Joint Transform Correlation for face tracking: elderly fall detection application

NASA Astrophysics Data System (ADS)

Katz, Philippe; Aron, Michael; Alfalou, Ayman

2013-03-01

In this paper, an iterative tracking algorithm based on a non-linear JTC (Joint Transform Correlator) architecture and enhanced by a digital image processing method is proposed and validated. This algorithm is based on the computation of a correlation plane where the reference image is updated at each frame. For that purpose, we use the JTC technique in real time to track a patient (target image) in a room fitted with a video camera. The correlation plane is used to localize the target image in the current video frame (frame i). Then, the reference image to be exploited in the next frame (frame i+1) is updated according to the previous one (frame i). In an effort to validate our algorithm, our work is divided into two parts: (i) a large study based on different sequences with several situations and different JTC parameters is achieved in order to quantify their effects on the tracking performances (decimation, non-linearity coefficient, size of the correlation plane, size of the region of interest...). (ii) the tracking algorithm is integrated into an application of elderly fall detection. The first reference image is a face detected by means of Haar descriptors, and then localized into the new video image thanks to our tracking method. In order to avoid a bad update of the reference frame, a method based on a comparison of image intensity histograms is proposed and integrated in our algorithm. This step ensures a robust tracking of the reference frame. This article focuses on face tracking step optimisation and evalutation. A supplementary step of fall detection, based on vertical acceleration and position, will be added and studied in further work.

Correlation Filters for Detection of Cellular Nuclei in Histopathology Images.

PubMed

Ahmad, Asif; Asif, Amina; Rajpoot, Nasir; Arif, Muhammad; Minhas, Fayyaz Ul Amir Afsar

2017-11-21

Nuclei detection in histology images is an essential part of computer aided diagnosis of cancers and tumors. It is a challenging task due to diverse and complicated structures of cells. In this work, we present an automated technique for detection of cellular nuclei in hematoxylin and eosin stained histopathology images. Our proposed approach is based on kernelized correlation filters. Correlation filters have been widely used in object detection and tracking applications but their strength has not been explored in the medical imaging domain up till now. Our experimental results show that the proposed scheme gives state of the art accuracy and can learn complex nuclear morphologies. Like deep learning approaches, the proposed filters do not require engineering of image features as they can operate directly on histopathology images without significant preprocessing. However, unlike deep learning methods, the large-margin correlation filters developed in this work are interpretable, computationally efficient and do not require specialized or expensive computing hardware. A cloud based webserver of the proposed method and its python implementation can be accessed at the following URL: http://faculty.pieas.edu.pk/fayyaz/software.html#corehist .

Edge-based correlation image registration for multispectral imaging

DOEpatents

Nandy, Prabal [Albuquerque, NM

2009-11-17

Registration information for images of a common target obtained from a plurality of different spectral bands can be obtained by combining edge detection and phase correlation. The images are edge-filtered, and pairs of the edge-filtered images are then phase correlated to produce phase correlation images. The registration information can be determined based on these phase correlation images.

Convective-diffusion-based fluorescence correlation spectroscopy for detection of a trace amount of E. coli in water.

PubMed

Qing, De-Kui; Mengüç, M Pinar; Payne, Fred A; Danao, Mary-Grace C

2003-06-01

Fluorescence correlation spectroscopy (FCS) is adapted for a new procedure to detect trace amounts of Escherichia coli in water. The present concept is based on convective diffusion rather than Brownian diffusion and employs confocal microscopy as in traditional FCS. With this system it is possible to detect concentrations as small as 1.5 x 10(5) E. coli per milliliter (2.5 x 10(-16) M). This concentration corresponds to an approximately 1.0-nM level of Rhodamine 6G dyes. A detailed analysis of the optical system is presented, and further improvements for the procedure are discussed.

Discriminative correlation filter tracking with occlusion detection

NASA Astrophysics Data System (ADS)

Zhang, Shuo; Chen, Zhong; Yu, XiPeng; Zhang, Ting; He, Jing

2018-03-01

Aiming at the problem that the correlation filter-based tracking algorithm can not track the target of severe occlusion, a target re-detection mechanism is proposed. First of all, based on the ECO, we propose the multi-peak detection model and the response value to distinguish the occlusion and deformation in the target tracking, which improve the success rate of tracking. And then we add the confidence model to update the mechanism to effectively prevent the model offset problem which due to similar targets or background during the tracking process. Finally, the redetection mechanism of the target is added, and the relocation is performed after the target is lost, which increases the accuracy of the target positioning. The experimental results demonstrate that the proposed tracker performs favorably against state-of-the-art methods in terms of robustness and accuracy.

Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method

PubMed Central

Zhang, Yang; Chen, Fuming; Xue, Huijun; Li, Zhao; An, Qiang; Wang, Jianqi; Zhang, Yang

2016-01-01

Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets. PMID:27801795

Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method.

PubMed

Zhang, Yang; Chen, Fuming; Xue, Huijun; Li, Zhao; An, Qiang; Wang, Jianqi; Zhang, Yang

2016-10-27

Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets.

Application of the differentiation process into the correlation-based leak detection in urban pipeline networks

NASA Astrophysics Data System (ADS)

Gao, Yan; Liu, Yuyou; Ma, Yifan; Cheng, Xiaobin; Yang, Jun

2018-11-01

One major challenge currently facing pipeline networks across the world is the improvement of leak detection technologies in urban environments. There is an imperative to locate accurately leaks in buried water pipes to avoid serious environmental, social and economic consequences. Much attention has been paid to time delay estimation (TDE) in determining the position of a leak by utilising cross-correlation, which has been proven to be effective with varying degrees of success over the past half century. Previous research in published literature has demonstrated the effectiveness of the pre-whitening process for accentuating the peak in the cross-correlation associated with the time delay. This paper is concerned with the implementation of the differentiation process for TDE, with particular focus on the problem of determining a leak in pipelines by means of pipe pressure measurements. Rather than the pre-whitening operation, the proposed cross-correlation via the differentiation process, termed here DIF, changes the characteristics of the pipe system so that the pipe effectively acts as a band-pass filter. This method has the potential to eliminate some ambiguity caused by the interference at low frequencies and to allow more high frequency information to pass. Given an appropriate differentiation order, a more pronounced and reliable peak is obtained in the cross-correlation result. The use of differentiation process may provide a viable cross-correlation method suited to water leak detection. Its performance in relation to leak detection is further compared to the basic cross-correlation and pre-whitening methods for TDE in detecting a leak from actual PVC water pipes. Experimental results are presented to show an additional property of the DIF compensating for the resonance effects that may exist in cross-spectral density measurements, and hence better performance for TDE.

Detection of diluted contaminants on chicken carcasses using a two-dimensional scatter plot based on a two-dimensional hyperspectral correlation spectrum.

PubMed

Wu, Wei; Chen, Gui-Yun; Wu, Ming-Qing; Yu, Zhen-Wei; Chen, Kun-Jie

2017-03-20

A two-dimensional (2D) scatter plot method based on the 2D hyperspectral correlation spectrum is proposed to detect diluted blood, bile, and feces from the cecum and duodenum on chicken carcasses. First, from the collected hyperspectral data, a set of uncontaminated regions of interest (ROIs) and four sets of contaminated ROIs were selected, whose average spectra were treated as the original spectrum and influenced spectra, respectively. Then, the difference spectra were obtained and used to conduct correlation analysis, from which the 2D hyperspectral correlation spectrum was constructed using the analogy method of 2D IR correlation spectroscopy. Two maximum auto-peaks and a pair of cross peaks appeared at 656 and 474 nm. Therefore, 656 and 474 nm were selected as the characteristic bands because they were most sensitive to the spectral change induced by the contaminants. The 2D scatter plots of the contaminants, clean skin, and background in the 474- and 656-nm space were used to distinguish the contaminants from the clean skin and background. The threshold values of the 474- and 656-nm bands were determined by receiver operating characteristic (ROC) analysis. According to the ROC results, a pixel whose relative reflectance at 656 nm was greater than 0.5 and relative reflectance at 474 nm was lower than 0.3 was judged as a contaminated pixel. A region with more than 50 pixels identified was marked in the detection graph. This detection method achieved a recognition rate of up to 95.03% at the region level and 31.84% at the pixel level. The false-positive rate was only 0.82% at the pixel level. The results of this study confirm that the 2D scatter plot method based on the 2D hyperspectral correlation spectrum is an effective method for detecting diluted contaminants on chicken carcasses.

A novel coefficient for detecting and quantifying asymmetry of California electricity market based on asymmetric detrended cross-correlation analysis.

PubMed

Wang, Fang

2016-06-01

In order to detect and quantify asymmetry of two time series, a novel cross-correlation coefficient is proposed based on recent asymmetric detrended cross-correlation analysis (A-DXA), which we called A-DXA coefficient. The A-DXA coefficient, as an important extension of DXA coefficient ρDXA, contains two directional asymmetric cross-correlated indexes, describing upwards and downwards asymmetric cross-correlations, respectively. By using the information of directional covariance function of two time series and directional variance function of each series itself instead of power-law between the covariance function and time scale, the proposed A-DXA coefficient can well detect asymmetry between the two series no matter whether the cross-correlation is significant or not. By means of the proposed A-DXA coefficient conducted over the asymmetry for California electricity market, we found that the asymmetry between the prices and loads is not significant for daily average data in 1999 yr market (before electricity crisis) but extremely significant for those in 2000 yr market (during the crisis). To further uncover the difference of asymmetry between the years 1999 and 2000, a modified H statistic (MH) and ΔMH statistic are proposed. One of the present contributions is that the high MH values calculated for hourly data exist in majority months in 2000 market. Another important conclusion is that the cross-correlation with downwards dominates over the whole 1999 yr in contrast to the cross-correlation with upwards dominates over the 2000 yr.

A novel coefficient for detecting and quantifying asymmetry of California electricity market based on asymmetric detrended cross-correlation analysis

NASA Astrophysics Data System (ADS)

Wang, Fang

2016-06-01

In order to detect and quantify asymmetry of two time series, a novel cross-correlation coefficient is proposed based on recent asymmetric detrended cross-correlation analysis (A-DXA), which we called A-DXA coefficient. The A-DXA coefficient, as an important extension of DXA coefficient ρ D X A , contains two directional asymmetric cross-correlated indexes, describing upwards and downwards asymmetric cross-correlations, respectively. By using the information of directional covariance function of two time series and directional variance function of each series itself instead of power-law between the covariance function and time scale, the proposed A-DXA coefficient can well detect asymmetry between the two series no matter whether the cross-correlation is significant or not. By means of the proposed A-DXA coefficient conducted over the asymmetry for California electricity market, we found that the asymmetry between the prices and loads is not significant for daily average data in 1999 yr market (before electricity crisis) but extremely significant for those in 2000 yr market (during the crisis). To further uncover the difference of asymmetry between the years 1999 and 2000, a modified H statistic (MH) and ΔMH statistic are proposed. One of the present contributions is that the high MH values calculated for hourly data exist in majority months in 2000 market. Another important conclusion is that the cross-correlation with downwards dominates over the whole 1999 yr in contrast to the cross-correlation with upwards dominates over the 2000 yr.

Change Point Detection in Correlation Networks

NASA Astrophysics Data System (ADS)

Barnett, Ian; Onnela, Jukka-Pekka

2016-01-01

Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure changes significantly as these may correspond to functional change points. We propose a method for detecting change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires minimal distributional assumptions. We investigate the difficulty of change point detection near the boundaries of the time series in correlation networks and study the power of our method and competing methods through simulation. We also show the generalizable nature of the method by applying it to stock price data as well as fMRI data.

Detection of Nitrogen Content in Rubber Leaves Using Near-Infrared (NIR) Spectroscopy with Correlation-Based Successive Projections Algorithm (SPA).

PubMed

Tang, Rongnian; Chen, Xupeng; Li, Chuang

2018-05-01

Near-infrared spectroscopy is an efficient, low-cost technology that has potential as an accurate method in detecting the nitrogen content of natural rubber leaves. Successive projections algorithm (SPA) is a widely used variable selection method for multivariate calibration, which uses projection operations to select a variable subset with minimum multi-collinearity. However, due to the fluctuation of correlation between variables, high collinearity may still exist in non-adjacent variables of subset obtained by basic SPA. Based on analysis to the correlation matrix of the spectra data, this paper proposed a correlation-based SPA (CB-SPA) to apply the successive projections algorithm in regions with consistent correlation. The result shows that CB-SPA can select variable subsets with more valuable variables and less multi-collinearity. Meanwhile, models established by the CB-SPA subset outperform basic SPA subsets in predicting nitrogen content in terms of both cross-validation and external prediction. Moreover, CB-SPA is assured to be more efficient, for the time cost in its selection procedure is one-twelfth that of the basic SPA.

Detecting subnetwork-level dynamic correlations.

PubMed

Yan, Yan; Qiu, Shangzhao; Jin, Zhuxuan; Gong, Sihong; Bai, Yun; Lu, Jianwei; Yu, Tianwei

2017-01-15

The biological regulatory system is highly dynamic. The correlations between many functionally related genes change over different biological conditions. Finding dynamic relations on the existing biological network may reveal important regulatory mechanisms. Currently no method is available to detect subnetwork-level dynamic correlations systematically on the genome-scale network. Two major issues hampered the development. The first is gene expression profiling data usually do not contain time course measurements to facilitate the analysis of dynamic relations, which can be partially addressed by using certain genes as indicators of biological conditions. Secondly, it is unclear how to effectively delineate subnetworks, and define dynamic relations between them. Here we propose a new method named LANDD (Liquid Association for Network Dynamics Detection) to find subnetworks that show substantial dynamic correlations, as defined by subnetwork A is concentrated with Liquid Association scouting genes for subnetwork B. The method produces easily interpretable results because of its focus on subnetworks that tend to comprise functionally related genes. Also, the collective behaviour of genes in a subnetwork is a much more reliable indicator of underlying biological conditions compared to using single genes as indicators. We conducted extensive simulations to validate the method's ability to detect subnetwork-level dynamic correlations. Using a real gene expression dataset and the human protein-protein interaction network, we demonstrate the method links subnetworks of distinct biological processes, with both confirmed relations and plausible new functional implications. We also found signal transduction pathways tend to show extensive dynamic relations with other functional groups. The R package is available at https://cran.r-project.org/web/packages/LANDD CONTACTS: yunba@pcom.edu, jwlu33@hotmail.com or tianwei.yu@emory.eduSupplementary information: Supplementary data

Detection of circuit-board components with an adaptive multiclass correlation filter

NASA Astrophysics Data System (ADS)

Diaz-Ramirez, Victor H.; Kober, Vitaly

2008-08-01

A new method for reliable detection of circuit-board components is proposed. The method is based on an adaptive multiclass composite correlation filter. The filter is designed with the help of an iterative algorithm using complex synthetic discriminant functions. The impulse response of the filter contains information needed to localize and classify geometrically distorted circuit-board components belonging to different classes. Computer simulation results obtained with the proposed method are provided and compared with those of known multiclass correlation based techniques in terms of performance criteria for recognition and classification of objects.

FPGA design of correlation-based pattern recognition

NASA Astrophysics Data System (ADS)

Jridi, Maher; Alfalou, Ayman

2017-05-01

Optical/Digital pattern recognition and tracking based on optical/digital correlation are a well-known techniques to detect, identify and localize a target object in a scene. Despite the limited number of treatments required by the correlation scheme, computational time and resources are relatively high. The most computational intensive treatment required by the correlation is the transformation from spatial to spectral domain and then from spectral to spatial domain. Furthermore, these transformations are used on optical/digital encryption schemes like the double random phase encryption (DRPE). In this paper, we present a VLSI architecture for the correlation scheme based on the fast Fourier transform (FFT). One interesting feature of the proposed scheme is its ability to stream image processing in order to perform correlation for video sequences. A trade-off between the hardware consumption and the robustness of the correlation can be made in order to understand the limitations of the correlation implementation in reconfigurable and portable platforms. Experimental results obtained from HDL simulations and FPGA prototype have demonstrated the advantages of the proposed scheme.

Detection of Impaired Cerebral Autoregulation Using Selected Correlation Analysis: A Validation Study

PubMed Central

Brawanski, Alexander

2017-01-01

Multimodal brain monitoring has been utilized to optimize treatment of patients with critical neurological diseases. However, the amount of data requires an integrative tool set to unmask pathological events in a timely fashion. Recently we have introduced a mathematical model allowing the simulation of pathophysiological conditions such as reduced intracranial compliance and impaired autoregulation. Utilizing a mathematical tool set called selected correlation analysis (sca), correlation patterns, which indicate impaired autoregulation, can be detected in patient data sets (scp). In this study we compared the results of the sca with the pressure reactivity index (PRx), an established marker for impaired autoregulation. Mean PRx values were significantly higher in time segments identified as scp compared to segments showing no selected correlations (nsc). The sca based approach predicted cerebral autoregulation failure with a sensitivity of 78.8% and a specificity of 62.6%. Autoregulation failure, as detected by the results of both analysis methods, was significantly correlated with poor outcome. Sca of brain monitoring data detects impaired autoregulation with high sensitivity and sufficient specificity. Since the sca approach allows the simultaneous detection of both major pathological conditions, disturbed autoregulation and reduced compliance, it may become a useful analysis tool for brain multimodal monitoring data. PMID:28255331

Detection of Impaired Cerebral Autoregulation Using Selected Correlation Analysis: A Validation Study.

PubMed

Proescholdt, Martin A; Faltermeier, Rupert; Bele, Sylvia; Brawanski, Alexander

2017-01-01

Multimodal brain monitoring has been utilized to optimize treatment of patients with critical neurological diseases. However, the amount of data requires an integrative tool set to unmask pathological events in a timely fashion. Recently we have introduced a mathematical model allowing the simulation of pathophysiological conditions such as reduced intracranial compliance and impaired autoregulation. Utilizing a mathematical tool set called selected correlation analysis (sca), correlation patterns, which indicate impaired autoregulation, can be detected in patient data sets (scp). In this study we compared the results of the sca with the pressure reactivity index (PRx), an established marker for impaired autoregulation. Mean PRx values were significantly higher in time segments identified as scp compared to segments showing no selected correlations (nsc). The sca based approach predicted cerebral autoregulation failure with a sensitivity of 78.8% and a specificity of 62.6%. Autoregulation failure, as detected by the results of both analysis methods, was significantly correlated with poor outcome. Sca of brain monitoring data detects impaired autoregulation with high sensitivity and sufficient specificity. Since the sca approach allows the simultaneous detection of both major pathological conditions, disturbed autoregulation and reduced compliance, it may become a useful analysis tool for brain multimodal monitoring data.

A new approach for SSVEP detection using PARAFAC and canonical correlation analysis.

PubMed

Tello, Richard; Pouryazdian, Saeed; Ferreira, Andre; Beheshti, Soosan; Krishnan, Sridhar; Bastos, Teodiano

2015-01-01

This paper presents a new way for automatic detection of SSVEPs through correlation analysis between tensor models. 3-way EEG tensor of channel × frequency × time is decomposed into constituting factor matrices using PARAFAC model. PARAFAC analysis of EEG tensor enables us to decompose multichannel EEG into constituting temporal, spectral and spatial signatures. SSVEPs characterized with localized spectral and spatial signatures are then detected exploiting a correlation analysis between extracted signatures of the EEG tensor and the corresponding simulated signatures of all target SSVEP signals. The SSVEP that has the highest correlation is selected as the intended target. Two flickers blinking at 8 and 13 Hz were used as visual stimuli and the detection was performed based on data packets of 1 second without overlapping. Five subjects participated in the experiments and the highest classification rate of 83.34% was achieved, leading to the Information Transfer Rate (ITR) of 21.01 bits/min.

Correlation between adenoma detection rate in colonoscopy- and fecal immunochemical testing-based colorectal cancer screening programs.

PubMed

Cubiella, Joaquín; Castells, Antoni; Andreu, Montserrat; Bujanda, Luis; Carballo, Fernando; Jover, Rodrigo; Lanas, Ángel; Morillas, Juan Diego; Salas, Dolores; Quintero, Enrique

2017-03-01

The adenoma detection rate (ADR) is the main quality indicator of colonoscopy. The ADR recommended in fecal immunochemical testing (FIT)-based colorectal cancer screening programs is unknown. Using the COLONPREV (NCT00906997) study dataset, we performed a post-hoc analysis to determine if there was a correlation between the ADR in primary and work-up colonoscopy, and the equivalent figure to the minimal 20% ADR recommended. Colonoscopy was performed in 5722 individuals: 5059 as primary strategy and 663 after a positive FIT result (OC-Sensor™; cut-off level 15 µg/g of feces). We developed a predictive model based on a multivariable lineal regression analysis including confounding variables. The median ADR was 31% (range, 14%-51%) in the colonoscopy group and 55% (range, 21%-83%) in the FIT group. There was a positive correlation in the ADR between primary and work-up colonoscopy (Pearson's coefficient 0.716; p  < 0.001). ADR in the FIT group was independently related to ADR in the colonoscopy group: regression coefficient for colonoscopy ADR, 0.71 ( p  = 0.009); sex, 0.09 ( p  = 0.09); age, 0.3 ( p  = 0.5); and region 0.00 ( p  = 0.9). The equivalent figure to the 20% ADR was 45% (95% confidence interval, 35%-56%). ADR in primary and work-up colonoscopy of a FIT-positive result are positively and significantly correlated.

Correlation between adenoma detection rate in colonoscopy- and fecal immunochemical testing-based colorectal cancer screening programs

PubMed Central

Castells, Antoni; Andreu, Montserrat; Bujanda, Luis; Carballo, Fernando; Jover, Rodrigo; Lanas, Ángel; Morillas, Juan Diego; Salas, Dolores; Quintero, Enrique

2016-01-01

Background The adenoma detection rate (ADR) is the main quality indicator of colonoscopy. The ADR recommended in fecal immunochemical testing (FIT)-based colorectal cancer screening programs is unknown. Methods Using the COLONPREV (NCT00906997) study dataset, we performed a post-hoc analysis to determine if there was a correlation between the ADR in primary and work-up colonoscopy, and the equivalent figure to the minimal 20% ADR recommended. Colonoscopy was performed in 5722 individuals: 5059 as primary strategy and 663 after a positive FIT result (OC-Sensor™; cut-off level 15 µg/g of feces). We developed a predictive model based on a multivariable lineal regression analysis including confounding variables. Results The median ADR was 31% (range, 14%–51%) in the colonoscopy group and 55% (range, 21%–83%) in the FIT group. There was a positive correlation in the ADR between primary and work-up colonoscopy (Pearson’s coefficient 0.716; p < 0.001). ADR in the FIT group was independently related to ADR in the colonoscopy group: regression coefficient for colonoscopy ADR, 0.71 (p = 0.009); sex, 0.09 (p = 0.09); age, 0.3 (p = 0.5); and region 0.00 (p = 0.9). The equivalent figure to the 20% ADR was 45% (95% confidence interval, 35%–56%). Conclusions ADR in primary and work-up colonoscopy of a FIT-positive result are positively and significantly correlated. PMID:28344793

Detection of virus in shrimp using digital color correlation

NASA Astrophysics Data System (ADS)

Alvarez-Borrego, Josue; Chavez-Sanchez, Cristina; Bueno-Ibarra, Mario A.

1999-07-01

Detection of virus in shrimp tissue using digital color correlation is presented. Phase filters in three channels (red, green and blue) were used in order to detect HPV virus like target. These first results obtained showed that is possible to detect virus in shrimp tissue. More research must be made with color correlation in order to consider natural morphology of the virus, color, scale and rotation and noise in the samples.

Trends in Correlation-Based Pattern Recognition and Tracking in Forward-Looking Infrared Imagery

PubMed Central

Alam, Mohammad S.; Bhuiyan, Sharif M. A.

2014-01-01

In this paper, we review the recent trends and advancements on correlation-based pattern recognition and tracking in forward-looking infrared (FLIR) imagery. In particular, we discuss matched filter-based correlation techniques for target detection and tracking which are widely used for various real time applications. We analyze and present test results involving recently reported matched filters such as the maximum average correlation height (MACH) filter and its variants, and distance classifier correlation filter (DCCF) and its variants. Test results are presented for both single/multiple target detection and tracking using various real-life FLIR image sequences. PMID:25061840

Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach

NASA Astrophysics Data System (ADS)

Lu, Feng; Liu, Kang; Duan, Yingying; Cheng, Shifen; Du, Fei

2018-07-01

A better characterization of the traffic influence among urban roads is crucial for traffic control and traffic forecasting. The existence of spatial heterogeneity imposes great influence on modeling the extent and degree of road traffic correlation, which is usually neglected by the traditional distance based method. In this paper, we propose a traffic-enhanced community detection approach to spatially reveal the traffic correlation in city road networks. First, the road network is modeled as a traffic-enhanced dual graph with the closeness between two road segments determined not only by their topological connection, but also by the traffic correlation between them. Then a flow-based community detection algorithm called Infomap is utilized to identify the road segment clusters. Evaluated by Moran's I, Calinski-Harabaz Index and the traffic interpolation application, we find that compared to the distance based method and the community based method, our proposed traffic-enhanced community based method behaves better in capturing the extent of traffic relevance as both the topological structure of the road network and the traffic correlations among urban roads are considered. It can be used in more traffic-related applications, such as traffic forecasting, traffic control and guidance.

Continuous detection of weak sensory signals in afferent spike trains: the role of anti-correlated interspike intervals in detection performance.

PubMed

Goense, J B M; Ratnam, R

2003-10-01

An important problem in sensory processing is deciding whether fluctuating neural activity encodes a stimulus or is due to variability in baseline activity. Neurons that subserve detection must examine incoming spike trains continuously, and quickly and reliably differentiate signals from baseline activity. Here we demonstrate that a neural integrator can perform continuous signal detection, with performance exceeding that of trial-based procedures, where spike counts in signal- and baseline windows are compared. The procedure was applied to data from electrosensory afferents of weakly electric fish (Apteronotus leptorhynchus), where weak perturbations generated by small prey add approximately 1 spike to a baseline of approximately 300 spikes s(-1). The hypothetical postsynaptic neuron, modeling an electrosensory lateral line lobe cell, could detect an added spike within 10-15 ms, achieving near ideal detection performance (80-95%) at false alarm rates of 1-2 Hz, while trial-based testing resulted in only 30-35% correct detections at that false alarm rate. The performance improvement was due to anti-correlations in the afferent spike train, which reduced both the amplitude and duration of fluctuations in postsynaptic membrane activity, and so decreased the number of false alarms. Anti-correlations can be exploited to improve detection performance only if there is memory of prior decisions.

Phase-detected Brillouin optical correlation-domain reflectometry

NASA Astrophysics Data System (ADS)

Mizuno, Yosuke; Hayashi, Neisei; Fukuda, Hideyuki; Nakamura, Kentaro

2018-05-01

Optical fiber sensing techniques based on Brillouin scattering have been extensively studied for structural health monitoring owing to their capability of distributed strain and temperature measurement. Although a higher signal-to-noise ratio (leading to high spatial resolution and high-speed measurement) is generally obtained for two-end-access systems, they reduce the degree of freedom in embedding the sensors into structures, and render the measurement no longer feasible when extremely high loss or breakage occurs at a point of the sensing fiber. To overcome these drawbacks, a one-end-access sensing technique called Brillouin optical correlation-domain reflectometry (BOCDR) has been developed. BOCDR has a high spatial resolution and cost efficiency, but its conventional configuration suffered from relatively low-speed operation. In this paper, we review the recently developed high-speed configurations of BOCDR, including phase-detected BOCDR, with which we demonstrate real-time distributed measurement by tracking a propagating mechanical wave. We also demonstrate breakage detection with a wide strain dynamic range.

Phase-detected Brillouin optical correlation-domain reflectometry

NASA Astrophysics Data System (ADS)

Mizuno, Yosuke; Hayashi, Neisei; Fukuda, Hideyuki; Nakamura, Kentaro

2018-06-01

Optical fiber sensing techniques based on Brillouin scattering have been extensively studied for structural health monitoring owing to their capability of distributed strain and temperature measurement. Although a higher signal-to-noise ratio (leading to high spatial resolution and high-speed measurement) is generally obtained for two-end-access systems, they reduce the degree of freedom in embedding the sensors into structures, and render the measurement no longer feasible when extremely high loss or breakage occurs at a point of the sensing fiber. To overcome these drawbacks, a one-end-access sensing technique called Brillouin optical correlation-domain reflectometry (BOCDR) has been developed. BOCDR has a high spatial resolution and cost efficiency, but its conventional configuration suffered from relatively low-speed operation. In this paper, we review the recently developed high-speed configurations of BOCDR, including phase-detected BOCDR, with which we demonstrate real-time distributed measurement by tracking a propagating mechanical wave. We also demonstrate breakage detection with a wide strain dynamic range.

Detection of periodicity based on independence tests - III. Phase distance correlation periodogram

NASA Astrophysics Data System (ADS)

Zucker, Shay

2018-02-01

I present the Phase Distance Correlation (PDC) periodogram - a new periodicity metric, based on the Distance Correlation concept of Gábor Székely. For each trial period, PDC calculates the distance correlation between the data samples and their phases. PDC requires adaptation of the Székely's distance correlation to circular variables (phases). The resulting periodicity metric is best suited to sparse data sets, and it performs better than other methods for sawtooth-like periodicities. These include Cepheid and RR-Lyrae light curves, as well as radial velocity curves of eccentric spectroscopic binaries. The performance of the PDC periodogram in other contexts is almost as good as that of the Generalized Lomb-Scargle periodogram. The concept of phase distance correlation can be adapted also to astrometric data, and it has the potential to be suitable also for large evenly spaced data sets, after some algorithmic perfection.

Phase-specific Surround suppression in Mouse Primary Visual Cortex Correlates with Figure Detection Behavior Based on Phase Discontinuity.

PubMed

Li, Fengling; Jiang, Weiqian; Wang, Tian-Yi; Xie, Taorong; Yao, Haishan

2018-05-21

In the primary visual cortex (V1), neuronal responses to stimuli within the receptive field (RF) are modulated by stimuli in the RF surround. A common effect of surround modulation is surround suppression, which is dependent on the feature difference between stimuli within and surround the RF and is suggested to be involved in the perceptual phenomenon of figure-ground segregation. In this study, we examined the relationship between feature-specific surround suppression of V1 neurons and figure detection behavior based on figure-ground feature difference. We trained freely moving mice to perform a figure detection task using figure and ground gratings that differed in spatial phase. The performance of figure detection increased with the figure-ground phase difference, and was modulated by stimulus contrast. Electrophysiological recordings from V1 in head-fixed mice showed that the increase in phase difference between stimuli within and surround the RF caused a reduction in surround suppression, which was associated with an increase in V1 neural discrimination between stimuli with and without RF-surround phase difference. Consistent with the behavioral performance, the sensitivity of V1 neurons to RF-surround phase difference could be influenced by stimulus contrast. Furthermore, inhibiting V1 by optogenetically activating either parvalbumin (PV)- or somatostatin (SOM)-expressing inhibitory neurons both decreased the behavioral performance of figure detection. Thus, the phase-specific surround suppression in V1 represents a neural correlate of figure detection behavior based on figure-ground phase discontinuity. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

Intrinsic Correlations for Flaring Blazars Detected by Fermi

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

Fan, J. H.; Xiao, H. B.; Lin, C.

2017-02-01

Blazars are an extreme subclass of active galactic nuclei. Their rapid variability, luminous brightness, superluminal motion, and high and variable polarization are probably due to a beaming effect. However, this beaming factor (or Doppler factor) is very difficult to measure. Currently, a good way to estimate it is to use the timescale of their radio flares. In this Letter, we use multiwavelength data and Doppler factors reported in the literature for a sample of 86 flaring blazars detected by Fermi to compute their intrinsic multiwavelength data and intrinsic spectral energy distributions and investigate the correlations among observed and intrinsic data.more » Quite interestingly, intrinsic data show a positive correlation between luminosity and peak frequency, in contrast with the behavior of observed data, and a tighter correlation between γ -ray luminosity and the lower-energy ones. For flaring blazars detected by Fermi , we conclude that (1) observed emissions are strongly beamed; (2) the anti-correlation between luminosity and peak frequency from the observed data is an apparent result, the correlation between intrinsic data being positive; and (3) intrinsic γ -ray luminosity is strongly correlated with other intrinsic luminosities.« less

Statistical characteristics of the sequential detection of signals in correlated noise

NASA Astrophysics Data System (ADS)

Averochkin, V. A.; Baranov, P. E.

1985-10-01

A solution is given to the problem of determining the distribution of the duration of the sequential two-threshold Wald rule for the time-discrete detection of determinate and Gaussian correlated signals on a background of Gaussian correlated noise. Expressions are obtained for the joint probability densities of the likelihood ratio logarithms, and an analysis is made of the effect of correlation and SNR on the duration distribution and the detection efficiency. Comparison is made with Neumann-Pearson detection.

Gas correlation lidar for methane detection

NASA Technical Reports Server (NTRS)

Galletti, E.; Zanzottera, E.; Draghi, S.; Garbi, M.; Petroni, R.

1986-01-01

A new type of DIAL system for the detection of methane in the atmosphere is being developed. The main feature of this lidar is the use of a gas correlation technique to obtain the reference signal by means of a single laser pulse, instead of two shots at different wavelengths. This fact is useful to make measurements on fast moving platforms. To meet the infrared absorption band of methane an optical parametric oscillator (OPO) was used with a LiNbO3 crystal as active element, and a tuning range between 1.5 divided by 4 microns. As known, the major problem to overcome in parametric oscillators are the pump beam quality and the difficulty in reducing the linewidth. The first requirement is met by using, as a pump, a Nd-YAG laser based on a new type of resonator cavity, named SFUR (Self Filtering Unstable Resonator). The laser emits, with high efficiency, near diffraction limited pulsed beams of about 250 mJ of energy, 20 ns of duration at 10 pps of frequency repetition rate. On the other hand, the gas correlation technique allows the operation with a bandwidth as large as 1/cm, which is obtainable using only a diffraction grating as a dispersive element in the OPO cavity.

Confined detection volume of fluorescence correlation spectroscopy by bare fiber probes.

PubMed

Lu, Guowei; Lei, Franck H; Angiboust, Jean-François; Manfait, Michel

2010-04-01

A fiber-tip-based near-field fluorescence correlation spectroscopy (FCS) has been developed for confining the detection volume to sub-diffraction-limited dimensions. This near-field FCS is based on near-field illumination by coupling a scanning near-field optical microscope (SNOM) to a conventional confocal FCS. Single-molecule FCS analysis at 100 nM Rhodamine 6G has been achieved by using bare chemically etched, tapered fiber tips. The detection volume under control of the SNOM system has been reduced over one order of magnitude compared to that of the conventional confocal FCS. Related factors influencing the near-field FCS performance are investigated and discussed in detail. In this proof-of-principle study, the preliminary experimental results suggest that the fiber-tip-based near-field FCS might be a good alternative to realize localized analysis at the single-molecule level.

Damage detection of structures with detrended fluctuation and detrended cross-correlation analyses

NASA Astrophysics Data System (ADS)

Lin, Tzu-Kang; Fajri, Haikal

2017-03-01

Recently, fractal analysis has shown its potential for damage detection and assessment in fields such as biomedical and mechanical engineering. For its practicability in interpreting irregular, complex, and disordered phenomena, a structural health monitoring (SHM) system based on detrended fluctuation analysis (DFA) and detrended cross-correlation analysis (DCCA) is proposed. First, damage conditions can be swiftly detected by evaluating ambient vibration signals measured from a structure through DFA. Damage locations can then be determined by analyzing the cross correlation of signals of different floors by applying DCCA. A damage index is also proposed based on multi-scale DCCA curves to improve the damage location accuracy. To verify the performance of the proposed SHM system, a four-story numerical model was used to simulate various damage conditions with different noise levels. Furthermore, an experimental verification was conducted on a seven-story benchmark structure to assess the potential damage. The results revealed that the DFA method could detect the damage conditions satisfactorily, and damage locations can be identified through the DCCA method with an accuracy of 75%. Moreover, damage locations can be correctly assessed by the damage index method with an improved accuracy of 87.5%. The proposed SHM system has promising application in practical implementations.

Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation

PubMed Central

Siraj, Maheyzah Md; Zainal, Anazida; Elshoush, Huwaida Tagelsir; Elhaj, Fatin

2016-01-01

Grouping and clustering alerts for intrusion detection based on the similarity of features is referred to as structurally base alert correlation and can discover a list of attack steps. Previous researchers selected different features and data sources manually based on their knowledge and experience, which lead to the less accurate identification of attack steps and inconsistent performance of clustering accuracy. Furthermore, the existing alert correlation systems deal with a huge amount of data that contains null values, incomplete information, and irrelevant features causing the analysis of the alerts to be tedious, time-consuming and error-prone. Therefore, this paper focuses on selecting accurate and significant features of alerts that are appropriate to represent the attack steps, thus, enhancing the structural-based alert correlation model. A two-tier feature selection method is proposed to obtain the significant features. The first tier aims at ranking the subset of features based on high information gain entropy in decreasing order. The‏ second tier extends additional features with a better discriminative ability than the initially ranked features. Performance analysis results show the significance of the selected features in terms of the clustering accuracy using 2000 DARPA intrusion detection scenario-specific dataset. PMID:27893821

Correlation dynamics and enhanced signals for the identification of serial biomolecules and DNA bases.

PubMed

Ahmed, Towfiq; Haraldsen, Jason T; Rehr, John J; Di Ventra, Massimiliano; Schuller, Ivan; Balatsky, Alexander V

2014-03-28

Nanopore-based sequencing has demonstrated a significant potential for the development of fast, accurate, and cost-efficient fingerprinting techniques for next generation molecular detection and sequencing. We propose a specific multilayered graphene-based nanopore device architecture for the recognition of single biomolecules. Molecular detection and analysis can be accomplished through the detection of transverse currents as the molecule or DNA base translocates through the nanopore. To increase the overall signal-to-noise ratio and the accuracy, we implement a new 'multi-point cross-correlation' technique for identification of DNA bases or other molecules on the single molecular level. We demonstrate that the cross-correlations between each nanopore will greatly enhance the transverse current signal for each molecule. We implement first-principles transport calculations for DNA bases surveyed across a multilayered graphene nanopore system to illustrate the advantages of the proposed geometry. A time-series analysis of the cross-correlation functions illustrates the potential of this method for enhancing the signal-to-noise ratio. This work constitutes a significant step forward in facilitating fingerprinting of single biomolecules using solid state technology.

An automated cross-correlation based event detection technique and its application to surface passive data set

USGS Publications Warehouse

Forghani-Arani, Farnoush; Behura, Jyoti; Haines, Seth S.; Batzle, Mike

2013-01-01

In studies on heavy oil, shale reservoirs, tight gas and enhanced geothermal systems, the use of surface passive seismic data to monitor induced microseismicity due to the fluid flow in the subsurface is becoming more common. However, in most studies passive seismic records contain days and months of data and manually analysing the data can be expensive and inaccurate. Moreover, in the presence of noise, detecting the arrival of weak microseismic events becomes challenging. Hence, the use of an automated, accurate and computationally fast technique for event detection in passive seismic data is essential. The conventional automatic event identification algorithm computes a running-window energy ratio of the short-term average to the long-term average of the passive seismic data for each trace. We show that for the common case of a low signal-to-noise ratio in surface passive records, the conventional method is not sufficiently effective at event identification. Here, we extend the conventional algorithm by introducing a technique that is based on the cross-correlation of the energy ratios computed by the conventional method. With our technique we can measure the similarities amongst the computed energy ratios at different traces. Our approach is successful at improving the detectability of events with a low signal-to-noise ratio that are not detectable with the conventional algorithm. Also, our algorithm has the advantage to identify if an event is common to all stations (a regional event) or to a limited number of stations (a local event). We provide examples of applying our technique to synthetic data and a field surface passive data set recorded at a geothermal site.

Hierarchical detection of red lesions in retinal images by multiscale correlation filtering

NASA Astrophysics Data System (ADS)

Zhang, Bob; Wu, Xiangqian; You, Jane; Li, Qin; Karray, Fakhri

2009-02-01

This paper presents an approach to the computer aided diagnosis (CAD) of diabetic retinopathy (DR) -- a common and severe complication of long-term diabetes which damages the retina and cause blindness. Since red lesions are regarded as the first signs of DR, there has been extensive research on effective detection and localization of these abnormalities in retinal images. In contrast to existing algorithms, a new approach based on Multiscale Correlation Filtering (MSCF) and dynamic thresholding is developed. This consists of two levels, Red Lesion Candidate Detection (coarse level) and True Red Lesion Detection (fine level). The approach was evaluated using data from Retinopathy On-line Challenge (ROC) competition website and we conclude our method to be effective and efficient.

Infants' Detection of Correlated Features among Social Stimuli: A Precursor to Stereotyping?

ERIC Educational Resources Information Center

Levy, Gary D.; And Others

This study examined the abilities of 10-month-old infants to detect correlations between objects and persons based on the characteristic of gender. A total of 32 infants were habituated to six stimuli in which a picture of a male or female face was paired with one of six objects such as a football or frying pan. Three objects were associated with…

Correlation fluorescence method of amine detection

NASA Astrophysics Data System (ADS)

Myslitsky, Valentin F.; Tkachuk, Svetlana S.; Rudeichuk, Volodimir M.; Strinadko, Miroslav T.; Slyotov, Mikhail M.; Strinadko, Marina M.

1997-12-01

The amines fluorescence spectra stimulated by UV laser radiation are investigated in this paper. The fluorescence is stimulated by the coherent laser beam with the wavelength 0.337 micrometers . At the sufficient energy of laser stimulation the narrow peaks of the fluorescence spectra are detected besides the wide maximum. The relationship between the fluorescence intensity and the concentration of amines solutions are investigated. The fluorescence intensity temporal dependence on wavelength 0.363 micrometers of the norepinephrine solution preliminarily radiated by UV laser with wavelength 0.337 micrometers was found. The computer stimulated and experimental investigations of adrenaline and norepinephrine mixtures fluorescence spectra were done. The correlation fluorescent method of amines detection is proposed.

Interference Cancellation Technique Based on Discovery of Spreading Codes of Interference Signals and Maximum Correlation Detection for DS-CDMA System

NASA Astrophysics Data System (ADS)

Hettiarachchi, Ranga; Yokoyama, Mitsuo; Uehara, Hideyuki

This paper presents a novel interference cancellation (IC) scheme for both synchronous and asynchronous direct-sequence code-division multiple-access (DS-CDMA) wireless channels. In the DS-CDMA system, the multiple access interference (MAI) and the near-far problem (NFP) are the two factors which reduce the capacity of the system. In this paper, we propose a new algorithm that is able to detect all interference signals as an individual MAI signal by maximum correlation detection. It is based on the discovery of all the unknowing spreading codes of the interference signals. Then, all possible MAI patterns so called replicas are generated as a summation of interference signals. And the true MAI pattern is found by taking correlation between the received signal and the replicas. Moreover, the receiver executes MAI cancellation in a successive manner, removing all interference signals by single-stage. Numerical results will show that the proposed IC strategy, which alleviates the detrimental effect of the MAI and the near-far problem, can significantly improve the system performance. Especially, we can obtain almost the same receiving characteristics as in the absense of interference for asynchrnous system when received powers are equal. Also, the same performances can be seen under any received power state for synchronous system.

Coincidence detection of spatially correlated photon pairs with a monolithic time-resolving detector array.

PubMed

Unternährer, Manuel; Bessire, Bänz; Gasparini, Leonardo; Stoppa, David; Stefanov, André

2016-12-12

We demonstrate coincidence measurements of spatially entangled photons by means of a multi-pixel based detection array. The sensor, originally developed for positron emission tomography applications, is a fully digital 8×16 silicon photomultiplier array allowing not only photon counting but also per-pixel time stamping of the arrived photons with an effective resolution of 265 ps. Together with a frame rate of 500 kfps, this property exceeds the capabilities of conventional charge-coupled device cameras which have become of growing interest for the detection of transversely correlated photon pairs. The sensor is used to measure a second-order correlation function for various non-collinear configurations of entangled photons generated by spontaneous parametric down-conversion. The experimental results are compared to theory.

Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission.

PubMed

Gao, Zheyu; Lin, Jing; Wang, Xiufeng; Xu, Xiaoqiang

2017-05-24

Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This paper utilizes Empirical Wavelet Transform (EWT) to decompose AE signals into mono-components adaptively followed by calculation of the correlated kurtosis (CK) at certain time intervals of these components. By comparing these CK values, the resonant frequency of the rolling bearing can be determined. Then the fault characteristic frequencies are found by spectrum envelope. Both simulation signal and rolling bearing AE signals are used to verify the effectiveness of the proposed method. The results show that the new method performs well in identifying bearing fault frequency under strong background noise.

Detection of correlated fragments in a sequence of images by superimposed Fourier holograms

NASA Astrophysics Data System (ADS)

Pavlov, A. V.

2016-08-01

The problem of detecting correlated fragments in a sequence of images recorded by the superimposing holograms within the Fourier holography scheme with angular multiplication of a spatially modulated reference beam is considered. The approach to the solution of this problem is based on the properties of the variance of the image sum. It is shown that this problem can be solved by providing a constant distance between the signal and reference images when recording superimposed holograms and a partial mutual correlatedness of reference images. The detection efficiency is analysed from the point of view of estimated image data capacity, the degree of mutual correlation of reference images, and the hologram recording conditions. The results of a numerical experiment under the most complicated conditions (representation of images by realisations of homogeneous random fields) confirm the theoretical conclusions.

Wear Detection of Drill Bit by Image-based Technique

NASA Astrophysics Data System (ADS)

Sukeri, Maziyah; Zulhilmi Paiz Ismadi, Mohd; Rahim Othman, Abdul; Kamaruddin, Shahrul

2018-03-01

Image processing for computer vision function plays an essential aspect in the manufacturing industries for the tool condition monitoring. This study proposes a dependable direct measurement method to measure the tool wear using image-based analysis. Segmentation and thresholding technique were used as the means to filter and convert the colour image to binary datasets. Then, the edge detection method was applied to characterize the edge of the drill bit. By using cross-correlation method, the edges of original and worn drill bits were correlated to each other. Cross-correlation graphs were able to detect the difference of the worn edge despite small difference between the graphs. Future development will focus on quantifying the worn profile as well as enhancing the sensitivity of the technique.

Assessing the performance of a motion tracking system based on optical joint transform correlation

NASA Astrophysics Data System (ADS)

Elbouz, M.; Alfalou, A.; Brosseau, C.; Ben Haj Yahia, N.; Alam, M. S.

2015-08-01

We present an optimized system specially designed for the tracking and recognition of moving subjects in a confined environment (such as an elderly remaining at home). In the first step of our study, we use a VanderLugt correlator (VLC) with an adapted pre-processing treatment of the input plane and a postprocessing of the correlation plane via a nonlinear function allowing us to make a robust decision. The second step is based on an optical joint transform correlation (JTC)-based system (NZ-NL-correlation JTC) for achieving improved detection and tracking of moving persons in a confined space. The proposed system has been found to have significantly superior discrimination and robustness capabilities allowing to detect an unknown target in an input scene and to determine the target's trajectory when this target is in motion. This system offers robust tracking performance of a moving target in several scenarios, such as rotational variation of input faces. Test results obtained using various real life video sequences show that the proposed system is particularly suitable for real-time detection and tracking of moving objects.

Noninvasive deep Raman detection with 2D correlation analysis

NASA Astrophysics Data System (ADS)

Kim, Hyung Min; Park, Hyo Sun; Cho, Youngho; Jin, Seung Min; Lee, Kang Taek; Jung, Young Mee; Suh, Yung Doug

2014-07-01

The detection of poisonous chemicals enclosed in daily necessaries is prerequisite essential for homeland security with the increasing threat of terrorism. For the detection of toxic chemicals, we combined a sensitive deep Raman spectroscopic method with 2D correlation analysis. We obtained the Raman spectra from concealed chemicals employing spatially offset Raman spectroscopy in which incident line-shaped light experiences multiple scatterings before being delivered to inner component and yielding deep Raman signal. Furthermore, we restored the pure Raman spectrum of each component using 2D correlation spectroscopic analysis with chemical inspection. Using this method, we could elucidate subsurface component under thick powder and packed contents in a bottle.

Improved target detection algorithm using Fukunaga-Koontz transform and distance classifier correlation filter

NASA Astrophysics Data System (ADS)

Bal, A.; Alam, M. S.; Aslan, M. S.

2006-05-01

Often sensor ego-motion or fast target movement causes the target to temporarily go out of the field-of-view leading to reappearing target detection problem in target tracking applications. Since the target goes out of the current frame and reenters at a later frame, the reentering location and variations in rotation, scale, and other 3D orientations of the target are not known thus complicating the detection algorithm has been developed using Fukunaga-Koontz Transform (FKT) and distance classifier correlation filter (DCCF). The detection algorithm uses target and background information, extracted from training samples, to detect possible candidate target images. The detected candidate target images are then introduced into the second algorithm, DCCF, called clutter rejection module, to determine the target coordinates are detected and tracking algorithm is initiated. The performance of the proposed FKT-DCCF based target detection algorithm has been tested using real-world forward looking infrared (FLIR) video sequences.

Robust Statistical Detection of Power-Law Cross-Correlation.

PubMed

Blythe, Duncan A J; Nikulin, Vadim V; Müller, Klaus-Robert

2016-06-02

We show that widely used approaches in statistical physics incorrectly indicate the existence of power-law cross-correlations between financial stock market fluctuations measured over several years and the neuronal activity of the human brain lasting for only a few minutes. While such cross-correlations are nonsensical, no current methodology allows them to be reliably discarded, leaving researchers at greater risk when the spurious nature of cross-correlations is not clear from the unrelated origin of the time series and rather requires careful statistical estimation. Here we propose a theory and method (PLCC-test) which allows us to rigorously and robustly test for power-law cross-correlations, correctly detecting genuine and discarding spurious cross-correlations, thus establishing meaningful relationships between processes in complex physical systems. Our method reveals for the first time the presence of power-law cross-correlations between amplitudes of the alpha and beta frequency ranges of the human electroencephalogram.

Robust Statistical Detection of Power-Law Cross-Correlation

PubMed Central

Blythe, Duncan A. J.; Nikulin, Vadim V.; Müller, Klaus-Robert

2016-01-01

We show that widely used approaches in statistical physics incorrectly indicate the existence of power-law cross-correlations between financial stock market fluctuations measured over several years and the neuronal activity of the human brain lasting for only a few minutes. While such cross-correlations are nonsensical, no current methodology allows them to be reliably discarded, leaving researchers at greater risk when the spurious nature of cross-correlations is not clear from the unrelated origin of the time series and rather requires careful statistical estimation. Here we propose a theory and method (PLCC-test) which allows us to rigorously and robustly test for power-law cross-correlations, correctly detecting genuine and discarding spurious cross-correlations, thus establishing meaningful relationships between processes in complex physical systems. Our method reveals for the first time the presence of power-law cross-correlations between amplitudes of the alpha and beta frequency ranges of the human electroencephalogram. PMID:27250630

An autonomous surface discontinuity detection and quantification method by digital image correlation and phase congruency

NASA Astrophysics Data System (ADS)

Cinar, A. F.; Barhli, S. M.; Hollis, D.; Flansbjer, M.; Tomlinson, R. A.; Marrow, T. J.; Mostafavi, M.

