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Sample records for based correlation detection

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

    Energy Technology Data Exchange (ETDEWEB)

    Tang Guanghui; Wang Jiangcheng [Department of Physics, Normal College of Shihezi University, Xinjiang 832003 (China)

    2005-09-01

    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.

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

    International Nuclear Information System (INIS)

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

    International Nuclear Information System (INIS)

    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

  4. 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.

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

    International Nuclear Information System (INIS)

    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

  6. Multi-component based cross correlation beat detection in electrocardiogram analysis

    Directory of Open Access Journals (Sweden)

    Owens Frank J

    2004-07-01

    Full Text Available Abstract Background The first stage in computerised processing of the electrocardiogram is beat detection. This involves identifying all cardiac cycles and locating the position of the beginning and end of each of the identifiable waveform components. The accuracy at which beat detection is performed has significant impact on the overall classification performance, hence efforts are still being made to improve this process. Methods A new beat detection approach is proposed based on the fundamentals of cross correlation and compared with two benchmarking approaches of non-syntactic and cross correlation beat detection. The new approach can be considered to be a multi-component based variant of traditional cross correlation where each of the individual inter-wave components are sought in isolation as opposed to being sought in one complete process. Each of three techniques were compared based on their performance in detecting the P wave, QRS complex and T wave in addition to onset and offset markers for 3000 cardiac cycles. Results Results indicated that the approach of multi-component based cross correlation exceeded the performance of the two benchmarking techniques by firstly correctly detecting more cardiac cycles and secondly provided the most accurate marker insertion in 7 out of the 8 categories tested. Conclusion The main benefit of the multi-component based cross correlation algorithm is seen to be firstly its ability to successfully detect cardiac cycles and secondly the accurate insertion of the beat markers based on pre-defined values as opposed to performing individual gradient searches for wave onsets and offsets following fiducial point location.

  7. Harmonic detection an AC excited generation system based on in-phase correlation filtering

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The paper reports results of investigation on the harmonic detection technique of a complicated power supply system such as an AC excited generation system, which has a variable fundamental frequency and low order harmonics with rich sub-harmonics whose frequencies are lower than the fundamental one. The in-phase correlation filtering technique, based on the frequency shifting principle, is proposed in this paper. Theoretical analysis and experimental results validate the effectiveness of this technique for the harmonic detections of AC excited generation systems.

  8. Correlation Detection Based on the Reconstructed Excitation Signal of Electromagnetic Seismic Vibrator

    Science.gov (United States)

    Yang, Z.; Jiang, T.; Xu, X.; Jia, H.

    2014-12-01

    Correlation detection method is generally used to detect seismic data of electromagnetic seismic vibrator, which is widely applicated for shallow mineral prospecting. By analyzing field seismic data from electromagnetic and hydraulic seismic vibrators in mining area, we find when media underground is complex or the base-plate of vibrator is coupled poorly with ground, there is a 9.30 m positioning precision error and false multiple waves in the electromagnetic vibrator data reference to hydraulic vibrator data. The paper analyzes the theoretical reason of above problems by studying how the signal of electromagnetic vibrator is excited, then proposes a new method of correlation detection based on the reconstructed excitation signal (CDBRES). CDBRES includes following steps. First, it extracts the direct wave signal from seismometer near base-plate of electromagnetic vibrator. Next, it reconstructs the excitation signal according to the extracted direct wave. Then, it detects the seismic data using cross-correlation with the reconstructed excitation signal as a reference. Finally, it uses spectrum whitening to improve detection quality. We simulate with ray-tracing method, and simulation results show that the reconstructed excitation signal is extremely consistence with the ideal excitation signal, the correlation coefficient between them is up to 0.9869. And the signal of electromagnetic vibrator is detected correctly with CDBRES method. Then a field comparison experiment between hydraulic vibrator MiniVib T15000 and electromagnetic vibrator PHVS 500 was carried out near a copper and nickel deposit area. Their output force are 30000N and 300N, respectively. Though there is a great output force difference, the detection result of PHVS 500 using CDBRES method is still consistent with MiniVib T15000. Reference to the MiniVib T15000, the positioning error of PHVS 500 is only 0.93m in relatively stronger noise level. In addition, false multiple waves are invisible. In

  9. Personalized Subject Learning Based on Topic Detection and Canonical Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Zhangzu SHI

    2015-10-01

    Full Text Available To keep pace with the time, learning from printed medium alone is no longer a comprehensive approach. Fresh digital contents can definitely be the complement of printed education medium. Although timely access to fresh contents is becoming increasingly important for education and gaining such access is no longer a problem, the capacity for human teachers to assimilate such huge amounts of contents is limited. Topic Detection (TD is then a promising research area that addresses speedy access of desired contents based on topic or subject. On the other hand, personalized education is getting more attention because it facilitates the improvement of creativity and subject learning of the students. This paper reveals a patented Personalized Subject Learning (PSL system that caters for the need of personalized education and efficiently provides subject based contents. An efficient topic detection algorithm for providing subject content is presented. Moreover, since education contents are multimedia based ones with multimodal, PSL introduces Canonical Correlation Analysis (CCA method to detect multimodal correlations across different types of media. Due to its novelty, PSL has been used as the key engine in a real world application of personalized education system as the smart education module sponsored by a Smart City project.

  10. Study on an auto-correlation-function-based damage index: Sensitivity analysis and structural damage detection

    Science.gov (United States)

    Zhang, Muyu; Schmidt, Rüdiger

    2015-12-01

    The damage index based on the auto correlation function to detect the damage of the structure under white noise excitation is studied in detail in this paper. The maximum values of the auto correlation function of the vibration response signals (displacement, velocity and acceleration) from different measurement points of the structure are collected and formulated as a vector called Auto Correlation Function at Maximum Point Value Vector (AMV), which is expressed as a weighted combination of the Hadamard product of two mode shapes. AMV is normalized by its root mean square value so that the influence of the excitation can be eliminated. Sensitivity analysis for the different parts of the normalized AMV shows that the sensitivity of the normalized AMV to the local stiffness is dependent most on the sensitivity of the Hadamard product of the two lower order mode shapes to the local stiffness, which has a sudden change of the value around the local stiffness change position. The sensitivity of the normalized AMV has the similar shape and same trend that shows it is a very good damage indicator even for the very small damage. The relative change of the normalized AMV before and after damage occurs in the structure is adopted as the damage index to show the damage location. Several examples of the stiffness reduction detection of a 12-story shear frame structure are utilized to validate the results in sensitivity analysis, illustrate the effectiveness and anti-noise ability of the AMV-based damage detection method and compare the effect of the response type on the detectability of the normalized AMV.

  11. 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.

  12. A canonical correlation analysis based method for contamination event detection in water sources.

    Science.gov (United States)

    Li, Ruonan; Liu, Shuming; Smith, Kate; Che, Han

    2016-06-15

    In this study, a general framework integrating a data-driven estimation model is employed for contamination event detection in water sources. Sequential canonical correlation coefficients are updated in the model using multivariate water quality time series. The proposed method utilizes canonical correlation analysis for studying the interplay between two sets of water quality parameters. The model is assessed by precision, recall and F-measure. The proposed method is tested using data from a laboratory contaminant injection experiment. The proposed method could detect a contamination event 1 minute after the introduction of 1.600 mg l(-1) acrylamide solution. With optimized parameter values, the proposed method can correctly detect 97.50% of all contamination events with no false alarms. The robustness of the proposed method can be explained using the Bauer-Fike theorem. PMID:27264637

  13. 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.

  14. Minimax distance transform correlation filter-based target detection in FLIR imagery

    Science.gov (United States)

    Khan, J. F.; Alam, M. S.; Adhami, R. R.; Bhuiyan, S. M. A.

    2005-08-01

    This paper proposes a method to detect objects of arbitrary poses and sizes from a complex forward looking infrared (FLIR) image scene exploiting image correlation technique along with the preprocessing of the scene using a class of morphological operators. This presented automatic target recognition (ATR) algorithm consists of two steps. In the first step, the image is preprocessed, by employing morphological reconstruction operators, to remove the background as well as clutter and to intensify the presence of both low or high contrast targets. This step also involves in finding the possible candidate target regions or region of interests (ROIs) and passing those ROIs to the second step for classification. The second step exploits template-matching technique such as minimax distance transform correlation filter (MDTCF) to identify the true target from the false alarms in the pre-selected ROIs after classification. The MDTCF minimizes the average squared distance from the filtered true-class training images to a filtered reference image while maximizing the mean squared distance of the filtered false-class training images to this filtered reference image. This approach increases the separation between the false-class correlation outputs and the true-class correlation outputs. Classification is performed using the squared distance of a filtered test image to the chosen filtered reference image. The proposed technique has been tested with real life FLIR image sequences supplied by the Army Missile Command (AMCOM). Experimental results, obtained with these real FLIR image sequences, illustrating a wide variety of target and clutter variability, demonstrate the effectiveness and robustness of the proposed method.

  15. 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.

  16. Spatiotemporal Correlation Based Fault-Tolerant Event Detection in Wireless Sensor Networks

    OpenAIRE

    Kezhong Liu; Yang Zhuang; Zhibo Wang; Jie Ma

    2015-01-01

    Reliable event detection is one of the most important objectives in wireless sensor networks (WSNs), especially in the presence of faulty nodes. Existing fault-tolerant event detection approaches usually take the probability of faulty nodes into account and fusion techniques to weaken the influence of faulty readings are usually developed. Through extensive experiments, we discover a phenomenon that event detection accuracy degrades quickly when the faulty sensors ratio reaches a critical val...

  17. 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%.

  18. Unbiased community detection for correlation matrices

    CERN Document Server

    MacMahon, Mel

    2013-01-01

    A challenging problem in the study of large 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 at identifying such modules and suffer from an unavoidable information loss. A promising alternative is that of employing community detection techniques developed in network theory. Unfortunately, the attempts made so far have merely replaced network data with correlation matrices, a procedure that we show to be fundamentally biased due to 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 unbiased correlation-based counterparts of the most popular community detection techniques. After successfully benchmarking our methods, we apply them to s...

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

    Science.gov (United States)

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

    2013-01-01

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

  20. 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

  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.; Woike, Mark R.; Abdul-Aziz, Ali

    2014-04-01

    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

  2. High efficiency cooperative spectrum detection based on spectral correlation function%基于谱相关函数的高效合作频谱检测

    Institute of Scientific and Technical Information of China (English)

    朱晓梅; 朱俊; 包亚萍

    2012-01-01

    Conventional cooperative spectrum detection method based on spectral correlation function has good detection performance in the case of low signal to noise ratio, but high computational complexity. It is difficult to use in practical application. A high efficiency cooperative spectrum detection method is proposed. Each cognitive user detects spectral correlation function of the received signal in single cycle frequency. The discrete-frequency smoothing method is used to estimate spectral correlation function and the amplitude of spectral correlation function is used as the test statistic quantity. In order to improve the detection performance, the fusion center uses OR criteria to fuse local decisions of all the cognitive users. The simulation results show that the method has the similar detection performance and greatly reduce the computational complexity.%传统的基于谱相关函数的合作频谱检测方法在低信噪比的情况下具有较好的检测性能,但是计算复杂度高,实际应用较困难.提出一种高效的合作频谱检测方法,采用认知用户对接收信号进行单循环频率的谱相关函数检测方法.采用离散频率平滑方法估计谱相关函数,将谱相关函数的幅度作为检测统计量.为了进一步提高检测性能,融合中心采用OR准则对所有认知用户的本地判决结果进行融合.仿真结果表明:在具有相近的检测性能的同时大大降低了计算复杂度.

  3. Detecting Structure-correlated Attributes on Graphs

    OpenAIRE

    Chen, Siheng; Yang, Yaoqing; Zong, Shi; Singh, Aarti; Kovačević, Jelena

    2016-01-01

    Do users from Carnegie Mellon University form social communities on Facebook? In this paper, we focus on a task of detecting structure-correlated attributes on a graph. A structure-correlated attribute means that the node set activated by the attribute form a community in the graph. This task is relevant to many applications including identifying structure-correlated attributes in social networks, special events in the urban traffic networks, unusual brain activity in the brain connectivity n...

  4. LDA及主题词相关性的新事件检测%New Event Detection Based on LDA and Correlation of Topic Terms

    Institute of Scientific and Technical Information of China (English)

    黄颖

    2012-01-01

    目前,话题检测与跟踪已被广泛应用,新事件检测作为话题检测与跟踪领域中的研究任务之一,为跟踪后续话题发展的先验知识,在话题检测与跟踪领域具有重要的理论研究意义.LDA主题模型不能自动识别新事件,其主题数需通过人工或反复实验来确定,识别效率低.本文提出基于LDA及主题词间的相关性新事件检测算法,同时结合报道发生的时间,确定合理的主题数目,从而探知新事件.实验证明,与传统LDA算法及Gibbs LDA算法相比,该方法具有一定优势,提高了对新事件检测的敏感度.%Topic detection and tracking(TDT) is widely used. As one of research tasks for TDT, new event detection can provide prior knowledge to TDT, so it is of great theoretical research significance in the field of TDT. Because LDA model can not automatically identify new events, and the number of LDA topic is determined by the artificial, or by repeated experiments, it is of low efficiency. This paper presents a new method based on LDA and correlation of topic terms, which considers the correlation of subject terms and report time, it can dynamically adapt updated topics and then detect the new event. Experiment results demonstrate that this method is of some advantages and the sensitivity of new events detection is increased.

  5. 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.

  6. Uncovering Quantum Correlations with Time-Multiplexed Click Detection

    Science.gov (United States)

    Sperling, J.; Bohmann, M.; Vogel, W.; Harder, G.; Brecht, B.; Ansari, V.; Silberhorn, C.

    2015-07-01

    We report on the implementation of a time-multiplexed click detection scheme to probe quantum correlations between different spatial optical modes. We demonstrate that such measurement setups can uncover nonclassical correlations in multimode light fields even if the single mode reductions are purely classical. The nonclassical character of correlated photon pairs, generated by a parametric down-conversion, is immediately measurable employing the theory of click counting instead of low-intensity approximations with photoelectric detection models. The analysis is based on second- and higher-order moments, which are directly retrieved from the measured click statistics, for relatively high mean photon numbers. No data postprocessing is required to demonstrate the effects of interest with high significance, despite low efficiencies and experimental imperfections. Our approach shows that such novel detection schemes are a reliable and robust way to characterize quantum-correlated light fields for practical applications in quantum communications.

  7. Cluster identification based on correlations

    Science.gov (United States)

    Schulman, L. S.

    2012-04-01

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

  8. Correlation methods in fingerprint detection studies

    Energy Technology Data Exchange (ETDEWEB)

    Santer, B.D.; Wigley, T.M.L.; Jones, P.D. (Lawrence Livermore National Laboratory, Livermore, CA (United States))

    1993-07-01

    This investigation addresses two general issues regarding the role of pattern similarity statistics in greenhouse warming detection studies: normalization, and the relative merits of centered versus uncentered statistics. A pattern correlation statistic is used to search for the greenhouse warming signals predicted by five different models in the observed records of land and ocean surface temperature changes. Two forms of this statistic were computed: R(t), which makes use of non-normalized data, and R(t), which employs point-wise normalized data in order to focus the search on regions where the signal-to-noise ratio is large. While there are no trends in the R(t) time series, the time series of R(t) show large positive trends. However, it is not possible to infer from the R(t) results that the observed pattern of temperature change is, in fact, becoming increasingly similar to the model-predicted signal. It is shown that trends in R(t) must arise almost completely from the observed data, and cannot be an indicator of increasing observed data/signal similarity. The most informative pattern correlation statistic for detection purposes is R(t), the standard product-moment correlation coefficient between the observed and model fields. Our failure to find meaningful trends in R(t) may be due to the fact that the signal is being obscured by the background noise of natural variability, and/or because of incorrect model signals or sensitivities.

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

    Science.gov (United States)

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

    2016-07-01

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

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

    Science.gov (United States)

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

    2016-07-01

    We present the design and preliminary characterization of the first detection module based on Silicon Photomultiplier (SiPM) tailored for single-photon timing applications. The aim of this work is to demonstrate, thanks to the design of a suitable module, the possibility to easily exploit SiPM in many applications as an interesting detector featuring large active area, similarly to photomultipliers tubes, but keeping the advantages of solid state detectors (high quantum efficiency, low cost, compactness, robustness, low bias voltage, and insensitiveness to magnetic field). The module integrates a cooled SiPM with a total photosensitive area of 1 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). PMID:27475542

  11. Decentralized Detection in Censoring Sensor Networks under Correlated Observations

    Directory of Open Access Journals (Sweden)

    Abdullah S. Abu-Romeh

    2010-01-01

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

  12. Edge-based correlation image registration for multispectral imaging

    Science.gov (United States)

    Nandy, Prabal

    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.

  13. Single molecule detection and fluorescence correlation spectroscopy on surfaces

    OpenAIRE

    Hassler, Kai; Lasser, Theo

    2008-01-01

    In this thesis a new approach for single molecule detection and analysis is explored. This approach is based on the combination of two well established methods, fluorescence correlation spectroscopy (FCS) and total internal reflection fluorescence microscopy (TIRFM). In contrast to most existing fluorescence spectroscopy techniques, the subject of primary interest in FCS is not the fluorescence intensity itself but the random intensity fluctuation around the mean value. Intensity fluctuations...

  14. Single molecule detection and fluorescence correlation spectroscopy on surfaces

    OpenAIRE

    Hassler, Kai

    2006-01-01

    In this thesis a new approach for single molecule detection and analysis is explored. This approach is based on the combination of two well established methods, fluorescence correlation spectroscopy (FCS) and total internal reflection fluorescence microscopy (TIRFM). In contrast to most existing fluorescence spectroscopy techniques, the subject of primary interest in FCS is not the fluorescence intensity itself but the random intensity fluctuation around the mean value. Intensity fluctuations...

  15. Waveform Correlation Based Detection of Aftershocks of the 6 August 2007 4.1 Mw Crandall Canyon Mine Collapse in Central Utah

    Science.gov (United States)

    Koper, K. D.; Kubacki, T. M.; McCarter, M. K.; Pankow, K. L.

    2012-12-01

    On 6 August 2007 at 08:48:40 (UTC) a 3.9 ML seismic event occurred about 22 km ESE of the town of Mount Pleasant in the coal mining district of central Utah [Pechmann et al., 2008]. An epicenter of 39.4675°N, 111.2249°W and source depth of 0.5 km were determined by University of Utah Seismograph Stations (UUSS). It quickly became clear that the seismic event was associated with a catastrophic collapse at the Crandall Canyon coal mine in which six miners were killed. Subsequent moment tensor inversion showed that a pure double-couple mechanism did not fit the observed waveforms and instead a mechanism dominated by a closing crack (which incorporates an isotropic component) and a smaller residual double-couple and/or CLVD source was preferred [Ford et al., 2008]. The full moment tensor had a scalar moment corresponding to 4.1 Mw. In the 60 days following the mine collapse UUSS located 42 seismic events in the immediate source region. These events had magnitudes of 0.8-2.5 Mc and were detected using standard network association procedures with data from permanent stations of the Utah Regional Seismic Network (URSN), as well as 5 temporary seismometers that UUSS installed in the source area within 2-3 days of the main event. Simple inspection of continuous data from the nearest station shows evidence for a much larger number of seismic events, especially in the hours immediately following the collapse. These events originally went undetected because they were too small to be recorded at a significant number of the permanent URSN stations. Here we use waveform correlation methods to formally detect and locate these tiny aftershocks. We performed multi-channel cross-correlation [vanDecar and Crosson, 1990] on the 42 catalog events using data recorded at the nearest permanent broadband URSN station, MTPU, which was located about 19 km to the south of the mine. A 15-s long window starting 5 s before the expected P arrival was used on data that had been bandpass filtered

  16. CORRELATION EXTREMAL PRINCIPLE OF OBJECT DETECTION ON GROUND SURFACE

    Directory of Open Access Journals (Sweden)

    Maryna Mukhina

    2013-04-01

    Full Text Available  Features of correlation extremal principle for detection of ground objects are considered for visual surveillance. The method of phase correlation is researched and variants of its optimization are proposed.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-08-15

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

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

    International Nuclear Information System (INIS)

    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

  19. Local Detection of Quantum Correlations with a Single Trapped Ion

    CERN Document Server

    Gessner, M; Pruttivarasin, T; Buchleitner, A; Breuer, H -P; Haeffner, H

    2014-01-01

    As one of the most striking features of quantum mechanics, quantum correlations are at the heart of quantum information science. Detection of correlations usually requires access to all the correlated subsystems. However, in many realistic scenarios this is not feasible as only some of the subsystems can be controlled and measured. Such cases can be treated as open quantum systems interacting with an inaccessible environment. Initial system-environment correlations play a fundamental role for the dynamics of the open system and often prevent one to use standard master equation methods. Following a recent proposal, we exploit the impact of the correlations on the open-system dynamics to detect system-environment quantum correlations without accessing the environment. We use two degrees of freedom of a trapped ion to model an open system and its environment. We identify the detected correlations as quantum discord, a resource for certain quantum information tasks. The presented method provides an important tool...

  20. 基于时空相关性的传感器网络离群点检测算法%SPATIOTEMPORAL CORRELATION-BASED OUTLIER DETECTION ALGORITHM IN WIRELESS SENSOR NETWORKS

    Institute of Scientific and Technical Information of China (English)

    林锋; 张红

    2013-01-01

    离群点检测是传感器网络中的一个重要问题,它主要包括事件检测、异常检测.提出一种基于时空相关性的、轻量级的局部离群点检测算法,它可利用节点的计算能力,降低节点之间的通信成本;同时,还能区分出离群点的类型.%Outlier detection is an important issue in sensor networks,which mainly comprises event detection and anomaly detection.The detection algorithm proposed in the paper is a lightweight one based on spatiotemporal correlation for local outliers.It can utilise the computational capacity of the sensors adequately and reduce the communication cost between the nodes.It can also differentiate outlier types.

  1. Detecting Positive Correlations in a Multivariate Sample

    CERN Document Server

    Castro, Ery Arias; Lugosi, Gábor

    2012-01-01

    We consider the problem of testing whether a correlation matrix of a multivariate normal population is the identity matrix. We focus on sparse classes of alternatives where only a few entries are nonzero and, in fact, positive. We derive a general lower bound applicable to various classes and study the performance of some near-optimal tests. We pay special attention to computational feasibility and construct near-optimal tests that can be computed efficiently. Finally, we apply our results to prove new lower bounds for the clique number of high-dimensional random geometric graphs.

  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. Markov chaotic sequences for correlation based watermarking schemes

    International Nuclear Information System (INIS)

    In this paper, statistical analysis of watermarking schemes based on correlation detection is presented. Statistical properties of watermark sequences generated by piecewise-linear Markov maps are exploited, resulting in superior watermark detection reliability. Correlation/spectral properties of such sequences are easily controllable, a fact that affects the watermarking system performance. A family of chaotic maps, namely the skew tent map family, is proposed for use in watermarking schemes

  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. Improving detection range via correlation of long PN codes

    Science.gov (United States)

    Subedi, Saurav; Wang, Zhonghai; Zheng, Y. Rosa

    2012-06-01

    This paper proposes a correlation method for detecting super-regenerative RF receivers via stimulation. Long PN sequences are used as to stimulate the unintended emissions from the RF receivers. High correlation between known PN sequence and stimulated unintended emissions from RF receivers helps improving the detection range compared to passive detection and power detection methods. Although RF receivers generate unintended emissions from their nonlinear devices, without stimulation, the power of these unintended emission is usually lower than --70dBm, as per the FCC regulations. Direct detection (passive detection) of these emissions is a challenging task specially in noisy conditions. When a stimulation signal is transmitted from distance, superregenerative receivers generate unintended emissions that contain the stimulation signal and its harmonics. Excellent correlation property of PN sequence enables us to improve the range and accuracy of detecting the super-regenerative receivers through stimulation method even in noisy conditions. The experiment involves detection of wireless doorbell, a commercially available super-regenerative receiver. USRP is used for transmitting the stimulant signal and receiving unintended stimulated emissions from the doorbell. Experiments show that the detection range of the proposed method with long PN sequences is much larger than passive detection and power detection methods.

  6. 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)

    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)

  7. Correlation-Based Burstiness for Logo Retrieval

    OpenAIRE

    Revaud, Jérôme; Douze, Matthijs; Schmid, Cordelia

    2012-01-01

    Detecting logos in photos is challenging. A reason is that logos locally resemble patterns frequently seen in random images. We propose to learn a statistical model for the distribution of incorrect detections output by an image matching algorithm. It results in a novel scoring criterion in which the weight of correlated keypoint matches is reduced, penalizing irrelevant logo detections. In experiments on two very diff erent logo retrieval benchmarks, our approach largely improves over the st...

  8. Theoretical analysis of a CO2 gas detection system using correlation spectroscopy

    OpenAIRE

    Chambers, Paul; Austin, Ed A.D.; Dakin, John P.

    2004-01-01

    We present a comprehensive model of a CO2 correlation spectroscopy based gas sensor. Predictions of the sensor response for typical fibre optic-coupled systems are made, taking into account effects of noise in detected signals.

  9. Detection of replication-defective hepatitis A virus based on the correlation between real-time polymerase chain reaction and ELISA in situ results

    Directory of Open Access Journals (Sweden)

    Alyne Moraes Costa

    2013-02-01

    Full Text Available ELISA in situ can be used to titrate hepatitis A virus (HAV particles and real-time polymerase chain reaction (RT-PCR has been shown to be a fast method to quantify the HAV genome. Precise quantification of viral concentration is necessary to distinguish between infectious and non-infectious particles. The purpose of this study was to compare cell culture and RT-PCR quantification results and determine whether HAV genome quantification can be correlated with infectivity. For this purpose, three stocks of undiluted, five-fold diluted and 10-fold diluted HAV were prepared to inoculate cells in a 96-well plate. Monolayers were then incubated for seven, 10 and 14 days and the correlation between the ELISA in situ and RT-PCR results was evaluated. At 10 days post-incubation, the highest viral load was observed in all stocks of HAV via RT-PCR (10(5 copies/mL (p = 0.0002, while ELISA revealed the highest quantity of particles after 14 days (optical density = 0.24, p < 0.001. At seven days post-infection, there was a significant statistical correlation between the results of the two methods, indicating equivalents titres of particles and HAV genome during this period of infection. The results reported here indicate that the duration of growth of HAV in cell culture must be taken into account to correlate genome quantification with infectivity.

  10. Robust Statistical Detection of Power-Law Cross-Correlation

    Science.gov (United States)

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

    2016-06-01

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

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

    Science.gov (United States)

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

    2011-08-01

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

  12. Speckle correlation method used to detect an object's surface slope

    Science.gov (United States)

    Smíd, Petr; Horváth, Pavel; Hrabovský, Miroslav

    2006-09-01

    We present a technique employing a speckle pattern correlation method for detection of the slope of an object's surface. Controlled translation of an object under investigation and numerical correlation of speckle patterns recorded during its motion give information used to evaluate the tilt of the object. The proposed optical setup uses a symmetrical arrangement of detection planes in the image field and enables one to detect the tilt of an object's surface within the interval (10°-30°). Simulation analysis shows how to control the measuring range. The presented theory, simulation analysis, and setup are verified through an experiment by measurement of the slope of a surface of a cube made out of steel.

  13. An object correlation and maneuver detection approach for space surveillance

    International Nuclear Information System (INIS)

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

  14. Features Based Text Similarity Detection

    CERN Document Server

    Kent, Chow Kok

    2010-01-01

    As the Internet help us cross cultural border by providing different information, plagiarism issue is bound to arise. As a result, plagiarism detection becomes more demanding in overcoming this issue. Different plagiarism detection tools have been developed based on various detection techniques. Nowadays, fingerprint matching technique plays an important role in those detection tools. However, in handling some large content articles, there are some weaknesses in fingerprint matching technique especially in space and time consumption issue. In this paper, we propose a new approach to detect plagiarism which integrates the use of fingerprint matching technique with four key features to assist in the detection process. These proposed features are capable to choose the main point or key sentence in the articles to be compared. Those selected sentence will be undergo the fingerprint matching process in order to detect the similarity between the sentences. Hence, time and space usage for the comparison process is r...

  15. Correlation-base feature selector and Bagging applied in intrusion detection%属性相关选择和Bagging算法在入侵检测中的应用

    Institute of Scientific and Technical Information of China (English)

    魏浩; 丁要军

    2014-01-01

    入侵事件的识别是入侵检测系统的关键,入侵事件的识别是一个网络数据的分类问题。通过基于相关的属性选择算法,选择出相关度高的属性子集,去除冗余度高的属性,在选择的属性子集上,使用Bagging算法对网络数据分类,识别入侵事件。实验结果表明,在选用的实验数据上,基于相关的属性选择算法和Bagging算法结合使用,提高了分类正确率和入侵事件的检出率,降低了入侵事件的误报率。%Intrusion event recognition is the key to intrusion detection systems, and it also is a network data classification problems. For high recognition rate,through the selection algorithm based on relevant attributes,select a subset of the attributes with low redundant,identification intrusion by event Bagging algorithm.Experiments showed that the correlation-based attribute selection algorithm and Bagging algorithm improve the classification accuracy and intrusion detection rate of events, reducing the false alarm rate of intrusion events.

  16. Signal detection by correlation of Fresnel diffraction patterns.

    Science.gov (United States)

    De, M; Lohmann, A W

    1967-12-01

    A typical problem of signal detection is the search for key words. This can be done automatically by means of optical matched filtering. Here we describe an alternative method in which the key word is used directly, not in the form of a matched filter. We compute optically the correlation integral of the intensity distributions of the Fresnel diffraction patterns from the input (printed page) and from the reference signal (key word). In the output plane a detection peak (light point) indicates the position of the key word on the input page. PMID:20062381

  17. Correlation detection as a general mechanism for multisensory integration

    OpenAIRE

    Parise, Cesare V.; Marc O. Ernst

    2016-01-01

    The brain efficiently processes multisensory information by selectively combining related signals across the continuous stream of multisensory inputs. To do so, it needs to detect correlation, lag and synchrony across the senses; optimally integrate related information; and dynamically adapt to spatiotemporal conflicts across the senses. Here we show that all these aspects of multisensory perception can be jointly explained by postulating an elementary processing unit akin to the Hassenstein–...

