Advancing Explosives Detection Capabilities: Vapor Detection

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

Atkinson, David

2012-10-15

A new, PNNL-developed method provides direct, real-time detection of trace amounts of explosives such as RDX, PETN and C-4. The method selectively ionizes a sample before passing the sample through a mass spectrometer to detect explosive vapors. The method could be used at airports to improve aviation security.

Dim target detection method based on salient graph fusion

NASA Astrophysics Data System (ADS)

Hu, Ruo-lan; Shen, Yi-yan; Jiang, Jun

2018-02-01

Dim target detection is one key problem in digital image processing field. With development of multi-spectrum imaging sensor, it becomes a trend to improve the performance of dim target detection by fusing the information from different spectral images. In this paper, one dim target detection method based on salient graph fusion was proposed. In the method, Gabor filter with multi-direction and contrast filter with multi-scale were combined to construct salient graph from digital image. And then, the maximum salience fusion strategy was designed to fuse the salient graph from different spectral images. Top-hat filter was used to detect dim target from the fusion salient graph. Experimental results show that proposal method improved the probability of target detection and reduced the probability of false alarm on clutter background images.

A Metric for Reducing False Positives in the Computer-Aided Detection of Breast Cancer from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Screening Examinations of High-Risk Women.

PubMed

Levman, Jacob E D; Gallego-Ortiz, Cristina; Warner, Ellen; Causer, Petrina; Martel, Anne L

2016-02-01

Magnetic resonance imaging (MRI)-enabled cancer screening has been shown to be a highly sensitive method for the early detection of breast cancer. Computer-aided detection systems have the potential to improve the screening process by standardizing radiologists to a high level of diagnostic accuracy. This retrospective study was approved by the institutional review board of Sunnybrook Health Sciences Centre. This study compares the performance of a proposed method for computer-aided detection (based on the second-order spatial derivative of the relative signal intensity) with the signal enhancement ratio (SER) on MRI-based breast screening examinations. Comparison is performed using receiver operating characteristic (ROC) curve analysis as well as free-response receiver operating characteristic (FROC) curve analysis. A modified computer-aided detection system combining the proposed approach with the SER method is also presented. The proposed method provides improvements in the rates of false positive markings over the SER method in the detection of breast cancer (as assessed by FROC analysis). The modified computer-aided detection system that incorporates both the proposed method and the SER method yields ROC results equal to that produced by SER while simultaneously providing improvements over the SER method in terms of false positives per noncancerous exam. The proposed method for identifying malignancies outperforms the SER method in terms of false positives on a challenging dataset containing many small lesions and may play a useful role in breast cancer screening by MRI as part of a computer-aided detection system.

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.

Community structure detection based on the neighbor node degree information

NASA Astrophysics Data System (ADS)

Tang, Li-Ying; Li, Sheng-Nan; Lin, Jian-Hong; Guo, Qiang; Liu, Jian-Guo

2016-11-01

Community structure detection is of great significance for better understanding the network topology property. By taking into account the neighbor degree information of the topological network as the link weight, we present an improved Nonnegative Matrix Factorization (NMF) method for detecting community structure. The results for empirical networks show that the largest improved ratio of the Normalized Mutual Information value could reach 63.21%. Meanwhile, for synthetic networks, the highest Normalized Mutual Information value could closely reach 1, which suggests that the improved method with the optimal λ can detect the community structure more accurately. This work is helpful for understanding the interplay between the link weight and the community structure detection.

Multiratio fusion change detection with adaptive thresholding

NASA Astrophysics Data System (ADS)

Hytla, Patrick C.; Balster, Eric J.; Vasquez, Juan R.; Neuroth, Robert M.

2017-04-01

A ratio-based change detection method known as multiratio fusion (MRF) is proposed and tested. The MRF framework builds on other change detection components proposed in this work: dual ratio (DR) and multiratio (MR). The DR method involves two ratios coupled with adaptive thresholds to maximize detected changes and minimize false alarms. The use of two ratios is shown to outperform the single ratio case when the means of the image pairs are not equal. MR change detection builds on the DR method by including negative imagery to produce four total ratios with adaptive thresholds. Inclusion of negative imagery is shown to improve detection sensitivity and to boost detection performance in certain target and background cases. MRF further expands this concept by fusing together the ratio outputs using a routine in which detections must be verified by two or more ratios to be classified as a true changed pixel. The proposed method is tested with synthetically generated test imagery and real datasets with results compared to other methods found in the literature. DR is shown to significantly outperform the standard single ratio method. MRF produces excellent change detection results that exhibit up to a 22% performance improvement over other methods from the literature at low false-alarm rates.

An improved PCA method with application to boiler leak detection.

PubMed

Sun, Xi; Marquez, Horacio J; Chen, Tongwen; Riaz, Muhammad

2005-07-01

Principal component analysis (PCA) is a popular fault detection technique. It has been widely used in process industries, especially in the chemical industry. In industrial applications, achieving a sensitive system capable of detecting incipient faults, which maintains the false alarm rate to a minimum, is a crucial issue. Although a lot of research has been focused on these issues for PCA-based fault detection and diagnosis methods, sensitivity of the fault detection scheme versus false alarm rate continues to be an important issue. In this paper, an improved PCA method is proposed to address this problem. In this method, a new data preprocessing scheme and a new fault detection scheme designed for Hotelling's T2 as well as the squared prediction error are developed. A dynamic PCA model is also developed for boiler leak detection. This new method is applied to boiler water/steam leak detection with real data from Syncrude Canada's utility plant in Fort McMurray, Canada. Our results demonstrate that the proposed method can effectively reduce false alarm rate, provide effective and correct leak alarms, and give early warning to operators.

A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals.

PubMed

Gold, Nathan; Frasch, Martin G; Herry, Christophe L; Richardson, Bryan S; Wang, Xiaogang

2017-01-01

Experimentally and clinically collected time series data are often contaminated with significant confounding noise, creating short, noisy time series. This noise, due to natural variability and measurement error, poses a challenge to conventional change point detection methods. We propose a novel and robust statistical method for change point detection for noisy biological time sequences. Our method is a significant improvement over traditional change point detection methods, which only examine a potential anomaly at a single time point. In contrast, our method considers all suspected anomaly points and considers the joint probability distribution of the number of change points and the elapsed time between two consecutive anomalies. We validate our method with three simulated time series, a widely accepted benchmark data set, two geological time series, a data set of ECG recordings, and a physiological data set of heart rate variability measurements of fetal sheep model of human labor, comparing it to three existing methods. Our method demonstrates significantly improved performance over the existing point-wise detection methods.

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

PubMed Central

Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

2016-01-01

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians. PMID:27548179

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images.

PubMed

Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

2016-08-19

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles' in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

Detecting Corresponding Vertex Pairs between Planar Tessellation Datasets with Agglomerative Hierarchical Cell-Set Matching.

PubMed

Huh, Yong; Yu, Kiyun; Park, Woojin

2016-01-01

This paper proposes a method to detect corresponding vertex pairs between planar tessellation datasets. Applying an agglomerative hierarchical co-clustering, the method finds geometrically corresponding cell-set pairs from which corresponding vertex pairs are detected. Then, the map transformation is performed with the vertex pairs. Since these pairs are independently detected for each corresponding cell-set pairs, the method presents improved matching performance regardless of locally uneven positional discrepancies between dataset. The proposed method was applied to complicated synthetic cell datasets assumed as a cadastral map and a topographical map, and showed an improved result with the F-measures of 0.84 comparing to a previous matching method with the F-measure of 0.48.

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

USGS Publications Warehouse

Clare, John; McKinney, Shawn T.; DePue, John E.; Loftin, Cynthia S.

2017-01-01

It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture–recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters.

Research on Aircraft Target Detection Algorithm Based on Improved Radial Gradient Transformation

NASA Astrophysics Data System (ADS)

Zhao, Z. M.; Gao, X. M.; Jiang, D. N.; Zhang, Y. Q.

2018-04-01

Aiming at the problem that the target may have different orientation in the unmanned aerial vehicle (UAV) image, the target detection algorithm based on the rotation invariant feature is studied, and this paper proposes a method of RIFF (Rotation-Invariant Fast Features) based on look up table and polar coordinate acceleration to be used for aircraft target detection. The experiment shows that the detection performance of this method is basically equal to the RIFF, and the operation efficiency is greatly improved.

On-line high-speed rail defect detection : part II.

DOT National Transportation Integrated Search

2012-03-01

The objectives of this project were (1) to improve the defect detection reliability and (2) to improve the inspection speed of conventional rail defect detection methods. The prototype developed in this work uses noncontact transducers, ultrasonic gu...

Imaging of underground karst water channels using an improved multichannel transient Rayleigh wave detecting method

PubMed Central

Zheng, Xuhui; Liu, Lei; Li, Gao; Zhou, Fubiao; Xu, Jiemin

2018-01-01

Geological and hydrogeological conditions in karst areas are complicated from the viewpoint of engineering. The construction of underground structures in these areas is often disturbed by the gushing of karst water, which may delay the construction schedule, result in economic losses, and even cause heavy casualties. In this paper, an innovative method of multichannel transient Rayleigh wave detecting is proposed by introducing the concept of arrival time difference phase between channels (TDP). Overcoming the restriction of the space-sampling law, the proposed method can extract the phase velocities of different frequency components from only two channels of transient Rayleigh wave recorded on two adjacent detecting points. This feature greatly improves the work efficiency and lateral resolution of transient Rayleigh wave detecting. The improved multichannel transient Rayleigh wave detecting method is applied to the detection of karst caves and fractures in rock mass of the foundation pit of Yan’an Road Station of Guiyang Metro. The imaging of the detecting results clearly reveals the distribution of karst water inflow channels, which provided significant guidance for water plugging and enabled good control over karst water gushing in the foundation pit. PMID:29883492

Imaging of underground karst water channels using an improved multichannel transient Rayleigh wave detecting method.

PubMed

Zheng, Xuhui; Liu, Lei; Sun, Jinzhong; Li, Gao; Zhou, Fubiao; Xu, Jiemin

2018-01-01

Geological and hydrogeological conditions in karst areas are complicated from the viewpoint of engineering. The construction of underground structures in these areas is often disturbed by the gushing of karst water, which may delay the construction schedule, result in economic losses, and even cause heavy casualties. In this paper, an innovative method of multichannel transient Rayleigh wave detecting is proposed by introducing the concept of arrival time difference phase between channels (TDP). Overcoming the restriction of the space-sampling law, the proposed method can extract the phase velocities of different frequency components from only two channels of transient Rayleigh wave recorded on two adjacent detecting points. This feature greatly improves the work efficiency and lateral resolution of transient Rayleigh wave detecting. The improved multichannel transient Rayleigh wave detecting method is applied to the detection of karst caves and fractures in rock mass of the foundation pit of Yan'an Road Station of Guiyang Metro. The imaging of the detecting results clearly reveals the distribution of karst water inflow channels, which provided significant guidance for water plugging and enabled good control over karst water gushing in the foundation pit.

Detecting persons concealed in a vehicle

DOEpatents

Tucker, Jr., Raymond W.

2005-03-29

An improved method for detecting the presence of humans or animals concealed within in a vehicle uses a combination of the continuous wavelet transform and a ratio-based energy calculation to determine whether the motion detected using seismic sensors placed on the vehicle is due to the presence of a heartbeat within the vehicle or is the result of motion caused by external factors such as the wind. The method performs well in the presence of light to moderate ambient wind levels, producing far fewer false alarm indications. The new method significantly improves the range of ambient environmental conditions under which human presence detection systems can reliably operate.

Novel use of a radiolabelled antibody against stage specific embryonic antigen for the detection of occult abscesses in mammals

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

Thakur, Madhukar L.

1990-01-01

The invention discloses improved reagents containing antibodies against stage specific embryonic antigen-1 antibodies and improved methods for detection of occult abscess and inflammation using the improved reagents.

Method for improving the limit of detection in a data signal

DOEpatents

Synovec, Robert E.; Yueng, Edward S.

1989-10-17

A method for improving the limit of detection for a data set in which experimental noise is uncorrelated along a given abscissa and an analytical signal is correlated to the abscissa, the steps comprising collecting the data set, converting the data set into a data signal including an analytical portion and the experimental noise portion, designating and adjusting a baseline of the data signal to center the experimental noise numerically about a zero reference, and integrating the data signal preserving the corresponding information for each point of the data signal. The steps of the method produce an enhanced integrated data signal which improves the limit of detection of the data signal.

Method for improving the limit of detection in a data signal

DOEpatents

Synovec, R.E.; Yueng, E.S.

1989-10-17

Disclosed is a method for improving the limit of detection for a data set in which experimental noise is uncorrelated along a given abscissa and an analytical signal is correlated to the abscissa, the steps comprising collecting the data set, converting the data set into a data signal including an analytical portion and the experimental noise portion, designating and adjusting a baseline of the data signal to center the experimental noise numerically about a zero reference, and integrating the data signal preserving the corresponding information for each point of the data signal. The steps of the method produce an enhanced integrated data signal which improves the limit of detection of the data signal. 8 figs.

Detection of fatigue cracks by nondestructive testing methods

NASA Technical Reports Server (NTRS)

Anderson, R. T.; Delacy, T. J.; Stewart, R. C.

1973-01-01

The effectiveness was assessed of various NDT methods to detect small tight cracks by randomly introducing fatigue cracks into aluminum sheets. The study included optimizing NDT methods calibrating NDT equipment with fatigue cracked standards, and evaluating a number of cracked specimens by the optimized NDT methods. The evaluations were conducted by highly trained personnel, provided with detailed procedures, in order to minimize the effects of human variability. These personnel performed the NDT on the test specimens without knowledge of the flaw locations and reported on the flaws detected. The performance of these tests was measured by comparing the flaws detected against the flaws present. The principal NDT methods utilized were radiographic, ultrasonic, penetrant, and eddy current. Holographic interferometry, acoustic emission monitoring, and replication methods were also applied on a reduced number of specimens. Generally, the best performance was shown by eddy current, ultrasonic, penetrant and holographic tests. Etching provided no measurable improvement, while proof loading improved flaw detectability. Data are shown that quantify the performances of the NDT methods applied.

Community detection enhancement using non-negative matrix factorization with graph regularization

NASA Astrophysics Data System (ADS)

Liu, Xiao; Wei, Yi-Ming; Wang, Jian; Wang, Wen-Jun; He, Dong-Xiao; Song, Zhan-Jie

2016-06-01

Community detection is a meaningful task in the analysis of complex networks, which has received great concern in various domains. A plethora of exhaustive studies has made great effort and proposed many methods on community detection. Particularly, a kind of attractive one is the two-step method which first makes a preprocessing for the network and then identifies its communities. However, not all types of methods can achieve satisfactory results by using such preprocessing strategy, such as the non-negative matrix factorization (NMF) methods. In this paper, rather than using the above two-step method as most works did, we propose a graph regularized-based model to improve, specialized, the NMF-based methods for the detection of communities, namely NMFGR. In NMFGR, we introduce the similarity metric which contains both the global and local information of networks, to reflect the relationships between two nodes, so as to improve the accuracy of community detection. Experimental results on both artificial and real-world networks demonstrate the superior performance of NMFGR to some competing methods.

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data.

PubMed

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-05-15

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection.

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data

PubMed Central

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-01-01

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection. PMID:28505135

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

NASA Astrophysics Data System (ADS)

Sa, Qila; Wang, Zhihui

2018-03-01

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

Detection, Isolation, and Identification of Vibrio cholerae from the Environment

PubMed Central

Huq, Anwar; Haley, Bradd J.; Taviani, Elisa; Chen, Arlene; Hasan, Nur A.; Colwell, Rita R.

2012-01-01

Recent molecular advances in microbiology have greatly improved the detection of bacterial pathogens in the environment. Improvement and a downward trend in the cost of molecular detection methods have contributed to increased frequency of detection of pathogenic microorganisms where traditional culture-based detection methods have failed. Culture methods also have been greatly improved and the confluence of the two suites of methods provides a powerful tool for detection, isolation, and characterization of pathogens. While molecular detection provides data on the presence and type of pathogens, culturing methods allow a researcher to preserve the organism of interest for “–omics” studies, such as genomic, metabolomic, secretomic, and transcriptomic analysis, which are rapidly becoming more affordable. This has yielded a clearer understanding of the ecology and epidemiology of microorganisms that cause disease. Specifically, important advances have been made over the past several years on isolation, detection, and identification of Vibrio cholerae, the causative agent of cholera in humans. In this unit, we present commonly accepted methods for isolation, detection, and characterization of V. cholerae, providing more extensive knowledge of the ecology and epidemiology of this organism. This unit has been fully revised and updated from the earlier unit (Huq, Grim et al. 2006) with the latest knowledge and additional information not previously included. We have also taken into account of cost of reagents and equipment that may be prohibitive for many researchers and have, therefore, included protocols for all laboratories, including those with limited resources, likely to be located in regions of cholera endemicity. PMID:22875567

Detection of wood failure by image processing method: influence of algorithm, adhesive and wood species

Treesearch

Lanying Lin; Sheng He; Feng Fu; Xiping Wang

2015-01-01

Wood failure percentage (WFP) is an important index for evaluating the bond strength of plywood. Currently, the method used for detecting WFP is visual inspection, which lacks efficiency. In order to improve it, image processing methods are applied to wood failure detection. The present study used thresholding and K-means clustering algorithms in wood failure detection...

An electromagnetic induction method for underground target detection and characterization

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

Bartel, L.C.; Cress, D.H.

1997-01-01

An improved capability for subsurface structure detection is needed to support military and nonproliferation requirements for inspection and for surveillance of activities of threatening nations. As part of the DOE/NN-20 program to apply geophysical methods to detect and characterize underground facilities, Sandia National Laboratories (SNL) initiated an electromagnetic induction (EMI) project to evaluate low frequency electromagnetic (EM) techniques for subsurface structure detection. Low frequency, in this case, extended from kilohertz to hundreds of kilohertz. An EMI survey procedure had already been developed for borehole imaging of coal seams and had successfully been applied in a surface mode to detect amore » drug smuggling tunnel. The SNL project has focused on building upon the success of that procedure and applying it to surface and low altitude airborne platforms. Part of SNL`s work has focused on improving that technology through improved hardware and data processing. The improved hardware development has been performed utilizing Laboratory Directed Research and Development (LDRD) funding. In addition, SNL`s effort focused on: (1) improvements in modeling of the basic geophysics of the illuminating electromagnetic field and its coupling to the underground target (partially funded using LDRD funds) and (2) development of techniques for phase-based and multi-frequency processing and spatial processing to support subsurface target detection and characterization. The products of this project are: (1) an evaluation of an improved EM gradiometer, (2) an improved gradiometer concept for possible future development, (3) an improved modeling capability, (4) demonstration of an EM wave migration method for target recognition, and a demonstration that the technology is capable of detecting targets to depths exceeding 25 meters.« less

Real time algorithms for sharp wave ripple detection.

PubMed

Sethi, Ankit; Kemere, Caleb

2014-01-01

Neural activity during sharp wave ripples (SWR), short bursts of co-ordinated oscillatory activity in the CA1 region of the rodent hippocampus, is implicated in a variety of memory functions from consolidation to recall. Detection of these events in an algorithmic framework, has thus far relied on simple thresholding techniques with heuristically derived parameters. This study is an investigation into testing and improving the current methods for detection of SWR events in neural recordings. We propose and profile methods to reduce latency in ripple detection. Proposed algorithms are tested on simulated ripple data. The findings show that simple realtime algorithms can improve upon existing power thresholding methods and can detect ripple activity with latencies in the range of 10-20 ms.

An Improved Text Localization Method for Natural Scene Images

NASA Astrophysics Data System (ADS)

Jiang, Mengdi; Cheng, Jianghua; Chen, Minghui; Ku, Xishu

2018-01-01

In order to extract text information effectively from natural scene image with complex background, multi-orientation perspective and multilingual languages, we present a new method based on the improved Stroke Feature Transform (SWT). Firstly, The Maximally Stable Extremal Region (MSER) method is used to detect text candidate regions. Secondly, the SWT algorithm is used in the candidate regions, which can improve the edge detection compared with tradition SWT method. Finally, the Frequency-tuned (FT) visual saliency is introduced to remove non-text candidate regions. The experiment results show that, the method can achieve good robustness for complex background with multi-orientation perspective, various characters and font sizes.

Novel use of a radiolabelled antibody against stage specific embryonic antigen for the detection of occult abscesses in mammals

DOEpatents

Thakur, M.L.

1990-04-17

The invention discloses improved reagents containing antibodies against stage specific embryonic antigen-1 antibodies and improved methods for detection of occult abscess and inflammation using the improved reagents. No Drawings

Rapid Methods for the Detection of General Fecal Indicators

EPA Science Inventory

Specified that EPA should develop: appropriate and effective indicators for improving detection in a timely manner of pathogens in coastal waters appropriate, accurate, expeditious and cost-effective methods for the timely detection of pathogens in coastal waters

Improvement of charge-pumping electrically detected magnetic resonance and its application to silicon metal-oxide-semiconductor field-effect transistor

NASA Astrophysics Data System (ADS)

Hori, Masahiro; Tsuchiya, Toshiaki; Ono, Yukinori

2017-01-01

Charge-pumping electrically detected magnetic resonance (CP EDMR), or EDMR in the CP mode, is improved and applied to a silicon metal-oxide-semiconductor field-effect transistor (MOSFET). Real-time monitoring of the CP process reveals that high-frequency transient currents are an obstacle to signal amplification for EDMR. Therefore, we introduce cutoff circuitry, leading to a detection limit for the number of spins as low as 103 for Si MOS interface defects. With this improved method, we demonstrate that CP EDMR inherits one of the most important features of the CP method: the gate control of the energy window of the detectable interface defects for spectroscopy.

Improvement of the ESR detection of irradiated food containing cellulose employing a simple extraction method

NASA Astrophysics Data System (ADS)

Delincée, Henry; Soika, Christiane

2002-03-01

Fruit may be irradiated at rather low doses, below 1 kGy in combination treatments or for quarantine purposes. To improve the ESR detection sensitivity of irradiated fruit de Jesus et al. (Int. J. Food Sci. Technol. 34 (1999) 173.) proposed extracting the fruit pulp with 80% ethanol and measuring the residue with ESR using low power (0.25 mW) for detection of 'cellulosic' radicals. An improvement in ESR sensitivity using the extraction procedure could be confirmed in this paper for strawberries and papayas. In most cases, a radiation dose of 0.5 kGy could be detected in both fruits even after 2-3 weeks storage. In addition, some herbs and spices were also tested, but only for a few of them the ESR detection of the 'cellulosic' signal was improved by previous alcoholic extraction. As an alternative to ESR measurements, other detection methods like DNA Comet Assay and thermoluminescence were also tested.

Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.

PubMed

Tang, Tianyu; Zhou, Shilin; Deng, Zhipeng; Zou, Huanxin; Lei, Lin

2017-02-10

Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance.

PubMed

Clare, John; McKinney, Shawn T; DePue, John E; Loftin, Cynthia S

2017-10-01

It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture-recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters. © 2017 by the Ecological Society of America.

Improving detection of low SNR targets using moment-based detection

NASA Astrophysics Data System (ADS)

Young, Shannon R.; Steward, Bryan J.; Hawks, Michael; Gross, Kevin C.

2016-05-01

Increases in the number of cameras deployed, frame rate, and detector array sizes have led to a dramatic increase in the volume of motion imagery data that is collected. Without a corresponding increase in analytical manpower, much of the data is not analyzed to full potential. This creates a need for fast, automated, and robust methods for detecting signals of interest. Current approaches fall into two categories: detect-before-track (DBT), which are fast but often poor at detecting dim targets, and track-before-detect (TBD) methods which can offer better performance but are typically much slower. This research seeks to contribute to the near real time detection of low SNR, unresolved moving targets through an extension of earlier work on higher order moments anomaly detection, a method that exploits both spatial and temporal information but is still computationally efficient and massively parallelizable. It was found that intelligent selection of parameters can improve probability of detection by as much as 25% compared to earlier work with higherorder moments. The present method can reduce detection thresholds by 40% compared to the Reed-Xiaoli anomaly detector for low SNR targets (for a given probability of detection and false alarm).

Ensemble empirical mode decomposition based fluorescence spectral noise reduction for low concentration PAHs

NASA Astrophysics Data System (ADS)

Wang, Shu-tao; Yang, Xue-ying; Kong, De-ming; Wang, Yu-tian

2017-11-01

A new noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed to improve the detection effect for fluorescence spectra. Polycyclic aromatic hydrocarbons (PAHs) pollutants, as a kind of important current environmental pollution source, are highly oncogenic. Using the fluorescence spectroscopy method, the PAHs pollutants can be detected. However, instrument will produce noise in the experiment. Weak fluorescent signals can be affected by noise, so we propose a way to denoise and improve the detection effect. Firstly, we use fluorescence spectrometer to detect PAHs to obtain fluorescence spectra. Subsequently, noises are reduced by EEMD algorithm. Finally, the experiment results show the proposed method is feasible.

Freeze-thaw method improves the detection of volatile compounds in insects using Headspace Solid-Phase Microextraction (HS-SPME)

USDA-ARS?s Scientific Manuscript database

Headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography–mass spectrometry (GC-MS) is commonly used in analyzing insect volatiles. In order to improve the detection of volatiles in insects, a freeze-thaw method was applied to insect samples before the HS-SPME-GC-MS analysis. ...

LEA Detection and Tracking Method for Color-Independent Visual-MIMO

PubMed Central

Kim, Jai-Eun; Kim, Ji-Won; Kim, Ki-Doo

2016-01-01

Communication performance in the color-independent visual-multiple input multiple output (visual-MIMO) technique is deteriorated by light emitting array (LEA) detection and tracking errors in the received image because the image sensor included in the camera must be used as the receiver in the visual-MIMO system. In this paper, in order to improve detection reliability, we first set up the color-space-based region of interest (ROI) in which an LEA is likely to be placed, and then use the Harris corner detection method. Next, we use Kalman filtering for robust tracking by predicting the most probable location of the LEA when the relative position between the camera and the LEA varies. In the last step of our proposed method, the perspective projection is used to correct the distorted image, which can improve the symbol decision accuracy. Finally, through numerical simulation, we show the possibility of robust detection and tracking of the LEA, which results in a symbol error rate (SER) performance improvement. PMID:27384563

LEA Detection and Tracking Method for Color-Independent Visual-MIMO.

PubMed

Kim, Jai-Eun; Kim, Ji-Won; Kim, Ki-Doo

2016-07-02

Communication performance in the color-independent visual-multiple input multiple output (visual-MIMO) technique is deteriorated by light emitting array (LEA) detection and tracking errors in the received image because the image sensor included in the camera must be used as the receiver in the visual-MIMO system. In this paper, in order to improve detection reliability, we first set up the color-space-based region of interest (ROI) in which an LEA is likely to be placed, and then use the Harris corner detection method. Next, we use Kalman filtering for robust tracking by predicting the most probable location of the LEA when the relative position between the camera and the LEA varies. In the last step of our proposed method, the perspective projection is used to correct the distorted image, which can improve the symbol decision accuracy. Finally, through numerical simulation, we show the possibility of robust detection and tracking of the LEA, which results in a symbol error rate (SER) performance improvement.

Adaptation of red blood cell lysis represents a fundamental breakthrough that improves the sensitivity of Salmonella detection in blood

PubMed Central

Boyd, MA; Tennant, SM; Melendez, JH; Toema, D; Galen, JE; Geddes, CD; Levine, MM

2015-01-01

Aims Isolation of Salmonella Typhi from blood culture is the standard diagnostic for confirming typhoid fever but it is unavailable in many developing countries. We previously described a Microwave Accelerated Metal Enhanced Fluorescence (MAMEF)-based assay to detect Salmonella in medium. Attempts to detect Salmonella in blood were unsuccessful, presumably due to the interference of erythrocytes. The objective of this study was to evaluate various blood treatment methods that could be used prior to PCR, real-time PCR or MAMEF to increase sensitivity of detection of Salmonella. Methods and Results We tested ammonium chloride and erythrocyte lysis buffer, water, Lymphocyte Separation Medium, BD Vacutainer® CPT™ Tubes and dextran. Erythrocyte lysis buffer was the best isolation method as it is fast, inexpensive and works with either fresh or stored blood. The sensitivity of PCR- and real-time PCR detection of Salmonella in spiked blood was improved when whole blood was first lysed using erythrocyte lysis buffer prior to DNA extraction. Removal of erythrocytes and clotting factors also enabled reproducible lysis of Salmonella and fragmentation of DNA, which are necessary for MAMEF sensing. Conclusions Use of the erythrocyte lysis procedure prior to DNA extraction has enabled improved sensitivity of Salmonella detection by PCR and real-time PCR and has allowed lysis and fragmentation of Salmonella using microwave radiation (for future detection by MAMEF). Significance and Impact of the Study Adaptation of the blood lysis method represents a fundamental breakthrough that improves the sensitivity of DNA-based detection of Salmonella in blood. PMID:25630831

An improved AE detection method of rail defect based on multi-level ANC with VSS-LMS

NASA Astrophysics Data System (ADS)

Zhang, Xin; Cui, Yiming; Wang, Yan; Sun, Mingjian; Hu, Hengshan

2018-01-01

In order to ensure the safety and reliability of railway system, Acoustic Emission (AE) method is employed to investigate rail defect detection. However, little attention has been paid to the defect detection at high speed, especially for noise interference suppression. Based on AE technology, this paper presents an improved rail defect detection method by multi-level ANC with VSS-LMS. Multi-level noise cancellation based on SANC and ANC is utilized to eliminate complex noises at high speed, and tongue-shaped curve with index adjustment factor is proposed to enhance the performance of variable step-size algorithm. Defect signals and reference signals are acquired by the rail-wheel test rig. The features of noise signals and defect signals are analyzed for effective detection. The effectiveness of the proposed method is demonstrated by comparing with the previous study, and different filter lengths are investigated to obtain a better noise suppression performance. Meanwhile, the detection ability of the proposed method is verified at the top speed of the test rig. The results clearly illustrate that the proposed method is effective in detecting rail defects at high speed, especially for noise interference suppression.

Corner detection and sorting method based on improved Harris algorithm in camera calibration

NASA Astrophysics Data System (ADS)

Xiao, Ying; Wang, Yonghong; Dan, Xizuo; Huang, Anqi; Hu, Yue; Yang, Lianxiang

2016-11-01

In traditional Harris corner detection algorithm, the appropriate threshold which is used to eliminate false corners is selected manually. In order to detect corners automatically, an improved algorithm which combines Harris and circular boundary theory of corners is proposed in this paper. After detecting accurate corner coordinates by using Harris algorithm and Forstner algorithm, false corners within chessboard pattern of the calibration plate can be eliminated automatically by using circular boundary theory. Moreover, a corner sorting method based on an improved calibration plate is proposed to eliminate false background corners and sort remaining corners in order. Experiment results show that the proposed algorithms can eliminate all false corners and sort remaining corners correctly and automatically.

Improved detectivity of pyroelectric detectors

NASA Technical Reports Server (NTRS)

Marshall, D. E.; Gelpey, J. C.; Marciniec, J. W.; Chiang, A. M.; Maciolek, R. B.

1978-01-01

High detectivity single-element SBN pyroelectric detectors were fabricated. The theory and technology developments related to improved detector performance were identified and formulated. Improved methods of material characterization, thinning, mounting, blackening and amplifier matching are discussed. Detectors with detectivities of 1.3 x 10 to the 9th power square root of Hz/watt at 1 Hz are reported. Factors limiting performance and recommendations for future work are discussed.

A multistage approach to improve performance of computer-aided detection of pulmonary embolisms depicted on CT images: preliminary investigation.

PubMed

Park, Sang Cheol; Chapman, Brian E; Zheng, Bin

2011-06-01

This study developed a computer-aided detection (CAD) scheme for pulmonary embolism (PE) detection and investigated several approaches to improve CAD performance. In the study, 20 computed tomography examinations with various lung diseases were selected, which include 44 verified PE lesions. The proposed CAD scheme consists of five basic steps: 1) lung segmentation; 2) PE candidate extraction using an intensity mask and tobogganing region growing; 3) PE candidate feature extraction; 4) false-positive (FP) reduction using an artificial neural network (ANN); and 5) a multifeature-based k-nearest neighbor for positive/negative classification. In this study, we also investigated the following additional methods to improve CAD performance: 1) grouping 2-D detected features into a single 3-D object; 2) selecting features with a genetic algorithm (GA); and 3) limiting the number of allowed suspicious lesions to be cued in one examination. The results showed that 1) CAD scheme using tobogganing, an ANN, and grouping method achieved the maximum detection sensitivity of 79.2%; 2) the maximum scoring method achieved the superior performance over other scoring fusion methods; 3) GA was able to delete "redundant" features and further improve CAD performance; and 4) limiting the maximum number of cued lesions in an examination reduced FP rate by 5.3 times. Combining these approaches, CAD scheme achieved 63.2% detection sensitivity with 18.4 FP lesions per examination. The study suggested that performance of CAD schemes for PE detection depends on many factors that include 1) optimizing the 2-D region grouping and scoring methods; 2) selecting the optimal feature set; and 3) limiting the number of allowed cueing lesions per examination.

Rhythm-based heartbeat duration normalization for atrial fibrillation detection.

PubMed

Islam, Md Saiful; Ammour, Nassim; Alajlan, Naif; Aboalsamh, Hatim

2016-05-01

Screening of atrial fibrillation (AF) for high-risk patients including all patients aged 65 years and older is important for prevention of risk of stroke. Different technologies such as modified blood pressure monitor, single lead ECG-based finger-probe, and smart phone using plethysmogram signal have been emerging for this purpose. All these technologies use irregularity of heartbeat duration as a feature for AF detection. We have investigated a normalization method of heartbeat duration for improved AF detection. AF is an arrhythmia in which heartbeat duration generally becomes irregularly irregular. From a window of heartbeat duration, we estimate the possible rhythm of the majority of heartbeats and normalize duration of all heartbeats in the window based on the rhythm so that we can measure the irregularity of heartbeats for both AF and non-AF rhythms in the same scale. Irregularity is measured by the entropy of distribution of the normalized duration. Then we classify a window of heartbeats as AF or non-AF by thresholding the measured irregularity. The effect of this normalization is evaluated by comparing AF detection performances using duration with the normalization, without normalization, and with other existing normalizations. Sensitivity and specificity of AF detection using normalized heartbeat duration were tested on two landmark databases available online and compared with results of other methods (with/without normalization) by receiver operating characteristic (ROC) curves. ROC analysis showed that the normalization was able to improve the performance of AF detection and it was consistent for a wide range of sensitivity and specificity for use of different thresholds. Detection accuracy was also computed for equal rates of sensitivity and specificity for different methods. Using normalized heartbeat duration, we obtained 96.38% accuracy which is more than 4% improvement compared to AF detection without normalization. The proposed normalization method was found useful for improving performance and robustness of AF detection. Incorporation of this method in a screening device could be crucial to reduce the risk of AF-related stroke. In general, the incorporation of the rhythm-based normalization in an AF detection method seems important for developing a robust AF screening device. Copyright © 2016 Elsevier Ltd. All rights reserved.

Vital sign sensing method based on EMD in terahertz band

NASA Astrophysics Data System (ADS)

Xu, Zhengwu; Liu, Tong

2014-12-01

Non-contact respiration and heartbeat rates detection could be applied to find survivors trapped in the disaster or the remote monitoring of the respiration and heartbeat of a patient. This study presents an improved algorithm that extracts the respiration and heartbeat rates of humans by utilizing the terahertz radar, which further lessens the effects of noise, suppresses the cross-term, and enhances the detection accuracy. A human target echo model for the terahertz radar is first presented. Combining the over-sampling method, low-pass filter, and Empirical Mode Decomposition improves the signal-to-noise ratio. The smoothed pseudo Wigner-Ville distribution time-frequency technique and the centroid of the spectrogram are used to estimate the instantaneous velocity of the target's cardiopulmonary motion. The down-sampling method is adopted to prevent serious distortion. Finally, a second time-frequency analysis is applied to the centroid curve to extract the respiration and heartbeat rates of the individual. Simulation results show that compared with the previously presented vital sign sensing method, the improved algorithm enhances the signal-to-noise ratio to 1 dB with a detection accuracy of 80%. The improved algorithm is an effective approach for the detection of respiration and heartbeat signal in a complicated environment.

A highly sensitive and specific method for the screening detection of genetically modified organisms based on digital PCR without pretreatment

PubMed Central

Fu, Wei; Zhu, Pengyu; Wang, Chenguang; Huang, Kunlun; Du, Zhixin; Tian, Wenying; Wang, Qin; Wang, Huiyu; Xu, Wentao; Zhu, Shuifang

2015-01-01

Digital PCR has developed rapidly since it was first reported in the 1990s. It was recently reported that an improved method facilitated the detection of genetically modified organisms (GMOs). However, to use this improved method, the samples must be pretreated, which could introduce inaccuracy into the results. In our study, we explored a pretreatment-free digital PCR detection method for the screening for GMOs. We chose the CaMV35s promoter and the NOS terminator as the templates in our assay. To determine the specificity of our method, 9 events of GMOs were collected, including MON810, MON863, TC1507, MIR604, MIR162, GA21, T25, NK603 and Bt176. Moreover, the sensitivity, intra-laboratory and inter-laboratory reproducibility of our detection method were assessed. The results showed that the limit of detection of our method was 0.1%, which was lower than the labeling threshold level of the EU. The specificity and stability among the 9 events were consistent, respectively. The intra-laboratory and inter-laboratory reproducibility were both good. Finally, the perfect fitness for the detection of eight double-blind samples indicated the good practicability of our method. In conclusion, the method in our study would allow more sensitive, specific and stable screening detection of the GMO content of international trading products. PMID:26239916

A highly sensitive and specific method for the screening detection of genetically modified organisms based on digital PCR without pretreatment.

PubMed

Fu, Wei; Zhu, Pengyu; Wang, Chenguang; Huang, Kunlun; Du, Zhixin; Tian, Wenying; Wang, Qin; Wang, Huiyu; Xu, Wentao; Zhu, Shuifang

2015-08-04

Digital PCR has developed rapidly since it was first reported in the 1990 s. It was recently reported that an improved method facilitated the detection of genetically modified organisms (GMOs). However, to use this improved method, the samples must be pretreated, which could introduce inaccuracy into the results. In our study, we explored a pretreatment-free digital PCR detection method for the screening for GMOs. We chose the CaMV35s promoter and the NOS terminator as the templates in our assay. To determine the specificity of our method, 9 events of GMOs were collected, including MON810, MON863, TC1507, MIR604, MIR162, GA21, T25, NK603 and Bt176. Moreover, the sensitivity, intra-laboratory and inter-laboratory reproducibility of our detection method were assessed. The results showed that the limit of detection of our method was 0.1%, which was lower than the labeling threshold level of the EU. The specificity and stability among the 9 events were consistent, respectively. The intra-laboratory and inter-laboratory reproducibility were both good. Finally, the perfect fitness for the detection of eight double-blind samples indicated the good practicability of our method. In conclusion, the method in our study would allow more sensitive, specific and stable screening detection of the GMO content of international trading products.

Research on vehicle detection based on background feature analysis in SAR images

NASA Astrophysics Data System (ADS)

Zhang, Bochuan; Tang, Bo; Zhang, Cong; Hu, Ruiguang; Yun, Hongquan; Xiao, Liping

2017-10-01

Aiming at vehicle detection on the ground through low resolution SAR images, a method is proposed for determining the region of the vehicles first and then detecting the target in the specific region. The experimental results show that this method not only reduces the target detection area, but also reduces the influence of terrain clutter on the detection, which greatly improves the reliability of the target detection.

SiPM electro-optical detection system noise suppression method

NASA Astrophysics Data System (ADS)

Bi, Xiangli; Yang, Suhui; Hu, Tao; Song, Yiheng

2014-11-01

In this paper, the single photon detection principle of Silicon Photomultipliers (SiPM) device is introduced. The main noise factors that infect the sensitivity of the electro-optical detection system are analyzed, including background light noise, detector dark noise, preamplifier noise and signal light noise etc. The Optical, electrical and thermodynamic methods are used to suppress the SiPM electro-optical detection system noise, which improved the response sensitivity of the detector. Using SiPM optoelectronic detector with a even high sensitivity, together with small field large aperture optical system, high cutoff narrow bandwidth filters, low-noise operational amplifier circuit, the modular design of functional circuit, semiconductor refrigeration technology, greatly improved the sensitivity of optical detection system, reduced system noise and achieved long-range detection of weak laser radiation signal. Theoretical analysis and experimental results show that the proposed methods are reasonable and efficient.

Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection

NASA Astrophysics Data System (ADS)

Li, Jiangtong; Luo, Yongdao; Dai, Honglin

2018-01-01

Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.

[An automatic peak detection method for LIBS spectrum based on continuous wavelet transform].

PubMed

Chen, Peng-Fei; Tian, Di; Qiao, Shu-Jun; Yang, Guang

2014-07-01

Spectrum peak detection in the laser-induced breakdown spectroscopy (LIBS) is an essential step, but the presence of background and noise seriously disturb the accuracy of peak position. The present paper proposed a method applied to automatic peak detection for LIBS spectrum in order to enhance the ability of overlapping peaks searching and adaptivity. We introduced the ridge peak detection method based on continuous wavelet transform to LIBS, and discussed the choice of the mother wavelet and optimized the scale factor and the shift factor. This method also improved the ridge peak detection method with a correcting ridge method. The experimental results show that compared with other peak detection methods (the direct comparison method, derivative method and ridge peak search method), our method had a significant advantage on the ability to distinguish overlapping peaks and the precision of peak detection, and could be be applied to data processing in LIBS.

An improved method to detect correct protein folds using partial clustering.

PubMed

Zhou, Jianjun; Wishart, David S

2013-01-16

Structure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient "partial" clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods. We propose a new scheme that performs rapid but incomplete clustering on protein decoys. Our method detects structurally similar decoys (measured using either C(α) RMSD or GDT-TS score) and extracts representatives from them without assigning every decoy to a cluster. We integrated our new clustering strategy with several different scoring functions to assess both the performance and speed in identifying correct or near-correct folds. Experimental results on 35 Rosetta decoy sets and 40 I-TASSER decoy sets show that our method can improve the correct fold detection rate as assessed by two different quality criteria. This improvement is significantly better than two recently published clustering methods, Durandal and Calibur-lite. Speed and efficiency testing shows that our method can handle much larger decoy sets and is up to 22 times faster than Durandal and Calibur-lite. The new method, named HS-Forest, avoids the computationally expensive task of clustering every decoy, yet still allows superior correct-fold selection. Its improved speed, efficiency and decoy-selection performance should enable structure prediction researchers to work with larger decoy sets and significantly improve their ab initio structure prediction performance.

An improved method to detect correct protein folds using partial clustering

PubMed Central

2013-01-01

Background Structure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient “partial“ clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods. Results We propose a new scheme that performs rapid but incomplete clustering on protein decoys. Our method detects structurally similar decoys (measured using either Cα RMSD or GDT-TS score) and extracts representatives from them without assigning every decoy to a cluster. We integrated our new clustering strategy with several different scoring functions to assess both the performance and speed in identifying correct or near-correct folds. Experimental results on 35 Rosetta decoy sets and 40 I-TASSER decoy sets show that our method can improve the correct fold detection rate as assessed by two different quality criteria. This improvement is significantly better than two recently published clustering methods, Durandal and Calibur-lite. Speed and efficiency testing shows that our method can handle much larger decoy sets and is up to 22 times faster than Durandal and Calibur-lite. Conclusions The new method, named HS-Forest, avoids the computationally expensive task of clustering every decoy, yet still allows superior correct-fold selection. Its improved speed, efficiency and decoy-selection performance should enable structure prediction researchers to work with larger decoy sets and significantly improve their ab initio structure prediction performance. PMID:23323835

An improved EMD method for modal identification and a combined static-dynamic method for damage detection

NASA Astrophysics Data System (ADS)

Yang, Jinping; Li, Peizhen; Yang, Youfa; Xu, Dian

2018-04-01

Empirical mode decomposition (EMD) is a highly adaptable signal processing method. However, the EMD approach has certain drawbacks, including distortions from end effects and mode mixing. In the present study, these two problems are addressed using an end extension method based on the support vector regression machine (SVRM) and a modal decomposition method based on the characteristics of the Hilbert transform. The algorithm includes two steps: using the SVRM, the time series data are extended at both endpoints to reduce the end effects, and then, a modified EMD method using the characteristics of the Hilbert transform is performed on the resulting signal to reduce mode mixing. A new combined static-dynamic method for identifying structural damage is presented. This method combines the static and dynamic information in an equilibrium equation that can be solved using the Moore-Penrose generalized matrix inverse. The combination method uses the differences in displacements of the structure with and without damage and variations in the modal force vector. Tests on a four-story, steel-frame structure were conducted to obtain static and dynamic responses of the structure. The modal parameters are identified using data from the dynamic tests and improved EMD method. The new method is shown to be more accurate and effective than the traditional EMD method. Through tests with a shear-type test frame, the higher performance of the proposed static-dynamic damage detection approach, which can detect both single and multiple damage locations and the degree of the damage, is demonstrated. For structures with multiple damage, the combined approach is more effective than either the static or dynamic method. The proposed EMD method and static-dynamic damage detection method offer improved modal identification and damage detection, respectively, in structures.

Concentration of enteric viruses from tap water using an anion exchange resin-based method.

PubMed

Pérez-Méndez, A; Chandler, J C; Bisha, B; Goodridge, L D

2014-09-01

Detecting low concentrations of enteric viruses in water is needed for public health-related monitoring and control purposes. Thus, there is a need for sensitive, rapid and cost effective enteric viral concentration methods compatible with downstream molecular detection. Here, a virus concentration method based on adsorption of the virus to an anion exchange resin and direct isolation of nucleic acids is presented. Ten liter samples of tap water spiked with different concentrations (10-10,000 TCID50/10 L) of human adenovirus 40 (HAdV-40), hepatitis A virus (HAV) or rotavirus (RV) were concentrated and detected by real time PCR or real time RT-PCR. This method improved viral detection compared to direct testing of spiked water samples where the ΔCt was 12.1 for AdV-40 and 4.3 for HAV. Direct detection of RV in water was only possible for one of the three replicates tested (Ct of 37), but RV detection was improved using the resin method (all replicates tested positive with an average Ct of 30, n=3). The limit of detection of the method was 10 TCID50/10 L for HAdV-40 and HAV, and 100 TCID50/10 L of water for RV. These results compare favorably with detection limits reported for more expensive and laborious methods. Copyright © 2014 Elsevier B.V. All rights reserved.

Combining spatial and spectral information to improve crop/weed discrimination algorithms

NASA Astrophysics Data System (ADS)

Yan, L.; Jones, G.; Villette, S.; Paoli, J. N.; Gée, C.

2012-01-01

Reduction of herbicide spraying is an important key to environmentally and economically improve weed management. To achieve this, remote sensors such as imaging systems are commonly used to detect weed plants. We developed spatial algorithms that detect the crop rows to discriminate crop from weeds. These algorithms have been thoroughly tested and provide robust and accurate results without learning process but their detection is limited to inter-row areas. Crop/Weed discrimination using spectral information is able to detect intra-row weeds but generally needs a prior learning process. We propose a method based on spatial and spectral information to enhance the discrimination and overcome the limitations of both algorithms. The classification from the spatial algorithm is used to build the training set for the spectral discrimination method. With this approach we are able to improve the range of weed detection in the entire field (inter and intra-row). To test the efficiency of these algorithms, a relevant database of virtual images issued from SimAField model has been used and combined to LOPEX93 spectral database. The developed method based is evaluated and compared with the initial method in this paper and shows an important enhancement from 86% of weed detection to more than 95%.

An Optimized Method to Detect BDS Satellites' Orbit Maneuvering and Anomalies in Real-Time.

PubMed

Huang, Guanwen; Qin, Zhiwei; Zhang, Qin; Wang, Le; Yan, Xingyuan; Wang, Xiaolei

2018-02-28

The orbital maneuvers of Global Navigation Satellite System (GNSS) Constellations will decrease the performance and accuracy of positioning, navigation, and timing (PNT). Because satellites in the Chinese BeiDou Navigation Satellite System (BDS) are in Geostationary Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO), maneuvers occur more frequently. Also, the precise start moment of the BDS satellites' orbit maneuvering cannot be obtained by common users. This paper presented an improved real-time detecting method for BDS satellites' orbit maneuvering and anomalies with higher timeliness and higher accuracy. The main contributions to this improvement are as follows: (1) instead of the previous two-steps method, a new one-step method with higher accuracy is proposed to determine the start moment and the pseudo random noise code (PRN) of the satellite orbit maneuvering in that time; (2) BDS Medium Earth Orbit (MEO) orbital maneuvers are firstly detected according to the proposed selection strategy for the stations; and (3) the classified non-maneuvering anomalies are detected by a new median robust method using the weak anomaly detection factor and the strong anomaly detection factor. The data from the Multi-GNSS Experiment (MGEX) in 2017 was used for experimental analysis. The experimental results and analysis showed that the start moment of orbital maneuvers and the period of non-maneuver anomalies can be determined more accurately in real-time. When orbital maneuvers and anomalies occur, the proposed method improved the data utilization for 91 and 95 min in 2017.

An Optimized Method to Detect BDS Satellites’ Orbit Maneuvering and Anomalies in Real-Time

PubMed Central

Huang, Guanwen; Qin, Zhiwei; Zhang, Qin; Wang, Le; Yan, Xingyuan; Wang, Xiaolei

2018-01-01

The orbital maneuvers of Global Navigation Satellite System (GNSS) Constellations will decrease the performance and accuracy of positioning, navigation, and timing (PNT). Because satellites in the Chinese BeiDou Navigation Satellite System (BDS) are in Geostationary Orbit (GEO) and Inclined Geosynchronous Orbit (IGSO), maneuvers occur more frequently. Also, the precise start moment of the BDS satellites’ orbit maneuvering cannot be obtained by common users. This paper presented an improved real-time detecting method for BDS satellites’ orbit maneuvering and anomalies with higher timeliness and higher accuracy. The main contributions to this improvement are as follows: (1) instead of the previous two-steps method, a new one-step method with higher accuracy is proposed to determine the start moment and the pseudo random noise code (PRN) of the satellite orbit maneuvering in that time; (2) BDS Medium Earth Orbit (MEO) orbital maneuvers are firstly detected according to the proposed selection strategy for the stations; and (3) the classified non-maneuvering anomalies are detected by a new median robust method using the weak anomaly detection factor and the strong anomaly detection factor. The data from the Multi-GNSS Experiment (MGEX) in 2017 was used for experimental analysis. The experimental results and analysis showed that the start moment of orbital maneuvers and the period of non-maneuver anomalies can be determined more accurately in real-time. When orbital maneuvers and anomalies occur, the proposed method improved the data utilization for 91 and 95 min in 2017. PMID:29495638

Digital PCR methods improve detection sensitivity and measurement precision of low abundance mtDNA deletions.

PubMed

Belmonte, Frances R; Martin, James L; Frescura, Kristin; Damas, Joana; Pereira, Filipe; Tarnopolsky, Mark A; Kaufman, Brett A

2016-04-28

Mitochondrial DNA (mtDNA) mutations are a common cause of primary mitochondrial disorders, and have also been implicated in a broad collection of conditions, including aging, neurodegeneration, and cancer. Prevalent among these pathogenic variants are mtDNA deletions, which show a strong bias for the loss of sequence in the major arc between, but not including, the heavy and light strand origins of replication. Because individual mtDNA deletions can accumulate focally, occur with multiple mixed breakpoints, and in the presence of normal mtDNA sequences, methods that detect broad-spectrum mutations with enhanced sensitivity and limited costs have both research and clinical applications. In this study, we evaluated semi-quantitative and digital PCR-based methods of mtDNA deletion detection using double-stranded reference templates or biological samples. Our aim was to describe key experimental assay parameters that will enable the analysis of low levels or small differences in mtDNA deletion load during disease progression, with limited false-positive detection. We determined that the digital PCR method significantly improved mtDNA deletion detection sensitivity through absolute quantitation, improved precision and reduced assay standard error.

Digital PCR methods improve detection sensitivity and measurement precision of low abundance mtDNA deletions

PubMed Central

Belmonte, Frances R.; Martin, James L.; Frescura, Kristin; Damas, Joana; Pereira, Filipe; Tarnopolsky, Mark A.; Kaufman, Brett A.

2016-01-01

Mitochondrial DNA (mtDNA) mutations are a common cause of primary mitochondrial disorders, and have also been implicated in a broad collection of conditions, including aging, neurodegeneration, and cancer. Prevalent among these pathogenic variants are mtDNA deletions, which show a strong bias for the loss of sequence in the major arc between, but not including, the heavy and light strand origins of replication. Because individual mtDNA deletions can accumulate focally, occur with multiple mixed breakpoints, and in the presence of normal mtDNA sequences, methods that detect broad-spectrum mutations with enhanced sensitivity and limited costs have both research and clinical applications. In this study, we evaluated semi-quantitative and digital PCR-based methods of mtDNA deletion detection using double-stranded reference templates or biological samples. Our aim was to describe key experimental assay parameters that will enable the analysis of low levels or small differences in mtDNA deletion load during disease progression, with limited false-positive detection. We determined that the digital PCR method significantly improved mtDNA deletion detection sensitivity through absolute quantitation, improved precision and reduced assay standard error. PMID:27122135

Salient man-made structure detection in infrared images

NASA Astrophysics Data System (ADS)

Li, Dong-jie; Zhou, Fu-gen; Jin, Ting

2013-09-01

Target detection, segmentation and recognition is a hot research topic in the field of image processing and pattern recognition nowadays, among which salient area or object detection is one of core technologies of precision guided weapon. Many theories have been raised in this paper; we detect salient objects in a series of input infrared images by using the classical feature integration theory and Itti's visual attention system. In order to find the salient object in an image accurately, we present a new method to solve the edge blur problem by calculating and using the edge mask. We also greatly improve the computing speed by improving the center-surround differences method. Unlike the traditional algorithm, we calculate the center-surround differences through rows and columns separately. Experimental results show that our method is effective in detecting salient object accurately and rapidly.

Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.

PubMed

Mansouri, Majdi; Nounou, Mohamed N; Nounou, Hazem N

2017-09-01

In our previous work, we have demonstrated the effectiveness of the linear multiscale principal component analysis (PCA)-based moving window (MW)-generalized likelihood ratio test (GLRT) technique over the classical PCA and multiscale principal component analysis (MSPCA)-based GLRT methods. The developed fault detection algorithm provided optimal properties by maximizing the detection probability for a particular false alarm rate (FAR) with different values of windows, and however, most real systems are nonlinear, which make the linear PCA method not able to tackle the issue of non-linearity to a great extent. Thus, in this paper, first, we apply a nonlinear PCA to obtain an accurate principal component of a set of data and handle a wide range of nonlinearities using the kernel principal component analysis (KPCA) model. The KPCA is among the most popular nonlinear statistical methods. Second, we extend the MW-GLRT technique to one that utilizes exponential weights to residuals in the moving window (instead of equal weightage) as it might be able to further improve fault detection performance by reducing the FAR using exponentially weighed moving average (EWMA). The developed detection method, which is called EWMA-GLRT, provides improved properties, such as smaller missed detection and FARs and smaller average run length. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. This provides a more accurate estimation of the GLRT statistic and provides a stronger memory that will enable better decision making with respect to fault detection. Therefore, in this paper, a KPCA-based EWMA-GLRT method is developed and utilized in practice to improve fault detection in biological phenomena modeled by S-systems and to enhance monitoring process mean. The idea behind a KPCA-based EWMA-GLRT fault detection algorithm is to combine the advantages brought forward by the proposed EWMA-GLRT fault detection chart with the KPCA model. Thus, it is used to enhance fault detection of the Cad System in E. coli model through monitoring some of the key variables involved in this model such as enzymes, transport proteins, regulatory proteins, lysine, and cadaverine. The results demonstrate the effectiveness of the proposed KPCA-based EWMA-GLRT method over Q , GLRT, EWMA, Shewhart, and moving window-GLRT methods. The detection performance is assessed and evaluated in terms of FAR, missed detection rates, and average run length (ARL 1 ) values.

Rapid detection of Salmonella in pet food: design and evaluation of integrated methods based on real-time PCR detection.

PubMed

Balachandran, Priya; Friberg, Maria; Vanlandingham, V; Kozak, K; Manolis, Amanda; Brevnov, Maxim; Crowley, Erin; Bird, Patrick; Goins, David; Furtado, Manohar R; Petrauskene, Olga V; Tebbs, Robert S; Charbonneau, Duane

2012-02-01

Reducing the risk of Salmonella contamination in pet food is critical for both companion animals and humans, and its importance is reflected by the substantial increase in the demand for pathogen testing. Accurate and rapid detection of foodborne pathogens improves food safety, protects the public health, and benefits food producers by assuring product quality while facilitating product release in a timely manner. Traditional culture-based methods for Salmonella screening are laborious and can take 5 to 7 days to obtain definitive results. In this study, we developed two methods for the detection of low levels of Salmonella in pet food using real-time PCR: (i) detection of Salmonella in 25 g of dried pet food in less than 14 h with an automated magnetic bead-based nucleic acid extraction method and (ii) detection of Salmonella in 375 g of composite dry pet food matrix in less than 24 h with a manual centrifugation-based nucleic acid preparation method. Both methods included a preclarification step using a novel protocol that removes food matrix-associated debris and PCR inhibitors and improves the sensitivity of detection. Validation studies revealed no significant differences between the two real-time PCR methods and the standard U.S. Food and Drug Administration Bacteriological Analytical Manual (chapter 5) culture confirmation method.

Edge detection of optical subaperture image based on improved differential box-counting method

NASA Astrophysics Data System (ADS)

Li, Yi; Hui, Mei; Liu, Ming; Dong, Liquan; Kong, Lingqin; Zhao, Yuejin

2018-01-01

Optical synthetic aperture imaging technology is an effective approach to improve imaging resolution. Compared with monolithic mirror system, the image of optical synthetic aperture system is often more complex at the edge, and as a result of the existence of gap between segments, which makes stitching becomes a difficult problem. So it is necessary to extract the edge of subaperture image for achieving effective stitching. Fractal dimension as a measure feature can describe image surface texture characteristics, which provides a new approach for edge detection. In our research, an improved differential box-counting method is used to calculate fractal dimension of image, then the obtained fractal dimension is mapped to grayscale image to detect edges. Compared with original differential box-counting method, this method has two improvements as follows: by modifying the box-counting mechanism, a box with a fixed height is replaced by a box with adaptive height, which solves the problem of over-counting the number of boxes covering image intensity surface; an image reconstruction method based on super-resolution convolutional neural network is used to enlarge small size image, which can solve the problem that fractal dimension can't be calculated accurately under the small size image, and this method may well maintain scale invariability of fractal dimension. The experimental results show that the proposed algorithm can effectively eliminate noise and has a lower false detection rate compared with the traditional edge detection algorithms. In addition, this algorithm can maintain the integrity and continuity of image edge in the case of retaining important edge information.

Improving Child Maltreatment Detection Systems: A Large-Scale Case Study Involving Health, Social Services, and School Professionals

ERIC Educational Resources Information Center

Cerezo, M.A.; Pons-Salvador, G.