2017-09-01

Digital image correlation has been routinely used to measure full-field displacements in many areas of solid mechanics, including fracture mechanics. Accurate segmentation of the crack path is needed to study its interaction with the microstructure and stress fields, and studies of crack behaviour, such as the effect of closure or residual stress in fatigue, require data on its opening displacement. Such information can be obtained from any digital image correlation analysis of cracked components, but it collection by manual methods is quite onerous, particularly for massive amounts of data. We introduce the novel application of Phase Congruency to detect and quantify cracks and their opening. Unlike other crack detection techniques, Phase Congruency does not rely on adjustable threshold values that require user interaction, and so allows large datasets to be treated autonomously. The accuracy of the Phase Congruency based algorithm in detecting cracks is evaluated and compared with conventional methods such as Heaviside function fitting. As Phase Congruency is a displacement-based method, it does not suffer from the noise intensification to which gradient-based methods (e.g. strain thresholding) are susceptible. Its application is demonstrated to experimental data for cracks in quasi-brittle (Granitic rock) and ductile (Aluminium alloy) materials.

Detection of elemental mercury by multimode diode laser correlation spectroscopy.

PubMed

Lou, Xiutao; Somesfalean, Gabriel; Svanberg, Sune; Zhang, Zhiguo; Wu, Shaohua

2012-02-27

We demonstrate a method for elemental mercury detection based on correlation spectroscopy employing UV laser radiation generated by sum-frequency mixing of two visible multimode diode lasers. Resonance matching of the multimode UV laser is achieved in a wide wavelength range and with good tolerance for various operating conditions. Large mode-hops provide an off-resonance baseline, eliminating interferences from other gas species with broadband absorption. A sensitivity of 1 μg/m3 is obtained for a 1-m path length and 30-s integration time. The performance of the system shows promise for mercury monitoring in industrial applications.

Improved maximum average correlation height filter with adaptive log base selection for object recognition

NASA Astrophysics Data System (ADS)

Tehsin, Sara; Rehman, Saad; Awan, Ahmad B.; Chaudry, Qaiser; Abbas, Muhammad; Young, Rupert; Asif, Afia

2016-04-01

Sensitivity to the variations in the reference image is a major concern when recognizing target objects. A combinational framework of correlation filters and logarithmic transformation has been previously reported to resolve this issue alongside catering for scale and rotation changes of the object in the presence of distortion and noise. In this paper, we have extended the work to include the influence of different logarithmic bases on the resultant correlation plane. The meaningful changes in correlation parameters along with contraction/expansion in the correlation plane peak have been identified under different scenarios. Based on our research, we propose some specific log bases to be used in logarithmically transformed correlation filters for achieving suitable tolerance to different variations. The study is based upon testing a range of logarithmic bases for different situations and finding an optimal logarithmic base for each particular set of distortions. Our results show improved correlation and target detection accuracies.

Detection of Cardiac Quiescence from B-Mode Echocardiography Using a Correlation-Based Frame-to-Frame Deviation Measure

PubMed Central

Mcclellan, James H.; Ravichandran, Lakshminarayan; Tridandapani, Srini

2013-01-01

Two novel methods for detecting cardiac quiescent phases from B-mode echocardiography using a correlation-based frame-to-frame deviation measure were developed. Accurate knowledge of cardiac quiescence is crucial to the performance of many imaging modalities, including computed tomography coronary angiography (CTCA). Synchronous electrocardiography (ECG) and echocardiography data were obtained from 10 healthy human subjects (four male, six female, 23–45 years) and the interventricular septum (IVS) was observed using the apical four-chamber echocardiographic view. The velocity of the IVS was derived from active contour tracking and verified using tissue Doppler imaging echocardiography methods. In turn, the frame-to-frame deviation methods for identifying quiescence of the IVS were verified using active contour tracking. The timing of the diastolic quiescent phase was found to exhibit both inter- and intra-subject variability, suggesting that the current method of CTCA gating based on the ECG is suboptimal and that gating based on signals derived from cardiac motion are likely more accurate in predicting quiescence for cardiac imaging. Two robust and efficient methods for identifying cardiac quiescent phases from B-mode echocardiographic data were developed and verified. The methods presented in this paper will be used to develop new CTCA gating techniques and quantify the resulting potential improvement in CTCA image quality. PMID:26609501

Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition.

PubMed

Wu, Panpan; Xia, Kewen; Yu, Hengyong

2016-11-01

Dimensionality reduction techniques are developed to suppress the negative effects of high dimensional feature space of lung CT images on classification performance in computer aided detection (CAD) systems for pulmonary nodule detection. An improved supervised locally linear embedding (SLLE) algorithm is proposed based on the concept of correlation coefficient. The Spearman's rank correlation coefficient is introduced to adjust the distance metric in the SLLE algorithm to ensure that more suitable neighborhood points could be identified, and thus to enhance the discriminating power of embedded data. The proposed Spearman's rank correlation coefficient based SLLE (SC(2)SLLE) is implemented and validated in our pilot CAD system using a clinical dataset collected from the publicly available lung image database consortium and image database resource initiative (LICD-IDRI). Particularly, a representative CAD system for solitary pulmonary nodule detection is designed and implemented. After a sequential medical image processing steps, 64 nodules and 140 non-nodules are extracted, and 34 representative features are calculated. The SC(2)SLLE, as well as SLLE and LLE algorithm, are applied to reduce the dimensionality. Several quantitative measurements are also used to evaluate and compare the performances. Using a 5-fold cross-validation methodology, the proposed algorithm achieves 87.65% accuracy, 79.23% sensitivity, 91.43% specificity, and 8.57% false positive rate, on average. Experimental results indicate that the proposed algorithm outperforms the original locally linear embedding and SLLE coupled with the support vector machine (SVM) classifier. Based on the preliminary results from a limited number of nodules in our dataset, this study demonstrates the great potential to improve the performance of a CAD system for nodule detection using the proposed SC(2)SLLE. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Reveal quantum correlation in complementary bases

PubMed Central

Wu, Shengjun; Ma, Zhihao; Chen, Zhihua; Yu, Sixia

2014-01-01

An essential feature of genuine quantum correlation is the simultaneous existence of correlation in complementary bases. We reveal this feature of quantum correlation by defining measures based on invariance under a basis change. For a bipartite quantum state, the classical correlation is the maximal correlation present in a certain optimum basis, while the quantum correlation is characterized as a series of residual correlations in the mutually unbiased bases. Compared with other approaches to quantify quantum correlation, our approach gives information-theoretical measures that directly reflect the essential feature of quantum correlation. PMID:24503595

Quantifying the momentum correlation between two light beams by detecting one

PubMed Central

Hochrainer, Armin; Lahiri, Mayukh; Lapkiewicz, Radek; Lemos, Gabriela Barreto; Zeilinger, Anton

2017-01-01

We report a measurement of the transverse momentum correlation between two photons by detecting only one of them. Our method uses two identical sources in an arrangement in which the phenomenon of induced coherence without induced emission is observed. In this way, we produce an interference pattern in the superposition of one beam from each source. We quantify the transverse momentum correlation by analyzing the visibility of this pattern. Our approach might be useful for the characterization of correlated photon pair sources and may lead to an experimental measure of continuous variable entanglement, which relies on the detection of only one of two entangled particles. PMID:28143940

Quantifying the momentum correlation between two light beams by detecting one.

PubMed

Hochrainer, Armin; Lahiri, Mayukh; Lapkiewicz, Radek; Lemos, Gabriela Barreto; Zeilinger, Anton

2017-02-14

We report a measurement of the transverse momentum correlation between two photons by detecting only one of them. Our method uses two identical sources in an arrangement in which the phenomenon of induced coherence without induced emission is observed. In this way, we produce an interference pattern in the superposition of one beam from each source. We quantify the transverse momentum correlation by analyzing the visibility of this pattern. Our approach might be useful for the characterization of correlated photon pair sources and may lead to an experimental measure of continuous variable entanglement, which relies on the detection of only one of two entangled particles.

Detection system for neutron β decay correlations in the UCNB and Nab experiments

DOE PAGES

Broussard, L. J.; Oak Ridge National Lab.; Zeck, B. A.; ...

2016-12-19

Here, we describe a detection system designed to precisely measure multiple correlations in neutron β decay. Furthermore, the system is based on thick, large area, highly segmented silicon detectors developed in collaboration with Micron Semiconductor, Ltd. The prototype system meets specifications of energy thresholds below 10 keV, energy resolution of ~3 keV FWHM, and rise time of ~50 ns with 19 of the 127 detector pixels instrumented. We have demonstrated the coincident detection of β particles and recoil protons from neutron β decay, using ultracold neutrons at the Los Alamos Neutron Science Center, . The fully instrumented detection system willmore » be implemented in the UCNB and Nab experiments, to determine the neutron β decay parameters B, a, and b.« less

Hierarchical clustering of EMD based interest points for road sign detection

NASA Astrophysics Data System (ADS)

Khan, Jesmin; Bhuiyan, Sharif; Adhami, Reza

2014-04-01

This paper presents an automatic road traffic signs detection and recognition system based on hierarchical clustering of interest points and joint transform correlation. The proposed algorithm consists of the three following stages: interest points detection, clustering of those points and similarity search. At the first stage, good discriminative, rotation and scale invariant interest points are selected from the image edges based on the 1-D empirical mode decomposition (EMD). We propose a two-step unsupervised clustering technique, which is adaptive and based on two criterion. In this context, the detected points are initially clustered based on the stable local features related to the brightness and color, which are extracted using Gabor filter. Then points belonging to each partition are reclustered depending on the dispersion of the points in the initial cluster using position feature. This two-step hierarchical clustering yields the possible candidate road signs or the region of interests (ROIs). Finally, a fringe-adjusted joint transform correlation (JTC) technique is used for matching the unknown signs with the existing known reference road signs stored in the database. The presented framework provides a novel way to detect a road sign from the natural scenes and the results demonstrate the efficacy of the proposed technique, which yields a very low false hit rate.

Remote detection of weak aftershocks of the DPRK underground explosions using waveform cross correlation

NASA Astrophysics Data System (ADS)

Le Bras, R.; Rozhkov, M.; Bobrov, D.; Kitov, I. O.; Sanina, I.

2017-12-01

Association of weak seismic signals generated by low-magnitude aftershocks of the DPRK underground tests into event hypotheses represent a challenge for routine automatic and interactive processing at the International Data Centre (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organization, due to the relatively low station density of the International Monitoring System (IMS) seismic network. Since 2011, as an alternative, the IDC has been testing various prototype techniques of signal detection and event creation based on waveform cross correlation. Using signals measured by seismic stations of the IMS from DPRK explosions as waveform templates, the IDC detected several small (estimated mb between 2.2 and 3.6) seismic events after two DPRK tests conducted on September 9, 2016 and September 3, 2017. The obtained detections were associated with reliable event hypothesis and then used to locate these events relative to the epicenters of the DPRK explosions. We observe high similarity of the detected signals with the corresponding waveform templates. The newly found signals also correlate well between themselves. In addition, the values of the signal-to-noise ratios (SNR) estimated using the traces of cross correlation coefficients, increase with template length (from 5 s to 150 s), providing strong evidence in favour of their spatial closeness, which allows interpreting them as explosion aftershocks. We estimated the relative magnitudes of all aftershocks using the ratio of RMS amplitudes of the master and slave signal in the cross correlation windows characterized by the highest SNR. Additional waveform data from regional non-IMS stations MDJ and SEHB provide independent validation of these aftershock hypotheses. Since waveform templates from any single master event may be sub-efficient at some stations, we have also developed a method of joint usage of the DPRK and the biggest aftershocks templates to build more robust event hypotheses.

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

PubMed

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

2017-06-01

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

Detection of Unexpected High Correlations between Balance Calibration Loads and Load Residuals

NASA Technical Reports Server (NTRS)

Ulbrich, N.; Volden, T.

2014-01-01

An algorithm was developed for the assessment of strain-gage balance calibration data that makes it possible to systematically investigate potential sources of unexpected high correlations between calibration load residuals and applied calibration loads. The algorithm investigates correlations on a load series by load series basis. The linear correlation coefficient is used to quantify the correlations. It is computed for all possible pairs of calibration load residuals and applied calibration loads that can be constructed for the given balance calibration data set. An unexpected high correlation between a load residual and a load is detected if three conditions are met: (i) the absolute value of the correlation coefficient of a residual/load pair exceeds 0.95; (ii) the maximum of the absolute values of the residuals of a load series exceeds 0.25 % of the load capacity; (iii) the load component of the load series is intentionally applied. Data from a baseline calibration of a six-component force balance is used to illustrate the application of the detection algorithm to a real-world data set. This analysis also showed that the detection algorithm can identify load alignment errors as long as repeat load series are contained in the balance calibration data set that do not suffer from load alignment problems.

Single neuron firing properties impact correlation-based population coding

PubMed Central

Hong, Sungho; Ratté, Stéphanie; Prescott, Steven A.; De Schutter, Erik

2012-01-01

Correlated spiking has been widely observed but its impact on neural coding remains controversial. Correlation arising from co-modulation of rates across neurons has been shown to vary with the firing rates of individual neurons. This translates into rate and correlation being equivalently tuned to the stimulus; under those conditions, correlated spiking does not provide information beyond that already available from individual neuron firing rates. Such correlations are irrelevant and can reduce coding efficiency by introducing redundancy. Using simulations and experiments in rat hippocampal neurons, we show here that pairs of neurons receiving correlated input also exhibit correlations arising from precise spike-time synchronization. Contrary to rate co-modulation, spike-time synchronization is unaffected by firing rate, thus enabling synchrony- and rate-based coding to operate independently. The type of output correlation depends on whether intrinsic neuron properties promote integration or coincidence detection: “ideal” integrators (with spike generation sensitive to stimulus mean) exhibit rate co-modulation whereas “ideal” coincidence detectors (with spike generation sensitive to stimulus variance) exhibit precise spike-time synchronization. Pyramidal neurons are sensitive to both stimulus mean and variance, and thus exhibit both types of output correlation proportioned according to which operating mode is dominant. Our results explain how different types of correlations arise based on how individual neurons generate spikes, and why spike-time synchronization and rate co-modulation can encode different stimulus properties. Our results also highlight the importance of neuronal properties for population-level coding insofar as neural networks can employ different coding schemes depending on the dominant operating mode of their constituent neurons. PMID:22279226

Progress of a Cross-correlation Based Optical Strain Measurement Technique for Detecting Radial Growth on a Rotating Disk

NASA Technical Reports Server (NTRS)

Clem, Michelle M.; Woike, Mark; Abdul-Aziz, Ali

2013-01-01

The Aeronautical Sciences Project under NASAs Fundamental Aeronautics Program is extremely interested in the development of fault detection technologies, such as optical surface measurements in the internal parts of a flow path, for in situ health monitoring of gas turbine engines. In situ health monitoring has the potential to detect flaws, i.e. cracks in key components, such as engine turbine disks, before the flaws lead to catastrophic failure. In the present study, a cross-correlation imaging technique is investigated in a proof-of-concept study as a possible optical technique to measure the radial growth and strain field on an already cracked sub-scale turbine engine disk under loaded conditions in the NASA Glenn Research Centers High Precision Rotordynamics Laboratory. The optical strain measurement technique under investigation offers potential fault detection using an applied background consisting of a high-contrast random speckle pattern and imaging the background under unloaded and loaded conditions with a CCD camera. Spinning the cracked disk at high speeds induces an external load, resulting in a radial growth of the disk of approximately 50.8-m in the flawed region and hence, a localized strain field. When imaging the cracked disk under static conditions, the disk will appear shifted. The resulting background displacements between the two images will then be measured using the two-dimensional cross-correlation algorithms implemented in standard Particle Image Velocimetry (PIV) software to track the disk growth, which facilitates calculation of the localized strain field. In order to develop and validate this optical strain measurement technique an initial proof-of-concept experiment is carried out in a controlled environment. Using PIV optimization principles and guidelines, three potential backgrounds, for future use on the rotating disk, are developed and investigated in the controlled experiment. A range of known shifts are induced on the

The detection error of thermal test low-frequency cable based on M sequence correlation algorithm

NASA Astrophysics Data System (ADS)

Wu, Dongliang; Ge, Zheyang; Tong, Xin; Du, Chunlin

2018-04-01

The problem of low accuracy and low efficiency of off-line detecting on thermal test low-frequency cable faults could be solved by designing a cable fault detection system, based on FPGA export M sequence code(Linear feedback shift register sequence) as pulse signal source. The design principle of SSTDR (Spread spectrum time-domain reflectometry) reflection method and hardware on-line monitoring setup figure is discussed in this paper. Testing data show that, this detection error increases with fault location of thermal test low-frequency cable.

Detection and location of small aftershocks using waveform cross correlation

NASA Astrophysics Data System (ADS)

Kitov, Ivan; Sanina, Irina; Sergeev, Sergey

2017-04-01

Aftershock sequences of earthquakes with magnitudes 5.0 and lower are difficult to detect and locate by sparse regional networks. Signals from aftershocks with magnitudes 2 to 3 are usually below detection thresholds of standard 3-C seismic stations at near regional distances. For seismic events close in space, the method waveform cross correlation (WCC) allows to reduce detection threshold by at least a unit of magnitude and to improve location precision to a few kilometers. Therefore, the WCC method is directly applicable to weak aftershock sequences. Here, we recover seismic activity after the earthquake near the town of Mariupol (Ukraine) occurred on August 7, 2016. The main shock was detected by many stations of the International monitoring system (IMS), including the closest primary IMS array stations AKASG (6.62 deg.) and BRTR (7.81), as well as 3-C station KBZ (5.00). The International data centre located this event (47.0013N, 37.5427E), estimated its origin time (08:15:4.1 UTC), magnitude (mb=4.5), and depth (6.8 km). This event was also detected by two array stations of the Institute for Dynamics of Geospheres (IDG) of the Russian Academy of Sciences: portable 3-C array RDON (3.28), which is the closest station, and MHVAR (7.96). Using signals from the main shock at five stations as waveform templates, we calculated continuous traces of cross correlation coefficient (CC) from the 7th to the 11th of August. We found that the best templates should include all regional phases, and thus, have the length from 80 s to 180 s. For detection, we used standard STA/LTA method with threshold depending on station. The accuracy of onset time estimation by the STA/LTA detector based on CC-traces is close to one sample, which varies from 0.05 s at BRTR to 0.005 s for RDON and MHVAR. Arrival times of all detected signals were reduced to origin times using the observed travel times from the main shock. Clusters of origin times are considered as event hypotheses in the

Neuroanatomical correlates of biological motion detection.

PubMed

Gilaie-Dotan, Sharon; Kanai, Ryota; Bahrami, Bahador; Rees, Geraint; Saygin, Ayse P

2013-02-01

Biological motion detection is both commonplace and important, but there is great inter-individual variability in this ability, the neural basis of which is currently unknown. Here we examined whether the behavioral variability in biological motion detection is reflected in brain anatomy. Perceptual thresholds for detection of biological motion and control conditions (non-biological object motion detection and motion coherence) were determined in a group of healthy human adults (n=31) together with structural magnetic resonance images of the brain. Voxel based morphometry analyzes revealed that gray matter volumes of left posterior superior temporal sulcus (pSTS) and left ventral premotor cortex (vPMC) significantly predicted individual differences in biological motion detection, but showed no significant relationship with performance on the control tasks. Our study reveals a neural basis associated with the inter-individual variability in biological motion detection, reliably linking the neuroanatomical structure of left pSTS and vPMC with biological motion detection performance. Copyright © 2012 Elsevier Ltd. All rights reserved.

Nonlocality in many-body quantum systems detected with two-body correlators

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

Tura, J., E-mail: jordi.tura@icfo.es; Augusiak, R.; Sainz, A.B.

Contemporary understanding of correlations in quantum many-body systems and in quantum phase transitions is based to a large extent on the recent intensive studies of entanglement in many-body systems. In contrast, much less is known about the role of quantum nonlocality in these systems, mostly because the available multipartite Bell inequalities involve high-order correlations among many particles, which are hard to access theoretically, and even harder experimentally. Standard, “theorist- and experimentalist-friendly” many-body observables involve correlations among only few (one, two, rarely three...) particles. Typically, there is no multipartite Bell inequality for this scenario based on such low-order correlations. Recently, however,more » we have succeeded in constructing multipartite Bell inequalities that involve two- and one-body correlations only, and showed how they revealed the nonlocality in many-body systems relevant for nuclear and atomic physics [Tura et al., Science 344 (2014) 1256]. With the present contribution we continue our work on this problem. On the one hand, we present a detailed derivation of the above Bell inequalities, pertaining to permutation symmetry among the involved parties. On the other hand, we present a couple of new results concerning such Bell inequalities. First, we characterize their tightness. We then discuss maximal quantum violations of these inequalities in the general case, and their scaling with the number of parties. Moreover, we provide new classes of two-body Bell inequalities which reveal nonlocality of the Dicke states—ground states of physically relevant and experimentally realizable Hamiltonians. Finally, we shortly discuss various scenarios for nonlocality detection in mesoscopic systems of trapped ions or atoms, and by atoms trapped in the vicinity of designed nanostructures.« less

Is comprehension necessary for error detection? A conflict-based account of monitoring in speech production

PubMed Central

Nozari, Nazbanou; Dell, Gary S.; Schwartz, Myrna F.

2011-01-01

Despite the existence of speech errors, verbal communication is successful because speakers can detect (and correct) their errors. The standard theory of speech-error detection, the perceptual-loop account, posits that the comprehension system monitors production output for errors. Such a comprehension-based monitor, however, cannot explain the double dissociation between comprehension and error-detection ability observed in the aphasic patients. We propose a new theory of speech-error detection which is instead based on the production process itself. The theory borrows from studies of forced-choice-response tasks the notion that error detection is accomplished by monitoring response conflict via a frontal brain structure, such as the anterior cingulate cortex. We adapt this idea to the two-step model of word production, and test the model-derived predictions on a sample of aphasic patients. Our results show a strong correlation between patients’ error-detection ability and the model’s characterization of their production skills, and no significant correlation between error detection and comprehension measures, thus supporting a production-based monitor, generally, and the implemented conflict-based monitor in particular. The successful application of the conflict-based theory to error-detection in linguistic, as well as non-linguistic domains points to a domain-general monitoring system. PMID:21652015

Raman spectroscopy-based detection of chemical contaminants in food powders

NASA Astrophysics Data System (ADS)

Chao, Kuanglin; Dhakal, Sagar; Qin, Jianwei; Kim, Moon; Bae, Abigail

2016-05-01

Raman spectroscopy technique has proven to be a reliable method for qualitative detection of chemical contaminants in food ingredients and products. For quantitative imaging-based detection, each contaminant particle in a food sample must be detected and it is important to determine the necessary spatial resolution needed to effectively detect the contaminant particles. This study examined the effective spatial resolution required for detection of maleic acid in tapioca starch and benzoyl peroxide in wheat flour. Each chemical contaminant was mixed into its corresponding food powder at a concentration of 1% (w/w). Raman spectral images were collected for each sample, leveled across a 45 mm x 45 mm area, using different spatial resolutions. Based on analysis of these images, a spatial resolution of 0.5mm was selected as effective spatial resolution for detection of maleic acid in starch and benzoyl peroxide in flour. An experiment was then conducted using the 0.5mm spatial resolution to demonstrate Raman imaging-based quantitative detection of these contaminants for samples prepared at 0.1%, 0.3%, and 0.5% (w/w) concentrations. The results showed a linear correlation between the detected numbers of contaminant pixels and the actual concentrations of contaminant.

Case-Based Multi-Sensor Intrusion Detection

NASA Astrophysics Data System (ADS)

Schwartz, Daniel G.; Long, Jidong

2009-08-01

Multi-sensor intrusion detection systems (IDSs) combine the alerts raised by individual IDSs and possibly other kinds of devices such as firewalls and antivirus software. A critical issue in building a multi-sensor IDS is alert-correlation, i.e., determining which alerts are caused by the same attack. This paper explores a novel approach to alert correlation using case-based reasoning (CBR). Each case in the CBR system's library contains a pattern of alerts raised by some known attack type, together with the identity of the attack. Then during run time, the alert streams gleaned from the sensors are compared with the patterns in the cases, and a match indicates that the attack described by that case has occurred. For this purpose the design of a fast and accurate matching algorithm is imperative. Two such algorithms were explored: (i) the well-known Hungarian algorithm, and (ii) an order-preserving matching of our own device. Tests were conducted using the DARPA Grand Challenge Problem attack simulator. These showed that the both matching algorithms are effective in detecting attacks; but the Hungarian algorithm is inefficient; whereas the order-preserving one is very efficient, in fact runs in linear time.

Parallel detecting super-resolution microscopy using correlation based image restoration

NASA Astrophysics Data System (ADS)

Yu, Zhongzhi; Liu, Shaocong; Zhu, Dazhao; Kuang, Cuifang; Liu, Xu

2017-12-01

A novel approach to achieve the image restoration is proposed in which each detector's relative position in the detector array is no longer a necessity. We can identify each detector's relative location by extracting a certain area from one of the detector's image and scanning it on other detectors' images. According to this location, we can generate the point spread functions (PSF) for each detector and perform deconvolution for image restoration. Equipped with this method, the microscope with discretionally designed detector array can be easily constructed without the concern of exact relative locations of detectors. The simulated results and experimental results show the total improvement in resolution with a factor of 1.7 compared to conventional confocal fluorescence microscopy. With the significant enhancement in resolution and easiness for application of this method, this novel method should have potential for a wide range of application in fluorescence microscopy based on parallel detecting.

A New Intrusion Detection Method Based on Antibody Concentration

NASA Astrophysics Data System (ADS)

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

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

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

Schemes of detecting nuclear spin correlations by dynamical decoupling based quantum sensing

NASA Astrophysics Data System (ADS)

Ma, Wen-Long Ma; Liu, Ren-Bao

Single-molecule sensitivity of nuclear magnetic resonance (NMR) and angstrom resolution of magnetic resonance imaging (MRI) are the highest challenges in magnetic microscopy. Recent development in dynamical decoupling (DD) enhanced diamond quantum sensing has enabled NMR of single nuclear spins and nanoscale NMR. Similar to conventional NMR and MRI, current DD-based quantum sensing utilizes the frequency fingerprints of target nuclear spins. Such schemes, however, cannot resolve different nuclear spins that have the same noise frequency or differentiate different types of correlations in nuclear spin clusters. Here we show that the first limitation can be overcome by using wavefunction fingerprints of target nuclear spins, which is much more sensitive than the ''frequency fingerprints'' to weak hyperfine interaction between the targets and a sensor, while the second one can be overcome by a new design of two-dimensional DD sequences composed of two sets of periodic DD sequences with different periods, which can be independently set to match two different transition frequencies. Our schemes not only offer an approach to breaking the resolution limit set by ''frequency gradients'' in conventional MRI, but also provide a standard approach to correlation spectroscopy for single-molecule NMR.

A novel fast phase correlation algorithm for peak wavelength detection of Fiber Bragg Grating sensors.

PubMed

Lamberti, A; Vanlanduit, S; De Pauw, B; Berghmans, F

2014-03-24

Fiber Bragg Gratings (FBGs) can be used as sensors for strain, temperature and pressure measurements. For this purpose, the ability to determine the Bragg peak wavelength with adequate wavelength resolution and accuracy is essential. However, conventional peak detection techniques, such as the maximum detection algorithm, can yield inaccurate and imprecise results, especially when the Signal to Noise Ratio (SNR) and the wavelength resolution are poor. Other techniques, such as the cross-correlation demodulation algorithm are more precise and accurate but require a considerable higher computational effort. To overcome these problems, we developed a novel fast phase correlation (FPC) peak detection algorithm, which computes the wavelength shift in the reflected spectrum of a FBG sensor. This paper analyzes the performance of the FPC algorithm for different values of the SNR and wavelength resolution. Using simulations and experiments, we compared the FPC with the maximum detection and cross-correlation algorithms. The FPC method demonstrated a detection precision and accuracy comparable with those of cross-correlation demodulation and considerably higher than those obtained with the maximum detection technique. Additionally, FPC showed to be about 50 times faster than the cross-correlation. It is therefore a promising tool for future implementation in real-time systems or in embedded hardware intended for FBG sensor interrogation.

Hotspot detection using image pattern recognition based on higher-order local auto-correlation

NASA Astrophysics Data System (ADS)

Maeda, Shimon; Matsunawa, Tetsuaki; Ogawa, Ryuji; Ichikawa, Hirotaka; Takahata, Kazuhiro; Miyairi, Masahiro; Kotani, Toshiya; Nojima, Shigeki; Tanaka, Satoshi; Nakagawa, Kei; Saito, Tamaki; Mimotogi, Shoji; Inoue, Soichi; Nosato, Hirokazu; Sakanashi, Hidenori; Kobayashi, Takumi; Murakawa, Masahiro; Higuchi, Tetsuya; Takahashi, Eiichi; Otsu, Nobuyuki

2011-04-01

Below 40nm design node, systematic variation due to lithography must be taken into consideration during the early stage of design. So far, litho-aware design using lithography simulation models has been widely applied to assure that designs are printed on silicon without any error. However, the lithography simulation approach is very time consuming, and under time-to-market pressure, repetitive redesign by this approach may result in the missing of the market window. This paper proposes a fast hotspot detection support method by flexible and intelligent vision system image pattern recognition based on Higher-Order Local Autocorrelation. Our method learns the geometrical properties of the given design data without any defects as normal patterns, and automatically detects the design patterns with hotspots from the test data as abnormal patterns. The Higher-Order Local Autocorrelation method can extract features from the graphic image of design pattern, and computational cost of the extraction is constant regardless of the number of design pattern polygons. This approach can reduce turnaround time (TAT) dramatically only on 1CPU, compared with the conventional simulation-based approach, and by distributed processing, this has proven to deliver linear scalability with each additional CPU.

A powerful score-based test statistic for detecting gene-gene co-association.

PubMed

Xu, Jing; Yuan, Zhongshang; Ji, Jiadong; Zhang, Xiaoshuai; Li, Hongkai; Wu, Xuesen; Xue, Fuzhong; Liu, Yanxun

2016-01-29

The genetic variants identified by Genome-wide association study (GWAS) can only account for a small proportion of the total heritability for complex disease. The existence of gene-gene joint effects which contains the main effects and their co-association is one of the possible explanations for the "missing heritability" problems. Gene-gene co-association refers to the extent to which the joint effects of two genes differ from the main effects, not only due to the traditional interaction under nearly independent condition but the correlation between genes. Generally, genes tend to work collaboratively within specific pathway or network contributing to the disease and the specific disease-associated locus will often be highly correlated (e.g. single nucleotide polymorphisms (SNPs) in linkage disequilibrium). Therefore, we proposed a novel score-based statistic (SBS) as a gene-based method for detecting gene-gene co-association. Various simulations illustrate that, under different sample sizes, marginal effects of causal SNPs and co-association levels, the proposed SBS has the better performance than other existed methods including single SNP-based and principle component analysis (PCA)-based logistic regression model, the statistics based on canonical correlations (CCU), kernel canonical correlation analysis (KCCU), partial least squares path modeling (PLSPM) and delta-square (δ (2)) statistic. The real data analysis of rheumatoid arthritis (RA) further confirmed its advantages in practice. SBS is a powerful and efficient gene-based method for detecting gene-gene co-association.

Detecting Noisy Events Using Waveform Cross-Correlation at Superarrays of Seismic Stations

NASA Astrophysics Data System (ADS)

von Seggern, D. H.; Tibuleac, I. M.

2007-12-01

Cross-correlation using master events, followed by stacking of the correlation series, has been shown to dramatically improve detection thresholds of small-to-medium seismic arrays. With the goal of lowering the detection threshold, determining relative magnitudes or moments, and characterizing sources by empirical Green's functions, we extend the cross-correlation methodology to include "superarrays" of seismic stations. The superarray concept naturally brings further benefits over conventional arrays and single-stations due to the fact that many distances and azimuths can be sampled. This extension is straightforward given the ease with which regional or global data from various stations or arrays can be currently accessed and combined into a single database. We demonstrate the capability of superarrays to detect and analyze events which lie below the detection threshold. This is aided by applying an F-statistic detector to the superarray cross-correlation stack and its components. Our first example illustrates the use of a superarray consisting of the Southern Great Basin Digital Seismic Network, a small-aperture array (NVAR) in Mina, Nevada and the Earthscope Transportable Array to detect events in California-Nevada areas. In our second example, we use a combination of small-to-medium arrays and single stations to study the rupture of the great Sumatra earthquake of 26 December 2004 and to detect its early aftershocks. The location and times of "detected" events are confirmed using a frequency- wavenumber method at the small-to-medium arrays. We propose that ad hoc superarrays can be used in many studies where conventional approaches previously used only single arrays or groups of single stations. The availability of near-real-time data from many networks and of archived data from, for instance, IRIS makes possible the easy assembly of superarrays. Furthermore, the continued improvement of seismic data availability and the continued growth in the number of

Improving Correlation Algorithms to Detect and Characterize Smaller Magnitude Induced Seismicity Swarms

NASA Astrophysics Data System (ADS)

Skoumal, R.; Brudzinski, M.; Currie, B.

2015-12-01

Induced seismic sequences often occur as swarms that can include thousands of small (< M 2) earthquakes. While the identification of this microseismicity would invariably aid in the characterization and modeling of induced sequences, traditional earthquake detection techniques often provide incomplete catalogs, even when local networks are deployed. Because induced sequences often include scores of micro-earthquakes that prelude larger magnitude events, the identification of these small magnitude events would be crucial for the early identification of induced sequences. By taking advantage of the repeating, swarm-like nature of induced seismicity, a more robust catalog can be created using complementary correlation algorithms in near real-time without the reliance on traditional earthquake detection and association routines. Since traditional earthquake catalog methodologies using regional networks have a relatively high detection threshold (M 2+), we have sought to develop correlation routines that can detect smaller magnitude sequences. While short-term/long-term amplitude average detection algorithms requires significant signal-to-noise ratio at multiple stations for confident identification, a correlation detector is capable of identifying earthquakes with high confidence using just a single station. The result is an embarrassingly parallel task that can be employed for a network to be used as an early warning system for potentially induced seismicity while also better characterizing tectonic sequences beyond what traditional methods allow.

Testing for the Presence of Correlation Changes in a Multivariate Time Series: A Permutation Based Approach.

PubMed

Cabrieto, Jedelyn; Tuerlinckx, Francis; Kuppens, Peter; Hunyadi, Borbála; Ceulemans, Eva

2018-01-15

Detecting abrupt correlation changes in multivariate time series is crucial in many application fields such as signal processing, functional neuroimaging, climate studies, and financial analysis. To detect such changes, several promising correlation change tests exist, but they may suffer from severe loss of power when there is actually more than one change point underlying the data. To deal with this drawback, we propose a permutation based significance test for Kernel Change Point (KCP) detection on the running correlations. Given a requested number of change points K, KCP divides the time series into K + 1 phases by minimizing the within-phase variance. The new permutation test looks at how the average within-phase variance decreases when K increases and compares this to the results for permuted data. The results of an extensive simulation study and applications to several real data sets show that, depending on the setting, the new test performs either at par or better than the state-of-the art significance tests for detecting the presence of correlation changes, implying that its use can be generally recommended.

Direct connections assist neurons to detect correlation in small amplitude noises

PubMed Central

Bolhasani, E.; Azizi, Y.; Valizadeh, A.

2013-01-01

We address a question on the effect of common stochastic inputs on the correlation of the spike trains of two neurons when they are coupled through direct connections. We show that the change in the correlation of small amplitude stochastic inputs can be better detected when the neurons are connected by direct excitatory couplings. Depending on whether intrinsic firing rate of the neurons is identical or slightly different, symmetric or asymmetric connections can increase the sensitivity of the system to the input correlation by changing the mean slope of the correlation transfer function over a given range of input correlation. In either case, there is also an optimum value for synaptic strength which maximizes the sensitivity of the system to the changes in input correlation. PMID:23966940

Fatigue crack detection by nonlinear spectral correlation with a wideband input

NASA Astrophysics Data System (ADS)

Liu, Peipei; Sohn, Hoon

2017-04-01

Due to crack-induced nonlinearity, ultrasonic wave can distort, create accompanying harmonics, multiply waves of different frequencies, and, under resonance conditions, change resonance frequencies as a function of driving amplitude. All these nonlinear ultrasonic features have been widely studied and proved capable of detecting fatigue crack at its very early stage. However, in noisy environment, the nonlinear features might be drown in the noise, therefore it is difficult to extract those features using a conventional spectral density function. In this study, nonlinear spectral correlation is defined as a new nonlinear feature, which considers not only nonlinear modulations in ultrasonic waves but also spectral correlation between the nonlinear modulations. The proposed nonlinear feature is associated with the following two advantages: (1) stationary noise in the ultrasonic waves has little effect on nonlinear spectral correlation; and (2) the contrast of nonlinear spectral correlation between damage and intact conditions can be enhanced simply by using a wideband input. To validate the proposed nonlinear feature, micro fatigue cracks are introduced to aluminum plates by repeated tensile loading, and the experiment is conducted using surface-mounted piezoelectric transducers for ultrasonic wave generation and measurement. The experimental results confirm that the nonlinear spectral correlation can successfully detect fatigue crack with a higher sensitivity than the classical nonlinear coefficient.

Brain tumor segmentation with Vander Lugt correlator based active contour.

PubMed

Essadike, Abdelaziz; Ouabida, Elhoussaine; Bouzid, Abdenbi

2018-07-01

The manual segmentation of brain tumors from medical images is an error-prone, sensitive, and time-absorbing process. This paper presents an automatic and fast method of brain tumor segmentation. In the proposed method, a numerical simulation of the optical Vander Lugt correlator is used for automatically detecting the abnormal tissue region. The tumor filter, used in the simulated optical correlation, is tailored to all the brain tumor types and especially to the Glioblastoma, which considered to be the most aggressive cancer. The simulated optical correlation, computed between Magnetic Resonance Images (MRI) and this filter, estimates precisely and automatically the initial contour inside the tumorous tissue. Further, in the segmentation part, the detected initial contour is used to define an active contour model and presenting the problematic as an energy minimization problem. As a result, this initial contour assists the algorithm to evolve an active contour model towards the exact tumor boundaries. Equally important, for a comparison purposes, we considered different active contour models and investigated their impact on the performance of the segmentation task. Several images from BRATS database with tumors anywhere in images and having different sizes, contrast, and shape, are used to test the proposed system. Furthermore, several performance metrics are computed to present an aggregate overview of the proposed method advantages. The proposed method achieves a high accuracy in detecting the tumorous tissue by a parameter returned by the simulated optical correlation. In addition, the proposed method yields better performance compared to the active contour based methods with the averages of Sensitivity=0.9733, Dice coefficient = 0.9663, Hausdroff distance = 2.6540, Specificity = 0.9994, and faster with a computational time average of 0.4119 s per image. Results reported on BRATS database reveal that our proposed system improves over the recently published

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.

A correlation analysis-based detection and delineation of ECG characteristic events using template waveforms extracted by ensemble averaging of clustered heart cycles.

PubMed

Homaeinezhad, M R; Erfanianmoshiri-Nejad, M; Naseri, H

2014-01-01

The goal of this study is to introduce a simple, standard and safe procedure to detect and to delineate P and T waves of the electrocardiogram (ECG) signal in real conditions. The proposed method consists of four major steps: (1) a secure QRS detection and delineation algorithm, (2) a pattern recognition algorithm designed for distinguishing various ECG clusters which take place between consecutive R-waves, (3) extracting template of the dominant events of each cluster waveform and (4) application of the correlation analysis in order to delineate automatically the P- and T-waves in noisy conditions. The performance characteristics of the proposed P and T detection-delineation algorithm are evaluated versus various ECG signals whose qualities are altered from the best to the worst cases based on the random-walk noise theory. Also, the method is applied to the MIT-BIH Arrhythmia and the QT databases for comparing some parts of its performance characteristics with a number of P and T detection-delineation algorithms. The conducted evaluations indicate that in a signal with low quality value of about 0.6, the proposed method detects the P and T events with sensitivity Se=85% and positive predictive value of P+=89%, respectively. In addition, at the same quality, the average delineation errors associated with those ECG events are 45 and 63ms, respectively. Stable delineation error, high detection accuracy and high noise tolerance were the most important aspects considered during development of the proposed method. © 2013 Elsevier Ltd. All rights reserved.

Inter-lead correlation analysis for automated detection of cable reversals in 12/16-lead ECG.

PubMed

Jekova, Irena; Krasteva, Vessela; Leber, Remo; Schmid, Ramun; Twerenbold, Raphael; Müller, Christian; Reichlin, Tobias; Abächerli, Roger

2016-10-01

A crucial factor for proper electrocardiogram (ECG) interpretation is the correct electrode placement in standard 12-lead ECG and extended 16-lead ECG for accurate diagnosis of acute myocardial infarctions. In the context of optimal patient care, we present and evaluate a new method for automated detection of reversals in peripheral and precordial (standard, right and posterior) leads, based on simple rules with inter-lead correlation dependencies. The algorithm for analysis of cable reversals relies on scoring of inter-lead correlations estimated over 4s snapshots with time-coherent data from multiple ECG leads. Peripheral cable reversals are detected by assessment of nine correlation coefficients, comparing V6 to limb leads: (I, II, III, -I, -II, -III, -aVR, -aVL, -aVF). Precordial lead reversals are detected by analysis of the ECG pattern cross-correlation progression within lead sets (V1-V6), (V4R, V3R, V3, V4), and (V4, V5, V6, V8, V9). Disturbed progression identifies the swapped leads. A test-set, including 2239 ECGs from three independent sources-public 12-lead (PTB, CSE) and proprietary 16-lead (Basel University Hospital) databases-is used for algorithm validation, reporting specificity (Sp) and sensitivity (Se) as true negative and true positive detection of simulated lead swaps. Reversals of limb leads are detected with Se = 95.5-96.9% and 100% when right leg is involved in the reversal. Among all 15 possible pairwise reversals in standard precordial leads, adjacent lead reversals are detected with Se = 93.8% (V5-V6), 95.6% (V2-V3), 95.9% (V3-V4), 97.1% (V1-V2), and 97.8% (V4-V5), increasing to 97.8-99.8% for reversals of anatomically more distant electrodes. The pairwise reversals in the four extra precordial leads are detected with Se = 74.7% (right-sided V4R-V3R), 91.4% (posterior V8-V9), 93.7% (V4R-V9), and 97.7% (V4R-V8, V3R-V9, V3R-V8). Higher true negative rate is achieved with Sp > 99% (standard 12-lead ECG), 81.9% (V4R-V3R), 91

Time-resolved single-photon detection module based on silicon photomultiplier: A novel building block for time-correlated measurement systems

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

Martinenghi, E., E-mail: edoardo.martinenghi@polimi.it; Di Sieno, L.; Contini, D.

2016-07-15

We present the design and preliminary characterization of the first detection module based on Silicon Photomultiplier (SiPM) tailored for single-photon timing applications. The aim of this work is to demonstrate, thanks to the design of a suitable module, the possibility to easily exploit SiPM in many applications as an interesting detector featuring large active area, similarly to photomultipliers tubes, but keeping the advantages of solid state detectors (high quantum efficiency, low cost, compactness, robustness, low bias voltage, and insensitiveness to magnetic field). The module integrates a cooled SiPM with a total photosensitive area of 1 mm{sup 2} together with themore » suitable avalanche signal read-out circuit, the signal conditioning, the biasing electronics, and a Peltier cooler driver for thermal stabilization. It is able to extract the single-photon timing information with resolution better than 100 ps full-width at half maximum. We verified the effective stabilization in response to external thermal perturbations, thus proving the complete insensitivity of the module to environment temperature variations, which represents a fundamental parameter to profitably use the instrument for real-field applications. We also characterized the single-photon timing resolution, the background noise due to both primary dark count generation and afterpulsing, the single-photon detection efficiency, and the instrument response function shape. The proposed module can become a reliable and cost-effective building block for time-correlated single-photon counting instruments in applications requiring high collection capability of isotropic light and detection efficiency (e.g., fluorescence decay measurements or time-domain diffuse optics systems).« less

Time-resolved single-photon detection module based on silicon photomultiplier: A novel building block for time-correlated measurement systems

NASA Astrophysics Data System (ADS)

Martinenghi, E.; Di Sieno, L.; Contini, D.; Sanzaro, M.; Pifferi, A.; Dalla Mora, A.

2016-07-01

We present the design and preliminary characterization of the first detection module based on Silicon Photomultiplier (SiPM) tailored for single-photon timing applications. The aim of this work is to demonstrate, thanks to the design of a suitable module, the possibility to easily exploit SiPM in many applications as an interesting detector featuring large active area, similarly to photomultipliers tubes, but keeping the advantages of solid state detectors (high quantum efficiency, low cost, compactness, robustness, low bias voltage, and insensitiveness to magnetic field). The module integrates a cooled SiPM with a total photosensitive area of 1 mm2 together with the suitable avalanche signal read-out circuit, the signal conditioning, the biasing electronics, and a Peltier cooler driver for thermal stabilization. It is able to extract the single-photon timing information with resolution better than 100 ps full-width at half maximum. We verified the effective stabilization in response to external thermal perturbations, thus proving the complete insensitivity of the module to environment temperature variations, which represents a fundamental parameter to profitably use the instrument for real-field applications. We also characterized the single-photon timing resolution, the background noise due to both primary dark count generation and afterpulsing, the single-photon detection efficiency, and the instrument response function shape. The proposed module can become a reliable and cost-effective building block for time-correlated single-photon counting instruments in applications requiring high collection capability of isotropic light and detection efficiency (e.g., fluorescence decay measurements or time-domain diffuse optics systems).

Application of interferential correlation of spectrum to the detection of atmospheric pollutants

NASA Technical Reports Server (NTRS)

Fortunato, G.

1979-01-01

The general correlation principles for spectra and spectra derivatives are studied by using the Fourier transform of the spectral distribution of energy from a source illuminating a double beam interferometer with transverse splitting by dividing luminance. In this correlation technique, the use of such an interferometer has the advantage of greater luminosity as compared with a slit spectrometer. However, the correlation example indicates that it is necessary to adapt the correlator to the particular case considered, in order to obtain the best gain in the signal to noise ratio. In the case of sulfur dioxide detection, a very simple mounting which could be used in some interesting industrial applications was developed. This mounting can be used each time that the substance to be analyzed has a quasi-periodic absorption spectrum: in particular this is often the case with absorption spectra of gases, and a mounting identical to the one described for sulfur dioxide proved to be effective in the detection of nitrogen oxides.

Tsunami Detection by High Frequency Radar Beyond the Continental Shelf: II. Extension of Time Correlation Algorithm and Validation on Realistic Case Studies

NASA Astrophysics Data System (ADS)

Grilli, Stéphan T.; Guérin, Charles-Antoine; Shelby, Michael; Grilli, Annette R.; Moran, Patrick; Grosdidier, Samuel; Insua, Tania L.

2017-08-01

In past work, tsunami detection algorithms (TDAs) have been proposed, and successfully applied to offline tsunami detection, based on analyzing tsunami currents inverted from high-frequency (HF) radar Doppler spectra. With this method, however, the detection of small and short-lived tsunami currents in the most distant radar ranges is challenging due to conflicting requirements on the Doppler spectra integration time and resolution. To circumvent this issue, in Part I of this work, we proposed an alternative TDA, referred to as time correlation (TC) TDA, that does not require inverting currents, but instead detects changes in patterns of correlations of radar signal time series measured in pairs of cells located along the main directions of tsunami propagation (predicted by geometric optics theory); such correlations can be maximized when one signal is time-shifted by the pre-computed long wave propagation time. We initially validated the TC-TDA based on numerical simulations of idealized tsunamis in a simplified geometry. Here, we further develop, extend, and apply the TC algorithm to more realistic tsunami case studies. These are performed in the area West of Vancouver Island, BC, where Ocean Networks Canada recently deployed a HF radar (in Tofino, BC), to detect tsunamis from far- and near-field sources, up to a 110 km range. Two case studies are considered, both simulated using long wave models (1) a far-field seismic, and (2) a near-field landslide, tsunami. Pending the availability of radar data, a radar signal simulator is parameterized for the Tofino HF radar characteristics, in particular its signal-to-noise ratio with range, and combined with the simulated tsunami currents to produce realistic time series of backscattered radar signal from a dense grid of cells. Numerical experiments show that the arrival of a tsunami causes a clear change in radar signal correlation patterns, even at the most distant ranges beyond the continental shelf, thus making an

Optical correlation based pose estimation using bipolar phase grayscale amplitude spatial light modulators

NASA Astrophysics Data System (ADS)

Outerbridge, Gregory John, II

Pose estimation techniques have been developed on both optical and digital correlator platforms to aid in the autonomous rendezvous and docking of spacecraft. This research has focused on the optical architecture, which utilizes high-speed bipolar-phase grayscale-amplitude spatial light modulators as the image and correlation filter devices. The optical approach has the primary advantage of optical parallel processing: an extremely fast and efficient way of performing complex correlation calculations. However, the constraints imposed on optically implementable filters makes optical correlator based posed estimation technically incompatible with the popular weighted composite filter designs successfully used on the digital platform. This research employs a much simpler "bank of filters" approach to optical pose estimation that exploits the inherent efficiency of optical correlation devices. A novel logarithmically mapped optically implementable matched filter combined with a pose search algorithm resulted in sub-degree standard deviations in angular pose estimation error. These filters were extremely simple to generate, requiring no complicated training sets and resulted in excellent performance even in the presence of significant background noise. Common edge detection and scaling of the input image was the only image pre-processing necessary for accurate pose detection at all alignment distances of interest.

