the multifaceted functional nature of TRAbs in patients with Graves' disease, with the identification of stimulating and blocking TRAbs, and even antibodies that activated pathways other than cAMP. After the cloning of the TSHR, the Kohn laboratory constructed human TSHR-rat luteinizing hormone/chorionic gonadotropin receptor chimeras. This paved the way to a new bioassay based on the use of non-thyroid cells transfected with the Mc4 chimera. The new Mc4 bioassay is characterized by high diagnostic and prognostic accuracy, greater than for other assays. The availability of a commercial kit based on the Mc4 chimera is spreading the use of this assay worldwide, indicating its benefits for these patients with Graves' disease. This review also describes the main contributions made by other researchers in TSHR molecular biology and TRAbs assay, especially with the development of highly potent moAbs. A comparison of the diagnostic accuracies of the main TRAbs assays, as both immunoassays and bioassays, is also provided.

Bioassays for TSH Receptor Autoantibodies, from FRTL-5 Cells to TSH Receptor–LH/CG Receptor Chimeras: The Contribution of Leonard D. Kohn

PubMed Central

Giuliani, Cesidio; Saji, Motoyasu; Bucci, Ines; Napolitano, Giorgio

2016-01-01

Since the discovery 60 years ago of the “long-acting thyroid stimulator” by Adams and Purves, great progress has been made in the detection of thyroid-stimulating hormone (TSH) receptor (TSHR) autoantibodies (TRAbs) in Graves’ disease. Today, commercial assays are available that can detect TRAbs with high accuracy and provide diagnostic and prognostic evaluation of patients with Graves’ disease. The present review focuses on the development of TRAbs bioassays, and particularly on the role that Leonard D. Kohn had in this. Indeed, 30 years ago, the Kohn group developed a bioassay based on the use of FRTL-5 cells that was characterized by high reproducibility, feasibility, and diagnostic accuracy. Using this FRTL-5 bioassay, Kohn and his colleagues were the first to develop monoclonal antibodies (moAbs) against the TSHR. Furthermore, they demonstrated the multifaceted functional nature of TRAbs in patients with Graves’ disease, with the identification of stimulating and blocking TRAbs, and even antibodies that activated pathways other than cAMP. After the cloning of the TSHR, the Kohn laboratory constructed human TSHR–rat luteinizing hormone/chorionic gonadotropin receptor chimeras. This paved the way to a new bioassay based on the use of non-thyroid cells transfected with the Mc4 chimera. The new Mc4 bioassay is characterized by high diagnostic and prognostic accuracy, greater than for other assays. The availability of a commercial kit based on the Mc4 chimera is spreading the use of this assay worldwide, indicating its benefits for these patients with Graves’ disease. This review also describes the main contributions made by other researchers in TSHR molecular biology and TRAbs assay, especially with the development of highly potent moAbs. A comparison of the diagnostic accuracies of the main TRAbs assays, as both immunoassays and bioassays, is also provided. PMID:27504107

A Star Image Extractor for the Nano-JASMINE satellite

NASA Astrophysics Data System (ADS)

Yamauchi, M.; Gouda, N.; Kobayashi, Y.; Tsujimoto, T.; Yano, T.; Suganuma, M.; Yamada, Y.; Nakasuka, S.; Sako, N.

2008-07-01

We have developped a software of Star-Image-Extractor (SIE) which works as the on-board real-time image processor. It detects and extracts only the object data from raw image data. SIE has two functions: reducing image data and providing data for the satellite's high accuracy attitude control system.

SU-G-201-03: Automation of High Dose Rate Brachytherapy Quality Assurance: Development of a Radioluminescent Detection System for Simultaneous Detection of Activity, Timing, and Positioning

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

Jenkins, C; Xing, L; Fahimian, B

Purpose: Accuracy of positioning, timing and activity is of critical importance for High Dose Rate (HDR) brachytherapy delivery. Respective measurements via film autoradiography, stop-watches and well chambers can be cumbersome, crude or lack dynamic source evaluation capabilities. To address such limitations, a single device radioluminescent detection system enabling automated real-time quantification of activity, position and timing accuracy is presented and experimentally evaluated. Methods: A radioluminescent sheet was fabricated by mixing Gd?O?S:Tb with PDMS and incorporated into a 3D printed device where it was fixated below a CMOS digital camera. An Ir-192 HDR source (VS2000, VariSource iX) with an effective activemore » length of 5 mm was introduced using a 17-gauge stainless steel needle below the sheet. Pixel intensity values for determining activity were taken from an ROI centered on the source location. A calibration curve relating intensity values to activity was generated and used to evaluate automated activity determination with data gathered over 6 weeks. Positioning measurements were performed by integrating images for an entire delivery and fitting peaks to the resulting profile. Timing measurements were performed by evaluating source location and timestamps from individual images. Results: Average predicted activity error over 6 weeks was .35 ± .5%. The distance between four dwell positions was determined by the automated system to be 1.99 ± .02 cm. The result from autoradiography was 2.00 ± .03 cm. The system achieved a time resolution of 10 msec and determined the dwell time to be 1.01 sec ± .02 sec. Conclusion: The system was able to successfully perform automated detection of activity, positioning and timing concurrently under a single setup. Relative to radiochromic and radiographic film-based autoradiography, which can only provide a static evaluation positioning, optical detection of temporary radiation induced luminescence enables dynamic detection of position enabling automated quantification of timing with millisecond accuracy.« less

Spectroscopic remote sensing of the distribution and persistence of oil from the Deepwater Horizon spill in Barataria Bay marshes

USGS Publications Warehouse

Kokaly, Raymond F.; Couvillion, Brady; Holloway, JoAnn M.; Roberts, Dar A.; Ustin, Susan L.; Peterson, Seth H.; Khanna, Shruti; Piazza, Sarai C.

2013-01-01

We applied a spectroscopic analysis to Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) data collected from low and medium altitudes during and after the Deepwater Horizon oil spill to delineate the distribution of oil-damaged canopies in the marshes of Barataria Bay, Louisiana. Spectral feature analysis compared the AVIRIS data to reference spectra of oiled marsh by using absorption features centered near 1.7 and 2.3 μm, which arise from CH bonds in oil. AVIRIS-derived maps of oiled shorelines from the individual dates of July 31, September 14, and October 4, 2010, were 89.3%, 89.8%, and 90.6% accurate, respectively. A composite map at 3.5 m grid spacing, accumulated from the three dates, was 93.4% accurate in detecting oiled shorelines. The composite map had 100% accuracy for detecting damaged plant canopy in oiled areas that extended more than 1.2 m into the marsh. Spatial resampling of the AVIRIS data to 30 m reduced the accuracy to 73.6% overall. However, detection accuracy remained high for oiled canopies that extended more than 4 m into the marsh (23 of 28 field reference points with oil were detected). Spectral resampling of the 3.5 m AVIRIS data to Landsat Enhanced Thematic Mapper (ETM) spectral response greatly reduced the detection of oil spectral signatures. With spatial resampling of simulated Landsat ETM data to 30 m, oil signatures were not detected. Overall, ~ 40 km of coastline, marsh comprised mainly of Spartina alterniflora and Juncus roemerianus, were found to be oiled in narrow zones at the shorelines. Zones of oiled canopies reached on average 11 m into the marsh, with a maximum reach of 21 m. The field and airborne data showed that, in many areas, weathered oil persisted in the marsh from the first field survey, July 10, to the latest airborne survey, October 4, 2010. The results demonstrate the applicability of high spatial resolution imaging spectrometer data to identifying contaminants in the environment for use in evaluating ecosystem disturbance and response.

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

PubMed

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

2013-04-02

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

A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data.

PubMed

Song, Hongchao; Jiang, Zhuqing; Men, Aidong; Yang, Bo

2017-01-01

Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE) and an ensemble k -nearest neighbor graphs- ( K -NNG-) based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity.

A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data

PubMed Central

Jiang, Zhuqing; Men, Aidong; Yang, Bo

2017-01-01

Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE) and an ensemble k-nearest neighbor graphs- (K-NNG-) based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity. PMID:29270197

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

NASA Astrophysics Data System (ADS)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Accuracy of routine magnetic resonance imaging in meniscal and ligamentous injuries of the knee: comparison with arthroscopy

PubMed Central

Behairy, Noha H.; Dorgham, Mohsen A.