2004-01-01

Objectives:: The purpose of this 5-year study was to improve detection in two consecutive phases: (a) To close the gap between the number of identified cases and the actual number of cases of child abuse by increasing detection; and (b) To increase the possibility of a broader spectrum of detection. Method:: The Balearic Islands (one of the…

Unsupervised change detection of multispectral images based on spatial constraint chi-squared transform and Markov random field model

NASA Astrophysics Data System (ADS)

Shi, Aiye; Wang, Chao; Shen, Shaohong; Huang, Fengchen; Ma, Zhenli

2016-10-01

Chi-squared transform (CST), as a statistical method, can describe the difference degree between vectors. The CST-based methods operate directly on information stored in the difference image and are simple and effective methods for detecting changes in remotely sensed images that have been registered and aligned. However, the technique does not take spatial information into consideration, which leads to much noise in the result of change detection. An improved unsupervised change detection method is proposed based on spatial constraint CST (SCCST) in combination with a Markov random field (MRF) model. First, the mean and variance matrix of the difference image of bitemporal images are estimated by an iterative trimming method. In each iteration, spatial information is injected to reduce scattered changed points (also known as "salt and pepper" noise). To determine the key parameter confidence level in the SCCST method, a pseudotraining dataset is constructed to estimate the optimal value. Then, the result of SCCST, as an initial solution of change detection, is further improved by the MRF model. The experiments on simulated and real multitemporal and multispectral images indicate that the proposed method performs well in comprehensive indices compared with other methods.

A Videotape-Based Training Method for Improving the Detection of Depression in Residents of Long-Term Care Facilities

ERIC Educational Resources Information Center

Wood, Stacey; Cummings, Jeffrey L.; Schnelle, Betha; Stephens, Mary

2002-01-01

Purpose: This article reviews the effectiveness of a new training program for improving nursing staffs' detection of depression within long-term care facilities. The course was designed to increase recognition of the Minimal Data Set (MDS) Mood Trigger items, to be brief, and to rely on images rather than didactics. Design and Methods: This study…

OZONATION BY-PRODUCTS 2. IMPROVEMENT OF AN AQUEOUS- PHASE DERIVITIZATION METHOD FOR THE DETECTION OF FORMALDEHYDE AND OTHER CARBONYL COMPOUNDS FORMED BY THE OZONATION OF DRINKING WATER

EPA Science Inventory

A method for the determination of low molecular weight aldehydes in water using aqueous-phase derivatization with O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine hydrochloride has been improved by the use of high-resolution capillary GC. Detection limits with GC/ECD and GC/MS with ...

An integrative 'omics' solution to the detection of recombinant human erythropoietin and blood doping.

PubMed

Pitsiladis, Yannis P; Durussel, Jérôme; Rabin, Olivier

2014-05-01

Administration of recombinant human erythropoietin (rHumanEPO) improves sporting performance and hence is frequently subject to abuse by athletes, although rHumanEPO is prohibited by the WADA. Approaches to detect rHumanEPO doping have improved significantly in recent years but remain imperfect. A new transcriptomic-based longitudinal screening approach is being developed that has the potential to improve the analytical performance of current detection methods. In particular, studies are being funded by WADA to identify a 'molecular signature' of rHumanEPO doping and preliminary results are promising. In the first systematic study to be conducted, the expression of hundreds of genes were found to be altered by rHumanEPO with numerous gene transcripts being differentially expressed after the first injection and further transcripts profoundly upregulated during and subsequently downregulated up to 4 weeks postadministration of the drug; with the same transcriptomic pattern observed in all participants. The identification of a blood 'molecular signature' of rHumanEPO administration is the strongest evidence to date that gene biomarkers have the potential to substantially improve the analytical performance of current antidoping methods such as the Athlete Biological Passport for rHumanEPO detection. Given the early promise of transcriptomics, research using an 'omics'-based approach involving genomics, transcriptomics, proteomics and metabolomics should be intensified in order to achieve improved detection of rHumanEPO and other doping substances and methods difficult to detect such a recombinant human growth hormone and blood transfusions.

Recombinant Temporal Aberration Detection Algorithms for Enhanced Biosurveillance

PubMed Central

Murphy, Sean Patrick; Burkom, Howard

2008-01-01

Objective Broadly, this research aims to improve the outbreak detection performance and, therefore, the cost effectiveness of automated syndromic surveillance systems by building novel, recombinant temporal aberration detection algorithms from components of previously developed detectors. Methods This study decomposes existing temporal aberration detection algorithms into two sequential stages and investigates the individual impact of each stage on outbreak detection performance. The data forecasting stage (Stage 1) generates predictions of time series values a certain number of time steps in the future based on historical data. The anomaly measure stage (Stage 2) compares features of this prediction to corresponding features of the actual time series to compute a statistical anomaly measure. A Monte Carlo simulation procedure is then used to examine the recombinant algorithms’ ability to detect synthetic aberrations injected into authentic syndromic time series. Results New methods obtained with procedural components of published, sometimes widely used, algorithms were compared to the known methods using authentic datasets with plausible stochastic injected signals. Performance improvements were found for some of the recombinant methods, and these improvements were consistent over a range of data types, outbreak types, and outbreak sizes. For gradual outbreaks, the WEWD MovAvg7+WEWD Z-Score recombinant algorithm performed best; for sudden outbreaks, the HW+WEWD Z-Score performed best. Conclusion This decomposition was found not only to yield valuable insight into the effects of the aberration detection algorithms but also to produce novel combinations of data forecasters and anomaly measures with enhanced detection performance. PMID:17947614

Experimental Detection and Characterization of Void using Time-Domain Reflection Wave

NASA Astrophysics Data System (ADS)

Zahari, M. N. H.; Madun, A.; Dahlan, S. H.; Joret, A.; Zainal Abidin, M. H.; Mohammad, A. H.; Omar, A. H.

2018-04-01

Recent technologies in engineering views have brought the significant improvement in terms of performance and precision. One of those improvements is in geophysics studies for underground detection. Reflection method has been demonstrated to able to detect and locate subsurface anomalies in previous studies, including voids. Conventional method merely involves field testing only for limited areas. This may lead to undiscovered of the void position. Problems arose when the voids were not recognised in early stage and thus, causing hazards, costs increment, and can lead to serious accidents and structural damages. Therefore, to achieve better certainty of the site investigation, a dynamic approach is needed to be implemented. To estimate and characterize the anomalies signal in a better way, an attempt has been made to model air-filled void as experimental testing at site. Robust detection and characterization of voids through inexpensive cost using reflection method are proposed to improve the detectability and characterization of the void. The result shows 2-Dimensional and 3-Dimensional analyses of void based on reflection data with P-waves velocity at 454.54 m/s.

Unsupervised Approaches for Post-Processing in Computationally Efficient Waveform-Similarity-Based Earthquake Detection

NASA Astrophysics Data System (ADS)

Bergen, K.; Yoon, C. E.; OReilly, O. J.; Beroza, G. C.

2015-12-01

Recent improvements in computational efficiency for waveform correlation-based detections achieved by new methods such as Fingerprint and Similarity Thresholding (FAST) promise to allow large-scale blind search for similar waveforms in long-duration continuous seismic data. Waveform similarity search applied to datasets of months to years of continuous seismic data will identify significantly more events than traditional detection methods. With the anticipated increase in number of detections and associated increase in false positives, manual inspection of the detection results will become infeasible. This motivates the need for new approaches to process the output of similarity-based detection. We explore data mining techniques for improved detection post-processing. We approach this by considering similarity-detector output as a sparse similarity graph with candidate events as vertices and similarities as weighted edges. Image processing techniques are leveraged to define candidate events and combine results individually processed at multiple stations. Clustering and graph analysis methods are used to identify groups of similar waveforms and assign a confidence score to candidate detections. Anomaly detection and classification are applied to waveform data for additional false detection removal. A comparison of methods will be presented and their performance will be demonstrated on a suspected induced and non-induced earthquake sequence.

PolyaPeak: Detecting Transcription Factor Binding Sites from ChIP-seq Using Peak Shape Information

PubMed Central

Wu, Hao; Ji, Hongkai

2014-01-01

ChIP-seq is a powerful technology for detecting genomic regions where a protein of interest interacts with DNA. ChIP-seq data for mapping transcription factor binding sites (TFBSs) have a characteristic pattern: around each binding site, sequence reads aligned to the forward and reverse strands of the reference genome form two separate peaks shifted away from each other, and the true binding site is located in between these two peaks. While it has been shown previously that the accuracy and resolution of binding site detection can be improved by modeling the pattern, efficient methods are unavailable to fully utilize that information in TFBS detection procedure. We present PolyaPeak, a new method to improve TFBS detection by incorporating the peak shape information. PolyaPeak describes peak shapes using a flexible Pólya model. The shapes are automatically learnt from the data using Minorization-Maximization (MM) algorithm, then integrated with the read count information via a hierarchical model to distinguish true binding sites from background noises. Extensive real data analyses show that PolyaPeak is capable of robustly improving TFBS detection compared with existing methods. An R package is freely available. PMID:24608116

Real-time fluorescence loop mediated isothermal amplification for the diagnosis of malaria.

PubMed

Lucchi, Naomi W; Demas, Allison; Narayanan, Jothikumar; Sumari, Deborah; Kabanywanyi, Abdunoor; Kachur, S Patrick; Barnwell, John W; Udhayakumar, Venkatachalam

2010-10-29

Molecular diagnostic methods can complement existing tools to improve the diagnosis of malaria. However, they require good laboratory infrastructure thereby restricting their use to reference laboratories and research studies. Therefore, adopting molecular tools for routine use in malaria endemic countries will require simpler molecular platforms. The recently developed loop-mediated isothermal amplification (LAMP) method is relatively simple and can be improved for better use in endemic countries. In this study, we attempted to improve this method for malaria diagnosis by using a simple and portable device capable of performing both the amplification and detection (by fluorescence) of LAMP in one platform. We refer to this as the RealAmp method. Published genus-specific primers were used to test the utility of this method. DNA derived from different species of malaria parasites was used for the initial characterization. Clinical samples of P. falciparum were used to determine the sensitivity and specificity of this system compared to microscopy and a nested PCR method. Additionally, directly boiled parasite preparations were compared with a conventional DNA isolation method. The RealAmp method was found to be simple and allowed real-time detection of DNA amplification. The time to amplification varied but was generally less than 60 minutes. All human-infecting Plasmodium species were detected. The sensitivity and specificity of RealAmp in detecting P. falciparum was 96.7% and 91.7% respectively, compared to microscopy and 98.9% and 100% respectively, compared to a standard nested PCR method. In addition, this method consistently detected P. falciparum from directly boiled blood samples. This RealAmp method has great potential as a field usable molecular tool for diagnosis of malaria. This tool can provide an alternative to conventional PCR based diagnostic methods for field use in clinical and operational programs.

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%.

Comparative study of protoporphyrin IX fluorescence image enhancement methods to improve an optical imaging system for oral cancer detection

NASA Astrophysics Data System (ADS)

Jiang, Ching-Fen; Wang, Chih-Yu; Chiang, Chun-Ping

2011-07-01

Optoelectronics techniques to induce protoporphyrin IX fluorescence with topically applied 5-aminolevulinic acid on the oral mucosa have been developed to noninvasively detect oral cancer. Fluorescence imaging enables wide-area screening for oral premalignancy, but the lack of an adequate fluorescence enhancement method restricts the clinical imaging application of these techniques. This study aimed to develop a reliable fluorescence enhancement method to improve PpIX fluorescence imaging systems for oral cancer detection. Three contrast features, red-green-blue reflectance difference, R/B ratio, and R/G ratio, were developed first based on the optical properties of the fluorescence images. A comparative study was then carried out with one negative control and four biopsy confirmed clinical cases to validate the optimal image processing method for the detection of the distribution of malignancy. The results showed the superiority of the R/G ratio in terms of yielding a better contrast between normal and neoplastic tissue, and this method was less prone to errors in detection. Quantitative comparison with the clinical diagnoses in the four neoplastic cases showed that the regions of premalignancy obtained using the proposed method accorded with the expert's determination, suggesting the potential clinical application of this method for the detection of oral cancer.

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

PubMed

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

1996-03-01

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

Improvement of retinal blood vessel detection by spur removal and Gaussian matched filtering compensation

NASA Astrophysics Data System (ADS)

Xiao, Di; Vignarajan, Janardhan; An, Dong; Tay-Kearney, Mei-Ling; Kanagasingam, Yogi

2016-03-01

Retinal photography is a non-invasive and well-accepted clinical diagnosis of ocular diseases. Qualitative and quantitative assessment of retinal images is crucial in ocular diseases related clinical application. In this paper, we proposed approaches for improving the quality of blood vessel detection based on our initial blood vessel detection methods. A blood vessel spur pruning method has been developed for removing the blood vessel spurs both on vessel medial lines and binary vessel masks, which are caused by artifacts and side-effect of Gaussian matched vessel enhancement. A Gaussian matched filtering compensation method has been developed for removing incorrect vessel branches in the areas of low illumination. The proposed approaches were applied and tested on the color fundus images from one publicly available database and our diabetic retinopathy screening dataset. A preliminary result has demonstrated the robustness and good performance of the proposed approaches and their potential application for improving retinal blood vessel detection.

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

PubMed

Mainali, Shraddha; Wahba, Mervat; Elijovich, Lucas

2014-01-01

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

Boosting instance prototypes to detect local dermoscopic features.

PubMed

Situ, Ning; Yuan, Xiaojing; Zouridakis, George

2010-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Detection of forced oscillations in power systems with multichannel methods

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

Follum, James D.

2015-09-30

The increasing availability of high fidelity, geographically dispersed measurements in power systems improves the ability of researchers and engineers to study dynamic behaviors in the grid. One such behavior that is garnering increased attention is the presence of forced oscillations. Power system engineers are interested in forced oscillations because they are often symptomatic of the malfunction or misoperation of equipment. Though the resulting oscillation is not always large in amplitude, the root cause may be serious. In this report, multi-channel forced oscillation detection methods are developed. These methods leverage previously developed detection approaches based on the periodogram and spectral-coherence. Makingmore » use of geographically distributed channels of data is shown to improved detection performance and shorten the delay before an oscillation can be detected in the online environment. Results from simulated and measured power system data are presented.« less

Extracting information from the text of electronic medical records to improve case detection: a systematic review

PubMed Central

Carroll, John A; Smith, Helen E; Scott, Donia; Cassell, Jackie A

2016-01-01

Background Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall). PMID:26911811

Review of recent advances in analytical techniques for the determination of neurotransmitters

PubMed Central

Perry, Maura; Li, Qiang; Kennedy, Robert T.

2009-01-01

Methods and advances for monitoring neurotransmitters in vivo or for tissue analysis of neurotransmitters over the last five years are reviewed. The review is organized primarily by neurotransmitter type. Transmitter and related compounds may be monitored by either in vivo sampling coupled to analytical methods or implanted sensors. Sampling is primarily performed using microdialysis, but low-flow push-pull perfusion may offer advantages of spatial resolution while minimizing the tissue disruption associated with higher flow rates. Analytical techniques coupled to these sampling methods include liquid chromatography, capillary electrophoresis, enzyme assays, sensors, and mass spectrometry. Methods for the detection of amino acid, monoamine, neuropeptide, acetylcholine, nucleoside, and soluable gas neurotransmitters have been developed and improved upon. Advances in the speed and sensitivity of these methods have enabled improvements in temporal resolution and increased the number of compounds detectable. Similar advances have enabled improved detection at tissue samples, with a substantial emphasis on single cell and other small samples. Sensors provide excellent temporal and spatial resolution for in vivo monitoring. Advances in application to catecholamines, indoleamines, and amino acids have been prominent. Improvements in stability, sensitivity, and selectivity of the sensors have been of paramount interest. PMID:19800472

A novel spatial-temporal detection method of dim infrared moving small target

NASA Astrophysics Data System (ADS)

Chen, Zhong; Deng, Tao; Gao, Lei; Zhou, Heng; Luo, Song

2014-09-01

Moving small target detection under complex background in infrared image sequence is one of the major challenges of modern military in Early Warning Systems (EWS) and the use of Long-Range Strike (LRS). However, because of the low SNR and undulating background, the infrared moving small target detection is a difficult problem in a long time. To solve this problem, a novel spatial-temporal detection method based on bi-dimensional empirical mode decomposition (EMD) and time-domain difference is proposed in this paper. This method is downright self-data decomposition and do not rely on any transition kernel function, so it has a strong adaptive capacity. Firstly, we generalized the 1D EMD algorithm to the 2D case. In this process, the project has solved serial issues in 2D EMD, such as large amount of data operations, define and identify extrema in 2D case, and two-dimensional signal boundary corrosion. The EMD algorithm studied in this project can be well adapted to the automatic detection of small targets under low SNR and complex background. Secondly, considering the characteristics of moving target, we proposed an improved filtering method based on three-frame difference on basis of the original difference filtering in time-domain, which greatly improves the ability of anti-jamming algorithm. Finally, we proposed a new time-space fusion method based on a combined processing of 2D EMD and improved time-domain differential filtering. And, experimental results show that this method works well in infrared small moving target detection under low SNR and complex background.

Effect of Treatment and Mammography Detection on Breast Cancer Survival Overtime: 1990–2011

PubMed Central

Kaplan, Henry G; Malmgren, Judith A; Atwood, Mary K; Calip, Gregory S.

2017-01-01

Background It is not known to what extent improvement over time in breast cancer survival is related to earlier detection by mammography or to more effective treatments. Methods At our comprehensive cancer care center we conducted a retrospective cohort study of women ages 50–69 years diagnosed with invasive stage I–III breast cancer and followed over three time periods: 1990–1994, 1995–1999 and 2000–2007. Data was chart abstracted on detection method, diagnosis, treatment, and follow up for vital status in our breast cancer registry (n=2998). Method of detection was categorized as patient or physician (Pt/PhysD) or mammography detected (MamD). Cox proportional hazards models to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CI) for five year disease specific survival (DSS) in relation to detection method and treatment factors, testing for differences in survival using the Kaplan-Meier method. Results 58% of cases were MamD and 42% were Pt/PhysD with 56% stage I, 31% stage II and 13% stage III. Average length of follow up was 10.71 years. Combined five year DSS was 89% 1990–94, 94% 1995–99, and 96% in 2000–2007 (p<.001). In an adjusted model, mammography detection (HR=0.43, 95% CI 0.27–0.70), hormone therapy (HR=0.47, 95% CI 0.30–0.75), and taxane-containing chemotherapy (HR=0.61, 95% CI 0.37–0.99) were significantly associated with a decreased risk of disease-specific mortality Conclusions Better breast cancer survival over time is related to mammography detection, hormonal therapy and taxane-containing chemotherapy treatment. Treatment improvements alone are not sufficient to explain the observed survival improvements over time. PMID:25872471

Using Policy-Capturing to Measure Attitudes in Organizational Diagnosis.

ERIC Educational Resources Information Center

Madden, Joseph M.

1981-01-01

Discusses an indirect method of attitude measurement, policy-capturing, that can be applied on an individual basis. In three experiments this method detected prejudicial attitudes toward females not detected with traditional methods. Can be used as a self-improvement diagnostic tool for developing awareness of behavior influences. (JAC)

IMPROVED METHODS FOR HEPATITIS A VIRUS AND ROTAVIRUS CONCENTRATION AND DETECTION IN RECREATIONAL, RAW POTABLE, AND FINISHED WATERS

EPA Science Inventory

The report contains procedures for detecting rotaviruses based upon an immunofluorescence test using a monoclonal antibody and fluorescein-isothiocyanate-conjugated antibody staining method to visualize virus-infected cells. Also contained in the report are test methods for detec...

High sensitivity leak detection method and apparatus

DOEpatents

Myneni, Ganapatic R.

1994-01-01

An improved leak detection method is provided that utilizes the cyclic adsorption and desorption of accumulated helium on a non-porous metallic surface. The method provides reliable leak detection at superfluid helium temperatures. The zero drift that is associated with residual gas analyzers in common leak detectors is virtually eliminated by utilizing a time integration technique. The sensitivity of the apparatus of this disclosure is capable of detecting leaks as small as 1.times.10.sup.-18 atm cc sec.sup.-1.

High sensitivity leak detection method and apparatus

DOEpatents

Myneni, G.R.

1994-09-06

An improved leak detection method is provided that utilizes the cyclic adsorption and desorption of accumulated helium on a non-porous metallic surface. The method provides reliable leak detection at superfluid helium temperatures. The zero drift that is associated with residual gas analyzers in common leak detectors is virtually eliminated by utilizing a time integration technique. The sensitivity of the apparatus of this disclosure is capable of detecting leaks as small as 1 [times] 10[sup [minus]18] atm cc sec[sup [minus]1]. 2 figs.

Improved astigmatic focus error detection method

NASA Technical Reports Server (NTRS)

Bernacki, Bruce E.

1992-01-01

All easy-to-implement focus- and track-error detection methods presently used in magneto-optical (MO) disk drives using pre-grooved media suffer from a side effect known as feedthrough. Feedthrough is the unwanted focus error signal (FES) produced when the optical head is seeking a new track, and light refracted from the pre-grooved disk produces an erroneous FES. Some focus and track-error detection methods are more resistant to feedthrough, but tend to be complicated and/or difficult to keep in alignment as a result of environmental insults. The astigmatic focus/push-pull tracking method is an elegant, easy-to-align focus- and track-error detection method. Unfortunately, it is also highly susceptible to feedthrough when astigmatism is present, with the worst effects caused by astigmatism oriented such that the tangential and sagittal foci are at 45 deg to the track direction. This disclosure outlines a method to nearly completely eliminate the worst-case form of feedthrough due to astigmatism oriented 45 deg to the track direction. Feedthrough due to other primary aberrations is not improved, but performance is identical to the unimproved astigmatic method.

Detection of Multiple Stationary Humans Using UWB MIMO Radar.

PubMed

Liang, Fulai; Qi, Fugui; An, Qiang; Lv, Hao; Chen, Fuming; Li, Zhao; Wang, Jianqi

2016-11-16

Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls.

Detection of Multiple Stationary Humans Using UWB MIMO Radar

PubMed Central

Liang, Fulai; Qi, Fugui; An, Qiang; Lv, Hao; Chen, Fuming; Li, Zhao; Wang, Jianqi

2016-01-01

Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls. PMID:27854356

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.

Trace gas detection in hyperspectral imagery using the wavelet packet subspace

NASA Astrophysics Data System (ADS)

Salvador, Mark A. Z.

This dissertation describes research into a new remote sensing method to detect trace gases in hyperspectral and ultra-spectral data. This new method is based on the wavelet packet transform. It attempts to improve both the computational tractability and the detection of trace gases in airborne and spaceborne spectral imagery. Atmospheric trace gas research supports various Earth science disciplines to include climatology, vulcanology, pollution monitoring, natural disasters, and intelligence and military applications. Hyperspectral and ultra-spectral data significantly increases the data glut of existing Earth science data sets. Spaceborne spectral data in particular significantly increases spectral resolution while performing daily global collections of the earth. Application of the wavelet packet transform to the spectral space of hyperspectral and ultra-spectral imagery data potentially improves remote sensing detection algorithms. It also facilities the parallelization of these methods for high performance computing. This research seeks two science goals, (1) developing a new spectral imagery detection algorithm, and (2) facilitating the parallelization of trace gas detection in spectral imagery data.

Improvement of ion chromatography with ultraviolet photometric detection and comparison with conductivity detection for the determination of serum cations.

PubMed

Shintani, H

1985-05-31

Studies were made of the analytical conditions required for indirect photometric ion chromatography using ultraviolet photometric detection (UV method) for the determination of serum cations following a previously developed serum pre-treatment. The sensitivities of the conductivity detection (CD) and UV methods and the amounts of serum cations determined by both methods were compared. Attempts to improve the sensitivity of the conventional UV method are reported. It was found that the mobile phase previously reported by Small and Miller showed no quantitative response when more than 4 mM copper(II) sulphate pentahydrate was used. As a result, there was no significant difference in the amounts of serum cations shown by the CD and UV methods. However, by adding 0.5-5 mM cobalt(II) sulphate heptahydrate, nickel(II) sulphate hexahydrate, zinc(II) sulphate heptahydrate or cobalt(II) diammonium sulphate hexahydrate to 0.5-1.5 mM copper(II) sulphate pentahydrate, higher sensitivity and a quantitative response were attained.

[Improvement of Phi bodies stain and its clinical significance].

PubMed

Gong, Xu-Bo; Lu, Xing-Guo; Yan, Li-Juan; Xiao, Xi-Bin; Wu, Dong; Xu, Gen-Bo; Zhang, Xiao-Hong; Zhao, Xiao-Ying

2009-02-01

The aim of this study was to improve the dyeing method of hydroperoxidase (HPO), to analyze the morphologic features of Phi bodies and to evaluate the clinical application of this method. 128 bone marrow or peripheral blood smears from patients with myeloid and lymphoid malignancies were stained by improved HPO staining. The Phi bodies were observed with detection rate of Phi bodies in different leukemias. 69 acute myeloid leukemia (AML) specimens were chosen randomly, the positive rate and the number of Phi bodies between the improved HPO and POX stain based on the same substrate of 3, 3'diaminobenzidine were compared. The results showed that the shape of bundle-like Phi bodies was variable, long or short. while the nubbly Phi bodies often presented oval and smooth. Club-like Phi bodies were found in M(3). The detection rates of bundle-like Phi bodies in AML M(1)-M(5) were 42.9% (6/14), 83.3% (15/18), 92.0% (23/25), 52.3% (11/21), 33.3% (5/15) respectively, and those of nubbly Phi bodies were 28.6% (4/14), 66.7% (12/18), 11.1% (3/25), 33.3% (7/21), 20.0% (3/15) respectively. The detection rate of bundle-like Phi bodies in M(3) was significantly higher than that in (M(1) + M(2)) or (M(4) + M(5)) groups. The detection rate of nubbly Phi bodies in (M(1) + M(2)) group was higher than that in M(3) group. In conclusion, after improvement of staining method, the HPO stain becomes simple, the detection rate of Phi bodies is higher than that by the previous method, the positive granules are more obvious, and the results become stable. This improved method plays an important role in differentiating AML from ALL, subtyping AML, and evaluating the therapeutic results.

Molecular Methods for the Detection of Mycoplasma and Ureaplasma Infections in Humans

PubMed Central

Waites, Ken B.; Xiao, Li; Paralanov, Vanya; Viscardi, Rose M.; Glass, John I.

2012-01-01

Mycoplasma and Ureaplasma species are well-known human pathogens responsible for a broad array of inflammatory conditions involving the respiratory and urogenital tracts of neonates, children, and adults. Greater attention is being given to these organisms in diagnostic microbiology, largely as a result of improved methods for their laboratory detection, made possible by powerful molecular-based techniques that can be used for primary detection in clinical specimens. For slow-growing species, such as Mycoplasma pneumoniae and Mycoplasma genitalium, molecular-based detection is the only practical means for rapid microbiological diagnosis. Most molecular-based methods used for detection and characterization of conventional bacteria have been applied to these organisms. A complete genome sequence is available for one or more strains of all of the important human pathogens in the Mycoplasma and Ureaplasma genera. Information gained from genome analyses and improvements in efficiency of DNA sequencing are expected to significantly advance the field of molecular detection and genotyping during the next few years. This review provides a summary and critical review of methods suitable for detection and characterization of mycoplasmas and ureaplasmas of humans, with emphasis on molecular genotypic techniques. PMID:22819362

An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System.

PubMed

Zhao, Anbang; Ma, Lin; Ma, Xuefei; Hui, Juan

2017-02-20

In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS) is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequencydomain and achieves computational complexity reduction.

Molecular diagnostics for human leptospirosis.

PubMed

Waggoner, Jesse J; Pinsky, Benjamin A

2016-10-01

The definitive diagnosis of leptospirosis, which results from infection with spirochetes of the genus Leptospira, currently relies on the use of culture, serological testing (microscopic agglutination testing), and molecular detection. The purpose of this review is to describe new molecular diagnostics for Leptospira and discuss advancements in the use of available methods. Efforts have been focused on improving the clinical sensitivity of Leptospira detection using molecular methods. In this review, we describe a reoptimized pathogenic species-specific real-time PCR (targeting lipL32) that has demonstrated improved sensitivity, findings by two groups that real-time reverse-transcription PCR assays targeting the 16S rrs gene can improve detection, and two new loop-mediated amplification techniques. Quantitation of leptospiremia, detection in different specimen types, and the complementary roles played by molecular detection and microscopic agglutination testing will be discussed. Finally, a protocol for Leptospira strain subtyping using variable number tandem repeat targets and high-resolution melting will be described. Molecular diagnostics have an established role for the diagnosis of leptospirosis and provide an actionable diagnosis in the acute setting. The use of real-time reverse-transcription PCR for testing serum/plasma and cerebrospinal fluid, when available, may improve the detection of Leptospira without decreasing clinical specificity.

Detecting crop growth stages of maize and soybeans by using time-series MODIS data

NASA Astrophysics Data System (ADS)

Sakamoto, T.; Wardlow, B. D.; Gitelson, A. A.; Verma, S. B.; Suyker, A. E.; Arkebauer, T. J.

2009-12-01

The crop phenological stages are one of essential parameters for evaluating crop productivity based on a crop simulation model. In this study, we improved a method named the Wavelet-based Filter for detecting Crop Phenology (WFCP) for detecting the specific phenological dates of maize and soybeans. The improved method was applied to MODIS-derived Wide Dynamic Range Vegetation Index (WDRVI) over a 6-year period (2003 to 2008) for three experimental fields planted to either maize or soybeans as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln (UNL). Using the ground-based crop growth stage observations collected by the CSP, it was confirmed that the improved method can estimate the specific phenological dates of maize (V2.5, R1, R5 and R6) and soybeans (V1, R5, R6 and R7) with reasonable accuracy.

Improved Conflict Detection for Reducing Operational Errors in Air Traffic Control

NASA Technical Reports Server (NTRS)

Paielli, Russell A.; Erzberger, Hainz

2003-01-01

An operational error is an incident in which an air traffic controller allows the separation between two aircraft to fall below the minimum separation standard. The rates of such errors in the US have increased significantly over the past few years. This paper proposes new detection methods that can help correct this trend by improving on the performance of Conflict Alert, the existing software in the Host Computer System that is intended to detect and warn controllers of imminent conflicts. In addition to the usual trajectory based on the flight plan, a "dead-reckoning" trajectory (current velocity projection) is also generated for each aircraft and checked for conflicts. Filters for reducing common types of false alerts were implemented. The new detection methods were tested in three different ways. First, a simple flightpath command language was developed t o generate precisely controlled encounters for the purpose of testing the detection software. Second, written reports and tracking data were obtained for actual operational errors that occurred in the field, and these were "replayed" to test the new detection algorithms. Finally, the detection methods were used to shadow live traffic, and performance was analysed, particularly with regard to the false-alert rate. The results indicate that the new detection methods can provide timely warnings of imminent conflicts more consistently than Conflict Alert.

4D numerical observer for lesion detection in respiratory-gated PET

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

Lorsakul, Auranuch; Li, Quanzheng; Ouyang, Jinsong

2014-10-15

Purpose: Respiratory-gated positron emission tomography (PET)/computed tomography protocols reduce lesion smearing and improve lesion detection through a synchronized acquisition of emission data. However, an objective assessment of image quality of the improvement gained from respiratory-gated PET is mainly limited to a three-dimensional (3D) approach. This work proposes a 4D numerical observer that incorporates both spatial and temporal informations for detection tasks in pulmonary oncology. Methods: The authors propose a 4D numerical observer constructed with a 3D channelized Hotelling observer for the spatial domain followed by a Hotelling observer for the temporal domain. Realistic {sup 18}F-fluorodeoxyglucose activity distributions were simulated usingmore » a 4D extended cardiac torso anthropomorphic phantom including 12 spherical lesions at different anatomical locations (lower, upper, anterior, and posterior) within the lungs. Simulated data based on Monte Carlo simulation were obtained using GEANT4 application for tomographic emission (GATE). Fifty noise realizations of six respiratory-gated PET frames were simulated by GATE using a model of the Siemens Biograph mMR scanner geometry. PET sinograms of the thorax background and pulmonary lesions that were simulated separately were merged to generate different conditions of the lesions to the background (e.g., lesion contrast and motion). A conventional ordered subset expectation maximization (OSEM) reconstruction (5 iterations and 6 subsets) was used to obtain: (1) gated, (2) nongated, and (3) motion-corrected image volumes (a total of 3200 subimage volumes: 2400 gated, 400 nongated, and 400 motion-corrected). Lesion-detection signal-to-noise ratios (SNRs) were measured in different lesion-to-background contrast levels (3.5, 8.0, 9.0, and 20.0), lesion diameters (10.0, 13.0, and 16.0 mm), and respiratory motion displacements (17.6–31.3 mm). The proposed 4D numerical observer applied on multiple-gated images was compared to the conventional 3D approach applied on the nongated and motion-corrected images. Results: On average, the proposed 4D numerical observer improved the detection SNR by 48.6% (p < 0.005), whereas the 3D methods on motion-corrected images improved by 31.0% (p < 0.005) as compared to the nongated method. For all different conditions of the lesions, the relative SNR measurement (Gain = SNR{sub Observed}/SNR{sub Nongated}) of the 4D method was significantly higher than one from the motion-corrected 3D method by 13.8% (p < 0.02), where Gain{sub 4D} was 1.49 ± 0.21 and Gain{sub 3D} was 1.31 ± 0.15. For the lesion with the highest amplitude of motion, the 4D numerical observer yielded the highest observer-performance improvement (176%). For the lesion undergoing the smallest motion amplitude, the 4D method provided superior lesion detectability compared with the 3D method, which provided a detection SNR close to the nongated method. The investigation on a structure of the 4D numerical observer showed that a Laguerre–Gaussian channel matrix with a volumetric 3D function yielded higher lesion-detection performance than one with a 2D-stack-channelized function, whereas a different kind of channels that have the ability to mimic the human visual system, i.e., difference-of-Gaussian, showed similar performance in detecting uniform and spherical lesions. The investigation of the detection performance when increasing noise levels yielded decreasing detection SNR by 27.6% and 41.5% for the nongated and gated methods, respectively. The investigation of lesion contrast and diameter showed that the proposed 4D observer preserved the linearity property of an optimal-linear observer while the motion was present. Furthermore, the investigation of the iteration and subset numbers of the OSEM algorithm demonstrated that these parameters had impact on the lesion detectability and the selection of the optimal parameters could provide the maximum lesion-detection performance. The proposed 4D numerical observer outperformed the other observers for the lesion-detection task in various lesion conditions and motions. Conclusions: The 4D numerical observer shows substantial improvement in lesion detectability over the 3D observer method. The proposed 4D approach could potentially provide a more reliable objective assessment of the impact of respiratory-gated PET improvement for lesion-detection tasks. On the other hand, the 4D approach may be used as an upper bound to investigate the performance of the motion correction method. In future work, the authors will validate the proposed 4D approach on clinical data for detection tasks in pulmonary oncology.« less

Flexible, multi-measurement guided wave damage detection under varying temperatures

NASA Astrophysics Data System (ADS)

Douglass, Alexander C. S.; Harley, Joel B.

2018-04-01

Temperature compensation in structural health monitoring helps identify damage in a structure by removing data variations due to environmental conditions, such as temperature. Stretch-based methods are one of the most commonly used temperature compensation methods. To account for variations in temperature, stretch-based methods optimally stretch signals in time to optimally match a measurement to a baseline. All of the data is then compared with the single baseline to determine the presence of damage. Yet, for these methods to be effective, the measurement and the baseline must satisfy the inherent assumptions of the temperature compensation method. In many scenarios, these assumptions are wrong, the methods generate error, and damage detection fails. To improve damage detection, a multi-measurement damage detection method is introduced. By using each measurement in the dataset as a baseline, error caused by imperfect temperature compensation is reduced. The multi-measurement method increases the detection effectiveness of our damage metric, or damage indicator, over time and reduces the presence of additional peaks caused by temperature that could be mistaken for damage. By using many baselines, the variance of the damage indicator is reduced and the effects from damage are amplified. Notably, the multi-measurement improves damage detection over single-measurement methods. This is demonstrated through an increase in the maximum of our damage signature from 0.55 to 0.95 (where large values, up to a maximum of one, represent a statistically significant change in the data due to damage).

Computer assisted diagnostic system in tumor radiography.

PubMed

Faisal, Ahmed; Parveen, Sharmin; Badsha, Shahriar; Sarwar, Hasan; Reza, Ahmed Wasif

2013-06-01

An improved and efficient method is presented in this paper to achieve a better trade-off between noise removal and edge preservation, thereby detecting the tumor region of MRI brain images automatically. Compass operator has been used in the fourth order Partial Differential Equation (PDE) based denoising technique to preserve the anatomically significant information at the edges. A new morphological technique is also introduced for stripping skull region from the brain images, which consequently leading to the process of detecting tumor accurately. Finally, automatic seeded region growing segmentation based on an improved single seed point selection algorithm is applied to detect the tumor. The method is tested on publicly available MRI brain images and it gives an average PSNR (Peak Signal to Noise Ratio) of 36.49. The obtained results also show detection accuracy of 99.46%, which is a significant improvement than that of the existing results.

Dangerous gas detection based on infrared video

NASA Astrophysics Data System (ADS)

Ding, Kang; Hong, Hanyu; Huang, Likun

2018-03-01

As the gas leak infrared imaging detection technology has significant advantages of high efficiency and remote imaging detection, in order to enhance the detail perception of observers and equivalently improve the detection limit, we propose a new type of gas leak infrared image detection method, which combines background difference methods and multi-frame interval difference method. Compared to the traditional frame methods, the multi-frame interval difference method we proposed can extract a more complete target image. By fusing the background difference image and the multi-frame interval difference image, we can accumulate the information of infrared target image of the gas leak in many aspect. The experiment demonstrate that the completeness of the gas leakage trace information is enhanced significantly, and the real-time detection effect can be achieved.

Detecting text in natural scenes with multi-level MSER and SWT

NASA Astrophysics Data System (ADS)

Lu, Tongwei; Liu, Renjun

2018-04-01

The detection of the characters in the natural scene is susceptible to factors such as complex background, variable viewing angle and diverse forms of language, which leads to poor detection results. Aiming at these problems, a new text detection method was proposed, which consisted of two main stages, candidate region extraction and text region detection. At first stage, the method used multiple scale transformations of original image and multiple thresholds of maximally stable extremal regions (MSER) to detect the text regions which could detect character regions comprehensively. At second stage, obtained SWT maps by using the stroke width transform (SWT) algorithm to compute the candidate regions, then using cascaded classifiers to propose non-text regions. The proposed method was evaluated on the standard benchmark datasets of ICDAR2011 and the datasets that we made our own data sets. The experiment results showed that the proposed method have greatly improved that compared to other text detection methods.

A new framework for analysing automated acoustic species-detection data: occupancy estimation and optimization of recordings post-processing

USGS Publications Warehouse

Chambert, Thierry A.; Waddle, J. Hardin; Miller, David A.W.; Walls, Susan; Nichols, James D.

2018-01-01

The development and use of automated species-detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide a cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors.We developed a hierarchical modelling framework for estimating species occupancy from automated species-detection data. We explore design and optimization of data post-processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false positive errors and utilizes information about both occurrence and abundance of detections to improve estimation.Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders.When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post-validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post-validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated.Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species-detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programs.

[Development of an automated processing method to detect coronary motion for coronary magnetic resonance angiography].

PubMed

Asou, Hiroya; Imada, N; Sato, T

2010-06-20

On coronary MR angiography (CMRA), cardiac motions worsen the image quality. To improve the image quality, detection of cardiac especially for individual coronary motion is very important. Usually, scan delay and duration were determined manually by the operator. We developed a new evaluation method to calculate static time of individual coronary artery. At first, coronary cine MRI was taken at the level of about 3 cm below the aortic valve (80 images/R-R). Chronological change of the signals were evaluated with Fourier transformation of each pixel of the images were done. Noise reduction with subtraction process and extraction process were done. To extract higher motion such as coronary arteries, morphological filter process and labeling process were added. Using these imaging processes, individual coronary motion was extracted and individual coronary static time was calculated automatically. We compared the images with ordinary manual method and new automated method in 10 healthy volunteers. Coronary static times were calculated with our method. Calculated coronary static time was shorter than that of ordinary manual method. And scan time became about 10% longer than that of ordinary method. Image qualities were improved in our method. Our automated detection method for coronary static time with chronological Fourier transformation has a potential to improve the image quality of CMRA and easy processing.

Search Radar Track-Before-Detect Using the Hough Transform.

DTIC Science & Technology

1995-03-01

before - detect processing method which allows previous data to help in target detection. The technique provides many advantages compared to...improved target detection scheme, applicable to search radars, using the Hough transform image processing technique. The system concept involves a track

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

NASA Astrophysics Data System (ADS)

Sun, Qianlai; Wang, Yin; Sun, Zhiyi

2018-05-01

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

A Facile Stable-Isotope Dilution Method for Determination of Sphingosine Phosphate Lyase Activity

PubMed Central

Suh, Jung H.; Eltanawy, Abeer; Rangan, Apoorva; Saba, Julie D.

2015-01-01

A new technique for quantifying sphingosine phosphate lyase activity in biological samples is described. In this procedure, 2-hydrazinoquinoline is used to convert (2E)-hexadecenal into the corresponding hydrazone derivative to improve ionization efficiency and selectivity of detection. Combined utilization of liquid chromatographic separation and multiple reaction monitoring-mass spectrometry allows for simultaneous quantification of the substrate S1P and product (2E)-hexadecenal. Incorporation of (2E)-d5-hexadecenal as an internal standard improves detection accuracy and precision. A simple one-step derivatization procedure eliminates the need for further extractions. Limits of quantification for (2E)-hexadecenal and sphingosine-1-phosphate are 100 and 50 fmol, respectively. The assay displays a wide dynamic detection range useful for detection of low basal sphingosine phosphate lyase activity in wild type cells, SPL-overexpressing cell lines, and wild type mouse tissues. Compared to current methods, the capacity for simultaneous detection of sphingosine-1-phosphate and (2E)-hexadecenal greatly improves the accuracy of results and shows excellent sensitivity and specificity for sphingosine phosphate lyase activity detection. PMID:26408264

A highly selective and simple fluorescent sensor for mercury (II) ion detection based on cysteamine-capped CdTe quantum dots synthesized by the reflux method.

PubMed

Ding, Xiaojie; Qu, Lingbo; Yang, Ran; Zhou, Yuchen; Li, Jianjun

2015-06-01

Cysteamine (CA)-capped CdTe quantum dots (QDs) (CA-CdTe QDs) were prepared by the reflux method and utilized as an efficient nano-sized fluorescent sensor to detect mercury (II) ions (Hg(2+) ). Under optimum conditions, the fluorescence quenching effect of CA-CdTe QDs was linear at Hg(2+) concentrations in the range of 6.0-450 nmol/L. The detection limit was calculated to be 4.0 nmol/L according to the 3σ IUPAC criteria. The influence of 10-fold Pb(2+) , Cu(2+) and Ag(+) on the determination of Hg(2+) was < 7% (superior to other reports based on crude QDs). Furthermore, the detection sensitivity and selectivity were much improved relative to a sensor based on the CA-CdTe QDs probe, which was prepared using a one-pot synthetic method. This CA-CdTe QDs sensor system represents a new feasibility to improve the detection performance of a QDs sensor by changing the synthesis method. Copyright © 2014 John Wiley & Sons, Ltd.

Airplane detection in remote sensing images using convolutional neural networks

NASA Astrophysics Data System (ADS)

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

2018-03-01

Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System

PubMed Central

Zhao, Anbang; Ma, Lin; Ma, Xuefei; Hui, Juan

2017-01-01

In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS) is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequency-domain and achieves computational complexity reduction. PMID:28230763

Integrated digital error suppression for improved detection of circulating tumor DNA

PubMed Central

Kurtz, David M.; Chabon, Jacob J.; Scherer, Florian; Stehr, Henning; Liu, Chih Long; Bratman, Scott V.; Say, Carmen; Zhou, Li; Carter, Justin N.; West, Robert B.; Sledge, George W.; Shrager, Joseph B.; Loo, Billy W.; Neal, Joel W.; Wakelee, Heather A.; Diehn, Maximilian; Alizadeh, Ash A.

2016-01-01

High-throughput sequencing of circulating tumor DNA (ctDNA) promises to facilitate personalized cancer therapy. However, low quantities of cell-free DNA (cfDNA) in the blood and sequencing artifacts currently limit analytical sensitivity. To overcome these limitations, we introduce an approach for integrated digital error suppression (iDES). Our method combines in silico elimination of highly stereotypical background artifacts with a molecular barcoding strategy for the efficient recovery of cfDNA molecules. Individually, these two methods each improve the sensitivity of cancer personalized profiling by deep sequencing (CAPP-Seq) by ~3 fold, and synergize when combined to yield ~15-fold improvements. As a result, iDES-enhanced CAPP-Seq facilitates noninvasive variant detection across hundreds of kilobases. Applied to clinical non-small cell lung cancer (NSCLC) samples, our method enabled biopsy-free profiling of EGFR kinase domain mutations with 92% sensitivity and 96% specificity and detection of ctDNA down to 4 in 105 cfDNA molecules. We anticipate that iDES will aid the noninvasive genotyping and detection of ctDNA in research and clinical settings. PMID:27018799

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

PubMed

Ge, Jing; Zhang, Guoping

2015-01-01

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

IMPROVED DETECTION OF HUMAN ENTERIC VIRUSES IN FOODS BY RT-PCR. (R826139)

EPA Science Inventory

Human enteric viruses (including hepatitis A virus (HAV) and Norwalk-like viruses (NLVs)) are now recognized as common causes of foodborne disease. While methods to detect these agents in clinical specimens have improved significantly over the last 10 years, applications to fo...

Advanced DNA- and Protein-based Methods for the Detection and Investigation of Food Allergens.

PubMed

Prado, M; Ortea, I; Vial, S; Rivas, J; Calo-Mata, P; Barros-Velázquez, J

2016-11-17

Currently, food allergies are an important health concern worldwide. The presence of undeclared allergenic ingredients or the presence of traces of allergens due to contamination during food processing poses a great health risk to sensitized individuals. Therefore, reliable analytical methods are required to detect and identify allergenic ingredients in food products. The present review addresses the recent developments regarding the application of DNA- and protein-based methods for the detection of allergenic ingredients in foods. The fitness-for-purpose of reviewed methodology will be discussed, and future trends will be highlighted. Special attention will be given to the evaluation of the potential of newly developed and promising technologies that can improve the detection and identification of allergenic ingredients in foods, such as the use of biosensors and/or nanomaterials to improve detection limits, specificity, ease of use, or to reduce the time of analysis. Such rapid food allergen test methods are required to facilitate the reliable detection of allergenic ingredients by control laboratories, to give the food industry the means to easily determine whether its product has been subjected to cross-contamination and, simultaneously, to identify how and when this cross-contamination occurred.

Evaluation of the morphology structure of meibomian glands based on mask dodging method

NASA Astrophysics Data System (ADS)

Yan, Huangping; Zuo, Yingbo; Chen, Yisha; Chen, Yanping

2016-10-01

Low contrast and non-uniform illumination of infrared (IR) meibography images make the detection of meibomian glands challengeable. An improved Mask dodging algorithm is proposed. To overcome the shortage of low contrast using traditional Mask dodging method, a scale factor is used to enhance the image after subtracting background image from an original one. Meibomian glands are detected and the ratio of the meibomian gland area to the measurement area is calculated. The results show that the improved Mask algorithm has ideal dodging effect, which can eliminate non-uniform illumination and improve contrast of meibography images effectively.

Android malware detection based on evolutionary super-network

NASA Astrophysics Data System (ADS)

Yan, Haisheng; Peng, Lingling

2018-04-01

In the paper, an android malware detection method based on evolutionary super-network is proposed in order to improve the precision of android malware detection. Chi square statistics method is used for selecting characteristics on the basis of analyzing android authority. Boolean weighting is utilized for calculating characteristic weight. Processed characteristic vector is regarded as the system training set and test set; hyper edge alternative strategy is used for training super-network classification model, thereby classifying test set characteristic vectors, and it is compared with traditional classification algorithm. The results show that the detection method proposed in the paper is close to or better than traditional classification algorithm. The proposed method belongs to an effective Android malware detection means.

Ultrasonic Imaging Techniques for Breast Cancer Detection

NASA Astrophysics Data System (ADS)

Goulding, N. R.; Marquez, J. D.; Prewett, E. M.; Claytor, T. N.; Nadler, B. R.

2008-02-01

Improving the resolution and specificity of current ultrasonic imaging technology is needed to enhance its relevance to breast cancer detection. A novel ultrasonic imaging reconstruction method is described that exploits classical straight-ray migration. This novel method improves signal processing for better image resolution and uses novel staging hardware options using a pulse-echo approach. A breast phantom with various inclusions is imaged using the classical migration method and is compared to standard computed tomography (CT) scans. These innovative ultrasonic methods incorporate ultrasound data acquisition, beam profile characterization, and image reconstruction. For an ultrasonic frequency of 2.25 MHz, imaged inclusions of approximately 1 cm are resolved and identified. Better resolution is expected with minor modifications. Improved image quality and resolution enables earlier detection and more accurate diagnoses of tumors thus reducing the number of biopsies performed, increasing treatment options, and lowering remission percentages. Using these new techniques the inclusions in the phantom are resolved and compared to the results of standard methods. Refinement of this application using other imaging techniques such as time-reversal mirrors (TRM), synthetic aperture focusing technique (SAFT), decomposition of the time reversal operator (DORT), and factorization methods is also discussed.

Automated grain extraction and classification by combining improved region growing segmentation and shape descriptors in electromagnetic mill classification system

NASA Astrophysics Data System (ADS)

Budzan, Sebastian

2018-04-01

In this paper, the automatic method of grain detection and classification has been presented. As input, it uses a single digital image obtained from milling process of the copper ore with an high-quality digital camera. The grinding process is an extremely energy and cost consuming process, thus granularity evaluation process should be performed with high efficiency and time consumption. The method proposed in this paper is based on the three-stage image processing. First, using Seeded Region Growing (SRG) segmentation with proposed adaptive thresholding based on the calculation of Relative Standard Deviation (RSD) all grains are detected. In the next step results of the detection are improved using information about the shape of the detected grains using distance map. Finally, each grain in the sample is classified into one of the predefined granularity class. The quality of the proposed method has been obtained by using nominal granularity samples, also with a comparison to the other methods.

Detecting breast microcalcifications using super-resolution ultrasound imaging: a clinical study

NASA Astrophysics Data System (ADS)

Huang, Lianjie; Labyed, Yassin; Hanson, Kenneth; Sandoval, Daniel; Pohl, Jennifer; Williamson, Michael

2013-03-01

Imaging breast microcalcifications is crucial for early detection and diagnosis of breast cancer. It is challenging for current clinical ultrasound to image breast microcalcifications. However, new imaging techniques using data acquired with a synthetic-aperture ultrasound system have the potential to significantly improve ultrasound imaging. We recently developed a super-resolution ultrasound imaging method termed the phase-coherent multiple-signal classification (PC-MUSIC). This signal subspace method accounts for the phase response of transducer elements to improve image resolution. In this paper, we investigate the clinical feasibility of our super-resolution ultrasound imaging method for detecting breast microcalcifications. We use our custom-built, real-time synthetic-aperture ultrasound system to acquire breast ultrasound data for 40 patients whose mammograms show the presence of breast microcalcifications. We apply our super-resolution ultrasound imaging method to the patient data, and produce clear images of breast calcifications. Our super-resolution ultrasound PC-MUSIC imaging with synthetic-aperture ultrasound data can provide a new imaging modality for detecting breast microcalcifications in clinic without using ionizing radiation.

A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images.

PubMed

Christodoulidis, Argyrios; Hurtut, Thomas; Tahar, Houssem Ben; Cheriet, Farida

2016-09-01

Segmenting the retinal vessels from fundus images is a prerequisite for many CAD systems for the automatic detection of diabetic retinopathy lesions. So far, research efforts have concentrated mainly on the accurate localization of the large to medium diameter vessels. However, failure to detect the smallest vessels at the segmentation step can lead to false positive lesion detection counts in a subsequent lesion analysis stage. In this study, a new hybrid method for the segmentation of the smallest vessels is proposed. Line detection and perceptual organization techniques are combined in a multi-scale scheme. Small vessels are reconstructed from the perceptual-based approach via tracking and pixel painting. The segmentation was validated in a high resolution fundus image database including healthy and diabetic subjects using pixel-based as well as perceptual-based measures. The proposed method achieves 85.06% sensitivity rate, while the original multi-scale line detection method achieves 81.06% sensitivity rate for the corresponding images (p<0.05). The improvement in the sensitivity rate for the database is 6.47% when only the smallest vessels are considered (p<0.05). For the perceptual-based measure, the proposed method improves the detection of the vasculature by 7.8% against the original multi-scale line detection method (p<0.05). Copyright © 2016 Elsevier Ltd. All rights reserved.

Double Threshold Energy Detection Based Cooperative Spectrum Sensing for Cognitive Radio Networks with QoS Guarantee

NASA Astrophysics Data System (ADS)

Hu, Hang; Yu, Hong; Zhang, Yongzhi

2013-03-01

Cooperative spectrum sensing, which can greatly improve the ability of discovering the spectrum opportunities, is regarded as an enabling mechanism for cognitive radio (CR) networks. In this paper, we employ a double threshold detection method in energy detector to perform spectrum sensing, only the CR users with reliable sensing information are allowed to transmit one bit local decision to the fusion center. Simulation results will show that our proposed double threshold detection method could not only improve the sensing performance but also save the bandwidth of the reporting channel compared with the conventional detection method with one threshold. By weighting the sensing performance and the consumption of system resources in a utility function that is maximized with respect to the number of CR users, it has been shown that the optimal number of CR users is related to the price of these Quality-of-Service (QoS) requirements.

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels

PubMed Central

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V.; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R.

2018-01-01

Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. Methods: In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. Results: The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. Conclusions: The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods. PMID:29619277

Dielectrophoretic separation of Bacillus subtilis spores from environmental diesel particles.

PubMed

Fatoyinbo, Henry O; Hughes, Michael P; Martin, Stacey P; Pashby, Paul; Labeed, Fatima H

2007-01-01

Isolation of pathogenic bacteria from non-biological material of similar size is a vital sample preparation step in the identification of such organisms, particularly in the context of detecting bio-terrorist attacks. However, many detection methods are impeded by particulate contamination from the environment such as those from engine exhausts. In this paper we use dielectrophoresis--the induced motion of particles in non-uniform fields--to successfully remove over 99% of diesel particulates acquired from environmental samples, whilst letting bacterial spores of B. subtilis pass through the chamber largely unimpeded. We believe that such a device has tremendous potential as a precursor to a range of detection methods, improving the signal-to-noise ratio and ultimately improving detection rates.

Improvements in an in vivo neutron activation analysis (NAA) method for the measurement of fluorine in human bone.

PubMed

Mostafaei, F; McNeill, F E; Chettle, D R; Prestwich, W V

2013-10-01

We previously published a method for the in vivo measurement of bone fluoride using neutron activation analysis (NAA) and demonstrated the utility of the technique in a pilot study of environmentally exposed people. The method involved activation of the hand in an irradiation cavity at the McMaster University Accelerator Laboratory and acquisition of the resultant γ-ray signals in a '4π' NaI(Tl) detector array of nine detectors. In this paper we describe a series of improvements to the method. This was investigated via measurement of hand simulating phantoms doped with varying levels of fluorine and fixed amounts of sodium, chlorine and calcium. Four improvements to the technique were tested since our first publication. The previously published detection limit for phantom measurements using this system was 0.66 mg F/g Ca. The accelerator irradiation and detection facilities were relocated to a new section of the laboratory and one more detector was added to the detection system. This was found to reduce the detection limit (possibly because of better detection shielding and additional detector) to 0.59 mg F/g Ca, a factor of 1.12. A new set of phantoms was developed and in this work we show that they improved the minimum detectable limit for fluoride in phantoms irradiated using neutrons produced by 2.15 MeV protons on lithium by a factor of 1.55. We compared the detection limits previously obtained using a summed signal from the nine detectors with the detection limit obtained by acquiring the spectra in anticoincidence mode for reduction of the disturbing signal from chlorine in bone. This was found to improve the ratio of the detection of fluorine to chlorine (an interfering signal) by a factor of 2.8 and the resultant minimum detection limit was found to be reduced by a factor of 1.2. We studied the effects of changing the timing of γ-ray acquisition. Our previously published data used a series of three 10 s acquisitions followed by a 300 s count. Changing the acquisition to a series of six 5 s acquisitions was found to further improve the detection limit by a factor of 1.4. We also present data showing that if the neutron dose is delivered to the phantom in a shorter time period, i.e. the dose rate is increased and irradiation shortened then the detection limit can be reduced by a further factor of 1.35.The overall improvement in detection limit by employing all of these changes was found to be a factor of 3.9. The technique now has an in phantom detection limit of 0.17 mg F/g Ca compared to a previous detection limit of 0.66 mg F/g Ca. The system can now be tested on human volunteers to see if individuals with diagnosed fluorosis can be distinguished from the general Canadian population using this technique.

Nanobarcoding for improved nanoparticle detection in nanomedical biodistribution studies

NASA Astrophysics Data System (ADS)

Eustaquio, Trisha

Determination of the fate of nanoparticles (NPs) in a biological system, or NP biodistribution, is critical in evaluating a NP formulation for nanomedicine. Unlike small-molecule drugs, NPs impose unique challenges in the design of appropriate biodistribution studies due to their small size and subsequent detection signal. Current methods to determine NP biodistribution are greatly inadequate due to their limited detection thresholds. There is an overwhelming need for a sensitive and efficient imaging-based method that can (1) detect and measure small numbers of NPs of various types, ideally single NPs, (2) associate preferential NP uptake with histological cell type by preserving spatial information in samples, and (3) allow for relatively quick and accurate NP detection in in vitro (and possibly ex vivo) samples for comprehensive NP biodistribution studies. Herein, a novel method for improved NP detection is proposed, coined "nanobarcoding." Nanobarcoding utilizes a non-endogenous oligonucleotide, or "nanobarcode" (NB), conjugated to the NP surface to amplify the detection signal from a single NP via in situ polymerase chain reaction (ISPCR), and this signal amplification will facilitate rapid and precise detection of single NPs inside cells over large areas of sample such that more sophisticated studies can be performed on the NP-positive subpopulation. Moreover, nanobarcoding has the potential to be applied to the detection of more than one NP type to study the effects of physicochemical properties, targeting mechanisms, and route of entry on NP biodistribution. The nanobarcoding method was validated in vitro using NB-functionalized superparamagnetic iron oxide NPs (NB-SPIONs) as the model NP type for improved NP detection inside HeLa human cervical cancer cells, a cell line commonly used for ISPCR-mediated detection of human papilloma virus (HPV). Nanotoxicity effects of NB-SPIONs were also evaluated at the single-cell level using LEAP (Laser-Enabled Analysis and Processing, Intrexon, San Diego, CA), and NB-SPIONs were found to be less toxic than its precursor, carboxylated SPIONs (COOH-SPIONs).

Improved signal recovery for flow cytometry based on ‘spatially modulated emission’

NASA Astrophysics Data System (ADS)

Quint, S.; Wittek, J.; Spang, P.; Levanon, N.; Walther, T.; Baßler, M.

2017-09-01

Recently, the technique of ‘spatially modulated emission’ has been introduced (Baßler et al 2008 US Patent 0080181827A1; Kiesel et al 2009 Appl. Phys. Lett. 94 041107; Kiesel et al 2011 Cytometry A 79A 317-24) improving the signal-to-noise ratio (SNR) for detecting bio-particles in the field of flow cytometry. Based on this concept, we developed two advanced signal processing methods which further enhance the SNR and selectivity for cell detection. The improvements are achieved by adapting digital filtering methods from RADAR technology and mainly address inherent offset elimination, increased signal dynamics and moreover reduction of erroneous detections due to processing artifacts. We present a comprehensive theory on SNR gain and provide experimental results of our concepts.

