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Sample records for based correlation detection

  1. Research on chronicles correlation based network intrusion detection techniques

    International Nuclear Information System (INIS)

    Han Zhengping; Jin Yan; Chen Taiwei; Xu Rongsheng

    2007-01-01

    According to some problems existed in network intrusion detection technique, such as alerts overwhelming, false-positives and lack of alert description, this paper introduces chronicle correlation method to alert events analysis by some correlative examples. With designed chronicle recognition language, portscan's alerts can be reduced, false-positives in buffer overflow's alerts can be detected, and NetBios DCERPC attack's alerts semantics can be improved. (authors)

  2. A correlation-based pulse detection technique for gamma-ray/neutron detectors

    International Nuclear Information System (INIS)

    Faisal, Muhammad; Schiffer, Randolph T.; Flaska, Marek; Pozzi, Sara A.; Wentzloff, David D.

    2011-01-01

    We present a correlation-based detection technique that significantly improves the probability of detection for low energy pulses. We propose performing a normalized cross-correlation of the incoming pulse data to a predefined pulse template, and using a threshold correlation value to trigger the detection of a pulse. This technique improves the detector sensitivity by amplifying the signal component of incoming pulse data and rejecting noise. Simulation results for various different templates are presented. Finally, the performance of the correlation-based detection technique is compared to the current state-of-the-art techniques.

  3. Intrusion detection method based on nonlinear correlation measure

    NARCIS (Netherlands)

    Ambusaidi, Mohammed A.; Tan, Zhiyuan; He, Xiangjian; Nanda, Priyadarsi; Lu, Liang Fu; Jamdagni, Aruna

    2014-01-01

    Cyber crimes and malicious network activities have posed serious threats to the entire internet and its users. This issue is becoming more critical, as network-based services, are more widespread and closely related to our daily life. Thus, it has raised a serious concern in individual internet

  4. Adaptive endpoint detection of seismic signal based on auto-correlated function

    International Nuclear Information System (INIS)

    Fan Wanchun; Shi Ren

    2001-01-01

    Based on the analysis of auto-correlation function, the notion of the distance between auto-correlation function was quoted, and the characterization of the noise and the signal with noise were discussed by using the distance. Then, the method of auto- adaptable endpoint detection of seismic signal based on auto-correlated similarity was summed up. The steps of implementation and determining of the thresholds were presented in detail. The experimental results that were compared with the methods based on artificial detecting show that this method has higher sensitivity even in a low signal with noise ratio circumstance

  5. Performance of Narrowband Signal Detection under Correlated Rayleigh Fading Based on Synthetic Array

    Directory of Open Access Journals (Sweden)

    Ali Broumandan

    2009-01-01

    design parameters of probability of detection (Pd and probability of false alarm (Pfa. An optimum detector based on Estimator-Correlator (EC is developed, and its performance is compared with that of suboptimal Equal-Gain (EG combiner in different channel correlation scenarios. It is shown that in moderate channel correlation scenarios the detection performance of EC and EG is identical. The sensitivity of the proposed method to knowledge of motion parameters is also investigated. An extensive set of measurements based on CDMA-2000 pilot signals using the static antenna and synthetic array are used to experimentally verify these theoretical findings.

  6. Adaptive endpoint detection of seismic signal based on auto-correlated function

    International Nuclear Information System (INIS)

    Fan Wanchun; Shi Ren

    2000-01-01

    There are certain shortcomings for the endpoint detection by time-waveform envelope and/or by checking the travel table (both labelled as the artificial detection method). Based on the analysis of the auto-correlation function, the notion of the distance between auto-correlation functions was quoted, and the characterizations of the noise and the signal with noise were discussed by using the distance. Then, the method of auto-adaptable endpoint detection of seismic signal based on auto-correlated similarity was summed up. The steps of implementation and determining of the thresholds were presented in detail. The experimental results that were compared with the methods based on artificial detecting show that this method has higher sensitivity even in a low SNR circumstance

  7. Fast region-based object detection and tracking using correlation of features

    CSIR Research Space (South Africa)

    Senekal, F

    2010-11-01

    Full Text Available A new method for object detection using region based characteristics is proposed. The method uses correlation between features over a region as a descriptor for the region. It is shown that this region descriptor can be successfully applied...

  8. A Detection Algorithm for the BOC Signal Based on Quadrature Channel Correlation

    Directory of Open Access Journals (Sweden)

    Bo Qian

    2018-01-01

    Full Text Available In order to solve the problem of detecting a BOC signal, which uses a long-period pseudo random sequence, an algorithm is presented based on quadrature channel correlation. The quadrature channel correlation method eliminates the autocorrelation component of the carrier wave, allowing for the extraction of the absolute autocorrelation peaks of the BOC sequence. If the same lag difference and height difference exist for the adjacent peaks, the BOC signal can be detected effectively using a statistical analysis of the multiple autocorrelation peaks. The simulation results show that the interference of the carrier wave component is eliminated and the autocorrelation peaks of the BOC sequence are obtained effectively without demodulation. The BOC signal can be detected effectively when the SNR is greater than −12 dB. The detection ability can be improved further by increasing the number of sampling points. The higher the ratio of the square wave subcarrier speed to the pseudo random sequence speed is, the greater the detection ability is with a lower SNR. The algorithm presented in this paper is superior to the algorithm based on the spectral correlation.

  9. Data-driven fault detection for industrial processes canonical correlation analysis and projection based methods

    CERN Document Server

    Chen, Zhiwen

    2017-01-01

    Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed. Contents A New Index for Performance Evaluation of FD Methods CCA-based FD Method for the Monitoring of Stationary Processes Projection-based FD Method for the Monitoring of Dynamic Processes Benchmark Study and Real-Time Implementat...

  10. Output-Based Structural Damage Detection by Using Correlation Analysis Together with Transmissibility.

    Science.gov (United States)

    Zhou, Yun-Lai; Cao, Hongyou; Liu, Quanmin; Wahab, Magd Abdel

    2017-07-27

    Output-based structural damage detection is becoming increasingly appealing due to its potential in real engineering applications without any restriction regarding excitation measurements. A new transmissibility-based damage detection approach is presented in this study by combining transmissibility with correlation analysis in order to strengthen its performance in discriminating damaged from undamaged scenarios. From this perspective, damage detection strategies are hereafter established by constructing damage-sensitive indicators from a derived transmissibility. A cantilever beam is numerically analyzed to verify the feasibility of the proposed damage detection procedure, and an ASCE (American Society of Civil Engineers) benchmark is henceforth used in the validation for its application in engineering structures. The results of both studies reveal a good performance of the proposed methodology in identifying damaged states from intact states. The comparison between the proposed indicator and the existing indicator also affirms its applicability in damage detection, which might be adopted in further structural health monitoring systems as a discrimination criterion. This study contributed an alternative criterion for transmissibility-based damage detection in addition to the conventional ones.

  11. Ionospheric earthquake effects detection based on Total Electron Content (TEC) GPS Correlation

    Science.gov (United States)

    Sunardi, Bambang; Muslim, Buldan; Eka Sakya, Andi; Rohadi, Supriyanto; Sulastri; Murjaya, Jaya

    2018-03-01

    Advances in science and technology showed that ground-based GPS receiver was able to detect ionospheric Total Electron Content (TEC) disturbances caused by various natural phenomena such as earthquakes. One study of Tohoku (Japan) earthquake, March 11, 2011, magnitude M 9.0 showed TEC fluctuations observed from GPS observation network spread around the disaster area. This paper discussed the ionospheric earthquake effects detection using TEC GPS data. The case studies taken were Kebumen earthquake, January 25, 2014, magnitude M 6.2, Sumba earthquake, February 12, 2016, M 6.2 and Halmahera earthquake, February 17, 2016, M 6.1. TEC-GIM (Global Ionosphere Map) correlation methods for 31 days were used to monitor TEC anomaly in ionosphere. To ensure the geomagnetic disturbances due to solar activity, we also compare with Dst index in the same time window. The results showed anomalous ratio of correlation coefficient deviation to its standard deviation upon occurrences of Kebumen and Sumba earthquake, but not detected a similar anomaly for the Halmahera earthquake. It was needed a continous monitoring of TEC GPS data to detect the earthquake effects in ionosphere. This study giving hope in strengthening the earthquake effect early warning system using TEC GPS data. The method development of continuous TEC GPS observation derived from GPS observation network that already exists in Indonesia is needed to support earthquake effects early warning systems.

  12. Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales

    Directory of Open Access Journals (Sweden)

    Alejandro Rodríguez-Molinero

    2017-09-01

    Full Text Available BackgroundOur group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson’s (On and Off state based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson’s Disease Rating Scale part-III (UPDRS-III.MethodSeventy-five patients suffering from Parkinson’s disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient’s home. Convergence between the algorithm and the scale was evaluated by using the Spearman’s correlation coefficient.ResultsCorrelation with the UPDRS-III was moderate (rho −0.56; p < 0.001. Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho −0.73; p < 0.001. The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: “axial function, balance, and gait.” The correlation between the algorithm outputs and this factor of the UPDRS-III was −0.67 (p < 0.01.ConclusionThe correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson’s disease and motor fluctuations.

  13. A Dynamically Configurable Log-based Distributed Security Event Detection Methodology using Simple Event Correlator

    Science.gov (United States)

    2010-06-01

    13 2.3 Security Information and Event Management . . . . . . 14 2.4 Insider Threat Detection . . . . . . . . . . . . . . . . . . 15...organizations fail to properly implement and properly resource Security Information and Event Management (SIEM) capa- bilities [32] [37]. Several...motivate the development of a distributed log event correlation methodology. Back- ground literature in the areas of log management , event correlation and

  14. A urinary biomarker-based risk score correlates with multiparametric MRI for prostate cancer detection.

    Science.gov (United States)

    Hendriks, Rianne J; van der Leest, Marloes M G; Dijkstra, Siebren; Barentsz, Jelle O; Van Criekinge, Wim; Hulsbergen-van de Kaa, Christina A; Schalken, Jack A; Mulders, Peter F A; van Oort, Inge M

    2017-10-01

    Prostate cancer (PCa) diagnostics would greatly benefit from more accurate, non-invasive techniques for the detection of clinically significant disease, leading to a reduction of over-diagnosis and over-treatment. The aim of this study was to determine the association between a novel urinary biomarker-based risk score (SelectMDx), multiparametric MRI (mpMRI) outcomes, and biopsy results for PCa detection. This retrospective observational study used data from the validation study of the SelectMDx score, in which urine was collected after digital rectal examination from men undergoing prostate biopsies. A subset of these patients also underwent a mpMRI scan of the prostate. The indications for performing mpMRI were based on persistent clinical suspicion of PCa or local staging after PCa was found upon biopsy. All mpMRI images were centrally reviewed in 2016 by an experienced radiologist blinded for the urine test results and biopsy outcome. The PI-RADS version 2 was used. In total, 172 patients were included for analysis. Hundred (58%) patients had PCa detected upon prostate biopsy, of which 52 (52%) had high-grade disease correlated with a significantly higher SelectMDx score (P < 0.01). The median SelectMDx score was significantly higher in patients with a suspicious significant lesion on mpMRI compared to no suspicion of significant PCa (P < 0.01). For the prediction of mpMRI outcome, the area-under-the-curve of SelectMDx was 0.83 compared to 0.66 for PSA and 0.65 for PCA3. There was a positive association between SelectMDx score and the final PI-RADS grade. There was a statistically significant difference in SelectMDx score between PI-RADS 3 and 4 (P < 0.01) and between PI-RADS 4 and 5 (P < 0.01). The novel urinary biomarker-based SelectMDx score is a promising tool in PCa detection. This study showed promising results regarding the correlation between the SelectMDx score and mpMRI outcomes, outperforming PCA3. Our results suggest that this risk

  15. Non-invasive Imaging based Detection and Mapping of Brain Oxidative Stress and its Correlation with Cognitive Functions

    Science.gov (United States)

    2017-05-14

    neuropsychological tests: cognitive performance, perceptual reasoning, working memory , processing speed and perceived stress scale were performed. Brain...AFRL-AFOSR-JP-TR-2017-0052 Non-invasive Imaging based Detection and Mapping of Brain Oxidative Stress and its Correlation with Cognative Functions...invasive Imaging based Detection and Mapping of Brain Oxidative Stress and its Correlation with Cognative Functions 5a.  CONTRACT NUMBER 5b.  GRANT

  16. Non invasive Imaging based Detection and Mapping of Brain Oxidative Stress and its Correlation with Cognative Functions

    Science.gov (United States)

    2017-05-14

    neuropsychological tests: cognitive performance, perceptual reasoning, working memory , processing speed and perceived stress scale were performed. Brain...AFRL-AFOSR-JP-TR-2017-0052 Non-invasive Imaging based Detection and Mapping of Brain Oxidative Stress and its Correlation with Cognative Functions...invasive Imaging based Detection and Mapping of Brain Oxidative Stress and its Correlation with Cognative Functions 5a.  CONTRACT NUMBER 5b.  GRANT

  17. Automatic detection of noisy channels in fNIRS signal based on correlation analysis.

    Science.gov (United States)

    Guerrero-Mosquera, Carlos; Borragán, Guillermo; Peigneux, Philippe

    2016-09-15

    fNIRS signals can be contaminated by distinct sources of noise. While most of the noise can be corrected using digital filters, optimized experimental paradigms or pre-processing methods, few approaches focus on the automatic detection of noisy channels. In the present study, we propose a new method that detect automatically noisy fNIRS channels by combining the global correlations of the signal obtained from sliding windows (Cui et al., 2010) with correlation coefficients extracted experimental conditions defined by triggers. The validity of the method was evaluated on test data from 17 participants, for a total of 16 NIRS channels per subject, positioned over frontal, dorsolateral prefrontal, parietal and occipital areas. Additionally, the detection of noisy channels was tested in the context of different levels of cognitive requirement in a working memory N-back paradigm. Bad channels detection accuracy, defined as the proportion of bad NIRS channels correctly detected among the total number of channels examined, was close to 91%. Under different cognitive conditions the area under the Receiver Operating Curve (AUC) increased from 60.5% (global correlations) to 91.2% (local correlations). Our results show that global correlations are insufficient for detecting potentially noisy channels when the whole data signal is included in the analysis. In contrast, adding specific local information inherent to the experimental paradigm (e.g., cognitive conditions in a block or event-related design), improved detection performance for noisy channels. Also, we show that automated fNIRS channel detection can be achieved with high accuracy at low computational cost. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Community Detection for Correlation Matrices

    Science.gov (United States)

    MacMahon, Mel; Garlaschelli, Diego

    2015-04-01

    A challenging problem in the study of complex systems is that of resolving, without prior information, the emergent, mesoscopic organization determined by groups of units whose dynamical activity is more strongly correlated internally than with the rest of the system. The existing techniques to filter correlations are not explicitly oriented towards identifying such modules and can suffer from an unavoidable information loss. A promising alternative is that of employing community detection techniques developed in network theory. Unfortunately, this approach has focused predominantly on replacing network data with correlation matrices, a procedure that we show to be intrinsically biased because of its inconsistency with the null hypotheses underlying the existing algorithms. Here, we introduce, via a consistent redefinition of null models based on random matrix theory, the appropriate correlation-based counterparts of the most popular community detection techniques. Our methods can filter out both unit-specific noise and system-wide dependencies, and the resulting communities are internally correlated and mutually anticorrelated. We also implement multiresolution and multifrequency approaches revealing hierarchically nested subcommunities with "hard" cores and "soft" peripheries. We apply our techniques to several financial time series and identify mesoscopic groups of stocks which are irreducible to a standard, sectorial taxonomy; detect "soft stocks" that alternate between communities; and discuss implications for portfolio optimization and risk management.

  19. Community Detection for Correlation Matrices

    Directory of Open Access Journals (Sweden)

    Mel MacMahon

    2015-04-01

    Full Text Available A challenging problem in the study of complex systems is that of resolving, without prior information, the emergent, mesoscopic organization determined by groups of units whose dynamical activity is more strongly correlated internally than with the rest of the system. The existing techniques to filter correlations are not explicitly oriented towards identifying such modules and can suffer from an unavoidable information loss. A promising alternative is that of employing community detection techniques developed in network theory. Unfortunately, this approach has focused predominantly on replacing network data with correlation matrices, a procedure that we show to be intrinsically biased because of its inconsistency with the null hypotheses underlying the existing algorithms. Here, we introduce, via a consistent redefinition of null models based on random matrix theory, the appropriate correlation-based counterparts of the most popular community detection techniques. Our methods can filter out both unit-specific noise and system-wide dependencies, and the resulting communities are internally correlated and mutually anticorrelated. We also implement multiresolution and multifrequency approaches revealing hierarchically nested subcommunities with “hard” cores and “soft” peripheries. We apply our techniques to several financial time series and identify mesoscopic groups of stocks which are irreducible to a standard, sectorial taxonomy; detect “soft stocks” that alternate between communities; and discuss implications for portfolio optimization and risk management.

  20. Denial-of-service attack detection based on multivariate correlation analysis

    NARCIS (Netherlands)

    Tan, Zhiyuan; Jamdagni, Aruna; He, Xiangjian; Nanda, Priyadarsi; Liu, Ren Ping; Lu, Bao-Liang; Zhang, Liqing; Kwok, James

    2011-01-01

    The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order

  1. Damage Detection of Structures for Ambient Loading Based on Cross Correlation Function Amplitude and SVM

    Directory of Open Access Journals (Sweden)

    Lin-sheng Huo

    2016-01-01

    Full Text Available An effective method for the damage detection of skeletal structures which combines the cross correlation function amplitude (CCFA with the support vector machine (SVM is presented in this paper. The proposed method consists of two stages. Firstly, the data features are extracted from the CCFA, which, calculated from dynamic responses and as a representation of the modal shapes of the structure, changes when damage occurs on the structure. The data features are then input into the SVM with the one-against-one (OAO algorithm to classify the damage status of the structure. The simulation data of IASC-ASCE benchmark model and a vibration experiment of truss structure are adopted to verify the feasibility of proposed method. The results show that the proposed method is suitable for the damage identification of skeletal structures with the limited sensors subjected to ambient excitation. As the CCFA based data features are sensitive to damage, the proposed method demonstrates its reliability in the diagnosis of structures with damage, especially for those with minor damage. In addition, the proposed method shows better noise robustness and is more suitable for noisy environments.

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

    Science.gov (United States)

    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.

  3. Neural correlates of tactile detection: a combined magnetoencephalography and biophysically based computational modeling study.

    Science.gov (United States)

    Jones, Stephanie R; Pritchett, Dominique L; Stufflebeam, Steven M; Hämäläinen, Matti; Moore, Christopher I

    2007-10-03

    Previous reports conflict as to the role of primary somatosensory neocortex (SI) in tactile detection. We addressed this question in normal human subjects using whole-head magnetoencephalography (MEG) recording. We found that the evoked signal (0-175 ms) showed a prominent equivalent current dipole that localized to the anterior bank of the postcentral gyrus, area 3b of SI. The magnitude and timing of peaks in the SI waveform were stimulus amplitude dependent and predicted perception beginning at approximately 70 ms after stimulus. To make a direct and principled connection between the SI waveform and underlying neural dynamics, we developed a biophysically realistic computational SI model that contained excitatory and inhibitory neurons in supragranular and infragranular layers. The SI evoked response was successfully reproduced from the intracellular currents in pyramidal neurons driven by a sequence of lamina-specific excitatory input, consisting of output from the granular layer (approximately 25 ms), exogenous input to the supragranular layers (approximately 70 ms), and a second wave of granular output (approximately 135 ms). The model also predicted that SI correlates of perception reflect stronger and shorter-latency supragranular and late granular drive during perceived trials. These findings strongly support the view that signatures of tactile detection are present in human SI and are mediated by local neural dynamics induced by lamina-specific synaptic drive. Furthermore, our model provides a biophysically realistic solution to the MEG signal and can predict the electrophysiological correlates of human perception.

  4. Detecting and locating volcanic tremors on the Klyuchevskoy group of volcanoes (Kamchatka) based on correlations of continuous seismic records

    Science.gov (United States)

    Droznin, D. V.; Shapiro, N. M.; Droznina, S. Ya.; Senyukov, S. L.; Chebrov, V. N.; Gordeev, E. I.

    2015-11-01

    We analyse daily cross-correlation computed from continuous records by permanent stations operating in vicinity of the Klyuchevskoy group of volcanoes (Kamchatka). Seismic waves generated by volcanic tremors are clearly seen on the cross-correlations between some pairs of stations as strong signals at frequencies between 0.2 and 2 Hz and with traveltimes typically shorter than those corresponding to interstation propagation. First, we develop a 2-D source-scanning algorithm based on summation of the envelops of cross-correlations to detect seismic tremors and to determine locations from which the strong seismic energy is continuously emitted. In an alternative approach, we explore the distinctive character of the cross-correlation waveforms corresponding to tremors emitted by different volcanoes and develop a phase-matching method for detecting volcanic tremors. Application of these methods allows us to detect and to distinguish tremors generated by the Klyuchevskoy and the Tolbachik, volcanoes and to monitor evolution of their intensity in time.

  5. Parallel detecting super-resolution microscopy using correlation based image restoration

    Science.gov (United States)

    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.

  6. Harmful intrusion detection algorithm of optical fiber pre-warning system based on correlation of orthogonal polarization signals

    Science.gov (United States)

    Bi, Fukun; Feng, Chong; Qu, Hongquan; Zheng, Tong; Wang, Chonglei

    2017-09-01

    At present, advanced researches of optical fiber intrusion measurement are based on the constant false alarm rate (CFAR) algorithm. Although these conventional methods overcome the interference of non-stationary random signals, there are still a large number of false alarms in practical applications. This is because there is no specific study on orthogonal polarization signals of false alarm and intrusion. In order to further reduce false alarms, we analyze the correlation of optical fiber signals using birefringence of single-mode fiber. This paper proposes the harmful intrusion detection algorithm based on the correlation of two orthogonal polarization signals. The proposed method uses correlation coefficient to distinguish false alarms and intrusions, which can decrease false alarms. Experiments on real data, which are collected from the practical environment, demonstrate that the difference in correlation is a robust feature. Furthermore, the results show that the proposed algorithm can reduce the false alarms and ensure the detection performance when it is used in optical fiber pre-warning system (OFPS).

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Yang Zhang

    2016-10-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  10. Detecting overlapping community structure of networks based on vertex–vertex correlations

    International Nuclear Information System (INIS)

    Zarei, Mina; Izadi, Dena; Samani, Keivan Aghababaei

    2009-01-01

    Using the NMF (non-negative matrix factorization) method, the structure of overlapping communities in complex networks is investigated. For the feature matrix of the NMF method we introduce a vertex–vertex correlation matrix. The method is applied to some computer-generated and real-world networks. Simulations show that this feature matrix gives more reasonable results

  11. A urinary biomarker-based risk score correlates with multiparametric MRI for prostate cancer detection

    NARCIS (Netherlands)

    Hendriks, R.J.; Leest, M.M.G. van der; Dijkstra, S.; Barentsz, J.O.; Criekinge, W. van; Kaa, C.A. van de; Schalken, J.A.; Mulders, P.F.A.; Oort, I.M. van

    2017-01-01

    BACKGROUND: Prostate cancer (PCa) diagnostics would greatly benefit from more accurate, non-invasive techniques for the detection of clinically significant disease, leading to a reduction of over-diagnosis and over-treatment. The aim of this study was to determine the association between a novel

  12. Real-time phase correlation based integrated system for seizure detection

    Science.gov (United States)

    Romaine, James B.; Delgado-Restituto, Manuel; Leñero-Bardallo, Juan A.; Rodríguez-Vázquez, Ángel

    2017-05-01

    This paper reports a low area, low power, integer-based digital processor for the calculation of phase synchronization between two neural signals. The processor calculates the phase-frequency content of a signal by identifying the specific time periods associated with two consecutive minima. The simplicity of this phase-frequency content identifier allows for the digital processor to utilize only basic digital blocks, such as registers, counters, adders and subtractors, without incorporating any complex multiplication and or division algorithms. In fact, the processor, fabricated in a 0.18μm CMOS process, only occupies an area of 0.0625μm2 and consumes 12.5nW from a 1.2V supply voltage when operated at 128kHz. These low-area, low-power features make the proposed processor a valuable computing element in closed loop neural prosthesis for the treatment of neural diseases, such as epilepsy, or for extracting functional connectivity maps between different recording sites in the brain.

  13. Sequence detection analysis based on canonical correlation for steady-state visual evoked potential brain computer interfaces.

    Science.gov (United States)

    Cao, Lei; Ju, Zhengyu; Li, Jie; Jian, Rongjun; Jiang, Changjun

    2015-09-30

    Steady-state visual evoked potential (SSVEP) has been widely applied to develop brain computer interface (BCI) systems. The essence of SSVEP recognition is to recognize the frequency component of target stimulus focused by a subject significantly present in EEG spectrum. In this paper, a novel statistical approach based on sequence detection (SD) is proposed for improving the performance of SSVEP recognition. This method uses canonical correlation analysis (CCA) coefficients to observe SSVEP signal sequence. And then, a threshold strategy is utilized for SSVEP recognition. The result showed the classification performance with the longer duration of time window achieved the higher accuracy for most subjects. And the average time costing per trial was lower than the predefined recognition time. It was implicated that our approach could improve the speed of BCI system in contrast to other methods. Comparison with existing method(s): In comparison with other resultful algorithms, experimental accuracy of SD approach was better than those using a widely used CCA-based method and two newly proposed algorithms, least absolute shrinkage and selection operator (LASSO) recognition model as well as multivariate synchronization index (MSI) method. Furthermore, the information transfer rate (ITR) obtained by SD approach was higher than those using other three methods for most participants. These conclusions demonstrated that our proposed method was promising for a high-speed online BCI. Copyright © 2015 Elsevier B.V. All rights reserved.

  14. A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines.

    Science.gov (United States)

    Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

    2016-12-13

    In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods.

  15. A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines

    Directory of Open Access Journals (Sweden)

    Qiyang Xiao

    2016-12-01

    Full Text Available In this study, a small leak detection method based on variational mode decomposition (VMD and ambiguity correlation classification (ACC is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF, an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM and back propagation neural network (BP methods.

  16. [Detection of human papilloma virus (HPV) in liquid-based cervical samples. Correlation with protein p16INK4a expression].

    Science.gov (United States)

    Toro de Méndez, Morelva; Ferrández Izquierdo, Antonio

    2011-03-01

    The liquid-based cervical cytology improves the quality of the sample and the residual sample could be used efficiently to carry out complementary tests, such as the detection of HPV DNA and the immunocytochemical biomarkers study. The purpose of this study was to correlate the presence of HPV and immunoexpression of p16INK4a in liquid-based cervical samples to examine the utility of these new tools in the detection of cervical cancer. The included patients (n = 67) presented an abnormal cytology or previous cervical pathology. The HPV detection and genotyping were carried out with PCR-SPF10/LiPA (INNOLiPA Extra Amp) and for p16INK4a immunodetection was used antibody clone E6H4. The conventional cytology provided the same cytologic interpretations that those of liquid-based cytology. The overall HPV prevalence was 43.3% (29/67). HPV16 was the most frequent viral type (31.03%) and 48.3% of the cases were infected with multiple HPV types. p16INK4a immunoexpression was observed in 35.8% of liquid-based cytological samples and this was significantly (p < 0.020) associated to the HPV presence. These results support the evidence that the implementation of new technologies in the daily routine of the laboratory, contribute significantly in the early detection of cervical cancer and provide important data to help in the patient's efficient management. The combined use of HPV detection and p16INK4a expression could be used for evaluation of patients with more risk to develop significant cervical lesions.

  17. A Universal High-Performance Correlation Analysis Detection Model and Algorithm for Network Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Hongliang Zhu

    2017-01-01

    Full Text Available In big data era, the single detection techniques have already not met the demand of complex network attacks and advanced persistent threats, but there is no uniform standard to make different correlation analysis detection be performed efficiently and accurately. In this paper, we put forward a universal correlation analysis detection model and algorithm by introducing state transition diagram. Based on analyzing and comparing the current correlation detection modes, we formalize the correlation patterns and propose a framework according to data packet timing and behavior qualities and then design a new universal algorithm to implement the method. Finally, experiment, which sets up a lightweight intrusion detection system using KDD1999 dataset, shows that the correlation detection model and algorithm can improve the performance and guarantee high detection rates.

  18. DInSAR-Based Detection of Land Subsidence and Correlation with Groundwater Depletion in Konya Plain, Turkey

    Directory of Open Access Journals (Sweden)

    Fabiana Caló

    2017-01-01

    Full Text Available In areas where groundwater overexploitation occurs, land subsidence triggered by aquifer compaction is observed, resulting in high socio-economic impacts for the affected communities. In this paper, we focus on the Konya region, one of the leading economic centers in the agricultural and industrial sectors in Turkey. We present a multi-source data approach aimed at investigating the complex and fragile environment of this area which is heavily affected by groundwater drawdown and ground subsidence. In particular, in order to analyze the spatial and temporal pattern of the subsidence process we use the Small BAseline Subset DInSAR technique to process two datasets of ENVISAT SAR images spanning the 2002–2010 period. The produced ground deformation maps and associated time-series allow us to detect a wide land subsidence extending for about 1200 km2 and measure vertical displacements reaching up to 10 cm in the observed time interval. DInSAR results, complemented with climatic, stratigraphic and piezometric data as well as with land-cover changes information, allow us to give more insights on the impact of climate changes and human activities on groundwater resources depletion and land subsidence.

  19. Local correlation detection with linearity enhancement in streaming data

    KAUST Repository

    Xie, Qing

    2013-01-01

    This paper addresses the challenges in detecting the potential correlation between numerical data streams, which facilitates the research of data stream mining and pattern discovery. We focus on local correlation with delay, which may occur in burst at different time in different streams, and last for a limited period. The uncertainty on the correlation occurrence and the time delay make it diff cult to monitor the correlation online. Furthermore, the conventional correlation measure lacks the ability of ref ecting visual linearity, which is more desirable in reality. This paper proposes effective methods to continuously detect the correlation between data streams. Our approach is based on the Discrete Fourier Transform to make rapid cross-correlation calculation with time delay allowed. In addition, we introduce a shape-based similarity measure into the framework, which ref nes the results by representative trend patterns to enhance the signif cance of linearity. The similarity of proposed linear representations can quickly estimate the correlation, and the window sliding strategy in segment level improves the eff ciency for online detection. The empirical study demonstrates the accuracy of our detection approach, as well as more than 30% improvement of eff ciency. Copyright 2013 ACM.

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

    Energy Technology Data Exchange (ETDEWEB)

    Martinenghi, E., E-mail: edoardo.martinenghi@polimi.it; Di Sieno, L.; Contini, D.; Dalla Mora, A. [Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano (Italy); Sanzaro, M. [Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano (Italy); Pifferi, A. [Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano (Italy); Istituto di Fotonica e Nanotecnologie, Consiglio Nazionale delle Ricerche, Piazza Leonardo da Vinci 32, 20133 Milano (Italy)

    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 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).

  1. Cluster identification based on correlations.

    Science.gov (United States)

    Schulman, L S

    2012-04-01

    The problem addressed is the identification of cooperating agents based on correlations created as a result of the joint action of these and other agents. A systematic method for using correlations beyond second moments is developed. The technique is applied to a didactic example, the identification of alphabet letters based on correlations among the pixels used in an image of the letter. As in this example, agents can belong to more than one cluster. Moreover, the identification scheme does not require that the patterns be known ahead of time.

  2. Cluster identification based on correlations

    Science.gov (United States)

    Schulman, L. S.

    2012-04-01

    The problem addressed is the identification of cooperating agents based on correlations created as a result of the joint action of these and other agents. A systematic method for using correlations beyond second moments is developed. The technique is applied to a didactic example, the identification of alphabet letters based on correlations among the pixels used in an image of the letter. As in this example, agents can belong to more than one cluster. Moreover, the identification scheme does not require that the patterns be known ahead of time.

  3. Decentralized Detection in Censoring Sensor Networks under Correlated Observations

    Directory of Open Access Journals (Sweden)

    Abdullah S. Abu-Romeh

    2010-01-01

    Full Text Available The majority of optimal rules derived for different decentralized detection application scenarios are based on an assumption that the sensors' observations are statistically independent. Deriving the optimal decision rule in the canonical decentralized setting with correlated observations was shown to be complicated even for the simple case of two sensors. We introduce an alternative suboptimal rule to deal with correlated observations in decentralized detection with censoring sensors using a modified generalized likelihood ratio test (mGLRT. In the censoring scheme, sensors either send or do not send their complete observations to the fusion center. Using ML estimation to estimate the censored values, the decentralized problem is converted to a centralized problem. Our simulation results indicate that, when sensor observations are correlated, the mGLRT gives considerably better performance in terms of probability of detection than does the optimal decision rule derived for uncorrelated observations.

  4. Improving Broadband Displacement Detection with Quantum Correlations

    Science.gov (United States)

    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.

  5. Edge-based correlation image registration for multispectral imaging

    Science.gov (United States)

    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.

  6. The waveform correlation event detection system global prototype software design

    Energy Technology Data Exchange (ETDEWEB)

    Beiriger, J.I.; Moore, S.G.; Trujillo, J.R.; Young, C.J.

    1997-12-01

    The WCEDS prototype software system was developed to investigate the usefulness of waveform correlation methods for CTBT monitoring. The WCEDS prototype performs global seismic event detection and has been used in numerous experiments. This report documents the software system design, presenting an overview of the system operation, describing the system functions, tracing the information flow through the system, discussing the software structures, and describing the subsystem services and interactions. The effectiveness of the software design in meeting project objectives is considered, as well as opportunities for code refuse and lessons learned from the development process. The report concludes with recommendations for modifications and additions envisioned for regional waveform-correlation-based detector.

  7. Object detection by correlation coefficients using azimuthally averaged reference projections.

    Science.gov (United States)

    Nicholson, William V

    2004-11-01

    A method of computing correlation coefficients for object detection that takes advantage of using azimuthally averaged reference projections is described and compared with two alternative methods-computing a cross-correlation function or a local correlation coefficient versus the azimuthally averaged reference projections. Two examples of an application from structural biology involving the detection of projection views of biological macromolecules in electron micrographs are discussed. It is found that a novel approach to computing a local correlation coefficient versus azimuthally averaged reference projections, using a rotational correlation coefficient, outperforms using a cross-correlation function and a local correlation coefficient in object detection from simulated images with a range of levels of simulated additive noise. The three approaches perform similarly in detecting macromolecular views in electron microscope images of a globular macrolecular complex (the ribosome). The rotational correlation coefficient outperforms the other methods in detection of keyhole limpet hemocyanin macromolecular views in electron micrographs.

  8. Correlation Filters for Detection of Cellular Nuclei in Histopathology Images.

    Science.gov (United States)

    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 .

  9. MR detection of retinal hemorrhages: correlation with graded ophthalmologic exam

    Energy Technology Data Exchange (ETDEWEB)

    Beavers, Angela J.; Allbery, Sandra M. [University of Nebraska Medical Center, Department of Radiology, Omaha, NE (United States); Children' s Hospital and Medical Center, Department of Radiology, Omaha, NE (United States); Stagner, Anna M.; Hejkal, Thomas W. [University of Nebraska Medical Center, Department of Ophthalmology, Omaha, NE (United States); Children' s Hospital and Medical Center, Department of Ophthalmology, Omaha, NE (United States); Lyden, Elizabeth R. [University of Nebraska Medical Center, College of Public Health, Omaha, NE (United States); Haney, Suzanne B. [Children' s Hospital and Medical Center, Department of Pediatrics, Omaha, NE (United States); University of Nebraska Medical Center, Department of Pediatrics, Omaha, NE (United States)

    2015-08-15

    Dilated fundoscopic exam is considered the gold standard for detecting retinal hemorrhage, but expertise in obtaining this exam is not always immediately available. MRI can detect retinal hemorrhages, but correlation of the grade or severity of retinal hemorrhage on dilated fundoscopic exam with retinal hemorrhage visibility on MRI has not been described. To determine the value of standard brain protocol MRI in detecting retinal hemorrhage and to determine whether there is any correlation with MR detection of retinal hemorrhage and the dilated fundoscopic exam grade of hemorrhage. We conducted a retrospective chart review of 77 children <2 years old who were seen for head trauma from April 2007 to July 2013 and had both brain MRI and dilated fundoscopic exam or retinal camera images. A staff pediatric radiologist and radiology resident reviewed the MR images. Retinal hemorrhages were graded by a chief ophthalmology resident on a 12-point scale based on the retinal hemorrhage type, size, location and extent as seen on review of retinal camera images and detailed reports by ophthalmologists. Higher scores indicated increased severity of retinal hemorrhages. There was a statistically significant difference in the median grade of retinal hemorrhage examination between children who had retinal hemorrhage detected on MRI and children who did not have retinal hemorrhage detected on MRI (P = 0.02). When examination grade was categorized as low-grade (1-4), moderate-grade (5-8) or high-grade (>8) hemorrhage, there was a statistically significant association between exam grade and diagnosis based on MRI (P = 0.008). For example, only 14% of children with low-grade retinal hemorrhages were identified on MRI compared to 76% of children with high-grade hemorrhages. MR detection of retinal hemorrhage demonstrated a sensitivity of 61%, specificity of 100%, positive predictive value of 100% and negative predictive value of 63%. Retinal hemorrhage was best seen on the gradient

  10. MR detection of retinal hemorrhages: correlation with graded ophthalmologic exam

    International Nuclear Information System (INIS)

    Beavers, Angela J.; Allbery, Sandra M.; Stagner, Anna M.; Hejkal, Thomas W.; Lyden, Elizabeth R.; Haney, Suzanne B.

    2015-01-01

    Dilated fundoscopic exam is considered the gold standard for detecting retinal hemorrhage, but expertise in obtaining this exam is not always immediately available. MRI can detect retinal hemorrhages, but correlation of the grade or severity of retinal hemorrhage on dilated fundoscopic exam with retinal hemorrhage visibility on MRI has not been described. To determine the value of standard brain protocol MRI in detecting retinal hemorrhage and to determine whether there is any correlation with MR detection of retinal hemorrhage and the dilated fundoscopic exam grade of hemorrhage. We conducted a retrospective chart review of 77 children <2 years old who were seen for head trauma from April 2007 to July 2013 and had both brain MRI and dilated fundoscopic exam or retinal camera images. A staff pediatric radiologist and radiology resident reviewed the MR images. Retinal hemorrhages were graded by a chief ophthalmology resident on a 12-point scale based on the retinal hemorrhage type, size, location and extent as seen on review of retinal camera images and detailed reports by ophthalmologists. Higher scores indicated increased severity of retinal hemorrhages. There was a statistically significant difference in the median grade of retinal hemorrhage examination between children who had retinal hemorrhage detected on MRI and children who did not have retinal hemorrhage detected on MRI (P = 0.02). When examination grade was categorized as low-grade (1-4), moderate-grade (5-8) or high-grade (>8) hemorrhage, there was a statistically significant association between exam grade and diagnosis based on MRI (P = 0.008). For example, only 14% of children with low-grade retinal hemorrhages were identified on MRI compared to 76% of children with high-grade hemorrhages. MR detection of retinal hemorrhage demonstrated a sensitivity of 61%, specificity of 100%, positive predictive value of 100% and negative predictive value of 63%. Retinal hemorrhage was best seen on the gradient

  11. Rate based failure detection

    Science.gov (United States)

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

    2018-01-02

    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 data paths may be adjusted in real-time to ensure that the power grid data network does not become overloaded and/or fail.

  12. Onconeural antibodies: improved detection and clinical correlations.

    Science.gov (United States)

    Storstein, Anette; Monstad, Sissel Evy; Haugen, Mette; Mazengia, Kibret; Veltman, Dana; Lohndal, Emilia; Aarseth, Jan; Vedeler, Christian

    2011-03-01

    Onconeural antibodies are found in many patients with paraneoplastic neurological syndromes (PNS) and define the disease as paraneoplastic. The study describes the presence of onconeural antibodies and PNS in 555 patients with neurological symptoms and confirmed cancer within five years, and compares the diagnostic accuracy of different antibody assays (immunoprecipitation, immunofluorescence and immunoblot). Onconeural antibodies were found in 11.9% of the patients by immunoprecipitation, in 7.0% by immunofluorescence and in 6.3% by immunoblot. PNS were present in 81.8% of the cancer patients that were seropositive by immunoprecipitation. Immunofluorescence and immunoblot failed to detect onconeural antibodies in almost one third of the PNS cases. Copyright © 2010 Elsevier B.V. All rights reserved.

  13. The Prevalence, Correlates, Detection and Control of Diabetes among Older People in Low and Middle Income Countries. A 10/66 Dementia Research Group Population-Based Survey.

    Directory of Open Access Journals (Sweden)

    Aquiles Salas

    Full Text Available Little is known of the epidemiology of diabetes among older people in low and middle income countries. We aimed to study and compare prevalence, social patterning, correlates, detection, treatment and control of diabetes among older people in Latin America, India, China and Nigeria.Cross-sectional surveys in 13 catchment area sites in nine countries. Diagnosed diabetes was assessed in all sites through self-reported diagnosis. Undiagnosed diabetes was assessed in seven Latin American sites through fasting blood samples (glucose > = 7 mmol/L.Total diabetes prevalence in catchment sites in Cuba (prevalence 24.2%, SMR 116, Puerto Rico (43.4%, 197, and urban (27.0%, 125, and rural Mexico (23.7%, 111 already exceeds that in the USA, while that in Venezuela (20.9%, 100 is similar. Diagnosed diabetes prevalence varied very widely, between low prevalences in sites in rural China (0.9%, rural India (6.6% and Nigeria (6.0%. and 32.1% in Puerto Rico, explained mainly by access to health services. Treatment coverage varied substantially between sites. Diabetes control (40 to 61% of those diagnosed was modest in the Latin American sites where this was studied. Diabetes was independently associated with less education, but more assets. Hypertension, central obesity and hypertriglyceridaemia, but not hypercholesterolaemia were consistently associated with total diabetes.Diabetes prevalence is already high in most sites. Identifying undiagnosed cases is essential to quantify population burden, particularly in least developed settings where diagnosis is uncommon. Metabolic risk factors and associated lifestyles may play an important part in aetiology, but this requires confirmation with longitudinal data. Given the high prevalence among older people, more population research is indicated to quantify the impact of diabetes, and to monitor the effect of prevention and health system strengthening on prevalence, treatment and control.

  14. The Prevalence, Correlates, Detection and Control of Diabetes among Older People in Low and Middle Income Countries. A 10/66 Dementia Research Group Population-Based Survey.

    Science.gov (United States)

    Salas, Aquiles; Acosta, Daisy; Ferri, Cleusa P; Guerra, Mariella; Huang, Yueqin; Jacob, K S; Jimenez-Velazquez, Ivonne Z; Llibre Rodriguez, Juan J; Sosa, Ana L; Uwakwe, Richard; Williams, Joseph D; Jotheeswaran, A T; Liu, Zhaorui; Lopez Medina, A M; Salinas-Contreras, Rosa Maria; Prince, Martin J

    2016-01-01

    Little is known of the epidemiology of diabetes among older people in low and middle income countries. We aimed to study and compare prevalence, social patterning, correlates, detection, treatment and control of diabetes among older people in Latin America, India, China and Nigeria. Cross-sectional surveys in 13 catchment area sites in nine countries. Diagnosed diabetes was assessed in all sites through self-reported diagnosis. Undiagnosed diabetes was assessed in seven Latin American sites through fasting blood samples (glucose > = 7 mmol/L). Total diabetes prevalence in catchment sites in Cuba (prevalence 24.2%, SMR 116), Puerto Rico (43.4%, 197), and urban (27.0%, 125), and rural Mexico (23.7%, 111) already exceeds that in the USA, while that in Venezuela (20.9%, 100) is similar. Diagnosed diabetes prevalence varied very widely, between low prevalences in sites in rural China (0.9%), rural India (6.6%) and Nigeria (6.0%). and 32.1% in Puerto Rico, explained mainly by access to health services. Treatment coverage varied substantially between sites. Diabetes control (40 to 61% of those diagnosed) was modest in the Latin American sites where this was studied. Diabetes was independently associated with less education, but more assets. Hypertension, central obesity and hypertriglyceridaemia, but not hypercholesterolaemia were consistently associated with total diabetes. Diabetes prevalence is already high in most sites. Identifying undiagnosed cases is essential to quantify population burden, particularly in least developed settings where diagnosis is uncommon. Metabolic risk factors and associated lifestyles may play an important part in aetiology, but this requires confirmation with longitudinal data. Given the high prevalence among older people, more population research is indicated to quantify the impact of diabetes, and to monitor the effect of prevention and health system strengthening on prevalence, treatment and control.

  15. Automatic particle detection in microscopy using temporal correlations.

    Science.gov (United States)

    Röding, Magnus; Deschout, Hendrik; Martens, Thomas; Notelaers, Kristof; Hofkens, Johan; Ameloot, Marcel; Braeckmans, Kevin; Särkkä, Aila; Rudemo, Mats

    2013-10-01

    One of the fundamental problems in the analysis of single particle tracking data is the detection of individual particle positions from microscopy images. Distinguishing true particles from noise with a minimum of false positives and false negatives is an important step that will have substantial impact on all further analysis of the data. A common approach is to obtain a plausible set of particles from a larger set of candidate particles by filtering using manually selected threshold values for intensity, size, shape, and other parameters describing a particle. This introduces subjectivity into the analysis and hinders reproducibility. In this paper, we introduce a method for automatic selection of these threshold values based on maximizing temporal correlations in particle count time series. We use Markov Chain Monte Carlo to find the threshold values corresponding to the maximum correlation, and we study several experimental data sets to assess the performance of the method in practice by comparing manually selected threshold values from several independent experts with automatically selected threshold values. We conclude that the method produces useful results, reducing subjectivity and the need for manual intervention, a great benefit being its easy integratability into many already existing particle detection algorithms. Copyright © 2013 Wiley Periodicals, Inc.

  16. Correlation Dimension-Based Classifier

    Czech Academy of Sciences Publication Activity Database

    Jiřina, Marcel; Jiřina jr., M.

    2014-01-01

    Roč. 44, č. 12 (2014), s. 2253-2263 ISSN 2168-2267 R&D Projects: GA MŠk(CZ) LG12020 Institutional support: RVO:67985807 Keywords : classifier * multidimensional data * correlation dimension * scaling exponent * polynomial expansion Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.469, year: 2014

  17. Spatial- and Time-Correlated Detection of Fission Fragments

    Directory of Open Access Journals (Sweden)

    Platkevic M.

    2012-02-01

    Full Text Available With the goal to measure angular correlations of fission fragments in rare fission decay (e.g. ternary and quaternary fission, a multi-detector coincidence system based on two and up to four position sensitive pixel detectors Timepix has been built. In addition to the high granularity, wide dynamic range and per pixel signal threshold, these devices are equipped with per pixel energy and time sensitivity providing more information (position, energy, time, enhances particle-type identification and selectivity of event-by-event detection. Operation of the device with the integrated USB 2.0 based readout interface FITPix and the control and data acquisition software tool Pixelman enables online visualization and flexible/adjustable operation for a different type of experiments. Spatially correlated fission fragments can be thus registered in coincidence. Similarly triggered measurements are performed using an integrated spectrometric module with analogue signal chain electronics. The current status of development together with demonstration of the technique with a 252Cf source is presented.

  18. A correlation-based fingerprint verification system

    NARCIS (Netherlands)

    Bazen, A.M.; Gerez, Sabih H.; Veelenturf, L.P.J.; van der Zwaag, B.J.; Verwaaijen, G.T.B.

    2000-01-01

    In this paper, a correlation-based fingerprint verification system is presented. Unlike the traditional minutiae-based systems, this system directly uses the richer gray-scale information of the fingerprints. The correlation-based fingerprint verification system first selects appropriate templates

  19. A correlation-based fingerprint verification system

    NARCIS (Netherlands)

    Bazen, A.M.; Gerez, Sabih H.; Veelenturf, L.P.J.; van der Zwaag, B.J.; Verwaaijen, G.T.B.

    In this paper, a correlation-based fingerprint verification system is presented. Unlike the traditional minutiae-based systems, this system directly uses the richer gray-scale information of the fingerprints. The correlation-based fingerprint verification system first selects appropriate templates

  20. Fluorescence correlation spectroscopy: Ultrasensitive detection in clear and turbid media

    Science.gov (United States)

    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

  1. Nonlinear spectral correlation for fatigue crack detection under noisy environments

    Science.gov (United States)

    Liu, Peipei; Sohn, Hoon; Jeon, Ikgeun

    2017-07-01

    When ultrasonic waves at two distinct frequencies are applied to a structure with a fatigue crack, crack-induced nonlinearity creates nonlinear ultrasonic modulations at the sum and difference of the two input frequencies. The amplitude of the nonlinear modulation components is typically one or two orders of magnitude smaller than that of the primary linear components. Therefore, the modulation components can be easily buried under noise levels and it becomes difficult to extract the nonlinear modulation components under noisy environments using a conventional spectral density function. In this study, nonlinear spectral correlation, which calculates the spectral correlation between nonlinear modulation components, is proposed to isolate the nonlinear modulation components from noisy environments and used for fatigue crack detection. The proposed nonlinear spectral correlation offers the following benefits: (1) Stationary noises have little effect on nonlinear spectral correlation; (2) By using a wideband high-frequency input and a single low-frequency input, the contrast of nonlinear spectral correlation between damage and intact conditions can be enhanced; and (3) The test efficiency can be also improved via reducing the data collection time. Validation tests are performed on aluminum plates and scaled steel shafts with real fatigue cracks. The experimental results demonstrate that the proposed nonlinear spectral correlation owns a higher sensitivity to fatigue crack than the classical nonlinear coefficient estimated from the spectral density function, and the usage of nonlinear spectral correlation allows the detection of fatigue crack even using noncontact air-coupled transducers with a low signal-to-noise ratio.

  2. Interevent Correlations from Avalanches Hiding Below the Detection Threshold

    Science.gov (United States)

    Janićević, Sanja; Laurson, Lasse; Mâløy, Knut Jørgen; Santucci, Stéphane; Alava, Mikko J.

    2016-12-01

    Numerous systems ranging from deformation of materials to earthquakes exhibit bursty dynamics, which consist of a sequence of events with a broad event size distribution. Very often these events are observed to be temporally correlated or clustered, evidenced by power-law-distributed waiting times separating two consecutive activity bursts. We show how such interevent correlations arise simply because of a finite detection threshold, created by the limited sensitivity of the measurement apparatus, or used to subtract background activity or noise from the activity signal. Data from crack-propagation experiments and numerical simulations of a nonequilibrium crack-line model demonstrate how thresholding leads to correlated bursts of activity by separating the avalanche events into subavalanches. The resulting temporal subavalanche correlations are well described by our general scaling description of thresholding-induced correlations in crackling noise.

  3. Study of the correlation between sensing performance and surface morphology of inkjet-printed aqueous graphene-based chemiresistors for NO2 detection

    Directory of Open Access Journals (Sweden)

    F. Villani

    2017-05-01

    Full Text Available The extremely high sensitivity to the external environment and the high specific surface area, as well as the absence of bulk phenomena that could interfere with the response signal, make graphene highly attractive for the applications in the field of sensing. Among the various methods for producing graphene over large areas, liquid phase exfoliation (LPE appears to be very promising, especially if combined with inkjet printing (IJP, which offers several advantages, including the selective and controlled deposition of small ink volumes and the versatility of the exploitable inks and substrates. Herein we present a feasibility study of chemiresistive gas sensors inkjet-printed onto paper substrates, in which a LPE graphene suspension dispersed in a water/isopropanol (H2O/IPA mixture is used as sensing ink. The device performances, in terms of relative conductance variations, upon exposure to NO2 at standard ambient temperature and pressure, are analysed. In addition, we examine the effect of the substrate morphology and, more specifically, of the ink/substrate interaction on the device performances, by comparing the response of different chemiresistors fabricated by dispensing the same suspension also onto Al2O3 and Si/SiO2 substrates and carrying out a supportive atomic force microscopy analysis. The results prove the possibility to produce sensor devices by means of a wholly environmentally friendly, low-cost process that meets the requests coming from the increasing field of paper-based electronics and paving the way towards a flexible, green-by-design mass production.