2008-01-01

The aim of this study was to detect the accuracy of routine magnetic resonance imaging (MRI) done in different centres and its agreement with arthroscopy in meniscal and ligamentous injuries of the knee. We prospectively examined 70 patients ranging in age between 22 and 59 years. History taking, plain X-ray, clinical examination, routine MRI and arthroscopy were done for all patients. Sensitivity, specificity, accuracy, positive and negative predictive values, P value and kappa agreement measures were calculated. We found a sensitivity of 47 and 100%, specificity of 95 and 75% and accuracy of 73 and 78.5%, respectively, for the medial and lateral meniscus. A sensitivity of 77.8%, specificity of 100% and accuracy of 94% was noted for the anterior cruciate ligament (ACL). We found good kappa agreements (0.43 and 0.45) for both menisci and excellent agreement (0.84) for the ACL. MRI shows high accuracy and should be used as the primary diagnostic tool for selection of candidates for arthroscopy. Level of evidence: 4. PMID:18506445

Detection of weapons of mass destruction

NASA Astrophysics Data System (ADS)

Bjorkholm, Paul J.

2003-07-01

High Energy X-ray cargo screening is a mature technology that has proven its value in the detection of contraband material hidden within cargo including fully loaded sea containers. To date high energy screening has been largely applied to manifest verification and to drug detection. However, the dramatic change in world terrorism has altered the application. Now it is essential that weapons of mass destruction (WMD"s) be interdicted with incredibly high accuracy. The implication of a missed detection has gone from loss of revenue or the lowering of the street price of drugs to potentially stopping, at least for some significant time, most world commerce. Screening containers with high energy x-rays (~250+ mm of steel penetration) is capable of detecting all nuclear threats at a fraction of the strategically important mass. The screening operation can be automated so that no human decisions are required with very low false alarms. Finally, the goal of 100% inspection of cargo inbound to the United States from the twenty largest international ports is an achievable goal with hardware costs in the area of that already spent on airport security.

Design of measuring system for wire diameter based on sub-pixel edge detection algorithm

NASA Astrophysics Data System (ADS)

Chen, Yudong; Zhou, Wang

2016-09-01

Light projection method is often used in measuring system for wire diameter, which is relatively simpler structure and lower cost, and the measuring accuracy is limited by the pixel size of CCD. Using a CCD with small pixel size can improve the measuring accuracy, but will increase the cost and difficulty of making. In this paper, through the comparative analysis of a variety of sub-pixel edge detection algorithms, polynomial fitting method is applied for data processing in measuring system for wire diameter, to improve the measuring accuracy and enhance the ability of anti-noise. In the design of system structure, light projection method with orthogonal structure is used for the detection optical part, which can effectively reduce the error caused by line jitter in the measuring process. For the electrical part, ARM Cortex-M4 microprocessor is used as the core of the circuit module, which can not only drive double channel linear CCD but also complete the sampling, processing and storage of the CCD video signal. In addition, ARM microprocessor can complete the high speed operation of the whole measuring system for wire diameter in the case of no additional chip. The experimental results show that sub-pixel edge detection algorithm based on polynomial fitting can make up for the lack of single pixel size and improve the precision of measuring system for wire diameter significantly, without increasing hardware complexity of the entire system.

Optically powered oil tank multichannel detection system with optical fiber link

NASA Astrophysics Data System (ADS)

Yu, Zhijing

1998-08-01

A novel oil tanks integrative parameters measuring system with optically powered are presented. To realize optical powered and micro-power consumption multiple channels and parameters detection, the system has taken the PWM/PPM modulation, ratio measurement, time division multiplexing and pulse width division multiplexing techniques. Moreover, the system also used special pulse width discriminator and single-chip microcomputer to accomplish signal pulse separation, PPM/PWM signal demodulation, the error correction of overlapping pulse and data processing. This new transducer has provided with high characteristics: experimental transmitting distance is 500m; total consumption of the probes is less than 150 (mu) W; measurement error: +/- 0.5 degrees C and +/- 0.2 percent FS. The measurement accuracy of the liquid level and reserves is mainly determined by the pressure accuracy. Finally, some points of the experiment are given.

Profiling of Arabidopsis secondary metabolites by capillary liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry.

PubMed

von Roepenack-Lahaye, Edda; Degenkolb, Thomas; Zerjeski, Michael; Franz, Mathias; Roth, Udo; Wessjohann, Ludger; Schmidt, Jürgen; Scheel, Dierk; Clemens, Stephan

2004-02-01

Large-scale metabolic profiling is expected to develop into an integral part of functional genomics and systems biology. The metabolome of a cell or an organism is chemically highly complex. Therefore, comprehensive biochemical phenotyping requires a multitude of analytical techniques. Here, we describe a profiling approach that combines separation by capillary liquid chromatography with the high resolution, high sensitivity, and high mass accuracy of quadrupole time-of-flight mass spectrometry. About 2000 different mass signals can be detected in extracts of Arabidopsis roots and leaves. Many of these originate from Arabidopsis secondary metabolites. Detection based on retention times and exact masses is robust and reproducible. The dynamic range is sufficient for the quantification of metabolites. Assessment of the reproducibility of the analysis showed that biological variability exceeds technical variability. Tools were optimized or established for the automatic data deconvolution and data processing. Subtle differences between samples can be detected as tested with the chalcone synthase deficient tt4 mutant. The accuracy of time-of-flight mass analysis allows to calculate elemental compositions and to tentatively identify metabolites. In-source fragmentation and tandem mass spectrometry can be used to gain structural information. This approach has the potential to significantly contribute to establishing the metabolome of Arabidopsis and other model systems. The principles of separation and mass analysis of this technique, together with its sensitivity and resolving power, greatly expand the range of metabolic profiling.

Computed Tomography-Derived Fractional Flow Reserve in the Detection of Lesion-Specific Ischemia: An Integrated Analysis of 3 Pivotal Trials.

PubMed

Xu, Rende; Li, Chenguang; Qian, Juying; Ge, Junbo

2015-11-01

Invasive fractional flow reserve (FFR) is the gold standard for the determination of physiologic stenosis severity and the need for revascularization. FFR computed from standard acquired coronary computed tomographic angiography datasets (FFRCT) is an emerging technology which allows calculation of FFR using resting image data from coronary computed tomographic angiography (CCTA). However, the diagnostic accuracy of FFRCT in the evaluation of lesion-specific myocardial ischemia remains to be confirmed, especially in patients with intermediate coronary stenosis. We performed an integrated analysis of data from 3 prospective, international, and multicenter trials, which assessed the diagnostic performance of FFRCT using invasive FFR as a reference standard. Three studies evaluating 609 patients and 1050 vessels were included. The total calculated sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of FFRCT were 82.8%, 77.7%, 60.8%, 91.6%, and 79.2%, respectively, for the per-vessel analysis, and 89.4%, 70.5%, 69.7%, 89.7%, and 78.7%, respectively, for the per-patient analysis. Compared with CCTA alone, FFRCT demonstrated significantly improved accuracy (P < 0.001) in detecting lesion-specific ischemia. In patients with intermediate coronary stenosis, FFRCT remained both highly sensitive and specific with respect to the diagnosis of ischemia. In conclusion, FFRCT appears to be a reliable noninvasive alternative to invasive FFR, as it demonstrates high accuracy in the determination of anatomy and lesion-specific ischemia, which justifies the performance of additional randomized controlled trials to evaluate both the clinical benefits and the cost-effectiveness of FFRCT-guided coronary revascularization.

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

Automated detection of retinal whitening in malarial retinopathy

NASA Astrophysics Data System (ADS)

Joshi, V.; Agurto, C.; Barriga, S.; Nemeth, S.; Soliz, P.; MacCormick, I.; Taylor, T.; Lewallen, S.; Harding, S.

2016-03-01

Cerebral malaria (CM) is a severe neurological complication associated with malarial infection. Malaria affects approximately 200 million people worldwide, and claims 600,000 lives annually, 75% of whom are African children under five years of age. Because most of these mortalities are caused by the high incidence of CM misdiagnosis, there is a need for an accurate diagnostic to confirm the presence of CM. The retinal lesions associated with malarial retinopathy (MR) such as retinal whitening, vessel discoloration, and hemorrhages, are highly specific to CM, and their detection can improve the accuracy of CM diagnosis. This paper will focus on development of an automated method for the detection of retinal whitening which is a unique sign of MR that manifests due to retinal ischemia resulting from CM. We propose to detect the whitening region in retinal color images based on multiple color and textural features. First, we preprocess the image using color and textural features of the CMYK and CIE-XYZ color spaces to minimize camera reflex. Next, we utilize color features of the HSL, CMYK, and CIE-XYZ channels, along with the structural features of difference of Gaussians. A watershed segmentation algorithm is used to assign each image region a probability of being inside the whitening, based on extracted features. The algorithm was applied to a dataset of 54 images (40 with whitening and 14 controls) that resulted in an image-based (binary) classification with an AUC of 0.80. This provides 88% sensitivity at a specificity of 65%. For a clinical application that requires a high specificity setting, the algorithm can be tuned to a specificity of 89% at a sensitivity of 82%. This is the first published method for retinal whitening detection and combining it with the detection methods for vessel discoloration and hemorrhages can further improve the detection accuracy for malarial retinopathy.