Detrended fluctuation analysis made flexible to detect range of cross-correlated fluctuations

NASA Astrophysics Data System (ADS)

Kwapień, Jarosław; Oświecimka, Paweł; DroŻdŻ, Stanisław

2015-11-01

The detrended cross-correlation coefficient ρDCCA has recently been proposed to quantify the strength of cross-correlations on different temporal scales in bivariate, nonstationary time series. It is based on the detrended cross-correlation and detrended fluctuation analyses (DCCA and DFA, respectively) and can be viewed as an analog of the Pearson coefficient in the case of the fluctuation analysis. The coefficient ρDCCA works well in many practical situations but by construction its applicability is limited to detection of whether two signals are generally cross-correlated, without the possibility to obtain information on the amplitude of fluctuations that are responsible for those cross-correlations. In order to introduce some related flexibility, here we propose an extension of ρDCCA that exploits the multifractal versions of DFA and DCCA: multifractal detrended fluctuation analysis and multifractal detrended cross-correlation analysis, respectively. The resulting new coefficient ρq not only is able to quantify the strength of correlations but also allows one to identify the range of detrended fluctuation amplitudes that are correlated in two signals under study. We show how the coefficient ρq works in practical situations by applying it to stochastic time series representing processes with long memory: autoregressive and multiplicative ones. Such processes are often used to model signals recorded from complex systems and complex physical phenomena like turbulence, so we are convinced that this new measure can successfully be applied in time-series analysis. In particular, we present an example of such application to highly complex empirical data from financial markets. The present formulation can straightforwardly be extended to multivariate data in terms of the q -dependent counterpart of the correlation matrices and then to the network representation.

Microbial Fuels Cell-Based Biosensor for Toxicity Detection: A Review

PubMed Central

Zhou, Tuoyu; Han, Huawen; Liu, Pu; Xiong, Jian; Tian, Fake; Li, Xiangkai

2017-01-01

With the unprecedented deterioration of environmental quality, rapid recognition of toxic compounds is paramount for performing in situ real-time monitoring. Although several analytical techniques based on electrochemistry or biosensors have been developed for the detection of toxic compounds, most of them are time-consuming, inaccurate, or cumbersome for practical applications. More recently, microbial fuel cell (MFC)-based biosensors have drawn increasing interest due to their sustainability and cost-effectiveness, with applications ranging from the monitoring of anaerobic digestion process parameters (VFA) to water quality detection (e.g., COD, BOD). When a MFC runs under correct conditions, the voltage generated is correlated with the amount of a given substrate. Based on this linear relationship, several studies have demonstrated that MFC-based biosensors could detect heavy metals such as copper, chromium, or zinc, as well as organic compounds, including p-nitrophenol (PNP), formaldehyde and levofloxacin. Both bacterial consortia and single strains can be used to develop MFC-based biosensors. Biosensors with single strains show several advantages over systems integrating bacterial consortia, such as selectivity and stability. One of the limitations of such sensors is that the detection range usually exceeds the actual pollution level. Therefore, improving their sensitivity is the most important for widespread application. Nonetheless, MFC-based biosensors represent a promising approach towards single pollutant detection. PMID:28956857

Microbial Fuels Cell-Based Biosensor for Toxicity Detection: A Review.

PubMed

Zhou, Tuoyu; Han, Huawen; Liu, Pu; Xiong, Jian; Tian, Fake; Li, Xiangkai

2017-09-28

With the unprecedented deterioration of environmental quality, rapid recognition of toxic compounds is paramount for performing in situ real-time monitoring. Although several analytical techniques based on electrochemistry or biosensors have been developed for the detection of toxic compounds, most of them are time-consuming, inaccurate, or cumbersome for practical applications. More recently, microbial fuel cell (MFC)-based biosensors have drawn increasing interest due to their sustainability and cost-effectiveness, with applications ranging from the monitoring of anaerobic digestion process parameters (VFA) to water quality detection (e.g., COD, BOD). When a MFC runs under correct conditions, the voltage generated is correlated with the amount of a given substrate. Based on this linear relationship, several studies have demonstrated that MFC-based biosensors could detect heavy metals such as copper, chromium, or zinc, as well as organic compounds, including p -nitrophenol (PNP), formaldehyde and levofloxacin. Both bacterial consortia and single strains can be used to develop MFC-based biosensors. Biosensors with single strains show several advantages over systems integrating bacterial consortia, such as selectivity and stability. One of the limitations of such sensors is that the detection range usually exceeds the actual pollution level. Therefore, improving their sensitivity is the most important for widespread application. Nonetheless, MFC-based biosensors represent a promising approach towards single pollutant detection.

Comparison of cyclic correlation and the wavelet method for symbol rate detection

NASA Astrophysics Data System (ADS)

Carr, Richard; Whitney, James

Software defined radio (SDR) is a relatively new technology that holds a great deal of promise in the communication field in general, and, in particular the area of space communications. Tra-ditional communication systems are comprised of a transmitter and a receiver, where through prior planning and scheduling, the transmitter and receiver are pre-configured for a particu-lar communication modality. For any particular modality the radio circuitry is configured to transmit, receive, and resolve one type of modulation at a certain data rate. Traditional radio's are limited by the fact that the circuitry is fixed. Software defined radios on the other hand do not suffer from this limitation. SDR's are comprised mainly of software modules which allow them to be flexible, in that they can resolve various types of modulation types that occur at different data rates. This ability is of very high importance in space where parameters of the communications link may need to be changed due to channel fading, reduced power, or other unforeseen events. In these cases the ability to autonomously change aspects of the radio's con-figuration becomes an absolute necessity in order to maintain communications. In order for the technology to work the receiver has to be able to determine the modulation type and the data rate of the signal. The data rate of the signal is one of the first parameters to be resolved, as it is needed to find the other signal parameters such as modulation type and the signal-to-noise ratio. There are a number of algorithms that have been developed to detect or estimate the data rate of a signal. This paper will investigate two of these algorithms, namely, the cyclic correlation algorithm and a wavelet-based detection algorithm. Both of these algorithms are feature-based algorithms, meaning that they make their estimations based on certain inherent features of the signals to which they are applied. The cyclic correlation algorithm takes advan-tage of the

Application of image recognition-based automatic hyphae detection in fungal keratitis.

PubMed

Wu, Xuelian; Tao, Yuan; Qiu, Qingchen; Wu, Xinyi

2018-03-01

The purpose of this study is to evaluate the accuracy of two methods in diagnosis of fungal keratitis, whereby one method is automatic hyphae detection based on images recognition and the other method is corneal smear. We evaluate the sensitivity and specificity of the method in diagnosis of fungal keratitis, which is automatic hyphae detection based on image recognition. We analyze the consistency of clinical symptoms and the density of hyphae, and perform quantification using the method of automatic hyphae detection based on image recognition. In our study, 56 cases with fungal keratitis (just single eye) and 23 cases with bacterial keratitis were included. All cases underwent the routine inspection of slit lamp biomicroscopy, corneal smear examination, microorganism culture and the assessment of in vivo confocal microscopy images before starting medical treatment. Then, we recognize the hyphae images of in vivo confocal microscopy by using automatic hyphae detection based on image recognition to evaluate its sensitivity and specificity and compare with the method of corneal smear. The next step is to use the index of density to assess the severity of infection, and then find the correlation with the patients' clinical symptoms and evaluate consistency between them. The accuracy of this technology was superior to corneal smear examination (p < 0.05). The sensitivity of the technology of automatic hyphae detection of image recognition was 89.29%, and the specificity was 95.65%. The area under the ROC curve was 0.946. The correlation coefficient between the grading of the severity in the fungal keratitis by the automatic hyphae detection based on image recognition and the clinical grading is 0.87. The technology of automatic hyphae detection based on image recognition was with high sensitivity and specificity, able to identify fungal keratitis, which is better than the method of corneal smear examination. This technology has the advantages when compared with

Wenchuan Event Detection And Localization Using Waveform Correlation Coupled With Double Difference

NASA Astrophysics Data System (ADS)

Slinkard, M.; Heck, S.; Schaff, D. P.; Young, C. J.; Richards, P. G.

2014-12-01

The well-studied Wenchuan aftershock sequence triggered by the May 12, 2008, Ms 8.0, mainshock offers an ideal test case for evaluating the effectiveness of using waveform correlation coupled with double difference relocation to detect and locate events in a large aftershock sequence. We use Sandia's SeisCorr detector to process 3 months of data recorded by permanent IRIS and temporary ASCENT stations using templates from events listed in a global catalog to find similar events in the raw data stream. Then we take the detections and relocate them using the double difference method. We explore both the performance that can be expected with using just a small number of stations, and, the benefits of reprocessing a well-studied sequence such as this one using waveform correlation to find even more events. We benchmark our results against previously published results describing relocations of regional catalog data. Before starting this project, we had examples where with just a few stations at far-regional distances, waveform correlation combined with double difference did and impressive job of detection and location events with precision at the few hundred and even tens of meters level.

Investigation of a Cross-Correlation Based Optical Strain Measurement Technique for Detecting radial Growth on a Rotating Disk

NASA Technical Reports Server (NTRS)

Clem, Michelle M.; Woike, Mark R.

2013-01-01

The Aeronautical Sciences Project under NASA`s Fundamental Aeronautics Program is extremely interested in the development of novel measurement technologies, such as optical surface measurements in the internal parts of a flow path, for in situ health monitoring of gas turbine engines. In situ health monitoring has the potential to detect flaws, i.e. cracks in key components, such as engine turbine disks, before the flaws lead to catastrophic failure. In the present study, a cross-correlation imaging technique is investigated in a proof-of-concept study as a possible optical technique to measure the radial growth and strain field on an already cracked sub-scale turbine engine disk under loaded conditions in the NASA Glenn Research Center`s High Precision Rotordynamics Laboratory. The optical strain measurement technique under investigation offers potential fault detection using an applied high-contrast random speckle pattern and imaging the pattern under unloaded and loaded conditions with a CCD camera. Spinning the cracked disk at high speeds induces an external load, resulting in a radial growth of the disk of approximately 50.0-im in the flawed region and hence, a localized strain field. When imaging the cracked disk under static conditions, the disk will be undistorted; however, during rotation the cracked region will grow radially, thus causing the applied particle pattern to be .shifted`. The resulting particle displacements between the two images will then be measured using the two-dimensional cross-correlation algorithms implemented in standard Particle Image Velocimetry (PIV) software to track the disk growth, which facilitates calculation of the localized strain field. In order to develop and validate this optical strain measurement technique an initial proof-of-concept experiment is carried out in a controlled environment. Using PIV optimization principles and guidelines, three potential speckle patterns, for future use on the rotating disk, are developed

Rapid and Robust Cross-Correlation-Based Seismic Phase Identification Using an Approximate Nearest Neighbor Method

NASA Astrophysics Data System (ADS)

Tibi, R.; Young, C. J.; Gonzales, A.; Ballard, S.; Encarnacao, A. V.

2016-12-01

The matched filtering technique involving the cross-correlation of a waveform of interest with archived signals from a template library has proven to be a powerful tool for detecting events in regions with repeating seismicity. However, waveform correlation is computationally expensive, and therefore impractical for large template sets unless dedicated distributed computing hardware and software are used. In this study, we introduce an Approximate Nearest Neighbor (ANN) approach that enables the use of very large template libraries for waveform correlation without requiring a complex distributed computing system. Our method begins with a projection into a reduced dimensionality space based on correlation with a randomized subset of the full template archive. Searching for a specified number of nearest neighbors is accomplished by using randomized K-dimensional trees. We used the approach to search for matches to each of 2700 analyst-reviewed signal detections reported for May 2010 for the IMS station MKAR. The template library in this case consists of a dataset of more than 200,000 analyst-reviewed signal detections for the same station from 2002-2014 (excluding May 2010). Of these signal detections, 60% are teleseismic first P, and 15% regional phases (Pn, Pg, Sn, and Lg). The analyses performed on a standard desktop computer shows that the proposed approach performs the search of the large template libraries about 20 times faster than the standard full linear search, while achieving recall rates greater than 80%, with the recall rate increasing for higher correlation values. To decide whether to confirm a match, we use a hybrid method involving a cluster approach for queries with two or more matches, and correlation score for single matches. Of the signal detections that passed our confirmation process, 52% were teleseismic first P, and 30% were regional phases.

Correlating nuclear frequencies by two-dimensional ELDOR-detected NMR spectroscopy.

PubMed

Kaminker, Ilia; Wilson, Tiffany D; Savelieff, Masha G; Hovav, Yonatan; Zimmermann, Herbert; Lu, Yi; Goldfarb, Daniella

2014-03-01

ELDOR (Electron Double Resonance)-detected NMR (EDNMR) is a pulse EPR experiment that is used to measure the transition frequencies of nuclear spins coupled to electron spins. These frequencies are further used to determine hyperfine and quadrupolar couplings, which are signatures of the electronic and spatial structures of paramagnetic centers. In recent years, EDNMR has been shown to be particularly useful at high fields/high frequencies, such as W-band (∼95 GHz, ∼3.5 T), for low γ quadrupolar nuclei. Although at high fields the nuclear Larmor frequencies are usually well resolved, the limited resolution of EDNMR still remains a major concern. In this work we introduce a two dimensional, triple resonance, correlation experiment based on the EDNMR pulse sequence, which we term 2D-EDNMR. This experiment allows circumventing the resolution limitation by spreading the signals in two dimensions and the observed correlations help in the assignment of the signals. First we demonstrate the utility of the 2D-EDNMR experiment on a nitroxide spin label, where we observe correlations between (14)N nuclear frequencies. Negative cross-peaks appear between lines belonging to different MS electron spin manifolds. We resolved two independent correlation patterns for nuclear frequencies arising from the EPR transitions corresponding to the (14)N mI=0 and mI=-1 nuclear spin states, which severely overlap in the one dimensional EDNMR spectrum. The observed correlations could be accounted for by considering changes in the populations of energy levels that S=1/2, I=1 spin systems undergo during the pulse sequence. In addition to these negative cross-peaks, positive cross-peaks appear as well. We present a theoretical model based on the Liouville equation and use it to calculate the time evolution of populations of the various energy levels during the 2D-EDNMR experiment and generated simulated 2D-EDMR spectra. These calculations show that the positive cross-peaks appear due to

Aptamer-Based Paper Strip Sensor for Detecting Vibrio fischeri.

PubMed

Shin, Woo-Ri; Sekhon, Simranjeet Singh; Rhee, Sung-Keun; Ko, Jung Ho; Ahn, Ji-Young; Min, Jiho; Kim, Yang-Hoon

2018-05-14

Aptamer-based paper strip sensor for detecting Vibrio fischeri was developed. Our method was based on the aptamer sandwich assay between whole live cells, V. fischeri and DNA aptamer probes. Following 9 rounds of Cell-SELEX and one of the negative-SELEX, V. fischeri Cell Aptamer (VFCA)-02 and -03 were isolated, with the former showing approximately 10-fold greater avidity (in the subnanomolar range) for the target cells when arrayed on a surface. The colorimetric response of a paper sensor based on VFCA-02 was linear in the range of 4 × 10 1 to 4 × 10 5 CFU/mL of target cell by using scanning reader. The linear regression correlation coefficient ( R 2 ) was 0.9809. This system shows promise for use in aptamer-conjugated gold nanoparticle probes in paper strip format for in-field detection of marine bioindicating bacteria.

Effects of threshold on single-target detection by using modified amplitude-modulated joint transform correlator

NASA Astrophysics Data System (ADS)

Kaewkasi, Pitchaya; Widjaja, Joewono; Uozumi, Jun

2007-03-01

Effects of threshold value on detection performance of the modified amplitude-modulated joint transform correlator are quantitatively studied using computer simulation. Fingerprint and human face images are used as test scenes in the presence of noise and a contrast difference. Simulation results demonstrate that this correlator improves detection performance for both types of image used, but moreso for human face images. Optimal detection of low-contrast human face images obscured by strong noise can be obtained by selecting an appropriate threshold value.

Correlation-based motion vector processing with adaptive interpolation scheme for motion-compensated frame interpolation.

PubMed

Huang, Ai-Mei; Nguyen, Truong

2009-04-01

In this paper, we address the problems of unreliable motion vectors that cause visual artifacts but cannot be detected by high residual energy or bidirectional prediction difference in motion-compensated frame interpolation. A correlation-based motion vector processing method is proposed to detect and correct those unreliable motion vectors by explicitly considering motion vector correlation in the motion vector reliability classification, motion vector correction, and frame interpolation stages. Since our method gradually corrects unreliable motion vectors based on their reliability, we can effectively discover the areas where no motion is reliable to be used, such as occlusions and deformed structures. We also propose an adaptive frame interpolation scheme for the occlusion areas based on the analysis of their surrounding motion distribution. As a result, the interpolated frames using the proposed scheme have clearer structure edges and ghost artifacts are also greatly reduced. Experimental results show that our interpolated results have better visual quality than other methods. In addition, the proposed scheme is robust even for those video sequences that contain multiple and fast motions.

[Detection of Heart Rate of Fetal ECG Based on STFT and BSS].

PubMed

Wang, Xu; Cai, Kun

2016-01-01

Changes in heart rate of fetal is function regulating performance of the circulatory system and the central nervous system, it is significant to detect heart rate of fetus in perinatal fetal. This paper puts forward the fetal heart rate detection method based on short time Fourier transform and blind source separation. First of all, the mixed ECG signal was preprocessed, and then the wavelet transform technique was used to separate the fetal ECG signal with noise from mixed ECG signal, after that, the short-time Fourier transform and the blind separation were carried on it, and then calculated the correlation coefficient of it, Finally, An independent component that it has strongest correlation with the original signal was selected to make FECG peak detection and calculated the fetal instantaneous heart rate. The experimental results show that the method can improve the detection rate of the FECG peak (R), and it has high accuracy in fixing peak(R) location in the case of low signal-noise ratio.

Tsunami detection by high-frequency radar in British Columbia: performance assessment of the time-correlation algorithm for synthetic and real events

NASA Astrophysics Data System (ADS)

Guérin, Charles-Antoine; Grilli, Stéphan T.; Moran, Patrick; Grilli, Annette R.; Insua, Tania L.

2018-05-01

The authors recently proposed a new method for detecting tsunamis using high-frequency (HF) radar observations, referred to as "time-correlation algorithm" (TCA; Grilli et al. Pure Appl Geophys 173(12):3895-3934, 2016a, 174(1): 3003-3028, 2017). Unlike standard algorithms that detect surface current patterns, the TCA is based on analyzing space-time correlations of radar signal time series in pairs of radar cells, which does not require inverting radial surface currents. This was done by calculating a contrast function, which quantifies the change in pattern of the mean correlation between pairs of neighboring cells upon tsunami arrival, with respect to a reference correlation computed in the recent past. In earlier work, the TCA was successfully validated based on realistic numerical simulations of both the radar signal and tsunami wave trains. Here, this algorithm is adapted to apply to actual data from a HF radar installed in Tofino, BC, for three test cases: (1) a simulated far-field tsunami generated in the Semidi Subduction Zone in the Aleutian Arc; (2) a simulated near-field tsunami from a submarine mass failure on the continental slope off of Tofino; and (3) an event believed to be a meteotsunami, which occurred on October 14th, 2016, off of the Pacific West Coast and was measured by the radar. In the first two cases, the synthetic tsunami signal is superimposed onto the radar signal by way of a current memory term; in the third case, the tsunami signature is present within the radar data. In light of these test cases, we develop a detection methodology based on the TCA, using a correlation contrast function, and show that in all three cases the algorithm is able to trigger a timely early warning.

Detecting PM2.5's Correlations between Neighboring Cities Using a Time-Lagged Cross-Correlation Coefficient.

PubMed

Wang, Fang; Wang, Lin; Chen, Yuming

2017-08-31

In order to investigate the time-dependent cross-correlations of fine particulate (PM2.5) series among neighboring cities in Northern China, in this paper, we propose a new cross-correlation coefficient, the time-lagged q-L dependent height crosscorrelation coefficient (denoted by p q (τ, L)), which incorporates the time-lag factor and the fluctuation amplitude information into the analogous height cross-correlation analysis coefficient. Numerical tests are performed to illustrate that the newly proposed coefficient ρ q (τ, L) can be used to detect cross-correlations between two series with time lags and to identify different range of fluctuations at which two series possess cross-correlations. Applying the new coefficient to analyze the time-dependent cross-correlations of PM2.5 series between Beijing and the three neighboring cities of Tianjin, Zhangjiakou, and Baoding, we find that time lags between the PM2.5 series with larger fluctuations are longer than those between PM2.5 series withsmaller fluctuations. Our analysis also shows that cross-correlations between the PM2.5 series of two neighboring cities are significant and the time lags between two PM2.5 series of neighboring cities are significantly non-zero. These findings providenew scientific support on the view that air pollution in neighboring cities can affect one another not simultaneously but with a time lag.

Aircraft target detection algorithm based on high resolution spaceborne SAR imagery

NASA Astrophysics Data System (ADS)

Zhang, Hui; Hao, Mengxi; Zhang, Cong; Su, Xiaojing

2018-03-01

In this paper, an image classification algorithm for airport area is proposed, which based on the statistical features of synthetic aperture radar (SAR) images and the spatial information of pixels. The algorithm combines Gamma mixture model and MRF. The algorithm using Gamma mixture model to obtain the initial classification result. Pixel space correlation based on the classification results are optimized by the MRF technique. Additionally, morphology methods are employed to extract airport (ROI) region where the suspected aircraft target samples are clarified to reduce the false alarm and increase the detection performance. Finally, this paper presents the plane target detection, which have been verified by simulation test.

Serum creatinine detection by a conducting-polymer-based electrochemical sensor to identify allograft dysfunction.

PubMed

Wei, Fang; Cheng, Scott; Korin, Yael; Reed, Elaine F; Gjertson, David; Ho, Chih-ming; Gritsch, H Albin; Veale, Jeffrey

2012-09-18

Kidney transplant recipients who have abnormally high creatinine levels in their blood often have allograft dysfunction secondary to rejection. Creatinine has become the preferred marker for renal dysfunction and is readily available in hospital clinical settings. We developed a rapid and accurate polymer-based electrochemical point-of-care (POC) assay for creatinine detection from whole blood to identify allograft dysfunction. The creatinine concentrations of 19 blood samples from transplant recipients were measured directly from clinical serum samples by the conducting polymer-based electrochemical (EC) sensor arrays. These measurements were compared to the traditional clinical laboratory assay. The time required for detection was <5 min from sample loading. Sensitivity of the detection was found to be 0.46 mg/dL of creatinine with only 40 μL sample in the creatinine concentration range of 0 mg/dL to 11.33 mg/dL. Signal levels that were detected electrochemically correlated closely with the creatinine blood concentration detected by the UCLA Ronald Reagan Medical Center traditional clinical laboratory assay (correlation coefficient = 0.94). This work is encouraging for the development of a rapid and accurate POC device for measuring creatinine levels in whole blood.

Rapid and Robust Cross-Correlation-Based Seismic Signal Identification Using an Approximate Nearest Neighbor Method

DOE PAGES

Tibi, Rigobert; Young, Christopher; Gonzales, Antonio; ...

2017-07-04

The matched filtering technique that uses the cross correlation of a waveform of interest with archived signals from a template library has proven to be a powerful tool for detecting events in regions with repeating seismicity. However, waveform correlation is computationally expensive and therefore impractical for large template sets unless dedicated distributed computing hardware and software are used. In this paper, we introduce an approximate nearest neighbor (ANN) approach that enables the use of very large template libraries for waveform correlation. Our method begins with a projection into a reduced dimensionality space, based on correlation with a randomized subset ofmore » the full template archive. Searching for a specified number of nearest neighbors for a query waveform is accomplished by iteratively comparing it with the neighbors of its immediate neighbors. We used the approach to search for matches to each of ~2300 analyst-reviewed signal detections reported in May 2010 for the International Monitoring System station MKAR. The template library in this case consists of a data set of more than 200,000 analyst-reviewed signal detections for the same station from February 2002 to July 2016 (excluding May 2010). Of these signal detections, 73% are teleseismic first P and 17% regional phases (Pn, Pg, Sn, and Lg). Finally, the analyses performed on a standard desktop computer show that the proposed ANN approach performs a search of the large template libraries about 25 times faster than the standard full linear search and achieves recall rates greater than 80%, with the recall rate increasing for higher correlation thresholds.« less

Rapid and Robust Cross-Correlation-Based Seismic Signal Identification Using an Approximate Nearest Neighbor Method

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

Tibi, Rigobert; Young, Christopher; Gonzales, Antonio

The matched filtering technique that uses the cross correlation of a waveform of interest with archived signals from a template library has proven to be a powerful tool for detecting events in regions with repeating seismicity. However, waveform correlation is computationally expensive and therefore impractical for large template sets unless dedicated distributed computing hardware and software are used. In this paper, we introduce an approximate nearest neighbor (ANN) approach that enables the use of very large template libraries for waveform correlation. Our method begins with a projection into a reduced dimensionality space, based on correlation with a randomized subset ofmore » the full template archive. Searching for a specified number of nearest neighbors for a query waveform is accomplished by iteratively comparing it with the neighbors of its immediate neighbors. We used the approach to search for matches to each of ~2300 analyst-reviewed signal detections reported in May 2010 for the International Monitoring System station MKAR. The template library in this case consists of a data set of more than 200,000 analyst-reviewed signal detections for the same station from February 2002 to July 2016 (excluding May 2010). Of these signal detections, 73% are teleseismic first P and 17% regional phases (Pn, Pg, Sn, and Lg). Finally, the analyses performed on a standard desktop computer show that the proposed ANN approach performs a search of the large template libraries about 25 times faster than the standard full linear search and achieves recall rates greater than 80%, with the recall rate increasing for higher correlation thresholds.« less

Neural Correlates of Coherence-Break Detection during Reading of Narratives

ERIC Educational Resources Information Center

Helder, Anne; van den Broek, Paul; Karlsson, Josefine; Van Leijenhorst, Linda

2017-01-01

This functional magnetic resonance imaging study examined the neural correlates of coherence-break detection during reading in the context of a contradiction paradigm. Young adults (N = 31, ages 19-27) read short narratives (half contained a break in coherence) that were presented sentence by sentence in a self-paced, slow event-related design.…

Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks.

PubMed

Martens, Marijn B; Houweling, Arthur R; E Tiesinga, Paul H

2017-02-01

Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks. We found that anti-correlation between the number of afferent (in-degree) and efferent (out-degree) synaptic connections of neurons increases stability against pathological bursting, relative to networks where the degrees were either positively correlated or uncorrelated. In the stable network state, stimulation of a few cells could lead to a detectable change in the firing rate. To quantify the ability of networks to detect the stimulation, we used a receiver operating characteristic (ROC) analysis. For a given level of background noise, networks with anti-correlated degrees displayed the lowest false positive rates, and consequently had the highest stimulus detection performance. We propose that anti-correlation in the degree distribution may be a computational strategy employed by sensory cortices to increase the detectability of external stimuli. We show that networks with anti-correlated degrees can in principle be formed by applying learning rules comprised of a combination of spike-timing dependent plasticity, homeostatic plasticity and pruning to networks with uncorrelated degrees. To test our prediction we suggest a novel experimental method to estimate correlations in the degree distribution.

Improving Broadband Displacement Detection with Quantum Correlations

NASA Astrophysics Data System (ADS)

Kampel, N. S.; Peterson, R. W.; Fischer, R.; Yu, P.-L.; Cicak, K.; Simmonds, R. W.; Lehnert, K. W.; Regal, C. A.

2017-04-01

Interferometers enable ultrasensitive measurement in a wide array of applications from gravitational wave searches to force microscopes. The role of quantum mechanics in the metrological limits of interferometers has a rich history, and a large number of techniques to surpass conventional limits have been proposed. In a typical measurement configuration, the trade-off between the probe's shot noise (imprecision) and its quantum backaction results in what is known as the standard quantum limit (SQL). In this work, we investigate how quantum correlations accessed by modifying the readout of the interferometer can access physics beyond the SQL and improve displacement sensitivity. Specifically, we use an optical cavity to probe the motion of a silicon nitride membrane off mechanical resonance, as one would do in a broadband displacement or force measurement, and observe sensitivity better than the SQL dictates for our quantum efficiency. Our measurement illustrates the core idea behind a technique known as variational readout, in which the optical readout quadrature is changed as a function of frequency to improve broadband displacement detection. And, more generally, our result is a salient example of how correlations can aid sensing in the presence of backaction.

Pulse Shape Correlation for Laser Detection and Ranging (LADAR)

DTIC Science & Technology

2010-03-01

with the incoming measured laser pulse [3]. All of these shapes are symmetric. Siegman and Liu’s findings indicate that the pulse is seldom symmetric...of Engineering, Air Force Institute of Technology (AETC), Wright Pat- terson AFB, OH, March 2007. 10. Siegman , Anthony E. Lasers . University Science...Pulse Shape Correlation for Laser Detection and Ranging (LADAR) THESIS Brian T. Deas, Major, USAF AFIT/GE/ENG/10-07 DEPARTMENT OF THE AIR FORCE AIR

Cellular telephone-based radiation detection instrument

DOEpatents

Craig, William W [Pittsburg, CA; Labov, Simon E [Berkeley, CA

2011-06-14

A network of radiation detection instruments, each having a small solid state radiation sensor module integrated into a cellular phone for providing radiation detection data and analysis directly to a user. The sensor module includes a solid-state crystal bonded to an ASIC readout providing a low cost, low power, light weight compact instrument to detect and measure radiation energies in the local ambient radiation field. In particular, the photon energy, time of event, and location of the detection instrument at the time of detection is recorded for real time transmission to a central data collection/analysis system. The collected data from the entire network of radiation detection instruments are combined by intelligent correlation/analysis algorithms which map the background radiation and detect, identify and track radiation anomalies in the region.

Technical Note: Detection of gas bubble leakage via correlation of water column multibeam images

NASA Astrophysics Data System (ADS)

Schneider von Deimling, J.; Papenberg, C.

2011-07-01

Hydroacoustic detection of natural gas release from the seafloor has been conducted in the past by using singlebeam echosounders. In contrast modern multibeam swath mapping systems allow much wider coverage, higher resolution, and offer 3-D spatial correlation. However, up to the present, the extremely high data rate hampers water column backscatter investigations. More sophisticated visualization and processing techniques for water column backscatter analysis are still under development. We here present such water column backscattering data gathered with a 50 kHz prototype multibeam system. Water column backscattering data is presented in videoframes grabbed over 75 s and a "re-sorted" singlebeam presentation. Thus individual gas bubbles rising from the 24 m deep seafloor clearly emerge in the acoustic images and rise velocities can be determined. A sophisticated processing scheme is introduced to identify those rising gas bubbles in the hydroacoustic data. It applies a cross-correlation technique similar to that used in Particle Imaging Velocimetry (PIV) to the acoustic backscatter images. Tempo-spatial drift patterns of the bubbles are assessed and match very well measured and theoretical rise patterns. The application of this processing scheme to our field data gives impressive results with respect to unambiguous bubble detection and remote bubble rise velocimetry. The method can identify and exclude the main driver for misinterpretations, i.e. fish-mediated echoes. Even though image-based cross-correlation techniques are well known in the field of fluid mechanics for high resolution and non-inversive current flow field analysis, this technique was never applied in the proposed sense for an acoustic bubble detector.

Technical Note: Detection of gas bubble leakage via correlation of water column multibeam images

NASA Astrophysics Data System (ADS)

Schneider von Deimling, J.; Papenberg, C.

2012-03-01

Hydroacoustic detection of natural gas release from the seafloor has been conducted in the past by using singlebeam echosounders. In contrast, modern multibeam swath mapping systems allow much wider coverage, higher resolution, and offer 3-D spatial correlation. Up to the present, the extremely high data rate hampers water column backscatter investigations and more sophisticated visualization and processing techniques are needed. Here, we present water column backscatter data acquired with a 50 kHz prototype multibeam system over a period of 75 seconds. Display types are of swath-images as well as of a "re-sorted" singlebeam presentation. Thus, individual and/or groups of gas bubbles rising from the 24 m deep seafloor clearly emerge in the acoustic images, making it possible to estimate rise velocities. A sophisticated processing scheme is introduced to identify those rising gas bubbles in the hydroacoustic data. We apply a cross-correlation technique adapted from particle imaging velocimetry (PIV) to the acoustic backscatter images. Temporal and spatial drift patterns of the bubbles are assessed and are shown to match very well to measured and theoretical rise patterns. The application of this processing to our field data gives clear results with respect to unambiguous bubble detection and remote bubble rise velocimetry. The method can identify and exclude the main source of misinterpretations, i.e. fish-mediated echoes. Although image-based cross-correlation techniques are well known in the field of fluid mechanics for high resolution and non-inversive current flow field analysis, we present the first application of this technique as an acoustic bubble detector.

Semiautomated tremor detection using a combined cross-correlation and neural network approach

USGS Publications Warehouse

Horstmann, Tobias; Harrington, Rebecca M.; Cochran, Elizabeth S.

2013-01-01

Despite observations of tectonic tremor in many locations around the globe, the emergent phase arrivals, low‒amplitude waveforms, and variable event durations make automatic detection a nontrivial task. In this study, we employ a new method to identify tremor in large data sets using a semiautomated technique. The method first reduces the data volume with an envelope cross‒correlation technique, followed by a Self‒Organizing Map (SOM) algorithm to identify and classify event types. The method detects tremor in an automated fashion after calibrating for a specific data set, hence we refer to it as being “semiautomated”. We apply the semiautomated detection algorithm to a newly acquired data set of waveforms from a temporary deployment of 13 seismometers near Cholame, California, from May 2010 to July 2011. We manually identify tremor events in a 3 week long test data set and compare to the SOM output and find a detection accuracy of 79.5%. Detection accuracy improves with increasing signal‒to‒noise ratios and number of available stations. We find detection completeness of 96% for tremor events with signal‒to‒noise ratios above 3 and optimal results when data from at least 10 stations are available. We compare the SOM algorithm to the envelope correlation method of Wech and Creager and find the SOM performs significantly better, at least for the data set examined here. Using the SOM algorithm, we detect 2606 tremor events with a cumulative signal duration of nearly 55 h during the 13 month deployment. Overall, the SOM algorithm is shown to be a flexible new method that utilizes characteristics of the waveforms to identify tremor from noise or other seismic signals.

Semiautomated tremor detection using a combined cross-correlation and neural network approach

NASA Astrophysics Data System (ADS)

Horstmann, T.; Harrington, R. M.; Cochran, E. S.

2013-09-01

Despite observations of tectonic tremor in many locations around the globe, the emergent phase arrivals, low-amplitude waveforms, and variable event durations make automatic detection a nontrivial task. In this study, we employ a new method to identify tremor in large data sets using a semiautomated technique. The method first reduces the data volume with an envelope cross-correlation technique, followed by a Self-Organizing Map (SOM) algorithm to identify and classify event types. The method detects tremor in an automated fashion after calibrating for a specific data set, hence we refer to it as being "semiautomated". We apply the semiautomated detection algorithm to a newly acquired data set of waveforms from a temporary deployment of 13 seismometers near Cholame, California, from May 2010 to July 2011. We manually identify tremor events in a 3 week long test data set and compare to the SOM output and find a detection accuracy of 79.5%. Detection accuracy improves with increasing signal-to-noise ratios and number of available stations. We find detection completeness of 96% for tremor events with signal-to-noise ratios above 3 and optimal results when data from at least 10 stations are available. We compare the SOM algorithm to the envelope correlation method of Wech and Creager and find the SOM performs significantly better, at least for the data set examined here. Using the SOM algorithm, we detect 2606 tremor events with a cumulative signal duration of nearly 55 h during the 13 month deployment. Overall, the SOM algorithm is shown to be a flexible new method that utilizes characteristics of the waveforms to identify tremor from noise or other seismic signals.

Machine Learning Based Malware Detection

DTIC Science & Technology

2015-05-18

A TRIDENT SCHOLAR PROJECT REPORT NO. 440 Machine Learning Based Malware Detection by Midshipman 1/C Zane A. Markel, USN...COVERED (From - To) 4. TITLE AND SUBTITLE Machine Learning Based Malware Detection 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...suitably be projected into realistic performance. This work explores several aspects of machine learning based malware detection . First, we

A Load-Based Temperature Prediction Model for Anomaly Detection

NASA Astrophysics Data System (ADS)

Sobhani, Masoud

Electric load forecasting, as a basic requirement for the decision-making in power utilities, has been improved in various aspects in the past decades. Many factors may affect the accuracy of the load forecasts, such as data quality, goodness of the underlying model and load composition. Due to the strong correlation between the input variables (e.g., weather and calendar variables) and the load, the quality of input data plays a vital role in forecasting practices. Even if the forecasting model were able to capture most of the salient features of the load, a low quality input data may result in inaccurate forecasts. Most of the data cleansing efforts in the load forecasting literature have been devoted to the load data. Few studies focused on weather data cleansing for load forecasting. This research proposes an anomaly detection method for the temperature data. The method consists of two components: a load-based temperature prediction model and a detection technique. The effectiveness of the proposed method is demonstrated through two case studies: one based on the data from the Global Energy Forecasting Competition 2014, and the other based on the data published by ISO New England. The results show that by removing the detected observations from the original input data, the final load forecast accuracy is enhanced.

Real-Time Model-Based Leak-Through Detection within Cryogenic Flow Systems

NASA Technical Reports Server (NTRS)

Walker, M.; Figueroa, F.

2015-01-01

The timely detection of leaks within cryogenic fuel replenishment systems is of significant importance to operators on account of the safety and economic impacts associated with material loss and operational inefficiencies. Associated loss in control of pressure also effects the stability and ability to control the phase of cryogenic fluids during replenishment operations. Current research dedicated to providing Prognostics and Health Management (PHM) coverage of such cryogenic replenishment systems has focused on the detection of leaks to atmosphere involving relatively simple model-based diagnostic approaches that, while effective, are unable to isolate the fault to specific piping system components. The authors have extended this research to focus on the detection of leaks through closed valves that are intended to isolate sections of the piping system from the flow and pressurization of cryogenic fluids. The described approach employs model-based detection of leak-through conditions based on correlations of pressure changes across isolation valves and attempts to isolate the faults to specific valves. Implementation of this capability is enabled by knowledge and information embedded in the domain model of the system. The approach has been used effectively to detect such leak-through faults during cryogenic operational testing at the Cryogenic Testbed at NASA's Kennedy Space Center.

Detection of Prostate Cancer: Quantitative Multiparametric MR Imaging Models Developed Using Registered Correlative Histopathology.

PubMed

Metzger, Gregory J; Kalavagunta, Chaitanya; Spilseth, Benjamin; Bolan, Patrick J; Li, Xiufeng; Hutter, Diane; Nam, Jung W; Johnson, Andrew D; Henriksen, Jonathan C; Moench, Laura; Konety, Badrinath; Warlick, Christopher A; Schmechel, Stephen C; Koopmeiners, Joseph S

2016-06-01

Purpose To develop multiparametric magnetic resonance (MR) imaging models to generate a quantitative, user-independent, voxel-wise composite biomarker score (CBS) for detection of prostate cancer by using coregistered correlative histopathologic results, and to compare performance of CBS-based detection with that of single quantitative MR imaging parameters. Materials and Methods Institutional review board approval and informed consent were obtained. Patients with a diagnosis of prostate cancer underwent multiparametric MR imaging before surgery for treatment. All MR imaging voxels in the prostate were classified as cancer or noncancer on the basis of coregistered histopathologic data. Predictive models were developed by using more than one quantitative MR imaging parameter to generate CBS maps. Model development and evaluation of quantitative MR imaging parameters and CBS were performed separately for the peripheral zone and the whole gland. Model accuracy was evaluated by using the area under the receiver operating characteristic curve (AUC), and confidence intervals were calculated with the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for the multiparametric model and the single best-performing quantitative MR imaging parameter at the individual level and in aggregate. Results Quantitative T2, apparent diffusion coefficient (ADC), volume transfer constant (K(trans)), reflux rate constant (kep), and area under the gadolinium concentration curve at 90 seconds (AUGC90) were significantly different between cancer and noncancer voxels (P < .001), with ADC showing the best accuracy (peripheral zone AUC, 0.82; whole gland AUC, 0.74). Four-parameter models demonstrated the best performance in both the peripheral zone (AUC, 0.85; P = .010 vs ADC alone) and whole gland (AUC, 0.77; P = .043 vs ADC alone). Individual-level analysis showed statistically significant improvement in AUC in 82% (23 of 28) and 71% (24 of 34

Target Detection of Quantum Illumination Receiver Based on Photon-subtracted Entanglement State

NASA Astrophysics Data System (ADS)

Chi, Jiao; Liu, HongJun; Huang, Nan; Wang, ZhaoLu

2017-12-01

We theoretically propose a quantum illumination receiver based on the ideal photon-subtracted two-mode squeezed state (PSTMSS) to efficiently detect the noise-hidden target. This receiver is generated by applying an optical parametric amplifier (OPA) to the cross correlation detection. With analyzing the output performance, it is found that OPA as a preposition technology of the receiver can contribute to the PSTMSS by significantly reducing the error probability than that of the general two-mode squeezed state (TMSS). Comparing with TMSS, the signal-to-noise ratio of quantum illumination based on ideal PSTMSS and OPA is improved more than 4 dB under an optimal gain of OPA. This work may provide a potential improvement in the application of accurate target detection when two kinds of resource have the identical real squeezing parameter.

Improving the resolution in proton-detected through-space heteronuclear multiple quantum correlation NMR spectroscopy.

PubMed

Shen, Ming; Trébosc, J; Lafon, O; Pourpoint, F; Hu, Bingwen; Chen, Qun; Amoureux, J-P

2014-08-01

Connectivities and proximities between protons and low-gamma nuclei can be probed in solid-state NMR spectroscopy using two-dimensional (2D) proton-detected heteronuclear correlation, through Heteronuclear Multiple Quantum Correlation (HMQC) pulse sequence. The indirect detection via protons dramatically enhances the sensitivity. However, the spectra are often broadened along the indirect F1 dimension by the decay of heteronuclear multiple-quantum coherences under the strong (1)H-(1)H dipolar couplings. This work presents a systematic comparison of the performances of various decoupling schemes during the indirect t1 evolution period of dipolar-mediated HMQC (D-HMQC) experiment. We demonstrate that (1)H-(1)H dipolar decoupling sequences during t1, such as symmetry-based schemes, phase-modulated Lee-Goldburg (PMLG) and Decoupling Using Mind-Boggling Optimization (DUMBO), provide better resolution than continuous wave (1)H irradiation. We also report that high resolution requires the preservation of (1)H isotropic chemical shifts during the decoupling sequences. When observing indirectly broad spectra presenting numerous spinning sidebands, the D-HMQC sequence must be fully rotor-synchronized owing to the rotor-synchronized indirect sampling and dipolar recoupling sequence employed. In this case, we propose a solution to reduce artefact sidebands caused by the modulation of window delays before and after the decoupling application during the t1 period. Moreover, we show that (1)H-(1)H dipolar decoupling sequence using Smooth Amplitude Modulation (SAM) minimizes the t1-noise. The performances of the various decoupling schemes are assessed via numerical simulations and compared to 2D (1)H-{(13)C} D-HMQC experiments on [U-(13)C]-L-histidine⋅HCl⋅H2O at various magnetic fields and Magic Angle spinning (MAS) frequencies. Great resolution and sensitivity enhancements resulting from decoupling during t1 period enable the detection of heteronuclear correlation between

Correlation-based perfusion mapping using time-resolved MR angiography: A feasibility study for patients with suspicions of steno-occlusive craniocervical arteries.

PubMed

Nam, Yoonho; Jang, Jinhee; Park, Sonya Youngju; Choi, Hyun Seok; Jung, So-Lyung; Ahn, Kook-Jin; Kim, Bum-Soo

2018-05-22

To explore the feasibility of using correlation-based time-delay (CTD) maps produced from time-resolved MR angiography (TRMRA) to diagnose perfusion abnormalities in patients suspected to have steno-occlusive lesions in the craniocervical arteries. Twenty-seven patients who were suspected to have steno-occlusive lesions in the craniocervical arteries underwent both TRMRA and brain single-photon emission computed tomography (SPECT). TRMRA was performed on the supra-aortic area after intravenous injection of a 0.03 mmol/kg gadolinium-based contrast agent. Time-to-peak (TTP) maps and CTD maps of the brain were automatically generated from TRMRA data, and their quality was assessed. Detection of perfusion abnormalities was compared between CTD maps and the time-series maximal intensity projection (MIP) images from TRMRA and TTP maps. Correlation coefficients between quantitative changes in SPECT and parametric maps for the abnormal perfusion areas were calculated. The CTD maps were of significantly superior quality than TTP maps (p < 0.01). For perfusion abnormality detection, CTD maps (kappa 0.84, 95% confidence interval [CI] 0.67-1.00) showed better agreement with SPECT than TTP maps (0.66, 0.46-0.85). For perfusion deficit detection, CTD maps showed higher accuracy (85.2%, 95% CI 66.3-95.8) than MIP images (66.7%, 46-83.5), with marginal significance (p = 0.07). In abnormal perfusion areas, correlation coefficients between SPECT and CTD (r = 0.74, 95% CI 0.34-0.91) were higher than those between SPECT and TTP (r = 0.66, 0.20-0.88). CTD maps generated from TRMRA were of high quality and offered good diagnostic performance for detecting perfusion abnormalities associated with steno-occlusive arterial lesions in the craniocervical area. • Generation of perfusion parametric maps from time-resolved MR angiography is clinically useful. • Correlation-based delay maps can be used to detect perfusion abnormalities associated with steno-occlusive craniocervical arteries

Fluorescence correlation spectroscopy: Ultrasensitive detection in clear and turbid media

NASA Astrophysics Data System (ADS)

Tahari, Abdel Kader

In this work, I describe the development of a simple, inexpensive, and powerful alternative technique to detect and analyze, without enrichment, extremely low concentrations of cells, bacteria, viruses, and protein aggregates in turbid fluids for clinical and biotechnological applications. The anticipated applications of this technique are many. They range from the determination of the somatic cell count in milk for the dairy industry, to the enumeration and characterization of microorganisms in environmental microbiology and the food industry, and to the fast and ultrasensitive detection of protein aggregates for the diagnosis of Alzheimer's and other neurodegenerative diseases in clinical medicine. A prototype instrument has been built and allowed the detection and quantification of particles down to a few per milliliter in short scanning times. It consists of a small microscope that has a horizontal geometry and a mechanical instrument that holds a cylindrical cuvette (1 cm in diameter) with two motors that provide a rotational and a slower vertical inversion motions. The illumination focus is centered about 200 mum from the wall of the cuvette inside the sample. The total volume that is explored is large (˜1ml/min for bright particles). The data is analyzed with a correlation filter program based on particle passage pattern recognition. I will also describe further work on improving the sensitivity of the technique, expanding it for multiple-species discrimination and enumeration, and testing the prototype device in actual clinical and biotechnological applications. The main clinical application of this project seeks to establish conditions and use this new technique to quantify and size-analyze oligomeric complexes of the Alzheimer's disease beta-peptide in cerebrospinal fluid and other body fluids as a molecular biomarker for persons at risk of Alzheimer's disease dementia. The technology could potentially be extended to the diagnosis and therapeutic

Quantitative Detection and Genotyping of Helicobacter pylori from Stool using Droplet Digital PCR Reveals Variation in Bacterial Loads that Correlates with cagA Virulence Gene Carriage.

PubMed

Talarico, Sarah; Safaeian, Mahboobeh; Gonzalez, Paula; Hildesheim, Allan; Herrero, Rolando; Porras, Carolina; Cortes, Bernal; Larson, Ann; Fang, Ferric C; Salama, Nina R

2016-08-01

Epidemiologic studies of the carcinogenic stomach bacterium Helicobacter pylori have been limited by the lack of noninvasive detection and genotyping methods. We developed a new stool-based method for detection, quantification, and partial genotyping of H. pylori using droplet digital PCR (ddPCR), which allows for increased sensitivity and absolute quantification by PCR partitioning. Stool-based ddPCR assays for H. pylori 16S gene detection and cagA virulence gene typing were tested using a collection of 50 matched stool and serum samples from Costa Rican volunteers and 29 H. pylori stool antigen-tested stool samples collected at a US hospital. The stool-based H. pylori 16S ddPCR assay had a sensitivity of 84% and 100% and a specificity of 100% and 71% compared to serology and stool antigen tests, respectively. The stool-based cagA genotyping assay detected cagA in 22 (88%) of 25 stools from CagA antibody-positive individuals and four (16%) of 25 stools from CagA antibody-negative individuals from Costa Rica. All 26 of these samples had a Western-type cagA allele. Presence of serum CagA antibodies was correlated with a significantly higher load of H. pylori in the stool. The stool-based ddPCR assays are a sensitive, noninvasive method for detection, quantification, and partial genotyping of H. pylori. The quantitative nature of ddPCR-based H. pylori detection revealed significant variation in bacterial load among individuals that correlates with presence of the cagA virulence gene. These stool-based ddPCR assays will facilitate future population-based epidemiologic studies of this important human pathogen. © 2015 John Wiley & Sons Ltd.

Perseveration effects in detection tasks with correlated decision intervals. [applied to pilot collision avoidance

NASA Technical Reports Server (NTRS)

Gai, E. G.; Curry, R. E.

1978-01-01

An investigation of the behavior of the human decisionmaker is described for a task related to the problem of a pilot using a traffic situation display to avoid collisions. This sequential signal detection task is characterized by highly correlated signals with time varying strength. Experimental results are presented and the behavior of the observers is analyzed using the theory of Markov processes and classical signal detection theory. Mathematical models are developed which describe the main result of the experiment: that correlation in sequential signals induced perseveration in the observer response and a strong tendency to repeat their previous decision, even when they were wrong.

Cross-correlation detection and analysis for California's electricity market based on analogous multifractal analysis

NASA Astrophysics Data System (ADS)

Wang, Fang; Liao, Gui-ping; Li, Jian-hui; Zou, Rui-biao; Shi, Wen

2013-03-01

A novel method, which we called the analogous multifractal cross-correlation analysis, is proposed in this paper to study the multifractal behavior in the power-law cross-correlation between price and load in California electricity market. In addition, a statistic ρAMF -XA, which we call the analogous multifractal cross-correlation coefficient, is defined to test whether the cross-correlation between two given signals is genuine or not. Our analysis finds that both the price and load time series in California electricity market express multifractal nature. While, as indicated by the ρAMF -XA statistical test, there is a huge difference in the cross-correlation behavior between the years 1999 and 2000 in California electricity markets.

Cross-correlation detection and analysis for California's electricity market based on analogous multifractal analysis.

PubMed

Wang, Fang; Liao, Gui-ping; Li, Jian-hui; Zou, Rui-biao; Shi, Wen

2013-03-01

A novel method, which we called the analogous multifractal cross-correlation analysis, is proposed in this paper to study the multifractal behavior in the power-law cross-correlation between price and load in California electricity market. In addition, a statistic ρAMF-XA, which we call the analogous multifractal cross-correlation coefficient, is defined to test whether the cross-correlation between two given signals is genuine or not. Our analysis finds that both the price and load time series in California electricity market express multifractal nature. While, as indicated by the ρAMF-XA statistical test, there is a huge difference in the cross-correlation behavior between the years 1999 and 2000 in California electricity markets.