  4. Evolution of worldwide stock markets, correlation structure, and correlation-based graphs.

    Science.gov (United States)

    Song, Dong-Ming; Tumminello, Michele; Zhou, Wei-Xing; Mantegna, Rosario N

    2011-08-01

    We investigate the daily correlation present among market indices of stock exchanges located all over the world in the time period January 1996 to July 2009. We discover that the correlation among market indices presents both a fast and a slow dynamics. The slow dynamics reflects the development and consolidation of globalization. The fast dynamics is associated with critical events that originate in a specific country or region of the world and rapidly affect the global system. We provide evidence that the short term time scale of correlation among market indices is less than 3 trading months (about 60 trading days). The average values of the nondiagonal elements of the correlation matrix, correlation-based graphs, and the spectral properties of the largest eigenvalues and eigenvectors of the correlation matrix are carrying information about the fast and slow dynamics of the correlation of market indices. We introduce a measure of mutual information based on link co-occurrence in networks in order to detect the fast dynamics of successive changes of correlation-based graphs in a quantitative way.

  5. Obstacle detection by stereo vision of fast correlation matching

    International Nuclear Information System (INIS)

    Jeon, Seung Hoon; Kim, Byung Kook

    1997-01-01

    Mobile robot navigation needs acquiring positions of obstacles in real time. A common method for performing this sensing is through stereo vision. In this paper, indoor images are acquired by binocular vision, which contains various shapes of obstacles. From these stereo image data, in order to obtain distances to obstacles, we must deal with the correspondence problem, or get the region in the other image corresponding to the projection of the same surface region. We present an improved correlation matching method enhancing the speed of arbitrary obstacle detection. The results are faster, simple matching, robustness to noise, and improvement of precision. Experimental results under actual surroundings are presented to reveal the performance. (author)

  6. Correlative Analysis of GRBs detected by Swift, Konus and HETE

    Science.gov (United States)

    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.

  7. On detecting and modeling periodic correlation in financial data

    Science.gov (United States)

    Broszkiewicz-Suwaj, E.; Makagon, A.; Weron, R.; Wyłomańska, A.

    2004-05-01

    For many economic problems standard statistical analysis, based on the notion of stationarity, is not adequate. These include modeling seasonal decisions of consumers, forecasting business cycles and-as we show in the present article-modeling wholesale power market prices. We apply standard methods and a novel spectral domain technique to conclude that electricity price returns exhibit periodic correlation with daily and weekly periods. As such they should be modeled with periodically correlated processes. We propose to apply periodic autoregression models which are closely related to the standard instruments in econometric analysis-vector autoregression models.

  8. VEHICLE LOCALIZATION BY LIDAR POINT CORRELATION IMPROVED BY CHANGE DETECTION

    Directory of Open Access Journals (Sweden)

    A. Schlichting

    2016-06-01

    Full Text Available 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.

  9. A robust correlation method to detect heterogeneous heart valve symptoms

    Science.gov (United States)

    Suboh, Mohd Zubir; Mansor, Muhammad Naufal; Junoh, Ahmad Kadri; Daud, Wan Suhana Wan; Muhamad, Wan Zuki Azman Wan; Idris, Azrini

    2015-05-01

    Heart valve disease affects a large number of patients. During the past decade, major advances have occurred in diagnostic techniques of heart valve disease. In this paper, we present an alternative method in classifying heart valve disease using correlation analysis and neural network classifier based on heart sound signal. The heart sound signals used in this study were taken from heart sound manipulator software. First, the signal was converted into frequency domain. Then, power spectrum of the sample is determined and cross-correlated with a reference sample (also in power spectrum form) to get different pattern of correlation plot. Seven different heart sounds of normal and other abnormal sounds from heart valve disease were classified into their classes. The result shows that 98.70% of the samples had been correctly classified by the system.

  10. Tools for Multimode Quantum Information: Modulation, Detection, and Spatial Quantum Correlations

    DEFF Research Database (Denmark)

    Lassen, Mikael Østergaard; Delaubert, Vincent; Janousek, Jirí

    2007-01-01

    We present here all the tools required for continuous variable parallel quantum information protocols based on spatial multi-mode quantum correlations and entanglement. We describe techniques for encoding and detecting this quantum information with high efficiency in the individual modes. We use...... parametric amplifier. By combining these modes we can now build a practical multi-mode optical quantum information system....

  11. FPGA design of correlation-based pattern recognition

    Science.gov (United States)

    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.

  12. Biometric Image Recognition Based on Optical Correlator

    Directory of Open Access Journals (Sweden)

    David Solus

    2017-01-01

    Full Text Available The aim of this paper is to design a biometric images recognition system able to recognize biometric images-eye and DNA marker. The input scenes are processed by user-friendly software created in C# programming language and then are compared with reference images stored in database. In this system, Cambridge optical correlator is used as an image comparator based on similarity of images in the recognition phase.

  13. Gait Correlation Analysis Based Human Identification

    Directory of Open Access Journals (Sweden)

    Jinyan Chen

    2014-01-01

    Full Text Available Human gait identification aims to identify people by a sequence of walking images. Comparing with fingerprint or iris based identification, the most important advantage of gait identification is that it can be done at a distance. In this paper, silhouette correlation analysis based human identification approach is proposed. By background subtracting algorithm, the moving silhouette figure can be extracted from the walking images sequence. Every pixel in the silhouette has three dimensions: horizontal axis (x, vertical axis (y, and temporal axis (t. By moving every pixel in the silhouette image along these three dimensions, we can get a new silhouette. The correlation result between the original silhouette and the new one can be used as the raw feature of human gait. Discrete Fourier transform is used to extract features from this correlation result. Then, these features are normalized to minimize the affection of noise. Primary component analysis method is used to reduce the features’ dimensions. Experiment based on CASIA database shows that this method has an encouraging recognition performance.

  14. Correlation measure to detect time series distances, whence economy globalization

    Science.gov (United States)

    Miśkiewicz, Janusz; Ausloos, Marcel

    2008-11-01

    An instantaneous time series distance is defined through the equal time correlation coefficient. The idea is applied to the Gross Domestic Product (GDP) yearly increments of 21 rich countries between 1950 and 2005 in order to test the process of economic globalisation. Some data discussion is first presented to decide what (EKS, GK, or derived) GDP series should be studied. Distances are then calculated from the correlation coefficient values between pairs of series. The role of time averaging of the distances over finite size windows is discussed. Three network structures are next constructed based on the hierarchy of distances. It is shown that the mean distance between the most developed countries on several networks actually decreases in time, -which we consider as a proof of globalization. An empirical law is found for the evolution after 1990, similar to that found in flux creep. The optimal observation time window size is found ≃15 years.

  15. Correlation between TAP detection and common digestive tract precancerous lesions.

    Science.gov (United States)

    Sun, Changqing; Deng, Fang; Meng, Lingjun; Chen, Guohua

    2018-02-01

    The aim of the present study was to investigate the clinical significance of abnormal sugar-chain glycoprotein tumor abnormal protein (TAP) in the screening of common digestive tract pre-cancer colon adenocarcinoma lesions. A total of 50 colitis patients, 50 colon polyp patients and 50 colon adenocarcinoma patients admitted to our hospital from March, 2012 to May, 2014 were included. Fresh blood from patient's fingertips was collected and condensation staining was used to detect TAP expression. Positive expressions of TAP in patients in the colitis, colon polyp and colon adenocarcinoma groups prior to treatment were 6,76 and 92%, respectively. The TAP-positive expression rate comparisons between the three groups were statistically significant (PTAP-positive expression showed an increasing trend. TAP-positive expression was not significantly correlated with sex, age or ethnic group (P>0.05). Patient follow-up revealed that the tumor incidence rate in TAP-positive patients was significantly higher than that in TAP-negative in the colitis and colon polyp groups (PTAP-positive patients was significantly higher than that in TAP-negative in the colon adenocarcinoma group (PTAP had a higher expression in colon pre-adenocarcinoma lesions. Additionally, TAP participated in the processes from intestinal mucosal inflammation to colon polyp formation to tissue canceration, and was correlated with these. Thus, TAP can be used for the screening of digestive tract precancerous lesions.

  16. Damage detection using sideband peak count in spectral correlation domain

    Science.gov (United States)

    Liu, Peipei; Sohn, Hoon

    2017-12-01

    Nonlinear ultrasonic techniques have been proven to be more sensitive to the presence of an early-stage damage than linear techniques. Among various nonlinear techniques, laser nonlinear wave modulation spectroscopy (LNWMS) utilizes a pulse laser to exert a broadband input and a damage on the target structure exhibits nonlinear wave modulation among various input frequency components. A sideband peak count (SPC) technique in the spectral frequency domain was proposed to estimate the damage-induced nonlinearity. In this study, the SPC operation is conducted in the spectral correlation domain so that noise has less influence on damage detection performance and a higher sensitivity to damage can be achieved. In addition, through spatial comparison of SPC over an inspection area, damage can be detected without relying on the baseline data obtained from a pristine condition. The performance of the proposed technique is validated using a numerical simulation performed on an aluminum plate with a simulated crack, and experiments performed on an aluminum plate with a fatigue crack and a carbon fiber reinforced polymer plate with delamination.

  17. Correlative Analysis of GRBs Detected by Swift and Suzaku- WAM

    Science.gov (United States)

    Krimm, H.A.; Sakamoto, T.; Yamaoka, K.; Sugita, S.; Ohno, M.; Sato, G.; Hara, R.; Ohmori, N.; Tanaka, H.; Yamauchi, M.; hide

    2009-01-01

    It is now well known that a complete understanding of the energetics of the prompt phase of gamma-ray bursts (GRBs) requires full knowledge of the spectrum, extending at least as high as the peak energy (Epeak) of the vF(v) spectrum. Since most gamma-ray bursts (GRBs) have Epeak above the energy range (15-150 keV) of the Burst Alert Telescope (BAT) on Swift, a full understanding of the prompt emission from Swift GRBs requires spectral fits over as broad an energy range as possible. This can be completed for bursts which are simultaneously detected by Swift BAT and the Suzaku Wide-band All-Sky Monitor (WAM), which covers the energy range from 50-5000 keV. Between the launch of Suzaku in July 2005 and the end of 2008, there were 44 gamma-ray bursts (GRBs) which triggered both Swift and WAM and an additional 41 bursts which triggered Swift and were detected by WAM, but did not trigger. A joint BAT-WAM team has cross-calibrated the two instruments using GRBs, and we are now able to perform joint fits on these bursts to determine spectral parameters including Epeak. The results of broad spectral fits allows us to understand the distribution of Epeak for Swift bursts and to calibrate Epeak estimators when Epeak is within the BAT energy range. For those bursts with spectroscopic redshifts, we can calculate the isotropic energy and study various correlations between Epeak and other global burst parameters. Here we present the results of joint Swift/BAT-Suzaku/WAM spectral fits for 77 of the bursts jointly detected by the two instruments. We show that the distribution of spectral fit parameters is consistent with distributions from earlier missions and confirm that Swift bursts are consistent with earlier reported relationships between Epeak and isotropic energy. We show through time-resolved spectroscopy that individual burst pulses are also consistent with this relationship.

  18. Prospects of Frequency-Time Correlation Analysis for Detecting Pipeline Leaks by Acoustic Emission Method

    International Nuclear Information System (INIS)

    Faerman, V A; Cheremnov, A G; Avramchuk, V V; Luneva, E E

    2014-01-01

    In the current work the relevance of nondestructive test method development applied for pipeline leak detection is considered. It was shown that acoustic emission testing is currently one of the most widely spread leak detection methods. The main disadvantage of this method is that it cannot be applied in monitoring long pipeline sections, which in its turn complicates and slows down the inspection of the line pipe sections of main pipelines. The prospects of developing alternative techniques and methods based on the use of the spectral analysis of signals were considered and their possible application in leak detection on the basis of the correlation method was outlined. As an alternative, the time-frequency correlation function calculation is proposed. This function represents the correlation between the spectral components of the analyzed signals. In this work, the technique of time-frequency correlation function calculation is described. The experimental data that demonstrate obvious advantage of the time-frequency correlation function compared to the simple correlation function are presented. The application of the time-frequency correlation function is more effective in suppressing the noise components in the frequency range of the useful signal, which makes maximum of the function more pronounced. The main drawback of application of the time- frequency correlation function analysis in solving leak detection problems is a great number of calculations that may result in a further increase in pipeline time inspection. However, this drawback can be partially reduced by the development and implementation of efficient algorithms (including parallel) of computing the fast Fourier transform using computer central processing unit and graphic processing unit

  19. Feature Selection Based on Mutual Correlation

    Czech Academy of Sciences Publication Activity Database

    Haindl, Michal; Somol, Petr; Ververidis, D.; Kotropoulos, C.

    2006-01-01

    Roč. 19, č. 4225 (2006), s. 569-577 ISSN 0302-9743. [Iberoamerican Congress on Pattern Recognition. CIARP 2006 /11./. Cancun, 14.11.2006-17.11.2006] R&D Projects: GA AV ČR 1ET400750407; GA MŠk 1M0572; GA AV ČR IAA2075302 EU Projects: European Commission(XE) 507752 - MUSCLE Institutional research plan: CEZ:AV0Z10750506 Keywords : feature selection Subject RIV: BD - Theory of Information Impact factor: 0.402, year: 2005 http://library.utia.cas.cz/separaty/historie/haindl-feature selection based on mutual correlation.pdf

  20. Ionizing particle detection based on phononic crystals

    Energy Technology Data Exchange (ETDEWEB)

    Aly, Arafa H., E-mail: arafa16@yahoo.com, E-mail: arafa.hussien@science.bsu.edu.eg; Mehaney, Ahmed; Eissa, Mostafa F. [Physics Department, Faculty of Science, Beni-Suef University, Beni-Suef (Egypt)

    2015-08-14

    Most conventional radiation detectors are based on electronic or photon collections. In this work, we introduce a new and novel type of ionizing particle detector based on phonon collection. Helium ion radiation treats tumors with better precision. There are nine known isotopes of helium, but only helium-3 and helium-4 are stable. Helium-4 is formed in fusion reactor technology and in enormous quantities during Big Bang nucleo-synthesis. In this study, we introduce a technique for helium-4 ion detection (sensing) based on the innovative properties of the new composite materials known as phononic crystals (PnCs). PnCs can provide an easy and cheap technique for ion detection compared with conventional methods. PnC structures commonly consist of a periodic array of two or more materials with different elastic properties. The two materials are polymethyl-methacrylate and polyethylene polymers. The calculations showed that the energies lost to target phonons are maximized at 1 keV helium-4 ion energy. There is a correlation between the total phonon energies and the transmittance of PnC structures. The maximum transmission for phonons due to the passage of helium-4 ions was found in the case of making polyethylene as a first layer in the PnC structure. Therefore, the concept of ion detection based on PnC structure is achievable.

  1. Limitations of correlation-based redatuming methods

    Science.gov (United States)

    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.

  2. A Comparison of Subpixel Edge Detection and Correlation Algorithms for the Measurement of Sprays

    Directory of Open Access Journals (Sweden)

    Daniel Duke

    2011-06-01

    Full Text Available Optical diagnostic techniques are commonly used to observe the breakup of dense sprays. In order to extract quantitative data from such images, edge detection algorithms have commonly been used. However, correlation image velocimetry techniques are now also becoming available for such applications. An empirical comparison between these two techniques is demonstrated for the high-speed velocimetry of the breakup of an annular air-assisted spray. A threshold based sub-pixel interpolating edge detection algorithm is employed. Both real and synthetic images are used to determine the sensitivity of the error in these techniques to changes in both image noise and defocus, the two leading causes of information loss. It is demonstrated that correlation image velocimetry techniques are generally superior in precision and accuracy as compared to edge detection techniques for the application of spray velocimetry within a reasonable parameter space of noise and defocus.

  3. On the Detection of Fake Certificates via Attribute Correlation

    Directory of Open Access Journals (Sweden)

    Xiaojing Gu

    2015-06-01

    Full Text Available Transport Layer Security (TLS and its predecessor, SSL, are important cryptographic protocol suites on the Internet. They both implement public key certificates and rely on a group of trusted certificate authorities (i.e., CAs for peer authentication. Unfortunately, the most recent research reveals that, if any one of the pre-trusted CAs is compromised, fake certificates can be issued to intercept the corresponding SSL/TLS connections. This security vulnerability leads to catastrophic impacts on SSL/TLS-based HTTPS, which is the underlying protocol to provide secure web services for e-commerce, e-mails, etc. To address this problem, we design an attribute dependency-based detection mechanism, called SSLight. SSLight can expose fake certificates by checking whether the certificates contain some attribute dependencies rarely occurring in legitimate samples. We conduct extensive experiments to evaluate SSLight and successfully confirm that SSLight can detect the vast majority of fake certificates issued from any trusted CAs if they are compromised. As a real-world example, we also implement SSLight as a Firefox add-on and examine its capability of exposing existent fake certificates from DigiNotar and Comodo, both of which have made a giant impact around the world.

  4. Spectral Correlation of Multicarrier Modulated Signals and Its Application for Signal Detection

    Directory of Open Access Journals (Sweden)

    Zhang Haijian

    2010-01-01

    Full Text Available Spectral correlation theory for cyclostationary time-series signals has been studied for decades. Explicit formulas of spectral correlation function for various types of analog-modulated and digital-modulated signals are already derived. In this paper, we investigate and exploit the cyclostationarity characteristics for two kinds of multicarrier modulated (MCM signals: conventional OFDM and filter bank based multicarrier (FBMC signals. The spectral correlation characterization of MCM signal can be described by a special linear periodic time-variant (LPTV system. Using this LPTV description, we have derived the explicit theoretical formulas of nonconjugate and conjugate cyclic autocorrelation function (CAF and spectral correlation function (SCF for OFDM and FBMC signals. According to theoretical spectral analysis, Cyclostationary Signatures (CS are artificially embedded into MCM signal and a low-complexity signature detector is, therefore, presented for detecting MCM signal. Theoretical analysis and simulation results demonstrate the efficiency and robustness of this CS detector compared to traditionary energy detector.

  5. Using amino acid correlation and community detection algorithms to identify functional determinants in protein families.

    Directory of Open Access Journals (Sweden)

    Lucas Bleicher

    Full Text Available Correlated mutation analysis has a long history of interesting applications, mostly in the detection of contact pairs in protein structures. Based on previous observations that, if properly assessed, amino acid correlation data can also provide insights about functional sub-classes in a protein family, we provide a complete framework devoted to this purpose. An amino acid specific correlation measure is proposed, which can be used to build networks summarizing all correlation and anti-correlation patterns in a protein family. These networks can be submitted to community structure detection algorithms, resulting in subsets of correlated amino acids which can be further assessed by specific parameters and procedures that provide insight into the relationship between different communities, the individual importance of community members and the adherence of a given amino acid sequence to a given community. By applying this framework to three protein families with contrasting characteristics (the Fe/Mn-superoxide dismutases, the peroxidase-catalase family and the C-type lysozyme/α-lactalbumin family, we show how our method and the proposed parameters and procedures are related to biological characteristics observed in these protein families, highlighting their potential use in protein characterization and gene annotation.

  6. Detecting long-range correlation with detrended fluctuation analysis: Application to BWR stability

    Energy Technology Data Exchange (ETDEWEB)

    Espinosa-Paredes, Gilberto [Departamento de Ingenieria de Procesos e Hidraulica, Universidad Autonoma Metropolitana-Iztapalapa, Apartado Postal 55-534, Mexico, DF 09340 (Mexico)]. E-mail: gepe@xanum.uam.mx; Alvarez-Ramirez, Jose [Departamento de Ingenieria de Procesos e Hidraulica, Universidad Autonoma Metropolitana-Iztapalapa, Apartado Postal 55-534, Mexico, DF 09340 (Mexico); Vazquez, Alejandro [Departamento de Ingenieria de Procesos e Hidraulica, Universidad Autonoma Metropolitana-Iztapalapa, Apartado Postal 55-534, Mexico, DF 09340 (Mexico)

    2006-11-15

    The aim of this paper is to explore the application of detrended fluctuation analysis (DFA) to study boiling water reactor stability. DFA is a scaling method commonly used for detecting long-range correlations in non-stationary time series. This method is based on the random walk theory and was applied to neutronic power signal of Forsmark stability benchmark. Our results shows that the scaling properties breakdown during unstable oscillations.

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

    Energy Technology Data Exchange (ETDEWEB)

    Broussard, L.J., E-mail: broussardlj@ornl.gov [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Zeck, B.A. [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); North Carolina State University, Raleigh, NC 27695 (United States); Adamek, E.R. [Indiana University, Bloomington, IN 47405 (United States); Baeßler, S. [University of Virginia, Charlottesville, VA 22904 (United States); Birge, N. [University of Tennessee, Knoxville, TN 37996 (United States); Blatnik, M. [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Cleveland State University, Cleveland, OH 44115 (United States); Bowman, J.D. [Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Brandt, A.E. [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); North Carolina State University, Raleigh, NC 27695 (United States); Brown, M. [University of Kentucky, Lexington, KY 40506 (United States); Burkhart, J. [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Callahan, N.B. [Indiana University, Bloomington, IN 47405 (United States); Clayton, S.M. [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Crawford, C. [University of Kentucky, Lexington, KY 40506 (United States); Cude-Woods, C. [North Carolina State University, Raleigh, NC 27695 (United States); Currie, S. [Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Dees, E.B. [North Carolina State University, Raleigh, NC 27695 (United States); Ding, X. [Virginia Polytechnic Institute & State University, Blacksburg, VA 24061 (United States); Fomin, N. [University of Tennessee, Knoxville, TN 37996 (United States); Frlez, E.; Fry, J. [University of Virginia, Charlottesville, VA 22904 (United States); and others

    2017-03-21

    We describe a detection system designed for precise measurements of angular correlations in neutron β decay. The system is based on thick, large area, highly segmented silicon detectors developed in collaboration with Micron Semiconductor, Ltd. The prototype system meets specifications for β electron detection with 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. Using ultracold neutrons at the Los Alamos Neutron Science Center, we have demonstrated the coincident detection of β particles and recoil protons from neutron β decay. The fully instrumented detection system will be implemented in the UCNB and Nab experiments to determine the neutron β decay parameters B, a, and b.

  8. DOM Based XSS Detecting Method Based on Phantomjs

    Science.gov (United States)

    Dong, Ri-Zhan; Ling, Jie; Liu, Yi

    Because malicious code does not appear in html source code, DOM based XSS cannot be detected by traditional methods. By analyzing the causes of DOM based XSS, this paper proposes a detection method of DOM based XSS based on phantomjs. This paper uses function hijacking to detect dangerous operation and achieves a prototype system. Comparing with existing tools shows that the system improves the detection rate and the method is effective to detect DOM based XSS.

  9. Multi-lane detection based on multiple vanishing points detection

    Science.gov (United States)

    Li, Chuanxiang; Nie, Yiming; Dai, Bin; Wu, Tao

    2015-03-01

    Lane detection plays a significant role in Advanced Driver Assistance Systems (ADAS) for intelligent vehicles. In this paper we present a multi-lane detection method based on multiple vanishing points detection. A new multi-lane model assumes that a single lane, which has two approximately parallel boundaries, may not parallel to others on road plane. Non-parallel lanes associate with different vanishing points. A biological plausibility model is used to detect multiple vanishing points and fit lane model. Experimental results show that the proposed method can detect both parallel lanes and non-parallel lanes.

  10. An Improved Wavelet‐Based Multivariable Fault Detection Scheme

    KAUST Repository

    Harrou, Fouzi

    2017-07-06

    Data observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using wavelets is a powerful feature extraction tool that is well suited to denoising and decorrelating time series data. In this chapter, we combine the advantages of multiscale partial least squares (MSPLSs) modeling with those of the univariate EWMA (exponentially weighted moving average) monitoring chart, which results in an improved fault detection system, especially for detecting small faults in highly correlated, multivariate data. Toward this end, we applied EWMA chart to the output residuals obtained from MSPLS model. It is shown through simulated distillation column data the significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional partial least square (PLS)‐based Q and EWMA methods and MSPLS‐based Q method.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  13. Potential fire detection based on Kalman-driven change detection

    CSIR Research Space (South Africa)

    Van Den Bergh, F

    2009-07-01

    Full Text Available A new active fire event detection algorithm for data collected with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, based on the extended Kalman filter, is introduced. Instead of using the observed temperatures of the spatial...

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

    Science.gov (United States)

    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.

  15. Cellular telephone-based radiation detection instrument

    Energy Technology Data Exchange (ETDEWEB)

    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.

  16. DSN Beowulf Cluster-Based VLBI Correlator

    Science.gov (United States)

    Rogstad, Stephen P.; Jongeling, Andre P.; Finley, Susan G.; White, Leslie A.; Lanyi, Gabor E.; Clark, John E.; Goodhart, Charles E.

    2009-01-01

    The NASA Deep Space Network (DSN) requires a broadband VLBI (very long baseline interferometry) correlator to process data routinely taken as part of the VLBI source Catalogue Maintenance and Enhancement task (CAT M&E) and the Time and Earth Motion Precision Observations task (TEMPO). The data provided by these measurements are a crucial ingredient in the formation of precision deep-space navigation models. In addition, a VLBI correlator is needed to provide support for other VLBI related activities for both internal and external customers. The JPL VLBI Correlator (JVC) was designed, developed, and delivered to the DSN as a successor to the legacy Block II Correlator. The JVC is a full-capability VLBI correlator that uses software processes running on multiple computers to cross-correlate two-antenna broadband noise data. Components of this new system (see Figure 1) consist of Linux PCs integrated into a Beowulf Cluster, an existing Mark5 data storage system, a RAID array, an existing software correlator package (SoftC) originally developed for Delta DOR Navigation processing, and various custom- developed software processes and scripts. Parallel processing on the JVC is achieved by assigning slave nodes of the Beowulf cluster to process separate scans in parallel until all scans have been processed. Due to the single stream sequential playback of the Mark5 data, some ramp-up time is required before all nodes can have access to required scan data. Core functions of each processing step are accomplished using optimized C programs. The coordination and execution of these programs across the cluster is accomplished using Pearl scripts, PostgreSQL commands, and a handful of miscellaneous system utilities. Mark5 data modules are loaded on Mark5 Data systems playback units, one per station. Data processing is started when the operator scans the Mark5 systems and runs a script that reads various configuration files and then creates an experiment-dependent status database

  17. Population-based screening versus case detection.

    Directory of Open Access Journals (Sweden)

    Thomas Ravi

    2002-01-01

    Full Text Available India has a large burden of blindness and population-based screening is a strategy commonly employed to detect disease and prevent morbidity. However, not all diseases are amenable to screening. This communication examines the issue of "population-based screening" versus "case detection" in the Indian scenario. Using the example of glaucoma, it demonstrates that given the poor infrastructure, for a "rare" disease, case detection is more effective than population-based screening.

  18. Correlation-based linear discriminant classification for gene expression data.

    Science.gov (United States)

    Pan, M; Zhang, J

    2017-01-23

    Microarray gene expression technology provides a systematic approach to patient classification. However, microarray data pose a great computational challenge owing to their large dimensionality, small sample sizes, and potential correlations among genes. A recent study has shown that gene-gene correlations have a positive effect on the accuracy of classification models, in contrast to some previous results. In this study, a recently developed correlation-based classifier, the ensemble of random subspace (RS) Fisher linear discriminants (FLDs), was utilized. The impact of gene-gene correlations on the performance of this classifier and other classifiers was studied using simulated datasets and real datasets. A cross-validation framework was used to evaluate the performance of each classifier using the simulated datasets or real datasets, and misclassification rates (MRs) were computed. Using the simulated data, the average MRs of the correlation-based classifiers decreased as the correlations increased when there were more correlated genes. Using real data, the correlation-based classifiers outperformed the non-correlation-based classifiers, especially when the gene-gene correlations were high. The ensemble RS-FLD classifier is a potential state-of-the-art computational method. The correlation-based ensemble RS-FLD classifier was effective and benefited from gene-gene correlations, particularly when the correlations were high.

  19. Radar-based hail detection

    Czech Academy of Sciences Publication Activity Database

    Skripniková, Kateřina; Řezáčová, Daniela

    2014-01-01

    Roč. 144, č. 1 (2014), s. 175-185 ISSN 0169-8095 R&D Projects: GA ČR(CZ) GAP209/11/2045; GA MŠk LD11044 Institutional support: RVO:68378289 Keywords : hail detection * weather radar * hail damage risk Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.844, year: 2014 http://www.sciencedirect.com/science/article/pii/S0169809513001804

  20. Audiovisual laughter detection based on temporal features

    NARCIS (Netherlands)

    Petridis, Stavros; Nijholt, Antinus; Nijholt, A.; Pantic, M.; Pantic, Maja; Poel, Mannes; Poel, M.; Hondorp, G.H.W.

    2008-01-01

    Previous research on automatic laughter detection has mainly been focused on audio-based detection. In this study we present an audiovisual approach to distinguishing laughter from speech based on temporal features and we show that the integration of audio and visual information leads to improved

  1. Pipeline leak detection and location by on-line-correlation with a process computer

    International Nuclear Information System (INIS)

    Siebert, H.; Isermann, R.

    1977-01-01

    A method for leak detection using a correlation technique in pipelines is described. For leak detection and also for leak localisation and estimation of the leak flow recursive estimation algorithms are used. The efficiency of the methods is demonstrated with a process computer and a pipeline model operating on-line. It is shown that very small leaks can be detected. (orig.) [de

  2. Estimating genetic correlations based on phenotypic data: a ...

    Indian Academy of Sciences (India)

    Knowledge of genetic correlations is essential to understand the joint evolution of traits through correlated responses to selection, a difficult and seldom, very precise task even with easy-to-breed species. Here, a simulation-based method to estimate genetic correlations and genetic covariances that relies only on ...

  3. Clusters of suicides and suicide attempts: detection, proximity and correlates.

    Science.gov (United States)

    Too, L S; Pirkis, J; Milner, A; Spittal, M J

    2017-10-01

    A suicide cluster is defined as a higher number of observed cases occurring in space and/or time than would typically be expected. Previous research has largely focused on identifying clusters of suicides, while there has been comparatively limited research on clusters of suicide attempts. We sought to identify clusters of both types of behaviour, and having done that, identify the factors that distinguish suicide attempts inside a cluster from those that were outside a cluster. We used data from Western Australia from 2000 to 2011. We defined suicide attempts as admissions to hospital for deliberate self-harm and suicides as deaths due to deliberate self-harm. Using an analytic strategy that accounted for the repetition of attempted suicide within a cluster, we performed spatial-temporal analysis using Poisson discrete scan statistics to detect clusters of suicide attempts and clusters of suicides. Logistic regression was then used to compare clustered attempts with non-clustered attempts to identify risk factors for an attempt being in a cluster. We detected 350 (1%) suicide attempts occurring within seven spatial-temporal clusters and 12 (0.6%) suicides occurring within two spatial-temporal clusters. Both of the suicide clusters were located within a larger but later suicide attempt cluster. In multivariate analysis, suicide attempts by individuals who lived in areas of low socioeconomic status had higher odds of being in a cluster than those living in areas of high socioeconomic status [odds ratio (OR) = 29.1, 95% confidence interval (CI) = 6.3-135.5]. A one percentage-point increase in the proportion of people who had changed address in the last year was associated with a 60% increase in the odds of the attempt being within a cluster (OR = 1.60, 95% CI = 1.29-1.98) and a one percentage-point increase in the proportion of Indigenous people in the area was associated with a 7% increase in the suicide being within a cluster (OR = 1.07, 95% CI = 1.00-1.13). Age

  4. Canonical correlation analysis for gene-based pleiotropy discovery.

    Directory of Open Access Journals (Sweden)

    Jose A Seoane

    2014-10-01

    Full Text Available Genome-wide association studies have identified a wealth of genetic variants involved in complex traits and multifactorial diseases. There is now considerable interest in testing variants for association with multiple phenotypes (pleiotropy and for testing multiple variants for association with a single phenotype (gene-based association tests. Such approaches can increase statistical power by combining evidence for association over multiple phenotypes or genetic variants respectively. Canonical Correlation Analysis (CCA measures the correlation between two sets of multidimensional variables, and thus offers the potential to combine these two approaches. To apply CCA, we must restrict the number of attributes relative to the number of samples. Hence we consider modules of genetic variation that can comprise a gene, a pathway or another biologically relevant grouping, and/or a set of phenotypes. In order to do this, we use an attribute selection strategy based on a binary genetic algorithm. Applied to a UK-based prospective cohort study of 4286 women (the British Women's Heart and Health Study, we find improved statistical power in the detection of previously reported genetic associations, and identify a number of novel pleiotropic associations between genetic variants and phenotypes. New discoveries include gene-based association of NSF with triglyceride levels and several genes (ACSM3, ERI2, IL18RAP, IL23RAP and NRG1 with left ventricular hypertrophy phenotypes. In multiple-phenotype analyses we find association of NRG1 with left ventricular hypertrophy phenotypes, fibrinogen and urea and pleiotropic relationships of F7 and F10 with Factor VII, Factor IX and cholesterol levels.

  5. Community-Based Intrusion Detection

    OpenAIRE

    Weigert, Stefan

    2017-01-01

    Today, virtually every company world-wide is connected to the Internet. This wide-spread connectivity has given rise to sophisticated, targeted, Internet-based attacks. For example, between 2012 and 2013 security researchers counted an average of about 74 targeted attacks per day. These attacks are motivated by economical, financial, or political interests and commonly referred to as “Advanced Persistent Threat (APT)” attacks. Unfortunately, many of these attacks are successful and the advers...

  6. Experimental detection of nonclassical correlations in mixed-state quantum computation

    International Nuclear Information System (INIS)

    Passante, G.; Moussa, O.; Trottier, D. A.; Laflamme, R.

    2011-01-01

    We report on an experiment to detect nonclassical correlations in a highly mixed state. The correlations are characterized by the quantum discord and are observed using four qubits in a liquid-state nuclear magnetic resonance quantum information processor. The state analyzed is the output of a DQC1 computation, whose input is a single quantum bit accompanied by n maximally mixed qubits. This model of computation outperforms the best known classical algorithms and, although it contains vanishing entanglement, it is known to have quantum correlations characterized by the quantum discord. This experiment detects nonvanishing quantum discord, ensuring the existence of nonclassical correlations as measured by the quantum discord.

  7. Daytime Water Detection Based on Sky Reflections

    Science.gov (United States)

    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.

  8. Optimized time alignment algorithm for LC-MS data: Correlation optimized warping using component detection algorithm-selected mass chromatograms

    NARCIS (Netherlands)

    Christin, C.; Smilde, A.K.; Hoefsloot, H.C.J.; Suits, F.; Bischoff, R.; Horvatovich, P.L.

    2008-01-01

    Correlation optimized warping (COW) based on the total ion current (TIC) is a widely used time alignment algorithm (COW-TIC). This approach works successfully on chromatograms containing few compounds and having a well-defined TIC. In this paper, we have combined COW with a component detection

  9. Optimized time alignment algorithm for LC-MS data : Correlation optimized warping using component detection algorithm-selected mass chromatograms

    NARCIS (Netherlands)

    Christin, Christin; Smilde, Age K.; Hoefsloot, Huub C. J.; Suits, Frank; Bischoff, Rainer; Horvatovich, Peter L.

    2008-01-01

    Correlation optimized warping (COW) based on the total ion current (TIC) is a widely used time alignment algorithm (COW-TIC). This approach works successfully on chromatograms containing few compounds and having a well-defined TIC. In this paper, we have combined COW with a component detection

  10. Detection of non-stationary leak signals at NPP primary circuit by cross-correlation analysis

    International Nuclear Information System (INIS)

    Shimanskij, S.B.

    2007-01-01

    A leak-detection system employing high-temperature microphones has been developed for the RBMK and ATR (Japan) reactors. Further improvement of the system focused on using cross-correlation analysis of the spectral components of the signal to detect a small leak at an early stage of development. Since envelope processes are less affected by distortions than are wave processes, they give a higher-degree of correlation and can be used to detect leaks with lower signal-noise ratios. Many simulation tests performed at nuclear power plants have shown that the proposed methods can be used to detect and find the location of a small leak [ru

  11. Visualizing confusion matrices for multidimensional signal detection correlational methods

    Science.gov (United States)

    Zhou, Yue; Wischgoll, Thomas; Blaha, Leslie M.; Smith, Ross; Vickery, Rhonda J.

    2013-12-01

    Advances in modeling and simulation for General Recognition Theory have produced more data than can be easily visualized using traditional techniques. In this area of psychological modeling, domain experts are struggling to find effective ways to compare large-scale simulation results. This paper describes methods that adapt the web-based D3 visualization framework combined with pre-processing tools to enable domain specialists to more easily interpret their data. The D3 framework utilizes Javascript and scalable vector graphics (SVG) to generate visualizations that can run readily within the web browser for domain specialists. Parallel coordinate plots and heat maps were developed for identification-confusion matrix data, and the results were shown to a GRT expert for an informal evaluation of their utility. There is a clear benefit to model interpretation from these visualizations when researchers need to interpret larger amounts of simulated data.

  12. Spike detection II: automatic, perception-based detection and clustering.

    Science.gov (United States)

    Wilson, S B; Turner, C A; Emerson, R G; Scheuer, M L

    1999-03-01

    We developed perception-based spike detection and clustering algorithms. The detection algorithm employs a novel, multiple monotonic neural network (MMNN). It is tested on two short-duration EEG databases containing 2400 spikes from 50 epilepsy patients and 10 control subjects. Previous studies are compared for database difficulty and reliability and algorithm accuracy. Automatic grouping of spikes via hierarchical clustering (using topology and morphology) is visually compared with hand marked grouping on a single record. The MMNN algorithm is found to operate close to the ability of a human expert while alleviating problems related to overtraining. The hierarchical and hand marked spike groupings are found to be strikingly similar. An automatic detection algorithm need not be as accurate as a human expert to be clinically useful. A user interface that allows the neurologist to quickly delete artifacts and determine whether there are multiple spike generators is sufficient.

  13. Power Consumption Based Android Malware Detection

    Directory of Open Access Journals (Sweden)

    Hongyu Yang

    2016-01-01

    Full Text Available In order to solve the problem that Android platform’s sand-box mechanism prevents security protection software from accessing effective information to detect malware, this paper proposes a malicious software detection method based on power consumption. Firstly, the mobile battery consumption status information was obtained, and the Gaussian mixture model (GMM was built by using Mel frequency cepstral coefficients (MFCC. Then, the GMM was used to analyze power consumption; malicious software can be classified and detected through classification processing. Experiment results demonstrate that the function of an application and its power consumption have a close relationship, and our method can detect some typical malicious application software accurately.

  14. Apriori-based network intrusion detection system

    International Nuclear Information System (INIS)

    Wang Wenjin; Liu Junrong; Liu Baoxu

    2012-01-01

    With the development of network communication technology, more and more social activities run by Internet. In the meantime, the network information security is getting increasingly serious. Intrusion Detection System (IDS) has greatly improved the general security level of whole network. But there are still many problem exists in current IDS, e.g. high leak rate detection/false alarm rates and feature library need frequently upgrade. This paper presents an association-rule based IDS. This system can detect unknown attack by generate rules from training data. Experiment in last chapter proved the system has great accuracy on unknown attack detection. (authors)

  15. Detection of gratings hidden by diffusers using photon-correlation techniques

    Science.gov (United States)

    Dainty, J. C.; Newman, D.

    1983-12-01

    Photon-correlation experiments have verified the theoretical prediction of Baltes et al. (1981) that a phase grating hidden by a diffuser can be detected by correlation measurements. It has additionally been demonstrated that a simpler method of detecting the presence of the grating, valid for arbitrarily fine diffusers, is to measure the temporal autocorrelation of the intensity of the scattered field at a single point.

  16. RANDOM FOREST BASED MISFIRE DETECTION USING KONONENKO DISCRETISER

    Directory of Open Access Journals (Sweden)

    S. Babu Devasenapati

    2012-01-01

    Full Text Available This paper evaluates the use of random forest (RF as a tool for misfire detection using statistical features. The engine block vibration contains hidden information about the events occurring inside the engine. Misfire detection was achieved by processing the vibration signals acquired from the engine using a piezoelectric accelerometer. The hidden information regarding misfire was decoded using feature extraction techniques. The effect of Kononenko based discretiser as feature size reduction tool and Correlation-based Feature Selection (CFS based feature subset selection is analysed for performance improvement in the RF model. The random forest based model is found to have a consistent high classification accuracy of around 90% when designed as a multi class ,ode and reaches 100% when the conditions are clubbed to simulate a two-class mode . From the results obtained the authors conclude that the combination of statistical features and RF algorithm is well suited for detection of misfire in spark ignition engines.

  17. A measurement-based technique for incipient anomaly detection

    KAUST Repository

    Harrou, Fouzi

    2016-06-13

    Fault detection is essential for safe operation of various engineering systems. Principal component analysis (PCA) has been widely used in monitoring highly correlated process variables. Conventional PCA-based methods, nevertheless, often fail to detect small or incipient faults. In this paper, we develop new PCA-based monitoring charts, combining PCA with multivariate memory control charts, such as the multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) monitoring schemes. The multivariate control charts with memory are sensitive to small and moderate faults in the process mean, which significantly improves the performance of PCA methods and widen their applicability in practice. Using simulated data, we demonstrate that the proposed PCA-based MEWMA and MCUSUM control charts are more effective in detecting small shifts in the mean of the multivariate process variables, and outperform the conventional PCA-based monitoring charts. © 2015 IEEE.

  18. Miniaturized Hollow-Waveguide Gas Correlation Radiometer (GCR) for Trace Gas Detection in the Martian Atmosphere

    Science.gov (United States)

    Wilson, Emily L.; Georgieva, E. M.; Melroy, H. R.

    2012-01-01

    Gas correlation radiometry (GCR) has been shown to be a sensitive and versatile method for detecting trace gases in Earth's atmosphere. Here, we present a miniaturized and simplified version of this instrument capable of mapping multiple trace gases and identifying active regions on the Mars surface. Reduction of the size and mass of the GCR instrument has been achieved by implementing a lightweight, 1 mm inner diameter hollow-core optical fiber (hollow waveguide) for the gas correlation cell. Based on a comparison with an Earth orbiting CO2 gas correlation instrument, replacement of the 10 meter mUltipass cell with hollow waveguide of equivalent pathlength reduces the cell mass from approx 150 kg to approx 0.5 kg, and reduces the volume from 1.9 m x 1.3 m x 0.86 m to a small bundle of fiber coils approximately I meter in diameter by 0.05 m in height (mass and volume reductions of >99%). This modular instrument technique can be expanded to include measurements of additional species of interest including nitrous oxide (N2O), hydrogen sulfide (H2S), methanol (CH3OH), and sulfur dioxide (SO2), as well as carbon dioxide (CO2) for a simultaneous measure of mass balance.

  19. Correlation-based nonlinear composite filters applied to image recognition

    Science.gov (United States)

    Martínez-Díaz, Saúl

    2010-08-01

    Correlation-based pattern recognition has been an area of extensive research in the past few decades. Recently, composite nonlinear correlation filters invariants to translation, rotation, and scale were proposed. The design of the filters is based on logical operations and nonlinear correlation. In this work nonlinear filters are designed and applied to non-homogeneously illuminated images acquired with an optical microscope. Images are embedded into cluttered background, non-homogeneously illuminated and corrupted by random noise, which makes difficult the recognition task. Performance of nonlinear composite filters is compared with performance of other composite correlation filters, in terms discrimination capability.

  20. Wear Detection of Drill Bit by Image-based Technique

    Science.gov (United States)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Baheza, Richard A. [Department of Biomedical Engineering and Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232-2310 (United States); Welch, E. Brian [Institute of Imaging Science and Departments of Radiology and Radiological Sciences and Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37232-2310 (United States); Gochberg, Daniel F. [Institute of Imaging Science and Departments of Radiology and Radiological Sciences, and Physics and Astronomy, Vanderbilt University, Nashville, Tennessee 37232-2310 (United States); Sanders, Melinda [Department of Pathology, Vanderbilt University, Nashville, Tennessee 37232-2310 (United States); Harvey, Sara [Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee 37232-2310 (United States); Gore, John C. [Institute of Imaging Science and Departments of Biomedical Engineering, Radiology and Radiological Sciences, Physics and Astronomy, and Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee 37232-2310 (United States); Yankeelov, Thomas E., E-mail: thomas.yankeelov@vanderbilt.edu [Institute of Imaging Science and Departments of Radiology and Radiological Sciences, Biomedical Engineering, Physics and Astronomy, and Cancer Biology, Vanderbilt University, Nashville, Tennessee 37232-2310 (United States)

    2015-03-15

    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){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 below 12

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

    Science.gov (United States)

    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

  3. Vision Based Obstacle Detection in Uav Imaging

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    J. Schneider von Deimling

    2012-03-01

    Full Text Available 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.

  5. Frontal Face Detection using Haar Wavelet Coefficients and Local Histogram Correlation

    Directory of Open Access Journals (Sweden)

    Iwan Setyawan

    2011-12-01

    Full Text Available Face detection is the main building block on which all automatic systems dealing with human faces is built. For example, a face recognition system must rely on face detection to process an input image and determine which areas contain human faces. These areas then become the input for the face recognition system for further processing. This paper presents a face detection system designed to detect frontal faces. The system uses Haar wavelet coefficients and local histogram correlation as differentiating features. Our proposed system is trained using 100 training images. Our experiments show that the proposed system performed well during testing, achieving a detection rate of 91.5%.

  6. Recent advances in biosensor based endotoxin detection.

    Science.gov (United States)

    Das, A P; Kumar, P S; Swain, S

    2014-01-15

    Endotoxins also referred to as pyrogens are chemically lipopolysaccharides habitually found in food, environment and clinical products of bacterial origin and are unavoidable ubiquitous microbiological contaminants. Pernicious issues of its contamination result in high mortality and severe morbidities. Standard traditional techniques are slow and cumbersome, highlighting the pressing need for evoking agile endotoxin detection system. The early and prompt detection of endotoxin assumes prime importance in health care, pharmacological and biomedical sectors. The unparalleled recognition abilities of LAL biosensors perched with remarkable sensitivity, high stability and reproducibility have bestowed it with persistent reliability and their possible fabrication for commercial applicability. This review paper entails an overview of various trends in current techniques available and other possible alternatives in biosensor based endotoxin detection together with its classification, epidemiological aspects, thrust areas demanding endotoxin control, commercially available detection sensors and a revolutionary unprecedented approach narrating the influence of omics for endotoxin detection. Copyright © 2013 Elsevier B.V. All rights reserved.

  7. Work hardening correlation for monotonic loading based on state variables

    International Nuclear Information System (INIS)

    Huang, F.H.; Li, C.Y.

    1977-01-01

    An absolute work hardening correlation in terms of the hardness parameter and the internal stress based on the state variable approach was developed. It was found applicable to a variety of metals and alloys. This correlation predicts strain rate insensitive work hardening properties at low homologous temperatures and produces strain rate effects at higher homologous temperatures without involving thermally induced recovery processes

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

    Science.gov (United States)

    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 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) of patients with peripheral-zone and whole-gland models, respectively, compared with ADC alone. Model-based CBS

  9. An SNMP based failure detection service

    OpenAIRE

    Wiesmann, Matthias; Urban, Peter; Defago, Xavier

    2006-01-01

    In this paper, we present the SNMP-FD system. This system is a novel failure detection service entirely based on the SNMP standard. The advantage of this approach is better interoperability, and the possibility to rely on different sources of information for failure detection, including network equipment. This, in turn, gives us more precise failure information. This paper presents the architecture of the SNMP-FD system and discusses its advantages, both from the system engineering and intero...

  10. Plagiarism Detection Based on SCAM Algorithm

    DEFF Research Database (Denmark)

    Anzelmi, Daniele; Carlone, Domenico; Rizzello, Fabio

    2011-01-01

    Plagiarism is a complex problem and considered one of the biggest in publishing of scientific, engineering and other types of documents. Plagiarism has also increased with the widespread use of the Internet as large amount of digital data is available. Plagiarism is not just direct copy but also...... paraphrasing, rewording, adapting parts, missing references or wrong citations. This makes the problem more difficult to handle adequately. Plagiarism detection techniques are applied by making a distinction between natural and programming languages. Our proposed detection process is based on natural language...... document. Our plagiarism detection system, like many Information Retrieval systems, is evaluated with metrics of precision and recall....

  11. Quantum Endpoint Detection Based on QRDA

    Science.gov (United States)

    Wang, Jian; Wang, Han; Song, Yan

    2017-10-01

    Speech recognition technology is widely used in many applications for man - machine interaction. To face more and more speech data, the computation of speech processing needs new approaches. The quantum computation is one of emerging computation technology and has been seen as useful computation model. So we focus on the basic operation of speech recognition processing, the voice activity detection, to present quantum endpoint detection algorithm. In order to achieve this algorithm, the n-bits quantum comparator circuit is given firstly. Then based on QRDA(Quantum Representation of Digital Audio), a quantum endpoint detection algorithm is presented. These quantum circuits could efficient process the audio data in quantum computer.

  12. Automatic tremor detection with a combined cross-correlation and neural network approach

    Science.gov (United States)

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

    2011-12-01

    Low-amplitude, long-duration, and ambiguous phase arrivals associated with crustal tremor make automatic detection difficult. We present a new detection method that combines cross-correlation with a neural network clustering algorithm. The approach is independent of any a priori assumptions regarding tremor event duration; instead, it examines frequency content, amplitude, and motion products of continuous data to distinguish tremor from earthquakes and background noise in an automated fashion. Because no assumptions regarding event duration are required, the clustering algorithm is therefore able to detect short, burst-like events which may be missed by many current methods. We detect roughly 130 seismic events occurring over 100 minutes, including earthquakes and tremor, in a three-week long test data set of waveforms recorded near Cholame, California. The detection has a success rate of over 90% when compared to visually selected events. We use continuous broadband data from 13 STS-2 seismometers deployed from May 2010 to July 2011 along the Cholame segment of the San Andreas Fault, as well as stations from the HRSN network. The large volume of waveforms requires first reducing the amount of data before applying the neural network algorithm. First, we filter the data between 2 Hz and 8 Hz, calculate envelopes, and decimate them to 0.2 Hz. We cross-correlate signals at each station with two master stations using a moving 520-second time window with a 5-sec time step. We calculate a mean cross-correlation coefficient value between all station pairs for each time window and each master station, and select the master station with the highest mean value. Time windows with mean coefficients exceeding 0.3 are used in the neural network approach, and windows separated by less than 300 seconds are grouped together. In the second step, we apply the neural network algorithm, i.e., Self Organized Map (SOM), to classify the reduced data set. We first calculate feature

  13. Correlation in the mechanical properties of acrylic denture base resins.

    Science.gov (United States)

    Lee, Hae-Hyoung; Lee, Chung-Jae; Asaoka, Kenzo

    2012-02-03

    The aim of the present study was to measure various mechanical properties of acrylic denture base resins, including flexural modulus, flexural strength, fracture toughness, Barcol and Vickers hardness and their related properties, and to investigate correlations between different mechanical properties. Resin specimens were prepared according to manufacturers' recommended instructions. The mechanical properties were measured under specified standards. Data from the mechanical tests were examined using correlation tests. In general, the mean results for mechanical properties of each specimen group were differently ranked depending on the tested mechanical property. The flexural modulus value showed strong or reasonable positive correlation with those of proportional limit, flexural strength, and surface hardness. In contrast, fracture toughness revealed strong negative correlations with the flexural parameters and hardness values. Results of correlation tests for the different parameters can be used for estimation of mechanical performance of acrylic denture bases in clinical situation and for quality control purposes.

