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Sample records for based correlation detection

  1. Digital Correlation based on Wavelet Transform for Image Detection

    International Nuclear Information System (INIS)

    Barba, L; Vargas, L; Torres, C; Mattos, L

    2011-01-01

    In this work is presented a method for the optimization of digital correlators to improve the characteristic detection on images using wavelet transform as well as subband filtering. It is proposed an approach of wavelet-based image contrast enhancement in order to increase the performance of digital correlators. The multiresolution representation is employed to improve the high frequency content of images taken into account the input contrast measured for the original image. The energy of correlation peaks and discrimination level of several objects are improved with this technique. To demonstrate the potentiality in extracting characteristics using the wavelet transform, small objects inside reference images are detected successfully.

  2. Sound card based digital correlation detection of weak photoelectrical signals

    International Nuclear Information System (INIS)

    Tang Guanghui; Wang Jiangcheng

    2005-01-01

    A simple and low-cost digital correlation method is proposed to investigate weak photoelectrical signals, using a high-speed photodiode as detector, which is directly connected to a programmably triggered sound card analogue-to-digital converter and a personal computer. Two testing experiments, autocorrelation detection of weak flickering signals from a computer monitor under background of noisy outdoor stray light and cross-correlation measurement of the surface velocity of a motional tape, are performed, showing that the results are reliable and the method is easy to implement

  3. 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.

  4. 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

  5. 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

  6. Abnormal Event Detection in Wireless Sensor Networks Based on Multiattribute Correlation

    Directory of Open Access Journals (Sweden)

    Mengdi Wang

    2017-01-01

    Full Text Available Abnormal event detection is one of the vital tasks in wireless sensor networks. However, the faults of nodes and the poor deployment environment have brought great challenges to abnormal event detection. In a typical event detection technique, spatiotemporal correlations are collected to detect an event, which is susceptible to noises and errors. To improve the quality of detection results, we propose a novel approach for abnormal event detection in wireless sensor networks. This approach considers not only spatiotemporal correlations but also the correlations among observed attributes. A dependency model of observed attributes is constructed based on Bayesian network. In this model, the dependency structure of observed attributes is obtained by structure learning, and the conditional probability table of each node is calculated by parameter learning. We propose a new concept named attribute correlation confidence to evaluate the fitting degree between the sensor reading and the abnormal event pattern. On the basis of time correlation detection and space correlation detection, the abnormal events are identified. Experimental results show that the proposed algorithm can reduce the impact of interference factors and the rate of the false alarm effectively; it can also improve the accuracy of event detection.

  7. 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.

  8. 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

  9. 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.

  10. 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...

  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 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

  14. A new method to detect event-related potentials based on Pearson's correlation.

    Science.gov (United States)

    Giroldini, William; Pederzoli, Luciano; Bilucaglia, Marco; Melloni, Simone; Tressoldi, Patrizio

    2016-12-01

    Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience.  Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise.  The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N , where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP's waveform, these waveforms being time- and phase-locked.  In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson's correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase-in consonance with the stimuli-in EEG signal correlation over all channels.  This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs.  These hidden components seem to be caused by variations (between each successive stimulus) of the ERP's inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology.  The method we are proposing can be directly used in the form of a process written in the well

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. 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.

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

    Science.gov (United States)

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

    2011-04-01

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

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

    Science.gov (United States)

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

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

  2. 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.

  3. Ultrasonic Detection Using Correlation Images (Preprint)

    National Research Council Canada - National Science Library

    Cepel, Raini; Ho, K. C; Rinker, Brett A; Palmer, Donald D; Neal, Steven P

    2006-01-01

    .... In this paper, we describe an amplitude independent approach for imaging and detection based on the similarity of adjacent signals, quantified by the correlation coefficient calculated between A-scans...

  4. 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.

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

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

    Science.gov (United States)

    2010-06-01

    Figures Figure Page 2.1. Verizon Data Breach Report: Detective Controls by percent of breach victims. [11...immature, operationally speaking. Figure 2.1: Verizon Data Breach Report: Detective Controls by percent of breach victims. [11] 7 The 2008 CSI Computer...Christopher Novak, Christopher Porter, Bryan Sartin, Peter Tippett, and J. Andrew Valentine. Data Breach Investigations Report. Technical report

  7. 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.

  8. 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.

  9. 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

  10. Temperature-Corrected Oxygen Detection Based on Multi-Mode Diode Laser Correlation Spectroscopy

    Directory of Open Access Journals (Sweden)

    Xiutao Lou

    2013-01-01

    Full Text Available Temperature-corrected oxygen measurements were performed by using multi-mode diode laser correlation spectroscopy at temperatures ranging between 300 and 473 K. The experiments simulate in situ monitoring of oxygen in coal-combustion exhaust gases at the tail of the flue. A linear relationship with a correlation coefficient of −0.999 was found between the evaluated concentration and the gas temperature. Temperature effects were either auto-corrected by keeping the reference gas at the same conditions as the sample gas, or rectified by using a predetermined effective temperature-correction coefficient calibrated for a range of absorption wavelengths. Relative standard deviations of the temperature-corrected oxygen concentrations obtained by different schemes and at various temperatures were estimated, yielding a measurement precision of 0.6%.

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

    CSIR Research Space (South Africa)

    Senekal, F

    2010-11-01

    Full Text Available and track a target object (or objects) over a series of digital images. Visual target tracking can be accomplished by feature-based or region-based approaches. In feature-based approaches, interest points are calculated in a digital image, and a local...-time performance based on the computational power that is available on a specific platform. To further reduce the computational requirements, process- ing is restricted to the region of interest (ROI). The region of interest is provided as an input parameter...

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

    Science.gov (United States)

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

    2018-05-21

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

  13. 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.

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

    Science.gov (United States)

    Clem, Michelle M.; Woike, Mark R.

    2013-01-01

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

  15. 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.

  16. 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.

  17. 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.

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

    Science.gov (United States)

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

    2014-01-01

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

  19. Structural analysis and incipient failure detection of primary circuit components based on correlation-analysis and finite-element models

    International Nuclear Information System (INIS)

    Olma, B.J.

    1977-01-01

    A method is presented to compute vibrational power spectral densities (VPSD's) of primary circuit components based on a finite-element representation of the primary circuit. First this method has been applied to the sodium cooled reactor KNK, Karlsruhe. Now a further application is being developed for a BWR-nuclear power plant. The experimentally determined VPSD's can be considered as the output of a multiple input-output system. They have to be explained as the frequency response of a multidimensional mechanical system, which is excited by stochastic and deterministic mechanical driving forces. The stochastic mechanical forces are generated by the dynamic pressure fluctuations of the fluid. The deterministic mechanical forces are caused by the pressure fluctuations, which are induced by the main coolant pumps or by standing waves. The excitation matrix can be obtained from measured pressure fluctuations. The vibration transfer function matrix can be computed from the mass matrix, damping matrix and stiffness matrix of a theoretical finite-element model or mass-spring model. Based on this theory the computer code 'STAMPO' has been established. This program has been applied to the KNK reactor. The excitation matrix was created from measured jet-noise pressure fluctuations. The mass-, stiffness- and damping matrix has been extracted from a SAP-IV-model of the primary system. Sequentially for each frequency point the complete VPSD matrix has been computed. The diagonal elements of this matrix represent the vibrational auto-power spectral densities, the off-diagonal elements represent the vibrational cross-power spectral densities. The calculations give good agreement with measured VPSD's. The comparison shows that the measured jet-noise pressure fluctuations act nearly uncorrelated on the structure, whereas the output VPSD's are well correlated

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

    Science.gov (United States)

    2017-05-14

    the impact of oxidative stress on brain function, but also enable development of reliable screening tools for cognitive performance of individuals in...of Brain Oxidative Stress and its Correlation with Cognitive Functions Date 04/20/2017 PI information: Dr. Pravat K. Mandal,Ph.D Professor...relationship between the brain oxidative status and stress at a cellular, physiological as well as a psychological level. These stressors, in turn, have

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

    Science.gov (United States)

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

    2015-01-01

    The modern turbine engine operates in a harsh environment at high speeds and is repeatedly exposed to combined high mechanical and thermal loads. The cumulative effects of these external forces lead to high stresses and strains on the engine components, such as the rotating turbine disks, which may eventually lead to a catastrophic failure if left undetected. The operating environment makes it difficult to use conventional strain gauges, therefore, non-contact strain measurement techniques is of interest to NASA and the turbine engine community. This presentation describes one such approach; the use of cross correlation analysis to measure strain experienced by the engine turbine disk with the goal of assessing potential faults and damage.

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

    Science.gov (United States)

    Hettiarachchi, Ranga; Yokoyama, Mitsuo; Uehara, Hideyuki

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

  3. 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.

  4. 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.

  5. Gas detection by correlation spectroscopy employing a multimode diode laser.

    Science.gov (United States)

    Lou, Xiutao; Somesfalean, Gabriel; Zhang, Zhiguo

    2008-05-01

    A gas sensor based on the gas-correlation technique has been developed using a multimode diode laser (MDL) in a dual-beam detection scheme. Measurement of CO(2) mixed with CO as an interfering gas is successfully demonstrated using a 1570 nm tunable MDL. Despite overlapping absorption spectra and occasional mode hops, the interfering signals can be effectively excluded by a statistical procedure including correlation analysis and outlier identification. The gas concentration is retrieved from several pair-correlated signals by a linear-regression scheme, yielding a reliable and accurate measurement. This demonstrates the utility of the unsophisticated MDLs as novel light sources for gas detection applications.

  6. Detecting subnetwork-level dynamic correlations.

    Science.gov (United States)

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

    2017-01-15

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

  7. A new digital correlation flaw detection system

    International Nuclear Information System (INIS)

    Lee, B.B.; Furgason, E.S.

    1981-01-01

    A new portable digital random signal flaw detection system is described which uses a digital delay line to replace the acoustic delay line of the original random signal system. Using this new system, a comparison was made between the two types of transmit signals which have been used in previous systems--m-sequences and random signals. This comparison has not been possible with these previous correlation flaw detection systems. Results indicated that for high-speed short code operation, the m-sequences produced slightly lower range sidelobes than typical samples of a clipped random signal. For normal long code operation, results indicated that system performance is essentially equivalent in resolution and signal-to-noise ratio using either m-sequences or clipped and sampled random signals. Further results also showed that for normal long code operation, the system produces outputs equivalent in resolution to pulse-echo systems, but with the added benefit of signal-to-noise ratio enhancement

  8. 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).

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

    International Nuclear Information System (INIS)

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

    2016-01-01

    We present the design and preliminary characterization of the first detection module based on Silicon Photomultiplier (SiPM) tailored for single-photon timing applications. The aim of this work is to demonstrate, thanks to the design of a suitable module, the possibility to easily exploit SiPM in many applications as an interesting detector featuring large active area, similarly to photomultipliers tubes, but keeping the advantages of solid state detectors (high quantum efficiency, low cost, compactness, robustness, low bias voltage, and insensitiveness to magnetic field). The module integrates a cooled SiPM with a total photosensitive area of 1 mm"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).

  10. Feynman-α correlation analysis by prompt-photon detection

    International Nuclear Information System (INIS)

    Hashimoto, Kengo; Yamada, Sumasu; Hasegawa, Yasuhiro; Horiguchi, Tetsuo

    1998-01-01

    Two-detector Feynman-α measurements were carried out using the UTR-KINKI reactor, a light-water-moderated and graphite-reflected reactor, by detecting high-energy, prompt gamma rays. For comparison, the conventional measurements by detecting neutrons were also performed. These measurements were carried out in the subcriticality range from 0 to $1.8. The gate-time dependence of the variance-and covariance-to-mean ratios measured by gamma-ray detection were nearly identical with those obtained using standard neutron-detection techniques. Consequently, the prompt-neutron decay constants inferred from the gamma-ray correlation data agreed with those from the neutron data. Furthermore, the correlated-to-uncorrelated amplitude ratios obtained by gamma-ray detection significantly depended on the low-energy discriminator level of the single-channel analyzer. The discriminator level was determined as optimum for obtaining a maximum value of the amplitude ratio. The maximum amplitude ratio was much larger than that obtained by neutron detection. The subcriticality dependence of the decay constant obtained by gamma-ray detection was consistent with that obtained by neutron detection and followed the linear relation based on the one-point kinetic model in the vicinity of delayed critical. These experimental results suggest that the gamma-ray correlation technique can be applied to measure reactor kinetic parameters more efficiently

  11. 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.

  12. 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.

  13. 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 .

  14. Reveal quantum correlation in complementary bases

    OpenAIRE

    Shengjun Wu; Zhihao Ma; Zhihua Chen; Sixia Yu

    2014-01-01

    An essential feature of genuine quantum correlation is the simultaneous existence of correlation in complementary bases. We reveal this feature of quantum correlation by defining measures based on invariance under a basis change. For a bipartite quantum state, the classical correlation is the maximal correlation present in a certain optimum basis, while the quantum correlation is characterized as a series of residual correlations in the mutually unbiased bases. Compared with other approaches ...

  15. Clock synchronization by remote detection of correlated photon pairs

    Energy Technology Data Exchange (ETDEWEB)

    Ho, Caleb; Lamas-Linares, AntIa; Kurtsiefer, Christian [Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, 117543 (Singapore)], E-mail: christian.kurtsiefer@gmail.com

    2009-04-15

    In this study, we present an algorithm to detect the time and frequency differences of independent clocks based on observation of time-correlated photon pairs. This enables remote coincidence identification in entanglement-based quantum key distribution schemes without dedicated coincidence hardware, pulsed sources with a timing structure or very stable reference clocks. We discuss the method for typical operating conditions and show that the requirement for reference clock accuracy can be relaxed by about five orders of magnitude in comparison with previous schemes.

  16. 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

  17. 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.

  18. Detection of elemental mercury by multimode diode laser correlation spectroscopy.

    Science.gov (United States)

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

    2012-02-27

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

  19. Brain correlates of automatic visual change detection.

    Science.gov (United States)

    Cléry, H; Andersson, F; Fonlupt, P; Gomot, M

    2013-07-15

    A number of studies support the presence of visual automatic detection of change, but little is known about the brain generators involved in such processing and about the modulation of brain activity according to the salience of the stimulus. The study presented here was designed to locate the brain activity elicited by unattended visual deviant and novel stimuli using fMRI. Seventeen adult participants were presented with a passive visual oddball sequence while performing a concurrent visual task. Variations in BOLD signal were observed in the modality-specific sensory cortex, but also in non-specific areas involved in preattentional processing of changing events. A degree-of-deviance effect was observed, since novel stimuli elicited more activity in the sensory occipital regions and at the medial frontal site than small changes. These findings could be compared to those obtained in the auditory modality and might suggest a "general" change detection process operating in several sensory modalities. Copyright © 2013 Elsevier Inc. All rights reserved.

  20. 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.

  1. 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.

  2. 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

    Background 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. Methods 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 > = 7mmol/L). Results 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. Conclusions 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

  3. Application of time-correlated single photon counting and stroboscopic detection methods with an evanescent-wave fibre-optic sensor for fluorescence-lifetime-based pH measurements

    International Nuclear Information System (INIS)

    Henning, Paul E; Geissinger, Peter

    2012-01-01

    Quasi-distributed optical fibre sensor arrays containing luminescent sensor molecules can be read out spatially resolved utilizing optical time-of-flight detection (OTOFD) methods, which employ pulsed laser interrogation of the luminosensors and time-resolved detection of the sensor signals. In many cases, sensing is based on a change in sensor luminescence intensity; however, sensing based on luminescence lifetime changes is preferable because it reduces the need for field calibration. Because in OTOFD detection is time-resolved, luminescence-lifetime information is already available through the signal pulses, although in practise applications were restricted to sensors with long luminescence lifetimes (hundreds of ns). To implement lifetime-based sensing in crossed-optical-fibre-sensor arrays for sensor molecules with lifetimes less than 10 ns, two time-domain methods, time-correlated single photon counting and stroboscopic detection, were used to record the pH-dependent emission of a fluorescein derivative covalently attached to a highly-porous polymer. A two-term nonexponential decay function yielded both a good fit for experimental lifetime data during reconvolution and a pH response that matches Henderson–Hasselbalch behaviour, yielding a sensor accuracy of 0.02 pH units. Moreover, strong agreement was obtained for the two lifetime determination methods and with intensity-based measurements taken previously. (paper)

  4. 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

  5. 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.

  6. Phase-detected Brillouin optical correlation-domain reflectometry

    Science.gov (United States)

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

    2018-05-01

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

  7. 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

  8. Local correlation detection with linearity enhancement in streaming data

    KAUST Repository

    Xie, Qing; Shang, Shuo; Yuan, Bo; Pang, Chaoyi; Zhang, Xiangliang

    2013-01-01

    -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

  9. 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)

  10. Exploiting large-scale correlations to detect continuous gravitational waves.

    Science.gov (United States)

    Pletsch, Holger J; Allen, Bruce

    2009-10-30

    Fully coherent searches (over realistic ranges of parameter space and year-long observation times) for unknown sources of continuous gravitational waves are computationally prohibitive. Less expensive hierarchical searches divide the data into shorter segments which are analyzed coherently, then detection statistics from different segments are combined incoherently. The novel method presented here solves the long-standing problem of how best to do the incoherent combination. The optimal solution exploits large-scale parameter-space correlations in the coherent detection statistic. Application to simulated data shows dramatic sensitivity improvements compared with previously available (ad hoc) methods, increasing the spatial volume probed by more than 2 orders of magnitude at lower computational cost.

  11. Detection of anomalous signals in temporally correlated data (Invited)

    Science.gov (United States)

    Langbein, J. O.