  18. Quotient correlation: A sample based alternative to Pearson's correlation

    OpenAIRE

    Zhang, Zhengjun

    2008-01-01

    The quotient correlation is defined here as an alternative to Pearson’s correlation that is more intuitive and flexible in cases where the tail behavior of data is important. It measures nonlinear dependence where the regular correlation coefficient is generally not applicable. One of its most useful features is a test statistic that has high power when testing nonlinear dependence in cases where the Fisher’s Z-transformation test may fail to reach a right conclusion. Unlike most asymptotic t...

  19. 一种矢量相关性的抗噪声边缘检测方法%An anti-noise edge detection method based on vector correlation

    Institute of Scientific and Technical Information of China (English)

    王文宁

    2014-01-01

    对图像中常见的高斯噪声特性进行了分析,对含有较强高斯噪声的数字图像的边缘检测方法进行了研究。分析了边缘的方向性相关特征和连续性相关特征,设计了图像特征矢量图,检测图像特征矢量相关的相似度,提取出抗噪声的图像边缘检测方法。仿真验证表明,对于含有较强高斯噪声的图像,本方法能够有效抑制噪声,而且能够提取出准确清晰的边缘。本算法在噪声图像中能很好地克服普通算子的噪声问题,而且算法简单,计算量较少。%This article analyses the Gauss noise characteristics of common image , studies the edge detection method based on digital image which contains a strong Gauss noise. It analyses directional correlation feature edges and continuity characteristics , design the image feature vector, detects similarity of image feature vector related, extract image edge detection method of anti-noise. The simulation results show that, for images with a strong Gauss noise, the proposed method can effectively suppress the noise, and can extract accurate and clear edge. This method can well overcome the noise problem of common operators in noise image , but also has a simple algorithm with less calculation.

  20. Behavioural Correlation for Detecting P2P Bots

    CERN Document Server

    Al-Hammadi, Yousof

    2010-01-01

    In the past few years, IRC bots, malicious programs which are remotely controlled by the attacker through IRC servers, have become a major threat to the Internet and users. These bots can be used in different malicious ways such as issuing distributed denial of services attacks to shutdown other networks and services, keystrokes logging, spamming, traffic sniffing cause serious disruption on networks and users. New bots use peer to peer (P2P) protocols start to appear as the upcoming threat to Internet security due to the fact that P2P bots do not have a centralized point to shutdown or traceback, thus making the detection of P2P bots is a real challenge. In response to these threats, we present an algorithm to detect an individual P2P bot running on a system by correlating its activities. Our evaluation shows that correlating different activities generated by P2P bots within a specified time period can detect these kind of bots.

  1. Design of EM-MWD signal detection system based on correlation and adaptive filter%基于相关自适应器的EM-MWD信号检测系统设计

    Institute of Scientific and Technical Information of China (English)

    王洪亮; 董浩斌; 蒋国盛

    2012-01-01

    The signal transmitted to ground surface through electromagnetic method measurement while drilling (EM-MWD) signal channel is very weak and suffers from the interference of white noise, pulse noise, power frequency noise and its harmonics, which results in the accuracy reduction of characteristic parameter extraction. In order to solve this problem, based on studying the electromagnetic wave transmission channel, according to the strength of received signal and characteristics of correlation and adaptive detection, the correlation and adaptive filter is studied and a detection system of EM-MWD is designed. Then the signal envelope is obtained using Hilbert transform, the data fitting and residual analysis are completed; and the root mean square error, signal to noise ratio and bit error rate are calculated. Experiment was carried out. Simulation and experiment results show that with this detection system, the accuracy of the signal characteristic parameters is improved; the purpose of effectively reducing noise is achieved, which provides a basis for the follow-up analysis and research.%经EM-MWD( electromagnetic method measurement while drilling)电磁通道传输至地表的信号很微弱并且极易受到白噪声、奇异噪声、工频噪声及其谐波等干扰,导致信号特征参数提取的准确度降低,为了解决这一难题,通过对电磁波传输信道的研究,根据接收初始信号强度以及自适应检测和相关检测的特点,研究并设计了相关自适应器,并基于此设计了电磁随钻地表信号检测系统.然后用Hilbert变换求信号包络,完成了数据拟合和残差分析,并计算了信噪比、均方根误差和误码率,最后做了实验.仿真和实验结果表明,利用该检测系统,能够提高信号特征参数的准确度,达到有效降噪目的,对后续分析和研究提供了保证.

  2. Upper Subcritical Calculations Based on Correlated Data

    Energy Technology Data Exchange (ETDEWEB)

    Sobes, Vladimir [ORNL; Rearden, Bradley T [ORNL; Mueller, Don [ORNL; Marshall, William BJ J [ORNL; Scaglione, John M [ORNL; Dunn, Michael E [ORNL

    2015-01-01

    The American National Standards Institute and American Nuclear Society standard for Validation of Neutron Transport Methods for Nuclear Criticality Safety Calculations defines the upper subcritical limit (USL) as “a limit on the calculated k-effective value established to ensure that conditions calculated to be subcritical will actually be subcritical.” Often, USL calculations are based on statistical techniques that infer information about a nuclear system of interest from a set of known/well-characterized similar systems. The work in this paper is part of an active area of research to investigate the way traditional trending analysis is used in the nuclear industry, and in particular, the research is assessing the impact of the underlying assumption that the experimental data being analyzed for USL calculations are statistically independent. In contrast, the multiple experiments typically used for USL calculations can be correlated because they are often performed at the same facilities using the same materials and measurement techniques. This paper addresses this issue by providing a set of statistical inference methods to calculate the bias and bias uncertainty based on the underlying assumption that the experimental data are correlated. Methods to quantify these correlations are the subject of a companion paper and will not be discussed here. The newly proposed USL methodology is based on the assumption that the integral experiments selected for use in the establishment of the USL are sufficiently applicable and that experimental correlations are known. Under the assumption of uncorrelated data, the new methods collapse directly to familiar USL equations currently used. We will demonstrate our proposed methods on real data and compare them to calculations of currently used methods such as USLSTATS and NUREG/CR-6698. Lastly, we will also demonstrate the effect experiment correlations can have on USL calculations.

  3. Obstacle detection by stereo vision of fast correlation matching

    International Nuclear Information System (INIS)

    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)

  4. 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 ...

  5. 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…

  6. 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.

  7. Gait correlation analysis based human identification.

    Science.gov (United States)

    Chen, Jinyan

    2014-01-01

    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. PMID:24592144

  8. 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.

  9. 基于灰色关联的运动训练背景下肌体行为特征检测%Body Behavior Characteristics Detection Based on Grey Correlation in Sports Training Background

    Institute of Scientific and Technical Information of China (English)

    蒙军; 黄梅; 王成科

    2015-01-01

    在复杂运动训练场背景下对运动员的肌体行为特征的准确检测是实现运动技术行为跟踪和识别的重要基础,在比赛监测和训练指导方法具有重要的应用价值。运动场景具有不确定性,图像背景具有跳变性,检测较为困难。提出一种基于灰色关联的运动训练场景背景下的运动员肌体行为特征检测算法,人体在进行复杂场景模式的训练和肌体作用下,特别是在如足球运动、跑步运动时,骨骼围绕着关节作为支撑进行矢状面内的运动,通过对人体肌体的驱动模式动力学进行建模分析,采用先分割再填充的方法,获得不重影、不抖动的图像,通过灰色关联分析准确识别运动训练背景下的人体肌体行为特征,编制运动员的肌体行为识别软件系统,通过运动录像解析系统准确及时地反馈运动信息,提高体育运动训练的科学化水平,促进训练和比赛质量的提高。实验结果表明,该算法能满足运动场景中的全方位多尺度信息采集的肌体动作识别的要求,误检率较传统算法低,性能较好。%Accurate detection of body behavior characteristic of athletes in sports training field is an important basis of mo⁃tion tracking and recognition, it has important application value in the competition and training methods monitoring. The sports scene is uncertain, and the image background has jumped degeneration, so the detection is difficult. A body behavior characteristics detection algorithm is proposed based on grey correlation in sports training background, especially in sports such as football, running, the segmentation of image is obtained after filling. The image without ghosting and jitter is collect⁃ed. The body behavior detection is taken based on grey relational analysis, and body behavior recognition software system is designed. The sports video analysis system can accurately and timely feedback motion

  10. 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

  11. Knowledge base of Nusselt number correlations

    International Nuclear Information System (INIS)

    This knowledge base was designed to identify various Nusselt Numbers given certain conditions and restrictions. The main criterion that was used in classifying them was the geometry of the flow. From these, the program then preceded to separate correlations by forced vs. free convection, laminar vs. turbulent flow, fully developed regimes vs. entrance regimes, constant wall temperature vs. constant wall flux, and different fluid types. Finally, the individual cases were classified according to particular ranges of Re, Pr, Pe, L/D ratio, etc. After this thorough search and elimination, the correct Nusselt Number was displayed

  12. Phase Correlation Based Iris Image Registration Model

    Institute of Scientific and Technical Information of China (English)

    Jun-Zhou Huang; Tie-Niu Tan; Li Ma; Yun-Hong Wang

    2005-01-01

    Iris recognition is one of the most reliable personal identification methods. In iris recognition systems, image registration is an important component. Accurately registering iris images leads to higher recognition rate for an iris recognition system. This paper proposes a phase correlation based method for iris image registration with sub-pixel accuracy.Compared with existing methods, it is insensitive to image intensity and can compensate to a certain extent the non-linear iris deformation caused by pupil movement. Experimental results show that the proposed algorithm has an encouraging performance.

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

    International Nuclear Information System (INIS)

    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

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

    Science.gov (United States)

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

    2009-01-01

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

  15. Ionizing particle detection based on phononic crystals

    Science.gov (United States)

    Aly, Arafa H.; Mehaney, Ahmed; Eissa, Mostafa F.

    2015-08-01

    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.

  16. 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.

  17. 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.

  18. 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.

  19. Detection for gene-gene co-association via kernel canonical correlation analysis

    Directory of Open Access Journals (Sweden)

    Yuan Zhongshang

    2012-10-01

    Full Text Available Abstract Background Currently, most methods for detecting gene-gene interaction (GGI in genomewide association studies (GWASs are limited in their use of single nucleotide polymorphism (SNP as the unit of association. One way to address this drawback is to consider higher level units such as genes or regions in the analysis. Earlier we proposed a statistic based on canonical correlations (CCU as a gene-based method for detecting gene-gene co-association. However, it can only capture linear relationship and not nonlinear correlation between genes. We therefore proposed a counterpart (KCCU based on kernel canonical correlation analysis (KCCA. Results Through simulation the KCCU statistic was shown to be a valid test and more powerful than CCU statistic with respect to sample size and interaction odds ratio. Analysis of data from regions involving three genes on rheumatoid arthritis (RA from Genetic Analysis Workshop 16 (GAW16 indicated that only KCCU statistic was able to identify interactions reported earlier. Conclusions KCCU statistic is a valid and powerful gene-based method for detecting gene-gene co-association.

  20. 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.

  1. Application of detecting algorithm based on network

    Institute of Scientific and Technical Information of China (English)

    张凤斌; 杨永田; 江子扬; 孙冰心

    2004-01-01

    Because currently intrusion detection systems cannot detect undefined intrusion behavior effectively,according to the robustness and adaptability of the genetic algorithms, this paper integrates the genetic algorithms into an intrusion detection system, and a detection algorithm based on network traffic is proposed. This algorithm is a real-time and self-study algorithm and can detect undefined intrusion behaviors effectively.

  2. Detection System for Neutron $\\beta$ Decay Correlations in the UCNB and Nab experiments

    CERN Document Server

    Broussard, L J; Adamek, E R; Baeßler, S; Birge, N; Blatnik, M; Bowman, J D; Brandt, A E; Brown, M; Burkhart, J; Callahan, N B; Clayton, S M; Crawford, C; Cude-Woods, C; Currie, S; Dees, E B; Ding, X; Fomin, N; Frlez, E; Fry, J; Gray, F E; Hasan, S; Hickerson, K P; Hoagland, J; Holley, A T; Ito, T M; Klein, A; Li, H; Liu, C -Y; Makela, M F; McGaughey, P L; Mirabal-Martinez, J; Morris, C L; Ortiz, J D; Pattie, R W; Penttilä, S I; Plaster, B; Počanić, D; Ramsey, J C; Salas-Bacci, A; Salvat, D J; Saunders, A; Seestrom, S J; Sjue, S K L; Sprow, A P; Tang, Z; Vogelaar, R B; Vorndick, B; Wang, Z; Wei, W; Wexler, J; Wilburn, W S; Womack, T L; Young, A R

    2016-01-01

    We describe a detection system designed for precise measurements of angular correlations in neutron $\\beta$ 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 $\\beta$ electron detection with energy thresholds below 10 keV, energy resolution of $\\sim$3 keV FWHM, and rise time of $\\sim$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 $\\beta$ particles and recoil protons from neutron $\\beta$ decay. The fully instrumented detection system will be implemented in the UCNB and Nab experiments, to determine the neutron $\\beta$ decay parameters $B$, $a$, and $b$.

  3. Dynamically reconfigurable multiple beam illumination based on optical correlation

    DEFF Research Database (Denmark)

    Glückstad, Jesper; Palima, Darwin; Dam, Jeppe Seidelin; Perch-Nielsen, Ivan R.

    2009-01-01

    We adapt concepts from optical correlation and optical pattern recognition to propose a method for generating reconfigurable multiple spots with high efficiency. The generated spots correspond to the correlation spikes in optical pattern recognition. In pattern recognition, optimizing the...... correlation filter is constrained by the target pattern to be detected. The reverse process of light projection grants the freedom to optimize both the target pattern and the correlation filters. Combined with contemporary spatial light modulation technologies, the proposed method can yield dynamically...

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

    CERN Document Server

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

    2016-01-01

    We demonstrate coincidence measurements of spatially entangled photons by means of a novel type of multi-pixel based detection array. The adopted sensor is a fully digital 8$\\times$16 silicon photomultiplier array allowing not only photon counting but also per-pixel time stamping of the arrived photons with a resolution of 65 ps. Together with a frame rate of 500 kfps, this property exceeds the capabilities of conventional charge-coupled device cameras which have become of growing interest for the detection of transversely correlated photon pairs. The sensor is used to measure a second-order correlation function for various non-collinear configurations of entangled photons generated by spontaneous parametric down-conversion. The experimental results are compared to theory.

  5. 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.

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

    CERN Document Server

    Kwapien, Jaroslaw; Drozdz, Stanislaw

    2015-01-01

    The detrended cross-correlation coefficient $\\rho_{\\rm DCCA}$ has recently been proposed to quantify the strength of cross-correlations on different temporal scales in bivariate, non-stationary time series. It is based on the detrended cross-correlation and detrended fluctuation analyses (DCCA and DFA, respectively) and can be viewed as an analogue of the Pearson coefficient in the case of the fluctuation analysis. The coefficient $\\rho_{\\rm DCCA}$ works well in many practical situations but by construction its applicability is limited to detection of whether two signals are generally cross-correlated, without 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 $\\rho_{\\rm DCCA}$ that exploits the multifractal versions of DFA and DCCA: MFDFA and MFCCA, respectively. The resulting new coefficient $\\rho_q$ not only is able to quantify the strength of correlations, but ...

  7. 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

  8. Adaptive MIMO radar detection algorithm in a spatially correlated clutter environment

    Science.gov (United States)

    Chen, Wei-Jen; Narayanan, Ram M.

    2008-04-01

    In recent years, multi-input and multi-output (MIMO) radar systems have captured the attention of many researchers. At the very beginning, these works focused on increasing the signal-to-noise power ratio (SNR) by transmitting coherent signals. In 2004, a new concept called spatial diversity was introduced for MIMO radar, which achieved the same objective using widely separated transmitters and/or receivers and independent transmitted signals. This technology dramatically enhances the detection probability and accuracy in estimating direction of arrival (DOA) by efficiently constraining target scintillations and collecting more information carried in distinguishable signals. Moreover, since the transmitted signals are independent in MIMO radar, considerable mechanisms such as beamforming technologies and coherent processing, can be developed and applied to improve its performance. However, MIMO radar detection performance in a spatially correlated clutter environment does not get adequate attention that it deserves. Therefore, in this paper, received signals considered include reflections from the target, K-distributed clutter, and thermal noise. Moreover, beamforming technology and coherent processing are applied to estimate the reflectivity of and distinguish between target and clutter reflections. As a result, when echoes from target and clutter are unresolvable, the detection problem can be formulated as a two hypotheses test. According to the Bayesian approach, we develop the ratio test for this situation. In addition, we observe that if highly correlated information is utilized by adaptively modifying clutter local mean power probability density function (PDF), the uncertainty of clutter local mean power decreases and detection performance can be further improved. Last, we also compare the power based detection algorithm with the ratio test. Even though the power based detection algorithm has advantage in simple computation, its comparable performance is

  9. Automatic Generation of Correlation Rules to Detect Complex Attack Scenarios

    OpenAIRE

    Godefroy, Erwan; Totel, Eric; Hurfin, Michel; Majorczyk, Frédéric

    2014-01-01

    In large distributed information systems, alert correlation systems are necessary to handle the huge amount of elementary security alerts and to identify complex multi-step attacks within the flow of low level events and alerts. In this paper, we show that, once a human expert has provided an action tree derived from an attack tree, a fully automated transformation process can generate exhaustive correlation rules that would be tedious and error prone to enumerate by hand. The transformation ...

  10. Trinocular Stereo Matching Based on Correlations Between Baselines and Disparities

    Institute of Scientific and Technical Information of China (English)

    GUANYepeng; GUWeikang

    2004-01-01

    Gray correlation technique is utilized to take the multi-peak feature points with gray correlation coefficients less than a certain range of maximal correlation coefficient as a potential candidate matching set. There exists the maximal correlation between the correct disparities and their corresponding baselines. A trinocular stereo matching algorithm is proposed based on correlations between the baselines and disparities. After computing the correlations between the baselines and disparities, the unique matches can be determined by maximal correlation coefficient. It is proved that the algorithm proposed is valid and credible by 3-D reconstruction on two pairs of actual natural stereo images.

  11. Peak detection in fiber Bragg grating using a fast phase correlation algorithm

    Science.gov (United States)

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

    2014-05-01

    Fiber Bragg grating sensing principle is based on the exact tracking of the peak wavelength location. Several peak detection techniques have already been proposed in literature. Among these, conventional peak detection (CPD) methods such as the maximum detection algorithm (MDA), do not achieve very high precision and accuracy, especially when the Signal to Noise Ratio (SNR) and the wavelength resolution are poor. On the other hand, recently proposed algorithms, like the cross-correlation demodulation algorithm (CCA), are more precise and accurate but require higher computational effort. To overcome these limitations, we developed a novel fast phase correlation algorithm (FPC) which performs as well as the CCA, being at the same time considerably faster. This paper presents the FPC technique and analyzes its performances for different SNR and wavelength resolutions. Using simulations and experiments, we compared the FPC with the MDA and CCA algorithms. The FPC detection capabilities were as precise and accurate as those of the CCA and considerably better than those of the CPD. The FPC computational time was up to 50 times lower than CCA, making the FPC a valid candidate for future implementation in real-time systems.

  12. Detecting temporal and spatial correlations in pseudoperiodic time series

    Science.gov (United States)

    Zhang, Jie; Luo, Xiaodong; Nakamura, Tomomichi; Sun, Junfeng; Small, Michael

    2007-01-01

    Recently there has been much attention devoted to exploring the complicated possibly chaotic dynamics in pseudoperiodic time series. Two methods [Zhang , Phys. Rev. E 73, 016216 (2006); Zhang and Small, Phys. Rev. Lett. 96, 238701 (2006)] have been forwarded to reveal the chaotic temporal and spatial correlations, respectively, among the cycles in the time series. Both these methods treat the cycle as the basic unit and design specific statistics that indicate the presence of chaotic dynamics. In this paper, we verify the validity of these statistics to capture the chaotic correlation among cycles by using the surrogate data method. In particular, the statistics computed for the original time series are compared with those from its surrogates. The surrogate data we generate is pseudoperiodic type (PPS), which preserves the inherent periodic components while destroying the subtle nonlinear (chaotic) structure. Since the inherent chaotic correlations among cycles, either spatial or temporal (which are suitably characterized by the proposed statistics), are eliminated through the surrogate generation process, we expect the statistics from the surrogate to take significantly different values than those from the original time series. Hence the ability of the statistics to capture the chaotic correlation in the time series can be validated. Application of this procedure to both chaotic time series and real world data clearly demonstrates the effectiveness of the statistics. We have found clear evidence of chaotic correlations among cycles in human electrocardiogram and vowel time series. Furthermore, we show that this framework is more sensitive to examine the subtle changes in the dynamics of the time series due to the match between PPS surrogate and the statistics adopted. It offers a more reliable tool to reveal the possible correlations among cycles intrinsic to the chaotic nature of the pseudoperiodic time series.

  13. Cellular telephone-based radiation detection instrument

    Science.gov (United States)

    Craig, William W.; Labov, Simon E.

    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.

  14. Correlative imaging in detecting post renal transplant urine leak

    International Nuclear Information System (INIS)

    Post transplant urinary leak is a common complication after kidney transplantation. There is no consensus on its most appropriate diagnostic and therapeutic methods. The objective of this study is to evaluate multiple imaging modalities in detecting symptomatic and asymptomatic urine leak. Seventeen cases of proven urine leak after renal transplantation were encountered and treated in our institution between November 1993 and September 2001. Diagnosis was made 7 to 41 days post transplantation. Ten cases were symptomatic and seven asymptomatic. Ultrasonography and radionuclide renography were performed for all patients. Contrast cystography was also performed in 7 patients. Radionuclide renography was obtained after injection of 10 mCi (370 MBq) of Tc99m-MAG-3. Flow study was acquired every one second for 60 seconds followed by sequential images obtained every 30 seconds for 29 minutes. Post void static image was then obtained. All studies were obtained while the urethral catheter is clamped to enhance the yield of the studies. Sixteen out of the 17 cases of leak were detected by radionuclide renography while only 8 were detected by ultrasonography. Among the 7 cases who had cystography leak was diagnosed in only 3. The case that was not detected by renography, was not detected by ultrasonography, was diagnosed by analyzing the wound leaky fluid in the laboratory and was further confirmed when treated surgically. Among the 7 asymptomatic cases only 3 had positive ultrasound findings while all were positive by radionuclide renography. Additionally, the findings of peri graft- fluid collections on ultrasonography were not as specific as those of the radionuclide renography for urine leak.Our experience suggests that radionuclide renography with clamping the urethral catheter is the modality of choice to detect both symptomatic and asymptomatic post renal transplant urine leak. (authors)

  15. PILOT-BASED FREQUENCY OFFSET DETECTION SCHEME IN OFDM SYSTEM

    Institute of Scientific and Technical Information of China (English)

    Du Zheng; Zhu Jinkang

    2003-01-01

    The frequency offset information is extracted from local pilot amplitude characteristics, which suffer much less distortion in frequency-selective fading channels than those utilizing frequency domain correlation techniques. Simulation shows that the performance of this scheme has better performance than the existing frequency domain pilot-based frequency offset detection scheme.

  16. Image Edge Detection Based on Oscillation

    Institute of Scientific and Technical Information of China (English)

    FAN Hong; WANG Zhi-jie

    2005-01-01

    A new method for image edge detection based on a pulse neural network is proposed in this paper. The network is locally connected. The external input of each neuron of the network is gray value of the corresponding pixel. The synchrony of the neuron and its neighbors is detected by detection neurons. The edge of the image can be read off at minima of the total activity of the detection neurons.

  17. Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems

    Institute of Scientific and Technical Information of China (English)

    YAO Yu; ZHU Shanfeng; CHEN Xinmeng

    2006-01-01

    In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users' ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.

  18. 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

  19. Asymptotic distributions in the projection pursuit based canonical correlation analysis

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    In this paper, associations between two sets of random variables based on the projection pursuit (PP) method are studied. The asymptotic normal distributions of estimators of the PP based canonical correlations and weighting vectors are derived.

  20. Visual-Based Transmedia Events Detection

    OpenAIRE

    Joly, Alexis; Champ, Julien; Letessier, Pierre; Hervé, Nicolas; Buisson, Olivier; Viaud, Marie-Luce

    2012-01-01

    This paper presents a visual-based media event detection system based on the automatic discovery of the most circulated images across the main news media (news websites, press agencies, TV news and newspapers). Its main originality is to rely on the transmedia contextual information to denoise the raw visual detections and consequently focus on the most salient transmedia events.

  1. Shifting-and-Scaling Correlation Based Biclustering Algorithm.

    Science.gov (United States)

    Ahmed, Hasin Afzal; Mahanta, Priyakshi; Bhattacharyya, Dhruba Kumar; Kalita, Jugal Kumar

    2014-01-01

    The existence of various types of correlations among the expressions of a group of biologically significant genes poses challenges in developing effective methods of gene expression data analysis. The initial focus of computational biologists was to work with only absolute and shifting correlations. However, researchers have found that the ability to handle shifting-and-scaling correlation enables them to extract more biologically relevant and interesting patterns from gene microarray data. In this paper, we introduce an effective shifting-and-scaling correlation measure named Shifting and Scaling Similarity (SSSim), which can detect highly correlated gene pairs in any gene expression data. We also introduce a technique named Intensive Correlation Search (ICS) biclustering algorithm, which uses SSSim to extract biologically significant biclusters from a gene expression data set. The technique performs satisfactorily with a number of benchmarked gene expression data sets when evaluated in terms of functional categories in Gene Ontology database. PMID:26357059

  2. Design of multiplexing photon correlator based on FPGA

    Science.gov (United States)

    Xu, Jisen; Wu, Xiaobin; Qiu, Jian; Luo, Kaiqing; Han, Peng

    2015-08-01

    We mainly study on hardware design and implementation of multiplexing photon correlator based on FPGA and the graphical user interface programmed by LabView to control it, and its application in submicron particle size analyzer. The study is based on the verification of the principle of sampling correlation calculation, the implementation of the multiplexing correlation operation, the hardware design of FPGA, and etc. Multiplexing photon correlator can calculate the auto-correlation function of multiplexing photon signals that were received by a single PMT at different times. Multiplexing photon correlator mainly composes of signal control module, photon counter module, shift register module, multiplier-accumulator module, communication module, etc. With appropriate optical set-up, it will change from the traditional measurement of the single point to the 2D or 3D measurement using a single photon detector, greatly expands the application range of photon correlation spectroscopy.

  3. Research on a Diagnosis Method Based on a Correlation Integral

    Institute of Scientific and Technical Information of China (English)

    ZHAO Hong; XIA Yong; LIANG Xiao-guo

    2003-01-01

    For the first time, the diagnosis idea based on a correlation integral is proposed, which regards the correlation integral as a feature set. The correlation dimension is contained in the double-log curve of the correlation integral to scale, so extracting features directly from the correlation integral can avoid the bottleneck problem of determining the range of non-scale length. Several features extracted from the correlation integral are better than the single feature of the correlation dimension when descri bing the signal. It is obvious that this method utilizes more information of the signal than does the correlation dimension. The diagnosis examples verify that this method is more accurate and more effective.

  4. Ghost imaging based on Pearson correlation coefficients

    Science.gov (United States)

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

    2015-05-01

    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. Project supported by the National Key Scientific Instrument and Equipment Development Project of China (Grant No. 2013YQ030595) and the National High Technology Research and Development Program of China (Grant No. 2013AA122902).

  5. Non Correlation DWT Based Watermarking Behavior in Different Color Spaces

    Directory of Open Access Journals (Sweden)

    Mehdi Khalili

    2016-01-01

    Full Text Available There are two digital watermarking techniques. Digital watermarking techniques based on correlation and digital watermarking techniques that are not based on correlation. In previous work, we proposed a DWT2 based CDMA image watermarking scheme to study the effects of using eight color spaces RGB, YCbCr, JPEG-YCbCr, YIQ, YUV, HSI, HSV and CIELab, on watermarking algorithms based on correlation techniques. This paper proposes a non correlation based image watermarking scheme in wavelet transform domain and tests it in the same color spaces, to develop studying, reach a comprehensive analysis and focus on satisfying the requirements of based non coloration watermarking algorithms. To achieve more security, imperceptibility and robustness of the proposed scheme, first, the binary watermark image encodes by applying ATM, CCM and exclusive OR. Then, the scrambled watermark embeds into intended quantized approximation coefficients of wavelet transform by LSB insertion technique.

  6. Image edge detection based on beamlet transform

    Institute of Scientific and Technical Information of China (English)

    Li Jing; Huang Peikang; Wang Xiaohu; Pan Xudong

    2009-01-01

    Combining beamlet transform with steerable filters, a new edge detection method based on line gra-dient is proposed. Compared with operators based on point local properties, the edge-detection results with this method achieve higher SNR and position accuracy, and are quite helpful for image registration, object identification, etc. Some edge-detection experiments on optical and SAR images that demonstrate the significant improvement over classical edge operators are also presented. Moreover, the template matching result based on edge information of optical reference image and SAR image also proves the validity of this method.