  14. On event-based optical flow detection

    Directory of Open Access Journals (Sweden)

    Tobias eBrosch

    2015-04-01

    Full Text Available 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.

  15. Improved biosensor-based detection system

    DEFF Research Database (Denmark)

    2015-01-01

    Described is a new biosensor-based detection system for effector compounds, useful for in vivo applications in e.g. screening and selecting of cells which produce a small molecule effector compound or which take up a small molecule effector compound from its environment. The detection system...... comprises a protein or RNA-based biosensor for the effector compound which indirectly regulates the expression of a reporter gene via two hybrid proteins, providing for fewer false signals or less 'noise', tuning of sensitivity or other advantages over conventional systems where the biosensor directly...

  16. Image denoising based on noise detection

    Science.gov (United States)

    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.

  17. Bioaerosol standoff detection and correlation assessment with concentration and viability point sensors

    Science.gov (United States)

    Buteau, Sylvie; Simard, Jean-Robert; Rowsell, Susan; Roy, Gilles

    2010-10-01

    A standoff bioaerosol sensor based on intensified range-gated spectrometric detection of Laser Induced Fluorescence was used to spectrally characterize bioaerosol simulants during in-chamber and open-air releases at Suffield, Canada, in August 2008 from a standoff position. In total, 42 in-chamber Bacillus atrophaeus (formerly Bacillus subtilis var globigii; BG) cloud and 27 open-air releases of either BG, Pantoea agglomerans (formerly Erwinia herbicola; EH), MS2 and ovalbumin (OV) were generated. The clouds were refereed by different point sensors including Aerodynamic Particle Sizer (APS) and slit or impingers samplers. The APS monitored the particle size distribution and concentration and the samplers characterized the viable portion of the cloud. The extracted spectral signatures show robustness to different degree. The correlation assessment showed good results in most cases where the LIF signal to noise ratio was significant. The sensor 4σ sensitivity was evaluated to 1 300, 600, 100 and 30 ppl for BG, OV, MS2 and EH respectively. Correlation results are presented by plotting the SINBAHD metric versus the corresponding particle concentration, in which case, the obtained slope is proportional to the material fluorescence cross-section. The different acquired signal is hence compared in terms of their fluorescence cross-section additionally to their spectral characteristics.

  18. Compressive Sensing-Based Detection With Multimodal Dependent Data

    Science.gov (United States)

    Wimalajeewa, Thakshila; Varshney, Pramod K.

    2018-02-01

    Detection with high dimensional multimodal data is a challenging problem when there are complex inter- and intra- modal dependencies. While several approaches have been proposed for dependent data fusion (e.g., based on copula theory), their advantages come at a high price in terms of computational complexity. In this paper, we treat the detection problem with compressive sensing (CS) where compression at each sensor is achieved via low dimensional random projections. CS has recently been exploited to solve detection problems under various assumptions on the signals of interest, however, its potential for dependent data fusion has not been explored adequately. We exploit the capability of CS to capture statistical properties of uncompressed data in order to compute decision statistics for detection in the compressed domain. First, a Gaussian approximation is employed to perform likelihood ratio (LR) based detection with compressed data. In this approach, inter-modal dependence is captured via a compressed version of the covariance matrix of the concatenated (temporally and spatially) uncompressed data vector. We show that, under certain conditions, this approach with a small number of compressed measurements per node leads to enhanced performance compared to detection with uncompressed data using widely considered suboptimal approaches. Second, we develop a nonparametric approach where a decision statistic based on the second order statistics of uncompressed data is computed in the compressed domain. The second approach is promising over other related nonparametric approaches and the first approach when multimodal data is highly correlated at the expense of slightly increased computational complexity.

  19. Hyperspectral anomaly detection based on stacked denoising autoencoders

    Science.gov (United States)

    Zhao, Chunhui; Li, Xueyuan; Zhu, Haifeng

    2017-10-01

    Hyperspectral anomaly detection (AD) is an important technique of unsupervised target detection and has significance in real situations. Due to the high dimensionality of hyperspectral data, AD will be influenced by noise, nonlinear correlation of band, or other factors that lead to the decline of detection accuracy. To overcome this problem, a method of hyperspectral AD based on stacked denoising autoencoders (AE) (HADSDA) is proposed. Simultaneously, two different feature detection models, spectral feature (SF) and fused feature by clustering (FFC), are constructed to verify the effectiveness of the proposed algorithm. The SF detection model uses the SF of each pixel. The FFC detection model uses a similar set of pixels constructed by clustering and then fuses the set of pixels by the stacked denoising autoencoders algorithm (SDA). The SDA is an algorithm that can automatically learn nonlinear deep features of the image. Compared with other linear or nonlinear feature extraction methods, the detection result of the proposed algorithm is greatly improved. Experiment results show that the proposed algorithm is an excellent feature learning method and can achieve higher detection performance.

  20. Seizure detection algorithms based on EMG signals

    DEFF Research Database (Denmark)

    Conradsen, Isa

    Background: the currently used non-invasive seizure detection methods are not reliable. Muscle fibers are directly connected to the nerves, whereby electric signals are generated during activity. Therefore, an alarm system on electromyography (EMG) signals is a theoretical possibility. Objective......: to show whether medical signal processing of EMG data is feasible for detection of epileptic seizures. Methods: EMG signals during generalised seizures were recorded from 3 patients (with 20 seizures in total). Two possible medical signal processing algorithms were tested. The first algorithm was based...... the frequency-based algorithm was efficient for detecting the seizures in the third patient. Conclusion: Our results suggest that EMG signals could be used to develop an automatic seizuredetection system. However, different patients might require different types of algorithms /approaches....

  1. Tornado Detection Based on Seismic Signal.

    Science.gov (United States)

    Tatom, Frank B.; Knupp, Kevin R.; Vitton, Stanley J.

    1995-02-01

    At the present time the only generally accepted method for detecting when a tornado is on the ground is human observation. Based on theoretical considerations combined with eyewitness testimony, there is strong reason to believe that a tornado in contact with the ground transfers a significant amount of energy into the ground. The amount of energy transferred depends upon the intensity of the tornado and the characteristics of the surface. Some portion of this energy takes the form of seismic waves, both body and surface waves. Surface waves (Rayleigh and possibly Love) represent the most likely type of seismic signal to be detected. Based on the existence of such a signal, a seismic tornado detector appears conceptually possible. The major concerns for designing such a detector are range of detection and discrimination between the tornadic signal and other types of surface waves generated by ground transportation equipment, high winds, or other nontornadic sources.

  2. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Science.gov (United States)

    Zonglin, Li; Guangmin, Hu; Xingmiao, Yao; Dan, Yang

    2008-12-01

    Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation). The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  3. Burst Detection and Localization using Discrete Wavelet Transform and Cross-Correlation

    Directory of Open Access Journals (Sweden)

    Eduardo Trutié-Carrero

    2018-03-01

    Full Text Available Burst in water distribution systems causes great loss of this natural resource, interrupts the water supply, damages the streets, builds and increases the transmission of infectious diseases. In this paper we propose a new algorithm that allows the detection and automatic localization of burst in water distribution systems. As for detection, the novelty is to use the wavelet correlation criterion to compute the statistical decision and compare it with a detection threshold. The novelty in the localization is to use the statistical operator cross-correlation. The algorithm was implemented in Octave and was validated with 32 signals acquired in the laboratory in a 26.7 m long steel pipe. In 16 signals burst were triggered which were detected under a false positive probability of 2 %. No false positives were present on the 16 signals where only noise was present.

  4. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Yang Dan

    2008-12-01

    Full Text Available Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation. The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  5. Water Detection Based on Object Reflections

    Science.gov (United States)

    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.

  6. Community detection based on network communicability

    Science.gov (United States)

    Estrada, Ernesto

    2011-03-01

    We propose a new method for detecting communities based on the concept of communicability between nodes in a complex network. This method, designated as N-ComBa K-means, uses a normalized version of the adjacency matrix to build the communicability matrix and then applies K-means clustering to find the communities in a graph. We analyze how this method performs for some pathological cases found in the analysis of the detection limit of communities and propose some possible solutions on the basis of the analysis of the ratio of local to global densities in graphs. We use four different quality criteria for detecting the best clustering and compare the new approach with the Girvan-Newman algorithm for the analysis of two "classical" networks: karate club and bottlenose dolphins. Finally, we analyze the more challenging case of homogeneous networks with community structure, for which the Girvan-Newman completely fails in detecting any clustering. The N-ComBa K-means approach performs very well in these situations and we applied it to detect the community structure in an international trade network of miscellaneous manufactures of metal having these characteristics. Some final remarks about the general philosophy of community detection are also discussed.

  7. Wavelet-frame-based microcalcification detection

    Science.gov (United States)

    Chang, Charles C.; Wu, Hsien-Hsun S.; Liu, Jyh-Charn S.; Chui, Charles K.

    1997-10-01

    As the leading cause of death for adult women under 54 years of age in the United States, breast cancer accounts for 29% of all cancers in women. Early diagnosis of breast cancer is the most effective approach to reduce death rate. The rapid climbing of the health care cost further reiterates the importance of cost-effective, accurate screening tools for breast cancer. This paper proposes a wavelet frame based computer algorithm for screening of microcalcifications on digitized mammographical imagery. Despite its simplicity, the discrete wavelet transform (DWT) of compactly supported wavelets has been effectively used for detection of various types of signals. However, the shifting variant property of DWT makes it very unstable for detection of minute microcalcifications. Although increasing the sampling rate will improve the detection probability, this approach will drastically increase the size of mammographical images. The wavelet frame transform can be easily derived from the DWT algorithm by eliminating its down sampling step. The subtle difference between DWT and WF in down sampling is shown to be critical to the accuracy of microcalcifications detection. Without any down sampling, local image information at different scales is preserved. By joint thresholding of wavelet coefficients at different scales, one can accurately pin point suspected microcalcifications. A simple partitioning technique enables the detection algorithm to process image blocks independently. Four different partitioning techniques have been compared, and the method of repeating the end value on each partition boundary has the least significant impact on the detection accuracy.

  8. Estimating genetic correlations based on phenotypic data: a ...

    Indian Academy of Sciences (India)

    effects. [Zintzaras E. 2011 Estimating genetic correlations based on phenotypic data: a simulation-based method. J. Genet. 90, 51–58]. Introduction. The evolutionary ... sibs); and (iii) shared environmental effects are absent, equa- tion (1) can ..... the averages of WL and WW in each fly following Becker. (1984). Table 1 gives ...

  9. Estimating genetic correlations based on phenotypic data: a ...

    Indian Academy of Sciences (India)

    effects. [Zintzaras E. 2011 Estimating genetic correlations based on phenotypic data: a simulation-based method. J. Genet. 90, 51–58]. Introduction. The evolutionary ... Also affiliated to Institute for Clinical. Research and Health Policy Studies, Tufts Medical Center, Tufts University. School of Medicine, 800 Washington Street, ...

  10. Full waveform inversion using envelope-based global correlation norm

    Science.gov (United States)

    Oh, Ju-Won; Alkhalifah, Tariq

    2018-01-01

    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 modeled 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.

  11. Evaluation of carbon nanotube fiber microelectrodes for neurotransmitter detection: Correlation of electrochemical performance and surface properties.

    Science.gov (United States)

    Yang, Cheng; Trikantzopoulos, Elefterios; Jacobs, Christopher B; Venton, B Jill

    2017-05-01

    Fibers made of CNTs are attractive microelectrode sensors because they can be directly fabricated into microelectrodes. Different protocols for making CNT fibers have been developed, but differences in surface structure and therefore electrochemical properties that result have not been studied. In this study, we correlated the surface and electrochemical properties for neurochemical detection at 3 types of materials: CNT fibers produced by wet spinning with (1) polyethylenimine (PEI/CNT) or (2) chlorosulfonic acid (CA/CNT), and (3) CNT yarns made by solid-based CNT drawing. CNT yarns had well-aligned, high purity CNTs, abundant oxygen functional groups, and moderate surface roughness which led to the highest dopamine current density (290 ± 65 pA/cm 2 ) and fastest electron transfer kinetics. The crevices of the CNT yarn and PEI/CNT fiber microelectrodes allow dopamine to be momentarily trapped during fast-scan cyclic voltammetry detection, leading to thin-layer cell conditions and a response that was independent of applied waveform frequency. The larger crevices on the PEI/CNT fibers led to a slower time response, showing too much roughness is detrimental to fast detection. CA/CNT fibers have a smoother surface and lower currents, but their negative surface charge results in high selectivity for dopamine over uric acid or ascorbic acid. Overall, small crevices, high conductivity, and abundant oxygen groups led to high sensitivity for amine neurotransmitters, such as dopamine and serotonin. Thus, different surfaces of CNT fibers result in altered electrochemical properties and could be used in the future to predict and control electrochemical performance. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Applied Neural Cross-Correlation into the Curved Trajectory Detection Process for Braitenberg Vehicles

    OpenAIRE

    Macktoobian, Matin; Jafari, Mohammad; Gh, Erfan Attarzadeh

    2014-01-01

    Curved Trajectory Detection (CTD) process could be considered among high-level planned capabilities for cognitive agents, has which been acquired under aegis of embedded artificial spiking neuronal circuits. In this paper, hard-wired implementation of the cross-correlation, as the most common comparison-driven scheme for both natural and artificial bionic constructions named Depth Detection Module(DDM), has been taken into account. It is manifestation of efficient handling upon epileptic seiz...

  13. Optical-Correlator Neural Network Based On Neocognitron

    Science.gov (United States)

    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.

  14. DIFFERENTIAL SEARCH ALGORITHM BASED EDGE DETECTION

    Directory of Open Access Journals (Sweden)

    M. A. Gunen

    2016-06-01

    Full Text Available In this paper, a new method has been presented for the extraction of edge information by using Differential Search Optimization Algorithm. The proposed method is based on using a new heuristic image thresholding method for edge detection. The success of the proposed method has been examined on fusion of two remote sensed images. The applicability of the proposed method on edge detection and image fusion problems have been analysed in detail and the empirical results exposed that the proposed method is useful for solving the mentioned problems.

  15. Microcomputer-based video motion detection system

    International Nuclear Information System (INIS)

    Howington, L.C.

    1979-01-01

    This system was developed to enhance the volumetric intrusion detection capability of the Oak Ridge Y-12 Plant's security program. Not only does the system exhibit an extended range of detection over present infrared, microwave, and ultrasonic devices, it also provides an instantaneous assessment capability by providing the operator with a closed-circuit television (CCTV) image of the alarm scene as soon as motion is detected. The system consists of a custom-built, microcomputer-based, video processor which analyzes the signals received from a network of video cameras. The operator can view the camera images as they are displayed on a CCTV monitor while alarm scenes are displayed on a second monitor. Motion is detected by digitizing and comparing successive video frames and making an alarm decision based on the degree of mismatch. The software-based nature of the microcomputer lends a great deal of flexibility and adaptability in making the alarm decision. Alarm decision variables which are easily adjusted through software are the percent change in gray level required to label a pixel (picture element) as suspect, the number of suspect pixels required to generate an alarm, the pixel pattern to be sampled from the image, and the rate at which a new reference frame is taken. The system is currently being evaluated in a warehouse for potential application in several areas of the Plant. This paper discusses the hardware and software design of the system as well as problems encountered in its implementation and results obtained

  16. Neutron detection with water Cerenkov based detectors

    International Nuclear Information System (INIS)

    Dazeley, S.; Bernstein, A.; Bowden, N.; Carr, D.; Ouedraogo, S.; Svoboda, R.; Sweany, M.; Tripathi, M.

    2009-01-01

    Legitimate cross border trade involves the transport of an enormous number of cargo containers. Especially following the September 11 attacks, it has become an international priority to verify that these containers are not transporting Special Nuclear Material (SNM) without impeding legitimate trade. Fission events from SNM produce a number of neutrons and MeV-scale gammas correlated in time. The observation of consistent time correlations between neutrons and gammas emitted from a cargo container could, therefore, constitute a robust signature for SNM, since this time coincident signature stands out strongly against the higher rate of uncorrelated gamma-ray backgrounds from the local environment. We are developing a cost effective way to build very large neutron detectors for this purpose. We have recently completed the construction of two new water Cherenkov detectors, a 250 liter prototype and a new 4-ton detector. The 250-liter prototype uses an ultra-pure water detection medium doped with a small amount of gadolinium tri-chloride (0.2%). A 55 μCi 252 Cf neutron source was placed at a distance of 1 meter from the detector behind a 2 inch thick wall of lead. The presence of the source is easily discernible from the background in both the uncorrelated count rate and the correlated one. The 4-ton detector will shortly undergo filling and testing

  17. Frequency Based Fault Detection in Wind Turbines

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2014-01-01

    In order to obtain lower cost of energy for wind turbines fault detection and accommodation is important. Expensive condition monitoring systems are often used to monitor the condition of rotating and vibrating system parts. One example is the gearbox in a wind turbine. This system is operated...... in parallel to the control system, using different computers and additional often expensive sensors. In this paper a simple filter based algorithm is proposed to detect changes in a resonance frequency in a system, exemplified with faults resulting in changes in the resonance frequency in the wind turbine...... turbine fault detection and fault tolerant control benchmark model, in which one of the included faults results in a change in the gear box resonance frequency. This evaluation shows the potential of the proposed scheme to monitor the condition of wind turbine gear boxes in the existing control system....

  18. Intelligent data analysis based on rough correlativity matrix

    Science.gov (United States)

    Geng, Zhiqiang; Zhu, Qunxiong

    2003-09-01

    This paper proposes a new data analysis method based on rough sets by rough correlativity matrix. In rough set theory, a table called information system or database is used as a special kind of formal language to represent knowledge, a rough correlativity matrix (RCM) can be seen as an internal representation of equivalence relations. Furthermore, this paper provides a new heuristic attributes reduction algorithm based on matrix computing, such as using matrix correlative implements to replace the relations computing between sets. Finally the paper adopts information transition matrix (ITM) of information theory to represent the certainty or uncertainty decision rules based on probability theory, namely, the information matrix composed of certainty factors gives the degree of belief of decision rules, on the contrary the "invert" ITM composed of coverage factor gives the interpretation of decision rules. The result of instance analysis is shown that it is an efficient and feasible method to deal with decision information table.

  19. Water Detection Based on Color Variation

    Science.gov (United States)

    Rankin, Arturo L.

    2012-01-01

    This software has been designed to detect water bodies that are out in the open on cross-country terrain at close range (out to 30 meters), using imagery acquired from a stereo pair of color cameras mounted on a terrestrial, unmanned ground vehicle (UGV). This detector exploits the fact that the color variation across water bodies is generally larger and more uniform than that of other naturally occurring types of terrain, such as soil and vegetation. Non-traversable water bodies, such as large puddles, ponds, and lakes, are detected based on color variation, image intensity variance, image intensity gradient, size, and shape. At ranges beyond 20 meters, water bodies out in the open can be indirectly detected by detecting reflections of the sky below the horizon in color imagery. But at closer range, the color coming out of a water body dominates sky reflections, and the water cue from sky reflections is of marginal use. Since there may be times during UGV autonomous navigation when a water body does not come into a perception system s field of view until it is at close range, the ability to detect water bodies at close range is critical. Factors that influence the perceived color of a water body at close range are the amount and type of sediment in the water, the water s depth, and the angle of incidence to the water body. Developing a single model of the mixture ratio of light reflected off the water surface (to the camera) to light coming out of the water body (to the camera) for all water bodies would be fairly difficult. Instead, this software detects close water bodies based on local terrain features and the natural, uniform change in color that occurs across the surface from the leading edge to the trailing edge.

  20. Skeleton-Based Abnormal Gait Detection

    Directory of Open Access Journals (Sweden)

    Trong-Nguyen Nguyen

    2016-10-01

    Full Text Available Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal human gait based on a normal gait model. Instead of employing the color image, silhouette, or spatio-temporal volume, our model is created based on human joint positions (skeleton in time series. We decompose each sequence of normal gait images into gait cycles. Each human instant posture is represented by a feature vector which describes relationships between pairs of bone joints located in the lower body. Such vectors are then converted into codewords using a clustering technique. The normal human gait model is created based on multiple sequences of codewords corresponding to different gait cycles. In the detection stage, a gait cycle with normality likelihood below a threshold, which is determined automatically in the training step, is assumed as an anomaly. The experimental results on both marker-based mocap data and Kinect skeleton show that our method is very promising in distinguishing normal and abnormal gaits with an overall accuracy of 90.12%.

  1. Early detection of the incidence of malignancy in mammograms using digital image correlation

    International Nuclear Information System (INIS)

    Espitia, J.; Jacome, J.; Torres, C.

    2016-01-01

    The digital image correlation has proved an effective way for Pattern Recognition, this research to identify the using Findings digitally extracted from a mammographic image, which is the means used by more specialists to determine if a person is a candidate or not, a Suffer Breast Cancer. This shown that early detection of symptom logy 'carcinogenic' is the key . (Author)

  2. US correlation for MRI-detected breast lesions in women with familial risk of breast cancer.

    NARCIS (Netherlands)

    Sim, L.S.; Hendriks, J.H.C.L.; Bult, P.; Fook-Chong, S.M.

    2005-01-01

    AIM: To examine the value of US correlation for MRI-detected breast lesions in women with familial risk of breast cancer. METHODS: From an initial dataset of 245 women with positive family history who had breast cancer surveillance involving mammography or MRI between November 1994 and February

  3. Neural Correlates of Coherence-Break Detection during Reading of Narratives

    Science.gov (United States)

    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.…

  4. An FPGA-Based People Detection System

    Directory of Open Access Journals (Sweden)

    James J. Clark

    2005-05-01

    Full Text Available 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 2.5 frames per second on a Virtex-II 2V1000 using a MicroBlaze processor running at 75 MHz, communicating with dedicated hardware over FSL links.

  5. Correlation filters for object detection in nonoverlapping background noise using a noisy reference image

    Science.gov (United States)

    Aguilar-González, Pablo Mario; Kober, Vitaly

    2009-08-01

    Classical correlation filters for object detection and location estimation are designed assuming that the appearance and the shape of the target are explicitly known. In this work we assume that the target is given at unknown coordinates in a reference image corrupted by additive noise. Optimal correlation filters, with respect to signal-to-noise ratio and peak-to-output energy, for object detection and location estimation are derived. Two mathematical models of observed images are used; the additive noise model for the reference image and the non-overlapping background model for the input scene. Computer simulation results obtained with the proposed filters are presented and compared with those of common correlation filters.

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

    Science.gov (United States)

    Achaibou, Amal; Loth, Eva; Bishop, Sonia J

    2016-02-01

    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. © The Author (2015). Published by Oxford University Press.

  7. Tensor-based spatiotemporal saliency detection

    Science.gov (United States)

    Dou, Hao; Li, Bin; Deng, Qianqian; Zhang, LiRui; Pan, Zhihong; Tian, Jinwen

    2018-03-01

    This paper proposes an effective tensor-based spatiotemporal saliency computation model for saliency detection in videos. First, we construct the tensor representation of video frames. Then, the spatiotemporal saliency can be directly computed by the tensor distance between different tensors, which can preserve the complete temporal and spatial structure information of object in the spatiotemporal domain. Experimental results demonstrate that our method can achieve encouraging performance in comparison with the state-of-the-art methods.

  8. Frequency-based Vehicle Idling Detection

    OpenAIRE

    Kai-Chao Yang; Chih-Ting Kuo; Chun-Yu Chen; Chih-Chyau Yang; Chien-Ming Wu; Chun-Ming Huang

    2014-01-01

    Continuous increases in fuel prices and environmental awareness have raised the importance of reducing vehicle emissions, with many national governments passing anti-idling laws. To reduce air pollution and fuel consumption, we propose a frequency-based vehicle idling detection method to remind drivers to turn off the engine vehicle idling exceeds a certain time threshold. The method is implemented in existing handheld devices without any modification to the car or engine, making the solution...

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

    Energy Technology Data Exchange (ETDEWEB)

    Mao, Kanmi [Iowa State Univ., Ames, IA (United States)

    2011-01-01

    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 (PMLG5m$\\bar{x}$, PMLG5mm$\\bar{x}$x and SAM3) were analyzed to maximize the performance of through-bond transfer based

  10. Fluorescence quenching based alkaline phosphatase activity detection.

    Science.gov (United States)

    Mei, Yaqi; Hu, Qiong; Zhou, Baojing; Zhang, Yonghui; He, Minhui; Xu, Ting; Li, Feng; Kong, Jinming

    2018-01-01

    Simple and fast detection of alkaline phosphatase (ALP) activity is of great importance for diagnostic and analytical applications. In this work, we report a turn-off approach for the real-time detection of ALP activity on the basis of the charge transfer induced fluorescence quenching of the Cu(BCDS) 2 2- (BCDS = bathocuproine disulfonate) probe. Initially, ALP can enzymatically hydrolyze the substrate ascorbic acid 2-phosphate to release ascorbic acid (AA). Subsequently, the AA-mediated reduction of the Cu(BCDS) 2 2- probe, which displays an intense photoluminescence band at the wavelength of 402nm, leads to the static quenching of fluorescence of the probe as a result of charge transfer. The underlying mechanism of the fluorescence quenching was demonstrated by quantum mechanical calculations. The Cu(BCDS) 2 2- probe features a large Stokes shift (86nm) and is highly immune to photo bleaching. In addition, this approach is free of elaborately designed fluorescent probes and allows the detection of ALP activity in a real-time manner. Under optimal conditions, it provides a fast and sensitive detection of ALP activity within the dynamic range of 0-220mUmL -1 , with a detection limit down to 0.27mUmL -1 . Results demonstrate that it is highly selective, and applicable to the screening of ALP inhibitors in drug discovery. More importantly, it shows a good analytical performance for the direct detection of the endogenous ALP levels of undiluted human serum and even whole blood samples. Therefore, the proposed charge transfer based approach has great potential in diagnostic and analytical applications. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Smartphone based scalable reverse engineering by digital image correlation

    Science.gov (United States)

    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.

  12. Detecting initial system-environment correlations: Performance of various distance measures for quantum states

    Science.gov (United States)

    Wißmann, S.; Leggio, B.; Breuer, H.-P.

    2013-08-01

    We study the time evolution of four distance measures in the presence of initial system-environment correlations. It is well known that the trace distance between two quantum states of an open system may increase due to initial correlations, which leads to a breakdown of the contractivity of the reduced dynamics. Here we compare and analyze, for two different models, the time evolution of the trace distance, the Bures metric, the Hellinger distance, and the Jensen-Shannon divergence regarding an increase above their initial values, witnessing initial correlations. This work generalizes, deepens, and corrects a part of the study performed by Dajka [Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.84.032120 84, 032120 (2011)] and thereby reveals generic features of the considered distance measures with respect to the capability of detecting initial system-environment correlations.

  13. Electrophysiological Correlates of Change Detection during Delayed Matching Task: A Comparison of Different References

    Directory of Open Access Journals (Sweden)

    Tengfei Liang

    2017-09-01

    Full Text Available Detecting the changed information between memory representation and incoming sensory inputs is a fundamental cognitive ability. By offering the promise of excellent temporal resolution, event-related potential (ERP technique has served as a primary tool for studying this process with reference of the linked mastoid (LM. However, given that LM may distort the ERP signals, it is still undetermined whether LM is the best reference choice. The goal of the current study was to systematically compare LM, reference electrode standardization technique (REST and average reference (AR for assessing the ERP correlates of change detection during a delayed matching task. Colored shapes were adopted as materials while both the task-relevant shape feature and -irrelevant color feature could be changed. The results of the ERP amplitude showed that both of the task-relevant and -conjunction feature changes elicited significantly more positive posterior P2 in REST and AR, but not in LM. Besides, significantly increased N270 was observed in task-relevant and -conjunction feature changes in both the REST and LM, but in the conjunction feature change in AR. Only the REST-obtained N270 revealed a significant increment in task-irrelevant feature change, which was compatible with the delayed behavioral performance. Statistical parametric scalp mapping (SPSM results showed a left posterior distribution for AR, an anterior distribution for LM, and both the anterior and left posterior distributions for REST. These results indicate that different types of references may provide distinct cognitive interpretations. Interestingly, only the SPSM of REST was consistent with previous fMRI findings. Combined with the evidence of simulation studies and the current observations, we take the REST-based results as the objective one, and recommend using REST technology in the future ERP data analysis.

  14. Normalized gradient fields cross-correlation for automated detection of prostate in magnetic resonance images

    Science.gov (United States)

    Fotin, Sergei V.; Yin, Yin; Periaswamy, Senthil; Kunz, Justin; Haldankar, Hrishikesh; Muradyan, Naira; Cornud, François; Turkbey, Baris; Choyke, Peter L.

    2012-02-01

    Fully automated prostate segmentation helps to address several problems in prostate cancer diagnosis and treatment: it can assist in objective evaluation of multiparametric MR imagery, provides a prostate contour for MR-ultrasound (or CT) image fusion for computer-assisted image-guided biopsy or therapy planning, may facilitate reporting and enables direct prostate volume calculation. Among the challenges in automated analysis of MR images of the prostate are the variations of overall image intensities across scanners, the presence of nonuniform multiplicative bias field within scans and differences in acquisition setup. Furthermore, images acquired with the presence of an endorectal coil suffer from localized high-intensity artifacts at the posterior part of the prostate. In this work, a three-dimensional method for fast automated prostate detection based on normalized gradient fields cross-correlation, insensitive to intensity variations and coil-induced artifacts, is presented and evaluated. The components of the method, offline template learning and the localization algorithm, are described in detail. The method was validated on a dataset of 522 T2-weighted MR images acquired at the National Cancer Institute, USA that was split in two halves for development and testing. In addition, second dataset of 29 MR exams from Centre d'Imagerie Médicale Tourville, France were used to test the algorithm. The 95% confidence intervals for the mean Euclidean distance between automatically and manually identified prostate centroids were 4.06 +/- 0.33 mm and 3.10 +/- 0.43 mm for the first and second test datasets respectively. Moreover, the algorithm provided the centroid within the true prostate volume in 100% of images from both datasets. Obtained results demonstrate high utility of the detection method for a fully automated prostate segmentation.

  15. PET-CT detection rate of primary breast cancer lesions. Correlation with the clinicopathological factors

    International Nuclear Information System (INIS)

    Ogawa, Tomoko; Tozaki, Mitsuhiro; Fukuma, Eisuke

    2008-01-01

    One hundred and forty lesions of primary breast cancer underwent positron emission tomography (PET)-CT between June 2006 and May 2007. The PET-CT detection rate of primary breast cancer lesions was 72.1%. The detection rate was 52.1% for invasive cancer ≤20 mm, 92.8% for invasive breast cancers >20 mm, and these results were significant. In the present study, no significant relationship was observed between tumor types, however, invasive lobular carcinoma showed a lower detection rate, 58.3%. The PET-CT results were not significantly affected by either estrogen and progesterone receptors or distant metastasis. A significant correlation regarding the detection rate of PET-CT was found with HER2 status, tumor grade, and axillary lymph node status. The detection rate was 100% for invasive cancer ≤20 mm when the interval between prior diagnostic Mammotome biopsies and PET-CT was less than 3 weeks, 18.8% for invasive cancer ≤20 mm when the interval was more than 3 weeks, and these results were significant. Mammotome biopsies may therefore affect the detection rate of PET-CT. Invasive cancers ≤20 mm showed a low detection rate, therefore, it is considered to be insufficient to use PET-CT for the detection of early breast cancer. (author)

  16. QRS detection based ECG quality assessment

    International Nuclear Information System (INIS)

    Hayn, Dieter; Jammerbund, Bernhard; Schreier, Günter

    2012-01-01

    Although immediate feedback concerning ECG signal quality during recording is useful, up to now not much literature describing quality measures is available. We have implemented and evaluated four ECG quality measures. Empty lead criterion (A), spike detection criterion (B) and lead crossing point criterion (C) were calculated from basic signal properties. Measure D quantified the robustness of QRS detection when applied to the signal. An advanced Matlab-based algorithm combining all four measures and a simplified algorithm for Android platforms, excluding measure D, were developed. Both algorithms were evaluated by taking part in the Computing in Cardiology Challenge 2011. Each measure's accuracy and computing time was evaluated separately. During the challenge, the advanced algorithm correctly classified 93.3% of the ECGs in the training-set and 91.6 % in the test-set. Scores for the simplified algorithm were 0.834 in event 2 and 0.873 in event 3. Computing time for measure D was almost five times higher than for other measures. Required accuracy levels depend on the application and are related to computing time. While our simplified algorithm may be accurate for real-time feedback during ECG self-recordings, QRS detection based measures can further increase the performance if sufficient computing power is available. (paper)

  17. Seismic network based detection, classification and location of volcanic tremors

    Science.gov (United States)

    Nikolai, S.; Soubestre, J.; Seydoux, L.; de Rosny, J.; Droznin, D.; Droznina, S.; Senyukov, S.; Gordeev, E.

    2017-12-01

    Volcanic tremors constitute an important attribute of volcanic unrest in many volcanoes, and their detection and characterization is a challenging issue of volcano monitoring. The main goal of the present work is to develop a network-based method to automatically classify volcanic tremors, to locate their sources and to estimate the associated wave speed. The method is applied to four and a half years of seismic data continuously recorded by 19 permanent seismic stations in the vicinity of the Klyuchevskoy volcanic group (KVG) in Kamchatka (Russia), where five volcanoes were erupting during the considered time period. The method is based on the analysis of eigenvalues and eigenvectors of the daily array covariance matrix. As a first step, following Seydoux et al. (2016), most coherent signals corresponding to dominating tremor sources are detected based on the width of the covariance matrix eigenvalues distribution. With this approach, the volcanic tremors of the two volcanoes known as most active during the considered period, Klyuchevskoy and Tolbachik, are efficiently detected. As a next step, we consider the array covariance matrix's first eigenvectors computed every day. The main hypothesis of our analysis is that these eigenvectors represent the principal component of the daily seismic wavefield and, for days with tremor activity, characterize the dominant tremor sources. Those first eigenvectors can therefore be used as network-based fingerprints of tremor sources. A clustering process is developed to analyze this collection of first eigenvectors, using correlation coefficient as a measure of their similarity. Then, we locate tremor sources based on cross-correlations amplitudes. We characterize seven tremor sources associated with different periods of activity of four volcanoes: Tolbachik, Klyuchevskoy, Shiveluch, and Kizimen. The developed method does not require a priori knowledge, is fully automatic and the database of network-based tremor fingerprints

  18. Reset Tree-Based Optical Fault Detection

    Directory of Open Access Journals (Sweden)

    Howon Kim

    2013-05-01

    Full Text Available In this paper, we present a new reset tree-based scheme to protect cryptographic hardware against optical fault injection attacks. As one of the most powerful invasive attacks on cryptographic hardware, optical fault attacks cause semiconductors to misbehave by injecting high-energy light into a decapped integrated circuit. The contaminated result from the affected chip is then used to reveal secret information, such as a key, from the cryptographic hardware. Since the advent of such attacks, various countermeasures have been proposed. Although most of these countermeasures are strong, there is still the possibility of attack. In this paper, we present a novel optical fault detection scheme that utilizes the buffers on a circuit’s reset signal tree as a fault detection sensor. To evaluate our proposal, we model radiation-induced currents into circuit components and perform a SPICE simulation. The proposed scheme is expected to be used as a supplemental security tool.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  1. Is Host-Based Anomaly Detection + Temporal Correlation = Worm Causality

    National Research Council Canada - National Science Library

    Sekar, Vyas; Xie, Yinglian; Reiter, Michael K; Zhang, Hui

    2007-01-01

    Epidemic-spreading attacks (e.g., worm and botnet propagation) have a natural notion of attack causality - a single network flow causes a victim host to get infected and subsequently spread the attack...

  2. Sodium boiling detection in LMFBRs by acoustic-neutronic cross correlation

    International Nuclear Information System (INIS)

    Wright, S.A.

    1977-01-01

    The acoustic and neutronic noise signals caused by boiling are the signals primarily considered likely to detect sodium boiling in an LMFBR. Unfortunately, these signals may have serious signal-to-noise problems due to strong background noise sources. Neutronic-acoustic cross correlation techniques are expected to provide a means of improving the signal-to-noise ratio. This technique can improve the signal-to-noise ratio because the neutronic and acoustic signals due to boiling are highly correlated near the bubble repetition frequency, while the background noise sources are expected to be uncorrelated (or at most weakly correlated). An experiment was designed to show that the neutronic and acoustic noise signals are indeed highly correlated. The experiment consisted of simulating the void and pressure effects of local sodium boiling in the core of a zero-power reactor (ARK). The analysis showed that the neutronic and acoustic noise signals caused by boiling are almost perfectly correlated in a wide frequency band about the bubble repetition frequency. The results of the experiments were generalized to full-scale reactors to compare the inherent effectiveness of the methods which use the neutronic or acoustic signals alone with a hybrid method, which cross correlates the neutronic and acoustic signals. It was concluded that over a zone of the reactor where the void coefficient is sufficiently large (approximately 85 percent the core volume), the cross correlation method can provide a more rapid detection system for a given signal-to-noise ratio. However, where the void coefficient is small, one must probably rely on the acoustic method alone

  3. Detecting protein complexes in living cells from laser scanning confocal image sequences by the cross correlation raster image spectroscopy method.

    Science.gov (United States)

    Digman, Michelle A; Wiseman, Paul W; Horwitz, Alan R; Gratton, Enrico

    2009-01-01

    We describe a general method for detecting molecular complexes based on the analysis of single molecule fluorescence fluctuations from laser scanning confocal images. The method detects and quantifies complexes of two different fluorescent proteins noninvasively in living cells. Because in a raster scanned image successive pixels are measured at different times, the spatial correlation of the image contains information about dynamic processes occurring over a large time range, from the microseconds to seconds. The correlation of intensity fluctuations measured simultaneously in two channels detects protein complexes that carry two molecules of different colors. This information is obtained from the entire image. A map of the spatial distribution of protein complexes in the cell and their diffusion and/or binding properties can be constructed. Using this cross correlation raster image spectroscopy method, specific locations in the cell can be visualized where dynamics of binding and unbinding of fluorescent protein complexes occur. This fluctuation imaging method can be applied to commercial laser scanning microscopes thereby making it accessible to a large community of scientists.

  4. GPU Based Software Correlators - Perspectives for VLBI2010

    Science.gov (United States)

    Hobiger, Thomas; Kimura, Moritaka; Takefuji, Kazuhiro; Oyama, Tomoaki; Koyama, Yasuhiro; Kondo, Tetsuro; Gotoh, Tadahiro; Amagai, Jun

    2010-01-01

    Caused by historical separation and driven by the requirements of the PC gaming industry, Graphics Processing Units (GPUs) have evolved to massive parallel processing systems which entered the area of non-graphic related applications. Although a single processing core on the GPU is much slower and provides less functionality than its counterpart on the CPU, the huge number of these small processing entities outperforms the classical processors when the application can be parallelized. Thus, in recent years various radio astronomical projects have started to make use of this technology either to realize the correlator on this platform or to establish the post-processing pipeline with GPUs. Therefore, the feasibility of GPUs as a choice for a VLBI correlator is being investigated, including pros and cons of this technology. Additionally, a GPU based software correlator will be reviewed with respect to energy consumption/GFlop/sec and cost/GFlop/sec.

  5. Correlates of gender and achievement in introductory algebra based physics

    Science.gov (United States)

    Smith, Rachel Clara

    The field of physics is heavily male dominated in America. Thus, half of the population of our country is underrepresented and underserved. The identification of factors that contribute to gender disparity in physics is necessary for educators to address the individual needs of students, and, in particular, the separate and specific needs of female students. In an effort to determine if any correlations could be established or strengthened between sex, gender identity, social network, algebra skill, scientific reasoning ability, and/or student attitude, a study was performed on a group of 82 students in an introductory algebra based physics course. The subjects each filled out a survey at the beginning of the semester of their first semester of algebra based physics. They filled out another survey at the end of that same semester. These surveys included physics content pretests and posttests, as well as questions about the students' habits, attitudes, and social networks. Correlates of posttest score were identified, in order of significance, as pretest score, emphasis on conceptual learning, preference for male friends, number of siblings (negatively correlated), motivation in physics, algebra score, and parents' combined education level. Number of siblings was also found to negatively correlate with, in order of significance, gender identity, preference for male friends, emphasis on conceptual learning, and motivation in physics. Preference for male friends was found to correlate with, in order of significance, emphasis on conceptual learning, gender identity, and algebra score. Also, gender identity was found to correlate with emphasis on conceptual learning, the strongest predictor of posttest score other than pretest score.

  6. Global contrast based salient region detection

    KAUST Repository

    Cheng, Ming-Ming

    2011-08-25

    Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their contents, and thus remains an important step in many computer vision tasks including image segmentation, object recognition, and adaptive compression. We propose a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence. The proposed algorithm is simple, efficient, and yields full resolution saliency maps. Our algorithm consistently outperformed existing saliency detection methods, yielding higher precision and better recall rates, when evaluated using one of the largest publicly available data sets. We also demonstrate how the extracted saliency map can be used to create high quality segmentation masks for subsequent image processing.

  7. DNA & Protein detection based on microbead agglutination

    KAUST Repository

    Kodzius, Rimantas

    2012-06-06

    We report a simple and rapid room temperature assay for point-of-care (POC) testing that is based on specific agglutination. Agglutination tests are based on aggregation of microparticles in the presence of a specific analyte thus enabling the macroscopic observation. Agglutination-based tests are most often used to explore the antibody-antigen reactions. Agglutination has been used for mode protein assays using a biotin/streptavidin two-component system, as well as a hybridization based two-component assay; however, as our work shows, two-component systems are prone to self-termination of the linking analyte and thus have a lower sensitivity. Three component systems have also been used with DNA hybridization, as in our work; however, their assay requires 48 hours for incubation, while our assay is performed in 5 minutes making it a real candidate for POC testing. We demonstrate three assays: a two-component biotin/streptavidin assay, a three-component hybridization assay using single stranded DNA (ssDNA) molecules and a stepped three-component hybridization assay. The comparison of these three assays shows our simple stepped three-component agglutination assay to be rapid at room temperature and more sensitive than the two-component version by an order of magnitude. An agglutination assay was also performed in a PDMS microfluidic chip where agglutinated beads were trapped by filter columns for easy observation. We developed a rapid (5 minute) room temperature assay, which is based on microbead agglutination. Our three-component assay solves the linker self-termination issue allowing an order of magnitude increase in sensitivity over two–component assays. Our stepped version of the three-component assay solves the issue with probe site saturation thus enabling a wider range of detection. Detection of the agglutinated beads with the naked eye by trapping in microfluidic channels has been shown.

  8. Scene change detection based on multimodal integration

    Science.gov (United States)

    Zhu, Yingying; Zhou, Dongru

    2003-09-01

    Scene change detection is an essential step to automatic and content-based video indexing, retrieval and browsing. In this paper, a robust scene change detection and classification approach is presented, which analyzes audio, visual and textual sources and accounts for their inter-relations and coincidence to semantically identify and classify video scenes. Audio analysis focuses on the segmentation of audio stream into four types of semantic data such as silence, speech, music and environmental sound. Further processing on speech segments aims at locating speaker changes. Video analysis partitions visual stream into shots. Text analysis can provide a supplemental source of clues for scene classification and indexing information. We integrate the video and audio analysis results to identify video scenes and use the text information detected by the video OCR technology or derived from transcripts available to refine scene classification. Results from single source segmentation are in some cases suboptimal. By combining visual, aural features adn the accessorial text information, the scence extraction accuracy is enhanced, and more semantic segmentations are developed. Experimental results are proven to rather promising.

  9. Waveguide-Based Biosensors for Pathogen Detection

    Directory of Open Access Journals (Sweden)

    Nile Hartman

    2009-07-01

    Full Text Available Optical phenomena such as fluorescence, phosphorescence, polarization, interference and non-linearity have been extensively used for biosensing applications. Optical waveguides (both planar and fiber-optic are comprised of a material with high permittivity/high refractive index surrounded on all sides by materials with lower refractive indices, such as a substrate and the media to be sensed. This arrangement allows coupled light to propagate through the high refractive index waveguide by total internal reflection and generates an electromagnetic wave—the evanescent field—whose amplitude decreases exponentially as the distance from the surface increases. Excitation of fluorophores within the evanescent wave allows for sensitive detection while minimizing background fluorescence from complex, “dirty” biological samples. In this review, we will describe the basic principles, advantages and disadvantages of planar optical waveguide-based biodetection technologies. This discussion will include already commercialized technologies (e.g., Corning’s EPIC® Ô, SRU Biosystems’ BIND™, Zeptosense®, etc. and new technologies that are under research and development. We will also review differing assay approaches for the detection of various biomolecules, as well as the thin-film coatings that are often required for waveguide functionalization and effective detection. Finally, we will discuss reverse-symmetry waveguides, resonant waveguide grating sensors and metal-clad leaky waveguides as alternative signal transducers in optical biosensing.

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

    Science.gov (United States)

    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.

  11. Time correlation measurements from extensive air showers detected by the EEE telescopes

    CERN Document Server

    Abbrescia, M; Fabbri, F L; Gnesi, I; Bressan, E; Tosello, F; Librizzi, F; Coccia, E; Paoletti, R; Yanez, G; Li, S; Votano, L; Scribano, A; Avanzini, C; Piragino, G; Perasso, L; Regano, A; Ferroli, R Baldini; De Gruttola, D; Sartorelli, G; Siddi, E; Cifarelli, L; Di Giovanni, A; Frolov, V; Serci, S; Selvi, M; Zouyevski, R; Dreucci, M; Squarcia, S; Righini, G C; Agocs, A; Zichichi, A; La Rocca, P; Pilo, F; Miozzi, S; Massai, M; Cicalo, C; D'Incecco, M; Panareo, M; Gemme, G; Garbini, M; Aiola, S; Riggi, F; Hatzifotiadou, D; Scapparone, E; Chiavassa, A; Maggiora, A; Bencivenni, G; Gustavino, C; Spandre, G; Taiuti, M; Williams, M C S; Bossini, E; De Pasquale, S

    2013-01-01

    Time correlated events due to cosmic muons from extensive air showers have been detected by means of telescope pairs of the EEE (Extreme Energy Events) Project array. The coincidence rate, properly normalized for detector acceptance, efficiency and altitude location, has been extracted as a function of the relative distance between the telescopes. The results have been also compared with additional measurements carried out by small scintillator detectors at various distances.

  12. Spectral methods and cluster structure in correlation-based networks

    Science.gov (United States)

    Heimo, Tapio; Tibély, Gergely; Saramäki, Jari; Kaski, Kimmo; Kertész, János

    2008-10-01

    We investigate how in complex systems the eigenpairs of the matrices derived from the correlations of multichannel observations reflect the cluster structure of the underlying networks. For this we use daily return data from the NYSE and focus specifically on the spectral properties of weight W=|-δ and diffusion matrices D=W/sj-δ, where C is the correlation matrix and si=∑jW the strength of node j. The eigenvalues (and corresponding eigenvectors) of the weight matrix are ranked in descending order. As in the earlier observations, the first eigenvector stands for a measure of the market correlations. Its components are, to first approximation, equal to the strengths of the nodes and there is a second order, roughly linear, correction. The high ranking eigenvectors, excluding the highest ranking one, are usually assigned to market sectors and industrial branches. Our study shows that both for weight and diffusion matrices the eigenpair analysis is not capable of easily deducing the cluster structure of the network without a priori knowledge. In addition we have studied the clustering of stocks using the asset graph approach with and without spectrum based noise filtering. It turns out that asset graphs are quite insensitive to noise and there is no sharp percolation transition as a function of the ratio of bonds included, thus no natural threshold value for that ratio seems to exist. We suggest that these observations can be of use for other correlation based networks as well.

  13. Patterns of trading profiles at the Nordic Stock Exchange. A correlation-based approach

    International Nuclear Information System (INIS)

    Musciotto, Federico; Marotta, Luca; Miccichè, Salvatore; Piilo, Jyrki; Mantegna, Rosario N.

    2016-01-01

    We investigate the trading behavior of Finnish individual investors trading the stocks selected to compute the OMXH25 index in 2003 by tracking the individual daily investment decisions. We verify that the set of investors is a highly heterogeneous system under many aspects. We introduce a correlation based method that is able to detect a hierarchical structure of the trading profiles of heterogeneous individual investors. We verify that the detected hierarchical structure is highly overlapping with the cluster structure obtained with the approach of statistically validated networks when an appropriate threshold of the hierarchical trees is used. We also show that the combination of the correlation based method and of the statistically validated method provides a way to expand the information about the clusters of investors with similar trading profiles in a robust and reliable way.

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

    Science.gov (United States)

    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.

  15. PCR-free detection of genetically modified organisms using magnetic capture technology and fluorescence cross-correlation spectroscopy.

    Science.gov (United States)

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

    2009-11-26

    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 microg 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.

  16. PCR-free detection of genetically modified organisms using magnetic capture technology and fluorescence cross-correlation spectroscopy.

    Directory of Open Access Journals (Sweden)

    Xiaoming Zhou

    2009-11-01

    Full Text Available 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 microg 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.

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

    Science.gov (United States)

    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

  18. A buffer overflow detection based on inequalities solution

    International Nuclear Information System (INIS)

    Xu Guoai; Zhang Miao; Yang Yixian

    2007-01-01

    A new buffer overflow detection model based on Inequalities Solution was designed, which is based on analyzing disadvantage of the old buffer overflow detection technique and successfully converting buffer overflow detection to Inequalities Solution. The new model can conquer the disadvantage of the old technique and improve efficiency of buffer overflow detection. (authors)

  19. Enhancement of Iris Recognition System Based on Phase Only Correlation

    Directory of Open Access Journals (Sweden)

    Nuriza Pramita

    2011-08-01

    Full Text Available Iris recognition system is one of biometric based recognition/identification systems. Numerous techniques have been implemented to achieve a good recognition rate, including the ones based on Phase Only Correlation (POC. Significant and higher correlation peaks suggest that the system recognizes iris images of the same subject (person, while lower and unsignificant peaks correspond to recognition of those of difference subjects. Current POC methods have not investigated minimum iris point that can be used to achieve higher correlation peaks. This paper proposed a method that used only one-fourth of full normalized iris size to achieve higher (or at least the same recognition rate. Simulation on CASIA version 1.0 iris image database showed that averaged recognition rate of the proposed method achieved 67%, higher than that of using one-half (56% and full (53% iris point. Furthermore, all (100% POC peak values of the proposed method was higher than that of the method with full iris points.

  20. Smell Detection Agent Based Optimization Algorithm

    Science.gov (United States)

    Vinod Chandra, S. S.

    2016-09-01

    In this paper, a novel nature-inspired optimization algorithm has been employed and the trained behaviour of dogs in detecting smell trails is adapted into computational agents for problem solving. The algorithm involves creation of a surface with smell trails and subsequent iteration of the agents in resolving a path. This algorithm can be applied in different computational constraints that incorporate path-based problems. Implementation of the algorithm can be treated as a shortest path problem for a variety of datasets. The simulated agents have been used to evolve the shortest path between two nodes in a graph. This algorithm is useful to solve NP-hard problems that are related to path discovery. This algorithm is also useful to solve many practical optimization problems. The extensive derivation of the algorithm can be enabled to solve shortest path problems.