Infrared target tracking via weighted correlation filter

NASA Astrophysics Data System (ADS)

He, Yu-Jie; Li, Min; Zhang, JinLi; Yao, Jun-Ping

2015-11-01

Design of an effective target tracker is an important and challenging task for many applications due to multiple factors which can cause disturbance in infrared video sequences. In this paper, an infrared target tracking method under tracking by detection framework based on a weighted correlation filter is presented. This method consists of two parts: detection and filtering. For the detection stage, we propose a sequential detection method for the infrared target based on low-rank representation. For the filtering stage, a new multi-feature weighted function which fuses different target features is proposed, which takes the importance of the different regions into consideration. The weighted function is then incorporated into a correlation filter to compute a confidence map more accurately, in order to indicate the best target location based on the detection results obtained from the first stage. Extensive experimental results on different video sequences demonstrate that the proposed method performs favorably for detection and tracking compared with baseline methods in terms of efficiency and accuracy.

An object correlation and maneuver detection approach for space surveillance

NASA Astrophysics Data System (ADS)

Huang, Jian; Hu, Wei-Dong; Xin, Qin; Du, Xiao-Yong

2012-10-01

Object correlation and maneuver detection are persistent problems in space surveillance and maintenance of a space object catalog. We integrate these two problems into one interrelated problem, and consider them simultaneously under a scenario where space objects only perform a single in-track orbital maneuver during the time intervals between observations. We mathematically formulate this integrated scenario as a maximum a posteriori (MAP) estimation. In this work, we propose a novel approach to solve the MAP estimation. More precisely, the corresponding posterior probability of an orbital maneuver and a joint association event can be approximated by the Joint Probabilistic Data Association (JPDA) algorithm. Subsequently, the maneuvering parameters are estimated by optimally solving the constrained non-linear least squares iterative process based on the second-order cone programming (SOCP) algorithm. The desired solution is derived according to the MAP criterions. The performance and advantages of the proposed approach have been shown by both theoretical analysis and simulation results. We hope that our work will stimulate future work on space surveillance and maintenance of a space object catalog.

Structural neural correlates of multitasking: A voxel-based morphometry study.

PubMed

Zhang, Rui-Ting; Yang, Tian-Xiao; Wang, Yi; Sui, Yuxiu; Yao, Jingjing; Zhang, Chen-Yuan; Cheung, Eric F C; Chan, Raymond C K

2016-12-01

Multitasking refers to the ability to organize assorted tasks efficiently in a short period of time, which plays an important role in daily life. However, the structural neural correlates of multitasking performance remain unclear. The present study aimed at exploring the brain regions associated with multitasking performance using global correlation analysis. Twenty-six healthy participants first underwent structural brain scans and then performed the modified Six Element Test, which required participants to attempt six subtasks in 10 min while obeying a specific rule. Voxel-based morphometry of the whole brain was used to detect the structural correlates of multitasking ability. Grey matter volume of the anterior cingulate cortex (ACC) was positively correlated with the overall performance and time monitoring in multitasking. In addition, white matter volume of the anterior thalamic radiation (ATR) was also positively correlated with time monitoring during multitasking. Other related brain regions associated with multitasking included the superior frontal gyrus, the inferior occipital gyrus, the lingual gyrus, and the inferior longitudinal fasciculus. No significant correlation was found between grey matter volume of the prefrontal cortex (Brodmann Area 10) and multitasking performance. Using a global correlation analysis to examine various aspects of multitasking performance, this study provided new insights into the structural neural correlates of multitasking ability. In particular, the ACC was identified as an important brain region that played both a general and a specific time-monitoring role in multitasking, extending the role of the ACC from lesioned populations to healthy populations. The present findings also support the view that the ATR may influence multitasking performance by affecting time-monitoring abilities. © 2016 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

The Correlation Function of Galaxy Clusters and Detection of Baryon Acoustic Oscillations

NASA Astrophysics Data System (ADS)

Hong, T.; Han, J. L.; Wen, Z. L.; Sun, L.; Zhan, H.

2012-04-01

We calculate the correlation function of 13,904 galaxy clusters of z <= 0.4 selected from the cluster catalog of Wen et al. The correlation function can be fitted with a power-law model ξ(r) = (r/R 0)-γ on the scales of 10 h -1 Mpc <= r <= 50 h -1 Mpc, with a larger correlation length of R 0 = 18.84 ± 0.27 h -1 Mpc for clusters with a richness of R >= 15 and a smaller length of R 0 = 16.15 ± 0.13 h -1 Mpc for clusters with a richness of R >= 5. The power-law index of γ = 2.1 is found to be almost the same for all cluster subsamples. A pronounced baryon acoustic oscillations (BAO) peak is detected at r ~ 110 h -1 Mpc with a significance of ~1.9σ. By analyzing the correlation function in the range of 20 h -1 Mpc <= r <= 200 h -1 Mpc, we find that the constraints on distance parameters are Dv (zm = 0.276) = 1077 ± 55(1σ) Mpc and h = 0.73 ± 0.039(1σ), which are consistent with the cosmology derived from Wilkinson Microwave Anisotropy Probe (WMAP) seven-year data. However, the BAO signal from the cluster sample is stronger than expected and leads to a rather low matter density Ω m h 2 = 0.093 ± 0.0077(1σ), which deviates from the WMAP7 result by more than 3σ. The correlation function of the GMBCG cluster sample is also calculated and our detection of the BAO feature is confirmed.

Detection of Fusarium verticillioides by PCR-ELISA based on FUM21 gene.

PubMed

Omori, Aline Myuki; Ono, Elisabete Yurie Sataque; Bordini, Jaqueline Gozzi; Hirozawa, Melissa Tiemi; Fungaro, Maria Helena Pelegrinelli; Ono, Mario Augusto

2018-08-01

Fusarium verticillioides is a primary corn pathogen and fumonisin producer which is associated with toxic effects in humans and animals. The traditional methods for detection of fungal contamination based on morphological characteristics are time-consuming and show low sensitivity and specificity. Therefore, the objective of this study was to develop a PCR-ELISA based on the FUM21 gene for F. verticillioides detection. The DNA of the F. verticillioides, Fusarium sp., Aspergillus sp. and Penicillium sp. isolates was analyzed by conventional PCR and PCR-ELISA to determine the specificity. The PCR-ELISA was specific to F. verticillioides isolates, showed a 2.5 pg detection limit and was 100-fold more sensitive than conventional PCR. In corn samples inoculated with F. verticillioides conidia, the detection limit of the PCR-ELISA was 1 × 10 4 conidia/g and was also 100-fold more sensitive than conventional PCR. Naturally contaminated corn samples were analyzed by PCR-ELISA based on the FUM21 gene and PCR-ELISA absorbance values correlated positively (p < 0.05) with Fusarium sp. counts (CFU/g). These results suggest that the PCR-ELISA developed in this study can be useful for F. verticillioides detection in corn samples. Copyright © 2018 Elsevier Ltd. All rights reserved.

Vehicle-borne IED detection using the ULTOR correlation processor

NASA Astrophysics Data System (ADS)

Burcham, Joel D.; Vachon, Joyce E.

2006-05-01

Advanced Optical Systems, Inc. developed the ULTOR(r) system, a real-time correlation processor that looks for improvised explosive devices (IED) by examining imagery of vehicles. The system determines the level of threat an approaching vehicle may represent. The system works on incoming video collected at different wavelengths, including visible, infrared, and synthetic aperture radar. Sensors that attach to ULTOR can be located wherever necessary to improve the safety around a checkpoint. When a suspect vehicle is detected, ULTOR can track the vehicle, alert personnel, check for previous instances of the vehicle, and update other networked systems with the threat information. The ULTOR processing engine focuses on the spatial frequency information available in the image. It correlates the imagery with templates that specify the criteria defining a suspect vehicle. It can perform full field correlations at a rate of 180 Hz or better. Additionally, the spatial frequency information is applied to a trained neural network to identify suspect vehicles. We have performed various laboratory and field experiments to verify the performance of the ULTOR system in a counter IED environment. The experiments cover tracking specific targets in video clips to demonstrating real-time ULTOR system performance. The selected targets in the experiments include various automobiles in both visible and infrared video.

Spatial correlation analysis of urban traffic state under a perspective of community detection

NASA Astrophysics Data System (ADS)

Yang, Yanfang; Cao, Jiandong; Qin, Yong; Jia, Limin; Dong, Honghui; Zhang, Aomuhan

2018-05-01

Understanding the spatial correlation of urban traffic state is essential for identifying the evolution patterns of urban traffic state. However, the distribution of traffic state always has characteristics of large spatial span and heterogeneity. This paper adapts the concept of community detection to the correlation network of urban traffic state and proposes a new perspective to identify the spatial correlation patterns of traffic state. In the proposed urban traffic network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding correlation of traffic state. Further, the process of community detection in the urban traffic network (named GWPA-K-means) is applied to analyze the spatial dependency of traffic state. The proposed method extends the traditional K-means algorithm in two steps: (i) redefines the initial cluster centers by two properties of nodes (the GWPA value and the minimum shortest path length); (ii) utilizes the weight signal propagation process to transfer the topological information of the urban traffic network into a node similarity matrix. Finally, numerical experiments are conducted on a simple network and a real urban road network in Beijing. The results show that GWPA-K-means algorithm is valid in spatial correlation analysis of traffic state. The network science and community structure analysis perform well in describing the spatial heterogeneity of traffic state on a large spatial scale.

Evaluation of three read-depth based CNV detection tools using whole-exome sequencing data.

PubMed

Yao, Ruen; Zhang, Cheng; Yu, Tingting; Li, Niu; Hu, Xuyun; Wang, Xiumin; Wang, Jian; Shen, Yiping

2017-01-01

Whole exome sequencing (WES) has been widely accepted as a robust and cost-effective approach for clinical genetic testing of small sequence variants. Detection of copy number variants (CNV) within WES data have become possible through the development of various algorithms and software programs that utilize read-depth as the main information. The aim of this study was to evaluate three commonly used, WES read-depth based CNV detection programs using high-resolution chromosomal microarray analysis (CMA) as a standard. Paired CMA and WES data were acquired for 45 samples. A total of 219 CNVs (size ranged from 2.3 kb - 35 mb) identified on three CMA platforms (Affymetrix, Agilent and Illumina) were used as standards. CNVs were called from WES data using XHMM, CoNIFER, and CNVnator with modified settings. All three software packages detected an elevated proportion of small variants (< 20 kb) compared to CMA. XHMM and CoNIFER had poor detection sensitivity (22.2 and 14.6%), which correlated with the number of capturing probes involved. CNVnator detected most variants and had better sensitivity (87.7%); however, suffered from an overwhelming detection of small CNVs below 20 kb, which required further confirmation. Size estimation of variants was exaggerated by CNVnator and understated by XHMM and CoNIFER. Low concordances of CNV, detected by three different read-depth based programs, indicate the immature status of WES-based CNV detection. Low sensitivity and uncertain specificity of WES-based CNV detection in comparison with CMA based CNV detection suggests that CMA will continue to play an important role in detecting clinical grade CNV in the NGS era, which is largely based on WES.

Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids.

PubMed

Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong

2017-04-28

Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability.

Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids

PubMed Central

Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong

2017-01-01

Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability. PMID:28452925

Data based abnormality detection

NASA Astrophysics Data System (ADS)

Purwar, Yashasvi

Data based abnormality detection is a growing research field focussed on extracting information from feature rich data. They are considered to be non-intrusive and non-destructive in nature which gives them a clear advantage over conventional methods. In this study, we explore different streams of data based anomalies detection. We propose extension and revisions to existing valve stiction detection algorithm supported with industrial case study. We also explored the area of image analysis and proposed a complete solution for Malaria diagnosis. The proposed method is tested over images provided by pathology laboratory at Alberta Health Service. We also address the robustness and practicality of the solution proposed.

Phone camera detection of glucose blood level based on magnetic particles entrapped inside bubble wrap.

PubMed

Martinkova, Pavla; Pohanka, Miroslav

2016-12-18

Glucose is an important diagnostic biochemical marker of diabetes but also for organophosphates, carbamates, acetaminophens or salicylates poisoning. Hence, innovation of accurate and fast detection assay is still one of priorities in biomedical research. Glucose sensor based on magnetic particles (MPs) with immobilized enzymes glucose oxidase (GOx) and horseradish peroxidase (HRP) was developed and the GOx catalyzed reaction was visualized by a smart-phone-integrated camera. Exponential decay concentration curve with correlation coefficient 0.997 and with limit of detection 0.4 mmol/l was achieved. Interfering and matrix substances were measured due to possibility of assay influencing and no effect of the tested substances was observed. Spiked plasma samples were also measured and no influence of plasma matrix on the assay was proved. The presented assay showed complying results with reference method (standard spectrophotometry based on enzymes glucose oxidase and peroxidase inside plastic cuvettes) with linear dependence and correlation coefficient 0.999 in concentration range between 0 and 4 mmol/l. On the grounds of measured results, method was considered as highly specific, accurate and fast assay for detection of glucose.

Combining satellite-based fire observations and ground-based lightning detections to identify lightning fires across the conterminous USA

USGS Publications Warehouse

Bar-Massada, A.; Hawbaker, T.J.; Stewart, S.I.; Radeloff, V.C.

2012-01-01

Lightning fires are a common natural disturbance in North America, and account for the largest proportion of the area burned by wildfires each year. Yet, the spatiotemporal patterns of lightning fires in the conterminous US are not well understood due to limitations of existing fire databases. Our goal here was to develop and test an algorithm that combined MODIS fire detections with lightning detections from the National Lightning Detection Network to identify lightning fires across the conterminous US from 2000 to 2008. The algorithm searches for spatiotemporal conjunctions of MODIS fire clusters and NLDN detected lightning strikes, given a spatiotemporal lag between lightning strike and fire ignition. The algorithm revealed distinctive spatial patterns of lightning fires in the conterminous US While a sensitivity analysis revealed that the algorithm is highly sensitive to the two thresholds that are used to determine conjunction, the density of fires it detected was moderately correlated with ground based fire records. When only fires larger than 0.4 km2 were considered, correlations were higher and the root-mean-square error between datasets was less than five fires per 625 km2 for the entire study period. Our algorithm is thus suitable for detecting broad scale spatial patterns of lightning fire occurrence, and especially lightning fire hotspots, but has limited detection capability of smaller fires because these cannot be consistently detected by MODIS. These results may enhance our understanding of large scale patterns of lightning fire activity, and can be used to identify the broad scale factors controlling fire occurrence.

PCR-Free Detection of Genetically Modified Organisms Using Magnetic Capture Technology and Fluorescence Cross-Correlation Spectroscopy

PubMed Central

Zhou, Xiaoming; Xing, Da; Tang, Yonghong; Chen, Wei R.

2009-01-01

The safety of genetically modified organisms (GMOs) has attracted much attention recently. Polymerase chain reaction (PCR) amplification is a common method used in the identification of GMOs. However, a major disadvantage of PCR is the potential amplification of non-target DNA, causing false-positive identification. Thus, there remains a need for a simple, reliable and ultrasensitive method to identify and quantify GMO in crops. This report is to introduce a magnetic bead-based PCR-free method for rapid detection of GMOs using dual-color fluorescence cross-correlation spectroscopy (FCCS). The cauliflower mosaic virus 35S (CaMV35S) promoter commonly used in transgenic products was targeted. CaMV35S target was captured by a biotin-labeled nucleic acid probe and then purified using streptavidin-coated magnetic beads through biotin-streptavidin linkage. The purified target DNA fragment was hybridized with two nucleic acid probes labeled respectively by Rhodamine Green and Cy5 dyes. Finally, FCCS was used to detect and quantify the target DNA fragment through simultaneously detecting the fluorescence emissions from the two dyes. In our study, GMOs in genetically engineered soybeans and tomatoes were detected, using the magnetic bead-based PCR-free FCCS method. A detection limit of 50 pM GMOs target was achieved and PCR-free detection of GMOs from 5 µg genomic DNA with magnetic capture technology was accomplished. Also, the accuracy of GMO determination by the FCCS method is verified by spectrophotometry at 260 nm using PCR amplified target DNA fragment from GM tomato. The new method is rapid and effective as demonstrated in our experiments and can be easily extended to high-throughput and automatic screening format. We believe that the new magnetic bead-assisted FCCS detection technique will be a useful tool for PCR-free GMOs identification and other specific nucleic acids. PMID:19956680

Correlation between self-reported and clinically based diagnoses of bruxism in temporomandibular disorders patients.

PubMed

Paesani, D A; Lobbezoo, F; Gelos, C; Guarda-Nardini, L; Ahlberg, J; Manfredini, D

2013-11-01

The present investigation was performed in a population of patients with temporomandibular disorders (TMD), and it was designed to assess the correlation between self-reported questionnaire-based bruxism diagnosis and a diagnosis based on history taking plus clinical examination. One-hundred-fifty-nine patients with TMD underwent an assessment including a questionnaire investigating five bruxism-related items (i.e. sleep grinding, sleep grinding referral by bed partner, sleep clenching, awake clenching, awake grinding) and an interview (i.e. oral history taking with specific focus on bruxism habits) plus a clinical examination to evaluate bruxism signs and symptoms. The correlation between findings of the questionnaire, viz., patients' report, and findings of the interview/oral history taking plus clinical examination, viz., clinicians' diagnosis, was assessed by means of φ coefficient. The highest correlations were achieved for the sleep grinding referral item (φ = 0·932) and for the awake clenching item (φ = 0·811), whilst lower correlation values were found for the other items (φ values ranging from 0·363 to 0·641). The percentage of disagreement between the two diagnostic approaches ranged between 1·8% and 18·2%. Within the limits of the present investigation, it can be suggested that a strong positive correlation between a self-reported and a clinically based approach to bruxism diagnosis can be achieved as for awake clenching, whilst lower levels of correlation were detected for sleep-time activities. © 2013 John Wiley & Sons Ltd.

Quantum correlation of fiber-based telecom-band photon pairs through standard loss and random media.

PubMed

Sua, Yong Meng; Malowicki, John; Lee, Kim Fook

2014-08-15

We study quantum correlation and interference of fiber-based telecom-band photon pairs with one photon of the pair experiencing multiple scattering in a random medium. We measure joint probability of two-photon detection for signal photon in a normal channel and idler photon in a channel, which is subjected to two independent conditions: standard loss (neutral density filter) and random media. We observe that both conditions degrade the correlation of signal and idler photons, and depolarization of the idler photon in random medium can enhance two-photon interference at certain relative polarization angles. Our theoretical calculation on two-photon polarization correlation and interference as a function of mean free path is in agreement with our experiment data. We conclude that quantum correlation of a polarization-entangled photon pair is better preserved than a polarization-correlated photon pair as one photon of the pair scatters through a random medium.

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.

Memory Detection 2.0: The First Web-Based Memory Detection Test

PubMed Central

Kleinberg, Bennett; Verschuere, Bruno

2015-01-01

There is accumulating evidence that reaction times (RTs) can be used to detect recognition of critical (e.g., crime) information. A limitation of this research base is its reliance upon small samples (average n = 24), and indications of publication bias. To advance RT-based memory detection, we report upon the development of the first web-based memory detection test. Participants in this research (Study1: n = 255; Study2: n = 262) tried to hide 2 high salient (birthday, country of origin) and 2 low salient (favourite colour, favourite animal) autobiographical details. RTs allowed to detect concealed autobiographical information, and this, as predicted, more successfully so than error rates, and for high salient than for low salient items. While much remains to be learned, memory detection 2.0 seems to offer an interesting new platform to efficiently and validly conduct RT-based memory detection research. PMID:25874966

Texture orientation-based algorithm for detecting infrared maritime targets.

PubMed

Wang, Bin; Dong, Lili; Zhao, Ming; Wu, Houde; Xu, Wenhai

2015-05-20

Infrared maritime target detection is a key technology for maritime target searching systems. However, in infrared maritime images (IMIs) taken under complicated sea conditions, background clutters, such as ocean waves, clouds or sea fog, usually have high intensity that can easily overwhelm the brightness of real targets, which is difficult for traditional target detection algorithms to deal with. To mitigate this problem, this paper proposes a novel target detection algorithm based on texture orientation. This algorithm first extracts suspected targets by analyzing the intersubband correlation between horizontal and vertical wavelet subbands of the original IMI on the first scale. Then the self-adaptive wavelet threshold denoising and local singularity analysis of the original IMI is combined to remove false alarms further. Experiments show that compared with traditional algorithms, this algorithm can suppress background clutter much better and realize better single-frame detection for infrared maritime targets. Besides, in order to guarantee accurate target extraction further, the pipeline-filtering algorithm is adopted to eliminate residual false alarms. The high practical value and applicability of this proposed strategy is backed strongly by experimental data acquired under different environmental conditions.

Robust Observation Detection for Single Object Tracking: Deterministic and Probabilistic Patch-Based Approaches

PubMed Central

Zulkifley, Mohd Asyraf; Rawlinson, David; Moran, Bill

2012-01-01

In video analytics, robust observation detection is very important as the content of the videos varies a lot, especially for tracking implementation. Contrary to the image processing field, the problems of blurring, moderate deformation, low illumination surroundings, illumination change and homogenous texture are normally encountered in video analytics. Patch-Based Observation Detection (PBOD) is developed to improve detection robustness to complex scenes by fusing both feature- and template-based recognition methods. While we believe that feature-based detectors are more distinctive, however, for finding the matching between the frames are best achieved by a collection of points as in template-based detectors. Two methods of PBOD—the deterministic and probabilistic approaches—have been tested to find the best mode of detection. Both algorithms start by building comparison vectors at each detected points of interest. The vectors are matched to build candidate patches based on their respective coordination. For the deterministic method, patch matching is done in 2-level test where threshold-based position and size smoothing are applied to the patch with the highest correlation value. For the second approach, patch matching is done probabilistically by modelling the histograms of the patches by Poisson distributions for both RGB and HSV colour models. Then, maximum likelihood is applied for position smoothing while a Bayesian approach is applied for size smoothing. The result showed that probabilistic PBOD outperforms the deterministic approach with average distance error of 10.03% compared with 21.03%. This algorithm is best implemented as a complement to other simpler detection methods due to heavy processing requirement. PMID:23202226

Vehicle Localization by LIDAR Point Correlation Improved by Change Detection

NASA Astrophysics Data System (ADS)

Schlichting, A.; Brenner, C.

2016-06-01

LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.

Full waveform inversion using envelope-based global correlation norm

NASA Astrophysics Data System (ADS)

Oh, Ju-Won; Alkhalifah, Tariq

2018-05-01

To increase the feasibility of full waveform inversion on real data, we suggest a new objective function, which is defined as the global correlation of the envelopes of modelled and observed data. The envelope-based global correlation norm has the advantage of the envelope inversion that generates artificial low-frequency information, which provides the possibility to recover long-wavelength structure in an early stage. In addition, the envelope-based global correlation norm maintains the advantage of the global correlation norm, which reduces the sensitivity of the misfit to amplitude errors so that the performance of inversion on real data can be enhanced when the exact source wavelet is not available and more complex physics are ignored. Through the synthetic example for 2-D SEG/EAGE overthrust model with inaccurate source wavelet, we compare the performance of four different approaches, which are the least-squares waveform inversion, least-squares envelope inversion, global correlation norm and envelope-based global correlation norm. Finally, we apply the envelope-based global correlation norm on the 3-D Ocean Bottom Cable (OBC) data from the North Sea. The envelope-based global correlation norm captures the strong reflections from the high-velocity caprock and generates artificial low-frequency reflection energy that helps us recover long-wavelength structure of the model domain in the early stages. From this long-wavelength model, the conventional global correlation norm is sequentially applied to invert for higher-resolution features of the model.

Silver nanoparticles-based colorimetric array for the detection of Thiophanate-methyl

NASA Astrophysics Data System (ADS)

Zheng, Mingda; Wang, Yingying; Wang, Chenge; Wei, Wei; Ma, Shuang; Sun, Xiaohan; He, Jiang

2018-06-01

A simple and selective colorimetric sensor based on citrate capped silver nanoparticles (Cit-AgNPs) is proposed for the detection of Thiophanate-methyl (TM) with high sensitivity and selectivity. The method based on the color change of Cit-AgNPs from yellow to cherry red with the addition of TM to Cit-AgNPs that caused a red-shift on the surface plasmon resonance (SPR) band from 394 nm to 525 nm due to the hydrogen-bonding and substitution. The density functional theory (DFT) method was also calculated the interactions between the TM and citrate ions. Under the optimized conditions, a linear relationship between the absorption ratio (A525nm/A394nm) and TM concentration was found in the range of 2-100 μM with correlation coefficient (R2) of 0.988. The detection limit of TM was 0.12 μM by UV-vis spectrometer. Moreover, the applicability of colorimetric sensor is successfully verified by the detection of TM in environmental samples with good recoveries.

Fuzzy logic and optical correlation-based face recognition method for patient monitoring application in home video surveillance

NASA Astrophysics Data System (ADS)

Elbouz, Marwa; Alfalou, Ayman; Brosseau, Christian

2011-06-01

Home automation is being implemented into more and more domiciles of the elderly and disabled in order to maintain their independence and safety. For that purpose, we propose and validate a surveillance video system, which detects various posture-based events. One of the novel points of this system is to use adapted Vander-Lugt correlator (VLC) and joint-transfer correlator (JTC) techniques to make decisions on the identity of a patient and his three-dimensional (3-D) positions in order to overcome the problem of crowd environment. We propose a fuzzy logic technique to get decisions on the subject's behavior. Our system is focused on the goals of accuracy, convenience, and cost, which in addition does not require any devices attached to the subject. The system permits one to study and model subject responses to behavioral change intervention because several levels of alarm can be incorporated according different situations considered. Our algorithm performs a fast 3-D recovery of the subject's head position by locating eyes within the face image and involves a model-based prediction and optical correlation techniques to guide the tracking procedure. The object detection is based on (hue, saturation, value) color space. The system also involves an adapted fuzzy logic control algorithm to make a decision based on information given to the system. Furthermore, the principles described here are applicable to a very wide range of situations and robust enough to be implementable in ongoing experiments.

Correlative Analysis of GRBs detected by Swift, Konus and HETE

NASA Technical Reports Server (NTRS)

Krimm, Hans A.; Barthelmy, S. D.; Gehrels, N.; Hullinger, D.; Sakamoto, T.; Donaghy, T.; Lamb, D. Q.; Pal'shin, V.; Golenetskii, S.; Ricker, G. R.

2005-01-01

Swift has now detected a large enough sample of gamma-ray bursts (GRBs) to allow correlation studies of burst parameters. Such studies of earlier data sets have yielded important results leading to further understanding of burst parameters and classifications. This work focuses on seventeen Swift bursts that have also been detected either by Konus-Wind or HETE-II, providing high energy spectra and fits to E(sub peak). Eight of these bursts have spectroscopic redshifts and for others we can estimate redshifts using the variability/luminosity relationship. We can also compare E(sub peak) with E(sub iso), and for those bursts for which a jet break was observed in the afterglow we can derive E(sub g) and test the relationship between E(peak) and E(sub gamma). For all bursts we can derive durations and hardness ratios from the prompt emission.

Rapid earthquake detection through GPU-Based template matching

NASA Astrophysics Data System (ADS)

Mu, Dawei; Lee, En-Jui; Chen, Po

2017-12-01

The template-matching algorithm (TMA) has been widely adopted for improving the reliability of earthquake detection. The TMA is based on calculating the normalized cross-correlation coefficient (NCC) between a collection of selected template waveforms and the continuous waveform recordings of seismic instruments. In realistic applications, the computational cost of the TMA is much higher than that of traditional techniques. In this study, we provide an analysis of the TMA and show how the GPU architecture provides an almost ideal environment for accelerating the TMA and NCC-based pattern recognition algorithms in general. So far, our best-performing GPU code has achieved a speedup factor of more than 800 with respect to a common sequential CPU code. We demonstrate the performance of our GPU code using seismic waveform recordings from the ML 6.6 Meinong earthquake sequence in Taiwan.

Within-Subject Correlation Analysis to Detect Functional Areas Associated With Response Inhibition.

PubMed

Yamasaki, Tomoko; Ogawa, Akitoshi; Osada, Takahiro; Jimura, Koji; Konishi, Seiki

2018-01-01

Functional areas in fMRI studies are often detected by brain-behavior correlation, calculating across-subject correlation between the behavioral index and the brain activity related to a function of interest. Within-subject correlation analysis is also employed in a single subject level, which utilizes cognitive fluctuations in a shorter time period by correlating the behavioral index with the brain activity across trials. In the present study, the within-subject analysis was applied to the stop-signal task, a standard task to probe response inhibition, where efficiency of response inhibition can be evaluated by the stop-signal reaction time (SSRT). Since the SSRT is estimated, by definition, not in a trial basis but from pooled trials, the correlation across runs was calculated between the SSRT and the brain activity related to response inhibition. The within-subject correlation revealed negative correlations in the anterior cingulate cortex and the cerebellum. Moreover, the dissociation pattern was observed in the within-subject analysis when earlier vs. later parts of the runs were analyzed: negative correlation was dominant in earlier runs, whereas positive correlation was dominant in later runs. Regions of interest analyses revealed that the negative correlation in the anterior cingulate cortex, but not in the cerebellum, was dominant in earlier runs, suggesting multiple mechanisms associated with inhibitory processes that fluctuate on a run-by-run basis. These results indicate that the within-subject analysis compliments the across-subject analysis by highlighting different aspects of cognitive/affective processes related to response inhibition.

Detection of sub-threshold periodic signal by multiplicative and additive cross-correlated sine-Wiener noises in the FitzHugh-Nagumo neuron

NASA Astrophysics Data System (ADS)

Yao, Yuangen; Ma, Chengzhang; Wang, Canjun; Yi, Ming; Gui, Rong

2018-02-01

We study the effects of multiplicative and additive cross-correlated sine-Wiener (CCSW) noises on the performance of sub-threshold periodic signal detection in the FitzHugh-Nagumo (FHN) neuron by calculating Fourier coefficients Q for measuring synchronization between sub-threshold input signal and the response of system. CCSW noises-induced transitions of electrical activity in the FHN neuron model can be observed. Moreover, the performance of sub-threshold periodic signal detection is achieved at moderate noise strength, cross-correlation time and cross-correlation strength of CCSW noises, which indicate the occurrence of CCSW noises-induced stochastic resonance. Furthermore, the performance of sub-threshold signal detection is strongly sensitive to cross-correlation time of CCSW noises. Therefore, the performance can be effectively controlled by regulating cross-correlation time of CCSW noises. These results provide a possible mechanism for amplifying or detecting the sub-threshold signal in the nervous system.

Development of a rapid optic bacteria detecting system based on ATP bioluminescence

NASA Astrophysics Data System (ADS)

Liu, Jun Tao; Luo, JinPing; Liu, XiaoHong; Cai, XinXia

2014-12-01

A rapid optic bacteria detecting system based on the principle of Adenosine triphosphate(ATP) bioluminescence was presented in this paper. This system consisted of bioluminescence-based biosensor and the high-sensitivity optic meter. A photon counting photomultiplier tube (PMT) module was used to improve the detection sensitivity, and a NIOS II/f processor based on a Field Programmable Gate Array(FPGA) was used to control the system. In this work, Micrococcus luteus were chosen as the test sample. Several Micrococcus luteus suspension with different concentration was tested by both T2011 and plate counting method. By comparing the two group results, an calibration curve was obtained from the bioluminescence intensity for Micrococcus luteus in the range of 2.3×102 ~ 2.3×106 CFU/mL with a good correlation coefficient of 0.960. An impacting Air microorganism sampler was used to capture Airborne Bacteria, and 8 samples were collected in different place. The TBC results of 8 samples by T2011 were between 10 ~ 2×103 cfu/mL, consistent with that of plate counting method, which indicated that 8 samples were between 10 ~ 3×103 cfu/mL. For total airborne bacteria count was small, correlation coefficient was poor. Also no significant difference was found between T2011 and plate counting method by statistical analyses.

Noise reduction in Lidar signal using correlation-based EMD combined with soft thresholding and roughness penalty

NASA Astrophysics Data System (ADS)

Chang, Jianhua; Zhu, Lingyan; Li, Hongxu; Xu, Fan; Liu, Binggang; Yang, Zhenbo

2018-01-01

Empirical mode decomposition (EMD) is widely used to analyze the non-linear and non-stationary signals for noise reduction. In this study, a novel EMD-based denoising method, referred to as EMD with soft thresholding and roughness penalty (EMD-STRP), is proposed for the Lidar signal denoising. With the proposed method, the relevant and irrelevant intrinsic mode functions are first distinguished via a correlation coefficient. Then, the soft thresholding technique is applied to the irrelevant modes, and the roughness penalty technique is applied to the relevant modes to extract as much information as possible. The effectiveness of the proposed method was evaluated using three typical signals contaminated by white Gaussian noise. The denoising performance was then compared to the denoising capabilities of other techniques, such as correlation-based EMD partial reconstruction, correlation-based EMD hard thresholding, and wavelet transform. The use of EMD-STRP on the measured Lidar signal resulted in the noise being efficiently suppressed, with an improved signal to noise ratio of 22.25 dB and an extended detection range of 11 km.

Modulation-format-free and automatic bias control for optical IQ modulators based on dither-correlation detection.

PubMed

Li, Xiaolei; Deng, Lei; Chen, Xiaoman; Cheng, Mengfan; Fu, Songnian; Tang, Ming; Liu, Deming

2017-04-17

A novel automatic bias control (ABC) method for optical in-phase and quadrature (IQ) modulator is proposed and experimentally demonstrated. In the proposed method, two different low frequency sine wave dither signals are generated and added on to the I/Q bias signal respectively. Instead of power monitoring of the harmonics of the dither signal, dither-correlation detection is proposed and used to adjust the bias voltages of the optical IQ modulator. By this way, not only frequency spectral analysis isn't required but also the directional bias adjustment could be realized, resulting in the decrease of algorithm complexity and the growth of convergence rate of ABC algorithm. The results show that the sensitivity of the proposed ABC method outperforms that of the traditional dither frequency monitoring method. Moreover, the proposed ABC method is proved to be modulation-format-free, and the transmission penalty caused by this method for both 10 Gb/s optical QPSK and 17.9 Gb/s optical 16QAM-OFDM signal transmission are negligible in our experiment.

Truncated feature representation for automatic target detection using transformed data-based decomposition

NASA Astrophysics Data System (ADS)

Riasati, Vahid R.

2016-05-01

In this work, the data covariance matrix is diagonalized to provide an orthogonal bases set using the eigen vectors of the data. The eigen-vector decomposition of the data is transformed and filtered in the transform domain to truncate the data for robust features related to a specified set of targets. These truncated eigen features are then combined and reconstructed to utilize in a composite filter and consequently utilized for the automatic target detection of the same class of targets. The results associated with the testing of the current technique are evaluated using the peak-correlation and peak-correlation energy metrics and are presented in this work. The inverse transformed eigen-bases of the current technique may be thought of as an injected sparsity to minimize data in representing the skeletal data structure information associated with the set of targets under consideration.

Cellular telephone-based wide-area radiation detection network

DOEpatents

Craig, William W [Pittsburg, CA; Labov, Simon E [Berkeley, CA

2009-06-09

A network of radiation detection instruments, each having a small solid state radiation sensor module integrated into a cellular phone for providing radiation detection data and analysis directly to a user. The sensor module includes a solid-state crystal bonded to an ASIC readout providing a low cost, low power, light weight compact instrument to detect and measure radiation energies in the local ambient radiation field. In particular, the photon energy, time of event, and location of the detection instrument at the time of detection is recorded for real time transmission to a central data collection/analysis system. The collected data from the entire network of radiation detection instruments are combined by intelligent correlation/analysis algorithms which map the background radiation and detect, identify and track radiation anomalies in the region.

Noise reduction algorithm with the soft thresholding based on the Shannon entropy and bone-conduction speech cross- correlation bands.

PubMed

Na, Sung Dae; Wei, Qun; Seong, Ki Woong; Cho, Jin Ho; Kim, Myoung Nam

2018-01-01

The conventional methods of speech enhancement, noise reduction, and voice activity detection are based on the suppression of noise or non-speech components of the target air-conduction signals. However, air-conduced speech is hard to differentiate from babble or white noise signals. To overcome this problem, the proposed algorithm uses the bone-conduction speech signals and soft thresholding based on the Shannon entropy principle and cross-correlation of air- and bone-conduction signals. A new algorithm for speech detection and noise reduction is proposed, which makes use of the Shannon entropy principle and cross-correlation with the bone-conduction speech signals to threshold the wavelet packet coefficients of the noisy speech. The proposed method can be get efficient result by objective quality measure that are PESQ, RMSE, Correlation, SNR. Each threshold is generated by the entropy and cross-correlation approaches in the decomposed bands using the wavelet packet decomposition. As a result, the noise is reduced by the proposed method using the MATLAB simulation. To verify the method feasibility, we compared the air- and bone-conduction speech signals and their spectra by the proposed method. As a result, high performance of the proposed method is confirmed, which makes it quite instrumental to future applications in communication devices, noisy environment, construction, and military operations.

A signal-based fault detection and classification method for heavy haul wagons

NASA Astrophysics Data System (ADS)

Li, Chunsheng; Luo, Shihui; Cole, Colin; Spiryagin, Maksym; Sun, Yanquan

2017-12-01

This paper proposes a signal-based fault detection and isolation (FDI) system for heavy haul wagons considering the special requirements of low cost and robustness. The sensor network of the proposed system consists of just two accelerometers mounted on the front left and rear right of the carbody. Seven fault indicators (FIs) are proposed based on the cross-correlation analyses of the sensor-collected acceleration signals. Bolster spring fault conditions are focused on in this paper, including two different levels (small faults and moderate faults) and two locations (faults in the left and right bolster springs of the first bogie). A fully detailed dynamic model of a typical 40t axle load heavy haul wagon is developed to evaluate the deterioration of dynamic behaviour under proposed fault conditions and demonstrate the detectability of the proposed FDI method. Even though the fault conditions considered in this paper did not deteriorate the wagon dynamic behaviour dramatically, the proposed FIs show great sensitivity to the bolster spring faults. The most effective and efficient FIs are chosen for fault detection and classification. Analysis results indicate that it is possible to detect changes in bolster stiffness of ±25% and identify the fault location.

Capillary-driven surface-enhanced Raman scattering (SERS)-based microfluidic chip for abrin detection

NASA Astrophysics Data System (ADS)

Yang, Hao; Deng, Min; Ga, Shan; Chen, Shouhui; Kang, Lin; Wang, Junhong; Xin, Wenwen; Zhang, Tao; You, Zherong; An, Yuan; Wang, Jinglin; Cui, Daxiang

2014-03-01

Herein, we firstly demonstrate the design and the proof-of-concept use of a capillary-driven surface-enhanced Raman scattering (SERS)-based microfluidic chip for abrin detection. The micropillar array substrate was etched and coated with a gold film by microelectromechanical systems (MEMS) process to integrate into a lateral flow test strip. The detection of abrin solutions of various concentrations was performed by the as-prepared microfluidic chip. It was shown that the correlation between the abrin concentration and SERS signal was found to be linear within the range of 0.1 ng/mL to 1 μg/mL with a limit of detection of 0.1 ng/mL. Our microfluidic chip design enhanced the operability of SERS-based immunodiagnostic techniques, significantly reducing the complication and cost of preparation as compared to previous SERS-based works. Meanwhile, this design proved the superiority to conventional lateral flow test strips in respect of both sensitivity and quantitation and showed great potential in the diagnosis and treatment for abrin poisoning as well as on-site screening of abrin-spiked materials.

Improvement of correlation-based centroiding methods for point source Shack-Hartmann wavefront sensor

NASA Astrophysics Data System (ADS)

Li, Xuxu; Li, Xinyang; wang, Caixia

2018-03-01

This paper proposes an efficient approach to decrease the computational costs of correlation-based centroiding methods used for point source Shack-Hartmann wavefront sensors. Four typical similarity functions have been compared, i.e. the absolute difference function (ADF), ADF square (ADF2), square difference function (SDF), and cross-correlation function (CCF) using the Gaussian spot model. By combining them with fast search algorithms, such as three-step search (TSS), two-dimensional logarithmic search (TDL), cross search (CS), and orthogonal search (OS), computational costs can be reduced drastically without affecting the accuracy of centroid detection. Specifically, OS reduces calculation consumption by 90%. A comprehensive simulation indicates that CCF exhibits a better performance than other functions under various light-level conditions. Besides, the effectiveness of fast search algorithms has been verified.

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

ERIC Educational Resources Information Center

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

2006-01-01

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

Experimental results for correlation-based wavefront sensing

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

Poyneer, L A; Palmer, D W; LaFortune, K N

2005-07-01

Correlation wave-front sensing can improve Adaptive Optics (AO) system performance in two keys areas. For point-source-based AO systems, Correlation is more accurate, more robust to changing conditions and provides lower noise than a centroiding algorithm. Experimental results from the Lick AO system and the SSHCL laser AO system confirm this. For remote imaging, Correlation enables the use of extended objects for wave-front sensing. Results from short horizontal-path experiments will show algorithm properties and requirements.

An estimation of distribution method for infrared target detection based on Copulas

NASA Astrophysics Data System (ADS)

Wang, Shuo; Zhang, Yiqun

2015-10-01

Track-before-detect (TBD) based target detection involves a hypothesis test of merit functions which measure each track as a possible target track. Its accuracy depends on the precision of the distribution of merit functions, which determines the threshold for a test. Generally, merit functions are regarded Gaussian, and on this basis the distribution is estimated, which is true for most methods such as the multiple hypothesis tracking (MHT). However, merit functions for some other methods such as the dynamic programming algorithm (DPA) are non-Guassian and cross-correlated. Since existing methods cannot reasonably measure the correlation, the exact distribution can hardly be estimated. If merit functions are assumed Guassian and independent, the error between an actual distribution and its approximation may occasionally over 30 percent, and is divergent by propagation. Hence, in this paper, we propose a novel estimation of distribution method based on Copulas, by which the distribution can be estimated precisely, where the error is less than 1 percent without propagation. Moreover, the estimation merely depends on the form of merit functions and the structure of a tracking algorithm, and is invariant to measurements. Thus, the distribution can be estimated in advance, greatly reducing the demand for real-time calculation of distribution functions.

Distinct frontal and amygdala correlates of change detection for facial identity and expression

PubMed Central

Achaibou, Amal; Loth, Eva

2016-01-01

Recruitment of ‘top-down’ frontal attentional mechanisms is held to support detection of changes in task-relevant stimuli. Fluctuations in intrinsic frontal activity have been shown to impact task performance more generally. Meanwhile, the amygdala has been implicated in ‘bottom-up’ attentional capture by threat. Here, 22 adult human participants took part in a functional magnetic resonance change detection study aimed at investigating the correlates of successful (vs failed) detection of changes in facial identity vs expression. For identity changes, we expected prefrontal recruitment to differentiate ‘hit’ from ‘miss’ trials, in line with previous reports. Meanwhile, we postulated that a different mechanism would support detection of emotionally salient changes. Specifically, elevated amygdala activation was predicted to be associated with successful detection of threat-related changes in expression, over-riding the influence of fluctuations in top-down attention. Our findings revealed that fusiform activity tracked change detection across conditions. Ventrolateral prefrontal cortical activity was uniquely linked to detection of changes in identity not expression, and amygdala activity to detection of changes from neutral to fearful expressions. These results are consistent with distinct mechanisms supporting detection of changes in face identity vs expression, the former potentially reflecting top-down attention, the latter bottom-up attentional capture by stimulus emotional salience. PMID:26245835

Detection of microcalcifications by characteristic magnetic susceptibility effects using MR phase image cross-correlation analysis

PubMed Central

Baheza, Richard A.; Welch, E. Brian; Gochberg, Daniel F.; Sanders, Melinda; Harvey, Sara; Gore, John C.; Yankeelov, Thomas E.

2015-01-01

Purpose: To develop and evaluate a new method for detecting calcium deposits using their characteristic magnetic susceptibility effects on magnetic resonance (MR) images at high fields and demonstrate its potential in practice for detecting breast microcalcifications. Methods: Characteristic dipole signatures of calcium deposits were detected in magnetic resonance phase images by computing the cross-correlation between the acquired data and a library of templates containing simulated phase patterns of spherical deposits. The influence of signal-to-noise ratio and various other MR parameters on the results were assessed using simulations and validated experimentally. The method was tested experimentally for detection of calcium fragments within gel phantoms and calcium-like inhomogeneities within chicken tissue at 7 T with optimized MR acquisition parameters. The method was also evaluated for detection of simulated microcalcifications, modeled from biopsy samples of malignant breast cancer, inserted in silico into breast magnetic resonance imaging (MRIs) of healthy subjects at 7 T. For both assessments of calcium fragments in phantoms and biopsy-based simulated microcalcifications in breast MRIs, receiver operator characteristic curve analyses were performed to determine the cross-correlation index cutoff, for achieving optimal sensitivity and specificity, and the area under the curve (AUC), for measuring the method’s performance. Results: The method detected calcium fragments with sizes of 0.14–0.79 mm, 1 mm calcium-like deposits, and simulated microcalcifications with sizes of 0.4–1.0 mm in images with voxel sizes between (0.2 mm)3 and (0.6 mm)3. In images acquired at 7 T with voxel sizes of (0.2 mm)3–(0.4 mm)3, calcium fragments (size 0.3–0.4 mm) were detected with a sensitivity, specificity, and AUC of 78%–90%, 51%–68%, and 0.77%–0.88%, respectively. In images acquired with a human 7 T scanner, acquisition times below 12 min, and voxel sizes of

Detection of microcalcifications by characteristic magnetic susceptibility effects using MR phase image cross-correlation analysis

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

Baheza, Richard A.; Welch, E. Brian; Gochberg, Daniel F.

Purpose: To develop and evaluate a new method for detecting calcium deposits using their characteristic magnetic susceptibility effects on magnetic resonance (MR) images at high fields and demonstrate its potential in practice for detecting breast microcalcifications. Methods: Characteristic dipole signatures of calcium deposits were detected in magnetic resonance phase images by computing the cross-correlation between the acquired data and a library of templates containing simulated phase patterns of spherical deposits. The influence of signal-to-noise ratio and various other MR parameters on the results were assessed using simulations and validated experimentally. The method was tested experimentally for detection of calcium fragmentsmore » within gel phantoms and calcium-like inhomogeneities within chicken tissue at 7 T with optimized MR acquisition parameters. The method was also evaluated for detection of simulated microcalcifications, modeled from biopsy samples of malignant breast cancer, inserted in silico into breast magnetic resonance imaging (MRIs) of healthy subjects at 7 T. For both assessments of calcium fragments in phantoms and biopsy-based simulated microcalcifications in breast MRIs, receiver operator characteristic curve analyses were performed to determine the cross-correlation index cutoff, for achieving optimal sensitivity and specificity, and the area under the curve (AUC), for measuring the method’s performance. Results: The method detected calcium fragments with sizes of 0.14–0.79 mm, 1 mm calcium-like deposits, and simulated microcalcifications with sizes of 0.4–1.0 mm in images with voxel sizes between (0.2 mm){sup 3} and (0.6 mm){sup 3}. In images acquired at 7 T with voxel sizes of (0.2 mm){sup 3}–(0.4 mm){sup 3}, calcium fragments (size 0.3–0.4 mm) were detected with a sensitivity, specificity, and AUC of 78%–90%, 51%–68%, and 0.77%–0.88%, respectively. In images acquired with a human 7 T scanner, acquisition times

A Method for Detecting Circulating Tumor Cells Based on the Measurement of Single-Cell Metabolism in Droplet-Based Microfluidics.

PubMed

Del Ben, Fabio; Turetta, Matteo; Celetti, Giorgia; Piruska, Aigars; Bulfoni, Michela; Cesselli, Daniela; Huck, Wilhelm T S; Scoles, Giacinto

2016-07-18

The number of circulating tumor cells (CTCs) in blood is strongly correlated with the progress of metastatic cancer. Current methods to detect CTCs are based on immunostaining or discrimination of physical properties. Herein, a label-free method is presented exploiting the abnormal metabolic behavior of cancer cells. A single-cell analysis technique is used to measure the secretion of acid from individual living tumor cells compartmentalized in microfluidically prepared, monodisperse, picoliter (pL) droplets. As few as 10 tumor cells can be detected in a background of 200 000 white blood cells and proof-of-concept data is shown on the detection of CTCs in the blood of metastatic patients. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

[A research in speech endpoint detection based on boxes-coupling generalization dimension].

PubMed

Wang, Zimei; Yang, Cuirong; Wu, Wei; Fan, Yingle

2008-06-01

In this paper, a new calculating method of generalized dimension, based on boxes-coupling principle, is proposed to overcome the edge effects and to improve the capability of the speech endpoint detection which is based on the original calculating method of generalized dimension. This new method has been applied to speech endpoint detection. Firstly, the length of overlapping border was determined, and through calculating the generalized dimension by covering the speech signal with overlapped boxes, three-dimension feature vectors including the box dimension, the information dimension and the correlation dimension were obtained. Secondly, in the light of the relation between feature distance and similarity degree, feature extraction was conducted by use of common distance. Lastly, bi-threshold method was used to classify the speech signals. The results of experiment indicated that, by comparison with the original generalized dimension (OGD) and the spectral entropy (SE) algorithm, the proposed method is more robust and effective for detecting the speech signals which contain different kinds of noise in different signal noise ratio (SNR), especially in low SNR.

Revisiting node-based SIR models in complex networks with degree correlations

NASA Astrophysics Data System (ADS)

Wang, Yi; Cao, Jinde; Alofi, Abdulaziz; AL-Mazrooei, Abdullah; Elaiw, Ahmed

2015-11-01

In this paper, we consider two growing networks which will lead to the degree-degree correlations between two nearest neighbors in the network. When the network grows to some certain size, we introduce an SIR-like disease such as pandemic influenza H1N1/09 to the population. Due to its rapid spread, the population size changes slowly, and thus the disease spreads on correlated networks with approximately fixed size. To predict the disease evolution on correlated networks, we first review two node-based SIR models incorporating degree correlations and an edge-based SIR model without considering degree correlation, and then compare the predictions of these models with stochastic SIR simulations, respectively. We find that the edge-based model, even without considering degree correlations, agrees much better than the node-based models incorporating degree correlations with stochastic SIR simulations in many respects. Moreover, simulation results show that for networks with positive correlation, the edge-based model provides a better upper bound of the cumulative incidence than the node-based SIR models, whereas for networks with negative correlation, it provides a lower bound of the cumulative incidence.

Airplane wing deformation and flight flutter detection method by using three-dimensional speckle image correlation technology.