Improved wheal detection from skin prick test images

NASA Astrophysics Data System (ADS)

Bulan, Orhan

2014-03-01

Skin prick test is a commonly used method for diagnosis of allergic diseases (e.g., pollen allergy, food allergy, etc.) in allergy clinics. The results of this test are erythema and wheal provoked on the skin where the test is applied. The sensitivity of the patient against a specific allergen is determined by the physical size of the wheal, which can be estimated from images captured by digital cameras. Accurate wheal detection from these images is an important step for precise estimation of wheal size. In this paper, we propose a method for improved wheal detection on prick test images captured by digital cameras. Our method operates by first localizing the test region by detecting calibration marks drawn on the skin. The luminance variation across the localized region is eliminated by applying a color transformation from RGB to YCbCr and discarding the luminance channel. We enhance the contrast of the captured images for the purpose of wheal detection by performing principal component analysis on the blue-difference (Cb) and red-difference (Cr) color channels. We finally, perform morphological operations on the contrast enhanced image to detect the wheal on the image plane. Our experiments performed on images acquired from 36 different patients show the efficiency of the proposed method for wheal detection from skin prick test images captured in an uncontrolled environment.

Improving the space surveillance telescope's performance using multi-hypothesis testing

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

Chris Zingarelli, J.; Cain, Stephen; Pearce, Eric

2014-05-01

The Space Surveillance Telescope (SST) is a Defense Advanced Research Projects Agency program designed to detect objects in space like near Earth asteroids and space debris in the geosynchronous Earth orbit (GEO) belt. Binary hypothesis test (BHT) methods have historically been used to facilitate the detection of new objects in space. In this paper a multi-hypothesis detection strategy is introduced to improve the detection performance of SST. In this context, the multi-hypothesis testing (MHT) determines if an unresolvable point source is in either the center, a corner, or a side of a pixel in contrast to BHT, which only testsmore » whether an object is in the pixel or not. The images recorded by SST are undersampled such as to cause aliasing, which degrades the performance of traditional detection schemes. The equations for the MHT are derived in terms of signal-to-noise ratio (S/N), which is computed by subtracting the background light level around the pixel being tested and dividing by the standard deviation of the noise. A new method for determining the local noise statistics that rejects outliers is introduced in combination with the MHT. An experiment using observations of a known GEO satellite are used to demonstrate the improved detection performance of the new algorithm over algorithms previously reported in the literature. The results show a significant improvement in the probability of detection by as much as 50% over existing algorithms. In addition to detection, the S/N results prove to be linearly related to the least-squares estimates of point source irradiance, thus improving photometric accuracy.« less

Detection method of flexion relaxation phenomenon based on wavelets for patients with low back pain

NASA Astrophysics Data System (ADS)

Nougarou, François; Massicotte, Daniel; Descarreaux, Martin

2012-12-01

The flexion relaxation phenomenon (FRP) can be defined as a reduction or silence of myoelectric activity of the lumbar erector spinae muscle during full trunk flexion. It is typically absent in patients with chronic low back pain (LBP). Before any broad clinical utilization of this neuromuscular response can be made, effective, standardized, and accurate methods of identifying FRP limits are needed. However, this phenomenon is clearly more difficult to detect for LBP patients than for healthy patients. The main goal of this study is to develop an automated method based on wavelet transformation that would improve time point limits detection of surface electromyography signals of the FRP in case of LBP patients. Conventional visual identification and proposed automated methods of time point limits detection of relaxation phase were compared on experimental data using criteria of accuracy and repeatability based on physiological properties. The evaluation demonstrates that the use of wavelet transform (WT) yields better results than methods without wavelet decomposition. Furthermore, methods based on wavelet per packet transform are more effective than algorithms employing discrete WT. Compared to visual detection, in addition to demonstrating an obvious saving of time, the use of wavelet per packet transform improves the accuracy and repeatability in the detection of the FRP limits. These results clearly highlight the value of the proposed technique in identifying onset and offset of the flexion relaxation response in LBP subjects.

Improved Sensor Fault Detection, Isolation, and Mitigation Using Multiple Observers Approach

PubMed Central

Wang, Zheng; Anand, D. M.; Moyne, J.; Tilbury, D. M.

2017-01-01

Traditional Fault Detection and Isolation (FDI) methods analyze a residual signal to detect and isolate sensor faults. The residual signal is the difference between the sensor measurements and the estimated outputs of the system based on an observer. The traditional residual-based FDI methods, however, have some limitations. First, they require that the observer has reached its steady state. In addition, residual-based methods may not detect some sensor faults, such as faults on critical sensors that result in an unobservable system. Furthermore, the system may be in jeopardy if actions required for mitigating the impact of the faulty sensors are not taken before the faulty sensors are identified. The contribution of this paper is to propose three new methods to address these limitations. Faults that occur during the observers' transient state can be detected by analyzing the convergence rate of the estimation error. Open-loop observers, which do not rely on sensor information, are used to detect faults on critical sensors. By switching among different observers, we can potentially mitigate the impact of the faulty sensor during the FDI process. These three methods are systematically integrated with a previously developed residual-based method to provide an improved FDI and mitigation capability framework. The overall approach is validated mathematically, and the effectiveness of the overall approach is demonstrated through simulation on a 5-state suspension system. PMID:28924303

Enhanced thermal and pyroelectric properties in 0-3 TGS:PVDF composites doped with graphene for infrared application

NASA Astrophysics Data System (ADS)

Feng, Xiaodong; Wang, Minqiang; Li, Le; Yang, Zhi; Cao, Minghui; Cheng, Z.-Y.

Pyroelectric composites of triglycine sulfate (TGS)-polyvinylidene difluoride (PVDF) doped with graphene are studied. It is found that the graphene can effectively improve the polling efficiency and thermal property of the composites so that the infrared detective performance can be significantly improved. For example, by adding about 0.83 wt.% of graphene, the infrared detective property can be improved by more than 30%. It is also found that the size of the graphene plays a critical role on the property improvement. For example, the small-sized graphene prepared by ultrasonic exfoliation (UE) method is more effective than the big-sized graphene prepared by electrochemical exfoliation (EE) method.

An Efficient Silent Data Corruption Detection Method with Error-Feedback Control and Even Sampling for HPC Applications

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

Di, Sheng; Berrocal, Eduardo; Cappello, Franck

The silent data corruption (SDC) problem is attracting more and more attentions because it is expected to have a great impact on exascale HPC applications. SDC faults are hazardous in that they pass unnoticed by hardware and can lead to wrong computation results. In this work, we formulate SDC detection as a runtime one-step-ahead prediction method, leveraging multiple linear prediction methods in order to improve the detection results. The contributions are twofold: (1) we propose an error feedback control model that can reduce the prediction errors for different linear prediction methods, and (2) we propose a spatial-data-based even-sampling method tomore » minimize the detection overheads (including memory and computation cost). We implement our algorithms in the fault tolerance interface, a fault tolerance library with multiple checkpoint levels, such that users can conveniently protect their HPC applications against both SDC errors and fail-stop errors. We evaluate our approach by using large-scale traces from well-known, large-scale HPC applications, as well as by running those HPC applications on a real cluster environment. Experiments show that our error feedback control model can improve detection sensitivity by 34-189% for bit-flip memory errors injected with the bit positions in the range [20,30], without any degradation on detection accuracy. Furthermore, memory size can be reduced by 33% with our spatial-data even-sampling method, with only a slight and graceful degradation in the detection sensitivity.« less

Enhancing detection of steady-state visual evoked potentials using individual training data.

PubMed

Wang, Yijun; Nakanishi, Masaki; Wang, Yu-Te; Jung, Tzyy-Ping

2014-01-01

Although the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has improved gradually in the past decades, it still does not meet the requirement of a high communication speed in many applications. A major challenge is the interference of spontaneous background EEG activities in discriminating SSVEPs. An SSVEP BCI using frequency coding typically does not have a calibration procedure since the frequency of SSVEPs can be recognized by power spectrum density analysis (PSDA). However, the detection rate can be deteriorated by the spontaneous EEG activities within the same frequency range because phase information of SSVEPs is ignored in frequency detection. To address this problem, this study proposed to incorporate individual SSVEP training data into canonical correlation analysis (CCA) to improve the frequency detection of SSVEPs. An eight-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment was used for performance evaluation. Compared to the standard CCA method, the proposed method obtained significantly improved detection accuracy (95.2% vs. 88.4%, p<0.05) and information transfer rates (ITR) (104.6 bits/min vs. 89.1 bits/min, p<0.05). The results suggest that the employment of individual SSVEP training data can significantly improve the detection rate and thereby facilitate the implementation of a high-speed BCI.

Stripline split-ring resonator with integrated optogalvanic sample cell

NASA Astrophysics Data System (ADS)

Persson, Anders; Berglund, Martin; Thornell, Greger; Possnert, Göran; Salehpour, Mehran

2014-04-01

Intracavity optogalvanic spectroscopy (ICOGS) has been proposed as a method for unambiguous detection of rare isotopes. Of particular interest is 14C, where detection of extremely low concentrations in the 1:1015 range (14C: 12C), is of interest in, e.g., radiocarbon dating and pharmaceutical sciences. However, recent reports show that ICOGS suffers from substantial problems with reproducibility. To qualify ICOGS as an analytical method, more stable and reliable plasma generation and signal detection are needed. In our proposed setup, critical parameters have been improved. We have utilized a stripline split-ring resonator microwave-induced microplasma source to excite and sustain the plasma. Such a microplasma source offers several advantages over conventional ICOGS plasma sources. For example, the stripline split-ring resonator concept employs separated plasma generation and signal detection, which enables sensitive detection at stable plasma conditions. The concept also permits in situ observation of the discharge conditions, which was found to improve reproducibility. Unique to the stripline split-ring resonator microplasma source in this study, is that the optogalvanic sample cell has been embedded in the device itself. This integration enables improved temperature control and more stable and accurate signal detection. Significant improvements are demonstrated, including reproducibility, signal-to-noise ratio, and precision.

Fluorescence detection system for microfluidic droplets

NASA Astrophysics Data System (ADS)

Chen, Binyu; Han, Xiaoming; Su, Zhen; Liu, Quanjun

2018-05-01

In microfluidic detection technology, because of the universality of optical methods in laboratory, optical detection is an attractive solution for microfluidic chip laboratory equipment. In addition, the equipment with high stability and low cost can be realized by integrating appropriate optical detection technology on the chip. This paper reports a detection system for microfluidic droplets. Photomultiplier tubes (PMT) is used as a detection device to improve the sensitivity of detection. This system improves the signal to noise ratio by software filtering and spatial filter. The fluorescence intensity is proportional to the concentration of the fluorescence and intensity of the laser. The fluorescence micro droplets of different concentrations can be distinguished by this system.

An Improved Harmonic Current Detection Method Based on Parallel Active Power Filter

NASA Astrophysics Data System (ADS)

Zeng, Zhiwu; Xie, Yunxiang; Wang, Yingpin; Guan, Yuanpeng; Li, Lanfang; Zhang, Xiaoyu

2017-05-01

Harmonic detection technology plays an important role in the applications of active power filter. The accuracy and real-time performance of harmonic detection are the precondition to ensure the compensation performance of Active Power Filter (APF). This paper proposed an improved instantaneous reactive power harmonic current detection algorithm. The algorithm uses an improved ip -iq algorithm which is combined with the moving average value filter. The proposed ip -iq algorithm can remove the αβ and dq coordinate transformation, decreasing the cost of calculation, simplifying the extraction process of fundamental components of load currents, and improving the detection speed. The traditional low-pass filter is replaced by the moving average filter, detecting the harmonic currents more precisely and quickly. Compared with the traditional algorithm, the THD (Total Harmonic Distortion) of the grid currents is reduced from 4.41% to 3.89% for the simulations and from 8.50% to 4.37% for the experiments after the improvement. The results show the proposed algorithm is more accurate and efficient.

Thin layer chromatographic method for the detection of uric acid: collaborative study.

PubMed

Thrasher, J J; Abadie, A

1978-07-01

A collaborative study has been completed on an improved method for the detection and confirmation of uric acid from bird and insect excreta. The proposed method involves the lithium carbonate solubilization of the suspect excreta material, followed by butanol-methanol-water-acetic acid thin layer chromatography, and trisodium phosphate-phosphotungstic acid color development. The collaborative tests resulted in 100% detection of uric acid standard at the 50 ng level and 75% detection at the 20-25 ng level. No false positives were reported during tests of compounds similar to uric acid. The proposed method has been adopted official first action; the present official final action method, 44.161, will be retained for screening purposes.

Kernel parameter variation-based selective ensemble support vector data description for oil spill detection on the ocean via hyperspectral imaging

NASA Astrophysics Data System (ADS)

Uslu, Faruk Sukru

2017-07-01

Oil spills on the ocean surface cause serious environmental, political, and economic problems. Therefore, these catastrophic threats to marine ecosystems require detection and monitoring. Hyperspectral sensors are powerful optical sensors used for oil spill detection with the help of detailed spectral information of materials. However, huge amounts of data in hyperspectral imaging (HSI) require fast and accurate computation methods for detection problems. Support vector data description (SVDD) is one of the most suitable methods for detection, especially for large data sets. Nevertheless, the selection of kernel parameters is one of the main problems in SVDD. This paper presents a method, inspired by ensemble learning, for improving performance of SVDD without tuning its kernel parameters. Additionally, a classifier selection technique is proposed to get more gain. The proposed approach also aims to solve the small sample size problem, which is very important for processing high-dimensional data in HSI. The algorithm is applied to two HSI data sets for detection problems. In the first HSI data set, various targets are detected; in the second HSI data set, oil spill detection in situ is realized. The experimental results demonstrate the feasibility and performance improvement of the proposed algorithm for oil spill detection problems.

Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique

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

Teramoto, Atsushi, E-mail: teramoto@fujita-hu.ac.jp; Fujita, Hiroshi; Yamamuro, Osamu

Purpose: Automated detection of solitary pulmonary nodules using positron emission tomography (PET) and computed tomography (CT) images shows good sensitivity; however, it is difficult to detect nodules in contact with normal organs, and additional efforts are needed so that the number of false positives (FPs) can be further reduced. In this paper, the authors propose an improved FP-reduction method for the detection of pulmonary nodules in PET/CT images by means of convolutional neural networks (CNNs). Methods: The overall scheme detects pulmonary nodules using both CT and PET images. In the CT images, a massive region is first detected using anmore » active contour filter, which is a type of contrast enhancement filter that has a deformable kernel shape. Subsequently, high-uptake regions detected by the PET images are merged with the regions detected by the CT images. FP candidates are eliminated using an ensemble method; it consists of two feature extractions, one by shape/metabolic feature analysis and the other by a CNN, followed by a two-step classifier, one step being rule based and the other being based on support vector machines. Results: The authors evaluated the detection performance using 104 PET/CT images collected by a cancer-screening program. The sensitivity in detecting candidates at an initial stage was 97.2%, with 72.8 FPs/case. After performing the proposed FP-reduction method, the sensitivity of detection was 90.1%, with 4.9 FPs/case; the proposed method eliminated approximately half the FPs existing in the previous study. Conclusions: An improved FP-reduction scheme using CNN technique has been developed for the detection of pulmonary nodules in PET/CT images. The authors’ ensemble FP-reduction method eliminated 93% of the FPs; their proposed method using CNN technique eliminates approximately half the FPs existing in the previous study. These results indicate that their method may be useful in the computer-aided detection of pulmonary nodules using PET/CT images.« less

Pornographic information of Internet views detection method based on the connected areas

NASA Astrophysics Data System (ADS)

Wang, Huibai; Fan, Ajie

2017-01-01

Nowadays online porn video broadcasting and downloading is very popular. In view of the widespread phenomenon of Internet pornography, this paper proposed a new method of pornographic video detection based on connected areas. Firstly, decode the video into a serious of static images and detect skin color on the extracted key frames. If the area of skin color reaches a certain threshold, use the AdaBoost algorithm to detect the human face. Judge the connectivity of the human face and the large area of skin color to determine whether detect the sensitive area finally. The experimental results show that the method can effectively remove the non-pornographic videos contain human who wear less. This method can improve the efficiency and reduce the workload of detection.

[Detection of lung nodules. New opportunities in chest radiography].

PubMed

Pötter-Lang, S; Schalekamp, S; Schaefer-Prokop, C; Uffmann, M

2014-05-01

Chest radiography still represents the most commonly performed X-ray examination because it is readily available, requires low radiation doses and is relatively inexpensive. However, as previously published, many initially undetected lung nodules are retrospectively visible in chest radiographs. The great improvements in detector technology with the increasing dose efficiency and improved contrast resolution provide a better image quality and reduced dose needs. The dual energy acquisition technique and advanced image processing methods (e.g. digital bone subtraction and temporal subtraction) reduce the anatomical background noise by reduction of overlapping structures in chest radiography. Computer-aided detection (CAD) schemes increase the awareness of radiologists for suspicious areas. The advanced image processing methods show clear improvements for the detection of pulmonary lung nodules in chest radiography and strengthen the role of this method in comparison to 3D acquisition techniques, such as computed tomography (CT). Many of these methods will probably be integrated into standard clinical treatment in the near future. Digital software solutions offer advantages as they can be easily incorporated into radiology departments and are often more affordable as compared to hardware solutions.

Regional fringe analysis for improving depth measurement in phase-shifting fringe projection profilometry

NASA Astrophysics Data System (ADS)

Chien, Kuang-Che Chang; Tu, Han-Yen; Hsieh, Ching-Huang; Cheng, Chau-Jern; Chang, Chun-Yen

2018-01-01

This study proposes a regional fringe analysis (RFA) method to detect the regions of a target object in captured shifted images to improve depth measurement in phase-shifting fringe projection profilometry (PS-FPP). In the RFA method, region-based segmentation is exploited to segment the de-fringed image of a target object, and a multi-level fuzzy-based classification with five presented features is used to analyze and discriminate the regions of an object from the segmented regions, which were associated with explicit fringe information. Then, in the experiment, the performance of the proposed method is tested and evaluated on 26 test cases made of five types of materials. The qualitative and quantitative results demonstrate that the proposed RFA method can effectively detect the desired regions of an object to improve depth measurement in the PS-FPP system.

Path length entropy analysis of diastolic heart sounds.

PubMed

Griffel, Benjamin; Zia, Mohammad K; Fridman, Vladamir; Saponieri, Cesare; Semmlow, John L

2013-09-01

Early detection of coronary artery disease (CAD) using the acoustic approach, a noninvasive and cost-effective method, would greatly improve the outcome of CAD patients. To detect CAD, we analyze diastolic sounds for possible CAD murmurs. We observed diastolic sounds to exhibit 1/f structure and developed a new method, path length entropy (PLE) and a scaled version (SPLE), to characterize this structure to improve CAD detection. We compare SPLE results to Hurst exponent, Sample entropy and Multiscale entropy for distinguishing between normal and CAD patients. SPLE achieved a sensitivity-specificity of 80%-81%, the best of the tested methods. However, PLE and SPLE are not sufficient to prove nonlinearity, and evaluation using surrogate data suggests that our cardiovascular sound recordings do not contain significant nonlinear properties. Copyright © 2013 Elsevier Ltd. All rights reserved.

Path Length Entropy Analysis of Diastolic Heart Sounds

PubMed Central

Griffel, B.; Zia, M. K.; Fridman, V.; Saponieri, C.; Semmlow, J. L.

2013-01-01

Early detection of coronary artery disease (CAD) using the acoustic approach, a noninvasive and cost-effective method, would greatly improve the outcome of CAD patients. To detect CAD, we analyze diastolic sounds for possible CAD murmurs. We observed diastolic sounds to exhibit 1/f structure and developed a new method, path length entropy (PLE) and a scaled version (SPLE), to characterize this structure to improve CAD detection. We compare SPLE results to Hurst exponent, Sample entropy and Multi-scale entropy for distinguishing between normal and CAD patients. SPLE achieved a sensitivity-specificity of 80%–81%, the best of the tested methods. However, PLE and SPLE are not sufficient to prove nonlinearity, and evaluation using surrogate data suggests that our cardiovascular sound recordings do not contain significant nonlinear properties. PMID:23930808

Feature Transformation Detection Method with Best Spectral Band Selection Process for Hyper-spectral Imaging

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike; Brickhouse, Mark

2015-11-01

We present a newly developed feature transformation (FT) detection method for hyper-spectral imagery (HSI) sensors. In essence, the FT method, by transforming the original features (spectral bands) to a different feature domain, may considerably increase the statistical separation between the target and background probability density functions, and thus may significantly improve the target detection and identification performance, as evidenced by the test results in this paper. We show that by differentiating the original spectral, one can completely separate targets from the background using a single spectral band, leading to perfect detection results. In addition, we have proposed an automated best spectral band selection process with a double-threshold scheme that can rank the available spectral bands from the best to the worst for target detection. Finally, we have also proposed an automated cross-spectrum fusion process to further improve the detection performance in lower spectral range (<1000 nm) by selecting the best spectral band pair with multivariate analysis. Promising detection performance has been achieved using a small background material signature library for concept-proving, and has then been further evaluated and verified using a real background HSI scene collected by a HYDICE sensor.

Improved detection of sugar addition to maple syrup using malic acid as internal standard and in 13C isotope ratio mass spectrometry (IRMS).

PubMed

Tremblay, Patrice; Paquin, Réal

2007-01-24

Stable carbon isotope ratio mass spectrometry (delta13C IRMS) was used to detect maple syrup adulteration by exogenous sugar addition (beet and cane sugar). Malic acid present in maple syrup is proposed as an isotopic internal standard to improve actual adulteration detection levels. A lead precipitation method has been modified to isolate quantitatively malic acid from maple syrup using preparative reversed-phase liquid chromatography. The stable carbon isotopic ratio of malic acid isolated from this procedure shows an excellent accuracy and repeatability of 0.01 and 0.1 per thousand respectively, confirming that the modified lead precipitation method is an isotopic fractionation-free process. A new approach is proposed to detect adulteration based on the correlation existing between the delta13Cmalic acid and the delta13Csugars-delta13Cmalic acid (r = 0.704). This technique has been tested on a set of 56 authentic maple syrup samples. Additionally, authentic samples were spiked with exogeneous sugars. The mean theoretical detection level was statistically lowered using this technique in comparison with the usual two-standard deviation approach, especially when maple syrup is adulterated with beet sugar : 24 +/- 12% of adulteration detection versus 48 +/- 20% (t-test, p = 7.3 x 10-15). The method was also applied to published data for pineapple juices and honey with the same improvement.

Structural and Functional Evaluations for the Early Detection of Glaucoma.

PubMed

Lucy, Katie A; Wollstein, Gadi

2016-01-01

The early detection of glaucoma is imperative in order to preserve functional vision. Structural and functional methods are utilized to detect and monitor glaucomatous damage and the vision loss it causes. The relationship between these detection measures is complex and differs between individuals, especially in early glaucoma. Using both measures together is advised in order to ensure the highest probability of glaucoma detection, and new testing methods are continuously developed with the goals of earlier disease detection and improvement of disease monitoring. The purpose of this review is to explore the relationship between structural and functional glaucoma detection and discuss important technological advances for early glaucoma detection.

Structural and Functional Evaluations for the Early Detection of Glaucoma

PubMed Central

Lucy, Katie A.; Wollstein, Gadi

2016-01-01

The early detection of glaucoma is imperative in order to preserve functional vision. Structural and functional methods are utilized to detect and monitor glaucomatous damage and the vision loss it causes. The relationship between these detection measures is complex and differs between individuals, especially in early glaucoma. Using both measures together is advised in order to ensure the highest probability of glaucoma detection, and new testing methods are continuously developed with the goals of earlier disease detection and improvement of disease monitoring. The purpose of this review is to explore the relationship between structural and functional glaucoma detection and discuss important technological advances for early glaucoma detection. PMID:28603546

Automated detection of retinal nerve fiber layer defects on fundus images: false positive reduction based on vessel likelihood

NASA Astrophysics Data System (ADS)

Muramatsu, Chisako; Ishida, Kyoko; Sawada, Akira; Hatanaka, Yuji; Yamamoto, Tetsuya; Fujita, Hiroshi

2016-03-01

Early detection of glaucoma is important to slow down or cease progression of the disease and for preventing total blindness. We have previously proposed an automated scheme for detection of retinal nerve fiber layer defect (NFLD), which is one of the early signs of glaucoma observed on retinal fundus images. In this study, a new multi-step detection scheme was included to improve detection of subtle and narrow NFLDs. In addition, new features were added to distinguish between NFLDs and blood vessels, which are frequent sites of false positives (FPs). The result was evaluated with a new test dataset consisted of 261 cases, including 130 cases with NFLDs. Using the proposed method, the initial detection rate was improved from 82% to 98%. At the sensitivity of 80%, the number of FPs per image was reduced from 4.25 to 1.36. The result indicates the potential usefulness of the proposed method for early detection of glaucoma.

Toward multimodal signal detection of adverse drug reactions.

PubMed

Harpaz, Rave; DuMouchel, William; Schuemie, Martijn; Bodenreider, Olivier; Friedman, Carol; Horvitz, Eric; Ripple, Anna; Sorbello, Alfred; White, Ryen W; Winnenburg, Rainer; Shah, Nigam H

2017-12-01

Improving mechanisms to detect adverse drug reactions (ADRs) is key to strengthening post-marketing drug safety surveillance. Signal detection is presently unimodal, relying on a single information source. Multimodal signal detection is based on jointly analyzing multiple information sources. Building on, and expanding the work done in prior studies, the aim of the article is to further research on multimodal signal detection, explore its potential benefits, and propose methods for its construction and evaluation. Four data sources are investigated; FDA's adverse event reporting system, insurance claims, the MEDLINE citation database, and the logs of major Web search engines. Published methods are used to generate and combine signals from each data source. Two distinct reference benchmarks corresponding to well-established and recently labeled ADRs respectively are used to evaluate the performance of multimodal signal detection in terms of area under the ROC curve (AUC) and lead-time-to-detection, with the latter relative to labeling revision dates. Limited to our reference benchmarks, multimodal signal detection provides AUC improvements ranging from 0.04 to 0.09 based on a widely used evaluation benchmark, and a comparative added lead-time of 7-22 months relative to labeling revision dates from a time-indexed benchmark. The results support the notion that utilizing and jointly analyzing multiple data sources may lead to improved signal detection. Given certain data and benchmark limitations, the early stage of development, and the complexity of ADRs, it is currently not possible to make definitive statements about the ultimate utility of the concept. Continued development of multimodal signal detection requires a deeper understanding the data sources used, additional benchmarks, and further research on methods to generate and synthesize signals. Copyright © 2017 Elsevier Inc. All rights reserved.

Application of a multiscale maximum entropy image restoration algorithm to HXMT observations

NASA Astrophysics Data System (ADS)

Guan, Ju; Song, Li-Ming; Huo, Zhuo-Xi

2016-08-01

This paper introduces a multiscale maximum entropy (MSME) algorithm for image restoration of the Hard X-ray Modulation Telescope (HXMT), which is a collimated scan X-ray satellite mainly devoted to a sensitive all-sky survey and pointed observations in the 1-250 keV range. The novelty of the MSME method is to use wavelet decomposition and multiresolution support to control noise amplification at different scales. Our work is focused on the application and modification of this method to restore diffuse sources detected by HXMT scanning observations. An improved method, the ensemble multiscale maximum entropy (EMSME) algorithm, is proposed to alleviate the problem of mode mixing exiting in MSME. Simulations have been performed on the detection of the diffuse source Cen A by HXMT in all-sky survey mode. The results show that the MSME method is adapted to the deconvolution task of HXMT for diffuse source detection and the improved method could suppress noise and improve the correlation and signal-to-noise ratio, thus proving itself a better algorithm for image restoration. Through one all-sky survey, HXMT could reach a capacity of detecting a diffuse source with maximum differential flux of 0.5 mCrab. Supported by Strategic Priority Research Program on Space Science, Chinese Academy of Sciences (XDA04010300) and National Natural Science Foundation of China (11403014)

Impedimetric detection of bacteria by using a microfluidic chip and silver nanoparticle based signal enhancement.

PubMed

Wang, Renjie; Xu, Yi; Sors, Thomas; Irudayaraj, Joseph; Ren, Wen; Wang, Rong

2018-02-19

The authors describe a method that can significantly improve the performance of impedimetric detection of bacteria. A multifunctional microfluidic chip was designed consisting of interdigitated microelectrodes and a micro-mixing zone with a Tesla structure. This maximizes the coating of bacterial surfaces with nanoparticles and results in improved impedimetric detection. The method was applied to the detection of Escherichia coli O157:H7 (E. coli). Silver enhancement was accomplished by coating E.coli with the cationic polymer diallyldimethylammonium chloride (PDDA) to form positively charged E. coli/PDDA complexes. Then, gold nanoparticles (AuNPs) were added, and the resulting E. coli/PDDA/AuNPs complexes were collected at interdigitated electrodes via positive dielectrophoresis (pDEP). A silver adduct was then formed on the E. coli/PDDA/AuNP complexes by using silver enhancement solutions and by using the AuNPs as catalysts. The combination of pDEP based capture and of using silver adducts reduces impedance by increasing the conductivity of the solution and the double layer capacitance around the microelectrodes. Impedance decreases linearly in the 2 × 10 3 -2 × 10 5  cfu·mL -1 E. coli concentration range, with a 500 cfu·mL -1 detection limit. Egg shell wash samples and tap water spiked with E. coli were successfully used for validation, and this demonstrates the practical application of this method. Graphical abstract Schematic representation of the AuNP@Ag enhancement method integrated with multifunctional microfluidic chip platform for impedimetric quantitation of bacteria. The method significantly improves the performance of impedimetric detection of bacteria.

Dual gated PET/CT imaging of small targets of the heart: method description and testing with a dynamic heart phantom.

PubMed

Kokki, Tommi; Sipilä, Hannu T; Teräs, Mika; Noponen, Tommi; Durand-Schaefer, Nicolas; Klén, Riku; Knuuti, Juhani

2010-01-01

In PET imaging respiratory and cardiac contraction motions interfere the imaging of heart. The aim was to develop and evaluate dual gating method for improving the detection of small targets of the heart. The method utilizes two independent triggers which are sent periodically into list mode data based on respiratory and ECG cycles. An algorithm for generating dual gated segments from list mode data was developed. The test measurements showed that rotational and axial movements of point source can be separated spatially to different segments with well-defined borders. The effect of dual gating on detection of small moving targets was tested with a moving heart phantom. Dual gated images showed 51% elimination (3.6 mm out of 7.0 mm) of contraction motion of hot spot (diameter 3 mm) and 70% elimination (14 mm out of 20 mm) of respiratory motion. Averaged activity value of hot spot increases by 89% when comparing to non-gated images. Patient study of suspected cardiac sarcoidosis shows sharper spatial myocardial uptake profile and improved detection of small myocardial structures such as papillary muscles. The dual gating method improves detection of small moving targets in a phantom and it is feasible in clinical situations.

Conflict management based on belief function entropy in sensor fusion.

PubMed

Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

2016-01-01

Wireless sensor network plays an important role in intelligent navigation. It incorporates a group of sensors to overcome the limitation of single detection system. Dempster-Shafer evidence theory can combine the sensor data of the wireless sensor network by data fusion, which contributes to the improvement of accuracy and reliability of the detection system. However, due to different sources of sensors, there may be conflict among the sensor data under uncertain environment. Thus, this paper proposes a new method combining Deng entropy and evidence distance to address the issue. First, Deng entropy is adopted to measure the uncertain information. Then, evidence distance is applied to measure the conflict degree. The new method can cope with conflict effectually and improve the accuracy and reliability of the detection system. An example is illustrated to show the efficiency of the new method and the result is compared with that of the existing methods.

Overview of MPLNET Version 3 Cloud Detection

NASA Technical Reports Server (NTRS)

Lewis, Jasper R.; Campbell, James; Welton, Ellsworth J.; Stewart, Sebastian A.; Haftings, Phillip

2016-01-01

The National Aeronautics and Space Administration Micro Pulse Lidar Network, version 3, cloud detection algorithm is described and differences relative to the previous version are highlighted. Clouds are identified from normalized level 1 signal profiles using two complementary methods. The first method considers vertical signal derivatives for detecting low-level clouds. The second method, which detects high-level clouds like cirrus, is based on signal uncertainties necessitated by the relatively low signal-to-noise ratio exhibited in the upper troposphere by eye-safe network instruments, especially during daytime. Furthermore, a multitemporal averaging scheme is used to improve cloud detection under conditions of a weak signal-to-noise ratio. Diurnal and seasonal cycles of cloud occurrence frequency based on one year of measurements at the Goddard Space Flight Center (Greenbelt, Maryland) site are compared for the new and previous versions. The largest differences, and perceived improvement, in detection occurs for high clouds (above 5 km, above MSL), which increase in occurrence by over 5%. There is also an increase in the detection of multilayered cloud profiles from 9% to 19%. Macrophysical properties and estimates of cloud optical depth are presented for a transparent cirrus dataset. However, the limit to which the cirrus cloud optical depth could be reliably estimated occurs between 0.5 and 0.8. A comparison using collocated CALIPSO measurements at the Goddard Space Flight Center and Singapore Micro Pulse Lidar Network (MPLNET) sites indicates improvements in cloud occurrence frequencies and layer heights.

Interlaboratory validation of an improved U.S. Food and Drug Administration method for detection of Cyclospora cayetanensis in produce using TaqMan real-time PCR

USDA-ARS?s Scientific Manuscript database

A collaborative validation study was performed to evaluate the performance of a new U.S. Food and Drug Administration method developed for detection of the protozoan parasite, Cyclospora cayetanensis, on cilantro and raspberries. The method includes a sample preparation step in which oocysts are re...

An evaluation of computer-aided disproportionality analysis for post-marketing signal detection.

PubMed

Lehman, H P; Chen, J; Gould, A L; Kassekert, R; Beninger, P R; Carney, R; Goldberg, M; Goss, M A; Kidos, K; Sharrar, R G; Shields, K; Sweet, A; Wiholm, B E; Honig, P K

2007-08-01

To understand the value of computer-aided disproportionality analysis (DA) in relation to current pharmacovigilance signal detection methods, four products were retrospectively evaluated by applying an empirical Bayes method to Merck's post-marketing safety database. Findings were compared with the prior detection of labeled post-marketing adverse events. Disproportionality ratios (empirical Bayes geometric mean lower 95% bounds for the posterior distribution (EBGM05)) were generated for product-event pairs. Overall (1993-2004 data, EBGM05> or =2, individual terms) results of signal detection using DA compared to standard methods were sensitivity, 31.1%; specificity, 95.3%; and positive predictive value, 19.9%. Using groupings of synonymous labeled terms, sensitivity improved (40.9%). More of the adverse events detected by both methods were detected earlier using DA and grouped (versus individual) terms. With 1939-2004 data, diagnostic properties were similar to those from 1993 to 2004. DA methods using Merck's safety database demonstrate sufficient sensitivity and specificity to be considered for use as an adjunct to conventional signal detection methods.

A surface acoustic wave response detection method for passive wireless torque sensor

NASA Astrophysics Data System (ADS)

Fan, Yanping; Kong, Ping; Qi, Hongli; Liu, Hongye; Ji, Xiaojun

2018-01-01

This paper presents an effective surface acoustic wave (SAW) response detection method for the passive wireless SAW torque sensor to improve the measurement accuracy. An analysis was conducted on the relationship between the response energy-entropy and the bandwidth of SAW resonator (SAWR). A self-correlation method was modified to suppress the blurred white noise and highlight the attenuation characteristic of wireless SAW response. The SAW response was detected according to both the variation and the duration of energy-entropy ascension of an acquired RF signal. Numerical simulation results showed that the SAW response can be detected even when the signal-to-noise ratio (SNR) is 6dB. The proposed SAW response detection method was evaluated with several experiments at different conditions. The SAW response can be well distinguished from the sinusoidal signal and the noise. The performance of the SAW torque measurement system incorporating the detection method was tested. The obtained repeatability error was 0.23% and the linearity was 0.9934, indicating the validity of the detection method.

Protein detection through different platforms of immuno-loop-mediated isothermal amplification

NASA Astrophysics Data System (ADS)

Pourhassan-Moghaddam, Mohammad; Rahmati-Yamchi, Mohammad; Akbarzadeh, Abolfazl; Daraee, Hadis; Nejati-Koshki, Kazem; Hanifehpour, Younes; Joo, Sang Woo

2013-11-01

Different immunoassay-based methods have been devised to detect protein targets. These methods have some challenges that make them inefficient for assaying ultra-low-amounted proteins. ELISA, iPCR, iRCA, and iNASBA are the common immunoassay-based methods of protein detection, each of which has specific and common technical challenges making it necessary to introduce a novel method in order to avoid their problems for detection of target proteins. Here we propose a new method nominated as `immuno-loop-mediated isothermal amplification' or `iLAMP'. This new method is free from the problems of the previous methods and has significant advantages over them. In this paper we also offer various configurations in order to improve the applicability of this method in real-world sample analyses. Important potential applications of this method are stated as well.

Multiple targets detection method in detection of UWB through-wall radar

NASA Astrophysics Data System (ADS)

Yang, Xiuwei; Yang, Chuanfa; Zhao, Xingwen; Tian, Xianzhong

2017-11-01

In this paper, the problems and difficulties encountered in the detection of multiple moving targets by UWB radar are analyzed. The experimental environment and the penetrating radar system are established. An adaptive threshold method based on local area is proposed to effectively filter out clutter interference The objective of the moving target is analyzed, and the false target is further filtered out by extracting the target feature. Based on the correlation between the targets, the target matching algorithm is proposed to improve the detection accuracy. Finally, the effectiveness of the above method is verified by practical experiment.

Low cost sensing technology for type 2 diabetes monitoring

NASA Astrophysics Data System (ADS)

Sarswat, Prashant; Free, Michael

2015-03-01

Alpha-hydroxybutyrate (2-hydroxybutyrate or α-HB) is becoming more widely recognized as an important metabolic biomarker that has been shown to be highly correlated with prediabetes and other metabolic diseases. In 2012 there were 86 million Americans with prediabetes, many of whom are not aware they have prediabetes, but could be diagnosed and treated to prevent type 2 diabetes if a simple, low-cost, convenient test were available. We have developed new, low-cost, accurate α-HB detection methods that can be used for the detection and monitoring of diseases such as prediabetes, type 2 diabetes, β-cell dysfunction, and early hyperglycemia. The new sensing method utilizes a diol recognition moiety, additives and a photoinitiator to detect α-HB at levels near 1 micro g/l in the presence of serum compounds such as lactic acid, sodium pyruvate, and glucose. The objective of this research is to improve the understanding of the interactions that enhance α-HB detection to enable additional improvements in α-HB detection as well as improvements in other biosensor applications.

Improved computer-aided detection of small polyps in CT colonography using interpolation for curvature estimationa

PubMed Central

Liu, Jiamin; Kabadi, Suraj; Van Uitert, Robert; Petrick, Nicholas; Deriche, Rachid; Summers, Ronald M.

2011-01-01

Purpose: Surface curvatures are important geometric features for the computer-aided analysis and detection of polyps in CT colonography (CTC). However, the general kernel approach for curvature computation can yield erroneous results for small polyps and for polyps that lie on haustral folds. Those erroneous curvatures will reduce the performance of polyp detection. This paper presents an analysis of interpolation’s effect on curvature estimation for thin structures and its application on computer-aided detection of small polyps in CTC. Methods: The authors demonstrated that a simple technique, image interpolation, can improve the accuracy of curvature estimation for thin structures and thus significantly improve the sensitivity of small polyp detection in CTC. Results: Our experiments showed that the merits of interpolating included more accurate curvature values for simulated data, and isolation of polyps near folds for clinical data. After testing on a large clinical data set, it was observed that sensitivities with linear, quadratic B-spline and cubic B-spline interpolations significantly improved the sensitivity for small polyp detection. Conclusions: The image interpolation can improve the accuracy of curvature estimation for thin structures and thus improve the computer-aided detection of small polyps in CTC. PMID:21859029

Near-Infrared Spectrum Detection of Wheat Gluten Protein Content Based on a Combined Filtering Method.

PubMed

Cai, Jian-Hua

2017-09-01

To eliminate the random error of the derivative near-IR (NIR) spectrum and to improve model stability and the prediction accuracy of the gluten protein content, a combined method is proposed for pretreatment of the NIR spectrum based on both empirical mode decomposition and the wavelet soft-threshold method. The principle and the steps of the method are introduced and the denoising effect is evaluated. The wheat gluten protein content is calculated based on the denoised spectrum, and the results are compared with those of the nine-point smoothing method and the wavelet soft-threshold method. Experimental results show that the proposed combined method is effective in completing pretreatment of the NIR spectrum, and the proposed method improves the accuracy of detection of wheat gluten protein content from the NIR spectrum.

Development of a Flow Cytometry-Based Method for Rapid Detection of Escherichia coli and Shigella Spp. Using an Oligonucleotide Probe

PubMed Central

Xue, Yong; Wilkes, Jon G.; Moskal, Ted J.; Williams, Anna J.; Cooper, Willie M.; Nayak, Rajesh; Rafii, Fatemeh; Buzatu, Dan A.

2016-01-01

Standard methods to detect Escherichia coli contamination in food use the polymerase chain reaction (PCR) and agar culture plates. These methods require multiple incubation steps and take a long time to results. An improved rapid flow-cytometry based detection method was developed, using a fluorescence-labeled oligonucleotide probe specifically binding a16S rRNA sequence. The method positively detected 51 E. coli isolates as well as 4 Shigella species. All 27 non-E. coli strains tested gave negative results. Comparison of the new genetic assay with a total plate count (TPC) assay and agar plate counting indicated similar sensitivity, agreement between cytometry cell and colony counts. This method can detect a small number of E.coli cells in the presence of large numbers of other bacteria. This method can be used for rapid, economical, and stable detection of E. coli and Shigella contamination in the food industry and other contexts. PMID:26913737

Development of a Flow Cytometry-Based Method for Rapid Detection of Escherichia coli and Shigella Spp. Using an Oligonucleotide Probe.

PubMed

Xue, Yong; Wilkes, Jon G; Moskal, Ted J; Williams, Anna J; Cooper, Willie M; Nayak, Rajesh; Rafii, Fatemeh; Buzatu, Dan A

2016-01-01

Standard methods to detect Escherichia coli contamination in food use the polymerase chain reaction (PCR) and agar culture plates. These methods require multiple incubation steps and take a long time to results. An improved rapid flow-cytometry based detection method was developed, using a fluorescence-labeled oligonucleotide probe specifically binding a16S rRNA sequence. The method positively detected 51 E. coli isolates as well as 4 Shigella species. All 27 non-E. coli strains tested gave negative results. Comparison of the new genetic assay with a total plate count (TPC) assay and agar plate counting indicated similar sensitivity, agreement between cytometry cell and colony counts. This method can detect a small number of E.coli cells in the presence of large numbers of other bacteria. This method can be used for rapid, economical, and stable detection of E. coli and Shigella contamination in the food industry and other contexts.

Addition of Carbon to the Culture Medium Improves the Detection Efficiency of Aflatoxin Synthetic Fungi

PubMed Central

Suzuki, Tadahiro; Iwahashi, Yumiko

2016-01-01

Aflatoxin (AF) is a harmful secondary metabolite that is synthesized by the Aspergillus species. Although AF detection techniques have been developed, techniques for detection of AF synthetic fungi are still required. Techniques such as plate culture methods are continually being modified for this purpose. However, plate culture methods require refinement because they suffer from several issues. In this study, activated charcoal powder (carbon) was added to a culture medium containing cyclodextrin (CD) to enhance the contrast of fluorescence and improve the detection efficiency for AF synthetic fungi. Two culture media, potato dextrose agar and yeast extract sucrose agar, were investigated using both plate and liquid cultures. The final concentrations of CD and carbon in the media were 3 mg/mL and 0.3 mg/mL, respectively. Addition of carbon improved the visibility of fluorescence by attenuating approximately 30% of light scattering. Several fungi that could not be detected with only CD in the medium were detected with carbon addition. The carbon also facilitated fungal growth in the potato dextrose liquid medium. The results suggest that addition of carbon to media can enhance the observation of AF-derived fluorescence. PMID:27854283

Detection of dechallenge in spontaneous reporting systems: a comparison of Bayes methods.

PubMed

Banu, A Bazila; Alias Balamurugan, S Appavu; Thirumalaikolundusubramanian, Ponniah

2014-01-01

Dechallenge is a response observed for the reduction or disappearance of adverse drug reactions (ADR) on withdrawal of a drug from a patient. Currently available algorithms to detect dechallenge have limitations. Hence, there is a need to compare available new methods. To detect dechallenge in Spontaneous Reporting Systems, data-mining algorithms like Naive Bayes and Improved Naive Bayes were applied for comparing the performance of the algorithms in terms of accuracy and error. Analyzing the factors of dechallenge like outcome and disease category will help medical practitioners and pharmaceutical industries to determine the reasons for dechallenge in order to take essential steps toward drug safety. Adverse drug reactions of the year 2011 and 2012 were downloaded from the United States Food and Drug Administration's database. The outcome of classification algorithms showed that Improved Naive Bayes algorithm outperformed Naive Bayes with accuracy of 90.11% and error of 9.8% in detecting the dechallenge. Detecting dechallenge for unknown samples are essential for proper prescription. To overcome the issues exposed by Naive Bayes algorithm, Improved Naive Bayes algorithm can be used to detect dechallenge in terms of higher accuracy and minimal error.

ERASE-Seq: Leveraging replicate measurements to enhance ultralow frequency variant detection in NGS data

PubMed Central

Kamps-Hughes, Nick; McUsic, Andrew; Kurihara, Laurie; Harkins, Timothy T.; Pal, Prithwish; Ray, Claire

2018-01-01

The accurate detection of ultralow allele frequency variants in DNA samples is of interest in both research and medical settings, particularly in liquid biopsies where cancer mutational status is monitored from circulating DNA. Next-generation sequencing (NGS) technologies employing molecular barcoding have shown promise but significant sensitivity and specificity improvements are still needed to detect mutations in a majority of patients before the metastatic stage. To address this we present analytical validation data for ERASE-Seq (Elimination of Recurrent Artifacts and Stochastic Errors), a method for accurate and sensitive detection of ultralow frequency DNA variants in NGS data. ERASE-Seq differs from previous methods by creating a robust statistical framework to utilize technical replicates in conjunction with background error modeling, providing a 10 to 100-fold reduction in false positive rates compared to published molecular barcoding methods. ERASE-Seq was tested using spiked human DNA mixtures with clinically realistic DNA input quantities to detect SNVs and indels between 0.05% and 1% allele frequency, the range commonly found in liquid biopsy samples. Variants were detected with greater than 90% sensitivity and a false positive rate below 0.1 calls per 10,000 possible variants. The approach represents a significant performance improvement compared to molecular barcoding methods and does not require changing molecular reagents. PMID:29630678

Improving the Space Surveillance Telescope's Performance Using Multi-Hypothesis Testing

NASA Astrophysics Data System (ADS)

Zingarelli, J. Chris; Pearce, Eric; Lambour, Richard; Blake, Travis; Peterson, Curtis J. R.; Cain, Stephen

2014-05-01

The Space Surveillance Telescope (SST) is a Defense Advanced Research Projects Agency program designed to detect objects in space like near Earth asteroids and space debris in the geosynchronous Earth orbit (GEO) belt. Binary hypothesis test (BHT) methods have historically been used to facilitate the detection of new objects in space. In this paper a multi-hypothesis detection strategy is introduced to improve the detection performance of SST. In this context, the multi-hypothesis testing (MHT) determines if an unresolvable point source is in either the center, a corner, or a side of a pixel in contrast to BHT, which only tests whether an object is in the pixel or not. The images recorded by SST are undersampled such as to cause aliasing, which degrades the performance of traditional detection schemes. The equations for the MHT are derived in terms of signal-to-noise ratio (S/N), which is computed by subtracting the background light level around the pixel being tested and dividing by the standard deviation of the noise. A new method for determining the local noise statistics that rejects outliers is introduced in combination with the MHT. An experiment using observations of a known GEO satellite are used to demonstrate the improved detection performance of the new algorithm over algorithms previously reported in the literature. The results show a significant improvement in the probability of detection by as much as 50% over existing algorithms. In addition to detection, the S/N results prove to be linearly related to the least-squares estimates of point source irradiance, thus improving photometric accuracy. The views expressed are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government.

Optimization of a Viability PCR Method for the Detection of Listeria monocytogenes in Food Samples.

PubMed

Agustí, Gemma; Fittipaldi, Mariana; Codony, Francesc

2018-06-01

Rapid detection of Listeria and other microbial pathogens in food is an essential part of quality control and it is critical for ensuring the safety of consumers. Culture-based methods for detecting foodborne pathogens are time-consuming, laborious and cannot detect viable but non-culturable microorganism, whereas viability PCR methodology provides quick results; it is able to detect viable but non-culturable cells, and allows for easier handling of large amount of samples. Although the most critical point to use viability PCR technique is achieving the complete exclusion of dead cell amplification signals, many improvements are being introduced to overcome this. In the present work, the yield of dead cell DNA neutralization was enhanced by incorporating two new sample treatment strategies: tube change combined with a double light treatment. This procedure was successfully tested using artificially contaminated food samples, showing improved neutralization of dead cell DNA.

Outlier and target detection in aerial hyperspectral imagery: a comparison of traditional and percentage occupancy hit or miss transform techniques

NASA Astrophysics Data System (ADS)

Young, Andrew; Marshall, Stephen; Gray, Alison

2016-05-01

The use of aerial hyperspectral imagery for the purpose of remote sensing is a rapidly growing research area. Currently, targets are generally detected by looking for distinct spectral features of the objects under surveillance. For example, a camouflaged vehicle, deliberately designed to blend into background trees and grass in the visible spectrum, can be revealed using spectral features in the near-infrared spectrum. This work aims to develop improved target detection methods, using a two-stage approach, firstly by development of a physics-based atmospheric correction algorithm to convert radiance into re ectance hyperspectral image data and secondly by use of improved outlier detection techniques. In this paper the use of the Percentage Occupancy Hit or Miss Transform is explored to provide an automated method for target detection in aerial hyperspectral imagery.

Improved Method for the Detection and Quantification of Naegleria fowleri in Water and Sediment Using Immunomagnetic Separation and Real-Time PCR

PubMed Central

Mull, Bonnie J.; Narayanan, Jothikumar; Hill, Vincent R.

2013-01-01

Primary amebic meningoencephalitis (PAM) is a rare and typically fatal infection caused by the thermophilic free-living ameba, Naegleria fowleri. In 2010, the first confirmed case of PAM acquired in Minnesota highlighted the need for improved detection and quantification methods in order to study the changing ecology of N. fowleri and to evaluate potential risk factors for increased exposure. An immunomagnetic separation (IMS) procedure and real-time PCR TaqMan assay were developed to recover and quantify N. fowleri in water and sediment samples. When one liter of lake water was seeded with N. fowleri strain CDC:V212, the method had an average recovery of 46% and detection limit of 14 amebas per liter of water. The method was then applied to sediment and water samples with unknown N. fowleri concentrations, resulting in positive direct detections by real-time PCR in 3 out of 16 samples and confirmation of N. fowleri culture in 6 of 16 samples. This study has resulted in a new method for detection and quantification of N. fowleri in water and sediment that should be a useful tool to facilitate studies of the physical, chemical, and biological factors associated with the presence and dynamics of N. fowleri in environmental systems. PMID:24228172

Recent Efforts to Improve the Near Real Time Forest Disturbance Monitoring Capabilities of the ForWarn System

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Hargrove, William; Gasser, Gerald

2013-01-01

This presentation discusses the development of anew method for computing NDVI temporal composites from near real time eMODIS data This research is being conducted to improve forest change products used in the ForWarn system for monitoring regional forest disturbances in the United States. ForWarn provides nation-wide NDVI-based forest disturbance detection products that are refreshed every 8 days. Current eMODIS and historical MOD13 24 day NDVI data are used to compute the disturbance detection products. The eMODIS 24 day NDVI data re-aggregated from 7 day NDVI products. The 24 day eMODIS NDVIs are generally cloud free, but do not necessarily use the freshest quality data. To shorten the disturbance detection time, a method has been developed that performs adaptive length/maximum value compositing of eMODIS NDVI, along with cloud and shadow "noise" mitigation. Tests indicate that this method can reduce detection rates by 8-16 days for known recent disturbance events, depending on the cloud frequencies and disturbance type. The noise mitigation in these tests, though imperfect, helped to improve quality of the resulting NDVI and forest change products.

New method for enhanced efficiency in detection of gravitational waves from supernovae using coherent network of detectors

NASA Astrophysics Data System (ADS)

Mukherjee, S.; Salazar, L.; Mittelstaedt, J.; Valdez, O.

2017-11-01

Supernovae in our universe are potential sources of gravitational waves (GW) that could be detected in a network of GW detectors like LIGO and Virgo. Core-collapse supernovae are rare, but the associated gravitational radiation is likely to carry profuse information about the underlying processes driving the supernovae. Calculations based on analytic models predict GW energies within the detection range of the Advanced LIGO detectors, out to tens of Mpc for certain types of signals e.g. coalescing binary neutron stars. For supernovae however, the corresponding distances are much less. Thus, methods that can improve the sensitivity of searches for GW signals from supernovae are desirable, especially in the advanced detector era. Several methods have been proposed based on various likelihood-based regulators that work on data from a network of detectors to detect burst-like signals (as is the case for signals from supernovae) from potential GW sources. To address this problem, we have developed an analysis pipeline based on a method of noise reduction known as the harmonic regeneration noise reduction (HRNR) algorithm. To demonstrate the method, sixteen supernova waveforms from the Murphy et al. 2009 catalog have been used in presence of LIGO science data. A comparative analysis is presented to show detection statistics for a standard network analysis as commonly used in GW pipelines and the same by implementing the new method in conjunction with the network. The result shows significant improvement in detection statistics.

Item Purification Does Not Always Improve DIF Detection: A Counterexample with Angoff's Delta Plot

ERIC Educational Resources Information Center

Magis, David; Facon, Bruno

2013-01-01

Item purification is an iterative process that is often advocated as improving the identification of items affected by differential item functioning (DIF). With test-score-based DIF detection methods, item purification iteratively removes the items currently flagged as DIF from the test scores to get purified sets of items, unaffected by DIF. The…

An effective hair detection algorithm for dermoscopic melanoma images of skin lesions

NASA Astrophysics Data System (ADS)

Chakraborti, Damayanti; Kaur, Ravneet; Umbaugh, Scott; LeAnder, Robert

2016-09-01

Dermoscopic images are obtained using the method of skin surface microscopy. Pigmented skin lesions are evaluated in terms of texture features such as color and structure. Artifacts, such as hairs, bubbles, black frames, ruler-marks, etc., create obstacles that prevent accurate detection of skin lesions by both clinicians and computer-aided diagnosis. In this article, we propose a new algorithm for the automated detection of hairs, using an adaptive, Canny edge-detection method, followed by morphological filtering and an arithmetic addition operation. The algorithm was applied to 50 dermoscopic melanoma images. In order to ascertain this method's relative detection accuracy, it was compared to the Razmjooy hair-detection method [1], using segmentation error (SE), true detection rate (TDR) and false positioning rate (FPR). The new method produced 6.57% SE, 96.28% TDR and 3.47% FPR, compared to 15.751% SE, 86.29% TDR and 11.74% FPR produced by the Razmjooy method [1]. Because of the 7.27-9.99% improvement in those parameters, we conclude that the new algorithm produces much better results for detecting thick, thin, dark and light hairs. The new method proposed here, shows an appreciable difference in the rate of detecting bubbles, as well.

Application of fluorescence spectroscopy and imaging in the detection of a photosensitizer in photodynamic therapy

NASA Astrophysics Data System (ADS)

Zang, Lixin; Zhao, Huimin; Zhang, Zhiguo; Cao, Wenwu

2017-02-01

Photodynamic therapy (PDT) is currently an advanced optical technology in medical applications. However, the application of PDT is limited by the detection of photosensitizers. This work focuses on the application of fluorescence spectroscopy and imaging in the detection of an effective photosenzitizer, hematoporphyrin monomethyl ether (HMME). Optical properties of HMME were measured and analyzed based on its absorption and fluorescence spectra. The production mechanism of its fluorescence emission was analyzed. The detection device for HMME based on fluorescence spectroscopy was designed. Ratiometric method was applied to eliminate the influence of intensity change of excitation sources, fluctuates of excitation sources and photo detectors, and background emissions. The detection limit of this device is 6 μg/L, and it was successfully applied to the diagnosis of the metabolism of HMME in the esophageal cancer cells. To overcome the limitation of the point measurement using fluorescence spectroscopy, a two-dimensional (2D) fluorescence imaging system was established. The algorithm of the 2D fluorescence imaging system is deduced according to the fluorescence ratiometric method using bandpass filters. The method of multiple pixel point addition (MPPA) was used to eliminate fluctuates of signals. Using the method of MPPA, SNR was improved by about 30 times. The detection limit of this imaging system is 1.9 μg/L. Our systems can be used in the detection of porphyrins to improve the PDT effect.

TU-H-206-04: An Effective Homomorphic Unsharp Mask Filtering Method to Correct Intensity Inhomogeneity in Daily Treatment MR Images

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

Yang, D; Gach, H; Li, H

Purpose: The daily treatment MRIs acquired on MR-IGRT systems, like diagnostic MRIs, suffer from intensity inhomogeneity issue, associated with B1 and B0 inhomogeneities. An improved homomorphic unsharp mask (HUM) filtering method, automatic and robust body segmentation, and imaging field-of-view (FOV) detection methods were developed to compute the multiplicative slow-varying correction field and correct the intensity inhomogeneity. The goal is to improve and normalize the voxel intensity so that the images could be processed more accurately by quantitative methods (e.g., segmentation and registration) that require consistent image voxel intensity values. Methods: HUM methods have been widely used for years. A bodymore » mask is required, otherwise the body surface in the corrected image would be incorrectly bright due to the sudden intensity transition at the body surface. In this study, we developed an improved HUM-based correction method that includes three main components: 1) Robust body segmentation on the normalized image gradient map, 2) Robust FOV detection (needed for body segmentation) using region growing and morphologic filters, and 3) An effective implementation of HUM using repeated Gaussian convolution. Results: The proposed method was successfully tested on patient images of common anatomical sites (H/N, lung, abdomen and pelvis). Initial qualitative comparisons showed that this improved HUM method outperformed three recently published algorithms (FCM, LEMS, MICO) in both computation speed (by 50+ times) and robustness (in intermediate to severe inhomogeneity situations). Currently implemented in MATLAB, it takes 20 to 25 seconds to process a 3D MRI volume. Conclusion: Compared to more sophisticated MRI inhomogeneity correction algorithms, the improved HUM method is simple and effective. The inhomogeneity correction, body mask, and FOV detection methods developed in this study would be useful as preprocessing tools for many MRI-related research and clinical applications in radiotherapy. Authors have received research grants from ViewRay and Varian.« less

Edge detection and localization with edge pattern analysis and inflection characterization

NASA Astrophysics Data System (ADS)

Jiang, Bo

2012-05-01

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

Mapping Diffuse Seismicity Using Empirical Matched Field Processing Techniques

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

Wang, J; Templeton, D C; Harris, D B

The objective of this project is to detect and locate more microearthquakes using the empirical matched field processing (MFP) method than can be detected using only conventional earthquake detection techniques. We propose that empirical MFP can complement existing catalogs and techniques. We test our method on continuous seismic data collected at the Salton Sea Geothermal Field during November 2009 and January 2010. In the Southern California Earthquake Data Center (SCEDC) earthquake catalog, 619 events were identified in our study area during this time frame and our MFP technique identified 1094 events. Therefore, we believe that the empirical MFP method combinedmore » with conventional methods significantly improves the network detection ability in an efficient matter.« less

Improvement of a picking algorithm real-time P-wave detection by kurtosis

NASA Astrophysics Data System (ADS)

Ishida, H.; Yamada, M.