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

USGS Publications Warehouse

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

2008-01-01

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

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

PubMed Central

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

2008-01-01

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

Mapping Vegetation Community Types in a Highly-Disturbed Landscape: Integrating Hiearchical Object-Based Image Analysis with Digital Surface Models

NASA Astrophysics Data System (ADS)

Snavely, Rachel A.

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island, California, this research tests the effectiveness of incorporating a hierarchal object-based image analysis (OBIA) approach with high-spatial resolution imagery and light detection and range (LiDAR) derived canopy height surfaces for mapping vegetation communities. The study is part of a large-scale research effort conducted by researchers at San Diego State University's (SDSU) Center for Earth Systems Analysis Research (CESAR) and Soil Ecology and Restoration Group (SERG), to develop an updated vegetation community map which will support both conservation and management decisions on Naval Auxiliary Landing Field (NALF) San Clemente Island. Trimble's eCognition Developer software was used to develop and generate vegetation community maps for two study sites, with and without vegetation height data as input. Overall and class-specific accuracies were calculated and compared across the two classifications. The highest overall accuracy (approximately 80%) was observed with the classification integrating airborne visible and near infrared imagery having very high spatial resolution with a LiDAR derived canopy height model. Accuracies for individual vegetation classes differed between both classification methods, but were highest when incorporating the LiDAR digital surface data. The addition of a canopy height model, however, yielded little difference in classification accuracies for areas of very dense shrub cover. Overall, the results show the utility of the OBIA approach for mapping vegetation with high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accuracy characterizing highly disturbed landscapes. The integrated imagery and digital canopy height model approach presented both advantages and limitations, which have to be considered prior to its operational use in mapping vegetation communities.

The Use of Remote Sensing to Resolve the Aerosol Radiative Forcing

NASA Technical Reports Server (NTRS)

Kaufman, Y. J.; Tanre, D.; Remer, Lorraine

1999-01-01

Satellites are used for remote sensing of aerosol optical thickness and optical properties in order to derive the aerosol direct and indirect radiative forcing of climate. Accuracy of the derived aerosol optical thickness is used as a measure of the accuracy in deriving the aerosol radiative forcing. Several questions can be asked to challenge this concept. Is the accuracy of the satellite-derived aerosol direct forcing limited to the accuracy of the measured optical thickness? What are the spectral bands needed to derive the total aerosol forcing? Does most of the direct or indirect aerosol forcing of climate originate from regions with aerosol concentrations that are high enough to be detected from space? What should be the synergism ground-based and space-borne remote sensing to solve the problem? We shall try to answer some of these questions, using AVIRIS airborne measurements and simulations.

The technology on noise reduction of the APD detection circuit

NASA Astrophysics Data System (ADS)

Wu, Xue-ying; Zheng, Yong-chao; Cui, Jian-yong

2013-09-01

The laser pulse detection is widely used in the field of laser range finders, laser communications, laser radar, laser Identification Friend or Foe, et al, for the laser pulse detection has the advantage of high accuracy, high sensitivity and strong anti-interference. The avalanche photodiodes (APD) has the advantage of high quantum efficiency, high response speed and huge gain. The APD is particularly suitable for weak signal detection. The technology that APD acts as the photodetector for weak signal reception and amplification is widely used in laser pulse detection. The APD will convert the laser signal to weak electrical signal. The weak signal is amplified, processed and exported by the circuit. In the circuit design, the optimal signal detection is one key point in photoelectric detection system. The issue discusses how to reduce the noise of the photoelectric signal detection circuit and how to improve the signal-to-noise ratio, related analysis and practice included. The essay analyzes the mathematical model of the signal-to-noise ratio for photoelectric conversion and the noise of the APD photoelectric detection system. By analysis the bandwidth of the detection system is determined, and the circuit devices are selected that match the APD. In the circuit design separated devices with low noise are combined with integrated operational amplifier for the purpose of noise reduction. The methods can effectively suppress the noise, and improve the detection sensitivity.

Multi-dynamic range compressional wave detection using optical-frequency comb

NASA Astrophysics Data System (ADS)

Minamikawa, Takeo; Masuoka, Takashi; Oe, Ryo; Nakajima, Yoshiaki; Yamaoka, Yoshihisa; Minoshima, Kaoru; Yasui, Takeshi

2018-02-01

Compressional wave detection is useful means for health monitoring of building, detection of abnormal vibration of moving objects, defect evaluation, and biomedical imaging such as echography and photoacoustic imaging. The frequency of the compressional wave is varied from quasi-static to a few tens of megahertz depending on applications. Since the dynamic range of general compressional wave detectors is limited, we need to choose a proper compressional wave detector depending on applications. For the compressional wave detection with wide dynamic range, two or more detectors with different detection ranges is required. However, these detectors with different detection ranges generally has different accuracy and precision, disabling the seamless detection over these detection ranges. In this study, we proposed a compressional wave detector employing optical frequency comb (OFC). The compressional wave was sensed with a part of an OFC cavity, being encoded into OFC. The spectrally encoded OFC was converted to radio-frequency by the frequency link nature of OFC. The compressional wave-encoded radio-frequency can therefore be directly measured with a high-speed photodetector. To enhance the dynamic range of the compressional wave detection, we developed a cavityfeedback-based system and a phase-sensitive detection system, both of which the accuracy and precision are coherently linked to these of the OFC. We provided a proof-of-principle demonstration of the detection of compressional wave from quasi-static to ultrasound wave by using the OFC-based compressional wave sensor. Our proposed approach will serve as a unique and powerful tool for detecting compressional wave versatile applications in the future.

A Dual-Mode Large-Arrayed CMOS ISFET Sensor for Accurate and High-Throughput pH Sensing in Biomedical Diagnosis.

PubMed

Huang, Xiwei; Yu, Hao; Liu, Xu; Jiang, Yu; Yan, Mei; Wu, Dongping

2015-09-01

The existing ISFET-based DNA sequencing detects hydrogen ions released during the polymerization of DNA strands on microbeads, which are scattered into microwell array above the ISFET sensor with unknown distribution. However, false pH detection happens at empty microwells due to crosstalk from neighboring microbeads. In this paper, a dual-mode CMOS ISFET sensor is proposed to have accurate pH detection toward DNA sequencing. Dual-mode sensing, optical and chemical modes, is realized by integrating a CMOS image sensor (CIS) with ISFET pH sensor, and is fabricated in a standard 0.18-μm CIS process. With accurate determination of microbead physical locations with CIS pixel by contact imaging, the dual-mode sensor can correlate local pH for one DNA slice at one location-determined microbead, which can result in improved pH detection accuracy. Moreover, toward a high-throughput DNA sequencing, a correlated-double-sampling readout that supports large array for both modes is deployed to reduce pixel-to-pixel nonuniformity such as threshold voltage mismatch. The proposed CMOS dual-mode sensor is experimentally examined to show a well correlated pH map and optical image for microbeads with a pH sensitivity of 26.2 mV/pH, a fixed pattern noise (FPN) reduction from 4% to 0.3%, and a readout speed of 1200 frames/s. A dual-mode CMOS ISFET sensor with suppressed FPN for accurate large-arrayed pH sensing is proposed and demonstrated with state-of-the-art measured results toward accurate and high-throughput DNA sequencing. The developed dual-mode CMOS ISFET sensor has great potential for future personal genome diagnostics with high accuracy and low cost.

Comparative Analysis of Daytime Fire Detection Algorithms, Using AVHRR Data for the 1995 Fire Season in Canda: Perspective for MODIS

NASA Technical Reports Server (NTRS)

Ichoku, Charles; Kaufman, Y. J.; Fraser, R. H.; Jin, J.-Z.; Park, W. M.; Lau, William K. M. (Technical Monitor)

2001-01-01

Two fixed-threshold Canada Centre for Remote Sensing and European Space Agency (CCRS and ESA) and three contextual GIGLIO, International Geosphere and Biosphere Project, and Moderate Resolution Imaging Spectroradiometer (GIGLIO, IGBP, and MODIS) algorithms were used for fire detection with Advanced Very High Resolution Radiometer (AVHRR) data acquired over Canada during the 1995 fire season. The CCRS algorithm was developed for the boreal ecosystem, while the other four are for global application. The MODIS algorithm, although developed specifically for use with the MODIS sensor data, was applied to AVHRR in this study for comparative purposes. Fire detection accuracy assessment for the algorithms was based on comparisons with available 1995 burned area ground survey maps covering five Canadian provinces. Overall accuracy estimations in terms of omission (CCRS=46%, ESA=81%, GIGLIO=75%, IGBP=51%, MODIS=81%) and commission (CCRS=0.35%, ESA=0.08%, GIGLIO=0.56%, IGBP=0.75%, MODIS=0.08%) errors over forested areas revealed large differences in performance between the algorithms, with no relevance to type (fixed-threshold or contextual). CCRS performed best in detecting real forest fires, with the least omission error, while ESA and MODIS produced the highest omission error, probably because of their relatively high threshold values designed for global application. The commission error values appear small because the area of pixels falsely identified by each algorithm was expressed as a ratio of the vast unburned forest area. More detailed study shows that most commission errors in all the algorithms were incurred in nonforest agricultural areas, especially on days with very high surface temperatures. The advantage of the high thresholds in ESA and MODIS was that they incurred the least commission errors.