PubMed

Wu, Jun; Yu, Zhijing; Wang, Tao; Zhuge, Jingchang; Ji, Yue; Xue, Bin

2017-06-01

Airplane wing deformation is an important element of aerodynamic characteristics, structure design, and fatigue analysis for aircraft manufacturing, as well as a main test content of certification regarding flutter for airplanes. This paper presents a novel real-time detection method for wing deformation and flight flutter detection by using three-dimensional speckle image correlation technology. Speckle patterns whose positions are determined through the vibration characteristic of the aircraft are coated on the wing; then the speckle patterns are imaged by CCD cameras which are mounted inside the aircraft cabin. In order to reduce the computation, a matching technique based on Geodetic Systems Incorporated coded points combined with the classical epipolar constraint is proposed, and a displacement vector map for the aircraft wing can be obtained through comparing the coordinates of speckle points before and after deformation. Finally, verification experiments containing static and dynamic tests by using an aircraft wing model demonstrate the accuracy and effectiveness of the proposed method.

Improved training for target detection using Fukunaga-Koontz transform and distance classifier correlation filter

NASA Astrophysics Data System (ADS)

Elbakary, M. I.; Alam, M. S.; Aslan, M. S.

2008-03-01

In a FLIR image sequence, a target may disappear permanently or may reappear after some frames and crucial information such as direction, position and size related to the target are lost. If the target reappears at a later frame, it may not be tracked again because the 3D orientation, size and location of the target might be changed. To obtain information about the target before disappearing and to detect the target after reappearing, distance classifier correlation filter (DCCF) is trained manualy by selecting a number of chips randomly. This paper introduces a novel idea to eliminates the manual intervention in training phase of DCCF. Instead of selecting the training chips manually and selecting the number of the training chips randomly, we adopted the K-means algorithm to cluster the training frames and based on the number of clusters we select the training chips such that a training chip for each cluster. To detect and track the target after reappearing in the field-ofview ,TBF and DCCF are employed. The contduced experiemnts using real FLIR sequences show results similar to the traditional agorithm but eleminating the manual intervention is the advantage of the proposed algorithm.

Improving the performance of lesion-based computer-aided detection schemes of breast masses using a case-based adaptive cueing method

NASA Astrophysics Data System (ADS)

Tan, Maxine; Aghaei, Faranak; Wang, Yunzhi; Qian, Wei; Zheng, Bin

2016-03-01

Current commercialized CAD schemes have high false-positive (FP) detection rates and also have high correlations in positive lesion detection with radiologists. Thus, we recently investigated a new approach to improve the efficacy of applying CAD to assist radiologists in reading and interpreting screening mammograms. Namely, we developed a new global feature based CAD approach/scheme that can cue the warning sign on the cases with high risk of being positive. In this study, we investigate the possibility of fusing global feature or case-based scores with the local or lesion-based CAD scores using an adaptive cueing method. We hypothesize that the information from the global feature extraction (features extracted from the whole breast regions) are different from and can provide supplementary information to the locally-extracted features (computed from the segmented lesion regions only). On a large and diverse full-field digital mammography (FFDM) testing dataset with 785 cases (347 negative and 438 cancer cases with masses only), we ran our lesion-based and case-based CAD schemes "as is" on the whole dataset. To assess the supplementary information provided by the global features, we used an adaptive cueing method to adaptively adjust the original CAD-generated detection scores (Sorg) of a detected suspicious mass region based on the computed case-based score (Scase) of the case associated with this detected region. Using the adaptive cueing method, better sensitivity results were obtained at lower FP rates (<= 1 FP per image). Namely, increases of sensitivities (in the FROC curves) of up to 6.7% and 8.2% were obtained for the ROI and Case-based results, respectively.

Curvature methods of damage detection using digital image correlation

NASA Astrophysics Data System (ADS)

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

2009-03-01

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

Neural correlates of change detection and change blindness in a working memory task.

PubMed

Pessoa, Luiz; Ungerleider, Leslie G

2004-05-01

Detecting changes in an ever-changing environment is highly advantageous, and this ability may be critical for survival. In the present study, we investigated the neural substrates of change detection in the context of a visual working memory task. Subjects maintained a sample visual stimulus in short-term memory for 6 s, and were asked to indicate whether a subsequent, test stimulus matched or did not match the original sample. To study change detection largely uncontaminated by attentional state, we compared correct change and correct no-change trials at test. Our results revealed that correctly detecting a change was associated with activation of a network comprising parietal and frontal brain regions, as well as activation of the pulvinar, cerebellum, and inferior temporal gyrus. Moreover, incorrectly reporting a change when none occurred led to a very similar pattern of activations. Finally, few regions were differentially activated by trials in which a change occurred but subjects failed to detect it (change blindness). Thus, brain activation was correlated with a subject's report of a change, instead of correlated with the physical change per se. We propose that frontal and parietal regions, possibly assisted by the cerebellum and the pulvinar, might be involved in controlling the deployment of attention to the location of a change, thereby allowing further processing of the visual stimulus. Visual processing areas, such as the inferior temporal gyrus, may be the recipients of top-down feedback from fronto-parietal regions that control the reactive deployment of attention, and thus exhibit increased activation when a change is reported (irrespective of whether it occurred or not). Whereas reporting that a change occurred, be it correctly or incorrectly, was associated with strong activation in fronto-parietal sites, change blindness appears to involve very limited territories.

Early Detection of Human Focal Seizures Based on Cortical Multiunit Activity

PubMed Central

Park, Yun S.; Hochberg, Leigh R.; Eskandar, Emad N.; Cash, Sydney S.; Truccolo, Wilson

2014-01-01

Approximately 50 million people in the world suffer from epileptic seizures. Reliable early seizure detection could bring significantly beneficial therapeutic alternatives. In recent decades, most approaches have relied on scalp EEG and intracranial EEG signals, but practical early detection for closed-loop seizure control remains challenging. In this study, we present preliminary analyses of an early detection approach based on intracortical neuronal multiunit activity (MUA) recorded from a 96-microelectrode array (MEA). The approach consists of (1) MUA detection from broadband field potentials recorded at 30 kHz by the MEA; (2) MUA feature extraction; (3) cost-sensitive support vector machine classification of ictal and interictal samples; and (4) Kalman-filtering postprocessing. MUA was here defined as the number of threshold crossing (spike counts) applied to the 300 Hz – 6 kHz bandpass filtered local field potentials in 0.1 sec time windows. MUA features explored in this study included the mean, variance, and Fano-factor, computed across the MEA channels. In addition, we used the leading eigenvalues of MUA spatial and temporal correlation matrices computed in 1-sec moving time windows. We assessed the seizure detection approach on out-of-sample data from one-participant recordings with six seizure events and 4.73-hour interictal data. The proposed MUA-based detection approach yielded a 100% sensitivity (6/6) and no false positives, and a latency of 4.17 ± 2.27 sec (mean ± SD) with respect to ECoG-identified seizure onsets. These preliminary results indicate intracortical MUA may be a useful signal for early detection of human epileptic seizures. PMID:25571313

The Maximum Cross-Correlation approach to detecting translational motions from sequential remote-sensing images

NASA Astrophysics Data System (ADS)

Gao, J.; Lythe, M. B.

1996-06-01

This paper presents the principle of the Maximum Cross-Correlation (MCC) approach in detecting translational motions within dynamic fields from time-sequential remotely sensed images. A C program implementing the approach is presented and illustrated in a flowchart. The program is tested with a pair of sea-surface temperature images derived from Advanced Very High Resolution Radiometer (AVHRR) images near East Cape, New Zealand. Results show that the mean currents in the region have been detected satisfactorily with the approach.

Fetal QRS detection and heart rate estimation: a wavelet-based approach.

PubMed

Almeida, Rute; Gonçalves, Hernâni; Bernardes, João; Rocha, Ana Paula

2014-08-01

Fetal heart rate monitoring is used for pregnancy surveillance in obstetric units all over the world but in spite of recent advances in analysis methods, there are still inherent technical limitations that bound its contribution to the improvement of perinatal indicators. In this work, a previously published wavelet transform based QRS detector, validated over standard electrocardiogram (ECG) databases, is adapted to fetal QRS detection over abdominal fetal ECG. Maternal ECG waves were first located using the original detector and afterwards a version with parameters adapted for fetal physiology was applied to detect fetal QRS, excluding signal singularities associated with maternal heartbeats. Single lead (SL) based marks were combined in a single annotator with post processing rules (SLR) from which fetal RR and fetal heart rate (FHR) measures can be computed. Data from PhysioNet with reference fetal QRS locations was considered for validation, with SLR outperforming SL including ICA based detections. The error in estimated FHR using SLR was lower than 20 bpm for more than 80% of the processed files. The median error in 1 min based FHR estimation was 0.13 bpm, with a correlation between reference and estimated FHR of 0.48, which increased to 0.73 when considering only records for which estimated FHR > 110 bpm. This allows us to conclude that the proposed methodology is able to provide a clinically useful estimation of the FHR.

Dissipation function and adaptive gradient reconstruction based smoke detection in video

NASA Astrophysics Data System (ADS)

Li, Bin; Zhang, Qiang; Shi, Chunlei

2017-11-01

A method for smoke detection in video is proposed. The camera monitoring the scene is assumed to be stationary. With the atmospheric scattering model, dissipation function is reflected transmissivity between the background objects in the scene and the camera. Dark channel prior and fast bilateral filter are used for estimating dissipation function which is only the function of the depth of field. Based on dissipation function, visual background extractor (ViBe) can be used for detecting smoke as a result of smoke's motion characteristics as well as detecting other moving targets. Since smoke has semi-transparent parts, the things which are covered by these parts can be recovered by poisson equation adaptively. The similarity between the recovered parts and the original background parts in the same position is calculated by Normalized Cross Correlation (NCC) and the original background's value is selected from the frame which is nearest to the current frame. The parts with high similarity are considered as smoke parts.

Detection of mercury compounds using invertase-glucose oxidase-based biosensor

NASA Astrophysics Data System (ADS)

Amine, A.; Cremisini, C.; Palleschi, G.

1995-10-01

Mercury compounds have been determined with an electrochemical biosensor based on invertase inhibition. When invertase is in the presence of mercury its activity decreases; this causes a decrease of glucose production which is monitored by the glucose sensor and correlated to the concentration of mercury in solution. Parameters as pH, enzyme concentration, substrate concentration, and reaction and incubation time were optimized. Mercury compounds determination using soluble or immobilized invertase were reported. Results show that the inhibition was competitive and reversible. Mercury compounds can be detected directly in aqueous solution in the range 2 - 10 ppb.

An attempt to determine the effect of increase of observation correlations on detectability and identifiability of a single gross error

NASA Astrophysics Data System (ADS)

Prószyński, Witold; Kwaśniak, Mieczysław

2016-12-01

The paper presents the results of investigating the effect of increase of observation correlations on detectability and identifiability of a single gross error, the outlier test sensitivity and also the response-based measures of internal reliability of networks. To reduce in a research a practically incomputable number of possible test options when considering all the non-diagonal elements of the correlation matrix as variables, its simplest representation was used being a matrix with all non-diagonal elements of equal values, termed uniform correlation. By raising the common correlation value incrementally, a sequence of matrix configurations could be obtained corresponding to the increasing level of observation correlations. For each of the measures characterizing the above mentioned features of network reliability the effect is presented in a diagram form as a function of the increasing level of observation correlations. The influence of observation correlations on sensitivity of the w-test for correlated observations (Förstner 1983, Teunissen 2006) is investigated in comparison with the original Baarda's w-test designated for uncorrelated observations, to determine the character of expected sensitivity degradation of the latter when used for correlated observations. The correlation effects obtained for different reliability measures exhibit mutual consistency in a satisfactory extent. As a by-product of the analyses, a simple formula valid for any arbitrary correlation matrix is proposed for transforming the Baarda's w-test statistics into the w-test statistics for correlated observations.

[Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].

PubMed

Zhou, Jinzhi; Tang, Xiaofang

2015-08-01

In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCD systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset IV a from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.

Standoff laser-based spectroscopy for explosives detection

NASA Astrophysics Data System (ADS)

Gaft, M.; Nagli, L.

2007-10-01

Real time detection and identification of explosives at a standoff distance is a major issue in efforts to develop defense against so-called Improvised Explosive Devices (IED). It is recognized that the only technique, which is potentially capable to standoff detection of minimal amounts of explosives is laser-based spectroscopy. LDS activity is based on a combination of laser-based spectroscopic methods with orthogonal capabilities. Our technique belongs to trace detection, namely to its micro-particles variety. It is based on commonly held belief that surface contamination was very difficult to avoid and could be exploited for standoff detection. We has applied optical techniques including gated Raman and time-resolved luminescence spectroscopy for detection of main explosive materials, both factory and homemade. We developed and tested a Raman system for the field remote detection and identification of minimal amounts of explosives on relevant surfaces at a distance of up to 30 meters.

Pickless event detection and location: The waveform correlation event detection system (WCEDS) revisited

DOE PAGES

Arrowsmith, Stephen John; Young, Christopher J.; Ballard, Sanford; ...

2016-01-01

The standard paradigm for seismic event monitoring breaks the event detection problem down into a series of processing stages that can be categorized at the highest level into station-level processing and network-level processing algorithms (e.g., Le Bras and Wuster (2002)). At the station-level, waveforms are typically processed to detect signals and identify phases, which may subsequently be updated based on network processing. At the network-level, phase picks are associated to form events, which are subsequently located. Furthermore, waveforms are typically directly exploited only at the station-level, while network-level operations rely on earth models to associate and locate the events thatmore » generated the phase picks.« less

A novel infrared small moving target detection method based on tracking interest points under complicated background

NASA Astrophysics Data System (ADS)

Dong, Xiabin; Huang, Xinsheng; Zheng, Yongbin; Bai, Shengjian; Xu, Wanying

2014-07-01

Infrared moving target detection is an important part of infrared technology. We introduce a novel infrared small moving target detection method based on tracking interest points under complicated background. Firstly, Difference of Gaussians (DOG) filters are used to detect a group of interest points (including the moving targets). Secondly, a sort of small targets tracking method inspired by Human Visual System (HVS) is used to track these interest points for several frames, and then the correlations between interest points in the first frame and the last frame are obtained. Last, a new clustering method named as R-means is proposed to divide these interest points into two groups according to the correlations, one is target points and another is background points. In experimental results, the target-to-clutter ratio (TCR) and the receiver operating characteristics (ROC) curves are computed experimentally to compare the performances of the proposed method and other five sophisticated methods. From the results, the proposed method shows a better discrimination of targets and clutters and has a lower false alarm rate than the existing moving target detection methods.

Temporal correlation measurements of pulsed dual CO2 lidar returns. [for atmospheric pollution detection

NASA Technical Reports Server (NTRS)

Menyuk, N.; Killinger, D. K.

1981-01-01

A pulsed dual-laser direct-detection differential-absorption lidar DIAL system, operating near 10.6 microns, is used to measure the temporal correlation and statistical properties of backscattered returns from specular and diffuse topographic targets. Results show that atmospheric-turbulence fluctuations can effectively be frozen for pulse separation times on the order of 1-3 msec or less. The diffuse target returns, however, yielded a much lower correlation than that obtained with the specular targets; this being due to uncorrelated system noise effects and different statistics for the two types of target returns.

Ladar-based IED detection

NASA Astrophysics Data System (ADS)

Engström, Philip; Larsson, Hâkan; Letalick, Dietmar

2014-05-01

An improvised explosive device (IED) is a bomb constructed and deployed in a non-standard manor. Improvised means that the bomb maker took whatever he could get his hands on, making it very hard to predict and detect. Nevertheless, the matters in which the IED's are deployed and used, for example as roadside bombs, follow certain patterns. One possible approach for early warning is to record the surroundings when it is safe and use this as reference data for change detection. In this paper a LADAR-based system for IED detection is presented. The idea is to measure the area in front of the vehicle when driving and comparing this to the previously recorded reference data. By detecting new, missing or changed objects the system can make the driver aware of probable threats.

Cross-correlation-based earthquake relocation and ambient noise imaging at Axial Seamount

NASA Astrophysics Data System (ADS)

Tan, Y. J.; Waldhauser, F.; Tolstoy, M.; Wilcock, W. S. D.

2016-12-01

The seismic network that was installed on Axial Seamount as part of the Ocean Observatory Initiative's Cabled Array has been streaming live data since November 2014, encompassing an eruption in April-May of 2015. The network includes two broadband and five short-period seismometers spanning the southern half of the caldera. Almost 200,000 local earthquakes were detected in the first year of operation. Earthquake locations based on phase picks delineate outward dipping ring faults inferred to have accommodated deflation and guided dike propagation during the eruption (Wilcock et al., submitted). We will present results from our current effort of computing cross-correlation-based double-difference hypocenter locations to derive a more detailed image of the structures that provide insight into the active processes leading up to, during, and after the volcano's eruption. The new high-resolution hypocenters will form the base catalog for real-time double-difference monitoring of the seismicity recorded by the Cabled Array, allowing for high-precision evaluation of variation in seismogenic properties. We will also present results of measurements of temporal velocity changes associated with the eruption using seismic noise cross-correlations. This method has the potential to reveal areas of dike injection and magma withdrawal, as well as for real-time monitoring of temporal velocity variations associated with active volcanic processes.

Noise-immune complex correlation for optical coherence angiography based on standard and Jones matrix optical coherence tomography

PubMed Central

Makita, Shuichi; Kurokawa, Kazuhiro; Hong, Young-Joo; Miura, Masahiro; Yasuno, Yoshiaki

2016-01-01

This paper describes a complex correlation mapping algorithm for optical coherence angiography (cmOCA). The proposed algorithm avoids the signal-to-noise ratio dependence and exhibits low noise in vasculature imaging. The complex correlation coefficient of the signals, rather than that of the measured data are estimated, and two-step averaging is introduced. Algorithms of motion artifact removal based on non perfusing tissue detection using correlation are developed. The algorithms are implemented with Jones-matrix OCT. Simultaneous imaging of pigmented tissue and vasculature is also achieved using degree of polarization uniformity imaging with cmOCA. An application of cmOCA to in vivo posterior human eyes is presented to demonstrate that high-contrast images of patients’ eyes can be obtained. PMID:27446673

Improving membrane based multiplex immunoassays for semi-quantitative detection of multiple cytokines in a single sample

PubMed Central

2014-01-01

Background Inflammatory mediators can serve as biomarkers for the monitoring of the disease progression or prognosis in many conditions. In the present study we introduce an adaptation of a membrane-based technique in which the level of up to 40 cytokines and chemokines can be determined in both human and rodent blood in a semi-quantitative way. The planar assay was modified using the LI-COR (R) detection system (fluorescence based) rather than chemiluminescence and semi-quantitative outcomes were achieved by normalizing the outcomes using the automated exposure settings of the Odyssey readout device. The results were compared to the gold standard assay, namely ELISA. Results The improved planar assay allowed the detection of a considerably higher number of analytes (n = 30 and n = 5 for fluorescent and chemiluminescent detection, respectively). The improved planar method showed high sensitivity up to 17 pg/ml and a linear correlation of the normalized fluorescence intensity with the results from the ELISA (r = 0.91). Conclusions The results show that the membrane-based technique is a semi-quantitative assay that correlates satisfactorily to the gold standard when enhanced by the use of fluorescence and subsequent semi-quantitative analysis. This promising technique can be used to investigate inflammatory profiles in multiple conditions, particularly in studies with constraints in sample sizes and/or budget. PMID:25022797

Vision Based Obstacle Detection in Uav Imaging

NASA Astrophysics Data System (ADS)

Badrloo, S.; Varshosaz, M.

2017-08-01

Detecting and preventing incidence with obstacles is crucial in UAV navigation and control. Most of the common obstacle detection techniques are currently sensor-based. Small UAVs are not able to carry obstacle detection sensors such as radar; therefore, vision-based methods are considered, which can be divided into stereo-based and mono-based techniques. Mono-based methods are classified into two groups: Foreground-background separation, and brain-inspired methods. Brain-inspired methods are highly efficient in obstacle detection; hence, this research aims to detect obstacles using brain-inspired techniques, which try to enlarge the obstacle by approaching it. A recent research in this field, has concentrated on matching the SIFT points along with, SIFT size-ratio factor and area-ratio of convex hulls in two consecutive frames to detect obstacles. This method is not able to distinguish between near and far obstacles or the obstacles in complex environment, and is sensitive to wrong matched points. In order to solve the above mentioned problems, this research calculates the dist-ratio of matched points. Then, each and every point is investigated for Distinguishing between far and close obstacles. The results demonstrated the high efficiency of the proposed method in complex environments.

Microcontroller based driver alertness detection systems to detect drowsiness

NASA Astrophysics Data System (ADS)

Adenin, Hasibah; Zahari, Rahimi; Lim, Tiong Hoo

2018-04-01

The advancement of embedded system for detecting and preventing drowsiness in a vehicle is a major challenge for road traffic accident systems. To prevent drowsiness while driving, it is necessary to have an alert system that can detect a decline in driver concentration and send a signal to the driver. Studies have shown that traffc accidents usually occur when the driver is distracted while driving. In this paper, we have reviewed a number of detection systems to monitor the concentration of a car driver and propose a portable Driver Alertness Detection System (DADS) to determine the level of concentration of the driver based on pixelated coloration detection technique using facial recognition. A portable camera will be placed at the front visor to capture facial expression and the eye activities. We evaluate DADS using 26 participants and have achieved 100% detection rate with good lighting condition and a low detection rate at night.

Pipeline Processing With an Iterative, Context-Based Detection Model

DTIC Science & Technology

2015-04-19

pattern detectors , correlation detectors , subspace detectors , matched field detectors , nuclear explosion monitoring 16. SECURITY CLASSIFICATION OF: 17...38 13. 3 days of SPAO-BHZ data which is dominated by signals from nearby icequakes. .... 39 14. (Top) 94 detections produced by detector ...92532 and (bottom) 148 detections from detector 92541 produced during the first run of the framework. .................................. 40 15. The 49

A powerful nonparametric method for detecting differentially co-expressed genes: distance correlation screening and edge-count test.

PubMed

Zhang, Qingyang

2018-05-16

Differential co-expression analysis, as a complement of differential expression analysis, offers significant insights into the changes in molecular mechanism of different phenotypes. A prevailing approach to detecting differentially co-expressed genes is to compare Pearson's correlation coefficients in two phenotypes. However, due to the limitations of Pearson's correlation measure, this approach lacks the power to detect nonlinear changes in gene co-expression which is common in gene regulatory networks. In this work, a new nonparametric procedure is proposed to search differentially co-expressed gene pairs in different phenotypes from large-scale data. Our computational pipeline consisted of two main steps, a screening step and a testing step. The screening step is to reduce the search space by filtering out all the independent gene pairs using distance correlation measure. In the testing step, we compare the gene co-expression patterns in different phenotypes by a recently developed edge-count test. Both steps are distribution-free and targeting nonlinear relations. We illustrate the promise of the new approach by analyzing the Cancer Genome Atlas data and the METABRIC data for breast cancer subtypes. Compared with some existing methods, the new method is more powerful in detecting nonlinear type of differential co-expressions. The distance correlation screening can greatly improve computational efficiency, facilitating its application to large data sets.

Optical-Correlator Neural Network Based On Neocognitron

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Stoner, William W.

1994-01-01

Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

Quadratic correlation filters for optical correlators

NASA Astrophysics Data System (ADS)

Mahalanobis, Abhijit; Muise, Robert R.; Vijaya Kumar, Bhagavatula V. K.

2003-08-01

Linear correlation filters have been implemented in optical correlators and successfully used for a variety of applications. The output of an optical correlator is usually sensed using a square law device (such as a CCD array) which forces the output to be the squared magnitude of the desired correlation. It is however not a traditional practice to factor the effect of the square-law detector in the design of the linear correlation filters. In fact, the input-output relationship of an optical correlator is more accurately modeled as a quadratic operation than a linear operation. Quadratic correlation filters (QCFs) operate directly on the image data without the need for feature extraction or segmentation. In this sense, the QCFs retain the main advantages of conventional linear correlation filters while offering significant improvements in other respects. Not only is more processing required to detect peaks in the outputs of multiple linear filters, but choosing a winner among them is an error prone task. In contrast, all channels in a QCF work together to optimize the same performance metric and produce a combined output that leads to considerable simplification of the post-processing. In this paper, we propose a novel approach to the design of quadratic correlation based on the Fukunaga Koontz transform. Although quadratic filters are known to be optimum when the data is Gaussian, it is expected that they will perform as well as or better than linear filters in general. Preliminary performance results are provided that show that quadratic correlation filters perform better than their linear counterparts.

An object-based classification method for automatic detection of lunar impact craters from topographic data

NASA Astrophysics Data System (ADS)

Vamshi, Gasiganti T.; Martha, Tapas R.; Vinod Kumar, K.

2016-05-01

Identification of impact craters is a primary requirement to study past geological processes such as impact history. They are also used as proxies for measuring relative ages of various planetary or satellite bodies and help to understand the evolution of planetary surfaces. In this paper, we present a new method using object-based image analysis (OBIA) technique to detect impact craters of wide range of sizes from topographic data. Multiresolution image segmentation of digital terrain models (DTMs) available from the NASA's LRO mission was carried out to create objects. Subsequently, objects were classified into impact craters using shape and morphometric criteria resulting in 95% detection accuracy. The methodology developed in a training area in parts of Mare Imbrium in the form of a knowledge-based ruleset when applied in another area, detected impact craters with 90% accuracy. The minimum and maximum sizes (diameters) of impact craters detected in parts of Mare Imbrium by our method are 29 m and 1.5 km, respectively. Diameters of automatically detected impact craters show good correlation (R2 > 0.85) with the diameters of manually detected impact craters.

Immunity-Based Aircraft Fault Detection System

NASA Technical Reports Server (NTRS)

Dasgupta, D.; KrishnaKumar, K.; Wong, D.; Berry, M.

2004-01-01

In the study reported in this paper, we have developed and applied an Artificial Immune System (AIS) algorithm for aircraft fault detection, as an extension to a previous work on intelligent flight control (IFC). Though the prior studies had established the benefits of IFC, one area of weakness that needed to be strengthened was the control dead band induced by commanding a failed surface. Since the IFC approach uses fault accommodation with no detection, the dead band, although it reduces over time due to learning, is present and causes degradation in handling qualities. If the failure can be identified, this dead band can be further A ed to ensure rapid fault accommodation and better handling qualities. The paper describes the application of an immunity-based approach that can detect a broad spectrum of known and unforeseen failures. The approach incorporates the knowledge of the normal operational behavior of the aircraft from sensory data, and probabilistically generates a set of pattern detectors that can detect any abnormalities (including faults) in the behavior pattern indicating unsafe in-flight operation. We developed a tool called MILD (Multi-level Immune Learning Detection) based on a real-valued negative selection algorithm that can generate a small number of specialized detectors (as signatures of known failure conditions) and a larger set of generalized detectors for unknown (or possible) fault conditions. Once the fault is detected and identified, an adaptive control system would use this detection information to stabilize the aircraft by utilizing available resources (control surfaces). We experimented with data sets collected under normal and various simulated failure conditions using a piloted motion-base simulation facility. The reported results are from a collection of test cases that reflect the performance of the proposed immunity-based fault detection algorithm.

A Portable Smart-Phone Readout Device for the Detection of Mercury Contamination Based on an Aptamer-Assay Nanosensor.

PubMed

Xiao, Wei; Xiao, Meng; Fu, Qiangqiang; Yu, Shiting; Shen, Haicong; Bian, Hongfen; Tang, Yong

2016-11-08

The detection of environmental mercury (Hg) contamination requires complex and expensive instruments and professional technicians. We present a simple, sensitive, and portable Hg 2+ detection system based on a smartphone and colorimetric aptamer nanosensor. A smartphone equipped with a light meter app was used to detect, record, and process signals from a smartphone-based microwell reader (MR S-phone), which is composed of a simple light source and a miniaturized assay platform. The colorimetric readout of the aptamer nanosensor is based on a specific interaction between the selected aptamer and Hg 2+ , which leads to a color change in the reaction solution due to an aggregation of gold nanoparticles (AuNPs). The MR S-phone-based AuNPs-aptamer colorimetric sensor system could reliably detect Hg 2+ in both tap water and Pearl River water samples and produced a linear colorimetric readout of Hg 2+ concentration in the range of 1 ng/mL-32 ng/mL with a correlation of 0.991, and a limit of detection (LOD) of 0.28 ng/mL for Hg 2+ . The detection could be quickly completed in only 20 min. Our novel mercury detection assay is simple, rapid, and sensitive, and it provides new strategies for the on-site detection of mercury contamination in any environment.

Force measurement-based discontinuity detection during friction stir welding

DOE PAGES

Shrivastava, Amber; Zinn, Michael; Duffie, Neil A.; ...

2017-02-23

Here, the objective of this work is to develop a method for detecting the creation of discontinuities ( i.e., voids, volume defects) during friction stir welding. Friction stir welding is inherently cost effective, however, the need for significant weld inspection can make the process cost prohibitive. A new approach to weld inspection is required in which an in situ characterization of weld quality can be obtained, reducing the need for postprocess inspection. To this end, friction stir welds with subsurface voids and without voids were created. The subsurface voids were generated by reducing the friction stir tool rotation frequency andmore » increasing the tool traverse speed in order to create “colder” welds. Process forces were measured during welding, and the void sizes were measured postprocess by computerized tomography ( i.e., 3D X-ray imaging). Two parameters, based on frequency domain content and time-domain average of the force signals, were found to be correlated with void size. Criteria for subsurface void detection and size prediction were developed and shown to be in good agreement with experimental observations. Furthermore, with the proper choice of data acquisition system and frequency analyzer the occurrence of subsurface voids can be detected in real time.« less

Force measurement-based discontinuity detection during friction stir welding

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

Shrivastava, Amber; Zinn, Michael; Duffie, Neil A.

Here, the objective of this work is to develop a method for detecting the creation of discontinuities ( i.e., voids, volume defects) during friction stir welding. Friction stir welding is inherently cost effective, however, the need for significant weld inspection can make the process cost prohibitive. A new approach to weld inspection is required in which an in situ characterization of weld quality can be obtained, reducing the need for postprocess inspection. To this end, friction stir welds with subsurface voids and without voids were created. The subsurface voids were generated by reducing the friction stir tool rotation frequency andmore » increasing the tool traverse speed in order to create “colder” welds. Process forces were measured during welding, and the void sizes were measured postprocess by computerized tomography ( i.e., 3D X-ray imaging). Two parameters, based on frequency domain content and time-domain average of the force signals, were found to be correlated with void size. Criteria for subsurface void detection and size prediction were developed and shown to be in good agreement with experimental observations. Furthermore, with the proper choice of data acquisition system and frequency analyzer the occurrence of subsurface voids can be detected in real time.« less

Feature-based alert correlation in security systems using self organizing maps

NASA Astrophysics Data System (ADS)

Kumar, Munesh; Siddique, Shoaib; Noor, Humera

2009-04-01

The security of the networks has been an important concern for any organization. This is especially important for the defense sector as to get unauthorized access to the sensitive information of an organization has been the prime desire for cyber criminals. Many network security techniques like Firewall, VPN Concentrator etc. are deployed at the perimeter of network to deal with attack(s) that occur(s) from exterior of network. But any vulnerability that causes to penetrate the network's perimeter of defense, can exploit the entire network. To deal with such vulnerabilities a system has been evolved with the purpose of generating an alert for any malicious activity triggered against the network and its resources, termed as Intrusion Detection System (IDS). The traditional IDS have still some deficiencies like generating large number of alerts, containing both true and false one etc. By automatically classifying (correlating) various alerts, the high-level analysis of the security status of network can be identified and the job of network security administrator becomes much easier. In this paper we propose to utilize Self Organizing Maps (SOM); an Artificial Neural Network for correlating large amount of logged intrusion alerts based on generic features such as Source/Destination IP Addresses, Port No, Signature ID etc. The different ways in which alerts can be correlated by Artificial Intelligence techniques are also discussed. . We've shown that the strategy described in the paper improves the efficiency of IDS by better correlating the alerts, leading to reduced false positives and increased competence of network administrator.

Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface

NASA Astrophysics Data System (ADS)

Chen, Xiaogang; Wang, Yijun; Gao, Shangkai; Jung, Tzyy-Ping; Gao, Xiaorong

2015-08-01

Objective. Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established. Approach. This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8-15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects. Main results. The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of ˜33.3 characters/min, the online BCI speller obtained an average ITR of 151.18 ± 20.34 bits min-1. Significance. By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.

On event-based optical flow detection

PubMed Central

Brosch, Tobias; Tschechne, Stephan; Neumann, Heiko

2015-01-01

Event-based sensing, i.e., the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition by standard cameras. Here, we systematically investigate the implications of event-based sensing in the context of visual motion, or flow, estimation. Starting from a common theoretical foundation, we discuss different principal approaches for optical flow detection ranging from gradient-based methods over plane-fitting to filter based methods and identify strengths and weaknesses of each class. Gradient-based methods for local motion integration are shown to suffer from the sparse encoding in address-event representations (AER). Approaches exploiting the local plane like structure of the event cloud, on the other hand, are shown to be well suited. Within this class, filter based approaches are shown to define a proper detection scheme which can also deal with the problem of representing multiple motions at a single location (motion transparency). A novel biologically inspired efficient motion detector is proposed, analyzed and experimentally validated. Furthermore, a stage of surround normalization is incorporated. Together with the filtering this defines a canonical circuit for motion feature detection. The theoretical analysis shows that such an integrated circuit reduces motion ambiguity in addition to decorrelating the representation of motion related activations. PMID:25941470

Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity.

PubMed

Van Hertem, T; Maltz, E; Antler, A; Romanini, C E B; Viazzi, S; Bahr, C; Schlageter-Tello, A; Lokhorst, C; Berckmans, D; Halachmi, I

2013-07-01

The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farm's daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cow's performance was measured with a milk yield meter in the milking parlor. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY=0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4d before diagnosis; the slope coefficient of the daily milk yield 4d before diagnosis; the nighttime to daytime neck activity ratio 6d before diagnosis; the milk yield week difference ratio 4d before diagnosis; the milk yield week difference 4d before diagnosis; the neck activity level during the daytime 7d before diagnosis; the ruminating time during nighttime 6d before diagnosis). After a 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame

Daytime Water Detection Based on Sky Reflections

NASA Technical Reports Server (NTRS)

Rankin, Arturo; Matthies, Larry; Bellutta, Paolo

2011-01-01

A water body s surface can be modeled as a horizontal mirror. Water detection based on sky reflections and color variation are complementary. A reflection coefficient model suggests sky reflections dominate the color of water at ranges > 12 meters. Water detection based on sky reflections: (1) geometrically locates the pixel in the sky that is reflecting on a candidate water pixel on the ground (2) predicts if the ground pixel is water based on color similarity and local terrain features. Water detection has been integrated on XUVs.

Indirectly detected chemical shift correlation NMR spectroscopy in solids under fast magic angle spinning

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

Mao, Kanmi

The development of fast magic angle spinning (MAS) opened up an opportunity for the indirect detection of insensitive low-γ nuclei (e.g., 13C and 15N) via the sensitive high-{gamma} nuclei (e.g., 1H and 19F) in solid-state NMR, with advanced sensitivity and resolution. In this thesis, new methodology utilizing fast MAS is presented, including through-bond indirectly detected heteronuclear correlation (HETCOR) spectroscopy, which is assisted by multiple RF pulse sequences for 1H- 1H homonuclear decoupling. Also presented is a simple new strategy for optimization of 1H- 1H homonuclear decoupling. As applications, various classes of materials, such as catalytic nanoscale materials, biomolecules, and organic complexes, are studied by combining indirect detection and other one-dimensional (1D) and two-dimensional (2D) NMR techniques. Indirectly detected through-bond HETCOR spectroscopy utilizing refocused INEPT (INEPTR) mixing was developed under fast MAS (Chapter 2). The time performance of this approach in 1H detected 2D 1H{l_brace} 13C{r_brace} spectra was significantly improved, by a factor of almost 10, compared to the traditional 13C detected experiments, as demonstrated by measuring naturally abundant organic-inorganic mesoporous hybrid materials. The through-bond scheme was demonstrated as a new analytical tool, which provides complementary structural information in solid-state systems in addition to through-space correlation. To further benefit the sensitivity of the INEPT transfer in rigid solids, the combined rotation and multiple-pulse spectroscopy (CRAMPS) was implemented for homonuclear 1H decoupling under fast MAS (Chapter 3). Several decoupling schemes (PMLG5 m more » $$\\bar{x}$$, PMLG5 mm $$\\bar{x}$$x and SAM3) were analyzed to maximize the performance of through-bond transfer based on decoupling efficiency as well as scaling factors. Indirect detection with assistance of PMLG m $$\\bar{x}$$ during INEPTR transfer proved to offer the highest

Patterns and Correlates of PrEP Drug Detection among MSM and Transgender Women in the Global iPrEx Study

PubMed Central

Liu, Albert; Glidden, David V.; Anderson, Peter L.; Amico, K.R.; McMahan, Vanessa; Mehrotra, Megha; Lama, Javier R.; MacRae, John; Hinojosa, Juan Carlos; Montoya, Orlando; Veloso, Valdilea G.; Schechter, Mauro; Kallas, Esper G.; Chariyalerstak, Suwat; Bekker, Linda-Gail; Mayer, Kenneth; Buchbinder, Susan; Grant, Robert

2014-01-01

Background Adherence to pre-exposure prophylaxis (PrEP) is critical for efficacy. Antiretroviral concentrations are an objective measure of PrEP use and correlate with efficacy. Understanding patterns and correlates of drug detection can identify populations at risk for non-adherence and inform design of PrEP adherence interventions. Methods Blood antiretroviral concentrations were assessed among active-arm participants in iPrEx, a randomized, placebo-controlled trial of emtricitabine/tenofovir in men who have sex with men (MSM) and transgender women in 6 countries. We evaluated rates and correlates of drug detection among a random sample of 470 participants at week 8 and a longitudinal cohort of 303 participants through 72 weeks of follow-up. Results Overall, 55% (95% CI 49–60%) of participants tested at week 8 had drug detected. Drug detection was associated with older age and varied by study site. In longitudinal analysis, 31% never had drug detected, 30% always had drug detected, and 39% had an inconsistent pattern. Overall detection rates declined over time. Drug detection at some or all visits was associated with older age; indices of sexual risk, including condomless receptive anal sex; and responding "don't know" to a question about belief of PrEP efficacy (0–10 scale). Conclusions Distinct patterns of study-product use were identified, with a significant proportion demonstrating no drug detection at any visit. Research literacy may explain greater drug detection among populations having greater research experience, such as older MSM in the US. Greater drug detection among those reporting highest-risk sexual practices is expected to increase the impact and cost-effectiveness of PrEP. PMID:25230290

Multivariate η-μ fading distribution with arbitrary correlation model

NASA Astrophysics Data System (ADS)

Ghareeb, Ibrahim; Atiani, Amani

2018-03-01

An extensive analysis for the multivariate ? distribution with arbitrary correlation is presented, where novel analytical expressions for the multivariate probability density function, cumulative distribution function and moment generating function (MGF) of arbitrarily correlated and not necessarily identically distributed ? power random variables are derived. Also, this paper provides exact-form expression for the MGF of the instantaneous signal-to-noise ratio at the combiner output in a diversity reception system with maximal-ratio combining and post-detection equal-gain combining operating in slow frequency nonselective arbitrarily correlated not necessarily identically distributed ?-fading channels. The average bit error probability of differentially detected quadrature phase shift keying signals with post-detection diversity reception system over arbitrarily correlated and not necessarily identical fading parameters ?-fading channels is determined by using the MGF-based approach. The effect of fading correlation between diversity branches, fading severity parameters and diversity level is studied.

Experimental Study on GFRP Surface Cracks Detection Using Truncated-Correlation Photothermal Coherence Tomography

NASA Astrophysics Data System (ADS)

Wang, Fei; Liu, Junyan; Mohummad, Oliullah; Wang, Yang

2018-04-01

In this paper, truncated-correlation photothermal coherence tomography (TC-PCT) was used as a nondestructive inspection technique to evaluate glass-fiber reinforced polymer (GFRP) composite surface cracks. Chirped-pulsed signal that combines linear frequency modulation and pulse excitation was proposed as an excitation signal to detect GFRP composite surface cracks. The basic principle of TC-PCT and extraction algorithm of the thermal wave signal feature was described. The comparison experiments between lock-in thermography, thermal wave radar imaging and chirped-pulsed photothermal radar for detecting GFRP artificial surface cracks were carried out. Experimental results illustrated that chirped-pulsed photothermal radar has the merits of high signal-to-noise ratio in detecting GFRP composite surface cracks. TC-PCT as a depth-resolved photothermal imaging modality was employed to enable three-dimensional visualization of GFRP composite surface cracks. The results showed that TC-PCT can effectively evaluate the cracks depth of GFRP composite.

Integrating physically based simulators with Event Detection Systems: Multi-site detection approach.

PubMed

Housh, Mashor; Ohar, Ziv

2017-03-01

The Fault Detection (FD) Problem in control theory concerns of monitoring a system to identify when a fault has occurred. Two approaches can be distinguished for the FD: Signal processing based FD and Model-based FD. The former concerns of developing algorithms to directly infer faults from sensors' readings, while the latter uses a simulation model of the real-system to analyze the discrepancy between sensors' readings and expected values from the simulation model. Most contamination Event Detection Systems (EDSs) for water distribution systems have followed the signal processing based FD, which relies on analyzing the signals from monitoring stations independently of each other, rather than evaluating all stations simultaneously within an integrated network. In this study, we show that a model-based EDS which utilizes a physically based water quality and hydraulics simulation models, can outperform the signal processing based EDS. We also show that the model-based EDS can facilitate the development of a Multi-Site EDS (MSEDS), which analyzes the data from all the monitoring stations simultaneously within an integrated network. The advantage of the joint analysis in the MSEDS is expressed by increased detection accuracy (higher true positive alarms and fewer false alarms) and shorter detection time. Copyright © 2016 Elsevier Ltd. All rights reserved.

Vision-based vehicle detection and tracking algorithm design

NASA Astrophysics Data System (ADS)

Hwang, Junyeon; Huh, Kunsoo; Lee, Donghwi

2009-12-01

The vision-based vehicle detection in front of an ego-vehicle is regarded as promising for driver assistance as well as for autonomous vehicle guidance. The feasibility of vehicle detection in a passenger car requires accurate and robust sensing performance. A multivehicle detection system based on stereo vision has been developed for better accuracy and robustness. This system utilizes morphological filter, feature detector, template matching, and epipolar constraint techniques in order to detect the corresponding pairs of vehicles. After the initial detection, the system executes the tracking algorithm for the vehicles. The proposed system can detect front vehicles such as the leading vehicle and side-lane vehicles. The position parameters of the vehicles located in front are obtained based on the detection information. The proposed vehicle detection system is implemented on a passenger car, and its performance is verified experimentally.

Infrared moving small target detection based on saliency extraction and image sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Xiaomin; Ren, Kan; Gao, Jin; Li, Chaowei; Gu, Guohua; Wan, Minjie

2016-10-01

Moving small target detection in infrared image is a crucial technique of infrared search and tracking system. This paper present a novel small target detection technique based on frequency-domain saliency extraction and image sparse representation. First, we exploit the features of Fourier spectrum image and magnitude spectrum of Fourier transform to make a rough extract of saliency regions and use a threshold segmentation system to classify the regions which look salient from the background, which gives us a binary image as result. Second, a new patch-image model and over-complete dictionary were introduced to the detection system, then the infrared small target detection was converted into a problem solving and optimization process of patch-image information reconstruction based on sparse representation. More specifically, the test image and binary image can be decomposed into some image patches follow certain rules. We select the target potential area according to the binary patch-image which contains salient region information, then exploit the over-complete infrared small target dictionary to reconstruct the test image blocks which may contain targets. The coefficients of target image patch satisfy sparse features. Finally, for image sequence, Euclidean distance was used to reduce false alarm ratio and increase the detection accuracy of moving small targets in infrared images due to the target position correlation between frames.

A novel sensitive pathogen detection system based on Microbead Quantum Dot System.

PubMed

Wu, Tzong-Yuan; Su, Yi-Yu; Shu, Wei-Hsien; Mercado, Augustus T; Wang, Shi-Kwun; Hsu, Ling-Yi; Tsai, Yow-Fu; Chen, Chung-Yung

2016-04-15

A fast and accurate detection system for pathogens can provide immediate measurements for the identification of infectious agents. Therefore, the Microbead Quantum-dots Detection System (MQDS) was developed to identify and measure target DNAs of pathogenic microorganisms and eliminated the need of PCR amplifications. This nanomaterial-based technique can detect different microorganisms by flow cytometry measurements. In MQDS, pathogen specific DNA probes were designed to form a hairpin structure and conjugated on microbeads. In the presence of the complementary target DNA sequence, the probes will compete for binding with the reporter probes but will not interfere with the binding between the probe and internal control DNA. To monitor the binding process by flow cytometry, both the reporter probes and internal control probes were conjugated with Quantum dots that fluoresce at different emission wavelengths using the click reaction. When MQDS was used to detect the pathogens in environmental samples, a high correlation coefficient (R=0.994) for Legionella spp., with a detection limit of 0.1 ng of the extracted DNAs and 10 CFU/test, can be achieved. Thus, this newly developed technique can also be applied to detect other pathogens, particularly viruses and other genetic diseases. Copyright © 2015 Elsevier B.V. All rights reserved.

Laser-based detection of chemical contraband

NASA Astrophysics Data System (ADS)

Clemmer, Robert G.; Kelly, James F.; Martin, Steven W.; Mong, Gary M.; Sharpe, Steven W.

1997-02-01

The goal of our work is tow fold; 1) develop a portable and rapid laser based air sampler for detection of specific chemical contraband and 2) compile a spectral data base in both the near- and mid-IR of sufficiently high quality to be useful for gas phase spectroscopic identification of chemical contraband. During the synthesis or 'cooking' of many illicit chemical substances, relatively high concentrations of volatile solvents, chemical precursors and byproducts are unavoidably released to the atmosphere. In some instances, the final product may have sufficient vapor pressure to be detectable in the surrounding air. The detection of a single high-value effluent or the simultaneous detection of two or more low-value effluents can be used as reliable indicators of a nearby clandestine cooking operation. The designation of high- versus low-value effluent reflects both the commercial availability and legitimate usage of a specific chemical. This paper will describe PNNL's progress and efforts towards the development of a portable laser based air sampling system for the detection of clandestine manufacturing of methamphetamine. Although our current efforts ar focused on methamphetamine, we see no fundamental limitations on detection of other forms of chemical contraband manufacturing. This also includes the synthesis of certain classes of chemical weapons that have recently been deployed by terrorist groups.

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.

Detecting particle dark matter signatures by cross-correlating γ-ray anisotropies with weak lensing

NASA Astrophysics Data System (ADS)

Camera, S.; Fornasa, M.; Fornengo, N.; Regis, M.

2016-05-01

The underlying nature of dark matter still represents one of the fundamental questions in contemporary cosmology. Although observations well agree with its description in terms of a new fundamental particle, neither direct nor indirect signatures of its particle nature have been detected so far, despite a strong experimental effort. Similarly, particle accelerators have hitherto failed at producing dark matter particles in collider physics experiments. Here, we illustrate how the cross-correlation between anisotropies in the diffuse γ-ray background and weak gravitational lensing effects represents a novel promising way in the quest of detecting particle dark matter signatures.

Determination of melamine of milk based on two-dimensional correlation infrared spectroscopy

NASA Astrophysics Data System (ADS)

Yang, Ren-jie; Liu, Rong; Xu, Kexin

2012-03-01

The adulteration of milk with harmful substances is a threat to public health and beyond question a serious crime. In order to develop a rapid, cost-effective, high-throughput analysis method for detecting of adulterants in milk, the discriminative analysis of melamine is established in milk based on the two-dimensional (2D) correlation infrared spectroscopy in present paper. Pure milk samples and adulterated milk samples with different content of melamine were prepared. Then the Fourier Transform Infrared spectra of all samples were measured at room temperature. The characteristics of pure milk and adulterated milk were studied by one-dimensional spectra. The 2D NIR and 2D IR correlation spectroscopy were calculated under the perturbation of adulteration concentration. In the range from 1400 to 1800 cm-1, two strong autopeaks were aroused by melamine in milk at 1464 cm-1 and 1560 cm-1 in synchronous spectrum. At the same time, the 1560 cm-1 band does not share cross peak with the 1464 cm-1 band, which further confirm that the two bands have the same origin. Also in the range from 4200 to 4800 cm-1, the autopeak was shown at 4648 cm-1 in synchronous spectrum of melamine in milk. 2D NIR-IR hetero-spectral correlation analysis confirmed that the bands at 1464, 1560 and 4648 cm-1 had the same origin. The results demonstrated that the adulterant can be discriminated correctly by 2D correlation infrared spectroscopy.

Adulteration detection in milk using infrared spectroscopy combined with two-dimensional correlation analysis

NASA Astrophysics Data System (ADS)

He, Bin; Liu, Rong; Yang, Renjie; Xu, Kexin

2010-02-01

Adulteration of milk and dairy products has brought serious threats to human health as well as enormous economic losses to the food industry. Considering the diversity of adulterants possibly mixed in milk, such as melamine, urea, tetracycline, sugar/salt and so forth, a rapid, widely available, high-throughput, cost-effective method is needed for detecting each of the components in milk at once. In this paper, a method using Fourier Transform Infrared spectroscopy (FTIR) combined with two-dimensional (2D) correlation spectroscopy is established for the discriminative analysis of adulteration in milk. Firstly, the characteristic peaks of the raw milk are found in the 4000-400 cm-1 region by its original spectra. Secondly, the adulterant samples are respectively detected with the same method to establish a spectral database for subsequent comparison. Then, 2D correlation spectra of the samples are obtained which have high time resolution and can provide information about concentration-dependent intensity changes not readily accessible from one-dimensional spectra. And the characteristic peaks in the synchronous 2D correlation spectra of the suspected samples are compared with those of raw milk. The differences among their synchronous spectra imply that the suspected milk sample must contain some kinds of adulterants. Melamine, urea, tetracycline and glucose adulterants in milk are identified respectively. This nondestructive method can be used for a correct discrimination on whether the milk and dairy products are adulterated with deleterious substances and it provides a new simple and cost-effective alternative to test the components of milk.

Electrochemical cell-based chip for the detection of toxic effects of bisphenol-A on neuroblastoma cells.

PubMed

Kafi, Md Abdul; Kim, Tae-Hyung; An, Jeung Hee; Choi, Jeong-Woo

2011-03-15

A cell-based chip was fabricated for the electrochemical detection of the dose-dependent effects of bisphenol-A (BPA) on neuroblastoma cells (SH-SY5Y), which showed dual-mode correlation as a standard curve. Toxicity assessment of BPA became very important in environmental toxicants detection since BPA can be reached out easily from various common plastic-based product and give negative cellular effects on living organism. Cell chip was fabricated by immobilizing cells on C(RGD)(4) peptide coated electrode to detect the cytotoxicity of BPA electrochemically. Redox properties in living cells were determined by cyclic voltammetry using a home-made three-electrode system, and the cathodic peak current (I(pc)) was used as a parameter for measurement of the effect of BPA on cell viability. The peak current, I(pc) value increased with the concentration of BPA up to 300 nM and then decreased because of the stimulation of cancer cell activity at the concentration of BPA below 300nM and cytotoxicity at the concentration of BPA above 300 nM, respectively. MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay and optical microscopy-based morphological analysis confirmed the results of electrochemical study. This dual-mode correlation between the concentration of BPA and voltammetric signal intensity should be firstly considered to analyze its dose-dependent stimulus and cytotoxic effects on neuroblastoma cells by cell chip. Copyright © 2010 Elsevier B.V. All rights reserved.