    2010-12-01

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

  12. 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.

  13. Vehicle Localization by LIDAR Point Correlation Improved by Change Detection

    Science.gov (United States)

    Schlichting, A.; Brenner, C.

    2016-06-01

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

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

    Science.gov (United States)

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

    2006-05-01

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

  15. 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 ...

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

    Science.gov (United States)

    Levy, Gary D.; And Others

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

  17. 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.

  18. 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.

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

    Science.gov (United States)

    Diaz-Ramirez, Victor H.; Kober, Vitaly

    2008-08-01

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

  20. 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

  1. 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.

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

    Science.gov (United States)

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

    2018-07-01

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

  3. 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

  4. 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.

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Martin A. Proescholdt

    2017-01-01

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

  7. 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.

  8. Domain similarity based orthology detection.

    Science.gov (United States)

    Bitard-Feildel, Tristan; Kemena, Carsten; Greenwood, Jenny M; Bornberg-Bauer, Erich

    2015-05-13

    Orthologous protein detection software mostly uses pairwise comparisons of amino-acid sequences to assert whether two proteins are orthologous or not. Accordingly, when the number of sequences for comparison increases, the number of comparisons to compute grows in a quadratic order. A current challenge of bioinformatic research, especially when taking into account the increasing number of sequenced organisms available, is to make this ever-growing number of comparisons computationally feasible in a reasonable amount of time. We propose to speed up the detection of orthologous proteins by using strings of domains to characterize the proteins. We present two new protein similarity measures, a cosine and a maximal weight matching score based on domain content similarity, and new software, named porthoDom. The qualities of the cosine and the maximal weight matching similarity measures are compared against curated datasets. The measures show that domain content similarities are able to correctly group proteins into their families. Accordingly, the cosine similarity measure is used inside porthoDom, the wrapper developed for proteinortho. porthoDom makes use of domain content similarity measures to group proteins together before searching for orthologs. By using domains instead of amino acid sequences, the reduction of the search space decreases the computational complexity of an all-against-all sequence comparison. We demonstrate that representing and comparing proteins as strings of discrete domains, i.e. as a concatenation of their unique identifiers, allows a drastic simplification of search space. porthoDom has the advantage of speeding up orthology detection while maintaining a degree of accuracy similar to proteinortho. The implementation of porthoDom is released using python and C++ languages and is available under the GNU GPL licence 3 at http://www.bornberglab.org/pages/porthoda .

  9. 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.

  10. Body-Sensor-Network-Based Spasticity Detection.

    Science.gov (United States)

    Misgeld, Berno J E; Luken, Markus; Heitzmann, Daniel; Wolf, Sebastian I; Leonhardt, Steffen

    2016-05-01

    Spasticity is a common disorder of the skeletal muscle with a high incidence in industrialised countries. A quantitative measure of spasticity using body-worn sensors is important in order to assess rehabilitative motor training and to adjust the rehabilitative therapy accordingly. We present a new approach to spasticity detection using the Integrated Posture and Activity Network by Medit Aachen body sensor network (BSN). For this, a new electromyography (EMG) sensor node was developed and employed in human locomotion. Following an analysis of the clinical gait data of patients with unilateral cerebral palsy, a novel algorithm was developed based on the idea to detect coactivation of antagonistic muscle groups as observed in the exaggerated stretch reflex with associated joint rigidity. The algorithm applies a cross-correlation function to the EMG signals of two antagonistically working muscles and subsequent weighting using a Blackman window. The result is a coactivation index which is also weighted by the signal equivalent energy to exclude positive detection of inactive muscles. Our experimental study indicates good performance in the detection of coactive muscles associated with spasticity from clinical data as well as measurements from a BSN in qualitative comparison with the Modified Ashworth Scale as classified by clinical experts. Possible applications of the new algorithm include (but are not limited to) use in robotic sensorimotor therapy to reduce the effect of spasticity.

  11. 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.

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

    International Nuclear Information System (INIS)

    Espinosa-Paredes, Gilberto; Alvarez-Ramirez, Jose; Vazquez, Alejandro

    2006-01-01

    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

  13. 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.

  14. Consistency based correlations for tailings consolidation

    Energy Technology Data Exchange (ETDEWEB)

    Azam, S.; Paul, A.C. [Regina Univ., Regina, SK (Canada). Environmental Systems Engineering

    2010-07-01

    The extraction of oil, uranium, metals and mineral resources from the earth generates significant amounts of tailings slurry. The tailings are contained in a disposal area with perimeter dykes constructed from the coarser fraction of the slurry. There are many unique challenges pertaining to the management of the containment facilities for several decades beyond mine closure that are a result of the slow settling rates of the fines and the high standing toxic waters. Many tailings dam failures in different parts of the world have been reported to result in significant contaminant releases causing public concern over the conventional practice of tailings disposal. Therefore, in order to reduce and minimize the environmental footprint, the fluid tailings need to undergo efficient consolidation. This paper presented an investigation into the consolidation behaviour of tailings in conjunction with soil consistency that captured physicochemical interactions. The paper discussed the large strain consolidation behaviour (volume compressibility and hydraulic conductivity) of six fine-grained soil slurries based on published data. The paper provided background information on the study and presented the research methodology. The geotechnical index properties of the selected materials were also presented. The large strain consolidation, volume compressibility correlations, and hydraulic conductivity correlations were provided. It was concluded that the normalized void ratio best described volume compressibility whereas liquidity index best explained the hydraulic conductivity. 17 refs., 3 tabs., 4 figs.

  15. Multiparty correlation measure based on the cumulant

    International Nuclear Information System (INIS)

    Zhou, D. L.; Zeng, B.; Xu, Z.; You, L.

    2006-01-01

    We propose a genuine multiparty correlation measure for a multiparty quantum system as the trace norm of the cumulant of the state. The legitimacy of our multiparty correlation measure is explicitly demonstrated by proving it satisfies the five basic conditions required for a correlation measure. As an application we construct an efficient algorithm for the calculation of our measures for all stabilizer states

  16. 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.

  17. Microcontroller based driver alertness detection systems to detect drowsiness

    Science.gov (United States)

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

    2018-04-01

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

  18. 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.

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

    Science.gov (United States)

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

    2010-04-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  1. 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.

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

    Science.gov (United States)

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

    2009-02-01

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

  3. 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.

  4. 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...

  5. 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.

  6. Cellular telephone-based radiation detection instrument

    Science.gov (United States)

    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.

  7. Vision-based Vehicle Detection Survey

    Directory of Open Access Journals (Sweden)

    Alex David S

    2016-03-01

    Full Text Available Nowadays thousands of drivers and passengers were losing their lives every year on road accident, due to deadly crashes between more than one vehicle. There are number of many research focuses were dedicated to the development of intellectual driver assistance systems and autonomous vehicles over the past decade, which reduces the danger by monitoring the on-road environment. In particular, researchers attracted towards the on-road detection of vehicles in recent years. Different parameters have been analyzed in this paper which includes camera placement and the various applications of monocular vehicle detection, common features and common classification methods, motion- based approaches and nighttime vehicle detection and monocular pose estimation. Previous works on the vehicle detection listed based on camera poisons, feature based detection and motion based detection works and night time detection.

  8. 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.

  9. 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

  10. 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

  11. Domain similarity based orthology detection

    OpenAIRE

    Bitard-Feildel, Tristan; Kemena, Carsten; Greenwood, Jenny M; Bornberg-Bauer, Erich

    2015-01-01

    Background Orthologous protein detection software mostly uses pairwise comparisons of amino-acid sequences to assert whether two proteins are orthologous or not. Accordingly, when the number of sequences for comparison increases, the number of comparisons to compute grows in a quadratic order. A current challenge of bioinformatic research, especially when taking into account the increasing number of sequenced organisms available, is to make this ever-growing number of comparisons computationa...

  12. 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

  13. Detection of gases and gas mixtures by correlation spectroscopy

    OpenAIRE

    Dakin, J.P.; Gunning, M.J.; Chambers, P.

    2002-01-01

    The reliable detection and monitoring of gases and gas mixtures is known to play a crucial role in many real-world environmental and industrial applications. It is of considerable importance to utilise techniques that are not susceptible to poisoning, are specific to a target gas in a mixture, are unaffected by contaminants, and can be adapted for in-process monitoring. Ever-more stringent requirements in this field dictate a need for ongoing research in this area. As many common gases exhibi...

  14. 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

  15. Laser-based optical detection of explosives

    CERN Document Server

    Pellegrino, Paul M; Farrell, Mikella E

    2015-01-01

    Laser-Based Optical Detection of Explosives offers a comprehensive review of past, present, and emerging laser-based methods for the detection of a variety of explosives. This book: Considers laser propagation safety and explains standard test material preparation for standoff optical-based detection system evaluation Explores explosives detection using deep ultraviolet native fluorescence, Raman spectroscopy, laser-induced breakdown spectroscopy, reflectometry, and hyperspectral imaging Examines photodissociation followed by laser-induced fluorescence, photothermal methods, cavity-enhanced absorption spectrometry, and short-pulse laser-based techniques Describes the detection and recognition of explosives using terahertz-frequency spectroscopic techniques Each chapter is authored by a leading expert on the respective technology, and is structured to supply historical perspective, address current advantages and challenges, and discuss novel research and applications. Readers are left with an in-depth understa...

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

    Energy Technology Data Exchange (ETDEWEB)

    Tura, J., E-mail: jordi.tura@icfo.es [ICFO—Institut de Ciencies Fotoniques, Mediterranean Technology Park, 08860 Castelldefels (Barcelona) (Spain); Augusiak, R.; Sainz, A.B. [ICFO—Institut de Ciencies Fotoniques, Mediterranean Technology Park, 08860 Castelldefels (Barcelona) (Spain); Lücke, B.; Klempt, C. [Institut für Quantenoptik, Leibniz Universität Hannover, Welfengarten 1, D-30167 Hannover (Germany); Lewenstein, M.; Acín, A. [ICFO—Institut de Ciencies Fotoniques, Mediterranean Technology Park, 08860 Castelldefels (Barcelona) (Spain); ICREA—Institució Catalana de Recerca i Estudis Avançats, Lluis Campanys 3, 08010 Barcelona (Spain)

    2015-11-15

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

  17. 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...

  18. 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.

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

    Science.gov (United States)

    Wang, Fang; Wang, Lin; Chen, Yuming

    2017-08-31

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

  20. Ghost imaging based on Pearson correlation coefficients

    International Nuclear Information System (INIS)

    Yu Wen-Kai; Yao Xu-Ri; Liu Xue-Feng; Li Long-Zhen; Zhai Guang-Jie

    2015-01-01

    Correspondence imaging is a new modality of ghost imaging, which can retrieve a positive/negative image by simple conditional averaging of the reference frames that correspond to relatively large/small values of the total intensity measured at the bucket detector. Here we propose and experimentally demonstrate a more rigorous and general approach in which a ghost image is retrieved by calculating a Pearson correlation coefficient between the bucket detector intensity and the brightness at a given pixel of the reference frames, and at the next pixel, and so on. Furthermore, we theoretically provide a statistical interpretation of these two imaging phenomena, and explain how the error depends on the sample size and what kind of distribution the error obeys. According to our analysis, the image signal-to-noise ratio can be greatly improved and the sampling number reduced by means of our new method. (paper)

  1. 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.

  2. 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.

  3. 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

  4. Photonic crystal fiber based antibody detection

    DEFF Research Database (Denmark)

    Duval, A; Lhoutellier, M; Jensen, J B

    2004-01-01

    An original approach for detecting labeled antibodies based on strong penetration photonic crystal fibers is introduced. The target antibody is immobilized inside the air-holes of a photonic crystal fiber and the detection is realized by the means of evanescent-wave fluorescence spectroscopy...

  5. Collaborative regression-based anatomical landmark detection

    International Nuclear Information System (INIS)

    Gao, Yaozong; Shen, Dinggang

    2015-01-01

    Anatomical landmark detection plays an important role in medical image analysis, e.g. for registration, segmentation and quantitative analysis. Among the various existing methods for landmark detection, regression-based methods have recently attracted much attention due to their robustness and efficiency. In these methods, landmarks are localised through voting from all image voxels, which is completely different from the classification-based methods that use voxel-wise classification to detect landmarks. Despite their robustness, the accuracy of regression-based landmark detection methods is often limited due to (1) the inclusion of uninformative image voxels in the voting procedure, and (2) the lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. (1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localise landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for informative voxels near the landmark, a spherical sampling strategy is also designed at the training stage to improve their prediction accuracy. (2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of ‘difficult-to-detect’ landmarks by using spatial guidance from ‘easy-to-detect’ landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head and neck landmarks in computed tomography images, and also dental landmarks in cone beam computed tomography images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods. (paper)

  6. 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)

  7. 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.

  8. A measurement-based technique for incipient anomaly detection

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2016-01-01

    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.

  9. 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.

  10. A study of metrics of distance and correlation between ranked lists for compositionality detection

    DEFF Research Database (Denmark)

    Lioma, Christina; Hansen, Niels Dalum

    2017-01-01

    affects the measurement of semantic similarity. We propose a new compositionality detection method that represents phrases as ranked lists of term weights. Our method approximates the semantic similarity between two ranked list representations using a range of well-known distance and correlation metrics...... of compositionality using any of the distance and correlation metrics considered....

  11. 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.

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

    Science.gov (United States)

    Oh, Ju-Won; Alkhalifah, Tariq

    2018-05-01

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

  13. VISION BASED OBSTACLE DETECTION IN UAV IMAGING

    Directory of Open Access Journals (Sweden)

    S. Badrloo

    2017-08-01

    Full Text Available 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.

  14. 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

  15. 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

    Millions of computers are infected with bot malware, form botnets and enable botmaster to perform malicious and criminal activities. Intrusion Detection Systems are deployed to detect infections, but they raise many correlated alerts for each infection, requiring a large manual investigation effort...

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

    Science.gov (United States)

    Schneider von Deimling, J.; Papenberg, C.

    2012-03-01

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

  17. 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.

  18. 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%.

  19. Pedestrian detection based on redundant wavelet transform

    Science.gov (United States)

    Huang, Lin; Ji, Liping; Hu, Ping; Yang, Tiejun

    2016-10-01

    Intelligent video surveillance is to analysis video or image sequences captured by a fixed or mobile surveillance camera, including moving object detection, segmentation and recognition. By using it, we can be notified immediately in an abnormal situation. Pedestrian detection plays an important role in an intelligent video surveillance system, and it is also a key technology in the field of intelligent vehicle. So pedestrian detection has very vital significance in traffic management optimization, security early warn and abnormal behavior detection. Generally, pedestrian detection can be summarized as: first to estimate moving areas; then to extract features of region of interest; finally to classify using a classifier. Redundant wavelet transform (RWT) overcomes the deficiency of shift variant of discrete wavelet transform, and it has better performance in motion estimation when compared to discrete wavelet transform. Addressing the problem of the detection of multi-pedestrian with different speed, we present an algorithm of pedestrian detection based on motion estimation using RWT, combining histogram of oriented gradients (HOG) and support vector machine (SVM). Firstly, three intensities of movement (IoM) are estimated using RWT and the corresponding areas are segmented. According to the different IoM, a region proposal (RP) is generated. Then, the features of a RP is extracted using HOG. Finally, the features are fed into a SVM trained by pedestrian databases and the final detection results are gained. Experiments show that the proposed algorithm can detect pedestrians accurately and efficiently.

  20. Study of relationship between MUF correlation and detection sensitivity of statistical analysis

    International Nuclear Information System (INIS)

    Tamura, Toshiaki; Ihara, Hitoshi; Yamamoto, Yoichi; Ikawa, Koji

    1989-11-01

    Various kinds of statistical analysis are proposed to NRTA (Near Real Time Materials Accountancy) which was devised to satisfy the timeliness goal of one of the detection goals of IAEA. It will be presumed that different statistical analysis results will occur between the case of considered rigorous error propagation (with MUF correlation) and the case of simplified error propagation (without MUF correlation). Therefore, measurement simulation and decision analysis were done using flow simulation of 800 MTHM/Y model reprocessing plant, and relationship between MUF correlation and detection sensitivity and false alarm of statistical analysis was studied. Specific character of material accountancy for 800 MTHM/Y model reprocessing plant was grasped by this simulation. It also became clear that MUF correlation decreases not only false alarm but also detection probability for protracted loss in case of CUMUF test and Page's test applied to NRTA. (author)

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

    Science.gov (United States)

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

    2014-03-24

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

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

    Science.gov (United States)

    Goense, J B M; Ratnam, R

    2003-10-01

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

  3. Power Consumption Based Android Malware Detection

    OpenAIRE

    Hongyu Yang; Ruiwen Tang

    2016-01-01

    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...

  4. 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....

  5. Detecting genuine multipartite correlations in terms of the rank of coefficient matrix

    International Nuclear Information System (INIS)

    Li Bo; Kwek, Leong Chuan; Fan Heng

    2012-01-01

    We propose a method to detect genuine quantum correlation for arbitrary quantum states in terms of the rank of coefficient matrices associated with the pure state. We then derive a necessary and sufficient condition for a quantum state to possess genuine correlation, namely that all corresponding coefficient matrices have rank larger than 1. We demonstrate an approach to decompose the genuine quantum correlated state with high rank coefficient matrix into the form of product states with no genuine quantum correlation for a pure state. (paper)

  6. 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

  7. 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...... on the amplitude of the signal. The other algorithm was based on information of the signal in the frequency domain, and it focused on synchronisation of the electrical activity in a single muscle during the seizure. Results: The amplitude-based algorithm reliably detected seizures in 2 of the patients, while...... 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....