  7. Adaptive Endpoint Detection Based on Subband Speech

    Institute of Scientific and Technical Information of China (English)

    张文军; 谢剑英

    2003-01-01

    An adaptive endpoint detection algorithm based on band energy and adaptive smoothing algorithm is described.This algorithm utilizes the capability of adaptive smoothing algorithm that intensifies the discontinuity between local areas. The band energy features are selected because of their usefulness in detecting high energy regions (in the incoming signal) and making the distinction between speech and noise. Heuristic "edge-focusing" is used to endpoint detection to save the time in iteration.

  8. Acoustic signal detection through the cross-correlation method in experiments with different signal to noise ratio and reverberation conditions

    CERN Document Server

    Adrián-Martínez, S; Bou-Cabo, M; Felis, I; Llorens, C; Martínez-Mora, J A; Saldaña, M

    2015-01-01

    The study and application of signal detection techniques based on cross-correlation method for acoustic transient signals in noisy and reverberant environments are presented. These techniques are shown to provide high signal to noise ratio, good signal discernment from very close echoes and accurate detection of signal arrival time. The proposed methodology has been tested on real data collected in environments and conditions where its benefits can be shown. This work focuses on the acoustic detection applied to tasks of positioning in underwater structures and calibration such those as ANTARES and KM3NeT deep-sea neutrino telescopes, as well as, in particle detection through acoustic events for the COUPP/PICO detectors. Moreover, a method for obtaining the real amplitude of the signal in time (voltage) by using cross correlation has been developed and tested and is described in this work.

  9. A local hidden variable model of quantum correlation exploiting the detection loophole

    OpenAIRE

    Gisin, N.; Gisin, B.

    1999-01-01

    A local hidden variable model exploiting the detection loophole to reproduce exactly the quantum correlation of the singlet state is presented. The model is shown to be compatible with both the CHSH and the CH Bell inequalities. Moreover, it bears the same rotational symmetry as spins. The reason why the model can reproduce the quantum correlation without violating the Bell theorem is that in the model the efficiency of the detectors depends on the local hidden variable. On average the detect...

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

    Science.gov (United States)

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

    2012-01-01

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

  11. Photonic crystal fiber based antibody detection

    DEFF Research Database (Denmark)

    Duval, A; Lhoutellier, M; Jensen, J B; Hoiby, P E; Missier, V; Pedersen, L H; Hansen, Theis Peter; Bjarklev, Anders Overgaard; Bang, Ole

    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 and...

  12. Immune based computer virus detection approaches

    Institute of Scientific and Technical Information of China (English)

    TAN Ying; ZHANG Pengtao

    2013-01-01

    The computer virus is considered one of the most horrifying threats to the security of computer systems worldwide.The rapid development of evasion techniques used in virus causes the signature based computer virus detection techniques to be ineffective.Many novel computer virus detection approaches have been proposed in the past to cope with the ineffectiveness,mainly classified into three categories:static,dynamic and heuristics techniques.As the natural similarities between the biological immune system (BIS),computer security system (CSS),and the artificial immune system (AIS) were all developed as a new prototype in the community of anti-virus research.The immune mechanisms in the BIS provide the opportunities to construct computer virus detection models that are robust and adaptive with the ability to detect unseen viruses.In this paper,a variety of classic computer virus detection approaches were introduced and reviewed based on the background knowledge of the computer virus history.Next,a variety of immune based computer virus detection approaches were also discussed in detail.Promising experimental results suggest that the immune based computer virus detection approaches were able to detect new variants and unseen viruses at lower false positive rates,which have paved a new way for the anti-virus research.

  13. Visualizing confusion matrices for multidimensional signal detection correlational methods

    Science.gov (United States)

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

    2013-12-01

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

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

    OpenAIRE

    Li, Bo; Kwek, Leong Chuan; Fan, Heng

    2012-01-01

    We propose a method to detect genuine quantum correlation for arbitrary quantum state 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 one. We demonstrate an approach to decompose the genuine quantum correlated state with high rank coefficient matrix into the form of product states with no g...

  15. Collaborative regression-based anatomical landmark detection

    Science.gov (United States)

    Gao, Yaozong; Shen, Dinggang

    2015-12-01

    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 & 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.

  16. Post detection Integration Analysis of Adaptive Detection of Partially-Correlated χ2 Targets in The Presence of Interferers

    Directory of Open Access Journals (Sweden)

    B., Mohamed

    2014-12-01

    Full Text Available The moderately fluctuating Rayleigh and 2 targets represent an important class of practical targets. The illumination of this class by a coherent pulse train will return a train of correlated pulses with a correlation coefficient in the range 01 (intermediate between SWII and SWI models in the case of Rayleigh targets.and (intermediate between SWIV and SWIII models in the case of 2 targets. Therefore, it is interesting to adaptively detect this class of partially-correlated targets. On the other hand, the constant false alarm rate in the presence of variable levels of noise is usually a requirement placed on any modern radar. The CA and OS schemes are the most familiar candidates in this category of detection techniques. Our goal in this paper is to analyze their detection performances for the case where the radar receiver post-detection integrates M pulses of an exponentially correlated signal from targets which exhibit 2 statistics with two and four degrees of freedom. Exact formulas for the detection probabilities are derived, in the absence as well as in the presence of spurious targets. As predicted, the CA detector has the best homogeneous performance while the OS scheme gives the best target multiplicity performance when the number of outlying targets is within its allowable values.

  17. Ontology-Based Textual Emotion Detection

    OpenAIRE

    Mohamed Haggag; Samar Fathy; Nahla Elhaggar

    2015-01-01

    Emotion Detection from text is a very important area of natural language processing. This paper shows a new method for emotion detection from text which depends on ontology. This method is depending on ontology extraction from the input sentence by using a triplet extraction algorithm by the OpenNLP parser, then make an ontology matching with the ontology base that we created by similarity and word sense disambiguation. This ontology base consists of ontologies and the emotion label related t...

  18. Biotoxin Detection Using Cell-Based Sensors

    OpenAIRE

    Pratik Banerjee; Spyridon Kintzios; Balabhaskar Prabhakarpandian

    2013-01-01

    Cell-based biosensors (CBBs) utilize the principles of cell-based assays (CBAs) by employing living cells for detection of different analytes from environment, food, clinical, or other sources. For toxin detection, CBBs are emerging as unique alternatives to other analytical methods. The main advantage of using CBBs for probing biotoxins and toxic agents is that CBBs respond to the toxic exposures in the manner related to actual physiologic responses of the vulnerable subjects. The results ob...

  19. 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.

  20. Post detection Integration Analysis of Adaptive Detection of Partially-Correlated χ2 Targets in The Presence of Interferers

    OpenAIRE

    B., Mohamed; El Mashade

    2014-01-01

    The moderately fluctuating Rayleigh and 2 targets represent an important class of practical targets. The illumination of this class by a coherent pulse train will return a train of correlated pulses with a correlation coefficient in the range 01 (intermediate between SWII and SWI models in the case of Rayleigh targets).and (intermediate between SWIV and SWIII models in the case of 2 targets). Therefore, it is interesting to adaptively detect this class of partially-correlated targets. On...

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

    International Nuclear Information System (INIS)

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

  2. 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

  3. 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%.

  4. Detection of a periodic structure embedded in surface roughness, for various correlation functions

    Indian Academy of Sciences (India)

    V C Vani; S Chatterjee

    2011-10-01

    This paper deals with surface profilometry, where we try to detect a periodic structure, hidden in randomness using the matched filter method of analysing the intensity of light, scattered from the surface. From the direct problem of light scattering from a composite rough surface of the above type, we find that the detectability of the periodic structure can be hindered by the randomness, being dependent on the correlation function of the random part. In our earlier works, we had concentrated mainly on the Cauchy-type correlation function for the rough part. In the present work, we show that this technique can determine the periodic structure of different kinds of correlation functions of the roughness, including Cauchy, Gaussian etc. We study the detection by the matched filter method as the nature of the correlation function is varied.

  5. Assessment of absolute added correlative coding in optical intensity modulation and direct detection channels

    Science.gov (United States)

    Dong-Nhat, Nguyen; Elsherif, Mohamed A.; Malekmohammadi, Amin

    2016-06-01

    The performance of absolute added correlative coding (AACC) modulation format with direct detection has been numerically and analytically reported, targeting metro data center interconnects. Hereby, the focus lies on the performance of the bit error rate, noise contributions, spectral efficiency, and chromatic dispersion tolerance. The signal space model of AACC, where the average electrical and optical power expressions are derived for the first time, is also delineated. The proposed modulation format was also compared to other well-known signaling, such as on-off-keying (OOK) and four-level pulse-amplitude modulation, at the same bit rate in a directly modulated vertical-cavity surface-emitting laser-based transmission system. The comparison results show a clear advantage of AACC in achieving longer fiber delivery distance due to the higher dispersion tolerance.

  6. Existence problem of optical correlation based pattern recognition

    Institute of Scientific and Technical Information of China (English)

    ZHANG; Yanxin(张延炘); LI; Sumei(李素梅)

    2003-01-01

    The existence problem of optical correlation based pattern recognition, namely its range of validity and its limitation, is discussed in this paper conjointly with the function approximation theory of neural networks. The conclusion is that only if the sets to be recognized are linearly separable (which is rare) or the subsets, in which a segmental sample of the targets is involved,are linearly separable, can the classical 4f optical correlation system carry out the task of recognition inerrably. The recognition principle of a joint transform correlator is the same as that of a 4f system, and so is its range of validities. Based on the demonstration of the existence problem of optical correlation based pattern recognition an evaluation on some important problems that were studied in this field over the past 40 years is presented explicitly.

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

    Science.gov (United States)

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

    2016-06-01

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

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

    Science.gov (United States)

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

    2014-03-01

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

  9. Web Based Cross Language Plagiarism Detection

    CERN Document Server

    Kent, Chow Kok

    2009-01-01

    As the Internet help us cross language and cultural border by providing different types of translation tools, cross language plagiarism, also known as translation plagiarism are bound to arise. Especially among the academic works, such issue will definitely affect the student's works including the quality of their assignments and paper works. In this paper, we propose a new approach in detecting cross language plagiarism. Our web based cross language plagiarism detection system is specially tuned to detect translation plagiarism by implementing different techniques and tools to assist the detection process. Google Translate API is used as our translation tool and Google Search API, which is used in our information retrieval process. Our system is also integrated with the fingerprint matching technique, which is a widely used plagiarism detection technique. In general, our proposed system is started by translating the input documents from Malay to English, followed by removal of stop words and stemming words, ...

  10. Dictionary based Approach to Edge Detection

    CERN Document Server

    Chandra, Nitish

    2015-01-01

    Edge detection is a very essential part of image processing, as quality and accuracy of detection determines the success of further processing. We have developed a new self learning technique for edge detection using dictionary comprised of eigenfilters constructed using features of the input image. The dictionary based method eliminates the need of pre or post processing of the image and accounts for noise, blurriness, class of image and variation of illumination during the detection process itself. Since, this method depends on the characteristics of the image, the new technique can detect edges more accurately and capture greater detail than existing algorithms such as Sobel, Prewitt Laplacian of Gaussian, Canny method etc which use generic filters and operators. We have demonstrated its application on various classes of images such as text, face, barcodes, traffic and cell images. An application of this technique to cell counting in a microscopic image is also presented.

  11. Recent advances in biosensor based endotoxin detection.

    Science.gov (United States)

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

    2014-01-15

    Endotoxins also referred to as pyrogens are chemically lipopolysaccharides habitually found in food, environment and clinical products of bacterial origin and are unavoidable ubiquitous microbiological contaminants. Pernicious issues of its contamination result in high mortality and severe morbidities. Standard traditional techniques are slow and cumbersome, highlighting the pressing need for evoking agile endotoxin detection system. The early and prompt detection of endotoxin assumes prime importance in health care, pharmacological and biomedical sectors. The unparalleled recognition abilities of LAL biosensors perched with remarkable sensitivity, high stability and reproducibility have bestowed it with persistent reliability and their possible fabrication for commercial applicability. This review paper entails an overview of various trends in current techniques available and other possible alternatives in biosensor based endotoxin detection together with its classification, epidemiological aspects, thrust areas demanding endotoxin control, commercially available detection sensors and a revolutionary unprecedented approach narrating the influence of omics for endotoxin detection. PMID:23934306

  12. Partially-Correlated χ2 Targets Detection Analysis of GTM-Adaptive Processor in the Presence of Outliers

    Directory of Open Access Journals (Sweden)

    Mohamed B. El Mashade

    2014-11-01

    Full Text Available This paper addresses the problem of detecting the partially-correlated χ2 fluctuating targets with two and four degrees of freedom. It presents the performance analysis, in its exact form, of GTM-CFAR processor when the operating environment is contaminated with extraneous targets and the radar receiver post-detection integrates M pulses of exponentially correlated targets. Mathematical formulas for the detection and false alarm probabilities are derived, in the absence as well as in the presence of spurious targets which are fluctuating in accordance with the so-called moderately fluctuating χ2 targets. A thorough performance assessment by several numerical examples, which has considered the role that each parameter can play in the processor performance, is also given. The results show that the processor performance improves, for weak SNR of the primary target, as the correlation coefficient ρs increases and this occurs either in the absence or in the presence of outlying targets. As the strength of the target return increases, the processor tends to invert this behavior. The SWI & SWII and SWIII & SWIV models enclose the correlated target cases when the target correlation follows χ2 fluctuation models with two and four degrees of freedom, respectively, and this behavior is common for all GTM based detectors.

  13. Partially-Correlated χ2 Targets Detection Analysis of GTM-Adaptive Processor in the Presence of Outliers

    Directory of Open Access Journals (Sweden)

    Mohamed B. El Mashade

    2014-10-01

    Full Text Available This paper addresses the problem of detecting the partially-correlated χ2 fluctuating targets with two and four degrees of freedom. It presents the performance analysis, in its exact form, of GTM-CFAR processor when the operating environment is contaminated with extraneous targets and the radar receiver post-detection integrates M pulses of exponentially correlated targets. Mathematical formulas for the detection and false alarm probabilities are derived, in the absence as well as in the presence of spurious targets which are fluctuating in accordance with the so-called moderately fluctuating χ2 targets. A thorough performance assessment by several numerical examples, which has considered the role that each parameter can play in the processor performance, is also given. The results show that the processor performance improves, for weak SNR of the primary target, as the correlation coefficient ρs increases and this occurs either in the absence or in the presence of outlying targets. As the strength of the target return increases, the processor tends to invert this behavior. The SWI & SWII and SWIII & SWIV models enclose the correlated target cases when the target correlation follows χ2 fluctuation models with two and four degrees of freedom, respectively, and this behavior is common for all GTM based detectors.

  14. 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...

  15. Web Based Cross Language Plagiarism Detection

    OpenAIRE

    Kent, Chow Kok; Salim, Naomie

    2009-01-01

    As the Internet help us cross language and cultural border by providing different types of translation tools, cross language plagiarism, also known as translation plagiarism are bound to arise. Especially among the academic works, such issue will definitely affect the student's works including the quality of their assignments and paper works. In this paper, we propose a new approach in detecting cross language plagiarism. Our web based cross language plagiarism detection system is specially t...

  16. Verification-based Software-fault Detection

    OpenAIRE

    Gladisch, Christoph David

    2011-01-01

    Software is used in many safety- and security-critical systems. Software development is, however, an error-prone task. In this dissertation new techniques for the detection of software faults (or software "bugs") are described which are based on a formal deductive verification technology. The described techniques take advantage of information obtained during verification and combine verification technology with deductive fault detection and test generation in a very unified way.

  17. 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...... registered document. Our plagiarism detection system, like many Information Retrieval systems, is evaluated with metrics of precision and recall....

  18. Anomaly Detection in Electroencephalogram Signals Using Unconstrained Minimum Average Correlation Energy Filter

    Directory of Open Access Journals (Sweden)

    Aini Hussain

    2009-01-01

    Full Text Available Problem statement: Electroencepharogram (EEG is an extremely complex signal with very low signal to noise ratio and these attributed to difficulty in analyzing the signal. Hence for detecting abnormal segment, a distinctive method is required to train the technologist to distinguish the anomalous in EEG data. The objective of this study was to create a framework to analyze EEG signals recorded from epileptic patients by evaluating the potential of UMACE filter to detect changes in single-channel EEG data during routine epilepsy monitoring. Approach: Normally, the peak to side lobe ratio (PSR of a UMACE filter was employed as an indicator if a test data is similar to an authentic class or vice versa, however in this study, the consistent changes of the correlation output known as Region Of Interest (ROI was plotted and monitored. Based on this approach, a novel method to analyze and distinguish variances in scalp EEG as well as comparing both normal and abnormal regions of the patient’s EEG was assessed. The performance of the novelty detection was examined based on the onset and end time of each seizure in the ROI plot. Results: Results showed that using ROI plot of variances one can distinguish irregularities in the EEG data. The advantage of the proposed technique was that it did not require large amount of data for training. Conclusion: As such, it was feasible to perform seizure analysis as well as localizing seizure onsets. In short, the technique can be used as a guideline for faster diagnosis in a lengthy EEG recording.

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

    Science.gov (United States)

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

    2010-10-01

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

  20. Color-image retrieval based on fuzzy correlation

    Institute of Scientific and Technical Information of China (English)

    ZHAI Hongchen; LIANG Yanmei; MU Guoguang

    2004-01-01

    We report a method of color-image retrieval based on fuzzy correlation, in which α-cut relations in fuzzy set theory are applied to defining color match and height match of color peaks for synthesizing fuzzy correlation of two color histograms, and RGB space is partitioned into six sub-regions in the experiment for the regional color comparisons. Experimental results show that the efficiency of the color-image retrieval can be effectively improved by this approach.

  1. Speedy Object Detection Based on Shape

    Directory of Open Access Journals (Sweden)

    Y. Jayanta Singh

    2013-07-01

    Full Text Available This study is a part of design of an audio system for in-house object detection system for visually impaired,low vision personnel by birth or by an accident ordue to old age. The input of the system will be scene andoutput as audio. Alert facility is provided based on severity levels of the objects (snake, broke glass etc andalso during difficulties. The study proposed techniques to provide speedy detection of objects based onshapes and its scale. Features are extraction to have minimum spaces using dynamic scaling. From ascene, clusters of objects are formed based on thescale and shape. Searching is performed among theclusters initially based on the shape, scale, meancluster value and index of object(s. The minimumoperation to detect the possible shape of the object is performed. In case the object does not have alikelymatching shape, scale etc, then the several operations required for an object detection will not perform;instead, it will declared as a new object. In suchway, this study finds a speedy way of detecting objects.

  2. SPEEDY OBJECT DETECTION BASED ON SHAPE

    Directory of Open Access Journals (Sweden)

    Y. Jayanta Singh

    2013-06-01

    Full Text Available This study is a part of design of an audio system for in-house object detection system for visually impaired, low vision personnel by birth or by an accident or due to old age. The input of the system will be scene and output as audio. Alert facility is provided based on severity levels of the objects (snake, broke glass etc and also during difficulties. The study proposed techniques to provide speedy detection of objects based on shapes and its scale. Features are extraction to have minimum spaces using dynamic scaling. From a scene, clusters of objects are formed based on the scale and shape. Searching is performed among the clusters initially based on the shape, scale, mean cluster value and index of object(s. The minimum operation to detect the possible shape of the object is performed. In case the object does not have a likely matching shape, scale etc, then the several operations required for an object detection will not perform; instead, it will declared as a new object. In such way, this study finds a speedy way of detecting objects.

  3. Effective information spreading based on local information in correlated networks

    CERN Document Server

    Gao, Lei; Pan, Liming; Tang, Ming; Zhang, Hai-Feng

    2016-01-01

    Using network-based information to facilitate information spreading is an essential task for spreading dynamics in complex networks, which will benefit the promotion of technical innovations, healthy behaviors, new products, etc. Focusing on degree correlated networks, we propose a preferential contact strategy based on the local network structure and local informed density to promote the information spreading. During the spreading process, an informed node will preferentially select a contact target among its neighbors, basing on their degrees or local informed densities. By extensively implementing numerical simulations in synthetic and empirical networks, we find that when only consider the local structure information, the convergence time of information spreading will be remarkably reduced if low-degree neighbors are favored as contact targets. Meanwhile, the minimum convergence time depends non-monotonically on degree-degree correlation, and moderate correlation coefficients result in most efficient info...

  4. 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.

  5. Experimental detection of non-classical correlations in mixed state quantum computation

    OpenAIRE

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

    2011-01-01

    We report on an experiment to detect non-classical 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 vanish...

  6. Repeat Sequences and Base Correlations in Human Y Chromosome Palindromes

    Institute of Scientific and Technical Information of China (English)

    Neng-zhi Jin; Zi-xian Liu; Yan-jiao Qi; Wen-yuan Qiu

    2009-01-01

    On the basis of information theory and statistical methods, we use mutual information, n-tuple entropy and conditional entropy, combined with biological characteristics, to analyze the long range correlation and short range correlation in human Y chromosome palindromes. The magnitude distribution of the long range correlation which can be reflected by the mutual information is P5>P5a>P5b (P5a and P5b are the sequences that replace solely Alu repeats and all interspersed repeats with random uncorrelated sequences in human Y chromosome palindrome 5, respectively); and the magnitude distribution of the short range correlation which can be reflected by the n-tuple entropy and the conditional entropy is P5>P5a>P5b>random uncorrelated sequence. In other words, when the Alu repeats and all interspersed repeats replace with random uncorrelated sequence, the long range and short range correlation decrease gradually. However, the random uncorrelated sequence has no correlation. This research indicates that more repeat sequences result in stronger correlation between bases in human Y chromosome. The analyses may be helpful to understand the special structures of human Y chromosome palindromes profoundly.

  7. 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.

  8. Low-cost microprocessor-based photon correlator

    Science.gov (United States)

    Murthy, N. S.; Choudhary, D. M.

    1983-04-01

    A simple cost-effective microprocessor-based correlator is described which can be used for Gaussian as well as non-Gaussian light sources. Error calculations are presented to show that there is no significant improvement in accuracy by adopting 4-bit word length in preference to 3-bit word length. The instrument can also be used in Raman and Raleigh scattering experiments. A few experimental results are presented bringing out the importance of correlation averaging in S/N enhancement. Some autocorrelograms for fluctuations in the scattered light from polystyrene spheres suspended in water are also presented. The instrument can sample 1500 points and calculate 85 correlations in each scan. All the parameters such as number of samples, number of correlations, number of scans, and the sampling time are user programmable.

  9. Expanded fluid based viscosity correlation : diluted heavy oil case study

    Energy Technology Data Exchange (ETDEWEB)

    Yarranton, H.; Satyro, M.A.; Schoeggl, F. [Calgary Univ., AB (Canada). Dept. of Chemical and Petroleum Engineering

    2009-07-01

    The viscosity of pure hydrocarbons has been correlated using a simple function involving fluid density, low pressure gas viscosity and pressure. The correlation was developed based on measured densities from the NIST database. The correlation has been refit to use densities predicted from the Advanced Peng-Robinson equation of state. The usefulness of the correlation was shown for pure hydrocarbons such as n-alkanes, branched alkanes, alkenes, cyclics and aromatics as well as binary mixtures of pure hydrocarbons. This presentation included a case study on the viscosity of heavy oil diluted with solvent. The newly proposed, simple and quick method for calculating liquid and vapour viscosities was found to be suitable for incorporation into process and reservoir simulators. tabs., figs.

  10. Ontology-Based Textual Emotion Detection

    Directory of Open Access Journals (Sweden)

    Mohamed Haggag

    2015-09-01

    Full Text Available Emotion Detection from text is a very important area of natural language processing. This paper shows a new method for emotion detection from text which depends on ontology. This method is depending on ontology extraction from the input sentence by using a triplet extraction algorithm by the OpenNLP parser, then make an ontology matching with the ontology base that we created by similarity and word sense disambiguation. This ontology base consists of ontologies and the emotion label related to each one. We choose the emotion label of the sentence with the highest score of matching. If the extracted ontology doesn’t match any ontology from the ontology base we use the keyword-based approach. This method doesn’t depend only on keywords like previous approaches; it depends on the meaning of sentence words and the syntax and semantic analysis of the context.

  11. Damage detection of metro tunnel structure through transmissibility function and cross correlation analysis using local excitation and measurement

    Science.gov (United States)

    Feng, Lei; Yi, Xiaohua; Zhu, Dapeng; Xie, Xiongyao; Wang, Yang

    2015-08-01

    In a modern metropolis, metro rail systems have become a dominant mode for mass transportation. The structural health of a metro tunnel is closely related to public safety. Many vibration-based techniques for detecting and locating structural damage have been developed in the past several decades. However, most damage detection techniques and validation tests are focused on bridge and building structures; very few studies have been reported on tunnel structures. Among these techniques, transmissibility function and cross correlation analysis are two well-known diagnostic approaches. The former operates in frequency domain and the latter in time domain. Both approaches can be applied to detect and locate damage through acceleration data obtained from sensor arrays. Furthermore, the two approaches can directly utilize structural response data without requiring excitation measurement, which offers advantages in field testing on a large structure. In this research, a numerical finite element model of a metro tunnel is built and different types of structural defects are introduced at multiple locations of the tunnel. Transmissibility function and cross correlation analysis are applied to perform structural damage detection and localization, based on simulated structural vibration data. Numerical results demonstrate that the introduced defects can be successfully identified and located. The sensitivity and feasibility of the two approaches have been verified when sufficient distribution of measurement locations is available. Damage detection results of the two different approaches are compared and discussed.

  12. Correlating hardware fault detection information from distributed control systems to isolate and diagnose a fault in pressurised water reactors

    International Nuclear Information System (INIS)

    Highlights: ► Attempt was to use available resources at a nuclear plant in a value added fashion. ► Includes plant measurement data and plant training and engineering simulator capabilities. ► Correlating fault detection data for systems to develop of a deterministic fault identifications system. ► After implementing a host of data manipulation algorithms, the results provided more information on the fault than expected. - Abstract: Early fault identification systems enable detecting and diagnosing early onset faults or fault causes which allow maintenance planning on the equipment showing signs of deterioration or failure. This includes valve and leaks and small cracks in steam generator tubes usually detected by means of ultrasonic inspection. We have shown (Cilliers and Mulder, 2012) that detecting faults early during transient operation in NPPs is possible when coupled with a reliable reference to compare plant measurements with during transients. The problem introduced by the distributed application of control systems operating independently to keep the plant operating within the safe operating boundaries was solved by re-introducing the fault information it into the measurement data, thereby improving plant diagnostic performance. This paper introduces the use of improved fault detection information received from all distributed systems in the plant control system and correlating the information to not only detect the fault but also to diagnose it based on the location and magnitude of the fault cause

  13. Combined zero-quantum and spin-diffusion mixing for efficient homonuclear correlation spectroscopy under fast MAS: broadband recoupling and detection of long-range correlations

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Xingyu, E-mail: luxingyu@udel.edu; Guo, Changmiao, E-mail: cmguo@udel.edu; Hou, Guangjin, E-mail: hou@udel.edu; Polenova, Tatyana, E-mail: tpolenov@udel.edu [University of Delaware, Department of Chemistry and Biochemistry (United States)

    2015-01-15

    Fast magic angle spinning (MAS) NMR spectroscopy is emerging as an essential analytical and structural biology technique. Large resolution and sensitivity enhancements observed under fast MAS conditions enable structural and dynamics analysis of challenging systems, such as large macromolecular assemblies and isotopically dilute samples, using only a fraction of material required for conventional experiments. Homonuclear dipolar-based correlation spectroscopy constitutes a centerpiece in the MAS NMR methodological toolbox, and is used essentially in every biological and organic system for deriving resonance assignments and distance restraints information necessary for structural analysis. Under fast MAS conditions (rotation frequencies above 35–40 kHz), dipolar-based techniques that yield multi-bond correlations and non-trivial distance information are ineffective and suffer from low polarization transfer efficiency. To overcome this limitation, we have developed a family of experiments, CORD–RFDR. These experiments exploit the advantages of both zero-quantum RFDR and spin-diffusion based CORD methods, and exhibit highly efficient and broadband dipolar recoupling across the entire spectrum, for both short-range and long-range correlations. We have verified the performance of the CORD–RFDR sequences experimentally on a U-{sup 13}C,{sup 15}N-MLF tripeptide and by numerical simulations. We demonstrate applications of 2D CORD–RFDR correlation spectroscopy in dynein light chain LC8 and HIV-1 CA tubular assemblies. In the CORD–RFDR spectra of LC8 acquired at the MAS frequency of 40 kHz, many new intra- and inter-residue correlations are detected, which were not observed with conventional dipolar recoupling sequences. At a moderate MAS frequency of 14 kHz, the CORD–RFDR experiment exhibits excellent performance as well, as demonstrated in the HIV-1 CA tubular assemblies. Taken together, the results indicate that CORD–RFDR experiment is beneficial in a

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

    OpenAIRE

    Jaroslaw Kwapien; Pawel Oswiecimka; Stanislaw Drozdz

    2015-01-01

    The detrended cross-correlation coefficient $\\rho_{\\rm DCCA}$ has recently been proposed to quantify the strength of cross-correlations on different temporal scales in bivariate, non-stationary time series. It is based on the detrended cross-correlation and detrended fluctuation analyses (DCCA and DFA, respectively) and can be viewed as an analogue of the Pearson coefficient in the case of the fluctuation analysis. The coefficient $\\rho_{\\rm DCCA}$ works well in many practical situations but ...

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

    OpenAIRE

    Haijian Zhang; Didier Le Ruyet; Michel Terré

    2010-01-01

    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 signa...