Search for long distance correlations between extensive air showers detected by the EEE network

NASA Astrophysics Data System (ADS)

Abbrescia, M.; Baldini, L.; Baldini Ferroli, R.; Batignani, G.; Battaglieri, M.; Boi, S.; Bossini, E.; Carnesecchi, F.; Chiavassa, A.; Cicalo, C.; Cifarelli, L.; Coccetti, F.; Coccia, E.; De Gruttola, D.; De Pasquale, S.; Fabbri, F. L.; Frolov, V.; Galeotti, P.; Garbini, M.; Gemme, G.; Gnesi, I.; Grazzi, S.; Gustavino, C.; Hatzifotiadou, D.; La Rocca, P.; Mandaglio, G.; Maragoto Rodriguez, O.; Maron, G.; Mazziotta, M. N.; Miozzi, S.; Nania, R.; Noferini, F.; Nozzoli, F.; Palmonari, F.; Panareo, M.; Panetta, M. P.; Paoletti, R.; Park, W.; Perasso, L.; Pilo, F.; Piragino, G.; Pisano, S.; Riggi, F.; Righini, G. C.; Ripoli, C.; Sartorelli, G.; Scapparone, E.; Schioppa, M.; Scribano, A.; Selvi, M.; Serci, S.; Squarcia, S.; Taiuti, M.; Terreni, G.; Trifirò, A.; Trimarchi, M.; Vistoli, M. C.; Votano, L.; Williams, M. C. S.; Zheng, L.; Zichichi, A.; Zuyeuski, R.

2018-02-01

A search for long distance correlations between individual Extensive Air Showers (EAS) detected by pairs of MRPC telescopes of the Extreme Energy Events (EEE) network was carried out. The search for an anomaly in these events is the purpose of our work. A dataset obtained by all the possible 45 pairs between 10 EEE cluster sites (hosting at least two telescopes), located at relative distances between 86 and 1200km, was analyzed, corresponding to an overall period of 3968 days time exposure. To estimate the possible event excess with respect to the spurious rate, the number of coincidence events was extracted as a function of the time difference between the arrival of the showers in the two sites, from ± 10 s to the smallest time interval where events are still observed. The analysis was done taking into account both the time and orientation correlation between the showers detected by the telescope pairs. A few candidate events with unusually small time difference and angular distance were observed, with a p-value sensibly smaller than a confidence level of 0.05.

Motion-Based Immunological Detection of Zika Virus Using Pt-Nanomotors and a Cellphone.

PubMed

Draz, Mohamed Shehata; Lakshminaraasimulu, Nivethitha Kota; Krishnakumar, Sanchana; Battalapalli, Dheerendranath; Vasan, Anish; Kanakasabapathy, Manoj Kumar; Sreeram, Aparna; Kallakuri, Shantanu; Thirumalaraju, Prudhvi; Li, Yudong; Hua, Stephane; Yu, Xu G; Kuritzkes, Daniel R; Shafiee, Hadi

2018-05-16

Zika virus (ZIKV) infection is an emerging pandemic threat to humans that can be fatal in newborns. Advances in digital health systems and nanoparticles can facilitate the development of sensitive and portable detection technologies for timely management of emerging viral infections. Here we report a nanomotor-based bead-motion cellphone (NBC) system for the immunological detection of ZIKV. The presence of virus in a testing sample results in the accumulation of platinum (Pt)-nanomotors on the surface of beads, causing their motion in H 2 O 2 solution. Then the virus concentration is detected in correlation with the change in beads motion. The developed NBC system was capable of detecting ZIKV in samples with virus concentrations as low as 1 particle/μL. The NBC system allowed a highly specific detection of ZIKV in the presence of the closely related dengue virus and other neurotropic viruses, such as herpes simplex virus type 1 and human cytomegalovirus. The NBC platform technology has the potential to be used in the development of point-of-care diagnostics for pathogen detection and disease management in developed and developing countries.

A Portable Smart-Phone Readout Device for the Detection of Mercury Contamination Based on an Aptamer-Assay Nanosensor

PubMed Central

Xiao, Wei; Xiao, Meng; Fu, Qiangqiang; Yu, Shiting; Shen, Haicong; Bian, Hongfen; Tang, Yong

2016-01-01

The detection of environmental mercury (Hg) contamination requires complex and expensive instruments and professional technicians. We present a simple, sensitive, and portable Hg2+ detection system based on a smartphone and colorimetric aptamer nanosensor. A smartphone equipped with a light meter app was used to detect, record, and process signals from a smartphone-based microwell reader (MR S-phone), which is composed of a simple light source and a miniaturized assay platform. The colorimetric readout of the aptamer nanosensor is based on a specific interaction between the selected aptamer and Hg2+, which leads to a color change in the reaction solution due to an aggregation of gold nanoparticles (AuNPs). The MR S-phone-based AuNPs-aptamer colorimetric sensor system could reliably detect Hg2+ in both tap water and Pearl River water samples and produced a linear colorimetric readout of Hg2+ concentration in the range of 1 ng/mL–32 ng/mL with a correlation of 0.991, and a limit of detection (LOD) of 0.28 ng/mL for Hg2+. The detection could be quickly completed in only 20 min. Our novel mercury detection assay is simple, rapid, and sensitive, and it provides new strategies for the on-site detection of mercury contamination in any environment. PMID:27834794

Transistor-based particle detection systems and methods

DOEpatents

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

2015-06-09

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

Analog Correlator Based on One Bit Digital Correlator

NASA Technical Reports Server (NTRS)

Prokop, Norman (Inventor); Krasowski, Michael (Inventor)

2017-01-01

A two input time domain correlator may perform analog correlation. In order to achieve high throughput rates with reduced or minimal computational overhead, the input data streams may be hard limited through adaptive thresholding to yield two binary bit streams. Correlation may be achieved through the use of a Hamming distance calculation, where the distance between the two bit streams approximates the time delay that separates them. The resulting Hamming distance approximates the correlation time delay with high accuracy.

Detection of rheumatoid arthritis by evaluation of normalized variances of fluorescence time correlation functions

NASA Astrophysics Data System (ADS)

Dziekan, Thomas; Weissbach, Carmen; Voigt, Jan; Ebert, Bernd; MacDonald, Rainer; Bahner, Malte L.; Mahler, Marianne; Schirner, Michael; Berliner, Michael; Berliner, Birgitt; Osel, Jens; Osel, Ilka

2011-07-01

Fluorescence imaging using the dye indocyanine green as a contrast agent was investigated in a prospective clinical study for the detection of rheumatoid arthritis. Normalized variances of correlated time series of fluorescence intensities describing the bolus kinetics of the contrast agent in certain regions of interest were analyzed to differentiate healthy from inflamed finger joints. These values are determined using a robust, parameter-free algorithm. We found that the normalized variance of correlation functions improves the differentiation between healthy joints of volunteers and joints with rheumatoid arthritis of patients by about 10% compared to, e.g., ratios of areas under the curves of raw data.

Genome-Scale Screen for DNA Methylation-Based Detection Markers for Ovarian Cancer

PubMed Central

Houshdaran, Sahar; Shen, Hui; Widschwendter, Martin; Daxenbichler, Günter; Long, Tiffany; Marth, Christian; Laird-Offringa, Ite A.; Press, Michael F.; Dubeau, Louis; Siegmund, Kimberly D.; Wu, Anna H.; Groshen, Susan; Chandavarkar, Uma; Roman, Lynda D.; Berchuck, Andrew; Pearce, Celeste L.; Laird, Peter W.

2011-01-01

Background The identification of sensitive biomarkers for the detection of ovarian cancer is of high clinical relevance for early detection and/or monitoring of disease recurrence. We developed a systematic multi-step biomarker discovery and verification strategy to identify candidate DNA methylation markers for the blood-based detection of ovarian cancer. Methodology/Principal Findings We used the Illumina Infinium platform to analyze the DNA methylation status of 27,578 CpG sites in 41 ovarian tumors. We employed a marker selection strategy that emphasized sensitivity by requiring consistency of methylation across tumors, while achieving specificity by excluding markers with methylation in control leukocyte or serum DNA. Our verification strategy involved testing the ability of identified markers to monitor disease burden in serially collected serum samples from ovarian cancer patients who had undergone surgical tumor resection compared to CA-125 levels. We identified one marker, IFFO1 promoter methylation (IFFO1-M), that is frequently methylated in ovarian tumors and that is rarely detected in the blood of normal controls. When tested in 127 serially collected sera from ovarian cancer patients, IFFO1-M showed post-resection kinetics significantly correlated with serum CA-125 measurements in six out of 16 patients. Conclusions/Significance We implemented an effective marker screening and verification strategy, leading to the identification of IFFO1-M as a blood-based candidate marker for sensitive detection of ovarian cancer. Serum levels of IFFO1-M displayed post-resection kinetics consistent with a reflection of disease burden. We anticipate that IFFO1-M and other candidate markers emerging from this marker development pipeline may provide disease detection capabilities that complement existing biomarkers. PMID:22163280

Relating quantum coherence and correlations with entropy-based measures.

PubMed

Wang, Xiao-Li; Yue, Qiu-Ling; Yu, Chao-Hua; Gao, Fei; Qin, Su-Juan

2017-09-21

Quantum coherence and quantum correlations are important quantum resources for quantum computation and quantum information. In this paper, using entropy-based measures, we investigate the relationships between quantum correlated coherence, which is the coherence between subsystems, and two main kinds of quantum correlations as defined by quantum discord as well as quantum entanglement. In particular, we show that quantum discord and quantum entanglement can be well characterized by quantum correlated coherence. Moreover, we prove that the entanglement measure formulated by quantum correlated coherence is lower and upper bounded by the relative entropy of entanglement and the entanglement of formation, respectively, and equal to the relative entropy of entanglement for all the maximally correlated states.

Detection of anomalous signals in temporally correlated data (Invited)

NASA Astrophysics Data System (ADS)

Langbein, J. O.

2010-12-01

Detection of transient tectonic signals in data obtained from large geodetic networks requires the ability to detect signals that are both temporally and spatially coherent. In this report I will describe a modification to an existing method that estimates both the coefficients of temporally correlated noise model and an efficient filter based on the noise model. This filter, when applied to the original time-series, effectively whitens (or flattens) the power spectrum. The filtered data provide the means to calculate running averages which are then used to detect deviations from the background trends. For large networks, time-series of signal-to-noise ratio (SNR) can be easily constructed since, by filtering, each of the original time-series has been transformed into one that is closer to having a Gaussian distribution with a variance of 1.0. Anomalous intervals may be identified by counting the number of GPS sites for which the SNR exceeds a specified value. For example, during one time interval, if there were 5 out of 20 time-series with SNR>2, this would be considered anomalous; typically, one would expect at 95% confidence that there would be at least 1 out of 20 time-series with an SNR>2. For time intervals with an anomalously large number of high SNR, the spatial distribution of the SNR is mapped to identify the location of the anomalous signal(s) and their degree of spatial clustering. Estimating the filter that should be used to whiten the data requires modification of the existing methods that employ maximum likelihood estimation to determine the temporal covariance of the data. In these methods, it is assumed that the noise components in the data are a combination of white, flicker and random-walk processes and that they are derived from three different and independent sources. Instead, in this new method, the covariance matrix is constructed assuming that only one source is responsible for the noise and that source can be represented as a white

Cold-Rolled Strip Steel Stress Detection Technology Based on a Magnetoresistance Sensor and the Magnetoelastic Effect.

PubMed

Guan, Ben; Zang, Yong; Han, Xiaohui; Zheng, Kailun

2018-05-21

Driven by the demands for contactless stress detection, technologies are being used for shape control when producing cold-rolled strips. This paper presents a novel contactless stress detection technology based on a magnetoresistance sensor and the magnetoelastic effect, enabling the detection of internal stress in manufactured cold-rolled strips. An experimental device was designed and produced. Characteristics of this detection technology were investigated through experiments assisted by theoretical analysis. Theoretically, a linear correlation exists between the internal stress of strip steel and the voltage output of a magneto-resistive sensor. Therefore, for this stress detection system, the sensitivity of the stress detection was adjusted by adjusting the supply voltage of the magnetoresistance sensor, detection distance, and other relevant parameters. The stress detection experimental results showed that this detection system has good repeatability and linearity. The detection error was controlled within 1.5%. Moreover, the intrinsic factors of the detected strip steel, including thickness, carbon percentage, and crystal orientation, also affected the sensitivity of the detection system. The detection technology proposed in this research enables online contactless detection and meets the requirements for cold-rolled steel strips.

Cold-Rolled Strip Steel Stress Detection Technology Based on a Magnetoresistance Sensor and the Magnetoelastic Effect

PubMed Central

Guan, Ben; Zang, Yong; Han, Xiaohui; Zheng, Kailun

2018-01-01

Driven by the demands for contactless stress detection, technologies are being used for shape control when producing cold-rolled strips. This paper presents a novel contactless stress detection technology based on a magnetoresistance sensor and the magnetoelastic effect, enabling the detection of internal stress in manufactured cold-rolled strips. An experimental device was designed and produced. Characteristics of this detection technology were investigated through experiments assisted by theoretical analysis. Theoretically, a linear correlation exists between the internal stress of strip steel and the voltage output of a magneto-resistive sensor. Therefore, for this stress detection system, the sensitivity of the stress detection was adjusted by adjusting the supply voltage of the magnetoresistance sensor, detection distance, and other relevant parameters. The stress detection experimental results showed that this detection system has good repeatability and linearity. The detection error was controlled within 1.5%. Moreover, the intrinsic factors of the detected strip steel, including thickness, carbon percentage, and crystal orientation, also affected the sensitivity of the detection system. The detection technology proposed in this research enables online contactless detection and meets the requirements for cold-rolled steel strips. PMID:29883387

Cellular telephone-based radiation sensor and wide-area detection network

DOEpatents

Craig, William W [Pittsburg, CA; Labov, Simon E [Berkeley, CA

2006-12-12

A network of radiation detection instruments, each having a small solid state radiation sensor module integrated into a cellular phone for providing radiation detection data and analysis directly to a user. The sensor module includes a solid-state crystal bonded to an ASIC readout providing a low cost, low power, light weight compact instrument to detect and measure radiation energies in the local ambient radiation field. In particular, the photon energy, time of event, and location of the detection instrument at the time of detection is recorded for real time transmission to a central data collection/analysis system. The collected data from the entire network of radiation detection instruments are combined by intelligent correlation/analysis algorithms which map the background radiation and detect, identify and track radiation anomalies in the region.

Automatic background updating for video-based vehicle detection

NASA Astrophysics Data System (ADS)

Hu, Chunhai; Li, Dongmei; Liu, Jichuan

2008-03-01

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

Full-waveform detection of non-impulsive seismic events based on time-reversal methods

NASA Astrophysics Data System (ADS)

Solano, Ericka Alinne; Hjörleifsdóttir, Vala; Liu, Qinya

2017-12-01

We present a full-waveform detection method for non-impulsive seismic events, based on time-reversal principles. We use the strain Green's tensor as a matched filter, correlating it with continuous observed seismograms, to detect non-impulsive seismic events. We show that this is mathematically equivalent to an adjoint method for detecting earthquakes. We define the detection function, a scalar valued function, which depends on the stacked correlations for a group of stations. Event detections are given by the times at which the amplitude of the detection function exceeds a given value relative to the noise level. The method can make use of the whole seismic waveform or any combination of time-windows with different filters. It is expected to have an advantage compared to traditional detection methods for events that do not produce energetic and impulsive P waves, for example glacial events, landslides, volcanic events and transform-fault earthquakes for events which velocity structure along the path is relatively well known. Furthermore, the method has advantages over empirical Greens functions template matching methods, as it does not depend on records from previously detected events, and therefore is not limited to events occurring in similar regions and with similar focal mechanisms as these events. The method is not specific to any particular way of calculating the synthetic seismograms, and therefore complicated structural models can be used. This is particularly beneficial for intermediate size events that are registered on regional networks, for which the effect of lateral structure on the waveforms can be significant. To demonstrate the feasibility of the method, we apply it to two different areas located along the mid-oceanic ridge system west of Mexico where non-impulsive events have been reported. The first study area is between Clipperton and Siqueiros transform faults (9°N), during the time of two earthquake swarms, occurring in March 2012 and May

A universal DNA-based protein detection system.

PubMed

Tran, Thua N N; Cui, Jinhui; Hartman, Mark R; Peng, Songming; Funabashi, Hisakage; Duan, Faping; Yang, Dayong; March, John C; Lis, John T; Cui, Haixin; Luo, Dan

2013-09-25

Protein immune detection requires secondary antibodies which must be carefully selected in order to avoid interspecies cross-reactivity, and is therefore restricted by the limited availability of primary/secondary antibody pairs. Here we present a versatile DNA-based protein detection system using a universal adapter to interface between IgG antibodies and DNA-modified reporter molecules. As a demonstration of this capability, we successfully used DNA nano-barcodes, quantum dots, and horseradish peroxidase enzyme to detect multiple proteins using our DNA-based labeling system. Our system not only eliminates secondary antibodies but also serves as a novel method platform for protein detection with modularity, high capacity, and multiplexed capability.

A Universal DNA-Based Protein Detection System

PubMed Central

Tran, Thua N. N.; Cui, Jinhui; Hartman, Mark R.; Peng, Songming; Funabashi, Hisakage; Duan, Faping; Yang, Dayong; March, John C.; Lis, John T.; Cui, Haixin; Luo, Dan

2014-01-01

Protein immune detection requires secondary antibodies which must be carefully selected in order to avoid interspecies cross-reactivity, and is therefore restricted by the limited availability of primary/secondary antibody pairs. Here we present a versatile DNA-based protein detection system using a universal adapter to interface between IgG antibodies and DNA-modified reporter molecules. As a demonstration of this capability, we successfully used DNA nano-barcodes, quantum dots, and horseradish peroxidase enzyme to detect multiple proteins using our DNA-based labeling system. Our system not only eliminates secondary antibodies but also serves as a novel method platform for protein detection with modularity, high capacity, and multiplexed capability. PMID:23978265

Gating based on internal/external signals with dynamic correlation updates.

PubMed

Wu, Huanmei; Zhao, Qingya; Berbeco, Ross I; Nishioka, Seiko; Shirato, Hiroki; Jiang, Steve B

2008-12-21

Precise localization of mobile tumor positions in real time is critical to the success of gated radiotherapy. Tumor positions are usually derived from either internal or external surrogates. Fluoroscopic gating based on internal surrogates, such as implanted fiducial markers, is accurate however requiring a large amount of imaging dose. Gating based on external surrogates, such as patient abdominal surface motion, is non-invasive however less accurate due to the uncertainty in the correlation between tumor location and external surrogates. To address these complications, we propose to investigate an approach based on hybrid gating with dynamic internal/external correlation updates. In this approach, the external signal is acquired at high frequency (such as 30 Hz) while the internal signal is sparsely acquired (such as 0.5 Hz or less). The internal signal is used to validate and update the internal/external correlation during treatment. Tumor positions are derived from the external signal based on the newly updated correlation. Two dynamic correlation updating algorithms are introduced. One is based on the motion amplitude and the other is based on the motion phase. Nine patients with synchronized internal/external motion signals are simulated retrospectively to evaluate the effectiveness of hybrid gating. The influences of different clinical conditions on hybrid gating, such as the size of gating windows, the optimal timing for internal signal acquisition and the acquisition frequency are investigated. The results demonstrate that dynamically updating the internal/external correlation in or around the gating window will reduce false positive with relatively diminished treatment efficiency. This improvement will benefit patients with mobile tumors, especially greater for early stage lung cancers, for which the tumors are less attached or freely floating in the lung.

Quantitative detection of glucose level based on radiofrequency patch biosensor combined with volume-fixed structures.

PubMed

Qiang, Tian; Wang, Cong; Kim, Nam-Young

2017-12-15

A concept for characterizing a radiofrequency (RF) patch biosensor combined with volume-fixed structures is presented for timely monitoring of an individual's glucose levels based on frequency variation. Two types of patch biosensors-separately integrated with a backside slot (0.53μL) and a front-side tank (0.70μL) structure-were developed to achieve precise and efficient detection while excluding the effects of interference due to the liquidity, shape, and thickness of the tested glucose sample. A glucose test analyte at different concentrations (50-600mg/dL) was dropped into the volume-fixed structures. It fully interacted with the RF patch electromagnetic field, effectively and sensitively changing the resonance frequency and magnitude of the reflection coefficient. Measurement results based on the resonance frequency showed high sensitivity up to 1.13MHz and 1.97MHz per mg/dL, and low detection limits of 26.54mg/dL and 15.22mg/dL, for the two types of patch biosensors, respectively, as well as a short response time of less than 1s. Excellent reusability of the proposed biosensors was verified through three sets of measurements for each individual glucose sample. Regression analysis revealed a good linear correlation between glucose concentrations and the resonance frequency shift. Moreover, to facilitate a multi-parameter-sensitive detection of glucose, the magnitude of the reflection coefficient was also tested, and it showed a good linear correlation with the glucose concentration. Thus, the proposed approach can be adopted for distinguishing glucose solution levels, and it is a potential candidate for early-stage detection of glucose levels in diabetes patients. Copyright © 2017 Elsevier B.V. All rights reserved.

A versatile software package for inter-subject correlation based analyses of fMRI.

PubMed

Kauppi, Jukka-Pekka; Pajula, Juha; Tohka, Jussi

2014-01-01

In the inter-subject correlation (ISC) based analysis of the functional magnetic resonance imaging (fMRI) data, the extent of shared processing across subjects during the experiment is determined by calculating correlation coefficients between the fMRI time series of the subjects in the corresponding brain locations. This implies that ISC can be used to analyze fMRI data without explicitly modeling the stimulus and thus ISC is a potential method to analyze fMRI data acquired under complex naturalistic stimuli. Despite of the suitability of ISC based approach to analyze complex fMRI data, no generic software tools have been made available for this purpose, limiting a widespread use of ISC based analysis techniques among neuroimaging community. In this paper, we present a graphical user interface (GUI) based software package, ISC Toolbox, implemented in Matlab for computing various ISC based analyses. Many advanced computations such as comparison of ISCs between different stimuli, time window ISC, and inter-subject phase synchronization are supported by the toolbox. The analyses are coupled with re-sampling based statistical inference. The ISC based analyses are data and computation intensive and the ISC toolbox is equipped with mechanisms to execute the parallel computations in a cluster environment automatically and with an automatic detection of the cluster environment in use. Currently, SGE-based (Oracle Grid Engine, Son of a Grid Engine, or Open Grid Scheduler) and Slurm environments are supported. In this paper, we present a detailed account on the methods behind the ISC Toolbox, the implementation of the toolbox and demonstrate the possible use of the toolbox by summarizing selected example applications. We also report the computation time experiments both using a single desktop computer and two grid environments demonstrating that parallelization effectively reduces the computing time. The ISC Toolbox is available in https://code.google.com/p/isc-toolbox/

A versatile software package for inter-subject correlation based analyses of fMRI

PubMed Central

Kauppi, Jukka-Pekka; Pajula, Juha; Tohka, Jussi

2014-01-01

In the inter-subject correlation (ISC) based analysis of the functional magnetic resonance imaging (fMRI) data, the extent of shared processing across subjects during the experiment is determined by calculating correlation coefficients between the fMRI time series of the subjects in the corresponding brain locations. This implies that ISC can be used to analyze fMRI data without explicitly modeling the stimulus and thus ISC is a potential method to analyze fMRI data acquired under complex naturalistic stimuli. Despite of the suitability of ISC based approach to analyze complex fMRI data, no generic software tools have been made available for this purpose, limiting a widespread use of ISC based analysis techniques among neuroimaging community. In this paper, we present a graphical user interface (GUI) based software package, ISC Toolbox, implemented in Matlab for computing various ISC based analyses. Many advanced computations such as comparison of ISCs between different stimuli, time window ISC, and inter-subject phase synchronization are supported by the toolbox. The analyses are coupled with re-sampling based statistical inference. The ISC based analyses are data and computation intensive and the ISC toolbox is equipped with mechanisms to execute the parallel computations in a cluster environment automatically and with an automatic detection of the cluster environment in use. Currently, SGE-based (Oracle Grid Engine, Son of a Grid Engine, or Open Grid Scheduler) and Slurm environments are supported. In this paper, we present a detailed account on the methods behind the ISC Toolbox, the implementation of the toolbox and demonstrate the possible use of the toolbox by summarizing selected example applications. We also report the computation time experiments both using a single desktop computer and two grid environments demonstrating that parallelization effectively reduces the computing time. The ISC Toolbox is available in https

Detection of greenbug infestation on wheat using ground-based radiometry

NASA Astrophysics Data System (ADS)

Yang, Zhiming

Scope of methods of study. The purpose of this greenhouse study was to characterize stress in wheat caused by greenbugs using ground-based radiometry. Experiments were conducted to (a) identify spectral bands and vegetation indices sensitive to greenbug infestation; (b) differentiate stress caused due to greenbugs from water stress; (c) examine the impacts of plant growth stage on detection of greenbug infestation; and (d) compare infestations due to greenbug and Russian wheat aphid. Wheat (variety-TAM 107) was planted (seed spacing 1 in. x 3 in.) in plastic flats with dimension 24 in. x 16 in. x 8.75 in. Fifteen days after sowing, wheat seedlings were infested with greenbugs (biotype-E). Nadir measurement of canopy reflectance started the day after infestation and lasted until most infested plants were dead. Using a 16-band Cropscan radiometer, spectral reflectance data were collected daily (between 13:00--14:00 hours) and 128 vegetation indices were derived in addition to greenbug counts per tiller. Using SAS PROC MIXED, sensitivity of band and vegetation indices was identified based on Threshold Day. Subsequent to Threshold Day there was a consistent significant spectral difference between control and infested plants. Sensitivity of band and vegetation indices was further examined using correlation and relative sensitivity analyses. Findings and conclusions. Results show that it is possible to detect greenbug-induced stress on wheat using hand-held radiometers, such as Cropscan. Band 694 nm and the ratio-based vegetation index (RVI) derived from the band 694 nm and 800 nm were identified as most sensitive to greenbug infestation. Landsat TM bands and their derived vegetation indices also show potential for detecting wheat stress caused by greenbug infestation. Also, RVIs particularly derived using spectral band 694 nm and 800 nm were found useful in differentiating greenbug infestation from water stress. Furthermore, vegetation indices such as Normalized total

Wear detection by means of wavelet-based acoustic emission analysis

NASA Astrophysics Data System (ADS)

Baccar, D.; Söffker, D.

2015-08-01

Wear detection and monitoring during operation are complex and difficult tasks especially for materials under sliding conditions. Due to the permanent contact and repetitive motion, the material surface remains during tests non-accessible for optical inspection so that attrition of the contact partners cannot be easily detected. This paper introduces the relevant scientific components of reliable and efficient condition monitoring system for online detection and automated classification of wear phenomena by means of acoustic emission (AE) and advanced signal processing approaches. The related experiments were performed using a tribological system consisting of two martensitic plates, sliding against each other. High sensitive piezoelectric transducer was used to provide the continuous measurement of AE signals. The recorded AE signals were analyzed mainly by time-frequency analysis. A feature extraction module using a novel combination of Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were used for the first time. A detailed correlation analysis between complex signal characteristics and the surface damage resulting from contact fatigue was investigated. Three wear process stages were detected and could be distinguished. To obtain quantitative and detailed information about different wear phases, the AE energy was calculated using STFT and decomposed into a suitable number of frequency levels. The individual energy distribution and the cumulative AE energy of each frequency components were analyzed using CWT. Results show that the behavior of individual frequency component changes when the wear state changes. Here, specific frequency ranges are attributed to the different wear states. The study reveals that the application of the STFT-/CWT-based AE analysis is an appropriate approach to distinguish and to interpret the different damage states occurred during sliding contact. Based on this results a new generation of condition monitoring

Faint Debris Detection by Particle Based Track-Before-Detect Method

NASA Astrophysics Data System (ADS)

Uetsuhara, M.; Ikoma, N.

2014-09-01

This study proposes a particle method to detect faint debris, which is hardly seen in single frame, from an image sequence based on the concept of track-before-detect (TBD). The most widely used detection method is detect-before-track (DBT), which firstly detects signals of targets from single frame by distinguishing difference of intensity between foreground and background then associate the signals for each target between frames. DBT is capable of tracking bright targets but limited. DBT is necessary to consider presence of false signals and is difficult to recover from false association. On the other hand, TBD methods try to track targets without explicitly detecting the signals followed by evaluation of goodness of each track and obtaining detection results. TBD has an advantage over DBT in detecting weak signals around background level in single frame. However, conventional TBD methods for debris detection apply brute-force search over candidate tracks then manually select true one from the candidates. To reduce those significant drawbacks of brute-force search and not-fully automated process, this study proposes a faint debris detection algorithm by a particle based TBD method consisting of sequential update of target state and heuristic search of initial state. The state consists of position, velocity direction and magnitude, and size of debris over the image at a single frame. The sequential update process is implemented by a particle filter (PF). PF is an optimal filtering technique that requires initial distribution of target state as a prior knowledge. An evolutional algorithm (EA) is utilized to search the initial distribution. The EA iteratively applies propagation and likelihood evaluation of particles for the same image sequences and resulting set of particles is used as an initial distribution of PF. This paper describes the algorithm of the proposed faint debris detection method. The algorithm demonstrates performance on image sequences acquired

A simple clot based assay for detection of procoagulant cell-derived microparticles.

PubMed

Patil, Rucha; Ghosh, Kanjaksha; Shetty, Shrimati

2016-05-01

Cell-derived microparticles (MPs) are important biomarkers in many facets of medicine. However, the MP detection methods used till date are costly and time consuming. The main aim of this study was to standardize an in-house clot based screening method for MP detection which would not only be specific and sensitive, but also inexpensive. Four different methods of MP assessment were performed and the results correlated. Using the flow cytometry technique as the gold standard, 25 samples with normal phosphatidylserine (PS) expressing MP levels and 25 samples with elevated levels were selected, which was cross checked by the commercial STA Procoag PPL clotting time (CT) assay. A simple recalcification time and an in-house clot assay were the remaining two tests. The in-house test measures the CT after the addition of calcium chloride to MP rich plasma, following incubation with Russell viper venom and phospholipid free plasma. The CT obtained by the in-house assay significantly correlated with the results obtained by flow cytometry (R2=0.87, p<0.01). Though preliminary, the in-house assay seems to be efficient, inexpensive and promising. It could definitely be utilized routinely for procoagulant MP assessment in various clinical settings.

Smile effect detection for dispersive hypersepctral imager based on the doped reflectance panel

NASA Astrophysics Data System (ADS)

Zhou, Jiankang; Liu, Xiaoli; Ji, Yiqun; Chen, Yuheng; Shen, Weimin

2012-11-01

Hyperspectral imager is now widely used in many regions, such as resource development, environmental monitoring and so on. The reliability of spectral data is based on the instrument calibration. The smile, wavelengths at the center pixels of imaging spectrometer detector array are different from the marginal pixels, is a main factor in the spectral calibration because it can deteriorate the spectral data accuracy. When the spectral resolution is high, little smile can result in obvious signal deviation near weak atmospheric absorption peak. The traditional method of detecting smile is monochromator wavelength scanning which is time consuming and complex and can not be used in the field or at the flying platform. We present a new smile detection method based on the holmium oxide panel which has the rich of absorbed spectral features. The higher spectral resolution spectrometer and the under-test imaging spectrometer acquired the optical signal from the Spectralon panel and the holmium oxide panel respectively. The wavelength absorption peak positions of column pixels are determined by curve fitting method which includes spectral response function sequence model and spectral resampling. The iteration strategy and Pearson coefficient together are used to confirm the correlation between the measured and modeled spectral curve. The present smile detection method is posed on our designed imaging spectrometer and the result shows that it can satisfy precise smile detection requirement of high spectral resolution imaging spectrometer.

Bio-inspired motion detection in an FPGA-based smart camera module.

PubMed

Köhler, T; Röchter, F; Lindemann, J P; Möller, R

2009-03-01

Flying insects, despite their relatively coarse vision and tiny nervous system, are capable of carrying out elegant and fast aerial manoeuvres. Studies of the fly visual system have shown that this is accomplished by the integration of signals from a large number of elementary motion detectors (EMDs) in just a few global flow detector cells. We developed an FPGA-based smart camera module with more than 10,000 single EMDs, which is closely modelled after insect motion-detection circuits with respect to overall architecture, resolution and inter-receptor spacing. Input to the EMD array is provided by a CMOS camera with a high frame rate. Designed as an adaptable solution for different engineering applications and as a testbed for biological models, the EMD detector type and parameters such as the EMD time constants, the motion-detection directions and the angle between correlated receptors are reconfigurable online. This allows a flexible and simultaneous detection of complex motion fields such as translation, rotation and looming, such that various tasks, e.g., obstacle avoidance, height/distance control or speed regulation can be performed by the same compact device.

Progress of a Cross-Correlation Based Optical Strain Measurement Technique for Detecting Radial Growth on a Rotating Disk

NASA Technical Reports Server (NTRS)

Clem, Michelle M.; Woike, Mark R.; Abdul-Aziz, Ali

2014-01-01

The Aeronautical Sciences Project under NASA's Fundamental Aeronautics Program is interested in the development of novel measurement technologies, such as optical surface measurements for the in situ health monitoring of critical constituents of the internal flow path. In situ health monitoring has the potential to detect flaws, i.e. cracks in key components, such as engine turbine disks, before the flaws lead to catastrophic failure. The present study, aims to further validate and develop an optical strain measurement technique to measure the radial growth and strain field of an already cracked disk, mimicking the geometry of a sub-scale turbine engine disk, under loaded conditions in the NASA Glenn Research Center's High Precision Rotordynamics Laboratory. The technique offers potential fault detection by imaging an applied high-contrast random speckle pattern under unloaded and loaded conditions with a CCD camera. Spinning the cracked disk at high speeds (loaded conditions) induces an external load, resulting in a radial growth of the disk of approximately 50.0-µm in the flawed region and hence, a localized strain field. When imaging the cracked disk under static conditions, the disk will be undistorted; however, during rotation the cracked region will grow radially, thus causing the applied particle pattern to be 'shifted'. The resulting particle displacements between the two images is measured using the two-dimensional cross-correlation algorithms implemented in standard Particle Image Velocimetry (PIV) software to track the disk growth, which facilitates calculation of the localized strain field. A random particle distribution is adhered onto the surface of the cracked disk and two bench top experiments are carried out to evaluate the technique's ability to measure the induced particle displacements. The disk is shifted manually using a translation stage equipped with a fine micrometer and a hotplate is used to induce thermal growth of the disk, causing the

Detection of the Wenchuan aftershock sequence using waveform correlation with a composite regional network

DOE PAGES

Slinkard, Megan; Heck, Stephen; Schaff, David; ...

2016-06-28

Using template waveforms from aftershocks of the Wenchuan earthquake (12 May 2008, M s 8.0) listed in a global bulletin and continuous data from eight regional stations, we detected more than 6000 additional events in the mainshock source region from 1 May to 12 August 2008. These new detections obey Omori’s law, extend the magnitude of completeness downward by 1.1 magnitude units, and lead to a more than fivefold increase in number of known aftershocks compared with the global bulletins published by the International Data Centre and the Inter national Seismological Centre. Moreover, we detected more M > 2 eventsmore » than were listed by the Sichuan Seismograph Network. Several clusters of these detections were then relocated using the double-difference method, yielding locations that reduced travel-time residuals by a factor of 32 compared with the initial bulletin locations. Finally, our results suggest that using waveform correlation on a few regional stations can find aftershock events very effectively and locate them with precision.« less

Fire flame detection based on GICA and target tracking

NASA Astrophysics Data System (ADS)

Rong, Jianzhong; Zhou, Dechuang; Yao, Wei; Gao, Wei; Chen, Juan; Wang, Jian

2013-04-01

To improve the video fire detection rate, a robust fire detection algorithm based on the color, motion and pattern characteristics of fire targets was proposed, which proved a satisfactory fire detection rate for different fire scenes. In this fire detection algorithm: (a) a rule-based generic color model was developed based on analysis on a large quantity of flame pixels; (b) from the traditional GICA (Geometrical Independent Component Analysis) model, a Cumulative Geometrical Independent Component Analysis (C-GICA) model was developed for motion detection without static background and (c) a BP neural network fire recognition model based on multi-features of the fire pattern was developed. Fire detection tests on benchmark fire video clips of different scenes have shown the robustness, accuracy and fast-response of the algorithm.

An immunity-based anomaly detection system with sensor agents.

PubMed

Okamoto, Takeshi; Ishida, Yoshiteru

2009-01-01

This paper proposes an immunity-based anomaly detection system with sensor agents based on the specificity and diversity of the immune system. Each agent is specialized to react to the behavior of a specific user. Multiple diverse agents decide whether the behavior is normal or abnormal. Conventional systems have used only a single sensor to detect anomalies, while the immunity-based system makes use of multiple sensors, which leads to improvements in detection accuracy. In addition, we propose an evaluation framework for the anomaly detection system, which is capable of evaluating the differences in detection accuracy between internal and external anomalies. This paper focuses on anomaly detection in user's command sequences on UNIX-like systems. In experiments, the immunity-based system outperformed some of the best conventional systems.

Experiments on Adaptive Techniques for Host-Based Intrusion Detection

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

DRAELOS, TIMOTHY J.; COLLINS, MICHAEL J.; DUGGAN, DAVID P.

2001-09-01

This research explores four experiments of adaptive host-based intrusion detection (ID) techniques in an attempt to develop systems that can detect novel exploits. The technique considered to have the most potential is adaptive critic designs (ACDs) because of their utilization of reinforcement learning, which allows learning exploits that are difficult to pinpoint in sensor data. Preliminary results of ID using an ACD, an Elman recurrent neural network, and a statistical anomaly detection technique demonstrate an ability to learn to distinguish between clean and exploit data. We used the Solaris Basic Security Module (BSM) as a data source and performed considerablemore » preprocessing on the raw data. A detection approach called generalized signature-based ID is recommended as a middle ground between signature-based ID, which has an inability to detect novel exploits, and anomaly detection, which detects too many events including events that are not exploits. The primary results of the ID experiments demonstrate the use of custom data for generalized signature-based intrusion detection and the ability of neural network-based systems to learn in this application environment.« less

A near-infrared fluorescence-based surgical navigation system imaging software for sentinel lymph node detection

NASA Astrophysics Data System (ADS)

Ye, Jinzuo; Chi, Chongwei; Zhang, Shuang; Ma, Xibo; Tian, Jie

2014-02-01

Sentinel lymph node (SLN) in vivo detection is vital in breast cancer surgery. A new near-infrared fluorescence-based surgical navigation system (SNS) imaging software, which has been developed by our research group, is presented for SLN detection surgery in this paper. The software is based on the fluorescence-based surgical navigation hardware system (SNHS) which has been developed in our lab, and is designed specifically for intraoperative imaging and postoperative data analysis. The surgical navigation imaging software consists of the following software modules, which mainly include the control module, the image grabbing module, the real-time display module, the data saving module and the image processing module. And some algorithms have been designed to achieve the performance of the software, for example, the image registration algorithm based on correlation matching. Some of the key features of the software include: setting the control parameters of the SNS; acquiring, display and storing the intraoperative imaging data in real-time automatically; analysis and processing of the saved image data. The developed software has been used to successfully detect the SLNs in 21 cases of breast cancer patients. In the near future, we plan to improve the software performance and it will be extensively used for clinical purpose.

Attribute and topology based change detection in a constellation of previously detected objects

DOEpatents

Paglieroni, David W.; Beer, Reginald N.

2016-01-19

A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

Consistently Sampled Correlation Filters with Space Anisotropic Regularization for Visual Tracking

PubMed Central

Shi, Guokai; Xu, Tingfa; Luo, Jiqiang; Li, Yuankun

2017-01-01

Most existing correlation filter-based tracking algorithms, which use fixed patches and cyclic shifts as training and detection measures, assume that the training samples are reliable and ignore the inconsistencies between training samples and detection samples. We propose to construct and study a consistently sampled correlation filter with space anisotropic regularization (CSSAR) to solve these two problems simultaneously. Our approach constructs a spatiotemporally consistent sample strategy to alleviate the redundancies in training samples caused by the cyclical shifts, eliminate the inconsistencies between training samples and detection samples, and introduce space anisotropic regularization to constrain the correlation filter for alleviating drift caused by occlusion. Moreover, an optimization strategy based on the Gauss-Seidel method was developed for obtaining robust and efficient online learning. Both qualitative and quantitative evaluations demonstrate that our tracker outperforms state-of-the-art trackers in object tracking benchmarks (OTBs). PMID:29231876

Colorimetric detection of Cr (VI) based on the leaching of gold nanoparticles using a paper-based sensor.

PubMed

Guo, Jian-Feng; Huo, Dan-Qun; Yang, Mei; Hou, Chang-Jun; Li, Jun-Jie; Fa, Huan-Bao; Luo, Hui-Bo; Yang, Ping

2016-12-01

Herein, we have developed a simple, sensitive and paper-based colorimetric sensor for the selective detection of Chromium (Ⅵ) ions (Cr (VI)). Silanization-titanium dioxide modified filter paper (STCP) was used to trap bovine serum albumin capped gold nanoparticles (BSA-Au NPs), leading to the fabrication of BSA-Au NPs decorated membrane (BSA-Au NPs/STCP). The BSA-Au NPs/STCP operated on the principle that BSA-Au NPs anchored on the STCP were gradually etched by Cr (VI) as the leaching process of gold in the presence of hydrobromic acid (HBr) and hence induced a visible color change. Under optimum conditions, the paper-based colorimetric sensor showed clear color change after reaction with Cr (VI) as well as with favorable selectivity to a variety of possible interfering counterparts. The amount-dependent colorimetric response was linearly correlated with the Cr (VI) concentrations ranging from 0.5µM to 50.0µM with a detection limit down to 280nM. Moreover, the developed cost-effective colorimetric sensor has been successfully applied to real environmental samples which demonstrated the potential for field applications. Copyright © 2016 Elsevier B.V. All rights reserved.

Exploring inter-frame correlation analysis and wavelet-domain modeling for real-time caption detection in streaming video

NASA Astrophysics Data System (ADS)

Li, Jia; Tian, Yonghong; Gao, Wen

2008-01-01

In recent years, the amount of streaming video has grown rapidly on the Web. Often, retrieving these streaming videos offers the challenge of indexing and analyzing the media in real time because the streams must be treated as effectively infinite in length, thus precluding offline processing. Generally speaking, captions are important semantic clues for video indexing and retrieval. However, existing caption detection methods often have difficulties to make real-time detection for streaming video, and few of them concern on the differentiation of captions from scene texts and scrolling texts. In general, these texts have different roles in streaming video retrieval. To overcome these difficulties, this paper proposes a novel approach which explores the inter-frame correlation analysis and wavelet-domain modeling for real-time caption detection in streaming video. In our approach, the inter-frame correlation information is used to distinguish caption texts from scene texts and scrolling texts. Moreover, wavelet-domain Generalized Gaussian Models (GGMs) are utilized to automatically remove non-text regions from each frame and only keep caption regions for further processing. Experiment results show that our approach is able to offer real-time caption detection with high recall and low false alarm rate, and also can effectively discern caption texts from the other texts even in low resolutions.

Detection of Antiferromagnetic Correlations in the Fermi-Hubbard Model

NASA Astrophysics Data System (ADS)

Hulet, Randall

2014-05-01

The Hubbard model, consisting of a cubic lattice with on-site interactions and kinetic energy arising from tunneling to nearest neighbors is a ``standard model'' of strongly correlated many-body physics, and it may also contain the essential ingredients of high-temperature superconductivity. While the Hamiltonian has only two terms it cannot be numerically solved for arbitrary density of spin-1/2 fermions due to exponential growth in the basis size. At a density of one spin-1/2 particle per site, however, the Hubbard model is known to exhibit antiferromagnetism at temperatures below the Néel temperature TN, a property shared by most of the undoped parent compounds of high-Tc superconductors. The realization of antiferromagnetism in a 3D optical lattice with atomic fermions has been impeded by the inability to attain sufficiently low temperatures. We have developed a method to perform evaporative cooling in a 3D cubic lattice by compensating the confinement envelope of the infrared optical lattice beams with blue-detuned laser beams. Evaporation can be controlled by the intensity of these non-retroreflected compensating beams. We observe significantly lower temperatures of a two-spin component gas of 6Li atoms in the lattice using this method. The cooling enables us to detect the development of short-range antiferromagnetic correlations using spin-sensitive Bragg scattering of light. Comparison with quantum Monte Carlo constrains the temperature in the lattice to 2-3 TN. We will discuss the prospects of attaining even lower temperatures with this method. Supported by DARPA/ARO, ONR, and NSF.

Research of detection depth for graphene-based optical sensor

NASA Astrophysics Data System (ADS)

Yang, Yong; Sun, Jialve; Liu, Lu; Zhu, Siwei; Yuan, Xiaocong

2018-03-01

Graphene-based optical sensors have been developed for research into the biological intercellular refractive index (RI) because they offer greater detection depths than those provided by the surface plasmon resonance technique. In this Letter, we propose an experimental approach for measurement of the detection depth in a graphene-based optical sensor system that uses transparent polydimethylsiloxane layers with different thicknesses. The experimental results show that detection depths of 2.5 μm and 3 μm can be achieved at wavelengths of 532 nm and 633 nm, respectively. These results prove that graphene-based optical sensors can realize long-range RI detection and are thus promising for use as tools in the biological cell detection field. Additionally, we analyze the factors that influence the detection depth and provide a feasible approach for detection depth control based on adjustment of the wavelength and the angle of incidence. We believe that this approach will be useful in RI tomography applications.

Mouse V1 population correlates of visual detection rely on heterogeneity within neuronal response patterns

PubMed Central

Montijn, Jorrit S; Goltstein, Pieter M; Pennartz, Cyriel MA

2015-01-01

Previous studies have demonstrated the importance of the primary sensory cortex for the detection, discrimination, and awareness of visual stimuli, but it is unknown how neuronal populations in this area process detected and undetected stimuli differently. Critical differences may reside in the mean strength of responses to visual stimuli, as reflected in bulk signals detectable in functional magnetic resonance imaging, electro-encephalogram, or magnetoencephalography studies, or may be more subtly composed of differentiated activity of individual sensory neurons. Quantifying single-cell Ca2+ responses to visual stimuli recorded with in vivo two-photon imaging, we found that visual detection correlates more strongly with population response heterogeneity rather than overall response strength. Moreover, neuronal populations showed consistencies in activation patterns across temporally spaced trials in association with hit responses, but not during nondetections. Contrary to models relying on temporally stable networks or bulk signaling, these results suggest that detection depends on transient differentiation in neuronal activity within cortical populations. DOI: http://dx.doi.org/10.7554/eLife.10163.001 PMID:26646184

Signal Detection Theory-Based Information Processing for the Detection of Breast Cancer at Microwave Frequencies

DTIC Science & Technology

2002-08-01

the measurement noise, as well as the physical model of the forward scattered electric field. The Bayesian algorithms for the Uncertain Permittivity...received at multiple sensors. In this research project a tissue- model -based signal-detection theory approach for the detection of mammary tumors in the...oriented information processors. In this research project a tissue- model - based signal detection theory approach for the detection of mammary tumors in the

Daytime Water Detection Based on Sky Reflections

NASA Technical Reports Server (NTRS)

Rankin, Arturo L.; Matthies, Larry H.; Bellutta, Paolo

2011-01-01

Robust water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation. This is particularly true in wide-open areas where water can collect in naturally occurring terrain depressions during periods of heavy precipitation and form large water bodies. One of the properties of water useful for detecting it is that its surface acts as a horizontal mirror at large incidence angles. Water bodies can be indirectly detected by detecting reflections of the sky below the horizon in color imagery. The Jet Propulsion Laboratory (JPL) has implemented a water detector based on sky reflections that geometrically locates the pixel in the sky that is reflecting on a candidate water pixel on the ground and predicts if the ground pixel is water based on color similarity and local terrain features. This software detects water bodies in wide-open areas on cross-country terrain at mid- to far-range using imagery acquired from a forward-looking stereo pair of color cameras mounted on a terrestrial UGV. In three test sequences approaching a pond under a clear, overcast, and cloudy sky, the true positive detection rate was 100% when the UGV was beyond 7 meters of the water's leading edge and the largest false positive detection rate was 0.58%. The sky reflection based water detector has been integrated on an experimental unmanned vehicle and field tested at Ft. Indiantown Gap, PA, USA.

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

DOEpatents

Paglieroni, David W.

2016-06-07

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

A SVM-based quantitative fMRI method for resting-state functional network detection.

PubMed

Song, Xiaomu; Chen, Nan-kuei

2014-09-01

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Bacteriophage-Based Pathogen Detection

NASA Astrophysics Data System (ADS)

Ripp, Steven

Considered the most abundant organism on Earth, at a population approaching 1031, bacteriophage, or phage for short, mediate interactions with myriad bacterial hosts that has for decades been exploited in phage typing schemes for signature identification of clinical, food-borne, and water-borne pathogens. With over 5,000 phage being morphologically characterized and grouped as to susceptible host, there exists an enormous cache of bacterial-specific sensors that has more recently been incorporated into novel bio-recognition assays with heightened sensitivity, specificity, and speed. These assays take many forms, ranging from straightforward visualization of labeled phage as they attach to their specific bacterial hosts to reporter phage that genetically deposit trackable signals within their bacterial hosts to the detection of progeny phage or other uniquely identifiable elements released from infected host cells. A comprehensive review of these and other phage-based detection assays, as directed towards the detection and monitoring of bacterial pathogens, will be provided in this chapter.