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.

Comparison of Microlife BP A200 Plus and Omron M6 blood pressure monitors to detect atrial fibrillation in hypertensive patients.

PubMed

Marazzi, Giuseppe; Iellamo, Ferdinando; Volterrani, Maurizio; Lombardo, Mauro; Pelliccia, Francesco; Righi, Daniela; Grieco, Fabrizia; Cacciotti, Luca; Iaia, Luigi; Caminiti, Giuseppe; Rosano, Giuseppe

2012-01-01

Self-monitoring home blood pressure (BP) devices are currently recommended for long-term follow-up of hypertension and its management. Some of these devices are integrated with algorithms aimed at detecting atrial fibrillation (AF), which is common essential hypertension. This study was designed to compare the diagnostic accuracy of two widely diffused home BP monitoring devices in detecting AF in an unselected population of outpatients referred to a hypertension clinic because of high BP. In 503 consecutive patients the authors simultaneously compared the accuracy of the Microlife(®) BP A200 Plus (Microlife) and the OMRON(®) M6 (OMRON) home BP devices, in detecting AF. Systolic and diastolic BP as well as heart rate (HR) values detected by the two devices were not significantly different. Pulse irregularity was detected in 124 and 112 patients with the OMRON M6 and Microlife BP A200 Plus devices, respectively. Simultaneous electrocardiogram (ECG) recording revealed that pulse irregularity was due to AF in 101 patients. Pulse irregularity detected by the OMRON M6 device corresponded to AF in 101, to supraventricular premature beats in 18, and to frequent premature ventricular beat in five patients, respectively. Pulse irregularity detected by the Microlife BP A200 Plus device corresponded to AF in 93, to supraventricular premature beats in 14, and to ventricular premature beats in five patients. The sensitivity for detecting AF was 100%, the specificity was 92%, and diagnostic accuracy 95% for the OMRON M6 and 100%, 92%, and 95 for the Microlife BP A200 Plus, respectively. AF was newly diagnosed by ECG recordings in 47 patients, and was detected in all patients by the OMRON device, and in 42 patients by the Microlife device. These results indicate that OMRON M6 is more accurate than Microlife BP A200 Plus in detecting AF in patients with essential hypertension. Widespread use of these devices in hypertensive patients could be of clinical benefit for the early diagnosis and treatment of this arrhythmia and related consequences.

Highly Sensitive Detection of Urinary Cadmium to Assess Personal Exposure

PubMed Central

Argun, Avni A.; Banks, Ashley; Merlen, Gwendolynne; Tempelman, Linda A.; Becker, Michael F.; Schuelke, Thomas; Dweik, Badawi

2013-01-01

A series of Boron-Doped Diamond (BDD) ultramicroelectrode arrays were fabricated and investigated for their performance as electrochemical sensors to detect trace level metals such as cadmium. The steady-state diffusion behavior of these sensors was validated using cyclic voltammetry followed by electrochemical detection of cadmium in water and in human urine to demonstrate high sensitivity (>200 μA/ppb/cm2) and low background current (<4 nA). When an array of ultramicroelectrodes was positioned with optimal spacing, these BDD sensors showed a sigmoidal diffusion behavior. They also demonstrated high accuracy with linear dose dependence for quantification of cadmium in a certified reference river water sample from the National Institute of Standards and Technology (NIST) as well as in a human urine sample spiked with 0.25–1 ppb cadmium. PMID:23561905

Understanding the detection of carbon in austenitic high-Mn steel using atom probe tomography.

PubMed

Marceau, R K W; Choi, P; Raabe, D

2013-09-01

A high-Mn TWIP steel having composition Fe-22Mn-0.6C (wt%) is considered in this study, where the need for accurate and quantitative analysis of clustering and short-range ordering by atom probe analysis requires a better understanding of the detection of carbon in this system. Experimental measurements reveal that a high percentage of carbon atoms are detected as molecular ion species and on multiple hit events, which is discussed with respect to issues such as optimal experimental parameters, correlated field evaporation and directional walk/migration of carbon atoms at the surface of the specimen tip during analysis. These phenomena impact the compositional and spatial accuracy of the atom probe measurement and thus require careful consideration for further cluster-finding analysis. Copyright © 2013 Elsevier B.V. All rights reserved.

Diagnosis and management of solitary pulmonary nodules.

PubMed

Jeong, Yeon Joo; Lee, Kyung Soo; Kwon, O Jung

2008-12-01

The advent of computed tomography (CT) screening with or without the help of computer-aided detection systems has increased the detection rate of solitary pulmonary nodules (SPNs), including that of early peripheral lung cancer. Helical dynamic (HD)CT, providing the information on morphologic and hemodynamic characteristics with high specificity and reasonably high accuracy, can be used for the initial assessment of SPNs. (18)F-fluorodeoxyglucose PET/CT is more sensitive at detecting malignancy than HDCT. Therefore, PET/CT may be selectively performed to characterize SPNs when HDCT gives an inconclusive diagnosis. Serial volume measurements are currently the most reliable methods for the tissue characterization of subcentimeter nodules. When malignant nodule is highly suspected for subcentimeter nodules, video-assisted thoracoscopic surgery nodule removal after nodule localization using the pulmonary nodule-marker system may be performed for diagnosis and treatment.

Quantitation of acrylamide in foods by high-resolution mass spectrometry.

PubMed

Troise, Antonio Dario; Fiore, Alberto; Fogliano, Vincenzo

2014-01-08

Acrylamide detection still represents one of the hottest topics in food chemistry. Solid phase cleanup coupled to liquid chromatography separation and tandem mass spectrometry detection along with GC-MS detection are nowadays the gold standard procedure for acrylamide quantitation thanks to high reproducibility, good recovery, and low relative standard deviation. High-resolution mass spectrometry (HRMS) is particularly suitable for the detection of low molecular weight amides, and it can provide some analytical advantages over other MS techniques. In this paper a liquid chromatography (LC) method for acrylamide determination using HRMS detection was developed and compared to LC coupled to tandem mass spectrometry. The procedure applied a simplified extraction, no cleanup steps, and a 4 min chromatography. It proved to be solid and robust with an acrylamide mass accuracy of 0.7 ppm, a limit of detection of 2.65 ppb, and a limit of quantitation of 5 ppb. The method was tested on four acrylamide-containing foods: cookies, French fries, ground coffee, and brewed coffee. Results were perfectly in line with those obtained by LC-MS/MS.

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

PubMed

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

2018-02-01

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

Quick, sensitive and specific detection and evaluation of quantification of minor variants by high-throughput sequencing.

PubMed

Leung, Ross Ka-Kit; Dong, Zhi Qiang; Sa, Fei; Chong, Cheong Meng; Lei, Si Wan; Tsui, Stephen Kwok-Wing; Lee, Simon Ming-Yuen

2014-02-01

Minor variants have significant implications in quasispecies evolution, early cancer detection and non-invasive fetal genotyping but their accurate detection by next-generation sequencing (NGS) is hampered by sequencing errors. We generated sequencing data from mixtures at predetermined ratios in order to provide insight into sequencing errors and variations that can arise for which simulation cannot be performed. The information also enables better parameterization in depth of coverage, read quality and heterogeneity, library preparation techniques, technical repeatability for mathematical modeling, theory development and simulation experimental design. We devised minor variant authentication rules that achieved 100% accuracy in both testing and validation experiments. The rules are free from tedious inspection of alignment accuracy, sequencing read quality or errors introduced by homopolymers. The authentication processes only require minor variants to: (1) have minimum depth of coverage larger than 30; (2) be reported by (a) four or more variant callers, or (b) DiBayes or LoFreq, plus SNVer (or BWA when no results are returned by SNVer), and with the interassay coefficient of variation (CV) no larger than 0.1. Quantification accuracy undermined by sequencing errors could neither be overcome by ultra-deep sequencing, nor recruiting more variant callers to reach a consensus, such that consistent underestimation and overestimation (i.e. low CV) were observed. To accommodate stochastic error and adjust the observed ratio within a specified accuracy, we presented a proof of concept for the use of a double calibration curve for quantification, which provides an important reference towards potential industrial-scale fabrication of calibrants for NGS.

Screening for bipolar spectrum disorders: A comprehensive meta-analysis of accuracy studies.