2016-12-01

Earthquake early warning (EEW) requires fast and accurate P-wave detection. The current EEW system in Japan uses the STA/LTAalgorithm (Allen, 1978) to detect P-wave arrival.However, some stations did not trigger during the 2011 Great Tohoku Earthquake due to the emergent onset. In addition, accuracy of the P-wave detection is very important: on August 1, 2016, the EEW issued a false alarm with M9 in Tokyo region due to a thunder noise.To solve these problems, we use a P-wave detection method using kurtosis statistics. It detects the change of statistic distribution of the waveform amplitude. This method was recently developed (Saragiotis et al., 2002) and used for off-line analysis such as making seismic catalogs. To apply this method for EEW, we need to remove an acausal calculation and enable a real-time processing. Here, we propose a real-time P-wave detection method using kurtosis statistics with a noise filter.To avoid false triggering by a noise, we incorporated a simple filter to classify seismic signal and noise. Following Kong et al. (2016), we used the interquartilerange and zero cross rate for the classification. The interquartile range is an amplitude measure that is equal to the middle 50% of amplitude in a certain time window. The zero cross rate is a simple frequency measure that counts the number of times that the signal crosses baseline zero. A discriminant function including these measures was constructed by the linear discriminant analysis.To test this kurtosis method, we used strong motion records for 62 earthquakes between April, 2005 and July, 2015, which recorded the seismic intensity greater equal to 6 lower in the JMA intensity scale. The records with hypocentral distance < 200km were used for the analysis. An attached figure shows the error of P-wave detection speed for STA/LTA and kurtosis methods against manual picks. It shows that the median error is 0.13 sec and 0.035 sec for STA/LTA and kurtosis method. The kurtosis method tends to be more sensitive to small changes in amplitude.Our approach will contribute to improve the accuracy of source location determination of earthquakes and improve the shaking intensity estimation for an earthquake early warning.

[A cloud detection algorithm for MODIS images combining Kmeans clustering and multi-spectral threshold method].

PubMed

Wang, Wei; Song, Wei-Guo; Liu, Shi-Xing; Zhang, Yong-Ming; Zheng, Hong-Yang; Tian, Wei

2011-04-01

An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.

Novel Method For Low-Rate Ddos Attack Detection

NASA Astrophysics Data System (ADS)

Chistokhodova, A. A.; Sidorov, I. D.

2018-05-01

The relevance of the work is associated with an increasing number of advanced types of DDoS attacks, in particular, low-rate HTTP-flood. Last year, the power and complexity of such attacks increased significantly. The article is devoted to the analysis of DDoS attacks detecting methods and their modifications with the purpose of increasing the accuracy of DDoS attack detection. The article details low-rate attacks features in comparison with conventional DDoS attacks. During the analysis, significant shortcomings of the available method for detecting low-rate DDoS attacks were found. Thus, the result of the study is an informal description of a new method for detecting low-rate denial-of-service attacks. The architecture of the stand for approbation of the method is developed. At the current stage of the study, it is possible to improve the efficiency of an already existing method by using a classifier with memory, as well as additional information.

Effect of time-of-flight and point spread function modeling on detectability of myocardial defects in PET

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

Schaefferkoetter, Joshua, E-mail: dnrjds@nus.edu.sg; Ouyang, Jinsong; Rakvongthai, Yothin

2014-06-15

Purpose: A study was designed to investigate the impact of time-of-flight (TOF) and point spread function (PSF) modeling on the detectability of myocardial defects. Methods: Clinical FDG-PET data were used to generate populations of defect-present and defect-absent images. Defects were incorporated at three contrast levels, and images were reconstructed by ordered subset expectation maximization (OSEM) iterative methods including ordinary Poisson, alone and with PSF, TOF, and PSF+TOF. Channelized Hotelling observer signal-to-noise ratio (SNR) was the surrogate for human observer performance. Results: For three iterations, 12 subsets, and no postreconstruction smoothing, TOF improved overall defect detection SNR by 8.6% as comparedmore » to its non-TOF counterpart for all the defect contrasts. Due to the slow convergence of PSF reconstruction, PSF yielded 4.4% less SNR than non-PSF. For reconstruction parameters (iteration number and postreconstruction smoothing kernel size) optimizing observer SNR, PSF showed larger improvement for faint defects. The combination of TOF and PSF improved mean detection SNR as compared to non-TOF and non-PSF counterparts by 3.0% and 3.2%, respectively. Conclusions: For typical reconstruction protocol used in clinical practice, i.e., less than five iterations, TOF improved defect detectability. In contrast, PSF generally yielded less detectability. For large number of iterations, TOF+PSF yields the best observer performance.« less

The review and results of different methods for facial recognition

NASA Astrophysics Data System (ADS)

Le, Yifan

2017-09-01

In recent years, facial recognition draws much attention due to its wide potential applications. As a unique technology in Biometric Identification, facial recognition represents a significant improvement since it could be operated without cooperation of people under detection. Hence, facial recognition will be taken into defense system, medical detection, human behavior understanding, etc. Several theories and methods have been established to make progress in facial recognition: (1) A novel two-stage facial landmark localization method is proposed which has more accurate facial localization effect under specific database; (2) A statistical face frontalization method is proposed which outperforms state-of-the-art methods for face landmark localization; (3) It proposes a general facial landmark detection algorithm to handle images with severe occlusion and images with large head poses; (4) There are three methods proposed on Face Alignment including shape augmented regression method, pose-indexed based multi-view method and a learning based method via regressing local binary features. The aim of this paper is to analyze previous work of different aspects in facial recognition, focusing on concrete method and performance under various databases. In addition, some improvement measures and suggestions in potential applications will be put forward.

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.

Detection of bacterial pathogens in Mongolia meningitis surveillance with a new real-time PCR assay to detect Haemophilus influenzae.

PubMed

Wang, Xin; Mair, Raydel; Hatcher, Cynthia; Theodore, M Jordan; Edmond, Karen; Wu, Henry M; Harcourt, Brian H; Carvalho, Maria da Gloria S; Pimenta, Fabiana; Nymadawa, Pagbajab; Altantsetseg, Dorjpurev; Kirsch, Mariah; Satola, Sarah W; Cohn, Amanda; Messonnier, Nancy E; Mayer, Leonard W

2011-04-01

Since the implementation of Haemophilus influenzae (Hi) serotype b vaccine, other serotypes and non-typeable strains have taken on greater importance as a cause of Hi diseases. A rapid and accurate method is needed to detect all Hi regardless of the encapsulation status. We developed 2 real-time PCR (rt-PCR) assays to detect specific regions of the protein D gene (hpd). Both hpd assays are very specific and sensitive for detection of Hi. Of the 63 non-Hi isolates representing 21 bacterial species, none was detected by the hpd #1 assay, and only one of 2 H. aphrophilus isolates was detected by the hpd #3 assay. The hpd #1 and #3 assays detected 97% (229/237) and 99% (234/237) of Hi isolates, respectively, and were superior for detection of both typeable and non-typeable Hi isolates, as compared to previously developed rt-PCR targeting ompP2 or bexA. The diagnostic sensitivity and specificity of these rt-PCR assays were assessed on cerebrospinal fluid specimens collected as part of meningitis surveillance in Ulaanbaatar, Mongolia. The etiology (Neisseria meningitidis, Hi, and Streptococcus pneumoniae) of 111 suspected meningitis cases was determined by conventional methods (culture and latex agglutination), previously developed rt-PCR assays, and the new hpd assays. The rt-PCR assays were more sensitive for detection of meningitis pathogens than other classical methods and improved detection from 50% (56/111) to 75% (83/111). The hpd #3 assay identified a non-b Hi that was missed by the bexA assay and other methods. A sensitive rt-PCR assay to detect both typeable and non-typeable Hi is a useful tool for improving Hi disease surveillance especially after Hib vaccine introduction. Published by Elsevier GmbH.

A Cyber-Attack Detection Model Based on Multivariate Analyses

NASA Astrophysics Data System (ADS)

Sakai, Yuto; Rinsaka, Koichiro; Dohi, Tadashi

In the present paper, we propose a novel cyber-attack detection model based on two multivariate-analysis methods to the audit data observed on a host machine. The statistical techniques used here are the well-known Hayashi's quantification method IV and cluster analysis method. We quantify the observed qualitative audit event sequence via the quantification method IV, and collect similar audit event sequence in the same groups based on the cluster analysis. It is shown in simulation experiments that our model can improve the cyber-attack detection accuracy in some realistic cases where both normal and attack activities are intermingled.

Application of core-shell-structured CdTe@SiO2 quantum dots synthesized via a facile solution method for improving latent fingerprint detection

NASA Astrophysics Data System (ADS)

Gao, Feng; Han, Jiaxing; Lv, Caifeng; Wang, Qin; Zhang, Jun; Li, Qun; Bao, Liru; Li, Xin

2012-10-01

Fingerprint detection is important in criminal investigation. This paper reports a facile powder brushing technique for improving latent fingerprint detection using core-shell-structured CdTe@SiO2 quantum dots (QDs) as fluorescent labeling marks. Core-shell-structured CdTe@SiO2 QDs are prepared via a simple solution-based approach using NH2NH2·H2O as pH adjustor and stabilizer, and their application for improving latent fingerprint detection is explored. The obtained CdTe@SiO2 QDs show spherical shapes with well-defined core-shell structures encapsulating different amounts of QDs depending on the type of the pH adjustor and stabilizer. Moreover, the fluorescence of CdTe@SiO2 QDs is largely enhanced by surface modification of the SiO2 shell. The CdTe@SiO2 QDs overcome the oxidation problem of pure CdTe QDs in air, thus affording better variability with strong adhesive ability, better resolution, and bright emission colors for practical application in latent fingerprint detection. In comparison with the conventional fluorescence powders, silver powders, and others, the effectiveness of CdTe@SiO2 QD powders for detection of latent fingerprints present on a large variety of object surfaces is greatly improved. The synthesis method for CdTe@SiO2 QDs is simple, cheap, and easy for large-scale production, and thus offers many advantages in the practical application of fingerprint detection.

Resolution-improved in situ DNA hybridization detection based on microwave photonic interrogation.

PubMed

Cao, Yuan; Guo, Tuan; Wang, Xudong; Sun, Dandan; Ran, Yang; Feng, Xinhuan; Guan, Bai-ou

2015-10-19

In situ bio-sensing system based on microwave photonics filter (MPF) interrogation method with improved resolution is proposed and experimentally demonstrated. A microfiber Bragg grating (mFBG) is used as sensing probe for DNA hybridization detection. Different from the traditional wavelength monitoring technique, we use the frequency interrogation scheme for resolution-improved bio-sensing detection. Experimental results show that the frequency shift of MPF notch presents a linear response to the surrounding refractive index (SRI) change over the range of 1.33 to 1.38, with a SRI resolution up to 2.6 × 10(-5) RIU, which has been increased for almost two orders of magnitude compared with the traditional fundamental mode monitoring technique (~3.6 × 10(-3) RIU). Due to the high Q value (about 27), the whole process of DNA hybridization can be in situ monitored. The proposed MPF-based bio-sensing system provides a new interrogation method over the frequency domain with improved sensing resolution and rapid interrogation rate for biochemical and environmental measurement.

Improved Detection of Local Earthquakes in the Vienna Basin (Austria), using Subspace Detectors

NASA Astrophysics Data System (ADS)

Apoloner, Maria-Theresia; Caffagni, Enrico; Bokelmann, Götz

2016-04-01

The Vienna Basin in Eastern Austria is densely populated and highly-developed; it is also a region of low to moderate seismicity, yet the seismological network coverage is relatively sparse. This demands improving our capability of earthquake detection by testing new methods, enlarging the existing local earthquake catalogue. This contributes to imaging tectonic fault zones for better understanding seismic hazard, also through improved earthquake statistics (b-value, magnitude of completeness). Detection of low-magnitude earthquakes or events for which the highest amplitudes slightly exceed the signal-to-noise-ratio (SNR), may be possible by using standard methods like the short-term over long-term average (STA/LTA). However, due to sparse network coverage and high background noise, such a technique may not detect all potentially recoverable events. Yet, earthquakes originating from the same source region and relatively close to each other, should be characterized by similarity in seismic waveforms, at a given station. Therefore, waveform similarity can be exploited by using specific techniques such as correlation-template based (also known as matched filtering) or subspace detection methods (based on the subspace theory). Matching techniques basically require a reference or template event, usually characterized by high waveform coherence in the array receivers, and high SNR, which is cross-correlated with the continuous data. Instead, subspace detection methods overcome in principle the necessity of defining template events as single events, but use a subspace extracted from multiple events. This approach theoretically should be more robust in detecting signals that exhibit a strong variability (e.g. because of source or magnitude). In this study we scan the continuous data recorded in the Vienna Basin with a subspace detector to identify additional events. This will allow us to estimate the increase of the seismicity rate in the local earthquake catalogue, therefore providing an evaluation of network performance and efficiency of the method.

Solid phase extraction of large volume of water and beverage samples to improve detection limits for GC-MS analysis of bisphenol A and four other bisphenols.

PubMed

Cao, Xu-Liang; Popovic, Svetlana

2018-01-01

Solid phase extraction (SPE) of large volumes of water and beverage products was investigated for the GC-MS analysis of bisphenol A (BPA), bisphenol AF (BPAF), bisphenol F (BPF), bisphenol E (BPE), and bisphenol B (BPB). While absolute recoveries of the method were improved for water and some beverage products (e.g. diet cola, iced tea), breakthrough may also have occurred during SPE of 200 mL of other beverages (e.g. BPF in cola). Improvements in method detection limits were observed with the analysis of large sample volumes for all bisphenols at ppt (pg/g) to sub-ppt levels. This improvement was found to be proportional to sample volumes for water and beverage products with less interferences and noise levels around the analytes. Matrix effects and interferences were observed during SPE of larger volumes (100 and 200 mL) of the beverage products, and affected the accurate analysis of BPF. This improved method was used to analyse bisphenols in various beverage samples, and only BPA was detected, with levels ranging from 0.022 to 0.030 ng/g for products in PET bottles, and 0.085 to 0.32 ng/g for products in cans.

Combined electrophoresis-electrospray interface and method

DOEpatents

Smith, Richard D.; Udseth, Harold R.; Barinaga, Charles J.

1995-01-01

An improvement to the system and method for analyzing molecular constituents of a composition sample that comprises improvements to an electrospray ionization source for interfacing to mass spectrometers and other detection devices. The improvement consists of establishing a unique electrical circuit pattern and nozzle configuration, a metallic coated and conical shaped capillary outlet, coupled with sizing of the capillary to obtain maximum sensitivity.

Thin wetting film lensless imaging

NASA Astrophysics Data System (ADS)

Allier, C. P.; Poher, V.; Coutard, J. G.; Hiernard, G.; Dinten, J. M.

2011-03-01

Lensless imaging has recently attracted a lot of attention as a compact, easy-to-use method to image or detect biological objects like cells, but failed at detecting micron size objects like bacteria that often do not scatter enough light. In order to detect single bacterium, we have developed a method based on a thin wetting film that produces a micro-lens effect. Compared with previously reported results, a large improvement in signal to noise ratio is obtained due to the presence of a micro-lens on top of each bacterium. In these conditions, standard CMOS sensors are able to detect single bacterium, e.g. E.coli, Bacillus subtilis and Bacillus thuringiensis, with a large signal to noise ratio. This paper presents our sensor optimization to enhance the SNR; improve the detection of sub-micron objects; and increase the imaging FOV, from 4.3 mm2 to 12 mm2 to 24 mm2, which allows the detection of bacteria contained in 0.5μl to 4μl to 10μl, respectively.

Predominant Bacteria Detected from the Middle Ear Fluid of Children Experiencing Otitis Media: A Systematic Review

PubMed Central

Ngo, Chinh C.; Massa, Helen M.; Thornton, Ruth B.; Cripps, Allan W.

2016-01-01

Background Otitis media (OM) is amongst the most common childhood diseases and is associated with multiple microbial pathogens within the middle ear. Global and temporal monitoring of predominant bacterial pathogens is important to inform new treatment strategies, vaccine development and to monitor the impact of vaccine implementation to improve progress toward global OM prevention. Methods A systematic review of published reports of microbiology of acute otitis media (AOM) and otitis media with effusion (OME) from January, 1970 to August 2014, was performed using PubMed databases. Results This review confirmed that Streptococcus pneumoniae and Haemophilus influenzae, remain the predominant bacterial pathogens, with S. pneumoniae the predominant bacterium in the majority reports from AOM patients. In contrast, H. influenzae was the predominant bacterium for patients experiencing chronic OME, recurrent AOM and AOM with treatment failure. This result was consistent, even where improved detection sensitivity from the use of polymerase chain reaction (PCR) rather than bacterial culture was conducted. On average, PCR analyses increased the frequency of detection of S. pneumoniae and H. influenzae 3.2 fold compared to culture, whilst Moraxella catarrhalis was 4.5 times more frequently identified by PCR. Molecular methods can also improve monitoring of regional changes in the serotypes and identification frequency of S. pneumoniae and H. influenzae over time or after vaccine implementation, such as after introduction of the 7-valent pneumococcal conjugate vaccine. Conclusions Globally, S. pneumoniae and H. influenzae remain the predominant otopathogens associated with OM as identified through bacterial culture; however, molecular methods continue to improve the frequency and accuracy of detection of individual serotypes. Ongoing monitoring with appropriate detection methods for OM pathogens can support development of improved vaccines to provide protection from the complex combination of otopathogens within the middle ear, ultimately aiming to reduce the risk of chronic and recurrent OM in vulnerable populations. PMID:26953891

Detecting Surgical Tools by Modelling Local Appearance and Global Shape.

PubMed

Bouget, David; Benenson, Rodrigo; Omran, Mohamed; Riffaud, Laurent; Schiele, Bernt; Jannin, Pierre

2015-12-01

Detecting tools in surgical videos is an important ingredient for context-aware computer-assisted surgical systems. To this end, we present a new surgical tool detection dataset and a method for joint tool detection and pose estimation in 2d images. Our two-stage pipeline is data-driven and relaxes strong assumptions made by previous works regarding the geometry, number, and position of tools in the image. The first stage classifies each pixel based on local appearance only, while the second stage evaluates a tool-specific shape template to enforce global shape. Both local appearance and global shape are learned from training data. Our method is validated on a new surgical tool dataset of 2 476 images from neurosurgical microscopes, which is made freely available. It improves over existing datasets in size, diversity and detail of annotation. We show that our method significantly improves over competitive baselines from the computer vision field. We achieve 15% detection miss-rate at 10(-1) false positives per image (for the suction tube) over our surgical tool dataset. Results indicate that performing semantic labelling as an intermediate task is key for high quality detection.

Wildfire Detection using by Multi Dimensional Histogram in Boreal Forest

NASA Astrophysics Data System (ADS)

Honda, K.; Kimura, K.; Honma, T.

2008-12-01

Early detection of wildfires is an issue for reduction of damage to environment and human. There are some attempts to detect wildfires by using satellite imagery, which are mainly classified into three methods: Dozier Method(1981-), Threshold Method(1986-) and Contextual Method(1994-). However, the accuracy of these methods is not enough: some commission and omission errors are included in the detected results. In addition, it is not so easy to analyze satellite imagery with high accuracy because of insufficient ground truth data. Kudoh and Hosoi (2003) developed the detection method by using three-dimensional (3D) histogram from past fire data with the NOAA-AVHRR imagery. But their method is impractical because their method depends on their handworks to pick up past fire data from huge data. Therefore, the purpose of this study is to collect fire points as hot spots efficiently from satellite imagery and to improve the method to detect wildfires with the collected data. As our method, we collect past fire data with the Alaska Fire History data obtained by the Alaska Fire Service (AFS). We select points that are expected to be wildfires, and pick up the points inside the fire area of the AFS data. Next, we make 3D histogram with the past fire data. In this study, we use Bands 1, 21 and 32 of MODIS. We calculate the likelihood to detect wildfires with the three-dimensional histogram. As our result, we select wildfires with the 3D histogram effectively. We can detect the troidally spreading wildfire. This result shows the evidence of good wildfire detection. However, the area surrounding glacier tends to rise brightness temperature. It is a false alarm. Burnt area and bare ground are sometimes indicated as false alarms, so that it is necessary to improve this method. Additionally, we are trying various combinations of MODIS bands as the better method to detect wildfire effectively. So as to adjust our method in another area, we are applying our method to tropical forest in Kalimantan, Indonesia and around Chiang Mai, Thailand. But the ground truth data in these areas is lesser than the one in Alaska. Our method needs lots of accurate observed data to make multi-dimensional histogram in the same area. In this study, we can show the system to select wildfire data efficiently from satellite imagery. Furthermore, the development of multi-dimensional histogram from past fire data makes it possible to detect wildfires accurately.

[The clinical value of sentinel lymph node detection in laryngeal and hypopharyngeal carcinoma patients with clinically negative neck by methylene blue method and radiolabeled tracer method].

PubMed

Zhao, Xin; Xiao, Dajiang; Ni, Jianming; Zhu, Guochen; Yuan, Yuan; Xu, Ting; Zhang, Yongsheng

2014-11-01

To investigate the clinical value of sentinel lymph node (SLN) detection in laryngeal and hypopharyngeal carcinoma patients with clinically negative neck (cN0) by methylene blue method, radiolabeled tracer method and combination of these two methods. Thirty-three patients with cN0 laryngeal carcinoma and six patients with cN0 hypopharyngeal carcinoma underwent SLN detection using both of methylene blue and radiolabeled tracer method. All these patients were accepted received the injection of radioactive isotope 99 Tc(m)-sulfur colloid (SC) and methylene blue into the carcinoma before surgery, then all these patients underwent intraopertive lymphatic mapping with a handheld gamma-detecting probe and blue-dyed SLN. After the mapping of SLN, selected neck dissections and tumor resections were peformed. The results of SLN detection by radiolabeled tracer, dye and combination of both methods were compared. The detection rate of SLN by radiolabeled tracer, methylene blue and combined method were 89.7%, 79.5%, 92.3% respectively. The number of detected SLN was significantly different between radiolabeled tracer method and combined method, and also between methylene blue method and combined method. The detection rate of methylene blue and radiolabeled tracer method were significantly different from combined method (P < 0.05). Nine patients were found to have lymph node metastasis by final pathological examination. The accuracy and negative rate of SLN detection of the combined method were 97.2% and 11.1%. The combined method using radiolabeled tracer and methylene blue can improve the detection rate and accuracy of sentinel lymph node detection. Furthermore, sentinel lymph node detection can accurately represent the cervical lymph node status in cN0 laryngeal and hypopharyngeal carcinoma.

Improved detection of multiple environmental antibiotics through an optimized sample extraction strategy in liquid chromatography-mass spectrometry analysis.

PubMed

Yi, Xinzhu; Bayen, Stéphane; Kelly, Barry C; Li, Xu; Zhou, Zhi

2015-12-01

A solid-phase extraction/liquid chromatography/electrospray ionization/multi-stage mass spectrometry (SPE-LC-ESI-MS/MS) method was optimized in this study for sensitive and simultaneous detection of multiple antibiotics in urban surface waters and soils. Among the seven classes of tested antibiotics, extraction efficiencies of macrolides, lincosamide, chloramphenicol, and polyether antibiotics were significantly improved under optimized sample extraction pH. Instead of only using acidic extraction in many existing studies, the results indicated that antibiotics with low pK a values (<7) were extracted more efficiently under acidic conditions and antibiotics with high pK a values (>7) were extracted more efficiently under neutral conditions. The effects of pH were more obvious on polar compounds than those on non-polar compounds. Optimization of extraction pH resulted in significantly improved sample recovery and better detection limits. Compared with reported values in the literature, the average reduction of minimal detection limits obtained in this study was 87.6% in surface waters (0.06-2.28 ng/L) and 67.1% in soils (0.01-18.16 ng/g dry wt). This method was subsequently applied to detect antibiotics in environmental samples in a heavily populated urban city, and macrolides, sulfonamides, and lincomycin were frequently detected. Antibiotics with highest detected concentrations were sulfamethazine (82.5 ng/L) in surface waters and erythromycin (6.6 ng/g dry wt) in soils. The optimized sample extraction strategy can be used to improve the detection of a variety of antibiotics in environmental surface waters and soils.

Canine Hip Dysplasia: Diagnostic Imaging.

PubMed

Butler, J Ryan; Gambino, Jennifer

2017-07-01

Diagnostic imaging is the principal method used to screen for and diagnose hip dysplasia in the canine patient. Multiple techniques are available, each having advantages, disadvantages, and limitations. Hip-extended radiography is the most used method and is best used as a screening tool and for assessment for osteoarthritis. Distraction radiographic methods such as the PennHip method allow for improved detection of laxity and improved ability to predict future osteoarthritis development. More advanced techniques such as MRI, although expensive and not widely available, may improve patient screening and allow for improved assessment of cartilage health. Copyright © 2017 Elsevier Inc. All rights reserved.

Reducing health risk assigned to organic emissions from a chemical weapons incinerator.

PubMed

Laman, David M; Weiler, B Douglas; Skeen, Rodney S

2013-03-01

Organic emissions from a chemical weapons incinerator have been characterized with an improved set of analytical methods to reduce the human health risk assigned to operations of the facility. A gas chromatography/mass selective detection method with substantially reduced detection limits has been used in conjunction with scanning electron microscopy/energy dispersive X-ray spectrometry and Fourier transform infrared microscopy to improve the speciation of semi-volatile and non-volatile organics emitted from the incinerator. The reduced detection limits have allowed a significant reduction in the assumed polycyclic aromatic hydrocarbon (PAH) and aminobiphenyl (ABP) emission rates used as inputs to the human health risk assessment for the incinerator. A mean factor of 17 decrease in assigned human health risk is realized for six common local exposure scenarios as a result of the reduced PAH and ABP detection limits.

Improved imaging algorithm for bridge crack detection

NASA Astrophysics Data System (ADS)

Lu, Jingxiao; Song, Pingli; Han, Kaihong

2012-04-01

This paper present an improved imaging algorithm for bridge crack detection, through optimizing the eight-direction Sobel edge detection operator, making the positioning of edge points more accurate than without the optimization, and effectively reducing the false edges information, so as to facilitate follow-up treatment. In calculating the crack geometry characteristics, we use the method of extracting skeleton on single crack length. In order to calculate crack area, we construct the template of area by making logical bitwise AND operation of the crack image. After experiment, the results show errors of the crack detection method and actual manual measurement are within an acceptable range, meet the needs of engineering applications. This algorithm is high-speed and effective for automated crack measurement, it can provide more valid data for proper planning and appropriate performance of the maintenance and rehabilitation processes of bridge.

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels.

PubMed

Sornapudi, Sudhir; Stanley, Ronald Joe; Stoecker, William V; Almubarak, Haidar; Long, Rodney; Antani, Sameer; Thoma, George; Zuna, Rosemary; Frazier, Shelliane R

2018-01-01

Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades. In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network. The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with demonstrated improvement over imaging-based and clustering-based benchmark techniques. The proposed DL-based nuclei segmentation Method with superpixel analysis has shown improved segmentation results in comparison to state-of-the-art methods.

An improved UPLC method for the detection of undeclared horse meat addition by using myoglobin as molecular marker.

PubMed

Di Giuseppe, Antonella M A; Giarretta, Nicola; Lippert, Martina; Severino, Valeria; Di Maro, Antimo

2015-02-15

In 2013, following the scandal of the presence of undeclared horse meat in various processed beef products across the Europe, several researches have been undertaken for the safety of consumer health. In this framework, an improved UPLC separation method has been developed to detect the presence of horse myoglobin in raw meat samples. The separation of both horse and beef myoglobins was achieved in only seven minutes. The methodology was improved by preparing mixtures with different composition percentages of horse and beef meat. By using myoglobin as marker, low amounts (0.50mg/0.50g, w/w; ∼0.1%) of horse meat can be detected and quantified in minced raw meat samples with high reproducibility and sensitivity, thus offering a valid alternative to conventional PCR techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.

Quantitative determination of Auramine O by terahertz spectroscopy with 2DCOS-PLSR model

NASA Astrophysics Data System (ADS)

Zhang, Huo; Li, Zhi; Chen, Tao; Qin, Binyi

2017-09-01

Residues of harmful dyes such as Auramine O (AO) in herb and food products threaten the health of people. So, fast and sensitive detection techniques of the residues are needed. As a powerful tool for substance detection, terahertz (THz) spectroscopy was used for the quantitative determination of AO by combining with an improved partial least-squares regression (PLSR) model in this paper. Absorbance of herbal samples with different concentrations was obtained by THz-TDS in the band between 0.2THz and 1.6THz. We applied two-dimensional correlation spectroscopy (2DCOS) to improve the PLSR model. This method highlighted the spectral differences of different concentrations, provided a clear criterion of the input interval selection, and improved the accuracy of detection result. The experimental result indicated that the combination of the THz spectroscopy and 2DCOS-PLSR is an excellent quantitative analysis method.

A spatial scan statistic for multiple clusters.

PubMed

Li, Xiao-Zhou; Wang, Jin-Feng; Yang, Wei-Zhong; Li, Zhong-Jie; Lai, Sheng-Jie

2011-10-01

Spatial scan statistics are commonly used for geographical disease surveillance and cluster detection. While there are multiple clusters coexisting in the study area, they become difficult to detect because of clusters' shadowing effect to each other. The recently proposed sequential method showed its better power for detecting the second weaker cluster, but did not improve the ability of detecting the first stronger cluster which is more important than the second one. We propose a new extension of the spatial scan statistic which could be used to detect multiple clusters. Through constructing two or more clusters in the alternative hypothesis, our proposed method accounts for other coexisting clusters in the detecting and evaluating process. The performance of the proposed method is compared to the sequential method through an intensive simulation study, in which our proposed method shows better power in terms of both rejecting the null hypothesis and accurately detecting the coexisting clusters. In the real study of hand-foot-mouth disease data in Pingdu city, a true cluster town is successfully detected by our proposed method, which cannot be evaluated to be statistically significant by the standard method due to another cluster's shadowing effect. Copyright © 2011 Elsevier Inc. All rights reserved.

Immunodiagnosis of Human Fascioliasis: An Update of Concepts and Performances of the Serological Assays

PubMed Central

Khabisi, Samaneh Abdolahi

2017-01-01

Human Fascioliasis (HF) is a foodborne neglected parasitic disease caused by Fasciola hepatica and Fasciola gigantica. New epidemiological data suggest that the endemic areas of the disease are expanding and HF is being reported from areas where it was previously not observed. Diagnosis of HF is challenging. Performances of parasitological approaches, based on the detection of parasite’s egg in the stool, are not satisfactory. Currently serological methods for the diagnosis of HF are mainly based on detection of anti-Fasciola antibodies in serum. Although, there have been some improvement in the development of immunological diagnostic tests for the diagnosis of HF, yet these tests suffer from insufficiency in sensitivity or/and specificity. Detection of antigens, rather than antibodies, seems to be a suitable approach in the diagnosis of HF. Antigen can be detected in sera or stool of the fascioliasis patients. Circulating antigen in serum disappears within a short time and most of the circulating antigens are in immune complex forms which are not freely available to be detected. Therefore, antigenemia might not be an appropriate method for the diagnosis of HF. Detection of antigen in stool (coproantigens) seems to be a suitable alternative method for the diagnosis of HF. Recent data provided convincing evidence that detection of coproantigen improved and simplified the diagnosis of HF. The present review highlights the new achievements in designing and improvement of diagnostic approaches for the immunodiagnosis of HF. Moreover, current status of the available immunodiagnostic techniques for the diagnosis of HF, their strengths and weaknesses has been discussed. PMID:28764235

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.

Comparison of a real-time PCR method with a culture method for the detection of Salmonella enterica serotype enteritidis in naturally contaminated environmental samples from integrated poultry houses.

PubMed

Lungu, Bwalya; Waltman, W Douglas; Berghaus, Roy D; Hofacre, Charles L

2012-04-01

Conventional culture methods have traditionally been considered the "gold standard" for the isolation and identification of foodborne bacterial pathogens. However, culture methods are labor-intensive and time-consuming. A Salmonella enterica serotype Enteritidis-specific real-time PCR assay that recently received interim approval by the National Poultry Improvement Plan for the detection of Salmonella Enteritidis was evaluated against a culture method that had also received interim National Poultry Improvement Plan approval for the analysis of environmental samples from integrated poultry houses. The method was validated with 422 field samples collected by either the boot sock or drag swab method. The samples were cultured by selective enrichment in tetrathionate broth followed by transfer onto a modified semisolid Rappaport-Vassiliadis medium and then plating onto brilliant green with novobiocin and xylose lysine brilliant Tergitol 4 plates. One-milliliter aliquots of the selective enrichment broths from each sample were collected for DNA extraction by the commercial PrepSEQ nucleic acid extraction assay and analysis by the Salmonella Enteritidis-specific real-time PCR assay. The real-time PCR assay detected no significant differences between the boot sock and drag swab samples. In contrast, the culture method detected a significantly higher number of positive samples from boot socks. The diagnostic sensitivity of the real-time PCR assay for the field samples was significantly higher than that of the culture method. The kappa value obtained was 0.46, indicating moderate agreement between the real-time PCR assay and the culture method. In addition, the real-time PCR method had a turnaround time of 2 days compared with 4 to 8 days for the culture method. The higher sensitivity as well as the reduction in time and labor makes this real-time PCR assay an excellent alternative to conventional culture methods for diagnostic purposes, surveillance, and research studies to improve food safety.

Magnetic force microscopy method and apparatus to detect and image currents in integrated circuits

DOEpatents

Campbell, Ann. N.; Anderson, Richard E.; Cole, Jr., Edward I.

1995-01-01

A magnetic force microscopy method and improved magnetic tip for detecting and quantifying internal magnetic fields resulting from current of integrated circuits. Detection of the current is used for failure analysis, design verification, and model validation. The interaction of the current on the integrated chip with a magnetic field can be detected using a cantilevered magnetic tip. Enhanced sensitivity for both ac and dc current and voltage detection is achieved with voltage by an ac coupling or a heterodyne technique. The techniques can be used to extract information from analog circuits.

Magnetic force microscopy method and apparatus to detect and image currents in integrated circuits

DOEpatents

Campbell, A.N.; Anderson, R.E.; Cole, E.I. Jr.

1995-11-07

A magnetic force microscopy method and improved magnetic tip for detecting and quantifying internal magnetic fields resulting from current of integrated circuits are disclosed. Detection of the current is used for failure analysis, design verification, and model validation. The interaction of the current on the integrated chip with a magnetic field can be detected using a cantilevered magnetic tip. Enhanced sensitivity for both ac and dc current and voltage detection is achieved with voltage by an ac coupling or a heterodyne technique. The techniques can be used to extract information from analog circuits. 17 figs.

Unsupervised iterative detection of land mines in highly cluttered environments.

PubMed

Batman, Sinan; Goutsias, John

2003-01-01

An unsupervised iterative scheme is proposed for land mine detection in heavily cluttered scenes. This scheme is based on iterating hybrid multispectral filters that consist of a decorrelating linear transform coupled with a nonlinear morphological detector. Detections extracted from the first pass are used to improve results in subsequent iterations. The procedure stops after a predetermined number of iterations. The proposed scheme addresses several weaknesses associated with previous adaptations of morphological approaches to land mine detection. Improvement in detection performance, robustness with respect to clutter inhomogeneities, a completely unsupervised operation, and computational efficiency are the main highlights of the method. Experimental results reveal excellent performance.

[Comparison between old and new methods for detection of allergenic substances (egg and milk)].

PubMed

Watanabe, Hiroko; Akaboshi, Chie; Saita, Kiyotaka; Sekido, Haruko; Hashiguchi, Shigeki; Watabe, Kenjiro; Tanaka, Kouki

2011-01-01

The old ELISA method for detection of allergenic substances (egg and milk) in Kanagawa prefecture from 2003 to 2007, employed before improvement of the food allergen labeling system, yielded detection rates of 20% for egg and 30% for milk. In 2005, after improvement of the labeling system, the detection rate using the new ELISA in solutions containing 1% SDS and 7% 2-mercaptoethanol increased by about 10% for egg, but decreased by half for milk. There were 4 positive samples (over 10 µg/g) for both egg and milk proteins, on account of contamination by ingredients at the manufacturing line and the lack of proper food labeling. In 2009, the contamination levels of egg and milk proteins in labeled commercial foods were low. In a comparison between the new and old methods with the same samples, both the new ELISA and Western-blot analyses showed an increase in the detection rate of egg protein. In relation to milk protein, the detection rates were decreased with the new ELISA, although the ELISA detection rate and consistency rates with Western-blot analysis were increased. On the other hand, in the case of a protein content below 5 µg/g, it was impossible to determine ovomucoid and casein by Western-blot analysis.

Cryo-balloon catheter localization in fluoroscopic images

NASA Astrophysics Data System (ADS)

Kurzendorfer, Tanja; Brost, Alexander; Jakob, Carolin; Mewes, Philip W.; Bourier, Felix; Koch, Martin; Kurzidim, Klaus; Hornegger, Joachim; Strobel, Norbert

2013-03-01

Minimally invasive catheter ablation has become the preferred treatment option for atrial fibrillation. Although the standard ablation procedure involves ablation points set by radio-frequency catheters, cryo-balloon catheters have even been reported to be more advantageous in certain cases. As electro-anatomical mapping systems do not support cryo-balloon ablation procedures, X-ray guidance is needed. However, current methods to provide support for cryo-balloon catheters in fluoroscopically guided ablation procedures rely heavily on manual user interaction. To improve this, we propose a first method for automatic cryo-balloon catheter localization in fluoroscopic images based on a blob detection algorithm. Our method is evaluated on 24 clinical images from 17 patients. The method successfully detected the cryoballoon in 22 out of 24 images, yielding a success rate of 91.6 %. The successful localization achieved an accuracy of 1.00 mm +/- 0.44 mm. Even though our methods currently fails in 8.4 % of the images available, it still offers a significant improvement over manual methods. Furthermore, detecting a landmark point along the cryo-balloon catheter can be a very important step for additional post-processing operations.

The research and development of the non-contact detection of the tubing internal thread with a line structured light

NASA Astrophysics Data System (ADS)

Hu, Yuanyuan; Xu, Yingying; Hao, Qun; Hu, Yao

2013-12-01

The tubing internal thread plays an irreplaceable role in the petroleum equipment. The unqualified tubing can directly lead to leakage, slippage and bring huge losses for oil industry. For the purpose of improving efficiency and precision of tubing internal thread detection, we develop a new non-contact tubing internal thread measurement system based on the laser triangulation principle. Firstly, considering that the tubing thread had a small diameter and relatively smooth surface, we built a set of optical system with a line structured light to irradiate the internal thread surface and obtain an image which contains the internal thread profile information through photoelectric sensor. Secondly, image processing techniques were used to do the edge detection of the internal thread from the obtained image. One key method was the sub-pixel technique which greatly improved the detection accuracy under the same hardware conditions. Finally, we restored the real internal thread contour information on the basis of laser triangulation method and calculated tubing thread parameters such as the pitch, taper and tooth type angle. In this system, the profile of several thread teeth can be obtained at the same time. Compared with other existing scanning methods using point light and stepper motor, this system greatly improves the detection efficiency. Experiment results indicate that this system can achieve the high precision and non-contact measurement of the tubing internal thread.

Detection capability of the IMS seismic network based on ambient seismic noise measurements

NASA Astrophysics Data System (ADS)

Gaebler, Peter J.; Ceranna, Lars

2016-04-01

All nuclear explosions - on the Earth's surface, underground, underwater or in the atmosphere - are banned by the Comprehensive Nuclear-Test-Ban Treaty (CTBT). As part of this treaty, a verification regime was put into place to detect, locate and characterize nuclear explosion testings at any time, by anyone and everywhere on the Earth. The International Monitoring System (IMS) plays a key role in the verification regime of the CTBT. Out of the different monitoring techniques used in the IMS, the seismic waveform approach is the most effective technology for monitoring nuclear underground testing and to identify and characterize potential nuclear events. This study introduces a method of seismic threshold monitoring to assess an upper magnitude limit of a potential seismic event in a certain given geographical region. The method is based on ambient seismic background noise measurements at the individual IMS seismic stations as well as on global distance correction terms for body wave magnitudes, which are calculated using the seismic reflectivity method. From our investigations we conclude that a global detection threshold of around mb 4.0 can be achieved using only stations from the primary seismic network, a clear latitudinal dependence for the detection threshold can be observed between northern and southern hemisphere. Including the seismic stations being part of the auxiliary seismic IMS network results in a slight improvement of global detection capability. However, including wave arrivals from distances greater than 120 degrees, mainly PKP-wave arrivals, leads to a significant improvement in average global detection capability. In special this leads to an improvement of the detection threshold on the southern hemisphere. We further investigate the dependence of the detection capability on spatial (latitude and longitude) and temporal (time) parameters, as well as on parameters such as source type and percentage of operational IMS stations.

A Review of Transmission Diagnostics Research at NASA Lewis Research Center

NASA Technical Reports Server (NTRS)

Zakajsek, James J.

1994-01-01

This paper presents a summary of the transmission diagnostics research work conducted at NASA Lewis Research Center over the last four years. In 1990, the Transmission Health and Usage Monitoring Research Team at NASA Lewis conducted a survey to determine the critical needs of the diagnostics community. Survey results indicated that experimental verification of gear and bearing fault detection methods, improved fault detection in planetary systems, and damage magnitude assessment and prognostics research were all critical to a highly reliable health and usage monitoring system. In response to this, a variety of transmission fault detection methods were applied to experimentally obtained fatigue data. Failure modes of the fatigue data include a variety of gear pitting failures, tooth wear, tooth fracture, and bearing spalling failures. Overall results indicate that, of the gear fault detection techniques, no one method can successfully detect all possible failure modes. The more successful methods need to be integrated into a single more reliable detection technique. A recently developed method, NA4, in addition to being one of the more successful gear fault detection methods, was also found to exhibit damage magnitude estimation capabilities.

Improving space debris detection in GEO ring using image deconvolution

NASA Astrophysics Data System (ADS)

Núñez, Jorge; Núñez, Anna; Montojo, Francisco Javier; Condominas, Marta

2015-07-01

In this paper we present a method based on image deconvolution to improve the detection of space debris, mainly in the geostationary ring. Among the deconvolution methods we chose the iterative Richardson-Lucy (R-L), as the method that achieves better goals with a reasonable amount of computation. For this work, we used two sets of real 4096 × 4096 pixel test images obtained with the Telescope Fabra-ROA at Montsec (TFRM). Using the first set of data, we establish the optimal number of iterations in 7, and applying the R-L method with 7 iterations to the images, we show that the astrometric accuracy does not vary significantly while the limiting magnitude of the deconvolved images increases significantly compared to the original ones. The increase is in average about 1.0 magnitude, which means that objects up to 2.5 times fainter can be detected after deconvolution. The application of the method to the second set of test images, which includes several faint objects, shows that, after deconvolution, up to four previously undetected faint objects are detected in a single frame. Finally, we carried out a study of some economic aspects of applying the deconvolution method, showing that an important economic impact can be envisaged.

Retinal hemorrhage detection by rule-based and machine learning approach.

PubMed

Di Xiao; Shuang Yu; Vignarajan, Janardhan; Dong An; Mei-Ling Tay-Kearney; Kanagasingam, Yogi

2017-07-01

Robust detection of hemorrhages (HMs) in color fundus image is important in an automatic diabetic retinopathy grading system. Detection of the hemorrhages that are close to or connected with retinal blood vessels was found to be challenge. However, most methods didn't put research on it, even some of them mentioned this issue. In this paper, we proposed a novel hemorrhage detection method based on rule-based and machine learning methods. We focused on the improvement of detection of the hemorrhages that are close to or connected with retinal blood vessels, besides detecting the independent hemorrhage regions. A preliminary test for detecting HM presence was conducted on the images from two databases. We achieved sensitivity and specificity of 93.3% and 88% as well as 91.9% and 85.6% on the two datasets.

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

PubMed

Nika, Varvara; Babyn, Paul; Zhu, Hongmei

2014-07-01

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

Development and evaluation of a culture-independent method for source determination of fecal wastes in surface and storm waters using reverse transcriptase-PCR detection of FRNA coliphage genogroup gene sequences.

EPA Science Inventory

A complete method, incorporating recently improved reverse transcriptase-PCR primer/probe assays and including controls for determining interferences to phage recoveries from water sample concentrates and for detecting interferences to their analysis, was developed for the direct...

Development and evaluation of a culture-independent method for source determination of fecal wastes in surface and storm waters using reverse transcriptase-PCR detection of FRNA coliphage genogroup gene sequences

EPA Science Inventory

A complete method, incorporating recently improved reverse transcriptase-PCR primer/probe assays and including controls for determining interferences to phage recoveries from water sample concentrates and for detecting interferences to their analysis, was developed for the direct...

A new method for skin color enhancement

NASA Astrophysics Data System (ADS)

Zeng, Huanzhao; Luo, Ronnier

2012-01-01

Skin tone is the most important color category in memory colors. Reproducing it pleasingly is an important factor in photographic color reproduction. Moving skin colors toward their preferred skin color center improves the skin color preference on photographic color reproduction. Two key factors to successfully enhance skin colors are: a method to detect original skin colors effectively even if they are shifted far away from the regular skin color region, and a method to morph skin colors toward a preferred skin color region properly without introducing artifacts. A method for skin color enhancement presented by the authors in the same conference last year applies a static skin color model for skin color detection, which may miss to detect skin colors that are far away from regular skin tones. In this paper, a new method using the combination of face detection and statistical skin color modeling is proposed to effectively detect skin pixels and to enhance skin colors more effectively.

Infrared small target detection technology based on OpenCV

NASA Astrophysics Data System (ADS)

Liu, Lei; Huang, Zhijian

2013-05-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, some basic principles and the implementing flow charts of a series of algorithms for target detection are described. These algorithms are traditional two-frame difference method, improved three-frame difference method, background estimate and frame difference fusion method, and building background with neighborhood mean method. On the foundation of above works, an infrared target detection software platform which is developed by OpenCV and MFC is introduced. Three kinds of tracking algorithms are integrated in this software. In order to explain the software clearly, the framework and the function are described in this paper. At last, the experiments are performed for some real-life IR images. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

Infrared small target detection technology based on OpenCV

NASA Astrophysics Data System (ADS)

Liu, Lei; Huang, Zhijian

2013-09-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, some basic principles and the implementing flow charts of a series of algorithms for target detection are described. These algorithms are traditional two-frame difference method, improved three-frame difference method, background estimate and frame difference fusion method, and building background with neighborhood mean method. On the foundation of above works, an infrared target detection software platform which is developed by OpenCV and MFC is introduced. Three kinds of tracking algorithms are integrated in this software. In order to explain the software clearly, the framework and the function are described in this paper. At last, the experiments are performed for some real-life IR images. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

Weak wide-band signal detection method based on small-scale periodic state of Duffing oscillator

NASA Astrophysics Data System (ADS)

Hou, Jian; Yan, Xiao-peng; Li, Ping; Hao, Xin-hong

2018-03-01

The conventional Duffing oscillator weak signal detection method, which is based on a strong reference signal, has inherent deficiencies. To address these issues, the characteristics of the Duffing oscillatorʼs phase trajectory in a small-scale periodic state are analyzed by introducing the theory of stopping oscillation system. Based on this approach, a novel Duffing oscillator weak wide-band signal detection method is proposed. In this novel method, the reference signal is discarded, and the to-be-detected signal is directly used as a driving force. By calculating the cosine function of a phase space angle, a single Duffing oscillator can be used for weak wide-band signal detection instead of an array of uncoupled Duffing oscillators. Simulation results indicate that, compared with the conventional Duffing oscillator detection method, this approach performs better in frequency detection intervals, and reduces the signal-to-noise ratio detection threshold, while improving the real-time performance of the system. Project supported by the National Natural Science Foundation of China (Grant No. 61673066).

Procedure for rapid concentration and detection of enteric viruses from berries and vegetables.

PubMed

Butot, S; Putallaz, T; Sánchez, G

2007-01-01

Several hepatitis A virus (HAV) and norovirus (NV) outbreaks due to consumption of berries and vegetables have been reported during recent years. To facilitate the detection of enteric viruses that may be present on different fresh and frozen products, we developed a rapid and sensitive detection method for HAV, NV, and rotavirus (RV). Initial experiments focused on optimizing the composition of the elution buffer, improving the viral concentration method, and evaluating the performance of various extraction kits. Viruses were extracted from the food surface by a direct elution method in a glycine-Tris (pH 9.5) buffer containing 1% beef extract and concentrated by ultrafiltration. Occasionally, PCR inhibitors were present in the processed berry samples, which gave relatively poor detection limits. However, this problem was overcome by adding a pectinase treatment in the protocol, which markedly improved the sensitivity of the method. After optimization, this concentration method was applied in combination with real-time reverse transcription-PCR (RT-PCR) using specific primers in various types of berries and vegetables. The average detection limits were 1 50% tissue culture infective dose (TCID(50)), 54 RT-PCR units, and 0.02 TCID(50) per 15 g of food for HAV, NV, and RV, respectively. Based on our results, it is concluded that this procedure is suitable to detect and quantify enteric viruses within 6 h and can be applied for surveillance of enteric viruses in fresh and frozen products.

Simulation and modeling of return waveforms from a ladar beam footprint in USU LadarSIM

NASA Astrophysics Data System (ADS)

Budge, Scott; Leishman, Brad; Pack, Robert

2006-05-01

Ladar systems are an emerging technology with applications in many fields. Consequently, simulations for these systems have become a valuable tool in the improvement of existing systems and the development of new ones. This paper discusses the theory and issues involved in reliably modeling the return waveform of a ladar beam footprint in the Utah State University LadarSIM simulation software. Emphasis is placed on modeling system-level effects that allow an investigation of engineering tradeoffs in preliminary designs, and validation of behaviors in fabricated designs. Efforts have been made to decrease the necessary computation time while still maintaining a usable model. A full waveform simulation is implemented that models optical signals received on detector followed by electronic signals and discriminators commonly encountered in contemporary direct-detection ladar systems. Waveforms are modeled using a novel hexagonal sampling process applied across the ladar beam footprint. Each sample is weighted using a Gaussian spatial profile for a well formed laser footprint. Model fidelity is also improved by using a bidirectional reflectance distribution function (BRDF) for target reflectance. Once photons are converted to electrons, waveform processing is used to detect first, last or multiple return pulses. The detection methods discussed in this paper are a threshold detection method, a constant fraction method, and a derivative zero-crossing method. Various detection phenomena, such as range error, walk error, drop outs and false alarms, can be studied using these detection methods.

DeepSig: deep learning improves signal peptide detection in proteins.

PubMed

Savojardo, Castrense; Martelli, Pier Luigi; Fariselli, Piero; Casadio, Rita

2018-05-15

The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website. pierluigi.martelli@unibo.it. Supplementary data are available at Bioinformatics online.

Combined electrophoresis-electrospray interface and method

DOEpatents

Smith, R.D.; Udseth, H.R.; Barinaga, C.J.

1995-06-13

An improvement to the system and method is disclosed for analyzing molecular constituents of a composition sample that comprises improvements to an electrospray ionization source for interfacing to mass spectrometers and other detection devices. The improvement consists of establishing a unique electrical circuit pattern and nozzle configuration, a metallic coated and conical shaped capillary outlet, coupled with sizing of the capillary to obtain maximum sensitivity. 10 figs.

B-Spline Filtering for Automatic Detection of Calcification Lesions in Mammograms

NASA Astrophysics Data System (ADS)

Bueno, G.; Sánchez, S.; Ruiz, M.

2006-10-01

Breast cancer continues to be an important health problem between women population. Early detection is the only way to improve breast cancer prognosis and significantly reduce women mortality. It is by using CAD systems that radiologist can improve their ability to detect, and classify lesions in mammograms. In this study the usefulness of using B-spline based on a gradient scheme and compared to wavelet and adaptative filtering has been investigated for calcification lesion detection and as part of CAD systems. The technique has been applied to different density tissues. A qualitative validation shows the success of the method.

Oil Spill Detection in Terma-Side-Looking Airborne Radar Images Using Image Features and Region Segmentation

PubMed Central

Alacid, Beatriz

2018-01-01

This work presents a method for oil-spill detection on Spanish coasts using aerial Side-Looking Airborne Radar (SLAR) images, which are captured using a Terma sensor. The proposed method uses grayscale image processing techniques to identify the dark spots that represent oil slicks on the sea. The approach is based on two steps. First, the noise regions caused by aircraft movements are detected and labeled in order to avoid the detection of false-positives. Second, a segmentation process guided by a map saliency technique is used to detect image regions that represent oil slicks. The results show that the proposed method is an improvement on the previous approaches for this task when employing SLAR images. PMID:29316716

Improving Balance Function Using Low Levels of Electrical Stimulation of the Balance Organs

NASA Technical Reports Server (NTRS)

Bloomberg, Jacob; Reschke, Millard; Mulavara, Ajitkumar; Wood, Scott; Serrador, Jorge; Fiedler, Matthew; Kofman, Igor; Peters, Brian T.; Cohen, Helen

2012-01-01

Crewmembers returning from long-duration space flight face significant challenges due to the microgravity-induced inappropriate adaptations in balance/ sensorimotor function. The Neuroscience Laboratory at JSC is developing a method based on stochastic resonance to enhance the brain s ability to detect signals from the balance organs of the inner ear and use them for rapid improvement in balance skill, especially when combined with balance training exercises. This method involves a stimulus delivery system that is wearable/portable providing imperceptible electrical stimulation to the balance organs of the human body. Stochastic resonance (SR) is a phenomenon whereby the response of a nonlinear system to a weak periodic input signal is optimized by the presence of a particular non-zero level of noise. This phenomenon of SR is based on the concept of maximizing the flow of information through a system by a non-zero level of noise. Application of imperceptible SR noise coupled with sensory input in humans has been shown to improve motor, cardiovascular, visual, hearing, and balance functions. SR increases contrast sensitivity and luminance detection; lowers the absolute threshold for tone detection in normal hearing individuals; improves homeostatic function in the human blood pressure regulatory system; improves noise-enhanced muscle spindle function; and improves detection of weak tactile stimuli using mechanical or electrical stimulation. SR noise has been shown to improve postural control when applied as mechanical noise to the soles of the feet, or when applied as electrical noise at the knee and to the back muscles.

Acoustic enhancement for photo detecting devices

DOEpatents

Thundat, Thomas G; Senesac, Lawrence R; Van Neste, Charles W

2013-02-19

Provided are improvements to photo detecting devices and methods for enhancing the sensitivity of photo detecting devices. A photo detecting device generates an electronic signal in response to a received light pulse. An electro-mechanical acoustic resonator, electrically coupled to the photo detecting device, damps the electronic signal and increases the signal noise ratio (SNR) of the electronic signal. Increased photo detector standoff distances and sensitivities will result.

Current and future molecular diagnostics for ocular infectious diseases.

PubMed

Doan, Thuy; Pinsky, Benjamin A

2016-11-01

Confirmation of ocular infections can pose great challenges to the clinician. A fundamental limitation is the small amounts of specimen that can be obtained from the eye. Molecular diagnostics can circumvent this limitation and have been shown to be more sensitive than conventional culture. The purpose of this review is to describe new molecular methods and to discuss the applications of next-generation sequencing-based approaches in the diagnosis of ocular infections. Efforts have focused on improving the sensitivity of pathogen detection using molecular methods. This review describes a new molecular target for Toxoplasma gondii-directed polymerase chain reaction assays. Molecular diagnostics for Chlamydia trachomatis and Acanthamoeba species are also discussed. Finally, we describe a hypothesis-free approach, metagenomic deep sequencing, which can detect DNA and RNA pathogens from a single specimen in one test. In some cases, this method can provide the geographic location and timing of the infection. Pathogen-directed PCRs have been powerful tools in the diagnosis of ocular infections for over 20 years. The use of next-generation sequencing-based approaches, when available, will further improve sensitivity of detection with the potential to improve patient care.

Improvement of LOD in Fluorescence Detection with Spectrally Nonuniform Background by Optimization of Emission Filtering.

PubMed

Galievsky, Victor A; Stasheuski, Alexander S; Krylov, Sergey N

2017-10-17

The limit-of-detection (LOD) in analytical instruments with fluorescence detection can be improved by reducing noise of optical background. Efficiently reducing optical background noise in systems with spectrally nonuniform background requires complex optimization of an emission filter-the main element of spectral filtration. Here, we introduce a filter-optimization method, which utilizes an expression for the signal-to-noise ratio (SNR) as a function of (i) all noise components (dark, shot, and flicker), (ii) emission spectrum of the analyte, (iii) emission spectrum of the optical background, and (iv) transmittance spectrum of the emission filter. In essence, the noise components and the emission spectra are determined experimentally and substituted into the expression. This leaves a single variable-the transmittance spectrum of the filter-which is optimized numerically by maximizing SNR. Maximizing SNR provides an accurate way of filter optimization, while a previously used approach based on maximizing a signal-to-background ratio (SBR) is the approximation that can lead to much poorer LOD specifically in detection of fluorescently labeled biomolecules. The proposed filter-optimization method will be an indispensable tool for developing new and improving existing fluorescence-detection systems aiming at ultimately low LOD.

Iterative nonlinear joint transform correlation for the detection of objects in cluttered scenes

NASA Astrophysics Data System (ADS)

Haist, Tobias; Tiziani, Hans J.

1999-03-01

An iterative correlation technique with digital image processing in the feedback loop for the detection of small objects in cluttered scenes is proposed. A scanning aperture is combined with the method in order to improve the immunity against noise and clutter. Multiple reference objects or different views of one object are processed in parallel. We demonstrate the method by detecting a noisy and distorted face in a crowd with a nonlinear joint transform correlator.

Driving With Hemianopia VI: Peripheral Prisms and Perceptual-Motor Training Improve Detection in a Driving Simulator

PubMed Central

Houston, Kevin E.; Peli, Eli; Goldstein, Robert B.; Bowers, Alex R.

2018-01-01

Purpose Drivers with homonymous hemianopia (HH) were previously found to have impaired detection of blind-side hazards, yet in many jurisdictions they may obtain a license. We evaluated whether oblique 57Δ peripheral prisms (p-prisms) and perceptual-motor training improved blind-side detection rates. Methods Patients with HH (n = 11) wore p-prisms for 2 weeks and then received perceptual-motor training (six visits) detecting and touching stimuli in the prism-expanded vision. In a driving simulator, patients drove and pressed the horn upon detection of pedestrians who ran toward the roadway (26 from each side): (1) without p-prisms at baseline; (2) with p-prisms after 2 weeks acclimation but before training; (3) with p-prisms after training; and (4) 3 months later. Results P-prisms improved blind-side detection from 42% to 56%, which further improved after training to 72% (all P < 0.001). Blind-side timely responses (adequate time to have stopped) improved from 31% without to 44% with p-prisms (P < 0.001) and further improved with training to 55% (P = 0.02). At the 3-month follow-up, improvements from training were maintained for detection (65%; P = 0.02) but not timely responses (P = 0.725). There was wide between-subject variability in baseline detection performance and response to p-prisms. There were no negative effects of p-prisms on vehicle control or seeing-side performance. Conclusions P-prisms improved detection with no negative effects, and training may provide additional benefit. Translational Relevance In jurisdictions where people with HH are legally driving, these data aid in clinical decision making by providing evidence that p-prisms improve performance without negative effects. PMID:29359111

Saturated fatty acid determination method using paired ion electrospray ionization mass spectrometry coupled with capillary electrophoresis.

PubMed

Lee, Ji-Hyun; Kim, Su-Jin; Lee, Sul; Rhee, Jin-Kyu; Lee, Soo Young; Na, Yun-Cheol

2017-09-01

A sensitive and selective capillary electrophoresis-mass spectrometry (CE-MS) method for determination of saturated fatty acids (FAs) was developed by using dicationic ion-pairing reagents forming singly charged complexes with anionic FAs. For negative ESI detection, 21 anionic FAs at pH 10 were separated using ammonium formate buffer containing 40% acetonitrile modifier in normal polarity mode in CE by optimizing various parameters. This method showed good separation efficiency, but the sensitivity of the method to short-chain fatty acids was quite low, causing acetic and propionic acids to be undetectable even at 100 mgL -1 in negative ESI-MS detection. Out of the four dicationic ion-pairing reagents tested, N,N'-dibutyl 1,1'-pentylenedipyrrolidium infused through a sheath-liquid ion source during CE separation was the best reagent regarding improved sensitivity and favorably complexed with anionic FAs for detection in positive ion ESI-MS. The monovalent complex showed improved ionization efficiency, providing the limits of detection (LODs) for 15 FAs ranging from 0.13 to 2.88 μg/mL and good linearity (R 2  > 0.99) up to 150 μg/mL. Compared to the negative detection results, the effect was remarkable for the detection of short- and medium-chain fatty acids. The optimized CE-paired ion electrospray (PIESI)-MS method was utilized for the determination of FAs in cheese and coffee with simple pretreatment. This method may be extended for sensitive analysis of unsaturated fatty acids. Copyright © 2017 Elsevier B.V. All rights reserved.