  1. Detecting Soft Errors in Stencil based Computations

    Energy Technology Data Exchange (ETDEWEB)

    Sharma, V. [Univ. of Utah, Salt Lake City, UT (United States); Gopalkrishnan, G. [Univ. of Utah, Salt Lake City, UT (United States); Bronevetsky, G. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-05-06

    Given the growing emphasis on system resilience, it is important to develop software-level error detectors that help trap hardware-level faults with reasonable accuracy while minimizing false alarms as well as the performance overhead introduced. We present a technique that approaches this idea by taking stencil computations as our target, and synthesizing detectors based on machine learning. In particular, we employ linear regression to generate computationally inexpensive models which form the basis for error detection. Our technique has been incorporated into a new open-source library called SORREL. In addition to reporting encouraging experimental results, we demonstrate techniques that help reduce the size of training data. We also discuss the efficacy of various detectors synthesized, as well as our future plans.

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

    Science.gov (United States)

    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

  3. Time Correlation Calculation Method Based on Delayed Coordinates

    Science.gov (United States)

    Morino, K.; Kobayashi, M. U.; Miyazaki, S.

    2009-06-01

    An approximate calculation method of time correlations by use of delayed coordinate is proposed. For a solvable piecewise linear hyperbolic chaotic map, this approximation is compared with the exact calculation, and an exponential convergence for the maximum time delay M is found. By use of this exponential convergence, the exact result for M &to ∞ is extrapolated from this approximation for the first few values of M. This extrapolation is shown to be much better than direct numerical simulations based on the definition of the time correlation function. As an application, the irregular dependence of diffusion coefficients similar to Takagi or Weierstrass functions is obtained from this approximation, which is indistinguishable from the exact result only at M = 2. The method is also applied to the dissipative Lozi and Hénon maps and the conservative standard map in order to show wide applicability.

  4. Quantum Image Encryption Algorithm Based on Image Correlation Decomposition

    Science.gov (United States)

    Hua, Tianxiang; Chen, Jiamin; Pei, Dongju; Zhang, Wenquan; Zhou, Nanrun

    2015-02-01

    A novel quantum gray-level image encryption and decryption algorithm based on image correlation decomposition is proposed. The correlation among image pixels is established by utilizing the superposition and measurement principle of quantum states. And a whole quantum image is divided into a series of sub-images. These sub-images are stored into a complete binary tree array constructed previously and then randomly performed by one of the operations of quantum random-phase gate, quantum revolving gate and Hadamard transform. The encrypted image can be obtained by superimposing the resulting sub-images with the superposition principle of quantum states. For the encryption algorithm, the keys are the parameters of random phase gate, rotation angle, binary sequence and orthonormal basis states. The security and the computational complexity of the proposed algorithm are analyzed. The proposed encryption algorithm can resist brute force attack due to its very large key space and has lower computational complexity than its classical counterparts.

  5. Concordance-based Kendall's Correlation for Computationally-Light vs. Computationally-Heavy Centrality Metrics: Lower Bound for Correlation

    Directory of Open Access Journals (Sweden)

    Natarajan Meghanathan

    2017-01-01

    Full Text Available We identify three different levels of correlation (pair-wise relative ordering, network-wide ranking and linear regression that could be assessed between a computationally-light centrality metric and a computationally-heavy centrality metric for real-world networks. The Kendall's concordance-based correlation measure could be used to quantitatively assess how well we could consider the relative ordering of two vertices vi and vj with respect to a computationally-light centrality metric as the relative ordering of the same two vertices with respect to a computationally-heavy centrality metric. We hypothesize that the pair-wise relative ordering (concordance-based assessment of the correlation between centrality metrics is the most strictest of all the three levels of correlation and claim that the Kendall's concordance-based correlation coefficient will be lower than the correlation coefficient observed with the more relaxed levels of correlation measures (linear regression-based Pearson's product-moment correlation coefficient and the network wide ranking-based Spearman's correlation coefficient. We validate our hypothesis by evaluating the three correlation coefficients between two sets of centrality metrics: the computationally-light degree and local clustering coefficient complement-based degree centrality metrics and the computationally-heavy eigenvector centrality, betweenness centrality and closeness centrality metrics for a diverse collection of 50 real-world networks.

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

    International Nuclear Information System (INIS)

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

    2014-01-01

    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. (paper)

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

    Science.gov (United States)

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

    2014-03-01

    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.

  8. Dramatyping: a generic algorithm for detecting reasonable temporal correlations between drug administration and lab value alterations

    Directory of Open Access Journals (Sweden)

    Axel Newe

    2016-03-01

    Full Text Available According to the World Health Organization, one of the criteria for the standardized assessment of case causality in adverse drug reactions is the temporal relationship between the intake of a drug and the occurrence of a reaction or a laboratory test abnormality. This article presents and describes an algorithm for the detection of a reasonable temporal correlation between the administration of a drug and the alteration of a laboratory value course. The algorithm is designed to process normalized lab values and is therefore universally applicable. It has a sensitivity of 0.932 for the detection of lab value courses that show changes in temporal correlation with the administration of a drug and it has a specificity of 0.967 for the detection of lab value courses that show no changes. Therefore, the algorithm is appropriate to screen the data of electronic health records and to support human experts in revealing adverse drug reactions. A reference implementation in Python programming language is available.

  9. Late Quaternary cryptotephra detection and correlation in loess in northeastern Japan using cummingtonite geochemistry

    Science.gov (United States)

    Matsu'ura, Tabito; Miyagi, Isoji; Furusawa, Akira

    2011-05-01

    We detected late Pleistocene cummingtonite-bearing cryptotephras in loess deposits in NE Japan and correlated them with known tephras elsewhere by using major-element compositions of the cummingtonite. This is the first time cryptotephras have been identified by analysis of a crystal phase rather than glass shards. In central NE Japan, four cummingtonite-bearing tephras, the Ichihasama pumice, the Dokusawa tephra, the Naruko-Nisaka tephra, and the Adachi-Medeshima tephra, are present in late Pleistocene loess deposits. Because the cummingtonite chemistry of each tephra is different and characteristic, it is potentially a powerful tool for detecting and identifying cryptotephras. An unidentified cummingtonite-bearing cryptotephra previously reported to be present in the late Pleistocene loess deposits at Kesennuma (Pacific coast) did not correlate with any of the known cummingtonite-bearing tephras in central NE Japan, but instead with the Numazawa-Kanayama tephra (erupted from the Numazawa caldera, southern NE Japan), although Kesennuma is well beyond the previously reported area of the distribution of the Numazawa-Kanayama tephra. Three new cummingtonite-bearing cryptotephras in the mid and late Pleistocene loess deposits (estimated to be less than 82 ka, 100-200 ka, and ca. 250 ka) on the Isawa upland were also detected.

  10. Single electron based binary multipliers with overflow detection ...

    African Journals Online (AJOL)

    electron based device. Multipliers with overflow detection based on serial and parallel prefix computation algorithm are elaborately discussed analytically and designed. The overflow detection circuits works in parallel with a simplified multiplier to ...

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

    Science.gov (United States)

    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.

  12. Rapid earthquake detection through GPU-Based template matching

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  14. Fault detection based on microseismic events

    Science.gov (United States)

    Yin, Chen

    2017-09-01

    In unconventional reservoirs, small faults allow the flow of oil and gas as well as act as obstacles to exploration; for, (1) fracturing facilitates fluid migration, (2) reservoir flooding, and (3) triggering of small earthquakes. These small faults are not generally detected because of the low seismic resolution. However, such small faults are very active and release sufficient energy to initiate a large number of microseismic events (MEs) during hydraulic fracturing. In this study, we identified microfractures (MF) from hydraulic fracturing and natural small faults based on microseismicity characteristics, such as the time-space distribution, source mechanism, magnitude, amplitude, and frequency. First, I identified the mechanism of small faults and MF by reservoir stress analysis and calibrated the ME based on the microseismic magnitude. The dynamic characteristics (frequency and amplitude) of MEs triggered by natural faults and MF were analyzed; moreover, the geometry and activity types of natural fault and MF were grouped according to the source mechanism. Finally, the differences among time-space distribution, magnitude, source mechanism, amplitude, and frequency were used to differentiate natural faults and manmade fractures.

  15. Passive and active correlation techniques for the detection of nuclear materials

    International Nuclear Information System (INIS)

    Deyglun, Clement; Carasco, Cedric; Perot, Bertrand; Eleon, Cyrille; Sannie, Guillaume; Boudergui, Karim; Corre, Gwenole; Konzdrasovs, Vladimir; Pras, Philippe

    2013-06-01

    In the frame of the French trans-governmental R and D program against CBRN-E threats, CEA (French Alternative Energies and Atomic Energy Commission) is studying the detection of Special Nuclear Materials (SNM) by neutron interrogation with the Associated Particle Technique (APT). Coincidences including at least 3 fission neutrons or gamma rays induced by tagged neutrons are used to detect and distinguish SNM from benign materials in which lower multiplicity events of 1 or 2 particles are produced by (n, 2n) or (n, n'γ) reactions. Coincidence are detected by fast plastic scintillators and correlated with tagged neutrons to improve the signal-to-noise ratio. Dedicated data acquisition electronics (DAQ) has been developed with independent FPGA cards associated to each detector, so that the acquisition window can be opened by any of the plastic scintillators. DAQ tests in passive mode are presented, in which acquisition is triggered by the sum signal of all detectors. The system time and energy calibration and resolution are reported, as well as the qualification of numerical simulations thanks to experimental data acquired with simple setups using a 252 Cf source. Numerical studies for the design and performance of cargo container inspection are also performed with the MCNP-PoliMi computer code and the ROOT data analysis package. SNM detection in iron cargo is quite straightforward but in organic matrix, data processing will need to combine more information to evidence SNM. (authors)

  16. A Correlation-Based Joint CFAR Detector Using Adaptively-Truncated Statistics in SAR Imagery.

    Science.gov (United States)

    Ai, Jiaqiu; Yang, Xuezhi; Zhou, Fang; Dong, Zhangyu; Jia, Lu; Yan, He

    2017-03-27

    Traditional constant false alarm rate (CFAR) detectors only use the contrast information between ship targets and clutter, and they suffer probability of detection (PD) degradation in multiple target situations. This paper proposes a correlation-based joint CFAR detector using adaptively-truncated statistics (hereafter called TS-2DLNCFAR) in SAR images. The proposed joint CFAR detector exploits the gray intensity correlation characteristics by building a two-dimensional (2D) joint log-normal model as the joint distribution (JPDF) of the clutter, so joint CFAR detection is realized. Inspired by the CFAR detection methodology, we design an adaptive threshold-based clutter truncation method to eliminate the high-intensity outliers, such as interfering ship targets, side-lobes, and ghosts in the background window, whereas the real clutter samples are preserved to the largest degree. A 2D joint log-normal model is accurately built using the adaptively-truncated clutter through simple parameter estimation, so the joint CFAR detection performance is greatly improved. Compared with traditional CFAR detectors, the proposed TS-2DLNCFAR detector achieves a high PD and a low false alarm rate (FAR) in multiple target situations. The superiority of the proposed TS-2DLNCFAR detector is validated on the multi-look Envisat-ASAR and TerraSAR-X data.

  17. A prototype of on-line digital flow rate meter based on cross-correlation principle

    International Nuclear Information System (INIS)

    Sun Xiaodong; Dai Zhenxi; Xu Jijun

    1997-01-01

    An on-line, digital prototype of flow rate measurement system based on cross-correlation principle is developed. Laboratory measurements using the prototype show that sufficiently large temperature fluctuations exist naturally and that measurements are possible. Temperature fluctuations are detected by two identical thermocouples spaced along the flow direction and are pre-processed by a thermocouple signal amplifier. The pre-processed temperature fluctuations are analyzed by a cross-correlator which measures the transit time of temperature fluctuations between two thermocouples directly. Thus, the so-called correlation velocity can be determined by a chip microprocessor 8031. Experimental results with single-phase under steady conditions also show that the distance between two thermocouples and the Reynolds number of fluid are the most important parameters to the measurement

  18. A family-based test for correlation between gene expression and trait values.

    Science.gov (United States)

    Kraft, Peter; Schadt, Eric; Aten, Jason; Horvath, Steve

    2003-05-01

    Advances in microarray technology have made it attractive to combine information on clinical traits, marker genotypes, and comprehensive gene expression from family studies to dissect complex disease genetics. Without accounting for family structure, methods that test for association between a trait and gene-expression levels can be misleading. We demonstrate that the standard unstratified test based on Pearson's correlation coefficient can produce spurious results when applied to family data, and we present a stratified family expression association test (FEXAT). We illustrate the utility of the FEXAT via simulation and an application to gene-expression data from lymphoblastoid cell lines from four CEPH families. The FEXAT has a smaller estimated false-discovery rate than the standard test when within-family correlations are of interest, and it detects biologically plausible correlations between beta catenin and genes in the WNT-activation pathway in humans that the standard test does not.

  19. Lesion size detection in geographic atrophy by polarization-sensitive optical coherence tomography and correlation to conventional imaging techniques.

    Science.gov (United States)

    Schütze, Christopher; Bolz, Matthias; Sayegh, Ramzi; Baumann, Bernhard; Pircher, Michael; Götzinger, Erich; Hitzenberger, Christoph K; Schmidt-Erfurth, Ursula

    2013-01-28

    To investigate the reproducibility of automated lesion size detection in patients with geographic atrophy (GA) using polarization-sensitive spectral-domain optical coherence tomography (PS-OCT) and to compare findings with scanning laser ophthalmoscopy (SLO), fundus autofluorescence (FAF), and intensity-based spectral-domain OCT (SD-OCT). Twenty-nine eyes of 22 patients with GA were examined by PS-OCT, selectively identifying the retinal pigment epithelium (RPE). A novel segmentation algorithm was applied, automatically detecting and quantifying areas of RPE atrophy. The reproducibility of the algorithm was assessed, and lesion sizes were correlated with manually delineated SLO, FAF, and intensity-based SD-OCT images to validate the clinical applicability of PS-OCT in GA evaluation. Mean GA lesion size of all patients was 5.28 mm(2) (SD: 4.92) in PS-OCT. Mean variability of individual repeatability measurements was 0.83 mm(2) (minimum: 0.05; maximum: 3.65). Mean coefficient of variation was 0.07 (min: 0.01; max: 0.19). Mean GA area in SLO (Spectralis OCT) was 5.15 mm(2) (SD: 4.72) and 2.5% smaller than in PS-OCT (P = 0.9, Pearson correlation coefficient = 0.98, P < 0.01). Mean GA area in intensity-based SD-OCT pseudo-SLO images (Cirrus OCT) was 5.14 mm(2) (SD: 4.67) and 2.7% smaller than in PS-OCT (P = 0.9, Pearson correlation coefficient = 0.98, P < 0.01). Mean GA area of all eyes measured 5.41 mm(2) (SD: 4.75) in FAF, deviating by 2.4% from PS-OCT results (P = 0.89, Pearson correlation coefficient = 0.99, P < 0.01). PS-OCT demonstrated high reproducibility of GA lesion size determination. Results correlated well with SLO, FAF, and intensity-based SD-OCT fundus imaging. PS-OCT may therefore be a valuable and specific imaging modality for automated GA lesion size determination in scientific studies and clinical practice.

  20. Parametric Roll Resonance Detection using Phase Correlation and Log-likelihood Testing Techniques

    DEFF Research Database (Denmark)

    Galeazzi, Roberto; Blanke, Mogens; Poulsen, Niels Kjølstad

    2009-01-01

    Real-time detection of parametric roll is still an open issue that is gathering an increasing attention. A first generation warning systems, based on guidelines and polar diagrams, showed their potential to face issues like long-term prediction and risk assessment. This paper presents a second ge...

  1. Research on Bridge Sensor Validation Based on Correlation in Cluster

    Directory of Open Access Journals (Sweden)

    Huang Xiaowei

    2016-01-01

    Full Text Available In order to avoid the false alarm and alarm failure caused by sensor malfunction or failure, it has been critical to diagnose the fault and analyze the failure of the sensor measuring system in major infrastructures. Based on the real time monitoring of bridges and the study on the correlation probability distribution between multisensors adopted in the fault diagnosis system, a clustering algorithm based on k-medoid is proposed, by dividing sensors of the same type into k clusters. Meanwhile, the value of k is optimized by a specially designed evaluation function. Along with the further study of the correlation of sensors within the same cluster, this paper presents the definition and corresponding calculation algorithm of the sensor’s validation. The algorithm is applied to the analysis of the sensor data from an actual health monitoring system. The result reveals that the algorithm can not only accurately measure the failure degree and orientate the malfunction in time domain but also quantitatively evaluate the performance of sensors and eliminate error of diagnosis caused by the failure of the reference sensor.

  2. Hand gesture recognition based on signals cross-correlation

    OpenAIRE

    Adda, Mo; Lekova, Anna

    2015-01-01

    Interactive gestures and body movements let us control and interact mobile devices, screens and robots. Vision-based gesture recognition systems analyze the detected infrared and visible light after converting them into some measurable signal, e.g. voltage or current. Since, infrared and visible light are electromagnetic waves (EMW) with particular wavelength between 0.4 and 1.6μm, we introduce a concept of a new kind of sensor for direct perception of EMW to see objects. We propose a novel f...

  3. Generation of interferogram for D-InSAR based on contoured correlation interferometry

    Science.gov (United States)

    Long, Xuejun; Fu, Sihua; Yu, Qifeng; Yang, Xia

    2009-10-01

    Synthetic Aperture Radar interferometry (InSAR) is a rapidly developing technique for earth observation. Differential InSAR (D-InSAR) technique, based on InSAR, is a new method for earthquake deformation detection and land subsidence monitoring. In this paper, an innovative method of generation of interferogram for D-InSAR based on contoured correlation interferometry (CCI) is presented, which may directly generate interferogram with almost no speckle noise or blurring. The data processing results of the Mani earthquake indicate that D-InSAR CCI method can effectively reduce or even remove the decorrelation noise, even in the area with serious decorrelation.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

  6. Water Pollution Detection Based on Hypothesis Testing in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xu Luo

    2017-01-01

    Full Text Available Water pollution detection is of great importance in water conservation. In this paper, the water pollution detection problems of the network and of the node in sensor networks are discussed. The detection problems in both cases of the distribution of the monitoring noise being normal and nonnormal are considered. The pollution detection problems are analyzed based on hypothesis testing theory firstly; then, the specific detection algorithms are given. Finally, two implementation examples are given to illustrate how the proposed detection methods are used in the water pollution detection in sensor networks and prove the effectiveness of the proposed detection methods.

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

    Science.gov (United States)

    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.

  8. Methods of estrus detection and correlates of the reproductive cycle in the sun bear (Helarctos malayanus).

    Science.gov (United States)

    Frederick, Cheryl; Kyes, Randall; Hunt, Kathleen; Collins, Darin; Durrant, Barbara; Wasser, Samuel K

    2010-10-15

    The objective was to explore multiple methods for detecting and characterizing the reproductive cycle of the sun bear (Helarctos malayanus). Thirteen H. m. euryspilus females, loaned from the Malaysian government to US zoos, were used. Fecal metabolite concentrations of estrogen and progesterone were compared to vaginal cytology, changes in genital appearance, and behavior (videotapes and zookeeper observations). Cytology and video behavior were characterized during five hormonally defined states: high, low, and baseline progesterone, estrus, and high estrogen. Among states, there were significant differences in cytology and behavior. Sexual, affiliative, and stereotypic behaviors were highest during estrus, whereas affiliative and social behaviors were lowest during high progesterone. In this captive breeding population, 30.8% of females cycled two or three times a year, 30.8% cycled once a year, and 38.5% did not cycle during this study. Inter-estrus intervals were (mean ± SEM) 115.7 ± 6.3 d (range, 101-131). Spearman rank correlations were significant between both ordinal sexual and affiliative behaviors and vulva swelling and color. Sexual behavior was significantly positively correlated with superficial and keratinized cells, but negatively correlated with parabasal and basophilic cells in cycling females (opposite pattern for appetitive behavior). In conclusion, data for cytology, vulva changes and behavior were consistent with, and complementary to, hormonal data; collectively, they delineated estrus and identified specific reproductive types. Copyright © 2010 Elsevier Inc. All rights reserved.

  9. Nonstationary weak signal detection based on normalization ...

    Indian Academy of Sciences (India)

    ... than the traditional stochastic resonance. The method develops the area of time-varying signal detection with stochastic resonance and presents new strategy for detection and denoising of a time-varying signal. It can be expected to be widely used in the areas of aperiodic signal processing, radar communication,etc ...

  10. Laser-Based Detection Methods for Explosives

    Science.gov (United States)

    2007-09-01

    Photofragmentation-Fragment Detection (SPF-FD) Cabalo and Sausa introduced a technique for detection of explosives with low vapor pressure called SPF-FD (149...1999, 38, 6447. 149. Cabalo , J.; Sausa, R. Appl. Spectrosc. 2003, 57, 1196. 150. Claspy, P. C.; Pao, Y.-H.; Kwong, S.; Nodov, E. IEEE J. Quant

  11. Network Anomaly Detection Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Ali A. Ghorbani

    2008-11-01

    Full Text Available Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

  12. Nuclear spin-echo detection by means of perturbed angular correlations

    International Nuclear Information System (INIS)

    Kopvillem, U.H.; Shakhmuratova, L.N.

    1973-01-01

    Recent progress in theory and experiment of NMR detection by means of registration of angular distribution of nuclear radiation (NMR/RD) has stimulated us to consider the problem of spin-echo detection by means of nuclear radiation (s-E/RD). We have considered the case when each decaying radioactive nucleus suffers two pulses of magnetic radiofrequency field in its oriented excited state. The time-differential function of radiation's angular distribution is important in the S-E/RD problem, as it displays the motion of nuclear spins in time. The obtained results show that after the first pulse the anisotropy of radiation's angular distribution strongly decreases because of the dephasing of nuclear spins. After the second pulse there exist certain moments of time, determined by the nuclear spin and geometry of experiment, when the anisotropy of radiation's angular distribution sharply increases, whereas before and after these moments the function is swept. It is possible to observe the spin-echo by means of γ-γ angular correlations and by means of registration of γ-radiation's angular distribution after the nuclear reaction, for example after the bombardment of nuclei by a pulsed particle beam. The spin-echo nuclear radiation detection gives the possibility to explore the relaxation processes by the use of a relatively small number nuclei, as it is a microscopic method. (author)

  13. Use of MR arthrography in detecting tears of the ligamentum teres with arthroscopic correlation

    Energy Technology Data Exchange (ETDEWEB)

    Chang, Connie Y.; Gill, Corey M.; Huang, Ambrose J.; Simeone, Frank J.; Torriani, Martin; Bredella, Miriam A. [Massachusetts General Hospital, Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Boston, MA (United States); McCarthy, Joseph C. [Massachusetts General Hospital, Department of Orthopedics, Boston, MA (United States)

    2014-12-20

    To demonstrate the normal appearance of the ligamentum teres on MR arthrography (MRA) and evaluate the accuracy of MRA in detecting ligamentum teres tears with arthroscopic correlation. Institutional Review Board approval was obtained with a waiver for informed consent because of the retrospective study design. A total of 165 cases in 159 patients (111 females, 48 males; mean age 41 ± 12 years) who underwent both MRA and hip arthroscopy were evaluated for appearance of the ligamentum teres, including the size, number of bundles, and ligamentum teres tears. Marrow edema of the fovea capitis adjacent to the ligamentum teres insertion and the presence of hip plicae were also recorded. The mean thickness and length of the ligamentum teres were 3.5 ± 1.5 mm and 25.2 ± 3.8 mm, respectively. Sensitivity, specificity, positive and negative predictive value, and accuracy of MRA for the detection of ligamentum teres tears were 78, 97, 74, 97, and 95 %, respectively. MRA is an accurate method to evaluate the normal morphology and to detect tears of the ligamentum teres. (orig.)

  14. Research on Fault Detection System of Power Equipment Based on UV and Infrared Image

    Science.gov (United States)

    Lu, Qiyu; Ding, Kun

    2017-09-01

    UV corona on power system can reflect the location of the fault and the severity of the fault, the traditional UV and infrared detection equipment can only use the band and the visible light band image of the power system fault detection. In this paper, a power system fault detection system based on ultraviolet and infrared dual-band images is designed. The principle of UV imaging detection and image fusion are introduced respectively. The software of the host computer is written by MFC. The software can acquire both ultraviolet and infrared, the two images are fused using the image fusion algorithm based on edge detection and cross correlation and the highest point temperature is plotted. Experiments show that the system can detect the failure of power equipment in time, and has a certain practical value, which puts forward a new idea for fault detection of power equipment.

  15. Shape based kinetic outlier detection in real-time PCR

    Directory of Open Access Journals (Sweden)

    D'Atri Mario

    2010-04-01

    Full Text Available Abstract Background Real-time PCR has recently become the technique of choice for absolute and relative nucleic acid quantification. The gold standard quantification method in real-time PCR assumes that the compared samples have similar PCR efficiency. However, many factors present in biological samples affect PCR kinetic, confounding quantification analysis. In this work we propose a new strategy to detect outlier samples, called SOD. Results Richards function was fitted on fluorescence readings to parameterize the amplification curves. There was not a significant correlation between calculated amplification parameters (plateau, slope and y-coordinate of the inflection point and the Log of input DNA demonstrating that this approach can be used to achieve a "fingerprint" for each amplification curve. To identify the outlier runs, the calculated parameters of each unknown sample were compared to those of the standard samples. When a significant underestimation of starting DNA molecules was found, due to the presence of biological inhibitors such as tannic acid, IgG or quercitin, SOD efficiently marked these amplification profiles as outliers. SOD was subsequently compared with KOD, the current approach based on PCR efficiency estimation. The data obtained showed that SOD was more sensitive than KOD, whereas SOD and KOD were equally specific. Conclusion Our results demonstrated, for the first time, that outlier detection can be based on amplification shape instead of PCR efficiency. SOD represents an improvement in real-time PCR analysis because it decreases the variance of data thus increasing the reliability of quantification.

  16. Dual channel detection of ultra low concentration of bacteria in real time by scanning fluorescence correlation spectroscopy

    Science.gov (United States)

    Altamore, Ilaria; Lanzano, Luca; Gratton, Enrico

    2013-06-01

    We describe a novel method to detect very low concentrations of bacteria in water. Our device consists of a portable horizontal geometry small confocal microscope with large pinhole and a holder for cylindrical cuvettes containing the sample. Two motors provide fast rotational and slow vertical motion of the cuvette so the device looks like a simplified flow cytometer without flow. To achieve high sensitivity, the design has two detection channels. Bacteria are stained by two different nucleic acid dyes and excited with two different lasers. Data are analyzed with a correlation filter based on particle passage pattern recognition. The passage of a particle through the illumination volume is compared with a Gaussian pattern in both channels. The width of the Gaussian correlates with the time of passage of the particle so one particle is counted when the algorithm finds a match with a Gaussian in both channels. The concentration of particles in the sample is deduced from the total number of coincident hits and the total volume scanned. This portable setup provides higher sensitivity, low-cost advantage, and it can have a wide use ranging from clinical applications to pollution monitors and water and air quality control.

  17. Dual channel detection of ultra low concentration of bacteria in real time by scanning fluorescence correlation spectroscopy

    International Nuclear Information System (INIS)

    Altamore, Ilaria; Lanzano, Luca; Gratton, Enrico

    2013-01-01

    We describe a novel method to detect very low concentrations of bacteria in water. Our device consists of a portable horizontal geometry small confocal microscope with large pinhole and a holder for cylindrical cuvettes containing the sample. Two motors provide fast rotational and slow vertical motion of the cuvette so the device looks like a simplified flow cytometer without flow. To achieve high sensitivity, the design has two detection channels. Bacteria are stained by two different nucleic acid dyes and excited with two different lasers. Data are analyzed with a correlation filter based on particle passage pattern recognition. The passage of a particle through the illumination volume is compared with a Gaussian pattern in both channels. The width of the Gaussian correlates with the time of passage of the particle so one particle is counted when the algorithm finds a match with a Gaussian in both channels. The concentration of particles in the sample is deduced from the total number of coincident hits and the total volume scanned. This portable setup provides higher sensitivity, low-cost advantage, and it can have a wide use ranging from clinical applications to pollution monitors and water and air quality control. (paper)

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

    Science.gov (United States)

    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.

  19. Miniaturized Gas Correlation Radiometer for the Detection of Trace Gases in the Martian Atmosphere

    Science.gov (United States)

    Melroy, H.; Wilson, E. L.; Georgieva, E.

    2012-12-01

    We present a miniaturized and simplified version of a gas correlation radiometer (GCR) capable of simultaneously mapping multiple trace gases and identifying active regions on the Mars surface. Gas correlation radiometry (GCR) has been shown to be a sensitive and versatile method for detecting trace gases in Earth's atmosphere. Reduction of the size and mass of the GCR was achieved by implementing compact, light-weight 1 mm inner diameter hollow-core optical fibers (hollow waveguides) as the gas correlation cells. In a comparison with an Earth orbiting CO2 GCR instrument, exchanging the 10 m multipass cells with hollow waveguide gas correlation cells of equivalent pathlength reduces the mass from ~150 kg to ~0.5 kg, and reduces the volume from 1.9 m x 1.3 m x 0.86 m to a small bundle of fiber coils approximately 1 meter in diameter by 0.05 m in height (mass and volume reductions of >99%). A unique feature of this instrument is its stackable module design, with a single module for each trace gas. Each of the modules is self-contained, and fundamentally identical; differing by the bandpass filter wavelength range and gas mixtures inside the hollow-waveguide absorption cells. The current configuration contains four stacked modules for simultaneous measurements of methane (CH4), formaldehyde (H2CO), water vapor (H2O), and deuterated water vapor (HDO) but could easily be expanded to include measurements of additional species of interest including nitrous oxide (N2O), hydrogen sulfide (H2S), methanol (CH3OH), and sulfur dioxide (SO2), as well as carbon dioxide (CO2) for a simultaneous measure of mass balance. Preliminary results indicate that a 1 ppb detection limit is possible for both formaldehyde and methane with one second of averaging. Using non-optimized components, we have demonstrated an instrument sensitivity equivalent to ~30 ppb for formaldehyde, and ~500 ppb for methane. We expect custom bandpass filters and 6 m long waveguides to significantly improve these

  20. Anomica: Fast Support Vector Based Novelty Detection

    Data.gov (United States)

    National Aeronautics and Space Administration — In this paper we propose ν-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the...

  1. Correlation of Dual Colour Single Particle Trajectories for Improved Detection and Analysis of Interactions in Living Cells

    Directory of Open Access Journals (Sweden)

    Kevin Braeckmans

    2013-08-01

    Full Text Available Interactions between objects inside living cells are often investigated by looking for colocalization between fluorescence microscopy images that are recorded in separate colours corresponding to the fluorescent label of each object. The fundamental limitation of this approach in the case of dynamic objects is that coincidental colocalization cannot be distinguished from true interaction. Instead, correlation between motion trajectories obtained by dual colour single particle tracking provides a much stronger indication of interaction. However, frequently occurring phenomena in living cells, such as immobile phases or transient interactions, can limit the correlation to small parts of the trajectories. The method presented here, developed for the detection of interaction, is based on the correlation inside a window that is scanned along the trajectories, covering different subsets of the positions. This scanning window method was validated by simulations and, as an experimental proof of concept, it was applied to the investigation of the intracellular trafficking of polymeric gene complexes by endosomes in living retinal pigment epithelium cells, which is of interest to ocular gene therapy.

  2. Correlation of immunosuppression scheme with renal graft complications detected by dynamic renal scintigraphy

    International Nuclear Information System (INIS)

    Martins, Flavia Paiva Proenca; Gutfilen, Bianca

    2001-01-01

    Dynamic renal scintigraphy allows the diagnosis of complications in patients submitted to organ transplantation, such as perfusion abnormalities, acute tubular necrosis and rejection. In this study we employed 99m Tc-DTPA scintigraphy to study patients submitted to kidney transplantation. The results obtained and the clinical findings were conjunctively analyzed in order to detect graft rejection or other complications. The type of immunosuppressive scheme used was also correlated with the observed complications. Fifty-five patients submitted to kidney transplantation from 1989 to 1999 were evaluated. All patients with nephrotoxicity received a 3-drug immunosuppressive scheme. In this study, acute rejection was the most frequent complication (40.4%) observed following transplantation. Thirteen of 15 recipients of cadaveric kidney grafts presented acute tubular necrosis. Only one false-positive case was observed when scintigraphy and clinical findings were not concordant. We suggest carrying out renal scintigraphy to follow-up post-transplantation patients. (author)

  3. Automated EEG detection algorithms and clinical semiology in epilepsy: importance of correlations.

    Science.gov (United States)

    Hogan, R Edward

    2011-12-01

    With advances in technological innovation, electroencephalography has remained the gold standard for classification and localization of epileptic seizures. Like other diagnostic modalities, technological advances have opened new avenues for assessment of data, and hold great promise to improve interpretive capabilities. However, proper overall interpretation and application of electroencephalographic findings relies on valid correlations of associated clinical semiology. This article addresses interpretation of clinical signs and symptoms in the context of the diagnostic predictive value of electroencephalographic, clinical, and electrographic definitions of seizures, and upcoming challenges of interpreting intracranial high-frequency electroencephalographic data. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction. Copyright © 2011 Elsevier Inc. All rights reserved.

  4. A Correlated Active Acoustic Leak Detection in a SFR Steam Generator

    International Nuclear Information System (INIS)

    Kim, Tae Joon; Jeong, Ji Young; Kim, Jong Man; Kim, Byung Ho; Kim, Yong Il

    2009-01-01

    The methods of acoustic leak detection are active acoustic leak detection and passive acoustic leak detection. The methods for passive acoustic leak detection are already established, but because our goal is development of passive acoustic leak detection for detecting a leakage range of small and micro leak rates, it is difficult detecting a leak in steam generator using this developed passive acoustic leak detection. Thus the acoustic leak detection system is required to be able to detect wide range of water leaks. From this view point we need to develop an active acoustic leak detection technology to be able to detect intermediate leak rates

  5. Methodology for correlations between doses and detectability in standard mammographic images: application in Sao Paulo state

    International Nuclear Information System (INIS)

    Furquim, Tania Aparecida Correia

    2005-01-01

    Measurements using mammography units were performed in loco in 50 health establishments, randomly sampled from an equipment list of the Cadastro Nacional de Estabelecimentos de Saude (Health Establishments Brazilian Catalog). For the measurements six phantoms were utilized to establish different quality criteria and to evaluate doses in different breast thicknesses. Two different methods of measuring average glandular doses (AGD) were applied, and measurements of entrance surface doses (ESD) were also realized, in order to obtain mean values to Sao Paulo State. A study relating distribution and properties of different mammography trademarks with doses was performed. The sensitometry of processors allowed a quantification of the film-processing contrast index, A g , establishing a state mean value. The phantom images allowed the evaluation of detection limits of structures as microcalcifications, fibers, and masses, and state mean values were established for: spatial resolution (on surface and glandular breast position); image contrast; and detection expert ability from phantom images in two situations: before knowing the image targets and after viewing of a target map. Then, the results were compared to target detections in laboratory environment. Based on dose results, A g , image contrast, maximum contrast, and detection ratio, a relationship between them was determined. The results show that, in Sao Paulo State, mean glandular doses were lower than reference levels considering the Wu method, and close to or above reference levels for ail phantoms considering the Dance method. The ESD was always close to or above reference levels. The A g presented a mean value of (10,42 ± 0,20) for Sao Paulo State, and the image contrast was lower than the required limits established by the phantom manufacturers. The high contrast resolution showed that mammography units presented the expected values of line pair per mm in the State. The detectability evaluation of local

  6. Cellular telephone-based wide-area radiation detection network

    Energy Technology Data Exchange (ETDEWEB)

    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.

  7. Spin correlation and entanglement detection in Cooper pair splitters by current measurements using magnetic detectors

    Science.gov (United States)

    Busz, Piotr; Tomaszewski, Damian; Martinek, Jan

    2017-08-01

    We analyze a model of a double quantum dot Cooper pair splitter coupled to two ferromagnetic detectors and demonstrate the possibility of determination of spin correlation by current measurements. We use perturbation theory, taking account of the exchange interaction with the detectors, which leads to complex spin dynamics in the dots. This affects the measured spin and restricts the use of ferromagnetic detectors to the nonlinear current-voltage characteristic regime at the current plateau, where the relevant spin projection is conserved, in contrast to the linear current-voltage characteristic regime, in which the spin information is distorted. Moreover, we show that for separable states the spin correlation can only be determined in a limited parameter regime, much more restricted than in the case of entangled states. We propose an entanglement test based on the Bell inequality.

  8. An Entropy-Based Network Anomaly Detection Method

    Directory of Open Access Journals (Sweden)

    Przemysław Bereziński

    2015-04-01

    Full Text Available Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i preparation of a concept of original entropy-based network anomaly detection method, (ii implementation of the method, (iii preparation of original dataset, (iv evaluation of the method.

  9. Vision-based vehicle detection and tracking algorithm design

    Science.gov (United States)

    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.

  10. Digital image correlation based on a fast convolution strategy

    Science.gov (United States)

    Yuan, Yuan; Zhan, Qin; Xiong, Chunyang; Huang, Jianyong

    2017-10-01

    In recent years, the efficiency of digital image correlation (DIC) methods has attracted increasing attention because of its increasing importance for many engineering applications. Based on the classical affine optical flow (AOF) algorithm and the well-established inverse compositional Gauss-Newton algorithm, which is essentially a natural extension of the AOF algorithm under a nonlinear iterative framework, this paper develops a set of fast convolution-based DIC algorithms for high-efficiency subpixel image registration. Using a well-developed fast convolution technique, the set of algorithms establishes a series of global data tables (GDTs) over the digital images, which allows the reduction of the computational complexity of DIC significantly. Using the pre-calculated GDTs, the subpixel registration calculations can be implemented efficiently in a look-up-table fashion. Both numerical simulation and experimental verification indicate that the set of algorithms significantly enhances the computational efficiency of DIC, especially in the case of a dense data sampling for the digital images. Because the GDTs need to be computed only once, the algorithms are also suitable for efficiently coping with image sequences that record the time-varying dynamics of specimen deformations.

  11. Nanopore-Based Target Sequence Detection.

    Directory of Open Access Journals (Sweden)

    Trevor J Morin

    Full Text Available The promise of portable diagnostic devices relies on three basic requirements: comparable sensitivity to established platforms, inexpensive manufacturing and cost of operations, and the ability to survive rugged field conditions. Solid state nanopores can meet all these requirements, but to achieve high manufacturing yields at low costs, assays must be tolerant to fabrication imperfections and to nanopore enlargement during operation. This paper presents a model for molecular engineering techniques that meets these goals with the aim of detecting target sequences within DNA. In contrast to methods that require precise geometries, we demonstrate detection using a range of pore geometries. As a result, our assay model tolerates any pore-forming method and in-situ pore enlargement. Using peptide nucleic acid (PNA probes modified for conjugation with synthetic bulk-adding molecules, pores ranging 15-50 nm in diameter are shown to detect individual PNA-bound DNA. Detection of the CFTRΔF508 gene mutation, a codon deletion responsible for ∼66% of all cystic fibrosis chromosomes, is demonstrated with a 26-36 nm pore size range by using a size-enhanced PNA probe. A mathematical framework for assessing the statistical significance of detection is also presented.

  12. Traffic Sign Recognition System based on Cambridge Correlator Image Comparator

    Directory of Open Access Journals (Sweden)

    J. Turan

    2012-06-01

    Full Text Available Paper presents basic information about application of Optical Correlator (OC, specifically Cambridge Correlator, in system to recognize of traffic sign. Traffic Sign Recognition System consists of three main blocks, Preprocessing, Optical Correlator and Traffic Sign Identification. The Region of Interest (ROI is defined and chosen in preprocessing block and then goes to Optical Correlator, where is compared with database of Traffic Sign. Output of Optical Correlation is correlation plane, which consist of highly localized intensities, know as correlation peaks. The intensity of spots provides a measure of similarity and position of spots, how images (traffic signs are relatively aligned in the input scene. Several experiments have been done with proposed system and results and conclusion are discussed.

  13. 3D Detection, Quantification and Correlation of Slope Failures with Geologic Structure in the Mont Blanc massif

    Science.gov (United States)

    Allan, Mark; Dunning, Stuart; Lim, Michael; Woodward, John

    2016-04-01

    A thorough understanding of supply from landslides and knowledge of their spatial distribution is of fundamental importance to high-mountain sediment budgets. Advances in 3D data acquisition techniques are heralding new opportunities to create high-resolution topographic models to aid our understanding of landscape change through time. In this study, we use a Structure-from-Motion Multi-View Stereo (SfM-MVS) approach to detect and quantify slope failures at selected sites in the Mont Blanc massif. Past and present glaciations along with its topographical characteristics have resulted in a high rate of geomorphological activity within the range. Data for SfM-MVS processing were captured across variable temporal scales to examine short-term (daily), seasonal and annual change from terrestrial, Unmanned Aerial Vehicle (UAV) and helicopter perspectives. Variable spatial scales were also examined ranging from small focussed slopes (~0.01 km2) to large valley-scale surveys (~3 km2). Alignment and registration were conducted using a series of Ground Control Points (GCPs) across the surveyed slope at various heights and slope aspects. GCPs were also used to optimise data and reduce non-linear distortions. 3D differencing was performed using a multiscale model-to-model comparison algorithm (M3C2) which uses variable thresholding across each slope based on local surface roughness and model alignment quality. Detected change was correlated with local slope structure and 3D discontinuity analysis was undertaken using a plane-detection and clustering approach (DSE). Computation of joint spacing was performed using the classified data and normal distances. Structural analysis allowed us to assign a Slope Mass Rating (SMR) and assess the stability of each slope relative to the detected change and determine likely failure modes. We demonstrate an entirely 3D workflow which preserves the complexity of alpine slope topography to compute volumetric loss using a variable threshold. A

  14. Adaptive skin detection based on online training

    Science.gov (United States)

    Zhang, Ming; Tang, Liang; Zhou, Jie; Rong, Gang

    2007-11-01

    Skin is a widely used cue for porn image classification. Most conventional methods are off-line training schemes. They usually use a fixed boundary to segment skin regions in the images and are effective only in restricted conditions: e.g. good lightness and unique human race. This paper presents an adaptive online training scheme for skin detection which can handle these tough cases. In our approach, skin detection is considered as a classification problem on Gaussian mixture model. For each image, human face is detected and the face color is used to establish a primary estimation of skin color distribution. Then an adaptive online training algorithm is used to find the real boundary between skin color and background color in current image. Experimental results on 450 images showed that the proposed method is more robust in general situations than the conventional ones.

  15. Laser spot detection based on reaction diffusion

    Czech Academy of Sciences Publication Activity Database

    Vázquez-Otero, Alejandro; Khikhlukha, Danila; Solano-Altamirano, J. M.; Dormido, R.; Duro, N.

    2016-01-01

    Roč. 16, č. 3 (2016), s. 1-11, č. článku 315. ISSN 1424-8220 R&D Projects: GA MŠk EF15_008/0000162 Grant - others:ELI Beamlines(XE) CZ.02.1.01/0.0/0.0/15_008/0000162 Institutional support: RVO:68378271 Keywords : laser spot detection * laser beam detection * reaction diffusion models * Fitzhugh-Nagumo model * reaction diffusion computation * Turing patterns Subject RIV: BL - Plasma and Gas Discharge Physics OBOR OECD: Fluids and plasma physics (including surface physics) Impact factor: 2.677, year: 2016

  16. A moving target detecting and tracking system based on DSP

    Science.gov (United States)

    Cai, Daonan; Zhao, Yuejin; Liu, Ming; Dong, Liquan; Liu, Xiaohua

    2018-01-01

    In order to solve the target fast tracking problem in embedded system, a moving target detecting and tracking algorithm based on a combination of three-frame difference and template matching is proposed. The system utilizes DSP to design a set of image processing equipment and DSP uses TI company's DM6437.Three-frame difference can detect a initial position of the target, then Mean Normalized Product Correlation(NNPROD) template matching algorithm was utilized in a partial area to achieve a precise position and reduce the amount of calculation. The algorithm utilized four templates and image compression to fit pose and scale changes when moving. To meet the real-time requirement, an improved algorithm of NNPROD was proposed under certain lighting conditions, what ' s more the C language code was optimized and TI company's highly optimized VLIB vision library was reasonably utilized. After several tests, the results showed that NNPROD can fit the changing of environmental light well, but more time was needed. The improved method can still work well with the changes of pose and scale when the light changes less intensely , and the processing speed of the improved method increased from the previous 11F / s to 23F / s.

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

    Science.gov (United States)

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

    2018-03-01

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

  18. Code-Aided Estimation and Detection on Time-Varying Correlated Mimo Channels: A Factor Graph Approach

    Directory of Open Access Journals (Sweden)

    Simoens Frederik

    2006-01-01

    Full Text Available This paper concerns channel tracking in a multiantenna context for correlated flat-fading channels obeying a Gauss-Markov model. It is known that data-aided tracking of fast-fading channels requires a lot of pilot symbols in order to achieve sufficient accuracy, and hence decreases the spectral efficiency. To overcome this problem, we design a code-aided estimation scheme which exploits information from both the pilot symbols and the unknown coded data symbols. The algorithm is derived based on a factor graph representation of the system and application of the sum-product algorithm. The sum-product algorithm reveals how soft information from the decoder should be exploited for the purpose of estimation and how the information bits can be detected. Simulation results illustrate the effectiveness of our approach.

  19. Contour Detection Operators Based on Surround Inhibition

    NARCIS (Netherlands)

    Grigorescu, Cosmin; Petkov, Nicolai; Westenberg, Michel A.

    2003-01-01

    We propose a biologically motivated computational step, called non-classical receptive field (non-CRF) inhibition, to improve contour detection in images of natural scenes. We augment a Gabor energy operator with non-CRF inhibition. The resulting contour operator responds strongly to isolated lines,

  20. Nonstationary weak signal detection based on normalization ...

    Indian Academy of Sciences (India)

    Haibin Zhang

    Time-varying signal; weak signal detection; varying parameters; stochastic resonance. 1. Introduction. In general view, noise ..... the numerical solution for the typical first-order differential equation as Eq. (2). The discrete fourth-rank Runge–Kutta method [27] as follows is applied to solve the equation numerically. x. 0 ¼ dx dt.

  1. Endogenous brain-machine interface based on the correlation of EEG maps.

    Science.gov (United States)

    Ubeda, Andrés; Iáñez, Eduardo; Azorín, José M; Perez-Vidal, Carlos

    2013-11-01

    In this paper, a non-invasive endogenous brain-machine interface (BMI) based on the correlation of EEG maps has been developed to work in real-time applications. The classifier is able to detect two mental tasks related to motor imagery with good success rates and stability. The BMI has been tested with four able-bodied volunteers. First, the users performed a training with visual feedback to adjust the classifier. Afterwards, the users carried out several trajectories in a visual interface controlling the cursor position with the BMI. In these tests, score and accuracy were measured. The results showed that the participants were able to follow the targets during the performed trajectory, proving that the EEG mapping correlation classifier is ready to work in more complex real-time applications aimed at helping people with a severe disability in their daily life. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

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

    Science.gov (United States)

    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.

  3. Research and Design on Trigger System Based on Acoustic Delay Correlation Filtering

    Directory of Open Access Journals (Sweden)

    Zhiyong Lei

    2014-01-01

    Full Text Available In the exterior trajectory test, there usually needs a muzzle or a gun muzzle trigger system to be used as start signal for other measuring device, the customary trigger systems include off- target, infrared and acoustic detection system. But inherent echo reflection of the acoustic detection system makes the original signal of sound trigger submerged in various echo interference for bursts and shooting in a closed room, so that it can’t produce accurate trigger. In order to solve this defect, this paper analyzed the mathematical model based on acoustic delay correlation filtering in detail, then put forward the constraint condition with minimum path for delay correlation filtering. In this constraint condition, delay correlation filtering can do de-noising operation accurately. In order to verify accuracy and actual performance of the model, a MEMS sound sensor was used to implement mathematical model onto project, experimental results show that this system can filter out the every path sound bounce echoes of muzzle shock wave signal and produce the desired trigger signal accurately.

  4. A Case Study Correlating Innovative Gamma Ray Scanning Detection Systems Data to Surface Soil Gamma Spectrometry Results - 13580

    International Nuclear Information System (INIS)

    Thompson, Shannon; Rodriguez, Rene; Billock, Paul; Lit, Peter

    2013-01-01

    HydroGeoLogic (HGL), Inc. completed a United States Environmental Protection Agency (USEPA) study to characterize radiological contamination at a site near Canoga Park, California. The characterized area contained 470 acres including the site of a prototype commercial nuclear reactor and other nuclear design, testing, and support operations from the 1950's until 1988 [1]. The site history included radiological releases during operation followed by D and D activities. The characterization was conducted under an accelerated schedule and the results will support the project remediation. The project has a rigorous cleanup to background agenda and does not allow for comparison to risk-based guidelines. To target soil sample locations, multiple lines of evidence were evaluated including a gamma radiation survey, geophysical surveys, historical site assessment, aerial photographs, and former worker interviews. Due to the time since production and decay, the primary gamma emitting radionuclide remaining is cesium-137 (Cs-137). The gamma ray survey covered diverse, rugged terrain using custom designed sodium iodide thallium-activated (NaI(Tl)) scintillation detection systems. The survey goals included attaining 100% ground surface coverage and detecting gamma radiation as sensitively as possible. The effectiveness of innovative gamma ray detection systems was tested by correlating field Cs-137 static count ratios to Cs-137 laboratory gamma spectrometry results. As a case study, the area encompassing the former location of the first nuclear power station in the U. S. was scanned, and second by second global positioning system (GPS)-linked gamma spectral data were evaluated by examining total count rate and nuclide-specific regions of interest. To compensate for Compton scattering from higher energy naturally occurring radionuclides (U-238, Th-232 and their progeny, and K-40), count rate ratios of anthropogenic nuclide-specific regions of interest to the total count rate were

  5. Memory detection 2.0: the first web-based memory detection test.

    Science.gov (United States)

    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.

  6. Memory detection 2.0: the first web-based memory detection test.

    Directory of Open Access Journals (Sweden)

    Bennett Kleinberg

    Full Text Available 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.

  7. A study of combined evaluation of suppliers based on correlation

    Directory of Open Access Journals (Sweden)

    Heting Qiu

    2013-03-01

    Full Text Available Purpose: The Selection of logistics service providers is an important issue in supply chain management. But different evaluation methods may lead to different results, which could cause inconsistent conclusions. This paper makes use of a new perspective to combine with a variety of methods to eliminate the deviation of different single evaluation methods. Design/methodology/approach: This paper expounds the application of the combined evaluation method based on correlation. Entropy method, factor analysis, grey colligation evaluation and AHP have been used for research. Findings: According to the evaluate result, the ranking of suppliers obtained by each method have obvious differences. The result shows that combined evaluation method can eliminate the deviation of different single evaluation methods. Originality/value: The combined evaluation method makes up for the defects of single evaluation methods and obtains a result that is more stable and creditable with smaller deviation. This study can provide the enterprise leaders with more scientific method to select their cooperative companies. 

  8. Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rupinder Singh

    2017-01-01

    Full Text Available In this paper, an Advanced Hybrid Intrusion Detection System (AHIDS that automatically detects the WSNs attacks is proposed. AHIDS makes use of cluster-based architecture with enhanced LEACH protocol that intends to reduce the level of energy consumption by the sensor nodes. AHIDS uses anomaly detection and misuse detection based on fuzzy rule sets along with the Multilayer Perceptron Neural Network. The Feed Forward Neural Network along with the Backpropagation Neural Network are utilized to integrate the detection results and indicate the different types of attackers (i.e., Sybil attack, wormhole attack, and hello flood attack. For detection of Sybil attack, Advanced Sybil Attack Detection Algorithm is developed while the detection of wormhole attack is done by Wormhole Resistant Hybrid Technique. The detection of hello flood attack is done by using signal strength and distance. An experimental analysis is carried out in a set of nodes; 13.33% of the nodes are determined as misbehaving nodes, which classified attackers along with a detection rate of the true positive rate and false positive rate. Sybil attack is detected at a rate of 99,40%; hello flood attack has a detection rate of 98, 20%; and wormhole attack has a detection rate of 99, 20%.