  8. 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.

  9. 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...

  10. Energy detection based on undecimated discrete wavelet transform and its application in magnetic anomaly detection.

    Directory of Open Access Journals (Sweden)

    Xinhua Nie

    Full Text Available Magnetic anomaly detection (MAD is a passive approach for detection of a ferromagnetic target, and its performance is often limited by external noises. In consideration of one major noise source is the fractal noise (or called 1/f noise with a power spectral density of 1/fa (0correlation. Meanwhile the orthonormal wavelet decomposition can play the role of a Karhunen-Loève-type expansion to the 1/f-type signal by its decorrelation abilities, an effective energy detection method based on undecimated discrete wavelet transform (UDWT is proposed in this paper. Firstly, the foundations of magnetic anomaly detection and UDWT are introduced in brief, while a possible detection system based on giant magneto-impedance (GMI magnetic sensor is also given out. Then our proposed energy detection based on UDWT is described in detail, and the probabilities of false alarm and detection for given the detection threshold in theory are presented. It is noticeable that no a priori assumptions regarding the ferromagnetic target or the magnetic noise probability are necessary for our method, and different from the discrete wavelet transform (DWT, the UDWT is shift invariant. Finally, some simulations are performed and the results show that the detection performance of our proposed detector is better than that of the conventional energy detector even utilized in the Gaussian white noise, especially when the spectral parameter α is less than 1.0. In addition, a real-world experiment was done to demonstrate the advantages of the proposed method.

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

    Science.gov (United States)

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

    2008-03-01

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

  12. Detecting Near-Earth Objects Using Cross-Correlation with a Point Spread Function

    Science.gov (United States)

    2009-03-01

    impact in the Yucatan Peninsula caused the extinction of the dinosaurs in the Cretaceous Period [Fix, 1995]. Even the Moon is pot marked by many...the atmosphere that the light traverses. For this reason , it is typically better to be at higher elevations to decrease the amount of atmosphere the...detection on average for the Rayleigh sampling with cross-correlation of a PSF than the Rayleigh sampling without cross- correlation. For this reason

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

    Science.gov (United States)

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

    1978-01-01

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

  14. 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.

  15. 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.

  16. 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.

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

    Science.gov (United States)

    Gao, J.; Lythe, M. B.

    1996-06-01

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

  18. Aptamer-Based Paper Strip Sensor for Detecting Vibrio fischeri.

    Science.gov (United States)

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

    2018-05-14

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

  19. [Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].

    Science.gov (United States)

    Zhou, Jinzhi; Tang, Xiaofang

    2015-08-01

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

  20. Nanomaterials based biosensors for cancer biomarker detection

    International Nuclear Information System (INIS)

    Malhotra, Bansi D; Kumar, Saurabh; Pandey, Chandra Mouli

    2016-01-01

    Biosensors have enormous potential to contribute to the evolution of new molecular diagnostic techniques for patients suffering with cancerous diseases. A major obstacle preventing faster development of biosensors pertains to the fact that cancer is a highly complex set of diseases. The oncologists currently rely on a few biomarkers and histological characterization of tumors. Some of the signatures include epigenetic and genetic markers, protein profiles, changes in gene expression, and post-translational modifications of proteins. These molecular signatures offer new opportunities for development of biosensors for cancer detection. In this context, conducting paper has recently been found to play an important role towards the fabrication of a biosensor for cancer biomarker detection. In this paper we will focus on results of some of the recent studies obtained in our laboratories relating to fabrication and application of nanomaterial modified paper based biosensors for cancer biomarker detection. (paper)

  1. 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.

  2. 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.

  3. 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%.

  4. An Improved Wavelet‐Based Multivariable Fault Detection Scheme

    KAUST Repository

    Harrou, Fouzi; Sun, Ying; Madakyaru, Muddu

    2017-01-01

    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

  5. 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)

  6. 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.

  7. IMAGE ANALYSIS BASED ON EDGE DETECTION TECHNIQUES

    Institute of Scientific and Technical Information of China (English)

    纳瑟; 刘重庆

    2002-01-01

    A method that incorporates edge detection technique, Markov Random field (MRF), watershed segmentation and merging techniques was presented for performing image segmentation and edge detection tasks. It first applies edge detection technique to obtain a Difference In Strength (DIS) map. An initial segmented result is obtained based on K-means clustering technique and the minimum distance. Then the region process is modeled by MRF to obtain an image that contains different intensity regions. The gradient values are calculated and then the watershed technique is used. DIS calculation is used for each pixel to define all the edges (weak or strong) in the image. The DIS map is obtained. This help as priority knowledge to know the possibility of the region segmentation by the next step (MRF), which gives an image that has all the edges and regions information. In MRF model,gray level l, at pixel location i, in an image X, depends on the gray levels of neighboring pixels. The segmentation results are improved by using watershed algorithm. After all pixels of the segmented regions are processed, a map of primitive region with edges is generated. The edge map is obtained using a merge process based on averaged intensity mean values. A common edge detectors that work on (MRF) segmented image are used and the results are compared. The segmentation and edge detection result is one closed boundary per actual region in the image.

  8. 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.

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

    Science.gov (United States)

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

    2017-02-01

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

  10. 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

  11. Automated image based prominent nucleoli detection.

    Science.gov (United States)

    Yap, Choon K; Kalaw, Emarene M; Singh, Malay; Chong, Kian T; Giron, Danilo M; Huang, Chao-Hui; Cheng, Li; Law, Yan N; Lee, Hwee Kuan

    2015-01-01

    Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

  12. Automated image based prominent nucleoli detection

    Directory of Open Access Journals (Sweden)

    Choon K Yap

    2015-01-01

    Full Text Available Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

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

    Directory of Open Access Journals (Sweden)

    Neha

    2016-01-01

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

  14. Advances in neutron based bulk explosive detection

    Science.gov (United States)

    Gozani, Tsahi; Strellis, Dan

    2007-08-01

    Neutron based explosive inspection systems can detect a wide variety of national security threats. The inspection is founded on the detection of characteristic gamma rays emitted as the result of neutron interactions with materials. Generally these are gamma rays resulting from thermal neutron capture and inelastic scattering reactions in most materials and fast and thermal neutron fission in fissile (e.g.235U and 239Pu) and fertile (e.g.238U) materials. Cars or trucks laden with explosives, drugs, chemical agents and hazardous materials can be detected. Cargo material classification via its main elements and nuclear materials detection can also be accomplished with such neutron based platforms, when appropriate neutron sources, gamma ray spectroscopy, neutron detectors and suitable decision algorithms are employed. Neutron based techniques can be used in a variety of scenarios and operational modes. They can be used as stand alones for complete scan of objects such as vehicles, or for spot-checks to clear (or validate) alarms indicated by another inspection system such as X-ray radiography. The technologies developed over the last two decades are now being implemented with good results. Further advances have been made over the last few years that increase the sensitivity, applicability and robustness of these systems. The advances range from the synchronous inspection of two sides of vehicles, increasing throughput and sensitivity and reducing imparted dose to the inspected object and its occupants (if any), to taking advantage of the neutron kinetic behavior of cargo to remove systematic errors, reducing background effects and improving fast neutron signals.

  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. 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.

  17. 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)

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

    Directory of Open Access Journals (Sweden)

    Tomoko Yamasaki

    2018-05-01

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

  19. 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

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

    Science.gov (United States)

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

    2018-05-01

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

  1. 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.

  2. 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...... gearbox. Only the generator speed measurement which is available in even simple wind turbine control systems is used as input. Consequently this proposed scheme does not need additional sensors and computers for monitoring the condition of the wind gearbox. The scheme is evaluated on a wide-spread wind...

  3. Correlation Based Testing for Passive Sonar Picture Rationalization

    National Research Council Canada - National Science Library

    Mellema, Garfield R

    2007-01-01

    .... The sample correlation coefficient, is a statistical measure of relatedness. This paper describes the application of a test based on that measure to compare tracks produced by a probabilistic data association filter from a set of towed array sonar data. Keywords.

  4. 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.

  5. 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.

  6. An iterative detection method of MIMO over spatial correlated frequency selective channel: using list sphere decoding for simplification

    Science.gov (United States)

    Shi, Zhiping; Yan, Bing

    2010-08-01

    In multiple-input multiple-output(MIMO) wireless systems, combining good channel codes(e.g., Non-binary Repeat Accumulate codes) with adaptive turbo equalization is a good option to get better performance and lower complexity under Spatial Correlated Frequency Selective(SCFS) Channel. The key of this method is after joint antennas MMSE detection (JAD/MMSE) based on interruption cancelling using soft information, considering the detection result as an output of a Gaussian equivalent flat fading channel, and performing maximum likelihood detection(ML) to get more correct estimated result. But the using of ML brings great complexity increase, which is not allowed. In this paper, a low complexity method called list sphere decoding is introduced and applied to replace the ML in order to simplify the adaptive iterative turbo equalization system.

  7. 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

  8. 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...

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

    Science.gov (United States)

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

    2016-05-01

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

  10. Correlation-based decimation in constraint satisfaction problems

    International Nuclear Information System (INIS)

    Higuchi, Saburo; Mezard, Marc

    2010-01-01

    We study hard constraint satisfaction problems using some decimation algorithms based on mean-field approximations. The message-passing approach is used to estimate, beside the usual one-variable marginals, the pair correlation functions. The identification of strongly correlated pairs allows to use a new decimation procedure, where the relative orientation of a pair of variables is fixed. We apply this novel decimation to locked occupation problems, a class of hard constraint satisfaction problems where the usual belief-propagation guided decimation performs poorly. The pair-decimation approach provides a significant improvement.

  11. 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.

  12. Global contrast based salient region detection

    KAUST Repository

    Cheng, Ming-Ming; Zhang, Guo-Xin; Mitra, Niloy J.; Huang, Xiaolei; Hu, Shi-Min

    2011-01-01

    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.

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

    Science.gov (United States)

    Zaripov, D. I.; Renfu, Li

    2018-05-01

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

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

    Science.gov (United States)

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

    2016-12-01

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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Cobalt: A GPU-based correlator and beamformer for LOFAR

    Science.gov (United States)

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

    2018-04-01

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

  4. 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.

  5. 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)

  6. Comic image understanding based on polygon detection

    Science.gov (United States)

    Li, Luyuan; Wang, Yongtao; Tang, Zhi; Liu, Dong

    2013-01-01

    Comic image understanding aims to automatically decompose scanned comic page images into storyboards and then identify the reading order of them, which is the key technique to produce digital comic documents that are suitable for reading on mobile devices. In this paper, we propose a novel comic image understanding method based on polygon detection. First, we segment a comic page images into storyboards by finding the polygonal enclosing box of each storyboard. Then, each storyboard can be represented by a polygon, and the reading order of them is determined by analyzing the relative geometric relationship between each pair of polygons. The proposed method is tested on 2000 comic images from ten printed comic series, and the experimental results demonstrate that it works well on different types of comic images.

  7. Low complexity pixel-based halftone detection

    Science.gov (United States)

    Ok, Jiheon; Han, Seong Wook; Jarno, Mielikainen; Lee, Chulhee

    2011-10-01

    With the rapid advances of the internet and other multimedia technologies, the digital document market has been growing steadily. Since most digital images use halftone technologies, quality degradation occurs when one tries to scan and reprint them. Therefore, it is necessary to extract the halftone areas to produce high quality printing. In this paper, we propose a low complexity pixel-based halftone detection algorithm. For each pixel, we considered a surrounding block. If the block contained any flat background regions, text, thin lines, or continuous or non-homogeneous regions, the pixel was classified as a non-halftone pixel. After excluding those non-halftone pixels, the remaining pixels were considered to be halftone pixels. Finally, documents were classified as pictures or photo documents by calculating the halftone pixel ratio. The proposed algorithm proved to be memory-efficient and required low computation costs. The proposed algorithm was easily implemented using GPU.

  8. Aptamer Based Microsphere Biosensor for Thrombin Detection

    Directory of Open Access Journals (Sweden)

    Xudong Fan

    2006-08-01

    Full Text Available We have developed an optical microsphere resonator biosensor using aptamer asreceptor for the measurement of the important biomolecule thrombin. The sphere surface ismodified with anti-thrombin aptamer, which has excellent binding affinity and selectivityfor thrombin. Binding of the thrombin at the sphere surface is monitored by the spectralposition of the microsphere’s whispering gallery mode resonances. A detection limit on theorder of 1 NIH Unit/mL is demonstrated. Control experiments with non-aptameroligonucleotide and BSA are also carried out to confirm the specific binding betweenaptamer and thrombin. We expect that this demonstration will lead to the development ofhighly sensitive biomarker sensors based on aptamer with lower cost and higher throughputthan current technology.

  9. 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.

  10. Texture orientation-based algorithm for detecting infrared maritime targets.

    Science.gov (United States)

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

    2015-05-20

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

  11. 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

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

    Science.gov (United States)

    Pessoa, Luiz; Ungerleider, Leslie G

    2004-05-01

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

  13. 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.

  14. 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 ...

  15. 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.

  16. 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)

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. Cosmic microwave and infrared backgrounds cross-correlation for ISW detection

    International Nuclear Information System (INIS)

    Ilić, S

    2014-01-01

    We have investigated the cross-correlation between the cosmic infrared and microwave backgrounds (CIB and CMB) anisotropies through the integrated Sachs-Wolfe effect. We have first described the CIB anisotropies using a linearly biased power spectrum, then derive the theoretical angular power spectrum of the CMB-CIB cross-correlation for different instruments and frequencies. We have discussed the detectability of the ISW signal by performing a signal-to-noise (SNR) analysis with our predicted spectra. The significances obtained range from 6σ to 7σ in an ideal case, depending on the frequency; in realistic cases which account for the presence of noise including astrophysical contaminants, the results span the range 2 – 5σ, depending strongly on the major contribution to the noise term

  3. Non-empirical exchange-correlation parameterizations based on exact conditions from correlated orbital theory.

    Science.gov (United States)

    Haiduke, Roberto Luiz A; Bartlett, Rodney J

    2018-05-14

    Some of the exact conditions provided by the correlated orbital theory are employed to propose new non-empirical parameterizations for exchange-correlation functionals from Density Functional Theory (DFT). This reparameterization process is based on range-separated functionals with 100% exact exchange for long-range interelectronic interactions. The functionals developed here, CAM-QTP-02 and LC-QTP, show mitigated self-interaction error, correctly predict vertical ionization potentials as the negative of eigenvalues for occupied orbitals, and provide nice excitation energies, even for challenging charge-transfer excited states. Moreover, some improvements are observed for reaction barrier heights with respect to the other functionals belonging to the quantum theory project (QTP) family. Finally, the most important achievement of these new functionals is an excellent description of vertical electron affinities (EAs) of atoms and molecules as the negative of appropriate virtual orbital eigenvalues. In this case, the mean absolute deviations for EAs in molecules are smaller than 0.10 eV, showing that physical interpretation can indeed be ascribed to some unoccupied orbitals from DFT.

  4. Non-empirical exchange-correlation parameterizations based on exact conditions from correlated orbital theory

    Science.gov (United States)

    Haiduke, Roberto Luiz A.; Bartlett, Rodney J.

    2018-05-01

    Some of the exact conditions provided by the correlated orbital theory are employed to propose new non-empirical parameterizations for exchange-correlation functionals from Density Functional Theory (DFT). This reparameterization process is based on range-separated functionals with 100% exact exchange for long-range interelectronic interactions. The functionals developed here, CAM-QTP-02 and LC-QTP, show mitigated self-interaction error, correctly predict vertical ionization potentials as the negative of eigenvalues for occupied orbitals, and provide nice excitation energies, even for challenging charge-transfer excited states. Moreover, some improvements are observed for reaction barrier heights with respect to the other functionals belonging to the quantum theory project (QTP) family. Finally, the most important achievement of these new functionals is an excellent description of vertical electron affinities (EAs) of atoms and molecules as the negative of appropriate virtual orbital eigenvalues. In this case, the mean absolute deviations for EAs in molecules are smaller than 0.10 eV, showing that physical interpretation can indeed be ascribed to some unoccupied orbitals from DFT.

  5. Case-Based Multi-Sensor Intrusion Detection

    Science.gov (United States)

    Schwartz, Daniel G.; Long, Jidong

    2009-08-01

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

  6. Application of cross-correlated delay shift rule in spiking neural networks for interictal spike detection.

    Science.gov (United States)

    Lilin Guo; Zhenzhong Wang; Cabrerizo, Mercedes; Adjouadi, Malek

    2016-08-01

    This study proposes a Cross-Correlated Delay Shift (CCDS) supervised learning rule to train neurons with associated spatiotemporal patterns to classify spike patterns. The objective of this study was to evaluate the feasibility of using the CCDS rule to automate the detection of interictal spikes in electroencephalogram (EEG) data on patients with epilepsy. Encoding is the initial yet essential step for spiking neurons to process EEG patterns. A new encoding method is utilized to convert the EEG signal into spike patterns. The simulation results show that the proposed algorithm identified 69 spikes out of 82 spikes, or 84% detection rate, which is quite high considering the subtleties of interictal spikes and the tediousness of monitoring long EEG records. This CCDS rule is also benchmarked by ReSuMe on the same task.