  16. Cytohistopathological correlation of Papanicolaou smears: a hospital based study

    Directory of Open Access Journals (Sweden)

    Purwa Rangrao Patil

    2016-06-01

    Conclusions: The study provides good cytohistopathological correlation especially for high grade lesions. So we believe that the success of screening for cervical cancer is based on collection of adequate materials and correct interpretation of abnormal cells. [Int J Reprod Contracept Obstet Gynecol 2016; 5(6.000: 1695-1699

  17. Decision Tree Based Algorithm for Intrusion Detection

    Directory of Open Access Journals (Sweden)

    Kajal Rai

    2016-01-01

    Full Text Available An Intrusion Detection System (IDS is a defense measure that supervises activities of the computer network and reports the malicious activities to the network administrator. Intruders do many attempts to gain access to the network and try to harm the organization’s data. Thus the security is the most important aspect for any type of organization. Due to these reasons, intrusion detection has been an important research issue. An IDS can be broadly classified as Signature based IDS and Anomaly based IDS. In our proposed work, the decision tree algorithm is developed based on C4.5 decision tree approach. Feature selection and split value are important issues for constructing a decision tree. In this paper, the algorithm is designed to address these two issues. The most relevant features are selected using information gain and the split value is selected in such a way that makes the classifier unbiased towards most frequent values. Experimentation is performed on NSL-KDD (Network Security Laboratory Knowledge Discovery and Data Mining dataset based on number of features. The time taken by the classifier to construct the model and the accuracy achieved is analyzed. It is concluded that the proposed Decision Tree Split (DTS algorithm can be used for signature based intrusion detection.

  18. Microwave-Based Biosensor for Glucose Detection

    Science.gov (United States)

    Salim, N. S. M.; Khalid, K.; Yusof, N. A.

    2010-07-01

    In this project, microwave-based biosensor for glucose detection has been studied. The study is based on the dielectric properties changes at microwave frequency for glucose-enzyme reaction. Glucose interaction with glucose oxidase (GOD) produced gluconic acid and hydrogen peroxide. The reaction of the glucose solutions with an enzyme was carried out in 1:3 of glucose and enzyme respectively. The measurements were done using the Open Ended Coaxial Probe (OECP) coupled with computer controlled software automated network analyzer (ANA) with frequency range from 200MHz to 20GHz at room temperature (25 °C). The differences of enzyme and glucose-enzyme reaction were calculated and plotted. In the microwave interaction with the glucose-enzyme reaction, ionic conduction and dipole molecules was detected at 0.99GHz and 16.44GHz respectively based on changes of dielectric loss factor.

  19. Microcomputer-based video motion detection system

    International Nuclear Information System (INIS)

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

  20. Differential Search Algorithm Based Edge Detection

    Science.gov (United States)

    Gunen, M. A.; Civicioglu, P.; Beşdok, E.

    2016-06-01

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

  1. Copula-based dynamic conditional correlation multiplicative error processes

    OpenAIRE

    Bodnar, Taras; Hautsch, Nikolaus

    2013-01-01

    We introduce a copula-based dynamic model for multivariate processes of (non-negative) high-frequency trading variables revealing time-varying conditional variances and correlations. Modeling the variables’ conditional mean processes using a multiplicative error model we map the resulting residuals into a Gaussian domain using a Gaussian copula. Based on high-frequency volatility, cumulative trading volumes, trade counts and market depth of various stocks traded at the NYSE, we show that the ...

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

    International Nuclear Information System (INIS)

    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)

  3. Combination of Micro-fluidic Chip with Fluorescence Correlation Spectroscopy for Single Molecule Detection

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A single molecule detection technique was developed by the combination of a single channel poly (dimethylsiloxane)/glass micro-fluidic chip and fluorescence correlation spectroscopy (FCS). This method was successfully used to determine the proportion of two model components in the mixture containing fluorescein and the rhodamine-green succinimidyl ester.

  4. CTA in the detection and quantification of vertebral artery pathologies: a correlation with color Doppler sonography

    Energy Technology Data Exchange (ETDEWEB)

    Puchner, Stefan; Rand, Thomas; Reiter, Markus; Lammer, Johannes; Bucek, Robert A. [Medical University Vienna, Department of Cardiovascular and Interventional Radiology, Vienna (Austria); Haumer, Markus; Minar, Erich [Medical University Vienna, Department of Angiology, Vienna (Austria)

    2007-08-15

    We evaluated the feasibility of multidetector CT angiography (MDCTA) in the examination of vertebral artery (VA) pathologies and correlated the results with those of color Doppler sonography (CDS). In this retrospective cohort analysis, we identified 65 patients with suspected cerebrovascular disease, who underwent MDCTA and CDS of the supraaortic vessels within a maximum period of 1 month. We evaluated the feasibility and image quality of MDCTA in this indication, compared the value of reformatted images and axial source images in the grading of stenoses and correlated these results with those of CDS. The image quality of the MDCTA examination was classified as good in 64 patients (98.5%) and as moderate in 1 patient (1.5%). Axial source images and reformatted images agreed perfectly in terms of stenosis detection and grading as well as the detection of hypoplastic VAs ({kappa} = 1). The correlation between MDCTA and CDS was moderate ({kappa} = 0.56) in terms of stenosis detection and quantification and poor ({kappa} = 0.35) in terms of detection of hypoplasia of the VA. MDCTA is a feasible method for the evaluation of VA pathologies providing a good image quality. Image reformatting does not add any diagnostic value to the interpretation of axial source images. The correlation between MDCTA and CDS is only moderate, reflecting the clinically important limitations of CDS in this indication. (orig.)

  5. Sideband correlation algorithm to detect phase shift and contrast variation in temporal phase-shifting interferometry

    International Nuclear Information System (INIS)

    Phase shift error and contrast variation caused by vibration lead to a large measurement error in temporal phase-shifting interferometry (PSI). To suppress the error, a sideband correlation algorithm is proposed to detect phase shift and contrast variation. The tilt factors and translational values of phase shift are determined by analyzing the correlations of spectral sidebands of interferograms. The contrast variations are determined by detecting the modulus of the baseband and sideband correlation result. A least-squares equation with contrast compensation is established to retrieve the wavefront phase. The algorithm requires a set of temporal phase-shifting interferograms, each one also containing a moderate amount of spatial-carrier; the interferograms may have an arbitrary aperture. Simulations demonstrate the reliability, and the experiments under vibration show the practical effectiveness of the algorithm. (paper)

  6. Detectability of trace gases in the Martian atmosphere using gas correlation filter radiometry

    Science.gov (United States)

    Sinclair, J.; Irwin, P. G. J.; Wilson, E.; Calcutt, S.

    2015-10-01

    We present the results of radiative transfer simulations of a gas correlation filter radiometer (GCFR) in the detection of trace species in the Martian atmosphere. We investigated two scenarios: 1) nadir and/or limb sounding from a Mars orbiter in the thermal infrared, 2) solar occultation measurements in the near-infrared from the Martian surface. In both scenarios, a GCFR would allow detection of trace gases at a lower concentration than that detectable by a conventional filter radiometer. In nadir/limb sounding, we find that CH4, SO2, N2O, C2H2 and CH3OH are detectable at concentrations lower than previously-derived upper limits. From solar occultation measurements, we find that CH4, SO2, C2H2, C2H6 are detectable at concentrations lower than previously-derived upper limits but only in low dust conditions.

  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......: to show whether medical signal processing of EMG data is feasible for detection of epileptic seizures. Methods: EMG signals during generalised seizures were recorded from 3 patients (with 20 seizures in total). Two possible medical signal processing algorithms were tested. The first algorithm was...... 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. A Web Based Cardiovascular Disease Detection System.

    Science.gov (United States)

    Alshraideh, Hussam; Otoom, Mwaffaq; Al-Araida, Aseel; Bawaneh, Haneen; Bravo, José

    2015-10-01

    Cardiovascular Disease (CVD) is one of the most catastrophic and life threatening health issue nowadays. Early detection of CVD is an important solution to reduce its devastating effects on health. In this paper, an efficient CVD detection algorithm is identified. The algorithm uses patient demographic data as inputs, along with several ECG signal features extracted automatically through signal processing techniques. Cross-validation results show a 98.29 % accuracy for the decision tree classification algorithm. The algorithm has been integrated into a web based system that can be used at anytime by patients to check their heart health status. At one end of the system is the ECG sensor attached to the patient's body, while at the other end is the detection algorithm. Communication between the two ends is done through an Android application. PMID:26293754

  9. Nanomaterials based biosensors for cancer biomarker detection

    Science.gov (United States)

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

    2016-04-01

    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.

  10. DATA-MINING BASED FAULT DETECTION

    Institute of Scientific and Technical Information of China (English)

    Ma Hongguang; Han Chongzhao; Wang Guohua; Xu Jianfeng; Zhu Xiaofei

    2005-01-01

    This paper presents a fault-detection method based on the phase space reconstruction and data mining approaches for the complex electronic system. The approach for the phase space reconstruction of chaotic time series is a combination algorithm of multiple autocorrelation and Γ-test, by which the quasi-optimal embedding dimension and time delay can be obtained.The data mining algorithm, which calculates the radius of gyration of unit-mass point around the centre of mass in the phase space, can distinguish the fault parameter from the chaotic time series output by the tested system. The experimental results depict that this fault detection method can correctly detect the fault phenomena of electronic system.

  11. Regional principal color based saliency detection.

    Science.gov (United States)

    Lou, Jing; Ren, Mingwu; Wang, Huan

    2014-01-01

    Saliency detection is widely used in many visual applications like image segmentation, object recognition and classification. In this paper, we will introduce a new method to detect salient objects in natural images. The approach is based on a regional principal color contrast modal, which incorporates low-level and medium-level visual cues. The method allows a simple computation of color features and two categories of spatial relationships to a saliency map, achieving higher F-measure rates. At the same time, we present an interpolation approach to evaluate resulting curves, and analyze parameters selection. Our method enables the effective computation of arbitrary resolution images. Experimental results on a saliency database show that our approach produces high quality saliency maps and performs favorably against ten saliency detection algorithms. PMID:25379960

  12. AGENT BASED INTRUSION DETECTION SYSTEM IN MANET

    Directory of Open Access Journals (Sweden)

    J. K. Mandal

    2013-02-01

    Full Text Available In this paper a technique for intrusion detection in MANET has been proposed where agents are fired from a node which traverses each node randomly and detect the malicious node. Detection is based on triangular encryption technique (TE where AODV is taken as routing protocol. For simulation we have taken NS2 (2.33 where two type of parameters are considered out of which number of nodes and percentage of node mobility are the attributes. For analysis purpose 20, 30, 30, 40, 50 and 60 nodes are taken with a variable percentage of malicious node as 0 %( no malicious, 10%, 20%, 30% and 40%. Analysis have been done taking generated packets, forwarded packets, delay, and average delay as parameters

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

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Kolodziejski, Christoph; Wörgötter, Florentin;

    2013-01-01

    associations. Based on these biological findings, we propose a new learning model to achieve successful control policies for artificial systems. This model combines correlation-based learning using input correlation learning (ICO learning) and reward-based learning using continuous actor–critic reinforcement...

  14. 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....... This paper presents a novel method with a goal of determining which alerts are correlated, by applying Neural Networks and clustering, thus reducing the number of alerts to manually process. The main advantage of the method is that no domain knowledge is required for designing feature extraction or any...... other part, as such knowledge is inferred by Neural Networks. Evaluation has been performed with traffic traces of real bot binaries executed in a lab setup. The method is trained on labelled Intrusion Detection System alerts and is capable of correctly predicting which of seven incidents an alert...

  15. Shearlet-based detection of flame fronts

    Science.gov (United States)

    Reisenhofer, Rafael; Kiefer, Johannes; King, Emily J.

    2016-03-01

    Identifying and characterizing flame fronts is the most common task in the computer-assisted analysis of data obtained from imaging techniques such as planar laser-induced fluorescence (PLIF), laser Rayleigh scattering (LRS), or particle imaging velocimetry (PIV). We present Complex Shearlet-Based Ridge and Edge Measure (CoShREM), a novel edge and ridge (line) detection algorithm based on complex-valued wavelet-like analyzing functions—so-called complex shearlets—displaying several traits useful for the extraction of flame fronts. In addition to providing a unified approach to the detection of edges and ridges, our method inherently yields estimates of local tangent orientations and local curvatures. To examine the applicability for high-frequency recordings of combustion processes, the algorithm is applied to mock images distorted with varying degrees of noise and real-world PLIF images of both OH and CH radicals. Furthermore, we compare the performance of the newly proposed complex shearlet-based measure to well-established edge and ridge detection techniques such as the Canny edge detector, another shearlet-based edge detector, and the phase congruency measure.

  16. Hydrocarbon microseepage mapping using signature based target detection techniques

    Science.gov (United States)

    Soydan, Hilal; Koz, Alper; Şebnem Düzgün, H.; Aydin Alatan, A.

    2015-10-01

    In this paper, we compare the conventional methods in hydrocarbon seepage anomalies with the signature based detection algorithms. The Crosta technique [1] is selected as a basement in the experimental comparisons for the conventional approach. The Crosta technique utilizes the characteristic bands of the searched target for principal component transformation in order to determine the components characterizing the target in interest. Desired Target Detection and Classification Algorithm (DTDCA), Spectral Matched Filter (SMF), and Normalized Correlation (NC) are employed for signature based target detection. Signature based target detection algorithms are applied to the whole spectrum benefiting from the information stored in all spectral bands. The selected methods are applied to a multispectral Advanced SpaceBorne Thermal Emission and Radiometer (ASTER) image of the study region, with an atmospheric correction prior to the realization of the algorithms. ASTER provides multispectral bands covering visible, short wave, and thermal infrared region, which serves as a useful tool for the interpretation of the areas with hydrocarbon anomalies. The exploration area is selected as Gemrik Anticline which is located in South East Anatolia, Adıyaman, Bozova Oil Field, where microseeps can be observed with almost no vegetation cover. The spectral signatures collected with Analytical Spectral Devices Inc. (ASD) spectrometer from the reference valley [2] have been utilized as an input to the signature based detection algorithms. The experiments have indicated that DTDCA and MF outperforms the Crosta technique by locating the microseepage patterns along the mitigation pathways with a better contrast. On the other hand, NC has not been able to map the searched target with a visible distinction. It is concluded that the signature based algorithms can be more effective than the conventional methods for the detection of microseepage induced anomalies.

  17. 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.

  18. Wavelet based detection of manatee vocalizations

    Science.gov (United States)

    Gur, Berke M.; Niezrecki, Christopher

    2005-04-01

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

  19. 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.

  20. 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

  1. Cluster Based Cost Efficient Intrusion Detection System For Manet

    OpenAIRE

    Kumarasamy, Saravanan; B, Hemalatha; P, Hashini

    2013-01-01

    Mobile ad-hoc networks are temporary wireless networks. Network resources are abnormally consumed by intruders. Anomaly and signature based techniques are used for intrusion detection. Classification techniques are used in anomaly based techniques. Intrusion detection techniques are used for the network attack detection process. Two types of intrusion detection systems are available. They are anomaly detection and signature based detection model. The anomaly detection model uses the historica...

  2. Detecting a stochastic background of gravitational waves by correlating n detectors

    International Nuclear Information System (INIS)

    We discuss the optimal detection strategy for a stochastic background of gravitational waves in the case n detectors are available. In the literature so far, only two cases have been considered: 2- and n-point correlators. We generalize these analyses to m-point correlators (with m < n) built out of the n detector signals, obtaining the result that the optimal choice is to combine 2-point correlators. Correlating n detectors in this optimal way will improve the (suitably defined) signal-to-noise ratio with respect to the n 2 case by a factor equal to the fourth root of n(n - 1)/2. Finally, we give an estimation of how this could improve the sensitivity for a network of multi-mode spherical antennas

  3. On the detectability of trace chemical species in the martian atmosphere using gas correlation filter radiometry

    Science.gov (United States)

    Sinclair, J. A.; Irwin, P. G. J.; Calcutt, S. B.; Wilson, E. L.

    2015-11-01

    The martian atmosphere is host to many trace gases including water (H2O) and its isotopologues, methane (CH4) and potentially sulphur dioxide (SO2), nitrous oxide (N2O) and further organic compounds, which would serve as indirect tracers of geological, chemical and biological processes on Mars. With exception of the recent detection of CH4 by Curiosity, previous detections of these species have been unsuccessful or considered tentative due to the low concentrations of these species in the atmosphere (∼10-9 partial pressures), limited spectral resolving power and/or signal-to-noise and the challenge of discriminating between telluric and martian features when observing from the Earth. In this study, we present radiative transfer simulations of an alternative method for detection of trace gas species - the gas correlation radiometry method. Two potential observing scenarios were explored where a gas correlation filter radiometer (GCFR) instrument: (1) performs nadir and/or limb sounding of the martian atmosphere in the thermal infrared (200-2000 cm-1 from an orbiting spacecraft or (2) performs solar occultation measurements in the near-infrared (2000-5000 cm-1) from a lander on the martian surface. In both scenarios, simulations of a narrowband filter radiometer (without gas correlation) were also generated to serve as a comparison. From a spacecraft, we find that a gas correlation filter radiometer, in comparison to a filter radiometer (FR), offers a greater discrimination between temperature and dust, a greater discrimination between H2O and HDO, and would allow detection of N2O and CH3OH at concentrations of ∼10 ppbv and ∼2 ppbv, respectively, which are lower than previously-derived upper limits. However, the lowest retrievable concentration of SO2 (approximately 2 ppbv) is comparable with previous upper limits and CH4 is only detectable at concentrations of approximately 10 ppbv, which is an order of magnitude higher than the concentration recently measured

  4. 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.

  5. General SIC measurement-based entanglement detection

    Science.gov (United States)

    Chen, Bin; Li, Tao; Fei, Shao-Ming

    2015-06-01

    We study the quantum separability problem by using general symmetric informationally complete measurements and present separability criteria for both -dimensional bipartite and multipartite systems. The criterion for bipartite quantum states is effective in detecting several well-known classes of quantum states. For isotropic states, it becomes both necessary and sufficient. Furthermore, our criteria can be experimentally implemented, and the criterion for two-qudit states requires less local measurements than the one based on mutually unbiased measurements.

  6. Raman LIDAR Detection of Cloud Base

    Science.gov (United States)

    Demoz, Belay; Starr, David; Whiteman, David; Evans, Keith; Hlavka, Dennis; Peravali, Ravindra

    1999-01-01

    Advantages introduced by Raman lidar systems for cloud base determination during precipitating periods are explored using two case studies of light rain and virga conditions. A combination of the Raman lidar derived profiles of water vapor mixing ratio and aerosol scattering ratio, together with the Raman scattered signals from liquid drops, can minimize or even eliminate some of the problems associated with cloud boundary detection using elastic backscatter lidars.

  7. Biotoxin detection using cell-based sensors.

    Science.gov (United States)

    Banerjee, Pratik; Kintzios, Spyridon; Prabhakarpandian, Balabhaskar

    2013-12-01

    Cell-based biosensors (CBBs) utilize the principles of cell-based assays (CBAs) by employing living cells for detection of different analytes from environment, food, clinical, or other sources. For toxin detection, CBBs are emerging as unique alternatives to other analytical methods. The main advantage of using CBBs for probing biotoxins and toxic agents is that CBBs respond to the toxic exposures in the manner related to actual physiologic responses of the vulnerable subjects. The results obtained from CBBs are based on the toxin-cell interactions, and therefore, reveal functional information (such as mode of action, toxic potency, bioavailability, target tissue or organ, etc.) about the toxin. CBBs incorporate both prokaryotic (bacteria) and eukaryotic (yeast, invertebrate and vertebrate) cells. To create CBB devices, living cells are directly integrated onto the biosensor platform. The sensors report the cellular responses upon exposures to toxins and the resulting cellular signals are transduced by secondary transducers generating optical or electrical signals outputs followed by appropriate read-outs. Examples of the layout and operation of cellular biosensors for detection of selected biotoxins are summarized. PMID:24335754

  8. Biotoxin Detection Using Cell-Based Sensors

    Directory of Open Access Journals (Sweden)

    Pratik Banerjee

    2013-11-01

    Full Text Available Cell-based biosensors (CBBs utilize the principles of cell-based assays (CBAs by employing living cells for detection of different analytes from environment, food, clinical, or other sources. For toxin detection, CBBs are emerging as unique alternatives to other analytical methods. The main advantage of using CBBs for probing biotoxins and toxic agents is that CBBs respond to the toxic exposures in the manner related to actual physiologic responses of the vulnerable subjects. The results obtained from CBBs are based on the toxin-cell interactions, and therefore, reveal functional information (such as mode of action, toxic potency, bioavailability, target tissue or organ, etc. about the toxin. CBBs incorporate both prokaryotic (bacteria and eukaryotic (yeast, invertebrate and vertebrate cells. To create CBB devices, living cells are directly integrated onto the biosensor platform. The sensors report the cellular responses upon exposures to toxins and the resulting cellular signals are transduced by secondary transducers generating optical or electrical signals outputs followed by appropriate read-outs. Examples of the layout and operation of cellular biosensors for detection of selected biotoxins are summarized.

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

    International Nuclear Information System (INIS)

    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)

  10. Advances in neutron based bulk explosive detection

    International Nuclear Information System (INIS)

    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

  11. 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.

  12. 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.

  13. 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.

  14. Vehicle Detection Research Based on USILTP Operator

    Directory of Open Access Journals (Sweden)

    Li Fei

    2015-01-01

    Full Text Available This paper presents a uniform SILTP operator based on the SILTP operator. In the vehicle detection, SILTP can solve the problems caused by the change of sunshine, the shadow of vehicle and the noise in the surrounding environment. But the algorithm has high dimensionality which can lead to error because of the deviation of the texture characteristics. The USILTP operator can reduce the dimensionality of the detection data which adapts to the problems caused by illumination variations and the noise in the surrounding environment. First, the method uses the SILTP operator to extract the vehicle image texture characteristics and reduce the dimensionality of the detection data, and then it uses the Gauss mixture model to do background modeling, and uses the texture characteristics of the new image to update background dynamically. At last, it gets the vehicle by contracting with the background model. It has been proved that this detection algorithm has a good performance with the test of the vehicle on public roads.

  15. Title Based Duplicate Detection of Web Documents

    Directory of Open Access Journals (Sweden)

    Mrs. M. Kiruthika

    2012-09-01

    Full Text Available In recent times, the concept of web crawling has received remarkable significance owing to extreme development of the World Wide Web. Very large amounts of web documents are swarming the web making the search engines less appropriate to the users. Among the vast number of web documents are many duplicates and near duplicates i.e. variants derived from the same original web document due to which additional overheads are created for search engines by which their performance and quality is significantly affected. Web crawling research community has extensively recognized the need for detection of duplicate and near duplicate web pages. Providing the users with relevant results for their queries in the first page without duplicates and redundant results is a vital requisite. Also, this problem of duplication should be avoided to save storage as well as to improve search quality. The near duplicate web pages are detected followed by the storage of crawled web pages in to repositories. The detection of near duplicates conserves network bandwidth, brings down storage cost and enhances the quality of search engines. In this paper, we have discussed a feasible method for detection of near-duplicate web documents based on the title of the documents which will help to reduce the overhead of search engines and improve their performance.

  16. Windows Volatile Memory Forensics Based on Correlation Analysis

    OpenAIRE

    Xiaolu Zhang; Liang Hu; Shinan Song; Zhenzhen Xie; Xiangyu Meng; Kuo Zhao

    2014-01-01

    In this paper, we present an integrated memory forensic solution for multiple Windows memory images. By calculation, the method can find out the correlation degree among the processes of volatile memory images and the hidden clues behind the events of computers, which is usually difficult to be obtained and easily ignored by analyzing one single memory image and forensic investigators. In order to test the validity, we performed an experiment based on two hosts' memory image which contains cr...

  17. Government bond market integration and the EMU: Correlation based evidence

    OpenAIRE

    Missio, Sebastian

    2012-01-01

    In July 1990, the project of the European Monetary Union (EMU) started and finally led to the introduction of the Euro in January 1999. This paper analyses the development of the government bond market integration during the three stages of the EMU. Based on the results from dynamic conditional correlation (DCC) models, the study shows that the integration process was highly advanced but not completed at any point in time and that the degree of integration differentiated between geographical ...

  18. QRS detection based ECG quality assessment

    International Nuclear Information System (INIS)

    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)

  19. Detection Loophole in Bell experiments: How post-selected local correlations can look non-local

    OpenAIRE

    Branciard, Cyril

    2010-01-01

    A common problem in Bell type experiments is the well-known detection loophole: if the detection efficiencies are not perfect and if one simply post-selects the conclusive events, one might observe a violation of a Bell inequality, even though a local model could have explained the experimental results. In this paper, we analyze the set of all post-selected correlations that can be explained by a local model, and show that it forms a polytope, larger than the Bell local polytope. We character...

  20. An immune based dynamic intrusion detection model

    Institute of Scientific and Technical Information of China (English)

    LI Tao

    2005-01-01

    With the dynamic description method for self and antigen, and the concept of dynamic immune tolerance for lymphocytes in network-security domain presented in this paper, a new immune based dynamic intrusion detection model (Idid) is proposed. In Idid, the dynamic models and the corresponding recursive equations of the lifecycle of mature lymphocytes, and the immune memory are built. Therefore, the problem of the dynamic description of self and nonself in computer immune systems is solved, and the defect of the low efficiency of mature lymphocyte generating in traditional computer immune systems is overcome. Simulations of this model are performed, and the comparison experiment results show that the proposed dynamic intrusion detection model has a better adaptability than the traditional methods.

  1. A Microcontroller Based Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Ewunonu Toochi

    2014-11-01

    Full Text Available A Microcontroller based Intrusion Detection System is designed and implemented. Rampant, Okintrusion to restricted zones have highlighted the need for embedded systems that can effectively monitor, instantly alert personnel of any breach in security and retrieve graphic evidence of any such activity in the secured area. At the heart of the intrusion detection system is the PIC 168F77A Microcontroller that transmits pulses at 38 KHz. It is suitably interfaced to a GSM modem that can send SMS on sight of infringement and a webcam that can take snapshots. The report also presents the system software which has been developed in two parts: one in C++ Language using MPLAB KIT and the other written in AT COMMAND resident in the GSM modem. The system is very cost-effective, uses easily available components and is adaptable to control systems.

  2. 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.

  3. [Progress in Application of Two-Dimensional Correlation Spectroscopy for Detection of Food Quality].

    Science.gov (United States)

    Yang, Ren-jie; Yang, Yan-rong; Liu, Hai-xue; Dong, Gui-mei; Du, Yan-hong; Shan, Hui-yong; Zhang, Wei-yu

    2015-08-01

    In recent years, the food safety and quality has always been a serious issue. Therefore, it is urgent to develop a rapid and widely available method to determine the quality of food. Due to high spectral resolution, good spectral selectivity and good ability of spectrogram analysis, the technology of two-dimensional (2D) correlation spectroscopy is an effective method for solving three major problems encountered by the conventional one-dimensional (1D) spectrum: low selectivity of the spectra, difficulty in extracting the information of the spectral feature and difficulty in spectrogram analysis. Therefore, 2D correlation spectroscopy, which is suited to distinguish similar samples hardly distinguished by the conventional 1D spectroscopy, has been successfully applied in many complex biological systems. The developmental process, the experimental way to obtain spectrum, the fundamental mathematical principle and the properties of 2D correlation spectroscopy were introduced in this paper. At the same time, it is pointed out that the origin of weak characteristic bands of substance can be verified in terms of the positive or negative corss peaks in synchronous 2D correlation spectrum combined with the existence or inexistence of corss peaks in asynchronous 2D correlation spectrum. The application of 2D near-infrared, mid-infrared, fluorescence, and raman correlation spectroscopy in the detection of food quality and adulteration, concentrated specifically on diary product, wine, oil, meat, honey, and rice were reviewed. Finally, the limitations and future development prospects were pointed out. PMID:26672279

  4. Detectability of Pore Defect in Wind Turbine Blade Composites Using Image Correlation Technique

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jong Il; Huh, Yong Hak; Lee, Gun Chang [Korea Research institute of Standard and Science, Daejeon (Korea, Republic of)

    2013-10-15

    Defects that occur during the manufacturing process or operation of a wind turbine blade have a great influence on its life and safety. Typically, defects such as delamination, pore, wrinkle and matrix crack are found in a blade. In this study, the detectability of the pores, a type of defect that frequently occur during manufacturing, was examined from the full field strain distribution determined with the image correlation technique. Pore defects were artificially introduced in four-ply laminated GFRP composites with 0 .deg/{+-}45 .deg fiber direction. The artificial pores were introduced in consideration of their size and location. Three different-sized pores with diameter of 1, 2 and 3 mm were located on the top and bottom surface and embedded. By applying static loads of 0-200 MPa, the strain distributions over the specimen with the pore defects were determined using image correlation technique. It was found the pores with diameter exceeding 2 mm can be detected in diameter.

  5. Detection of hyaluronidase activity using fluorescein labeled hyaluronic acid and fluorescence correlation spectroscopy

    OpenAIRE

    Rich, Ryan M.; Mummert, Mark; Foldes-Papp, Zeno; Gryczynski, Zygmunt; Borejdo, Julian; Gryczynski, Ignacy; Fudala, Rafal

    2012-01-01

    The over-expression of hyaluronidase has been observed in many types of cancer, suggesting that it may have utility for diagnosis. Here we present a technique for the detection of hyaluronidase using Fluorescence Correlation Spectroscopy (FCS). Hyaluronan macromolecules (HAs) have been heavily labeled with fluorescein amine resulting in strong self-quenching. In the presence of hyaluronidase, HA is cleaved into smaller, fluorescein-labeled fragments and the self-quenching is released. Such cl...

  6. Photonic Methods to Enhance Fluorescence Correlation Spectroscopy and Single Molecule Fluorescence Detection

    OpenAIRE

    Hervé Rigneault; Jérome Wenger

    2010-01-01

    Recent advances in nanophotonics open the way for promising applications towards efficient single molecule fluorescence analysis. In this review, we discuss how photonic methods bring innovative solutions for two essential questions: how to detect a single molecule in a highly concentrated solution, and how to enhance the faint optical signal emitted per molecule? The focus is set primarily on the widely used technique of fluorescence correlation spectroscopy (FCS), yet the discussion can be ...