  9. Anomaly-based Network Intrusion Detection Methods

    Directory of Open Access Journals (Sweden)

    Pavel Nevlud

    2013-01-01

    Full Text Available The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization of our results. The WEKA is a collection of machine learning algorithms for data mining tasks.

  10. Cable-Based Water Leak Detection Technology

    OpenAIRE

    ECT Team, Purdue

    2007-01-01

    Water leaks can be considered as a serious problem from many sources such as water supply and return chains, air conditioning units, cold-water chillers, clogged drains, damaged skylights or windows, or even construction errors. The new water leak detection technologies can provide significant advantages in cost, reliability, and easy adoption have continued since the traditional technology mainly focusing on a spot detector revealed several limitations.

  11. Carbon-Based Electrodes for Parabens Detection

    OpenAIRE

    Aniela Pop; Ianina Birsan; Corina Orha; Rodica Pode; Florica Manea

    2016-01-01

    Carbon nanofiber-epoxy composite electrode has been investigated through voltammetric and amperometric techniques in order to detect parabens from aqueous solutions. The occurrence into environment as emerging pollutants of these preservative compounds has been extensively studied in the last decades, and consequently, a rapid and reliable method for their quantitative quantification is required. In this study, methylparaben (MP) and propylparaben (PP) were chosen as representatives for parab...

  12. Parkinson's disease detection based on dysphonia measurements

    Science.gov (United States)

    Lahmiri, Salim

    2017-04-01

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

  13. Daytime Water Detection Based on Color Variation

    Science.gov (United States)

    Rankin, Arturo L.; Matthies, Larry H.

    2010-01-01

    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 (such as ponds). At far range, reflections of the sky provide a strong cue for water. But at close range, the color coming out of a water body dominates sky reflections and the water cue from sky reflections is of marginal use. We model this behavior by using water body intensity data from multiple frames of RGB imagery to estimate the total reflection coefficient contribution from surface reflections and the combination of all other factors. Then we describe an algorithm that uses one of the color cameras in a forward- looking, UGV-mounted stereo-vision perception system to detect water bodies in wide open areas. This detector exploits the knowledge that the change in saturation-to-brightness ratio across a water body from the leading to trailing edge is uniform and distinct from other terrain types. In test sequences approaching a pond under clear, overcast, and cloudy sky conditions, the true positive and false negative water detection rates were (95.76%, 96.71%, 98.77%) and (0.45%, 0.60%, 0.62%), respectively. This software has been integrated on an experimental unmanned vehicle and field tested at Ft. Indiantown Gap, PA.

  14. Memory detection 2.0: The first web-based memory detection test

    NARCIS (Netherlands)

    Kleinberg, B.; Verschuere, B.

    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

  15. Principal Component Analysis Based Two-Dimensional (PCA-2D) Correlation Spectroscopy: PCA Denoising for 2D Correlation Spectroscopy

    International Nuclear Information System (INIS)

    Jung, Young Mee

    2003-01-01

    Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra

  16. Dynamically reconfigurable multiple beam illumination based on optical correlation

    DEFF Research Database (Denmark)

    Glückstad, Jesper; Palima, Darwin; Dam, Jeppe Seidelin

    2009-01-01

    We adapt concepts from optical correlation and optical pattern recognition to propose a method for generating reconfigurable multiple spots with high efficiency. The generated spots correspond to the correlation spikes in optical pattern recognition. In pattern recognition, optimizing...... reconfigurable optical patterns with high efficiency for optical micromanipulation and other applications....

  17. An Immunity-Based Anomaly Detection System with Sensor Agents

    Directory of Open Access Journals (Sweden)

    Yoshiteru Ishida

    2009-11-01

    Full Text Available 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.

  18. An immunity-based anomaly detection system with sensor agents.

    Science.gov (United States)

    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.

  19. Suspicious amorphous microcalcifications detected on full-field digital mammography: correlation with histopathology

    Directory of Open Access Journals (Sweden)

    Vera Christina Camargo de Siqueira Ferreira

    2018-03-01

    Full Text Available Abstract Objective: To evaluate suspicious amorphous calcifications diagnosed on full-field digital mammography (FFDM and establish correlations with histopathology findings. Materials and Methods: This was a retrospective study of 78 suspicious amorphous calcifications (all classified as BI-RADS® 4 detected on FFDM. Vacuum-assisted breast biopsy (VABB was performed. The histopathological classification of VABB core samples was as follows: pB2 (benign; pB3 (uncertain malignant potential; pB4 (suspicion of malignancy; and pB5 (malignant. Treatment was recommended for pB5 lesions. To rule out malignancy, surgical excision was recommended for pB3 and pB4 lesions. Patients not submitted to surgery were followed for at least 6 months. Results: Among the 78 amorphous calcifications evaluated, the histopathological analysis indicated that 8 (10.3% were malignant/suspicious (6 classified as pB5 and 2 classified as pB4 and 36 (46.2% were benign (classified as pB2. The remaining 34 lesions (43.6% were classified as pB3: 33.3% were precursor lesions (atypical ductal hyperplasia, lobular neoplasia, or flat epithelial atypia and 10.3% were high-risk lesions. For the pB3 lesions, the underestimation rate was zero. Conclusion: The diagnosis of precursor lesions (excluding atypical ductal hyperplasia, which can be pB4 depending on the severity and extent of the lesion should not necessarily be considered indicative of underestimation of malignancy. Suspicious amorphous calcifications correlated more often with precursor lesions than with malignant lesions, at a ratio of 3:1.

  20. The Proof of Statistical Criteria for Hardware Virtualization-Based Rootkits Detection in Computer Systems

    Directory of Open Access Journals (Sweden)

    I. Y. Korkin

    2012-06-01

    Full Text Available This article is devoted to hardware virtualization-based rootkits (HVBR detection. A statistical criterion of HVBR presence has been suggested. The criterion has been proved for theoretical correlations of processor instructions execution latency. In this paper variation series, the 2nd and 4th order moments and line of instructions execution latency have been applied.

  1. Automatic hearing loss detection system based on auditory brainstem response

    International Nuclear Information System (INIS)

    Aldonate, J; Mercuri, C; Reta, J; Biurrun, J; Bonell, C; Gentiletti, G; Escobar, S; Acevedo, R

    2007-01-01

    Hearing loss is one of the pathologies with the highest prevalence in newborns. If it is not detected in time, it can affect the nervous system and cause problems in speech, language and cognitive development. The recommended methods for early detection are based on otoacoustic emissions (OAE) and/or auditory brainstem response (ABR). In this work, the design and implementation of an automated system based on ABR to detect hearing loss in newborns is presented. Preliminary evaluation in adults was satisfactory

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

    Science.gov (United States)

    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.

  3. Correlation between theoretical anatomical patterns of lymphatic drainage and lymphoscintigraphy findings during sentinel node detection in head and neck melanomas

    Energy Technology Data Exchange (ETDEWEB)

    Vidal, Monica; Ruiz, Diana Milena [Hospital Clinic de Barcelona, Nuclear Medicine Department, Barcelona (Spain); Vidal-Sicart, Sergi; Paredes, Pilar; Pons, Francesca [Hospital Clinic de Barcelona, Nuclear Medicine Department, Barcelona (Spain); Institut d' Investigacions Biomediques Agusti Pi i Sunyer (IDIBAPS), Barcelona (Spain); Torres, Ferran [Hospital Clinic Barcelona, Statistical of Biostatistics and Data Management Core Facility, IDIBAPS, Barcelona (Spain); Universitat Autonoma de Barcelona, Biostatistics Unit, Faculty of Medicine, Barcelona (Spain)

    2016-04-15

    In the diagnosis of head and neck melanoma, lymphatic drainage is complex and highly variable. As regional lymph node metastasis is one of the most important prognostic factors, lymphoscintigraphy can help map individual drainage patterns. The aim of this study was to compare the results of lymphoscintigraphy and sentinel lymph node (SLN) detection with theoretical anatomical patterns of lymphatic drainage based on the location of the primary tumour lesion in patients with head and neck melanoma. We also determined the percentage of discrepancies between our lymphoscintigraphy and the theoretical location of nodal drainage predicted by a large lymphoscintigraphic database, in order to explain recurrence and false-negative SLN biopsies. In this retrospective study of 152 patients with head and neck melanoma, the locations of the SLNs on lymphoscintigraphy and detected intraoperatively were compared with the lymphatic drainage predicted by on-line software based on a large melanoma database. All patients showed lymphatic drainage and in all patients at least one SLN was identified by lymphoscintigraphy. Of the 152 patients, 4 had a primary lesion in areas that were not described in the Sydney Melanoma Unit database, so agreement could only be evaluated in 148 patients. Agreement between lymphoscintigraphic findings and the theoretical lymphatic drainage predicted by the software was completely concordant in 119 of the 148 patients (80.4 %, 95 % CI 73.3 - 86 %). However, this concordance was partial (some concordant nodes and others not) in 18 patients (12.2 %, 95 % CI 7.8 - 18.4 %). Discordance was complete in 11 patients (7.4 %, 95 % CI 4.2 - 12.8 %). In melanoma of the head and neck there is a high correlation between lymphatic drainage found by lymphoscintigraphy and the predicted drainage pattern and basins provided by a large reference database. Due to unpredictable drainage, preoperative lymphoscintigraphy is essential to accurately detect the SLNs in head and

  4. Neutron Detection with Water Cerenkov Based Detectors

    Energy Technology Data Exchange (ETDEWEB)

    Dazeley, S; Bernstein, A; Bowden, N; Carr, D; Ouedraogo, S; Svoboda, R; Sweany, M; Tripathi, M

    2009-05-13

    Legitimate cross border trade involves the transport of an enormous number of cargo containers. Especially following the September 11 attacks, it has become an international priority to verify that these containers are not transporting Special Nuclear Material (SNM) without impeding legitimate trade. Fission events from SNM produce a number of neutrons and MeV-scale gammas correlated in time. The observation of consistent time correlations between neutrons and gammas emitted from a cargo container could, therefore, constitute a robust signature for SNM, since this time coincident signature stands out strongly against the higher rate of uncorrelated gamma-ray backgrounds from the local environment. We are developing a cost effective way to build very large neutron detectors for this purpose. We have recently completed the construction of two new water Cherenkov detectors, a 250 liter prototype and a new 4 ton detector. We present both the results from our prototype detector and an update on the newly commissioned large detector. We will also present pictures from the construction and outline our future detector development plans.

  5. DNA based methods used for characterization and detection of food ...

    African Journals Online (AJOL)

    Detection of food borne pathogen is of outmost importance in the food industries and related agencies. For the last few decades conventional methods were used to detect food borne pathogens based on phenotypic characters. At the advent of complementary base pairing and amplification of DNA, the diagnosis of food ...

  6. Poseidon: A 2-tier Anomaly-based Intrusion Detection System

    NARCIS (Netherlands)

    Bolzoni, D.; Zambon, Emmanuele; Etalle, Sandro; Hartel, Pieter H.

    2005-01-01

    We present Poseidon, a new anomaly based intrusion detection system. Poseidon is payload-based, and presents a two-tier architecture: the first stage consists of a Self-Organizing Map, while the second one is a modified PAYL system. Our benchmarks on the 1999 DARPA data set show a higher detection

  7. Neural correlates of own name and own face detection in autism spectrum disorder.

    Directory of Open Access Journals (Sweden)

    Hanna B Cygan

    Full Text Available Autism spectrum disorder (ASD is a heterogeneous neurodevelopmental condition clinically characterized by social interaction and communication difficulties. To date, the majority of research efforts have focused on brain mechanisms underlying the deficits in interpersonal social cognition associated with ASD. Recent empirical and theoretical work has begun to reveal evidence for a reduced or even absent self-preference effect in patients with ASD. One may hypothesize that this is related to the impaired attentional processing of self-referential stimuli. The aim of our study was to test this hypothesis. We investigated the neural correlates of face and name detection in ASD. Four categories of face/name stimuli were used: own, close-other, famous, and unknown. Event-related potentials were recorded from 62 electrodes in 23 subjects with ASD and 23 matched control subjects. P100, N170, and P300 components were analyzed. The control group clearly showed a significant self-preference effect: higher P300 amplitude to the presentation of own face and own name than to the close-other, famous, and unknown categories, indicating preferential attentional engagement in processing of self-related information. In contrast, detection of both own and close-other's face and name in the ASD group was associated with enhanced P300, suggesting similar attention allocation for self and close-other related information. These findings suggest that attention allocation in the ASD group is modulated by the personal significance factor, and that the self-preference effect is absent if self is compared to close-other. These effects are similar for physical and non-physical aspects of the autistic self. In addition, lateralization of face and name processing is attenuated in ASD, suggesting atypical brain organization.

  8. Neural correlates of own name and own face detection in autism spectrum disorder.

    Science.gov (United States)

    Cygan, Hanna B; Tacikowski, Pawel; Ostaszewski, Pawel; Chojnicka, Izabela; Nowicka, Anna

    2014-01-01

    Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition clinically characterized by social interaction and communication difficulties. To date, the majority of research efforts have focused on brain mechanisms underlying the deficits in interpersonal social cognition associated with ASD. Recent empirical and theoretical work has begun to reveal evidence for a reduced or even absent self-preference effect in patients with ASD. One may hypothesize that this is related to the impaired attentional processing of self-referential stimuli. The aim of our study was to test this hypothesis. We investigated the neural correlates of face and name detection in ASD. Four categories of face/name stimuli were used: own, close-other, famous, and unknown. Event-related potentials were recorded from 62 electrodes in 23 subjects with ASD and 23 matched control subjects. P100, N170, and P300 components were analyzed. The control group clearly showed a significant self-preference effect: higher P300 amplitude to the presentation of own face and own name than to the close-other, famous, and unknown categories, indicating preferential attentional engagement in processing of self-related information. In contrast, detection of both own and close-other's face and name in the ASD group was associated with enhanced P300, suggesting similar attention allocation for self and close-other related information. These findings suggest that attention allocation in the ASD group is modulated by the personal significance factor, and that the self-preference effect is absent if self is compared to close-other. These effects are similar for physical and non-physical aspects of the autistic self. In addition, lateralization of face and name processing is attenuated in ASD, suggesting atypical brain organization.

  9. Correlations Between Life-Detection Techniques and Implications for Sampling Site Selection in Planetary Analog Missions

    Science.gov (United States)

    Gentry, Diana M.; Amador, Elena S.; Cable, Morgan L.; Chaudry, Nosheen; Cullen, Thomas; Jacobsen, Malene B.; Murukesan, Gayathri; Schwieterman, Edward W.; Stevens, Adam H.; Stockton, Amanda; Tan, George; Yin, Chang; Cullen, David C.; Geppert, Wolf

    2017-10-01

    We conducted an analog sampling expedition under simulated mission constraints to areas dominated by basaltic tephra of the Eldfell and Fimmvörðuháls lava fields (Iceland). Sites were selected to be "homogeneous" at a coarse remote sensing resolution (10-100 m) in apparent color, morphology, moisture, and grain size, with best-effort realism in numbers of locations and replicates. Three different biomarker assays (counting of nucleic-acid-stained cells via fluorescent microscopy, a luciferin/luciferase assay for adenosine triphosphate, and quantitative polymerase chain reaction (qPCR) to detect DNA associated with bacteria, archaea, and fungi) were characterized at four nested spatial scales (1 m, 10 m, 100 m, and >1 km) by using five common metrics for sample site representativeness (sample mean variance, group F tests, pairwise t tests, and the distribution-free rank sum H and u tests). Correlations between all assays were characterized with Spearman's rank test. The bioluminescence assay showed the most variance across the sites, followed by qPCR for bacterial and archaeal DNA; these results could not be considered representative at the finest resolution tested (1 m). Cell concentration and fungal DNA also had significant local variation, but they were homogeneous over scales of >1 km. These results show that the selection of life detection assays and the number, distribution, and location of sampling sites in a low biomass environment with limited a priori characterization can yield both contrasting and complementary results, and that their interdependence must be given due consideration to maximize science return in future biomarker sampling expeditions.

  10. Detection and localization of deep endometriosis by means of MRI and correlation with the ENZIAN score

    International Nuclear Information System (INIS)

    Di Paola, V.; Manfredi, R.; Castelli, F.; Negrelli, R.; Mehrabi, S.; Pozzi Mucelli, R.

    2015-01-01

    %, 95%, 99%, 86%, respectively. The highest accuracy was for adenomyosis (100%) and endometriosis of utero-sacral ligaments (USLs) (98%), slightly lower for vagina-rectovaginal septum an colo-rectal walls (96%), and the lowest for bladder endometriosis (92%). The concordance between histopathological and MRI ENZIAN score was excellent (k = 0.824); in particular it was 0.812 for lesions in vagina-rectovaginal space, 0.890 for lesions in USL, 0.822 for lesions in rectum–sigmoid colon, 1.000 for uterine adenomyosis, and 0.367 for lesions located in the bladder wall. Conclusion: MRI correlates with the ENZIAN score and has an accuracy of 95% in the detection and localization of deep endometriosis, allowing to minimize false negative results (4%) in patients with deep endometriosis and to obtain a correct preoperative staging

  11. Detection and localization of deep endometriosis by means of MRI and correlation with the ENZIAN score

    Energy Technology Data Exchange (ETDEWEB)

    Di Paola, V., E-mail: dipaola.valerio@libero.it; Manfredi, R.; Castelli, F.; Negrelli, R.; Mehrabi, S.; Pozzi Mucelli, R.

    2015-04-15

    %, 95%, 99%, 86%, respectively. The highest accuracy was for adenomyosis (100%) and endometriosis of utero-sacral ligaments (USLs) (98%), slightly lower for vagina-rectovaginal septum an colo-rectal walls (96%), and the lowest for bladder endometriosis (92%). The concordance between histopathological and MRI ENZIAN score was excellent (k = 0.824); in particular it was 0.812 for lesions in vagina-rectovaginal space, 0.890 for lesions in USL, 0.822 for lesions in rectum–sigmoid colon, 1.000 for uterine adenomyosis, and 0.367 for lesions located in the bladder wall. Conclusion: MRI correlates with the ENZIAN score and has an accuracy of 95% in the detection and localization of deep endometriosis, allowing to minimize false negative results (4%) in patients with deep endometriosis and to obtain a correct preoperative staging.

  12. A review and development of correlations for base pressure and base heating in supersonic flow

    Energy Technology Data Exchange (ETDEWEB)

    Lamb, J.P. [Texas Univ., Austin, TX (United States). Dept. of Mechanical Engineering; Oberkampf, W.L. [Sandia National Labs., Albuquerque, NM (United States)

    1993-11-01

    A comprehensive review of experimental base pressure and base heating data related to supersonic and hypersonic flight vehicles has been completed. Particular attention was paid to free-flight data as well as wind tunnel data for models without rear sting support. Using theoretically based correlation parameters, a series of internally consistent, empirical prediction equations has been developed for planar and axisymmetric geometries (wedges, cones, and cylinders). These equations encompass the speed range from low supersonic to hypersonic flow and laminar and turbulent forebody boundary layers. A wide range of cone and wedge angles and cone bluntness ratios was included in the data base used to develop the correlations. The present investigation also included preliminary studies of the effect of angle of attack and specific-heat ratio of the gas.

  13. Correlative Analysis of GRBs detected by Swift and Suzaku-WAM

    International Nuclear Information System (INIS)

    Krimm, Hans; Yamaoka, Kazutaka; Sugita, Satoshi; Ohno, Masanori; Tashiro, Makoto; Onda, Kaori; Sato, Goro; Sakamoto, Takanori

    2008-01-01

    Since most gamma-ray bursts (GRBs) have a peak energy (Epeak) above the energy range (15-150 keV) of the Burst Alert Telescope (BAT) on Swift, a full understanding of the prompt emission from Swift GRBs requires spectral fits over as broad an energy range as possible. This can be done for bursts which are simultaneously detected by Swift BAT and the Suzaku Wide-band All-Sky Monitor (WAM), which covers the energy range from 50-5000 keV. Since the launch of Suzaku in July 2005, there have been 33 gamma-ray bursts (GRBs) which have triggered both Swift and WAM. A joint BAT-WAM team has cross-calibrated the two instruments using GRBs, and we are now able to perform joint fits on these bursts to determine spectral parameters including Epeak. The results of broad spectral fits allows us to understand the distribution of Epeak for Swift bursts and to calibrate Epeak estimators when Epeak is within the BAT energy range. For those bursts with spectroscopic redshifts, we can calculate the isotropic energy and study various correlations between Epeak and other global burst parameters. Here we present preliminary results of joint Swift/BAT-Suzaku/WAM spectral fits

  14. Functional cine MR imaging for the detection and mapping of intraabdominal adhesions: method and surgical correlation

    Energy Technology Data Exchange (ETDEWEB)

    Buhmann-Kirchhoff, Sonja; Reiser, Maximilian; Lienemann, Andreas [University Hospital Grosshadern, Ludwig-Maximilians-University Munich, Department of Clinical Radiology, Munich (Germany); Lang, Reinhold; Steitz, Heinrich O.; Jauch, Karl W. [University Hospital Munich-Grosshadern, Department of Surgery, Munich (Germany); Kirchhoff, Chlodwig [University Hospital Munich-Innenstadt, Department of Surgery, Munich (Germany)

    2008-06-15

    The purpose of this study was to evaluate the presence and localization of intraabdominal adhesions using functional cine magnetic resonance imaging (MRI) and to correlate the MR findings with intraoperative results. In a retrospective study, patients who had undergone previous abdominal surgery with suspected intraabdominal adhesions were examined. A true fast imaging with steady state precession sequence in transverse/sagittal orientation was used for a section-by-section dynamic depiction of visceral slide on a 1.5-Tesla system. After MRI, all patients underwent anew surgery. A nine-segment abdominal map was used to document the location and type of the adhesions. The intraoperative results were taken as standard of reference. Ninety patients were enrolled. During surgery 71 adhesions were detected, MRI depicted 68 intraabdominal adhesions. The most common type of adhesion in MRI was found between the anterior abdominal wall and small bowel loops (n = 22, 32.5%) and between small bowel loops and pelvic organs (n = 14, 20.6%). Comparing MRI with the intraoperative findings, sensitivity varied between 31 and 75% with a varying specificity between 65 and 92% in the different segments leading to an overall MRI accuracy of 89%. Functional cine MRI proved to be a useful examination technique for the identification of intraabdominal adhesions in patients with acute or chronic pain and corresponding clinical findings providing accurate results. However, no differentiation for symptomatic versus asymptomatic adhesions is possible. (orig.)

  15. Serial diffusion tensor imaging detects white matter changes that correlate with motor outcome in premature infants.

    Science.gov (United States)

    Drobyshevsky, Alexander; Bregman, Joanne; Storey, Pippa; Meyer, Joel; Prasad, P V; Derrick, Matthew; MacKendrick, William; Tan, Sidhartha

    2007-01-01

    The objective of the study was to assess predictive value of serial diffusion tensor MRI (DTI) for the white matter injury and neurodevelopmental outcome in a cohort of premature infants. Twenty-four infants less than 32 weeks' gestation were stratified to a control group (n = 11), mild brain injury with grades 1-2 of intraventricular hemorrhage (n = 6) and severe brain injury with grades 3-4 intraventricular hemorrhage (n = 4). Serial DTI studies were performed at around 30 and 36 weeks' gestation. Fractional anisotropy (FA) and apparent diffusion coefficient were calculated. Twelve infants were followed up for developmental outcome. Developmental testing was performed with the Bayley Scales of Infant Development to obtain psychomotor index (Performance Developmental Index). Apparent diffusion coefficient was higher in the severe injury group at the second MRI in the central and occipital white matter, and corona radiata; FA was lower in optic radiation compared to controls. Performance Developmental Index score correlated with FA on the scan taken at the 30th week and inversely with the change of FA between scans in internal capsule and occipital white matter. A low value of FA at 30 weeks and a higher change of FA predicted less favorable motor outcome at 2 years and suggests that early subtle white matter injury can be detected in premature infants even without obvious signs of injury. 2007 S. Karger AG, Basel

  16. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi

    2015-12-07

    Proper operation of antenna arrays requires continuously monitoring their performances. When a fault occurs in an antenna array, the radiation pattern changes and can significantly deviate from the desired design performance specifications. In this paper, the problem of fault detection in linear antenna arrays is addressed within a statistical framework. Specifically, a statistical fault detection method based on the generalized likelihood ratio (GLR) principle is utilized for detecting potential faults in linear antenna arrays. The proposed method relies on detecting deviations in the radiation pattern of the monitored array with respect to a reference (fault-free) one. To assess the abilities of the GLR based fault detection method, three case studies involving different types of faults have been performed. The simulation results clearly illustrate the effectiveness of the GLR-based fault detection method in monitoring the performance of linear antenna arrays.

  17. A Vehicle Detection Algorithm Based on Deep Belief Network

    Directory of Open Access Journals (Sweden)

    Hai Wang

    2014-01-01

    Full Text Available Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. Traditional shallow model based vehicle detection algorithm still cannot meet the requirement of accurate vehicle detection in these applications. In this work, a novel deep learning based vehicle detection algorithm with 2D deep belief network (2D-DBN is proposed. In the algorithm, the proposed 2D-DBN architecture uses second-order planes instead of first-order vector as input and uses bilinear projection for retaining discriminative information so as to determine the size of the deep architecture which enhances the success rate of vehicle detection. On-road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets.

  18. A fuzzy rule-based system for epileptic seizure detection in intracranial EEG.

    Science.gov (United States)

    Aarabi, A; Fazel-Rezai, R; Aghakhani, Y

    2009-09-01

    We present a method for automatic detection of seizures in intracranial EEG recordings from patients suffering from medically intractable focal epilepsy. We designed a fuzzy rule-based seizure detection system based on knowledge obtained from experts' reasoning. Temporal, spectral, and complexity features were extracted from IEEG segments, and spatio-temporally integrated using the fuzzy rule-based system for seizure detection. A total of 302.7h of intracranial EEG recordings from 21 patients having 78 seizures was used for evaluation of the system. The system yielded a sensitivity of 98.7%, a false detection rate of 0.27/h, and an average detection latency of 11s. There was only one missed seizure. Most of false detections were caused by high-amplitude rhythmic activities. The results from the system correlate well with those from expert visual analysis. The fuzzy rule-based seizure detection system enabled us to deal with imprecise boundaries between interictal and ictal IEEG patterns. This system may serve as a good seizure detection tool with high sensitivity and low false detection rate for monitoring long-term IEEG.

  19. Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG.

    Science.gov (United States)

    Rabbi, Ahmed F; Aarabi, Ardalan; Fazel-Rezai, Reza

    2010-01-01

    In this paper, we present a method for epileptic seizure prediction from intracranial EEG recordings. We applied correlation dimension, a nonlinear dynamics based univariate characteristic measure for extracting features from EEG segments. Finally, we designed a fuzzy rule-based system for seizure prediction. The system is primarily designed based on expert's knowledge and reasoning. A spatial-temporal filtering method was used in accordance with the fuzzy rule-based inference system for issuing forecasting alarms. The system was evaluated on EEG data from 10 patients having 15 seizures.

  20. Muon Detection Based on a Hadronic Calorimeter

    CERN Document Server

    Ciodaro, T; Abreu, R; Achenbach, R; Adragna, P; Aharrouche, M; Aielli, G; Al-Shabibi, A; Aleksandrov, I; Alexandrov, E; Aloisio, A; Alviggi, M G; Amorim, A; Amram, N; Andrei, V; Anduaga, X; Angelaszek, D; Anjos, N; Annovi, A; Antonelli, S; Anulli, F; Apolle, R; Aracena, I; Ask, S; Åsman, B; Avolio, G; Baak, M; Backes, M; Backlund, S; Badescu, E; Baines, J; Ballestrero, S; Banerjee, S; Bansil, H S; Barnett, B M; Bartoldus, R; Bartsch, V; Batraneanu, S; Battaglia, A; Bauss, B; Beauchemin, P; Beck, H P; Bee, C; Begel, M; Behera, P K; Bell, P; Bell, W H; Bellagamba, L; Bellomo, M; Ben Ami, S; Bendel, M; Benhammou, Y; Benslama, K; Berge, D; Bernius, C; Berry, T; Bianco, M; Biglietti, M; Blair, R E; Bogaerts, A; Bohm, C; Boisvert, V; Bold, T; Bondioli, M; Borer, C; Boscherini, D; Bosman, M; Bossini, E; Boveia, A; Bracinik, J; Brandt, A G; Brawn, I P; Brelier, B; Brenner, R; Bressler, S; Brock, R; Brooks, W K; Brown, G; Brunet, S; Bruni, A; Bruni, G; Bucci, F; Buda, S; Burckhart-Chromek, D; Buscher, V; Buttinger, W; Calvet, S; Camarri, P; Campanelli, M; Canale, V; Canelli, F; Capasso, L; Caprini, M; Caracinha, D; Caramarcu, C; Cardarelli, R; Carlino, G; Casadei, D; Casado, M P; Cattani, G; Cerri, A; Cerrito, L; Chapleau, B; Childers, J T; Chiodini, G; Christidi, I; Ciapetti, G; Cimino, D; Ciobotaru, M; Coccaro, A; Cogan, J; Collins, N J; Conde Muino, P; Conidi, C; Conventi, F; Corradi, M; Corso-Radu, A; Coura Torres, R; Cranmer, K; Crescioli, F; Crone, G; Crupi, R; Cuenca Almenar, C; Cummings, J T; Curtis, C J; Czyczula, Z; Dam, M; Damazio, D; Dao, V; Darlea, G L; Davis, A O; De Asmundis, R; De Pedis, D; De Santo, A; de Seixas, J M; Degenhardt, J; Della Pietra, M; Della Volpe, D; Demers, S; Demirkoz, B; Di Ciaccio, A; Di Mattia, A; Di Nardo, R; Di Simone, A; Diaz, M A; Dietzsch, T A; Dionisi, C; Dobson, E; Dobson, M; dos Anjos, A; Dotti, A; Dova, M T; Drake, G; Dufour, M-A; Dumitru, I; Eckweiler, S; Ehrenfeld, W; Eifert, T; Eisenhandler, E; Ellis, K V; Ellis, N; Emeliyanov, D; Enoque Ferreira de Lima, D; Ermoline, Y; Ernst, J; Etzion, E; Falciano, S; Farrington, S; Farthouat, P; Faulkner , P J W; Fedorko, W; Fellmann, D; Feng, E; Ferrag, S; Ferrari, R; Ferrer, M L; Fiorini, L; Fischer, G; Flowerdew, M J; Fonseca Martin, T; Francis, D; Fratina, S; French, S T; Front, D; Fukunaga, C; Gadomski, S; Garelli, N; Garitaonandia Elejabarrieta, H; Gaudio, G; Gee, C N P; George, S; Giagu, S; Giannetti, P; Gillman, A R; Giorgi, M; Giunta, M; Giusti, P; Goebel, M; Gonçalo, R; Gonzalez Silva, L; Göringer, C; Gorini, B; Gorini, E; Grabowska-Bold, I; Green, B; Groll, M; Guida, A; Guler, H; Haas, S; Hadavand, H; Hadley, D R; Haller, J; Hamilton, A; Hanke, P; Hansen, J R; Hasegawa, S; Hasegawa, Y; Hauser, R; Hayakawa, T; Hayden, D; Head, S; Heim, S; Hellman, S; Henke, M; Hershenhorn, A; Hidvégi, A; Hillert, S; Hillier, S J; Hirayama, S; Hod, N; Hoffmann, D; Hong, T M; Hryn'ova, T; Huston, J; Iacobucci, G; Igonkina, O; Ikeno, M; Ilchenko, Y; Ishikawa, A; Ishino, M; Iwasaki, H; Izzo, V; Jez, P; Jimenez Otero, S; Johansen, M; Johns, K; Jones, G; Joos, M; Kadlecik, P; Kajomovitz, E; Kanaya, N; Kanega, F; Kanno, T; Kapliy, A; Kaushik, V; Kawagoe, K; Kawamoto, T; Kazarov, A; Kehoe, R; Kessoku, K; Khomich, A; Khoriauli, G; Kieft, G; Kirk, J; Klemetti, M; Klofver, P; Klous, S; Kluge, E-E; Kobayashi, T; Koeneke, K; Koletsou, I; Koll, J D; Kolos, S; Kono, T; Konoplich, R; Konstantinidis, N; Korcyl, K; Kordas, K; Kotov, V; Kowalewski, R V; Krasznahorkay, A; Kraus, J; Kreisel, A; Kubota, T; Kugel, A; Kunkle, J; Kurashige, H; Kuze, M; Kwee, R; Laforge, B; Landon, M; Lane, J; Lankford, A J; Laranjeira Lima, S M; Larner, A; Leahu, L; Lehmann Miotto, G; Lei, X; Lellouch, D; Levinson, L; Li, S; Liberti, B; Lilley, J N; Linnemann, J T; Lipeles, E; Lohse, T; Losada, M; Lowe, A; Luci, C; Luminari, L; Lundberg, J; Lupu, N; Machado Miguéns, J; Mackeprang, R; Maettig, S; Magnoni, L; Maiani, C; Maltrana, D; Mangeard, P-S; Männer, R; Mapelli, L; Marchese, F; Marino, C; Martin, B; Martin, B T; Martin, T; Martyniuk, A; Marzano, F; Masik, J; Mastrandrea, P; Matsushita, T; McCarn, A; Mechnich, J; Medinnis, M; Meier, K; Melachrinos, C; Mendoza Nava, L M; Merola, L; Messina, A; Meyer, C P; Middleton, R P; Mikenberg, G; Mills, C M; Mincer, A; Mineev, M; Misiejuk, A; Moa, T; Moenig, K; Monk, J; Monticelli, F; Mora Herrera, C; Morettini, P; Morris, J D; Müller, F; Munwes, Y; Murillo Garcia, R; Nagano, K; Nagasaka, Y; Navarro, G A; Negri, A; Nelson, S; Nemethy, P; Neubauer, M S; Neusiedl, A; Newman, P; Nisati, A; Nomoto, H; Nozaki, M; Nozicka, M; Nurse, E; Ochando, C; Ochi, A; Oda, S; Oh, A; Ohm, C; Okumura, Y; Olivito, D; Omachi, C; Osculati, B; Oshita, H; Ospanov, R; Owen, M A; Özcan, V E; Ozone, K; Padilla, C; Panes, B; Panikashvili, N; Paramonov, A; Parodi, F; Pasqualucci, E; Pastore, F; Patricelli, S; Pauly, T; Perera, V J O; Perez, E; Petcu, M; Petersen, B A; Petersen, J; Petrolo, E; Phan, A; Piegaia, R; Pilkington, A; Pinder, A; Poddar, S; Polini, A; Pope, B G; Potter, C T; Primavera, M; Prokoshin, F; Ptacek, E; Qian, W; Quinonez, F; Rajagopalan, S; Ramos Dos Santos Neves, R; Reinherz-Aronis, E; Reinsch, A; Renkel, P; Rescigno, M; Rieke, S; Riu, I; Robertson, S H; Robinson, M; Rodriguez, D; Roich, A; Romeo, G; Romero, R; Roos, L; Ruiz Martinez, A; Ryabov, Y; Ryan, P; Saavedra, A; Safai Tehrani, F; Sakamoto, H; Salamanna, G; Salamon, A; Saland, J; Salnikov, A; Salvatore, F; Sankey, D P C; Santamarina, C; Santonico, R; Sarkisyan-Grinbaum, E; Sasaki, O; Savu, D; Scannicchio, D A; Schäfer, U; Scharf, V L; Scheirich, D; Schiavi, C; Schlereth, J; Schmitt, K; Schroder, C; Schroer, N; Schultz-Coulon, H-C; Schwienhorst, R; Sekhniaidze, G; Sfyrla, A; Shamim, M; Sherman, D; Shimojima, M; Shochet, M; Shooltz, D; Sidoti, A; Silbert, O; Silverstein, S; Sinev, N; Siragusa, G; Sivoklokov, S; Sjoen, R; Sjölin, J; Slagle, K; Sloper, J E; Smith, B C; Soffer, A; Soloviev, I; Spagnolo, S; Spiwoks, R; Staley, R J; Stamen, R; Stancu, S; Steinberg, P; Stelzer, J; Stockton, M C; Straessner, A; Strauss, E A; Strom, D; Su, D; Sugaya, Y; Sugimoto, T; Sushkov, S; Sutton, M R; Suzuki, Y; Taffard, A; Taiblum, N; Takahashi, Y; Takeda, H; Takeshita, T; Tamsett, M; Tan, C L A; Tanaka, S; Tapprogge, S; Tarem, S; Tarem, Z; Taylor, C; Teixeira-Dias, P; Thomas, J P; Thompson, P D; Thomson, M A; Tokushuku, K; Tollefson, K; Tomoto, M; Topfel, C; Torrence, E; Touchard, F; Traynor, D; Tremblet, L; Tricoli, A; Tripiana, M; Triplett, N; True, P; Tsiakiris, M; Tsuno, S; Tuggle, J; Ünel, G; Urquijo, P; Urrejola, P; Usai, G; Vachon, B; Vallecorsa, S; Valsan, L; Vandelli, W; Vari, R; Vaz Gil Lopes, L; Veneziano, S; Ventura, A; Venturi, N; Vercesi, V; Vermeulen, J C; Volpi, G; Vorwerk, V; Wagner, P; Wang, M; Warburton, A; Watkins, P M; Watson, A T; Watson, M; Weber, P; Weidberg, A R; Wengler, T; Werner, P; Werth, M; Wessels, M; White, M; Whiteson, D; Wickens, F J; Wiedenmann, W; Wielers, M; Winklmeier, F; Woods, K S; Wu, S-L; Wu, X; Xaplanteris Karampatsos, L; Xella, S; Yakovlev, A; Yamazaki, Y; Yang, U; Yasu, Y; Yuan, L; Zaitsev, A; Zanello, L; Zhang, H; Zhang, J; Zhao, L; Zobernig, H; zur Nedden, M

    2010-01-01

    The ATLAS Tile hadronic calorimeter (TileCal) provides highly-segmented energy measurements of incoming particles. The information from TileCal's last segmentation layer can assist in muon tagging and it is being considered for a near future upgrade of the level-one trigger, mainly for rejecting triggers due to cavern background at the barrel region. A muon receiver for the TileCal muon signals is being designed in order to interface with the ATLAS level-one trigger. This paper addresses the preliminary studies concerning the muon discrimination capability for the muon receiver. Monte Carlo simulations for single muons from the interaction point were used to study the effectiveness of hadronic calorimeter information on muon detection.

  1. Muon Detection Based on a Hadronic Calorimeter

    CERN Document Server

    Ciodaro, Thiago; Abreu, R; Achenbach, R; Adragna, P; Aharrouche, M; Aielli, G; Al-Shabibi, A; Aleksandrov, I; Alexandrov, E; Aloisio, A; Alviggi, M G; Amorim, A; Amram, N; Andrei, V; Anduaga, X; Angelaszek, D; Anjos, N; Annovi, A; Antonelli, S; Anulli, F; Apolle, R; Aracena, I; Ask, S; Åsman, B; Avolio, G; Baak, M; Backes, M; Backlund, S; Badescu, E; Baines, J; Ballestrero, S; Banerjee, S; Bansil, H S; Barnett, B M; Bartoldus, R; Bartsch, V; Batraneanu, S; Battaglia, A; Bauss, B; Beauchemin, P; Beck, H P; Bee, C; Begel, M; Behera, P K; Bell, P; Bell, W H; Bellagamba, L; Bellomo, M; Ben Ami, S; Bendel, M; Benhammou, Y; Benslama, K; Berge, D; Bernius, C; Berry, T; Bianco, M; Biglietti, M; Blair, R E; Bogaerts, A; Bohm, C; Boisvert, V; Bold, T; Bondioli, M; Borer, C; Boscherini, D; Bosman, M; Bossini, E; Boveia, A; Bracinik, J; Brandt, A G; Brawn, I P; Brelier, B; Brenner, R; Bressler, S; Brock, R; Brooks, W K; Brown, G; Brunet, S; Bruni, A; Bruni, G; Bucci, F; Buda, S; Burckhart-Chromek, D; Buscher, V; Buttinger, W; Calvet, S; Camarri, P; Campanelli, M; Canale, V; Canelli, F; Capasso, L; Caprini, M; Caracinha, D; Caramarcu, C; Cardarelli, R; Carlino, G; Casadei, D; Casado, M P; Cattani, G; Cerri, A; Cerrito, L; Chapleau, B; Childers, J T; Chiodini, G; Christidi, I; Ciapetti, G; Cimino, D; Ciobotaru, M; Coccaro, A; Cogan, J; Collins, N J; Conde Muino, P; Conidi, C; Conventi, F; Corradi, M; Corso-Radu, A; Coura Torres, R; Cranmer, K; Crescioli, F; Crone, G; Crupi, R; Cuenca Almenar, C; Cummings, J T; Curtis, C J; Czyczula, Z; Dam, M; Damazio, D; Dao, V; Darlea, G L; Davis, A O; De Asmundis, R; De Pedis, D; De Santo, A; de Seixas, J M; Degenhardt, J; Della Pietra, M; Della Volpe, D; Demers, S; Demirkoz, B; Di Ciaccio, A; Di Mattia, A; Di Nardo, R; Di Simone, A; Diaz, M A; Dietzsch, T A; Dionisi, C; Dobson, E; Dobson, M; dos Anjos, A; Dotti, A; Dova, M T; Drake, G; Dufour, M-A; Dumitru, I; Eckweiler, S; Ehrenfeld, W; Eifert, T; Eisenhandler, E; Ellis, K V; Ellis, N; Emeliyanov, D; Enoque Ferreira de Lima, D; Ermoline, Y; Ernst, J; Etzion, E; Falciano, S; Farrington, S; Farthouat, P; Faulkner, P J W; Fedorko, W; Fellmann, D; Feng, E; Ferrag, S; Ferrari, R; Ferrer, M L; Fiorini, L; Fischer, G; Flowerdew, M J; Fonseca Martin, T; Francis, D; Fratina, S; French, S T; Front, D; Fukunaga, C; Gadomski, S; Garelli, N; Garitaonandia Elejabarrieta, H; Gaudio, G; Gee, C N P; George, S; Giagu, S; Giannetti, P; Gillman, A R; Giorgi, M; Giunta, M; Giusti, P; Goebel, M; Gonçalo, R; Gonzalez Silva, L; Göringer, C; Gorini, B; Gorini, E; Grabowska-Bold, I; Green, B; Groll, M; Guida, A; Guler, H; Haas, S; Hadavand, H; Hadley, D R; Haller, J; Hamilton, A; Hanke, P; Hansen, J R; Hasegawa, S; Hasegawa, Y; Hauser, R; Hayakawa, T; Hayden, D; Head, S; Heim, S; Hellman, S; Henke, M; Hershenhorn, A; Hidvégi, A; Hillert, S; Hillier, S J; Hirayama, S; Hod, N; Hoffmann, D; Hong, T M; Hryn'ova, T; Huston, J; Iacobucci, G; Igonkina, O; Ikeno, M; Ilchenko, Y; Ishikawa, A; Ishino, M; Iwasaki, H; Izzo, V; Jez, P; Jimenez Otero, S; Johansen, M; Johns, K; Jones, G; Joos, M; Kadlecik, P; Kajomovitz, E; Kanaya, N; Kanega, F; Kanno, T; Kapliy, A; Kaushik, V; Kawagoe, K; Kawamoto, T; Kazarov, A; Kehoe, R; Kessoku, K; Khomich, A; Khoriauli, G; Kieft, G; Kirk, J; Klemetti, M; Klofver, P; Klous, S; Kluge, E-E; Kobayashi, T; Koeneke, K; Koletsou, I; Koll, J D; Kolos, S; Kono, T; Konoplich, R; Konstantinidis, N; Korcyl, K; Kordas, K; Kotov, V; Kowalewski, R V; Krasznahorkay, A; Kraus, J; Kreisel, A; Kubota, T; Kugel, A; Kunkle, J; Kurashige, H; Kuze, M; Kwee, R; Laforge, B; Landon, M; Lane, J; Lankford, A J; Laranjeira Lima, S M; Larner, A; Leahu, L; Lehmann Miotto, G; Lei, X; Lellouch, D; Levinson, L; Li, S; Liberti, B; Lilley, J N; Linnemann, J T; Lipeles, E; Lohse, T; Losada, M; Lowe, A; Luci, C; Luminari, L; Lundberg, J; Lupu, N; Machado Miguéns, J; Mackeprang, R; Maettig, S; Magnoni, L; Maiani, C; Maltrana, D; Mangeard, P-S; Männer, R; Mapelli, L; Marchese, F; Marino, C; Martin, B; Martin, B T; Martin, T; Martyniuk, A; Marzano, F; Masik, J; Mastrandrea, P; Matsushita, T; McCarn, A; Mechnich, J; Medinnis, M; Meier, K; Melachrinos, C; Mendoza Nava, L M; Merola, L; Messina, A; Meyer, C P; Middleton, R P; Mikenberg, G; Mills, C M; Mincer, A; Mineev, M; Misiejuk, A; Moa, T; Moenig, K; Monk, J; Monticelli, F; Mora Herrera, C; Morettini, P; Morris, J D; Müller, F; Munwes, Y; Murillo Garcia, R; Nagano, K; Nagasaka, Y; Navarro, G A; Negri, A; Nelson, S; Nemethy, P; Neubauer, M S; Neusiedl, A; Newman, P; Nisati, A; Nomoto, H; Nozaki, M; Nozicka, M; Nurse, E; Ochando, C; Ochi, A; Oda, S; Oh, A; Ohm, C; Okumura, Y; Olivito, D; Omachi, C; Osculati, B; Oshita, H; Ospanov, R; Owen, M A; Özcan, V E; Ozone, K; Padilla, C; Panes, B; Panikashvili, N; Paramonov, A; Parodi, F; Pasqualucci, E; Pastore, F; Patricelli, S; Pauly, T; Perera, V J O; Perez, E; Petcu, M; Petersen, B A; Petersen, J; Petrolo, E; Phan, A; Piegaia, R; Pilkington, A; Pinder, A; Poddar, S; Polini, A; Pope, B G; Potter, C T; Primavera, M; Prokoshin, F; Ptacek, E; Qian, W; Quinonez, F; Rajagopalan, S; Ramos Dos Santos Neves, R; Reinherz-Aronis, E; Reinsch, A; Renkel, P; Rescigno, M; Rieke, S; Riu, I; Robertson, S H; Robinson, M; Rodriguez, D; Roich, A; Romeo, G; Romero, R; Roos, L; Ruiz Martinez, A; Ryabov, Y; Ryan, P; Saavedra, A; Safai Tehrani, F; Sakamoto, H; Salamanna, G; Salamon, A; Saland, J; Salnikov, A; Salvatore, F; Sankey, D P C; Santamarina, C; Santonico, R; Sarkisyan-Grinbaum, E; Sasaki, O; Savu, D; Scannicchio, D A; Schäfer, U; Scharf, V L; Scheirich, D; Schiavi, C; Schlereth, J; Schmitt, K; Schroder, C; Schroer, N; Schultz-Coulon, H-C; Schwienhorst, R; Sekhniaidze, G; Sfyrla, A; Shamim, M; Sherman, D; Shimojima, M; Shochet, M; Shooltz, D; Sidoti, A; Silbert, O; Silverstein, S; Sinev, N; Siragusa, G; Sivoklokov, S; Sjoen, R; Sjölin, J; Slagle, K; Sloper, J E; Smith, B C; Soffer, A; Soloviev, I; Spagnolo, S; Spiwoks, R; Staley, R J; Stamen, R; Stancu, S; Steinberg, P; Stelzer, J; Stockton, M C; Straessner, A; Strauss, E A; Strom, D; Su, D; Sugaya, Y; Sugimoto, T; Sushkov, S; Sutton, M R; Suzuki, Y; Taffard, A; Taiblum, N; Takahashi, Y; Takeda, H; Takeshita, T; Tamsett, M; Tan, C L A; Tanaka, S; Tapprogge, S; Tarem, S; Tarem, Z; Taylor, C; Teixeira-Dias, P; Thomas, J P; Thompson, P D; Thomson, M A; Tokushuku, K; Tollefson, K; Tomoto, M; Topfel, C; Torrence, E; Touchard, F; Traynor, D; Tremblet, L; Tricoli, A; Tripiana, M; Triplett, N; True, P; Tsiakiris, M; Tsuno, S; Tuggle, J; Ünel, G; Urquijo, P; Urrejola, P; Usai, G; Vachon, B; Vallecorsa, S; Valsan, L; Vandelli, W; Vari, R; Vaz Gil Lopes, L; Veneziano, S; Ventura, A; Venturi, N; Vercesi, V; Vermeulen, J C; Volpi, G; Vorwerk, V; Wagner, P; Wang, M; Warburton, A; Watkins, P M; Watson, A T; Watson, M; Weber, P; Weidberg, A R; Wengler, T; Werner, P; Werth, M; Wessels, M; White, M; Whiteson, D; Wickens, F J; Wiedenmann, W; Wielers, M; Winklmeier, F; Woods, K S; Wu, S-L; Wu, X; Xaplanteris Karampatsos, L; Xella, S; Yakovlev, A; Yamazaki, Y; Yang, U; Yasu, Y; Yuan, L; Zaitsev, A; Zanello, L; Zhang, H; Zhang, J; Zhao, L; Zobernig, H; zur Nedden, M

    2010-01-01

    The TileCal hadronic calorimeter provides a muon signal which can be used to assist in muon tagging at the ATLAS level-one trigger. Originally, the muon signal was conceived to be combined with the RPC trigger in order to reduce unforeseen high trigger rates due to cavern background. Nevertheless, the combined trigger cannot significantly deteriorate the muon detection performance at the barrel region. This paper presents preliminary studies concerning the impact in muon identification at the ATLAS level-one trigger, through the use of Monte Carlo simulations with single muons with 40 GeV/c momentum. Further, different trigger scenarios were proposed, together with an approach for matching both TileCal and RPC geometries.

  2. Humidity detection using chitosan film based sensor

    Science.gov (United States)

    Nasution, T. I.; Nainggolan, I.; Dalimunthe, D.; Balyan, M.; Cuana, R.; Khanifah, S.

    2018-02-01

    A humidity sensor made of the natural polymer chitosan has been successfully fabricated in the film form by a solution casting method. Humidity testing was performed by placing a chitosan film sensor in a cooling machine room, model KT-2000 Ahu. The testing results showed that the output voltage values of chitosan film sensor increased with the increase in humidity percentage. For the increase in humidity percentage from 30 to 90% showed that the output voltage of chitosan film sensor increased from 32.19 to 138.75 mV. It was also found that the sensor evidenced good repeatability and stability during the testing. Therefore, chitosan has a great potential to be used as new sensing material for the humidity detection of which was cheaper and environmentally friendly.

  3. Detecting Outlier Microarray Arrays by Correlation and Percentage of Outliers Spots

    Directory of Open Access Journals (Sweden)

    Song Yang

    2006-01-01

    Full Text Available We developed a quality assurance (QA tool, namely microarray outlier filter (MOF, and have applied it to our microarray datasets for the identification of problematic arrays. Our approach is based on the comparison of the arrays using the correlation coefficient and the number of outlier spots generated on each array to reveal outlier arrays. For a human universal reference (HUR dataset, which is used as a technical control in our standard hybridization procedure, 3 outlier arrays were identified out of 35 experiments. For a human blood dataset, 12 outlier arrays were identified from 185 experiments. In general, arrays from human blood samples displayed greater variation in their gene expression profiles than arrays from HUR samples. As a result, MOF identified two distinct patterns in the occurrence of outlier arrays. These results demonstrate that this methodology is a valuable QA practice to identify questionable microarray data prior to downstream analysis.