  7. Detection of Target ssDNA Using a Microfabricated Hall Magnetometer with Correlated Optical Readout

    Directory of Open Access Journals (Sweden)

    Steven M. Hira

    2012-01-01

    Full Text Available Sensing biological agents at the genomic level, while enhancing the response time for biodetection over commonly used, optics-based techniques such as nucleic acid microarrays or enzyme-linked immunosorbent assays (ELISAs, is an important criterion for new biosensors. Here, we describe the successful detection of a 35-base, single-strand nucleic acid target by Hall-based magnetic transduction as a mimic for pathogenic DNA target detection. The detection platform has low background, large signal amplification following target binding and can discriminate a single, 350 nm superparamagnetic bead labeled with DNA. Detection of the target sequence was demonstrated at 364 pM (<2 target DNA strands per bead target DNA in the presence of 36 μM nontarget (noncomplementary DNA (<10 ppm target DNA using optical microscopy detection on a GaAs Hall mimic. The use of Hall magnetometers as magnetic transduction biosensors holds promise for multiplexing applications that can greatly improve point-of-care (POC diagnostics and subsequent medical care.

  8. 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)

  9. Phase transition transistors based on strongly-correlated materials

    Science.gov (United States)

    Nakano, Masaki

    2013-03-01

    The field-effect transistor (FET) provides electrical switching functions through linear control of the number of charges at a channel surface by external voltage. Controlling electronic phases of condensed matters in a FET geometry has long been a central issue of physical science. In particular, FET based on a strongly correlated material, namely ``Mott transistor,'' has attracted considerable interest, because it potentially provides gigantic and diverse electronic responses due to a strong interplay between charge, spin, orbital and lattice. We have investigated electric-field effects on such materials aiming at novel physical phenomena and electronic functions originating from strong correlation effects. Here we demonstrate electrical switching of bulk state of matter over the first-order metal-insulator transition. We fabricated FETs based on VO2 with use of a recently developed electric-double-layer transistor technique, and found that the electrostatically induced carriers at a channel surface drive all preexisting localized carriers of 1022 cm-3 even inside a bulk to motion, leading to bulk carrier delocalization beyond the electrostatic screening length. This non-local switching of bulk phases is achieved with just around 1 V, and moreover, a novel non-volatile memory like character emerges in a voltage-sweep measurement. These observations are apparently distinct from those of conventional FETs based on band insulators, capturing the essential feature of collective interactions in strongly correlated materials. This work was done in collaboration with K. Shibuya, D. Okuyama, T. Hatano, S. Ono, M. Kawasaki, Y. Iwasa, and Y. Tokura. This work was supported by the Japan Society for the Promotion of Science (JSAP) through its ``Funding Program for World-Leading Innovative R&D on Science and Technology (FIRST Program).''

  10. 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.

  11. 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

  12. Lagrangian based methods for coherent structure detection

    Energy Technology Data Exchange (ETDEWEB)

    Allshouse, Michael R., E-mail: mallshouse@chaos.utexas.edu [Center for Nonlinear Dynamics and Department of Physics, University of Texas at Austin, Austin, Texas 78712 (United States); Peacock, Thomas, E-mail: tomp@mit.edu [Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States)

    2015-09-15

    There has been a proliferation in the development of Lagrangian analytical methods for detecting coherent structures in fluid flow transport, yielding a variety of qualitatively different approaches. We present a review of four approaches and demonstrate the utility of these methods via their application to the same sample analytic model, the canonical double-gyre flow, highlighting the pros and cons of each approach. Two of the methods, the geometric and probabilistic approaches, are well established and require velocity field data over the time interval of interest to identify particularly important material lines and surfaces, and influential regions, respectively. The other two approaches, implementing tools from cluster and braid theory, seek coherent structures based on limited trajectory data, attempting to partition the flow transport into distinct regions. All four of these approaches share the common trait that they are objective methods, meaning that their results do not depend on the frame of reference used. For each method, we also present a number of example applications ranging from blood flow and chemical reactions to ocean and atmospheric flows.

  13. Intelligent-based Structural Damage Detection Model

    International Nuclear Information System (INIS)

    Lee, Eric Wai Ming; Yu, K.F.

    2010-01-01

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  14. Intelligent-based Structural Damage Detection Model

    Science.gov (United States)

    Lee, Eric Wai Ming; Yu, Kin Fung

    2010-05-01

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  15. 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.

  16. 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.

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

    Science.gov (United States)

    Wu, Panpan; Xia, Kewen; Yu, Hengyong

    2016-11-01

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

  18. 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.

  19. Dangerous gas detection based on infrared video

    Science.gov (United States)

    Ding, Kang; Hong, Hanyu; Huang, Likun

    2018-03-01

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

  20. Network Anomaly Detection Based on Wavelet Analysis

    Science.gov (United States)

    Lu, Wei; Ghorbani, Ali A.

    2008-12-01

    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.

  1. 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.)

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

    Science.gov (United States)

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

    2018-04-01

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

  3. Quantification of transuranic elements by time interval correlation spectroscopy of the detected neutrons

    Science.gov (United States)

    Baeten; Bruggeman; Paepen; Carchon

    2000-03-01

    The non-destructive quantification of transuranic elements in nuclear waste management or in safeguards verifications is commonly performed by passive neutron assay techniques. To minimise the number of unknown sample-dependent parameters, Neutron Multiplicity Counting (NMC) is applied. We developed a new NMC-technique, called Time Interval Correlation Spectroscopy (TICS), which is based on the measurement of Rossi-alpha time interval distributions. Compared to other NMC-techniques, TICS offers several advantages.

  4. Analysis method of high-order collective-flow correlations based on the concept of correlative degree

    International Nuclear Information System (INIS)

    Zhang Weigang

    2000-01-01

    Based on the concept of correlative degree, a new method of high-order collective-flow measurement is constructed, with which azimuthal correlations, correlations of final state transverse momentum magnitude and transverse correlations can be inspected respectively. Using the new method the contributions of the azimuthal correlations of particles distribution and the correlations of transverse momentum magnitude of final state particles to high-order collective-flow correlations are analyzed respectively with 4π experimental events for 1.2 A GeV Ar + BaI 2 collisions at the Bevalac stream chamber. Comparing with the correlations of transverse momentum magnitude, the azimuthal correlations of final state particles distribution dominate high-order collective-flow correlations in experimental samples. The contributions of correlations of transverse momentum magnitude of final state particles not only enhance the strength of the high-order correlations of particle group, but also provide important information for the measurement of the collectivity of collective flow within the more constraint district

  5. 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.

  6. Improving Seroreactivity-Based Detection of Glioma

    Directory of Open Access Journals (Sweden)

    Nicole Ludwig

    2009-12-01

    Full Text Available Seroreactivity profiling emerges as valuable technique for minimal invasive cancer detection. Recently, we provided first evidence for the applicability of serum profiling of glioma using a limited number of immunogenic antigens. Here, we screened 57 glioma and 60 healthy sera for autoantibodies against 1827 Escherichia coli expressed clones, including 509 in-frame peptide sequences. By a linear support vector machine approach, we calculated mean specificity, sensitivity, and accuracy of 100 repetitive classifications. We were able to differentiate glioma sera from sera of the healthy controls with a specificity of 90.28%, a sensitivity of 87.31% and an accuracy of 88.84%. We were also able to differentiate World Health Organization grade IV glioma sera from healthy sera with a specificity of 98.45%, a sensitivity of 80.93%, and an accuracy of 92.88%. To rank the antigens according to their information content, we computed the area under the receiver operator characteristic curve value for each clone. Altogether, we found 46 immunogenic clones including 16 in-frame clones that were informative for the classification of glioma sera versus healthy sera. For the separation of glioblastoma versus healthy sera, we found 91 informative clones including 26 in-frame clones. The best-suited in-frame clone for the classification glioma sera versus healthy sera corresponded to the vimentin gene (VIM that was previously associated with glioma. In the future, autoantibody signatures in glioma not only may prove useful for diagnosis but also offer the prospect for a personalized immune-based therapy.

  7. 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.

  8. 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)

  9. Prevalence and correlates of beta human papillomavirus detection in fingernail samples from mid-adult women

    Directory of Open Access Journals (Sweden)

    Rachel L. Winer

    2018-06-01

    Full Text Available Cutaneous human papillomaviruses (HPVs have not been evaluated in fingernails from healthy individuals. To determine prevalence and correlates of β-HPVs in fingernails from healthy mid-adult women, we tested archived samples collected from 2011 to 2012 using a multiplex PCR combined with Luminex technology for 46 β-HPV genotypes. One hundred thirteen (61.1% of 185 fingernail samples were positive for β-HPV, and the median number of types detected in positive samples was 2 (interquartile range: 1–4. The most common genotypes detected were HPV-23 (β−2 (13.5%, HPV-38 (β−2 (13.0%, HPV-5 (β−1 (9.2%, HPV-107 (β−2 (8.7%, and HPV-120 (β−2 (8.7%. In multivariate analysis, β-HPV detection was associated with age (prevalence ratio [PR] for women 40–51 years versus 30–39 years = 1.30, 95% CI: 1.05–1.62 and race (PR for non-white versus white race = 0.65, 95% CI: 0.45–0.94. The prevalence of β-HPV in fingernail samples from healthy mid-adult women was similar to the prevalence of β-HPV reported at other cutaneous sites in prior studies. We did not identify any significant health or sexual behavior predictors of β-HPV detection in fingernails. Our results support the hypothesis that fingers may serve as a source of transmission or autoinoculation of cutaneous HPVs to other anatomic sites. Keywords: Fingernails, Women, Beta-HPV, Prevalence, Mid-adult, Risk factor

  10. Trojan detection model based on network behavior analysis

    International Nuclear Information System (INIS)

    Liu Junrong; Liu Baoxu; Wang Wenjin

    2012-01-01

    Based on the analysis of existing Trojan detection technology, this paper presents a Trojan detection model based on network behavior analysis. First of all, we abstract description of the Trojan network behavior, then according to certain rules to establish the characteristic behavior library, and then use the support vector machine algorithm to determine whether a Trojan invasion. Finally, through the intrusion detection experiments, shows that this model can effectively detect Trojans. (authors)

  11. Estimation of viscosity based on transverse momentum correlations

    Science.gov (United States)

    Sharma, Monika

    2010-02-01

    The heavy ion program at RHIC created a paradigm shift in the exploration of strongly interacting hot and dense matter. An important milestone achieved is the discovery of the formation of strongly interacting matter which seemingly flows like a perfect liquid at temperatures on the scale of T ˜ 2 x10^12 K [1]. As a next step, we consider measurements of transport coefficients such as kinematic, shear or bulk viscosity? Many calculations based on event anisotropy measurements indicate that the shear viscosity to the entropy density ratio (η/s) of the fluid formed at RHIC is significantly below that of all known fluids including the superfluid ^4He [2]. Precise determination of η/s ratio is currently a subject of extensive study. We present an alternative technique for the determination of medium viscosity proposed by Gavin and Aziz [3]. Preliminary results of measurements of the evolution of the transverse momentum correlation function with collision centrality of Au + Au interactions at √sNN = 200 GeV will be shown. We present results on differential version of the correlation measure and describe its use for the experimental determination of η/s.[4pt] [1] J. Adams et al., [STAR Collaboration], Nucl. Phys. A 757 (2005) 102.[0pt] [2] R. A. Lacey et al., Phys. Rev. Lett. 98 (2007) 092301.[0pt] [3] S. Gavin and M. Abdel-Aziz, Phys. Rev. Lett. 97 (2006) 162302. )

  12. QRS Detection Based on Improved Adaptive Threshold

    Directory of Open Access Journals (Sweden)

    Xuanyu Lu

    2018-01-01

    Full Text Available Cardiovascular disease is the first cause of death around the world. In accomplishing quick and accurate diagnosis, automatic electrocardiogram (ECG analysis algorithm plays an important role, whose first step is QRS detection. The threshold algorithm of QRS complex detection is known for its high-speed computation and minimized memory storage. In this mobile era, threshold algorithm can be easily transported into portable, wearable, and wireless ECG systems. However, the detection rate of the threshold algorithm still calls for improvement. An improved adaptive threshold algorithm for QRS detection is reported in this paper. The main steps of this algorithm are preprocessing, peak finding, and adaptive threshold QRS detecting. The detection rate is 99.41%, the sensitivity (Se is 99.72%, and the specificity (Sp is 99.69% on the MIT-BIH Arrhythmia database. A comparison is also made with two other algorithms, to prove our superiority. The suspicious abnormal area is shown at the end of the algorithm and RR-Lorenz plot drawn for doctors and cardiologists to use as aid for diagnosis.

  13. 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

    generation warning system the purpose of which is to provide the master with an onboard system able to trigger an alarm when parametric roll is likely to happen within the immediate future. A detection scheme is introduced, which is able to issue a warning within five roll periods after a resonant motion......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...... started. After having determined statistical properties of the signals at hand, a detector based on the generalised log-likelihood ratio test (GLRT) is designed to look for variation in signal power. The ability of the detector to trigger alarms when parametric roll is going to onset is evaluated on two...

  14. Overview of frequency bandwidth determination techniques of useful signal in case of leaks detection by correlation method

    International Nuclear Information System (INIS)

    Faerman, V A; Avramchuk, V S; Luneva, E E

    2014-01-01

    In this paper an overview of useful signal detection methods on the background of intense noise and limits determination methods of useful signal is presented. The following features are considered: peculiarities of usage of correlation analysis, cross-amplitude spectrum, coherence function, cross-phase spectrum, time-frequency correlation function in case of frequency limits determination as well as leaks detection in pipelines. The possibility of using time-frequency correlation function for solving above named issues is described. Time- frequency correlation function provides information about the signals correlation for each of the investigated frequency bands. Data about location of peaks on the surface plot of a time- frequency correlation function allows making an assumption about the spectral composition of useful signal and its frequency boundaries

  15. 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.

  16. Automatic detection and classification of damage zone(s) for incorporating in digital image correlation technique

    Science.gov (United States)

    Bhattacharjee, Sudipta; Deb, Debasis

    2016-07-01

    Digital image correlation (DIC) is a technique developed for monitoring surface deformation/displacement of an object under loading conditions. This method is further refined to make it capable of handling discontinuities on the surface of the sample. A damage zone is referred to a surface area fractured and opened in due course of loading. In this study, an algorithm is presented to automatically detect multiple damage zones in deformed image. The algorithm identifies the pixels located inside these zones and eliminate them from FEM-DIC processes. The proposed algorithm is successfully implemented on several damaged samples to estimate displacement fields of an object under loading conditions. This study shows that displacement fields represent the damage conditions reasonably well as compared to regular FEM-DIC technique without considering the damage zones.

  17. 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)

  18. A Method for Vibration-Based Structural Interrogation and Health Monitoring Based on Signal Cross-Correlation

    International Nuclear Information System (INIS)

    Trendafilova, I

    2011-01-01

    Vibration-based structural interrogation and health monitoring is a field which is concerned with the estimation of the current state of a structure or a component from its vibration response with regards to its ability to perform its intended function appropriately. One way to approach this problem is through damage features extracted from the measured structural vibration response. This paper suggests to use a new concept for the purposes of vibration-based health monitoring. The correlation between two signals, an input and an output, measured on the structure is used to develop a damage indicator. The paper investigates the applicability of the signal cross-correlation and a nonlinear alternative, the average mutual information between the two signals, for the purposes of structural health monitoring and damage assessment. The suggested methodology is applied and demonstrated for delamination detection in a composite beam.

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

    Science.gov (United States)

    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.

  20. 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

  1. 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

  2. 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

    Classical conditioning (conventionally modeled as correlation-based learning) and operant conditioning (conventionally modeled as reinforcement learning or reward-based learning) have been found in biological systems. Evidence shows that these two mechanisms strongly involve learning about...... 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...

  3. 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.

  4. 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.

  5. 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.

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

    Science.gov (United States)

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

    2013-11-01

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

  7. 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.

  8. 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

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

    Science.gov (United States)

    Straube, Sirko; Fahle, Manfred

    2010-01-11

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

  10. Heat shock-induced interactions among nuclear HSFs detected by fluorescence cross-correlation spectroscopy

    Energy Technology Data Exchange (ETDEWEB)

    Pack, Chan-Gi, E-mail: changipack@amc.seoul.kr [Asan Institute for Life Sciences, University of Ulsan, College of Medicine, Asan Medical Center, Seoul 138-736 (Korea, Republic of); Ahn, Sang-Gun [Dept. of Pathology, College of Dentistry, Chosun University, Seosuk-dong, Dong-gu, Gwangju 501-759 (Korea, Republic of)

    2015-07-31

    The cellular response to stress is primarily controlled in cells via transcriptional activation by heat shock factor 1 (HSF1). HSF1 is well-known to form homotrimers for activation upon heat shock and subsequently bind to target DNAs, such as heat-shock elements, by forming stress granules. A previous study demonstrated that nuclear HSF1 and HSF2 molecules in live cells interacted with target DNAs on the stress granules. However, the process underlying the binding interactions of HSF family in cells upon heat shock remains unclear. This study demonstrate for the first time that the interaction kinetics among nuclear HSF1, HSF2, and HSF4 upon heat shock can be detected directly in live cells using dual color fluorescence cross-correlation spectroscopy (FCCS). FCCS analyses indicated that the binding between HSFs was dramatically changed by heat shock. Interestingly, the recovery kinetics of interaction between HSF1 molecules after heat shock could be represented by changes in the relative interaction amplitude and mobility. - Highlights: • The binding interactions among nuclear HSFs were successfully detected. • The binding kinetics between HSF1s during recovery was quantified. • HSF2 and HSF4 strongly formed hetero-complex, even before heat shock. • Nuclear HSF2 and HSF4 bound to HSF1 only after heat shock.