PubMed

Carvalho, André F; Takwoingi, Yemisi; Sales, Paulo Marcelo G; Soczynska, Joanna K; Köhler, Cristiano A; Freitas, Thiago H; Quevedo, João; Hyphantis, Thomas N; McIntyre, Roger S; Vieta, Eduard

2015-02-01

Bipolar spectrum disorders are frequently under-recognized and/or misdiagnosed in various settings. Several influential publications recommend the routine screening of bipolar disorder. A systematic review and meta-analysis of accuracy studies for the bipolar spectrum diagnostic scale (BSDS), the hypomania checklist (HCL-32) and the mood disorder questionnaire (MDQ) were performed. The Pubmed, EMBASE, Cochrane, PsycINFO and SCOPUS databases were searched. Studies were included if the accuracy properties of the screening measures were determined against a DSM or ICD-10 structured diagnostic interview. The QUADAS-2 tool was used to rate bias. Fifty three original studies met inclusion criteria (N=21,542). At recommended cutoffs, summary sensitivities were 81%, 66% and 69%, while specificities were 67%, 79% and 86% for the HCL-32, MDQ, and BSDS in psychiatric services, respectively. The HCL-32 was more accurate than the MDQ for the detection of type II bipolar disorder in mental health care centers (P=0.018). At a cutoff of 7, the MDQ had a summary sensitivity of 43% and a summary specificity of 95% for detection of bipolar disorder in primary care or general population settings. Most studies were performed in mental health care settings. Several included studies had a high risk of bias. Although accuracy properties of the three screening instruments did not consistently differ in mental health care services, the HCL-32 was more accurate than the MDQ for the detection of type II BD. More studies in other settings (for example, in primary care) are necessary. Copyright © 2014 Elsevier B.V. All rights reserved.

A comparative study on accuracy of liquid crystal forehead, digital electronic axillary, infrared tympanic with glass-mercury rectal thermometer in infants and young children.

PubMed

Kongpanichkul, A; Bunjongpak, S

2000-09-01

This study was carried out to assess the accuracy of three devices namely, liquid crystal forehead, digital electronic axillary and infrared tympanic thermometer, using a glass-mercury rectal thermometer as the control. The subjects were two hundred children aged 0-48 months. The mean rectal temperature was 38.0 +/- 0.91 degrees C; forehead, 37.83 +/- 0.94 degrees C; tympanic, 37.77 +/- 0.95 degrees C, and axillary, 37.71 +/- 0.86 degrees C. Compared to the rectal temperature, all values were significantly lower (p < 0.05). Forehead, tympanic and axillary temperature differed from rectal temperature by at least 0.5 degrees C in 33.33 per cent, 23.5 per cent and 31.5 per cent of subjects, and at least 1 degrees C in 22 per cent, 1 per cent and 6 per cent of subjects respectively. Accuracy in detection of fever was 79 per cent for forehead, 85.5 per cent for tympanic and 84 per cent for axillary thermometry. Sensitivity of the three devices was 67-83 per cent in detection of fever and 64-77 per cent in detection of high fever. Tympanic thermometry had the best performance while forehead thermometry had the poorest. After using revised diagnostic threshold temperature by ROC curves, sensitivity of each device improved but accuracy was nearly the same. It is concluded that the three devices are not suitable as a substitute for a glass-mercury rectal thermometer in assessment of fever in infants and young children.

Direct Detection of Drug-Resistant Hepatitis B Virus in Serum Using a Dendron-Modified Microarray

PubMed Central

Kim, Doo Hyun; Kang, Hong Seok; Hur, Seong-Suk; Sim, Seobo; Ahn, Sung Hyun; Park, Yong Kwang; Park, Eun-Sook; Lee, Ah Ram; Park, Soree; Kwon, So Young; Lee, Jeong-Hoon

2018-01-01

Background/Aims Direct sequencing is the gold standard for the detection of drug-resistance mutations in hepatitis B virus (HBV); however, this procedure is time-consuming, labor-intensive, and difficult to adapt to high-throughput screening. In this study, we aimed to develop a dendron-modified DNA microarray for the detection of genotypic resistance mutations and evaluate its efficiency. Methods The specificity, sensitivity, and selectivity of dendron-modified slides for the detection of representative drug-resistance mutations were evaluated and compared to those of conventional slides. The diagnostic accuracy was validated using sera obtained from 13 patients who developed viral breakthrough during lamivudine, adefovir, or entecavir therapy and compared with the accuracy of restriction fragment mass polymorphism and direct sequencing data. Results The dendron-modified slides significantly outperformed the conventional microarray slides and were able to detect HBV DNA at a very low level (1 copy/μL). Notably, HBV mutants could be detected in the chronic hepatitis B patient sera without virus purification. The validation of our data revealed that this technique is fully compatible with sequencing data of drug-resistant HBV. Conclusions We developed a novel diagnostic technique for the simultaneous detection of several drug-resistance mutations using a dendron-modified DNA microarray. This technique can be directly applied to sera from chronic hepatitis B patients who show resistance to several nucleos(t)ide analogues. PMID:29271185

Simultaneous Quantitative Detection of Helicobacter Pylori Based on a Rapid and Sensitive Testing Platform using Quantum Dots-Labeled Immunochromatiographic Test Strips

NASA Astrophysics Data System (ADS)

Zheng, Yu; Wang, Kan; Zhang, Jingjing; Qin, Weijian; Yan, Xinyu; Shen, Guangxia; Gao, Guo; Pan, Fei; Cui, Daxiang

2016-02-01

Quantum dots-labeled urea-enzyme antibody-based rapid immunochromatographic test strips have been developed as quantitative fluorescence point-of-care tests (POCTs) to detect helicobacter pylori. Presented in this study is a new test strip reader designed to run on tablet personal computers (PCs), which is portable for outdoor detection even without an alternating current (AC) power supply. A Wi-Fi module was integrated into the reader to improve its portability. Patient information was loaded by a barcode scanner, and an application designed to run on tablet PCs was developed to handle the acquired images. A vision algorithm called Kmeans was used for picture processing. Different concentrations of various human blood samples were tested to evaluate the stability and accuracy of the fabricated device. Results demonstrate that the reader can provide an easy, rapid, simultaneous, quantitative detection for helicobacter pylori. The proposed test strip reader has a lighter weight than existing detection readers, and it can run for long durations without an AC power supply, thus verifying that it possesses advantages for outdoor detection. Given its fast detection speed and high accuracy, the proposed reader combined with quantum dots-labeled test strips is suitable for POCTs and owns great potential in applications such as screening patients with infection of helicobacter pylori, etc. in near future.

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

PubMed

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

2014-09-01

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

A Shellcode Detection Method Based on Full Native API Sequence and Support Vector Machine

NASA Astrophysics Data System (ADS)

Cheng, Yixuan; Fan, Wenqing; Huang, Wei; An, Jing

2017-09-01

Dynamic monitoring the behavior of a program is widely used to discriminate between benign program and malware. It is usually based on the dynamic characteristics of a program, such as API call sequence or API call frequency to judge. The key innovation of this paper is to consider the full Native API sequence and use the support vector machine to detect the shellcode. We also use the Markov chain to extract and digitize Native API sequence features. Our experimental results show that the method proposed in this paper has high accuracy and low detection rate.

Remote detection of insect epidemics in conifers

NASA Technical Reports Server (NTRS)

Heller, R. C.

1970-01-01

With properly exposed color or infrared color film, discolored foliage caused by insect infestations in ponderosa pine is detectable on moderately small-scale photographs with acceptable accuracies. Black and white photographs which matched the wavebands of the ERTS multispectral scanner were combined into one additive color photo. This imagery was not as useful as photographs taken on color, color infrared, or color film with a minus blue filter. Based on the high-altitude color and color infrared photos obtained, it is concluded that only insect infestations larger than 100 meters in diameter are detectable on ERTS imagery.

The wisdom of crowds for visual search

PubMed Central

Juni, Mordechai Z.; Eckstein, Miguel P.

2017-01-01

Decision-making accuracy typically increases through collective integration of people’s judgments into group decisions, a phenomenon known as the wisdom of crowds. For simple perceptual laboratory tasks, classic signal detection theory specifies the upper limit for collective integration benefits obtained by weighted averaging of people’s confidences, and simple majority voting can often approximate that limit. Life-critical perceptual decisions often involve searching large image data (e.g., medical, security, and aerial imagery), but the expected benefits and merits of using different pooling algorithms are unknown for such tasks. Here, we show that expected pooling benefits are significantly greater for visual search than for single-location perceptual tasks and the prediction given by classic signal detection theory. In addition, we show that simple majority voting obtains inferior accuracy benefits for visual search relative to averaging and weighted averaging of observers’ confidences. Analysis of gaze behavior across observers suggests that the greater collective integration benefits for visual search arise from an interaction between the foveated properties of the human visual system (high foveal acuity and low peripheral acuity) and observers’ nonexhaustive search patterns, and can be predicted by an extended signal detection theory framework with trial to trial sampling from a varying mixture of high and low target detectabilities across observers (SDT-MIX). These findings advance our theoretical understanding of how to predict and enhance the wisdom of crowds for real world search tasks and could apply more generally to any decision-making task for which the minority of group members with high expertise varies from decision to decision. PMID:28490500

Detecting very low allele fraction variants using targeted DNA sequencing and a novel molecular barcode-aware variant caller.