Accurate detection of blood vessels improves the detection of exudates in color fundus images.

PubMed

Youssef, Doaa; Solouma, Nahed H

2012-12-01

Exudates are one of the earliest and most prevalent symptoms of diseases leading to blindness such as diabetic retinopathy and macular degeneration. Certain areas of the retina with such conditions are to be photocoagulated by laser to stop the disease progress and prevent blindness. Outlining these areas is dependent on outlining the lesions and the anatomic structures of the retina. In this paper, we provide a new method for the detection of blood vessels that improves the detection of exudates in fundus photographs. The method starts with an edge detection algorithm which results in a over segmented image. Then the new feature-based algorithm can be used to accurately detect the blood vessels. This algorithm considers the characteristics of a retinal blood vessel such as its width range, intensities and orientations for the purpose of selective segmentation. Because of its bulb shape and its color similarity with exudates, the optic disc can be detected using the common Hough transform technique. The extracted blood vessel tree and optic disc could be subtracted from the over segmented image to get an initial estimate of exudates. The final estimation of exudates can then be obtained by morphological reconstruction based on the appearance of exudates. This method is shown to be promising since it increases the sensitivity and specificity of exudates detection to 80% and 100% respectively. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Detection of no-model input-output pairs in closed-loop systems.

PubMed

Potts, Alain Segundo; Alvarado, Christiam Segundo Morales; Garcia, Claudio

2017-11-01

The detection of no-model input-output (IO) pairs is important because it can speed up the multivariable system identification process, since all the pairs with null transfer functions are previously discarded and it can also improve the identified model quality, thus improving the performance of model based controllers. In the available literature, the methods focus just on the open-loop case, since in this case there is not the effect of the controller forcing the main diagonal in the transfer matrix to one and all the other terms to zero. In this paper, a modification of a previous method able to detect no-model IO pairs in open-loop systems is presented, but adapted to perform this duty in closed-loop systems. Tests are performed by using the traditional methods and the proposed one to show its effectiveness. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Deep neural network-based bandwidth enhancement of photoacoustic data.

PubMed

Gutta, Sreedevi; Kadimesetty, Venkata Suryanarayana; Kalva, Sandeep Kumar; Pramanik, Manojit; Ganapathy, Sriram; Yalavarthy, Phaneendra K

2017-11-01

Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network was proposed to enhance the bandwidth (BW) of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square-based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed network. The proposed method was evaluated using both numerical and experimental data. The results indicate that the proposed method was capable of enhancing the BW of the detected PA signal, which inturn improves the contrast recovery and quality of reconstructed PA images without adding any significant computational burden. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Improving Control System Cyber-State Awareness using Known Secure Sensor Measurements

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

Ondrej Linda; Milos Manic; Miles McQueen

Abstract—This paper presents design and simulation of a low cost and low false alarm rate method for improved cyber-state awareness of critical control systems - the Known Secure Sensor Measurements (KSSM) method. The KSSM concept relies on physical measurements to detect malicious falsification of the control systems state. The KSSM method can be incrementally integrated with already installed control systems for enhanced resilience. This paper reviews the previously developed theoretical KSSM concept and then describes a simulation of the KSSM system. A simulated control system network is integrated with the KSSM components. The effectiveness of detection of various intrusion scenariosmore » is demonstrated on several control system network topologies.« less

A high-throughput LC-MS/MS screen for GHRP in equine and human urine, featuring peptide derivatization for improved chromatography.

PubMed

Timms, Mark; Hall, Nikki; Levina, Vita; Vine, John; Steel, Rohan

2014-10-01

The growth hormone releasing peptides (GHRPs) hexarelin, ipamorelin, alexamorelin, GHRP-1, GHRP-2, GHRP-4, GHRP-5, and GHRP-6 are all synthetic met-enkephalin analogues that include unnatural D-amino acids. They were designed specifically for their ability to stimulate growth hormone release and may serve as performance enhancing drugs. To regulate the use of these peptides within the horse racing industry and by human athletes, a method is presented for the extraction, derivatization, and detection of GHRPs from equine and human urine. This method takes advantage of a highly specific solid-phase extraction combined with a novel derivatization method to improve the chromatography of basic peptides. The method was validated with respect to linearity, repeatability, intermediate precision, specificity, limits of detection, limits of confirmation, ion suppression, and stability. As proof of principle, all eight GHRPs or their metabolites could be detected in urine collected from rats after intravenous administration. Copyright © 2014 John Wiley & Sons, Ltd.

Optimization of OT-MACH Filter Generation for Target Recognition

NASA Technical Reports Server (NTRS)

Johnson, Oliver C.; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

2009-01-01

An automatic Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter generator for use in a gray-scale optical correlator (GOC) has been developed for improved target detection at JPL. While the OT-MACH filter has been shown to be an optimal filter for target detection, actually solving for the optimum is too computationally intensive for multiple targets. Instead, an adaptive step gradient descent method was tested to iteratively optimize the three OT-MACH parameters, alpha, beta, and gamma. The feedback for the gradient descent method was a composite of the performance measures, correlation peak height and peak to side lobe ratio. The automated method generated and tested multiple filters in order to approach the optimal filter quicker and more reliably than the current manual method. Initial usage and testing has shown preliminary success at finding an approximation of the optimal filter, in terms of alpha, beta, gamma values. This corresponded to a substantial improvement in detection performance where the true positive rate increased for the same average false positives per image.

Practical Considerations for Optic Nerve Estimation in Telemedicine

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

Karnowski, Thomas Paul; Aykac, Deniz; Chaum, Edward

The projected increase in diabetes in the United States and worldwide has created a need for broad-based, inexpensive screening for diabetic retinopathy (DR), an eye disease which can lead to vision impairment. A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion / anomaly detection is a low-cost way of achieving broad-based screening. In this work we report on the effect of quality estimation on an optic nerve (ON) detection method with a confidence metric. We report on an improvement of the fusion technique using a data set from an ophthalmologists practice then show themore » results of the method as a function of image quality on a set of images from an on-line telemedicine network collected in Spring 2009 and another broad-based screening program. We show that the fusion method, combined with quality estimation processing, can improve detection performance and also provide a method for utilizing a physician-in-the-loop for images that may exceed the capabilities of automated processing.« less

Development of a fast and efficient method for hepatitis A virus concentration from green onion.

PubMed

Zheng, Yan; Hu, Yuan

2017-11-01

Hepatitis A virus (HAV) can cause serious liver disease and even death. HAV outbreaks are associated with the consumption of raw or minimally processed produce, making it a major public health concern. Infections have occurred despite the fact that effective HAV vaccine has been available. Development of a rapid and sensitive HAV detection method is necessary for an investigation of an HAV outbreak. Detection of HAV is complicated by the lack of a reliable culture method. In addition, due to the low infectious dose of HAV, these methods must be very sensitive. Current methods rely on efficient sample preparation and concentration steps followed by sensitive molecular detection techniques. Using green onions which was involved in most recent HAV outbreaks as a representative produce, a method of capturing virus particles was developed using carboxyl-derivatized magnetic beads in this study. Carboxyl beads, like antibody-coated beads or cationic beads, detect HAV at a level as low as 100 pfu/25g of green onions. RNA from virus concentrated in this manner can be released by heat-shock (98°C 5min) for molecular detection without sacrificing sensitivity. Bypassing the RNA extraction procedure saves time and removes multiple manipulation steps, which makes large scale HAV screening possible. In addition, the inclusion of beef extract and pectinase rather than NP40 in the elution buffer improved the HAV liberation from the food matrix over current methods by nearly 10 fold. The method proposed in this study provides a promising tool to improve food risk assessment and protect public health. Published by Elsevier B.V.

Structured product labeling improves detection of drug-intolerance issues.

PubMed

Schadow, Gunther

2009-01-01

This study sought to assess the value of the Health Level 7/U.S. Food and Drug Administration Structured Product Labeling (SPL) drug knowledge representation standard and its associated terminology sources for drug-intolerance (allergy) decision support in computerized provider order entry (CPOE) systems. The Regenstrief Institute CPOE drug-intolerance issue detection system and its knowledge base was compared with a method based on existing SPL label content enriched with knowledge sources used with SPL (NDF-RT/MeSH). Both methods were applied to a large set of drug-intolerance (allergy) records, drug orders, and medication dispensing records covering >50,000 patients over 30 years. The number of drug-intolerance issues detected by both methods was counted, as well as the number of patients with issues, number of distinct drugs, and number of distinct intolerances. The difference between drug-intolerance issues detected or missed by either method was qualitatively analyzed. Although <70% of terms were mapped to SPL, the new approach detected four times as many drug-intolerance issues on twice as many patients. The SPL-based approach is more sensitive and suggests that mapping local dictionaries to SPL, and enhancing the depth and breadth of coverage of SPL content are worth accelerating. The study also highlights specificity problems known to trouble drug-intolerance decision support and suggests how terminology and methods of recording drug intolerances could be improved.

Structured Product Labeling Improves Detection of Drug-intolerance Issues

PubMed Central

Schadow, Gunther

2009-01-01

Objectives This study sought to assess the value of the Health Level 7/U.S. Food and Drug Administration Structured Product Labeling (SPL) drug knowledge representation standard and its associated terminology sources for drug-intolerance (allergy) decision support in computerized provider order entry (CPOE) systems. Design The Regenstrief Institute CPOE drug-intolerance issue detection system and its knowledge base was compared with a method based on existing SPL label content enriched with knowledge sources used with SPL (NDF-RT/MeSH). Both methods were applied to a large set of drug-intolerance (allergy) records, drug orders, and medication dispensing records covering >50,000 patients over 30 years. Measurements The number of drug-intolerance issues detected by both methods was counted, as well as the number of patients with issues, number of distinct drugs, and number of distinct intolerances. The difference between drug-intolerance issues detected or missed by either method was qualitatively analyzed. Results Although <70% of terms were mapped to SPL, the new approach detected four times as many drug-intolerance issues on twice as many patients. Conclusion The SPL-based approach is more sensitive and suggests that mapping local dictionaries to SPL, and enhancing the depth and breadth of coverage of SPL content are worth accelerating. The study also highlights specificity problems known to trouble drug-intolerance decision support and suggests how terminology and methods of recording drug intolerances could be improved. PMID:18952933

Major improvement of the reference method of the French drug resistance network for P-glycoprotein detection in human haematological malignancies.

PubMed

Huet, Sylvie; Marie, Jean-Pierre; Laurand, Armelle; Robert, Jacques

2005-09-01

The aim of this study was to improve significantly the sensitivity and specificity of the flow cytometric assay of P-glycoprotein (Pgp) implemented and validated by the laboratories of the French Drug Resistance Network [Huet S, Marie JP, Gualde N, Robert J. Reference method for detection of Pgp mediated multidrug resistance in human hematological malignancies: a method validated by the laboratories of the French Drug Resistance Network. Cytometry 1998;34:248-56] in cells displaying low level of resistance. Fluoresceine-conjugated monoclonal antibodies (Mabs) and propidium iodide were respectively replaced by phycoerythrin-conjugated Mabs and Sytox green. The removal of erythrocytes and granulocytes by density gradient was replaced by the lysis of erythrocytes after Mab incubation. Using these conditions, Pgp could be detected in the K-H30 line, which was negative in former studies, with Mab/Control ratios increasing by 3.7- to 5.9-fold, and Mab/Control ratios in the parental sensitive K562 line still ranging between 0.8 and 1.2. When tested on 16 blood samples from patients presenting haematological malignancies, six samples presented low positivity, which was not detected with the former method, while 10 samples remained negative with the two methods. Pgp was specifically detected in pathological blood cells in the six positive samples.

Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

PubMed

Melendez, Jaime; Sánchez, Clara I; van Ginneken, Bram; Karssemeijer, Nico

2014-08-01

Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed. The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together. The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature discrepancies and prove advantageous not only at the candidate detection level, but also at subsequent steps of a CAD system.

Automating Cell Detection and Classification in Human Brain Fluorescent Microscopy Images Using Dictionary Learning and Sparse Coding

PubMed Central

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A.; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M.

2017-01-01

Background Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. New method Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Results Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. Comparison with existing methods We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. Conclusion The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. PMID:28267565

Background Adjusted Alignment-Free Dissimilarity Measures Improve the Detection of Horizontal Gene Transfer.

PubMed

Tang, Kujin; Lu, Yang Young; Sun, Fengzhu

2018-01-01

Horizontal gene transfer (HGT) plays an important role in the evolution of microbial organisms including bacteria. Alignment-free methods based on single genome compositional information have been used to detect HGT. Currently, Manhattan and Euclidean distances based on tetranucleotide frequencies are the most commonly used alignment-free dissimilarity measures to detect HGT. By testing on simulated bacterial sequences and real data sets with known horizontal transferred genomic regions, we found that more advanced alignment-free dissimilarity measures such as CVTree and [Formula: see text] that take into account the background Markov sequences can solve HGT detection problems with significantly improved performance. We also studied the influence of different factors such as evolutionary distance between host and donor sequences, size of sliding window, and host genome composition on the performances of alignment-free methods to detect HGT. Our study showed that alignment-free methods can predict HGT accurately when host and donor genomes are in different order levels. Among all methods, CVTree with word length of 3, [Formula: see text] with word length 3, Markov order 1 and [Formula: see text] with word length 4, Markov order 1 outperform others in terms of their highest F 1 -score and their robustness under the influence of different factors.

Dual-model automatic detection of nerve-fibres in corneal confocal microscopy images.

PubMed

Dabbah, M A; Graham, J; Petropoulos, I; Tavakoli, M; Malik, R A

2010-01-01

Corneal Confocal Microscopy (CCM) imaging is a non-invasive surrogate of detecting, quantifying and monitoring diabetic peripheral neuropathy. This paper presents an automated method for detecting nerve-fibres from CCM images using a dual-model detection algorithm and compares the performance to well-established texture and feature detection methods. The algorithm comprises two separate models, one for the background and another for the foreground (nerve-fibres), which work interactively. Our evaluation shows significant improvement (p approximately 0) in both error rate and signal-to-noise ratio of this model over the competitor methods. The automatic method is also evaluated in comparison with manual ground truth analysis in assessing diabetic neuropathy on the basis of nerve-fibre length, and shows a strong correlation (r = 0.92). Both analyses significantly separate diabetic patients from control subjects (p approximately 0).

Improved flaw detection and characterization with difference thermography

NASA Astrophysics Data System (ADS)

Winfree, William P.; Zalameda, Joseph N.; Howell, Patricia A.

2011-05-01

Flaw detection and characterization with thermographic techniques in graphite polymer composites is often limited by localized variations in the thermographic response. Variations in properties such as acceptable porosity, variations in fiber volume content and surface polymer thickness result in variations in the thermal response that in general cause significant variations in the initial thermal response. These variations result in a noise floor that increases the difficulty of detecting and characterizing deeper flaws. The paper investigates comparing thermographic responses taken before and after a change in state in a composite to improve the detection of subsurface flaws. A method is presented for registration of the responses before finding the difference. A significant improvement in the detectability is achieved by comparing the differences in response. Examples of changes in state due to application of a load and impact are presented.

Improved Flaw Detection and Characterization with Difference Thermography

NASA Technical Reports Server (NTRS)

Winfree, William P.; Zalameda, Joseph N.; Howell, Patricia A.

2011-01-01

Flaw detection and characterization with thermographic techniques in graphite polymer composites is often limited by localized variations in the thermographic response. Variations in properties such as acceptable porosity, variations in fiber volume content and surface polymer thickness result in variations in the thermal response that in general cause significant variations in the initial thermal response. These variations result in a noise floor that increases the difficulty of detecting and characterizing deeper flaws. The paper investigates comparing thermographic responses taken before and after a change in state in a composite to improve the detection of subsurface flaws. A method is presented for registration of the responses before finding the difference. A significant improvement in the detectability is achieved by comparing the differences in response. Examples of changes in state due to application of a load and impact are presented.

Bounds on the minimum number of recombination events in a sample history.

PubMed Central

Myers, Simon R; Griffiths, Robert C

2003-01-01

Recombination is an important evolutionary factor in many organisms, including humans, and understanding its effects is an important task facing geneticists. Detecting past recombination events is thus important; this article introduces statistics that give a lower bound on the number of recombination events in the history of a sample, on the basis of the patterns of variation in the sample DNA. Such lower bounds are appropriate, since many recombination events in the history are typically undetectable, so the true number of historical recombinations is unobtainable. The statistics can be calculated quickly by computer and improve upon the earlier bound of Hudson and Kaplan 1985. A method is developed to combine bounds on local regions in the data to produce more powerful improved bounds. The method is flexible to different models of recombination occurrence. The approach gives recombination event bounds between all pairs of sites, to help identify regions with more detectable recombinations, and these bounds can be viewed graphically. Under coalescent simulations, there is a substantial improvement over the earlier method (of up to a factor of 2) in the expected number of recombination events detected by one of the new minima, across a wide range of parameter values. The method is applied to data from a region within the lipoprotein lipase gene and the amount of detected recombination is substantially increased. Further, there is strong clustering of detected recombination events in an area near the center of the region. A program implementing these statistics, which was used for this article, is available from http://www.stats.ox.ac.uk/mathgen/programs.html. PMID:12586723

A Multi-Channel Method for Detecting Periodic Forced Oscillations in Power Systems

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

Follum, James D.; Tuffner, Francis K.

2016-11-14

Forced oscillations in electric power systems are often symptomatic of equipment malfunction or improper operation. Detecting and addressing the cause of the oscillations can improve overall system operation. In this paper, a multi-channel method of detecting forced oscillations and estimating their frequencies is proposed. The method operates by comparing the sum of scaled periodograms from various channels to a threshold. A method of setting the threshold to specify the detector's probability of false alarm while accounting for the correlation between channels is also presented. Results from simulated and measured power system data indicate that the method outperforms its single-channel counterpartmore » and is suitable for real-world applications.« less

A Frequency-Domain Adaptive Matched Filter for Active Sonar Detection.

PubMed

Zhao, Zhishan; Zhao, Anbang; Hui, Juan; Hou, Baochun; Sotudeh, Reza; Niu, Fang

2017-07-04

The most classical detector of active sonar and radar is the matched filter (MF), which is the optimal processor under ideal conditions. Aiming at the problem of active sonar detection, we propose a frequency-domain adaptive matched filter (FDAMF) with the use of a frequency-domain adaptive line enhancer (ALE). The FDAMF is an improved MF. In the simulations in this paper, the signal to noise ratio (SNR) gain of the FDAMF is about 18.6 dB higher than that of the classical MF when the input SNR is -10 dB. In order to improve the performance of the FDAMF with a low input SNR, we propose a pre-processing method, which is called frequency-domain time reversal convolution and interference suppression (TRC-IS). Compared with the classical MF, the FDAMF combined with the TRC-IS method obtains higher SNR gain, a lower detection threshold, and a better receiver operating characteristic (ROC) in the simulations in this paper. The simulation results show that the FDAMF has higher processing gain and better detection performance than the classical MF under ideal conditions. The experimental results indicate that the FDAMF does improve the performance of the MF, and can adapt to actual interference in a way. In addition, the TRC-IS preprocessing method works well in an actual noisy ocean environment.

Molecular-Based Identification and Detection of Salmonella in Food Production Systems: Current Perspectives.

PubMed

Ricke, Steven C; Kim, Sun Ae; Shi, Zhaohao; Park, Si Hong

2018-04-19

Salmonella remains a prominent cause of foodborne illnesses and can originate from a wide range of food products. Given the continued presence of pathogenic Salmonella in food production systems, there is a consistent need to improve identification and detection methods that can identify this pathogen at all stages in food systems. Methods for subtyping have evolved over the years, and the introduction of whole genome sequencing and advancements in PCR technologies has greatly improved the resolution for differentiating strains within a particular serovar. This, in turn, has led to the continued improvement in Salmonella detection technologies for utilization in food production systems. In this review, the focus will be on recent advancements in these technologies, as well as potential issues associated with the application of these tools in food production. In addition, the recent and emerging research developments on Salmonella detection and identification methodologies and their potential application in food production systems will be discussed. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Multi-Target Tracking Using an Improved Gaussian Mixture CPHD Filter.

PubMed

Si, Weijian; Wang, Liwei; Qu, Zhiyu

2016-11-23

The cardinalized probability hypothesis density (CPHD) filter is an alternative approximation to the full multi-target Bayesian filter for tracking multiple targets. However, although the joint propagation of the posterior intensity and cardinality distribution in its recursion allows more reliable estimates of the target number than the PHD filter, the CPHD filter suffers from the spooky effect where there exists arbitrary PHD mass shifting in the presence of missed detections. To address this issue in the Gaussian mixture (GM) implementation of the CPHD filter, this paper presents an improved GM-CPHD filter, which incorporates a weight redistribution scheme into the filtering process to modify the updated weights of the Gaussian components when missed detections occur. In addition, an efficient gating strategy that can adaptively adjust the gate sizes according to the number of missed detections of each Gaussian component is also presented to further improve the computational efficiency of the proposed filter. Simulation results demonstrate that the proposed method offers favorable performance in terms of both estimation accuracy and robustness to clutter and detection uncertainty over the existing methods.

Automated Fall Detection With Quality Improvement “Rewind” to Reduce Falls in Hospital Rooms

PubMed Central

Rantz, Marilyn J.; Banerjee, Tanvi S.; Cattoor, Erin; Scott, Susan D.; Skubic, Marjorie; Popescu, Mihail

2014-01-01

The purpose of this study was to test the implementation of a fall detection and “rewind” privacy-protecting technique using the Microsoft® Kinect™ to not only detect but prevent falls from occurring in hospitalized patients. Kinect sensors were placed in six hospital rooms in a step-down unit and data were continuously logged. Prior to implementation with patients, three researchers performed a total of 18 falls (walking and then falling down or falling from the bed) and 17 non-fall events (crouching down, stooping down to tie shoe laces, and lying on the floor). All falls and non-falls were correctly identified using automated algorithms to process Kinect sensor data. During the first 8 months of data collection, processing methods were perfected to manage data and provide a “rewind” method to view events that led to falls for post-fall quality improvement process analyses. Preliminary data from this feasibility study show that using the Microsoft Kinect sensors provides detection of falls, fall risks, and facilitates quality improvement after falls in real hospital environments unobtrusively, while taking into account patient privacy. PMID:24296567

Mobile phones improve case detection and management of malaria in rural Bangladesh

PubMed Central

2013-01-01

Background The recent introduction of mobile phones into the rural Bandarban district of Bangladesh provided a resource to improve case detection and treatment of patients with malaria. Methods During studies to define the epidemiology of malaria in villages in south-eastern Bangladesh, an area with hypoendemic malaria, the project recorded 986 mobile phone calls from families because of illness suspected to be malaria between June 2010 and June 2012. Results Based on phone calls, field workers visited the homes with ill persons, and collected blood samples for malaria on 1,046 people. 265 (25%) of the patients tested were positive for malaria. Of the 509 symptomatic malaria cases diagnosed during this study period, 265 (52%) were detected because of an initial mobile phone call. Conclusion Mobile phone technology was found to be an efficient and effective method for rapidly detecting and treating patients with malaria in this remote area. This technology, when combined with local knowledge and field support, may be applicable to other hard-to-reach areas to improve malaria control. PMID:23374585

A neurite quality index and machine vision software for improved quantification of neurodegeneration.

PubMed

Romero, Peggy; Miller, Ted; Garakani, Arman

2009-12-01

Current methods to assess neurodegradation in dorsal root ganglion cultures as a model for neurodegenerative diseases are imprecise and time-consuming. Here we describe two new methods to quantify neuroprotection in these cultures. The neurite quality index (NQI) builds upon earlier manual methods, incorporating additional morphological events to increase detection sensitivity for the detection of early degeneration events. Neurosight is a machine vision-based method that recapitulates many of the strengths of NQI while enabling high-throughput screening applications with decreased costs.

An Improved Image Matching Method Based on Surf Algorithm

NASA Astrophysics Data System (ADS)

Chen, S. J.; Zheng, S. Z.; Xu, Z. G.; Guo, C. C.; Ma, X. L.

2018-04-01

Many state-of-the-art image matching methods, based on the feature matching, have been widely studied in the remote sensing field. These methods of feature matching which get highly operating efficiency, have a disadvantage of low accuracy and robustness. This paper proposes an improved image matching method which based on the SURF algorithm. The proposed method introduces color invariant transformation, information entropy theory and a series of constraint conditions to increase feature points detection and matching accuracy. First, the model of color invariant transformation is introduced for two matching images aiming at obtaining more color information during the matching process and information entropy theory is used to obtain the most information of two matching images. Then SURF algorithm is applied to detect and describe points from the images. Finally, constraint conditions which including Delaunay triangulation construction, similarity function and projective invariant are employed to eliminate the mismatches so as to improve matching precision. The proposed method has been validated on the remote sensing images and the result benefits from its high precision and robustness.

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.

Imaging and Elastometry of Blood Clots Using Magnetomotive Optical Coherence Tomography and Labeled Platelets.

PubMed

Oldenburg, Amy L; Wu, Gongting; Spivak, Dmitry; Tsui, Frank; Wolberg, Alisa S; Fischer, Thomas H

2011-07-21

Improved methods for imaging and assessment of vascular defects are needed for directing treatment of cardiovascular pathologies. In this paper, we employ magnetomotive optical coherence tomography (MMOCT) as a platform both to detect and to measure the elasticity of blood clots. Detection is enabled through the use of rehydrated, lyophilized platelets loaded with superparamagnetic iron oxides (SPIO-RL platelets) that are functional infusion agents that adhere to sites of vascular endothelial damage. Evidence suggests that the sensitivity for detection is improved over threefold by magnetic interactions between SPIOs inside RL platelets. Using the same MMOCT system, we show how elastometry of simulated clots, using resonant acoustic spectroscopy, is correlated with the fibrin content of the clot. Both methods are based upon magnetic actuation and phase-sensitive optical monitoring of nanoscale displacements using MMOCT, underscoring its utility as a broad-based platform to detect and measure the molecular structure and composition of blood clots.

Method for outlier detection: a tool to assess the consistency between laboratory data and ultraviolet-visible absorbance spectra in wastewater samples.

PubMed

Zamora, D; Torres, A

2014-01-01

Reliable estimations of the evolution of water quality parameters by using in situ technologies make it possible to follow the operation of a wastewater treatment plant (WWTP), as well as improving the understanding and control of the operation, especially in the detection of disturbances. However, ultraviolet (UV)-Vis sensors have to be calibrated by means of a local fingerprint laboratory reference concentration-value data-set. The detection of outliers in these data-sets is therefore important. This paper presents a method for detecting outliers in UV-Vis absorbances coupled to water quality reference laboratory concentrations for samples used for calibration purposes. Application to samples from the influent of the San Fernando WWTP (Medellín, Colombia) is shown. After the removal of outliers, improvements in the predictability of the influent concentrations using absorbance spectra were found.

Joint detection and tracking of size-varying infrared targets based on block-wise sparse decomposition

NASA Astrophysics Data System (ADS)

Li, Miao; Lin, Zaiping; Long, Yunli; An, Wei; Zhou, Yiyu

2016-05-01

The high variability of target size makes small target detection in Infrared Search and Track (IRST) a challenging task. A joint detection and tracking method based on block-wise sparse decomposition is proposed to address this problem. For detection, the infrared image is divided into overlapped blocks, and each block is weighted on the local image complexity and target existence probabilities. Target-background decomposition is solved by block-wise inexact augmented Lagrange multipliers. For tracking, label multi-Bernoulli (LMB) tracker tracks multiple targets taking the result of single-frame detection as input, and provides corresponding target existence probabilities for detection. Unlike fixed-size methods, the proposed method can accommodate size-varying targets, due to no special assumption for the size and shape of small targets. Because of exact decomposition, classical target measurements are extended and additional direction information is provided to improve tracking performance. The experimental results show that the proposed method can effectively suppress background clutters, detect and track size-varying targets in infrared images.

Effective DQE (eDQE) for monoscopic and stereoscopic chest radiography imaging systems with the incorporation of anatomical noise

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

Boyce, Sarah J.; Choudhury, Kingshuk Roy; Samei, Ehsan

2013-09-15

Purpose: Stereoscopic chest biplane correlation imaging (stereo/BCI) has been proposed as an alternative modality to single view chest x-ray (CXR). The metrics effective modulation transfer function (eMTF), effective normalized noise power spectrum (eNNPS), and effective detective quantum efficiency (eDQE) have been proposed as clinically relevant metrics for assessing clinical system performance taking into consideration the magnification and scatter effects. This study compared the metrics eMTF, eNNPS, eDQE, and detectability index for stereo/BCI and single view CXR under isodose conditions at two magnifications for two anthropomorphic phantoms of differing sizes.Methods: Measurements for the eMTF were taken for two phantom sizes withmore » an opaque edge test device using established techniques. The eNNPS was measured at two isodose conditions for two phantoms using established techniques. The scatter was measured for two phantoms using an established beam stop method. All measurements were also taken at two different magnifications with two phantoms. A geometrical phantom was used for comparison with prior results for CXR although the results for an anatomy free phantom are not expected to vary for BCI.Results: Stereo/BCI resulted in improved metrics compared to single view CXR. Results indicated that magnification can potentially improve the detection performance primarily due to the air gap which reduced scatter by ∼20%. For both phantoms, at isodose, eDQE(0) for stereo/BCI was ∼100 times higher than that for CXR. Magnification at isodose improved eDQE(0) by ∼10 times for stereo/BCI. Increasing the dose did not improve eDQE. The detectability index for stereo/BCI was ∼100 times better than single view CXR for all conditions. The detectability index was also not improved with increased dose.Conclusions: The findings indicate that stereo/BCI with magnification may improve detectability of subtle lung nodules compared to single view CXR. Results were improved with magnification for the smaller phantom but not for the larger phantom. The effective DQE and the detectability index did not improve with increasing dose.« less

Method and Pd/V2 O5 device for H2 detection

DOEpatents

Liu, Ping [San Diego, CA; Tracy, C Edwin [Golden, CO; Pitts, J Roland [Lakewood, CO; Smith, II, R. Davis; Lee, Se-Hee [Lakewood, CO

2011-12-27

Methods and Pd/V.sub.2O.sub.5 devices for hydrogen detection are disclosed. An exemplary method of preparing an improved sensor for chemochromic detection of hydrogen gas over a wide response range exhibits stability during repeated coloring/bleaching cycles upon exposure and removal of hydrogen gas. The method may include providing a substrate. The method may also include depositing a V.sub.20.sub.5 layer that functions as a H.sub.2 insertion host in a Pd/V.sub.20.sub.5 hydrogen sensor to be formed on said substrate. The method may also include depositing a Pd layer onto said V.sub.20.sub.5 layer; said Pd layer functioning as an optical modulator.

The ability of endotoxin adsorption during a longer duration of direct hemoperfusion with a polymyxin B-immobilized fiber column in patients with septic shock.

PubMed

Shimizu, Tomoharu; Obata, Toru; Sonoda, Hiromichi; Akabori, Hiroya; Tabata, Takahisa; Eguchi, Yutaka; Endo, Yoshihiro; Tani, Tohru

2013-12-01

The patients' hemodynamic conditions of septic shock due to intra-abdominal infection were improved by the longer duration of direct hemoperfusion with a polymyxin B-immobilized fiber column (PMX), reducing plasma endotoxins measured by the novel endotoxin detection method, named endotoxin scattering photometry (ESP) method; however, turbidimetric method could not detect endotoxins. We also observed the reduction in the endotoxin after passing through column by ESP method even after the longer duration of PMX. ESP method may more sensitively detect endotoxins than the ordinary turbidimetric method. Moreover, we demonstrated the ability of endotoxin adsorption in spite of the longer duration of PMX. Copyright © 2013 Elsevier Ltd. All rights reserved.

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.

Intelligent transient transitions detection of LRE test bed

NASA Astrophysics Data System (ADS)

Zhu, Fengyu; Shen, Zhengguang; Wang, Qi

2013-01-01

Health Monitoring Systems is an implementation of monitoring strategies for complex systems whereby avoiding catastrophic failure, extending life and leading to improved asset management. A Health Monitoring Systems generally encompasses intelligence at many levels and sub-systems including sensors, actuators, devices, etc. In this paper, a smart sensor is studied, which is use to detect transient transitions of liquid-propellant rocket engines test bed. In consideration of dramatic changes of variable condition, wavelet decomposition is used to work real time in areas. Contrast to traditional Fourier transform method, the major advantage of adding wavelet analysis is the ability to detect transient transitions as well as obtaining the frequency content using a much smaller data set. Historically, transient transitions were only detected by offline analysis of the data. The methods proposed in this paper provide an opportunity to detect transient transitions automatically as well as many additional data anomalies, and provide improved data-correction and sensor health diagnostic abilities. The developed algorithms have been tested on actual rocket test data.

A multi-view face recognition system based on cascade face detector and improved Dlib

NASA Astrophysics Data System (ADS)

Zhou, Hongjun; Chen, Pei; Shen, Wei

2018-03-01

In this research, we present a framework for multi-view face detect and recognition system based on cascade face detector and improved Dlib. This method is aimed to solve the problems of low efficiency and low accuracy in multi-view face recognition, to build a multi-view face recognition system, and to discover a suitable monitoring scheme. For face detection, the cascade face detector is used to extracted the Haar-like feature from the training samples, and Haar-like feature is used to train a cascade classifier by combining Adaboost algorithm. Next, for face recognition, we proposed an improved distance model based on Dlib to improve the accuracy of multiview face recognition. Furthermore, we applied this proposed method into recognizing face images taken from different viewing directions, including horizontal view, overlooks view, and looking-up view, and researched a suitable monitoring scheme. This method works well for multi-view face recognition, and it is also simulated and tested, showing satisfactory experimental results.

Detection of shallow buried objects using an autoregressive model on the ground penetrating radar signal

NASA Astrophysics Data System (ADS)

Nabelek, Daniel P.; Ho, K. C.

2013-06-01

The detection of shallow buried low-metal content objects using ground penetrating radar (GPR) is a challenging task. This is because these targets are right underneath the ground and the ground bounce reflection interferes with their detections. They do not create distinctive hyperbolic signatures as required by most existing GPR detection algorithms due to their special geometric shapes and low metal content. This paper proposes the use of the Autoregressive (AR) modeling method for the detection of these targets. We fit an A-scan of the GPR data to an AR model. It is found that the fitting error will be small when such a target is present and large when it is absent. The ratio of the energy in an Ascan before and after AR model fitting is used as the confidence value for detection. We also apply AR model fitting over scans and utilize the fitting residual energies over several scans to form a feature vector for improving the detections. Using the data collected from a government test site, the proposed method can improve the detection of this kind of targets by 30% compared to the pre-screener, at a false alarm rate of 0.002/m2.

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.

Abnormality detection of mammograms by discriminative dictionary learning on DSIFT descriptors.

PubMed

Tavakoli, Nasrin; Karimi, Maryam; Nejati, Mansour; Karimi, Nader; Reza Soroushmehr, S M; Samavi, Shadrokh; Najarian, Kayvan

2017-07-01

Detection and classification of breast lesions using mammographic images are one of the most difficult studies in medical image processing. A number of learning and non-learning methods have been proposed for detecting and classifying these lesions. However, the accuracy of the detection/classification still needs improvement. In this paper we propose a powerful classification method based on sparse learning to diagnose breast cancer in mammograms. For this purpose, a supervised discriminative dictionary learning approach is applied on dense scale invariant feature transform (DSIFT) features. A linear classifier is also simultaneously learned with the dictionary which can effectively classify the sparse representations. Our experimental results show the superior performance of our method compared to existing approaches.

Replica amplification of nucleic acid arrays

DOEpatents

Church, George M.; Mitra, Robi D.

2010-08-31

Disclosed are improved methods of making and using immobilized arrays of nucleic acids, particularly methods for producing replicas of such arrays. Included are methods for producing high density arrays of nucleic acids and replicas of such arrays, as well as methods for preserving the resolution of arrays through rounds of replication. Also included are methods which take advantage of the availability of replicas of arrays for increased sensitivity in detection of sequences on arrays. Improved methods of sequencing nucleic acids immobilized on arrays utilizing single copies of arrays and methods taking further advantage of the availability of replicas of arrays are disclosed. The improvements lead to higher fidelity and longer read lengths of sequences immobilized on arrays. Methods are also disclosed which improve the efficiency of multiplex PCR using arrays of immobilized nucleic acids.

Application of COLD-PCR for improved detection of KRAS mutations in clinical samples.

PubMed

Zuo, Zhuang; Chen, Su S; Chandra, Pranil K; Galbincea, John M; Soape, Matthew; Doan, Steven; Barkoh, Bedia A; Koeppen, Hartmut; Medeiros, L Jeffrey; Luthra, Rajyalakshmi

2009-08-01

KRAS mutations have been detected in approximately 30% of all human tumors, and have been shown to predict response to some targeted therapies. The most common KRAS mutation-detection strategy consists of conventional PCR and direct sequencing. This approach has a 10-20% detection sensitivity depending on whether pyrosequencing or Sanger sequencing is used. To improve detection sensitivity, we compared our conventional method with the recently described co-amplification-at-lower denaturation-temperature PCR (COLD-PCR) method, which selectively amplifies minority alleles. In COLD-PCR, the critical denaturation temperature is lowered to 80 degrees C (vs 94 degrees C in conventional PCR). The sensitivity of COLD-PCR was determined by assessing serial dilutions. Fifty clinical samples were used, including 20 fresh bone-marrow aspirate specimens and the formalin-fixed paraffin-embedded (FFPE) tissue of 30 solid tumors. Implementation of COLD-PCR was straightforward and required no additional cost for reagents or instruments. The method was specific and reproducible. COLD-PCR successfully detected mutations in all samples that were positive by conventional PCR, and enhanced the mutant-to-wild-type ratio by >4.74-fold, increasing the mutation detection sensitivity to 1.5%. The enhancement of mutation detection by COLD-PCR inversely correlated with the tumor-cell percentage in a sample. In conclusion, we validated the utility and superior sensitivity of COLD-PCR for detecting KRAS mutations in a variety of hematopoietic and solid tumors using either fresh or fixed, paraffin-embedded tissue.

Using parallel computing methods to improve log surface defect detection methods

Treesearch

R. Edward Thomas; Liya Thomas

2013-01-01

Determining the size and location of surface defects is crucial to evaluating the potential yield and value of hardwood logs. Recently a surface defect detection algorithm was developed using the Java language. This algorithm was developed around an earlier laser scanning system that had poor resolution along the length of the log (15 scan lines per foot). A newer...

Detection of neuron membranes in electron microscopy images using a serial neural network architecture.

PubMed

Jurrus, Elizabeth; Paiva, Antonio R C; Watanabe, Shigeki; Anderson, James R; Jones, Bryan W; Whitaker, Ross T; Jorgensen, Erik M; Marc, Robert E; Tasdizen, Tolga

2010-12-01

Study of nervous systems via the connectome, the map of connectivities of all neurons in that system, is a challenging problem in neuroscience. Towards this goal, neurobiologists are acquiring large electron microscopy datasets. However, the shear volume of these datasets renders manual analysis infeasible. Hence, automated image analysis methods are required for reconstructing the connectome from these very large image collections. Segmentation of neurons in these images, an essential step of the reconstruction pipeline, is challenging because of noise, anisotropic shapes and brightness, and the presence of confounding structures. The method described in this paper uses a series of artificial neural networks (ANNs) in a framework combined with a feature vector that is composed of image intensities sampled over a stencil neighborhood. Several ANNs are applied in series allowing each ANN to use the classification context provided by the previous network to improve detection accuracy. We develop the method of serial ANNs and show that the learned context does improve detection over traditional ANNs. We also demonstrate advantages over previous membrane detection methods. The results are a significant step towards an automated system for the reconstruction of the connectome. Copyright 2010 Elsevier B.V. All rights reserved.

Real-time method for establishing a detection map for a network of sensors

DOEpatents

Nguyen, Hung D; Koch, Mark W; Giron, Casey; Rondeau, Daniel M; Russell, John L

2012-09-11

A method for establishing a detection map of a dynamically configurable sensor network. This method determines an appropriate set of locations for a plurality of sensor units of a sensor network and establishes a detection map for the network of sensors while the network is being set up; the detection map includes the effects of the local terrain and individual sensor performance. Sensor performance is characterized during the placement of the sensor units, which enables dynamic adjustment or reconfiguration of the placement of individual elements of the sensor network during network set-up to accommodate variations in local terrain and individual sensor performance. The reconfiguration of the network during initial set-up to accommodate deviations from idealized individual sensor detection zones improves the effectiveness of the sensor network in detecting activities at a detection perimeter and can provide the desired sensor coverage of an area while minimizing unintentional gaps in coverage.

Detecting Signage and Doors for Blind Navigation and Wayfinding

PubMed Central

Wang, Shuihua; Yang, Xiaodong; Tian, Yingli

2013-01-01

Signage plays a very important role to find destinations in applications of navigation and wayfinding. In this paper, we propose a novel framework to detect doors and signage to help blind people accessing unfamiliar indoor environments. In order to eliminate the interference information and improve the accuracy of signage detection, we first extract the attended areas by using a saliency map. Then the signage is detected in the attended areas by using a bipartite graph matching. The proposed method can handle multiple signage detection. Furthermore, in order to provide more information for blind users to access the area associated with the detected signage, we develop a robust method to detect doors based on a geometric door frame model which is independent to door appearances. Experimental results on our collected datasets of indoor signage and doors demonstrate the effectiveness and efficiency of our proposed method. PMID:23914345

Detecting Signage and Doors for Blind Navigation and Wayfinding.

PubMed

Wang, Shuihua; Yang, Xiaodong; Tian, Yingli

2013-07-01

Signage plays a very important role to find destinations in applications of navigation and wayfinding. In this paper, we propose a novel framework to detect doors and signage to help blind people accessing unfamiliar indoor environments. In order to eliminate the interference information and improve the accuracy of signage detection, we first extract the attended areas by using a saliency map. Then the signage is detected in the attended areas by using a bipartite graph matching. The proposed method can handle multiple signage detection. Furthermore, in order to provide more information for blind users to access the area associated with the detected signage, we develop a robust method to detect doors based on a geometric door frame model which is independent to door appearances. Experimental results on our collected datasets of indoor signage and doors demonstrate the effectiveness and efficiency of our proposed method.

Enhancement of optic cup detection through an improved vessel kink detection framework

NASA Astrophysics Data System (ADS)

Wong, Damon W. K.; Liu, Jiang; Tan, Ngan Meng; Zhang, Zhuo; Lu, Shijian; Lim, Joo Hwee; Li, Huiqi; Wong, Tien Yin

2010-03-01

Glaucoma is a leading cause of blindness. The presence and extent of progression of glaucoma can be determined if the optic cup can be accurately segmented from retinal images. In this paper, we present a framework which improves the detection of the optic cup. First, a region of interest is obtained from the retinal fundus image, and a pallor-based preliminary cup contour estimate is determined. Patches are then extracted from the ROI along this contour. To improve the usability of the patches, adaptive methods are introduced to ensure the patches are within the optic disc and to minimize redundant information. The patches are then analyzed for vessels by an edge transform which generates pixel segments of likely vessel candidates. Wavelet, color and gradient information are used as input features for a SVM model to classify the candidates as vessel or non-vessel. Subsequently, a rigourous non-parametric method is adopted in which a bi-stage multi-resolution approach is used to probe and localize the location of kinks along the vessels. Finally, contenxtual information is used to fuse pallor and kink information to obtain an enhanced optic cup segmentation. Using a batch of 21 images obtained from the Singapore Eye Research Institute, the new method results in a 12.64% reduction in the average overlap error against a pallor only cup, indicating viable improvements in the segmentation and supporting the use of kinks for optic cup detection.

Research on Daily Objects Detection Based on Deep Neural Network

NASA Astrophysics Data System (ADS)

Ding, Sheng; Zhao, Kun

2018-03-01

With the rapid development of deep learning, great breakthroughs have been made in the field of object detection. In this article, the deep learning algorithm is applied to the detection of daily objects, and some progress has been made in this direction. Compared with traditional object detection methods, the daily objects detection method based on deep learning is faster and more accurate. The main research work of this article: 1. collect a small data set of daily objects; 2. in the TensorFlow framework to build different models of object detection, and use this data set training model; 3. the training process and effect of the model are improved by fine-tuning the model parameters.

One improved LSB steganography algorithm

NASA Astrophysics Data System (ADS)

Song, Bing; Zhang, Zhi-hong

2013-03-01

It is easy to be detected by X2 and RS steganalysis with high accuracy that using LSB algorithm to hide information in digital image. We started by selecting information embedded location and modifying the information embedded method, combined with sub-affine transformation and matrix coding method, improved the LSB algorithm and a new LSB algorithm was proposed. Experimental results show that the improved one can resist the X2 and RS steganalysis effectively.

Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor.

PubMed

Hoang, Toan Minh; Hong, Hyung Gil; Vokhidov, Husan; Park, Kang Ryoung

2016-08-18

With the increasing need for road lane detection used in lane departure warning systems and autonomous vehicles, many studies have been conducted to turn road lane detection into a virtual assistant to improve driving safety and reduce car accidents. Most of the previous research approaches detect the central line of a road lane and not the accurate left and right boundaries of the lane. In addition, they do not discriminate between dashed and solid lanes when detecting the road lanes. However, this discrimination is necessary for the safety of autonomous vehicles and the safety of vehicles driven by human drivers. To overcome these problems, we propose a method for road lane detection that distinguishes between dashed and solid lanes. Experimental results with the Caltech open database showed that our method outperforms conventional methods.

Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor

PubMed Central

Hoang, Toan Minh; Hong, Hyung Gil; Vokhidov, Husan; Park, Kang Ryoung

2016-01-01

With the increasing need for road lane detection used in lane departure warning systems and autonomous vehicles, many studies have been conducted to turn road lane detection into a virtual assistant to improve driving safety and reduce car accidents. Most of the previous research approaches detect the central line of a road lane and not the accurate left and right boundaries of the lane. In addition, they do not discriminate between dashed and solid lanes when detecting the road lanes. However, this discrimination is necessary for the safety of autonomous vehicles and the safety of vehicles driven by human drivers. To overcome these problems, we propose a method for road lane detection that distinguishes between dashed and solid lanes. Experimental results with the Caltech open database showed that our method outperforms conventional methods. PMID:27548176

in silico Surveillance: evaluating outbreak detection with simulation models

PubMed Central

2013-01-01

Background Detecting outbreaks is a crucial task for public health officials, yet gaps remain in the systematic evaluation of outbreak detection protocols. The authors’ objectives were to design, implement, and test a flexible methodology for generating detailed synthetic surveillance data that provides realistic geographical and temporal clustering of cases and use to evaluate outbreak detection protocols. Methods A detailed representation of the Boston area was constructed, based on data about individuals, locations, and activity patterns. Influenza-like illness (ILI) transmission was simulated, producing 100 years of in silico ILI data. Six different surveillance systems were designed and developed using gathered cases from the simulated disease data. Performance was measured by inserting test outbreaks into the surveillance streams and analyzing the likelihood and timeliness of detection. Results Detection of outbreaks varied from 21% to 95%. Increased coverage did not linearly improve detection probability for all surveillance systems. Relaxing the decision threshold for signaling outbreaks greatly increased false-positives, improved outbreak detection slightly, and led to earlier outbreak detection. Conclusions Geographical distribution can be more important than coverage level. Detailed simulations of infectious disease transmission can be configured to represent nearly any conceivable scenario. They are a powerful tool for evaluating the performance of surveillance systems and methods used for outbreak detection. PMID:23343523

Direct Detection of Biotinylated Proteins by Mass Spectrometry

PubMed Central

2015-01-01

Mass spectrometric strategies to identify protein subpopulations involved in specific biological functions rely on covalently tagging biotin to proteins using various chemical modification methods. The biotin tag is primarily used for enrichment of the targeted subpopulation for subsequent mass spectrometry (MS) analysis. A limitation of these strategies is that MS analysis does not easily discriminate unlabeled contaminants from the labeled protein subpopulation under study. To solve this problem, we developed a flexible method that only relies on direct MS detection of biotin-tagged proteins called “Direct Detection of Biotin-containing Tags” (DiDBiT). Compared with conventional targeted proteomic strategies, DiDBiT improves direct detection of biotinylated proteins ∼200 fold. We show that DiDBiT is applicable to several protein labeling protocols in cell culture and in vivo using cell permeable NHS-biotin and incorporation of the noncanonical amino acid, azidohomoalanine (AHA), into newly synthesized proteins, followed by click chemistry tagging with biotin. We demonstrate that DiDBiT improves the direct detection of biotin-tagged newly synthesized peptides more than 20-fold compared to conventional methods. With the increased sensitivity afforded by DiDBiT, we demonstrate the MS detection of newly synthesized proteins labeled in vivo in the rodent nervous system with unprecedented temporal resolution as short as 3 h. PMID:25117199

A new method for detecting cerebral hemorrhage in rabbits by magnetic inductive phase shift.

PubMed

Jin, Gui; Sun, Jian; Qin, Mingxin; Tang, Qinghua; Xu, Lin; Ning, Xu; Xu, Jia; Pu, Xianjie; Chen, Mingsheng

2014-02-15

Cerebral hemorrhage, which is an important clinical problem, is often monitored and studied using expensive devices, such as magnetic resonance imaging (MRI) and positron emission tomography (PET) that are unavailable in economically underdeveloped regions. Magnetic induction tomography (MIT) is a new type of non-contact, non-invasive, and low-cost detection technology, and exhibits prospects for wide application, especially for the detection of brain diseases. However, the previous studies on MIT have focused on laboratory models and rarely on in vivo applications because the induced signals produced by biological tissues are notably weak. Based on the symmetry between the two brain hemispheres and the fact that a local brain hemorrhage will not affect the contra-lateral hemisphere, a symmetric cancellation-type sensor detection system, which is characterized by one excitation coil and two receiving coils, was designed to improve the detection sensitivity of MIT. This method was subsequently used to detect the occurrence of cerebral hematomas in rabbits. The average phase drift induced by a 3-ml injection of autologous blood was 1.885°, which is a fivefold improvement compared with the traditional single excitation coil and single receiving coil method. The results indicate that this system has high sensitivity and anti-interference ability and high practical value. © 2013 Published by Elsevier B.V.

Using occupancy modelling to compare environmental DNA to traditional field methods for regional-scale monitoring of an endangered aquatic species.

PubMed

Schmelzle, Molly C; Kinziger, Andrew P

2016-07-01

Environmental DNA (eDNA) monitoring approaches promise to greatly improve detection of rare, endangered and invasive species in comparison with traditional field approaches. Herein, eDNA approaches and traditional seining methods were applied at 29 research locations to compare method-specific estimates of detection and occupancy probabilities for endangered tidewater goby (Eucyclogobius newberryi). At each location, multiple paired seine hauls and water samples for eDNA analysis were taken, ranging from two to 23 samples per site, depending upon habitat size. Analysis using a multimethod occupancy modelling framework indicated that the probability of detection using eDNA was nearly double (0.74) the rate of detection for seining (0.39). The higher detection rates afforded by eDNA allowed determination of tidewater goby occupancy at two locations where they have not been previously detected and at one location considered to be locally extirpated. Additionally, eDNA concentration was positively related to tidewater goby catch per unit effort, suggesting eDNA could potentially be used as a proxy for local tidewater goby abundance. Compared to traditional field sampling, eDNA provided improved occupancy parameter estimates and can be applied to increase management efficiency across a broad spatial range and within a diversity of habitats. © 2015 John Wiley & Sons Ltd.

A Fully Automated Method to Detect and Segment a Manufactured Object in an Underwater Color Image

NASA Astrophysics Data System (ADS)

Barat, Christian; Phlypo, Ronald

2010-12-01

We propose a fully automated active contours-based method for the detection and the segmentation of a moored manufactured object in an underwater image. Detection of objects in underwater images is difficult due to the variable lighting conditions and shadows on the object. The proposed technique is based on the information contained in the color maps and uses the visual attention method, combined with a statistical approach for the detection and an active contour for the segmentation of the object to overcome the above problems. In the classical active contour method the region descriptor is fixed and the convergence of the method depends on the initialization. With our approach, this dependence is overcome with an initialization using the visual attention results and a criterion to select the best region descriptor. This approach improves the convergence and the processing time while providing the advantages of a fully automated method.

Detection of respiratory viruses and bacteria in children using a twenty-two target reverse-transcription real-time PCR (RT-qPCR) panel.

PubMed

Ellis, Chelsey; Misir, Amita; Hui, Charles; Jabbour, Mona; Barrowman, Nicholas; Langill, Jonathan; Bowes, Jennifer; Slinger, Robert

2016-05-01

Rapid detection of the wide range of viruses and bacteria that cause respiratory infection in children is important for patient care and antibiotic stewardship. We therefore designed and evaluated a ready-to-use 22 target respiratory infection reverse-transcription real-time polymerase chain reaction (RT-qPCR) panel to determine if this would improve detection of these agents at our pediatric hospital. RT-qPCR assays for twenty-two target organisms were dried-down in individual wells of 96 well plates and saved at room temperature. Targets included 18 respiratory viruses and 4 bacteria. After automated nucleic acid extraction of nasopharyngeal aspirate (NPA) samples, rapid qPCR was performed. RT-qPCR results were compared with those obtained by the testing methods used at our hospital laboratories. One hundred fifty-nine pediatric NPA samples were tested with the RT-qPCR panel. One or more respiratory pathogens were detected in 132/159 (83%) samples. This was significantly higher than the detection rate of standard methods (94/159, 59%) (P<0.001). This difference was mainly due to improved RT-qPCR detection of rhinoviruses, parainfluenza viruses, bocavirus, and coronaviruses. The panel internal control assay performance remained stable at room temperature storage over a two-month testing period. The RT-qPCR panel was able to identify pathogens in a high proportion of respiratory samples. The panel detected more positive specimens than the methods in use at our hospital. The pre-made panel format was easy to use and rapid, with results available in approximately 90 minutes. We now plan to determine if use of this panel improves patient care and antibiotic stewardship.

Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke

PubMed Central

Song, Sunbin; Luby, Marie; Edwardson, Matthew A.; Brown, Tyler; Shah, Shreyansh; Cox, Robert W.; Saad, Ziad S.; Reynolds, Richard C.; Glen, Daniel R.; Cohen, Leonardo G.; Latour, Lawrence L.

2017-01-01

Introduction Interpretation of the extent of perfusion deficits in stroke MRI is highly dependent on the method used for analyzing the perfusion-weighted signal intensity time-series after gadolinium injection. In this study, we introduce a new model-free standardized method of temporal similarity perfusion (TSP) mapping for perfusion deficit detection and test its ability and reliability in acute ischemia. Materials and methods Forty patients with an ischemic stroke or transient ischemic attack were included. Two blinded readers compared real-time generated interactive maps and automatically generated TSP maps to traditional TTP/MTT maps for presence of perfusion deficits. Lesion volumes were compared for volumetric inter-rater reliability, spatial concordance between perfusion deficits and healthy tissue and contrast-to-noise ratio (CNR). Results Perfusion deficits were correctly detected in all patients with acute ischemia. Inter-rater reliability was higher for TSP when compared to TTP/MTT maps and there was a high similarity between the lesion volumes depicted on TSP and TTP/MTT (r(18) = 0.73). The Pearson's correlation between lesions calculated on TSP and traditional maps was high (r(18) = 0.73, p<0.0003), however the effective CNR was greater for TSP compared to TTP (352.3 vs 283.5, t(19) = 2.6, p<0.03.) and MTT (228.3, t(19) = 2.8, p<0.03). Discussion TSP maps provide a reliable and robust model-free method for accurate perfusion deficit detection and improve lesion delineation compared to traditional methods. This simple method is also computationally faster and more easily automated than model-based methods. This method can potentially improve the speed and accuracy in perfusion deficit detection for acute stroke treatment and clinical trial inclusion decision-making. PMID:28973000

On-line/on-site analysis of heavy metals in water and soils by laser induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Meng, Deshuo; Zhao, Nanjing; Wang, Yuanyuan; Ma, Mingjun; Fang, Li; Gu, Yanhong; Jia, Yao; Liu, Jianguo

2017-11-01

The enrichment method of heavy metal in water with graphite and aluminum electrode was studied, and combined with plasma restraint device for improving the sensitivity of detection and reducing the limit of detection (LOD) of elements. For aluminum electrode enrichment, the LODs of Cd, Pb and Ni can be as low as several ppb. For graphite enrichment, the measurement time can be less than 3 min. The results showed that the graphite enrichment and aluminum electrode enrichment method can effectively improve the LIBS detection ability. The graphite enrichment method combined with plasma spatial confinement is more suitable for on-line monitoring of industrial waste water, the aluminum electrode enrichment method can be used for trace heavy metal detection in water. A LIBS method and device for soil heavy metals analysis was also developed, and a mobile LIBS system was tested in outfield. The measurement results deduced from LIBS and ICP-MS had a good consistency. The results provided an important application support for rapid and on-site monitoring of heavy metals in soil. (Left: the mobile LIBS system for analysis of heavy metals in soils. Top right: the spatial confinement device. Bottom right: automatic graphite enrichment device for on0line analysis of heavy metals in water).

Rectal swab sampling followed by an enrichment culture-based real-time PCR assay to detect Salmonella enterocolitis in children.

PubMed

Lin, L-H; Tsai, C-Y; Hung, M-H; Fang, Y-T; Ling, Q-D

2011-09-01

Although routine bacterial culture is the traditional reference standard method for the detection of Salmonella infection in children with diarrhoea, it is a time-consuming procedure that usually only gives results after 3-4 days. Some molecular detection methods can improve the turn-around time to within 24 h, but these methods are not applied directly from stool or rectal swab specimens as routine diagnostic methods for the detection of gastrointestinal pathogens. In this study, we tested the feasibility of a bacterial enrichment culture-based real-time PCR assay method for detecting and screening for diarrhoea in children caused by Salmonella. Our results showed that the minimum real-time PCR assay time required to detect enriched bacterial culture from a swab was 3 h. In all children with suspected Salmonella diarrhoea, the enrichment culture-based real-time PCR achieved 85.4% sensitivity and 98.1% specificity, as compared with the 53.7% sensitivity and 100% specificity of detection with the routine bacterial culture method. We suggest that rectal swab sampling followed by enrichment culture-based real-time PCR is suitable as a rapid method for detecting and screening for Salmonella in paediatric patients. © 2011 The Authors. Clinical Microbiology and Infection © 2011 European Society of Clinical Microbiology and Infectious Diseases.

Wind profiling for a coherent wind Doppler lidar by an auto-adaptive background subtraction approach.

PubMed

Wu, Yanwei; Guo, Pan; Chen, Siying; Chen, He; Zhang, Yinchao

2017-04-01

Auto-adaptive background subtraction (AABS) is proposed as a denoising method for data processing of the coherent Doppler lidar (CDL). The method is proposed specifically for a low-signal-to-noise-ratio regime, in which the drifting power spectral density of CDL data occurs. Unlike the periodogram maximum (PM) and adaptive iteratively reweighted penalized least squares (airPLS), the proposed method presents reliable peaks and is thus advantageous in identifying peak locations. According to the analysis results of simulated and actually measured data, the proposed method outperforms the airPLS method and the PM algorithm in the furthest detectable range. The proposed method improves the detection range approximately up to 16.7% and 40% when compared to the airPLS method and the PM method, respectively. It also has smaller mean wind velocity and standard error values than the airPLS and PM methods. The AABS approach improves the quality of Doppler shift estimates and can be applied to obtain the whole wind profiling by the CDL.