  11. A damage detection method for instrumented civil structures using prerecorded Green’s functions and cross-correlation

    OpenAIRE

    Heckman, Vanessa; Kohler, Monica; Heaton, Thomas

    2011-01-01

    Automated damage detection methods have application to instrumented structures that are susceptible to types of damage that are difficult or costly to detect. The presented method has application to the detection of brittle fracture of welded beam-column connections in steel moment-resisting frames (MRFs), where locations of potential structural damage are known a priori. The method makes use of a prerecorded catalog of Green’s function templates and a cross-correlation method ...

  12. 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.

  13. 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.

  14. Flow-Based Detection of DNS Tunnels

    NARCIS (Netherlands)

    Ellens, W.; Żuraniewski, P.; Sperotto, A.; Schotanus, H.; Mandjes, M.; Meeuwissen, E.

    2013-01-01

    DNS tunnels allow circumventing access and security policies in firewalled networks. Such a security breach can be misused for activities like free web browsing, but also for command & control traffic or cyber espionage, thus motivating the search for effective automated DNS tunnel detection

  15. Flow-based detection of DNS tunnels

    NARCIS (Netherlands)

    Ellens, W.; Zuraniewski, P.; Schotanus, H.; Mandjes, M.R.H.; Meeuwissen, E.; Doyen, Guillaume; Waldburger, Martin; Celeda, Pavel; Sperotto, Anna; Stiller, Burkhard

    DNS tunnels allow circumventing access and security policies in firewalled networks. Such a security breach can be misused for activities like free web browsing, but also for command & control traffic or cyber espionage, thus motivating the search for effective automated DNS tunnel detection

  16. Flow-based detection of DNS tunnels

    NARCIS (Netherlands)

    Ellens, W.; Zuraniewski, P.W.; Sperotto, A.; Schotanus, H.A.; Mandjes, M.; Meeuwissen, H.B.

    2013-01-01

    DNS tunnels allow circumventing access and security policies in firewalled networks. Such a security breach can be misused for activities like free web browsing, but also for command & control traffic or cyber espionage, thus motivating the search for effective automated DNS tunnel detection

  17. 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.

  18. 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.

  19. 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.

  20. Underwater electric field detection system based on weakly electric fish

    Science.gov (United States)

    Xue, Wei; Wang, Tianyu; Wang, Qi

    2018-04-01

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

  1. 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%.

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

    Science.gov (United States)

    Huang, Ai-Mei; Nguyen, Truong

    2009-04-01

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

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. Detection of Subsurface Defects in Levees in Correlation to Weather Conditions Utilizing Ground Penetrating Radar

    Science.gov (United States)

    Martinez, I. A.; Eisenmann, D.

    2012-12-01

    Ground Penetrating Radar (GPR) has been used for many years in successful subsurface detection of conductive and non-conductive objects in all types of material including different soils and concrete. Typical defect detection is based on subjective examination of processed scans using data collection and analysis software to acquire and analyze the data, often requiring a developed expertise or an awareness of how a GPR works while collecting data. Processing programs, such as GSSI's RADAN analysis software are then used to validate the collected information. Iowa State University's Center for Nondestructive Evaluation (CNDE) has built a test site, resembling a typical levee used near rivers, which contains known sub-surface targets of varying size, depth, and conductivity. Scientist at CNDE have developed software with the enhanced capabilities, to decipher a hyperbola's magnitude and amplitude for GPR signal processing. With this enhanced capability, the signal processing and defect detection capabilities for GPR have the potential to be greatly enhanced. This study will examine the effects of test parameters, antenna frequency (400MHz), data manipulation methods (which include data filters and restricting the range of depth in which the chosen antenna's signal can reach), and real-world conditions using this test site (such as varying weather conditions) , with the goal of improving GPR tests sensitivity for differing soil conditions.

  10. 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. 

  11. STOCK Market Differences in Correlation-Based Weighted Network

    Science.gov (United States)

    Youn, Janghyuk; Lee, Junghoon; Chang, Woojin

    We examined the sector dynamics of Korean stock market in relation to the market volatility. The daily price data of 360 stocks for 5019 trading days (from January, 1990 to August, 2008) in Korean stock market are used. We performed the weighted network analysis and employed four measures: the average, the variance, the intensity, and the coherence of network weights (absolute values of stock return correlations) to investigate the network structure of Korean stock market. We performed regression analysis using the four measures in the seven major industry sectors and the market (seven sectors combined). We found that the average, the intensity, and the coherence of sector (subnetwork) weights increase as market becomes volatile. Except for the "Financials" sector, the variance of sector weights also grows as market volatility increases. Based on the four measures, we can categorize "Financials," "Information Technology" and "Industrials" sectors into one group, and "Materials" and "Consumer Discretionary" sectors into another group. We investigated the distributions of intrasector and intersector weights for each sector and found the differences in "Financials" sector are most distinct.

  12. 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

  13. 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.

  14. 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 ...

  15. Approaches in anomaly-based network intrusion detection systems

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, S.; Di Pietro, R.; Mancini, L.V.

    2008-01-01

    Anomaly-based network intrusion detection systems (NIDSs) can take into consideration packet headers, the payload, or a combination of both. We argue that payload-based approaches are becoming the most effective methods to detect attacks. Nowadays, attacks aim mainly to exploit vulnerabilities at

  16. Approaches in Anomaly-based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, Sandro

    Anomaly-based network intrusion detection systems (NIDSs) can take into consideration packet headers, the payload, or a combination of both. We argue that payload-based approaches are becoming the most effective methods to detect attacks. Nowadays, attacks aim mainly to exploit vulnerabilities at

  17. 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

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. DNA & Protein detection based on microbead agglutination

    KAUST Repository

    Kodzius, Rimantas; Castro, David; Foulds, Ian G.; Parameswaran, Ash M.; Sumanpreet, K. Chhina

    2012-01-01

    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

  3. Leak detection for city gas pipelines based on instantaneous energy distribution characteristics

    Energy Technology Data Exchange (ETDEWEB)

    Zhigang, Chen [Deijing University of Civil Engineering and Architecture, Beijing, (China)

    2010-07-01

    Many natural gas pipelines are used in our cities. The development of efficient leakage detection systems is fundamental for safety issues avoidance. This paper investigated a new solution to the leak detection problem in city gas pipelines based on instantaneous energy distribution. In a theoretical approach, the Hilbert-Huang transform (HHT) was used to provide the instantaneous energy distribution feature of an unstable pressure signal. The signal noise was eliminated thanks to the instantaneous energy contribution. A leakage detection model with instantaneous energy distribution (IED) was then established. The correlation coefficients of instantaneous energy distribution were through correlation analysis. It was found that in different pipeline states, the instantaneous energy distribution characteristics are different. A strong correlation of IED signal characteristics was found of the two ends of a city gas pipeline in the same operation. The test results demonstrated the reliability and validity of the method.

  4. Wireless Falling Detection System Based on Community.

    Science.gov (United States)

    Xia, Yun; Wu, Yanqi; Zhang, Bobo; Li, Zhiyang; He, Nongyue; Li, Song

    2015-06-01

    The elderly are more likely to suffer the aches or pains from the accidental falls, and both the physiology and psychology of patients would subject to a long-term disturbance, especially when the emergency treatment was not given timely and properly. Although many methods and devices have been developed creatively and shown their efficiency in experiments, few of them are suitable for commercial applications routinely. Here, we design a wearable falling detector as a mobile terminal, and utilize the wireless technology to transfer and monitor the activity data of the host in a relatively small community. With the help of the accelerometer sensor and the Google Mapping service, information of the location and the activity data will be send to the remote server for the downstream processing. The experimental result has shown that SA (Sum-vector of all axes) value of 2.5 g is the threshold value to distinguish the falling from other activities. A three-stage detection algorithm was adopted to increase the accuracy of the real alarm, and the accuracy rate of our system was more than 95%. With the further improvement, the falling detecting device which is low-cost, accurate and user-friendly would become more and more common in everyday life.

  5. Accounting for binaural detection as a function of masker interaural correlation: effects of center frequency and bandwidth.

    Science.gov (United States)

    Bernstein, Leslie R; Trahiotis, Constantine

    2014-12-01

    Binaural detection was measured as a function of the center frequency, bandwidth, and interaural correlation of masking noise. Thresholds were obtained for 500-Hz or 125-Hz Sπ tonal signals and for the latter stimuli (noise or signal-plus-noise) transposed to 4 kHz. A primary goal was assessment of the generality of van der Heijden and Trahiotis' [J. Acoust. Soc. Am. 101, 1019-1022 (1997)] hypothesis that thresholds could be accounted for by the "additive" masking effects of the underlying No and Nπ components of a masker having an interaural correlation of ρ. Results indicated that (1) the overall patterning of the data depended neither upon center frequency nor whether information was conveyed via the waveform or by its envelope; (2) thresholds for transposed stimuli improved relative to their low-frequency counterparts as bandwidth of the masker was increased; (3) the additivity approach accounted well for the data across stimulus conditions but consistently overestimated MLDs, especially for narrowband maskers; (4) a quantitative approach explicitly taking into account the distributions of time-varying ITD-based lateral positions produced by masker-alone and signal-plus-masker waveforms proved more successful, albeit while employing a larger set of assumptions, parameters, and computational complexity.

  6. 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

  7. 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.

  8. A new binaural detection model based on contralateral inhibition

    NARCIS (Netherlands)

    Breebaart, D.J.; Kohlrausch, A.G.; Dau, T.; Hohmann, V.; Kollmeier, B.

    1999-01-01

    Binaural models attempt to explain binaural phenomena in terms of neural mechanisms that extract binaural information from accoustic stimuli. In this paper, a model setup is presented that can be used to simulate binaural detection tasks. In contrast to the most often used cross correlation between

  9. 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.

  10. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2015-01-01

    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.

  11. 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

  12. 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.)

  13. Neural Correlates of Temporal Complexity and Synchrony during Audiovisual Correspondence Detection.

    Science.gov (United States)

    Baumann, Oliver; Vromen, Joyce M G; Cheung, Allen; McFadyen, Jessica; Ren, Yudan; Guo, Christine C

    2018-01-01

    We often perceive real-life objects as multisensory cues through space and time. A key challenge for audiovisual integration is to match neural signals that not only originate from different sensory modalities but also that typically reach the observer at slightly different times. In humans, complex, unpredictable audiovisual streams lead to higher levels of perceptual coherence than predictable, rhythmic streams. In addition, perceptual coherence for complex signals seems less affected by increased asynchrony between visual and auditory modalities than for simple signals. Here, we used functional magnetic resonance imaging to determine the human neural correlates of audiovisual signals with different levels of temporal complexity and synchrony. Our study demonstrated that greater perceptual asynchrony and lower signal complexity impaired performance in an audiovisual coherence-matching task. Differences in asynchrony and complexity were also underpinned by a partially different set of brain regions. In particular, our results suggest that, while regions in the dorsolateral prefrontal cortex (DLPFC) were modulated by differences in memory load due to stimulus asynchrony, areas traditionally thought to be involved in speech production and recognition, such as the inferior frontal and superior temporal cortex, were modulated by the temporal complexity of the audiovisual signals. Our results, therefore, indicate specific processing roles for different subregions of the fronto-temporal cortex during audiovisual coherence detection.

  14. 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.

  15. 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.

  16. 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.

  17. Detection of human effluents by a MOS gas sensor in correlation to VOC quantification by GC/MS

    Energy Technology Data Exchange (ETDEWEB)

    Herberger, S.; Herold, M.; Ulmer, H. [Research and Development, AppliedSensor GmbH, Gerhard-Kindler-Str. 8, D-72770 Reutlingen (Germany); Burdack-Freitag, A.; Mayer, F. [Fraunhofer-Institute for Building Physics (IBP), Fraunhoferstr. 10, D-83626 Valley/Oberlaindern (Germany)

    2010-11-15

    Due to increasing interest in indoor air quality (IAQ) monitoring for demand controlled ventilation (DCV) aiming at improved perceived air quality, health, energy and cost saving, the objective of this study has been the development of a sensor module based on a single microelectromechanical-system (MEMS) metal oxide semiconductor (MOS) gas sensor for IAQ monitoring as close as possible to the human sensory impression in indoor environments. Based on the results of a statistical evaluation on human induced volatile organic compounds (VOCs) in the ambient air of indoor environments correlating with human presence and perceived air quality, the performance of differently doped SnO{sub 2} thick film gas sensor materials has been investigated in laboratory and by means of field tests in order to find the most promising sensor material for IAQ monitoring based on the detection of changes of human induced VOCs in indoor air. Implementation of an empirical evaluation algorithm reversing proportionality of anthropogenic CO{sub 2} production and other bio-effluent generation allows prediction of CO{sub 2} equivalent units. Analytical instrumentation and reference sensors served to evaluate the effectiveness of the developed sensor module in real-life. (author)

  18. 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.

  19. Composite correlation filter for O-ring detection in stationary colored noise

    Science.gov (United States)

    Hassebrook, Laurence G.

    2009-04-01

    O-rings are regularly replaced in aircraft and if they are not replaced or if they are installed improperly, they can result in catastrophic failure of the aircraft. It is critical that the o-rings be packaged correctly to avoid mistakes made by technicians during routine maintenance. For this reason, fines may be imposed on the o-ring manufacturer if the o-rings are packaged incorrectly. That is, a single o-ring must be packaged and labeled properly. No o-rings or more than one o-ring per package is not acceptable. We present an industrial inspection system based on real-time composite correlation filtering that has successfully solved this problem in spite of opaque paper o-ring packages. We present the system design including the composite filter design.

  20. Laser Spot Detection Based on Reaction Diffusion.

    Science.gov (United States)

    Vázquez-Otero, Alejandro; Khikhlukha, Danila; Solano-Altamirano, J M; Dormido, Raquel; Duro, Natividad

    2016-03-01

    Center-location of a laser spot is a problem of interest when the laser is used for processing and performing measurements. Measurement quality depends on correctly determining the location of the laser spot. Hence, improving and proposing algorithms for the correct location of the spots are fundamental issues in laser-based measurements. In this paper we introduce a Reaction Diffusion (RD) system as the main computational framework for robustly finding laser spot centers. The method presented is compared with a conventional approach for locating laser spots, and the experimental results indicate that RD-based computation generates reliable and precise solutions. These results confirm the flexibility of the new computational paradigm based on RD systems for addressing problems that can be reduced to a set of geometric operations.

  1. Laser Spot Detection Based on Reaction Diffusion

    Directory of Open Access Journals (Sweden)

    Alejandro Vázquez-Otero

    2016-03-01

    Full Text Available Center-location of a laser spot is a problem of interest when the laser is used for processing and performing measurements. Measurement quality depends on correctly determining the location of the laser spot. Hence, improving and proposing algorithms for the correct location of the spots are fundamental issues in laser-based measurements. In this paper we introduce a Reaction Diffusion (RD system as the main computational framework for robustly finding laser spot centers. The method presented is compared with a conventional approach for locating laser spots, and the experimental results indicate that RD-based computation generates reliable and precise solutions. These results confirm the flexibility of the new computational paradigm based on RD systems for addressing problems that can be reduced to a set of geometric operations.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Correlation-based Transition Modeling for External Aerodynamic Flows

    Science.gov (United States)

    Medida, Shivaji

    Conventional turbulence models calibrated for fully turbulent boundary layers often over-predict drag and heat transfer on aerodynamic surfaces with partially laminar boundary layers. A robust correlation-based model is developed for use in Reynolds-Averaged Navier-Stokes simulations to predict laminar-to-turbulent transition onset of boundary layers on external aerodynamic surfaces. The new model is derived from an existing transition model for the two-equation k-omega Shear Stress Transport (SST) turbulence model, and is coupled with the one-equation Spalart-Allmaras (SA) turbulence model. The transition model solves two transport equations for intermittency and transition momentum thickness Reynolds number. Experimental correlations and local mean flow quantities are used in the model to account for effects of freestream turbulence level and pressure gradients on transition onset location. Transition onset is triggered by activating intermittency production using a vorticity Reynolds number criterion. In the new model, production and destruction terms of the intermittency equation are modified to improve consistency in the fully turbulent boundary layer post-transition onset, as well as ensure insensitivity to freestream eddy viscosity value specified in the SA model. In the original model, intermittency was used to control production and destruction of turbulent kinetic energy. Whereas, in the new model, only the production of eddy viscosity in SA model is controlled, and the destruction term is not altered. Unlike the original model, the new model does not use an additional correction to intermittency for separation-induced transition. Accuracy of drag predictions are improved significantly with the use of the transition model for several two-dimensional single- and multi-element airfoil cases over a wide range of Reynolds numbers. The new model is able to predict the formation of stable and long laminar separation bubbles on low-Reynolds number airfoils that

  9. 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.

  10. Laser Spot Detection Based on Reaction Diffusion

    OpenAIRE

    Alejandro Vázquez-Otero; Danila Khikhlukha; J. M. Solano-Altamirano; Raquel Dormido; Natividad Duro

    2016-01-01

    Center-location of a laser spot is a problem of interest when the laser is used for processing and performing measurements. Measurement quality depends on correctly determining the location of the laser spot. Hence, improving and proposing algorithms for the correct location of the spots are fundamental issues in laser-based measurements. In this paper we introduce a Reaction Diffusion (RD) system as the main computational framework for robustly finding laser spot centers. The method presente...

  11. Similarity-based Polymorphic Shellcode Detection

    Directory of Open Access Journals (Sweden)

    Denis Yurievich Gamayunov

    2013-02-01

    Full Text Available In the work the method for polymorphic shellcode dedection based on the set of known shellcodes is proposed. The method’s main idea is in sequential applying of deobfuscating transformations to a data analyzed and then recognizing similarity with malware samples. The method has been tested on the sets of shellcodes generated using Metasploit Framework v.4.1.0 and PELock Obfuscator and shows 87 % precision with zero false positives rate.