  4. Robust face detection based on components and their topology

    Science.gov (United States)

    Goldmann, Lutz; Mönich, Ullrich; Sikora, Thomas

    2006-01-01

    This paper presents a novel approach for automatic and robust object detection. It utilizes a component-based approach that combines techniques from both statistical and structural pattern recognition domain. While the component detection relies on Haar-like features and an AdaBoost trained classifier cascade, the topology verification is based on graph matching techniques. The system was applied to face detection and the experiments show its outstanding performance in comparison to other face detection approaches. Especially in the presence of partial occlusions, uneven illumination and out-of-plane rotations it yields higher robustness.

  5. Segment-based change detection for polarimetric SAR data

    DEFF Research Database (Denmark)

    Skriver, Henning; Nielsen, Allan Aasbjerg; Conradsen, Knut

    2006-01-01

    single-channel SAR images but multi-channel algorithms have also been described. Different approaches have been used for image segmentation. Edge detection combined with region growing is one approach, where segments are created by growing regions from a previously edge detected and edge thinned image....... This method relies primarily on a robust edge detector, which preferably provides a constant false alarm rate. For single-channel SAR images this is fulfilled by the ratio edge detector, and for polarimetric SAR data, an edge detector based on the above mentioned test statistic fulfils this. Another approach......, wetlands, lakes, and urban areas. Also, other test sites over for instance urban areas have been used to assess the improvement by the segment-based change detection method. In the paper, results from pixel-based change detection, i.e. without segmentation, and from segment-based change detection, where...

  6. Research on moving object detection based on frog's eyes

    Science.gov (United States)

    Fu, Hongwei; Li, Dongguang; Zhang, Xinyuan

    2008-12-01

    On the basis of object's information processing mechanism with frog's eyes, this paper discussed a bionic detection technology which suitable for object's information processing based on frog's vision. First, the bionics detection theory by imitating frog vision is established, it is an parallel processing mechanism which including pick-up and pretreatment of object's information, parallel separating of digital image, parallel processing, and information synthesis. The computer vision detection system is described to detect moving objects which has special color, special shape, the experiment indicates that it can scheme out the detecting result in the certain interfered background can be detected. A moving objects detection electro-model by imitating biologic vision based on frog's eyes is established, the video simulative signal is digital firstly in this system, then the digital signal is parallel separated by FPGA. IN the parallel processing, the video information can be caught, processed and displayed in the same time, the information fusion is taken by DSP HPI ports, in order to transmit the data which processed by DSP. This system can watch the bigger visual field and get higher image resolution than ordinary monitor systems. In summary, simulative experiments for edge detection of moving object with canny algorithm based on this system indicate that this system can detect the edge of moving objects in real time, the feasibility of bionic model was fully demonstrated in the engineering system, and it laid a solid foundation for the future study of detection technology by imitating biologic vision.

  7. Aptamer-Based Technologies in Foodborne Pathogen Detection

    Directory of Open Access Journals (Sweden)

    Jun Teng

    2016-09-01

    Full Text Available Aptamers are single stranded DNA or RNA ligands, which can be selected by a method called systematic evolution of ligands by exponential enrichment (SELEX; and they can specifically recognize and bind to their targets. These unique characteristics of aptamers offer great potentials in applications such as pathogen detection and biomolecular screening. Pathogen detection is the first and critical means in detecting and identifying the problems related to public health and food safety; and only the rapid, sensitive and efficient detection technologies can enable the users to make to accurate assessments on the risk of infections (humans and animals or contaminations (foods and other commodities caused by various pathogens. This article reviews the developments in the field of the aptamer-based approaches for pathogen detection, including whole-cell SELEX and Genomic SELEX. Nowadays, a variety of aptamer-based biosensors have been developed for pathogen detection. Thus, in this review, we also cover the development of aptamer-based biosensors including optical biosensors for multiple pathogen detection in multiple-labeling or label-free models such as fluorescence detection and surface plasmon resonance, electrochemical biosensors, and lateral chromatography test strips, and their applications in the pathogen detection and biomolecular screening. While notable progress has been made in the field in the last decade, challenges or drawbacks in their applications such as pathogen detection and biomolecular screening, remain to be overcome.

  8. Aptamer-Based Technologies in Foodborne Pathogen Detection.

    Science.gov (United States)

    Teng, Jun; Yuan, Fang; Ye, Yingwang; Zheng, Lei; Yao, Li; Xue, Feng; Chen, Wei; Li, Baoguang

    2016-01-01

    Aptamers are single stranded DNA or RNA ligands, which can be selected by a method called systematic evolution of ligands by exponential enrichment (SELEX); and they can specifically recognize and bind to their targets. These unique characteristics of aptamers offer great potentials in applications such as pathogen detection and biomolecular screening. Pathogen detection is the critical means in detecting and identifying the problems related to public health and food safety; and only the rapid, sensitive and efficient detection technologies can enable the users to make the accurate assessments on the risks of infections (humans and animals) or contaminations (foods and other commodities) caused by various pathogens. This article reviews the development in the field of the aptamer-based approaches for pathogen detection, including whole-cell SELEX and Genomic SELEX. Nowadays, a variety of aptamer-based biosensors have been developed for pathogen detection. Thus, in this review, we also cover the development in aptamer-based biosensors including optical biosensors for multiple pathogen detection by multiple-labeling or label-free models such as fluorescence detection and surface plasmon resonance, electrochemical biosensors and lateral chromatography test strips, and their applications in pathogen detection and biomolecular screening. While notable progress has been made in the field in the last decade, challenges or drawbacks in their applications such as pathogen detection and biomolecular screening remain to be overcome.

  9. White matter structure in the right planum temporale region correlates with visual motion detection thresholds in deaf people.

    Science.gov (United States)

    Shiell, Martha M; Zatorre, Robert J

    2017-01-01

    The right planum temporale region is typically involved in higher-order auditory processing. After deafness, this area reorganizes to become sensitive to visual motion. This plasticity is thought to support compensatory enhancements to visual ability. In earlier work we showed that enhanced visual motion detection abilities in early-deaf people correlate with cortical thickness in a subregion of the right planum temporale. In the current study, we build on this earlier result by examining the relationship between enhanced visual motion detection ability and white matter structure in this area in the same sample. We used diffusion-weighted magnetic resonance imaging and extracted the measures of white matter structure from a region-of-interest just below the grey matter surface where cortical thickness correlates with visual motion detection ability. We also tested control regions-of-interest in the auditory and visual cortices where we did not expect to find a relationship between visual motion detection ability and white matter. We found that in the right planum temporale subregion, and in no other tested regions, fractional anisotropy, radial diffusivity, and mean diffusivity correlated with visual motion detection thresholds. We interpret this change as further evidence of a structural correlate of cross-modal reorganization after deafness. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. Accelerator based techniques for contraband detection

    Science.gov (United States)

    Vourvopoulos, George

    1994-05-01

    It has been shown that narcotics, explosives, and other contraband materials, contain various chemical elements such as H, C, N, O, P, S, and Cl in quantities and ratios that differentiate them from each other and from other innocuous substances. Neutrons and γ-rays have the ability to penetrate through various materials at large depths. They are thus able, in a non-intrusive way, to interrogate volumes ranging from suitcases to Sea-Land containers, and have the ability to image the object with an appreciable degree of reliability. Neutron induced reactions such as (n, γ), (n, n') (n, p) or proton induced γ-resonance absorption are some of the reactions currently investigated for the identification of the chemical elements mentioned above. Various DC and pulsed techniques are discussed and their advantages, characteristics, and current progress are shown. Areas where use of these methods is currently under evaluation are detection of hidden explosives, illicit drug interdiction, chemical war agents identification, nuclear waste assay, nuclear weapons destruction and others.

  11. Novel gas-based detection techniques

    International Nuclear Information System (INIS)

    Graaf, Harry van der

    2009-01-01

    This year we celebrate the 100th birthday of gaseous detectors: Hans Geiger operated the first gas-filled counter in Manchester in 1908. The thin wires, essential for obtaining gas amplification, have been replaced by Micro Pattern Gas Detectors (MPGDs): Micromegas (1995) and GEM (1996). In the GridPix detector, each of the grid holes of a MPGD is equipped with its own electronic readout channel in the form of an active pixel in suitable pixel CMOS chips. By means of MEMS technology, the grid has been integrated with the chip, forming a monolithic readout device for gas volumes. By applying a protection layer made of hydrogenated amorphous silicon, the chips can be made spark proof. New protection layers have been made of silicon nitride. The use of gas as detection material for trackers is compared to Si, and the issue of chamber aging is addressed. New developments are set out: the development of Micro Channel Plates, integrated on pixel chips, the development of electron emission foil, and the realization of TimePix-2: a general-purpose pixel chip with time and amplitude measurement, per pixel, of charge signals.

  12. Combining Correlation-Based and Reward-Based Learning in Neural Control for Policy Improvement

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Kolodziejski, Christoph; Wörgötter, Florentin

    2013-01-01

    associations. Based on these biological findings, we propose a new learning model to achieve successful control policies for artificial systems. This model combines correlation-based learning using input correlation learning (ICO learning) and reward-based learning using continuous actor–critic reinforcement...... learning (RL), thereby working as a dual learner system. The model performance is evaluated by simulations of a cart-pole system as a dynamic motion control problem and a mobile robot system as a goal-directed behavior control problem. Results show that the model can strongly improve pole balancing control...... policy, i.e., it allows the controller to learn stabilizing the pole in the largest domain of initial conditions compared to the results obtained when using a single learning mechanism. This model can also find a successful control policy for goal-directed behavior, i.e., the robot can effectively learn...

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

    Science.gov (United States)

    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/

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

    Science.gov (United States)

    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.

  15. Testing for time-based correlates of perceived gender discrimination.

    Science.gov (United States)

    Blau, Gary; Tatum, Donna Surges; Ward-Cook, Kory; Dobria, Lidia; McCoy, Keith

    2005-01-01

    Using a sample of 201 medical technologists (MTs) over a five-year period, this study extends initial findings on perceived gender discrimination (PGD) by Blau and Tatum (2000) by applying organizational justice variables and internal-external locus of control as hypothesized correlates of PGD. Three types of organizational justice were measured: distributive, procedural, and interactional. General relationships found include locus of control being related to PGD such that internals perceived lower PGD. Also, distributive, procedural, and interactional justice were negatively related to PGD. However, increasing the time interval between these correlates weakened their relationships. The relationship of interactional justice to PGD remained the most "resistant" to attenuation over time.

  16. GNSS Spoofing Detection Based on Signal Power Measurements: Statistical Analysis

    Directory of Open Access Journals (Sweden)

    V. Dehghanian

    2012-01-01

    Full Text Available A threat to GNSS receivers is posed by a spoofing transmitter that emulates authentic signals but with randomized code phase and Doppler values over a small range. Such spoofing signals can result in large navigational solution errors that are passed onto the unsuspecting user with potentially dire consequences. An effective spoofing detection technique is developed in this paper, based on signal power measurements and that can be readily applied to present consumer grade GNSS receivers with minimal firmware changes. An extensive statistical analysis is carried out based on formulating a multihypothesis detection problem. Expressions are developed to devise a set of thresholds required for signal detection and identification. The detection processing methods developed are further manipulated to exploit incidental antenna motion arising from user interaction with a GNSS handheld receiver to further enhance the detection performance of the proposed algorithm. The statistical analysis supports the effectiveness of the proposed spoofing detection technique under various multipath conditions.

  17. Comparison of three T-Wave Delineation Algorithms based on Wavelet Filterbank, Correlation and PCA.

    Science.gov (United States)

    Baas, T; Gravenhorst, F; Fischer, R; Khawaja, A; Dössel, O

    2010-09-26

    There is a large interest in analysing the QT-interval, as a prolonged QT-interval can cause the development of ventricular tachyarrhythmias such as Torsade de Pointes. One major part of QT-analysis is T-end detection. Three automatic T-end delineation methods based on wavelet filterbanks (WAM), correlation (CORM) and Principal Component Analysis PCA (PCAM) have been developed and applied to Physionet QT database.All algorithms tested on Physionet QT database showed good results, while PCAM produced better results than WAM and CORM achieved best results. Standard deviation in sampling points (f(s)=250Hz) have been 33.3 (WAM), 8.0 (PTDM) and 7.8 (CORM). It could be shown that WAM is prone to interference while CORM is the most stable method even under bad conditions. Furthermore it was possible to detect significant QT-prolongation caused by Moxifloxacin in Thorough QT Study # 2 using CORM. QT-prolongation is significantly correlated to blood plasma concentration of Moxifloxacin.

  18. Combining voxel-based morphometry and diffusion tensor imaging to detect age-related brain changes.

    Science.gov (United States)

    Lehmbeck, Jan T; Brassen, Stefanie; Weber-Fahr, Wolfgang; Braus, Dieter F

    2006-04-03

    The present study combined optimized voxel-based morphometry and diffusion tensor imaging to detect age-related brain changes. We compared grey matter density maps (grey matter voxel-based morphometry) and white matter fractional anisotropy maps (diffusion tensor imaging-voxel-based morphometry) between two groups of 17 younger and 17 older women. Older women exhibited reduced white matter fractional anisotropy as well as decreased grey matter density most prominently in the frontal, limbic, parietal and temporal lobes. A discriminant analysis identified four frontal and limbic grey and white matter areas that separated the two groups most effectively. We conclude that grey matter voxel-based morphometry and diffusion tensor imaging voxel-based morphometry are well suited for the detection of age-related changes and their combination provides high accuracy when detecting the neural correlates of aging.

  19. Research about Memory Detection Based on the Embedded Platform

    Science.gov (United States)

    Sun, Hao; Chu, Jian

    As is known to us all, the resources of memory detection of the embedded systems are very limited. Taking the Linux-based embedded arm as platform, this article puts forward two efficient memory detection technologies according to the characteristics of the embedded software. Especially for the programs which need specific libraries, the article puts forwards portable memory detection methods to help program designers to reduce human errors,improve programming quality and therefore make better use of the valuable embedded memory resource.

  20. For early detection of ''potential patients with depression''. Correlation of sleep disorder with frontal lobe dysfunction and depression symptoms

    International Nuclear Information System (INIS)

    Koyama, Fumihiko; Kubuki, Yukiko; Uragami, Ikuko

    2011-01-01

    In Phase I of the research field of ''mental health of workers'' among the 13 research fields for work-related injuries/illness etc. promoted by the Japan Labour Health and Welfare Organization, a statistical image analysis of cerebral blood flow single photon emission computed tomography (SPECT) ( 99 mTc-ECD) was performed for 45 workers (a group of 25 patients with depression and a control group of 20 healthy workers) to perform objective assessment of the features of depression. In the depression and remission periods, we obtained findings regarding characteristic changes in cerebral blood flow, and local decreases in cerebral blood flow that correlated with the level of cumulative fatigue and subjective feelings of fatigue. Based on these image analysis results, it was suggested that for the prevention and early detection of depression, we should focus on the fact that patients with more severe sleep disorder(s) might show a decrease in blood flow in the dorsal frontal lobe, and that a close relationship between sleep disorder and depression was suggested in the images of cerebral function. Among 17 items of the Structured Interview Guide for the Hamilton Depression Rating Scale (SIGH-D) for the general evaluation of depression state, the patients with higher scores of sleep disorder, Insomnia Score (IS), showed a significant decrease in blood flow in the dorsal frontal lobe, suggesting a decrease in attentiveness/concentration. Focusing on the biological finding that showed a correlation between sleep disorder (IS) and frontal lobe dysfunction, we further examined the correlation between the level of sleep disorder, shown in IS, and the data related to depression (total SIGH-D score and the points of individual items; total score of the self-rating depressive scale [SDS] and points of individual items) in 108 workers (57 in the depression undergoing follow-up observation group and 51 in the healthy control group). As a result, IS in 57 subjects in the

  1. Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge

    Directory of Open Access Journals (Sweden)

    Huang Chia-Ling

    2012-03-01

    Full Text Available Abstract Background Identification of active causal regulators is a crucial problem in understanding mechanism of diseases or finding drug targets. Methods that infer causal regulators directly from primary data have been proposed and successfully validated in some cases. These methods necessarily require very large sample sizes or a mix of different data types. Recent studies have shown that prior biological knowledge can successfully boost a method's ability to find regulators. Results We present a simple data-driven method, Correlation Set Analysis (CSA, for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and a specific type of literature-derived causal relationships. Instead of investigating the co-expression level between regulators and their regulatees, we focus on coherence of regulatees of a regulator. Using simulated datasets we show that our method performs very well at recovering even weak regulatory relationships with a low false discovery rate. Using three separate real biological datasets we were able to recover well known and as yet undescribed, active regulators for each disease population. The results are represented as a rank-ordered list of regulators, and reveals both single and higher-order regulatory relationships. Conclusions CSA is an intuitive data-driven way of selecting directed perturbation experiments that are relevant to a disease population of interest and represent a starting point for further investigation. Our findings demonstrate that combining co-expression analysis on regulatee sets with a literature-derived network can successfully identify causal regulators and help develop possible hypothesis to explain disease progression.

  2. Clustering and information in correlation based financial networks

    Science.gov (United States)

    Onnela, J.-P.; Kaski, K.; Kertész, J.

    2004-03-01

    Networks of companies can be constructed by using return correlations. A crucial issue in this approach is to select the relevant correlations from the correlation matrix. In order to study this problem, we start from an empty graph with no edges where the vertices correspond to stocks. Then, one by one, we insert edges between the vertices according to the rank of their correlation strength, resulting in a network called asset graph. We study its properties, such as topologically different growth types, number and size of clusters and clustering coefficient. These properties, calculated from empirical data, are compared against those of a random graph. The growth of the graph can be classified according to the topological role of the newly inserted edge. We find that the type of growth which is responsible for creating cycles in the graph sets in much earlier for the empirical asset graph than for the random graph, and thus reflects the high degree of networking present in the market. We also find the number of clusters in the random graph to be one order of magnitude higher than for the asset graph. At a critical threshold, the random graph undergoes a radical change in topology related to percolation transition and forms a single giant cluster, a phenomenon which is not observed for the asset graph. Differences in mean clustering coefficient lead us to conclude that most information is contained roughly within 10% of the edges.

  3. Neural Correlates of Familiarity-Based Associative Retrieval

    Science.gov (United States)

    Ford, Jaclyn Hennessey; Verfaellie, Mieke; Giovanello, Kelly S.

    2010-01-01

    The current study compared the neural correlates of associative retrieval of compound (unitized) stimuli and unrelated (non-unitized) stimuli. Although associative recognition was nearly identical for compounds and unrelated pairs, accurate recognition of these different pair types was associated with activation in distinct regions within the…

  4. Biopolymer-based material used in optical image correlation

    Czech Academy of Sciences Publication Activity Database

    Mysliwiec, J.; Kochalska, Anna; Miniewicz, A.

    2008-01-01

    Roč. 47, č. 11 (2008), s. 1902-1906 ISSN 0003-6935 Institutional research plan: CEZ:AV0Z40500505 Keywords : biopolymer * DNA * optical correlation Subject RIV: CD - Macromolecular Chemistry Impact factor: 1.763, year: 2008

  5. Developing nucleic acid-based electrical detection systems

    Directory of Open Access Journals (Sweden)

    Gabig-Ciminska Magdalena

    2006-03-01

    Full Text Available Abstract Development of nucleic acid-based detection systems is the main focus of many research groups and high technology companies. The enormous work done in this field is particularly due to the broad versatility and variety of these sensing devices. From optical to electrical systems, from label-dependent to label-free approaches, from single to multi-analyte and array formats, this wide range of possibilities makes the research field very diversified and competitive. New challenges and requirements for an ideal detector suitable for nucleic acid analysis include high sensitivity and high specificity protocol that can be completed in a relatively short time offering at the same time low detection limit. Moreover, systems that can be miniaturized and automated present a significant advantage over conventional technology, especially if detection is needed in the field. Electrical system technology for nucleic acid-based detection is an enabling mode for making miniaturized to micro- and nanometer scale bio-monitoring devices via the fusion of modern micro- and nanofabrication technology and molecular biotechnology. The electrical biosensors that rely on the conversion of the Watson-Crick base-pair recognition event into a useful electrical signal are advancing rapidly, and recently are receiving much attention as a valuable tool for microbial pathogen detection. Pathogens may pose a serious threat to humans, animal and plants, thus their detection and analysis is a significant element of public health. Although different conventional methods for detection of pathogenic microorganisms and their toxins exist and are currently being applied, improvements of molecular-based detection methodologies have changed these traditional detection techniques and introduced a new era of rapid, miniaturized and automated electrical chip detection technologies into pathogen identification sector. In this review some developments and current directions in

  6. Scintillation particle detection based on microfluidics

    CERN Document Server

    Mapelli, A; Renaud, P; Gorini, B; Trivino, N Vico; Jiguet, S; Vandelli, W; Haguenauer, M

    2010-01-01

    A novel type of particle detector based on scintillation, with precise spatial resolution and high radiation hardness, is being studied. It consists of a single microfluidic channel filled with a liquid scintillator and is designed to define an array of scintillating waveguides each independently coupled to a photodetector. Prototype detectors built using an SU-8 epoxy resin have been tested with electrons from a radioactive source. The experimental results show a light yield compatible with the theoretical expectations and confirm the validity of the approach. (C) 2010 Elsevier B.V. All rights reserved.

  7. Forward collision warning based on kernelized correlation filters

    Science.gov (United States)

    Pu, Jinchuan; Liu, Jun; Zhao, Yong

    2017-07-01

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

  8. Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral

    Directory of Open Access Journals (Sweden)

    Wenhui Li

    2014-01-01

    Full Text Available Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS and Blind Spot Detection Systems (BSDS. The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicle detection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: feature similarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicle detection method, and then vehicle taillight feature similarity measure is defined. Second, combining with the definition of Choquet integral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, these three features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, the experimental results show that our method has achieved effective and robust multivehicle detection in complicated environments. Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS.

  9. Video-Based Affect Detection in Noninteractive Learning Environments

    Science.gov (United States)

    Chen, Yuxuan; Bosch, Nigel; D'Mello, Sidney

    2015-01-01

    The current paper explores possible solutions to the problem of detecting affective states from facial expressions during text/diagram comprehension, a context devoid of interactive events that can be used to infer affect. These data present an interesting challenge for face-based affect detection because likely locations of affective facial…

  10. Revisiting Anomaly-based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.

    2009-01-01

    Intrusion detection systems (IDSs) are well-known and widely-deployed security tools to detect cyber-attacks and malicious activities in computer systems and networks. A signature-based IDS works similar to anti-virus software. It employs a signature database of known attacks, and a successful match

  11. Model Based Fault Detection in a Centrifugal Pump Application

    DEFF Research Database (Denmark)

    Kallesøe, Carsten; Cocquempot, Vincent; Izadi-Zamanabadi, Roozbeh

    2006-01-01

    A model based approach for fault detection in a centrifugal pump, driven by an induction motor, is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, observer design and Analytical Redundancy Relation (ARR) design. Structural considerations...

  12. Vibration Based Sun Gear Damage Detection

    Science.gov (United States)

    Hood, Adrian; LaBerge, Kelsen; Lewicki, David; Pines, Darryll

    2013-01-01

    Seeded fault experiments were conducted on the planetary stage of an OH-58C helicopter transmission. Two vibration based methods are discussed that isolate the dynamics of the sun gear from that of the planet gears, bearings, input spiral bevel stage, and other components in and around the gearbox. Three damaged sun gears: two spalled and one cracked, serve as the focus of this current work. A non-sequential vibration separation algorithm was developed and the resulting signals analyzed. The second method uses only the time synchronously averaged data but takes advantage of the signal/source mapping required for vibration separation. Both algorithms were successful in identifying the spall damage. Sun gear damage was confirmed by the presence of sun mesh groups. The sun tooth crack condition was inconclusive.

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

    Science.gov (United States)

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

    2018-02-01

    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.

  14. An experiment-based comparative investigation of correlations for ...

    Indian Academy of Sciences (India)

    Before measurements, leakage detection was done by means of coating the piping connections with soap and water with no evident leakage point observed. Each time before running, the system was charged with gas to the pressure of over 1 MPa. The experiment was allowed to be carried out, if no observable change ...

  15. Biclustering of Gene Expression Data by Correlation-Based Scatter Search

    Science.gov (United States)

    2011-01-01

    Background The analysis of data generated by microarray technology is very useful to understand how the genetic information becomes functional gene products. Biclustering algorithms can determine a group of genes which are co-expressed under a set of experimental conditions. Recently, new biclustering methods based on metaheuristics have been proposed. Most of them use the Mean Squared Residue as merit function but interesting and relevant patterns from a biological point of view such as shifting and scaling patterns may not be detected using this measure. However, it is important to discover this type of patterns since commonly the genes can present a similar behavior although their expression levels vary in different ranges or magnitudes. Methods Scatter Search is an evolutionary technique that is based on the evolution of a small set of solutions which are chosen according to quality and diversity criteria. This paper presents a Scatter Search with the aim of finding biclusters from gene expression data. In this algorithm the proposed fitness function is based on the linear correlation among genes to detect shifting and scaling patterns from genes and an improvement method is included in order to select just positively correlated genes. Results The proposed algorithm has been tested with three real data sets such as Yeast Cell Cycle dataset, human B-cells lymphoma dataset and Yeast Stress dataset, finding a remarkable number of biclusters with shifting and scaling patterns. In addition, the performance of the proposed method and fitness function are compared to that of CC, OPSM, ISA, BiMax, xMotifs and Samba using Gene the Ontology Database. PMID:21261986

  16. Ensemble of classifiers based network intrusion detection system performance bound

    CSIR Research Space (South Africa)

    Mkuzangwe, Nenekazi NP

    2017-11-01

    Full Text Available This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance...

  17. Adaptive, Model-Based Monitoring and Threat Detection

    National Research Council Canada - National Science Library

    Valdes, Alfonso

    2002-01-01

    .... We describe a network intrusion detection system (IDS) using Bayes inference, wherein the knowledge base is encoded not as rules but as conditional probability relations between observables and hypotheses of normal and malicious usage...

  18. Algorithms for Speeding up Distance-Based Outlier Detection

    Data.gov (United States)

    National Aeronautics and Space Administration — The problem of distance-based outlier detection is difficult to solve efficiently in very large datasets because of potential quadratic time complexity. We address...

  19. Functional MRI-based lie detection: scientific and societal challenges.

    Science.gov (United States)

    Farah, Martha J; Hutchinson, J Benjamin; Phelps, Elizabeth A; Wagner, Anthony D

    2014-02-01

    Functional MRI (fMRI)-based lie detection has been marketed as a tool for enhancing personnel selection, strengthening national security and protecting personal reputations, and at least three US courts have been asked to admit the results of lie detection scans as evidence during trials. How well does fMRI-based lie detection perform, and how should the courts, and society more generally, respond? Here, we address various questions — some of which are based on a meta-analysis of published studies — concerning the scientific state of the art in fMRI-based lie detection and its legal status, and discuss broader ethical and societal implications. We close with three general policy recommendations.

  20. Knowledge-Base Application to Ground Moving Target Detection

    National Research Council Canada - National Science Library

    Adve, R

    2001-01-01

    This report summarizes a multi-year in-house effort to apply knowledge-base control techniques and advanced Space-Time Adaptive Processing algorithms to improve detection performance and false alarm...

  1. Extracting Coherent Information from Noise Based Correlation Processing

    Science.gov (United States)

    2013-09-30

    Correlation Processing W. A. Kuperman Marine Physical Laboratory of the Scripps Institution of Ocenaography Univeritiy of California, San Diego La...plotted using the Seismic Analysis Code (SAC). R: Radial, Z: Vertical, T: Transverse. (top) Blue lines represent the DGN hydroacoustic data, (middle...green lines represent the DGS hydroacoustic data, and (bottom) red lines represent the DGAR seismic data. The x-axis corresponds to the time after

  2. Vehicle Detection Based on Probability Hypothesis Density Filter

    Directory of Open Access Journals (Sweden)

    Feihu Zhang

    2016-04-01

    Full Text Available In the past decade, the developments of vehicle detection have been significantly improved. By utilizing cameras, vehicles can be detected in the Regions of Interest (ROI in complex environments. However, vision techniques often suffer from false positives and limited field of view. In this paper, a LiDAR based vehicle detection approach is proposed by using the Probability Hypothesis Density (PHD filter. The proposed approach consists of two phases: the hypothesis generation phase to detect potential objects and the hypothesis verification phase to classify objects. The performance of the proposed approach is evaluated in complex scenarios, compared with the state-of-the-art.

  3. USING MORPHLET-BASED IMAGE REPRESENTATION FOR OBJECT DETECTION

    Directory of Open Access Journals (Sweden)

    V. S. Gorbatsevich

    2016-06-01

    Full Text Available In this paper, we propose an original method for objects detection based on a special tree-structured image representation – the trees of morphlets. The method provides robust detection of various types of objects in an image without employing a machine learning procedure. Along with a bounding box creation on a detection step, the method makes pre-segmentation, which can be further used for recognition purposes. Another important feature of the proposed approach is that there are no needs to use a running window as well as a features pyramid in order to detect the objects of different sizes.

  4. Chord Recognition Based on Temporal Correlation Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhongyang Rao

    2016-05-01

    Full Text Available In this paper, we propose a method called temporal correlation support vector machine (TCSVM for automatic major-minor chord recognition in audio music. We first use robust principal component analysis to separate the singing voice from the music to reduce the influence of the singing voice and consider the temporal correlations of the chord features. Using robust principal component analysis, we expect the low-rank component of the spectrogram matrix to contain the musical accompaniment and the sparse component to contain the vocal signals. Then, we extract a new logarithmic pitch class profile (LPCP feature called enhanced LPCP from the low-rank part. To exploit the temporal correlation among the LPCP features of chords, we propose an improved support vector machine algorithm called TCSVM. We perform this study using the MIREX’09 (Music Information Retrieval Evaluation eXchange Audio Chord Estimation dataset. Furthermore, we conduct comprehensive experiments using different pitch class profile feature vectors to examine the performance of TCSVM. The results of our method are comparable to the state-of-the-art methods that entered the MIREX in 2013 and 2014 for the MIREX’09 Audio Chord Estimation task dataset.

  5. Validation of a raw data-based synchronization signal (kymogram) for phase-correlated cardiac image reconstruction

    International Nuclear Information System (INIS)

    Ertel, Dirk; Kachelriess, Marc; Kalender, Willi A.; Pflederer, Tobias; Achenbach, Stephan; Steffen, Peter

    2008-01-01

    Phase-correlated reconstruction is commonly used in computed tomography (CT)-based cardiac imaging. Alternatively to the commonly used ECG, the raw data-based kymogram function can be used as a synchronization signal. We used raw data of 100 consecutive patient exams to compare the performance of kymogram function to the ECG signal. For objective validation the correlation of the ECG and the kymogram was assessed. Additionally, we performed a double-blinded comparison of ECG-based and kymogram-based phase-correlated images. The two synchronization signals showed good correlation indicated by a mean difference in the detected heart rate of negligible 0.2 bpm. The mean image quality score was 2.0 points for kymogram-correlated images and 2.3 points for ECG-correlated images, respectively (3: best; 0: worst). The kymogram and the ECG provided images adequate for diagnosis for 93 and 97 patients, respectively. For 50% of the datasets the kymogram provided an equivalent or even higher image quality compared with the ECG signal. We conclude that an acceptable image quality can be assured in most cases by the kymogram. Improvements of image quality by the kymogram function were observed in a noticeable number of cases. The kymogram can serve as a backup solution when an ECG is not available or lacking in quality. (orig.)

  6. Correlation filter design using a single cluttered training image for detecting a noisy target in a nonoverlapping scene

    Science.gov (United States)

    Aguilar-González, Pablo Mario; Kober, Vitaly

    2010-08-01

    Classical correlation filters for object detection and location estimation are designed under the assumption that the shape and intensity values of the object of interest are explicitly known. In this work we assume that the target is given at unknown coordinates in a reference image with a cluttered background corrupted by additive noise. We consider the nonoverlapping signal model for both the reference image and the input scene. Optimal correlation filters, with respect to signal-to-noise ratio and peak-to-output energy, for object detection and location estimation are derived. Estimation techniques are proposed for the parameters required for filter design. Computer simulation results obtained with the proposed filters are presented and compared with those of common correlation filters.

  7. Upconversion based continuous-wave mid-infrared detection

    DEFF Research Database (Denmark)

    Tidemand-Lichtenberg, Peter; Dam, Jeppe Seidelin; Pedersen, Christian

    2013-01-01

    We present theoretical and experimental work on upconversion based mid-wavelength infrared detection using silicon detectors without the need for cryogenic cooling. We consider both multi-spectral imaging and point spectroscopy targeting several specific applications.......We present theoretical and experimental work on upconversion based mid-wavelength infrared detection using silicon detectors without the need for cryogenic cooling. We consider both multi-spectral imaging and point spectroscopy targeting several specific applications....

  8. Voltage Sag Source Location Based on Instantaneous Energy Detection

    DEFF Research Database (Denmark)

    Chen, Zhe; Kong, Wei; Dong, Xinzhou

    2008-01-01

    Voltage sag is a major power quality problem, which could disrupt the operation of voltage-sensitive equipment. This paper presents the method based on variation components-based instantaneous energy for voltage sag source detection. Simulations have been performed to provide the thorough analysis...... for system with distributed generation units. The studies show that the presented method can effectively detect the location of voltage sag source....

  9. Machine Learning Based Classifier for Falsehood Detection

    Science.gov (United States)

    Mallikarjun, H. M.; Manimegalai, P., Dr.; Suresh, H. N., Dr.

    2017-08-01

    The investigation of physiological techniques for Falsehood identification tests utilizing the enthusiastic aggravations started as a part of mid 1900s. The need of Falsehood recognition has been a piece of our general public from hundreds of years back. Different requirements drifted over the general public raising the need to create trick evidence philosophies for Falsehood identification. The established similar addressing tests have been having a tendency to gather uncertain results against which new hearty strategies are being explored upon for acquiring more productive Falsehood discovery set up. Electroencephalography (EEG) is a non-obtrusive strategy to quantify the action of mind through the anodes appended to the scalp of a subject. Electroencephalogram is a record of the electric signs produced by the synchronous activity of mind cells over a timeframe. The fundamental goal is to accumulate and distinguish the important information through this action which can be acclimatized for giving surmising to Falsehood discovery in future analysis. This work proposes a strategy for Falsehood discovery utilizing EEG database recorded on irregular people of various age gatherings and social organizations. The factual investigation is directed utilizing MATLAB v-14. It is a superior dialect for specialized registering which spares a considerable measure of time with streamlined investigation systems. In this work center is made on Falsehood Classification by Support Vector Machine (SVM). 72 Samples are set up by making inquiries from standard poll with a Wright and wrong replies in a diverse era from the individual in wearable head unit. 52 samples are trained and 20 are tested. By utilizing Bluetooth based Neurosky’s Mindwave kit, brain waves are recorded and qualities are arranged appropriately. In this work confusion matrix is derived by matlab programs and accuracy of 56.25 % is achieved.

  10. Cross-correlation in flow-injection analysis with parallel flow streams and amperometric detection.

    Science.gov (United States)

    McKean, R E; Curran, D J

    1992-03-01

    Cross-correlation was implemented for flow-injection analysis by using two parallel flow lines, each with amperometric detectors, and driven by peristaltic pumps. One flow line was used to generate the reference signal for an analog correlator circuit and the other to generate the analyte signal. Cross-correlation was performed by multiplying these signals together at a time delay of zero, followed by low pass filtering. Using dopamine as a test system, improvements in signal-to-noise ratios of about two orders of magnitude were found for the correlation signal over the direct measurement of the electrode current.

  11. [Detecting fire smoke based on the multispectral image].

    Science.gov (United States)

    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.

  12. Transistor-based particle detection systems and methods

    Science.gov (United States)

    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.

  13. Region duplication forgery detection technique based on SURF and HAC.

    Science.gov (United States)

    Mishra, Parul; Mishra, Nishchol; Sharma, Sanjeev; Patel, Ravindra

    2013-01-01

    Region duplication forgery detection is a special type of forgery detection approach and widely used research topic under digital image forensics. In copy move forgery, a specific area is copied and then pasted into any other region of the image. Due to the availability of sophisticated image processing tools, it becomes very hard to detect forgery with naked eyes. From the forged region of an image no visual clues are often detected. For making the tampering more robust, various transformations like scaling, rotation, illumination changes, JPEG compression, noise addition, gamma correction, and blurring are applied. So there is a need for a method which performs efficiently in the presence of all such attacks. This paper presents a detection method based on speeded up robust features (SURF) and hierarchical agglomerative clustering (HAC). SURF detects the keypoints and their corresponding features. From these sets of keypoints, grouping is performed on the matched keypoints by HAC that shows copied and pasted regions.

  14. DDoS Attack Detection Algorithms Based on Entropy Computing

    Science.gov (United States)

    Li, Liying; Zhou, Jianying; Xiao, Ning

    Distributed Denial of Service (DDoS) attack poses a severe threat to the Internet. It is difficult to find the exact signature of attacking. Moreover, it is hard to distinguish the difference of an unusual high volume of traffic which is caused by the attack or occurs when a huge number of users occasionally access the target machine at the same time. The entropy detection method is an effective method to detect the DDoS attack. It is mainly used to calculate the distribution randomness of some attributes in the network packets' headers. In this paper, we focus on the detection technology of DDoS attack. We improve the previous entropy detection algorithm, and propose two enhanced detection methods based on cumulative entropy and time, respectively. Experiment results show that these methods could lead to more accurate and effective DDoS detection.

  15. Laser-based instrumentation for the detection of chemical agents

    International Nuclear Information System (INIS)

    Hartford, A. Jr.; Sander, R.K.; Quigley, G.P.; Radziemski, L.J.; Cremers, D.A.

    1982-01-01

    Several laser-based techniques are being evaluated for the remote, point, and surface detection of chemical agents. Among the methods under investigation are optoacoustic spectroscopy, laser-induced breakdown spectroscopy (LIBS), and synchronous detection of laser-induced fluorescence (SDLIF). Optoacoustic detection has already been shown to be capable of extremely sensitive point detection. Its application to remote sensing of chemical agents is currently being evaluated. Atomic emission from the region of a laser-generated plasma has been used to identify the characteristic elements contained in nerve (P and F) and blister (S and Cl) agents. Employing this LIBS approach, detection of chemical agent simulants dispersed in air and adsorbed on a variety of surfaces has been achieved. Synchronous detection of laser-induced fluorescence provides an attractive alternative to conventional LIF, in that an artificial narrowing of the fluorescence emission is obtained. The application of this technique to chemical agent simulants has been successfully demonstrated. 19 figures

  16. Image Fakery Detection Based on Singular Value Decomposition

    Directory of Open Access Journals (Sweden)

    T. Basaruddin

    2009-11-01

    Full Text Available The growing of image processing technology nowadays make it easier for user to modify and fake the images. Image fakery is a process to manipulate part or whole areas of image either in it content or context with the help of digital image processing techniques. Image fakery is barely unrecognizable because the fake image is looking so natural. Yet by using the numerical computation technique it is able to detect the evidence of fake image. This research is successfully applied the singular value decomposition method to detect image fakery. The image preprocessing algorithm prior to the detection process yields two vectors orthogonal to the singular value vector which are important to detect fake image. The result of experiment to images in several conditions successfully detects the fake images with threshold value 0.2. Singular value decomposition-based detection of image fakery can be used to investigate fake image modified from original image accurately.

  17. Region Duplication Forgery Detection Technique Based on SURF and HAC

    Directory of Open Access Journals (Sweden)

    Parul Mishra

    2013-01-01

    Full Text Available Region duplication forgery detection is a special type of forgery detection approach and widely used research topic under digital image forensics. In copy move forgery, a specific area is copied and then pasted into any other region of the image. Due to the availability of sophisticated image processing tools, it becomes very hard to detect forgery with naked eyes. From the forged region of an image no visual clues are often detected. For making the tampering more robust, various transformations like scaling, rotation, illumination changes, JPEG compression, noise addition, gamma correction, and blurring are applied. So there is a need for a method which performs efficiently in the presence of all such attacks. This paper presents a detection method based on speeded up robust features (SURF and hierarchical agglomerative clustering (HAC. SURF detects the keypoints and their corresponding features. From these sets of keypoints, grouping is performed on the matched keypoints by HAC that shows copied and pasted regions.

  18. An Incremental Support Vector Machine based Speech Activity Detection Algorithm.

    Science.gov (United States)

    Xianbo, Xiao; Guangshu, Hu

    2005-01-01

    Traditional voice activity detection algorithms are mostly threshold-based or statistical model-based. All those methods are absent of the ability to react quickly to variations of environments. This paper describes an incremental SVM (Support Vector Machine) method for speech activity detection. The proposed incremental procedure makes it adaptive to variation of environments and the special construction of incremental training data set decreases computing consumption effectively. Experiments results demonstrated its higher end point detection accuracy. Further work will be focused on decreasing computing consumption and importing multi-class SVM classifiers.

  19. Computer-based instrumentation for partial discharge detection in GIS

    International Nuclear Information System (INIS)

    Md Enamul Haque; Ahmad Darus; Yaacob, M.M.; Halil Hussain; Feroz Ahmed

    2000-01-01

    Partial discharge is one of the prominent indicators of defects and insulation degradation in a Gas Insulated Switchgear (GIS). Partial discharges (PD) have a harmful effect on the life of insulation of high voltage equipment. The PD detection using acoustic technique and subsequent analysis is currently an efficient method of performing non-destructive testing of GIS apparatus. A low cost PC-based acoustic PD detection instrument has been developed for the non-destructive diagnosis of GIS. This paper describes the development of a PC-based instrumentation system for partial discharge detection in GIS and some experimental results have also presented. (Author)

  20. A novel line segment detection algorithm based on graph search

    Science.gov (United States)

    Zhao, Hong-dan; Liu, Guo-ying; Song, Xu

    2018-02-01

    To overcome the problem of extracting line segment from an image, a method of line segment detection was proposed based on the graph search algorithm. After obtaining the edge detection result of the image, the candidate straight line segments are obtained in four directions. For the candidate straight line segments, their adjacency relationships are depicted by a graph model, based on which the depth-first search algorithm is employed to determine how many adjacent line segments need to be merged. Finally we use the least squares method to fit the detected straight lines. The comparative experimental results verify that the proposed algorithm has achieved better results than the line segment detector (LSD).

  1. Islanding detection scheme based on adaptive identifier signal estimation method.

    Science.gov (United States)

    Bakhshi, M; Noroozian, R; Gharehpetian, G B

    2017-11-01

    This paper proposes a novel, passive-based anti-islanding method for both inverter and synchronous machine-based distributed generation (DG) units. Unfortunately, when the active/reactive power mismatches are near to zero, majority of the passive anti-islanding methods cannot detect the islanding situation, correctly. This study introduces a new islanding detection method based on exponentially damped signal estimation method. The proposed method uses adaptive identifier method for estimating of the frequency deviation of the point of common coupling (PCC) link as a target signal that can detect the islanding condition with near-zero active power imbalance. Main advantage of the adaptive identifier method over other signal estimation methods is its small sampling window. In this paper, the adaptive identifier based islanding detection method introduces a new detection index entitled decision signal by estimating of oscillation frequency of the PCC frequency and can detect islanding conditions, properly. In islanding conditions, oscillations frequency of PCC frequency reach to zero, thus threshold setting for decision signal is not a tedious job. The non-islanding transient events, which can cause a significant deviation in the PCC frequency are considered in simulations. These events include different types of faults, load changes, capacitor bank switching, and motor starting. Further, for islanding events, the capability of the proposed islanding detection method is verified by near-to-zero active power mismatches. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  2. Research of detection depth for graphene-based optical sensor

    Science.gov (United States)

    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.

  3. Comparison of Subset-Based Local and Finite Element-Based Global Digital Image Correlation

    KAUST Repository

    Pan, Bing

    2015-02-12

    Digital image correlation (DIC) techniques require an image matching algorithm to register the same physical points represented in different images. Subset-based local DIC and finite element-based (FE-based) global DIC are the two primary image matching methods that have been extensively investigated and regularly used in the field of experimental mechanics. Due to its straightforward implementation and high efficiency, subset-based local DIC has been used in almost all commercial DIC packages. However, it is argued by some researchers that FE-based global DIC offers better accuracy because of the enforced continuity between element nodes. We propose a detailed performance comparison between these different DIC algorithms both in terms of measurement accuracy and computational efficiency. Then, by measuring displacements of the same calculation points using the same calculation algorithms (e.g., correlation criterion, initial guess estimation, subpixel interpolation, optimization algorithm and convergence conditions) and identical calculation parameters (e.g., subset or element size), the performances of subset-based local DIC and two FE-based global DIC approaches are carefully compared in terms of measurement error and computational efficiency using both numerical tests and real experiments. A detailed examination of the experimental results reveals that, when subset (element) size is not very small and the local deformation within a subset (element) can be well approximated by the shape function used, standard subset-based local DIC approach not only provides better results in measured displacements, but also demonstrates much higher computation efficiency. However, several special merits of FE-based global DIC approaches are indicated.

  4. Smart phone based bacterial detection using bio functionalized fluorescent nanoparticles

    International Nuclear Information System (INIS)

    Rajendran, Vinoth Kumar; Bakthavathsalam, Padmavathy; Ali, Baquir Mohammed Jaffar

    2014-01-01

    We are describing immunochromatographic test strips with smart phone-based fluorescence readout. They are intended for use in the detection of the foodborne bacterial pathogens Salmonella spp. and Escherichia coli O157. Silica nanoparticles (SiNPs) were doped with FITC and Ru(bpy), conjugated to the respective antibodies, and then used in a conventional lateral flow immunoassay (LFIA). Fluorescence was recorded by inserting the nitrocellulose strip into a smart phone-based fluorimeter consisting of a light weight (40 g) optical module containing an LED light source, a fluorescence filter set and a lens attached to the integrated camera of the cell phone in order to acquire high-resolution fluorescence images. The images were analysed by exploiting the quick image processing application of the cell phone and enable the detection of pathogens within few minutes. This LFIA is capable of detecting pathogens in concentrations as low as 10 5 cfu mL −1 directly from test samples without pre-enrichment. The detection is one order of magnitude better compared to gold nanoparticle-based LFIAs under similar condition. The successful combination of fluorescent nanoparticle-based pathogen detection by LFIAs with a smart phone-based detection platform has resulted in a portable device with improved diagnosis features and having potential application in diagnostics and environmental monitoring. (author)

  5. ERP Correlates of Pitch Error Detection in Complex Tone and Voice Auditory Feedback with Missing Fundamental

    Science.gov (United States)

    Behroozmand, Roozbeh; Korzyukov, Oleg; Larson, Charles R.

    2012-01-01

    Previous studies have shown that the pitch of a sound is perceived in the absence of its fundamental frequency (F0), suggesting that a distinct mechanism may resolve pitch based on a pattern that exists between harmonic frequencies. The present study investigated whether such a mechanism is active during voice pitch control. ERPs were recorded in response to +200 cents pitch shifts in the auditory feedback of self-vocalizations and complex tones with and without the F0. The absence of the fundamental induced no difference in ERP latencies. However, a right-hemisphere difference was found in the N1 amplitudes with larger responses to complex tones that included the fundamental compared to when it was missing. The P1 and N1 latencies were shorter in the left hemisphere, and the N1 and P2 amplitudes were larger bilaterally for pitch shifts in voice and complex tones compared with pure tones. These findings suggest hemispheric differences in neural encoding of pitch in sounds with missing fundamental. Data from the present study suggest that the right cortical auditory areas, thought to be specialized for spectral processing, may utilize different mechanisms to resolve pitch in sounds with missing fundamental. The left hemisphere seems to perform faster processing to resolve pitch based on the rate of temporal variations in complex sounds compared with pure tones. These effects indicate that the differential neural processing of pitch in the left and right hemispheres may enable the audio-vocal system to detect temporal and spectral variations in the auditory feedback for vocal pitch control. PMID:22386045

  6. [The study in detection of microcalcification in early breast cancer by ultrasound and its correlation with pathohistology].

    Science.gov (United States)

    Han, Xiu-jie; Ren, Jun-hong; Ma, Na; Tan, Qing-ting; Wang, Si-yu

    2012-09-04

    To discuss the role of ultrasound in examining microcalcification of early breast cancer and its correlation with pathohistological type and grade. 178 lesions in 165 cases of early breast cancer confirmed by pathology after surgical resection were examine by high frequency ultrasound, meanwhile microcalcification were detected and reported. 39 lesions in 32 cases are carcinoma in situ and microinvasive carcinoma of breast. 139 lesions in 133 cases are early invasive breast carcinoma that is below 2 cm in diameter and doesn't invasive the lymph node and other parts of the body. To analyse the sensitivity of detection micro-calcification of early breast cancer by ultrasound and its correlation with pathohistological type and grade. The sensitivity is 81.6% in detecting microcalcification of early breast cancer by ultrasound. There is no significant statistical difference in detecting microcalcification between the two group (P = 0.217). There is no significant statistical difference in detecting microcalcification of early invasive breast cancer between the different pathologic types (P > 0.05), and there are no significant differences in detecting microcalcification of early breast cancer between the different pathologic grades (group I: P = 0.202, group II: P = 0.415). There is significant difference in detecting microcalcification of solid tumor by ultrasonic examination in group I between the different pathologic grades (P = 0.029). There is higher sensitivity in detecting microcalcification of early breast cancer by ultrasonography. Microcalcification of early breast cancer may be no closely related to pathologic grades. US has a certain value to clinic in detecting microcalcification of early breast cancer.

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

    Science.gov (United States)

    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.

  8. Combining Host-based and network-based intrusion detection system

    African Journals Online (AJOL)

    These attacks were simulated using hping. The proposed system is implemented in Java. The results show that the proposed system is able to detect attacks both from within (host-based) and outside sources (network-based). Key Words: Intrusion Detection System (IDS), Host-based, Network-based, Signature, Security log.

  9. Study of Threat Scenario Reconstruction based on Multiple Correlation

    Science.gov (United States)

    Yuan, Xuejun; Du, Jing; Qin, Futong; Zhou, Yunyan

    2017-10-01

    The emergence of intrusion detection technology has solved many network attack problems, ensuring the safety of computer systems. However, because of the isolated output alarm information, large amount of data, and mixed events, it is difficult for the managers to understand the deep logic relationship between the alarm information, thus they cannot deduce the attacker’s true intentions. This paper presents a method of online threat scene reconstruction to handle the alarm information, which reconstructs of the threat scene. For testing, the standard data set is used.

  10. An Algorithm for Detection of DVB-T Signals Based on Their Second-Order Statistics

    Directory of Open Access Journals (Sweden)

    Jallon Pierre

    2008-01-01

    Full Text Available Abstract We propose in this paper a detection algorithm based on a cost function that jointly tests the correlation induced by the cyclic prefix and the fact that this correlation is time-periodic. In the first part of the paper, the cost function is introduced and some analytical results are given. In particular, the noise and multipath channel impacts on its values are theoretically analysed. In a second part of the paper, some asymptotic results are derived. A first exploitation of these results is used to build a detection test based on the false alarm probability. These results are also used to evaluate the impact of the number of cycle frequencies taken into account in the cost function on the detection performances. Thanks to numerical estimations, we have been able to estimate that the proposed algorithm detects DVB-T signals with an SNR of  dB. As a comparison, and in the same context, the detection algorithm proposed by the 802.22 WG in 2006 is able to detect these signals with an SNR of  dB.