Intraoperative detection of 18F-FDG-avid tissue sites using the increased probe counting efficiency of the K-alpha probe design and variance-based statistical analysis with the three-sigma criteria

PubMed Central

2013-01-01

Background Intraoperative detection of 18F-FDG-avid tissue sites during 18F-FDG-directed surgery can be very challenging when utilizing gamma detection probes that rely on a fixed target-to-background (T/B) ratio (ratiometric threshold) for determination of probe positivity. The purpose of our study was to evaluate the counting efficiency and the success rate of in situ intraoperative detection of 18F-FDG-avid tissue sites (using the three-sigma statistical threshold criteria method and the ratiometric threshold criteria method) for three different gamma detection probe systems. Methods Of 58 patients undergoing 18F-FDG-directed surgery for known or suspected malignancy using gamma detection probes, we identified nine 18F-FDG-avid tissue sites (from amongst seven patients) that were seen on same-day preoperative diagnostic PET/CT imaging, and for which each 18F-FDG-avid tissue site underwent attempted in situ intraoperative detection concurrently using three gamma detection probe systems (K-alpha probe, and two commercially-available PET-probe systems), and then were subsequently surgical excised. Results The mean relative probe counting efficiency ratio was 6.9 (± 4.4, range 2.2–15.4) for the K-alpha probe, as compared to 1.5 (± 0.3, range 1.0–2.1) and 1.0 (± 0, range 1.0–1.0), respectively, for two commercially-available PET-probe systems (P < 0.001). Successful in situ intraoperative detection of 18F-FDG-avid tissue sites was more frequently accomplished with each of the three gamma detection probes tested by using the three-sigma statistical threshold criteria method than by using the ratiometric threshold criteria method, specifically with the three-sigma statistical threshold criteria method being significantly better than the ratiometric threshold criteria method for determining probe positivity for the K-alpha probe (P = 0.05). Conclusions Our results suggest that the improved probe counting efficiency of the K-alpha probe design used in conjunction with the three-sigma statistical threshold criteria method can allow for improved detection of 18F-FDG-avid tissue sites when a low in situ T/B ratio is encountered. PMID:23496877

Laser-ultrasound spectroscopy apparatus and method with detection of shear resonances for measuring anisotropy, thickness, and other properties

DOEpatents

Levesque, Daniel; Moreau, Andre; Dubois, Marc; Monchalin, Jean-Pierre; Bussiere, Jean; Lord, Martin; Padioleau, Christian

2000-01-01

Apparatus and method for detecting shear resonances includes structure and steps for applying a radiation pulse from a pulsed source of radiation to an object to generate elastic waves therein, optically detecting the elastic waves generated in the object, and analyzing the elastic waves optically detected in the object. These shear resonances, alone or in combination with other information, may be used in the present invention to improve thickness measurement accuracy and to determine geometrical, microstructural, and physical properties of the object. At least one shear resonance in the object is detected with the elastic waves optically detected in the object. Preferably, laser-ultrasound spectroscopy is utilized to detect the shear resonances.

Research on Abnormal Detection Based on Improved Combination of K - means and SVDD

NASA Astrophysics Data System (ADS)

Hao, Xiaohong; Zhang, Xiaofeng

2018-01-01

In order to improve the efficiency of network intrusion detection and reduce the false alarm rate, this paper proposes an anomaly detection algorithm based on improved K-means and SVDD. The algorithm first uses the improved K-means algorithm to cluster the training samples of each class, so that each class is independent and compact in class; Then, according to the training samples, the SVDD algorithm is used to construct the minimum superspheres. The subordinate relationship of the samples is determined by calculating the distance of the minimum superspheres constructed by SVDD. If the test sample is less than the center of the hypersphere, the test sample belongs to this class, otherwise it does not belong to this class, after several comparisons, the final test of the effective detection of the test sample.In this paper, we use KDD CUP99 data set to simulate the proposed anomaly detection algorithm. The results show that the algorithm has high detection rate and low false alarm rate, which is an effective network security protection method.

Research on the method of improving the accuracy of CMM (coordinate measuring machine) testing aspheric surface

NASA Astrophysics Data System (ADS)

Cong, Wang; Xu, Lingdi; Li, Ang

2017-10-01

Large aspheric surface which have the deviation with spherical surface are being used widely in various of optical systems. Compared with spherical surface, Large aspheric surfaces have lots of advantages, such as improving image quality, correcting aberration, expanding field of view, increasing the effective distance and make the optical system compact, lightweight. Especially, with the rapid development of space optics, space sensor resolution is required higher and viewing angle is requred larger. Aspheric surface will become one of the essential components in the optical system. After finishing Aspheric coarse Grinding surface profile error is about Tens of microns[1].In order to achieve the final requirement of surface accuracy,the aspheric surface must be quickly modified, high precision testing is the basement of rapid convergence of the surface error . There many methods on aspheric surface detection[2], Geometric ray detection, hartmann detection, ronchi text, knifeedge method, direct profile test, interferometry, while all of them have their disadvantage[6]. In recent years the measure of the aspheric surface become one of the import factors which are restricting the aspheric surface processing development. A two meter caliber industrial CMM coordinate measuring machine is avaiable, but it has many drawbacks such as large detection error and low repeatability precision in the measurement of aspheric surface coarse grinding , which seriously affects the convergence efficiency during the aspherical mirror processing. To solve those problems, this paper presents an effective error control, calibration and removal method by calibration mirror position of the real-time monitoring and other effective means of error control, calibration and removal by probe correction and the measurement mode selection method to measure the point distribution program development. This method verified by real engineer examples, this method increases the original industrial-grade coordinate system nominal measurement accuracy PV value of 7 microns to 4microns, Which effectively improves the grinding efficiency of aspheric mirrors and verifies the correctness of the method. This paper also investigates the error detection and operation control method, the error calibration of the CMM and the random error calibration of the CMM .

Improvement of antigen detection efficiency with the use of two-dimensional photonic crystal as a substrate

NASA Astrophysics Data System (ADS)

Dovzhenko, Dmitriy; Terekhin, Vladimir; Vokhmincev, Kirill; Sukhanova, Alyona; Nabiev, Igor

2017-01-01

Multiplex detection of different antigens in human serum in order to reveal diseases at the early stage is of interest nowadays. There are a lot of biosensors, which use the fluorescent labels for specific detection of analytes. For instance, common method for detection of antigens in human serum samples is enzyme-linked immunosorbent assay (ELISA). One of the most effective ways to improve the sensitivity of this detection method is the use of a substrate that could enhance the fluorescent signal and make it easier to collect. Two-dimensional (2D) photonic crystals are very suitable structures for these purposes because of the ability to enhance the luminescent signal, control the light propagation and perform the analysis directly on its surface. In our study we have calculated optimal parameters for 2D-dimensional photonic crystal consisting of the array of silicon nano-rods, fabricated such photonic crystal on a silicon substrate using reactive ion etching and showed the possibility of its efficient application as a substrate for ELISA detection of human cancer antigens.

An improved method for detecting circulating microRNAs with S-Poly(T) Plus real-time PCR

PubMed Central

Niu, Yanqin; Zhang, Limin; Qiu, Huiling; Wu, Yike; Wang, Zhiwei; Zai, Yujia; Liu, Lin; Qu, Junle; Kang, Kang; Gou, Deming

2015-01-01

We herein describe a simple, sensitive and specific method for analysis of circulating microRNAs (miRNA), termed S-Poly(T) Plus real-time PCR assay. This new method is based on our previously developed S-Poly(T) method, in which a unique S-Poly(T) primer is used during reverse-transcription to increase sensitivity and specificity. Further increased sensitivity and simplicity of S-Poly(T) Plus, in comparison with the S-Poly(T) method, were achieved by a single-step, multiple-stage reaction, where RNAs were polyadenylated and reverse-transcribed at the same time. The sensitivity of circulating miRNA detection was further improved by a modified method of total RNA isolation from serum/plasma, S/P miRsol, in which glycogen was used to increase the RNA yield. We validated our methods by quantifying miRNA expression profiles in the sera of the patients with pulmonary arterial hypertension associated with congenital heart disease. In conclusion, we developed a simple, sensitive, and specific method for detecting circulating miRNAs that allows the measurement of 266 miRNAs from 100 μl of serum or plasma. This method presents a promising tool for basic miRNA research and clinical diagnosis of human diseases based on miRNA biomarkers. PMID:26459910

Delayed entanglement echo for individual control of a large number of nuclear spins

PubMed Central

Wang, Zhen-Yu; Casanova, Jorge; Plenio, Martin B.

2017-01-01

Methods to selectively detect and manipulate nuclear spins by single electrons of solid-state defects play a central role for quantum information processing and nanoscale nuclear magnetic resonance (NMR). However, with standard techniques, no more than eight nuclear spins have been resolved by a single defect centre. Here we develop a method that improves significantly the ability to detect, address and manipulate nuclear spins unambiguously and individually in a broad frequency band by using a nitrogen-vacancy (NV) centre as model system. On the basis of delayed entanglement control, a technique combining microwave and radio frequency fields, our method allows to selectively perform robust high-fidelity entangling gates between hardly resolved nuclear spins and the NV electron. Long-lived qubit memories can be naturally incorporated to our method for improved performance. The application of our ideas will increase the number of useful register qubits accessible to a defect centre and improve the signal of nanoscale NMR. PMID:28256508

Delayed entanglement echo for individual control of a large number of nuclear spins.

PubMed

Wang, Zhen-Yu; Casanova, Jorge; Plenio, Martin B

2017-03-03

Methods to selectively detect and manipulate nuclear spins by single electrons of solid-state defects play a central role for quantum information processing and nanoscale nuclear magnetic resonance (NMR). However, with standard techniques, no more than eight nuclear spins have been resolved by a single defect centre. Here we develop a method that improves significantly the ability to detect, address and manipulate nuclear spins unambiguously and individually in a broad frequency band by using a nitrogen-vacancy (NV) centre as model system. On the basis of delayed entanglement control, a technique combining microwave and radio frequency fields, our method allows to selectively perform robust high-fidelity entangling gates between hardly resolved nuclear spins and the NV electron. Long-lived qubit memories can be naturally incorporated to our method for improved performance. The application of our ideas will increase the number of useful register qubits accessible to a defect centre and improve the signal of nanoscale NMR.

A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors

PubMed Central

Xu, Zhengyi; Wei, Jianming; Zhang, Bo; Yang, Weijun

2015-01-01

This paper proposes a robust zero velocity (ZV) detector algorithm to accurately calculate stationary periods in a gait cycle. The proposed algorithm adopts an effective gait cycle segmentation method and introduces a Bayesian network (BN) model based on the measurements of inertial sensors and kinesiology knowledge to infer the ZV period. During the detected ZV period, an Extended Kalman Filter (EKF) is used to estimate the error states and calibrate the position error. The experiments reveal that the removal rate of ZV false detections by the proposed method increases 80% compared with traditional method at high walking speed. Furthermore, based on the detected ZV, the Personal Inertial Navigation System (PINS) algorithm aided by EKF performs better, especially in the altitude aspect. PMID:25831086

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

PubMed

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M; Grinberg, Lea T

2017-04-15

Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. Copyright © 2017 Elsevier B.V. All rights reserved.

Improved method increases sensitivity for circulating hepatocellular carcinoma cells

PubMed Central

Liu, Hui-Ying; Qian, Hai-Hua; Zhang, Xiao-Feng; Li, Jun; Yang, Xia; Sun, Bin; Ma, Jun-Yong; Chen, Lei; Yin, Zheng-Feng

2015-01-01

AIM: To improve an asialoglycoprotein receptor (ASGPR)-based enrichment method for detection of circulating tumor cells (CTCs) of hepatocellular carcinoma (HCC). METHODS: Peripheral blood samples were collected from healthy subjects, patients with HCC or various other cancers, and patients with hepatic lesions or hepatitis. CTCs were enriched from whole blood by extracting CD45-expressing leukocytes with monoclonal antibody coated-beads following density gradient centrifugation. The remaining cells were cytocentrifuged on polylysine-coated slides. Isolated cells were treated by triple immunofluorescence staining with CD45 antibody and a combination of antibodies against ASGPR and carbamoyl phosphate synthetase 1 (CPS1), used as liver-specific markers, and costained with DAPI. The cell slide was imaged and stained tumor cells that met preset criteria were counted. Recovery, sensitivity and specificity of the detection methods were determined and compared by spiking experiments with various types of cultured human tumor cell lines. Expression of ASGPR and CPS1 in cultured tumor cells and tumor tissue specimens was analyzed by flow cytometry and triple immunofluorescence staining, respectively. RESULTS: CD45 depletion of leukocytes resulted in a significantly greater recovery of multiple amounts of spiked HCC cells than the ASGPR+ selection (Ps < 0.05). The expression rates of either ASGPR or CPS1 were different in various liver cancer cell lines, ranging between 18% and 99% for ASGPR and between 9% and 98% for CPS1. In both human HCC tissues and liver cancer cell lines, there were a few HCC cells that did not stain positive for ASGPR or CPS1. The mixture of monoclonal antibodies against ASGPR and CPS1 identified more HCC cells than either antibody alone. However, these antibodies did not detect any tumor cells in blood samples spiked with the human breast cancer cell line MCF-7 and the human renal cancer cell line A498. ASGPR+ or/and CPS1+ CTCs were detected in 29/32 (91%) patients with HCC, but not in patients with any other kind of cancer or any of the other test subjects. Furthermore, the improved method detected a higher CTC count in all patients examined than did the previous method (P = 0.001), and consistently achieved 12%-21% higher sensitivity of CTC detection in all seven HCC patients with more than 40 CTCs. CONCLUSION: Negative depletion enrichment combined with identification using a mixture of antibodies against ASGPR and CPS1 improves sensitivity and specificity for detecting circulating HCC cells. PMID:25780289

An automatic fall detection framework using data fusion of Doppler radar and motion sensor network.

PubMed

Liu, Liang; Popescu, Mihail; Skubic, Marjorie; Rantz, Marilyn

2014-01-01

This paper describes the ongoing work of detecting falls in independent living senior apartments. We have developed a fall detection system with Doppler radar sensor and implemented ceiling radar in real senior apartments. However, the detection accuracy on real world data is affected by false alarms inherent in the real living environment, such as motions from visitors. To solve this issue, this paper proposes an improved framework by fusing the Doppler radar sensor result with a motion sensor network. As a result, performance is significantly improved after the data fusion by discarding the false alarms generated by visitors. The improvement of this new method is tested on one week of continuous data from an actual elderly person who frequently falls while living in her senior home.

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

Bueno, G.; Ruiz, M.; Sanchez, S

Breast cancer continues to be an important health problem between women population. Early detection is the only way to improve breast cancer prognosis and significantly reduce women mortality. It is by using CAD systems that radiologist can improve their ability to detect, and classify lesions in mammograms. In this study the usefulness of using B-spline based on a gradient scheme and compared to wavelet and adaptative filtering has been investigated for calcification lesion detection and as part of CAD systems. The technique has been applied to different density tissues. A qualitative validation shows the success of the method.

Brightness-preserving fuzzy contrast enhancement scheme for the detection and classification of diabetic retinopathy disease.

PubMed

Datta, Niladri Sekhar; Dutta, Himadri Sekhar; Majumder, Koushik

2016-01-01

The contrast enhancement of retinal image plays a vital role for the detection of microaneurysms (MAs), which are an early sign of diabetic retinopathy disease. A retinal image contrast enhancement method has been presented to improve the MA detection technique. The success rate on low-contrast noisy retinal image analysis shows the importance of the proposed method. Overall, 587 retinal input images are tested for performance analysis. The average sensitivity and specificity are obtained as 95.94% and 99.21%, respectively. The area under curve is found as 0.932 for the receiver operating characteristics analysis. The classifications of diabetic retinopathy disease are also performed here. The experimental results show that the overall MA detection method performs better than the current state-of-the-art MA detection algorithms.

Development and evaluation of modified envelope correlation method for deep tectonic tremor

NASA Astrophysics Data System (ADS)

Mizuno, N.; Ide, S.

2017-12-01

We develop a new location method for deep tectonic tremors, as an improvement of widely used envelope correlation method, and applied it to construct a tremor catalog in western Japan. Using the cross-correlation functions as objective functions and weighting components of data by the inverse of error variances, the envelope cross-correlation method is redefined as a maximum likelihood method. This method is also capable of multiple source detection, because when several events occur almost simultaneously, they appear as local maxima of likelihood.The average of weighted cross-correlation functions, defined as ACC, is a nonlinear function whose variable is a position of deep tectonic tremor. The optimization method has two steps. First, we fix the source depth to 30 km and use a grid search with 0.2 degree intervals to find the maxima of ACC, which are candidate event locations. Then, using each of the candidate locations as initial values, we apply a gradient method to determine horizontal and vertical components of a hypocenter. Sometimes, several source locations are determined in a time window of 5 minutes. We estimate the resolution, which is defined as a distance of sources to be detected separately by the location method, is about 100 km. The validity of this estimation is confirmed by a numerical test using synthetic waveforms. Applying to continuous seismograms in western Japan for over 10 years, the new method detected 27% more tremors than a previous method, owing to the multiple detection and improvement of accuracy by appropriate weighting scheme.

An improved method for direct estimation of free cyanide in drinking water by Ion Chromatography-Pulsed Amperometry Detection (IC-PAD) on gold working electrode.

PubMed

Kumar Meher, Alok; Labhsetwar, Nitin; Bansiwal, Amit

2018-02-01

In the present work a fast, reliable and safe Ion Exchange Chromatography-Pulsed Amperometry Detection (IC-PAD) method for direct determination of free cyanide in drinking water has been reported. To the best of our knowledge for the first time we are reporting the application of Gold working electrode for detection of free cyanide in a chromatography system. The system shows a wide linear range up to 8000µg/L. The electrode was found to have improved sensitivity and selectivity in the presence of interfering ions. The detection limit of the system was calculated to be 2µg/L. Long term evaluation of the electrode was found to be stable. Reproducible results were obtained from analysis of drinking water samples with recoveries of 98.3-101.2% and Relative Standard Deviations (RSD) of <2%. This study proves the potential application of the newly developed method for the analysis of free cyanide in drinking water. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Improvement of Forest Fire Detection Algorithm Using Brightness Temperature Lapse Rate Correction in HIMAWARI-8 IR Channels: Application to the 6 may 2017 Samcheok City, Korea

NASA Astrophysics Data System (ADS)

Park, S. H.; Park, W.; Jung, H. S.

2018-04-01

Forest fires are a major natural disaster that destroys a forest area and a natural environment. In order to minimize the damage caused by the forest fire, it is necessary to know the location and the time of day and continuous monitoring is required until fire is fully put out. We have tried to improve the forest fire detection algorithm by using a method to reduce the variability of surrounding pixels. We focused that forest areas of East Asia, part of the Himawari-8 AHI coverage, are mostly located in mountainous areas. The proposed method was applied to the forest fire detection in Samcheok city, Korea on May 6 to 10, 2017.

Water-Tree Modelling and Detection for Underground Cables

NASA Astrophysics Data System (ADS)

Chen, Qi

In recent years, aging infrastructure has become a major concern for the power industry. Since its inception in early 20th century, the electrical system has been the cornerstone of an industrial society. Stable and uninterrupted delivery of electrical power is now a base necessity for the modern world. As the times march-on, however, the electrical infrastructure ages and there is the inevitable need to renew and replace the existing system. Unfortunately, due to time and financial constraints, many electrical systems today are forced to operate beyond their original design and power utilities must find ways to prolong the lifespan of older equipment. Thus, the concept of preventative maintenance arises. Preventative maintenance allows old equipment to operate longer and at better efficiency, but in order to implement preventative maintenance, the operators must know minute details of the electrical system, especially some of the harder to assess issues such water-tree. Water-tree induced insulation degradation is a problem typically associated with older cable systems. It is a very high impedance phenomenon and it is difficult to detect using traditional methods such as Tan-Delta or Partial Discharge. The proposed dissertation studies water-tree development in underground cables, potential methods to detect water-tree location and water-tree severity estimation. The dissertation begins by developing mathematical models of water-tree using finite element analysis. The method focuses on surface-originated vented tree, the most prominent type of water-tree fault in the field. Using the standard operation parameters of North American electrical systems, the water-tree boundary conditions are defined. By applying finite element analysis technique, the complex water-tree structure is broken down to homogeneous components. The result is a generalized representation of water-tree capacitance at different stages of development. The result from the finite element analysis is used to model water-tree in large system. Both empirical measurements and the mathematical model show that the impedance of early-stage water-tree is extremely large. As the result, traditional detection methods such Tan-Delta or Partial Discharge are not effective due to the excessively high accuracy requirement. A high-frequency pulse detection method is developed instead. The water-tree impedance is capacitive in nature and it can be reduced to manageable level by high-frequency inputs. The method is able to determine the location of early-stage water-tree in long-distance cables using economically feasible equipment. A pattern recognition method is developed to estimate the severity of water-tree using its pulse response from the high-frequency test method. The early-warning system for water-tree appearance is a tool developed to assist the practical implementation of the high-frequency pulse detection method. Although the equipment used by the detection method is economically feasible, it is still a specialized test and not designed for constant monitoring of the system. The test also place heavy stress on the cable and it is most effective when the cable is taken offline. As the result, utilities need a method to estimate the likelihood of water-tree presence before subjecting the cable to the specialized test. The early-warning system takes advantage of naturally occurring high-frequency events in the system and uses a deviation-comparison method to estimate the probability of water-tree presence on the cable. If the likelihood is high, then the utility can use the high-frequency pulse detection method to obtain accurate results. Specific pulse response patterns can be used to calculate the capacitance of water-tree. The calculated result, however, is subjected to margins of error due to limitations from the real system. There are both long-term and short-term methods to improve the accuracy. Computation algorithm improvement allows immediate improvement on accuracy of the capacitance estimation. The probability distribution of the calculation solution showed that improvements in waveform time-step measurement allow fundamental improves to the overall result.

Assessing the accuracy of TDR-based water leak detection system

NASA Astrophysics Data System (ADS)

Fatemi Aghda, S. M.; GanjaliPour, K.; Nabiollahi, K.

2018-03-01

The use of TDR system to detect leakage locations in underground pipes has been developed in recent years. In this system, a bi-wire is installed in parallel with the underground pipes and is considered as a TDR sensor. This approach greatly covers the limitations arisen with using the traditional method of acoustic leak positioning. TDR based leak detection method is relatively accurate when the TDR sensor is in contact with water in just one point. Researchers have been working to improve the accuracy of this method in recent years. In this study, the ability of TDR method was evaluated in terms of the appearance of multi leakage points simultaneously. For this purpose, several laboratory tests were conducted. In these tests in order to simulate leakage points, the TDR sensor was put in contact with water at some points, then the number and the dimension of the simulated leakage points were gradually increased. The results showed that with the increase in the number and dimension of the leakage points, the error rate of the TDR-based water leak detection system increases. The authors tried, according to the results obtained from the laboratory tests, to develop a method to improve the accuracy of the TDR-based leak detection systems. To do that, they defined a few reference points on the TDR sensor. These points were created via increasing the distance between two conductors of TDR sensor and were easily identifiable in the TDR waveform. The tests were repeated again using the TDR sensor having reference points. In order to calculate the exact distance of the leakage point, the authors developed an equation in accordance to the reference points. A comparison between the results obtained from both tests (with and without reference points) showed that using the method and equation developed by the authors can significantly improve the accuracy of positioning the leakage points.

Task-based statistical image reconstruction for high-quality cone-beam CT

NASA Astrophysics Data System (ADS)

Dang, Hao; Webster Stayman, J.; Xu, Jennifer; Zbijewski, Wojciech; Sisniega, Alejandro; Mow, Michael; Wang, Xiaohui; Foos, David H.; Aygun, Nafi; Koliatsos, Vassilis E.; Siewerdsen, Jeffrey H.

2017-11-01

Task-based analysis of medical imaging performance underlies many ongoing efforts in the development of new imaging systems. In statistical image reconstruction, regularization is often formulated in terms to encourage smoothness and/or sharpness (e.g. a linear, quadratic, or Huber penalty) but without explicit formulation of the task. We propose an alternative regularization approach in which a spatially varying penalty is determined that maximizes task-based imaging performance at every location in a 3D image. We apply the method to model-based image reconstruction (MBIR—viz., penalized weighted least-squares, PWLS) in cone-beam CT (CBCT) of the head, focusing on the task of detecting a small, low-contrast intracranial hemorrhage (ICH), and we test the performance of the algorithm in the context of a recently developed CBCT prototype for point-of-care imaging of brain injury. Theoretical predictions of local spatial resolution and noise are computed via an optimization by which regularization (specifically, the quadratic penalty strength) is allowed to vary throughout the image to maximize local task-based detectability index ({{d}\\prime} ). Simulation studies and test-bench experiments were performed using an anthropomorphic head phantom. Three PWLS implementations were tested: conventional (constant) penalty; a certainty-based penalty derived to enforce constant point-spread function, PSF; and the task-based penalty derived to maximize local detectability at each location. Conventional (constant) regularization exhibited a fairly strong degree of spatial variation in {{d}\\prime} , and the certainty-based method achieved uniform PSF, but each exhibited a reduction in detectability compared to the task-based method, which improved detectability up to ~15%. The improvement was strongest in areas of high attenuation (skull base), where the conventional and certainty-based methods tended to over-smooth the data. The task-driven reconstruction method presents a promising regularization method in MBIR by explicitly incorporating task-based imaging performance as the objective. The results demonstrate improved ICH conspicuity and support the development of high-quality CBCT systems.

Multiple-Bit Differential Detection of OQPSK

NASA Technical Reports Server (NTRS)

Simon, Marvin

2005-01-01

A multiple-bit differential-detection method has been proposed for the reception of radio signals modulated with offset quadrature phase-shift keying (offset QPSK or OQPSK). The method is also applicable to other spectrally efficient offset quadrature modulations. This method is based partly on the same principles as those of a multiple-symbol differential-detection method for M-ary QPSK, which includes QPSK (that is, non-offset QPSK) as a special case. That method was introduced more than a decade ago by the author of the present method as a means of improving performance relative to a traditional (two-symbol observation) differential-detection scheme. Instead of symbol-by-symbol detection, both that method and the present one are based on a concept of maximum-likelihood sequence estimation (MLSE). As applied to the modulations in question, MLSE involves consideration of (1) all possible binary data sequences that could have been received during an observation time of some number, N, of symbol periods and (2) selection of the sequence that yields the best match to the noise-corrupted signal received during that time. The performance of the prior method was shown to range from that of traditional differential detection for short observation times (small N) to that of ideal coherent detection (with differential encoding) for long observation times (large N).

Impedance-based structural health monitoring of wind turbine blades

NASA Astrophysics Data System (ADS)

Pitchford, Corey; Grisso, Benjamin L.; Inman, Daniel J.

2007-04-01

Wind power is a fast-growing source of non-polluting, renewable energy with vast potential. However, current wind turbine technology must be improved before the potential of wind power can be fully realized. Wind turbine blades are one of the key components in improving this technology. Blade failure is very costly because it can damage other blades, the wind turbine itself, and possibly other wind turbines. A successful damage detection system incorporated into wind turbines could extend blade life and allow for less conservative designs. A damage detection method which has shown promise on a wide variety of structures is impedance-based structural health monitoring. The technique utilizes small piezoceramic (PZT) patches attached to a structure as self-sensing actuators to both excite the structure with high-frequency excitations, and monitor any changes in structural mechanical impedance. By monitoring the electrical impedance of the PZT, assessments can be made about the integrity of the mechanical structure. Recently, advances in hardware systems with onboard computing, including actuation and sensing, computational algorithms, and wireless telemetry, have improved the accessibility of the impedance method for in-field measurements. This paper investigates the feasibility of implementing such an onboard system inside of turbine blades as an in-field method of damage detection. Viability of onboard detection is accomplished by running a series of tests to verify the capability of the method on an actual wind turbine blade section from an experimental carbon/glass/balsa composite blade developed at Sandia National Laboratories.

SISGR: Room Temperature Single-Molecule Detection and Imaging by Stimulated Emission Microscopy

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

Xie, Xiaoliang Sunney

Single-molecule spectroscopy has made considerable impact on many disciplines including chemistry, physics, and biology. To date, most single-molecule spectroscopy work is accomplished by detecting fluorescence. On the other hand, many naturally occurring chromophores, such as retinal, hemoglobin and cytochromes, do not have detectable fluorescence. There is an emerging need for single-molecule spectroscopy techniques that do not require fluorescence. In the last proposal period, we have successfully demonstrated stimulated emission microscopy, single molecule absorption, and stimulated Raman microscopy based on a high-frequency modulation transfer technique. These first-of-a- kind new spectroscopy/microscopy methods tremendously improved our ability to observe molecules that fluorescence weakly,more » even to the limit of single molecule detection for absorption measurement. All of these methods employ two laser beams: one (pump beam) excites a single molecule to a real or virtual excited state, and the other (probe beam) monitors the absorption/emission property of the single. We extract the intensity change of the probe beam with high sensitivity by implementing a high-frequency phase-sensitive detection scheme, which offers orders of magnitude improvement in detection sensitivity over direct absorption/emission measurement. However, single molecule detection based on fluorescence or absorption is fundamentally limited due to their broad spectral response. It is important to explore other avenues in single molecule detection and imaging which provides higher molecular specificity for studying a wide variety of heterogeneous chemical and biological systems. This proposal aimed to achieve single-molecule detection sensitivity with near resonance stimulated Raman scattering (SRS) microscopy. SRS microscopy was developed in our lab as a powerful technique for imaging heterogeneous samples based on their intrinsic vibrational contrasts, which provides much higher molecular specificity than absorption and fluorescence. Current sensitivity limit of SRS microscopy has not yet reached single molecule detection. We proposed to capitalize on our state-of-the-art SRS microscopy and develop near-resonance enhanced SRS for single molecule detection of carotenoids and heme proteins. The specific aims we pursued are: (1) building the next SRS generation microscope that utilizes near resonance enhancement to allow detection and imaging of single molecules with undetectable fluorescence, such as -carotene. (2) using near-resonance SRS as a contrast mechanism to study dye-sensitize semiconductor interface, elucidating the heterogeneous electron ejection kinetics with high spatial and temporal resolution. (3) studying the binding and unbinding of oxygen in single hemoglobin molecules in order to gain molecular level understanding of the long-standing issue of cooperativity. The new methods developed in the fund period of this grant have advanced the detection sensitivity in many aspects. Near-resonance SRS improved the signal by using shorter wavelengths for SRS microscopy. Frequency modulation and multi-color SRS target the reduction of background to improve the chemical specificity of SRS while maintaining the high imaging speed. Time-domain coherent Raman scattering microscopy targets to reduce the noise floor of coherent Raman microscopy. These methods have already demonstrated first-of-a-kind new applications in biology and medical research. However, we are still one order of magnitude away from single molecule limit. It is important to continue to improve the laser specification and develop new imaging methods to finally achieve label-free single molecule microscopy.« less

Object detection system based on multimodel saliency maps

NASA Astrophysics Data System (ADS)

Guo, Ya'nan; Luo, Chongfan; Ma, Yide

2017-03-01

Detection of visually salient image regions is extensively applied in computer vision and computer graphics, such as object detection, adaptive compression, and object recognition, but any single model always has its limitations to various images, so in our work, we establish a method based on multimodel saliency maps to detect the object, which intelligently absorbs the merits of various individual saliency detection models to achieve promising results. The method can be roughly divided into three steps: in the first step, we propose a decision-making system to evaluate saliency maps obtained by seven competitive methods and merely select the three most valuable saliency maps; in the second step, we introduce heterogeneous PCNN algorithm to obtain three prime foregrounds; and then a self-designed nonlinear fusion method is proposed to merge these saliency maps; at last, the adaptive improved and simplified PCNN model is used to detect the object. Our proposed method can constitute an object detection system for different occasions, which requires no training, is simple, and highly efficient. The proposed saliency fusion technique shows better performance over a broad range of images and enriches the applicability range by fusing different individual saliency models, this proposed system is worthy enough to be called a strong model. Moreover, the proposed adaptive improved SPCNN model is stemmed from the Eckhorn's neuron model, which is skilled in image segmentation because of its biological background, and in which all the parameters are adaptive to image information. We extensively appraise our algorithm on classical salient object detection database, and the experimental results demonstrate that the aggregation of saliency maps outperforms the best saliency model in all cases, yielding highest precision of 89.90%, better recall rates of 98.20%, greatest F-measure of 91.20%, and lowest mean absolute error value of 0.057, the value of proposed saliency evaluation EHA reaches to 215.287. We deem our method can be wielded to diverse applications in the future.

A New Reassigned Spectrogram Method in Interference Detection for GNSS Receivers.

PubMed

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-09-02

Interference detection is very important for Global Navigation Satellite System (GNSS) receivers. Current work on interference detection in GNSS receivers has mainly focused on time-frequency (TF) analysis techniques, such as spectrogram and Wigner-Ville distribution (WVD), where the spectrogram approach presents the TF resolution trade-off problem, since the analysis window is used, and the WVD method suffers from the very serious cross-term problem, due to its quadratic TF distribution nature. In order to solve the cross-term problem and to preserve good TF resolution in the TF plane at the same time, in this paper, a new TF distribution by using a reassigned spectrogram has been proposed in interference detection for GNSS receivers. This proposed reassigned spectrogram method efficiently combines the elimination of the cross-term provided by the spectrogram itself according to its inherent nature and the improvement of the TF aggregation property achieved by the reassignment method. Moreover, a notch filter has been adopted in interference mitigation for GNSS receivers, where receiver operating characteristics (ROCs) are used as metrics for the characterization of interference mitigation performance. The proposed interference detection method by using a reassigned spectrogram is evaluated by experiments on GPS L1 signals in the disturbing scenarios in comparison to the state-of-the-art TF analysis approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-term problem and also keeps good TF localization properties, which has been proven to be valid and effective to enhance the interference Sensors 2015, 15 22168 detection performance; in addition, the adoption of the notch filter in interference mitigation has shown a significant acquisition performance improvement in terms of ROC curves for GNSS receivers in jamming environments.

A New Reassigned Spectrogram Method in Interference Detection for GNSS Receivers

PubMed Central

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-01-01

Interference detection is very important for Global Navigation Satellite System (GNSS) receivers. Current work on interference detection in GNSS receivers has mainly focused on time-frequency (TF) analysis techniques, such as spectrogram and Wigner–Ville distribution (WVD), where the spectrogram approach presents the TF resolution trade-off problem, since the analysis window is used, and the WVD method suffers from the very serious cross-term problem, due to its quadratic TF distribution nature. In order to solve the cross-term problem and to preserve good TF resolution in the TF plane at the same time, in this paper, a new TF distribution by using a reassigned spectrogram has been proposed in interference detection for GNSS receivers. This proposed reassigned spectrogram method efficiently combines the elimination of the cross-term provided by the spectrogram itself according to its inherent nature and the improvement of the TF aggregation property achieved by the reassignment method. Moreover, a notch filter has been adopted in interference mitigation for GNSS receivers, where receiver operating characteristics (ROCs) are used as metrics for the characterization of interference mitigation performance. The proposed interference detection method by using a reassigned spectrogram is evaluated by experiments on GPS L1 signals in the disturbing scenarios in comparison to the state-of-the-art TF analysis approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-term problem and also keeps good TF localization properties, which has been proven to be valid and effective to enhance the interference detection performance; in addition, the adoption of the notch filter in interference mitigation has shown a significant acquisition performance improvement in terms of ROC curves for GNSS receivers in jamming environments. PMID:26364637

Highly sensitive detection for proteins using graphene oxide-aptamer based sensors.

PubMed

Gao, Li; Li, Qin; Li, Raoqi; Yan, Lirong; Zhou, Yang; Chen, Keping; Shi, Haixia

2015-07-07

In recent years, the detection of proteins by using bare graphene oxide (GO) to quench the fluorescence of fluorescein-labeled aptamers has been reported. However, the proteins can be adsorbed on the surface of bare GO to prevent the sensitivity from further being improved. In order to solve this problem, polyethylene glycol (PEG)-protected GO was used to prevent the proteins using thrombin as an example from nonspecific binding. The detection limit was improved compared to bare GO under the optimized ratio of GO to PEG concentration. The results show that our method is a promising technique for the detection of proteins.

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.

Immunocapture reverse transcription-polymerase chain reaction combined with nested PCR greatly increases the detection of Prunus necrotic ring spot virus in the peach.

PubMed

Helguera, P R; Taborda, R; Docampo, D M; Ducasse, D A

2001-06-01

A detection system based on nested PCR after IC-RT-PCR (IC-RT-PCR-Nested PCR) was developed to improve indexing of Prunus necrotic ringspot virus in peach trees. Inhibitory effects and inconsistencies of the standard IC-RT-PCR were overcome by this approach. IC-RT-PCR-Nested PCR improved detection by three orders of magnitude compared with DAS-ELISA for the detection of PNRSV in leaves. Several different tissues were evaluated and equally consistent results were observed. The main advantages of the method are its consistency, high sensitivity and easy application in quarantine programs.

An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation

PubMed Central

Yuan, Xin; Martínez, José-Fernán; Eckert, Martina; López-Santidrián, Lourdes

2016-01-01

The main focus of this paper is on extracting features with SOund Navigation And Ranging (SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping (SLAM). According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. In combination with a contour detection algorithm, the foreground objects, although presenting different feature shapes, are separated much faster and more precisely than by other segmentation methods. Tests have been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and the maximum entropy TSM. For all the sonar images presented in this work, the computational time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which achieves the highest segmentation precision among the four above mentioned TSMs. As a result of the segmentations, the centroids of the main extracted regions have been computed to represent point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stage (as in classical Extended Kalman Filter (EKF)), includes an augmentation stage. During navigation, the robot localizes the centroids of different segments of features in sonar images, which are detected by our improved Otsu TSM, as point landmarks. Using them with the AEKF achieves more accurate and robust estimations of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM. Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM has achieved reliable detection of cycles in the map and consistent map update on loop closure, which is shown in simulated experiments. PMID:27455279

An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation.

PubMed

Yuan, Xin; Martínez, José-Fernán; Eckert, Martina; López-Santidrián, Lourdes

2016-07-22

The main focus of this paper is on extracting features with SOund Navigation And Ranging (SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping (SLAM). According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. In combination with a contour detection algorithm, the foreground objects, although presenting different feature shapes, are separated much faster and more precisely than by other segmentation methods. Tests have been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and the maximum entropy TSM. For all the sonar images presented in this work, the computational time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which achieves the highest segmentation precision among the four above mentioned TSMs. As a result of the segmentations, the centroids of the main extracted regions have been computed to represent point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stage (as in classical Extended Kalman Filter (EKF)), includes an augmentation stage. During navigation, the robot localizes the centroids of different segments of features in sonar images, which are detected by our improved Otsu TSM, as point landmarks. Using them with the AEKF achieves more accurate and robust estimations of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM. Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM has achieved reliable detection of cycles in the map and consistent map update on loop closure, which is shown in simulated experiments.

Design of an Evolutionary Approach for Intrusion Detection

PubMed Central

2013-01-01

A novel evolutionary approach is proposed for effective intrusion detection based on benchmark datasets. The proposed approach can generate a pool of noninferior individual solutions and ensemble solutions thereof. The generated ensembles can be used to detect the intrusions accurately. For intrusion detection problem, the proposed approach could consider conflicting objectives simultaneously like detection rate of each attack class, error rate, accuracy, diversity, and so forth. The proposed approach can generate a pool of noninferior solutions and ensembles thereof having optimized trade-offs values of multiple conflicting objectives. In this paper, a three-phase, approach is proposed to generate solutions to a simple chromosome design in the first phase. In the first phase, a Pareto front of noninferior individual solutions is approximated. In the second phase of the proposed approach, the entire solution set is further refined to determine effective ensemble solutions considering solution interaction. In this phase, another improved Pareto front of ensemble solutions over that of individual solutions is approximated. The ensemble solutions in improved Pareto front reported improved detection results based on benchmark datasets for intrusion detection. In the third phase, a combination method like majority voting method is used to fuse the predictions of individual solutions for determining prediction of ensemble solution. Benchmark datasets, namely, KDD cup 1999 and ISCX 2012 dataset, are used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover individual solutions and ensemble solutions thereof with a good support and a detection rate from benchmark datasets (in comparison with well-known ensemble methods like bagging and boosting). In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives, and a dataset can be represented in the form of labelled instances in terms of its features. PMID:24376390

Mammographic enhancement with combining local statistical measures and sliding band filter for improved mass segmentation in mammograms

NASA Astrophysics Data System (ADS)

Kim, Dae Hoe; Choi, Jae Young; Choi, Seon Hyeong; Ro, Yong Man

2012-03-01

In this study, a novel mammogram enhancement solution is proposed, aiming to improve the quality of subsequent mass segmentation in mammograms. It has been widely accepted that characteristics of masses are usually hyper-dense or uniform density with respect to its background. Also, their core parts are likely to have high-intensity values while the values of intensity tend to be decreased as the distance to core parts increases. Based on the aforementioned observations, we develop a new and effective mammogram enhancement method by combining local statistical measurements and Sliding Band Filtering (SBF). By effectively combining local statistical measurements and SBF, we are able to improve the contrast of the bright and smooth regions (which represent potential mass regions), as well as, at the same time, the regions where their surrounding gradients are converging to the centers of regions of interest. In this study, 89 mammograms were collected from the public MAIS database (DB) to demonstrate the effectiveness of the proposed enhancement solution in terms of improving mass segmentation. As for a segmentation method, widely used contour-based segmentation approach was employed. The contour-based method in conjunction with the proposed enhancement solution achieved overall detection accuracy of 92.4% with a total of 85 correct cases. On the other hand, without using our enhancement solution, overall detection accuracy of the contour-based method was only 78.3%. In addition, experimental results demonstrated the feasibility of our enhancement solution for the purpose of improving detection accuracy on mammograms containing dense parenchymal patterns.

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

PubMed Central

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

2016-01-01

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

Detection method of visible and invisible nipples on digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

Bias correction for estimated QTL effects using the penalized maximum likelihood method.

PubMed

Zhang, J; Yue, C; Zhang, Y-M

2012-04-01

A penalized maximum likelihood method has been proposed as an important approach to the detection of epistatic quantitative trait loci (QTL). However, this approach is not optimal in two special situations: (1) closely linked QTL with effects in opposite directions and (2) small-effect QTL, because the method produces downwardly biased estimates of QTL effects. The present study aims to correct the bias by using correction coefficients and shifting from the use of a uniform prior on the variance parameter of a QTL effect to that of a scaled inverse chi-square prior. The results of Monte Carlo simulation experiments show that the improved method increases the power from 25 to 88% in the detection of two closely linked QTL of equal size in opposite directions and from 60 to 80% in the identification of QTL with small effects (0.5% of the total phenotypic variance). We used the improved method to detect QTL responsible for the barley kernel weight trait using 145 doubled haploid lines developed in the North American Barley Genome Mapping Project. Application of the proposed method to other shrinkage estimation of QTL effects is discussed.

Improving prokaryotic transposable elements identification using a combination of de novo and profile HMM methods.

PubMed

Kamoun, Choumouss; Payen, Thibaut; Hua-Van, Aurélie; Filée, Jonathan

2013-10-11

Insertion Sequences (ISs) and their non-autonomous derivatives (MITEs) are important components of prokaryotic genomes inducing duplication, deletion, rearrangement or lateral gene transfers. Although ISs and MITEs are relatively simple and basic genetic elements, their detection remains a difficult task due to their remarkable sequence diversity. With the advent of high-throughput genome and metagenome sequencing technologies, the development of fast, reliable and sensitive methods of ISs and MITEs detection become an important challenge. So far, almost all studies dealing with prokaryotic transposons have used classical BLAST-based detection methods against reference libraries. Here we introduce alternative methods of detection either taking advantages of the structural properties of the elements (de novo methods) or using an additional library-based method using profile HMM searches. In this study, we have developed three different work flows dedicated to ISs and MITEs detection: the first two use de novo methods detecting either repeated sequences or presence of Inverted Repeats; the third one use 28 in-house transposase alignment profiles with HMM search methods. We have compared the respective performances of each method using a reference dataset of 30 archaeal and 30 bacterial genomes in addition to simulated and real metagenomes. Compared to a BLAST-based method using ISFinder as library, de novo methods significantly improve ISs and MITEs detection. For example, in the 30 archaeal genomes, we discovered 30 new elements (+20%) in addition to the 141 multi-copies elements already detected by the BLAST approach. Many of the new elements correspond to ISs belonging to unknown or highly divergent families. The total number of MITEs has even doubled with the discovery of elements displaying very limited sequence similarities with their respective autonomous partners (mainly in the Inverted Repeats of the elements). Concerning metagenomes, with the exception of short reads data (<300 bp) for which both techniques seem equally limited, profile HMM searches considerably ameliorate the detection of transposase encoding genes (up to +50%) generating low level of false positives compare to BLAST-based methods. Compared to classical BLAST-based methods, the sensitivity of de novo and profile HMM methods developed in this study allow a better and more reliable detection of transposons in prokaryotic genomes and metagenomes. We believed that future studies implying ISs and MITEs identification in genomic data should combine at least one de novo and one library-based method, with optimal results obtained by running the two de novo methods in addition to a library-based search. For metagenomic data, profile HMM search should be favored, a BLAST-based step is only useful to the final annotation into groups and families.

Optical Detection of Ultrasound in Photoacoustic Imaging

PubMed Central

Dong, Biqin; Sun, Cheng; Zhang, Hao F.

2017-01-01

Objective Photoacoustic (PA) imaging emerges as a unique tool to study biological samples based on optical absorption contrast. In PA imaging, piezoelectric transducers are commonly used to detect laser-induced ultrasonic waves. However, they typically lack adequate broadband sensitivity at ultrasonic frequency higher than 100 MHz while their bulky size and optically opaque nature cause technical difficulties in integrating PA imaging with conventional optical imaging modalities. To overcome these limitations, optical methods of ultrasound detection were developed and shown their unique applications in photoacoustic imaging. Methods We provide an overview of recent technological advances in optical methods of ultrasound detection and their applications in PA imaging. A general theoretical framework describing sensitivity, bandwidth, and angular responses of optical ultrasound detection is also introduced. Results Optical methods of ultrasound detection can provide improved detection angle and sensitivity over significantly extended bandwidth. In addition, its versatile variants also offer additional advantages, such as device miniaturization, optical transparency, mechanical flexibility, minimal electrical/mechanical crosstalk, and potential noncontact PA imaging. Conclusion The optical ultrasound detection methods discussed in this review and their future evolution may play an important role in photoacoustic imaging for biomedical study and clinical diagnosis. PMID:27608445

Comprehensive Detection of Gas Plumes from Multibeam Water Column Images with Minimisation of Noise Interferences

PubMed Central

Zhao, Jianhu; Zhang, Hongmei; Wang, Shiqi

2017-01-01

Multibeam echosounder systems (MBES) can record backscatter strengths of gas plumes in the water column (WC) images that may be an indicator of possible occurrence of gas at certain depths. Manual or automatic detection is generally adopted in finding gas plumes, but frequently results in low efficiency and high false detection rates because of WC images that are polluted by noise. To improve the efficiency and reliability of the detection, a comprehensive detection method is proposed in this paper. In the proposed method, the characteristics of WC background noise are first analyzed and given. Then, the mean standard deviation threshold segmentations are respectively used for the denoising of time-angle and depth-angle images, an intersection operation is performed for the two segmented images to further weaken noise in the WC data, and the gas plumes in the WC data are detected from the intersection image by the morphological constraint. The proposed method was tested by conducting shallow-water and deepwater experiments. In these experiments, the detections were conducted automatically and higher correct detection rates than the traditional methods were achieved. The performance of the proposed method is analyzed and discussed. PMID:29186014

Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

NASA Astrophysics Data System (ADS)

Li, Hong; Ding, Xue

2017-03-01

This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

Round-robin comparison of methods for the detection of human enteric viruses in lettuce.

PubMed

Le Guyader, Françoise S; Schultz, Anna-Charlotte; Haugarreau, Larissa; Croci, Luciana; Maunula, Leena; Duizer, Erwin; Lodder-Verschoor, Froukje; von Bonsdorff, Carl-Henrik; Suffredini, Elizabetha; van der Poel, Wim M M; Reymundo, Rosanna; Koopmans, Marion

2004-10-01

Five methods that detect human enteric virus contamination in lettuce were compared. To mimic multiple contaminations as observed after sewage contamination, artificial contamination was with human calicivirus and poliovirus and animal calicivirus strains at different concentrations. Nucleic acid extractions were done at the same time in the same laboratory to reduce assay-to-assay variability. Results showed that the two critical steps are the washing step and removal of inhibitors. The more reliable methods (sensitivity, simplicity, low cost) included an elution/concentration step and a commercial kit. Such development of sensitive methods for viral detection in foods other than shellfish is important to improve food safety.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Dermoscopy, Digital Dermoscopy and Other Diagnostic Tools in the Early Detection of Melanoma and Follow-up of High-risk Skin Cancer Patients.

PubMed

Thomas, Luc; Puig, Susana

2017-07-05

Early detection is a key strategy for reducing the mortality and economic burden associated with melanoma. Dermoscopy is a non-invasive and cost-effective tool for melanoma diagnosis, which has been shown to be a reliable and sensitive method for detecting early-stage skin cancer and reducing the number of unnecessary excisions. Patients at high risk of developing melanoma require long-term surveillance. Use of digital dermoscopy follow-up of these patients has led to improved outcomes. Combined follow-up programmes using total-body photography and digital dermoscopy have led to further improvements in early diagnosis and diagnostic accuracy. Dermoscopy is now widely used by dermatologists, but the public health impact of this tool is yet to be evaluated. Despite the clear advantages of dermoscopy and digital follow-up meth-ods, dermoscopy training and access to digital dermoscopy among dermatologists and general practitioners needs to be improved.

Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection.

PubMed

Bogaarts, J G; Hilkman, D M W; Gommer, E D; van Kranen-Mastenbroek, V H J M; Reulen, J P H

2016-12-01

Continuous electroencephalographic monitoring of critically ill patients is an established procedure in intensive care units. Seizure detection algorithms, such as support vector machines (SVM), play a prominent role in this procedure. To correct for inter-human differences in EEG characteristics, as well as for intra-human EEG variability over time, dynamic EEG feature normalization is essential. Recently, the median decaying memory (MDM) approach was determined to be the best method of normalization. MDM uses a sliding baseline buffer of EEG epochs to calculate feature normalization constants. However, while this method does include non-seizure EEG epochs, it also includes EEG activity that can have a detrimental effect on the normalization and subsequent seizure detection performance. In this study, EEG data that is to be incorporated into the baseline buffer are automatically selected based on a novelty detection algorithm (Novelty-MDM). Performance of an SVM-based seizure detection framework is evaluated in 17 long-term ICU registrations using the area under the sensitivity-specificity ROC curve. This evaluation compares three different EEG normalization methods, namely a fixed baseline buffer (FB), the median decaying memory (MDM) approach, and our novelty median decaying memory (Novelty-MDM) method. It is demonstrated that MDM did not improve overall performance compared to FB (p < 0.27), partly because seizure like episodes were included in the baseline. More importantly, Novelty-MDM significantly outperforms both FB (p = 0.015) and MDM (p = 0.0065).

Location detection and tracking of moving targets by a 2D IR-UWB radar system.

PubMed

Nguyen, Van-Han; Pyun, Jae-Young

2015-03-19

In indoor environments, the Global Positioning System (GPS) and long-range tracking radar systems are not optimal, because of signal propagation limitations in the indoor environment. In recent years, the use of ultra-wide band (UWB) technology has become a possible solution for object detection, localization and tracking in indoor environments, because of its high range resolution, compact size and low cost. This paper presents improved target detection and tracking techniques for moving objects with impulse-radio UWB (IR-UWB) radar in a short-range indoor area. This is achieved through signal-processing steps, such as clutter reduction, target detection, target localization and tracking. In this paper, we introduce a new combination consisting of our proposed signal-processing procedures. In the clutter-reduction step, a filtering method that uses a Kalman filter (KF) is proposed. Then, in the target detection step, a modification of the conventional CLEAN algorithm which is used to estimate the impulse response from observation region is applied for the advanced elimination of false alarms. Then, the output is fed into the target localization and tracking step, in which the target location and trajectory are determined and tracked by using unscented KF in two-dimensional coordinates. In each step, the proposed methods are compared to conventional methods to demonstrate the differences in performance. The experiments are carried out using actual IR-UWB radar under different scenarios. The results verify that the proposed methods can improve the probability and efficiency of target detection and tracking.

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

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system.

PubMed

Wang, Baofeng; Qi, Zhiquan; Chen, Sizhong; Liu, Zhaodu; Ma, Guocheng

2017-01-01

Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.

Rapid and improved gas-liquid chromatography technique for detection of hippurate hydrolysis by Campylobacter jejuni and Campylobacter coli.

PubMed Central

Bär, W; Fricke, G

1987-01-01

A gas-liquid chromatographic method which requires no chloroform extraction of the split products has been investigated for the detection of hippurate hydrolysis by Campylobacter spp. This technique gave better reproducibility than other tests also used in this study and allows the routine use of the gas-liquid chromatographic method for identification of Campylobacter isolates. PMID:3654950

Accelerated SPECT Monte Carlo Simulation Using Multiple Projection Sampling and Convolution-Based Forced Detection

NASA Astrophysics Data System (ADS)

Liu, Shaoying; King, Michael A.; Brill, Aaron B.; Stabin, Michael G.; Farncombe, Troy H.

2008-02-01

Monte Carlo (MC) is a well-utilized tool for simulating photon transport in single photon emission computed tomography (SPECT) due to its ability to accurately model physical processes of photon transport. As a consequence of this accuracy, it suffers from a relatively low detection efficiency and long computation time. One technique used to improve the speed of MC modeling is the effective and well-established variance reduction technique (VRT) known as forced detection (FD). With this method, photons are followed as they traverse the object under study but are then forced to travel in the direction of the detector surface, whereby they are detected at a single detector location. Another method, called convolution-based forced detection (CFD), is based on the fundamental idea of FD with the exception that detected photons are detected at multiple detector locations and determined with a distance-dependent blurring kernel. In order to further increase the speed of MC, a method named multiple projection convolution-based forced detection (MP-CFD) is presented. Rather than forcing photons to hit a single detector, the MP-CFD method follows the photon transport through the object but then, at each scatter site, forces the photon to interact with a number of detectors at a variety of angles surrounding the object. This way, it is possible to simulate all the projection images of a SPECT simulation in parallel, rather than as independent projections. The result of this is vastly improved simulation time as much of the computation load of simulating photon transport through the object is done only once for all projection angles. The results of the proposed MP-CFD method agrees well with the experimental data in measurements of point spread function (PSF), producing a correlation coefficient (r2) of 0.99 compared to experimental data. The speed of MP-CFD is shown to be about 60 times faster than a regular forced detection MC program with similar results.

Breast mass detection in mammography and tomosynthesis via fully convolutional network-based heatmap regression

NASA Astrophysics Data System (ADS)

Zhang, Jun; Cain, Elizabeth Hope; Saha, Ashirbani; Zhu, Zhe; Mazurowski, Maciej A.

2018-02-01

Breast mass detection in mammography and digital breast tomosynthesis (DBT) is an essential step in computerized breast cancer analysis. Deep learning-based methods incorporate feature extraction and model learning into a unified framework and have achieved impressive performance in various medical applications (e.g., disease diagnosis, tumor detection, and landmark detection). However, these methods require large-scale accurately annotated data. Unfortunately, it is challenging to get precise annotations of breast masses. To address this issue, we propose a fully convolutional network (FCN) based heatmap regression method for breast mass detection, using only weakly annotated mass regions in mammography images. Specifically, we first generate heat maps of masses based on human-annotated rough regions for breast masses. We then develop an FCN model for end-to-end heatmap regression with an F-score loss function, where the mammography images are regarded as the input and heatmaps for breast masses are used as the output. Finally, the probability map of mass locations can be estimated with the trained model. Experimental results on a mammography dataset with 439 subjects demonstrate the effectiveness of our method. Furthermore, we evaluate whether we can use mammography data to improve detection models for DBT, since mammography shares similar structure with tomosynthesis. We propose a transfer learning strategy by fine-tuning the learned FCN model from mammography images. We test this approach on a small tomosynthesis dataset with only 40 subjects, and we show an improvement in the detection performance as compared to training the model from scratch.

Assisted inhibition effect of acetylcholinesterase with n-octylphosphonic acid and application in high sensitive detection of organophosphorous pesticides by matrix-assisted laser desorption/ionization Fourier transform mass spectrometry.

PubMed

Cai, Tingting; Zhang, Li; Wang, Haoyang; Zhang, Jing; Guo, Yinlong

2011-11-14

A simple and practical approach to improve the sensitivity of acetylcholinesterase (AChE)-inhibited method has been developed for monitoring organophosphorous (OP) pesticide residues. In this work, matrix-assisted laser desorption/ionization Fourier transform mass spectrometry (MALDI-FTMS) was used to detect AChE activity. Due to its good salt-tolerance and low sample consumption, MALDI-FTMS facilitates rapid and high-throughput screening of OP pesticides. Here we describe a new method to obtain low detection limits via employing external reagents. Among candidate compounds, n-octylphosphonic acid (n-Octyl-PA) displays assistant effect to enhance AChE inhibition by OP pesticides. In presence of n-Octyl-PA, the percentages of AChE inhibition still kept correlation with OP pesticide concentrations. The detection limits were improved significantly even by 10(2)-10(3) folds in comparison with conventional enzyme-inhibited methods. Different detection limits of OP pesticides with different toxicities were as low as 0.005 μg L(-1) for high toxic pesticides and 0.05 μg L(-1) for low toxic pesticides. Besides, the reliability of results from this method to analyze cowpea samples had been demonstrated by liquid-chromatography tandem mass spectrometry (LC-MS/MS). The application of this commercial available assistant agent shows great promise to detect OP compounds in complicated biological matrix and broadens the mind for high sensitivity detection of OP pesticide residues in agricultural products. Copyright © 2011 Elsevier B.V. All rights reserved.

A simple method for low-contrast detectability, image quality and dose optimisation with CT iterative reconstruction algorithms and model observers.

PubMed

Bellesi, Luca; Wyttenbach, Rolf; Gaudino, Diego; Colleoni, Paolo; Pupillo, Francesco; Carrara, Mauro; Braghetti, Antonio; Puligheddu, Carla; Presilla, Stefano

2017-01-01

The aim of this work was to evaluate detection of low-contrast objects and image quality in computed tomography (CT) phantom images acquired at different tube loadings (i.e. mAs) and reconstructed with different algorithms, in order to find appropriate settings to reduce the dose to the patient without any image detriment. Images of supraslice low-contrast objects of a CT phantom were acquired using different mAs values. Images were reconstructed using filtered back projection (FBP), hybrid and iterative model-based methods. Image quality parameters were evaluated in terms of modulation transfer function; noise, and uniformity using two software resources. For the definition of low-contrast detectability, studies based on both human (i.e. four-alternative forced-choice test) and model observers were performed across the various images. Compared to FBP, image quality parameters were improved by using iterative reconstruction (IR) algorithms. In particular, IR model-based methods provided a 60% noise reduction and a 70% dose reduction, preserving image quality and low-contrast detectability for human radiological evaluation. According to the model observer, the diameters of the minimum detectable detail were around 2 mm (up to 100 mAs). Below 100 mAs, the model observer was unable to provide a result. IR methods improve CT protocol quality, providing a potential dose reduction while maintaining a good image detectability. Model observer can in principle be useful to assist human performance in CT low-contrast detection tasks and in dose optimisation.