TCSPC based approaches for multiparameter detection in living cells

NASA Astrophysics Data System (ADS)

Jahn, Karolina; Buschmann, Volker; Koberling, Felix; Hille, Carsten

2014-03-01

In living cells a manifold of processes take place simultaneously. This implies a precise regulation of intracellular ion homeostasis. In order to understand their spatio-temporal pattern comprehensively, the development of multiplexing concepts is essential. Due to the multidimensional characteristics of fluorescence dyes (absorption and emission spectra, decay time, anisotropy), the highly sensitive and non-invasive fluorescence microscopy is a versatile tool for realising multiplexing concepts. A prerequisite are analyte-specific fluorescence dyes with low cross-sensitivity to other dyes and analytes, respectively. Here, two approaches for multiparameter detection in living cells are presented. Insect salivary glands are well characterised secretory active tissues which were used as model systems to evaluate multiplexing concepts. Salivary glands secrete a KCl-rich or NaCl-rich fluid upon stimulation which is mainly regulated by intracellular Ca2+ as second messenger. Thus, pairwise detection of intracellular Na+, Cl- and Ca2+ with the fluorescent dyes ANG2, MQAE and ACR were tested. Therefore, the dyes were excited simultaneously (2-photon excitation) and their corresponding fluorescence decay times were recorded within two spectral ranges using time-correlated singlephoton counting (TCSPC). A second approach presented here is based on a new TCSPC-platform covering decay time detection from picoseconds to milliseconds. Thereby, nanosecond decaying cellular fluorescence and microsecond decaying phosphorescence of Ruthenium-complexes, which is quenched by oxygen, were recorded simultaneously. In both cases changes in luminescence decay times can be linked to changes in analyte concentrations. In consequence of simultaneous excitation as well as detection, it is possible to get a deeper insight into spatio-temporal pattern in living tissues.

Ghost detection and removal based on super-pixel grouping in exposure fusion

NASA Astrophysics Data System (ADS)

Jiang, Shenyu; Xu, Zhihai; Li, Qi; Chen, Yueting; Feng, Huajun

2014-09-01

A novel multi-exposure images fusion method for dynamic scenes is proposed. The commonly used techniques for high dynamic range (HDR) imaging are based on the combination of multiple differently exposed images of the same scene. The drawback of these methods is that ghosting artifacts will be introduced into the final HDR image if the scene is not static. In this paper, a super-pixel grouping based method is proposed to detect the ghost in the image sequences. We introduce the zero mean normalized cross correlation (ZNCC) as a measure of similarity between a given exposure image and the reference. The calculation of ZNCC is implemented in super-pixel level, and the super-pixels which have low correlation with the reference are excluded by adjusting the weight maps for fusion. Without any prior information on camera response function or exposure settings, the proposed method generates low dynamic range (LDR) images which can be shown on conventional display devices directly with details preserving and ghost effects reduced. Experimental results show that the proposed method generates high quality images which have less ghost artifacts and provide a better visual quality than previous approaches.

Bruxism force detection by a piezoelectric film-based recording device in sleeping humans.

PubMed

Baba, Kazuyoshi; Clark, Glenn T; Watanabe, Tatsutomi; Ohyama, Takashi

2003-01-01

To test the reliability and utility of a force-based bruxism detection system (Intra-Splint Force Detector [ISFD]) for multiple night recordings of forceful tooth-to-splint contacts in sleeping human subjects in their home environment. Bruxism-type forces, i.e., forceful tooth-to-splint contacts, during the night were recorded with this system in 12 subjects (6 bruxers and 6 controls) for 5 nights in their home environment; a laboratory-based nocturnal polysomnogram (NPSG) study was also performed on 1 of these subjects. All 12 subjects were able to use the device without substantial difficulty on a nightly basis. The bruxer group exhibited bruxism events of significantly longer duration than the control group (27 seconds/hour versus 7.4 seconds/hour, P < .01). A NPSG study performed on 1 subject revealed that, when the masseter muscle electromyogram (EMG) was used as a "gold standard," the ISFD had a sensitivity of 0.89. The correlation coefficient between the duration of events detected by the ISFD and the EMG was also 0.89. These results suggest that the ISFD is a system that can be used easily by the subjects and that has a reasonable reliability for bruxism detection as reflected in forceful tooth-to-splint contacts during sleep.

Towards a global-scale ambient noise cross-correlation data base

NASA Astrophysics Data System (ADS)

Ermert, Laura; Fichtner, Andreas; Sleeman, Reinoud

2014-05-01

We aim to obtain a global-scale data base of ambient seismic noise correlations. This database - to be made publicly available at ORFEUS - will enable us to study the distribution of microseismic and hum sources, and to perform multi-scale full waveform inversion for crustal and mantle structure. Ambient noise tomography has developed into a standard technique. According to theory, cross-correlations equal inter-station Green's functions only if the wave field is equipartitioned or the sources are isotropically distributed. In an attempt to circumvent these assumptions, we aim to investigate possibilities to directly model noise cross-correlations and invert for their sources using adjoint techniques. A data base containing correlations of 'gently' preprocessed noise, excluding preprocessing steps which are explicitly taken to reduce the influence of a non-isotropic source distribution like spectral whitening, is a key ingredient in this undertaking. Raw data are acquired from IRIS/FDSN and ORFEUS. We preprocess and correlate the time series using a tool based on the Python package Obspy which is run in parallel on a cluster of the Swiss National Supercomputing Centre. Correlation is done in two ways: Besides the classical cross-correlation function, the phase cross-correlation is calculated, which is an amplitude-independent measure of waveform similarity and therefore insensitive to high-energy events. Besides linear stacks of these correlations, instantaneous phase stacks are calculated which can be applied as optional weight, enhancing coherent portions of the traces and facilitating the emergence of a meaningful signal. The _STS1 virtual network by IRIS contains about 250 globally distributed stations, several of which have been operating for more than 20 years. It is the first data collection we will use for correlations in the hum frequency range, as the STS-1 instrument response is flat in the largest part of the period range where hum is observed, up to a

Feasibility of the simultaneous determination of polycyclic aromatic hydrocarbons based on two-dimensional fluorescence correlation spectroscopy

NASA Astrophysics Data System (ADS)

Yang, Renjie; Dong, Guimei; Sun, Xueshan; Yang, Yanrong; Yu, Yaping; Liu, Haixue; Zhang, Weiyu

2018-02-01

A new approach for quantitative determination of polycyclic aromatic hydrocarbons (PAHs) in environment was proposed based on two-dimensional (2D) fluorescence correlation spectroscopy in conjunction with multivariate method. 40 mixture solutions of anthracene and pyrene were prepared in the laboratory. Excitation-emission matrix (EEM) fluorescence spectra of all samples were collected. And 2D fluorescence correlation spectra were calculated under the excitation perturbation. The N-way partial least squares (N-PLS) models were developed based on 2D fluorescence correlation spectra, showing a root mean square error of calibration (RMSEC) of 3.50 μg L- 1 and root mean square error of prediction (RMSEP) of 4.42 μg L- 1 for anthracene and of 3.61 μg L- 1 and 4.29 μg L- 1 for pyrene, respectively. Also, the N-PLS models were developed for quantitative analysis of anthracene and pyrene using EEM fluorescence spectra. The RMSEC and RMSEP were 3.97 μg L- 1 and 4.63 μg L- 1 for anthracene, 4.46 μg L- 1 and 4.52 μg L- 1 for pyrene, respectively. It was found that the N-PLS model using 2D fluorescence correlation spectra could provide better results comparing with EEM fluorescence spectra because of its low RMSEC and RMSEP. The methodology proposed has the potential to be an alternative method for detection of PAHs in environment.

Functional network connectivity analysis based on partial correlation in Alzheimer's disease

NASA Astrophysics Data System (ADS)

Zhang, Nan; Guan, Xiaoting; Zhang, Yumei; Li, Jingjing; Chen, Hongyan; Chen, Kewei; Fleisher, Adam; Yao, Li; Wu, Xia

2009-02-01

Functional network connectivity (FNC) measures the temporal dependency among the time courses of functional networks. However, the marginal correlation between two networks used in the classic FNC analysis approach doesn't separate the FNC from the direct/indirect effects of other networks. In this study, we proposed an alternative approach based on partial correlation to evaluate the FNC, since partial correlation based FNC can reveal the direct interaction between a pair of networks, removing dependencies or influences from others. Previous studies have demonstrated less task-specific activation and less rest-state activity in Alzheimer's disease (AD). We applied present approach to contrast FNC differences of resting state network (RSN) between AD and normal controls (NC). The fMRI data under resting condition were collected from 15 AD and 16 NC. FNC was calculated for each pair of six RSNs identified using Group ICA, thus resulting in 15 (2 out of 6) pairs for each subject. Partial correlation based FNC analysis indicated 6 pairs significant differences between groups, while marginal correlation only revealed 2 pairs (involved in the partial correlation results). Additionally, patients showed lower correlation than controls among most of the FNC differences. Our results provide new evidences for the disconnection hypothesis in AD.

Detection of the baryon acoustic peak in the large-scale correlation function of SDSS luminous red galaxies

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

Eisenstein, Daniel J.; Zehavi, Idit; Hogg, David W.

2005-01-01

We present the large-scale correlation function measured from a spectroscopic sample of 46,748 luminous red galaxies from the Sloan Digital Sky Survey. The survey region covers 0.72h{sup -3} Gpc{sup 3} over 3816 square degrees and 0.16 < z < 0.47, making it the best sample yet for the study of large-scale structure. We find a well-detected peak in the correlation function at 100h{sup -1} Mpc separation that is an excellent match to the predicted shape and location of the imprint of the recombination-epoch acoustic oscillations on the low-redshift clustering of matter. This detection demonstrates the linear growth of structure bymore » gravitational instability between z {approx} 1000 and the present and confirms a firm prediction of the standard cosmological theory. The acoustic peak provides a standard ruler by which we can measure the ratio of the distances to z = 0.35 and z = 1089 to 4% fractional accuracy and the absolute distance to z = 0.35 to 5% accuracy. From the overall shape of the correlation function, we measure the matter density {Omega}{sub m}h{sup 2} to 8% and find agreement with the value from cosmic microwave background (CMB) anisotropies. Independent of the constraints provided by the CMB acoustic scale, we find {Omega}{sub m} = 0.273 {+-} 0.025 + 0.123(1 + w{sub 0}) + 0.137{Omega}{sub K}. Including the CMB acoustic scale, we find that the spatial curvature is {Omega}{sub K} = -0.010 {+-} 0.009 if the dark energy is a cosmological constant. More generally, our results provide a measurement of cosmological distance, and hence an argument for dark energy, based on a geometric method with the same simple physics as the microwave background anisotropies. The standard cosmological model convincingly passes these new and robust tests of its fundamental properties.« less

The relationship study between image features and detection probability based on psychology experiments

NASA Astrophysics Data System (ADS)

Lin, Wei; Chen, Yu-hua; Wang, Ji-yuan; Gao, Hong-sheng; Wang, Ji-jun; Su, Rong-hua; Mao, Wei

2011-04-01

Detection probability is an important index to represent and estimate target viability, which provides basis for target recognition and decision-making. But it will expend a mass of time and manpower to obtain detection probability in reality. At the same time, due to the different interpretation of personnel practice knowledge and experience, a great difference will often exist in the datum obtained. By means of studying the relationship between image features and perception quantity based on psychology experiments, the probability model has been established, in which the process is as following.Firstly, four image features have been extracted and quantified, which affect directly detection. Four feature similarity degrees between target and background were defined. Secondly, the relationship between single image feature similarity degree and perception quantity was set up based on psychological principle, and psychological experiments of target interpretation were designed which includes about five hundred people for interpretation and two hundred images. In order to reduce image features correlativity, a lot of artificial synthesis images have been made which include images with single brightness feature difference, images with single chromaticity feature difference, images with single texture feature difference and images with single shape feature difference. By analyzing and fitting a mass of experiments datum, the model quantitys have been determined. Finally, by applying statistical decision theory and experimental results, the relationship between perception quantity with target detection probability has been found. With the verification of a great deal of target interpretation in practice, the target detection probability can be obtained by the model quickly and objectively.

Train integrity detection risk analysis based on PRISM

NASA Astrophysics Data System (ADS)

Wen, Yuan

2018-04-01

GNSS based Train Integrity Monitoring System (TIMS) is an effective and low-cost detection scheme for train integrity detection. However, as an external auxiliary system of CTCS, GNSS may be influenced by external environments, such as uncertainty of wireless communication channels, which may lead to the failure of communication and positioning. In order to guarantee the reliability and safety of train operation, a risk analysis method of train integrity detection based on PRISM is proposed in this article. First, we analyze the risk factors (in GNSS communication process and the on-board communication process) and model them. Then, we evaluate the performance of the model in PRISM based on the field data. Finally, we discuss how these risk factors influence the train integrity detection process.

High-speed technique based on a parallel projection correlation procedure for digital image correlation

NASA Astrophysics Data System (ADS)

Zaripov, D. I.; Renfu, Li

2018-05-01

The implementation of high-efficiency digital image correlation methods based on a zero-normalized cross-correlation (ZNCC) procedure for high-speed, time-resolved measurements using a high-resolution digital camera is associated with big data processing and is often time consuming. In order to speed-up ZNCC computation, a high-speed technique based on a parallel projection correlation procedure is proposed. The proposed technique involves the use of interrogation window projections instead of its two-dimensional field of luminous intensity. This simplification allows acceleration of ZNCC computation up to 28.8 times compared to ZNCC calculated directly, depending on the size of interrogation window and region of interest. The results of three synthetic test cases, such as a one-dimensional uniform flow, a linear shear flow and a turbulent boundary-layer flow, are discussed in terms of accuracy. In the latter case, the proposed technique is implemented together with an iterative window-deformation technique. On the basis of the results of the present work, the proposed technique is recommended to be used for initial velocity field calculation, with further correction using more accurate techniques.

Development of an Early Warning Fire Detection System using Correlation Spectroscopy

NASA Technical Reports Server (NTRS)

Goswami, K.; Voevodkin, G.; Rubstov, V.; Lieberman, R.; Piltch, N.

2001-01-01

Combustion byproducts are numerous. A few examples of the gaseous byproducts include carbon dioxide, carbon monoxide, hydrogen chloride, hydrogen cyanide and ammonia. For detecting these chemical species, classic absorption spectroscopy has been used for many decades, but the sensitivity of steady-state methods is often unsuitable for the detection of trace compounds at the low levels (parts per million to parts per billion) appropriate for scientific purposes. This is particularly so for monitoring equipment, which must be compact and cost-effective, and which is often subjected to shock, vibration, and other environmental effects that can severely degrade the performance of high-sensitivity spectrometers in an aircraft. Steady-state techniques also suffer from a lack of specificity; the deconvolution of the spectra of complex mixtures is a laborious and error prone process. These problems are exacerbated in remote fiber-optic monitoring where, for practical reasons, the fundamental absorbance region of the spectrum (often between 3 and 8 microns) is inaccessible, and the low-strength, closely spaced, near-infrared overtone absorbance bands must be used. We circumvented these challenges by employing correlation spectroscopy, a variation of modulation spectroscopy.

Correlation between quantitative traits and correlation between corresponding LOD scores: detection of pleiotropic effects.

PubMed

Ulgen, Ayse; Han, Zhihua; Li, Wentian

2003-12-31

We address the question of whether statistical correlations among quantitative traits lead to correlation of linkage results of these traits. Five measured quantitative traits (total cholesterol, fasting glucose, HDL cholesterol, blood pressure, and triglycerides), and one derived quantitative trait (total cholesterol divided by the HDL cholesterol) are used for phenotype correlation studies. Four of them are used for linkage analysis. We show that although correlation among phenotypes partially reflects the correlation among linkage analysis results, the LOD-score correlations are on average low. The most significant peaks found by using different traits do not often overlap. Studying covariances at specific locations in LOD scores may provide clues for further bivariate linkage analyses.

Current-based detection of nonlocal spin transport in graphene for spin-based logic applications

NASA Astrophysics Data System (ADS)

Wen, Hua; Zhu, Tiancong; Luo, Yunqiu Kelly; Amamou, Walid; Kawakami, Roland K.

2014-05-01

Graphene has been proposed for novel spintronic devices due to its robust and efficient spin transport properties at room temperature. Some of the most promising proposals require current-based readout for integration purposes, but the current-based detection of spin accumulation has not yet been developed. In this work, we demonstrate current-based detection of spin transport in graphene using a modified nonlocal geometry. By adding a variable shunt resistor in parallel to the nonlocal voltmeter, we are able to systematically cross over from the conventional voltage-based detection to current-based detection. As the shunt resistor is reduced, the output current from the spin accumulation increases as the shunt resistance drops below a characteristic value R*. We analyze this behavior using a one-dimensional drift-diffusion model, which accounts well for the observed behavior. These results provide the experimental and theoretical foundation for current-based detection of nonlocal spin transport.

Limitations of correlation-based redatuming methods

NASA Astrophysics Data System (ADS)

Barrera P, D. F.; Schleicher, J.; van der Neut, J.

2017-12-01

Redatuming aims to correct seismic data for the consequences of an acquisition far from the target. That includes the effects of an irregular acquisition surface and of complex geological structures in the overburden such as strong lateral heterogeneities or layers with low or very high velocity. Interferometric techniques can be used to relocate sources to positions where only receivers are available and have been used to move acquisition geometries to the ocean bottom or transform data between surface-seismic and vertical seismic profiles. Even if no receivers are available at the new datum, the acquisition system can be relocated to any datum in the subsurface to which the propagation of waves can be modeled with sufficient accuracy. By correlating the modeled wavefield with seismic surface data, one can carry the seismic acquisition geometry from the surface closer to geologic horizons of interest. Specifically, we show the derivation and approximation of the one-sided seismic interferometry equation for surface-data redatuming, conveniently using Green’s theorem for the Helmholtz equation with density variation. Our numerical examples demonstrate that correlation-based single-boundary redatuming works perfectly in a homogeneous overburden. If the overburden is inhomogeneous, primary reflections from deeper interfaces are still repositioned with satisfactory accuracy. However, in this case artifacts are generated as a consequence of incorrectly redatumed overburden multiples. These artifacts get even worse if the complete wavefield is used instead of the direct wavefield. Therefore, we conclude that correlation-based interferometric redatuming of surface-seismic data should always be applied using direct waves only, which can be approximated with sufficient quality if a smooth velocity model for the overburden is available.

Binaural comodulation masking release: Effects of masker interaural correlation

PubMed Central

Hall, Joseph W.; Buss, Emily; Grose, John H.

2007-01-01

Binaural detection was examined for a signal presented in a narrow band of noise centered on the on-signal masking band (OSB) or in the presence of flanking noise bands that were random or comodulated with respect to the OSB. The noise had an interaural correlation of 1.0 (No), 0.99 or 0.95. In No noise, random flanking bands worsened Sπ detection and comodulated bands improved Sπ detection for some listeners but had no effect for other listeners. For the 0.99 or 0.95 interaural correlation conditions, random flanking bands were less detrimental to Sπ detection and comodulated flanking bands improved Sπ detection for all listeners. Analyses based on signal detection theory indicated that the improvement in Sπ thresholds obtained with comodulated bands was not compatible with an optimal combination of monaural and binaural cues or to across-frequency analyses of dynamic interaural phase differences. Two accounts consistent with the improvement in Sπ thresholds in comodulated noise were (1) envelope information carried by the flanking bands improves the weighting of binaural cues associated with the signal; (2) the auditory system is sensitive to across-frequency differences in ongoing interaural correlation. PMID:17225415

A new approach based on the median filter to T-wave detection in ECG signal.

PubMed

Kholkhal, Mourad; Bereksi Reguig, Fethi

2014-07-01

The electrocardiogram (ECG) is one of the most used signals in the diagnosis of heart disease. It contains different waves which directly correlate to heart activity. Different methods have been used in order to detect these waves and consequently lead to heart activity diagnosis. This paper is interested more particularly to the detection of the T-wave. Such a wave represents the re-polarization state of the heart activity. The proposed approach is based on the algorithm procedure which allows the detection of the T-wave using a lot of filter including mean and median filter. The proposed algorithm is implemented and tested on a set of ECG recordings taken from, respectively, the European STT, MITBIH and MITBIH ST databases. The results are found to be very satisfactory in terms of sensitivity, predictivity and error compared to other works in the field.

Detection of cat-eye effect echo based on unit APD

NASA Astrophysics Data System (ADS)

Wu, Dong-Sheng; Zhang, Peng; Hu, Wen-Gang; Ying, Jia-Ju; Liu, Jie

2016-10-01

The cat-eye effect echo of optical system can be detected based on CCD, but the detection range is limited within several kilometers. In order to achieve long-range even ultra-long-range detection, it ought to select APD as detector because of the high sensitivity of APD. The detection system of cat-eye effect echo based on unit APD is designed in paper. The implementation scheme and key technology of the detection system is presented. The detection performances of the detection system including detection range, detection probability and false alarm probability are modeled. Based on the model, the performances of the detection system are analyzed using typical parameters. The results of numerical calculation show that the echo signal-to-noise ratio is greater than six, the detection probability is greater than 99.9% and the false alarm probability is less tan 0.1% within 20 km detection range. In order to verify the detection effect, we built the experimental platform of detection system according to the design scheme and carry out the field experiments. The experimental results agree well with the results of numerical calculation, which prove that the detection system based on the unit APD is feasible to realize remote detection for cat-eye effect echo.

Water Detection Based on Object Reflections

NASA Technical Reports Server (NTRS)

Rankin, Arturo L.; Matthies, Larry H.

2012-01-01

Water bodies are challenging terrain hazards for terrestrial unmanned ground vehicles (UGVs) for several reasons. Traversing through deep water bodies could cause costly damage to the electronics of UGVs. Additionally, a UGV that is either broken down due to water damage or becomes stuck in a water body during an autonomous operation will require rescue, potentially drawing critical resources away from the primary operation and increasing the operation cost. Thus, robust water detection is a critical perception requirement for UGV autonomous navigation. One of the properties useful for detecting still water bodies is that their surface acts as a horizontal mirror at high incidence angles. Still water bodies in wide-open areas can be detected by geometrically locating the exact pixels in the sky that are reflecting on candidate water pixels on the ground, predicting if ground pixels are water based on color similarity to the sky and local terrain features. But in cluttered areas where reflections of objects in the background dominate the appearance of the surface of still water bodies, detection based on sky reflections is of marginal value. Specifically, this software attempts to solve the problem of detecting still water bodies on cross-country terrain in cluttered areas at low cost.

Bell Correlations in a Many-Body System with Finite Statistics

NASA Astrophysics Data System (ADS)

Wagner, Sebastian; Schmied, Roman; Fadel, Matteo; Treutlein, Philipp; Sangouard, Nicolas; Bancal, Jean-Daniel

2017-10-01

A recent experiment reported the first violation of a Bell correlation witness in a many-body system [Science 352, 441 (2016)]. Following discussions in this Letter, we address here the question of the statistics required to witness Bell correlated states, i.e., states violating a Bell inequality, in such experiments. We start by deriving multipartite Bell inequalities involving an arbitrary number of measurement settings, two outcomes per party and one- and two-body correlators only. Based on these inequalities, we then build up improved witnesses able to detect Bell correlated states in many-body systems using two collective measurements only. These witnesses can potentially detect Bell correlations in states with an arbitrarily low amount of spin squeezing. We then establish an upper bound on the statistics needed to convincingly conclude that a measured state is Bell correlated.

Bell Correlations in a Many-Body System with Finite Statistics.

PubMed

Wagner, Sebastian; Schmied, Roman; Fadel, Matteo; Treutlein, Philipp; Sangouard, Nicolas; Bancal, Jean-Daniel

2017-10-27

A recent experiment reported the first violation of a Bell correlation witness in a many-body system [Science 352, 441 (2016)]. Following discussions in this Letter, we address here the question of the statistics required to witness Bell correlated states, i.e., states violating a Bell inequality, in such experiments. We start by deriving multipartite Bell inequalities involving an arbitrary number of measurement settings, two outcomes per party and one- and two-body correlators only. Based on these inequalities, we then build up improved witnesses able to detect Bell correlated states in many-body systems using two collective measurements only. These witnesses can potentially detect Bell correlations in states with an arbitrarily low amount of spin squeezing. We then establish an upper bound on the statistics needed to convincingly conclude that a measured state is Bell correlated.

Saliency detection algorithm based on LSC-RC

NASA Astrophysics Data System (ADS)

Wu, Wei; Tian, Weiye; Wang, Ding; Luo, Xin; Wu, Yingfei; Zhang, Yu

2018-02-01

Image prominence is the most important region in an image, which can cause the visual attention and response of human beings. Preferentially allocating the computer resources for the image analysis and synthesis by the significant region is of great significance to improve the image area detecting. As a preprocessing of other disciplines in image processing field, the image prominence has widely applications in image retrieval and image segmentation. Among these applications, the super-pixel segmentation significance detection algorithm based on linear spectral clustering (LSC) has achieved good results. The significance detection algorithm proposed in this paper is better than the regional contrast ratio by replacing the method of regional formation in the latter with the linear spectral clustering image is super-pixel block. After combining with the latest depth learning method, the accuracy of the significant region detecting has a great promotion. At last, the superiority and feasibility of the super-pixel segmentation detection algorithm based on linear spectral clustering are proved by the comparative test.

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.

Reference geometry-based detection of (4D-)CT motion artifacts: a feasibility study

NASA Astrophysics Data System (ADS)

Werner, René; Gauer, Tobias

2015-03-01

Respiration-correlated computed tomography (4D or 3D+t CT) can be considered as standard of care in radiation therapy treatment planning for lung and liver lesions. The decision about an application of motion management devices and the estimation of patient-specific motion effects on the dose distribution relies on precise motion assessment in the planning 4D CT data { which is impeded in case of CT motion artifacts. The development of image-based/post-processing approaches to reduce motion artifacts would benefit from precise detection and localization of the artifacts. Simple slice-by-slice comparison of intensity values and threshold-based analysis of related metrics suffer from- depending on the threshold- high false-positive or -negative rates. In this work, we propose exploiting prior knowledge about `ideal' (= artifact free) reference geometries to stabilize metric-based artifact detection by transferring (multi-)atlas-based concepts to this specific task. Two variants are introduced and evaluated: (S1) analysis and comparison of warped atlas data obtained by repeated non-linear atlas-to-patient registration with different levels of regularization; (S2) direct analysis of vector field properties (divergence, curl magnitude) of the atlas-to-patient transformation. Feasibility of approaches (S1) and (S2) is evaluated by motion-phantom data and intra-subject experiments (four patients) as well as - adopting a multi-atlas strategy- inter-subject investigations (twelve patients involved). It is demonstrated that especially sorting/double structure artifacts can be precisely detected and localized by (S1). In contrast, (S2) suffers from high false positive rates.

Integrator or Coincidence Detector: A Novel Measure Based on the Discrete Reverse Correlation to Determine a Neuron's Operational Mode.

PubMed

Kanev, Jacob; Koutsou, Achilleas; Christodoulou, Chris; Obermayer, Klaus

2016-10-01

In this letter, we propose a definition of the operational mode of a neuron, that is, whether a neuron integrates over its input or detects coincidences. We complete the range of possible operational modes by a new mode we call gap detection, which means that a neuron responds to gaps in its stimulus. We propose a measure consisting of two scalar values, both ranging from -1 to +1: the neural drive, which indicates whether its stimulus excites the neuron, serves as background noise, or inhibits it; the neural mode, which indicates whether the neuron's response is the result of integration over its input, of coincidence detection, or of gap detection; with all three modes possible for all neural drive values. This is a pure spike-based measure and can be applied to measure the influence of either all or subset of a neuron's stimulus. We derive the measure by decomposing the reverse correlation, test it in several artificial and biological settings, and compare it to other measures, finding little or no correlation between them. We relate the results of the measure to neural parameters and investigate the effect of time delay during spike generation. Our results suggest that a neuron can use several different modes simultaneously on different subsets of its stimulus to enable it to respond to its stimulus in a complex manner.

Sella size and jaw bases - Is there a correlation???

PubMed

Neha; Mogra, Subraya; Shetty, Vorvady Surendra; Shetty, Siddarth

2016-01-01

Sella turcica is an important cephalometric structure and attempts have been made in the past to correlate its dimensions to the malocclusion. However, no study has so far compared the size of sella to the jaw bases that determine the type of malocclusion. The present study was undertaken to find out any such correlation if it exists. Lateral cephalograms of 110 adults consisting of 40 Class I, 40 Class II, and 30 Class III patients were assessed for the measurement of sella length, width, height, and area. The maxillary length, mandibular ramus height, and body length were also measured. The sella dimensions were compared among three malocclusion types by one-way ANOVA. Pearson correlation was calculated between the jaw size and sella dimensions. Furthermore, the ratio of jaw base lengths and sella area were calculated. Mean sella length, width and area were found to be greatest in Class III, followed by Class I and least in Class II though the results were not statistically significant. 3 out of 4 measured dimensions of sella, correlated significantly with mandibular ramus and body length each. However, only one dimension of sella showed significant correlation with maxilla. The mandibular ramus and body length show a nearly constant ratio to sella area (0.83-0.85, 0.64-0.65, respectively) in all the three malocclusions. Thus, mandible has a definite and better correlation to the size of sella turcica.

Gear Tooth Wear Detection Algorithm

NASA Technical Reports Server (NTRS)

Delgado, Irebert R.

2015-01-01

Vibration-based condition indicators continue to be developed for Health Usage Monitoring of rotorcraft gearboxes. Testing performed at NASA Glenn Research Center have shown correlations between specific condition indicators and specific types of gear wear. To speed up the detection and analysis of gear teeth, an image detection program based on the Viola-Jones algorithm was trained to automatically detect spiral bevel gear wear pitting. The detector was tested using a training set of gear wear pictures and a blind set of gear wear pictures. The detector accuracy for the training set was 75 percent while the accuracy for the blind set was 15 percent. Further improvements on the accuracy of the detector are required but preliminary results have shown its ability to automatically detect gear tooth wear. The trained detector would be used to quickly evaluate a set of gear or pinion pictures for pits, spalls, or abrasive wear. The results could then be used to correlate with vibration or oil debris data. In general, the program could be retrained to detect features of interest from pictures of a component taken over a period of time.

Two-Way Gene Interaction From Microarray Data Based on Correlation Methods.

PubMed

Alavi Majd, Hamid; Talebi, Atefeh; Gilany, Kambiz; Khayyer, Nasibeh

2016-06-01

Gene networks have generated a massive explosion in the development of high-throughput techniques for monitoring various aspects of gene activity. Networks offer a natural way to model interactions between genes, and extracting gene network information from high-throughput genomic data is an important and difficult task. The purpose of this study is to construct a two-way gene network based on parametric and nonparametric correlation coefficients. The first step in constructing a Gene Co-expression Network is to score all pairs of gene vectors. The second step is to select a score threshold and connect all gene pairs whose scores exceed this value. In the foundation-application study, we constructed two-way gene networks using nonparametric methods, such as Spearman's rank correlation coefficient and Blomqvist's measure, and compared them with Pearson's correlation coefficient. We surveyed six genes of venous thrombosis disease, made a matrix entry representing the score for the corresponding gene pair, and obtained two-way interactions using Pearson's correlation, Spearman's rank correlation, and Blomqvist's coefficient. Finally, these methods were compared with Cytoscape, based on BIND, and Gene Ontology, based on molecular function visual methods; R software version 3.2 and Bioconductor were used to perform these methods. Based on the Pearson and Spearman correlations, the results were the same and were confirmed by Cytoscape and GO visual methods; however, Blomqvist's coefficient was not confirmed by visual methods. Some results of the correlation coefficients are not the same with visualization. The reason may be due to the small number of data.

Anomalous Quantum Correlations of Squeezed Light

NASA Astrophysics Data System (ADS)

Kühn, B.; Vogel, W.; Mraz, M.; Köhnke, S.; Hage, B.

2017-04-01

Three different noise moments of field strength, intensity, and their correlations are simultaneously measured. For this purpose a homodyne cross-correlation measurement [1] is implemented by superimposing the signal field and a weak local oscillator on an unbalanced beam splitter. The relevant information is obtained via the intensity noise correlation of the output modes. Detection details like quantum efficiencies or uncorrelated dark noise are meaningless for our technique. Yet unknown insight in the quantumness of a squeezed signal field is retrieved from the anomalous moment, correlating field strength with intensity noise. A classical inequality including this moment is violated for almost all signal phases. Precognition on quantum theory is superfluous, as our analysis is solely based on classical physics.

Water Detection Based on Sky Reflections

NASA Technical Reports Server (NTRS)

Rankin, Arturo L.; Matthies, Larry H.

2010-01-01

This software has been designed to detect water bodies that are out in the open on cross-country terrain at mid- to far-range (approximately 20 100 meters), using imagery acquired from a stereo pair of color cameras mounted on a terrestrial, unmanned ground vehicle (UGV). Non-traversable water bodies, such as large puddles, ponds, and lakes, are indirectly detected by detecting reflections of the sky below the horizon in color imagery. The appearance of water bodies in color imagery largely depends on the ratio of light reflected off the water surface to the light coming out of the water body. When a water body is far away, the angle of incidence is large, and the light reflected off the water surface dominates. We have exploited this behavior to detect water bodies out in the open at mid- to far-range. When a water body is detected at far range, a UGV s path planner can begin to look for alternate routes to the goal position sooner, rather than later. As a result, detecting water hazards at far range generally reduces the time required to reach a goal position during autonomous navigation. This software implements a new water detector based on sky reflections that geometrically locates the exact pixel in the sky that is reflecting on a candidate water pixel on the ground, and predicts if the ground pixel is water based on color similarity and local terrain features

System for critical infrastructure security based on multispectral observation-detection module

NASA Astrophysics Data System (ADS)

Trzaskawka, Piotr; Kastek, Mariusz; Życzkowski, Marek; Dulski, Rafał; Szustakowski, Mieczysław; Ciurapiński, Wiesław; Bareła, Jarosław

2013-10-01

Recent terrorist attacks and possibilities of such actions in future have forced to develop security systems for critical infrastructures that embrace sensors technologies and technical organization of systems. The used till now perimeter protection of stationary objects, based on construction of a ring with two-zone fencing, visual cameras with illumination are efficiently displaced by the systems of the multisensor technology that consists of: visible technology - day/night cameras registering optical contrast of a scene, thermal technology - cheap bolometric cameras recording thermal contrast of a scene and active ground radars - microwave and millimetre wavelengths that record and detect reflected radiation. Merging of these three different technologies into one system requires methodology for selection of technical conditions of installation and parameters of sensors. This procedure enables us to construct a system with correlated range, resolution, field of view and object identification. Important technical problem connected with the multispectral system is its software, which helps couple the radar with the cameras. This software can be used for automatic focusing of cameras, automatic guiding cameras to an object detected by the radar, tracking of the object and localization of the object on the digital map as well as target identification and alerting. Based on "plug and play" architecture, this system provides unmatched flexibility and simplistic integration of sensors and devices in TCP/IP networks. Using a graphical user interface it is possible to control sensors and monitor streaming video and other data over the network, visualize the results of data fusion process and obtain detailed information about detected intruders over a digital map. System provide high-level applications and operator workload reduction with features such as sensor to sensor cueing from detection devices, automatic e-mail notification and alarm triggering. The paper presents

Colorimetric detection of 1,5-anhydroglucitol based on graphene quantum dots and enzyme-catalyzed reaction.

PubMed

Zhou, Zhide; Zhao, Le; Wang, Zhihong; Xue, Wen; Wang, Yunxiao; Huang, Yong; Liang, Jintao; Chen, Jiejing; Li, Guiyin

2018-06-01

Early diagnosis of diabetes yields significant clinical benefits. The serum level of 1,5‑anhydroglucitol (1,5‑AG) has been a new biochemical marker for postprandial hyperglycemia. In this study, a simple colorimetric method for 1,5‑AG detection has been designed based on highly efficient peroxidase mimetic activity of GQDs and enzyme-catalyzed reaction. By the catalytic action of pyranose oxidase (PROD), the 1,5‑AG was oxidized to 1,5‑anhydrofuctose and H 2 O 2 . The GQDs in the presence of H 2 O 2 exhibited highly efficient catalytic activity toward the oxidation of 3, 3', 5, 5'‑tetramethylbenzidine (TMB) to a blue colored product. The influence of relevant experimental variables was optimized. A linear relationship of optical signal with the concentration of 1,5‑AG in the range of 20.0-100.0μg/mL with the regression correlation coefficient of 0.9985 was obtained which could be monitored by colorimetry detection. The limit of detection (LOD) for 1,5‑AG detection was approximately 0.144μg/mL. All in all, the proposed 1,5‑AG detection system based on GQDs and PROD-catalyzed reaction showed better performances with simple operation, low-cost, higher selectivity. Copyright © 2018 Elsevier B.V. All rights reserved.

Wavelet-based image compression using shuffling and bit plane correlation

NASA Astrophysics Data System (ADS)

Kim, Seungjong; Jeong, Jechang

2000-12-01

In this paper, we propose a wavelet-based image compression method using shuffling and bit plane correlation. The proposed method improves coding performance in two steps: (1) removing the sign bit plane by shuffling process on quantized coefficients, (2) choosing the arithmetic coding context according to maximum correlation direction. The experimental results are comparable or superior for some images with low correlation, to existing coders.

Python Waveform Cross-Correlation

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

Templeton, Dennise

PyWCC is a tool to compute seismic waveform cross-correlation coefficients on single-component or multiple-component seismic data across a network of seismic sensors. PyWCC compares waveform data templates with continuous seismic data, associates the resulting detections, identifies the template with the highest cross-correlation coefficient, and outputs a catalog of detections above a user-defined absolute cross-correlation threshold value.

Autocorrel I: A Neural Network Based Network Event Correlation Approach

DTIC Science & Technology

2005-05-01

which concern any component of the network. 2.1.1 Existing Intrusion Detection Systems EMERALD [8] is a distributed, scalable, hierarchal, customizable...writing this paper, the updaters of this system had not released their correlation unit to the public. EMERALD ex- plicitly divides statistical analysis... EMERALD , NetSTAT is scalable and composi- ble. QuidSCOR [12] is an open-source IDS, though it requires a subscription from its publisher, Qualys Inc

Hair cortisol detection in dairy cattle by using EIA: protocol validation and correlation with faecal cortisol metabolites.

PubMed

Tallo-Parra, O; Manteca, X; Sabes-Alsina, M; Carbajal, A; Lopez-Bejar, M

2015-06-01

Hair may be a useful matrix to detect cumulative cortisol concentrations in studies of animal welfare and chronic stress. The aim of this study was to validate a protocol for cortisol detection in hair from dairy cattle by enzyme immunoassay (EIA). Seventeen adult Holstein-Friesian dairy cows were used during the milking period. Hair cortisol concentration was assessed in 25-day-old hair samples taken from the frontal region of the head, analysing black and white coloured hair separately. Concentrations of cortisol metabolites were determined in faeces collected twice a week during the same period of time. There was a high correlation between cortisol values in faeces and cortisol in white colour hair samples but such correlation was not significant with the black colour hair samples. The intra- and inter-assay coefficients of variation were 4.9% and 10.6%, respectively. The linearity showed R 2=0.98 and mean percentage error of -10.8 ± 1.55%. The extraction efficiency was 89.0 ± 23.52% and the parallelism test showed similar slopes. Cortisol detection in hair by using EIA seems to be a valid method to represent long-term circulating cortisol levels in dairy cattle.

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

NASA Astrophysics Data System (ADS)

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

2013-06-01

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

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.

[Detecting fire smoke based on the multispectral image].

PubMed

Wei, Ying-Zhuo; Zhang, Shao-Wu; Liu, Yan-Wei

2010-04-01

Smoke detection is very important for preventing forest-fire in the fire early process. Because the traditional technologies based on video and image processing are easily affected by the background dynamic information, three limitations exist in these technologies, i. e. lower anti-interference ability, higher false detection rate and the fire smoke and water fog being not easily distinguished. A novel detection method for detecting smoke based on the multispectral image was proposed in the present paper. Using the multispectral digital imaging technique, the multispectral image series of fire smoke and water fog were obtained in the band scope of 400 to 720 nm, and the images were divided into bins. The Euclidian distance among the bins was taken as a measurement for showing the difference of spectrogram. After obtaining the spectral feature vectors of dynamic region, the regions of fire smoke and water fog were extracted according to the spectrogram feature difference between target and background. The indoor and outdoor experiments show that the smoke detection method based on multispectral image can be applied to the smoke detection, which can effectively distinguish the fire smoke and water fog. Combined with video image processing method, the multispectral image detection method can also be applied to the forest fire surveillance, reducing the false alarm rate in forest fire detection.

Advanced driver assistance system: Road sign identification using VIAPIX system and a correlation technique

NASA Astrophysics Data System (ADS)

Ouerhani, Y.; Alfalou, A.; Desthieux, M.; Brosseau, C.

2017-02-01

We present a three-step approach based on the commercial VIAPIX® module for road traffic sign recognition and identification. Firstly, detection in a scene of all objects having characteristics of traffic signs is performed. This is followed by a first-level recognition based on correlation which consists in making a comparison between each detected object with a set of reference images of a database. Finally, a second level of identification allows us to confirm or correct the previous identification. In this study, we perform a correlation-based analysis by combining and adapting the Vander Lugt correlator with the nonlinear joint transformation correlator (JTC). Of particular significance, this approach permits to make a reliable decision on road traffic sign identification. We further discuss a robust scheme allowing us to track a detected road traffic sign in a video sequence for the purpose of increasing the decision performance of our system. This approach can have broad practical applications in the maintenance and rehabilitation of transportation infrastructure, or for drive assistance.

Dysregulated Pathway Identification of Alzheimer's Disease Based on Internal Correlation Analysis of Genes and Pathways.

PubMed

Kong, Wei; Mou, Xiaoyang; Di, Benteng; Deng, Jin; Zhong, Ruxing; Wang, Shuaiqun

2017-11-20

Dysregulated pathway identification is an important task which can gain insight into the underlying biological processes of disease. Current pathway-identification methods focus on a set of co-expression genes and single pathways and ignore the correlation between genes and pathways. The method proposed in this study, takes into account the internal correlations not only between genes but also pathways to identifying dysregulated pathways related to Alzheimer's disease (AD), the most common form of dementia. In order to find the significantly differential genes for AD, mutual information (MI) is used to measure interdependencies between genes other than expression valves. Then, by integrating the topology information from KEGG, the significant pathways involved in the feature genes are identified. Next, the distance correlation (DC) is applied to measure the pairwise pathway crosstalks since DC has the advantage of detecting nonlinear correlations when compared to Pearson correlation. Finally, the pathway pairs with significantly different correlations between normal and AD samples are known as dysregulated pathways. The molecular biology analysis demonstrated that many dysregulated pathways related to AD pathogenesis have been discovered successfully by the internal correlation detection. Furthermore, the insights of the dysregulated pathways in the development and deterioration of AD will help to find new effective target genes and provide important theoretical guidance for drug design. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Rate based failure detection

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

Johnson, Brett Emery Trabun; Gamage, Thoshitha Thanushka; Bakken, David Edward

This disclosure describes, in part, a system management component and failure detection component for use in a power grid data network to identify anomalies within the network and systematically adjust the quality of service of data published by publishers and subscribed to by subscribers within the network. In one implementation, subscribers may identify a desired data rate, a minimum acceptable data rate, desired latency, minimum acceptable latency and a priority for each subscription. The failure detection component may identify an anomaly within the network and a source of the anomaly. Based on the identified anomaly, data rates and or datamore » paths may be adjusted in real-time to ensure that the power grid data network does not become overloaded and/or fail.« less

Direct Generation and Detection of Quantum Correlated Photons with 3.2 um Wavelength Spacing.

PubMed

Sua, Yong Meng; Fan, Heng; Shahverdi, Amin; Chen, Jia-Yang; Huang, Yu-Ping

2017-12-13

Quantum correlated, highly non-degenerate photons can be used to synthesize disparate quantum nodes and link quantum processing over incompatible wavelengths, thereby constructing heterogeneous quantum systems for otherwise unattainable superior performance. Existing techniques for correlated photons have been concentrated in the visible and near-IR domains, with the photon pairs residing within one micron. Here, we demonstrate direct generation and detection of high-purity photon pairs at room temperature with 3.2 um wavelength spacing, one at 780 nm to match the rubidium D2 line, and the other at 3950 nm that falls in a transparent, low-scattering optical window for free space applications. The pairs are created via spontaneous parametric downconversion in a lithium niobate waveguide with specially designed geometry and periodic poling. The 780 nm photons are measured with a silicon avalanche photodiode, and the 3950 nm photons are measured with an upconversion photon detector using a similar waveguide, which attains 34% internal conversion efficiency. Quantum correlation measurement yields a high coincidence-to-accidental ratio of 54, which indicates the strong correlation with the extremely non-degenerate photon pairs. Our system bridges existing quantum technology to the challenging mid-IR regime, where unprecedented applications are expected in quantum metrology and sensing, quantum communications, medical diagnostics, and so on.

DNA aptamer-based colorimetric detection platform for Salmonella Enteritidis.

PubMed

Bayraç, Ceren; Eyidoğan, Füsun; Avni Öktem, Hüseyin

2017-12-15

Food safety is a major issue to protect public health and a key challenge is to find detection methods for identification of hazards in food. Food borne infections affects millions of people each year and among pathogens, Salmonella Enteritidis is most widely found bacteria causing food borne diseases. Therefore, simple, rapid, and specific detection methods are needed for food safety. In this study, we demonstrated the selection of DNA aptamers with high affinity and specificity against S. Enteritidis via Cell Systematic Evolution of Ligands by Exponential Enrichment (Cell-SELEX) and development of sandwich type aptamer-based colorimetric platforms for its detection. Two highly specific aptamers, crn-1 and crn-2, were developed through 12 rounds of selection with K d of 0.971µM and 0.309µM, respectively. Both aptamers were used to construct sandwich type capillary detection platforms. With the detection limit of 10 3 CFU/mL, crn-1 and crn-2 based platforms detected target bacteria specifically based on color change. This platform is also suitable for detection of S. Enteritidis in complex food matrix. Thus, this is the first to demonstrate use of Salmonella aptamers for development of the colorimetric aptamer-based detection platform in its identification and detection with naked eye in point-of-care. Copyright © 2017 Elsevier B.V. All rights reserved.

Volatility and correlation-based systemic risk measures in the US market

NASA Astrophysics Data System (ADS)

Civitarese, Jamil

2016-10-01

This paper deals with the problem of how to use simple systemic risk measures to assess portfolio risk characteristics. Using three simple examples taken from previous literature, one based on raw and partial correlations, another based on the eigenvalue decomposition of the covariance matrix and the last one based on an eigenvalue entropy, a Granger-causation analysis revealed some of them are not always a good measure of risk in the S&P 500 and in the VIX. The measures selected do not Granger-cause the VIX index in all windows selected; therefore, in the sense of risk as volatility, the indicators are not always suitable. Nevertheless, their results towards returns are similar to previous works that accept them. A deeper analysis has shown that any symmetric measure based on eigenvalue decomposition of correlation matrices, however, is not useful as a measure of "correlation" risk. The empirical counterpart analysis of this proposition stated that negative correlations are usually small and, therefore, do not heavily distort the behavior of the indicator.

A dual-channel fluorescent chemosensor for discriminative detection of glutathione based on functionalized carbon quantum dots.

PubMed

Huang, Yuanyuan; Zhou, Jin; Feng, Hui; Zheng, Jieyu; Ma, Hui-Min; Liu, Weidong; Tang, Cong; Ao, Hang; Zhao, Meizhi; Qian, Zhaosheng

2016-12-15

A convenient, fluorescent dual-channel chemosensor on the basis of bis(3-pyridylmethyl)amine-functionalized carbon quantum dots (BPMA-CQDs) nanoprobe was constructed, and it can discriminatively detect glutathione from its analogues cysteine and homocysteine based on two distinctive strategies. Two distinct fluorescence responses of BPMA-CQDs probe to Cu(II) and Ag(I) were identified and further employed to achieve selective detection of Cu(II) and Ag(I) respectively. Based on the BPMA-CQDs/Cu(II) conjugate, discriminative detection of GSH was achieved in terms of correlation between the amounts of GSH and fluorescence recovery. The addition of GSH into BPMA-CQDs/Cu(II) system induces the reduction of Cu(II) to Cu(I), which could efficiently block PET process resulting in the following fluorescence recovery. Based on the BPMA-CQDs/Ag(I) conjugate, GSH assay could also be established on the basis of fluorescence response to GSH. The introduction of GSH into the preceding system triggers the competitive coordination to Ag(I) between BPMA and GSH, and silver ions are finally taken away by GSH from the probe, where the fluorescence is restored to its original weak state. Both of the detection strategies can achieve discriminative detection of GSH from Cys and Hcy. The assays showed good stability and repeatability, and covered a broad linear range of up to 13.3μM with a lowest detection limit of 42.0nM. Moreover, both of them were utilized to monitor GSH level in live cells. Copyright © 2016 Elsevier B.V. All rights reserved.

EEG-based mild depressive detection using feature selection methods and classifiers.

PubMed

Li, Xiaowei; Hu, Bin; Sun, Shuting; Cai, Hanshu

2016-11-01

Depression has become a major health burden worldwide, and effectively detection of such disorder is a great challenge which requires latest technological tool, such as Electroencephalography (EEG). This EEG-based research seeks to find prominent frequency band and brain regions that are most related to mild depression, as well as an optimal combination of classification algorithms and feature selection methods which can be used in future mild depression detection. An experiment based on facial expression viewing task (Emo_block and Neu_block) was conducted, and EEG data of 37 university students were collected using a 128 channel HydroCel Geodesic Sensor Net (HCGSN). For discriminating mild depressive patients and normal controls, BayesNet (BN), Support Vector Machine (SVM), Logistic Regression (LR), k-nearest neighbor (KNN) and RandomForest (RF) classifiers were used. And BestFirst (BF), GreedyStepwise (GSW), GeneticSearch (GS), LinearForwordSelection (LFS) and RankSearch (RS) based on Correlation Features Selection (CFS) were applied for linear and non-linear EEG features selection. Independent Samples T-test with Bonferroni correction was used to find the significantly discriminant electrodes and features. Data mining results indicate that optimal performance is achieved using a combination of feature selection method GSW based on CFS and classifier KNN for beta frequency band. Accuracies achieved 92.00% and 98.00%, and AUC achieved 0.957 and 0.997, for Emo_block and Neu_block beta band data respectively. T-test results validate the effectiveness of selected features by search method GSW. Simplified EEG system with only FP1, FP2, F3, O2, T3 electrodes was also explored with linear features, which yielded accuracies of 91.70% and 96.00%, AUC of 0.952 and 0.972, for Emo_block and Neu_block respectively. Classification results obtained by GSW + KNN are encouraging and better than previously published results. In the spatial distribution of features, we find

Correlation between systemic lupus erythematosus and cytomegalovirus infection detected by different methods.