  12. Autocorrel I: A Neural Network Based Network Event Correlation Approach

    National Research Council Canada - National Science Library

    Japkowicz, Nathalie; Smith, Reuben

    2005-01-01

    .... We use the autoassociator to build prototype software to cluster network alerts generated by a Snort intrusion detection system, and discuss how the results are significant, and how they can be applied to other types of network events.

  13. 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.

  14. 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

  15. Special Nuclear Material Detection with a Water Cherenkov based Detector

    International Nuclear Information System (INIS)

    Sweany, M.; Bernstein, A.; Bowden, N.; Dazeley, S.; Svoboda, R.

    2008-01-01

    Fission events from Special Nuclear Material (SNM), such as highly enriched uranium or plutonium, produce a number of neutrons and high energy gamma-rays. Assuming the neutron multiplicity is approximately Poissonian with an average of 2 to 3, the observation of time correlations between these particles from a cargo container would constitute a robust signature of the presence of SNM inside. However, in order to be sensitive to the multiplicity, one would require a high total efficiency. There are two approaches to maximize the total efficiency; maximizing the detector efficiency or maximizing the detector solid angle coverage. The advanced detector group at LLNL is investigating one way to maximize the detector size. We are designing and building a water Cerenkov based gamma and neutron detector for the purpose of developing an efficient and cost effective way to deploy a large solid angle car wash style detector. We report on our progress in constructing a larger detector and also present preliminary results from our prototype detector that indicates detection of neutrons

  16. 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

  17. 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.

  18. Estimation of shear viscosity based on transverse momentum correlations

    International Nuclear Information System (INIS)

    Sharma, Monika

    2009-01-01

    Event anisotropy measurements at RHIC suggest the strongly interacting matter created in heavy ion collisions flows with very little shear viscosity. Precise determination of 'shear viscosity-to-entropy' ratio is currently a subject of extensive study [S. Gavin and M. Abdel-Aziz, Phys. Rev. Lett. 97 (2006) 162302]. We present preliminary results of measurements of the evolution of transverse momentum correlation function with collision centrality of Au+Au interactions at √(s NN )=200 GeV. We compare two differential correlation functions, namely inclusive [J. Adams et al. (STAR Collaboration), Phys. Rev. C 72 (2005) 044902] and a differential version of the correlation measure C introduced by Gavin et al. [S. Gavin and M. Abdel-Aziz, Phys. Rev. Lett. 97 (2006) 162302; M. Sharma and C. A. Pruneau, Phys. Rev. C 79 (2009) 024905.]. These observables can be used for the experimental study of the shear viscosity per unit entropy.

  19. Estimation of shear viscosity based on transverse momentum correlations

    Science.gov (United States)

    STAR Collaboration; Sharma, Monika; STAR Collaboration

    2009-11-01

    Event anisotropy measurements at RHIC suggest the strongly interacting matter created in heavy ion collisions flows with very little shear viscosity. Precise determination of “shear viscosity-to-entropy” ratio is currently a subject of extensive study [S. Gavin and M. Abdel-Aziz, Phys. Rev. Lett. 97 (2006) 162302]. We present preliminary results of measurements of the evolution of transverse momentum correlation function with collision centrality of Au+Au interactions at s=200 GeV. We compare two differential correlation functions, namely inclusive [J. Adams et al. (STAR Collaboration), Phys. Rev. C 72 (2005) 044902] and a differential version of the correlation measure C˜ introduced by Gavin et al. [S. Gavin and M. Abdel-Aziz, Phys. Rev. Lett. 97 (2006) 162302; M. Sharma and C. A. Pruneau, Phys. Rev. C 79 (2009) 024905.]. These observables can be used for the experimental study of the shear viscosity per unit entropy.

  20. 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.

  1. A Versatile Software Package for Inter-subject Correlation Based Analyses of fMRI

    Directory of Open Access Journals (Sweden)

    Jukka-Pekka eKauppi

    2014-01-01

    Full Text Available 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 modelling 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/.

  2. 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/

  3. 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.

  4. 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

  5. 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...

  6. On Emulation-Based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Abbasi, Ali; Wetzel, Jos; Bokslag, Wouter; Zambon, Emmanuele; Etalle, Sandro

    2014-01-01

    Emulation-based network intrusion detection systems have been devised to detect the presence of shellcode in network traffic by trying to execute (portions of) the network packet payloads in an in- strumented environment and checking the execution traces for signs of shellcode activity.

  7. On emulation-based network intrusion detection systems

    NARCIS (Netherlands)

    Abbasi, A.; Wetzels, J.; Bokslag, W.; Zambon, E.; Etalle, S.; Stavrou, A.; Bos, H.; Portokalidis, G.

    2014-01-01

    Emulation-based network intrusion detection systems have been devised to detect the presence of shellcode in network traffic by trying to execute (portions of) the network packet payloads in an instrumented environment and checking the execution traces for signs of shellcode activity.

  8. Active Fault Detection Based on a Statistical Test

    DEFF Research Database (Denmark)

    Sekunda, André Krabdrup; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2016-01-01

    In this paper active fault detection of closed loop systems using dual Youla-Jabr-Bongiorno-Kucera(YJBK) parameters is presented. Until now all detector design for active fault detection using the dual YJBK parameters has been based on CUSUM detectors. Here a method for design of a matched filter...

  9. Nonlinear Model-Based Fault Detection for a Hydraulic Actuator

    NARCIS (Netherlands)

    Van Eykeren, L.; Chu, Q.P.

    2011-01-01

    This paper presents a model-based fault detection algorithm for a specific fault scenario of the ADDSAFE project. The fault considered is the disconnection of a control surface from its hydraulic actuator. Detecting this type of fault as fast as possible helps to operate an aircraft more cost

  10. 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.

  11. Ground-based Polarization Remote Sensing of Atmospheric Aerosols and the Correlation between Polarization Degree and PM2.5

    International Nuclear Information System (INIS)

    Cheng, Chen; Zhengqiang, Li; Weizhen, Hou; Yisong, Xie; Donghui, Li; Kaitao, Li; Ying, Zhang

    2014-01-01

    The ground-based polarization remote sensing adds the polarization dimension information to traditional intensity detection, which provides a new method to detect atmospheric aerosols properties. In this paper, the polarization measurements achieved by a new multi-wavelength sun photometer, CE318-DP, are used for the ground-based remote sensing of atmospheric aerosols. In addition, a polarized vector radiative transfer model is introduced to simulate the DOLP (Degree Of Linear Polarization) under different sky conditions. At last, the correlative analysis between mass density of PM 2.5 and multi-wavelength and multi-angular DOLP is carried out. The result shows that DOLP has a high correlation with mass density of PM 2.5 , R 2 >0.85. As a consequence, this work provides a new method to estimate the mass density of PM 2.5 by using the comprehensive network of ground-based sun photometer

  12. 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

  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. 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.

    2018-05-01

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

  15. 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.

  16. 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...

  17. 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...

  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. Exact, almost and delayed fault detection: An observer based approach

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Saberi, Ali; Stoorvogel, Anton A.

    1999-01-01

    This paper consider the problem of fault detection and isolation in continuous- and discrete-time systems while using zero or almost zero threshold. A number of different fault detections and isolation problems using exact or almost exact disturbance decoupling are formulated. Solvability...... conditions are given for the formulated design problems together with methods for appropriate design of observer based fault detectors. The l-step delayed fault detection problem is also considered for discrete-time systems . Moreover, certain indirect fault detection methods such as unknown input observers...

  20. Abnormal traffic flow data detection based on wavelet analysis

    Directory of Open Access Journals (Sweden)

    Xiao Qian

    2016-01-01

    Full Text Available In view of the traffic flow data of non-stationary, the abnormal data detection is difficult.proposed basing on the wavelet analysis and least squares method of abnormal traffic flow data detection in this paper.First using wavelet analysis to make the traffic flow data of high frequency and low frequency component and separation, and then, combined with least square method to find abnormal points in the reconstructed signal data.Wavelet analysis and least square method, the simulation results show that using wavelet analysis of abnormal traffic flow data detection, effectively reduce the detection results of misjudgment rate and false negative rate.

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

    Science.gov (United States)

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

    2016-01-29

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

  2. Aircraft target detection algorithm based on high resolution spaceborne SAR imagery

    Science.gov (United States)

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

    2018-03-01

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

  3. Blu-ray based optomagnetic aptasensor for detection of small molecules

    DEFF Research Database (Denmark)

    Yang, Jaeyoung; Donolato, Marco; Pinto, Alessandro

    2016-01-01

    This paper describes an aptamer-based optomagnetic biosensor for detection of a small molecule based on target binding-induced inhibition of magnetic nanoparticle (MNP) clustering. For the detection of a target small molecule, two mutually exclusive binding reactions (aptamer-target binding...... the hydrodynamic size distribution of MNPs and their clusters. A commercial Blu-ray optical pickup unit is used for optical signal acquisition, which enables the establishment of a low-cost and miniaturized biosensing platform. Experimental results show that the degree of MNP clustering correlates well...

  4. LSTM-Based Hierarchical Denoising Network for Android Malware Detection

    OpenAIRE

    Yan, Jinpei; Qi, Yong; Rao, Qifan

    2018-01-01

    Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequence...

  5. 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.

  6. Fluorescence-Based Multiplex Protein Detection Using Optically Encoded Microbeads

    Directory of Open Access Journals (Sweden)

    Dae Hong Jeong

    2012-03-01

    Full Text Available Potential utilization of proteins for early detection and diagnosis of various diseases has drawn considerable interest in the development of protein-based multiplex detection techniques. Among the various techniques for high-throughput protein screening, optically-encoded beads combined with fluorescence-based target monitoring have great advantages over the planar array-based multiplexing assays. This review discusses recent developments of analytical methods of screening protein molecules on microbead-based platforms. These include various strategies such as barcoded microbeads, molecular beacon-based techniques, and surface-enhanced Raman scattering-based techniques. Their applications for label-free protein detection are also addressed. Especially, the optically-encoded beads such as multilayer fluorescence beads and SERS-encoded beads are successful for generating a large number of coding.

  7. CSI Frequency Domain Fingerprint-Based Passive Indoor Human Detection

    Directory of Open Access Journals (Sweden)

    Chong Han

    2018-04-01

    Full Text Available Passive indoor personnel detection technology is now a hot topic. Existing methods have been greatly influenced by environmental changes, and there are problems with the accuracy and robustness of detection. Passive personnel detection based on Wi-Fi not only solves the above problems, but also has the advantages of being low cost and easy to implement, and can be better applied to elderly care and safety monitoring. In this paper, we propose a passive indoor personnel detection method based on Wi-Fi, which we call FDF-PIHD (Frequency Domain Fingerprint-based Passive Indoor Human Detection. Through this method, fine-grained physical layer Channel State Information (CSI can be extracted to generate feature fingerprints so as to help determine the state in the scene by matching online fingerprints with offline fingerprints. In order to improve accuracy, we combine the detection results of three receiving antennas to obtain the final test result. The experimental results show that the detection rates of our proposed scheme all reach above 90%, no matter whether the scene is human-free, stationary or a moving human presence. In addition, it can not only detect whether there is a target indoors, but also determine the current state of the target.

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

    Indian Academy of Sciences (India)

    5.92 to 6.23 cm, which is long enough for the flow to become fully developed. ... carried out, if no observable change was detected by the pressure sensor after 24 hours. ... According to Eq. (8), for certain working fluid in determined flow geometry, Reynolds number .... Cryogenic and Superconductivity Society of Japan, p.

  9. 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

  10. Gold Nanoparticles-Based Barcode Analysis for Detection of Norepinephrine.

    Science.gov (United States)

    An, Jeung Hee; Lee, Kwon-Jai; Choi, Jeong-Woo

    2016-02-01

    Nanotechnology-based bio-barcode amplification analysis offers an innovative approach for detecting neurotransmitters. We evaluated the efficacy of this method for detecting norepinephrine in normal and oxidative-stress damaged dopaminergic cells. Our approach use a combination of DNA barcodes and bead-based immunoassays for detecting neurotransmitters with surface-enhanced Raman spectroscopy (SERS), and provides polymerase chain reaction (PCR)-like sensitivity. This method relies on magnetic Dynabeads containing antibodies and nanoparticles that are loaded both with DNA barcords and with antibodies that can sandwich the target protein captured by the Dynabead-bound antibodies. The aggregate sandwich structures are magnetically separated from the solution and treated to remove the conjugated barcode DNA. The DNA barcodes are then identified by SERS and PCR analysis. The concentration of norepinephrine in dopaminergic cells can be readily detected using the bio-barcode assay, which is a rapid, high-throughput screening tool for detecting neurotransmitters.

  11. 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.

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

    Science.gov (United States)

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

    2016-06-01

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

  13. 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)

  14. 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).

  15. Copula-based modeling of degree-correlated networks

    International Nuclear Information System (INIS)

    Raschke, Mathias; Schläpfer, Markus; Trantopoulos, Konstantinos

    2014-01-01

    Dynamical processes on complex networks such as information exchange, innovation diffusion, cascades in financial networks or epidemic spreading are highly affected by their underlying topologies as characterized by, for instance, degree–degree correlations. Here, we introduce the concept of copulas in order to generate random networks with an arbitrary degree distribution and a rich a priori degree–degree correlation (or ‘association’) structure. The accuracy of the proposed formalism and corresponding algorithm is numerically confirmed, while the method is tested on a real-world network of yeast protein–protein interactions. The derived network ensembles can be systematically deployed as proper null models, in order to unfold the complex interplay between the topology of real-world networks and the dynamics on top of them. (paper)

  16. 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.

  17. 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.)

  18. 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.

  19. Train integrity detection risk analysis based on PRISM

    Science.gov (United States)

    Wen, Yuan

    2018-04-01

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

  20. Learning-Based Detection of Harmful Data in Mobile Devices

    Directory of Open Access Journals (Sweden)

    Seok-Woo Jang

    2016-01-01

    Full Text Available The Internet has supported diverse types of multimedia content flowing freely on smart phones and tablet PCs based on its easy accessibility. However, multimedia content that can be emotionally harmful for children is also easily spread, causing many social problems. This paper proposes a method to assess the harmfulness of input images automatically based on an artificial neural network. The proposed method first detects human face areas based on the MCT features from the input images. Next, based on color characteristics, this study identifies human skin color areas along with the candidate areas of nipples, one of the human body parts representing harmfulness. Finally, the method removes nonnipple areas among the detected candidate areas using the artificial neural network. The experimental results show that the suggested neural network learning-based method can determine the harmfulness of various types of images more effectively by detecting nipple regions from input images robustly.

  1. Two-Stage Part-Based Pedestrian Detection

    DEFF Research Database (Denmark)

    Møgelmose, Andreas; Prioletti, Antonio; Trivedi, Mohan M.

    2012-01-01

    Detecting pedestrians is still a challenging task for automotive vision system due the extreme variability of targets, lighting conditions, occlusions, and high speed vehicle motion. A lot of research has been focused on this problem in the last 10 years and detectors based on classifiers has...... gained a special place among the different approaches presented. This work presents a state-of-the-art pedestrian detection system based on a two stages classifier. Candidates are extracted with a Haar cascade classifier trained with the DaimlerDB dataset and then validated through part-based HOG...... of several metrics, such as detection rate, false positives per hour, and frame rate. The novelty of this system rely in the combination of HOG part-based approach, tracking based on specific optimized feature and porting on a real prototype....

  2. 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)

  3. 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.

  4. 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.

  5. 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.

  6. Trained neurons-based motion detection in optical camera communications

    Science.gov (United States)

    Teli, Shivani; Cahyadi, Willy Anugrah; Chung, Yeon Ho

    2018-04-01

    A concept of trained neurons-based motion detection (TNMD) in optical camera communications (OCC) is proposed. The proposed TNMD is based on neurons present in a neural network that perform repetitive analysis in order to provide efficient and reliable motion detection in OCC. This efficient motion detection can be considered another functionality of OCC in addition to two traditional functionalities of illumination and communication. To verify the proposed TNMD, the experiments were conducted in an indoor static downlink OCC, where a mobile phone front camera is employed as the receiver and an 8 × 8 red, green, and blue (RGB) light-emitting diode array as the transmitter. The motion is detected by observing the user's finger movement in the form of centroid through the OCC link via a camera. Unlike conventional trained neurons approaches, the proposed TNMD is trained not with motion itself but with centroid data samples, thus providing more accurate detection and far less complex detection algorithm. The experiment results demonstrate that the TNMD can detect all considered motions accurately with acceptable bit error rate (BER) performances at a transmission distance of up to 175 cm. In addition, while the TNMD is performed, a maximum data rate of 3.759 kbps over the OCC link is obtained. The OCC with the proposed TNMD combined can be considered an efficient indoor OCC system that provides illumination, communication, and motion detection in a convenient smart home environment.

  7. 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.

  8. 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.

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

    KAUST Repository

    Pan, Bing; Wang, B.; Lubineau, Gilles; Moussawi, Ali

    2015-01-01

    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.

  10. Image Registration-Based Bolt Loosening Detection of Steel Joints

    Science.gov (United States)

    2018-01-01

    Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts. PMID:29597264

  11. Image Registration-Based Bolt Loosening Detection of Steel Joints.

    Science.gov (United States)

    Kong, Xiangxiong; Li, Jian

    2018-03-28

    Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts.