PubMed

Xu, Chang; Nezami Ranjbar, Mohammad R; Wu, Zhong; DiCarlo, John; Wang, Yexun

2017-01-03

Detection of DNA mutations at very low allele fractions with high accuracy will significantly improve the effectiveness of precision medicine for cancer patients. To achieve this goal through next generation sequencing, researchers need a detection method that 1) captures rare mutation-containing DNA fragments efficiently in the mix of abundant wild-type DNA; 2) sequences the DNA library extensively to deep coverage; and 3) distinguishes low level true variants from amplification and sequencing errors with high accuracy. Targeted enrichment using PCR primers provides researchers with a convenient way to achieve deep sequencing for a small, yet most relevant region using benchtop sequencers. Molecular barcoding (or indexing) provides a unique solution for reducing sequencing artifacts analytically. Although different molecular barcoding schemes have been reported in recent literature, most variant calling has been done on limited targets, using simple custom scripts. The analytical performance of barcode-aware variant calling can be significantly improved by incorporating advanced statistical models. We present here a highly efficient, simple and scalable enrichment protocol that integrates molecular barcodes in multiplex PCR amplification. In addition, we developed smCounter, an open source, generic, barcode-aware variant caller based on a Bayesian probabilistic model. smCounter was optimized and benchmarked on two independent read sets with SNVs and indels at 5 and 1% allele fractions. Variants were called with very good sensitivity and specificity within coding regions. We demonstrated that we can accurately detect somatic mutations with allele fractions as low as 1% in coding regions using our enrichment protocol and variant caller.

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

PubMed

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

2016-01-01

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

Classification of large-scale fundus image data sets: a cloud-computing framework.

PubMed

Roychowdhury, Sohini

2016-08-01

Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce computational time complexity while maximizing overall classification accuracy for detection of diabetic retinopathy (DR). First, region-based and pixel-based features are extracted from fundus images for classification of DR lesions and vessel-like structures. Next, feature ranking strategies are used to distinguish the optimal classification feature sets. DR lesion and vessel classification accuracies are computed using the boosted decision tree and decision forest classifiers in the Microsoft Azure Machine Learning Studio platform, respectively. For images from the DIARETDB1 data set, 40 of its highest-ranked features are used to classify four DR lesion types with an average classification accuracy of 90.1% in 792 seconds. Also, for classification of red lesion regions and hemorrhages from microaneurysms, accuracies of 85% and 72% are observed, respectively. For images from STARE data set, 40 high-ranked features can classify minor blood vessels with an accuracy of 83.5% in 326 seconds. Such cloud-based fundus image analysis systems can significantly enhance the borderline classification performances in automated screening systems.

Personality and attention: Levels of neuroticism and extraversion can predict attentional performance during a change detection task.

PubMed

Hahn, Sowon; Buttaccio, Daniel R; Hahn, Jungwon; Lee, Taehun

2015-01-01

The present study demonstrates that levels of extraversion and neuroticism can predict attentional performance during a change detection task. After completing a change detection task built on the flicker paradigm, participants were assessed for personality traits using the Revised Eysenck Personality Questionnaire (EPQ-R). Multiple regression analyses revealed that higher levels of extraversion predict increased change detection accuracies, while higher levels of neuroticism predict decreased change detection accuracies. In addition, neurotic individuals exhibited decreased sensitivity A' and increased fixation dwell times. Hierarchical regression analyses further revealed that eye movement measures mediate the relationship between neuroticism and change detection accuracies. Based on the current results, we propose that neuroticism is associated with decreased attentional control over the visual field, presumably due to decreased attentional disengagement. Extraversion can predict increased attentional performance, but the effect is smaller than the relationship between neuroticism and attention.

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

NASA Astrophysics Data System (ADS)

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

2010-12-01

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

Test of the FDTD accuracy in the analysis of the scattering resonances associated with high-Q whispering-gallery modes of a circular cylinder.

PubMed

Boriskin, Artem V; Boriskina, Svetlana V; Rolland, Anthony; Sauleau, Ronan; Nosich, Alexander I

2008-05-01

Our objective is the assessment of the accuracy of a conventional finite-difference time-domain (FDTD) code in the computation of the near- and far-field scattering characteristics of a circular dielectric cylinder. We excite the cylinder with an electric or magnetic line current and demonstrate the failure of the two-dimensional FDTD algorithm to accurately characterize the emission rate and the field patterns near high-Q whispering-gallery-mode resonances. This is proven by comparison with the exact series solutions. The computational errors in the emission rate are then studied at the resonances still detectable with FDTD, i.e., having Q-factors up to 10(3).

Implementation of High Time Delay Accuracy of Ultrasonic Phased Array Based on Interpolation CIC Filter

PubMed Central

Liu, Peilu; Li, Xinghua; Li, Haopeng; Su, Zhikun; Zhang, Hongxu

2017-01-01

In order to improve the accuracy of ultrasonic phased array focusing time delay, analyzing the original interpolation Cascade-Integrator-Comb (CIC) filter, an 8× interpolation CIC filter parallel algorithm was proposed, so that interpolation and multichannel decomposition can simultaneously process. Moreover, we summarized the general formula of arbitrary multiple interpolation CIC filter parallel algorithm and established an ultrasonic phased array focusing time delay system based on 8× interpolation CIC filter parallel algorithm. Improving the algorithmic structure, 12.5% of addition and 29.2% of multiplication was reduced, meanwhile the speed of computation is still very fast. Considering the existing problems of the CIC filter, we compensated the CIC filter; the compensated CIC filter’s pass band is flatter, the transition band becomes steep, and the stop band attenuation increases. Finally, we verified the feasibility of this algorithm on Field Programming Gate Array (FPGA). In the case of system clock is 125 MHz, after 8× interpolation filtering and decomposition, time delay accuracy of the defect echo becomes 1 ns. Simulation and experimental results both show that the algorithm we proposed has strong feasibility. Because of the fast calculation, small computational amount and high resolution, this algorithm is especially suitable for applications with high time delay accuracy and fast detection. PMID:29023385

Implementation of High Time Delay Accuracy of Ultrasonic Phased Array Based on Interpolation CIC Filter.

PubMed

Liu, Peilu; Li, Xinghua; Li, Haopeng; Su, Zhikun; Zhang, Hongxu

2017-10-12

In order to improve the accuracy of ultrasonic phased array focusing time delay, analyzing the original interpolation Cascade-Integrator-Comb (CIC) filter, an 8× interpolation CIC filter parallel algorithm was proposed, so that interpolation and multichannel decomposition can simultaneously process. Moreover, we summarized the general formula of arbitrary multiple interpolation CIC filter parallel algorithm and established an ultrasonic phased array focusing time delay system based on 8× interpolation CIC filter parallel algorithm. Improving the algorithmic structure, 12.5% of addition and 29.2% of multiplication was reduced, meanwhile the speed of computation is still very fast. Considering the existing problems of the CIC filter, we compensated the CIC filter; the compensated CIC filter's pass band is flatter, the transition band becomes steep, and the stop band attenuation increases. Finally, we verified the feasibility of this algorithm on Field Programming Gate Array (FPGA). In the case of system clock is 125 MHz, after 8× interpolation filtering and decomposition, time delay accuracy of the defect echo becomes 1 ns. Simulation and experimental results both show that the algorithm we proposed has strong feasibility. Because of the fast calculation, small computational amount and high resolution, this algorithm is especially suitable for applications with high time delay accuracy and fast detection.

High sensitivity and accuracy dissolved oxygen (DO) detection by using PtOEP/poly(MMA-co-TFEMA) sensing film.