An improved partial least-squares regression method for Raman spectroscopy

NASA Astrophysics Data System (ADS)

Momenpour Tehran Monfared, Ali; Anis, Hanan

2017-10-01

It is known that the performance of partial least-squares (PLS) regression analysis can be improved using the backward variable selection method (BVSPLS). In this paper, we further improve the BVSPLS based on a novel selection mechanism. The proposed method is based on sorting the weighted regression coefficients, and then the importance of each variable of the sorted list is evaluated using root mean square errors of prediction (RMSEP) criterion in each iteration step. Our Improved BVSPLS (IBVSPLS) method has been applied to leukemia and heparin data sets and led to an improvement in limit of detection of Raman biosensing ranged from 10% to 43% compared to PLS. Our IBVSPLS was also compared to the jack-knifing (simpler) and Genetic Algorithm (more complex) methods. Our method was consistently better than the jack-knifing method and showed either a similar or a better performance compared to the genetic algorithm.

Improved MIMO radar GMTI via cyclic-shift transmission of orthogonal frequency division signals

NASA Astrophysics Data System (ADS)

Li, Fuyou; He, Feng; Dong, Zhen; Wu, Manqing

2018-05-01

Minimum detectable velocity (MDV) and maximum detectable velocity are both important in ground moving target indication (GMTI) systems. Smaller MDV can be achieved by longer baseline via multiple-input multiple-output (MIMO) radar. Maximum detectable velocity is decided by blind velocities associated with carrier frequencies, and blind velocities can be mitigated by orthogonal frequency division signals. However, the scattering echoes from different carrier frequencies are independent, which is not good for improving MDV performance. An improved cyclic-shift transmission is applied in MIMO GMTI system in this paper. MDV performance is improved due to the longer baseline, and maximum detectable velocity performance is improved due to the mitigation of blind velocities via multiple carrier frequencies. The signal model for this mode is established, the principle of mitigating blind velocities with orthogonal frequency division signals is presented; the performance of different MIMO GMTI waveforms is analysed; and the performance of different array configurations is analysed. Simulation results by space-time-frequency adaptive processing proves that our proposed method is a valid way to improve GMTI performance.

Development of a Sensitive Luciferase-Based Sandwich ELISA System for the Detection of Human Extracellular Matrix 1 Protein.

PubMed

Li, Ya; Li, Yanqing; Zhao, Junli; Zheng, Xiaojing; Mao, Qinwen; Xia, Haibin

2016-12-01

Enzyme-linked immunosorbent assay (ELISA) has been one of the main methods for detecting an antigen in an aqueous sample for more than four decades. Nowadays, one of the biggest concerns for ELISA is still how to improve the sensitivity of the assay, and the luciferase-luciferin reaction system has been noticed as a new detection method with high sensitivity. In this study, a luciferin-luciferase reaction system was used as the detection method for a sandwich ELISA system. It was shown that this new system led to an increase in the detection sensitivity of at least two times when compared with the traditional horseradish peroxidase (HRP) detection method. Lastly, the serum levels of the human extracellular matrix 1 protein of breast cancer patients were determined by the new system, which were overall similar to the HRP chemiluminescent system. Furthermore, this new luciferase reporter can be implemented into other ELISA systems for the purpose of increasing the assay sensitivity.

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

NASA Astrophysics Data System (ADS)

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

2013-09-01

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

Where to restore ecological connectivity? Detecting barriers and quantifying restoration benefits.

PubMed

McRae, Brad H; Hall, Sonia A; Beier, Paul; Theobald, David M

2012-01-01

Landscape connectivity is crucial for many ecological processes, including dispersal, gene flow, demographic rescue, and movement in response to climate change. As a result, governmental and non-governmental organizations are focusing efforts to map and conserve areas that facilitate movement to maintain population connectivity and promote climate adaptation. In contrast, little focus has been placed on identifying barriers-landscape features which impede movement between ecologically important areas-where restoration could most improve connectivity. Yet knowing where barriers most strongly reduce connectivity can complement traditional analyses aimed at mapping best movement routes. We introduce a novel method to detect important barriers and provide example applications. Our method uses GIS neighborhood analyses in conjunction with effective distance analyses to detect barriers that, if removed, would significantly improve connectivity. Applicable in least-cost, circuit-theoretic, and simulation modeling frameworks, the method detects both complete (impermeable) barriers and those that impede but do not completely block movement. Barrier mapping complements corridor mapping by broadening the range of connectivity conservation alternatives available to practitioners. The method can help practitioners move beyond maintaining currently important areas to restoring and enhancing connectivity through active barrier removal. It can inform decisions on trade-offs between restoration and protection; for example, purchasing an intact corridor may be substantially more costly than restoring a barrier that blocks an alternative corridor. And it extends the concept of centrality to barriers, highlighting areas that most diminish connectivity across broad networks. Identifying which modeled barriers have the greatest impact can also help prioritize error checking of land cover data and collection of field data to improve connectivity maps. Barrier detection provides a different way to view the landscape, broadening thinking about connectivity and fragmentation while increasing conservation options.

Where to Restore Ecological Connectivity? Detecting Barriers and Quantifying Restoration Benefits

PubMed Central

McRae, Brad H.; Hall, Sonia A.; Beier, Paul; Theobald, David M.

2012-01-01

Landscape connectivity is crucial for many ecological processes, including dispersal, gene flow, demographic rescue, and movement in response to climate change. As a result, governmental and non-governmental organizations are focusing efforts to map and conserve areas that facilitate movement to maintain population connectivity and promote climate adaptation. In contrast, little focus has been placed on identifying barriers—landscape features which impede movement between ecologically important areas—where restoration could most improve connectivity. Yet knowing where barriers most strongly reduce connectivity can complement traditional analyses aimed at mapping best movement routes. We introduce a novel method to detect important barriers and provide example applications. Our method uses GIS neighborhood analyses in conjunction with effective distance analyses to detect barriers that, if removed, would significantly improve connectivity. Applicable in least-cost, circuit-theoretic, and simulation modeling frameworks, the method detects both complete (impermeable) barriers and those that impede but do not completely block movement. Barrier mapping complements corridor mapping by broadening the range of connectivity conservation alternatives available to practitioners. The method can help practitioners move beyond maintaining currently important areas to restoring and enhancing connectivity through active barrier removal. It can inform decisions on trade-offs between restoration and protection; for example, purchasing an intact corridor may be substantially more costly than restoring a barrier that blocks an alternative corridor. And it extends the concept of centrality to barriers, highlighting areas that most diminish connectivity across broad networks. Identifying which modeled barriers have the greatest impact can also help prioritize error checking of land cover data and collection of field data to improve connectivity maps. Barrier detection provides a different way to view the landscape, broadening thinking about connectivity and fragmentation while increasing conservation options. PMID:23300719

Development and validation of a 48-target analytical method for high-throughput monitoring of genetically modified organisms.

PubMed

Li, Xiaofei; Wu, Yuhua; Li, Jun; Li, Yunjing; Long, Likun; Li, Feiwu; Wu, Gang

2015-01-05

The rapid increase in the number of genetically modified (GM) varieties has led to a demand for high-throughput methods to detect genetically modified organisms (GMOs). We describe a new dynamic array-based high throughput method to simultaneously detect 48 targets in 48 samples on a Fludigm system. The test targets included species-specific genes, common screening elements, most of the Chinese-approved GM events, and several unapproved events. The 48 TaqMan assays successfully amplified products from both single-event samples and complex samples with a GMO DNA amount of 0.05 ng, and displayed high specificity. To improve the sensitivity of detection, a preamplification step for 48 pooled targets was added to enrich the amount of template before performing dynamic chip assays. This dynamic chip-based method allowed the synchronous high-throughput detection of multiple targets in multiple samples. Thus, it represents an efficient, qualitative method for GMO multi-detection.

Development and Validation of A 48-Target Analytical Method for High-throughput Monitoring of Genetically Modified Organisms

PubMed Central

Li, Xiaofei; Wu, Yuhua; Li, Jun; Li, Yunjing; Long, Likun; Li, Feiwu; Wu, Gang

2015-01-01

The rapid increase in the number of genetically modified (GM) varieties has led to a demand for high-throughput methods to detect genetically modified organisms (GMOs). We describe a new dynamic array-based high throughput method to simultaneously detect 48 targets in 48 samples on a Fludigm system. The test targets included species-specific genes, common screening elements, most of the Chinese-approved GM events, and several unapproved events. The 48 TaqMan assays successfully amplified products from both single-event samples and complex samples with a GMO DNA amount of 0.05 ng, and displayed high specificity. To improve the sensitivity of detection, a preamplification step for 48 pooled targets was added to enrich the amount of template before performing dynamic chip assays. This dynamic chip-based method allowed the synchronous high-throughput detection of multiple targets in multiple samples. Thus, it represents an efficient, qualitative method for GMO multi-detection. PMID:25556930

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

Automatic Defect Detection for TFT-LCD Array Process Using Quasiconformal Kernel Support Vector Data Description

PubMed Central

Liu, Yi-Hung; Chen, Yan-Jen

2011-01-01

Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms. PMID:22016625

Automatic defect detection for TFT-LCD array process using quasiconformal kernel support vector data description.

PubMed

Liu, Yi-Hung; Chen, Yan-Jen

2011-01-01

Defect detection has been considered an efficient way to increase the yield rate of panels in thin film transistor liquid crystal display (TFT-LCD) manufacturing. In this study we focus on the array process since it is the first and key process in TFT-LCD manufacturing. Various defects occur in the array process, and some of them could cause great damage to the LCD panels. Thus, how to design a method that can robustly detect defects from the images captured from the surface of LCD panels has become crucial. Previously, support vector data description (SVDD) has been successfully applied to LCD defect detection. However, its generalization performance is limited. In this paper, we propose a novel one-class machine learning method, called quasiconformal kernel SVDD (QK-SVDD) to address this issue. The QK-SVDD can significantly improve generalization performance of the traditional SVDD by introducing the quasiconformal transformation into a predefined kernel. Experimental results, carried out on real LCD images provided by an LCD manufacturer in Taiwan, indicate that the proposed QK-SVDD not only obtains a high defect detection rate of 96%, but also greatly improves generalization performance of SVDD. The improvement has shown to be over 30%. In addition, results also show that the QK-SVDD defect detector is able to accomplish the task of defect detection on an LCD image within 60 ms.

Predominant Bacteria Detected from the Middle Ear Fluid of Children Experiencing Otitis Media: A Systematic Review.

PubMed

Ngo, Chinh C; Massa, Helen M; Thornton, Ruth B; Cripps, Allan W

2016-01-01

Otitis media (OM) is amongst the most common childhood diseases and is associated with multiple microbial pathogens within the middle ear. Global and temporal monitoring of predominant bacterial pathogens is important to inform new treatment strategies, vaccine development and to monitor the impact of vaccine implementation to improve progress toward global OM prevention. A systematic review of published reports of microbiology of acute otitis media (AOM) and otitis media with effusion (OME) from January, 1970 to August 2014, was performed using PubMed databases. This review confirmed that Streptococcus pneumoniae and Haemophilus influenzae, remain the predominant bacterial pathogens, with S. pneumoniae the predominant bacterium in the majority reports from AOM patients. In contrast, H. influenzae was the predominant bacterium for patients experiencing chronic OME, recurrent AOM and AOM with treatment failure. This result was consistent, even where improved detection sensitivity from the use of polymerase chain reaction (PCR) rather than bacterial culture was conducted. On average, PCR analyses increased the frequency of detection of S. pneumoniae and H. influenzae 3.2 fold compared to culture, whilst Moraxella catarrhalis was 4.5 times more frequently identified by PCR. Molecular methods can also improve monitoring of regional changes in the serotypes and identification frequency of S. pneumoniae and H. influenzae over time or after vaccine implementation, such as after introduction of the 7-valent pneumococcal conjugate vaccine. Globally, S. pneumoniae and H. influenzae remain the predominant otopathogens associated with OM as identified through bacterial culture; however, molecular methods continue to improve the frequency and accuracy of detection of individual serotypes. Ongoing monitoring with appropriate detection methods for OM pathogens can support development of improved vaccines to provide protection from the complex combination of otopathogens within the middle ear, ultimately aiming to reduce the risk of chronic and recurrent OM in vulnerable populations.

High resolution colonoscopy in a bowel cancer screening program improves polyp detection

PubMed Central

Banks, Matthew R; Haidry, Rehan; Butt, M Adil; Whitley, Lisa; Stein, Judith; Langmead, Louise; Bloom, Stuart L; O’Bichere, Austin; McCartney, Sara; Basherdas, Kalpesh; Rodriguez-Justo, Manuel; Lovat, Laurence B

2011-01-01

AIM: To compare high resolution colonoscopy (Olympus Lucera) with a megapixel high resolution system (Pentax HiLine) as an in-service evaluation. METHODS: Polyp detection rates and measures of performance were collected for 269 colonoscopy procedures. Five colonoscopists conducted the study over a three month period, as part of the United Kingdom bowel cancer screening program. RESULTS:There were no differences in procedure duration (χ2 P = 0.98), caecal intubation rates (χ2 P = 0.67), or depth of sedation (χ2 P = 0.64). Mild discomfort was more common in the Pentax group (χ2 P = 0.036). Adenoma detection rate was significantly higher in the Pentax group (χ2 test for trend P = 0.01). Most of the extra polyps detected were flat or sessile adenomas. CONCLUSION: Megapixel definition colonoscopes improve adenoma detection without compromising other measures of endoscope performance. Increased polyp detection rates may improve future outcomes in bowel cancer screening programs. PMID:22090787

a Single-Exposure Dual-Energy Computed Radiography Technique for Improved Nodule Detection and Classification in Chest Imaging

NASA Astrophysics Data System (ADS)

Zink, Frank Edward

The detection and classification of pulmonary nodules is of great interest in chest radiography. Nodules are often indicative of primary cancer, and their detection is particularly important in asymptomatic patients. The ability to classify nodules as calcified or non-calcified is important because calcification is a positive indicator that the nodule is benign. Dual-energy methods offer the potential to improve both the detection and classification of nodules by allowing the formation of material-selective images. Tissue-selective images can improve detection by virtue of the elimination of obscuring rib structure. Bone -selective images are essentially calcium images, allowing classification of the nodule. A dual-energy technique is introduced which uses a computed radiography system to acquire dual-energy chest radiographs in a single-exposure. All aspects of the dual-energy technique are described, with particular emphasis on scatter-correction, beam-hardening correction, and noise-reduction algorithms. The adaptive noise-reduction algorithm employed improves material-selective signal-to-noise ratio by up to a factor of seven with minimal sacrifice in selectivity. A clinical comparison study is described, undertaken to compare the dual-energy technique to conventional chest radiography for the tasks of nodule detection and classification. Observer performance data were collected using the Free Response Observer Characteristic (FROC) method and the bi-normal Alternative FROC (AFROC) performance model. Results of the comparison study, analyzed using two common multiple observer statistical models, showed that the dual-energy technique was superior to conventional chest radiography for detection of nodules at a statistically significant level (p < .05). Discussion of the comparison study emphasizes the unique combination of data collection and analysis techniques employed, as well as the limitations of comparison techniques in the larger context of technology assessment.

Improving Fall Detection Using an On-Wrist Wearable Accelerometer

PubMed Central

Chira, Camelia; González, Víctor M.; de la Cal, Enrique

2018-01-01

Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The feature extraction is extended in order to balance the dataset for the minority class. Alternative models have been analyzed to reduce the computational constraints so the solution can be embedded in smart-phones or smart wristbands. Several published datasets have been used in the Materials and Methods section. Although these datasets do not include data from real falls of elderly people, a complete comparison study of fall-related datasets shows statistical differences between the simulated falls and real falls from participants suffering from impairment diseases. Given the obtained results, the rule-based systems represent a promising research line as they perform similarly to neural networks, but with a reduced computational cost. Furthermore, support vector machines performed with a high specificity. However, further research to validate the proposal in real on-line scenarios is needed. Furthermore, a slight improvement should be made to reduce the number of false alarms. PMID:29701721

Improved relocatable over-the-horizon radar detection and tracking using the maximum likelihood adaptive neural system algorithm

NASA Astrophysics Data System (ADS)

Perlovsky, Leonid I.; Webb, Virgil H.; Bradley, Scott R.; Hansen, Christopher A.

1998-07-01

An advanced detection and tracking system is being developed for the U.S. Navy's Relocatable Over-the-Horizon Radar (ROTHR) to provide improved tracking performance against small aircraft typically used in drug-smuggling activities. The development is based on the Maximum Likelihood Adaptive Neural System (MLANS), a model-based neural network that combines advantages of neural network and model-based algorithmic approaches. The objective of the MLANS tracker development effort is to address user requirements for increased detection and tracking capability in clutter and improved track position, heading, and speed accuracy. The MLANS tracker is expected to outperform other approaches to detection and tracking for the following reasons. It incorporates adaptive internal models of target return signals, target tracks and maneuvers, and clutter signals, which leads to concurrent clutter suppression, detection, and tracking (track-before-detect). It is not combinatorial and thus does not require any thresholding or peak picking and can track in low signal-to-noise conditions. It incorporates superresolution spectrum estimation techniques exceeding the performance of conventional maximum likelihood and maximum entropy methods. The unique spectrum estimation method is based on the Einsteinian interpretation of the ROTHR received energy spectrum as a probability density of signal frequency. The MLANS neural architecture and learning mechanism are founded on spectrum models and maximization of the "Einsteinian" likelihood, allowing knowledge of the physical behavior of both targets and clutter to be injected into the tracker algorithms. The paper describes the addressed requirements and expected improvements, theoretical foundations, engineering methodology, and results of the development effort to date.

Wavelet Fusion for Concealed Object Detection Using Passive Millimeter Wave Sequence Images

NASA Astrophysics Data System (ADS)

Chen, Y.; Pang, L.; Liu, H.; Xu, X.

2018-04-01

PMMW imaging system can create interpretable imagery on the objects concealed under clothing, which gives the great advantage to the security check system. Paper addresses wavelet fusion to detect concealed objects using passive millimeter wave (PMMW) sequence images. According to PMMW real-time imager acquired image characteristics and storage methods firstly, using the sum of squared difference (SSD) as the image-related parameters to screen the sequence images. Secondly, the selected images are optimized using wavelet fusion algorithm. Finally, the concealed objects are detected by mean filter, threshold segmentation and edge detection. The experimental results show that this method improves the detection effect of concealed objects by selecting the most relevant images from PMMW sequence images and using wavelet fusion to enhance the information of the concealed objects. The method can be effectively applied to human body concealed object detection in millimeter wave video.

Method for detecting an image of an object

DOEpatents

Chapman, Leroy Dean; Thomlinson, William C.; Zhong, Zhong

1999-11-16

A method for detecting an absorption, refraction and scatter image of an object by independently analyzing, detecting, digitizing, and combining images acquired on a high and a low angle side of a rocking curve of a crystal analyzer. An x-ray beam which is generated by any suitable conventional apparatus can be irradiated upon either a Bragg type crystal analyzer or a Laue type crystal analyzer. Images of the absorption, refraction and scattering effects are detected, such as on an image plate, and then digitized. The digitized images are simultaneously solved, preferably on a pixel-by-pixel basis, to derive a combined visual image which has dramatically improved contrast and spatial resolution over an image acquired through conventional radiology methods.

A novel approach to describing and detecting performance anti-patterns

NASA Astrophysics Data System (ADS)

Sheng, Jinfang; Wang, Yihan; Hu, Peipei; Wang, Bin

2017-08-01

Anti-pattern, as an extension to pattern, describes a widely used poor solution which can bring negative influence to application systems. Aiming at the shortcomings of the existing anti-pattern descriptions, an anti-pattern description method based on first order predicate is proposed. This method synthesizes anti-pattern forms and symptoms, which makes the description more accurate and has good scalability and versatility as well. In order to improve the accuracy of anti-pattern detection, a Bayesian classification method is applied in validation for detection results, which can reduce false negatives and false positives of anti-pattern detection. Finally, the proposed approach in this paper is applied to a small e-commerce system, the feasibility and effectiveness of the approach is demonstrated further through experiments.

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.

Homogenization optics to improve detectability of a fluorescence response to a single laser pulse: Detection of feces on apples

USDA-ARS?s Scientific Manuscript database

Fecal contamination of produce is a known food safety risk. Measuring fluorescence responses to UV excitation is an established method for detecting such contamination. One measurement system utilizes a pulsed UV laser to induce a fluorescence response from fecal material and a gated intensified cam...

Eye gazing direction inspection based on image processing technique

NASA Astrophysics Data System (ADS)

Hao, Qun; Song, Yong

2005-02-01

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

Balanced detection for self-mixing interferometry to improve signal-to-noise ratio

NASA Astrophysics Data System (ADS)

Zhao, Changming; Norgia, Michele; Li, Kun

2018-01-01

We apply balanced detection to self-mixing interferometry for displacement and vibration measurement, using two photodiodes for implementing a differential acquisition. The method is based on the phase opposition of the self-mixing signal measured between the two laser diode facet outputs. The balanced signal obtained by enlarging the self-mixing signal, also by canceling of the common-due noises mainly due to disturbances on laser supply and transimpedance amplifier. Experimental results demonstrate the signal-to-noise ratio significantly improves, with almost twice signals enhancement and more than half noise decreasing. This method allows for more robust, longer-distance measurement systems, especially using fringe-counting.

An FP7 "Space" project: Aphorism "Advanced PRocedures for volcanic and Seismic Monitoring"

NASA Astrophysics Data System (ADS)

Di Iorio, A., Sr.; Stramondo, S.; Bignami, C.; Corradini, S.; Merucci, L.

2014-12-01

APHORISM project proposes the development and testing of two new methods to combine Earth Observation satellite data from different sensors, and ground data. The aim is to demonstrate that this two types of data, appropriately managed and integrated, can provide new improved GMES products useful for seismic and volcanic crisis management. The first method, APE - A Priori information for Earthquake damage mapping, concerns the generation of maps to address the detection and estimate of damage caused by a seism. The use of satellite data to investigate earthquake damages is not an innovative issue. We can find a wide literature and projects concerning such issue, but usually the approach is only based on change detection techniques and classifications algorithms. The novelty of APE relies on the exploitation of a priori information derived by InSAR time series to measure surface movements, shake maps obtained from seismological data, and vulnerability information. This a priori information is then integrated with change detection map to improve accuracy and to limit false alarms. The second method deals with volcanic crisis management. The method, MACE - Multi-platform volcanic Ash Cloud Estimation, concerns the exploitation of GEO (Geosynchronous Earth Orbit) sensor platform, LEO (Low Earth Orbit) satellite sensors and ground measures to improve the ash detection and retrieval and to characterize the volcanic ash clouds. The basic idea of MACE consists of an improvement of volcanic ash retrievals at the space-time scale by using both the LEO and GEO estimations and in-situ data. Indeed the standard ash thermal infrared retrieval is integrated with data coming from a wider spectral range from visible to microwave. The ash detection is also extended in case of cloudy atmosphere or steam plumes. APE and MACE methods have been defined in order to provide products oriented toward the next ESA Sentinels satellite missions.The project is funded under the European Union FP7 program and the Kick-Off meeting has been held at INGV premises in Rome on 18th December 2013.

Improving signal-to-noise in the direct imaging of exoplanets and circumstellar disks with MLOCI

NASA Astrophysics Data System (ADS)

Wahhaj, Zahed; Cieza, Lucas A.; Mawet, Dimitri; Yang, Bin; Canovas, Hector; de Boer, Jozua; Casassus, Simon; Ménard, François; Schreiber, Matthias R.; Liu, Michael C.; Biller, Beth A.; Nielsen, Eric L.; Hayward, Thomas L.

2015-09-01

We present a new algorithm designed to improve the signal-to-noise ratio (S/N) of point and extended source detections around bright stars in direct imaging data.One of our innovations is that we insert simulated point sources into the science images, which we then try to recover with maximum S/N. This improves the S/N of real point sources elsewhere in the field. The algorithm, based on the locally optimized combination of images (LOCI) method, is called Matched LOCI or MLOCI. We show with Gemini Planet Imager (GPI) data on HD 135344 B and Near-Infrared Coronagraphic Imager (NICI) data on several stars that the new algorithm can improve the S/N of point source detections by 30-400% over past methods. We also find no increase in false detections rates. No prior knowledge of candidate companion locations is required to use MLOCI. On the other hand, while non-blind applications may yield linear combinations of science images that seem to increase the S/N of true sources by a factor >2, they can also yield false detections at high rates. This is a potential pitfall when trying to confirm marginal detections or to redetect point sources found in previous epochs. These findings are relevant to any method where the coefficients of the linear combination are considered tunable, e.g., LOCI and principal component analysis (PCA). Thus we recommend that false detection rates be analyzed when using these techniques. Based on observations obtained at the Gemini Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (USA), the Science and Technology Facilities Council (UK), the National Research Council (Canada), CONICYT (Chile), the Australian Research Council (Australia), Ministério da Ciência e Tecnologia (Brazil) and Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina).

An improved procedure for detection and enumeration of walrus signatures in airborne thermal imagery

USGS Publications Warehouse

Burn, Douglas M.; Udevitz, Mark S.; Speckman, Suzann G.; Benter, R. Bradley

2009-01-01

In recent years, application of remote sensing to marine mammal surveys has been a promising area of investigation for wildlife managers and researchers. In April 2006, the United States and Russia conducted an aerial survey of Pacific walrus (Odobenus rosmarus divergens) using thermal infrared sensors to detect groups of animals resting on pack ice in the Bering Sea. The goal of this survey was to estimate the size of the Pacific walrus population. An initial analysis of the U.S. data using previously-established methods resulted in lower detectability of walrus groups in the imagery and higher variability in calibration models than was expected based on pilot studies. This paper describes an improved procedure for detection and enumeration of walrus groups in airborne thermal imagery. Thermal images were first subdivided into smaller 200 x 200 pixel "tiles." We calculated three statistics to represent characteristics of walrus signatures from the temperature histogram for each the. Tiles that exhibited one or more of these characteristics were examined further to determine if walrus signatures were present. We used cluster analysis on tiles that contained walrus signatures to determine which pixels belonged to each group. We then calculated a thermal index value for each walrus group in the imagery and used generalized linear models to estimate detection functions (the probability of a group having a positive index value) and calibration functions (the size of a group as a function of its index value) based on counts from matched digital aerial photographs. The new method described here improved our ability to detect walrus groups at both 2 m and 4 m spatial resolution. In addition, the resulting calibration models have lower variance than the original method. We anticipate that the use of this new procedure will greatly improve the quality of the population estimate derived from these data. This procedure may also have broader applicability to thermal infrared surveys of other wildlife species. Published by Elsevier B.V.

Magnetic Separation Methods for the Detection of Mycobacterium avium subsp. paratuberculosis in Various Types of Matrices: A Review

PubMed Central

Dziedzinska, Radka

2017-01-01

The main reasons to improve the detection of Mycobacterium avium subsp. paratuberculosis (MAP) are animal health and monitoring of MAP entering the food chain via meat, milk, and/or dairy products. Different approaches can be used for the detection of MAP, but the use of magnetic separation especially in conjunction with PCR as an end-point detection method has risen in past years. However, the extraction of DNA which is a crucial step prior to PCR detection can be complicated due to the presence of inhibitory substances. Magnetic separation methods involving either antibodies or peptides represent a powerful tool for selective separation of target bacteria from other nontarget microorganisms and inhibitory sample components. These methods enable the concentration of pathogens present in the initial matrix into smaller volume and facilitate the isolation of sufficient quantities of pure DNA. The purpose of this review was to summarize the methods based on the magnetic separation approach that are currently available for the detection of MAP in a broad range of matrices. PMID:28642876

Improvement of retinal blood vessel detection using morphological component analysis.

PubMed

Imani, Elaheh; Javidi, Malihe; Pourreza, Hamid-Reza

2015-03-01

Detection and quantitative measurement of variations in the retinal blood vessels can help diagnose several diseases including diabetic retinopathy. Intrinsic characteristics of abnormal retinal images make blood vessel detection difficult. The major problem with traditional vessel segmentation algorithms is producing false positive vessels in the presence of diabetic retinopathy lesions. To overcome this problem, a novel scheme for extracting retinal blood vessels based on morphological component analysis (MCA) algorithm is presented in this paper. MCA was developed based on sparse representation of signals. This algorithm assumes that each signal is a linear combination of several morphologically distinct components. In the proposed method, the MCA algorithm with appropriate transforms is adopted to separate vessels and lesions from each other. Afterwards, the Morlet Wavelet Transform is applied to enhance the retinal vessels. The final vessel map is obtained by adaptive thresholding. The performance of the proposed method is measured on the publicly available DRIVE and STARE datasets and compared with several state-of-the-art methods. An accuracy of 0.9523 and 0.9590 has been respectively achieved on the DRIVE and STARE datasets, which are not only greater than most methods, but are also superior to the second human observer's performance. The results show that the proposed method can achieve improved detection in abnormal retinal images and decrease false positive vessels in pathological regions compared to other methods. Also, the robustness of the method in the presence of noise is shown via experimental result. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Optoelectronic scanning system upgrade by energy center localization methods

NASA Astrophysics Data System (ADS)

Flores-Fuentes, W.; Sergiyenko, O.; Rodriguez-Quiñonez, J. C.; Rivas-López, M.; Hernández-Balbuena, D.; Básaca-Preciado, L. C.; Lindner, L.; González-Navarro, F. F.

2016-11-01

A problem of upgrading an optoelectronic scanning system with digital post-processing of the signal based on adequate methods of energy center localization is considered. An improved dynamic triangulation analysis technique is proposed by an example of industrial infrastructure damage detection. A modification of our previously published method aimed at searching for the energy center of an optoelectronic signal is described. Application of the artificial intelligence algorithm of compensation for the error of determining the angular coordinate in calculating the spatial coordinate through dynamic triangulation is demonstrated. Five energy center localization methods are developed and tested to select the best method. After implementation of these methods, digital compensation for the measurement error, and statistical data analysis, a non-parametric behavior of the data is identified. The Wilcoxon signed rank test is applied to improve the result further. For optical scanning systems, it is necessary to detect a light emitter mounted on the infrastructure being investigated to calculate its spatial coordinate by the energy center localization method.

puma: a Bioconductor package for propagating uncertainty in microarray analysis.

PubMed

Pearson, Richard D; Liu, Xuejun; Sanguinetti, Guido; Milo, Marta; Lawrence, Neil D; Rattray, Magnus

2009-07-09

Most analyses of microarray data are based on point estimates of expression levels and ignore the uncertainty of such estimates. By determining uncertainties from Affymetrix GeneChip data and propagating these uncertainties to downstream analyses it has been shown that we can improve results of differential expression detection, principal component analysis and clustering. Previously, implementations of these uncertainty propagation methods have only been available as separate packages, written in different languages. Previous implementations have also suffered from being very costly to compute, and in the case of differential expression detection, have been limited in the experimental designs to which they can be applied. puma is a Bioconductor package incorporating a suite of analysis methods for use on Affymetrix GeneChip data. puma extends the differential expression detection methods of previous work from the 2-class case to the multi-factorial case. puma can be used to automatically create design and contrast matrices for typical experimental designs, which can be used both within the package itself but also in other Bioconductor packages. The implementation of differential expression detection methods has been parallelised leading to significant decreases in processing time on a range of computer architectures. puma incorporates the first R implementation of an uncertainty propagation version of principal component analysis, and an implementation of a clustering method based on uncertainty propagation. All of these techniques are brought together in a single, easy-to-use package with clear, task-based documentation. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. These methods can be used to improve results from more traditional analyses of microarray data. puma also offers improvements in terms of scope and speed of execution over previously available methods. puma is recommended for anyone working with the Affymetrix GeneChip platform for gene expression analysis and can also be applied more generally.

Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models?

USGS Publications Warehouse

Graves, Tabitha A.; Royle, J. Andrew; Kendall, Katherine C.; Beier, Paul; Stetz, Jeffrey B.; Macleod, Amy C.

2012-01-01

Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic) and bear rubs (opportunistic). We used hierarchical abundance models (N-mixture models) with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1) lead to the selection of the same variables as important and (2) provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3) yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight), and (4) improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed against those risks. The analysis framework presented here will be useful for other species exhibiting heterogeneity by detection method.

Noise-tolerant instantaneous heart rate and R-peak detection using short-term autocorrelation for wearable healthcare systems.

PubMed

Fujii, Takahide; Nakano, Masanao; Yamashita, Ken; Konishi, Toshihiro; Izumi, Shintaro; Kawaguchi, Hiroshi; Yoshimoto, Masahiko

2013-01-01

This paper describes a robust method of Instantaneous Heart Rate (IHR) and R-peak detection from noisy electrocardiogram (ECG) signals. Generally, the IHR is calculated from the R-wave interval. Then, the R-waves are extracted from the ECG using a threshold. However, in wearable bio-signal monitoring systems, noise increases the incidence of misdetection and false detection of R-peaks. To prevent incorrect detection, we introduce a short-term autocorrelation (STAC) technique and a small-window autocorrelation (SWAC) technique, which leverages the similarity of QRS complex waveforms. Simulation results show that the proposed method improves the noise tolerance of R-peak detection.

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

PubMed Central

Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

2015-01-01

Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280

Hierarchical Nanogold Labels to Improve the Sensitivity of Lateral Flow Immunoassay

NASA Astrophysics Data System (ADS)

Serebrennikova, Kseniya; Samsonova, Jeanne; Osipov, Alexander

2018-06-01

Lateral flow immunoassay (LFIA) is a widely used express method and offers advantages such as a short analysis time, simplicity of testing and result evaluation. However, an LFIA based on gold nanospheres lacks the desired sensitivity, thereby limiting its wide applications. In this study, spherical nanogold labels along with new types of nanogold labels such as gold nanopopcorns and nanostars were prepared, characterized, and applied for LFIA of model protein antigen procalcitonin. It was found that the label with a structure close to spherical provided more uniform distribution of specific antibodies on its surface, indicative of its suitability for this type of analysis. LFIA using gold nanopopcorns as a label allowed procalcitonin detection over a linear range of 0.5-10 ng mL-1 with the limit of detection of 0.1 ng mL-1, which was fivefold higher than the sensitivity of the assay with gold nanospheres. Another approach to improve the sensitivity of the assay included the silver enhancement method, which was used to compare the amplification of LFIA for procalcitonin detection. The sensitivity of procalcitonin determination by this method was 10 times better the sensitivity of the conventional LFIA with gold nanosphere as a label. The proposed approach of LFIA based on gold nanopopcorns improved the detection sensitivity without additional steps and prevented the increased consumption of specific reagents (antibodies).

Multi-laboratory evaluations of the performance of Catellicoccus marimammalium PCR assays developed to target gull fecal sources

USGS Publications Warehouse

Sinigalliano, Christopher D.; Ervin, Jared S.; Van De Werfhorst, Laurie C.; Badgley, Brian D.; Ballestée, Elisenda; Bartkowiaka, Jakob; Boehm, Alexandria B.; Byappanahalli, Muruleedhara N.; Goodwin, Kelly D.; Gourmelon, Michèle; Griffith, John; Holden, Patricia A.; Jay, Jenny; Layton, Blythe; Lee, Cheonghoon; Lee, Jiyoung; Meijer, Wim G.; Noble, Rachel; Raith, Meredith; Ryu, Hodon; Sadowsky, Michael J.; Schriewer, Alexander; Wang, Dan; Wanless, David; Whitman, Richard; Wuertz, Stefan; Santo Domingo, Jorge W.

2013-01-01

Here we report results from a multi-laboratory (n = 11) evaluation of four different PCR methods targeting the 16S rRNA gene of Catellicoccus marimammalium originally developed to detect gull fecal contamination in coastal environments. The methods included a conventional end-point PCR method, a SYBR® Green qPCR method, and two TaqMan® qPCR methods. Different techniques for data normalization and analysis were tested. Data analysis methods had a pronounced impact on assay sensitivity and specificity calculations. Across-laboratory standardization of metrics including the lower limit of quantification (LLOQ), target detected but not quantifiable (DNQ), and target not detected (ND) significantly improved results compared to results submitted by individual laboratories prior to definition standardization. The unit of measure used for data normalization also had a pronounced effect on measured assay performance. Data normalization to DNA mass improved quantitative method performance as compared to enterococcus normalization. The MST methods tested here were originally designed for gulls but were found in this study to also detect feces from other birds, particularly feces composited from pigeons. Sequencing efforts showed that some pigeon feces from California contained sequences similar to C. marimammalium found in gull feces. These data suggest that the prevalence, geographic scope, and ecology of C. marimammalium in host birds other than gulls require further investigation. This study represents an important first step in the multi-laboratory assessment of these methods and highlights the need to broaden and standardize additional evaluations, including environmentally relevant target concentrations in ambient waters from diverse geographic regions.

Correlation Study Of Diffenrential Skin Temperatures (DST) For Ovulation Detection Using Infra-Red Thermography

NASA Astrophysics Data System (ADS)

Rao, K. H. S.; Shah, A. v.; Ruedi, B.

1982-11-01

The importance of ovulation time detection in the Practice of Natural Birth Control (NBC) as a contraceptive tool, and for natural/artificial insemination among women having the problem of in-fertility, is well known. The simple Basal Body Temperature (BBT) method of ovulation detection is so far unreliable. A newly proposed Differential Skin Temperature (DST) method may help minimize disturbing physiological effects and improve reliability. This paper explains preliminary results of a detailed correlative study on the DST method, using Infra-Red Thermography (IRT) imaging, and computer analysis techniques. Results obtained with five healthy, normally menstruating women volunteers will be given.

Probabilistic double guarantee kidnapping detection in SLAM.

PubMed

Tian, Yang; Ma, Shugen

2016-01-01

For determining whether kidnapping has happened and which type of kidnapping it is while a robot performs autonomous tasks in an unknown environment, a double guarantee kidnapping detection (DGKD) method has been proposed. The good performance of DGKD in a relative small environment is shown. However, a limitation of DGKD is found in a large-scale environment by our recent work. In order to increase the adaptability of DGKD in a large-scale environment, an improved method called probabilistic double guarantee kidnapping detection is proposed in this paper to combine probability of features' positions and the robot's posture. Simulation results demonstrate the validity and accuracy of the proposed method.

Automatic cell detection and segmentation from H and E stained pathology slides using colorspace decorrelation stretching

NASA Astrophysics Data System (ADS)

Peikari, Mohammad; Martel, Anne L.

2016-03-01

Purpose: Automatic cell segmentation plays an important role in reliable diagnosis and prognosis of patients. Most of the state-of-the-art cell detection and segmentation techniques focus on complicated methods to subtract foreground cells from the background. In this study, we introduce a preprocessing method which leads to a better detection and segmentation results compared to a well-known state-of-the-art work. Method: We transform the original red-green-blue (RGB) space into a new space defined by the top eigenvectors of the RGB space. Stretching is done by manipulating the contrast of each pixel value to equalize the color variances. New pixel values are then inverse transformed to the original RGB space. This altered RGB image is then used to segment cells. Result: The validation of our method with a well-known state-of-the-art technique revealed a statistically significant improvement on an identical validation set. We achieved a mean F1-score of 0.901. Conclusion: Preprocessing steps to decorrelate colorspaces may improve cell segmentation performances.

Determination of Trace Available Heavy Metals in Soil Using Laser-Induced Breakdown Spectroscopy Assisted with Phase Transformation Method.

PubMed

Yi, Rongxing; Yang, Xinyan; Zhou, Ran; Li, Jiaming; Yu, Huiwu; Hao, Zhongqi; Guo, Lianbo; Li, Xiangyou; Lu, Yongfeng; Zeng, Xiaoyan

2018-05-18

To detect available heavy metals in soil using laser-induced breakdown spectroscopy (LIBS) and improve its poor detection sensitivity, a simple and low cost sample pretreatment method named solid-liquid-solid transformation was proposed. By this method, available heavy metals were extracted from soil through ultrasonic vibration and centrifuging and then deposited on a glass slide. Utilization of this solid-liquid-solid transformation method, available Cd and Pb elements in soil were detected successfully. The results show that the regression coefficients of calibration curves for soil analyses reach to more than 0.98. The limits of detection could reach to 0.067 and 0.94 ppm for available Cd and Pb elements in soil under optimized conditions, respectively, which are much better than those obtained by conventional LIBS.

Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

PubMed

Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

2017-01-01

Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

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

PubMed

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

2017-01-01

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

Research on infrared ship detection method in sea-sky background

NASA Astrophysics Data System (ADS)

Tang, Da; Sun, Gang; Wang, Ding-he; Niu, Zhao-dong; Chen, Zeng-ping

2013-09-01

An approach to infrared ship detection based on sea-sky-line(SSL) detection, ROI extraction and feature recognition is proposed in this paper. Firstly, considering that far ships are expected to be adjacent to the SSL, SSL is detected to find potential target areas. Radon transform is performed on gradient image to choose candidate SSLs, and detection result is given by fuzzy synthetic evaluation values. Secondly, in view of recognizable condition that there should be enough differences between target and background in infrared image, two gradient masks have been created and improved as practical guidelines in eliminating false alarm. Thirdly, extract ROI near the SSL by using multi-grade segmentation and fusion method after image sharpening, and unsuitable candidates are screened out according to the gradient masks and ROI shape. Finally, we segment the rest of ROIs by two-stage modified OTSU, and calculate target confidence as a standard measuring the facticity of target. Compared with other ship detection methods, proposed method is suitable for bipolar targets, which offers a good practicability and accuracy, and achieves a satisfying detection speed. Detection experiments with 200 thousand frames show that the proposed method is widely applicable, powerful in resistance to interferences and noises with a detection rate of above 95%, which satisfies the engineering needs commendably.

Data fusion for QRS complex detection in multi-lead electrocardiogram recordings

NASA Astrophysics Data System (ADS)

Ledezma, Carlos A.; Perpiñan, Gilberto; Severeyn, Erika; Altuve, Miguel

2015-12-01

Heart diseases are the main cause of death worldwide. The first step in the diagnose of these diseases is the analysis of the electrocardiographic (ECG) signal. In turn, the ECG analysis begins with the detection of the QRS complex, which is the one with the most energy in the cardiac cycle. Numerous methods have been proposed in the bibliography for QRS complex detection, but few authors have analyzed the possibility of taking advantage of the information redundancy present in multiple ECG leads (simultaneously acquired) to produce accurate QRS detection. In our previous work we presented such an approach, proposing various data fusion techniques to combine the detections made by an algorithm on multiple ECG leads. In this paper we present further studies that show the advantages of this multi-lead detection approach, analyzing how many leads are necessary in order to observe an improvement in the detection performance. A well known QRS detection algorithm was used to test the fusion techniques on the St. Petersburg Institute of Cardiological Technics database. Results show improvement in the detection performance with as little as three leads, but the reliability of these results becomes interesting only after using seven or more leads. Results were evaluated using the detection error rate (DER). The multi-lead detection approach allows an improvement from DER = 3:04% to DER = 1:88%. Further works are to be made in order to improve the detection performance by implementing further fusion steps.

Saliency detection by conditional generative adversarial network

NASA Astrophysics Data System (ADS)

Cai, Xiaoxu; Yu, Hui

2018-04-01

Detecting salient objects in images has been a fundamental problem in computer vision. In recent years, deep learning has shown its impressive performance in dealing with many kinds of vision tasks. In this paper, we propose a new method to detect salient objects by using Conditional Generative Adversarial Network (GAN). This type of network not only learns the mapping from RGB images to salient regions, but also learns a loss function for training the mapping. To the best of our knowledge, this is the first time that Conditional GAN has been used in salient object detection. We evaluate our saliency detection method on 2 large publicly available datasets with pixel accurate annotations. The experimental results have shown the significant and consistent improvements over the state-of-the-art method on a challenging dataset, and the testing speed is much faster.

COMPARISON OF FILTRATION METHODS FOR PRIMARY RECOVERY OF CRYPTOSPORIIDUM PARVUM FROM WATER

EPA Science Inventory

Waterborne disease outbreaks from contaminated drinking water have been linked to the protozoan parasite, Cryptosporidium parvum. To improve monitoring for this agent, the USEPA developed Method 1622 for isolation and detection of Cryptosporidium oocysts in water. Method 1622 i...

Evaluation of Total Nitrite Pattern Visualization as an Improved Method for Gunshot Residue Detection and its Application to Casework Samples.

PubMed

Berger, Jason; Upton, Colin; Springer, Elyah

2018-04-23

Visualization of nitrite residues is essential in gunshot distance determination. Current protocols for the detection of nitrites include, among other tests, the Modified Griess Test (MGT). This method is limited as nitrite residues are unstable in the environment and limited to partially burned gunpowder. Previous research demonstrated the ability of alkaline hydrolysis to convert nitrates to nitrites, allowing visualization of unburned gunpowder particles using the MGT. This is referred to as Total Nitrite Pattern Visualization (TNV). TNV techniques were modified and a study conducted to streamline the procedure outlined in the literature to maximize the efficacy of the TNV in casework, while reducing the required time from 1 h to 5 min, and enhancing effectiveness on blood-soiled samples. The TNV method was found to provide significant improvement in the ability to detect significant nitrite residues, without sacrificing efficiency, that would allow for the determination of the muzzle-to-target distance. © 2018 American Academy of Forensic Sciences.

Technological advances in diagnostic testing for von Willebrand disease: new approaches and challenges.

PubMed

Hayward, C P M; Moffat, K A; Graf, L

2014-06-01

Diagnostic tests for von Willebrand disease (VWD) are important for the assessment of VWD, which is a commonly encountered bleeding disorder worldwide. Technical innovations have been applied to improve the precision and lower limit of detection of von Willebrand factor (VWF) assays, including the ristocetin cofactor activity assay (VWF:RCo) that uses the antibiotic ristocetin to induce plasma VWF binding to glycoprotein (GP) IbIXV on target platelets. VWF-collagen-binding assays, depending on the type of collagen used, can improve the detection of forms of VWD with high molecular weight VWF multimer loss, although the best method is debatable. A number of innovations have been applied to VWF:RCo (which is commonly performed on an aggregometer), including replacing the target platelets with immobilized GPIbα, and quantification by an enzyme-linked immunosorbent assay (ELISA), immunoturbidimetric, or chemiluminescent end-point. Some common polymorphisms in the VWF gene that do not cause bleeding are associated with falsely low VWF activity by ristocetin-dependent methods. To overcome the need for ristocetin, some new VWF activity assays use gain-of-function GPIbα mutants that bind VWF without the need for ristocetin, with an improved precision and lower limit of detection than measuring VWF:RCo by aggregometry. ELISA of VWF binding to mutated GPIbα shows promise as a method to identify gain-of-function defects from type 2B VWD. The performance characteristics of many new VWF activity assays suggest that the detection of VWD, and monitoring of VWD therapy, by clinical laboratories could be improved through adopting newer generation VWF assays. © 2014 John Wiley & Sons Ltd.

The Global Detection Capability of the IMS Seismic Network in 2013 Inferred from Ambient Seismic Noise Measurements

NASA Astrophysics Data System (ADS)

Gaebler, P. J.; Ceranna, L.

2016-12-01

All nuclear explosions - on the Earth's surface, underground, underwater or in the atmosphere - are banned by the Comprehensive Nuclear-Test-Ban Treaty (CTBT). As part of this treaty, a verification regime was put into place to detect, locate and characterize nuclear explosion testings at any time, by anyone and everywhere on the Earth. The International Monitoring System (IMS) plays a key role in the verification regime of the CTBT. Out of the different monitoring techniques used in the IMS, the seismic waveform approach is the most effective technology for monitoring nuclear underground testing and to identify and characterize potential nuclear events. This study introduces a method of seismic threshold monitoring to assess an upper magnitude limit of a potential seismic event in a certain given geographical region. The method is based on ambient seismic background noise measurements at the individual IMS seismic stations as well as on global distance correction terms for body wave magnitudes, which are calculated using the seismic reflectivity method. From our investigations we conclude that a global detection threshold of around mb 4.0 can be achieved using only stations from the primary seismic network, a clear latitudinal dependence for the detection thresholdcan be observed between northern and southern hemisphere. Including the seismic stations being part of the auxiliary seismic IMS network results in a slight improvement of global detection capability. However, including wave arrivals from distances greater than 120 degrees, mainly PKP-wave arrivals, leads to a significant improvement in average global detection capability. In special this leads to an improvement of the detection threshold on the southern hemisphere. We further investigate the dependence of the detection capability on spatial (latitude and longitude) and temporal (time) parameters, as well as on parameters such as source type and percentage of operational IMS stations.

X-ray detection capabilities of plastic scintillators incorporated with hafnium oxide nanoparticles surface-modified with phenyl propionic acid

NASA Astrophysics Data System (ADS)

Hiyama, Fumiyuki; Noguchi, Takio; Koshimizu, Masanori; Kishimoto, Shunji; Haruki, Rie; Nishikido, Fumihiko; Yanagida, Takayuki; Fujimoto, Yutaka; Aida, Tsutomu; Takami, Seiichi; Adschiri, Tadafumi; Asai, Keisuke

2018-01-01

We synthesized plastic scintillators incorporated with HfO2 nanoparticles as detectors for X-ray synchrotron radiation. Nanoparticles with sizes of less than 10 nm were synthesized with the subcritical hydrothermal method. The detection efficiency of high-energy X-ray photons improved by up to 3.3 times because of the addition of the nanoparticles. Nanosecond time resolution was successfully achieved for all the scintillators. These results indicate that this method is applicable for the preparation of plastic scintillators to detect X-ray synchrotron radiation.

Evaluation of the efficiency of three extraction conditions for the immunochemical detection of allergenic soy proteins in different food matrices.

PubMed

Amponsah, Amma; Nayak, Balunkeswar

2018-04-01

Recent studies have shown the need to improve soy allergen extraction using different extraction conditions to ensure more accurate results in allergen detection. This study investigated some of these extraction conditions to confirm that these methods, especially ultrasound-assisted extraction (UAE) and the use of Laemmli buffer instead of the conventional extraction with phosphate-buffered saline (PBS), could be helpful in improving the extraction step in allergen detection. Higher total soluble protein was obtained in all samples extracted with Laemmli buffer alone and in combination with ultrasound. For immunochemical detection of soy proteins by enzyme-linked immunosorbent assay (ELISA), comparable detection was observed in extracts from all extraction conditions in all commercial samples with the exception of table cracker and veggie burger, where significantly higher detection was seen in extracts from Laemmli buffer only. For the dry mix and cookie samples, the degree of soy protein detection with ELISA varied among the different extraction conditions, but overall, extraction with only Laemmli buffer showed higher detection. Laemmli buffer with conventional extraction and UAE may be better alternatives or additional extraction methods in soy allergen detection. Different food matrices performed differently (whether it was for the recovery of total proteins or detection by ELISA) under different extraction conditions. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Targeted Analyte Detection by Standard Addition Improves Detection Limits in MALDI Mass Spectrometry

PubMed Central

Eshghi, Shadi Toghi; Li, Xingde; Zhang, Hui

2014-01-01

Matrix-assisted laser desorption/ionization has proven an effective tool for fast and accurate determination of many molecules. However, the detector sensitivity and chemical noise compromise the detection of many invaluable low-abundance molecules from biological and clinical samples. To challenge this limitation, we developed a targeted analyte detection (TAD) technique. In TAD, the target analyte is selectively elevated by spiking a known amount of that analyte into the sample, thereby raising its concentration above the noise level, where we take advantage of the improved sensitivity to detect the presence of the endogenous analyte in the sample. We assessed TAD on three peptides in simple and complex background solutions with various exogenous analyte concentrations in two MALDI matrices. TAD successfully improved the limit of detection (LOD) of target analytes when the target peptides were added to the sample in a concentration close to optimum concentration. The optimum exogenous concentration was estimated through a quantitative method to be approximately equal to the original LOD for each target. Also, we showed that TAD could achieve LOD improvements on an average of 3-fold in a simple and 2-fold in a complex sample. TAD provides a straightforward assay to improve the LOD of generic target analytes without the need for costly hardware modifications. PMID:22877355

Targeted analyte detection by standard addition improves detection limits in matrix-assisted laser desorption/ionization mass spectrometry.

PubMed

Toghi Eshghi, Shadi; Li, Xingde; Zhang, Hui

2012-09-18

Matrix-assisted laser desorption/ionization (MALDI) has proven an effective tool for fast and accurate determination of many molecules. However, the detector sensitivity and chemical noise compromise the detection of many invaluable low-abundance molecules from biological and clinical samples. To challenge this limitation, we developed a targeted analyte detection (TAD) technique. In TAD, the target analyte is selectively elevated by spiking a known amount of that analyte into the sample, thereby raising its concentration above the noise level, where we take advantage of the improved sensitivity to detect the presence of the endogenous analyte in the sample. We assessed TAD on three peptides in simple and complex background solutions with various exogenous analyte concentrations in two MALDI matrices. TAD successfully improved the limit of detection (LOD) of target analytes when the target peptides were added to the sample in a concentration close to optimum concentration. The optimum exogenous concentration was estimated through a quantitative method to be approximately equal to the original LOD for each target. Also, we showed that TAD could achieve LOD improvements on an average of 3-fold in a simple and 2-fold in a complex sample. TAD provides a straightforward assay to improve the LOD of generic target analytes without the need for costly hardware modifications.

Particle swarm optimization method for small retinal vessels detection on multiresolution fundus images.

PubMed

Khomri, Bilal; Christodoulidis, Argyrios; Djerou, Leila; Babahenini, Mohamed Chaouki; Cheriet, Farida

2018-05-01

Retinal vessel segmentation plays an important role in the diagnosis of eye diseases and is considered as one of the most challenging tasks in computer-aided diagnosis (CAD) systems. The main goal of this study was to propose a method for blood-vessel segmentation that could deal with the problem of detecting vessels of varying diameters in high- and low-resolution fundus images. We proposed to use the particle swarm optimization (PSO) algorithm to improve the multiscale line detection (MSLD) method. The PSO algorithm was applied to find the best arrangement of scales in the MSLD method and to handle the problem of multiscale response recombination. The performance of the proposed method was evaluated on two low-resolution (DRIVE and STARE) and one high-resolution fundus (HRF) image datasets. The data include healthy (H) and diabetic retinopathy (DR) cases. The proposed approach improved the sensitivity rate against the MSLD by 4.7% for the DRIVE dataset and by 1.8% for the STARE dataset. For the high-resolution dataset, the proposed approach achieved 87.09% sensitivity rate, whereas the MSLD method achieves 82.58% sensitivity rate at the same specificity level. When only the smallest vessels were considered, the proposed approach improved the sensitivity rate by 11.02% and by 4.42% for the healthy and the diabetic cases, respectively. Integrating the proposed method in a comprehensive CAD system for DR screening would allow the reduction of false positives due to missed small vessels, misclassified as red lesions. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Feasibility of fast neutron analysis for the detection of explosives buried in soil

NASA Astrophysics Data System (ADS)

Faust, A. A.; McFee, J. E.; Bowman, C. L.; Mosquera, C.; Andrews, H. R.; Kovaltchouk, V. D.; Ing, H.

2011-12-01

A commercialized thermal neutron analysis (TNA) sensor has been developed to confirm the presence of buried bulk explosives as part of a multi-sensor anti-tank landmine detection system. Continuing improvements to the TNA system have included the use of an electronic pulsed neutron generator that offers the possibility of applying fast neutron analysis (FNA) methods to improve the system's detection capability. This paper describes an investigation into the use of FNA as a complementary component in such a TNA system. The results of a modeling study using simple geometries and a full model of the TNA sensor head are presented, as well as preliminary results from an experimental associated particle imaging (API) system that supports the modeling study results. The investigation has concluded that the pulsed beam FNA approach would not improve the detection performance of a TNA system for landmine or buried IED detection in a confirmation role, and could not be made into a practical stand-alone detection system for buried anti-tank landmines. Detection of buried landmines and IEDs by FNA remains a possibility, however, through the use of the API technique.

Using distances between Top-n-gram and residue pairs for protein remote homology detection.

PubMed

Liu, Bin; Xu, Jinghao; Zou, Quan; Xu, Ruifeng; Wang, Xiaolong; Chen, Qingcai

2014-01-01

Protein remote homology detection is one of the central problems in bioinformatics, which is important for both basic research and practical application. Currently, discriminative methods based on Support Vector Machines (SVMs) achieve the state-of-the-art performance. Exploring feature vectors incorporating the position information of amino acids or other protein building blocks is a key step to improve the performance of the SVM-based methods. Two new methods for protein remote homology detection were proposed, called SVM-DR and SVM-DT. SVM-DR is a sequence-based method, in which the feature vector representation for protein is based on the distances between residue pairs. SVM-DT is a profile-based method, which considers the distances between Top-n-gram pairs. Top-n-gram can be viewed as a profile-based building block of proteins, which is calculated from the frequency profiles. These two methods are position dependent approaches incorporating the sequence-order information of protein sequences. Various experiments were conducted on a benchmark dataset containing 54 families and 23 superfamilies. Experimental results showed that these two new methods are very promising. Compared with the position independent methods, the performance improvement is obvious. Furthermore, the proposed methods can also provide useful insights for studying the features of protein families. The better performance of the proposed methods demonstrates that the position dependant approaches are efficient for protein remote homology detection. Another advantage of our methods arises from the explicit feature space representation, which can be used to analyze the characteristic features of protein families. The source code of SVM-DT and SVM-DR is available at http://bioinformatics.hitsz.edu.cn/DistanceSVM/index.jsp.

Breast cancer detection via Hu moment invariant and feedforward neural network

NASA Astrophysics Data System (ADS)

Zhang, Xiaowei; Yang, Jiquan; Nguyen, Elijah

2018-04-01

One of eight women can get breast cancer during all her life. This study used Hu moment invariant and feedforward neural network to diagnose breast cancer. With the help of K-fold cross validation, we can test the out-of-sample accuracy of our method. Finally, we found that our methods can improve the accuracy of detecting breast cancer and reduce the difficulty of judging.

Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions

PubMed Central

Tan, Maxine; Aghaei, Faranak; Wang, Yunzhi; Zheng, Bin

2017-01-01

The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography (FFDM) images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a “scoring fusion” artificial neural network (ANN) classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC = 0.793±0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions. PMID:27997380

Improving surface EMG burst detection in infrahyoid muscles during swallowing using digital filters and discrete wavelet analysis.

PubMed

Restrepo-Agudelo, Sebastian; Roldan-Vasco, Sebastian; Ramirez-Arbelaez, Lina; Cadavid-Arboleda, Santiago; Perez-Giraldo, Estefania; Orozco-Duque, Andres

2017-08-01

The visual inspection is a widely used method for evaluating the surface electromyographic signal (sEMG) during deglutition, a process highly dependent of the examiners expertise. It is desirable to have a less subjective and automated technique to improve the onset detection in swallowing related muscles, which have a low signal-to-noise ratio. In this work, we acquired sEMG measured in infrahyoid muscles with high baseline noise of ten healthy adults during water swallowing tasks. Two methods were applied to find the combination of cutoff frequencies that achieve the most accurate onset detection: discrete wavelet decomposition based method and fixed steps variations of low and high cutoff frequencies of a digital bandpass filter. Teager-Kaiser Energy operator, root mean square and simple threshold method were applied for both techniques. Results show a narrowing of the effective bandwidth vs. the literature recommended parameters for sEMG acquisition. Both level 3 decomposition with mother wavelet db4 and bandpass filter with cutoff frequencies between 130 and 180Hz were optimal for onset detection in infrahyoid muscles. The proposed methodologies recognized the onset time with predictive power above 0.95, that is similar to previous findings but in larger and more superficial muscles in limbs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dual Energy Method for Breast Imaging: A Simulation Study.

PubMed

Koukou, V; Martini, N; Michail, C; Sotiropoulou, P; Fountzoula, C; Kalyvas, N; Kandarakis, I; Nikiforidis, G; Fountos, G

2015-01-01

Dual energy methods can suppress the contrast between adipose and glandular tissues in the breast and therefore enhance the visibility of calcifications. In this study, a dual energy method based on analytical modeling was developed for the detection of minimum microcalcification thickness. To this aim, a modified radiographic X-ray unit was considered, in order to overcome the limited kVp range of mammographic units used in previous DE studies, combined with a high resolution CMOS sensor (pixel size of 22.5 μm) for improved resolution. Various filter materials were examined based on their K-absorption edge. Hydroxyapatite (HAp) was used to simulate microcalcifications. The contrast to noise ratio (CNR tc ) of the subtracted images was calculated for both monoenergetic and polyenergetic X-ray beams. The optimum monoenergetic pair was 23/58 keV for the low and high energy, respectively, resulting in a minimum detectable microcalcification thickness of 100 μm. In the polyenergetic X-ray study, the optimal spectral combination was 40/70 kVp filtered with 100 μm cadmium and 1000 μm copper, respectively. In this case, the minimum detectable microcalcification thickness was 150 μm. The proposed dual energy method provides improved microcalcification detectability in breast imaging with mean glandular dose values within acceptable levels.