  11. An Algorithm for Detection of DVB-T Signals Based on Their Second-Order Statistics

    Directory of Open Access Journals (Sweden)

    Pierre Jallon

    2008-03-01

    Full Text Available We propose in this paper a detection algorithm based on a cost function that jointly tests the correlation induced by the cyclic prefix and the fact that this correlation is time-periodic. In the first part of the paper, the cost function is introduced and some analytical results are given. In particular, the noise and multipath channel impacts on its values are theoretically analysed. In a second part of the paper, some asymptotic results are derived. A first exploitation of these results is used to build a detection test based on the false alarm probability. These results are also used to evaluate the impact of the number of cycle frequencies taken into account in the cost function on the detection performances. Thanks to numerical estimations, we have been able to estimate that the proposed algorithm detects DVB-T signals with an SNR of −12 dB. As a comparison, and in the same context, the detection algorithm proposed by the 802.22 WG in 2006 is able to detect these signals with an SNR of −8 dB.

  12. Multicriteria Similarity-Based Anomaly Detection Using Pareto Depth Analysis.

    Science.gov (United States)

    Hsiao, Ko-Jen; Xu, Kevin S; Calder, Jeff; Hero, Alfred O

    2016-06-01

    We consider the problem of identifying patterns in a data set that exhibits anomalous behavior, often referred to as anomaly detection. Similarity-based anomaly detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g., as measured by the nearest neighbor Euclidean distances between a test sample and the training samples. In many application domains, there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such cases, multiple dissimilarity measures can be defined, including nonmetric measures, and one can test for anomalies by scalarizing using a nonnegative linear combination of them. If the relative importance of the different dissimilarity measures are not known in advance, as in many anomaly detection applications, the anomaly detection algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we propose a method for similarity-based anomaly detection using a novel multicriteria dissimilarity measure, the Pareto depth. The proposed Pareto depth analysis (PDA) anomaly detection algorithm uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach is provably better than using linear combinations of the criteria, and shows superior performance on experiments with synthetic and real data sets.

  13. A physiology-based seizure detection system for multichannel EEG.

    Directory of Open Access Journals (Sweden)

    Chia-Ping Shen

    Full Text Available BACKGROUND: Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable. METHODOLOGY: This study proposes a physiology-based detection system for epileptic seizures that uses multichannel EEG signals. The proposed technique was tested on two EEG data sets acquired from 18 patients. Both unipolar and bipolar EEG signals were analyzed. We employed sample entropy (SampEn, statistical values, and concepts used in clinical neurophysiology (e.g., phase reversals and potential fields of a bipolar EEG to extract the features. We further tested the performance of a genetic algorithm cascaded with a support vector machine and post-classification spike matching. PRINCIPAL FINDINGS: We obtained 86.69% spike detection and 99.77% seizure detection for Data Set I. The detection system was further validated using the model trained by Data Set I on Data Set II. The system again showed high performance, with 91.18% detection of spikes and 99.22% seizure detection. CONCLUSION: We report a de novo EEG classification system for seizure and spike detection on multichannel EEG that includes physiology-based knowledge to enhance the performance of this type of system.

  14. Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks.

    Science.gov (United States)

    Chen, Xiaoguang; Jiang, Xu; Wu, Zhilu; Zhuang, Shufeng

    2017-02-16

    High throughput, low latency and reliable communication has always been a hot topic for wireless sensor networks (WSNs) in various applications. Multiuser detection is widely used to suppress the bad effect of multiple access interference in WSNs. In this paper, a novel multiuser detection method based on differential characteristics is proposed to suppress multiple access interference. The proposed iterative receive method consists of three stages. Firstly, a differential characteristics function is presented based on the optimal multiuser detection decision function; then on the basis of differential characteristics, a preliminary threshold detection is utilized to find the potential wrongly received bits; after that an error bit corrector is employed to correct the wrong bits. In order to further lower the bit error ratio (BER), the differential characteristics calculation, threshold detection and error bit correction process described above are iteratively executed. Simulation results show that after only a few iterations the proposed multiuser detection method can achieve satisfactory BER performance. Besides, BER and near far resistance performance are much better than traditional suboptimal multiuser detection methods. Furthermore, the proposed iterative multiuser detection method also has a large system capacity.

  15. Two-Dimensional UV Absorption Correlation Spectroscopy as a Method for the Detection of Thiamethoxam Residue in Tea

    Science.gov (United States)

    Zhang, J.; Zhao, Zh.; Wang, L.; Zhu, X.; Shen, L.; Yu, Y.

    2015-05-01

    Two-dimensional correlation spectroscopy (2D-COS) combined with UV absorption spectroscopy was evaluated as a technique for the identification of spectral regions associated with the residues of thiamethoxam in tea. There is only one absorption peak at 275 nm in the absorption spectrum of a mixture of thiamethoxam and tea, which is the absorption peak of tea. Based on 2D-COS, the absorption peak of thiamethoxam at 250 nm is extracted from the UV spectra of the mixture. To determine the residue of thiamethoxam in tea, 250 nm is selected as the measured wavelength, at which the fitting result is as follows: the residual sum of squares is 0.01375, standard deviation R2 is 0.99068, and F value is 426. Statistical analysis shows that there is a significant linear relationship between the concentration of thiamethoxam in tea and the absorbance at 250 nm in the UV spectra of the mixture. Moreover, the average prediction error is 0.0033 and the prediction variance is 0.1654, indicating good predictive result. Thus, the UV absorption spectrum can be used as a measurement method for rapid detection of thiamethoxam residues in tea.

  16. A Portable Pesticide Residues Detection Instrument Based on Impedance Immunosensor

    Directory of Open Access Journals (Sweden)

    Jiang Ding

    2014-06-01

    Full Text Available In this paper, a design of portable pesticide residues detection instrument was presented based on an impedance immunosensor. The immunosensor exploited the novel multilayer films based on Au nanoparticles (AuNPs and polyaniline/carboxylated multiwall carbon nanotubes- chitosan nanocomposite (PANI/MWCNTs/CS. The detection principle of the instrument was based on the electrochemical characteristic of antigen-specific antibody immune response. With a stronger signal generated from the antigen-specific antibody immune response, the signal detection circuit was designed more easily. We integrated immunosensor and signal detection circuit to fabricate pesticide residues detection instrument. This proposed instrument could realize the rapid detection of pesticide residues in fruits and vegetables with automatic data processing and presented the result on the spot. The impedance test error was less than 5 %. The results showed that the proposed instrument had a good consistence compared with the traditional analytical methods. Thus, it would be a promising rapid detection instrument for pesticide residues in agricultural products.

  17. [A cell-based detection of ciguatoxin using sodium fluorescence probe].

    Science.gov (United States)

    Yuan, Jian-hui; Yang, Hui; Tang, Huan-wen; Huang, Wei; Xu, Xin-yun; Liu, Jian-jun; Ke, Yue-bin; Cheng, Jin-quan; Zhuang, Zhi-xiong

    2011-04-01

    To establish a cell-based detection method of ciguatoxin using fluorescence assay. Mouse neuroblastoma N-2A cells were exposed to ouabain and veratridine and different concentrations of standard ciguatoxin samples (P-CTX-1) to establish the curvilinear relationship between the toxin dosage and fluorescence intensity using the sodium fluorescence probe CoroNaTM Green. The toxicity curvilinear relationship was also generated between the toxin dosage and cell survival using CCK-8 method. Based on these standard curves, the presence of ciguatoxin was detected in 33 samples of deep-sea coral fish. A correlation was found between the detection results of cell-based fluorescence assay and cytotoxicity assay, whose detection limit reached 103 g/ml and 1012 g/ml, respectively. The cell-based fluorescent assay sensitivity showed a higher sensitivity than cytotoxicity assay with a 2-4 h reduction of the detection time. The cell-based fluorescent assay can quickly and sensitively detect ciguatoxin and may serve as a good option for preliminary screening of the toxin.

  18. Novel ultrasonic distance measuring system based on correlation method

    Directory of Open Access Journals (Sweden)

    Gądek K.

    2014-09-01

    Full Text Available This paper presents an innovative method for measuring the time delay of ultrasonic waves. Pulse methods used in the previous studies was characterized by latency. The method of phase correlation, presented in this article is free from this disadvantages. Due to the phase encoding with the use of Walsh functions the presented method allows to obtain better precision than previous methods. The algorithm to measure delay of the reflected wave with the use of microprocessor ARM Cortex M4 linked to a PC has been worked out and tested. This method uses the signal from the ultrasonic probe to precisely determine the time delay, caused by the propagation in medium, possible. In order to verify the effectiveness of the method a part of the measuring system was implemented in LabVIEW. The presented method proved to be effective, as it is shown in presented simulation results

  19. Distributed Iterative Multiuser Detection through Base Station Cooperation

    Directory of Open Access Journals (Sweden)

    Shahid Khattak

    2008-08-01

    Full Text Available This paper deals with multiuser detection through base station cooperation in an uplink, interference-limited, high frequency reuse scenario. Distributed iterative detection (DID is an interference mitigation technique in which the base stations at different geographical locations exchange detected data iteratively while performing separate detection and decoding of their received data streams. This paper explores possible DID receive strategies and proposes to exchange between base stations only the processed information for their associated mobile terminals. The resulting backhaul traffic is considerably lower than that of existing cooperative multiuser detection strategies. Single-antenna interference cancellation techniques are employed to generate local estimates of the dominant interferers at each base station, which are then combined with their independent received copies from other base stations, resulting in more effective interference suppression. Since hard information bits or quantized log-likelihood ratios (LLRs are transferred, we investigate the effect of quantization of the LLR values with the objective of further reducing the backhaul traffic. Our findings show that schemes based on nonuniform quantization of the “soft bits” allow for reducing the backhaul to 1–2 exchanged bits/coded bit.

  20. Distributed Iterative Multiuser Detection through Base Station Cooperation

    Directory of Open Access Journals (Sweden)

    Khattak Shahid

    2008-01-01

    Full Text Available Abstract This paper deals with multiuser detection through base station cooperation in an uplink, interference-limited, high frequency reuse scenario. Distributed iterative detection (DID is an interference mitigation technique in which the base stations at different geographical locations exchange detected data iteratively while performing separate detection and decoding of their received data streams. This paper explores possible DID receive strategies and proposes to exchange between base stations only the processed information for their associated mobile terminals. The resulting backhaul traffic is considerably lower than that of existing cooperative multiuser detection strategies. Single-antenna interference cancellation techniques are employed to generate local estimates of the dominant interferers at each base station, which are then combined with their independent received copies from other base stations, resulting in more effective interference suppression. Since hard information bits or quantized log-likelihood ratios (LLRs are transferred, we investigate the effect of quantization of the LLR values with the objective of further reducing the backhaul traffic. Our findings show that schemes based on nonuniform quantization of the "soft bits" allow for reducing the backhaul to 1–2 exchanged bits/coded bit.

  1. Detection of breast abnormalities on enhanced chest CT: Correlation with breast composition on mammography

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Eun Mi; Kang, Hee; Shin, Young Gyung; Yun, Jong Hyouk; Oh, Kyung Seung [Dept. of Radiology, Kosin University Gospel Hospital, Kosin University College of Medicine, Busan (Korea, Republic of)

    2017-02-15

    To investigate the capability of enhanced chest computed tomography (CT) for detecting breast abnormalities and to assess the influence of breast composition on this detectability. From 2000 to 2013, 75 patients who underwent mammography, breast sonography, and enhanced chest CT within one month and had abnormalities on sonography were included. Detection rate of breast abnormality on enhanced chest CT was compared among 4 types of breast composition by the Breast Imaging Reporting and Data System. Contribution of breast composition, size and enhancement of target lesions to detectability of enhanced chest CT was assessed using logistic regression and chi-square test. Of the 75 target lesions, 34 (45.3%) were detected on enhanced chest CT, corresponding with those on breast sonography; there were no significantly different detection rates among the 4 types of breast composition (p = 0.078). Breast composition [odds ratio (OR) = 1.07, p = 0.206] and enhancement (OR = 21.49, p = 0.998) had no significant effect, but size (OR = 1.23, p = 0.004) was a significant contributing factor influencing the detectability of enhanced chest CT for breast lesions. About half of the cases (45.3%) demonstrated breast lesions on chest CT corresponding with target lesions on sonography. Breast composition defined on mammography did not affect the detectability of enhanced chest CT for breast lesions.

  2. Home Camera-Based Fall Detection System for the Elderly

    Directory of Open Access Journals (Sweden)

    Koldo de Miguel

    2017-12-01

    Full Text Available Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.

  3. Home Camera-Based Fall Detection System for the Elderly.

    Science.gov (United States)

    de Miguel, Koldo; Brunete, Alberto; Hernando, Miguel; Gambao, Ernesto

    2017-12-09

    Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.

  4. A vision based row detection system for sugar beet

    NARCIS (Netherlands)

    Bakker, T.; Wouters, H.; Asselt, van C.J.; Bontsema, J.; Tang, L.; Müller, J.; Straten, van G.

    2008-01-01

    One way of guiding autonomous vehicles through the field is using a vision based row detection system. A new approach for row recognition is presented which is based on grey-scale Hough transform on intelligently merged images resulting in a considerable improvement of the speed of image processing.

  5. A Labeled Data Set For Flow-based Intrusion Detection

    NARCIS (Netherlands)

    Sperotto, Anna; Sadre, R.; van Vliet, Frank; Pras, Aiko; Nunzi, Giorgio; Scoglio, Caterina; Li, Xing

    2009-01-01

    Flow-based intrusion detection has recently become a promising security mechanism in high speed networks (1-10 Gbps). Despite the richness in contributions in this field, benchmarking of flow-based IDS is still an open issue. In this paper, we propose the first publicly available, labeled data set

  6. Dust storm detection using random forests and physical-based ...

    Indian Academy of Sciences (India)

    This paper investigates the capability of two physical-based methods, and random forests (RF) classifier, for the first time, to detect dust storms using MODIS imagery. Since the physical-based approaches are empirical, they suffer from certain drawbacks such as high variability of thresholds depending on the underlying ...

  7. Smartphone-based low light detection for bioluminescence application

    Science.gov (United States)

    We report a smartphone-based device and associated imaging-processing algorithm to maximize the sensitivity of standard smartphone cameras, that can detect the presence of single-digit pW of radiant flux intensity. The proposed hardware and software, called bioluminescent-based analyte quantitation ...

  8. An Android malware detection system based on machine learning

    Science.gov (United States)

    Wen, Long; Yu, Haiyang

    2017-08-01

    The Android smartphone, with its open source character and excellent performance, has attracted many users. However, the convenience of the Android platform also has motivated the development of malware. The traditional method which detects the malware based on the signature is unable to detect unknown applications. The article proposes a machine learning-based lightweight system that is capable of identifying malware on Android devices. In this system we extract features based on the static analysis and the dynamitic analysis, then a new feature selection approach based on principle component analysis (PCA) and relief are presented in the article to decrease the dimensions of the features. After that, a model will be constructed with support vector machine (SVM) for classification. Experimental results show that our system provides an effective method in Android malware detection.

  9. Robust facial landmark detection based on initializing multiple poses

    Directory of Open Access Journals (Sweden)

    Xin Chai

    2016-10-01

    Full Text Available For robot systems, robust facial landmark detection is the first and critical step for face-based human identification and facial expression recognition. In recent years, the cascaded-regression-based method has achieved excellent performance in facial landmark detection. Nevertheless, it still has certain weakness, such as high sensitivity to the initialization. To address this problem, regression based on multiple initializations is established in a unified model; face shapes are then estimated independently according to these initializations. With a ranking strategy, the best estimate is selected as the final output. Moreover, a face shape model based on restricted Boltzmann machines is built as a constraint to improve the robustness of ranking. Experiments on three challenging datasets demonstrate the effectiveness of the proposed facial landmark detection method against state-of-the-art methods.

  10. Detection and correction of blinking bias in image correlation transport measurements of quantum dot tagged macromolecules

    DEFF Research Database (Denmark)

    Durisic, Nela; Bachir, Alexia I; Kolin, David L

    2007-01-01

    Semiconductor nanocrystals or quantum dots (QDs) are becoming widely used as fluorescent labels for biological applications. Here we demonstrate that fluorescence fluctuation analysis of their diffusional mobility using temporal image correlation spectroscopy is highly susceptible to systematic...... application of the image correlation methods for measurement of the diffusion coefficient of glycosyl phosphatidylinositol-anchored proteins tagged with QDs as imaged on living fibroblasts Udgivelsesdato: 2007-Aug-15...

  11. Target Detection Based on EBPSK Satellite Passive Radar

    Directory of Open Access Journals (Sweden)

    Lu Zeyuan

    2015-05-01

    Full Text Available Passive radar is a topic anti stealth technology with simple structure, and low cost. Radiation source model, signal transmission model, and target detection are the key points of passive radar technology research. The paper analyzes the characteristics of EBPSK signal modulation and target detection method aspect of spaceborne radiant source. By comparison with other satellite navigation and positioning system, the characteristics of EBPSK satellite passive radar system are analyzed. It is proved that the maximum detection range of EBPSK satellite signal can satisfy the needs of the proposed model. In the passive radar model, sparse representation is used to achieve high resolution DOA detection. The comparison with the real target track by simulation demonstrates that effective detection of airborne target using EBPSK satellite passive radar system based on sparse representation is efficient.

  12. Dim target detection method based on salient graph fusion

    Science.gov (United States)

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

    2018-02-01

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

  13. Edge detection based on computational ghost imaging with structured illuminations

    Science.gov (United States)

    Yuan, Sheng; Xiang, Dong; Liu, Xuemei; Zhou, Xin; Bing, Pibin

    2018-03-01

    Edge detection is one of the most important tools to recognize the features of an object. In this paper, we propose an optical edge detection method based on computational ghost imaging (CGI) with structured illuminations which are generated by an interference system. The structured intensity patterns are designed to make the edge of an object be directly imaged from detected data in CGI. This edge detection method can extract the boundaries for both binary and grayscale objects in any direction at one time. We also numerically test the influence of distance deviations in the interference system on edge extraction, i.e., the tolerance of the optical edge detection system to distance deviation. Hopefully, it may provide a guideline for scholars to build an experimental system.

  14. Research on Daily Objects Detection Based on Deep Neural Network

    Science.gov (United States)

    Ding, Sheng; Zhao, Kun

    2018-03-01

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

  15. Automated detection and classification for craters based on geometric matching

    Science.gov (United States)

    Chen, Jian-qing; Cui, Ping-yuan; Cui, Hui-tao

    2011-08-01

    Crater detection and classification are critical elements for planetary mission preparations and landing site selection. This paper presents a methodology for the automated detection and matching of craters on images of planetary surface such as Moon, Mars and asteroids. For craters usually are bowl shaped depression, craters can be figured as circles or circular arc during landing phase. Based on the hypothesis that detected crater edges is related to craters in a template by translation, rotation and scaling, the proposed matching method use circles to fitting craters edge, and align circular arc edges from the image of the target body with circular features contained in a model. The approach includes edge detection, edge grouping, reference point detection and geometric circle model matching. Finally we simulate planetary surface to test the reasonableness and effectiveness of the proposed method.

  16. Wavelet Packet based Detection of Surface Faults on Compact Discs

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Wickerhauser, Mladen Victor

    2006-01-01

    In this paper the detection of faults on the surface of a compact disc is addressed. Surface faults like scratches and fingerprints disturb the on-line measurement of the pick-up position relative to the track. This is critical since the pick-up is focused on and tracked at the information track...... based on these measurements. A precise detection of the surface fault is a prerequisite to a correct handling of the faults in order to protect the pick-up of the compact disc player from audible track losses. The actual fault handling which is addressed in other publications can be carried out...... by the use of dedicated filters adapted to remove the faults from the measurements. In this paper detection using wavelet packet filters is demonstrated. The filters are designed using the joint best basis method. Detection using these filters shows a distinct improvement compared to detection using ordinary...

  17. Arduino-based noise robust online heart-rate detection.

    Science.gov (United States)

    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.

  18. Stratification-Based Outlier Detection over the Deep Web.

    Science.gov (United States)

    Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

    2016-01-01

    For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web.

  19. A UAV-BASED ROE DEER FAWN DETECTION SYSTEM

    Directory of Open Access Journals (Sweden)

    M. Israel

    2012-09-01

    Full Text Available This paper presents a UAV based remote sensing system for the detection of fawns in the meadows. There is a high demand because during pasture mowing many wild animals, especially roe deer fawns are killed by mowing machines. The system was tested in several real situations especially with differing weather and iluminating conditions. Its primary sensor is a lightweight thermal infrared camera. The images are captured onboard of the flight system and also transmitted as analog video stream to the ground station, where the user can follow the camera live stream on a monitor for manual animal detection. Beside a high detection rate a fast workflow is another very important objective for this application. Therefore a waypoint planning software was developed that accelerates the workflow. At adequate illuminating and weather conditions the presented UAV-based fawn detection via thermal imaging is a comfortable, fast and reliable method.

  20. a Uav-Based ROE Deer Fawn Detection System

    Science.gov (United States)

    Israel, M.

    2011-09-01

    This paper presents a UAV based remote sensing system for the detection of fawns in the meadows. There is a high demand because during pasture mowing many wild animals, especially roe deer fawns are killed by mowing machines. The system was tested in several real situations especially with differing weather and iluminating conditions. Its primary sensor is a lightweight thermal infrared camera. The images are captured onboard of the flight system and also transmitted as analog video stream to the ground station, where the user can follow the camera live stream on a monitor for manual animal detection. Beside a high detection rate a fast workflow is another very important objective for this application. Therefore a waypoint planning software was developed that accelerates the workflow. At adequate illuminating and weather conditions the presented UAV-based fawn detection via thermal imaging is a comfortable, fast and reliable method.

  1. The Unknown Computer Viruses Detection Based on Similarity

    Science.gov (United States)

    Liu, Zhongda; Nakaya, Naoshi; Koui, Yuuji

    New computer viruses are continually being generated and they cause damage all over the world. In general, current anti-virus software detects viruses by matching a pattern based on the signature; thus, unknown viruses without any signature cannot be detected. Although there are some static analysis technologies that do not depend on signatures, virus writers often use code obfuscation techniques, which make it difficult to execute a code analysis. As is generally known, unknown viruses and known viruses share a common feature. In this paper we propose a new static analysis technology that can circumvent code obfuscation to extract the common feature and detect unknown viruses based on similarity. The results of evaluation experiments demonstrated that this technique is able to detect unknown viruses without false positives.

  2. Enhancing Community Detection By Affinity-based Edge Weighting Scheme

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Andy [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Sanders, Geoffrey [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Henson, Van [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Vassilevski, Panayot [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-10-05

    Community detection refers to an important graph analytics problem of finding a set of densely-connected subgraphs in a graph and has gained a great deal of interest recently. The performance of current community detection algorithms is limited by an inherent constraint of unweighted graphs that offer very little information on their internal community structures. In this paper, we propose a new scheme to address this issue that weights the edges in a given graph based on recently proposed vertex affinity. The vertex affinity quantifies the proximity between two vertices in terms of their clustering strength, and therefore, it is ideal for graph analytics applications such as community detection. We also demonstrate that the affinity-based edge weighting scheme can improve the performance of community detection algorithms significantly.

  3. [Medical image processing based on wavelet characteristics and edge blur detection].

    Science.gov (United States)

    Zhu, Baihui; Wan, Zhiping

    2014-06-01

    To solve the problems of noise interference and edge signal weakness for the existing medical image, we used two-dimensional wavelet transform to process medical images. Combined the directivity of the image edges and the correlation of the wavelet coefficients, we proposed a medical image processing algorithm based on wavelet characteristics and edge blur detection. This algorithm improved noise reduction capabilities and the edge effect due to wavelet transformation and edge blur detection. The experimental results showed that directional correlation improved edge based on wavelet transform fuzzy algorithm could effectively reduce the noise signal in the medical image and save the image edge signal. It has the advantage of the high-definition and de-noising ability.

  4. Pipeline Processing with an Iterative, Context-Based Detection Model

    Science.gov (United States)

    2016-01-22

    unlimited. 59 optimum choice for steering vectors is the adaptive beamformer weighting [ Capon et al ., 1967] also known as the minimum variance...AFRL-RV-PS- AFRL-RV-PS- TR-2016-0080 TR-2016-0080 PIPELINE PROCESSING WITH AN ITERATIVE, CONTEXT-BASED DETECTION MODEL T. Kværna, et al ...conditions, the capability to detect events down to magnitude 2.0 [Gibbons et al ., 2011]. Figure 3. Location of the SPITS array in relation to Novaya

  5. An Automated Energy Detection Algorithm Based on Consecutive Mean Excision

    Science.gov (United States)

    2018-01-01

    ARL-TR-8268 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Consecutive Mean Excision...not return it to the originator. ARL-TR-8268 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm...2018 2. REPORT TYPE Technical Report 3. DATES COVERED (From - To) 1 October 2016–30 September 2017 4. TITLE AND SUBTITLE An Automated Energy

  6. Flow-based Brute-force Attack Detection

    OpenAIRE

    Drašar, Martin; Vykopal, Jan; Winter, Philipp

    2013-01-01

    Brute-force attacks are a prevalent phenomenon that is getting harderto successfully detect on a network level due to increasing volume and en-cryption of network traffic and growing ubiquity of high-speed networks.Although the research in this field advanced considerably, there still remainclasses of attacks that are undetectable. In this chapter, we present sev-eral methods for the detection of brute-force attacks based on the analysisof network flows. We discuss their strengths and shortco...

  7. Computer-vision-based car logotype detection and recognition

    OpenAIRE

    Tomažič, Gašper

    2015-01-01

    This thesis addresses the problem of image-based logotype detection and recognition. A new algorithm for logotype detection in images of cars is proposed. In the first stage, the algorithm localizes all maximally-stable extremal regions as candidates of logotype parts. In the next stage, the regions are combined to create logotype candidates, which are encoded by histograms of gradients. A random forest classifier is then used to verify the candidate regions as being logotypes or not and simu...

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

    Directory of Open Access Journals (Sweden)

    Tuoyu Zhou

    2017-09-01

    Full Text Available 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.

  9. Fiber-Optic Based Compact Gas Leak Detection System

    Science.gov (United States)

    deGroot, Wim A.

    1995-01-01

    A propellant leak detection system based on Raman scattering principles is introduced. The proposed system is flexible and versatile as the result of the use of optical fibers. It is shown that multiple species can be monitored simultaneously. In this paper oxygen, nitrogen, carbon monoxide, and hydrogen are detected and monitored. The current detection sensitivity for both hydrogen and carbon monoxide is 1% partial pressure at ambient conditions. The sensitivity for oxygen and nitrogen is 0.5% partial pressure. The response time to changes in species concentration is three minutes. This system can be used to monitor multiple species at several locations.

  10. Statistical Outlier Detection for Jury Based Grading Systems

    DEFF Research Database (Denmark)

    Thompson, Mary Kathryn; Clemmensen, Line Katrine Harder; Rosas, Harvey

    2013-01-01

    This paper presents an algorithm that was developed to identify statistical outliers from the scores of grading jury members in a large project-based first year design course. The background and requirements for the outlier detection system are presented. The outlier detection algorithm...... and the follow-up procedures for score validation and appeals are described in detail. Finally, the impact of various elements of the outlier detection algorithm, their interactions, and the sensitivity of their numerical values are investigated. It is shown that the difference in the mean score produced...

  11. Gold nanoparticle-based microfluidic sensor for mercury detection

    DEFF Research Database (Denmark)

    Lafleur, Josiane P.; Jensen, Thomas Glasdam; Kutter, Jörg Peter

    2011-01-01

    The contamination of natural resources by human activity can have severe socio-economical impacts. Conventional methods of environmental analysis can be significantly improved by the development of portable microscale technologies for remote/field sensing. A gold nanoparticle-based lab-on-a-chip ......-on-a-chip device was developed for the rapid, in-field detection and quantification of mercury in aquatic environments. Rhodamine 6G functionalized gold nanoparticles allowed the on-chip fluorescence detection of mercury in aqueous samples with a limit of detection of 7 nM....

  12. A new Expert Finding model based on Term Correlation Matrix

    Directory of Open Access Journals (Sweden)

    Ehsan Pornour

    2015-09-01

    Full Text Available Due to the enormous volume of unstructured information available on the Web and inside organization, finding an answer to the knowledge need in a short time is difficult. For this reason, beside Search Engines which don’t consider users individual characteristics, Recommender systems were created which use user’s previous activities and other individual characteristics to help users find needed knowledge. Recommender systems usage is increasing every day. Expert finder systems also by introducing expert people instead of recommending information to users have provided this facility for users to ask their questions form experts. Having relation with experts not only causes information transition, but also with transferring experiences and inception causes knowledge transition. In this paper we used university professors academic resume as expert people profile and then proposed a new expert finding model that recommends experts to users query. We used Term Correlation Matrix, Vector Space Model and PageRank algorithm and proposed a new hybrid model which outperforms conventional methods. This model can be used in internet environment, organizations and universities that experts have resume dataset.

  13. Correlation of Aggregatibacter actinomycetemcomitans detection with clinical/immunoinflammatory profile of localized aggressive periodontitis using a 16S rRNA microarray method: a cross-sectional study.

    Directory of Open Access Journals (Sweden)

    Patricia F Gonçalves

    Full Text Available The objective of this study was to determine whether the detection of Aggregatibacter actinomycetemcomitans (Aa correlates with the clinical and immunoinflammatory profile of Localized Aggressive Periodontitis (LAP, as determined by by 16S rRNA gene-based microarray.Subgingival plaque samples from the deepest diseased site of 30 LAP patients [PD ≥ 5 mm, BoP and bone loss] were analyzed by 16S rRNA gene-based microarrays. Gingival crevicular fluid (GCF samples were analyzed for 14 cyto/chemokines. Peripheral blood was obtained and stimulated in vitro with P.gingivalis and E.coli to evaluate inflammatory response profiles. Plasma lipopolysaccharide (LPS levels were also measured.Aa was detected in 56% of LAP patients and was shown to be an indicator for different bacterial community structures (p0.05. Clinical parameters and serum LPS levels were similar between groups. However, Aa-non-detected GCF contained higher concentration of IL-8 than Aa-detected sites (p<0.05. TNFα and IL1β were elevated upon E.coli LPS stimulation of peripheral blood cells derived from patients with Aa-detected sites.Our findings demonstrate that the detection of Aa in LAP affected sites, did not correlate with clinical severity of the disease at the time of sampling in this cross-sectional study, although it did associate with lower local levels of IL-8, a different subgingival bacterial profile and elevated LPS-induced levels of TNFα and IL1β.

  14. Kullback-Leibler distance-based enhanced detection of incipient anomalies

    KAUST Repository

    Harrou, Fouzi

    2016-09-09

    Accurate and effective anomaly detection and diagnosis of modern engineering systems by monitoring processes ensure reliability and safety of a product while maintaining desired quality. In this paper, an innovative method based on Kullback-Leibler divergence for detecting incipient anomalies in highly correlated multivariate data is presented. We use a partial least square (PLS) method as a modeling framework and a symmetrized Kullback-Leibler distance (KLD) as an anomaly indicator, where it is used to quantify the dissimilarity between current PLS-based residual and reference probability distributions obtained using fault-free data. Furthermore, this paper reports the development of two monitoring charts based on the KLD. The first approach is a KLD-Shewhart chart, where the Shewhart monitoring chart with a three sigma rule is used to monitor the KLD of the response variables residuals from the PLS model. The second approach integrates the KLD statistic into the exponentially weighted moving average monitoring chart. The performance of the PLS-based KLD anomaly-detection methods is illustrated and compared to that of conventional PLS-based anomaly detection methods. Using synthetic data and simulated distillation column data, we demonstrate the greater sensitivity and effectiveness of the developed method over the conventional PLS-based methods, especially when data are highly correlated and small anomalies are of interest. Results indicate that the proposed chart is a very promising KLD-based method because KLD-based charts are, in practice, designed to detect small shifts in process parameters. © 2016 Elsevier Ltd

  15. Tactile sensor of hardness recognition based on magnetic anomaly detection

    Science.gov (United States)

    Xue, Lingyun; Zhang, Dongfang; Chen, Qingguang; Rao, Huanle; Xu, Ping

    2018-03-01

    Hardness, as one kind of tactile sensing, plays an important role in the field of intelligent robot application such as gripping, agricultural harvesting, prosthetic hand and so on. Recently, with the rapid development of magnetic field sensing technology with high performance, a number of magnetic sensors have been developed for intelligent application. The tunnel Magnetoresistance(TMR) based on magnetoresistance principal works as the sensitive element to detect the magnetic field and it has proven its excellent ability of weak magnetic detection. In the paper, a new method based on magnetic anomaly detection was proposed to detect the hardness in the tactile way. The sensor is composed of elastic body, ferrous probe, TMR element, permanent magnet. When the elastic body embedded with ferrous probe touches the object under the certain size of force, deformation of elastic body will produce. Correspondingly, the ferrous probe will be forced to displace and the background magnetic field will be distorted. The distorted magnetic field was detected by TMR elements and the output signal at different time can be sampled. The slope of magnetic signal with the sampling time is different for object with different hardness. The result indicated that the magnetic anomaly sensor can recognize the hardness rapidly within 150ms after the tactile moment. The hardness sensor based on magnetic anomaly detection principal proposed in the paper has the advantages of simple structure, low cost, rapid response and it has shown great application potential in the field of intelligent robot.

  16. Deceiving entropy-based DoS detection

    Science.gov (United States)

    Özçelik, Ä.°lker; Brooks, Richard R.

    2014-06-01

    Denial of Service (DoS) attacks disable network services for legitimate users. A McAfee report shows that eight out of ten Critical Infrastructure Providers (CIPs) surveyed had a significant Distributed DoS (DDoS) attack in 2010.1 Researchers proposed many approaches for detecting these attacks in the past decade. Anomaly based DoS detection is the most common. In this approach, the detector uses statistical features; such as the entropy of incoming packet header fields like source IP addresses or protocol type. It calculates the observed statistical feature and triggers an alarm if an extreme deviation occurs. However, intrusion detection systems (IDS) using entropy based detection can be fooled by spoofing. An attacker can sniff the network to collect header field data of network packets coming from distributed nodes on the Internet and fuses them to calculate the entropy of normal background traffic. Then s/he can spoof attack packets to keep the entropy value in the expected range during the attack. In this study, we present a proof of concept entropy spoofing attack that deceives entropy based detection approaches. Our preliminary results show that spoofing attacks cause significant detection performance degradation.

  17. Vision-Based People Detection System for Heavy Machine Applications

    Directory of Open Access Journals (Sweden)

    Vincent Fremont

    2016-01-01

    Full Text Available This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye images has not been well studied, we focus on investigating and quantifying the impact that strong radial distortions have on the appearance of people, and we propose approaches for handling this specificity, adapted from state-of-the-art people detection approaches. These adaptive approaches nevertheless have the drawback of high computational cost and complexity. Consequently, we also present a framework for harnessing the LiDAR modality in order to enhance the detection algorithm for different camera positions. A sequential LiDAR-based fusion architecture is used, which addresses directly the problem of reducing false detections and computational cost in an exclusively vision-based system. A heavy machine dataset was built, and different experiments were carried out to evaluate the performance of the system. The results are promising, in terms of both processing speed and performance.

  18. Vision-Based People Detection System for Heavy Machine Applications.

    Science.gov (United States)

    Fremont, Vincent; Bui, Manh Tuan; Boukerroui, Djamal; Letort, Pierrick

    2016-01-20

    This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye images has not been well studied, we focus on investigating and quantifying the impact that strong radial distortions have on the appearance of people, and we propose approaches for handling this specificity, adapted from state-of-the-art people detection approaches. These adaptive approaches nevertheless have the drawback of high computational cost and complexity. Consequently, we also present a framework for harnessing the LiDAR modality in order to enhance the detection algorithm for different camera positions. A sequential LiDAR-based fusion architecture is used, which addresses directly the problem of reducing false detections and computational cost in an exclusively vision-based system. A heavy machine dataset was built, and different experiments were carried out to evaluate the performance of the system. The results are promising, in terms of both processing speed and performance.

  19. How to detect Edgar Allan Poe's 'purloined letter,' or cross-correlation algorithms in digitized video images for object identification, movement evaluation, and deformation analysis

    Science.gov (United States)

    Dost, Michael; Vogel, Dietmar; Winkler, Thomas; Vogel, Juergen; Erb, Rolf; Kieselstein, Eva; Michel, Bernd

    2003-07-01

    Cross correlation analysis of digitised grey scale patterns is based on - at least - two images which are compared one to each other. Comparison is performed by means of a two-dimensional cross correlation algorithm applied to a set of local intensity submatrices taken from the pattern matrices of the reference and the comparison images in the surrounding of predefined points of interest. Established as an outstanding NDE tool for 2D and 3D deformation field analysis with a focus on micro- and nanoscale applications (microDAC and nanoDAC), the method exhibits an additional potential for far wider applications, that could be used for advancing homeland security. Cause the cross correlation algorithm in some kind seems to imitate some of the "smart" properties of human vision, this "field-of-surface-related" method can provide alternative solutions to some object and process recognition problems that are difficult to solve with more classic "object-related" image processing methods. Detecting differences between two or more images using cross correlation techniques can open new and unusual applications in identification and detection of hidden objects or objects with unknown origin, in movement or displacement field analysis and in some aspects of biometric analysis, that could be of special interest for homeland security.

  20. Prevalence and correlates of gender-based violence among female ...

    African Journals Online (AJOL)

    The overall prevalence of gender-based violence was 58.8% [95% Confidence Interval (CI) = 52.9% to 64.5%]. Specifically, 22.8%, 22.2% and 50.8% of students experienced physical, sexual or emotional violence respectively. Religious affiliation, ethnicity, indigeneship, marital status, campus residence and faculty ...

  1. Prevalence and Correlates of Gender-based Violence among ...

    African Journals Online (AJOL)

    Erah

    Nigeria; 4Chiles Center for Healthy Mothers and Babies, University of South Florida, Tampa, Florida, USA. *For correspondence: Email: ... valued human resources. Graduates have added value if they are not only ..... Prevalence and risk factors of gender-based violence among female college students in Awassa,. Ethiopia.

  2. Osteochondroma of the skull base: MRI and histological correlation

    International Nuclear Information System (INIS)

    Sato, K.; Kodera, T.; Kitai, R.; Kubota, T.

    1996-01-01

    A skull base osteochondroma (benign exostosis) in a 38-year-old man is reported. MRI was not only very useful for determining the extent of the tumour, but also showed its far content and, on contrast-enhanced fat-suppressed images, its vascularity. (orig.)

  3. MIMO Performance and Decoupling Network: Analysis of Uniform Rectangular array Using Correlated-Based Stochastic Models

    OpenAIRE

    Obour Agyekum Kwame O-B; Maxwell Oppong Afriyie; Paul Oswald Kwasi Anane; Affum Emmanuel Ampoma; Matthew Seddoh Akatey

    2017-01-01

    We explore the dependency of MIMO performance on azimuthal spread (AS) and elevation spread (ES) using correlated-based stochastic models (CBSMs). We represent the transmitter as uniform rectangular array (URA), and derive an analytical function for spatial correlation, in terms of maximum power when phase gradient of the incident wave follows a Student’s t-distribution. We model the correlated-based stochastic MIMO system to investigate the usefulness of the analytical function, under the co...

  4. Detection Optimization of the Progressive Multi-Channel Correlation Algorithm Used in Infrasound Nuclear Treaty Monitoring

    Science.gov (United States)

    2013-03-01

    also thank Dr. Arrowsmith at Los Alamos National Laboratory for providing me with ground truth data he collected using his own impressive detection...Centre IMS International Monitoring System LANL Los Alamos National Laboratory LRT Likelihood ratio test LTA Long-term-power-average MAP Maximum a...IMS likewise uses 4 different sensor networks to ensure the detection of explosions by anyone, anywhere. Seismic, hydroacoustic , infrasound, and

  5. Waveform correlation and coherence of short-period seismic noise within Gauribidanur array with implications for event detection

    International Nuclear Information System (INIS)

    Bhadauria, Y.S.; Arora, S.K.

    1995-01-01

    In continuation with our effort to model the short-period micro seismic noise at the seismic array at Gauribidanur (GBA), we have examined in detail time-correlation and spectral coherence of the noise field within the array space. This has implications of maximum possible improvement in signal-to-noise ratio (SNR) relevant to event detection. The basis of this study is about a hundred representative wide-band noise samples collected from GBA throughout the year 1992. Both time-structured correlation as well as coherence of the noise waveforms are found to be practically independent of the inter element distances within the array, and they exhibit strong temporal and spectral stability. It turns out that the noise is largely incoherent at frequencies ranging upwards from 2 Hz; the coherency coefficient tends to increase in the lower frequency range attaining a maximum of 0.6 close to 0.5 Hz. While the maximum absolute cross-correlation also diminishes with increasing frequency, the zero-lag cross-correlation is found to be insensitive to frequency filtering regardless of the pass band. An extremely small value of -0.01 of the zero-lag correlation and a comparatively higher year-round average estimate at 0.15 of the maximum absolute time-lagged correlation yields an SNR improvement varying between a probable high of 4.1 and a low of 2.3 for the full 20-element array. 19 refs., 6 figs

  6. Highly sensitive fluorescence resonance energy transfer (FRET)-based nanosensor for rapid detection of clenbuterol

    Science.gov (United States)

    Nghia Nguyen, Duc; Ngo, Trinh Tung; Liem Nguyen, Quang

    2012-09-01

    In this study we investigate the fabrication of a fluorescence resonance energy transfer (FRET)-based nanosensor for the detection of clenbuterol. The nanosensor consists of CdTe quantum dots coated by clenbuterol recognizable agent naphthol and diazotized clenbuterol. Changes in maximal photoluminescent intensities of the nanosensor were utilized to measure clenbuterol concentrations. The maximal photoluminescent intensities of the nanosensor were found to decrease with increasing clenbuterol concentrations, following a linear correlation. We have successfully fabricated a nanosensor for detection of clenbuterol with sensitivity up to 10 pg ml-1.

  7. Analysis of Vehicle Detection with WSN-Based Ultrasonic Sensors

    Directory of Open Access Journals (Sweden)

    Youngtae Jo

    2014-08-01

    Full Text Available Existing traffic information acquisition systems suffer from high cost and low scalability. To address these problems, the application of wireless sensor networks (WSNs has been studied, as WSN-based systems are highly scalable and have a low cost of installing and replacing the systems. Magnetic, acoustic and accelerometer sensors have been considered for WSN-based traffic surveillance, but the use of ultrasonic sensors has not been studied. The limitations of WSN-based systems make it necessary to employ power saving methods and vehicle detection algorithms with low computational complexity. In this paper, we model and analyze optimal power saving methodologies for an ultrasonic sensor and present a computationally-efficient vehicle detection algorithm using ultrasonic data. The proposed methodologies are implemented and evaluated with a tiny microprocessor on real roads. The evaluation results show that the low computational complexity of our algorithm does not compromise the accuracy of vehicle detection.

  8. DNA methylation detection based on difference of base content

    Science.gov (United States)

    Sato, Shinobu; Ohtsuka, Keiichi; Honda, Satoshi; Sato, Yusuke; Takenaka, Shigeori

    2016-04-01

    Methylation frequently occurs in cytosines of CpG sites to regulate gene expression. The identification of aberrant methylation of certain genes is important for cancer marker analysis. The aim of this study was to determine the methylation frequency in DNA samples of unknown length and/or concentration. Unmethylated cytosine is known to be converted to thymine following bisulfite treatment and subsequent PCR. For this reason, the AT content in DNA increases with an increasing number of methylation sites. In this study, the fluorescein-carrying bis-acridinyl peptide (FKA) molecule was used for the detection of methylation frequency. FKA contains fluorescein and two acridine moieties, which together allow for the determination of the AT content of double-stranded DNA fragments. Methylated and unmethylated human genomes were subjected to bisulfide treatment and subsequent PCR using primers specific for the CFTR, CDH4, DBC1, and NPY genes. The AT content in the resulting PCR products was estimated by FKA, and AT content estimations were found to be in good agreement with those determined by DNA sequencing. This newly developed method may be useful for determining methylation frequencies of many PCR products by measuring the fluorescence in samples excited at two different wavelengths.

  9. State-based Event Detection Optimization for Complex Event Processing

    Directory of Open Access Journals (Sweden)

    Shanglian PENG

    2014-02-01

    Full Text Available Detection of patterns in high speed, large volume of event streams has been an important paradigm in many application areas of Complex Event Processing (CEP including security monitoring, financial markets analysis and health-care monitoring. To assure real-time responsive complex pattern detection over high volume and speed event streams, efficient event detection techniques have to be designed. Unfortunately evaluation of the Nondeterministic Finite Automaton (NFA based event detection model mainly considers single event query and its optimization. In this paper, we propose multiple event queries evaluation on event streams. In particular, we consider scalable multiple event detection model that shares NFA transfer states of different event queries. For each event query, the event query is parse into NFA and states of the NFA are partitioned into different units. With this partition, the same individual state of NFA is run on different processing nodes, providing states sharing and reducing partial matches maintenance. We compare our state-based approach with Stream-based And Shared Event processing (SASE. Our experiments demonstrate that state-based approach outperforms SASE both on CPU time usage and memory consumption.

  10. The detection of bulk explosives using nuclear-based techniques

    Energy Technology Data Exchange (ETDEWEB)

    Morgado, R.E.; Gozani, T.; Seher, C.C.

    1988-01-01

    In 1986 we presented a rationale for the detection of bulk explosives based on nuclear techniques that addressed the requirements of civil aviation security in the airport environment. Since then, efforts have intensified to implement a system based on thermal neutron activation (TNA), with new work developing in fast neutron and energetic photon reactions. In this paper we will describe these techniques and present new results from laboratory and airport testing. Based on preliminary results, we contended in our earlier paper that nuclear-based techniques did provide sufficiently penetrating probes and distinguishable detectable reaction products to achieve the FAA operational goals; new data have supported this contention. The status of nuclear-based techniques for the detection of bulk explosives presently under investigation by the US Federal Aviation Administration (FAA) is reviewed. These include thermal neutron activation (TNA), fast neutron activation (FNA), the associated particle technique, nuclear resonance absorption, and photoneutron activation. The results of comprehensive airport testing of the TNA system performed during 1987-88 are summarized. From a technical point of view, nuclear-based techniques now represent the most comprehensive and feasible approach for meeting the operational criteria of detection, false alarms, and throughput. 9 refs., 5 figs., 2 tabs.

  11. On the Use of Cross-Correlation between Volume Scattering and Helix Scattering from Polarimetric SAR Data for the Improvement of Ship Detection

    Directory of Open Access Journals (Sweden)

    Jujie Wei

    2016-01-01

    Full Text Available Synthetic Aperture Radar (SAR ship detection is an important maritime application. However, azimuth ambiguities caused by the finite sampling of the Doppler spectrum are often visible in SAR images and are always mistaken as ships by classic detection techniques, like the Constant False Alarm Rate (CFAR. It is known that radar targets and azimuth ambiguities have different characteristics in polarimetric SAR (PolSAR data, i.e., first ambiguities usually have strong odd- or double-bounce scattering and the maximum amplitude of the first ambiguity in SHV is always considerably smaller than that of the corresponding target for zero or high velocity. On the basis of this characteristics, this paper finds that first ambiguities usually have low volume scattering power relative to ships and almost have no helix scattering by Yamaguchi decomposition. But some residual ambiguities still exit in the volume scattering power and have similar scattering intensity to small ships, and some parts of a ship also have zero helix scattering owing to some physical factors (e.g., ship structure, radar incidence angle, etc.. Thus, for high-precision ship detection, a new ship detection method based on cross-correlation between the volume and helix scattering mechanisms derived from Yamaguchi decomposition is proposed to avoid false alarms caused by azimuth ambiguities and enhance Target-to-Clutter Ratio (TCR for improving the miss detection rate of small ships. By experiments, it is proved that our method can work effectively and has high detection accuracy.

  12. Role of ultrasonography in the detection of intraductal spread of breast cancer: correlation with pathologic findings, mammography and MR imaging

    Energy Technology Data Exchange (ETDEWEB)

    Satake, H.; Sawaki, A.; Niimi, R.; Ando, Y.; Ishiguchi, T.; Ishigaki, T. [Dept. of Radiology, Nagoya University School of Medicine (Japan); Shimamoto, K. [Dept. of Radiological Technology, Nagoya University School of Health Sciences (Japan); Yamakawa, K. [Dept. of Radiology, Tousei General Hospital, Aichi (Japan); Nagasaka, T. [Dept. of Clinical Laboratory, Nagoya University Hospital (Japan); Funahashi, H. [Department of Surgery II, Nagoya University School of Medicine (Japan)

    2000-11-01

    The purpose of this study was to assess the role of US in the detection of intraductal spread of breast cancer in comparison with mammography (MMG) and MRI. In 46 patients with breast cancer, US features of the intraductal component were classified as ductal type or distorted type. Histopathologically, 29 of 46 (63 %) cases had intraductal components, and the sensitivity, specificity, and accuracy rates in detection of intraductal spread were 89, 76, and 85 %, respectively. Each US pattern demonstrated good correspondence to the histologic components, and the distorted type correlated well with comedo-type carcinoma. Mammography was performed in all cases, and the sensitivity, specificity, and accuracy rates in detection of intraductal spread were 55, 100, and 72 %, respectively. In comedo type, MMG could diagnose the extent of intraductal spread more accurately compared with US examination. Magnetic resonance imaging comparison was available in 25 cases. Magnetic resonance imaging depicted intraductal extension as an enhanced area during the early phase of a contrast enhancement study with a sensitivity of 93 %. Ultrasound and MRI were closely related in terms of morphologic characteristics: the ductal type of US image correlated well with linear enhancement on MRI, whereas the distorted type correlated with regional or segmental enhancement. Current US examination is useful in depicting the intraductal spread of breast cancer; however, US has a tendency to underestimate intraductal component of comedo type compared with MMG and MRI. (orig.)

  13. Contrast-enhanced ultrasonography for the detection and characterization of prostate cancer: correlation with microvessel density and Gleason score.

    Science.gov (United States)

    Jiang, J; Chen, Y; Zhu, Y; Yao, X; Qi, J

    2011-08-01

    To determine whether there is a correlation between the peak intensity of the lesion at contrast-enhanced ultrasonography and the microvessel density (MVD) and Gleason score in biopsy specimens of prostate cancer. Contrast-enhanced ultrasonography using cadence-contrast pulse sequence (CPS) technology was performed in 147 patients with suspected prostate cancer before biopsy. An auto-tracking contrast quantification (ACQ) software was used to analyse the peak intensity (PI) of the lesion. The Gleason score and MVD immunoreactivity were determined in the prostate biopsy specimens. Ultrasound findings were correlated with biopsy findings. Prostate cancer was detected in 73 of 147 patients. The PI values of prostate cancer patients were significantly higher than those of non-malignant patients [9.81 (4.23) versus 5.69 (3.19) dB; pPI value increased significantly with a higher Gleason score (pPI and MVD in prostate cancer, with a correlation coefficient of 0.617. No correlation was found between PI value and age, prostate specific antigen (PSA) or prostate specific antigen density (PSAD) level (p>0.05). The PI obtained by CPS harmonic ultrasonography appears to be of value as an indicator of MVD and increases with a higher Gleason score. CPS harmonic ultrasonography could be promising as a useful imaging technique in the detection and characterization of prostate cancer. Copyright © 2011 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

  14. [A Hyperspectral Imagery Anomaly Detection Algorithm Based on Gauss-Markov Model].