  7. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision.

    Science.gov (United States)

    Weiss, Sophie; Van Treuren, Will; Lozupone, Catherine; Faust, Karoline; Friedman, Jonathan; Deng, Ye; Xia, Li Charlie; Xu, Zhenjiang Zech; Ursell, Luke; Alm, Eric J; Birmingham, Amanda; Cram, Jacob A; Fuhrman, Jed A; Raes, Jeroen; Sun, Fengzhu; Zhou, Jizhong; Knight, Rob

    2016-07-01

    Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques. PMID:26905627

  8. 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.

  9. Plagiarism Detection Using Graph-Based Representation

    CERN Document Server

    Osman, Ahmed Hamza; Binwahlan, Mohammed Salem

    2010-01-01

    Plagiarism of material from the Internet is a widespread and growing problem. Several methods used to detect the plagiarism and similarity between the source document and suspected documents such as fingerprint based on character or n-gram. In this paper, we discussed a new method to detect the plagiarism based on graph representation; however, Preprocessing for each document is required such as breaking down the document into its constituent sentences. Segmentation of each sentence into separated terms and stop word removal. We build the graph by grouping each sentence terms in one node, the resulted nodes are connected to each other based on order of sentence within the document, all nodes in graph are also connected to top level node "Topic Signature". Topic signature node is formed by extracting the concepts of each sentence terms and grouping them in such node. The main advantage of the proposed method is the topic signature which is main entry for the graph is used as quick guide to the relevant nodes. ...

  10. Automated Signature Creator for a Signature Based Intrusion Detection System with Network Attack Detection Capabilities (Pancakes

    Directory of Open Access Journals (Sweden)

    Frances Bernadette C. De Ocampo

    2015-05-01

    Full Text Available Signature-based Intrusion Detection System (IDS helps in maintaining the integrity of data in a network controlled environment. Unfortunately, this type of IDS depends on predetermined intrusion patterns that are manually created. If the signature database of the Signature-based IDS is not updated, network attacks just pass through this type of IDS without being noticed. To avoid this, an Anomaly-based IDS is used in order to countercheck if a network traffic that is not detected by Signature-based IDS is a true malicious traffic or not. In doing so, the Anomaly-based IDS might come up with several numbers of logs containing numerous network attacks which could possibly be a false positive. This is the reason why the Anomaly-based IDS is not perfect, it would readily alarm the system that a network traffic is an attack just because it is not on its baseline. In order to resolve the problem between these two IDSs, the goal is to correlate data between the logs of the Anomaly-based IDS and the packet that has been captured in order to determine if a network traffic is really malicious or not. With the supervision of a security expert, the malicious network traffic would be verified as malicious. Using machine learning, the researchers can identify which algorithm is better than the other algorithms in classifying if a certain network traffic is really malicious. Upon doing so, the creation of signatures would follow by basing the automated creation of signatures from the detected malicious traffic.

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

    Science.gov (United States)

    Fortunato, G.

    1979-01-01

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

  12. Correlation-based temperature and emissivity separation algorithm

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Based on analyzing the relationship between the atmospheric downward radiance and surface emis- sivity, this paper proposes a correlation criterion to optimize surface temperature during the process of temperature and emissivity separation from thermal infrared hyperspectral data, and puts forward the correlation-based temperature and emissivity separation algorithm (CBTES). The algorithm uses the correlation between the atmospheric downward radiance and surface emissivity to optimize surface temperature, and obtains surface emissivity with this temperature. The accuracy of CBTES was evalu- ated by the simulated thermal infrared hyperspectral data. The simulated results show that the CBTES can achieve high accuracy of temperature and emissivity inversion. CBTES has been compared with the iterative spectrally smooth temperature/emissivity separation (ISSTES), and the comparison results show that they have relative accuracy. Besides, CBTES is insensitive to the instrumental random noise and the change of atmospheric downward radiance during the measurements. As regards the noniso- thermal pixel, its radiometric temperature changes slowly with the wavenumber when its emissivity is defined as r-emissivity. The CBTES can be used to derive the equivalent temperature of nonisothermal pixel in a narrow spectral region when we assumed that the radiometric temperature is invariable in the narrow spectral region. The derived equivalent temperatures in multi-spectral regions in 714―1250 cm?1 can characterize the change trend of nonisothermal pixel’s radiometric temperature.

  13. SVM multiuser detection based on heuristic kernel

    Institute of Scientific and Technical Information of China (English)

    Yang Tao; Hu Bo

    2007-01-01

    A support vector machine (SVM) based multiuser detection (MUD) scheme in code-division multiple-access (CDMA) system is proposed. In this scheme, the equivalent support vector (SV) is obtained through a kernel sparsity approximation algorithm, which avoids the conventional costly quadratic programming (QP) procedure in SVM. Besides, the coefficient of the SV is attained through the solution to a generalized eigenproblem. Simulation results show that the proposed scheme has almost the same bit error rate (BER) as the standard SVM and is better than minimum mean square error (MMSE) scheme. Meanwhile, it has a low computation complexity.

  14. 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.

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

    International Nuclear Information System (INIS)

    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)

  16. Robust Reservoir Generation by Correlation-Based Learning

    Directory of Open Access Journals (Sweden)

    Tadashi Yamazaki

    2009-01-01

    Full Text Available Reservoir computing (RC is a new framework for neural computation. A reservoir is usually a recurrent neural network with fixed random connections. In this article, we propose an RC model in which the connections in the reservoir are modifiable. Specifically, we consider correlation-based learning (CBL, which modifies the connection weight between a given pair of neurons according to the correlation in their activities. We demonstrate that CBL enables the reservoir to reproduce almost the same spatiotemporal activity patterns in response to an identical input stimulus in the presence of noise. This result suggests that CBL enhances the robustness in the generation of the spatiotemporal activity pattern against noise in input signals. We apply our RC model to trace eyeblink conditioning. The reservoir bridged the gap of an interstimulus interval between the conditioned and unconditioned stimuli, and a readout neuron was able to learn and express the timed conditioned response.

  17. Laser-diode-based joint transform correlator for fingerprint identification

    Science.gov (United States)

    Lal, Amit K.; Zang, De Yu; Millerd, James E.

    1999-01-01

    A laser-diode-based joint transform correlator (JTC) is reported here for the identification and discrimination of fingerprints. The system employs compact and inexpensive laser diodes as the light sources and a bacteriorhodopsin (BR) film in the Fourier plane, which can record the joint power spectrum without the need for expensive spatial light modulators or CCD cameras. The BR film also introduces nonlinearities in the Fourier plane which can improve JTC performance. In addition, real-time, all-optical programmable spatial filtering is demonstrated to improve the discrimination of the system. We present computer modeling and experimental results of this optical correlator, which shows excellent potential for the identification and discrimination of fingerprints.

  18. 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.

  19. Overlapping Community Detection based on Network Decomposition

    Science.gov (United States)

    Ding, Zhuanlian; Zhang, Xingyi; Sun, Dengdi; Luo, Bin

    2016-04-01

    Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent drawbacks to discover overlapping communities. Node clustering is inadequate to capture the pervasive overlaps, while link clustering is often criticized due to the high computational cost and ambiguous definition of communities. So, overlapping community detection is still a formidable challenge. In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD. Specifically, NDOCD iteratively splits the network by removing all links in derived link communities, which are identified by utilizing node clustering technique. The network decomposition contributes to reducing the computation time and noise link elimination conduces to improving the quality of obtained communities. Besides, we employ node clustering technique rather than link similarity measure to discover link communities, thus NDOCD avoids an ambiguous definition of community and becomes less time-consuming. We test our approach on both synthetic and real-world networks. Results demonstrate the superior performance of our approach both in computation time and accuracy compared to state-of-the-art algorithms.

  20. Network Intrusion Detection based on GMKL Algorithm

    Directory of Open Access Journals (Sweden)

    Li Yuxiang

    2013-06-01

    Full Text Available According to the 31th statistical reports of China Internet network information center (CNNIC, by the end of December 2012, the number of Chinese netizens has reached 564 million, and the scale of mobile Internet users also reached 420 million. But when the network brings great convenience to people's life, it also brings huge threat in the life of people. So through collecting and analyzing the information in the computer system or network we can detect any possible behaviors that can damage the availability, integrity and confidentiality of the computer resource, and make timely treatment to these behaviors which have important research significance to improve the operation environment of network and network service. At present, the Neural Network, Support Vector machine (SVM and Hidden Markov Model, Fuzzy inference and Genetic Algorithms are introduced into the research of network intrusion detection, trying to build a healthy and secure network operation environment. But most of these algorithms are based on the total sample and it also hypothesizes that the number of the sample is infinity. But in the field of network intrusion the collected data often cannot meet the above requirements. It often shows high latitudes, variability and small sample characteristics. For these data using traditional machine learning methods are hard to get ideal results. In view of this, this paper proposed a Generalized Multi-Kernel Learning method to applied to network intrusion detection. The Generalized Multi-Kernel Learning method can be well applied to large scale sample data, dimension complex, containing a large number of heterogeneous information and so on. The experimental results show that applying GMKL to network attack detection has high classification precision and low abnormal practical precision.

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

    Energy Technology Data Exchange (ETDEWEB)

    Paglieroni, David W.

    2016-06-07

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

  2. Detection Loophole in Bell experiments: How post-selected local correlations can look non-local

    CERN Document Server

    Branciard, Cyril

    2010-01-01

    A common problem in Bell type experiments is the well-known detection loophole: if the detection efficiencies are not perfect and if one simply post-selects the conclusive events, one might observe a violation of a Bell inequality, even though a local model could have explained the experimental results. In this paper, we analyze the set of all post-selected correlations that can be explained by a local model, and show that it forms a polytope, larger than the Bell local polytope. We characterize the facets of this post-selected local polytope in the CHSH scenario, where two parties have binary inputs and outcomes. Our approach gives new insights on the detection loophole problem.

  3. Correlation-based imaging technique for fatigue monitoring of riveted lap-joint structure

    International Nuclear Information System (INIS)

    In the present study, a correlation-based imaging technique called Excitelet is assessed to monitor fatigue crack propagation in a riveted aluminum lap-joint, representative of an aircraft component. For this purpose, a micro-machined piezoceramic array is used to generate guided waves into the structure and measure the reflections induced by potential damage. The method uses a propagation model to correlate measured signals with a bank of signals and imaging is performed using a round-robin procedure (full-matrix capture). This allows taking into account the transducer dynamics and finite dimensions, multi-modal and dispersive characteristics of the guided wave propagation and complex interaction between with damage. Experimental validation has been conducted on an aluminum lap-joint instrumented with a compact linear piezoceramic array of 8 circular elements of 3 mm diameter each. The imaging technique is applied to detect crack propagation after fatigue cycling. Imaging results obtained using A0 mode at 300 and 450 kHz are presented for different crack sizes. It is demonstrated that crack detection and localization can be achieved, while the correlation level indicates the level of reflected energy, and thus damage severity. An accuracy below 5 mm on damage location can be achieved, demonstrating the potential of the correlation-based imaging technique for damage monitoring of complex aerospace structures. (paper)

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

    Science.gov (United States)

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

    2009-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Xiaoming Zhou

    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.

  6. Photoemission Electronic States and Correlation Energies of Magnetite Based Compounds

    International Nuclear Information System (INIS)

    The photoemission spectra (XPS/UPS) for iron oxides, stoichiometric magnetite and for selected Ti and Zn doped magnetite single crystals are presented. From the Fe-3s split lines the exchange energies for FeO, Fe2O3 and magnetite based samples were estimated. It was shown that Ti and Zn ions are of 4+ and 2+ valency, respectively. The correlation energies were estimated from the Fe2p3/2 core-level spectra and from the L3- M4,5, M4,5 Auger lines. The type of insulating gap in these compounds was discussed. (author)

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  8. Mitigating ground-based sensor failures with video motion detection

    Science.gov (United States)

    Macior, Robert E.; Knauth, Jonathan P.; Walter, Sharon M.; Evans, Richard

    2008-10-01

    Unattended Ground Sensor (UGS) systems typically employ distributed sensor nodes utilizing seismic, magnetic or passive IR sensing modalities to alarm if activity is present. The use of an imaging component to verify sensor events is beneficial to create actionable intelligence. Integration of the ground-based images with other ISR data requires that the images contain valid activity and are appropriately formatted, such as prescribed by Standard NATO Agreement (STANAG) 4545 or the National Imagery Transmission Format, version 2.1 (NITF 2.1). Ground activity sensors suffer from false alarms due to meteorological or biological activity. The addition of imaging allows the analyst to differentiate valid threats from nuisance alarms. Images are prescreened based on target size and temperature difference relative to the background. The combination of video motion detection based on thermal imaging with seismic, magnetic or passive IR sensing modalities improves data quality through multi-phenomenon combinatorial logic. The ground-based images having a nominally vertical aspect are transformed to the horizontal geospatial domain for exploitation and correlation of UGS imagery with other ISR data and for efficient archive and retrieval purposes. The description of an UGS system utilized and solutions that were developed and implemented during an experiment to correlate and fuse IR still imagery with ground moving target information, forming real-time, actionable, coalition intelligence, are presented.

  9. Track infrared point targets based on projection coefficient templates and non-linear correlation combined with Kalman prediction

    Science.gov (United States)

    Liu, Ruiming; Li, Xuelong; Han, Lei; Meng, Jiao

    2013-03-01

    For a long time, tracking IR point targets is a great challenge task. We propose a tracking framework based on template matching combined with Kalman prediction. Firstly, a novel template matching method for detecting infrared point targets is presented. Different from the classic template matching, the projection coefficients obtained from principal component analysis are used as templates and the non-linear correlation coefficient is used to measure the matching degree. The non-linear correlation can capture the higher-order statistics. So the detection performance is improved greatly. Secondly, a framework of tracking point targets, based on the proposed detection method and Kalman prediction, is developed. Kalman prediction reduces the searching region for the detection method and, in turn, the detection method provides the more precise measurement for Kalman prediction. They bring out the best in each other. Results of experiments show that this framework is competent to track infrared point targets.

  10. Use of time-correlated single photon counting detection to measure the speed of light in water

    CERN Document Server

    Muino, P L; Buenker, R J; Muino, Pedro L.; Thompson, Aaron M.; Buenker, Robert J.

    2004-01-01

    Traditional methods for measuring the speed of light in dispersive media have been based on the detection of interference between light waves emitted from the same source. In the present study the elapsed times for single photons to move from a laser to a photomultiplier tube are measured electronically. Time-correlated single photon counting detection produces a characteristic instrument response which has the same shape independent of both the path length the light travels and the nature of the transparent media through which it passes. This allows for an accurate calibration of the chronograph by observing shifts in the location of the instrument response for different distances traveled by the light. Measurement of the corresponding shift which occurs when light moves the same distance through air and water then enables an accurate determination of the ratio of the photon velocities in these two media. Three different wavelengths of light have been used. In two cases good agreement is found between the pr...

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

    Science.gov (United States)

    Newe, Axel

    2016-01-01

    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. PMID:27042396

  12. Edge detection based on directional space

    Institute of Scientific and Technical Information of China (English)

    YUAN Wei-qi; LI De-sheng

    2006-01-01

    A new method for edge detection based on directional space is proposed.The principle is that:firstly,the directional differential space is set up in which the ridge edge pixels and valley edge pixels are abstracted with the help of the method of logical judgments along the direction of differential function,forming a directional roof edge map;secondly,step edge pixels are abstracted between the neighboring directional ridge edge and directional valley edge along the direction of differential function;finally,the ridge edge map,valley edge map and step edge map gained along different directions are combined into corresponding ridge edge map,valley edge map and step edge map.This method is different from classical algorithms in which the gray differential values of the mutual vertical direction are combined into one gradient value.The experiment of edge detection is made for the images of nature scenery,human body and accumulative raw material,whose result is compared with the one of classical algorithms and showing the robustness of the proposed method.

  13. On exploiting interbeat correlation in compressive sensing-based ECG compression

    Science.gov (United States)

    Polania, Luisa F.; Carrillo, Rafael E.; Blanco-Velasco, Manuel; Barner, Kenneth E.

    2012-06-01

    Compressive Sensing (CS) is an emerging data acquisition scheme with the potential to reduce the number of measurements required by the Nyquist sampling theorem to acquire sparse signals. We recently used the interbeat correlation to find the common support between jointly sparse adjacent heartbeats. In this paper, we fully exploit this correlation to find the magnitude, in addition to the support of the significant coefficients in the sparse domain. The approach used for this purpose is based on sparse Bayesian learning algorithms due to its superior performance compared to other reconstruction algorithms and the fact that being a probabilistic approach facilitates the incorporation of correlation information. The reconstruction includes, in the first place, the detection of the R peaks and the length normalization of ECG cycles to take advantage of the quasi-periodic structure. Since the common support reduces as the number of heartbeats increases, we propose the use of a sliding window where the support maintains approximately constant across cycles. The sparse Bayesian algorithm adaptively learns and exploits the high correlation between the heartbeats in the constructed window. Experimental results show that the proposed method reduces significantly the number of measurements required to achieve good reconstruction quality, validating the potential of using correlation information in compressed sensing-based ECG compression.

  14. Frequency-based Vehicle Idling Detection

    Directory of Open Access Journals (Sweden)

    Kai-Chao Yang

    2014-02-01

    Full Text Available Continuous increases in fuel prices and environmental awareness have raised the importance of reducing vehicle emissions, with many national governments passing anti-idling laws. To reduce air pollution and fuel consumption, we propose a frequency-based vehicle idling detection method to remind drivers to turn off the engine vehicle idling exceeds a certain time threshold. The method is implemented in existing handheld devices without any modification to the car or engine, making the solution cheap and simple to implement in any type of motor vehicle. Experimental results show the proposed method can effectively differentiate idling, engine-off, and moving conditions. Two implementation cases are presented to demonstrate the feasibility and performance of the proposed method.

  15. 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.

  16. Texture-Based Polyp Detection in Colonoscopy

    Science.gov (United States)

    Ameling, Stefan; Wirth, Stephan; Paulus, Dietrich; Lacey, Gerard; Vilarino, Fernando

    Colonoscopy is one of the best methods for screening colon cancer. A variety of research groups have proposed methods for automatic detection of polyps in colonoscopic images to support the doctors during examination. However, the problem can still not be assumed as solved. The major drawback of many approaches is the amount and quality of images used for classifier training and evaluation. Our database consists of more than four hours of high resolution video from colonoscopies which were examined and labeled by medical experts. We applied four methods of texture feature extraction based on Grey-Level-Co-occurence and Local-Binary-Patterns. Using this data, we achieved classification results with an area under the ROC-curve of up to 0.96.

  17. 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.

  18. Detection of Thermal SZ -- CMB Lensing Cross-Correlation in Planck Nominal Mission Data

    CERN Document Server

    Hill, J Colin

    2013-01-01

    The nominal mission maps from the Planck satellite contain a wealth of information about secondary anisotropies in the cosmic microwave background (CMB), including those induced by the thermal Sunyaev-Zel'dovich (tSZ) effect and gravitational lensing. As both the tSZ and CMB lensing signals trace the large-scale matter density field, the anisotropies sourced by these processes are expected to be correlated. We report the first detection of this cross-correlation signal, which we measure at 6.2 sigma significance using the Planck data. We take advantage of Planck's multifrequency coverage to construct a tSZ map using internal linear combination techniques, which we subsequently cross-correlate with the publicly-released Planck CMB lensing potential map. [Abridged] We interpret the signal using halo model calculations, which indicate that the tSZ -- CMB lensing cross-correlation is a unique probe of the physics of intracluster gas in high-redshift, low-mass groups and clusters. Our results are consistent with e...

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

    International Nuclear Information System (INIS)

    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

  20. Cosmic Microwave and Infrared Backgrounds cross-correlation for ISW detection

    CERN Document Server

    Ilic, Stéphane

    2012-01-01

    We investigate the cross-correlation between the cosmic infrared and microwave backgrounds (CIB & CMB) anisotropies through the integrated Sachs-Wolfe effect. We first describe 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 discuss the detectability of the ISW signal by performing a signal-to-noise (SNR) analysis with our predicted spectra. The significances obtained range from 6{\\sigma} to 7{\\sigma} 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 2-5{\\sigma}, depending strongly on the major contribution to the noise term.

  1. Correlated Detection of sub-mHz Gravitational Waves by Two Optical-Fiber Interferometers

    Directory of Open Access Journals (Sweden)

    Cahill R. T.

    2008-04-01

    Full Text Available Results from two optical-fiber gravitational-wave interferometric detectors are reported. The detector design is very small, cheap and simple to build and operate. Using two de- tectors has permitted various tests of the design principles as well as demonstrating the first simultaneous detection of correlated gravitational waves from detectors spatially separated by 1.1 km. The frequency spectrum of the detected gravitational waves is sub-mHz with a strain spectral index alpha = -1.4 +-0.1. As well as characterising the wave effects the detectors also show, from data collected over some 80 days in the latter part of 2007, the dominant earth rotation e ect and the earth orbit effect. The detectors op- erate by exploiting light speed anisotropy in optical-fibers. The data confirms previous observations of light speed anisotropy, earth rotation and orbit eff ects, and gravitational waves.

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

    International Nuclear Information System (INIS)

    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 252Cf 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)

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

    International Nuclear Information System (INIS)

    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.

  4. Traffic Prediction Based on Correlation of Road Sections

    Directory of Open Access Journals (Sweden)

    Xiaodan Huang

    2013-10-01

    Full Text Available Road section data packet is very necessary for the estimation and prediction in short-time traffic condition. However, previous researches on this problem are lack of quantitative analysis. A section correlation analyzing method with traffic flow microwave data is proposed for this problem. It is based on the metric multidimensional scaling theory. With a dissimilarity matrix, scalar product matrix can be calculated. Subsequently, a reconstructing matrix of section traffic flow could be got with principal components factor analysis, which could display section groups in low dimension. It is verified that the new method is reliable and effective. After that, Auto Regressive Moving Average (A RMA model is used for forecasting traffic flow and lane occupancy. Finally, a simulated example has shown that the technique is effective and exact. The theoretical analysis indicates that the forecasting model and algorithms have a broad prospect for practical application.  

  5. Windows Volatile Memory Forensics Based on Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Xiaolu Zhang

    2014-03-01

    Full Text Available In this paper, we present an integrated memory forensic solution for multiple Windows memory images. By calculation, the method can find out the correlation degree among the processes of volatile memory images and the hidden clues behind the events of computers, which is usually difficult to be obtained and easily ignored by analyzing one single memory image and forensic investigators. In order to test the validity, we performed an experiment based on two hosts' memory image which contains criminal incidents. According to the experimental result, we find that the event chains reconstructed by our method are similar to the actual actions in the criminal scene. Investigators can review the digital crime scenario which is contained in the data set by analyzing the experimental results. This paper is aimed at finding the valid actions with illegal attempt and making the memory analysis not to be utterly dependent on the operating system and relevant experts.

  6. Preserving Differential Privacy in Degree-Correlation based Graph Generation.

    Science.gov (United States)

    Wang, Yue; Wu, Xintao

    2013-08-01

    Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as cluster coefficient often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data. In this paper, we study the problem of enforcing edge differential privacy in graph generation. The idea is to enforce differential privacy on graph model parameters learned from the original network and then generate the graphs for releasing using the graph model with the private parameters. In particular, we develop a differential privacy preserving graph generator based on the dK-graph generation model. We first derive from the original graph various parameters (i.e., degree correlations) used in the dK-graph model, then enforce edge differential privacy on the learned parameters, and finally use the dK-graph model with the perturbed parameters to generate graphs. For the 2K-graph model, we enforce the edge differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We conduct experiments on four real networks and compare the performance of our private dK-graph models with the stochastic Kronecker graph generation model in terms of utility and privacy tradeoff. Empirical evaluations show the developed private dK-graph generation models significantly outperform the approach based on the stochastic Kronecker generation model. PMID:24723987

  7. Preserving Differential Privacy in Degree-Correlation based Graph Generation

    Directory of Open Access Journals (Sweden)

    Yue Wang

    2013-08-01

    Full Text Available Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as cluster coefficient often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum on tabular data. In this paper, we study the problem of enforcing edge differential privacy in graph generation. The idea is to enforce differential privacy on graph model parameters learned from the original network and then generate the graphs for releasing using the graph model with the private parameters. In particular, we develop a differential privacy preserving graph generator based on the dK-graph generation model. We first derive from the original graph various parameters (i.e., degree correlations used in the dK-graph model, then enforce edge differential privacy on the learned parameters, and finally use the dKgraph model with the perturbed parameters to generate graphs. For the 2K-graph model, we enforce the edge differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We conduct experiments on four real networks and compare the performance of our private dK-graph models with the stochastic Kronecker graph generation model in terms of utility and privacy tradeoff. Empirical evaluations show the developed private dK-graph generation models significantly outperform the approach based on the stochastic Kronecker generation model.

  8. A multivariate correlation method as a tool for fault detection and its application to feedwater system in a nuclear power plant

    International Nuclear Information System (INIS)

    A new method based on multivariate correlation has been developed for surveillance and anomaly detection in nuclear power plants. Natural signal noises observed under normal plant operations are applied in the method, thus the method is suitable for online surveillance. In the method, correlation of signals and noise properties in the process are estimated. Eigenvalues of normalized covariance matrix and correlation function among signals are used for surveillance of process dynamics and operational condition. The effectiveness of the method has been demonstrated with an application to feedwater system of a BWR plant. (author)

  9. Detecting Differential and Correlated Protein Expression in Label-Free Shotgun Proteomics

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Bing [ORNL; Verberkmoes, Nathan C [ORNL; Langston, Michael A [ORNL; Uberbacher, Edward C [ORNL; Hettich, Robert {Bob} L [ORNL; Samatova, Nagiza F [ORNL

    2006-01-01

    Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomycescerevisiaeand prokaryotic Rhodopseudomonaspalustrislabel free LC-MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests: Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S.cerevisiaedata set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R.palustris proteins in the LC-MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly

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

    Science.gov (United States)

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

    2010-02-01

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

  11. The Waveform Correlation Event Detection System project, Phase II: Testing with the IDC primary network

    Energy Technology Data Exchange (ETDEWEB)

    Young, C.J.; Beiriger, J.I.; Moore, S.G. [and others

    1998-04-01

    Further improvements to the Waveform Correlation Event Detection System (WCEDS) developed by Sandia Laboratory have made it possible to test the system on the accepted Comprehensive Test Ban Treaty (CTBT) seismic monitoring network. For our test interval we selected a 24-hour period from December 1996, and chose to use the Reviewed Event Bulletin (REB) produced by the Prototype International Data Center (PIDC) as ground truth for evaluating the results. The network is heterogeneous, consisting of array and three-component sites, and as a result requires more flexible waveform processing algorithms than were available in the first version of the system. For simplicity and superior performance, we opted to use the spatial coherency algorithm of Wagner and Owens (1996) for both types of sites. Preliminary tests indicated that the existing version of WCEDS, which ignored directional information, could not achieve satisfactory detection or location performance for many of the smaller events in the REB, particularly those in the south Pacific where the network coverage is unusually sparse. To achieve an acceptable level of performance, we made modifications to include directional consistency checks for the correlations, making the regions of high correlation much less ambiguous. These checks require the production of continuous azimuth and slowness streams for each station, which is accomplished by means of FK processing for the arrays and power polarization processing for the three-component sites. In addition, we added the capability to use multiple frequency-banded data streams for each site to increase sensitivity to phases whose frequency content changes as a function of distance.

  12. Detection of gravitational waves in Michelson interferometer by the use of second order correlation functions

    OpenAIRE

    Ben-Aryeh, Y.

    2006-01-01

    The possibility of measuring the second order correlation function of the gravitational waves detectors' currents or photonumbers, and the observation of the gravitational signals by using a spectrum analyzer is discussed. The method is based on complicated data processing and is expected to be efficient for coherent periodic gravitational waves. It is suggested as an alternative method to the conventional one which is used now in the gravitational waves observatories.

  13. Lagrangian based methods for coherent structure detection

    International Nuclear Information System (INIS)

    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

  14. 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.

  15. Biomimetic visual detection based on insect neurobiology

    Science.gov (United States)

    O'Carroll, David C.

    2001-11-01

    With a visual system that accounts for as much as 30% of the lifted mass, flying insects such as dragonflies and hoverflies invest more in vision than any other animal. Impressive visual performance is subserved by a surprisingly simple visual system. In a typical insect eye, between 2,000 and 30,000 pixels in the image are analyzed by fewer than 200,000 neurons in underlying neural circuits. The combination of sophisticated visual processing with an approachable level of complexity has made the insect visual system a leading model for biomimetic approaches to computer vision. Much neurobiological research has focused on neural circuits used for detection of moving patterns (e.g. optical flow during flight) and moving targets (e.g. prey). Research from several labs has led to great advances in our understanding of the neural mechanisms involved, and has spawned neuromorphic hardware based on key processes identified in neurobiological experiments. Despite its attractions, the highly non-linear nature of several key stages in insect visual processing presents a challenge to understanding. I will describe examples of adaptive elements of neural circuits in the fly visual system which analyze the direction and velocity of wide-field optical flow patterns and the result of experiments that suggest that these non-linearities may contribute to robust responses to natural image motion.