PubMed

Zhang, Ke; Zhang, Honglin; Wang, Ying; Tian, Yanqing; Zhao, Jiupeng; Li, Yao

2017-01-05

Fluorinated acrylate polymer has received great interest in recent years due to its extraordinary characteristics such as high oxygen permeability, good stability, low surface energy and refractive index. In this work, platinum octaethylporphyrin/poly(methylmethacrylate-co-trifluoroethyl methacrylate) (PtOEP/poly(MMA-co-TFEMA)) oxygen sensing film was prepared by the immobilizing of PtOEP in a poly(MMA-co-TFEMA) matrix and the technological readiness of optical properties was established based on the principle of luminescence quenching. It was found that the oxygen-sensing performance could be improved by optimizing the monomer ratio (MMA/TFEMA=1:1), tributylphosphate(TBP, 0.05mL) and PtOEP (5μg) content. Under this condition, the maximum quenching ratio I0/I100 of the oxygen sensing film is obtained to be about 8.16, Stern-Volmer equation is I0/I=1.003+2.663[O2] (R(2)=0.999), exhibiting a linear relationship, good photo-stability, high sensitivity and accuracy. Finally, the synthesized PtOEP/poly(MMA-co-TFEMA) sensing film was used for DO detection in different water samples. Copyright © 2016 Elsevier B.V. All rights reserved.

High sensitivity and accuracy dissolved oxygen (DO) detection by using PtOEP/poly(MMA-co-TFEMA) sensing film

NASA Astrophysics Data System (ADS)

Zhang, Ke; Zhang, Honglin; Wang, Ying; Tian, Yanqing; Zhao, Jiupeng; Li, Yao

2017-01-01

Fluorinated acrylate polymer has received great interest in recent years due to its extraordinary characteristics such as high oxygen permeability, good stability, low surface energy and refractive index. In this work, platinum octaethylporphyrin/poly(methylmethacrylate-co-trifluoroethyl methacrylate) (PtOEP/poly(MMA-co-TFEMA)) oxygen sensing film was prepared by the immobilizing of PtOEP in a poly(MMA-co-TFEMA) matrix and the technological readiness of optical properties was established based on the principle of luminescence quenching. It was found that the oxygen-sensing performance could be improved by optimizing the monomer ratio (MMA/TFEMA = 1:1), tributylphosphate(TBP, 0.05 mL) and PtOEP (5 μg) content. Under this condition, the maximum quenching ratio I0/I100 of the oxygen sensing film is obtained to be about 8.16, Stern-Volmer equation is I0/I = 1.003 + 2.663[O2] (R2 = 0.999), exhibiting a linear relationship, good photo-stability, high sensitivity and accuracy. Finally, the synthesized PtOEP/poly(MMA-co-TFEMA) sensing film was used for DO detection in different water samples.

Force monitoring transducers with more than 100,000 scale intervals

NASA Astrophysics Data System (ADS)

Stavrov, Vladimir; Shulev, Assen; Chakarov, Dimiter; Stavreva, Galina

2015-05-01

This paper presents the results obtained at characterization of novel, high performing force transducers to be employed into monitoring systems with very high accuracy. Each force transducer comprises of a coherently designed mechanical transducer and a position microsensor with very high accuracy. The range of operation for the mechanical transducer has been optimized to fit the 500μm travel range of the position microsensor. Respectively, the flexures' stiffness corresponds to achieve the maximum displacement at 70N load force. The position microsensor is a MEMS device, comprising of two rigid elements: an anchored and an actuated ones connected via one monolithic micro-flexure. Additionally, the micro-flexure comprises of two strain detecting cantilevers having four sidewall embedded piezoresistors connected in a Wheatstone bridge. The particular sensor provides a voltage signal having sensitivity in the range of 240μV/μm at 1V DC voltage supply. The experimental set-up for measurement of the load curve of the force transducer has demonstrated an overall force resolution of about 0.6mN. As a result, more than 100,000 scale intervals have been experimentally assessed. The present work forms development of a common approach for accurate measurement of various physical values, when they are transduced in a multi-D displacement. Due to the demonstrated high accuracy, the force transducers with piezoresistive MEMS sensors remove most of the constraints in force monitoring with ppm-accuracy.

Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images.

PubMed

Knauer, Uwe; Matros, Andrea; Petrovic, Tijana; Zanker, Timothy; Scott, Eileen S; Seiffert, Udo

2017-01-01

Hyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of the full spectral information, appropriate selection of individual bands, or calculation of spectral indices. Limitations of such approaches are reduced classification accuracy, reduced robustness due to spatial variation of the spectral information across the surface of the objects measured as well as a loss of information intrinsic to band selection and use of spectral indices. In this paper we present an improved spatial-spectral segmentation approach for the analysis of hyperspectral imaging data and its application for the prediction of powdery mildew infection levels (disease severity) of intact Chardonnay grape bunches shortly before veraison. Instead of calculating texture features (spatial features) for the huge number of spectral bands independently, dimensionality reduction by means of Linear Discriminant Analysis (LDA) was applied first to derive a few descriptive image bands. Subsequent classification was based on modified Random Forest classifiers and selective extraction of texture parameters from the integral image representation of the image bands generated. Dimensionality reduction, integral images, and the selective feature extraction led to improved classification accuracies of up to [Formula: see text] for detached berries used as a reference sample (training dataset). Our approach was validated by predicting infection levels for a sample of 30 intact bunches. Classification accuracy improved with the number of decision trees of the Random Forest classifier. These results corresponded with qPCR results. An accuracy of 0.87 was achieved in classification of healthy, infected, and severely diseased bunches. However, discrimination between visually healthy and infected bunches proved to be challenging for a few samples, perhaps due to colonized berries or sparse mycelia hidden within the bunch or airborne conidia on the berries that were detected by qPCR. An advanced approach to hyperspectral image classification based on combined spatial and spectral image features, potentially applicable to many available hyperspectral sensor technologies, has been developed and validated to improve the detection of powdery mildew infection levels of Chardonnay grape bunches. The spatial-spectral approach improved especially the detection of light infection levels compared with pixel-wise spectral data analysis. This approach is expected to improve the speed and accuracy of disease detection once the thresholds for fungal biomass detected by hyperspectral imaging are established; it can also facilitate monitoring in plant phenotyping of grapevine and additional crops.

Cervical vertebral maturation as a biologic indicator of skeletal maturity.

PubMed

Santiago, Rodrigo César; de Miranda Costa, Luiz Felipe; Vitral, Robert Willer Farinazzo; Fraga, Marcelo Reis; Bolognese, Ana Maria; Maia, Lucianne Cople

2012-11-01

To identify and review the literature regarding the reliability of cervical vertebrae maturation (CVM) staging to predict the pubertal spurt. The selection criteria included cross-sectional and longitudinal descriptive studies in humans that evaluated qualitatively or quantitatively the accuracy and reproducibility of the CVM method on lateral cephalometric radiographs, as well as the correlation with a standard method established by hand-wrist radiographs. The searches retrieved 343 unique citations. Twenty-three studies met the inclusion criteria. Six articles had moderate to high scores, while 17 of 23 had low scores. Analysis also showed a moderate to high statistically significant correlation between CVM and hand-wrist maturation methods. There was a moderate to high reproducibility of the CVM method, and only one specific study investigated the accuracy of the CVM index in detecting peak pubertal growth. This systematic review has shown that the studies on CVM method for radiographic assessment of skeletal maturation stages suffer from serious methodological failures. Better-designed studies with adequate accuracy, reproducibility, and correlation analysis, including studies with appropriate sensitivity-specificity analysis, should be performed.

A high accuracy ultrasonic distance measurement system using binary frequency shift-keyed signal and phase detection

NASA Astrophysics Data System (ADS)

Huang, S. S.; Huang, C. F.; Huang, K. N.; Young, M. S.

2002-10-01

A highly accurate binary frequency shift-keyed (BFSK) ultrasonic distance measurement system (UDMS) for use in isothermal air is described. This article presents an efficient algorithm which combines both the time-of-flight (TOF) method and the phase-shift method. The proposed method can obtain larger range measurement than the phase-shift method and also get higher accuracy compared with the TOF method. A single-chip microcomputer-based BFSK signal generator and phase detector was designed to record and compute the TOF, two phase shifts, and the resulting distance, which were then sent to either an LCD to display or a PC to calibrate. Experiments were done in air using BFSK with the frequencies of 40 and 41 kHz. Distance resolution of 0.05% of the wavelength corresponding to the frequency of 40 kHz was obtained. The range accuracy was found to be within ±0.05 mm at a range of over 6000 mm. The main advantages of this UDMS system are high resolution, low cost, narrow bandwidth requirement, and ease of implementation.

Integrated multi-ISE arrays with improved sensitivity, accuracy and precision

NASA Astrophysics Data System (ADS)

Wang, Chunling; Yuan, Hongyan; Duan, Zhijuan; Xiao, Dan

2017-03-01

Increasing use of ion-selective electrodes (ISEs) in the biological and environmental fields has generated demand for high-sensitivity ISEs. However, improving the sensitivities of ISEs remains a challenge because of the limit of the Nernstian slope (59.2/n mV). Here, we present a universal ion detection method using an electronic integrated multi-electrode system (EIMES) that bypasses the Nernstian slope limit of 59.2/n mV, thereby enabling substantial enhancement of the sensitivity of ISEs. The results reveal that the response slope is greatly increased from 57.2 to 1711.3 mV, 57.3 to 564.7 mV and 57.7 to 576.2 mV by electronic integrated 30 Cl- electrodes, 10 F- electrodes and 10 glass pH electrodes, respectively. Thus, a tiny change in the ion concentration can be monitored, and correspondingly, the accuracy and precision are substantially improved. The EIMES is suited for all types of potentiometric sensors and may pave the way for monitoring of various ions with high accuracy and precision because of its high sensitivity.