    Science.gov (United States)

    Gao, Kun; Liu, Ying; Wang, Li-jing; Zhu, Zhen-yu; Cheng, Hao-bo

    2015-10-01

    With the development of spectral imaging technology, hyperspectral anomaly detection is getting more and more widely used in remote sensing imagery processing. The traditional RX anomaly detection algorithm neglects spatial correlation of images. Besides, it does not validly reduce the data dimension, which costs too much processing time and shows low validity on hyperspectral data. The hyperspectral images follow Gauss-Markov Random Field (GMRF) in space and spectral dimensions. The inverse matrix of covariance matrix is able to be directly calculated by building the Gauss-Markov parameters, which avoids the huge calculation of hyperspectral data. This paper proposes an improved RX anomaly detection algorithm based on three-dimensional GMRF. The hyperspectral imagery data is simulated with GMRF model, and the GMRF parameters are estimated with the Approximated Maximum Likelihood method. The detection operator is constructed with GMRF estimation parameters. The detecting pixel is considered as the centre in a local optimization window, which calls GMRF detecting window. The abnormal degree is calculated with mean vector and covariance inverse matrix, and the mean vector and covariance inverse matrix are calculated within the window. The image is detected pixel by pixel with the moving of GMRF window. The traditional RX detection algorithm, the regional hypothesis detection algorithm based on GMRF and the algorithm proposed in this paper are simulated with AVIRIS hyperspectral data. Simulation results show that the proposed anomaly detection method is able to improve the detection efficiency and reduce false alarm rate. We get the operation time statistics of the three algorithms in the same computer environment. The results show that the proposed algorithm improves the operation time by 45.2%, which shows good computing efficiency.

  15. Temperament and Mood Detection Using Case-Based Reasoning

    OpenAIRE

    Adebayo Kolawole John; Adekoya Adewale M.; Ekwonna Chinnasa

    2014-01-01

    Case-Based Reasoning (CBR) is a branch of AI that is employed to solving problems which emphasizes the use of previous solutions in solving similar new problems. This work presents TAMDS, a Temperament and Mood Detection system which employs Case-Based Reasoning technique. The proposed system is adapted to the field of psychology to help psychologists solve part of the problems in their complex domain. We have designed TAMDS to detect temperament and moods of individuals. A major aim of our s...

  16. VoIP attacks detection engine based on neural network

    Science.gov (United States)

    Safarik, Jakub; Slachta, Jiri

    2015-05-01

    The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

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

    Science.gov (United States)

    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.

  18. The Correlation Of Learning Independence Attitudes And Student's Learning Achievement On Physics Learning Based-portfolio

    OpenAIRE

    Saefullah, Asep; Siahaan, Parsaoran; Sari, Ika Mustika

    2017-01-01

    This study aimed to determine correlation between learning independence attitudes and student’s learning achievement. Type of this research is a correlation study to detect the connection of learning independence attitude’s variance in relation to learning achievement variance. This study used an attitude scale to measure the student’s learning independence attitude and objective multiple-choice questions to measure the student’s learning achievement. The results showed that there is a posit...

  19. Generation of Quasi-Gaussian Pulses Based on Correlation Techniques

    Directory of Open Access Journals (Sweden)

    POHOATA, S.

    2012-02-01

    Full Text Available The Gaussian pulses have been mostly used within communications, where some applications can be emphasized: mobile telephony (GSM, where GMSK signals are used, as well as the UWB communications, where short-period pulses based on Gaussian waveform are generated. Since the Gaussian function signifies a theoretical concept, which cannot be accomplished from the physical point of view, this should be expressed by using various functions, able to determine physical implementations. New techniques of generating the Gaussian pulse responses of good precision are approached, proposed and researched in this paper. The second and third order derivatives with regard to the Gaussian pulse response are accurately generated. The third order derivates is composed of four individual rectangular pulses of fixed amplitudes, being easily to be generated by standard techniques. In order to generate pulses able to satisfy the spectral mask requirements, an adequate filter is necessary to be applied. This paper emphasizes a comparative analysis based on the relative error and the energy spectra of the proposed pulses.

  20. Effective and Efficient Correlation Analysis with Application to Market Basket Analysis and Network Community Detection

    Science.gov (United States)

    Duan, Lian

    2012-01-01

    Finding the most interesting correlations among items is essential for problems in many commercial, medical, and scientific domains. For example, what kinds of items should be recommended with regard to what has been purchased by a customer? How to arrange the store shelf in order to increase sales? How to partition the whole social network into…

  1. A-Posteriori Detection of Sensor Infrastructure Errors in Correlated Sensor Data and Business Workflows

    NARCIS (Netherlands)

    Wombacher, Andreas

    2011-01-01

    Sensor data can be interpreted as a view on physical objects effected by business processes. Since both sensor infrastructures and business workflows must deal with imprecise information, the correlation of sensor data and business workflow data might be used a-posteriori to determine the source of

  2. A-Posteriori Detection of Sensor Infrastructure Errors in Correlated Sensor Data and Business Workflows

    NARCIS (Netherlands)

    Wombacher, Andreas; Rinderle-Ma, Stefanie; Toumani, Farouk; Wolf, Karsten

    Some physical objects are influenced by business workflows and are observed by sensors. Since both sensor infrastructures and business workflows must deal with imprecise information, the correlation of sensor data and business workflow data related to physical objects might be used a-posteriori to

  3. A Frequency-Based Approach to Intrusion Detection

    Directory of Open Access Journals (Sweden)

    Mian Zhou

    2004-06-01

    Full Text Available Research on network security and intrusion detection strategies presents many challenging issues to both theoreticians and practitioners. Hackers apply an array of intrusion and exploit techniques to cause disruption of normal system operations, but on the defense, firewalls and intrusion detection systems (IDS are typically only effective in defending known intrusion types using their signatures, and are far less than mature when faced with novel attacks. In this paper, we adapt the frequency analysis techniques such as the Discrete Fourier Transform (DFT used in signal processing to the design of intrusion detection algorithms. We demonstrate the effectiveness of the frequency-based detection strategy by running synthetic network intrusion data in simulated networks using the OPNET software. The simulation results indicate that the proposed intrusion detection strategy is effective in detecting anomalous traffic data that exhibit patterns over time, which include several types of DOS and probe attacks. The significance of this new strategy is that it does not depend on the prior knowledge of attack signatures, thus it has the potential to be a useful supplement to existing signature-based IDS and firewalls.

  4. Analysis of Android Device-Based Solutions for Fall Detection.

    Science.gov (United States)

    Casilari, Eduardo; Luque, Rafael; Morón, María-José

    2015-07-23

    Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.

  5. Semantic-based technique for thai documents plagiarism detection

    Directory of Open Access Journals (Sweden)

    Sorawat Prapanitisatian

    2014-03-01

    Full Text Available Plagiarism is the act of taking another person's writing or idea without referring to the source of information. This is one of major problems in educational institutes. There is a number of plagiarism detection software available on the Internet. However, a few numbers of them works. Typically, they use a simple method for plagiarism detection e.g. string matching. The main weakness of this method is it cannot detect the plagiarism when the author replaces some words using synonyms. As such, this paper presents a new technique for a semantic-based plagiarism detection using Semantic Role Labeling (SRL and term weighting. SRL is deployed in order to calculate the semantic-based similarity. The main different from the existing framework is terms in a sentence are weighted dynamically depending on their roles in the sentence e.g. subject, verb or object. This technique enhances the plagiarism detection mechanism more efficiently than existing system although positions of terms in a sentence are reordered. The experimental results show that the proposed method can detect the plagiarism document more effective than the existing methods, Anti-kobpae, Turnit-in and Traditional Semantic Role Labeling.

  6. An Effective Conversation-Based Botnet Detection Method

    Directory of Open Access Journals (Sweden)

    Ruidong Chen

    2017-01-01

    Full Text Available A botnet is one of the most grievous threats to network security since it can evolve into many attacks, such as Denial-of-Service (DoS, spam, and phishing. However, current detection methods are inefficient to identify unknown botnet. The high-speed network environment makes botnet detection more difficult. To solve these problems, we improve the progress of packet processing technologies such as New Application Programming Interface (NAPI and zero copy and propose an efficient quasi-real-time intrusion detection system. Our work detects botnet using supervised machine learning approach under the high-speed network environment. Our contributions are summarized as follows: (1 Build a detection framework using PF_RING for sniffing and processing network traces to extract flow features dynamically. (2 Use random forest model to extract promising conversation features. (3 Analyze the performance of different classification algorithms. The proposed method is demonstrated by well-known CTU13 dataset and nonmalicious applications. The experimental results show our conversation-based detection approach can identify botnet with higher accuracy and lower false positive rate than flow-based approach.

  7. Fast Detection of Compressively Sensed IR Targets Using Stochastically Trained Least Squares and Compressed Quadratic Correlation Filters

    KAUST Repository

    Millikan, Brian

    2017-05-02

    Target detection of potential threats at night can be deployed on a costly infrared focal plane array with high resolution. Due to the compressibility of infrared image patches, the high resolution requirement could be reduced with target detection capability preserved. For this reason, a compressive midwave infrared imager (MWIR) with a low-resolution focal plane array has been developed. As the most probable coefficient indices of the support set of the infrared image patches could be learned from the training data, we develop stochastically trained least squares (STLS) for MWIR image reconstruction. Quadratic correlation filters (QCF) have been shown to be effective for target detection and there are several methods for designing a filter. Using the same measurement matrix as in STLS, we construct a compressed quadratic correlation filter (CQCF) employing filter designs for compressed infrared target detection. We apply CQCF to the U.S. Army Night Vision and Electronic Sensors Directorate dataset. Numerical simulations show that the recognition performance of our algorithm matches that of the standard full reconstruction methods, but at a fraction of the execution time.

  8. Accounting for detectability in fish distribution models: an approach based on time-to-first-detection

    Directory of Open Access Journals (Sweden)

    Mário Ferreira

    2015-12-01

    Full Text Available Imperfect detection (i.e., failure to detect a species when the species is present is increasingly recognized as an important source of uncertainty and bias in species distribution modeling. Although methods have been developed to solve this problem by explicitly incorporating variation in detectability in the modeling procedure, their use in freshwater systems remains limited. This is probably because most methods imply repeated sampling (≥ 2 of each location within a short time frame, which may be impractical or too expensive in most studies. Here we explore a novel approach to control for detectability based on the time-to-first-detection, which requires only a single sampling occasion and so may find more general applicability in freshwaters. The approach uses a Bayesian framework to combine conventional occupancy modeling with techniques borrowed from parametric survival analysis, jointly modeling factors affecting the probability of occupancy and the time required to detect a species. To illustrate the method, we modeled large scale factors (elevation, stream order and precipitation affecting the distribution of six fish species in a catchment located in north-eastern Portugal, while accounting for factors potentially affecting detectability at sampling points (stream depth and width. Species detectability was most influenced by depth and to lesser extent by stream width and tended to increase over time for most species. Occupancy was consistently affected by stream order, elevation and annual precipitation. These species presented a widespread distribution with higher uncertainty in tributaries and upper stream reaches. This approach can be used to estimate sampling efficiency and provide a practical framework to incorporate variations in the detection rate in fish distribution models.

  9. A stereo vision-based obstacle detection system in vehicles

    Science.gov (United States)

    Huh, Kunsoo; Park, Jaehak; Hwang, Junyeon; Hong, Daegun

    2008-02-01

    Obstacle detection is a crucial issue for driver assistance systems as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision with the front vehicle. The vision-based obstacle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, an obstacle detection system using stereo vision sensors is developed. This system utilizes feature matching, epipoplar constraint and feature aggregation in order to robustly detect the initial corresponding pairs. After the initial detection, the system executes the tracking algorithm for the obstacles. The proposed system can detect a front obstacle, a leading vehicle and a vehicle cutting into the lane. Then, the position parameters of the obstacles and leading vehicles can be obtained. The proposed obstacle detection system is implemented on a passenger car and its performance is verified experimentally.

  10. Ground-based detection of G star superflares with NGTS

    Science.gov (United States)

    Jackman, James A. G.; Wheatley, Peter J.; Pugh, Chloe E.; Gänsicke, Boris T.; Gillen, Edward; Broomhall, Anne-Marie; Armstrong, David J.; Burleigh, Matthew R.; Chaushev, Alexander; Eigmüller, Philipp; Erikson, Anders; Goad, Michael R.; Grange, Andrew; Günther, Maximilian N.; Jenkins, James S.; McCormac, James; Raynard, Liam; Thompson, Andrew P. G.; Udry, Stéphane; Walker, Simon; Watson, Christopher A.; West, Richard G.

    2018-04-01

    We present high cadence detections of two superflares from a bright G8 star (V = 11.56) with the Next Generation Transit Survey (NGTS). We improve upon previous superflare detections by resolving the flare rise and peak, allowing us to fit a solar flare inspired model without the need for arbitrary break points between rise and decay. Our data also enables us to identify substructure in the flares. From changing starspot modulation in the NGTS data we detect a stellar rotation period of 59 hours, along with evidence for differential rotation. We combine this rotation period with the observed ROSAT X-ray flux to determine that the star's X-ray activity is saturated. We calculate the flare bolometric energies as 5.4^{+0.8}_{-0.7}× 10^{34}and 2.6^{+0.4}_{-0.3}× 10^{34}erg and compare our detections with G star superflares detected in the Kepler survey. We find our main flare to be one of the largest amplitude superflares detected from a bright G star. With energies more than 100 times greater than the Carrington event, our flare detections demonstrate the role that ground-based instruments such as NGTS can have in assessing the habitability of Earth-like exoplanets, particularly in the era of PLATO.

  11. Physicochemical properties determining the detection probability of tryptic peptides in Fourier transform mass spectrometry. A correlation study

    DEFF Research Database (Denmark)

    Nielsen, Michael L; Savitski, Mikhail M; Kjeldsen, Frank

    2004-01-01

    Sequence verification and mapping of posttranslational modifications require nearly 100% sequence coverage in the "bottom-up" protein analysis. Even in favorable cases, routine liquid chromatography-mass spectrometry detects from protein digests peptides covering 50-90% of the sequence. Here we...... investigated the reasons for limited peptide detection, considering various physicochemical aspects of peptide behavior in liquid chromatography-Fourier transform mass spectrometry (LC-FTMS). No overall correlation was found between the detection probability and peptide mass. In agreement with literature data...... between pI and signal response. An explanation of this paradoxal behavior was found through the observation that more acidic tryptic peptide lengths tend to be longer. Longer peptides tend to acquire higher average charge state in positive mode electrospray ionization than more basic but shorter...

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

    Science.gov (United States)

    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.

  13. Full waveform inversion using envelope-based global correlation norm

    KAUST Repository

    Oh, Juwon

    2018-01-28

    Various parameterizations have been suggested to simplify inversions of first arrivals, or P −waves, in orthorhombic anisotropic media, but the number and type of retrievable parameters have not been decisively determined. We show that only six parameters can be retrieved from the dynamic linearized inversion of P −waves. These parameters are different from the six parameters needed to describe the kinematics of P −waves. Reflection-based radiation patterns from the P − P scattered waves are remapped into the spectral domain to allow for our resolution analysis based on the effective angle of illumination concept. Singular value decomposition of the spectral sensitivities from various azimuths, offset coverage scenarios, and data bandwidths allows us to quantify the resolution of different parameterizations, taking into account the signal-to-noise ratio in a given experiment. According to our singular value analysis, when the primary goal of inversion is determining the velocity of the P −waves, gradually adding anisotropy of lower orders (isotropic, vertically transversally isotropic, orthorhombic) in hierarchical parameterization is the best choice. Hierarchical parametrization reduces the tradeoff between the parameters and makes gradual introduction of lower anisotropy orders straightforward. When all the anisotropic parameters affecting P −wave propagation need to be retrieved simultaneously, the classic parameterization of orthorhombic medium with elastic stiffness matrix coefficients and density is a better choice for inversion. We provide estimates of the number and set of parameters that can be retrieved from surface seismic data in different acquisition scenarios. To set up an inversion process, the singular values determine the number of parameters that can be inverted and the resolution matrices from the parameterizations can be used to ascertain the set of parameters that can be resolved.

  14. Colour based fire detection method with temporal intensity variation filtration

    International Nuclear Information System (INIS)

    Trambitckii, K; Musalimov, V; Anding, K; Linß, G

    2015-01-01

    Development of video, computing technologies and computer vision gives a possibility of automatic fire detection on video information. Under that project different algorithms was implemented to find more efficient way of fire detection. In that article colour based fire detection algorithm is described. But it is not enough to use only colour information to detect fire properly. The main reason of this is that in the shooting conditions may be a lot of things having colour similar to fire. A temporary intensity variation of pixels is used to separate them from the fire. These variations are averaged over the series of several frames. This algorithm shows robust work and was realised as a computer program by using of the OpenCV library

  15. Local Community Detection Algorithm Based on Minimal Cluster

    Directory of Open Access Journals (Sweden)

    Yong Zhou

    2016-01-01

    Full Text Available In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.

  16. A DFIG Islanding Detection Scheme Based on Reactive Power Infusion

    Science.gov (United States)

    Wang, M.; Liu, C.; He, G. Q.; Li, G. H.; Feng, K. H.; Sun, W. W.

    2017-07-01

    A lot of research has been done on photovoltaic (the “PV”) power system islanding detection in recent years. As a comparison, much less attention has been paid to islanding in wind turbines. Meanwhile, wind turbines can work in islanding conditions for quite a long period, which can be harmful to equipments and cause safety hazards. This paper presents and examines a double fed introduction generation (the “DFIG”) islanding detection scheme based on feedback of reactive power and frequency and uses a trigger signal of reactive power infusion which can be obtained by dividing the voltage total harmonic distortion (the "THD") by the voltage THD of last cycle to avoid the deterioration of power quality. This DFIG islanding detection scheme uses feedback of reactive power current loop to amplify the frequency differences in islanding and normal conditions. Simulation results show that the DFIG islanding detection scheme is effective.

  17. Engineering nanomaterials-based biosensors for food safety detection.

    Science.gov (United States)

    Lv, Man; Liu, Yang; Geng, Jinhui; Kou, Xiaohong; Xin, Zhihong; Yang, Dayong

    2018-05-30

    Food safety always remains a grand global challenge to human health, especially in developing countries. To solve food safety pertained problems, numerous strategies have been developed to detect biological and chemical contaminants in food. Among these approaches, nanomaterials-based biosensors provide opportunity to realize rapid, sensitive, efficient and portable detection, overcoming the restrictions and limitations of traditional methods such as complicated sample pretreatment, long detection time, and relying on expensive instruments and well-trained personnel. In this review article, we provide a cross-disciplinary perspective to review the progress of nanomaterials-based biosensors for the detection of food contaminants. The review article is organized by the category of food contaminants including pathogens/toxins, heavy metals, pesticides, veterinary drugs and illegal additives. In each category of food contaminant, the biosensing strategies are summarized including optical, colorimetric, fluorescent, electrochemical, and immune- biosensors; the relevant analytes, nanomaterials and biosensors are analyzed comprehensively. Future perspectives and challenges are also discussed briefly. We envision that our review could bridge the gap between the fields of food science and nanotechnology, providing implications for the scientists or engineers in both areas to collaborate and promote the development of nanomaterials-based biosensors for food safety detection. Copyright © 2018 Elsevier B.V. All rights reserved.

  18. Ensemble regression model-based anomaly detection for cyber-physical intrusion detection in smart grids

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena; Gehrke, Oliver

    2016-01-01

    on an ensemble of non-linear artificial neural network DER models which detect and evaluate anomalies in DER operation. The proposed method is validated against measurement data which yields a precision of 0.947 and an accuracy of 0.976. This improves the precision and accuracy of a classic model-based anomaly...

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

    Science.gov (United States)

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

    2016-04-01

    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.

  20. Correlating intrusion detection alerts on bot malware infections using neural network

    DEFF Research Database (Denmark)

    Kidmose, Egon; Stevanovic, Matija; Pedersen, Jens Myrup

    2016-01-01

    part, as such knowledge is inferred by Neural Networks. Evaluation has been performed with traffic traces of real bot binaries executed in a lab setup. The method is trained on labelled Intrusion Detection System alerts and is capable of correctly predicting which of seven incidents an alert pertains...

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

    NARCIS (Netherlands)

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

    2016-01-01

    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,

  2. Detection of Cytomegalovirus DNA in Serum Correlates with Clinical Cytomegalovirus Retinitis in AIDS

    DEFF Research Database (Denmark)

    Hansen, K.K.; Ricksten, A.; Hofmann, B.

    1994-01-01

    The high sensitivity of nested polymerase chain reaction (PCR) offers the possibility of rapid detection of cytomegalovirus (CMV) DNA in serum. Five consecutive serum samples were examined from 52 human immunodeficiency virus (HIV)-seropositive patients (19 of whom had clinically presumed diagnosis...

  3. Correlation between cationic lipid-based transfection and cell division

    Energy Technology Data Exchange (ETDEWEB)

    Kirchenbuechler, Inka; Kirchenbuechler, David; Elbaum, Michael, E-mail: michael@elbaum.ac.il

    2016-07-01

    We evaluate the temporal relation between protein expression by cationic lipid-mediated transfection and cell division using time lapse fluorescence microscopy. Detailed image analysis provides new insights on the single cell level while simultaneously achieving appropriate statistics. Earlier evidence by less direct methods such as flow cytometry indicates a primary route for transfection involving nuclear envelope breakdown, but also suggests the existence of a pathway independent of mitosis. We confirm and quantify both mechanisms. We found the timing for successful transfection to be unexpectedly flexible, contrary to assertions of a narrow time window. Specifically, cells dividing more than 24 h after exposure to the transfection medium express the probed protein at a comparable level to cells in a mitotic state during or shortly after transfection. This finding can have a profound impact on the guidance and development of non-viral gene delivery materials. - Highlights: • Cationic lipid-based transfection supports protein expression without cell division. • Protein expression is unrelated to cell cycle status at the time of transfection. • Time-lapse imaging provides direct evaluation without statistical averaging. • Lipoplex dissociation is a likely target for improvement of transfection efficiency.

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

    Science.gov (United States)

    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.

  5. Observer-Based and Regression Model-Based Detection of Emerging Faults in Coal Mills

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Lin, Bao; Jørgensen, Sten Bay

    2006-01-01

    In order to improve the reliability of power plants it is important to detect fault as fast as possible. Doing this it is interesting to find the most efficient method. Since modeling of large scale systems is time consuming it is interesting to compare a model-based method with data driven ones....... In this paper three different fault detection approaches are compared using a example of a coal mill, where a fault emerges. The compared methods are based on: an optimal unknown input observer, static and dynamic regression model-based detections. The conclusion on the comparison is that observer-based scheme...... detects the fault 13 samples earlier than the dynamic regression model-based method, and that the static regression based method is not usable due to generation of far too many false detections....

  6. Abnormal Appearance Detection of Substation Based on Image Comparison

    Directory of Open Access Journals (Sweden)

    Zhang Xu

    2016-01-01

    Full Text Available Based on image comparison, a novel algorithm for abnormal appearance detection of substation is proposed. Previous spatial states of an object are compared to its current representation in a digital image. Firstly, saliency maps are acquired using a fast implementation method of salient region detection. Based on saliency maps, image registration was completed by ORB (Oriented Fast and Rotated Brief. Then, sliding widow algorithm is applied to transform the whole image comparison problem into sub-image comparison problem. Textural feature and shape feature vectors (TSFVs representing contents of images are generated by feature level fusion. Finally, decisions are automatically made as to whether or not change at the outline has occurred by the Euclidean distance of TEFVs. Experimental results show that the proposed method has good performance in abnormal appearance detection of substation.

  7. DNA-based species detection capabilities using laser transmission spectroscopy.

    Science.gov (United States)

    Mahon, A R; Barnes, M A; Li, F; Egan, S P; Tanner, C E; Ruggiero, S T; Feder, J L; Lodge, D M

    2013-01-06

    Early detection of invasive species is critical for effective biocontrol to mitigate potential ecological and economic damage. Laser transmission spectroscopy (LTS) is a powerful solution offering real-time, DNA-based species detection in the field. LTS can measure the size, shape and number of nanoparticles in a solution and was used here to detect size shifts resulting from hybridization of the polymerase chain reaction product to nanoparticles functionalized with species-specific oligonucleotide probes or with the species-specific oligonucleotide probes alone. We carried out a series of DNA detection experiments using the invasive freshwater quagga mussel (Dreissena bugensis) to evaluate the capability of the LTS platform for invasive species detection. Specifically, we tested LTS sensitivity to (i) DNA concentrations of a single target species, (ii) the presence of a target species within a mixed sample of other closely related species, (iii) species-specific functionalized nanoparticles versus species-specific oligonucleotide probes alone, and (iv) amplified DNA fragments versus unamplified genomic DNA. We demonstrate that LTS is a highly sensitive technique for rapid target species detection, with detection limits in the picomolar range, capable of successful identification in multispecies samples containing target and non-target species DNA. These results indicate that the LTS DNA detection platform will be useful for field application of target species. Additionally, we find that LTS detection is effective with species-specific oligonucleotide tags alone or when they are attached to polystyrene nanobeads and with both amplified and unamplified DNA, indicating that the technique may also have versatility for broader applications.

  8. Chemical analysis of industrial scale deposits by combined use of correlation coefficients with emission line detection of laser induced breakdown spectroscopy spectra

    International Nuclear Information System (INIS)

    Siozos, P.; Philippidis, A.; Hadjistefanou, M.; Gounarakis, C.; Anglos, D.

    2013-01-01

    Laser-induced breakdown spectroscopy (LIBS) was used to determine the mineral composition of various industrial scale samples. The aim of the study has been to investigate the capacity of LIBS to provide a fast, reliable analytical tool for carrying out routine analysis of inorganic scales, potentially on site, as a means to facilitate decision making concerning scale removal procedures. LIBS spectra collected in the range of 200–660 nm conveyed information about the metal content of the minerals. Via a straightforward analysis based on linear correlation of LIBS spectra it was possible to successfully discriminate scale samples into three main groups, Fe-rich, Ca-rich and Ba-rich, on the basis of correlation coefficients. By combining correlation coefficients with spectral data collected in the NIR, 860–960 nm, where sulfur emissions are detected, it became further possible to discriminate sulfates from carbonates as confirmed by independent analysis based on Raman spectroscopy. It is emphasized that the proposed LIBS-based method successfully identifies the major mineral or minerals present in the samples classifying the scales into relevant groups hence enabling process engineers to select appropriate scale dissolution strategies. - Highlights: • LIBS was used to determine the mineral composition of industrial scale samples. • Three groups of inorganic scales were identified: Ca rich, Ba rich and Fe rich. • A method that combines correlation coefficients and line detection is proposed. • The method successfully identifies the main mineral, or minerals, in the samples. • The results were compared with results obtained by use of Raman analysis

  9. Feature selection for fMRI-based deception detection

    Science.gov (United States)

    Jin, Bo; Strasburger, Alvin; Laken, Steven J; Kozel, F Andrew; Johnson, Kevin A; George, Mark S; Lu, Xinghua

    2009-01-01

    Background Functional magnetic resonance imaging (fMRI) is a technology used to detect brain activity. Patterns of brain activation have been utilized as biomarkers for various neuropsychiatric applications. Detecting deception based on the pattern of brain activation characterized with fMRI is getting attention – with machine learning algorithms being applied to this field in recent years. The high dimensionality of fMRI data makes it a difficult task to directly utilize the original data as input for classification algorithms in detecting deception. In this paper, we investigated the procedures of feature selection to enhance fMRI-based deception detection. Results We used the t-statistic map derived from the statistical parametric mapping analysis of fMRI signals to construct features that reflect brain activation patterns. We subsequently investigated various feature selection methods including an ensemble method to identify discriminative features to detect deception. Using 124 features selected from a set of 65,166 original features as inputs for a support vector machine classifier, our results indicate that feature selection significantly enhanced the classification accuracy of the support vector machine in comparison to the models trained using all features and dimension reduction based models. Furthermore, the selected features are shown to form anatomic clusters within brain regions, which supports the hypothesis that specific brain regions may play a role during deception processes. Conclusion Feature selection not only enhances classification accuracy in fMRI-based deception detection but also provides support for the biological hypothesis that brain activities in certain regions of the brain are important for discrimination of deception. PMID:19761569

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

    Science.gov (United States)

    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.

  11. Fast Content-Based Packet Handling for Intrusion Detection

    National Research Council Canada - National Science Library

    Fisk, Mike

    2001-01-01

    ... use of Royer-Moore currently used in the popular intrusion detection platform Snort. We then measure the actual performance of several search algorithms on actual packet traces and rulesets. Our results provide lessons on the structuring of content-based handlers.

  12. An Astigmatic Detection System for Polymeric Cantilever-based Sensors

    DEFF Research Database (Denmark)

    Hwu, En-Te; Liao, Hsien-Shun; Bosco, Filippo

    2012-01-01

    We demonstrate the use of an astigmatic detection system (ADS) for resonance frequency identification of polymer microcantilever sensors. The ADS technology is based on a DVD optical head combined with an optical microscope (OM). The optical head has a signal bandwidth of 80 MHz, allowing thermal...

  13. REGION BASED FOREST CHANGE DETECTION FROM CARTOSAT-1 STEREO IMAGERY

    Directory of Open Access Journals (Sweden)

    J. Tian

    2012-09-01

    Full Text Available Tree height is a fundamental parameter for describing the forest situation and changes. The latest development of automatic Digital Surface Model (DSM generation techniques allows new approaches of forest change detection from satellite stereo imagery. This paper shows how DSMs can support the change detection in forest area. A novel region based forest change detection method is proposed using single-channel CARTOSAT-1 stereo imagery. In the first step, DSMs from two dates are generated based on automatic matching technology. After co-registration and normalising by using LiDAR data, the mean-shift segmentation is applied to the original pan images, and the images of both dates are classified to forest and non-forest areas by analysing their histograms and height differences. In the second step, a rough forest change detection map is generated based on the comparison of the two forest map. Then the GLCM texture from the nDSM and the Cartosat-1 images of the resulting regions are analyzed and compared, the real changes are extracted by SVM based classification.

  14. GIS-based Integration of Interdisciplinary Ecological Data to Detect ...

    African Journals Online (AJOL)

    GIS-based Integration of Interdisciplinary Ecological Data to Detect Land-cover Changes in Creek Mangroves at Gazi Bay, Kenya. ... Very low values of basal area (7.7 m2/ha and 4.9 m2/ha) and complexity indices (1.86 and 1.12) at Makongeni and Kinondo 1, respectively, reflected intense human pressure in these areas.

  15. PCR-based detection of allergenic mackerel ingredients in seafood

    Indian Academy of Sciences (India)

    Administrator

    Keywords. Scomber mackerel; allergy; seafood; PCR; genus identification. RESEARCH NOTE. PCR-based detection of allergenic mackerel ingredients in seafood. FUTOSHI ARANISHI 1,* and TAKANE OKIMOTO 1,2. 1Department of Biological and Environmental Sciences, Miyazaki University,. Miyazaki 889-2192, Japan.

  16. The harmonics detection method based on neural network applied ...

    African Journals Online (AJOL)

    The harmonics detection method based on neural network applied to harmonics compensation. R Dehini, A Bassou, B Ferdi. Abstract. Several different methods have been used to sense load currents and extract its harmonic component in order to produce a reference current in shunt active power filters (SAPF), and to ...

  17. Detecting Hacked Twitter Accounts based on Behavioural Change

    NARCIS (Netherlands)

    Nauta, Meike; Habib, Mena Badieh; van Keulen, Maurice

    Social media accounts are valuable for hackers for spreading phishing links, malware and spam. Furthermore, some people deliberately hack an acquaintance to damage his or her image. This paper describes a classification for detecting hacked Twitter accounts. The model is mainly based on features

  18. Machine learning for network-based malware detection

    DEFF Research Database (Denmark)

    Stevanovic, Matija

    using traffic traces from honeypots and malware testing environments as well as operational ISP networks. Based on the evaluation, the novel detection methods provide accurate and efficient identification of malicious network traffic, thus being promising in the light of operational deployment...

  19. A proposed data base system for detection, classification and ...

    African Journals Online (AJOL)

    A proposed data base system for detection, classification and location of fault on electricity company of Ghana electrical distribution system. Isaac Owusu-Nyarko, Mensah-Ananoo Eugine. Abstract. No Abstract. Keywords: database, classification of fault, power, distribution system, SCADA, ECG. Full Text: EMAIL FULL TEXT ...

  20. Phishing Detection: Analysis of Visual Similarity Based Approaches

    Directory of Open Access Journals (Sweden)

    Ankit Kumar Jain

    2017-01-01

    Full Text Available Phishing is one of the major problems faced by cyber-world and leads to financial losses for both industries and individuals. Detection of phishing attack with high accuracy has always been a challenging issue. At present, visual similarities based techniques are very useful for detecting phishing websites efficiently. Phishing website looks very similar in appearance to its corresponding legitimate website to deceive users into believing that they are browsing the correct website. Visual similarity based phishing detection techniques utilise the feature set like text content, text format, HTML tags, Cascading Style Sheet (CSS, image, and so forth, to make the decision. These approaches compare the suspicious website with the corresponding legitimate website by using various features and if the similarity is greater than the predefined threshold value then it is declared phishing. This paper presents a comprehensive analysis of phishing attacks, their exploitation, some of the recent visual similarity based approaches for phishing detection, and its comparative study. Our survey provides a better understanding of the problem, current solution space, and scope of future research to deal with phishing attacks efficiently using visual similarity based approaches.

  1. Section based traffic detection on motorways for incident management

    NARCIS (Netherlands)

    Noort, M. van; Klunder, G.

    2007-01-01

    Current vehicle detection on motorways is based generally on either inductive loop systems or various alternatives such as video cameras. Recently, we encountered two new developments that take a different approach: one from The Netherlands using microwave sensors, and the other from Sweden using

  2. Correlates of HIV infection among street-based and venue-based sex workers in Vietnam.

    Science.gov (United States)

    Le, Thuy Tc; Nguyen, Quoc C; Tran, Ha Tt; Schwandt, Michael; Lim, Hyun J

    2016-10-01

    Commercial sex work is one of the driving forces of the HIV epidemic across the world. In Vietnam, although female sex workers (FSWs) carry a disproportionate burden of HIV, little is known about the risk profile and associated factors for HIV infection among this population. There is a need for large-scale research to obtain reliable and representative estimates of the measures of association. This study involved secondary data analysis of the 'HIV/STI Integrated Biological and Behavioral Surveillance' study in Vietnam in 2009-2010 to examine the correlates of HIV among FSWs. Data collected from 5298 FSWs, including 2530 street-based sex workers and 2768 venue-based sex workers from 10 provinces in Vietnam, were analyzed using descriptive statistics and bivariate and multivariate logistic regression analyses. HIV prevalence among the overall FSW population was 8.6% (n = 453). However, when stratified by FSW subpopulations, HIV prevalence was 10.6% (n = 267) for street-based sex workers and 6.7% (n = 186) for venue-based sex workers. Factors independently associated with HIV infection in the multivariate analysis, regardless of sex work types, were injecting drug use, high self-perceived HIV risk, and age ≥ 25 years. Additional factors independently associated with HIV risk within each FSW subpopulation included having ever been married among street-based sex workers and inconsistent condom use with clients and having sex partners who injected drugs among venue-based sex workers. Apart from strategies addressing modifiable risk behaviours among all FSWs, targeted strategies to address specific risk behaviours within each FSW subpopulation should be adopted. © The Author(s) 2016.

  3. Citation-based plagiarism detection detecting disguised and cross-language plagiarism using citation pattern analysis

    CERN Document Server

    Gipp, Bela

    2014-01-01

    Plagiarism is a problem with far-reaching consequences for the sciences. However, even today's best software-based systems can only reliably identify copy & paste plagiarism. Disguised plagiarism forms, including paraphrased text, cross-language plagiarism, as well as structural and idea plagiarism often remain undetected. This weakness of current systems results in a large percentage of scientific plagiarism going undetected. Bela Gipp provides an overview of the state-of-the art in plagiarism detection and an analysis of why these approaches fail to detect disguised plagiarism forms. The aut

  4. Heteronuclear Correlation SSNMR Spectroscopy with Indirect Detection under Fast Magic-Angle Spinning [Book Chapter

    Energy Technology Data Exchange (ETDEWEB)

    Kobayshi, Takeshi [Ames Laboratory (AMES), Ames, IA (United States); Nishiyama, Yusuke [Ames Laboratory (AMES), Ames, IA (United States); Pruski, Marek [Ames Laboratory (AMES), Ames, IA (United States)

    2018-01-01

    The main focus of this chapter is to address experimental strategies on the subject by providing a hands-on guide to fast MAS experiments, with a particular focus on indirect detection. Although our experience is limited to our respective laboratories in Ames and Yokohama, we hope that our descriptions of experimental setups and optimization procedures are sufficiently general to be applicable to all modern instruments. The chapter is organized as follows. Section 2 below introduces briefly the fast MAS technology and its main advantages. In Section 3, we describe the hardware associated with this remarkable technology and provide practical advices on its use, including procedures for loading and unloading the samples, maintaining the probe, reducing t1 noise, etc. In Section 4, we describe the principles and hands-on aspects of experiments involving the indirect detection of spin-1/2 and 14N nuclei

  5. Family-Based Benchmarking of Copy Number Variation Detection Software.

    Science.gov (United States)

    Nutsua, Marcel Elie; Fischer, Annegret; Nebel, Almut; Hofmann, Sylvia; Schreiber, Stefan; Krawczak, Michael; Nothnagel, Michael

    2015-01-01

    The analysis of structural variants, in particular of copy-number variations (CNVs), has proven valuable in unraveling the genetic basis of human diseases. Hence, a large number of algorithms have been developed for the detection of CNVs in SNP array signal intensity data. Using the European and African HapMap trio data, we undertook a comparative evaluation of six commonly used CNV detection software tools, namely Affymetrix Power Tools (APT), QuantiSNP, PennCNV, GLAD, R-gada and VEGA, and assessed their level of pair-wise prediction concordance. The tool-specific CNV prediction accuracy was assessed in silico by way of intra-familial validation. Software tools differed greatly in terms of the number and length of the CNVs predicted as well as the number of markers included in a CNV. All software tools predicted substantially more deletions than duplications. Intra-familial validation revealed consistently low levels of prediction accuracy as measured by the proportion of validated CNVs (34-60%). Moreover, up to 20% of apparent family-based validations were found to be due to chance alone. Software using Hidden Markov models (HMM) showed a trend to predict fewer CNVs than segmentation-based algorithms albeit with greater validity. PennCNV yielded the highest prediction accuracy (60.9%). Finally, the pairwise concordance of CNV prediction was found to vary widely with the software tools involved. We recommend HMM-based software, in particular PennCNV, rather than segmentation-based algorithms when validity is the primary concern of CNV detection. QuantiSNP may be used as an additional tool to detect sets of CNVs not detectable by the other tools. Our study also reemphasizes the need for laboratory-based validation, such as qPCR, of CNVs predicted in silico.

  6. Pedestrian detection from thermal images: A sparse representation based approach

    Science.gov (United States)

    Qi, Bin; John, Vijay; Liu, Zheng; Mita, Seiichi

    2016-05-01

    Pedestrian detection, a key technology in computer vision, plays a paramount role in the applications of advanced driver assistant systems (ADASs) and autonomous vehicles. The objective of pedestrian detection is to identify and locate people in a dynamic environment so that accidents can be avoided. With significant variations introduced by illumination, occlusion, articulated pose, and complex background, pedestrian detection is a challenging task for visual perception. Different from visible images, thermal images are captured and presented with intensity maps based objects' emissivity, and thus have an enhanced spectral range to make human beings perceptible from the cool background. In this study, a sparse representation based approach is proposed for pedestrian detection from thermal images. We first adopted the histogram of sparse code to represent image features and then detect pedestrian with the extracted features in an unimodal and a multimodal framework respectively. In the unimodal framework, two types of dictionaries, i.e. joint dictionary and individual dictionary, are built by learning from prepared training samples. In the multimodal framework, a weighted fusion scheme is proposed to further highlight the contributions from features with higher separability. To validate the proposed approach, experiments were conducted to compare with three widely used features: Haar wavelets (HWs), histogram of oriented gradients (HOG), and histogram of phase congruency (HPC) as well as two classification methods, i.e. AdaBoost and support vector machine (SVM). Experimental results on a publicly available data set demonstrate the superiority of the proposed approach.

  7. A Wavelet-Based Approach to Fall Detection

    Directory of Open Access Journals (Sweden)

    Luca Palmerini

    2015-05-01

    Full Text Available Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the “prototype fall”.In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases in order to improve the performance of fall detection algorithms.

  8. Saliency detection algorithm based on LSC-RC

    Science.gov (United States)

    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.

  9. A wavelet-based approach to fall detection.

    Science.gov (United States)

    Palmerini, Luca; Bagalà, Fabio; Zanetti, Andrea; Klenk, Jochen; Becker, Clemens; Cappello, Angelo

    2015-05-20

    Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the "prototype fall".In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases) in order to improve the performance of fall detection algorithms.

  10. Vision Sensor-Based Road Detection for Field Robot Navigation

    Directory of Open Access Journals (Sweden)

    Keyu Lu

    2015-11-01

    Full Text Available Road detection is an essential component of field robot navigation systems. Vision sensors play an important role in road detection for their great potential in environmental perception. In this paper, we propose a hierarchical vision sensor-based method for robust road detection in challenging road scenes. More specifically, for a given road image captured by an on-board vision sensor, we introduce a multiple population genetic algorithm (MPGA-based approach for efficient road vanishing point detection. Superpixel-level seeds are then selected in an unsupervised way using a clustering strategy. Then, according to the GrowCut framework, the seeds proliferate and iteratively try to occupy their neighbors. After convergence, the initial road segment is obtained. Finally, in order to achieve a globally-consistent road segment, the initial road segment is refined using the conditional random field (CRF framework, which integrates high-level information into road detection. We perform several experiments to evaluate the common performance, scale sensitivity and noise sensitivity of the proposed method. The experimental results demonstrate that the proposed method exhibits high robustness compared to the state of the art.

  11. Colorimetric detection of cholesterol based on enzyme modified gold nanoparticles

    Science.gov (United States)

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

    2018-02-01

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

  12. Wideband Array Signal Detection Algorithm Based on Power Focusing

    Directory of Open Access Journals (Sweden)

    Gong Bin

    2012-09-01

    Full Text Available Aiming at the requirement of real-time signal detection in the passive surveillance system, a wideband array signal detection algorithm is proposed based on the concept of power focusing. By making use of the phase difference of the signal received by a uniform linear array, the algorithm makes the power of the received signal focused in the Direction Of Arrival (DOA with improved cascade FFT. Subsequently, the probability density function of the output noise at each angle is derived. Furthermore, a Constant False Alarm Rate (CFAR test statistic and the corresponding detection threshold are constructed. The theoretical probability of detection is also derived for different false alarm rate and Signal-to-Noise Ratio (SNR. The proposed algorithm is computationally efficient, and the detection process is independent of the prior information. Meanwhile, the results can act as the initial value for other algorithms with higher precision. Simulation results show that the proposed algorithm achieves good performance for weak signal detection.

  13. A Viola-Jones based hybrid face detection framework

    Science.gov (United States)

    Murphy, Thomas M.; Broussard, Randy; Schultz, Robert; Rakvic, Ryan; Ngo, Hau

    2013-12-01

    Improvements in face detection performance would benefit many applications. The OpenCV library implements a standard solution, the Viola-Jones detector, with a statistically boosted rejection cascade of binary classifiers. Empirical evidence has shown that Viola-Jones underdetects in some instances. This research shows that a truncated cascade augmented by a neural network could recover these undetected faces. A hybrid framework is constructed, with a truncated Viola-Jones cascade followed by an artificial neural network, used to refine the face decision. Optimally, a truncation stage that captured all faces and allowed the neural network to remove the false alarms is selected. A feedforward backpropagation network with one hidden layer is trained to discriminate faces based upon the thresholding (detection) values of intermediate stages of the full rejection cascade. A clustering algorithm is used as a precursor to the neural network, to group significant overlappings. Evaluated on the CMU/VASC Image Database, comparison with an unmodified OpenCV approach shows: (1) a 37% increase in detection rates if constrained by the requirement of no increase in false alarms, (2) a 48% increase in detection rates if some additional false alarms are tolerated, and (3) an 82% reduction in false alarms with no reduction in detection rates. These results demonstrate improved face detection and could address the need for such improvement in various applications.

  14. Disposable amperometric biosensor based on nanostructured bacteriophages for glucose detection

    International Nuclear Information System (INIS)

    Kang, Yu Ri; Kim, Ju Hwan; Kim, Soo Won; Hwang, Kyung Hoon; Nam, Chang Hoon

    2010-01-01

    The selection of electrode material profoundly influences biosensor science and engineering, as it heavily influences biosensor sensitivity. Here we propose a novel electrochemical detection method using a working electrode consisting of bio-nanowires from genetically modified filamentous phages and nanoparticles. fd-tet p8MMM filamentous phages displaying a three-methionine (MMM) peptide on the major coat protein pVIII (designated p8MMM phages) were immobilized on the active area of an electrochemical sensor through physical adsorption and chemical bonding. Bio-nanowires composed of p8MMM phages and silver nanoparticles facilitated sensitive, rapid and selective detection of particular molecules. We explored whether the composite electrode with bio-nanowires was an effective platform to detect the glucose oxidase. The current response of the bio-nanowire sensor was high at various glucose concentrations (0.1 µm–0.1 mM). This method provides a considerable advantage to demonstrate analyte detection over low concentration ranges. Especially, phage-enabled bio-nanowires can serve as receptors with high affinity and specificity for the detection of particular biomolecules and provide a convenient platform for designing site-directed multifunctional scaffolds based on bacteriophages and may serve as a simple method for label-free detection

  15. Immunomagnetic nanoparticle based quantitative PCR for rapid detection of Salmonella

    International Nuclear Information System (INIS)

    Bakthavathsalam, Padmavathy; Rajendran, Vinoth Kumar; Saran, Uttara; Chatterjee, Suvro; Ali, Baquir Mohammed Jaffar

    2013-01-01

    We have developed a rapid and sensitive method for immunomagnetic separation (IMS) of Salmonella along with their real time detection via PCR. Silica-coated magnetic nanoparticles were functionalized with carboxy groups to which anti-Salmonella antibody raised against heat-inactivated whole cells of Salmonella were covalently attached. The immuno-captured target cells were detected in beverages like milk and lemon juice by multiplex PCR and real time PCR with a detection limit of 10 4 cfu.mL −1 and 10 3 cfu.mL −1 , respectively. We demonstrate that IMS can be used for selective concentration of target bacteria from beverages for subsequent use in PCR detection. PCR also enables differentiation of Salmonella typhi and Salmonella paratyphi A using a set of four specific primers. In addition, IMS—PCR can be used as a screening tool in the food and beverage industry for the detection of Salmonella within 3–4 h which compares favorably to the time of several days that is needed in case of conventional detection based on culture and biochemical methods. (author)

  16. Paper-based Platform for Urinary Creatinine Detection.

    Science.gov (United States)

    Sittiwong, Jarinya; Unob, Fuangfa

    2016-01-01

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

  17. Detection of CSF leak in spinal CSF leak syndrome using MR myelography: correlation with radioisotope cisternography.

    Science.gov (United States)

    Yoo, H-M; Kim, S J; Choi, C G; Lee, D H; Lee, J H; Suh, D C; Choi, J W; Jeong, K S; Chung, S J; Kim, J S; Yun, S-C

    2008-04-01

    Spinal CSF leak syndrome is a unique disorder caused by spinal CSF leak. In this study, we attempted to determine whether MR myelography (MRM) can detect the leakage site in the spine. We performed both MRM and radioisotope cisternography (RIC) in 15 patients with spinal CSF leak syndrome. Patients were included in this study if they had at least 2 of the following criteria: 1) orthostatic headache, 2) low CSF opening pressure, and 3) diffuse pachymeningeal enhancement on brain MR imaging. For comparison, we performed MRM in 15 subjects without symptoms of spinal CSF leak syndrome. MRM was performed with the 2D turbo spin-echo technique in the entire spine by using a 1.5T scanner. Two blinded radiologists evaluated the MRM findings in a total of 30 cases, composed of patient and control groups, with regard to the presence of leakage and the level of leakage if present. RIC was performed only in the patient group and was assessed by consensus among 3 physicians experienced in nuclear medicine. The diagnostic performance of MRM and RIC was evaluated on the basis of the clinical diagnosis of spinal CSF leak syndrome. The sensitivity, specificity, and accuracy of MR myelography for detecting CSF leak was 86.7%, 86.7%, and 86.7% for reader 1, respectively, and 80.0%, 93.3%, and 86.7% for reader 2, respectively. The sensitivity of RIC was 93.3%. Agreement between the 2 techniques for the detection of CSF leak was substantial in reader 1 and moderate in reader 2 (kappa = 0.634 and 0.444, respectively). MRM is an effective tool for detecting CSF leak in the spine in patients with spinal CSF leak syndrome.

  18. Vision-based threat detection in dynamic environments.

    Energy Technology Data Exchange (ETDEWEB)

    Carlson, Jeffrey J.

    2007-08-01

    This report addresses the development of automated video-screening technology to assist security forces in protecting our homeland against terrorist threats. A prevailing threat is the covert placement of bombs inside crowded public facilities. Although video-surveillance systems are increasingly common, current systems cannot detect the placement of bombs. It is also unlikely that security personnel could detect a bomb or its placement by observing video from surveillance cameras. The problems lie in the large number of cameras required to monitor large areas, the limited number of security personnel employed to protect these areas, and the intense diligence required to effectively screen live video from even a single camera. Different from existing video-detection systems designed to operate in nearly static environments, we are developing technology to detect changes in the background of dynamic environments: environments where motion and human activities are persistent over long periods. Our goal is to quickly detect background changes, even if the background is visible to the camera less than 5 percent of the time and possibly never free from foreground activity. Our approach employs statistical scene models based on mixture densities. We hypothesized that the background component of the mixture has a small variance compared to foreground components. Experiments demonstrate this hypothesis is true under a wide variety of operating conditions. A major focus involved the development of robust background estimation techniques that exploit this property. We desire estimation algorithms that can rapidly produce accurate background estimates and detection algorithms that can reliably detect background changes with minimal nuisance alarms. Another goal is to recognize unusual activities or foreground conditions that could signal an attack (e.g., large numbers of running people, people falling to the floor, etc.). Detection of background changes and/or unusual

  19. Detecting epileptic regions based on global brain connectivity patterns.

    Science.gov (United States)

    Sweet, Andrew; Venkataraman, Archana; Stufflebeam, Steven M; Liu, Hesheng; Tanaka, Naoro; Madsen, Joseph; Golland, Polina

    2013-01-01

    We present a method to detect epileptic regions based on functional connectivity differences between individual epilepsy patients and a healthy population. Our model assumes that the global functional characteristics of these differences are shared across patients, but it allows for the epileptic regions to vary between individuals. We evaluate the detection performance against intracranial EEG observations and compare our approach with two baseline methods that use standard statistics. The baseline techniques are sensitive to the choice of thresholds, whereas our algorithm automatically estimates the appropriate model parameters and compares favorably with the best baseline results. This suggests the promise of our approach for pre-surgical planning in epilepsy.

  20. Digital Printing Quality Detection and Analysis Technology Based on CCD

    Science.gov (United States)

    He, Ming; Zheng, Liping

    2017-12-01

    With the help of CCD digital printing quality detection and analysis technology, it can carry out rapid evaluation and objective detection of printing quality, and can play a certain control effect on printing quality. It can be said CDD digital printing quality testing and analysis of the rational application of technology, its digital printing and printing materials for a variety of printing equipments to improve the quality of a very positive role. In this paper, we do an in-depth study and discussion based on the CCD digital print quality testing and analysis technology.