  16. Neutron detection based on superheated materials

    International Nuclear Information System (INIS)

    The environmental and radiation responses of the Active Personnel Dosimeter/Superheated Drop Detector (APD/SDD) combination have been evaluated at the Pacific Northwest Laboratory (PNL) for the US Department of Energy's Neutron Measurement and Evaluation Program. This paper provides results of the evaluation and discusses possible improvements for the current system. Radiation detection based on the radiation sensitivity of superheated liquids has been studied for some time. A liquid is superheated if it exists as a liquid at a temperature-pressure state normally associated with the vapor phase of that material. The liquid does not vaporize because there are no bubble nucleation sites in the sample. These sites usually exist (1) in microscopic cracks on solid container surfaces, (2) in crevices of imperfectly wetted solid particles suspended in the liquid, or (3) as a result of the radiation-matter interaction producing a microbubble that is large enough for bubble growth to be thermodynamically favorable. By suspending small drops of superheated liquid in an immiscible, inert, impurity-free medium, potential for bubble nucleation by the first two mechanisms is eliminated. Therefore, each drop becomes a miniature radiation detector

  17. Neutron detection based on superheated materials

    International Nuclear Information System (INIS)

    The environmental and radiation responses of the Active Personnel Dosimeter/Superheated Drop Detector (APD/SDD) combination have been evaluated at the Pacific Northwest Laboratory (PNL) for the U.S. Department of Energy's Neutron Measurement and Evaluation Program. This paper provides results of the evaluation and discusses possible improvements for the current system. Radiation detection based on the radiation sensitivity of superheated liquids has been studied for some time. A liquid is superheated if it exists as a liquid at a temperature-pressure state normally associated with the vapor phase of that material. The liquid does not vaporize because there are no bubble nucleation sits in the sample. These sites usually exist in microscopic cracks on solid container surfaces, in crevices of imperfectly wetted solid particles suspended in the liquid, or as a result of the radiation-matter interaction producing a microbubble that is large enough for bubble growth to be thermodynamically favorable. By suspending small drops of superheated liquid in an immiscible, inert, impurity-free medium, potential for bubble nucleation by the first two mechanisms is eliminated. Therefore, each drop becomes a miniature radiation detector

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

    International Nuclear Information System (INIS)

    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)

  19. Estimating genetic correlations based on phenotypic data: a simulation-based method

    Indian Academy of Sciences (India)

    Elias Zintzaras

    2011-04-01

    Knowledge of genetic correlations is essential to understand the joint evolution of traits through correlated responses to selection, a difficult and seldom, very precise task even with easy-to-breed species. Here, a simulation-based method to estimate genetic correlations and genetic covariances that relies only on phenotypic measurements is proposed. The method does not require any degree of relatedness in the sampled individuals. Extensive numerical results suggest that the propose method may provide relatively efficient estimates regardless of sample sizes and contributions from common environmental effects.

  20. Energy Based Correlation Method for Location of Partial Discharge in Transformer Winding

    Directory of Open Access Journals (Sweden)

    JEYABALAN, V.

    2009-02-01

    Full Text Available Partial discharge (PD is the major source of insulation failure in power transformer. When transformers are subjected to electrical stress during operation, PD can occur. PD identification is an important diagnostic tool for the reliable operation of transformers. The PD signal detection and location is one of the main challenges for system utilities and equipment manufacturers. In this paper energy based correlation method is proposed for locating the source of PD for different pulse durations. Simulation and experiment are performed on lumped physical layer winding to prove the feasibility of the method and also verified with distributed model of 22kV prototype interleaved winding.

  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. Prevalence, Detection and Correlates of PTSD in the Primary Care Setting: A Systematic Review.

    Science.gov (United States)

    Greene, Talya; Neria, Yuval; Gross, Raz

    2016-06-01

    Research suggests that posttraumatic stress disorder (PTSD) is common, debilitating and frequently associated with comorbid health conditions, including poor functioning, and increased health care utilization. This article systematically reviewed the empirical literature on PTSD in primary care settings, focusing on prevalence, detection and correlates. Twenty-seven studies were identified for inclusion. Current PTSD prevalence in primary care patients ranged widely between 2 % to 39 %, with significant heterogeneity in estimates explained by samples with different levels of trauma exposure. Six studies found detection of PTSD by primary care physicians (PCPs) ranged from 0 % to 52 %. Studies examining associations between PTSD and sociodemographic variables yielded equivocal results. High comorbidity was reported between PTSD and other psychiatric disorders including depression and anxiety, and PTSD was associated with functional impairment or disability. Exposure to multiple types of trauma also raised the risk of PTSD. While some studies indicated that primary care patients with PTSD report higher levels of substance and alcohol abuse, somatic symptoms, pain, health complaints, and healthcare utilization, other studies did not find these associations. This review proposes that primary care settings are important for the early detection of PTSD, which can be improved through indicated screening and PCP education. PMID:26868222

  3. 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.

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

    Directory of Open Access Journals (Sweden)

    J. Turan

    2012-06-01

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

  5. HerMES: detection of cosmic magnification of sub-mm galaxies using angular cross-correlation

    CERN Document Server

    Wang, L; Farrah, D; Amblard, A; Auld, R; Bock, J; Brisbin, D; Burgarella, D; Chanial, P; Clements, D L; Eales, S; Franceschini, A; Glenn, J; Gong, Y; Griffin, M; Heinis, S; Ibar, E; Ivison, R J; Mortier, A M J; Oliver, S J; Page, M J; Papageorgiou, A; Pearson, C P; Pérez-Fournon, I; Pohlen, M; Rawlings, J I; Raymond, G; Rodighiero, G; Roseboom, I G; Rowan-Robinson, M; Scott, Douglas; Serra, P; Seymour, N; Smith, A J; Symeonidis, M; Tugwell, K E; Vaccari, M; Vieira, J D; Vigroux, L; Wright, G

    2011-01-01

    Cosmic magnification is due to the weak gravitational lensing of sources in the distant Universe by foreground large-scale structure leading to coherent changes in the observed number density of the background sources. Depending on the slope of the background source number counts, cosmic magnification causes a correlation between the background and foreground galaxies, which is unexpected in the absence of lensing if the two populations are spatially disjoint. Previous attempts using submillimetre (sub-mm) sources have been hampered by small number statistics. The large number of sources detected in the {\\it Herschel} Multi-tiered Extra-galactic Survey (HerMES) Lockman-SWIRE field enables us to carry out the first robust study of the cross-correlation between sub-mm sources and sources at lower redshifts. Using ancillary data we compile two low-redshift samples from SDSS and SWIRE with ~ 0.2 and 0.4, respectively, and cross-correlate with two sub-mm samples based on flux density and colour criteria, selectin...

  6. Effective Steganography Detection Based On Data Compression

    CERN Document Server

    Nechta, Ivan

    2011-01-01

    This article describes novel text steganalysis method. The archiver "Bzip2" used for detection stegotext generated by Texto stegosystem. Experiments show that proposed approach gets better performance than typical existing methods. The detection accuracy exceeds 99.98% for text segments with size 400 bytes.

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

    Science.gov (United States)

    Melroy, Hilary R.; Wilson, Emily L.; Georgieva, Elena

    2012-01-01

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

  8. 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.

  9. 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.

  10. Correlation of expertise with error detection skills of force application during spinal manipulation learning*

    Science.gov (United States)

    Loranger, Michel; Treboz, Julien; Boucher, Jean-Alexandre; Nougarou, François; Dugas, Claude; Descarreaux, Martin

    2016-01-01

    Objective: Most studies on spinal manipulation learning demonstrate the relevance of including motor learning strategies in chiropractic curricula. Two outcomes of practice are the production of movement in an efficient manner and the improved capability of learners to evaluate their own motor performance. The goals of this study were to evaluate if expertise is associated with increased spinal manipulation proficiency and if error detection skills of force application during a high-velocity low-amplitude spinal manipulation are related to expertise. Methods: Three groups of students and 1 group of expert chiropractors completed 10 thoracic spine manipulations on an instrumented device with the specific goal of reaching a maximum peak force of 300 N after a brief period of practice. After each trial, participants were asked to give an estimate of their maximal peak force. Force-time profiles were analyzed to determine the biomechanical parameters of each participant and the participant's capacity to estimate his or her own performance. Results: Significant between-group differences were found for each biomechanical parameter. No significant difference was found between groups for the error detection variables (p > .05). The lack of significant effects related to the error detection capabilities with expertise could be related to the specificity of the task and how the training process was structured. Conclusion: This study confirms that improvements in biomechanical parameters of spinal manipulation are related to expertise. Feedback based on error detection could be implemented in chiropractic curricula to improve trainee abilities in detecting motor execution errors. PMID:26270897

  11. A network-based realtime intrusion detection system

    International Nuclear Information System (INIS)

    The author first reviews the background of Intrusion Detection (ID), then discusses the models and classifications of Intrusion Detection System (IDS). After detail the basic concepts to realize network-based realtime IDS, the analysis of authors' work are presented

  12. Improving seroreactivity-based detection of glioma.

    Science.gov (United States)

    Ludwig, Nicole; Keller, Andreas; Heisel, Sabrina; Leidinger, Petra; Klein, Veronika; Rheinheimer, Stefanie; Andres, Claudia U; Stephan, Bernhard; Steudel, Wolf-Ingo; Graf, Norbert M; Burgeth, Bernhard; Weickert, Joachim; Lenhof, Hans-Peter; Meese, Eckart

    2009-12-01

    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. PMID:20019846

  13. 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.

  14. BASE: Bayesian Astrometric and Spectroscopic Exoplanet Detection and Characterization Tool

    Science.gov (United States)

    Schulze-Hartung, Tim

    2012-08-01

    BASE is a novel program for the combined or separate Bayesian analysis of astrometric and radial-velocity measurements of potential exoplanet hosts and binary stars. The tool fulfills two major tasks of exoplanet science, namely the detection of exoplanets and the characterization of their orbits. BASE was developed to provide the possibility of an integrated Bayesian analysis of stellar astrometric and Doppler-spectroscopic measurements with respect to their binary or planetary companions’ signals, correctly treating the astrometric measurement uncertainties and allowing to explore the whole parameter space without the need for informative prior constraints. The tool automatically diagnoses convergence of its Markov chain Monte Carlo (MCMC[2]) sampler to the posterior and regularly outputs status information. For orbit characterization, BASE delivers important results such as the probability densities and correlations of model parameters and derived quantities. BASE is a highly configurable command-line tool developed in Fortran 2008 and compiled with GFortran. Options can be used to control the program’s behaviour and supply information such as the stellar mass or prior information. Any option can be supplied in a configuration file and/or on the command line.

  15. 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.

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

    International Nuclear Information System (INIS)

    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)

  17. CFHTLenS and RCSLenS cross-correlation with Planck lensing detected in fourier and configuration space

    Science.gov (United States)

    Harnois-Déraps, Joachim; Tröster, Tilman; Hojjati, Alireza; van Waerbeke, Ludovic; Asgari, Marika; Choi, Ami; Erben, Thomas; Heymans, Catherine; Hildebrandt, Hendrik; Kitching, Thomas D.; Miller, Lance; Nakajima, Reiko; Viola, Massimo; Arnouts, Stéphane; Coupon, Jean; Moutard, Thibaud

    2016-07-01

    We measure the cross-correlation signature between the Planck cosmic microwave background (CMB) lensing map and the weak lensing observations from both the Red-sequence Cluster Lensing Survey and the Canada-France-Hawaii Telescope Lensing Survey. In addition to a Fourier analysis, we include the first configuration-space detection, based on the estimators and . Combining 747.2 deg2 from both surveys, we find a detection significance that exceeds 4.2σ in both Fourier- and configuration-space analyses. Scaling the predictions by a free parameter A, we obtain A^Planck_CFHT= 0.68± 0.31 and A^Planck_RCS= 1.31± 0.33. In preparation for the next generation of measurements similar to these, we quantify the impact of different analysis choices on these results. First, since none of these estimators probes the exact same dynamical range, we improve our detection by combining them. Secondly, we carry out a detailed investigation on the effect of apodization, zero-padding and mask multiplication, validated on a suite of high-resolution simulations, and find that the latter produces the largest systematic bias in the cosmological interpretation. Finally, we show that residual contamination from intrinsic alignment and the effect of photometric redshift error are both largely degenerate with the characteristic signal from massive neutrinos, however the signature of baryon feedback might be easier to distinguish. The three lensing data sets are publicly available.

  18. CFHTLenS and RCSLenS Cross-Correlation with Planck Lensing Detected in Fourier and Configuration Space

    CERN Document Server

    Harnois-Déraps, Joachim; Hojjati, Alireza; van Waerbeke, Ludovic; Asgari, Marika; Choi, Ami; Erben, Thomas; Heymans, Catherine; Hildebrandt, Hendrik; Kitching, Thomas D; Miller, Lance; Nakajima, Reiko; Viola, Massimo; Arnouts, Stéphane; Coupon, Jean; Moutard, Thibaud

    2016-01-01

    We measure the cross-correlation signature between the Planck CMB lensing map and the weak lensing observations from both the Red-sequence Cluster Lensing Survey (RCSLenS) and the Canada-France-Hawai Telescope Lensing Survey (CFHTLenS). In addition to a Fourier analysis, we include the first configuration-space detection, based on the estimators $\\langle \\kappa_{\\rm CMB} \\kappa_{\\rm gal} \\rangle$ and $\\langle \\kappa_{\\rm CMB} \\gamma_{t} \\rangle$. Combining 747.2 deg$^2$ from both surveys, we find a detection significance that exceeds $4.2\\sigma$ in both Fourier- and configuration-space analyses. Scaling the predictions by a free parameter $A$, we obtain $A^{\\rm Planck}_{\\rm CFHT}= 0.68\\pm 0.31 $ and $A^{\\rm Planck}_{\\rm RCS}= 1.31\\pm 0.33$. In preparation for the next generation of measurements similar to these, we quantify the impact of different analysis choices on these results. First, since none of these estimators probes the exact same dynamical range, we improve our detection by combining them. Second, ...

  19. Correlation methods of base-level cycle based on wavelet neural network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The authors discussed the method of wavelet neural network (WNN) for correlation of base-level cycle. A new vectored method of well log data was proposed. Through the training with the known data set, the WNN can remenber the cycle pattern characteristic of the well log curves. By the trained WNN to identify the cycle pattern in the vectored log data, the ocrrelation process among the well cycles was completed. The application indicates that it is highly efficient and reliable in base-level cycle correlation.

  20. 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. 

  1. Anomica: Fast Support Vector Based Novelty Detection

    Data.gov (United States)

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

  2. Cancer core modules identification through genomic and transcriptomic changes correlation detection at network level

    Directory of Open Access Journals (Sweden)

    Li Wenting

    2012-06-01

    Full Text Available Abstract Background Identification of driver mutations among numerous genomic alternations remains a critical challenge to the elucidation of the underlying mechanisms of cancer. Because driver mutations by definition are associated with a greater number of cancer phenotypes compared to other mutations, we hypothesized that driver mutations could more easily be identified once the genotype-phenotype correlations are detected across tumor samples. Results In this study, we describe a novel network analysis to identify the driver mutation through integrating both cancer genomes and transcriptomes. Our method successfully identified a significant genotype-phenotype change correlation in all six solid tumor types and revealed core modules that contain both significantly enriched somatic mutations and aberrant expression changes specific to tumor development. Moreover, we found that the majority of these core modules contained well known cancer driver mutations, and that their mutated genes tended to occur at hub genes with central regulatory roles. In these mutated genes, the majority were cancer-type specific and exhibited a closer relationship within the same cancer type rather than across cancer types. The remaining mutated genes that exist in multiple cancer types led to two cancer type clusters, one cluster consisted of three neural derived or related cancer types, and the other cluster consisted of two adenoma cancer types. Conclusions Our approach can successfully identify the candidate drivers from the core modules. Comprehensive network analysis on the core modules potentially provides critical insights into convergent cancer development in different organs.

  3. Improving Spam Detection Based on Structural Similarity

    OpenAIRE

    Gomes, Luiz H.; Castro, Fernando D. O.; Almeida, Rodrigo B.; Bettencourt, Luis M. A.; VIRGILIO A. F. ALMEIDA; Almeida, Jussara M.

    2005-01-01

    We propose a new detection algorithm that uses structural relationships between senders and recipients of email as the basis for the identification of spam messages. Users and receivers are represented as vectors in their reciprocal spaces. A measure of similarity between vectors is constructed and used to group users into clusters. Knowledge of their classification as past senders/receivers of spam or legitimate mail, comming from an auxiliary detection algorithm, is then used to label these...

  4. Detection of Atmospheric Composition Based on Lidar

    Energy Technology Data Exchange (ETDEWEB)

    Zhang Jinye; Tong Yala; Yang Xiaoling; Gong Jiaoli [School of science, Hubei University of Technology, Wuhan 430068 (China); Gong Wei, E-mail: yezi.zh@163.com [State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079 (China)

    2011-02-01

    A summary overview about the types of lidar and their own applications on atmosphere detection is presented. Measurement of atmospheric aerosols by Mie lidar and Raman lidar is focused. The vertical profiles of aerosols in the atmosphere are retrieved. And at the same time, through analyzing aerosol vertical content distribution, the atmosphere boundary layer and the cloud are also observed. All the results show that the lidar has good performance on detecting the atmospheric composition.

  5. Detection of Atmospheric Composition Based on Lidar

    International Nuclear Information System (INIS)

    A summary overview about the types of lidar and their own applications on atmosphere detection is presented. Measurement of atmospheric aerosols by Mie lidar and Raman lidar is focused. The vertical profiles of aerosols in the atmosphere are retrieved. And at the same time, through analyzing aerosol vertical content distribution, the atmosphere boundary layer and the cloud are also observed. All the results show that the lidar has good performance on detecting the atmospheric composition.

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

    Science.gov (United States)

    Craig, William W.; Labov, Simon E.

    2009-06-09

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

  7. 3D Detection, Quantification and Correlation of Slope Failures with Geologic Structure in the Mont Blanc massif

    Science.gov (United States)

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

    2016-04-01

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

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

    International Nuclear Information System (INIS)

    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, Ag, 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, Ag, 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 Ag 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 experts was

  9. 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.

  10. 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.

  11. 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.

  12. Fault diagnosis hybrid system using a Luenberger-based detection filter and neural networks

    Science.gov (United States)

    Tarantino, Rocco; Cabezas, Kathiusca; Rivas-Echeverria, Francklin; Colina-Morles, Eliezer

    2001-03-01

    The present paper proposes a new layout for failure detection and diagnosis in industrial dynamic systems in which, failure vector decoupling is not always possible, due to the failure intrinsic propagation. In this case diagnosis can be determined due to the existing correlation between the failure vector and residual vector time patterns. The greatest benefit of this study is the failure detection method, Luenberger observer based detection filter, through vectorial residual generation combined with the pattern recognition technique based on neural networks theory. The synergy of both methods offer a wider application range to diagnosis problem solutions, in systems under presence of non-decoupled failures.

  13. 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.

  14. Model based traffic congestion detection in optical remote sensing imagery

    OpenAIRE

    Palubinskas, Gintautas; Kurz, Franz; Reinartz, Peter

    2010-01-01

    Purpose A new model based approach for the traffic congestion detection in time series of airborne optical digital camera images is proposed. Methods It is based on the estimation of the average vehicle speed on road segments. The method puts various techniques together: the vehicle detection on road segments by change detection between two images with a short time lag, the usage of a priori information such as road data base, vehicle sizes and road parameters and a si...

  15. The imprecision of heterozygosity-fitness correlations hinders the detection of inbreeding and inbreeding depression in a threatened species.

    Science.gov (United States)

    Grueber, Catherine E; Waters, Jonathan M; Jamieson, Ian G

    2011-01-01

    In nonpedigreed wild populations, inbreeding depression is often quantified through the use of heterozygosity-fitness correlations (HFCs), based on molecular estimates of relatedness. Although such correlations are typically interpreted as evidence of inbreeding depression, by assuming that the marker heterozygosity is a proxy for genome-wide heterozygosity, theory predicts that these relationships should be difficult to detect. Until now, the vast majority of empirical research in this area has been performed on generally outbred, nonbottlenecked populations, but differences in population genetic processes may limit extrapolation of results to threatened populations. Here, we present an analysis of HFCs, and their implications for the interpretation of inbreeding, in a free-ranging pedigreed population of a bottlenecked species: the endangered takahe (Porphyrio hochstetteri). Pedigree-based inbreeding depression has already been detected in this species. Using 23 microsatellite loci, we observed only weak evidence of the expected relationship between multilocus heterozygosity and fitness at individual life-history stages (such as survival to hatching and fledging), and parameter estimates were imprecise (had high error). Furthermore, our molecular data set could not accurately predict the inbreeding status of individuals (as 'inbred' or 'outbred', determined from pedigrees), nor could we show that the observed HFCs were the result of genome-wide identity disequilibrium. These results may be attributed to high variance in heterozygosity within inbreeding classes. This study is an empirical example from a free-ranging endangered species, suggesting that even relatively large numbers (>20) of microsatellites may give poor precision for estimating individual genome-wide heterozygosity. We argue that pedigree methods remain the most effective method of quantifying inbreeding in wild populations, particularly those that have gone through severe bottlenecks. PMID

  16. Nanopore-Based Target Sequence Detection.

    Science.gov (United States)

    Morin, Trevor J; Shropshire, Tyler; Liu, Xu; Briggs, Kyle; Huynh, Cindy; Tabard-Cossa, Vincent; Wang, Hongyun; Dunbar, William B

    2016-01-01

    The promise of portable diagnostic devices relies on three basic requirements: comparable sensitivity to established platforms, inexpensive manufacturing and cost of operations, and the ability to survive rugged field conditions. Solid state nanopores can meet all these requirements, but to achieve high manufacturing yields at low costs, assays must be tolerant to fabrication imperfections and to nanopore enlargement during operation. This paper presents a model for molecular engineering techniques that meets these goals with the aim of detecting target sequences within DNA. In contrast to methods that require precise geometries, we demonstrate detection using a range of pore geometries. As a result, our assay model tolerates any pore-forming method and in-situ pore enlargement. Using peptide nucleic acid (PNA) probes modified for conjugation with synthetic bulk-adding molecules, pores ranging 15-50 nm in diameter are shown to detect individual PNA-bound DNA. Detection of the CFTRΔF508 gene mutation, a codon deletion responsible for ∼66% of all cystic fibrosis chromosomes, is demonstrated with a 26-36 nm pore size range by using a size-enhanced PNA probe. A mathematical framework for assessing the statistical significance of detection is also presented. PMID:27149679

  17. Nanopore-Based Target Sequence Detection

    Science.gov (United States)

    Morin, Trevor J.; Shropshire, Tyler; Liu, Xu; Briggs, Kyle; Huynh, Cindy; Tabard-Cossa, Vincent; Wang, Hongyun; Dunbar, William B.

    2016-01-01

    The promise of portable diagnostic devices relies on three basic requirements: comparable sensitivity to established platforms, inexpensive manufacturing and cost of operations, and the ability to survive rugged field conditions. Solid state nanopores can meet all these requirements, but to achieve high manufacturing yields at low costs, assays must be tolerant to fabrication imperfections and to nanopore enlargement during operation. This paper presents a model for molecular engineering techniques that meets these goals with the aim of detecting target sequences within DNA. In contrast to methods that require precise geometries, we demonstrate detection using a range of pore geometries. As a result, our assay model tolerates any pore-forming method and in-situ pore enlargement. Using peptide nucleic acid (PNA) probes modified for conjugation with synthetic bulk-adding molecules, pores ranging 15-50 nm in diameter are shown to detect individual PNA-bound DNA. Detection of the CFTRΔF508 gene mutation, a codon deletion responsible for ∼66% of all cystic fibrosis chromosomes, is demonstrated with a 26-36 nm pore size range by using a size-enhanced PNA probe. A mathematical framework for assessing the statistical significance of detection is also presented. PMID:27149679

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

    International Nuclear Information System (INIS)

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

  19. Biosensor based on Butyrylcholinesterase for Detection of Carbofuran

    Science.gov (United States)

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

    2015-04-01

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

  20. 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.

  1. Performance of VBLAST Systems Based on Spatial Correlated MIMO Channels

    Institute of Scientific and Technical Information of China (English)

    WANG Zhong-peng; QIU Zhong-yuan; WU Wei-ling

    2004-01-01

    Vertically-layered Bell Laboratories Layered Space-Time (VBLAST) is one of the most promising techniques for realizing high spectral efficiencies over wireless link. In previously published work, the performance of VBLAST has been primarily investigated in uncorrelated Rayleigh fading channels. However in real environments some correlation between antenna elements can be presented. In this paper, we study the impact of transmit correlation on the performance of VBLAST systems. Finally we provide simulation results demonstrating the impact of spatial fading correlation on the symbol error rate of VBLAST.

  2. 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...... the algorithm is capable of detecting four different faults in the mechanical and hydraulic parts of the pump....

  3. Brain Tumor Detection Based On Symmetry Information

    OpenAIRE

    G., Narkhede Sachin; Khairnar, Vaishali

    2013-01-01

    Advances in computing technology have allowed researchers across many fields of endeavor to collect and maintain vast amounts of observational statistical data such as clinical data, biological patient data, data regarding access of web sites, financial data, and the like. This paper addresses some of the challenging issues on brain magnetic resonance (MR) image tumor segmentation caused by the weak correlation between magnetic resonance imaging (MRI) intensity and anatomical meaning. With th...

  4. Thresholding-based reconstruction of compressed correlated signals

    CERN Document Server

    Fawzi, Alhussein; Frossard, Pascal

    2011-01-01

    We consider the problem of recovering a set of correlated signals (e.g., images from different viewpoints) from a few linear measurements per signal. We assume that each sensor in a network acquires a compressed signal in the form of linear measurements and sends it to a joint decoder for reconstruction. We propose a novel joint reconstruction algorithm that exploits correlation among underlying signals. Our correlation model considers geometrical transformations between the supports of the different signals. The proposed joint decoder estimates the correlation and reconstructs the signals using a simple thresholding algorithm. We give both theoretical and experimental evidence to show that our method largely outperforms independent decoding in terms of support recovery and reconstruction quality.

  5. Flow-Based Detection of DNS Tunnels

    NARCIS (Netherlands)

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

    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 techniq

  6. Contour Detection Operators Based on Surround Inhibition

    NARCIS (Netherlands)

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

    2003-01-01

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

  7. 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 techniq

  8. Sampling of general correlators in worm-algorithm based simulations

    Science.gov (United States)

    Rindlisbacher, Tobias; Åkerlund, Oscar; de Forcrand, Philippe

    2016-08-01

    Using the complex ϕ4-model as a prototype for a system which is simulated by a worm algorithm, we show that not only the charged correlator , but also more general correlators such as or , as well as condensates like , can be measured at every step of the Monte Carlo evolution of the worm instead of on closed-worm configurations only. The method generalizes straightforwardly to other systems simulated by worms, such as spin or sigma models.

  9. Sampling of General Correlators in Worm Algorithm-based Simulations

    CERN Document Server

    Rindlisbacher, Tobias; de Forcrand, Philippe

    2016-01-01

    Using the complex $\\phi^4$-model as a prototype for a system which is simulated by a (bosonic) worm algorithm, we show that not only the charged correlator $$, but also more general correlators such as $$ or $$ as well as condensates like $$ can be measured at every step of the Monte Carlo evolution of the worm instead of on closed-worm configurations only. The method generalizes straightforwardly to other systems simulated by (bosonic) worms, such as spin or sigma models.

  10. Financial Fraud Detection Model Based on Random Forest

    OpenAIRE

    Liu, Chengwei; Chan, Yixiang; Alam Kazmi, Syed Hasnain; Fu, Hao

    2015-01-01

    Business's accelerated globalization has weakened regulatory capacity of the law and scholars have been paid attention to fraud detection in recent years. In this study, we introduced Random Forest (RF) for financial fraud technique detection and detailed features selection, variables’ importance measurement, partial correlation analysis and Multidimensional analysis. The results show that a combination of eight variables has the highest accuracy. The ratio of debt to equity (DEQUTY) is the m...

  11. A Novel Datamining Based Approach for Remote Intrusion Detection

    Directory of Open Access Journals (Sweden)

    Renu Deepti.S, Loshma.G

    2012-06-01

    Full Text Available Today, as information systems are more open to the Internet,attacks and intrusions are also increasing rapidly so the importance of secure networks is also vital. New intelligent Intrusion Detection Systems which are based on sophisticated algorithms are in demand.Intrusion Detection System (IDS is an important detection used as a countermeasure to preserve data integrity and system availability from attacks. It is a combination of software and hardware that attempts to perform intrusion detection.In data mining based intrusion detection system, we should make use of particular domain knowledge in relation to intrusion detection in order to efficiently extract relative rules from large amounts of records.This paper proposes boosting method for intrusion detection and it is possible to detect the intrusions in all the Systems, without installing the Software in client System (like client-server via Web service (Apache tomcat by using the ip address of the client system.

  12. The switch rail detection system based on laser sensor

    Directory of Open Access Journals (Sweden)

    Sa Ji Ming

    2016-01-01

    Full Text Available As a carrier, turnout is an extremely important part of transport, when railways is operating. So the detection of turnout should meet the requirement. However, the detection effort is mainly completed manually at the present stage, which is low accuracy. Thus the study of the switch rail detection system based on laser sensor is necessary. In this paper, we discuss the scheme of the switch rail detection by using Gocator 2030 laser sensor and SIMENS 840D numerical control system. We study the algorithm and data collection of the switch rail detection based on laser sensor. This detection system provides accurate data collection and information display for the enterprise of producing turnout. After the test, the system has a faster detection speed and higher detection accuracy.

  13. 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.

  14. 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

  15. Generalization of GLRT-Based Magnetic Anomaly Detection

    OpenAIRE

    Pepe, Pascal; Zozor, Steeve; Rouve, Laure-Line; Coulomb, Jean-Louis; Servière, Christine; Muley, Jean

    2015-01-01

    International audience Magnetic anomaly detection (MAD) refers to a passive method used to reveal hidden magnetic masses and is most commonly based on a dipolar target model. This paper proposes a generalization of the MAD through a multipolar model that provides a more precise description of the anomaly and serves a twofold objective: to improve the detection performance , and to widen the variety of detectable targets. The dipole detection strategy – namely an orthonormal decomposition o...