  12. Sequential backbone assignment based on dipolar amide-to-amide correlation experiments

    Energy Technology Data Exchange (ETDEWEB)

    Xiang, ShengQi; Grohe, Kristof; Rovó, Petra; Vasa, Suresh Kumar; Giller, Karin; Becker, Stefan; Linser, Rasmus, E-mail: rali@nmr.mpibpc.mpg.de [Max Planck Institute for Biophysical Chemistry, Department for NMR-Based Structural Biology (Germany)

    2015-07-15

    Proton detection in solid-state NMR has seen a tremendous increase in popularity in the last years. New experimental techniques allow to exploit protons as an additional source of information on structure, dynamics, and protein interactions with their surroundings. In addition, sensitivity is mostly improved and ambiguity in assignment experiments reduced. We show here that, in the solid state, sequential amide-to-amide correlations turn out to be an excellent, complementary way to exploit amide shifts for unambiguous backbone assignment. For a general assessment, we compare amide-to-amide experiments with the more common {sup 13}C-shift-based methods. Exploiting efficient CP magnetization transfers rather than less efficient INEPT periods, our results suggest that the approach is very feasible for solid-state NMR.

  13. Sequential backbone assignment based on dipolar amide-to-amide correlation experiments

    International Nuclear Information System (INIS)

    Xiang, ShengQi; Grohe, Kristof; Rovó, Petra; Vasa, Suresh Kumar; Giller, Karin; Becker, Stefan; Linser, Rasmus

    2015-01-01

    Proton detection in solid-state NMR has seen a tremendous increase in popularity in the last years. New experimental techniques allow to exploit protons as an additional source of information on structure, dynamics, and protein interactions with their surroundings. In addition, sensitivity is mostly improved and ambiguity in assignment experiments reduced. We show here that, in the solid state, sequential amide-to-amide correlations turn out to be an excellent, complementary way to exploit amide shifts for unambiguous backbone assignment. For a general assessment, we compare amide-to-amide experiments with the more common 13 C-shift-based methods. Exploiting efficient CP magnetization transfers rather than less efficient INEPT periods, our results suggest that the approach is very feasible for solid-state NMR

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

    Science.gov (United States)

    Zhang, Qingyang

    2018-05-16

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

  15. A Survey on Anomaly Based Host Intrusion Detection System

    Science.gov (United States)

    Jose, Shijoe; Malathi, D.; Reddy, Bharath; Jayaseeli, Dorathi

    2018-04-01

    An intrusion detection system (IDS) is hardware, software or a combination of two, for monitoring network or system activities to detect malicious signs. In computer security, designing a robust intrusion detection system is one of the most fundamental and important problems. The primary function of system is detecting intrusion and gives alerts when user tries to intrusion on timely manner. In these techniques when IDS find out intrusion it will send alert massage to the system administrator. Anomaly detection is an important problem that has been researched within diverse research areas and application domains. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. From the existing anomaly detection techniques, each technique has relative strengths and weaknesses. The current state of the experiment practice in the field of anomaly-based intrusion detection is reviewed and survey recent studies in this. This survey provides a study of existing anomaly detection techniques, and how the techniques used in one area can be applied in another application domain.

  16. [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.

  17. 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.

  18. Comparison of subset-based local and FE-based global digital image correlation: Theoretical error analysis and validation

    KAUST Repository

    Pan, B.; Wang, Bo; Lubineau, Gilles

    2016-01-01

    Subset-based local and finite-element-based (FE-based) global digital image correlation (DIC) approaches are the two primary image matching algorithms widely used for full-field displacement mapping. Very recently, the performances

  19. CINCH (confocal incoherent correlation holography) super resolution fluorescence microscopy based upon FINCH (Fresnel incoherent correlation holography).

    Science.gov (United States)

    Siegel, Nisan; Storrie, Brian; Bruce, Marc; Brooker, Gary

    2015-02-07

    FINCH holographic fluorescence microscopy creates high resolution super-resolved images with enhanced depth of focus. The simple addition of a real-time Nipkow disk confocal image scanner in a conjugate plane of this incoherent holographic system is shown to reduce the depth of focus, and the combination of both techniques provides a simple way to enhance the axial resolution of FINCH in a combined method called "CINCH". An important feature of the combined system allows for the simultaneous real-time image capture of widefield and holographic images or confocal and confocal holographic images for ready comparison of each method on the exact same field of view. Additional GPU based complex deconvolution processing of the images further enhances resolution.

  20. 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.

  1. Nanoscale protein diffusion by STED-based pair correlation analysis.

    Directory of Open Access Journals (Sweden)

    Paolo Bianchini

    Full Text Available We describe for the first time the combination between cross-pair correlation function analysis (pair correlation analysis or pCF and stimulated emission depletion (STED to obtain diffusion maps at spatial resolution below the optical diffraction limit (super-resolution. Our approach was tested in systems characterized by high and low signal to noise ratio, i.e. Capsid Like Particles (CLPs bearing several (>100 active fluorescent proteins and monomeric fluorescent proteins transiently expressed in living Chinese Hamster Ovary cells, respectively. The latter system represents the usual condition encountered in living cell studies on fluorescent protein chimeras. Spatial resolution of STED-pCF was found to be about 110 nm, with a more than twofold improvement over conventional confocal acquisition. We successfully applied our method to highlight how the proximity to nuclear envelope affects the mobility features of proteins actively imported into the nucleus in living cells. Remarkably, STED-pCF unveiled the existence of local barriers to diffusion as well as the presence of a slow component at distances up to 500-700 nm from either sides of nuclear envelope. The mobility of this component is similar to that previously described for transport complexes. Remarkably, all these features were invisible in conventional confocal mode.

  2. Optical nonclassicality test based on third-order intensity correlations

    Science.gov (United States)

    Rigovacca, L.; Kolthammer, W. S.; Di Franco, C.; Kim, M. S.

    2018-03-01

    We develop a nonclassicality criterion for the interference of three delayed, but otherwise identical, light fields in a three-mode Bell interferometer. We do so by comparing the prediction of quantum mechanics with those of a classical framework in which independent sources emit electric fields with random phases. In particular, we evaluate third-order correlations among output intensities as a function of the delays, and show how the presence of a correlation revival for small delays cannot be explained by the classical model of light. The observation of a revival is thus a nonclassicality signature, which can be achieved only by sources with a photon-number statistics that is highly sub-Poissonian. Our analysis provides strong evidence for the nonclassicality of the experiment discussed by Menssen et al. [Phys. Rev. Lett. 118, 153603 (2017), 10.1103/PhysRevLett.118.153603], and shows how a collective "triad" phase affects the interference of any three or more light fields, irrespective of their quantum or classical character.

  3. Analysis of neutron multiplicity measurements with allowance for dead-time losses between time-correlated detections

    International Nuclear Information System (INIS)

    Vincent, C.H.

    1992-01-01

    An exact solution is found for dead-time losses between detections occurring within a gate interval, with constant dead time and with allowance for time correlation between detections from the same spontaneous initial event. This is used to obtain a close approximation to the losses with a multi-channel detection system, with allowance for dead times briding the gate opening. This is applied, inversely, to calculate the true detection multiplicity rates from the distribution of the recorded counts within that interval. A suggestion is made for a circuit change to give a major reduction in dead-time effects. The unavoidable statistical errors that would remain are calculated. Their minimization and the limits of such minimization are discussed. (orig.)

  4. LSTM-Based Hierarchical Denoising Network for Android Malware Detection

    Directory of Open Access Journals (Sweden)

    Jinpei Yan

    2018-01-01

    Full Text Available Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN, a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than N-gram-based malware detection which is similar to our method.

  5. Hyperspectral Based Skin Detection for Person of Interest Identification

    Science.gov (United States)

    2015-03-01

    short-wave infrared VIS visible spectrum PCA principal component analysis FCBF Fast Correlation- Based Filter POI person of interest ANN artificial neural...an artificial neural network (ANN) that is created in MATLAB® using the Neural Network Toolbox to identify a POI based on their skin spectral data. A...identifying a POI based on skin spectral data. She identified an optimal feature subset to be used with the hyperspectral data she collected using a

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

    Science.gov (United States)

    Housh, Mashor; Ohar, Ziv

    2017-03-01

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

  7. 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%.

  8. 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%.

  9. 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

  10. 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 ...

  11. 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.

  12. 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.

  13. 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.

  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. 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.

  16. Cosmic String Detection with Tree-Based Machine Learning

    Science.gov (United States)

    Vafaei Sadr, A.; Farhang, M.; Movahed, S. M. S.; Bassett, B.; Kunz, M.

    2018-05-01

    We explore the use of random forest and gradient boosting, two powerful tree-based machine learning algorithms, for the detection of cosmic strings in maps of the cosmic microwave background (CMB), through their unique Gott-Kaiser-Stebbins effect on the temperature anisotropies. The information in the maps is compressed into feature vectors before being passed to the learning units. The feature vectors contain various statistical measures of the processed CMB maps that boost cosmic string detectability. Our proposed classifiers, after training, give results similar to or better than claimed detectability levels from other methods for string tension, Gμ. They can make 3σ detection of strings with Gμ ≳ 2.1 × 10-10 for noise-free, 0.9΄-resolution CMB observations. The minimum detectable tension increases to Gμ ≳ 3.0 × 10-8 for a more realistic, CMB S4-like (II) strategy, improving over previous results.

  17. 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.

  18. 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.

  19. 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 e...

  20. A dynamic bead-based microarray for parallel DNA detection

    International Nuclear Information System (INIS)

    Sochol, R D; Lin, L; Casavant, B P; Dueck, M E; Lee, L P

    2011-01-01

    A microfluidic system has been designed and constructed by means of micromachining processes to integrate both microfluidic mixing of mobile microbeads and hydrodynamic microbead arraying capabilities on a single chip to simultaneously detect multiple bio-molecules. The prototype system has four parallel reaction chambers, which include microchannels of 18 × 50 µm 2 cross-sectional area and a microfluidic mixing section of 22 cm length. Parallel detection of multiple DNA oligonucleotide sequences was achieved via molecular beacon probes immobilized on polystyrene microbeads of 16 µm diameter. Experimental results show quantitative detection of three distinct DNA oligonucleotide sequences from the Hepatitis C viral (HCV) genome with single base-pair mismatch specificity. Our dynamic bead-based microarray offers an effective microfluidic platform to increase parallelization of reactions and improve microbead handling for various biological applications, including bio-molecule detection, medical diagnostics and drug screening

  1. A universal DNA-based protein detection system.

    Science.gov (United States)

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

    2013-09-25

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

  2. Live face detection based on the analysis of Fourier spectra

    Science.gov (United States)

    Li, Jiangwei; Wang, Yunhong; Tan, Tieniu; Jain, Anil K.

    2004-08-01

    Biometrics is a rapidly developing technology that is to identify a person based on his or her physiological or behavioral characteristics. To ensure the correction of authentication, the biometric system must be able to detect and reject the use of a copy of a biometric instead of the live biometric. This function is usually termed "liveness detection". This paper describes a new method for live face detection. Using structure and movement information of live face, an effective live face detection algorithm is presented. Compared to existing approaches, which concentrate on the measurement of 3D depth information, this method is based on the analysis of Fourier spectra of a single face image or face image sequences. Experimental results show that the proposed method has an encouraging performance.

  3. 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.

  4. Random Valued Impulse Noise Removal Using Region Based Detection Approach

    Directory of Open Access Journals (Sweden)

    S. Banerjee

    2017-12-01

    Full Text Available Removal of random valued noisy pixel is extremely challenging when the noise density is above 50%. The existing filters are generally not capable of eliminating such noise when density is above 70%. In this paper a region wise density based detection algorithm for random valued impulse noise has been proposed. On the basis of the intensity values, the pixels of a particular window are sorted and then stored into four regions. The higher density based region is considered for stepwise detection of noisy pixels. As a result of this detection scheme a maximum of 75% of noisy pixels can be detected. For this purpose this paper proposes a unique noise removal algorithm. It was experimentally proved that the proposed algorithm not only performs exceptionally when it comes to visual qualitative judgment of standard images but also this filter combination outsmarts the existing algorithm in terms of MSE, PSNR and SSIM comparison even up to 70% noise density level.

  5. 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.

  6. 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.

  7. Fluorescence detection of single molecules using pulsed near-field optical excitation and time correlated photon counting

    International Nuclear Information System (INIS)

    Ambrose, W.P.; Goodwin, P.M.; Martin, J.C.; Keller, R.A.

    1994-01-01

    Pulsed excitation, time correlated single photon counting and time gated detection are used in near-field optical microscopy to enhance fluorescence images and measure the fluorescence lifetimes of single molecules of Rhodamine 6G on silica surfaces. Time gated detection is used to reject prompt scattered background and to improve the image signal to noise ratio. The excited state lifetime of a single Rhodamine 6G molecule is found to depend on the position of the near-field probe. We attribute the lifetime variations to spontaneous emission rate alterations by the fluorescence reflected from and quenching by the aluminum coated probe

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

    African Journals Online (AJOL)

    Erah

    The extent of this problem in educational institutions has not been explored in Northern Nigeria. ... Keywords: Gender-based violence female university students. Introduction ..... depression, while one attributed poor academic achievement to ...

  10. 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

  11. 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.

  12. 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

  13. Distance Based Method for Outlier Detection of Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Haibin Zhang

    2016-01-01

    Full Text Available We propose a distance based method for the outlier detection of body sensor networks. Firstly, we use a Kernel Density Estimation (KDE to calculate the probability of the distance to k nearest neighbors for diagnosed data. If the probability is less than a threshold, and the distance of this data to its left and right neighbors is greater than a pre-defined value, the diagnosed data is decided as an outlier. Further, we formalize a sliding window based method to improve the outlier detection performance. Finally, to estimate the KDE by training sensor readings with errors, we introduce a Hidden Markov Model (HMM based method to estimate the most probable ground truth values which have the maximum probability to produce the training data. Simulation results show that the proposed method possesses a good detection accuracy with a low false alarm rate.

  14. Correlations between power and test reactor data bases

    International Nuclear Information System (INIS)

    Guthrie, G.L.; Simonen, E.P.

    1989-02-01

    Differences between power reactor and test reactor data bases have been evaluated. Charpy shift data has been assembled from specimens irradiated in both high-flux test reactors and low-flux power reactors. Preliminary tests for the existence of a bias between test and power reactor data bases indicate a possible bias between the weld data bases. The bias is nonconservative for power predictive purposes, using test reactor data. The lesser shift for test reactor data compared to power reactor data is interpreted primarily in terms of greater point defect recombination for test reactor fluxes compared to power reactor fluxes. The possibility of greater thermal aging effects during lower damage rates is also discussed. 15 refs., 5 figs., 2 tabs

  15. 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.

  16. Atoms as nonlinear mixers for detection of quantum correlations at ultrahigh frequencies

    International Nuclear Information System (INIS)

    Georgiades, N.P.; Polzik, E.S.; Kimble, H.J.

    1997-01-01

    Measurements of quantum correlations are reported for a frequency difference of 25 THz between the signal and idler output fields generated by a subthreshold nondegenerate optical parametric oscillator. By simultaneously exciting a two-photon transition in atomic Cs by a combination of signal, idler, and open-quotes references oscillatorclose quotes fields, we record modulation of the excited-state population due to quantum interference between two alternative excitation pathways. The observed phase-sensitive modulation is proportional to the correlation function left-angle E s E i right-angle for the quantized signal and idler fields. copyright 1997 The American Physical Society

  17. 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.)

  18. 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.

  19. A Cyber-Attack Detection Model Based on Multivariate Analyses

    Science.gov (United States)

    Sakai, Yuto; Rinsaka, Koichiro; Dohi, Tadashi

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

  20. Detection of polyaromatic compounds using antibody-based fiberoptics fluoroimmunosensors

    International Nuclear Information System (INIS)

    Vo-Dinh, T.; Tromberg, B.J.; Griffin, G.D.; Ambrose, K.R.; Sepaniak, M.J.; Alarie, J.P.

    1987-01-01

    In this work we have investigated the performance of an antibody-based fiberoptics sensor for the detection of the carcinogen benzo(a)pyrene and its DNA-adduct product BP-tetrol. The excellent sensitivity of this device - femtomole limits of detection for BP - illustrates that it has considerable potential to perform analyses of chemical and biological samples at trace levels in complex matrices. The results indicate that fiberoptics-based fluoroimmunosensors can be useful in a wide spectrum of biochemical and clinical analyses. 17 refs., 4 figs., 1 tab

  1. Detection of polyaromatic compounds using antibody-based fiberoptics fluoroimmunosensors

    Energy Technology Data Exchange (ETDEWEB)

    Vo-Dinh, T.; Tromberg, B.J.; Griffin, G.D.; Ambrose, K.R.; Sepaniak, M.J.; Alarie, J.P.

    1987-01-01

    In this work we have investigated the performance of an antibody-based fiberoptics sensor for the detection of the carcinogen benzo(a)pyrene and its DNA-adduct product BP-tetrol. The excellent sensitivity of this device - femtomole limits of detection for BP - illustrates that it has considerable potential to perform analyses of chemical and biological samples at trace levels in complex matrices. The results indicate that fiberoptics-based fluoroimmunosensors can be useful in a wide spectrum of biochemical and clinical analyses. 17 refs., 4 figs., 1 tab.

  2. Nanobiosensors Based on Localized Surface Plasmon Resonance for Biomarker Detection

    Directory of Open Access Journals (Sweden)

    Yoochan Hong

    2012-01-01

    Full Text Available Localized surface plasmon resonance (LSPR is induced by incident light when it interacts with noble metal nanoparticles that have smaller sizes than the wavelength of the incident light. Recently, LSPR-based nanobiosensors were developed as tools for highly sensitive, label-free, and flexible sensing techniques for the detection of biomolecular interactions. In this paper, we describe the basic principles of LSPR-based nanobiosensing techniques and LSPR sensor system for biomolecule sensing. We also discuss the challenges using LSPR nanobiosensors for detection of biomolecules as a biomarker.