The detection of the coal roof interface by use of high pressure water

NASA Technical Reports Server (NTRS)

1981-01-01

A device whereby water jets can be used to detect the interface between coal and the overlying roof rock is described. Once this identification is made this distance can be measured using instruments such as the autofocus systems recently developed in the photographic industry. Experiments carried out show that the device can discriminate between coal and rock at coal thicknesses up to 8 inches. An autofocus system was examined which indicates accuracies of better than 0.1 inches.

Chromatic dispersion estimation based on heterodyne detection for coherent optical communication systems

NASA Astrophysics Data System (ADS)

Li, Yong; Yang, Aiying; Guo, Peng; Qiao, Yaojun; Lu, Yueming

2018-01-01

We propose an accurate and nondata-aided chromatic dispersion (CD) estimation method involving the use of the cross-correlation function of two heterodyne detection signals for coherent optical communication systems. Simulations are implemented to verify the feasibility of the proposed method for 28-GBaud coherent systems with different modulation formats. The results show that the proposed method has high accuracy for measuring CD and has good robustness against laser phase noise, amplified spontaneous emission noise, and nonlinear impairments.

Deep learning applications in ophthalmology.

PubMed

Rahimy, Ehsan

2018-05-01

To describe the emerging applications of deep learning in ophthalmology. Recent studies have shown that various deep learning models are capable of detecting and diagnosing various diseases afflicting the posterior segment of the eye with high accuracy. Most of the initial studies have centered around detection of referable diabetic retinopathy, age-related macular degeneration, and glaucoma. Deep learning has shown promising results in automated image analysis of fundus photographs and optical coherence tomography images. Additional testing and research is required to clinically validate this technology.

Profile parameters of wheelset detection for high speed freight train

NASA Astrophysics Data System (ADS)

Yang, Kai; Ma, Li; Gao, Xiaorong; Wang, Li

2012-04-01

Because of freight train, in China, transports goods on railway freight line throughout the country, it does not depart from or return to engine shed during a long phase, thus we cannot monitor the quality of wheel set effectively. This paper provides a system which uses leaser and high speed camera, applies no-contact light section technology to get precise wheel set profile parameters. The paper employs clamping-track method to avoid complex railway ballast modification project. And detailed descript an improved image-tracking algorithm to extract central line from profile curve. For getting one pixel width and continuous line of the profile curve, uses local gray maximum points as direction control points to direct tracking direction. The results based on practical experiment show the system adapted to detection environment of high speed and high vibration, and it can effectively detect the wheelset geometric parameters with high accuracy. The system fills the gaps in wheel set detection for freight train in main line and has an enlightening function on monitoring the quality of wheel set.

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

PubMed

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

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

Application of Fuzzy c-Means and Joint-Feature-Clustering to Detect Redundancies of Image-Features in Drug Combinations Studies of Breast Cancer

NASA Astrophysics Data System (ADS)

Brandl, Miriam B.; Beck, Dominik; Pham, Tuan D.

2011-06-01

The high dimensionality of image-based dataset can be a drawback for classification accuracy. In this study, we propose the application of fuzzy c-means clustering, cluster validity indices and the notation of a joint-feature-clustering matrix to find redundancies of image-features. The introduced matrix indicates how frequently features are grouped in a mutual cluster. The resulting information can be used to find data-derived feature prototypes with a common biological meaning, reduce data storage as well as computation times and improve the classification accuracy.

A novel ultra-wideband 80 GHz FMCW radar system for contactless monitoring of vital signs.

PubMed

Wang, Siying; Pohl, Antje; Jaeschke, Timo; Czaplik, Michael; Köny, Marcus; Leonhardt, Steffen; Pohl, Nils

2015-01-01

In this paper an ultra-wideband 80 GHz FMCW-radar system for contactless monitoring of respiration and heart rate is investigated and compared to a standard monitoring system with ECG and CO(2) measurements as reference. The novel FMCW-radar enables the detection of the physiological displacement of the skin surface with submillimeter accuracy. This high accuracy is achieved with a large bandwidth of 10 GHz and the combination of intermediate frequency and phase evaluation. This concept is validated with a radar system simulation and experimental measurements are performed with different radar sensor positions and orientations.

Rapid detection of health-care-associated bloodstream infection in critical care using multipathogen real-time polymerase chain reaction technology: a diagnostic accuracy study and systematic review.

PubMed

Warhurst, Geoffrey; Dunn, Graham; Chadwick, Paul; Blackwood, Bronagh; McAuley, Daniel; Perkins, Gavin D; McMullan, Ronan; Gates, Simon; Bentley, Andrew; Young, Duncan; Carlson, Gordon L; Dark, Paul

2015-05-01

There is growing interest in the potential utility of real-time polymerase chain reaction (PCR) in diagnosing bloodstream infection by detecting pathogen deoxyribonucleic acid (DNA) in blood samples within a few hours. SeptiFast (Roche Diagnostics GmBH, Mannheim, Germany) is a multipathogen probe-based system targeting ribosomal DNA sequences of bacteria and fungi. It detects and identifies the commonest pathogens causing bloodstream infection. As background to this study, we report a systematic review of Phase III diagnostic accuracy studies of SeptiFast, which reveals uncertainty about its likely clinical utility based on widespread evidence of deficiencies in study design and reporting with a high risk of bias. Determine the accuracy of SeptiFast real-time PCR for the detection of health-care-associated bloodstream infection, against standard microbiological culture. Prospective multicentre Phase III clinical diagnostic accuracy study using the standards for the reporting of diagnostic accuracy studies criteria. Critical care departments within NHS hospitals in the north-west of England. Adult patients requiring blood culture (BC) when developing new signs of systemic inflammation. SeptiFast real-time PCR results at species/genus level compared with microbiological culture in association with independent adjudication of infection. Metrics of diagnostic accuracy were derived including sensitivity, specificity, likelihood ratios and predictive values, with their 95% confidence intervals (CIs). Latent class analysis was used to explore the diagnostic performance of culture as a reference standard. Of 1006 new patient episodes of systemic inflammation in 853 patients, 922 (92%) met the inclusion criteria and provided sufficient information for analysis. Index test assay failure occurred on 69 (7%) occasions. Adult patients had been exposed to a median of 8 days (interquartile range 4-16 days) of hospital care, had high levels of organ support activities and recent antibiotic exposure. SeptiFast real-time PCR, when compared with culture-proven bloodstream infection at species/genus level, had better specificity (85.8%, 95% CI 83.3% to 88.1%) than sensitivity (50%, 95% CI 39.1% to 60.8%). When compared with pooled diagnostic metrics derived from our systematic review, our clinical study revealed lower test accuracy of SeptiFast real-time PCR, mainly as a result of low diagnostic sensitivity. There was a low prevalence of BC-proven pathogens in these patients (9.2%, 95% CI 7.4% to 11.2%) such that the post-test probabilities of both a positive (26.3%, 95% CI 19.8% to 33.7%) and a negative SeptiFast test (5.6%, 95% CI 4.1% to 7.4%) indicate the potential limitations of this technology in the diagnosis of bloodstream infection. However, latent class analysis indicates that BC has a low sensitivity, questioning its relevance as a reference test in this setting. Using this analysis approach, the sensitivity of the SeptiFast test was low but also appeared significantly better than BC. Blood samples identified as positive by either culture or SeptiFast real-time PCR were associated with a high probability (> 95%) of infection, indicating higher diagnostic rule-in utility than was apparent using conventional analyses of diagnostic accuracy. SeptiFast real-time PCR on blood samples may have rapid rule-in utility for the diagnosis of health-care-associated bloodstream infection but the lack of sensitivity is a significant limiting factor. Innovations aimed at improved diagnostic sensitivity of real-time PCR in this setting are urgently required. Future work recommendations include technology developments to improve the efficiency of pathogen DNA extraction and the capacity to detect a much broader range of pathogens and drug resistance genes and the application of new statistical approaches able to more reliably assess test performance in situation where the reference standard (e.g. blood culture in the setting of high antimicrobial use) is prone to error. The systematic review is registered as PROSPERO CRD42011001289. The National Institute for Health Research Health Technology Assessment programme. Professor Daniel McAuley and Professor Gavin D Perkins contributed to the systematic review through their funded roles as codirectors of the Intensive Care Foundation (UK).