  16. 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

  17. Hybrid Collision Detection Algorithm based on Image Space

    Directory of Open Access Journals (Sweden)

    XueLi Shen

    2013-07-01

    Full Text Available Collision detection is an important application in the field of virtual reality, and efficiently completing collision detection has become the research focus. For the poorly real-time defect of collision detection, this paper has presented an algorithm based on the hybrid collision detection, detecting the potential collision object sets quickly with the mixed bounding volume hierarchy tree, and then using the streaming pattern collision detection algorithm to make an accurate detection. With the above methods, it can achieve the purpose of balancing load of the CPU and GPU and speeding up the detection rate. The experimental results show that compared with the classic Rapid algorithm, this algorithm can effectively improve the efficiency of collision detection.

  18. BER analysis of MPSK space-time code with differential detection over correlated block-fading Rayleigh channel

    Institute of Scientific and Technical Information of China (English)

    ZOU Yu-long; ZHENG Bao-yu

    2008-01-01

    MIMO technology proposed in recent years can effectively combat the multipath fading of wireless channel and can considerably enlarge the channel capacity, which has been investigated widely by researchers. However, its performance analysis over correlated block-fading Rayleigh channel is still an open and challenging objective. In this article, an analytic expression of bit error rate (BER) is presented for multiple phase shift keying (MPSK) space-time code, with differential detection over correlated block-fading Rayleigh channel. Through theoretical analysis of BER, it can be found that the differential space-time scheme without the need for channel state information (CSI) at receiver achieves distinct performance gain compared with the traditional nonspace-time system. And then, the system simulation is complimented to verify the above result, showing that the diversity system based on the differential space-time block coding (DSTBC) outperforms the traditional nonspace- time system with diversity gain in terms of BER. Furthermore, the numerical results also demonstrate that the error floor of the differential space-time system is much lower than that of the differential nonspace-time system.

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

    Directory of Open Access Journals (Sweden)

    Yoshiteru Ishida

    2009-11-01

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

  20. 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.

  1. Aptamer Based Microsphere Biosensor for Thrombin Detection

    OpenAIRE

    Xudong Fan; White, Ian M.; Suter, Jonathan D.; Hongying Zhu

    2006-01-01

    We have developed an optical microsphere resonator biosensor using aptamer as receptor for the measurement of the important biomolecule thrombin. The sphere surface is modified with anti-thrombin aptamer, which has excellent binding affinity and selectivity for thrombin. Binding of the thrombin at the sphere surface is monitored by the spectral position of the microsphere's whispering gallery mode resonances. A detection limit on the order of 1 NIH Unit/mL is demonstrated. Control experiments...

  2. Testing radar-based hail detection criteria

    Czech Academy of Sciences Publication Activity Database

    Skripniková, Kateřina; Řezáčová, Daniela

    Toulouse : Météo France, 2012. [ERAD 2012 - European Conference on Radar in Meteorology and Hydrology /7./. Toulouse (FR), 24.06.2012-29.06.2012] R&D Projects: GA ČR(CZ) GAP209/11/2045; GA MŠk LD11044 Institutional support: RVO:68378289 Keywords : hail detection * weather radar * damaging hailstorms Subject RIV: DG - Athmosphere Sciences, Meteorology http://www.meteo.fr/cic/meetings/2012/ERAD/extended_abs/NOW_367_ext_abs.pdf

  3. Improving Seroreactivity-Based Detection of Glioma

    OpenAIRE

    Nicole Ludwig; Andreas Keller; Sabrina Heisel; Petra Leidinger; Veronika Klein; Stefanie Rheinheimer; Andres, Claudia U; Bernhard Stephan; Wolf-Ingo Steudel; Graf, Norbert M; Bernhard Burgeth; Joachim Weickert; Hans-Peter Lenhof; Eckart Meese

    2009-01-01

    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 ...

  4. FACIAL EXPRESSION RECOGNITION BASED ON EDGE DETECTION

    OpenAIRE

    Chen, Xiaoming; Cheng, Wushan

    2015-01-01

    Relational Over the last two decades, the advances in computer vision and pattern recognition power have opened the door to new opportunity of automatic facial expression recognition system[1]. This paper use Canny edge detection method for facial expression recognition. Image color space transformation in the first place and then to identify and locate human face .Next pick up the edge of eyes and mouth's features extraction. Last we judge the facial expressions after compared wi...

  5. Coupled-cluster based basis sets for valence correlation calculations

    Science.gov (United States)

    Claudino, Daniel; Gargano, Ricardo; Bartlett, Rodney J.

    2016-03-01

    Novel basis sets are generated that target the description of valence correlation in atoms H through Ar. The new contraction coefficients are obtained according to the Atomic Natural Orbital (ANO) procedure from CCSD(T) (coupled-cluster singles and doubles with perturbative triples correction) density matrices starting from the primitive functions of Dunning et al. [J. Chem. Phys. 90, 1007 (1989); ibid. 98, 1358 (1993); ibid. 100, 2975 (1993)] (correlation consistent polarized valence X-tuple zeta, cc-pVXZ). The exponents of the primitive Gaussian functions are subject to uniform scaling in order to ensure satisfaction of the virial theorem for the corresponding atoms. These new sets, named ANO-VT-XZ (Atomic Natural Orbital Virial Theorem X-tuple Zeta), have the same number of contracted functions as their cc-pVXZ counterparts in each subshell. The performance of these basis sets is assessed by the evaluation of the contraction errors in four distinct computations: correlation energies in atoms, probing the density in different regions of space via (-3 ≤ n ≤ 3) in atoms, correlation energies in diatomic molecules, and the quality of fitting potential energy curves as measured by spectroscopic constants. All energy calculations with ANO-VT-QZ have contraction errors within "chemical accuracy" of 1 kcal/mol, which is not true for cc-pVQZ, suggesting some improvement compared to the correlation consistent series of Dunning and co-workers.

  6. Fuzzy Based Anomaly Intrusion Detection System for Clustered WSN

    OpenAIRE

    Sumathy Murugan; Sundara Rajan, M.

    2015-01-01

    In Wireless Sensor Networks (WSN), the intrusion detection technique may result in increased computational cost, packet loss, performance degradation and so on. In order to overcome these issues, in this study, we propose a fuzzy based anomaly intrusion detection system for clustered WSN. Initially the cluster heads are selected based on the parameters such as link quality, residual energy and coverage. Then the anomaly intrusion is detected using fuzzy logic technique. This technique conside...

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

    Science.gov (United States)

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

    2007-11-01

    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.

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

    International Nuclear Information System (INIS)

    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

  9. Method for detecting software anomalies based on recurrence plot analysis

    Directory of Open Access Journals (Sweden)

    Michał Mosdorf

    2012-03-01

    Full Text Available Presented paper evaluates method for detecting software anomalies based on recurrence plot analysis of trace log generated by software execution. Described method for detecting software anomalies is based on windowed recurrence quantification analysis for selected measures (e.g. Recurrence rate - RR or Determinism - DET. Initial results show that proposed method is useful in detecting silent software anomalies that do not result in typical crashes (e.g. exceptions.

  10. Seizure Onset Detection based on one sEMG channel

    DEFF Research Database (Denmark)

    Conradsen, Isa; Beniczky, Sandor; Hoppe, Karsten; Wolf, Peter; Sams, Thomas; Sørensen, Helge Bjarup Dissing

    We present a new method to detect seizure onsets of tonic-clonic epileptic seizures based on surface electromyography (sEMG) data. The proposed method is generic and based on a single channel making it ideal for a small detection or monitoring device. The sEMG signal is high-pass filtered with a...

  11. Brief Communication: Contrast stretching and histogram smoothness based flood detection

    Directory of Open Access Journals (Sweden)

    F. Nazir

    2014-08-01

    Full Text Available Synthetic aperture radar images used for flood detection often have degraded contrast, which consequently leads to inaccurate flood maps. A technique for flood detection based on contrast stretching and histogram smoothing is proposed. The proposed technique applies different processing steps (based on contrast stretching and histogram smoothness on pre, post and difference images to improve visualization by maintaining the natural smoothness.

  12. Brief Communication: Contrast stretching and histogram smoothness based flood detection

    OpenAIRE

    F. Nazir; Riaz, M. M.; GHAFOOR, A.; F. Arif

    2014-01-01

    Synthetic aperture radar images used for flood detection often have degraded contrast, which consequently leads to inaccurate flood maps. A technique for flood detection based on contrast stretching and histogram smoothing is proposed. The proposed technique applies different processing steps (based on contrast stretching and histogram smoothness) on pre, post and difference images to improve visualization by maintaining the natural smoothness.

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

    International Nuclear Information System (INIS)

    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 PM2.5 and multi-wavelength and multi-angular DOLP is carried out. The result shows that DOLP has a high correlation with mass density of PM2.5, R2>0.85. As a consequence, this work provides a new method to estimate the mass density of PM2.5 by using the comprehensive network of ground-based sun photometer

  14. A novel pitch evaluation of one-dimensional gratings based on a cross-correlation filter

    International Nuclear Information System (INIS)

    If one-dimensional (1D), p-period and arbitrarily structured grating position-related topographical signals coexist with noise, it is difficult to evaluate the pitch practically using the center-of-gravity (CG) method. The Fourier-transform-based (FT) method is the most precise to evaluate pitches; nevertheless, it cannot give the uniformity of pitches. If a cross-correlation filter—a half period of sinusoidal waveform sequence (pT period), cross-correlates with the signals, the noise can be eliminated if pT ≈ p. After cross-correlation filtering, the distance between any two adjacent waveform peaks along the direction perpendicular to 1D grating lines is one pitch value. The pitch evaluation based on the cross-correlation filtering together with the detection of the peak's position is described as the peak detection (PD) method in this paper. The pitch average and uniformity can be calculated by using the PD method. The computer simulation has indicated that the average of pitch deviations from the true pitch and the pitch variations are less than 0.2% and 0.2% for the sinusoidal and rectangular waveform signals with up to 50% uniform white noise, less than 0.1% and 1% for the sinusoidal and rectangular waveform signals and 0.6% and 2.5% for the triangular waveform signal if three waveform signals are mixed with Gaussian white, binomial and Bernoulli noise up to 50% in standard deviation, one probability and trial probability, respectively. As examples, a highly oriented pyrolytic graphite (HOPG) with a 0.246 nm distance between second nearest neighbour atoms and a 1D grating with 3000 nm nominal pitch are measured by a ultra-high vacuum scanning tunneling microscope (UHV STM) and a metrological atomic force microscope (AFM), respectively. After the position-related topographical signals are cross-correlation filtered, the 0.240 and 3004.11 nm pitches calculated by using the PD method are very close to the 0.240 and 3003.34 nm results evaluated by the

  15. 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.

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

    International Nuclear Information System (INIS)

    %, 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

  17. Background Subtraction Algorithm Based Human Behavior Detection

    Directory of Open Access Journals (Sweden)

    Prof. D. D. Dighe

    2014-07-01

    Full Text Available Consider all the features of subset information in video streaming there is a tremendous processes with real time applications. In this paper we introduce and develop a new video surveillance system. Using this technique we detect human normal and exponential behaviors in realistic format, and also we categories data event generation of human tracking in real time applications. In this technique we apply differencing, threshold segmentation, morphological operations and object tracking. The experimental result show efficient human tracking in video streaming operations.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ertel, Dirk; Kachelriess, Marc; Kalender, Willi A. [University of Erlangen-Nuernberg, Institute of Medical Physics (IMP), Erlangen (Germany); Pflederer, Tobias; Achenbach, Stephan [University of Erlangen-Nuernberg, Department of Internal Medicine II, Erlangen (Germany); Steffen, Peter [University of Erlangen-Nuernberg, Multimedia Communications and Signal Processing, Erlangen (Germany)

    2008-02-15

    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.)

  19. 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.

  20. 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

  1. Clustering and information in correlation based financial networks

    Science.gov (United States)

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

    2004-03-01

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

  2. Wavelet-Based Correlation Analysis of the Key Traded Assets

    Czech Academy of Sciences Publication Activity Database

    Baruník, J.; Kočenda, Evžen; Vácha, L.

    Vol. 20. Cham: Springer, 2014, s. 157-183. ISBN 978-3-319-07060-5 R&D Projects: GA ČR GA14-24129S Institutional support: PRVOUK-P23 Keywords : time-frequency dynamics * high-frequency data * dynamic correlation Subject RIV: AH - Economics

  3. 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.

  4. A Vehicle Detection Algorithm Based on Deep Belief Network

    Directory of Open Access Journals (Sweden)

    Hai Wang

    2014-01-01

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

  5. A vehicle detection algorithm based on deep belief network.

    Science.gov (United States)

    Wang, Hai; Cai, Yingfeng; Chen, Long

    2014-01-01

    Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. Traditional shallow model based vehicle detection algorithm still cannot meet the requirement of accurate vehicle detection in these applications. In this work, a novel deep learning based vehicle detection algorithm with 2D deep belief network (2D-DBN) is proposed. In the algorithm, the proposed 2D-DBN architecture uses second-order planes instead of first-order vector as input and uses bilinear projection for retaining discriminative information so as to determine the size of the deep architecture which enhances the success rate of vehicle detection. On-road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets. PMID:24959617

  6. 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.

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

    Directory of Open Access Journals (Sweden)

    Nepomuceno Juan A

    2011-01-01

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

  8. 2D Satellite Image Registration Using Transform Based and Correlation Based Methods

    OpenAIRE

    Dr. H.B. Kekre, Dr. Tanuja K. Sarode, Ms. Ruhina B. Karani

    2012-01-01

    Image registration is the process of geometrically aligning one image to another image of the same scene taken from different viewpoints or by different sensors. It is a fundamental image processing technique and is very useful in integrating information from different sensors, finding changes in images taken at different times and inferring three-dimensional information from stereo images. Image registration can be done by using two matching method: transform based methods and correlation ba...

  9. 2D Satellite Image Registration Using Transform Based and Correlation Based Methods

    Directory of Open Access Journals (Sweden)

    Dr. H.B. Kekre, Dr. Tanuja K. Sarode, Ms. Ruhina B. Karani

    2012-05-01

    Full Text Available Image registration is the process of geometrically aligning one image to another image of the same scene taken from different viewpoints or by different sensors. It is a fundamental image processing technique and is very useful in integrating information from different sensors, finding changes in images taken at different times and inferring three-dimensional information from stereo images. Image registration can be done by using two matching method: transform based methods and correlation based methods. When image registration is done using correlation based methods like normalized cross correlation, the results are slow. They are also computationally complex and sensitive to the image intensity changes which are caused by noise and varying illumination. In this paper, an unusual form of image registration is proposed which focuses upon using various transforms for fast and accurate image registration. The data set can be a set of photographs, data from various sensors, from different times, or from different viewpoints. The applications of image registration are in the field of computer vision, medical imaging, military automatic target recognition, and in analyzing images and data from satellites. The proposed technique works on satellite images. It tries to find out area of interest by comparing the unregistered image with source image and finding the part that has highest similarity matching. The paper mainly works on the concept of seeking water or land in the stored image. The proposed technique uses different transforms like Discrete Cosine Transform, Discrete Wavelet Transform, HAAR Transform and Walsh transform to achieve accurate image registration. The paper also focuses upon using normalized cross correlation as an area based technique of image registration for the purpose of comparison. The root mean square error is used as similarity measure. Experimental results show that the proposed algorithm can successfully register the

  10. Multi-features Based Approach for Moving Shadow Detection

    Institute of Scientific and Technical Information of China (English)

    ZHOU Ning; ZHOU Man-li; XU Yi-ping; FANG Bao-hong

    2004-01-01

    In the video-based surveillance application, moving shadows can affect the correct localization and detection of moving objects. This paper aims to present a method for shadow detection and suppression used for moving visual object detection. The major novelty of the shadow suppression is the integration of several features including photometric invariant color feature, motion edge feature, and spatial feature etc. By modifying process for false shadow detected, the averaging detection rate of moving object reaches above 90% in the test of Hall-Monitor sequence.

  11. Towards aerial natural gas leak detection system based on TDLAS

    Science.gov (United States)

    Liu, Shuyang; Zhou, Tao; Jia, Xiaodong

    2014-11-01

    Pipeline leakage is a complex scenario for sensing system due to the traditional high cost, low efficient and labor intensive detection scheme. TDLAS has been widely accepted as industrial trace gas detection method and, thanks to its high accuracy and reasonable size, it has the potential to meet pipeline gas leakage detection requirements if it combines with the aerial platform. Based on literature study, this paper discussed the possibility of applying aerial TDLAS principle in pipeline gas leak detection and the key technical foundation of implementing it. Such system is able to result in a high efficiency and accuracy measurement which will provide sufficient data in time for the pipeline leakage detection.

  12. Metabolic syndrome correlates intracoronary stenosis detected by multislice computed tomography in male subjects with sleep-disordered breathing

    Directory of Open Access Journals (Sweden)

    Nakanishi-Minami Tomoko

    2012-03-01

    Full Text Available Abstract Background Sleep-disordered breathing (SDB, especially obstructive sleep apnea (OSA, has frequent complications include hypertension, dyslipidemia and insulin resistance based on abdominal obesity or excess visceral fat (called Syndrome Z. OSA is a potential risk factor for cardiovascular diseases. The clinical characteristics of Japanese OSA subjects with OSA remain unclear. The present study investigated prevalence and predictive factors of intracoronary stenosis detected by multislice computed tomography (MSCT in Japanese male subjects with SDB/OSA. Findings The study (O-VFStudy subjects were 39 Japanese men with SDB/OSA who underwent all-night cardiorespiratory monitoring with fully attended polysomnography, and moreover both fat computed tomography (CT scan and 64-row MSCT coronary angiography. The prevalence of coronary stenosis in this selected population with SDB/OSA was 15%. Logistic regression analysis showed a significant relationship between age-adjusted CAD and metabolic syndrome (p p = 0.033, and lower levels of serum adiponectin (4.5 ± 0.6 versus 6.4 ± 0.6 μg/mL, p = 0.014, compared with groups without the metabolic syndrome. Conclusions The present study describes that the prevalence of greater than 50% intracoronary stenotic lesions detected by MSCT was 15% and the metabolic syndrome was correlated with intracoronary stenosis detected by MSCT in Japanese SDB/OSA subjects. Trial Registration UMIN 000002997 https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr.cgi?function=brows&action=brows&type=summary&recptno=R000003633&language=E.

  13. An audio-based sports video segmentation and event detection algorithm

    OpenAIRE

    Baillie, M.; Jose, J.M.

    2004-01-01

    In this paper, we present an audio-based event detection algorithm shown to be effective when applied to Soccer video. The main benefit of this approach is the ability to recognise patterns that display high levels of crowd response correlated to key events. The soundtrack from a Soccer sequence is first parameterised using Mel-frequency Cepstral coefficients. It is then segmented into homogenous components using a windowing algorithm with a decision process based on Bayesian model selection....

  14. Intrusion Detection Systems Based On Packet Sniffing

    Directory of Open Access Journals (Sweden)

    Ushus Maria Joseph

    2013-01-01

    Full Text Available In the present era of networks, security of network systems is becoming increasingly important, as more and more sensitive information is being stored and manipulated online. The paper entitled ’Packet Sniffing’ is a IDS where it monitors packets on the network wire and attempts to the discovery of hacker/cracker who is attempting to break into system. Packet Sniffing also finds the contents and tracks the data packet in the network system. This sniffing is being performed by comparing the captured packet with the intruder details stored in the database .If the packet is found to be an intruder it is then forwarded to the firewall with the respective message for blocking. The Emotional Ants module contains the sender and receiver .The sender will inform all the other Ants running in other machines about the detection of intruder through his pheromone (Messages. The receiver in Ants will listen for the messages from other Ants

  15. 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.

  16. 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.

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

    International Nuclear Information System (INIS)

    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) (99mTc-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 depression

  18. Color night vision method based on the correlation between natural color and dual band night image

    Science.gov (United States)

    Zhang, Yi; Bai, Lian-fa; Zhang, Chuang; Chen, Qian; Gu, Guo-hua

    2009-07-01

    Color night vision technology can effectively improve the detection and identification probability. Current color night vision method based on gray scale modulation fusion, spectrum field fusion, special component fusion and world famous NRL method, TNO method will bring about serious color distortion, and the observers will be visual tired after long time observation. Alexander Toet of TNO Human Factors presents a method to fuse multiband night image a natural day time color appearance, but it need the true color image of the scene to be observed. In this paper we put forward a color night vision method based on the correlation between natural color image and dual band night image. Color display is attained through dual-band low light level images and their fusion image. Actual color image of the similar scene is needed to obtain color night vision image, the actual color image is decomposed to three gray-scale images of RGB color module, and the short wave LLL image, long wave LLL image and their fusion image are compared to them through gray-scale spatial correlation method, and the color space mapping scheme is confirmed by correlation. Gray-scale LLL images and their fusion image are adjusted through the variation of HSI color space coefficient, and the coefficient matrix is built. Color display coefficient matrix of LLL night vision system is obtained by multiplying the above coefficient matrix and RGB color space mapping matrix. Emulation experiments on general scene dual-band color night vision indicate that the color display effect is approving. This method was experimented on dual channel dual spectrum LLL color night vision experimental apparatus based on Texas Instruments digital video processing device DM642.

  19. 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.

  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

    2016-01-01

    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 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. PMID:26938537

  1. 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.

  2. Wavelet-Based Correlation Analysis of the Key Traded Assets

    Czech Academy of Sciences Publication Activity Database

    Baruník, Jozef; Kočenda, Evžen; Vácha, Lukáš

    Vol. 20. Cham: Springer, 2014, s. 157-183. ISBN 978-3-319-07060-5 R&D Projects: GA ČR(CZ) GA13-24313S; GA ČR(CZ) GA14-24129S Institutional support: RVO:67985556 ; RVO:67985998 Keywords : time-frequency dynamics * high-frequency data * dynamic correlation Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2014/E/barunik-0434205.pdf

  3. Correlation Preserved Indexing Based Approach For Document Clustering

    Directory of Open Access Journals (Sweden)

    Meena.S.U, P.Parthasarathi

    2013-02-01

    Full Text Available Document clustering is the act of collecting similar documents into clusters, where similarity is some function on a document. Document clustering method achieves 1 a high accuracy for documents 2 document frequency can be calculated 3 term weight is calculated with the term frequency vector. Document clustering is closely related to the concept of data clustering. Document clustering is a more specific technique for unsupervised document organization, automatic topic extraction and fast information retrieval or filtering. Clustering methods can be used to automatically group the retrieved documents into a list of meaningful categories. The correlation preserving indexing method is performed to find the correlation between the documents. The Term Frequency-Inverse Document Frequency (TF-IDF method is used to find the frequency of occurrence of words in each document. The disadvantage of this method is computation complexity. In this paper Significant Score Calculation method is introduced, where similarity between the words are calculated using word net tool. Here the related words are identified. The 98% accuracy is occurred with significant score calculation for finding correlation preserving indexing.

  4. Chord Recognition Based on Temporal Correlation Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhongyang Rao

    2016-05-01

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

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

    OpenAIRE

    Kleinberg, B; 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...

  6. 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.

  7. Digital image restoration based on pixel simultaneous detection probabilities

    CERN Document Server

    Grabskii, V

    2008-01-01

    Here an image restoration on the basis of pixel simultaneous detection probabilities (PSDP) is proposed. These probabilities can be precisely determined by means of correlations measurement [NIMA 586 (2008) 314-326]. The proposed image restoration is based on the solution of matrix equation. Non-zero elements of Toeplitz block matrix with ones on the main diagonal, is determined using PSDP. The number of non zero descending diagonals depends on the detector construction and is not always smaller than 8. To solve the matrix equation, the Gaussian elimination algorithm is used. The proposed restoration algorithm is studied by means of the simulated images (with and without additive noise using PSDP for General Electric Senographe 2000D mammography device detector) and a small area (160x160 pixels) of real images acquired by the above mentioned device. The estimation errors of PSDP and the additive noise magnitude permits to restore images with the precision better than 2% for the above mentioned detector. The a...

  8. Experiments on Adaptive Techniques for Host-Based Intrusion Detection

    International Nuclear Information System (INIS)

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

  9. Shape-Based Plagiarism Detection for Flowchart Figures in Texts

    Directory of Open Access Journals (Sweden)

    Senosy Arrish

    2014-02-01

    Full Text Available Plagiarism detection is well known phenomenon in the academic arena. Copying other people is considered as serious offence that needs to be checked. There are many plagiarism detection systems such as turn-it-in that has been developed to provide this checks. Most, if not all, discard the figures and charts before checking for plagiarism. Discarding the figures and charts results in look holes that people can take advantage. That means people can plagiarized figures and charts easily without the current plagiarism systems detecting it. There are very few papers which talks about flowcharts plagiarism detection. Therefore, there is a need to develop a system that will detect plagiarism in figures and charts. This paper presents a method for detecting flow chart figure plagiarism based on shape-based image processing and multimedia retrieval. The method managed to retrieve flowcharts with ranked similarity according to different matching sets.

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

    Directory of Open Access Journals (Sweden)

    V. Dehghanian

    2012-01-01

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

  11. Color pattern recognition based on the joint fractional Fourier transform correlator

    Institute of Scientific and Technical Information of China (English)

    Weimin Jin; Yupei Zhang

    2007-01-01

    A new system of multi-channel single-output joint fractional Fourier transform correlator (JFRTC) for color pattern recognition is proposed based on the conventional system of multi-channel single-output joint transform correlator (JTC). The theoretical analysis and optical experiments are performed. With this method, one can obtain three correlation peaks at the output plane which show a pair of desired cross-correlation peaks and one auto-correlation peak. In comparison, the conventional system leads to more correlation peaks playing a noise role in color pattern recognition.

  12. Minimum variance imaging based on correlation analysis of Lamb wave signals.

    Science.gov (United States)

    Hua, Jiadong; Lin, Jing; Zeng, Liang; Luo, Zhi

    2016-08-01

    In Lamb wave imaging, MVDR (minimum variance distortionless response) is a promising approach for the detection and monitoring of large areas with sparse transducer network. Previous studies in MVDR use signal amplitude as the input damage feature, and the imaging performance is closely related to the evaluation accuracy of the scattering characteristic. However, scattering characteristic is highly dependent on damage parameters (e.g. type, orientation and size), which are unknown beforehand. The evaluation error can degrade imaging performance severely. In this study, a more reliable damage feature, LSCC (local signal correlation coefficient), is established to replace signal amplitude. In comparison with signal amplitude, one attractive feature of LSCC is its independence of damage parameters. Therefore, LSCC model in the transducer network could be accurately evaluated, the imaging performance is improved subsequently. Both theoretical analysis and experimental investigation are given to validate the effectiveness of the LSCC-based MVDR algorithm in improving imaging performance. PMID:27155349

  13. Parallelization Research of Circle Detection Based on Hough Transform

    OpenAIRE

    Suping Wu; Xiangjiao Liu

    2012-01-01

    There is a problem of too long computation time in Circle detection of Hough transform. In this paper, two paralleled methods are given based on Threading Building Blocks (TBB) and CUDA, by utilizing multi-core and GPU, the most timeconsuming part of circle detection is coped with parallelization. Experimental results show that the circle detection algorithms proposed in this paper has extremely good result of acceleration.

  14. Evidence-based detection of pulmonary arterial hypertension in systemic sclerosis: the DETECT study

    NARCIS (Netherlands)

    Coghlan, J.G.; Denton, C.P.; Grunig, E.; Bonderman, D.; Distler, O.; Khanna, D.; Muller-Ladner, U.; Pope, J.E.; Vonk, M.C.; Doelberg, M.; Chadha-Boreham, H.; Heinzl, H.; Rosenberg, D.M.; McLaughlin, V.V.; Seibold, J.R.

    2014-01-01

    OBJECTIVE: Earlier detection of pulmonary arterial hypertension (PAH), a leading cause of death in systemic sclerosis (SSc), facilitates earlier treatment. The objective of this study was to develop the first evidence-based detection algorithm for PAH in SSc. METHODS: In this cross-sectional, intern

  15. 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

  16. Emergent Disorder Phenomena in Correlated Fe-Based Superconductors

    DEFF Research Database (Denmark)

    Navarro Gastiasoro, Maria

    assumption that the observed electronic phases are captured within the itinerant electron scenario. We investigate competing spin density wave phases in homogeneous systems, but also in disordered systems where the interaction between impurity moments becomes relevant. The theory of emergent states around...... potentials in multi-band systems is introduced, and we propose strongly anisotropic defect states as a source of the reported transport anisotropy. Finally, we discuss unconventional correlation-driven disorder phenomena in the superconducting state, revealing a highly unusual impurity response....

  17. SCREEN photometric property detection system based on area CCD

    Science.gov (United States)

    Yan, Fu-cai; Ye, Wei; Xu, Yu; Wang, Chao; Zhang, Yu-wei

    2011-08-01

    The photometric property detection of screen display is crucial for screen display quality test. Traditional photometry detection technologies were based on photoelectric sensors such as silicon photocell, photo-electric multiplier and CdS, which can detect only some isolated points. To break the limitation of randomness, incompleteness and detection accuracy in current technologies, we designed a screen photometric detection system based on area CCD. The system consists of photometric image sensor, photometric image acquisition hardware and photometric image analyzing software. The photometric image sensor, which adopts optical lens, optical filters and area CCD, adapts its spectrum response property to fit the spectrum luminous efficiency curve V (λ) by adjusting the thickness and quantity of appropriate optical filters. photometric image acquisition hardware adopts the DSP as a core processor to drive the area CCD, to sample, acquire , process and save the image from image sensor, to transmit the image to computer. For real-time performance of transmitting, the hardware system adopts the transmission protocol of USB2.0. The uploaded image will be processed by photometric image analyzing software, and then displayed in real time with detection results. The screen photometric detection technology based on area CCD can detect specifications of the whole screen such as luminance, contrast, onoff ratio and uniformity, breaks the limitation of randomness and incompleteness in current detection technology, exactly and fully reflects the integrated display quality of the whole screen. According to the test results, the accuracy of this system has reached the accuracy level one in China.