PubMed

Chen, Jing; Zhang, Huidi; Chen, Peirong; Lin, Qiaoai; Zhu, Xiaochun; Zhang, Lifang; Xue, Xiangyang

2015-04-01

Human cytomegalovirus (HCMV), a β-herpes virus subfamily member, leads to a lifelong, latent infection in most humans, but the correlation between HCMV infection and systemic lupus erythematosus (SLE) remains controversial. We analyzed the relevance of HCMV infection in SLE by analyzing the peripheral blood leukocytes (PBLs) and serum samples of 60 patients with SLE and 111 healthy individuals. HCMV genes UL55 and UL138 were detected in PBLs by polymerase chain reaction (PCR), and HCMV-specific serum IgG and IgM antibodies were investigated by enzyme-linked immunosorbent assay. The relationship between cellular HCMV infection in PBLs and common clinical indicators of SLE was further explored. Data indicated that the frequency of positive IgG and IgM anti-CMV antibodies was not significantly different in SLE patients and controls. However, compared to the healthy controls, the titers of IgG and IgM anti-CMV antibodies in SLE patients were significantly higher. The detection of cellular HCMV infection showed that almost all subjects were positive for UL138 gene in PBLs, but the positivity for UL55 gene was lower in PBLs. HCMV UL138 detection in PBLs was highly consistent with the frequency of the HCMV-specific IgG test and did not show significant difference in SLE patients and healthy controls. However, compared with that in healthy people, the positivity rate for cellular HCMV UL55 detection was significantly higher in SLE patients (P < 0.001). In addition, cellular HCMV UL55 with positive detection in PBLs was associated with significantly different clinical characteristics of SLE than that with negative detection. In conclusion, our data confirmed that the HCMV infection was related to the development of SLE. Especially, some clinical strains or substrains of HCMV, such as containing the UL55 gene in HCMV's genome, might play a vital role in the development of SLE.

Correlation of crAssphage-based qPCR markers with culturable and molecular indicators of human fecal pollution in an impacted urban watershed.

PubMed

Stachler, Elyse; Akyon, Benay; Aquino de Carvalho, Nathalia; Ference, Christian; Bibby, Kyle

2018-06-06

Environmental waters are monitored for fecal pollution to protect public health. Many previously developed human-specific fecal pollution indicators lack adequate sensitivity to be reliably detected in environmental waters or do not correlate well with viral pathogens. Recently, two novel human sewage-associated source tracking qPCR markers were developed based on the bacteriophage crAssphage, CPQ_056 and CPQ_064. These assays are highly human specific, abundant in sewage, and are viral-based, suggesting great promise for environmental application as human fecal pollution indicators. A 30-day sampling study was conducted in an urban stream impacted by combined sewer overflows to evaluate the crAssphage markers' performance in an environmental system. The crAssphage markers were present at concentrations of 4.02-6.04 log10 copies/100 mL throughout the study period, indicating their high abundance and ease of detection in polluted environmental waters. In addition, the crAssphage assays were correlated with rain events, molecular markers for human polyomavirus and HF183, as well as culturable E. coli, enterococci, and somatic coliphage. The CPQ_064 assay correlated strongly to a greater number of biological indicators than the CPQ_056 assay. This study is the first to evaluate both crAssphage qPCR assays in an extended environmental application of crAssphage markers for monitoring of environmental waters. It is also the first study to compare crAssphage marker concentration with other viral-based indicators.

An FPGA-Based People Detection System

NASA Astrophysics Data System (ADS)

Nair, Vinod; Laprise, Pierre-Olivier; Clark, James J.

2005-12-01

This paper presents an FPGA-based system for detecting people from video. The system is designed to use JPEG-compressed frames from a network camera. Unlike previous approaches that use techniques such as background subtraction and motion detection, we use a machine-learning-based approach to train an accurate detector. We address the hardware design challenges involved in implementing such a detector, along with JPEG decompression, on an FPGA. We also present an algorithm that efficiently combines JPEG decompression with the detection process. This algorithm carries out the inverse DCT step of JPEG decompression only partially. Therefore, it is computationally more efficient and simpler to implement, and it takes up less space on the chip than the full inverse DCT algorithm. The system is demonstrated on an automated video surveillance application and the performance of both hardware and software implementations is analyzed. The results show that the system can detect people accurately at a rate of about[InlineEquation not available: see fulltext.] frames per second on a Virtex-II 2V1000 using a MicroBlaze processor running at[InlineEquation not available: see fulltext.], communicating with dedicated hardware over FSL links.

Wavelet based detection of manatee vocalizations

NASA Astrophysics Data System (ADS)

Gur, Berke M.; Niezrecki, Christopher

2005-04-01

The West Indian manatee (Trichechus manatus latirostris) has become endangered partly because of watercraft collisions in Florida's coastal waterways. Several boater warning systems, based upon manatee vocalizations, have been proposed to reduce the number of collisions. Three detection methods based on the Fourier transform (threshold, harmonic content and autocorrelation methods) were previously suggested and tested. In the last decade, the wavelet transform has emerged as an alternative to the Fourier transform and has been successfully applied in various fields of science and engineering including the acoustic detection of dolphin vocalizations. As of yet, no prior research has been conducted in analyzing manatee vocalizations using the wavelet transform. Within this study, the wavelet transform is used as an alternative to the Fourier transform in detecting manatee vocalizations. The wavelet coefficients are analyzed and tested against a specified criterion to determine the existence of a manatee call. The performance of the method presented is tested on the same data previously used in the prior studies, and the results are compared. Preliminary results indicate that using the wavelet transform as a signal processing technique to detect manatee vocalizations shows great promise.

Hypothesis testing for differentially correlated features.

PubMed

Sheng, Elisa; Witten, Daniela; Zhou, Xiao-Hua

2016-10-01

In a multivariate setting, we consider the task of identifying features whose correlations with the other features differ across conditions. Such correlation shifts may occur independently of mean shifts, or differences in the means of the individual features across conditions. Previous approaches for detecting correlation shifts consider features simultaneously, by computing a correlation-based test statistic for each feature. However, since correlations involve two features, such approaches do not lend themselves to identifying which feature is the culprit. In this article, we instead consider a serial testing approach, by comparing columns of the sample correlation matrix across two conditions, and removing one feature at a time. Our method provides a novel perspective and favorable empirical results compared with competing approaches. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A determinant-based criterion for working correlation structure selection in generalized estimating equations.

PubMed

Jaman, Ajmery; Latif, Mahbub A H M; Bari, Wasimul; Wahed, Abdus S

2016-05-20

In generalized estimating equations (GEE), the correlation between the repeated observations on a subject is specified with a working correlation matrix. Correct specification of the working correlation structure ensures efficient estimators of the regression coefficients. Among the criteria used, in practice, for selecting working correlation structure, Rotnitzky-Jewell, Quasi Information Criterion (QIC) and Correlation Information Criterion (CIC) are based on the fact that if the assumed working correlation structure is correct then the model-based (naive) and the sandwich (robust) covariance estimators of the regression coefficient estimators should be close to each other. The sandwich covariance estimator, used in defining the Rotnitzky-Jewell, QIC and CIC criteria, is biased downward and has a larger variability than the corresponding model-based covariance estimator. Motivated by this fact, a new criterion is proposed in this paper based on the bias-corrected sandwich covariance estimator for selecting an appropriate working correlation structure in GEE. A comparison of the proposed and the competing criteria is shown using simulation studies with correlated binary responses. The results revealed that the proposed criterion generally performs better than the competing criteria. An example of selecting the appropriate working correlation structure has also been shown using the data from Madras Schizophrenia Study. Copyright © 2015 John Wiley & Sons, Ltd.

Two-Way Gene Interaction From Microarray Data Based on Correlation Methods

PubMed Central

Alavi Majd, Hamid; Talebi, Atefeh; Gilany, Kambiz; Khayyer, Nasibeh

2016-01-01

Background Gene networks have generated a massive explosion in the development of high-throughput techniques for monitoring various aspects of gene activity. Networks offer a natural way to model interactions between genes, and extracting gene network information from high-throughput genomic data is an important and difficult task. Objectives The purpose of this study is to construct a two-way gene network based on parametric and nonparametric correlation coefficients. The first step in constructing a Gene Co-expression Network is to score all pairs of gene vectors. The second step is to select a score threshold and connect all gene pairs whose scores exceed this value. Materials and Methods In the foundation-application study, we constructed two-way gene networks using nonparametric methods, such as Spearman’s rank correlation coefficient and Blomqvist’s measure, and compared them with Pearson’s correlation coefficient. We surveyed six genes of venous thrombosis disease, made a matrix entry representing the score for the corresponding gene pair, and obtained two-way interactions using Pearson’s correlation, Spearman’s rank correlation, and Blomqvist’s coefficient. Finally, these methods were compared with Cytoscape, based on BIND, and Gene Ontology, based on molecular function visual methods; R software version 3.2 and Bioconductor were used to perform these methods. Results Based on the Pearson and Spearman correlations, the results were the same and were confirmed by Cytoscape and GO visual methods; however, Blomqvist’s coefficient was not confirmed by visual methods. Conclusions Some results of the correlation coefficients are not the same with visualization. The reason may be due to the small number of data. PMID:27621916

Correlation between detection rates of periodontopathic bacterial DNA in coronary stenotic artery plaque [corrected] and in dental plaque samples.

PubMed

Ishihara, Kazuyuki; Nabuchi, Akihiro; Ito, Rieko; Miyachi, Kouji; Kuramitsu, Howard K; Okuda, Katsuji

2004-03-01

Utilizing PCR, the 16S rRNA detection rates for Porphyromonas gingivalis, Actinobacillus actinomycetemcomitans, Bacteroides forsythus, Treponema denticola, and Campylobacter rectus in samples of stenotic coronary artery plaques were determined to be 21.6, 23.3, 5.9, 23.5, and 15.7%, respectively. The detection rates for P. gingivalis and C. rectus correlated with their presence in subgingival plaque.

Change Detection via Selective Guided Contrasting Filters

NASA Astrophysics Data System (ADS)

Vizilter, Y. V.; Rubis, A. Y.; Zheltov, S. Y.

2017-05-01

Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample) as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the non-similar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map) could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO) is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC). The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC), mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All implemented

Image denoising based on noise detection

NASA Astrophysics Data System (ADS)

Jiang, Yuanxiang; Yuan, Rui; Sun, Yuqiu; Tian, Jinwen

2018-03-01

Because of the noise points in the images, any operation of denoising would change the original information of non-noise pixel. A noise detection algorithm based on fractional calculus was proposed to denoise in this paper. Convolution of the image was made to gain direction gradient masks firstly. Then, the mean gray was calculated to obtain the gradient detection maps. Logical product was made to acquire noise position image next. Comparisons in the visual effect and evaluation parameters after processing, the results of experiment showed that the denoising algorithms based on noise were better than that of traditional methods in both subjective and objective aspects.

Biosensor based on Butyrylcholinesterase for Detection of Carbofuran

NASA Astrophysics Data System (ADS)

Dey, Mousumi; Bhuvanagayathri, R.; Daniel, David K.

2015-04-01

Esterase enzymes play an important role in biology because they are responsible for the hydrolysis of choline esters. In their absence, the original state of the post synaptic membranes cannot be reestablished. Therefore, the aim of the work is to study the inhibiting action exerted by the group of compounds on these enzymes. Among these class of inhibiting compounds, pesticides are important because of the potential danger as a result of their large scale use in agriculture. Pesticides are generally determined using liquid or gas chromatography methods with various detection techniques. These methods are very sensitive and discriminating, however they require sample pretreatment such as extraction, preconcentration and clean up, which are skilled techniques and high cost treatment and also time consuming. In this study, acetyl cholinesterase and butyrylcholinesterase based biosensors have emerged as a promising tool for the detection and characterization of pesticides which are inhibitors of these enzymes. Although the physiological function of butyrylcholinesterase in comparison with acetyl cholinesterase is ambiguous, it has larger substrate specificity towards choline esters. Therefore, the development of a more selective electrode against choline, can lead to more sensitive determination of the inhibitor being investigated. Hence in the present work, a method based on inhibition of butyrylcholinesterase was attempted for quantification of carbofuran on the basis of cholinesterase inhibition. Butyrylcholinesterase with an activity of 10.2 units/mg was immobilized on a solid surface by cross linking with glutaraldehyde. The immobilized system was calibrated by correlating the inhibition of the butyrylcholinesterase activity with varying concentrations of the butyryl choline chloride and carbofuran. The sensing mechanism was investigated for its response to carbofuran concentrations ranging from 125 to 1,000 ppm. The effects of butyryl choline chloride

Background-Error Correlation Model Based on the Implicit Solution of a Diffusion Equation

DTIC Science & Technology

2010-01-01

1 Background- Error Correlation Model Based on the Implicit Solution of a Diffusion Equation Matthew J. Carrier* and Hans Ngodock...4. TITLE AND SUBTITLE Background- Error Correlation Model Based on the Implicit Solution of a Diffusion Equation 5a. CONTRACT NUMBER 5b. GRANT...2001), which sought to model error correlations based on the explicit solution of a generalized diffusion equation. The implicit solution is

Cobalt: A GPU-based correlator and beamformer for LOFAR

NASA Astrophysics Data System (ADS)

Broekema, P. Chris; Mol, J. Jan David; Nijboer, R.; van Amesfoort, A. S.; Brentjens, M. A.; Loose, G. Marcel; Klijn, W. F. A.; Romein, J. W.

2018-04-01

For low-frequency radio astronomy, software correlation and beamforming on general purpose hardware is a viable alternative to custom designed hardware. LOFAR, a new-generation radio telescope centered in the Netherlands with international stations in Germany, France, Ireland, Poland, Sweden and the UK, has successfully used software real-time processors based on IBM Blue Gene technology since 2004. Since then, developments in technology have allowed us to build a system based on commercial off-the-shelf components that combines the same capabilities with lower operational cost. In this paper, we describe the design and implementation of a GPU-based correlator and beamformer with the same capabilities as the Blue Gene based systems. We focus on the design approach taken, and show the challenges faced in selecting an appropriate system. The design, implementation and verification of the software system show the value of a modern test-driven development approach. Operational experience, based on three years of operations, demonstrates that a general purpose system is a good alternative to the previous supercomputer-based system or custom-designed hardware.

Detection of 2-mm-long strained section in silica fiber using slope-assisted Brillouin optical correlation-domain reflectometry

NASA Astrophysics Data System (ADS)

Lee, Heeyoung; Mizuno, Yosuke; Nakamura, Kentaro

2018-02-01

Slope-assisted Brillouin optical correlation-domain reflectometry is a single-end-access distributed Brillouin sensing technique with high spatial resolution and high-speed operation. We have recently discovered its unique feature, that is, strained or heated sections even shorter than nominal resolution can be detected, but its detailed characterization has not been carried out. Here, after experimentally characterizing this “beyond-nominal-resolution” effect, we show its usefulness by demonstrating the detection of a 2-mm-long strained section along a silica fiber. We also demonstrate the detection of a 5-mm-long heated section along a polymer optical fiber. The lengths of these detected sections are smaller than those of the other demonstrations reported so far.

A cyber-event correlation framework and metrics

NASA Astrophysics Data System (ADS)

Kang, Myong H.; Mayfield, Terry

2003-08-01

In this paper, we propose a cyber-event fusion, correlation, and situation assessment framework that, when instantiated, will allow cyber defenders to better understand the local, regional, and global cyber-situation. This framework, with associated metrics, can be used to guide assessment of our existing cyber-defense capabilities, and to help evaluate the state of cyber-event correlation research and where we must focus our future cyber-event correlation research. The framework, based on the cyber-event gathering activities and analysis functions, consists of five operational steps, each of which provides a richer set of contextual information to support greater situational understanding. The first three steps are categorically depicted as increasingly richer and broader-scoped contexts achieved through correlation activity, while in the final two steps, these richer contexts are achieved through analytical activities (situation assessment, and threat analysis & prediction). Category 1 Correlation focuses on the detection of suspicious activities and the correlation of events from a single cyber-event source. Category 2 Correlation clusters the same or similar events from multiple detectors that are located at close proximity and prioritizes them. Finally, the events from different time periods and event sources at different location/regions are correlated at Category 3 to recognize the relationship among different events. This is the category that focuses on the detection of large-scale and coordinated attacks. The situation assessment step (Category 4) focuses on the assessment of cyber asset damage and the analysis of the impact on missions. The threat analysis and prediction step (Category 5) analyzes attacks based on attack traces and predicts the next steps. Metrics that can distinguish correlation and cyber-situation assessment tools for each category are also proposed.

A PLSPM-based test statistic for detecting gene-gene co-association in genome-wide association study with case-control design.

PubMed

Zhang, Xiaoshuai; Yang, Xiaowei; Yuan, Zhongshang; Liu, Yanxun; Li, Fangyu; Peng, Bin; Zhu, Dianwen; Zhao, Jinghua; Xue, Fuzhong

2013-01-01

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods.

A PLSPM-Based Test Statistic for Detecting Gene-Gene Co-Association in Genome-Wide Association Study with Case-Control Design

PubMed Central

Zhang, Xiaoshuai; Yang, Xiaowei; Yuan, Zhongshang; Liu, Yanxun; Li, Fangyu; Peng, Bin; Zhu, Dianwen; Zhao, Jinghua; Xue, Fuzhong

2013-01-01

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods. PMID:23620809

Accurate mask-based spatially regularized correlation filter for visual tracking

NASA Astrophysics Data System (ADS)

Gu, Xiaodong; Xu, Xinping

2017-01-01

Recently, discriminative correlation filter (DCF)-based trackers have achieved extremely successful results in many competitions and benchmarks. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier. However, this assumption will produce unwanted boundary effects, which severely degrade the tracking performance. Correlation filters with limited boundaries and spatially regularized DCFs were proposed to reduce boundary effects. However, their methods used the fixed mask or predesigned weights function, respectively, which was unsuitable for large appearance variation. We propose an accurate mask-based spatially regularized correlation filter for visual tracking. Our augmented objective can reduce the boundary effect even in large appearance variation. In our algorithm, the masking matrix is converted into the regularized function that acts on the correlation filter in frequency domain, which makes the algorithm fast convergence. Our online tracking algorithm performs favorably against state-of-the-art trackers on OTB-2015 Benchmark in terms of efficiency, accuracy, and robustness.

Smartphone based scalable reverse engineering by digital image correlation

NASA Astrophysics Data System (ADS)

Vidvans, Amey; Basu, Saurabh

2018-03-01

There is a need for scalable open source 3D reconstruction systems for reverse engineering. This is because most commercially available reconstruction systems are capital and resource intensive. To address this, a novel reconstruction technique is proposed. The technique involves digital image correlation based characterization of surface speeds followed by normalization with respect to angular speed during rigid body rotational motion of the specimen. Proof of concept of the same is demonstrated and validated using simulation and empirical characterization. Towards this, smart-phone imaging and inexpensive off the shelf components along with those fabricated additively using poly-lactic acid polymer with a standard 3D printer are used. Some sources of error in this reconstruction methodology are discussed. It is seen that high curvatures on the surface suppress accuracy of reconstruction. Reasons behind this are delineated in the nature of the correlation function. Theoretically achievable resolution during smart-phone based 3D reconstruction by digital image correlation is derived.

The electrophysiological correlate of saliency: evidence from a figure-detection task.

PubMed

Straube, Sirko; Fahle, Manfred

2010-01-11

Although figure-ground segregation in a natural environment usually relies on multiple cues, we experience a coherent figure without usually noticing the individual single cues. It is still unclear how various cues interact to achieve this unified percept and whether this interaction depends on task demands. Studies investigating the effect of cue combination on the human EEG are still lacking. In the present study, we combined psychophysics, ERP and time-frequency analysis to investigate the interaction of orientation and spatial frequency as visual cues in a figure detection task. The figure was embedded in a matrix of Gabor elements, and we systematically varied figure saliency by changing the underlying cue configuration. We found a strong correlation between the posterior P2 amplitude and the perceived saliency of the figure: the P2 amplitude decreased with increasing saliency. Analogously, the power of the theta-band decreased for more salient figures. At longer latencies, the posterior P3 component was modulated in amplitude and latency, possibly reflecting increased decision confidence at higher saliencies. In conclusion, when the cue composition (e.g. one or two cues) or cue strength is changed in a figure detection task, first differences in the electrophysiological response reflect the perceived saliency and not directly the underlying cue configuration.

Detection of heavy metal by paper-based microfluidics.

PubMed

Lin, Yang; Gritsenko, Dmitry; Feng, Shaolong; Teh, Yi Chen; Lu, Xiaonan; Xu, Jie

2016-09-15

Heavy metal pollution has shown great threat to the environment and public health worldwide. Current methods for the detection of heavy metals require expensive instrumentation and laborious operation, which can only be accomplished in centralized laboratories. Various microfluidic paper-based analytical devices have been developed recently as simple, cheap and disposable alternatives to conventional ones for on-site detection of heavy metals. In this review, we first summarize current development of paper-based analytical devices and discuss the selection of paper substrates, methods of device fabrication, and relevant theories in these devices. We then compare and categorize recent reports on detection of heavy metals using paper-based microfluidic devices on the basis of various detection mechanisms, such as colorimetric, fluorescent, and electrochemical methods. To finalize, the future development and trend in this field are discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

A novel way to detect correlations on multi-time scales, with temporal evolution and for multi-variables

NASA Astrophysics Data System (ADS)

Yuan, Naiming; Xoplaki, Elena; Zhu, Congwen; Luterbacher, Juerg

2016-06-01

In this paper, two new methods, Temporal evolution of Detrended Cross-Correlation Analysis (TDCCA) and Temporal evolution of Detrended Partial-Cross-Correlation Analysis (TDPCCA), are proposed by generalizing DCCA and DPCCA. Applying TDCCA/TDPCCA, it is possible to study correlations on multi-time scales and over different periods. To illustrate their properties, we used two climatological examples: i) Global Sea Level (GSL) versus North Atlantic Oscillation (NAO); and ii) Summer Rainfall over Yangtze River (SRYR) versus previous winter Pacific Decadal Oscillation (PDO). We find significant correlations between GSL and NAO on time scales of 60 to 140 years, but the correlations are non-significant between 1865-1875. As for SRYR and PDO, significant correlations are found on time scales of 30 to 35 years, but the correlations are more pronounced during the recent 30 years. By combining TDCCA/TDPCCA and DCCA/DPCCA, we proposed a new correlation-detection system, which compared to traditional methods, can objectively show how two time series are related (on which time scale, during which time period). These are important not only for diagnosis of complex system, but also for better designs of prediction models. Therefore, the new methods offer new opportunities for applications in natural sciences, such as ecology, economy, sociology and other research fields.

Application of a molecular beacon based real-time isothermal amplification (MBRTIA) technology for simultaneous detection of Bacillus cereus and Staphylococcus aureus.

PubMed

Mandappa, I M; Joglekar, Prasanna; Manonmani, H K

2015-07-01

A multiplex real-time isothermal amplification assay was developed using molecular beacons for the detection of Bacillus cereus and Staphylococcus aureus by targeting four important virulence genes. A correlation between targeting highly accessible DNA sequences and isothermal amplification based molecular beacon efficiency and sensitivity was demonstrated using phi(Φ)29 DNA polymerase at a constant isothermal temperature of 30 °C. It was very selective and consistently detected down to 10(1) copies of DNA. The specificity and sensitivity of this assay, when tested with pure culture were high, surpassing those of currently used PCR assays for the detection of these organisms. The molecular beacon based real-time isothermal amplification (MBRTIA) assay could be carried out entirely in 96 well plates or well strips, enabling a rapid and high-throughput detection of food borne pathogens.

Arduino-based noise robust online heart-rate detection.

PubMed

Das, Sangita; Pal, Saurabh; Mitra, Madhuchhanda

2017-04-01

This paper introduces a noise robust real time heart rate detection system from electrocardiogram (ECG) data. An online data acquisition system is developed to collect ECG signals from human subjects. Heart rate is detected using window-based autocorrelation peak localisation technique. A low-cost Arduino UNO board is used to implement the complete automated process. The performance of the system is compared with PC-based heart rate detection technique. Accuracy of the system is validated through simulated noisy ECG data with various levels of signal to noise ratio (SNR). The mean percentage error of detected heart rate is found to be 0.72% for the noisy database with five different noise levels.

Detectability of exoplanet transits with Athena's WFI instrument: testing for white and correlated noise

NASA Astrophysics Data System (ADS)

Carpano, Stefania; Wilms, Jörn; Rau, Arne

2016-07-01

One of the science goal of the Athena mission is to detect and characterise, in the X-ray domain, transits of hot Jupiter-like planets orbiting their parent stars. To date, the only candidate for this kind of studies is HD 189733b, a Jupiter-size planet in a 2d orbit, for which a transit depth of 6-8% has been observed accumulating several Chandra and XMM-Newton observations. We simulate in this work realistic light curves of exoplanet transits using the Athena end-to-end simulator, SIXTE, and derive the expected signal-to-noise ratios (SNR) for different instrument configurations and planetary system parameters. We first produce at light curves for the currently existing WFI instrument designs and for different source fluxes to extract the expected (white noise) standard deviation. Next, moderate levels of correlated noise and transits of different depths are added to the light curves. As expected, for pure white noise the SNR is proportional to the square root of the flux, to the light curve bin size and to the number of co-added transits, and by definition proportional to the transit depth. When correlated noise starts to be significant, rebinning the data will only slightly increase the SNR, depending on the noise characteristics. Considering only white noise, a transit observed in a source like HD 189733, that has a flux around 5x10-13 erg s-1 cm-2 and a transit depth of about 5% can be detected with a SNR>3 in a unique transit. With correlated noise, several transits might be necessary. We also simulate trapezoidal shaped transits and try to recover the ingress/egress times after addition of noise. The relative error on the fitted ingress times is below 10% for most of the light curves with SNR>1.

Long-range correlation and market segmentation in bond market

NASA Astrophysics Data System (ADS)

Wang, Zhongxing; Yan, Yan; Chen, Xiaosong

2017-09-01

This paper investigates the long-range auto-correlations and cross-correlations in bond market. Based on Detrended Moving Average (DMA) method, empirical results present a clear evidence of long-range persistence that exists in one year scale. The degree of long-range correlation related to maturities has an upward tendency with a peak in short term. These findings confirm the expectations of fractal market hypothesis (FMH). Furthermore, we have developed a method based on a complex network to study the long-range cross-correlation structure and applied it to our data, and found a clear pattern of market segmentation in the long run. We also detected the nature of long-range correlation in the sub-period 2007-2012 and 2011-2016. The result from our research shows that long-range auto-correlations are decreasing in the recent years while long-range cross-correlations are strengthening.

Deviance detection based on regularity encoding along the auditory hierarchy: electrophysiological evidence in humans.

PubMed

Escera, Carles; Leung, Sumie; Grimm, Sabine

2014-07-01

Detection of changes in the acoustic environment is critical for survival, as it prevents missing potentially relevant events outside the focus of attention. In humans, deviance detection based on acoustic regularity encoding has been associated with a brain response derived from the human EEG, the mismatch negativity (MMN) auditory evoked potential, peaking at about 100-200 ms from deviance onset. By its long latency and cerebral generators, the cortical nature of both the processes of regularity encoding and deviance detection has been assumed. Yet, intracellular, extracellular, single-unit and local-field potential recordings in rats and cats have shown much earlier (circa 20-30 ms) and hierarchically lower (primary auditory cortex, medial geniculate body, inferior colliculus) deviance-related responses. Here, we review the recent evidence obtained with the complex auditory brainstem response (cABR), the middle latency response (MLR) and magnetoencephalography (MEG) demonstrating that human auditory deviance detection based on regularity encoding-rather than on refractoriness-occurs at latencies and in neural networks comparable to those revealed in animals. Specifically, encoding of simple acoustic-feature regularities and detection of corresponding deviance, such as an infrequent change in frequency or location, occur in the latency range of the MLR, in separate auditory cortical regions from those generating the MMN, and even at the level of human auditory brainstem. In contrast, violations of more complex regularities, such as those defined by the alternation of two different tones or by feature conjunctions (i.e., frequency and location) fail to elicit MLR correlates but elicit sizable MMNs. Altogether, these findings support the emerging view that deviance detection is a basic principle of the functional organization of the auditory system, and that regularity encoding and deviance detection is organized in ascending levels of complexity along the auditory

Expert systems for automated correlation and interpretation of wireline logs

USGS Publications Warehouse

Olea, R.A.

1994-01-01

CORRELATOR is an interactive computer program for lithostratigraphic correlation of wireline logs able to store correlations in a data base with a consistency, accuracy, speed, and resolution that are difficult to obtain manually. The automatic determination of correlations is based on the maximization of a weighted correlation coefficient using two wireline logs per well. CORRELATOR has an expert system to scan and flag incongruous correlations in the data base. The user has the option to accept or disregard the advice offered by the system. The expert system represents knowledge through production rules. The inference system is goal-driven and uses backward chaining to scan through the rules. Work in progress is used to illustrate the potential that a second expert system with a similar architecture for interpreting dip diagrams could have to identify episodes-as those of interest in sequence stratigraphy and fault detection- and annotate them in the stratigraphic column. Several examples illustrate the presentation. ?? 1994 International Association for Mathematical Geology.

Design of fire detection equipment based on ultraviolet detection technology

NASA Astrophysics Data System (ADS)

Liu, Zhenji; Liu, Jin; Chu, Sheng; Ping, Chao; Yuan, Xiaobing

2015-03-01

Utilized the feature of wide bandgap semiconductor of MgZnO, researched and developed a kind of Mid-Ultraviolet-Band(MUV) ultraviolet detector which has passed the simulation experiment in the sun circumstance. Based on the ultraviolet detector, it gives out a design scheme of gun-shot detection device, which is composed of twelve ultraviolet detectors, signal amplifier, processor, annunciator , azimuth indicator and the bracket. Through Analysing the feature of solar blind, ultraviolet responsivity, fire feature of gunshots and detection distance, the feasibility of this design scheme is proved.

DNA-based nanobiostructured devices: The role of quasiperiodicity and correlation effects

NASA Astrophysics Data System (ADS)

Albuquerque, E. L.; Fulco, U. L.; Freire, V. N.; Caetano, E. W. S.; Lyra, M. L.; de Moura, F. A. B. F.

2014-02-01

The purpose of this review is to present a comprehensive and up-to-date account of the main physical properties of DNA-based nanobiostructured devices, stressing the role played by their quasi-periodicity arrangement and correlation effects. Although the DNA-like molecule is usually described as a short-ranged correlated random ladder, artificial segments can be grown following quasiperiodic sequences as, for instance, the Fibonacci and Rudin-Shapiro ones. They have interesting properties like a complex fractal spectra of energy, which can be considered as their indelible mark, and collective properties that are not shared by their constituents. These collective properties are due to the presence of long-range correlations, which are expected to be reflected somehow in their various spectra (electronic transmission, density of states, etc.) defining another description of disorder. Although long-range correlations are responsible for the effective electronic transport at specific resonant energies of finite DNA segments, much of the anomalous spread of an initially localized electron wave-packet can be accounted by short-range pair correlations, suggesting that an approach based on the inclusion of further short-range correlations on the nucleotide distribution leads to an adequate description of the electronic properties of DNA segments. The introduction of defects may generate states within the gap, and substantially improves the conductance, specially of finite branches. They usually become exponentially localized for any amount of disorder, and have the property to tailor the electronic transport properties of DNA-based nanoelectronic devices. In particular, symmetric and antisymmetric correlations have quite distinct influence on the nature of the electronic states, and a diluted distribution of defects lead to an anomalous diffusion of the electronic wave-packet. Nonlinear contributions, arising from the coupling between electrons and the molecular vibrations

Automatic food intake detection based on swallowing sounds.

PubMed

Makeyev, Oleksandr; Lopez-Meyer, Paulo; Schuckers, Stephanie; Besio, Walter; Sazonov, Edward

2012-11-01

This paper presents a novel fully automatic food intake detection methodology, an important step toward objective monitoring of ingestive behavior. The aim of such monitoring is to improve our understanding of eating behaviors associated with obesity and eating disorders. The proposed methodology consists of two stages. First, acoustic detection of swallowing instances based on mel-scale Fourier spectrum features and classification using support vector machines is performed. Principal component analysis and a smoothing algorithm are used to improve swallowing detection accuracy. Second, the frequency of swallowing is used as a predictor for detection of food intake episodes. The proposed methodology was tested on data collected from 12 subjects with various degrees of adiposity. Average accuracies of >80% and >75% were obtained for intra-subject and inter-subject models correspondingly with a temporal resolution of 30s. Results obtained on 44.1 hours of data with a total of 7305 swallows show that detection accuracies are comparable for obese and lean subjects. They also suggest feasibility of food intake detection based on swallowing sounds and potential of the proposed methodology for automatic monitoring of ingestive behavior. Based on a wearable non-invasive acoustic sensor the proposed methodology may potentially be used in free-living conditions.

Automatic food intake detection based on swallowing sounds

PubMed Central

Makeyev, Oleksandr; Lopez-Meyer, Paulo; Schuckers, Stephanie; Besio, Walter; Sazonov, Edward

2012-01-01

This paper presents a novel fully automatic food intake detection methodology, an important step toward objective monitoring of ingestive behavior. The aim of such monitoring is to improve our understanding of eating behaviors associated with obesity and eating disorders. The proposed methodology consists of two stages. First, acoustic detection of swallowing instances based on mel-scale Fourier spectrum features and classification using support vector machines is performed. Principal component analysis and a smoothing algorithm are used to improve swallowing detection accuracy. Second, the frequency of swallowing is used as a predictor for detection of food intake episodes. The proposed methodology was tested on data collected from 12 subjects with various degrees of adiposity. Average accuracies of >80% and >75% were obtained for intra-subject and inter-subject models correspondingly with a temporal resolution of 30s. Results obtained on 44.1 hours of data with a total of 7305 swallows show that detection accuracies are comparable for obese and lean subjects. They also suggest feasibility of food intake detection based on swallowing sounds and potential of the proposed methodology for automatic monitoring of ingestive behavior. Based on a wearable non-invasive acoustic sensor the proposed methodology may potentially be used in free-living conditions. PMID:23125873

Laser-based standoff detection of explosives: a critical review.

PubMed

Wallin, Sara; Pettersson, Anna; Ostmark, Henric; Hobro, Alison

2009-09-01

A review of standoff detection technologies for explosives has been made. The review is focused on trace detection methods (methods aiming to detect traces from handling explosives or the vapours surrounding an explosive charge due to the vapour pressure of the explosive) rather than bulk detection methods (methods aiming to detect the bulk explosive charge). The requirements for standoff detection technologies are discussed. The technologies discussed are mostly laser-based trace detection technologies, such as laser-induced-breakdown spectroscopy, Raman spectroscopy, laser-induced-fluorescence spectroscopy and IR spectroscopy but the bulk detection technologies millimetre wave imaging and terahertz spectroscopy are also discussed as a complement to the laser-based methods. The review includes novel techniques, not yet tested in realistic environments, more mature technologies which have been tested outdoors in realistic environments as well as the most mature millimetre wave imaging technique.

Aptamer-based SERRS Sensor for Thrombin Detection

PubMed Central

Cho, Hansang; Baker, Brian R.; Wachsmann-Hogiu, Sebastian; Pagba, Cynthia V.; Laurence, Ted A.; Lane, Stephen M.; Lee, Luke P.; Tok, Jeffrey B.-H.

2012-01-01

We describe an aptamer-based Surface Enhanced Resonance Raman Scattering (SERRS) sensor with high sensitivity, specificity, and stability for the detection of a coagulation protein, human α-thrombin. The sensor achieves high sensitivity and a limit of detection of 100 pM by monitoring the SERRS signal change upon the single step of thrombin binding to immobilized thrombin binding aptamer. The selectivity of the sensor is demonstrated by the specific discrimination of thrombin from other protein analytes. The specific recognition and binding of thrombin by the thrombin binding aptamer is essential to the mechanism of the aptamer-based sensor, as shown through measurements using negative control oligonucleotides. In addition, the sensor can detect 1 nM thrombin in the presence of complex biofluids, such as 10% fetal calf serum, demonstrating that the immobilized, 5'-capped, 3'-capped aptamer is sufficiently robust for clinical diagnostic applications. Furthermore, the proposed sensor may be implemented for multiplexed detection using different aptamer-Raman probe complexes. PMID:19367849

Shadow-Based Vehicle Detection in Urban Traffic

PubMed Central

Ibarra-Arenado, Manuel; Tjahjadi, Tardi; Pérez-Oria, Juan; Robla-Gómez, Sandra; Jiménez-Avello, Agustín

2017-01-01

Vehicle detection is a fundamental task in Forward Collision Avoiding Systems (FACS). Generally, vision-based vehicle detection methods consist of two stages: hypotheses generation and hypotheses verification. In this paper, we focus on the former, presenting a feature-based method for on-road vehicle detection in urban traffic. Hypotheses for vehicle candidates are generated according to the shadow under the vehicles by comparing pixel properties across the vertical intensity gradients caused by shadows on the road, and followed by intensity thresholding and morphological discrimination. Unlike methods that identify the shadow under a vehicle as a road region with intensity smaller than a coarse lower bound of the intensity for road, the thresholding strategy we propose determines a coarse upper bound of the intensity for shadow which reduces false positives rates. The experimental results are promising in terms of detection performance and robustness in day time under different weather conditions and cluttered scenarios to enable validation for the first stage of a complete FACS. PMID:28448465

Magnetic wire trap arrays for biomarker-based molecular detection

NASA Astrophysics Data System (ADS)

Vieira, Gregory; Mahajan, Kalpesh; Ruan, Gang; Winter, Jessica; Sooryakumar, R.

2012-02-01

Submicrometer-scale magnetic devices built on chip-based platforms have recently been shown to present opportunities for new particle trapping and manipulation technologies. Meanwhile, advances in nanoparticle fabrication allow for the building of custom-made particles with precise control of their size, composition, and other properties such as magnetism, fluorescence, and surface biomarker characteristics. In particular, carefully tailored surface biomarkers facilitate precise binding to targeted molecules, self-actuated construction of hybrid structures, and fluorescence-based detection schemes. Based on these progresses, we present an on-chip detection mechanism for molecules with known surface markers. Hybrid nanostructures consisting of micelle nanoparticles, fluorescent quantum dots, and superparamagnetic iron oxide nanoparticles are used to detect proteins or DNA molecules. The target is detected by the magnetic and fluorescent functionalities of the composite nanostructure, whereas in the absence of the target these signals are not present. Underlying this approach is the simultaneous manipulation via ferromagnetic zigzag nanowire arrays and imaging via quantum dot excitation. This chip-based detection technique could provide a powerful, low cost tool for ultrasensitive molecule detection with ramifications in healthcare diagnostics and small-scale chemical synthesis.

Detection of non-classical space-time correlations with a novel type of single-photon camera.

PubMed

Just, Felix; Filipenko, Mykhaylo; Cavanna, Andrea; Michel, Thilo; Gleixner, Thomas; Taheri, Michael; Vallerga, John; Campbell, Michael; Tick, Timo; Anton, Gisela; Chekhova, Maria V; Leuchs, Gerd

2014-07-14

During the last decades, multi-pixel detectors have been developed capable of registering single photons. The newly developed hybrid photon detector camera has a remarkable property that it has not only spatial but also temporal resolution. In this work, we apply this device to the detection of non-classical light from spontaneous parametric down-conversion and use two-photon correlations for the absolute calibration of its quantum efficiency.

Accounting for binaural detection as a function of masker interaural correlation: effects of center frequency and bandwidth.

PubMed

Bernstein, Leslie R; Trahiotis, Constantine

2014-12-01

Binaural detection was measured as a function of the center frequency, bandwidth, and interaural correlation of masking noise. Thresholds were obtained for 500-Hz or 125-Hz Sπ tonal signals and for the latter stimuli (noise or signal-plus-noise) transposed to 4 kHz. A primary goal was assessment of the generality of van der Heijden and Trahiotis' [J. Acoust. Soc. Am. 101, 1019-1022 (1997)] hypothesis that thresholds could be accounted for by the "additive" masking effects of the underlying No and Nπ components of a masker having an interaural correlation of ρ. Results indicated that (1) the overall patterning of the data depended neither upon center frequency nor whether information was conveyed via the waveform or by its envelope; (2) thresholds for transposed stimuli improved relative to their low-frequency counterparts as bandwidth of the masker was increased; (3) the additivity approach accounted well for the data across stimulus conditions but consistently overestimated MLDs, especially for narrowband maskers; (4) a quantitative approach explicitly taking into account the distributions of time-varying ITD-based lateral positions produced by masker-alone and signal-plus-masker waveforms proved more successful, albeit while employing a larger set of assumptions, parameters, and computational complexity.

Improving correlations between MODIS aerosol optical thickness and ground-based PM 2.5 observations through 3D spatial analyses

NASA Astrophysics Data System (ADS)

Hutchison, Keith D.; Faruqui, Shazia J.; Smith, Solar

The Center for Space Research (CSR) continues to focus on developing methods to improve correlations between satellite-based aerosol optical thickness (AOT) values and ground-based, air pollution observations made at continuous ambient monitoring sites (CAMS) operated by the Texas commission on environmental quality (TCEQ). Strong correlations and improved understanding of the relationships between satellite and ground observations are needed to formulate reliable real-time predictions of air quality using data accessed from the moderate resolution imaging spectroradiometer (MODIS) at the CSR direct-broadcast ground station. In this paper, improvements in these correlations are demonstrated first as a result of the evolution in the MODIS retrieval algorithms. Further improvement is then shown using procedures that compensate for differences in horizontal spatial scales between the nominal 10-km MODIS AOT products and CAMS point measurements. Finally, airborne light detection and ranging (lidar) observations, collected during the Texas Air Quality Study of 2000, are used to examine aerosol profile concentrations, which may vary greatly between aerosol classes as a result of the sources, chemical composition, and meteorological conditions that govern transport processes. Further improvement in correlations is demonstrated with this limited dataset using insights into aerosol profile information inferred from the vertical motion vectors in a trajectory-based forecast model. Analyses are ongoing to verify these procedures on a variety of aerosol classes using data collected by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (Calipso) lidar.

Use of petroleum-based correlations and estimation methods for synthetic fuels

NASA Technical Reports Server (NTRS)

Antoine, A. C.

1980-01-01

Correlations of hydrogen content with aromatics content, heat of combustion, and smoke point are derived for some synthetic fuels prepared from oil and coal syncrudes. Comparing the results of the aromatics content with correlations derived for petroleum fuels shows that the shale-derived fuels fit the petroleum-based correlations, but the coal-derived fuels do not. The correlations derived for heat of combustion and smoke point are comparable to some found for petroleum-based correlations. Calculated values of hydrogen content and of heat of combustion are obtained for the synthetic fuels by use of ASTM estimation methods. Comparisons of the measured and calculated values show biases in the equations that exceed the critical statistics values. Comparison of the measured hydrogen content by the standard ASTM combustion method with that by a nuclear magnetic resonance (NMR) method shows a decided bias. The comparison of the calculated and measured NMR hydrogen contents shows a difference similar to that found with petroleum fuels.

The structural and functional correlates of the efficiency in fearful face detection.

PubMed

Wang, Yongchao; Guo, Nana; Zhao, Li; Huang, Hui; Yao, Xiaonan; Sang, Na; Hou, Xin; Mao, Yu; Bi, Taiyong; Qiu, Jiang

2017-06-01

Human visual system is found to be much efficient in searching for a fearful face. Some individuals are more sensitive to this threat-related stimulus. However, we still know little about the neural correlates of such variability. In the current study, we exploited a visual search paradigm, and asked the subjects to search for a fearful face or a target gender. Every subject showed a shallower search function for fearful face search than face gender search, indicating a stable fearful face advantage. We then used voxel-based morphometry (VBM) analysis and correlated this advantage to the gray matter volume (GMV) of some presumably face related cortical areas. The result revealed that only the left fusiform gyrus showed a significant positive correlation. Next, we defined the left fusiform gyrus as the seed region and calculated its resting state functional connectivity to the whole brain. Correlations were also calculated between fearful face advantage and these connectivities. In this analysis, we found positive correlations in the inferior parietal lobe and the ventral medial prefrontal cortex. These results suggested that the anatomical structure of the left fusiform gyrus might determine the search efficiency of fearful face, and frontoparietal attention network involved in this process through top-down attentional modulation. Copyright © 2017. Published by Elsevier Ltd.

Theoretical NMR correlations based Structure Discussion.

PubMed

Junker, Jochen

2011-07-28

The constitutional assignment of natural products by NMR spectroscopy is usually based on 2D NMR experiments like COSY, HSQC, and HMBC. The actual difficulty of the structure elucidation problem depends more on the type of the investigated molecule than on its size. The moment HMBC data is involved in the process or a large number of heteroatoms is present, a possibility of multiple solutions fitting the same data set exists. A structure elucidation software can be used to find such alternative constitutional assignments and help in the discussion in order to find the correct solution. But this is rarely done. This article describes the use of theoretical NMR correlation data in the structure elucidation process with WEBCOCON, not for the initial constitutional assignments, but to define how well a suggested molecule could have been described by NMR correlation data. The results of this analysis can be used to decide on further steps needed to assure the correctness of the structural assignment. As first step the analysis of the deviation of carbon chemical shifts is performed, comparing chemical shifts predicted for each possible solution with the experimental data. The application of this technique to three well known compounds is shown. Using NMR correlation data alone for the description of the constitutions is not always enough, even when including 13C chemical shift prediction.

Human visual system-based smoking event detection

NASA Astrophysics Data System (ADS)

Odetallah, Amjad D.; Agaian, Sos S.

2012-06-01

Human action (e.g. smoking, eating, and phoning) analysis is an important task in various application domains like video surveillance, video retrieval, human-computer interaction systems, and so on. Smoke detection is a crucial task in many video surveillance applications and could have a great impact to raise the level of safety of urban areas, public parks, airplanes, hospitals, schools and others. The detection task is challenging since there is no prior knowledge about the object's shape, texture and color. In addition, its visual features will change under different lighting and weather conditions. This paper presents a new scheme of a system for detecting human smoking events, or small smoke, in a sequence of images. In developed system, motion detection and background subtraction are combined with motion-region-saving, skin-based image segmentation, and smoke-based image segmentation to capture potential smoke regions which are further analyzed to decide on the occurrence of smoking events. Experimental results show the effectiveness of the proposed approach. As well, the developed method is capable of detecting the small smoking events of uncertain actions with various cigarette sizes, colors, and shapes.

International Monitoring System Correlation Detection at the North Korean Nuclear Test Site at Punggye-ri with Insights from the Source Physics Experiment

DOE PAGES

Ford, Sean R.; Walter, William R.

2015-05-06

Seismic waveform correlation offers the prospect of greatly reducing event detection thresholds when compared with more conventional processing methods. Correlation is applicable for seismic events that in some sense repeat, that is they have very similar waveforms. A number of recent studies have shown that correlated seismic signals may form a significant fraction of seismicity at regional distances. For the particular case of multiple nuclear explosions at the same test site, regional distance correlation also allows very precise relative location measurements and could offer the potential to lower thresholds when multiple events exist. Using the Comprehensive Nuclear-Test-Ban Treaty (CTBT) Internationalmore » Monitoring System (IMS) seismic array at Matsushiro, Japan (MJAR), Gibbons and Ringdal (2012) were able to create a multichannel correlation detector with a very low false alarm rate and a threshold below magnitude 3.0. They did this using the 2006 or 2009 Democratic People’s Republic of Korea (DPRK) nuclear explosion as a template to search through a data stream from the same station to find a match via waveform correlation. In this paper, we extend the work of Gibbons and Ringdal (2012) and measure the correlation detection threshold at several other IMS arrays. We use this to address three main points. First, we show the IMS array station at Mina, Nevada (NVAR), which is closest to the Nevada National Security Site (NNSS), is able to detect a chemical explosion that is well under 1 ton with the right template. Second, we examine the two IMS arrays closest to the North Korean (DPRK) test site (at Ussuriysk, Russian Federation [USRK] and Wonju, Republic of Korea [KSRS]) to show that similarly low thresholds are possible when the right templates exist. We also extend the work of Schaff et al. (2012) and measure the correlation detection threshold at the nearest Global Seismic Network (GSN) three-component station (MDJ) at Mudanjiang, Heilongjiang

Comparison of co-expression measures: mutual information, correlation, and model based indices.

PubMed

Song, Lin; Langfelder, Peter; Horvath, Steve

2012-12-09

Co-expression measures are often used to define networks among genes. Mutual information (MI) is often used as a generalized correlation measure. It is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. Further, it is important to assess what transformations of these and other co-expression measures lead to biologically meaningful modules (clusters of genes). We provide a comprehensive comparison between mutual information and several correlation measures in 8 empirical data sets and in simulations. We also study different approaches for transforming an adjacency matrix, e.g. using the topological overlap measure. Overall, we confirm close relationships between MI and correlation in all data sets which reflects the fact that most gene pairs satisfy linear or monotonic relationships. We discuss rare situations when the two measures disagree. We also compare correlation and MI based approaches when it comes to defining co-expression network modules. We show that a robust measure of correlation (the biweight midcorrelation transformed via the topological overlap transformation) leads to modules that are superior to MI based modules and maximal information coefficient (MIC) based modules in terms of gene ontology enrichment. We present a function that relates correlation to mutual information which can be used to approximate the mutual information from the corresponding correlation coefficient. We propose the use of polynomial or spline regression models as an alternative to MI for capturing non-linear relationships between quantitative variables. The biweight midcorrelation outperforms MI in terms of elucidating gene pairwise relationships. Coupled with the topological overlap matrix transformation, it often leads to more significantly enriched co-expression modules. Spline and polynomial networks form attractive alternatives to MI in case of non-linear relationships. Our results indicate that MI

Detection limit of a VCO based detection chain dedicated to particles recognition and tracking

NASA Astrophysics Data System (ADS)

Coulié, K.; Rahajandraibe, W.; Aziza, H.; Micolau, G.; Vauché, R.

2018-01-01

A particle detection chain based on CMOS-SOI VCO circuit is presented. The solution is used for the recognition and the tracking of a given particle at circuit level. TCAD simulation of the detector has been performed on a 3×3 matrix of diodes based detector for particles recognition and tracking. The current response of the detector has been used for a case study in order to determine the ability of the chain to recognize an alpha particle crossing a 3×3 detection cell. The detection limit of the proposed solution is investigated and discussed in this paper.

Eye gaze tracking using correlation filters

NASA Astrophysics Data System (ADS)

Karakaya, Mahmut; Bolme, David; Boehnen, Chris

2014-03-01

In this paper, we studied a method for eye gaze tracking that provide gaze estimation from a standard webcam with a zoom lens and reduce the setup and calibration requirements for new users. Specifically, we have developed a gaze estimation method based on the relative locations of points on the top of the eyelid and eye corners. Gaze estimation method in this paper is based on the distances between top point of the eyelid and eye corner detected by the correlation filters. Advanced correlation filters were found to provide facial landmark detections that are accurate enough to determine the subjects gaze direction up to angle of approximately 4-5 degrees although calibration errors often produce a larger overall shift in the estimates. This is approximately a circle of diameter 2 inches for a screen that is arm's length from the subject. At this accuracy it is possible to figure out what regions of text or images the subject is looking but it falls short of being able to determine which word the subject has looked at.