Dual Energy Method for Breast Imaging: A Simulation Study

PubMed Central

2015-01-01

Dual energy methods can suppress the contrast between adipose and glandular tissues in the breast and therefore enhance the visibility of calcifications. In this study, a dual energy method based on analytical modeling was developed for the detection of minimum microcalcification thickness. To this aim, a modified radiographic X-ray unit was considered, in order to overcome the limited kVp range of mammographic units used in previous DE studies, combined with a high resolution CMOS sensor (pixel size of 22.5 μm) for improved resolution. Various filter materials were examined based on their K-absorption edge. Hydroxyapatite (HAp) was used to simulate microcalcifications. The contrast to noise ratio (CNRtc) of the subtracted images was calculated for both monoenergetic and polyenergetic X-ray beams. The optimum monoenergetic pair was 23/58 keV for the low and high energy, respectively, resulting in a minimum detectable microcalcification thickness of 100 μm. In the polyenergetic X-ray study, the optimal spectral combination was 40/70 kVp filtered with 100 μm cadmium and 1000 μm copper, respectively. In this case, the minimum detectable microcalcification thickness was 150 μm. The proposed dual energy method provides improved microcalcification detectability in breast imaging with mean glandular dose values within acceptable levels. PMID:26246848

Improved assessment of multiple sclerosis lesion segmentation agreement via detection and outline error estimates

PubMed Central

2012-01-01

Background Presented is the method “Detection and Outline Error Estimates” (DOEE) for assessing rater agreement in the delineation of multiple sclerosis (MS) lesions. The DOEE method divides operator or rater assessment into two parts: 1) Detection Error (DE) -- rater agreement in detecting the same regions to mark, and 2) Outline Error (OE) -- agreement of the raters in outlining of the same lesion. Methods DE, OE and Similarity Index (SI) values were calculated for two raters tested on a set of 17 fluid-attenuated inversion-recovery (FLAIR) images of patients with MS. DE, OE, and SI values were tested for dependence with mean total area (MTA) of the raters' Region of Interests (ROIs). Results When correlated with MTA, neither DE (ρ = .056, p=.83) nor the ratio of OE to MTA (ρ = .23, p=.37), referred to as Outline Error Rate (OER), exhibited significant correlation. In contrast, SI is found to be strongly correlated with MTA (ρ = .75, p < .001). Furthermore, DE and OER values can be used to model the variation in SI with MTA. Conclusions The DE and OER indices are proposed as a better method than SI for comparing rater agreement of ROIs, which also provide specific information for raters to improve their agreement. PMID:22812697

Functional magnetic resonance imaging activation detection: fuzzy cluster analysis in wavelet and multiwavelet domains.

PubMed

Jahanian, Hesamoddin; Soltanian-Zadeh, Hamid; Hossein-Zadeh, Gholam-Ali

2005-09-01

To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents. Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared. The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis. More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features. (c) 2005 Wiley-Liss, Inc.

Integration of biomimicry and nanotechnology for significantly improved detection of circulating tumor cells (CTCs).

PubMed

Myung, Ja Hye; Park, Sin-Jung; Wang, Andrew Z; Hong, Seungpyo

2017-12-13

Circulating tumor cells (CTCs) have received a great deal of scientific and clinical attention as a biomarker for diagnosis and prognosis of many types of cancer. Given their potential significance in clinics, a variety of detection methods, utilizing the recent advances in nanotechnology and microfluidics, have been introduced in an effort of achieving clinically significant detection of CTCs. However, effective detection and isolation of CTCs still remain a tremendous challenge due to their extreme rarity and phenotypic heterogeneity. Among many approaches that are currently under development, this review paper focuses on a unique, promising approach that takes advantages of naturally occurring processes achievable through application of nanotechnology to realize significant improvement in sensitivity and specificity of CTC capture. We provide an overview of successful outcome of this biomimetic CTC capture system in detection of tumor cells from in vitro, in vivo, and clinical pilot studies. We also emphasize the clinical impact of CTCs as biomarkers in cancer diagnosis and predictive prognosis, which provides a cost-effective, minimally invasive method that potentially replaces or supplements existing methods such as imaging technologies and solid tissue biopsy. In addition, their potential prognostic values as treatment guidelines and that ultimately help to realize personalized therapy are discussed. Copyright © 2017. Published by Elsevier B.V.

Optic disc detection and boundary extraction in retinal images.

PubMed

Basit, A; Fraz, Muhammad Moazam

2015-04-10

With the development of digital image processing, analysis and modeling techniques, automatic retinal image analysis is emerging as an important screening tool for early detection of ophthalmologic disorders such as diabetic retinopathy and glaucoma. In this paper, a robust method for optic disc detection and extraction of the optic disc boundary is proposed to help in the development of computer-assisted diagnosis and treatment of such ophthalmic disease. The proposed method is based on morphological operations, smoothing filters, and the marker controlled watershed transform. Internal and external markers are used to first modify the gradient magnitude image and then the watershed transformation is applied on this modified gradient magnitude image for boundary extraction. This method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc. The proposed method has optic disc detection success rate of 100%, 100%, 100% and 98.9% for the DRIVE, Shifa, CHASE_DB1, and DIARETDB1 databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 61.88%, 70.96%, 45.61%, and 54.69% for these databases, respectively, which are higher than currents methods.

Impact of transition from microscopy to molecular screening for detection of intestinal protozoa in Dutch patients.

PubMed

Svraka-Latifovic, S; Bouter, S; Naus, H; Bakker, L J; Timmerman, C P; Dorigo-Zetsma, J W

2014-11-01

Detection of intestinal protozoa by PCR methods has been described as being sensitive and specific, and as improving the diagnostic yield. Here we present the outcome of the transition from microscopy to molecular screening for detection of a select group of intestinal protozoa in faeces in our laboratory. Introduction of molecular screening for intestinal protozoa resulted in higher sensitivity, reduced hands-on-time, reduced time-to-results, leading to improved diagnostic efficiency. © 2014 The Authors Clinical Microbiology and Infection © 2014 European Society of Clinical Microbiology and Infectious Diseases.

Bearing failure detection of micro wind turbine via power spectral density analysis for stator current signals spectrum

NASA Astrophysics Data System (ADS)

Mahmood, Faleh H.; Kadhim, Hussein T.; Resen, Ali K.; Shaban, Auday H.

2018-05-01

The failure such as air gap weirdness, rubbing, and scrapping between stator and rotor generator arise unavoidably and may cause extremely terrible results for a wind turbine. Therefore, we should pay more attention to detect and identify its cause-bearing failure in wind turbine to improve the operational reliability. The current paper tends to use of power spectral density analysis method of detecting internal race and external race bearing failure in micro wind turbine by estimation stator current signal of the generator. The failure detector method shows that it is well suited and effective for bearing failure detection.

Four-Wave-Mixing Approach to In Situ Detection of Nanoparticles

NASA Astrophysics Data System (ADS)

Gerakis, Alexandros; Yeh, Yao-Wen; Shneider, Mikhail N.; Mitrani, James M.; Stratton, Brentley C.; Raitses, Yevgeny

2018-01-01

We report on the development and experimental validation of a laser-based technique which uses coherent Rayleigh-Brillouin scattering (CRBS) to detect nanoparticles with characteristic sizes ranging from the atomic scale to tens of nanometers. This technique is aimed (nonexclusively) at the detection of nanoparticles produced by volumetric nanoparticle synthesis methods. Using CRBS, carbon nanoparticles of dimensions less than 10 nm and concentrations of 1010 cm-3 are detected in situ in a carbon arc discharge with graphite electrodes. This four-wave-mixing approach should enable advances in the understanding of nanoparticle growth that could potentially lead to improved modeling of the growth mechanisms, and thus to improve synthesis selectivity of nanoparticles and yield.

Flash memory management system and method utilizing multiple block list windows

NASA Technical Reports Server (NTRS)

Chow, James (Inventor); Gender, Thomas K. (Inventor)

2005-01-01

The present invention provides a flash memory management system and method with increased performance. The flash memory management system provides the ability to efficiently manage and allocate flash memory use in a way that improves reliability and longevity, while maintaining good performance levels. The flash memory management system includes a free block mechanism, a disk maintenance mechanism, and a bad block detection mechanism. The free block mechanism provides efficient sorting of free blocks to facilitate selecting low use blocks for writing. The disk maintenance mechanism provides for the ability to efficiently clean flash memory blocks during processor idle times. The bad block detection mechanism provides the ability to better detect when a block of flash memory is likely to go bad. The flash status mechanism stores information in fast access memory that describes the content and status of the data in the flash disk. The new bank detection mechanism provides the ability to automatically detect when new banks of flash memory are added to the system. Together, these mechanisms provide a flash memory management system that can improve the operational efficiency of systems that utilize flash memory.

Acquiring 3-D information about thick objects from differential interference contrast images using texture extraction

NASA Astrophysics Data System (ADS)

Sierra, Heidy; Brooks, Dana; Dimarzio, Charles

2010-07-01

The extraction of 3-D morphological information about thick objects is explored in this work. We extract this information from 3-D differential interference contrast (DIC) images by applying a texture detection method. Texture extraction methods have been successfully used in different applications to study biological samples. A 3-D texture image is obtained by applying a local entropy-based texture extraction method. The use of this method to detect regions of blastocyst mouse embryos that are used in assisted reproduction techniques such as in vitro fertilization is presented as an example. Results demonstrate the potential of using texture detection methods to improve morphological analysis of thick samples, which is relevant to many biomedical and biological studies. Fluorescence and optical quadrature microscope phase images are used for validation.

Can Detectability Analysis Improve the Utility of Point Counts for Temperate Forest Raptors?

EPA Science Inventory

Temperate forest breeding raptors are poorly represented in typical point count surveys because these birds are cryptic and typically breed at low densities. In recent years, many new methods for estimating detectability during point counts have been developed, including distanc...

Improved and Robust Detection of Cell Nuclei from Four Dimensional Fluorescence Images

PubMed Central

Bashar, Md. Khayrul; Yamagata, Kazuo; Kobayashi, Tetsuya J.

2014-01-01

Segmentation-free direct methods are quite efficient for automated nuclei extraction from high dimensional images. A few such methods do exist but most of them do not ensure algorithmic robustness to parameter and noise variations. In this research, we propose a method based on multiscale adaptive filtering for efficient and robust detection of nuclei centroids from four dimensional (4D) fluorescence images. A temporal feedback mechanism is employed between the enhancement and the initial detection steps of a typical direct method. We estimate the minimum and maximum nuclei diameters from the previous frame and feed back them as filter lengths for multiscale enhancement of the current frame. A radial intensity-gradient function is optimized at positions of initial centroids to estimate all nuclei diameters. This procedure continues for processing subsequent images in the sequence. Above mechanism thus ensures proper enhancement by automated estimation of major parameters. This brings robustness and safeguards the system against additive noises and effects from wrong parameters. Later, the method and its single-scale variant are simplified for further reduction of parameters. The proposed method is then extended for nuclei volume segmentation. The same optimization technique is applied to final centroid positions of the enhanced image and the estimated diameters are projected onto the binary candidate regions to segment nuclei volumes.Our method is finally integrated with a simple sequential tracking approach to establish nuclear trajectories in the 4D space. Experimental evaluations with five image-sequences (each having 271 3D sequential images) corresponding to five different mouse embryos show promising performances of our methods in terms of nuclear detection, segmentation, and tracking. A detail analysis with a sub-sequence of 101 3D images from an embryo reveals that the proposed method can improve the nuclei detection accuracy by 9 over the previous methods, which used inappropriate large valued parameters. Results also confirm that the proposed method and its variants achieve high detection accuracies ( 98 mean F-measure) irrespective of the large variations of filter parameters and noise levels. PMID:25020042

SAR Image Change Detection Based on Fuzzy Markov Random Field Model

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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.

Mapping Health Data: Improved Privacy Protection With Donut Method Geomasking

PubMed Central

Hampton, Kristen H.; Fitch, Molly K.; Allshouse, William B.; Doherty, Irene A.; Gesink, Dionne C.; Leone, Peter A.; Serre, Marc L.; Miller, William C.

2010-01-01

A major challenge in mapping health data is protecting patient privacy while maintaining the spatial resolution necessary for spatial surveillance and outbreak identification. A new adaptive geomasking technique, referred to as the donut method, extends current methods of random displacement by ensuring a user-defined minimum level of geoprivacy. In donut method geomasking, each geocoded address is relocated in a random direction by at least a minimum distance, but less than a maximum distance. The authors compared the donut method with current methods of random perturbation and aggregation regarding measures of privacy protection and cluster detection performance by masking multiple disease field simulations under a range of parameters. Both the donut method and random perturbation performed better than aggregation in cluster detection measures. The performance of the donut method in geoprivacy measures was at least 42.7% higher and in cluster detection measures was less than 4.8% lower than that of random perturbation. Results show that the donut method provides a consistently higher level of privacy protection with a minimal decrease in cluster detection performance, especially in areas where the risk to individual geoprivacy is greatest. PMID:20817785

Mapping health data: improved privacy protection with donut method geomasking.

PubMed

Hampton, Kristen H; Fitch, Molly K; Allshouse, William B; Doherty, Irene A; Gesink, Dionne C; Leone, Peter A; Serre, Marc L; Miller, William C

2010-11-01

A major challenge in mapping health data is protecting patient privacy while maintaining the spatial resolution necessary for spatial surveillance and outbreak identification. A new adaptive geomasking technique, referred to as the donut method, extends current methods of random displacement by ensuring a user-defined minimum level of geoprivacy. In donut method geomasking, each geocoded address is relocated in a random direction by at least a minimum distance, but less than a maximum distance. The authors compared the donut method with current methods of random perturbation and aggregation regarding measures of privacy protection and cluster detection performance by masking multiple disease field simulations under a range of parameters. Both the donut method and random perturbation performed better than aggregation in cluster detection measures. The performance of the donut method in geoprivacy measures was at least 42.7% higher and in cluster detection measures was less than 4.8% lower than that of random perturbation. Results show that the donut method provides a consistently higher level of privacy protection with a minimal decrease in cluster detection performance, especially in areas where the risk to individual geoprivacy is greatest.

Approach to explosive hazard detection using sensor fusion and multiple kernel learning with downward-looking GPR and EMI sensor data

NASA Astrophysics Data System (ADS)

Pinar, Anthony; Masarik, Matthew; Havens, Timothy C.; Burns, Joseph; Thelen, Brian; Becker, John

2015-05-01

This paper explores the effectiveness of an anomaly detection algorithm for downward-looking ground penetrating radar (GPR) and electromagnetic inductance (EMI) data. Threat detection with GPR is challenged by high responses to non-target/clutter objects, leading to a large number of false alarms (FAs), and since the responses of target and clutter signatures are so similar, classifier design is not trivial. We suggest a method based on a Run Packing (RP) algorithm to fuse GPR and EMI data into a composite confidence map to improve detection as measured by the area-under-ROC (NAUC) metric. We examine the value of a multiple kernel learning (MKL) support vector machine (SVM) classifier using image features such as histogram of oriented gradients (HOG), local binary patterns (LBP), and local statistics. Experimental results on government furnished data show that use of our proposed fusion and classification methods improves the NAUC when compared with the results from individual sensors and a single kernel SVM classifier.

Imaging and Elastometry of Blood Clots Using Magnetomotive Optical Coherence Tomography and Labeled Platelets

PubMed Central

Oldenburg, Amy L.; Wu, Gongting; Spivak, Dmitry; Tsui, Frank; Wolberg, Alisa S.; Fischer, Thomas H.

2013-01-01

Improved methods for imaging and assessment of vascular defects are needed for directing treatment of cardiovascular pathologies. In this paper, we employ magnetomotive optical coherence tomography (MMOCT) as a platform both to detect and to measure the elasticity of blood clots. Detection is enabled through the use of rehydrated, lyophilized platelets loaded with superparamagnetic iron oxides (SPIO-RL platelets) that are functional infusion agents that adhere to sites of vascular endothelial damage. Evidence suggests that the sensitivity for detection is improved over threefold by magnetic interactions between SPIOs inside RL platelets. Using the same MMOCT system, we show how elastometry of simulated clots, using resonant acoustic spectroscopy, is correlated with the fibrin content of the clot. Both methods are based upon magnetic actuation and phase-sensitive optical monitoring of nanoscale displacements using MMOCT, underscoring its utility as a broad-based platform to detect and measure the molecular structure and composition of blood clots. PMID:23833549

Incorporating profile information in community detection for online social networks

NASA Astrophysics Data System (ADS)

Fan, W.; Yeung, K. H.

2014-07-01

Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users' profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.

Improving Cardiac Action Potential Measurements: 2D and 3D Cell Culture.

PubMed

Daily, Neil J; Yin, Yue; Kemanli, Pinar; Ip, Brian; Wakatsuki, Tetsuro

2015-11-01

Progress in the development of assays for measuring cardiac action potential is crucial for the discovery of drugs for treating cardiac disease and assessing cardiotoxicity. Recently, high-throughput methods for assessing action potential using induced pluripotent stem cell (iPSC) derived cardiomyocytes in both two-dimensional monolayer cultures and three-dimensional tissues have been developed. We describe an improved method for assessing cardiac action potential using an ultra-fast cost-effective plate reader with commercially available dyes. Our methods improve dramatically the detection of the fluorescence signal from these dyes and make way for the development of more high-throughput methods for cardiac drug discovery and cardiotoxicity.

Enzymatic Digestion for Improved Bacteria Separation from Leafy Green Vegetables.

PubMed

Wang, Danhui; Wang, Ziyuan; He, Fei; Kinchla, Amanda J; Nugen, Sam R

2016-08-01

An effective and rapid method for the separation of bacteria from food matrix remains a bottleneck for rapid bacteria detection for food safety. Bacteria can strongly attach to a food surface or internalize within the matrix, making their isolation extremely difficult. Traditional methods of separating bacteria from food routinely involve stomaching, blending, and shaking. However, these methods may not be efficient at removing all the bacteria from complex matrices. Here, we investigate the benefits of using enzyme digestion followed by immunomagnetic separation to isolate Salmonella from spinach and lettuce. Enzymatic digestion using pectinase and cellulase was able to break down the structure of the leafy green vegetables, resulting in the detachment and release of Salmonella from the leaves. Immunomagnetic separation of Salmonella from the liquefied sample allowed an additional separation step to achieve a more pure sample without leaf debris that may benefit additional downstream applications. We have investigated the optimal combination of pectinase and cellulase for the digestion of spinach and lettuce to improve sample detection yields. The concentrations of enzymes used to digest the leaves were confirmed to have no significant effect on the viability of the inoculated Salmonella. Results reported that the recovery of the Salmonella from the produce after enzyme digestion of the leaves was significantly higher (P < 0.05) than traditional sample preparation methods to separate bacteria (stomaching and manually shaking). The results demonstrate the potential for use of enzyme digestion prior to separation can improve the efficiency of bacteria separation and increase the likelihood of detecting pathogens in the final detection assay.

Refining historical limits method to improve disease cluster detection, New York City, New York, USA.

PubMed

Levin-Rector, Alison; Wilson, Elisha L; Fine, Annie D; Greene, Sharon K

2015-02-01

Since the early 2000s, the Bureau of Communicable Disease of the New York City Department of Health and Mental Hygiene has analyzed reportable infectious disease data weekly by using the historical limits method to detect unusual clusters that could represent outbreaks. This method typically produced too many signals for each to be investigated with available resources while possibly failing to signal during true disease outbreaks. We made method refinements that improved the consistency of case inclusion criteria and accounted for data lags and trends and aberrations in historical data. During a 12-week period in 2013, we prospectively assessed these refinements using actual surveillance data. The refined method yielded 74 signals, a 45% decrease from what the original method would have produced. Fewer and less biased signals included a true citywide increase in legionellosis and a localized campylobacteriosis cluster subsequently linked to live-poultry markets. Future evaluations using simulated data could complement this descriptive assessment.

[Improvement of 2-mercaptoimidazoline analysis in rubber products containing chlorine].

PubMed

Kaneko, Reiko; Haneishi, Nahoko; Kawamura, Yoko

2012-01-01

An improved analysis method for 2-mercaptoimidazoline in rubber products containing chlorine was developed. 2-Mercaptoimidazoline (20 µg/mL) is detected by means of TLC with two developing solvents in the official method. But, this method is not quantitative. Instead, we employed HPLC using water-methanol (9 : 1) as the mobile phase. This procedure decreased interfering peaks, and the quantitation limit was 2 µg/mL of standard solution. 2-Mercaptoimidazoline was confirmed by GC-MS (5 µg/mL) and LC/MS (1 µg/mL) in the scan mode. For preparation of test solution, a soaking extraction method, in which 20 mL of methanol was added to the sample and allowed to stand overnight at about 40°C, was used. This gave similar values to the Soxhlet extraction method (official method) and was more convenient. The results indicate that our procedure is suitable for analysis of 2-mercaptoimidazoline. When 2-mercaptoimidazoline is detected, it is confirmed by either GC/MS or LC/MS.

A geometrical defect detection method for non-silicon MEMS part based on HU moment invariants of skeleton image

NASA Astrophysics Data System (ADS)

Cheng, Xu; Jin, Xin; Zhang, Zhijing; Lu, Jun

2014-01-01

In order to improve the accuracy of geometrical defect detection, this paper presented a method based on HU moment invariants of skeleton image. This method have four steps: first of all, grayscale images of non-silicon MEMS parts are collected and converted into binary images, secondly, skeletons of binary images are extracted using medialaxis- transform method, and then HU moment invariants of skeleton images are calculated, finally, differences of HU moment invariants between measured parts and qualified parts are obtained to determine whether there are geometrical defects. To demonstrate the availability of this method, experiments were carried out between skeleton images and grayscale images, and results show that: when defects of non-silicon MEMS part are the same, HU moment invariants of skeleton images are more sensitive than that of grayscale images, and detection accuracy is higher. Therefore, this method can more accurately determine whether non-silicon MEMS parts qualified or not, and can be applied to nonsilicon MEMS part detection system.

Seismic data fusion anomaly detection

NASA Astrophysics Data System (ADS)

Harrity, Kyle; Blasch, Erik; Alford, Mark; Ezekiel, Soundararajan; Ferris, David

2014-06-01

Detecting anomalies in non-stationary signals has valuable applications in many fields including medicine and meteorology. These include uses such as identifying possible heart conditions from an Electrocardiography (ECG) signals or predicting earthquakes via seismographic data. Over the many choices of anomaly detection algorithms, it is important to compare possible methods. In this paper, we examine and compare two approaches to anomaly detection and see how data fusion methods may improve performance. The first approach involves using an artificial neural network (ANN) to detect anomalies in a wavelet de-noised signal. The other method uses a perspective neural network (PNN) to analyze an arbitrary number of "perspectives" or transformations of the observed signal for anomalies. Possible perspectives may include wavelet de-noising, Fourier transform, peak-filtering, etc.. In order to evaluate these techniques via signal fusion metrics, we must apply signal preprocessing techniques such as de-noising methods to the original signal and then use a neural network to find anomalies in the generated signal. From this secondary result it is possible to use data fusion techniques that can be evaluated via existing data fusion metrics for single and multiple perspectives. The result will show which anomaly detection method, according to the metrics, is better suited overall for anomaly detection applications. The method used in this study could be applied to compare other signal processing algorithms.

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

PubMed

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

2018-02-12

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

Improving colorectal cancer screening: fact and fantasy

NASA Astrophysics Data System (ADS)

Van Dam, Jacques

2008-02-01

Premalignant diseases of the gastrointestinal tract, such as Barrett's esophagus, long-standing ulcerative colitis, and adenomatous polyps, have a significantly increased risk for development of adenocarcinoma, most often through an intermediate stage of dysplasia. Adenocarcinoma of the colon is the second most common cancer in the United States. Because patients with colorectal cancer often present with advanced disease, the outcomes are associated with significant morbidity and mortality. Effective methods of early detection are essential. As non-polypoid dysplasia is not visible using conventional endoscopy, surveillance of patients with Barrett's esophagus and ulcerative colitis is performed via a system in which multiple random biopsies are obtained at prescribed intervals. Sampling error and missed diagnoses occur frequently and render current screening methods inadequate. Also, the examination of a tissue biopsy is time consuming and costly, and significant intra- and inter-observer variation may occur. The newer methods discussed herein demonstrate the potential to solve these problems by early detection of disease with high sensitivity and specificity. Conventional endoscopy is based on the observation of white light reflected off the tissue surface. Subtle changes in color and shadow reveal structural changes. New developments in optical imaging go beyond white light, exploiting other properties of light. Several promising methods will be discussed at this meeting and shall be briefly discussed below. However, few such imaging modalities have arrived at our clinical practice. Some much more practical methods to improve colorectal cancer screening are currently being evaluated for their clinical impact. These methods seek to overcome limitations other than those of detecting dysplasia not visible under white light endoscopy. The current standard practice of colorectal cancer screening utilizes colonoscopy, an uncomfortable, sometimes difficult medical procedure. Efforts to improve the practice of colonoscopy will be described. Another limitation of the current practice is the inability to detect polypoid neoplasia that is hidden from view under white light imaging by the natural folds that occur within the colon. A device to overcome this limitation will also be described. Efforts to improve colorectal cancer screening (and thereby decrease the death rate of this second leading cause of cancer death in the United States) are progressing in many arenas. The researcher, basic or clinical, should maintain an up to date overview of the field and how each new technological advance is likely to have a role in the screening and early detection of colorectal cancer.

Hands-on resonance-enhanced photoacoustic detection

NASA Astrophysics Data System (ADS)

Euler, Manfred

2001-10-01

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

Improved Diffuse Fluorescence Flow Cytometer Prototype for High Sensitivity Detection of Rare Circulating Cells In Vivo

NASA Astrophysics Data System (ADS)

Pestana, Noah Benjamin

Accurate quantification of circulating cell populations is important in many areas of pre-clinical and clinical biomedical research, for example, in the study of cancer metastasis or the immune response following tissue and organ transplants. Normally this is done "ex-vivo" by drawing and purifying a small volume of blood and then analyzing it with flow cytometry, hemocytometry or microfludic devices, but the sensitivity of these techniques are poor and the process of handling samples has been shown to affect cell viability and behavior. More recently "in vivo flow cytometry" (IVFC) techniques have been developed where fluorescently-labeled cells flowing in a small blood vessel in the ear or retina are analyzed, but the sensitivity is generally poor due to the small sampling volume. To address this, our group recently developed a method known as "Diffuse Fluorescence Flow Cytometry" (DFFC) that allows detection and counting of rare circulating cells with diffuse photons, offering extremely high single cell counting sensitivity. In this thesis, an improved DFFC prototype was designed and validated. The chief improvements were three-fold, i) improved optical collection efficiency, ii) improved detection electronics, and iii) development of a method to mitigate motion artifacts during in vivo measurements. In combination, these improvements yielded an overall instrument detection sensitivity better than 1 cell/mL in vivo, which is the most sensitive IVFC system reported to date. Second, development and validation of a low-cost microfluidic device reader for analysis of ocular fluids is described. We demonstrate that this device has equivalent or better sensitivity and accuracy compared a fluorescence microscope, but at an order-of-magnitude reduced cost with simplified operation. Future improvements to both instruments are also discussed.

On precise phase difference measurement approach using border stability of detection resolution.

PubMed

Bai, Lina; Su, Xin; Zhou, Wei; Ou, Xiaojuan

2015-01-01

For the precise phase difference measurement, this paper develops an improved dual phase coincidence detection method. The measurement resolution of the digital phase coincidence detection circuits is always limited, for example, only at the nanosecond level. This paper reveals a new way to improve the phase difference measurement precision by using the border stability of the circuit detection fuzzy areas. When a common oscillator signal is used to detect the phase coincidence with the two comparison signals, there will be two detection fuzzy areas for the reason of finite detection resolution surrounding the strict phase coincidence. Border stability of fuzzy areas and the fluctuation difference of the two fuzzy areas can be even finer than the picoseconds level. It is shown that the system resolution obtained only depends on the stability of the circuit measurement resolution which is much better than the measurement device resolution itself.

Homeland Security Research Improves the Nation's Ability to ...

EPA Pesticide Factsheets

Technical Brief Homeland Security (HS) Research develops data, tools, and technologies to minimize the impact of accidents, natural disasters, terrorist attacks, and other incidents that can result in toxic chemical, biological or radiological (CBR) contamination. HS Research develops ways to detect contamination, sampling strategies, sampling and analytical methods, cleanup methods, waste management approaches, exposure assessment methods, and decision support tools (including water system models). These contributions improve EPA’s response to a broad range of environmental disasters.

Accurate template-based modeling in CASP12 using the IntFOLD4-TS, ModFOLD6, and ReFOLD methods.

PubMed

McGuffin, Liam J; Shuid, Ahmad N; Kempster, Robert; Maghrabi, Ali H A; Nealon, John O; Salehe, Bajuna R; Atkins, Jennifer D; Roche, Daniel B

2018-03-01

Our aim in CASP12 was to improve our Template-Based Modeling (TBM) methods through better model selection, accuracy self-estimate (ASE) scores and refinement. To meet this aim, we developed two new automated methods, which we used to score, rank, and improve upon the provided server models. Firstly, the ModFOLD6_rank method, for improved global Quality Assessment (QA), model ranking and the detection of local errors. Secondly, the ReFOLD method for fixing errors through iterative QA guided refinement. For our automated predictions we developed the IntFOLD4-TS protocol, which integrates the ModFOLD6_rank method for scoring the multiple-template models that were generated using a number of alternative sequence-structure alignments. Overall, our selection of top models and ASE scores using ModFOLD6_rank was an improvement on our previous approaches. In addition, it was worthwhile attempting to repair the detected errors in the top selected models using ReFOLD, which gave us an overall gain in performance. According to the assessors' formula, the IntFOLD4 server ranked 3rd/5th (average Z-score > 0.0/-2.0) on the server only targets, and our manual predictions (McGuffin group) ranked 1st/2nd (average Z-score > -2.0/0.0) compared to all other groups. © 2017 Wiley Periodicals, Inc.

Novel Automated Blood Separations Validate Whole Cell Biomarkers

PubMed Central

Burger, Douglas E.; Wang, Limei; Ban, Liqin; Okubo, Yoshiaki; Kühtreiber, Willem M.; Leichliter, Ashley K.; Faustman, Denise L.

2011-01-01

Background Progress in clinical trials in infectious disease, autoimmunity, and cancer is stymied by a dearth of successful whole cell biomarkers for peripheral blood lymphocytes (PBLs). Successful biomarkers could help to track drug effects at early time points in clinical trials to prevent costly trial failures late in development. One major obstacle is the inaccuracy of Ficoll density centrifugation, the decades-old method of separating PBLs from the abundant red blood cells (RBCs) of fresh blood samples. Methods and Findings To replace the Ficoll method, we developed and studied a novel blood-based magnetic separation method. The magnetic method strikingly surpassed Ficoll in viability, purity and yield of PBLs. To reduce labor, we developed an automated platform and compared two magnet configurations for cell separations. These more accurate and labor-saving magnet configurations allowed the lymphocytes to be tested in bioassays for rare antigen-specific T cells. The automated method succeeded at identifying 79% of patients with the rare PBLs of interest as compared with Ficoll's uniform failure. We validated improved upfront blood processing and show accurate detection of rare antigen-specific lymphocytes. Conclusions Improving, automating and standardizing lymphocyte detections from whole blood may facilitate development of new cell-based biomarkers for human diseases. Improved upfront blood processes may lead to broad improvements in monitoring early trial outcome measurements in human clinical trials. PMID:21799852

Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy.

PubMed

Akram, Usman M; Khan, Shoab A

2012-10-01

There is an ever-increasing interest in the development of automatic medical diagnosis systems due to the advancement in computing technology and also to improve the service by medical community. The knowledge about health and disease is required for reliable and accurate medical diagnosis. Diabetic Retinopathy (DR) is one of the most common causes of blindness and it can be prevented if detected and treated early. DR has different signs and the most distinctive are microaneurysm and haemorrhage which are dark lesions and hard exudates and cotton wool spots which are bright lesions. Location and structure of blood vessels and optic disk play important role in accurate detection and classification of dark and bright lesions for early detection of DR. In this article, we propose a computer aided system for the early detection of DR. The article presents algorithms for retinal image preprocessing, blood vessel enhancement and segmentation and optic disk localization and detection which eventually lead to detection of different DR lesions using proposed hybrid fuzzy classifier. The developed methods are tested on four different publicly available databases. The presented methods are compared with recently published methods and the results show that presented methods outperform all others.

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

DOE PAGES

Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

2016-04-12

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

Hydrogeology and human health

USDA-ARS?s Scientific Manuscript database

Over the past 50 years, significant progress has been made in improving our understanding of the extent and potential consequences of groundwater contamination, with research advancing on several fronts including groundwater sampling methods, laboratory detection methods, subsurface transport (and m...

Mass type-specific sparse representation for mass classification in computer-aided detection on mammograms

PubMed Central

2013-01-01

Background Breast cancer is the leading cause of both incidence and mortality in women population. For this reason, much research effort has been devoted to develop Computer-Aided Detection (CAD) systems for early detection of the breast cancers on mammograms. In this paper, we propose a new and novel dictionary configuration underpinning sparse representation based classification (SRC). The key idea of the proposed algorithm is to improve the sparsity in terms of mass margins for the purpose of improving classification performance in CAD systems. Methods The aim of the proposed SRC framework is to construct separate dictionaries according to the types of mass margins. The underlying idea behind our method is that the separated dictionaries can enhance the sparsity of mass class (true-positive), leading to an improved performance for differentiating mammographic masses from normal tissues (false-positive). When a mass sample is given for classification, the sparse solutions based on corresponding dictionaries are separately solved and combined at score level. Experiments have been performed on both database (DB) named as Digital Database for Screening Mammography (DDSM) and clinical Full Field Digital Mammogram (FFDM) DBs. In our experiments, sparsity concentration in the true class (SCTC) and area under the Receiver operating characteristic (ROC) curve (AUC) were measured for the comparison between the proposed method and a conventional single dictionary based approach. In addition, a support vector machine (SVM) was used for comparing our method with state-of-the-arts classifier extensively used for mass classification. Results Comparing with the conventional single dictionary configuration, the proposed approach is able to improve SCTC of up to 13.9% and 23.6% on DDSM and FFDM DBs, respectively. Moreover, the proposed method is able to improve AUC with 8.2% and 22.1% on DDSM and FFDM DBs, respectively. Comparing to SVM classifier, the proposed method improves AUC with 2.9% and 11.6% on DDSM and FFDM DBs, respectively. Conclusions The proposed dictionary configuration is found to well improve the sparsity of dictionaries, resulting in an enhanced classification performance. Moreover, the results show that the proposed method is better than conventional SVM classifier for classifying breast masses subject to various margins from normal tissues. PMID:24564973

Label-Free Immuno-Sensors for the Fast Detection of Listeria in Food.

PubMed

Morlay, Alexandra; Roux, Agnès; Templier, Vincent; Piat, Félix; Roupioz, Yoann

2017-01-01

Foodborne diseases are a major concern for both food industry and health organizations due to the economic costs and potential threats for human lives. For these reasons, specific regulations impose the research of pathogenic bacteria in food products. Nevertheless, current methods, references and alternatives, take up to several days and require many handling steps. In order to improve pathogen detection in food, we developed an immune-sensor, based on Surface Plasmon Resonance imaging (SPRi) and bacterial growth which allows the detection of a very low number of Listeria monocytogenes in food sample in one day. Adequate sensitivity is achieved by the deposition of several antibodies in a micro-array format allowing real-time detection. This label-free method thus reduces handling and time to result compared with current methods.

Spectrophotometric analyses of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in water.

PubMed

Shi, Cong; Xu, Zhonghou; Smolinski, Benjamin L; Arienti, Per M; O'Connor, Gregory; Meng, Xiaoguang

2015-07-01

A simple and accurate spectrophotometric method for on-site analysis of royal demolition explosive (RDX) in water samples was developed based on the Berthelot reaction. The sensitivity and accuracy of an existing spectrophotometric method was improved by: replacing toxic chemicals with more stable and safer reagents; optimizing the reagent dose and reaction time; improving color stability; and eliminating the interference from inorganic nitrogen compounds in water samples. Cation and anion exchange resin cartridges were developed and used for sample pretreatment to eliminate the effect of ammonia and nitrate on RDX analyses. The detection limit of the method was determined to be 100 μg/L. The method was used successfully for analysis of RDX in untreated industrial wastewater samples. It can be used for on-site monitoring of RDX in wastewater for early detection of chemical spills and failure of wastewater treatment systems. Copyright © 2015. Published by Elsevier B.V.

Improved method for the on-line metal chelate affinity chromatography-high-performance liquid chromatographic determination of tetracycline antibiotics in animal products.

PubMed

Cooper, A D; Stubbings, G W; Kelly, M; Tarbin, J A; Farrington, W H; Shearer, G

1998-07-03

An improved on-line metal chelate affinity chromatography-high-performance liquid chromatography (MCAC-HPLC) method for the determination of tetracycline antibiotics in animal tissues and egg has been developed. Extraction was carried out with ethyl acetate. The extract was then evaporated to dryness and reconstituted in methanol prior to on-line MCAC clean-up and HPLC-UV determination. Recoveries of tetracycline, oxytetracycline, demeclocycline and chlortetracycline in the range 42% to 101% were obtained from egg, poultry, fish and venison tissues spiked at 25 micrograms kg-1. Limits of detection less than 10 microgram kg-1 were estimated for all four analytes. This method has higher throughput, higher recovery and lower limits of detection than a previously reported on-line MCAC-HPLC method which involved aqueous extraction and solid-phase extraction clean-up.

Combining multiple ChIP-seq peak detection systems using combinatorial fusion.

PubMed

Schweikert, Christina; Brown, Stuart; Tang, Zuojian; Smith, Phillip R; Hsu, D Frank

2012-01-01

Due to the recent rapid development in ChIP-seq technologies, which uses high-throughput next-generation DNA sequencing to identify the targets of Chromatin Immunoprecipitation, there is an increasing amount of sequencing data being generated that provides us with greater opportunity to analyze genome-wide protein-DNA interactions. In particular, we are interested in evaluating and enhancing computational and statistical techniques for locating protein binding sites. Many peak detection systems have been developed; in this study, we utilize the following six: CisGenome, MACS, PeakSeq, QuEST, SISSRs, and TRLocator. We define two methods to merge and rescore the regions of two peak detection systems and analyze the performance based on average precision and coverage of transcription start sites. The results indicate that ChIP-seq peak detection can be improved by fusion using score or rank combination. Our method of combination and fusion analysis would provide a means for generic assessment of available technologies and systems and assist researchers in choosing an appropriate system (or fusion method) for analyzing ChIP-seq data. This analysis offers an alternate approach for increasing true positive rates, while decreasing false positive rates and hence improving the ChIP-seq peak identification process.

Improving the performance of US Environmental Protection Agency Method 300.1 for monitoring drinking water compliance.

PubMed

Wagner, Herbert P; Pepich, Barry V; Hautman, Daniel P; Munch, David J

2003-09-05

In 1998, the United States Environmental Protection Agency (EPA) promulgated the maximum contaminant level (MCL) for bromate in drinking water at 10 microg/l, and the method for compliance monitoring of bromate in drinking water was established under Stage 1 of the Disinfectants/Disinfection By-Products Rule (D/DBP) as EPA Method 300.1. In January 2002, the United States Food and Drug Administration (FDA) regulated the bromate concentration in bottled waters at 10 microg/l. EPA anticipates proposing additional methods, which have improved performance for bromate monitoring, in addition to EPA Method 300.1, in the Stage 2 DBP Rule. Until the Stage 2 Rule is promulgated, EPA Method 300.1 will continue to be the only method approved for compliance monitoring of bromate. This manuscript describes the work completed at EPA's Technical Support Center (TSC) to assess the performance of recently developed suppressor technologies toward improving the trace level performance of EPA Method 300.1, specifically for the analysis of trace levels of bromate in high ionic matrices. Three different types of Dionex suppressors were evaluated. The baseline noise, return to baseline after the water dip, detection limits, precision and accuracy, and advantages/disadvantages of each suppressor are discussed. Performance data for the three different suppressors indicates that chemical suppression of the eluent, using the AMMS III suppressor, is the most effective means to reduce baseline noise, resulting in the best resolution and the lowest bromate detection limits, even when a high ionic matrix is analyzed. Incorporation of the AMMS III suppressor improves the performance of EPA Method 300.1 at and below 5.0 microg/l and is a quick way for laboratories to improve their bromate compliance monitoring.

Detection of expression quantitative trait Loci in complex mouse crosses: impact and alleviation of data quality and complex population substructure.

PubMed

Iancu, Ovidiu D; Darakjian, Priscila; Kawane, Sunita; Bottomly, Daniel; Hitzemann, Robert; McWeeney, Shannon

2012-01-01

Complex Mus musculus crosses, e.g., heterogeneous stock (HS), provide increased resolution for quantitative trait loci detection. However, increased genetic complexity challenges detection methods, with discordant results due to low data quality or complex genetic architecture. We quantified the impact of theses factors across three mouse crosses and two different detection methods, identifying procedures that greatly improve detection quality. Importantly, HS populations have complex genetic architectures not fully captured by the whole genome kinship matrix, calling for incorporating chromosome specific relatedness information. We analyze three increasingly complex crosses, using gene expression levels as quantitative traits. The three crosses were an F(2) intercross, a HS formed by crossing four inbred strains (HS4), and a HS (HS-CC) derived from the eight lines found in the collaborative cross. Brain (striatum) gene expression and genotype data were obtained using the Illumina platform. We found large disparities between methods, with concordance varying as genetic complexity increased; this problem was more acute for probes with distant regulatory elements (trans). A suite of data filtering steps resulted in substantial increases in reproducibility. Genetic relatedness between samples generated overabundance of detected eQTLs; an adjustment procedure that includes the kinship matrix attenuates this problem. However, we find that relatedness between individuals is not evenly distributed across the genome; information from distinct chromosomes results in relatedness structure different from the whole genome kinship matrix. Shared polymorphisms from distinct chromosomes collectively affect expression levels, confounding eQTL detection. We suggest that considering chromosome specific relatedness can result in improved eQTL detection.

Virtual-Lattice Based Intrusion Detection Algorithm over Actuator-Assisted Underwater Wireless Sensor Networks

PubMed Central

Yan, Jing; Li, Xiaolei; Luo, Xiaoyuan; Guan, Xinping

2017-01-01

Due to the lack of a physical line of defense, intrusion detection becomes one of the key issues in applications of underwater wireless sensor networks (UWSNs), especially when the confidentiality has prime importance. However, the resource-constrained property of UWSNs such as sparse deployment and energy constraint makes intrusion detection a challenging issue. This paper considers a virtual-lattice-based approach to the intrusion detection problem in UWSNs. Different from most existing works, the UWSNs consist of two kinds of nodes, i.e., sensor nodes (SNs), which cannot move autonomously, and actuator nodes (ANs), which can move autonomously according to the performance requirement. With the cooperation of SNs and ANs, the intruder detection probability is defined. Then, a virtual lattice-based monitor (VLM) algorithm is proposed to detect the intruder. In order to reduce the redundancy of communication links and improve detection probability, an optimal and coordinative lattice-based monitor patrolling (OCLMP) algorithm is further provided for UWSNs, wherein an equal price search strategy is given for ANs to find the shortest patrolling path. Under VLM and OCLMP algorithms, the detection probabilities are calculated, while the topology connectivity can be guaranteed. Finally, simulation results are presented to show that the proposed method in this paper can improve the detection accuracy and save the energy consumption compared with the conventional methods. PMID:28531127

Evaluation of an Improved U.S. Food and Drug Administration Method for the Detection of Cyclospora cayetanensis in Produce Using Real-Time PCR.

PubMed

Murphy, Helen R; Lee, Seulgi; da Silva, Alexandre J

2017-07-01

Cyclospora cayetanensis is a protozoan parasite that causes human diarrheal disease associated with the consumption of fresh produce or water contaminated with C. cayetanensis oocysts. In the United States, foodborne outbreaks of cyclosporiasis have been linked to various types of imported fresh produce, including cilantro and raspberries. An improved method was developed for identification of C. cayetanensis in produce at the U.S. Food and Drug Administration. The method relies on a 0.1% Alconox produce wash solution for efficient recovery of oocysts, a commercial kit for DNA template preparation, and an optimized TaqMan real-time PCR assay with an internal amplification control for molecular detection of the parasite. A single laboratory validation study was performed to assess the method's performance and compare the optimized TaqMan real-time PCR assay and a reference nested PCR assay by examining 128 samples. The samples consisted of 25 g of cilantro or 50 g of raspberries seeded with 0, 5, 10, or 200 C. cayetanensis oocysts. Detection rates for cilantro seeded with 5 and 10 oocysts were 50.0 and 87.5%, respectively, with the real-time PCR assay and 43.7 and 94.8%, respectively, with the nested PCR assay. Detection rates for raspberries seeded with 5 and 10 oocysts were 25.0 and 75.0%, respectively, with the real-time PCR assay and 18.8 and 68.8%, respectively, with the nested PCR assay. All unseeded samples were negative, and all samples seeded with 200 oocysts were positive. Detection rates using the two PCR methods were statistically similar, but the real-time PCR assay is less laborious and less prone to amplicon contamination and allows monitoring of amplification and analysis of results, making it more attractive to diagnostic testing laboratories. The improved sample preparation steps and the TaqMan real-time PCR assay provide a robust, streamlined, and rapid analytical procedure for surveillance, outbreak response, and regulatory testing of foods for detection of C. cayetanensis.

Satellite-based assessment of grassland yields

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Developing accurate survey methods for estimating population sizes and trends of the critically endangered Nihoa Millerbird and Nihoa Finch.

USGS Publications Warehouse

Gorresen, P. Marcos; Camp, Richard J.; Brinck, Kevin W.; Farmer, Chris

2012-01-01

Point-transect surveys indicated that millerbirds were more abundant than shown by the striptransect method, and were estimated at 802 birds in 2010 (95%CI = 652 – 964) and 704 birds in 2011 (95%CI = 579 – 837). Point-transect surveys yielded population estimates with improved precision which will permit trends to be detected in shorter time periods and with greater statistical power than is available from strip-transect survey methods. Mean finch population estimates and associated uncertainty were not markedly different among the three survey methods, but the performance of models used to estimate density and population size are expected to improve as the data from additional surveys are incorporated. Using the pointtransect survey, the mean finch population size was estimated at 2,917 birds in 2010 (95%CI = 2,037 – 3,965) and 2,461 birds in 2011 (95%CI = 1,682 – 3,348). Preliminary testing of the line-transect method in 2011 showed that it would not generate sufficient detections to effectively model bird density, and consequently, relatively precise population size estimates. Both species were fairly evenly distributed across Nihoa and appear to occur in all or nearly all available habitat. The time expended and area traversed by observers was similar among survey methods; however, point-transect surveys do not require that observers walk a straight transect line, thereby allowing them to avoid culturally or biologically sensitive areas and minimize the adverse effects of recurrent travel to any particular area. In general, pointtransect surveys detect more birds than strip-survey methods, thereby improving precision and resulting population size and trend estimation. The method is also better suited for the steep and uneven terrain of Nihoa

Extraction and height estimation of artificial vertical structures based on the wrapped interferometric phase difference within their layovers

NASA Astrophysics Data System (ADS)

Uemoto, Jyunpei; Nadai, Akitsugu; Kojima, Shoichiro; Kobayashi, Tatsuharu; Umehara, Toshihiko; Matsuoka, Takeshi; Uratsuka, Seiho; Satake, Makoto

2018-05-01

The geometric modulation of synthetic aperture radar (SAR) imagery such as radar shadow, foreshortening, and layover often complicates image interpretation while it contains useful information about targets. Recently, some methods for automatic building detection utilizing a peculiar pattern of phase differences (PDs) within building layovers on SAR interferograms have been proposed. One of the merits of these methods is the capability to detect buildings even taller than the height of ambiguity without incorporating any external data. In this paper, we propose a new method that has achieved the following improvements while maintaining the merit mentioned above. The first improvement is freedom from the dependence of target heights; without changing any parameters and thresholds, the proposed method can detect low-rise apartments to skyscrapers. The second one is the prevention of the false grouping of vertical structure constituents by considering relationships between their PDs. In addition, the method can measure the height of vertical structures without assuming their shape to be simple ones such as a parallelogram. These improvements have been verified by applying the method to real datasets acquired from an airborne X-band SAR. The quantitative assessment for apartment complexes has demonstrated the high performance of the method; the correctness and completeness are 94% and 83%, respectively. The mean error in the measured height is -0.2 m, while the standard deviation is 1.8 m. The verification using real datasets has revealed at the same time that the performance of the method can be degraded due to the crowdedness in dense urban areas including skyscrapers and owing to the poor discriminability between artificial vertical structures and trees. Overcoming these limitations is necessary in future studies.

In silico simulation of liver crack detection using ultrasonic shear wave imaging.

PubMed

Nie, Erwei; Yu, Jiao; Dutta, Debaditya; Zhu, Yanying

2018-05-16

Liver trauma is an important source of morbidity and mortality worldwide. A timely detection and precise evaluation of traumatic liver injury and the bleeding site is necessary. There is a need to develop better imaging modalities of hepatic injuries to increase the sensitivity of ultrasonic imaging techniques for sites of hemorrhage caused by cracks. In this study, we conduct an in silico simulation of liver crack detection and delineation using an ultrasonic shear wave imaging (USWI) based method. We simulate the generation and propagation of the shear wave in a liver tissue medium having a crack using COMSOL. Ultrasound radio frequency (RF) signal synthesis and the two-dimensional speckle tracking algorithm are applied to simulate USWI in a medium with randomly distributed scatterers. Crack detection is performed using the directional filter and the edge detection algorithm rather than the conventional inversion algorithm. Cracks with varied sizes and locations are studied with our method and the crack localization results are compared with the given crack. Our pilot simulation study shows that, by using USWI combined with a directional filter cum edge detection technique, the near-end edge of the crack can be detected in all the three cracks that we studied. The detection errors are within 5%. For a crack of 1.6 mm thickness, little shear wave can pass through it and the far-end edge of the crack cannot be detected. The detected crack lengths using USWI are all slightly shorter than the actual crack length. The robustness of our method in detecting a straight crack, a curved crack and a subtle crack of 0.5 mm thickness is demonstrated. In this paper, we simulate the use of a USWI based method for the detection and delineation of the crack in liver. The in silico simulation helps to improve understanding and interpretation of USWI measurements in a physical scattered liver medium with a crack. This pilot study provides a basis for improved insights in future crack detection studies in a tissue phantom or liver.

Crack image segmentation based on improved DBC method

NASA Astrophysics Data System (ADS)

Cao, Ting; Yang, Nan; Wang, Fengping; Gao, Ting; Wang, Weixing

2017-11-01

With the development of computer vision technology, crack detection based on digital image segmentation method arouses global attentions among researchers and transportation ministries. Since the crack always exhibits the random shape and complex texture, it is still a challenge to accomplish reliable crack detection results. Therefore, a novel crack image segmentation method based on fractal DBC (differential box counting) is introduced in this paper. The proposed method can estimate every pixel fractal feature based on neighborhood information which can consider the contribution from all possible direction in the related block. The block moves just one pixel every time so that it could cover all the pixels in the crack image. Unlike the classic DBC method which only describes fractal feature for the related region, this novel method can effectively achieve crack image segmentation according to the fractal feature of every pixel. The experiment proves the proposed method can achieve satisfactory results in crack detection.

Network anomaly detection system with optimized DS evidence theory.

PubMed

Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

2014-01-01

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network-complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each sensor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.

Detection of nuclear resonance signals: modification of the receiver operating characteristics using feedback.

PubMed

Blauch, A J; Schiano, J L; Ginsberg, M D

2000-06-01

The performance of a nuclear resonance detection system can be quantified using binary detection theory. Within this framework, signal averaging increases the probability of a correct detection and decreases the probability of a false alarm by reducing the variance of the noise in the average signal. In conjunction with signal averaging, we propose another method based on feedback control concepts that further improves detection performance. By maximizing the nuclear resonance signal amplitude, feedback raises the probability of correct detection. Furthermore, information generated by the feedback algorithm can be used to reduce the probability of false alarm. We discuss the advantages afforded by feedback that cannot be obtained using signal averaging. As an example, we show how this method is applicable to the detection of explosives using nuclear quadrupole resonance. Copyright 2000 Academic Press.

Network Anomaly Detection System with Optimized DS Evidence Theory

PubMed Central

Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

2014-01-01

Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly. PMID:25254258

A novel method of forceps biopsy improves the diagnosis of proximal biliary malignancies.

PubMed

Kulaksiz, Hasan; Strnad, Pavel; Römpp, Achim; von Figura, Guido; Barth, Thomas; Esposito, Irene; Schirmacher, Peter; Henne-Bruns, Doris; Adler, Guido; Stiehl, Adolf

2011-02-01

Tissue specimen collection represents a cornerstone in diagnosis of proximal biliary tract malignancies offering great specificity, but only limited sensitivity. To improve the tumor detection rate, we developed a new method of forceps biopsy and compared it prospectively with endoscopic transpapillary brush cytology. 43 patients with proximal biliary stenoses, which were suspect for malignancy, undergoing endoscopic retrograde cholangiography were prospectively recruited and subjected to both biopsy [using a double-balloon enteroscopy (DBE) forceps under a guidance of a pusher and guiding catheter with guidewire] and transpapillary brush cytology. The cytological/histological findings were compared with the final clinical diagnosis. 35 out of 43 patients had a malignant disease (33 cholangiocarcinomas, 1 hepatocellular carcinoma, 1 gallbladder carcinoma). The sensitivity of cytology and biopsy in these patients was 49 and 69%, respectively. The method with DBE forceps allowed a pinpoint biopsy of the biliary stenoses. Both methods had 100% specificity, and, when combined, 80% of malignant processes were detected. All patients with non-malignant conditions were correctly assigned by both methods. No clinically relevant complications were observed. The combination of forceps biopsy and transpapillary brush cytology is safe and offers superior detection rates compared to both methods alone, and therefore represents a promising approach in evaluation of proximal biliary tract processes.

Optimized signal detection and analysis methods for in vivo photoacoustic flow cytometry

NASA Astrophysics Data System (ADS)

Wang, Qiyan; Zhou, Quanyu; Yang, Ping; Wang, Xiaoling; Niu, Zhenyu; Suo, Yuanzhen; He, Hao; Gao, Wenyuan; Tang, Shuo; Wei, Xunbin

2017-02-01

Melanoma is known as a malignant tumor of melanocytes, which usually appear in the blood circulation at the metastasis stage of cancer. Thus the detection of circulating melanoma cells is useful for early diagnosis and therapy of cancer. Here we have developed an in vivo photoacoustic flow cytometry (PAFC) based on the photoacoustic effect to detect melanoma cells. However, the raw signals we obtain from the target cells contain noises such as environmental sonic noises and electronic noises. Therefore we apply correlation comparison and feature separation methods to the detection and verification of the in vivo signals. Due to similar shape and structure of cells, the photoacoustic signals usually have similar vibration mode. By analyzing the correlations and the signal features in time domain and frequency domain, we are able to provide a method for separating photoacoustic signals generated by target cells from background noises. The method introduced here has proved to optimize the signal acquisition and signal processing, which can improve the detection accuracy in PAFC.

A rapid low-cost high-density DNA-based multi-detection test for routine inspection of meat species.

PubMed

Lin, Chun Chi; Fung, Lai Ling; Chan, Po Kwok; Lee, Cheuk Man; Chow, Kwok Fai; Cheng, Shuk Han

2014-02-01

The increasing occurrence of food frauds suggests that species identification should be part of food authentication. Current molecular-based species identification methods have their own limitations or drawbacks, such as relatively time-consuming experimental steps, expensive equipment and, in particular, these methods cannot identify mixed species in a single experiment. This project proposes an improved method involving PCR amplification of the COI gene and detection of species-specific sequences by hybridisation. Major innovative breakthrough lies in the detection of multiple species, including pork, beef, lamb, horse, cat, dog and mouse, from a mixed sample within a single experiment. The probes used are species-specific either in sole or mixed species samples. As little as 5 pg of DNA template in the PCR is detectable in the proposed method. By designing species-specific probes and adopting reverse dot blot hybridisation and flow-through hybridisation, a low-cost high-density DNA-based multi-detection test suitable for routine inspection of meat species was developed. © 2013.

Detecting and enumerating soil-transmitted helminth eggs in soil: New method development and results from field testing in Kenya and Bangladesh.

PubMed

Steinbaum, Lauren; Kwong, Laura H; Ercumen, Ayse; Negash, Makeda S; Lovely, Amira J; Njenga, Sammy M; Boehm, Alexandria B; Pickering, Amy J; Nelson, Kara L

2017-04-01

Globally, about 1.5 billion people are infected with at least one species of soil-transmitted helminth (STH). Soil is a critical environmental reservoir of STH, yet there is no standard method for detecting STH eggs in soil. We developed a field method for enumerating STH eggs in soil and tested the method in Bangladesh and Kenya. The US Environmental Protection Agency (EPA) method for enumerating Ascaris eggs in biosolids was modified through a series of recovery efficiency experiments; we seeded soil samples with a known number of Ascaris suum eggs and assessed the effect of protocol modifications on egg recovery. We found the use of 1% 7X as a surfactant compared to 0.1% Tween 80 significantly improved recovery efficiency (two-sided t-test, t = 5.03, p = 0.007) while other protocol modifications-including different agitation and flotation methods-did not have a significant impact. Soil texture affected the egg recovery efficiency; sandy samples resulted in higher recovery compared to loamy samples processed using the same method (two-sided t-test, t = 2.56, p = 0.083). We documented a recovery efficiency of 73% for the final improved method using loamy soil in the lab. To field test the improved method, we processed soil samples from 100 households in Bangladesh and 100 households in Kenya from June to November 2015. The prevalence of any STH (Ascaris, Trichuris or hookworm) egg in soil was 78% in Bangladesh and 37% in Kenya. The median concentration of STH eggs in soil in positive samples was 0.59 eggs/g dry soil in Bangladesh and 0.15 eggs/g dry soil in Kenya. The prevalence of STH eggs in soil was significantly higher in Bangladesh than Kenya (chi-square, χ2 = 34.39, p < 0.001) as was the concentration (Mann-Whitney, z = 7.10, p < 0.001). This new method allows for detecting STH eggs in soil in low-resource settings and could be used for standardizing soil STH detection globally.

Coherent scattering noise reduction method with wavelength diversity detection for holographic data storage system

NASA Astrophysics Data System (ADS)

Nakamura, Yusuke; Hoshizawa, Taku; Takashima, Yuzuru

2017-09-01

A new method, wavelength diversity detection (WDD), for improving signal quality is proposed and its effectiveness is numerically confirmed. We consider that WDD is especially effective for high-capacity systems having low hologram diffraction efficiencies. In such systems, the signal quality is primarily limited by coherent scattering noise; thus, effective improvement of the signal quality under a scattering-limited system is of great interest. WDD utilizes a new degree of freedom, the spectrum width, and scattering by molecules to improve the signal quality of the system. We found that WDD improves the quality by counterbalancing the degradation of the quality due to Bragg mismatch. With WDD, a higher-scattering-coefficient medium can improve the quality. The result provides an interesting insight into the requirements for material characteristics, especially for a large-M/# material. In general, a larger-M/# material contains more molecules; thus, the system is subject to more scattering, which actually improves the quality with WDD. We propose a pathway for a future holographic data storage system (HDSS) using WDD, which can record a larger amount of data than a conventional HDSS.