  3. 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.

  4. 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.

  5. Analysis of Android Device-Based Solutions for Fall Detection

    Directory of Open Access Journals (Sweden)

    Eduardo Casilari

    2015-07-01

    Full Text Available 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.

  6. 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.

  7. Analysis of Android Device-Based Solutions for Fall Detection

    Science.gov (United States)

    Casilari, Eduardo; Luque, Rafael; Morón, María-José

    2015-01-01

    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. PMID:26213928

  8. 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

  9. 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

  10. Detectability of the 21-cm CMB cross-correlation from the epoch of reionization

    NARCIS (Netherlands)

    Tashiro, Hiroyuki; Aghanim, Nabila; Langer, Mathieu; Douspis, Marian; Zaroubi, Saleem; Jelic, Vibor

    The 21-cm line fluctuations and the cosmic microwave background (CMB) are powerful probes of the epoch of reionization of the Universe. We study the potential of the cross-correlation between 21-cm line fluctuations and CMB anisotropy to obtain further constraints on the reionization history. We

  11. 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…

  12. 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.

  13. 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.

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

    Science.gov (United States)

    Li, Jia; Tian, Yonghong; Gao, Wen

    2008-01-01

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

  15. Fast Detection of Compressively Sensed IR Targets Using Stochastically Trained Least Squares and Compressed Quadratic Correlation Filters

    KAUST Repository

    Millikan, Brian; Dutta, Aritra; Sun, Qiyu; Foroosh, Hassan

    2017-01-01

    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.

  16. 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.

  17. 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...

  18. 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.

  19. Colour based fire detection method with temporal intensity variation filtration

    Science.gov (United States)

    Trambitckii, K.; Anding, K.; Musalimov, V.; Linß, G.

    2015-02-01

    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.

  20. 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

  1. 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...... by a grading jury before and after a suspected outlier is removed from the mean is the single most effective criterion for identifying potential outliers but that all of the criteria included in the algorithm have an effect on the outlier detection process....

  2. Android malware detection based on evolutionary super-network

    Science.gov (United States)

    Yan, Haisheng; Peng, Lingling

    2018-04-01

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

  3. 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.

  4. Microcontroller-based real-time QRS detection.

    Science.gov (United States)

    Sun, Y; Suppappola, S; Wrublewski, T A

    1992-01-01

    The authors describe the design of a system for real-time detection of QRS complexes in the electrocardiogram based on a single-chip microcontroller (Motorola 68HC811). A systematic analysis of the instrumentation requirements for QRS detection and of the various design techniques is also given. Detection algorithms using different nonlinear transforms for the enhancement of QRS complexes are evaluated by using the ECG database of the American Heart Association. The results show that the nonlinear transform involving multiplication of three adjacent, sign-consistent differences in the time domain gives a good performance and a quick response. When implemented with an appropriate sampling rate, this algorithm is also capable of rejecting pacemaker spikes. The eight-bit single-chip microcontroller provides sufficient throughput and shows a satisfactory performance. Implementation of multiple detection algorithms in the same system improves flexibility and reliability. The low chip count in the design also favors maintainability and cost-effectiveness.

  5. 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.

  6. 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.

  7. Optical character recognition based on nonredundant correlation measurements.

    Science.gov (United States)

    Braunecker, B; Hauck, R; Lohmann, A W

    1979-08-15

    The essence of character recognition is a comparison between the unknown character and a set of reference patterns. Usually, these reference patterns are all possible characters themselves, the whole alphabet in the case of letter characters. Obviously, N analog measurements are highly redundant, since only K = log(2)N binary decisions are enough to identify one out of N characters. Therefore, we devised K reference patterns accordingly. These patterns, called principal components, are found by digital image processing, but used in an optical analog computer. We will explain the concept of principal components, and we will describe experiments with several optical character recognition systems, based on this concept.

  8. 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.

  9. Detection of material property errors in handbooks and databases using artificial neural networks with hidden correlations

    Science.gov (United States)

    Zhang, Y. M.; Evans, J. R. G.; Yang, S. F.

    2010-11-01

    The authors have discovered a systematic, intelligent and potentially automatic method to detect errors in handbooks and stop their transmission using unrecognised relationships between materials properties. The scientific community relies on the veracity of scientific data in handbooks and databases, some of which have a long pedigree covering several decades. Although various outlier-detection procedures are employed to detect and, where appropriate, remove contaminated data, errors, which had not been discovered by established methods, were easily detected by our artificial neural network in tables of properties of the elements. We started using neural networks to discover unrecognised relationships between materials properties and quickly found that they were very good at finding inconsistencies in groups of data. They reveal variations from 10 to 900% in tables of property data for the elements and point out those that are most probably correct. Compared with the statistical method adopted by Ashby and co-workers [Proc. R. Soc. Lond. Ser. A 454 (1998) p. 1301, 1323], this method locates more inconsistencies and could be embedded in database software for automatic self-checking. We anticipate that our suggestion will be a starting point to deal with this basic problem that affects researchers in every field. The authors believe it may eventually moderate the current expectation that data field error rates will persist at between 1 and 5%.

  10. Tomosynthesis-detected Architectural Distortion: Management Algorithm with Radiologic-Pathologic Correlation.

    Science.gov (United States)

    Durand, Melissa A; Wang, Steven; Hooley, Regina J; Raghu, Madhavi; Philpotts, Liane E

    2016-01-01

    As use of digital breast tomosynthesis becomes increasingly widespread, new management challenges are inevitable because tomosynthesis may reveal suspicious lesions not visible at conventional two-dimensional (2D) full-field digital mammography. Architectural distortion is a mammographic finding associated with a high positive predictive value for malignancy. It is detected more frequently at tomosynthesis than at 2D digital mammography and may even be occult at conventional 2D imaging. Few studies have focused on tomosynthesis-detected architectural distortions to date, and optimal management of these distortions has yet to be well defined. Since implementing tomosynthesis at our institution in 2011, we have learned some practical ways to assess architectural distortion. Because distortions may be subtle, tomosynthesis localization tools plus improved visualization of adjacent landmarks are crucial elements in guiding mammographic identification of elusive distortions. These same tools can guide more focused ultrasonography (US) of the breast, which facilitates detection and permits US-guided tissue sampling. Some distortions may be sonographically occult, in which case magnetic resonance imaging may be a reasonable option, both to increase diagnostic confidence and to provide a means for image-guided biopsy. As an alternative, tomosynthesis-guided biopsy, conventional stereotactic biopsy (when possible), or tomosynthesis-guided needle localization may be used to achieve tissue diagnosis. Practical uses for tomosynthesis in evaluation of architectural distortion are highlighted, potential complications are identified, and a working algorithm for management of tomosynthesis-detected architectural distortion is proposed. (©)RSNA, 2016.

  11. Ensemble method: Community detection based on game theory

    Science.gov (United States)

    Zhang, Xia; Xia, Zhengyou; Xu, Shengwu; Wang, J. D.

    2014-08-01

    Timely and cost-effective analytics over social network has emerged as a key ingredient for success in many businesses and government endeavors. Community detection is an active research area of relevance to analyze online social network. The problem of selecting a particular community detection algorithm is crucial if the aim is to unveil the community structure of a network. The choice of a given methodology could affect the outcome of the experiments because different algorithms have different advantages and depend on tuning specific parameters. In this paper, we propose a community division model based on the notion of game theory, which can combine advantages of previous algorithms effectively to get a better community classification result. By making experiments on some standard dataset, it verifies that our community detection model based on game theory is valid and better.

  12. SERS-based pesticide detection by using nanofinger sensors

    Science.gov (United States)

    Kim, Ansoon; Barcelo, Steven J.; Li, Zhiyong

    2015-01-01

    Simple, sensitive, and rapid detection of trace levels of extensively used and highly toxic pesticides are in urgent demand for public health. Surface-enhanced Raman scattering (SERS)-based sensor was designed to achieve ultrasensitive and simple pesticide sensing. We developed a portable sensor system composed of high performance and reliable gold nanofinger sensor strips and a custom-built portable Raman spectrometer. Compared to the general procedure and previously reported studies that are limited to laboratory settings, our analytical method is simple, sensitive, rapid, and cost-effective. Based on the SERS results, the chemical interaction of two pesticides, chlorpyrifos (CPF) and thiabendazole (TBZ), with gold nanofingers was studied to determine a fingerprint for each pesticide. The portable SERS-sensor system was successfully demonstrated to detect CPF and TBZ pesticides within 15 min with a detection limit of 35 ppt in drinking water and 7 ppb on apple skin, respectively.

  13. Vision based speed breaker detection for autonomous vehicle

    Science.gov (United States)

    C. S., Arvind; Mishra, Ritesh; Vishal, Kumar; Gundimeda, Venugopal

    2018-04-01

    In this paper, we are presenting a robust and real-time, vision-based approach to detect speed breaker in urban environments for autonomous vehicle. Our method is designed to detect the speed breaker using visual inputs obtained from a camera mounted on top of a vehicle. The method performs inverse perspective mapping to generate top view of the road and segment out region of interest based on difference of Gaussian and median filter images. Furthermore, the algorithm performs RANSAC line fitting to identify the possible speed breaker candidate region. This initial guessed region via RANSAC, is validated using support vector machine. Our algorithm can detect different categories of speed breakers on cement, asphalt and interlock roads at various conditions and have achieved a recall of 0.98.

  14. Identifying Threats Using Graph-based Anomaly Detection

    Science.gov (United States)

    Eberle, William; Holder, Lawrence; Cook, Diane

    Much of the data collected during the monitoring of cyber and other infrastructures is structural in nature, consisting of various types of entities and relationships between them. The detection of threatening anomalies in such data is crucial to protecting these infrastructures. We present an approach to detecting anomalies in a graph-based representation of such data that explicitly represents these entities and relationships. The approach consists of first finding normative patterns in the data using graph-based data mining and then searching for small, unexpected deviations to these normative patterns, assuming illicit behavior tries to mimic legitimate, normative behavior. The approach is evaluated using several synthetic and real-world datasets. Results show that the approach has high truepositive rates, low false-positive rates, and is capable of detecting complex structural anomalies in real-world domains including email communications, cellphone calls and network traffic.

  15. Detecting weather radar clutter using satellite-based nowcasting products

    DEFF Research Database (Denmark)

    Jensen, Thomas B.S.; Gill, Rashpal S.; Overgaard, Søren

    2006-01-01

    This contribution presents the initial results from experiments with detection of weather radar clutter by information fusion with satellite based nowcasting products. Previous studies using information fusion of weather radar data and first generation Meteosat imagery have shown promising results...... for the detecting and removal of clutter. Naturally, the improved spatio-temporal resolution of the Meteosat Second Generation sensors, coupled with its increased number of spectral bands, is expected to yield even better detection accuracies. Weather radar data from three C-band Doppler weather radars...... Application Facility' of EUMETSAT and is based on multispectral images from the SEVIRI sensor of the Meteosat-8 platform. Of special interest is the 'Precipitating Clouds' product, which uses the spectral information coupled with surface temperatures from Numerical Weather Predictions to assign probabilities...

  16. Microstructure taxonomy based on spatial correlations: Application to microstructure coarsening

    International Nuclear Information System (INIS)

    Fast, Tony; Wodo, Olga; Ganapathysubramanian, Baskar; Kalidindi, Surya R.

    2016-01-01

    To build materials knowledge, rigorous description of the material structure and associated tools to explore and exploit information encoded in the structure are needed. These enable recognition, categorization and identification of different classes of microstructure and ultimately enable to link structure with properties of materials. Particular interest lies in the protocols capable of mining the essential information in large microstructure datasets and building robust knowledge systems that can be easily accessed, searched, and shared by the broader materials community. In this paper, we develop a protocol based on automated tools to classify microstructure taxonomies in the context of coarsening behavior which is important for long term stability of materials. Our new concepts for enhanced description of the local microstructure state provide flexibility of description. The mathematical description of microstructure that capture crucial attributes of the material, although central to building materials knowledge, is still elusive. The new description captures important higher order spatial information, but at the same time, allows down sampling if less information is needed. We showcase the classification protocol by studying coarsening of binary polymer blends and classifying steady state structures. We study several microstructure descriptions by changing the microstructure local state order and discretization and critically evaluate their efficacy. Our analysis revealed the superior properties of microstructure representation is based on the first order-gradient of the atomic fraction.

  17. 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.

  18. 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.

  19. 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

  20. Comparison of two Simon tasks: neuronal correlates of conflict resolution based on coherent motion perception.

    Science.gov (United States)

    Wittfoth, Matthias; Buck, Daniela; Fahle, Manfred; Herrmann, Manfred

    2006-08-15

    The present study aimed at characterizing the neural correlates of conflict resolution in two variations of the Simon effect. We introduced two different Simon tasks where subjects had to identify shapes on the basis of form-from-motion perception (FFMo) within a randomly moving dot field, while (1) motion direction (motion-based Simon task) or (2) stimulus location (location-based Simon task) had to be ignored. Behavioral data revealed that both types of Simon tasks induced highly significant interference effects. Using event-related fMRI, we could demonstrate that both tasks share a common cluster of activated brain regions during conflict resolution (pre-supplementary motor area (pre-SMA), superior parietal lobule (SPL), and cuneus) but also show task-specific activation patterns (left superior temporal cortex in the motion-based, and the left fusiform gyrus in the location-based Simon task). Although motion-based and location-based Simon tasks are conceptually very similar (Type 3 stimulus-response ensembles according to the taxonomy of [Kornblum, S., Stevens, G. (2002). Sequential effects of dimensional overlap: findings and issues. In: Prinz, W., Hommel., B. (Eds.), Common mechanism in perception and action. Oxford University Press, Oxford, pp. 9-54]) conflict resolution in both tasks results in the activation of different task-specific regions probably related to the different sources of task-irrelevant information. Furthermore, the present data give evidence those task-specific regions are most likely to detect the relationship between task-relevant and task-irrelevant information.

  1. Data acquisition card for fluctuation correlation spectroscopy allowing full access to the detected photon sequence

    OpenAIRE

    Eid, JS; Muller, JD; Gratton, E

    2000-01-01

    Typically, fluctuation correlation spectroscopy (FCS) data acquisition cards measure the number of photon events per time interval (i.e., bin) - time mode. Commercial FCS cards combine the bins through hardware in order to calculate the autocorrelation function. Such a design therefore does not yield the time resolved photon sequence, but only the autocorrelation of that sequence. A different acquisition method which measures the number of time intervals between photon events has been impleme...

  2. 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.

  3. 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

  4. Logic based feature detection on incore neutron spectra

    Energy Technology Data Exchange (ETDEWEB)

    Racz, A.; Kiss, S.; Bende-Farkas, S. (Hungarian Academy of Sciences, Budapest (Hungary). Central Research Inst. for Physics)

    1993-04-01

    A general framework for detecting features of incore neutron spectra with a rule-based methodology is presented. As an example, we determine the meaningful peaks in the APSD-s. This work is part of a larger project, aimed at developing a noise diagnostic expert system. (Author).

  5. 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 ...

  6. 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.

  7. 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

  8. Smartphone-based low light detection for bioluminescence application

    OpenAIRE

    Kim, Huisung; Jung, Youngkee; Doh, Iyll-Joon; Lozano-Mahecha, Roxana Andrea; Applegate, Bruce; Bae, Euiwon

    2017-01-01

    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 by smartphone (BAQS), provides an opportunity for onsite analysis and quantitation of luminescent signals from biological and non-biological sensing elements which emit photons in response to an an...

  9. 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.

  10. 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

    2016-01-01

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

  11. 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

  12. 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

    The shift from centralised large production to distributed energy production has several consequences for current power system operation. The replacement of large power plants by growing numbers of distributed energy resources (DERs) increases the dependency of the power system on small scale......, distributed production. Many of these DERs can be accessed and controlled remotely, posing a cybersecurity risk. This paper investigates an intrusion detection system which evaluates the DER operation in order to discover unauthorized control actions. The proposed anomaly detection method is based...

  13. 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.

  14. 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.

  15. 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.

  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. 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)

  18. Miniaturized Aptamer-Based Assays for Protein Detection

    Directory of Open Access Journals (Sweden)

    Alessandro Bosco

    2016-09-01

    Full Text Available The availability of devices for cancer biomarker detection at early stages of the disease is one of the most critical issues in biomedicine. Towards this goal, to increase the assay sensitivity, device miniaturization strategies empowered by the employment of high affinity protein binders constitute a valuable approach. In this work we propose two different surface-based miniaturized platforms for biomarker detection in body fluids: the first platform is an atomic force microscopy (AFM-based nanoarray, where AFM is used to generate functional nanoscale areas and to detect biorecognition through careful topographic measurements; the second platform consists of a miniaturized electrochemical cell to detect biomarkers through electrochemical impedance spectroscopy (EIS analysis. Both devices rely on robust and highly-specific protein binders as aptamers, and were tested for thrombin detection. An active layer of DNA-aptamer conjugates was immobilized via DNA directed immobilization on complementary single-stranded DNA self-assembled monolayers confined on a nano/micro area of a gold surface. Results obtained with these devices were compared with the output of surface plasmon resonance (SPR assays used as reference. We succeeded in capturing antigens in concentrations as low as a few nM. We put forward ideas to push the sensitivity further to the pM range, assuring low biosample volume (μL range assay conditions.

  19. 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.

  20. 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.