  18. 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.

  19. Simple and easy estimation of network properties based on linear correlation analysis

    OpenAIRE

    Yanhong Qi

    2015-01-01

    An ecological network can be constructed by calculating the sampling data of taxon by sample type. A statistically significant Pearson linear correlation means an indirect or direct linear interaction between two taxa, and a statistically significant partial (net, or pure) correlation based on Pearson linear correlation means a candidate direct linear interaction between two taxa. In many cases, statistically significant partial correlations are not available, or we only need to estimate some...

  20. 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.

  1. Weak Signal Frequency Detection Method Based on Generalized Duffing Oscillator

    Institute of Scientific and Technical Information of China (English)

    SHI Si-Hong; YUAN Yong; WANG Hui-Qi; LUO Mao-Kang

    2011-01-01

    @@ The sensitive characteristic to the initial value of chaos system sufficiently demonstrates the superiority in weak signal parameters detection.Analyzing the current chaos-based frequency detection method, a novel generalized Duffing equation is proposed to detect weak signal frequency.By choosing a suitable adjusting factor, when the outside driving force frequency is equal to that of the detected signal, the generalized Duffing oscillator is in great period state, which can obtain the frequency information of the detected signal.The simulation results indicate this method is rapidly convenient and shows better accuracy.%The sensitive characteristic to the initial value of chaos system sufficiently demonstrates the superiority in weak signal parameters detection. Analyzing the current chaos-based frequency detection method, a novel generalized Duffing equation is proposed to detect weak signal frequency. By choosing a suitable adjusting factor, when the outside driving force frequency is equal to that of the detected signal, the generalized Duffing oscillator is in great period state, which can obtain the frequency information of the detected signal. The simulation results indicate this method is rapidly convenient and shows better accuracy.

  2. 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 effect

  3. Pixel based skin detection: asurvey and suggestions for implementation

    NARCIS (Netherlands)

    Monaci, G.

    2009-01-01

    Skin detection is one of the building blocks of many image processing and computer vision algorithms. Within the Video Processing group at Philips Research we are investigating the use of skin detection algorithms for a number of core applications. In this report we focus on skin color based methods

  4. 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...... correlation 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...

  5. Detecting weak seismicity in urban areas using waveform stacking and cross correlation: Application to a stimulation experiment

    Science.gov (United States)

    Plenkers, K.; Ritter, J.; Schindler, M.

    2012-04-01

    Urban noise often prevents the detection of microseismicity (ML sites, where the need to perform reliable hazard assessment has increased the interest in small seismic events. We study the microseismicity (ML searched for. Therefore, the most promising detections, selected by coinciding triggers and high amplitude measures, are reviewed manually. In this way we are able to identify 13 seismic events. Finally, we conduct a cross correlation analysis. As master template we use the stacked waveforms of five manually detected seismic events with a repeating waveform. This search reveals additional 194 events with a cross correlation coefficient exceeding 0.65 which ensures a stable identification. Our analysis shows that the repeating events occurred during the stimulation of a geothermal reservoir within a source region of only about (0.5 km)3.

  6. 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.

  7. 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...

  8. Pilot study of automated bullet signature identification based on topography measurements and correlations.

    Science.gov (United States)

    Chu, Wei; Song, John; Vorburger, Theodore; Yen, James; Ballou, Susan; Bachrach, Benjamin

    2010-03-01

    A procedure for automated bullet signature identification is described based on topography measurements using confocal microscopy and correlation calculation. Automated search and retrieval systems are widely used for comparison of firearms evidence. In this study, 48 bullets fired from six different barrel manufacturers are classified into different groups based on the width class characteristic for each land engraved area of the bullets. Then the cross-correlation function is applied both for automatic selection of the effective correlation area, and for the extraction of a 2D bullet profile signature. Based on the cross-correlation maximum values, a list of top ranking candidates against a ballistics signature database of bullets fired from the same model firearm is developed. The correlation results show a 9.3% higher accuracy rate compared with a currently used commercial system based on optical reflection. This suggests that correlation results can be improved using the sequence of methods described here. PMID:20102451

  9. Upconversion based continuous-wave mid-infrared detection

    DEFF Research Database (Denmark)

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

    2013-01-01

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

  10. Metamorphic computer virus detection by Case- Based Reasoning (CBR) methods

    OpenAIRE

    Abdellatif Berkat

    2011-01-01

    Metamorphic virus employs code obfuscation techniques to mutate itself. It absconds from signaturebaseddetection system by modifying internal structure without compromising original functionality.In this paper, we propose a new method, for detecting metamorphic computer viruses, that is based on thetechnique of Case-Based Reasoning (CBR). In this method:-Can detect similar viruses with high probability.- The updating of the virus database is done automatically without connecting to the Intern...

  11. Deep Feature-based Face Detection on Mobile Devices

    OpenAIRE

    Sarkar, Sayantan; Patel, Vishal M.; Chellappa, Rama

    2016-01-01

    We propose a deep feature-based face detector for mobile devices to detect user's face acquired by the front facing camera. The proposed method is able to detect faces in images containing extreme pose and illumination variations as well as partial faces. The main challenge in developing deep feature-based algorithms for mobile devices is the constrained nature of the mobile platform and the non-availability of CUDA enabled GPUs on such devices. Our implementation takes into account the speci...

  12. Vehicle Detection Based on Probability Hypothesis Density Filter

    Science.gov (United States)

    Zhang, Feihu; Knoll, Alois

    2016-01-01

    In the past decade, the developments of vehicle detection have been significantly improved. By utilizing cameras, vehicles can be detected in the Regions of Interest (ROI) in complex environments. However, vision techniques often suffer from false positives and limited field of view. In this paper, a LiDAR based vehicle detection approach is proposed by using the Probability Hypothesis Density (PHD) filter. The proposed approach consists of two phases: the hypothesis generation phase to detect potential objects and the hypothesis verification phase to classify objects. The performance of the proposed approach is evaluated in complex scenarios, compared with the state-of-the-art. PMID:27070621

  13. 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.

  14. 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...

  15. Vehicle Detection Based on Probability Hypothesis Density Filter.

    Science.gov (United States)

    Zhang, Feihu; Knoll, Alois

    2016-01-01

    In the past decade, the developments of vehicle detection have been significantly improved. By utilizing cameras, vehicles can be detected in the Regions of Interest (ROI) in complex environments. However, vision techniques often suffer from false positives and limited field of view. In this paper, a LiDAR based vehicle detection approach is proposed by using the Probability Hypothesis Density (PHD) filter. The proposed approach consists of two phases: the hypothesis generation phase to detect potential objects and the hypothesis verification phase to classify objects. The performance of the proposed approach is evaluated in complex scenarios, compared with the state-of-the-art. PMID:27070621

  16. Analysis of fault detection method based on predictive filter approach

    Institute of Scientific and Technical Information of China (English)

    LI Ji; ZHANG Hongyue

    2005-01-01

    A new detection method for component faults based on predictive filters together with the fault detectability, false alarm rate, missed alarm rate and upper bound of detection time are proposed. The efficiency of the method is illustrated by a simulation example of a second-order system. It is shown that the fault detection method using predictive filters has a small delay, a low false alarm rate and a low missed alarm rate. Furthermore the filter can give accurate estimates of states even after a fault occurs. The real-time estimation provided by this method can also be used for fault tolerant control.

  17. Using Morphlet-Based Image Representation for Object Detection

    Science.gov (United States)

    Gorbatsevich, V. S.; Vizilter, Yu. V.

    2016-06-01

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

  18. A COPULA-BASED CORRELATION MEASURE AND ITS APPLICATION IN CHINESE STOCK MARKET

    OpenAIRE

    FENGHUA WEN; ZHIFENG LIU

    2009-01-01

    In this paper, a copula-based correlation measure is proposed to test the interdependence among stochastic variables in terms of copula function. Based on a geometric analysis of copula function, a new derivation method is introduced to derive the Gini correlation coefficient. Meantime theoretical analysis finds that the Gini correlation coefficient tends to overestimate the tail interdependence in the case of stochastic variables clustering at the tails. For this overestimation issue, a full...

  19. Theoretical detection ranges for acoustic based manatee avoidance technology.

    Science.gov (United States)

    Phillips, Richard; Niezrecki, Christopher; Beusse, Diedrich O

    2006-07-01

    The West Indian manatee (Trichechus manatus latirostris) has become endangered partly because of watercraft collisions in Florida's coastal waterways. To reduce the number of collisions, warning systems based upon detecting manatee vocalizations have been proposed. One aspect of the feasibility of an acoustically based warning system relies upon the distance at which a manatee vocalization is detectable. Assuming a mixed spreading model, this paper presents a theoretical analysis of the system detection capabilities operating within various background and watercraft noise conditions. This study combines measured source levels of manatee vocalizations with the modeled acoustic properties of manatee habitats to develop a method for determining the detection range and hydrophone spacing requirements for acoustic based manatee avoidance technologies. In quiet environments (background noise approximately 70 dB) it was estimated that manatee vocalizations are detectable at approximately 250 m, with a 6 dB detection threshold, In louder environments (background noise approximately 100dB) the detection range drops to 2.5 m. In a habitat with 90 dB of background noise, a passing boat with a maximum noise floor of 120 dB would be the limiting factor when it is within approximately 100 m of a hydrophone. The detection range was also found to be strongly dependent on the manatee vocalization source level. PMID:16875213

  20. 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.

  1. 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.

  2. 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. PMID:27305769

  3. 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.

  4. A Smartphone Malware Detection Framework Based on Artificial Immunology

    Directory of Open Access Journals (Sweden)

    Min Zhao

    2013-02-01

    Full Text Available With the sharp increase in the number of smartphones, the Android platform pose to becoming a market leader that makes the need for malware analysis on this platform an urgent issue. The current Artificial Immune-Based malware detection systems research focus on traditional computers that uses information from OS or network, but the smartphone software behavior has its own structure and semantics. Current research cannot detect malware in smartphone exactly and efficiently. To address these problems, in this paper, we capitalize on earlier approaches for dynamic analysis of application behavior as a means for detecting malware in the smartphone. An Artificial Immune-Based Smartphone Malware Detection Framework is brought forwards and a prototype system is implemented, the experiment result show that the system can obtain higher detection rate and decrease the false positive rate.

  5. Node Attribute Behavior Based Intrusion Detection in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Radhika Baskar

    2013-10-01

    Full Text Available Security is one of the important problem in wireless sensor networks. With limited energy resources and processing resources, this paper focus on node attribute behavior based anomaly detection system and deals only with attributes of layered sensor node. It introduces node attribute behavioral index. The detection uses genetic algorithm which evaluates the behavior of sensor node with node attributes and threshold technique have been used to detect abnormal behavior of sensor node based on behavioral index. The performance has been evaluated for MAC and network layer feature set of wireless nodes.

  6. Using Case-Based Reasoning for detecting computer virus

    OpenAIRE

    Abdellatif Berkat

    2011-01-01

    The typical antivirus approach consists of waiting for a number of computers to be infected, detecting the virus, designing a solution, delivering and deploying a solution. In such a situation, it is very difficult to prevent every machine from being compromised by viruses. In this paper, we propose a new method, for detecting computer viruses, that is based on the technique of Case-Based Reasoning (CBR). In this method: (1) even new viruses that do not exist in the database can be detected (...

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

    International Nuclear Information System (INIS)

    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)

  8. Outlier Mining Based Abnormal Machine Detection in Intelligent Maintenance

    Institute of Scientific and Technical Information of China (English)

    ZHANG Lei; CAO Qi-xin; LEE Jay

    2009-01-01

    Assessing machine's performance through comparing the same or similar machines is important to implement intelligent maintenance for swarm machine. In this paper, an outlier mining based abnormal machine detection algorithm is proposed for this purpose. Firstly, the outlier mining based on clustering is introduced and the definition of cluster-based global outlier factor (CBGOF) is presented. Then the modified swarm intelligence clustering(MSIC) algorithm is suggested and the outlier mining algorithm based on MSIC is proposed. The algorithm can not only cluster machines according to their performance but also detect possible abnormal machines. Finally, a comparison of mobile soccer robots' performance proves the algorithm is feasible and effective.

  9. Image Fakery Detection Based on Singular Value Decomposition

    Directory of Open Access Journals (Sweden)

    T. Basaruddin

    2009-11-01

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

  10. Design and implementation of photon correlator based on C8051F

    Science.gov (United States)

    Shen, Jin; Li, Yuming; Liu, Wei; Yang, Yan; Cheng, Yanting

    2008-02-01

    Correlation techniques are widely used to extract spectral information from light scattering and other stochastic processes. Within the photon correlation system, the correlating operation must work at a high speed. In this paper, a photon correlator based on microcontroller C8051F was developed. In the photon correlator, the work of counting and scratch is completed by the two 4-bits binary adder 74F161, which is connected to form an 8-bits adder., and the correlation operation of every channel is carried out by the software of C8051F. By probably choosing high speed devices counting of 10ns in width pulses can be counted. The correlation operations including multiplying and addition operation of 56 channels with the circulation program within 3μs were made in interrupt service routine of the C8051F. The work in this paper can be applied in the ultra-fine particle sizing with photon correlation spectroscopy.

  11. REIONIZATION ON LARGE SCALES. II. DETECTING PATCHY REIONIZATION THROUGH CROSS-CORRELATION OF THE COSMIC MICROWAVE BACKGROUND

    Energy Technology Data Exchange (ETDEWEB)

    Natarajan, A.; Battaglia, N.; Trac, H. [McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 (United States); Pen, U.-L. [Canadian Institute for Theoretical Astrophysics, University of Toronto, 60 St. George Street, Toronto, ON M5S 3H8 (Canada); Loeb, A. [Institute for Theory and Computation, Harvard University, 60 Garden Street, Cambridge, MA 02138 (United States)

    2013-10-20

    We investigate the effect of patchy reionization on the cosmic microwave background (CMB) temperature. An anisotropic optical depth τ( n-hat ) alters the TT power spectrum on small scales l > 2000. We make use of the correlation between the matter density and the reionization redshift fields to construct full sky maps of τ( n-hat ). Patchy reionization transfers CMB power from large scales to small scales, resulting in a non-zero cross correlation between large and small angular scales. We show that the patchy τ correlator is sensitive to small root mean square (rms) values τ{sub rms} ∼ 0.003 seen in our maps. We include frequency-independent secondaries such as CMB lensing and kinetic Sunyaev-Zel'dovich (kSZ) terms, and show that patchy τ may still be detected at high significance. Reionization models that predict different values of τ{sub rms} may be distinguished even for the same mean value (τ). It is more difficult to detect patchy τ in the presence of larger secondaries such as the thermal Sunyaev-Zel'dovich, radio background, and the cosmic infrared background. In this case, we show that patchy τ may be detected if these frequency-dependent secondaries are minimized to ∼< 5 μK (rms) by means of a multi-frequency analysis. We show that the patchy τ correlator provides information that is complementary to what may be obtained from the polarization and the kSZ power spectra.

  12. A new approach toward object-based change detection

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Object-based change detection has been the hotspot in remote sensing image processing.A new approach toward object-based change detection is proposed.The two different temporal images are unitedly segmented using the mean shift procedure to obtain corresponding objects.Then change detection is implemented based on the integration of corresponding objects’ intensity and texture differences.Experiments are conducted on both panchromatic images and multispectral images and the results show that the integrated measure is robust with respect to illumination changes and noise.Supplementary color detection is conducted to determine whether the color of the unchanged objects changes or not when dealing with multispectral images.Some verification work is carried out to show the accuracy of the proposed approach.

  13. Signalprint-Based Intrusion Detection in Wireless Networks

    Science.gov (United States)

    Mitchell, Rob; Chen, Ing-Ray; Eltoweissy, Mohamed

    Wireless networks are a critical part of global communication for which intrusion detection techniques should be applied to secure network access, or the cost associated with successful attacks will overshadow the benefits that wireless networks offer. In this paper we investigate a new scheme called Nodeprints to extend the existing centralized Signalprints design for authentication to a distributed voting-based design for intrusion detection. We analyze the effect of voting-based intrusion detection designs, the probability of an individual node voting incorrectly, the ratio of mobile nodes to base stations, and the rate at which nodes are compromised, on the system performance measured by the probability that the intrusion detection system yields a false result. We develop a performance model for evaluating our Nodeprints design and identify conditions under which Nodeprints outperforms the existing Signalprints design.

  14. An Efficient Fuzzy Clustering-Based Approach for Intrusion Detection

    CERN Document Server

    Nguyen, Huu Hoa; Darmont, Jérôme

    2011-01-01

    The need to increase accuracy in detecting sophisticated cyber attacks poses a great challenge not only to the research community but also to corporations. So far, many approaches have been proposed to cope with this threat. Among them, data mining has brought on remarkable contributions to the intrusion detection problem. However, the generalization ability of data mining-based methods remains limited, and hence detecting sophisticated attacks remains a tough task. In this thread, we present a novel method based on both clustering and classification for developing an efficient intrusion detection system (IDS). The key idea is to take useful information exploited from fuzzy clustering into account for the process of building an IDS. To this aim, we first present cornerstones to construct additional cluster features for a training set. Then, we come up with an algorithm to generate an IDS based on such cluster features and the original input features. Finally, we experimentally prove that our method outperform...

  15. Moving Foreground Detection Based On Spatio-temporal Saliency

    Directory of Open Access Journals (Sweden)

    Yang Xia

    2013-01-01

    Full Text Available Detection of moving foreground in video is very important for many applications, such as visual surveillance, object-based video coding, etc. When objects move with different speeds and under illumination changes, the robustness of moving object detection methods proposed so far is still not satisfactory. In this paper, we use the semantic information to adjust the pixel-wise learning rate adaptively for more robust detection performance, which are obtained by spatial saliency map based on Gaussian mixture model (GMM in luma space and temporal saliency map obtained by background subtraction. In addition, we design a two-pass background estimation framework, in which the initial estimation is used for temporal saliency estimation, and the other is to detect foreground and update model parameters. The experimental results show that our method can achieve better moving object extraction performance than the existing background subtraction method based on GMM.

  16. Detection of heavy metal by paper-based microfluidics.

    Science.gov (United States)

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

    2016-09-15

    Heavy metal pollution has shown great threat to the environment and public health worldwide. Current methods for the detection of heavy metals require expensive instrumentation and laborious operation, which can only be accomplished in centralized laboratories. Various microfluidic paper-based analytical devices have been developed recently as simple, cheap and disposable alternatives to conventional ones for on-site detection of heavy metals. In this review, we first summarize current development of paper-based analytical devices and discuss the selection of paper substrates, methods of device fabrication, and relevant theories in these devices. We then compare and categorize recent reports on detection of heavy metals using paper-based microfluidic devices on the basis of various detection mechanisms, such as colorimetric, fluorescent, and electrochemical methods. To finalize, the future development and trend in this field are discussed. PMID:27131999

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

    International Nuclear Information System (INIS)

    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 105 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)

  18. 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.

  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; Hebert, Benedict; Lagerholm, B Christoffer; Grutter, Peter; Wiseman, Paul W

    2007-01-01

    model, but the transport coefficients can have significant systematic errors in the measurements due to blinking. Image correlation measurements of the diffusing QD samples measured at different laser excitation powers and analysis of computer simulated image time series verified that the effect we...... application of the image correlation methods for measurement of the diffusion coefficient of glycosyl phosphatidylinositol-anchored proteins tagged with QDs as imaged on living fibroblasts Udgivelsesdato: 2007-Aug-15......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...

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

    Directory of Open Access Journals (Sweden)

    Chia-Ping Shen

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

  1. 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. PMID:26336154

  2. A unified framework for model-based clustering, linear regression and multiple cluster structure detection

    OpenAIRE

    Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele

    2015-01-01

    A general framework for dealing with both linear regression and clustering problems is described. It includes Gaussian clusterwise linear regression analysis with random covariates and cluster analysis via Gaussian mixture models with variable selection. It also admits a novel approach for detecting multiple clusterings from possibly correlated sub-vectors of variables, based on a model defined as the product of conditionally independent Gaussian mixture models. A necessary condition for the ...

  3. Cross-correlation between the cosmic microwave and infrared backgrounds for integrated Sachs-Wolfe detection

    OpenAIRE

    Ilic, S.; Douspis, M.; Langer, M.; Pénin, A.; Lagache, G.

    2011-01-01

    We investigate the cross-correlation between the cosmic infrared background (CIB) and cosmic microwave background (CMB) anisotropies due to the integrated Sachs-Wolfe (ISW) effect. We first describe the CIB anisotropies using a linearly biased power spectrum, valid on the angular scales of interest. From this, we derive the theoretical angular power spectrum of the CMB-CIB cross-correlation for different instruments and frequencies. Our cross-spectra show similarities in shape with usual CMB/...

  4. Signal detection, modularity and the correlation between extrinsic and intrinsic noise in biochemical networks

    OpenAIRE

    Tanase-Nicola, Sorin; Warren, Patrick B.; Wolde, Pieter Rein ten

    2005-01-01

    Understanding cell function requires an accurate description of how noise is transmitted through biochemical networks. We present an analytical result for the power spectrum of the output signal of a biochemical network that takes into account the correlations between the noise in the input signal (the extrinsic noise) and the noise in the reactions that constitute the network (the intrinsic noise). These correlations arise from the fact that the reactions by which biochemical signals are det...

  5. A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation

    OpenAIRE

    SAHA, AMRITA; Khapra, Mitesh M.; Chandar, Sarath; Rajendran, Janarthanan; Cho, Kyunghyun

    2016-01-01

    Interlingua based Machine Translation (MT) aims to encode multiple languages into a common linguistic representation and then decode sentences in multiple target languages from this representation. In this work we explore this idea in the context of neural encoder decoder architectures, albeit on a smaller scale and without MT as the end goal. Specifically, we consider the case of three languages or modalities X, Z and Y wherein we are interested in generating sequences in Y starting from inf...

  6. Word Embedding based Correlation Model for Question/Answer Matching

    OpenAIRE

    Shen, Yikang; Rong, Wenge; Jiang, Nan; Peng, Baolin; Tang, Jie; Xiong, Zhang

    2015-01-01

    With the development of community based question answering (Q\\&A) services, a large scale of Q\\&A archives have been accumulated and are an important information and knowledge resource on the web. Question and answer matching has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing user experience with recurrent questions. In this paper, we try to improve the matching accuracy by overcoming the lexical gap between question ...

  7. A visual saliency based method for vehicle logo detection

    Science.gov (United States)

    Zhang, Fan; Shen, Yiping; Chang, Hongxing

    2013-07-01

    This paper presents a novel method based on visual saliency and template matching for detecting vehicle logo from images captured by cross-road cameras. To detect the logo, such method first generates a saliency map based on the modified Itti's saliency model, and then obtains regions of interest (ROI) by thresholding the saliency map, at last performs an edge-based template matching to locate the logo. Experiments on more than 2400 images validate both high accuracy and efficiency of the proposed method, and demonstrates our method suitable for real-time application.

  8. 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

  9. Infrared Image Small Target Detection Based on Bi-orthogonal Wavelet and Morphology

    Institute of Scientific and Technical Information of China (English)

    CHI Jian-nan; ZHANG Zhao-hui; WANG Dong-shu; HAO Yan-shuang

    2007-01-01

    An image multi-scale edge detection method based on anti-symmetrical bi-orthogonal wavelet is given in theory. Convolution operation property and function as a differential operator are analyzed,which anti-symmetrical bi-orthogonal wavelet transform have. An algorithm for wavelet reconstruction in which multi-scale edge can be detected is put forward. Based on it, a detection method for small target in infrared image with sea or sky background based on the anti-symmetrical bi-orthogonal wavelet and morphology is proposed. The small target detection is considered as a process in which structural background is removed, correlative background is suppressed, and noise is restrained. In this approach, the multi-scale edge is extracted by means of the anti-symmetrical bi-orthogonal wavelet decomposition. Then, module maximum chains formed by complicated background of clouds, sea wave and sea-sky-line are removed, and the image background becomes smoother. Finally, the morphology based edge detection method is used to get small target and restrain undulate background and noise. Experiment results show that the approach can suppress clutter background and detect the small target effectively.

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

    International Nuclear Information System (INIS)

    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

  11. Highly sensitive fluorescence resonance energy transfer (FRET)-based nanosensor for rapid detection of clenbuterol

    International Nuclear Information System (INIS)

    In this study we investigate the fabrication of a fluorescence resonance energy transfer (FRET)-based nanosensor for the detection of clenbuterol. The nanosensor consists of CdTe quantum dots coated by clenbuterol recognizable agent naphthol and diazotized clenbuterol. Changes in maximal photoluminescent intensities of the nanosensor were utilized to measure clenbuterol concentrations. The maximal photoluminescent intensities of the nanosensor were found to decrease with increasing clenbuterol concentrations, following a linear correlation. We have successfully fabricated a nanosensor for detection of clenbuterol with sensitivity up to 10 pg ml−1. (paper)

  12. Highly sensitive fluorescence resonance energy transfer (FRET)-based nanosensor for rapid detection of clenbuterol

    Science.gov (United States)

    Nghia Nguyen, Duc; Ngo, Trinh Tung; Liem Nguyen, Quang

    2012-09-01

    In this study we investigate the fabrication of a fluorescence resonance energy transfer (FRET)-based nanosensor for the detection of clenbuterol. The nanosensor consists of CdTe quantum dots coated by clenbuterol recognizable agent naphthol and diazotized clenbuterol. Changes in maximal photoluminescent intensities of the nanosensor were utilized to measure clenbuterol concentrations. The maximal photoluminescent intensities of the nanosensor were found to decrease with increasing clenbuterol concentrations, following a linear correlation. We have successfully fabricated a nanosensor for detection of clenbuterol with sensitivity up to 10 pg ml‑1.

  13. Parametric roll resonance monitoring using signal-based detection

    DEFF Research Database (Denmark)

    Galeazzi, Roberto; Blanke, Mogens; Falkenberg, Thomas;

    2015-01-01

    detection algorithms in real conditions, and to evaluate the frequency of parametric roll events on the selected vessels. Detection performance is scrutinised through the validation of the detected events using owners’ standard methods, and supported by available wave radar data. Further, a bivariate...... a curiosity, others have concerns. This study employs novel signalbased detection algorithms to analyse logged motion data from a container vessel (2800 TEU) and a large car and truck carrier (LCTC) during one year at sea. The scope of the study is to assess the performance and robustness of the...... statistical analysis of the outcome of the signal-based detectors is performed to assess the real life false alarm probability. It is shown that detection robustness and very low false warning rates are obtained. The study concludes that small parametric roll events are occurring, and that the proposed signal...

  14. Graph-based pigment network detection in skin images

    Science.gov (United States)

    Sadeghi, M.; Razmara, M.; Ester, M.; Lee, T. K.; Atkins, M. S.

    2010-03-01

    Detecting pigmented network is a crucial step for melanoma diagnosis. In this paper, we present a novel graphbased pigment network detection method that can find and visualize round structures belonging to the pigment network. After finding sharp changes of the luminance image by an edge detection function, the resulting binary image is converted to a graph, and then all cyclic sub-graphs are detected. Theses cycles represent meshes that belong to the pigment network. Then, we create a new graph of the cyclic structures based on their distance. According to the density ratio of the new graph of the pigment network, the image is classified as "Absent" or "Present". Being Present means that a pigment network is detected in the skin lesion. Using this approach, we achieved an accuracy of 92.6% on five hundred unseen images.

  15. Edge detection of noisy images based on cellular neural networks

    Science.gov (United States)

    Li, Huaqing; Liao, Xiaofeng; Li, Chuandong; Huang, Hongyu; Li, Chaojie

    2011-09-01

    This paper studies a technique employing both cellular neural networks (CNNs) and linear matrix inequality (LMI) for edge detection of noisy images. Our main work focuses on training templates of noise reduction and edge detection CNNs. Based on the Lyapunov stability theorem, we derive a criterion for global asymptotical stability of a unique equilibrium of the noise reduction CNN. Then we design an approach to train edge detection templates, and this approach can detect the edge precisely and efficiently, i.e., by only one iteration. Finally, we illustrate performance of the proposed methodology from the aspect of peak signal to noise ratio (PSNR) through computer simulations. Moreover, some comparisons are also given to prove that our method outperforms classical operators in gray image edge detection.

  16. Multi-core Processors based Network Intrusion Detection Method

    Directory of Open Access Journals (Sweden)

    Ziqian Wan

    2012-09-01

    Full Text Available It is becoming increasingly hard to build an intrusion detection system (IDS, because of the higher traffic throughput and the rising sophistication of attacking. Scale will be an important issue to address in the intrusion detection area. For hardware, tomorrow’s performance gains will come from multi-core architectures in which a number of CPU executes concurrently. We take the advantage of multi-core processors’ full power for intrusion detection in this work. We present an intrusion detection system based on the Snort open-source IDS that exploits the computational power of MIPS multi-core architecture to offload the costly pattern matching operations from the CPU, and thus increase the system’s processing throughput. A preliminary experiment demonstrates the potential of this system. The experiment results indicate that this method can be used effectively to speed up intrusion detection systems.

  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. [Mutual information-based correlation analysis of herbs against insomnia].

    Science.gov (United States)

    Tian, Jin; Liu, Ren-quan

    2015-10-01

    This paper aims to analyze Professor Guo Rongjuan's medication experience on insomnia therapy based on the Traditional Chinese Medicine (TCM) Inheritance Support Plat. First, TCM formulae prescribed by Professor Guo for insomnia therapy were collected from the TCM Inheritance Support Plat. Next, unsupervised data mining algorithms, including apriori, modified mutual-information, and entropy clustering of complex system were applied to obtain the frequencies for different herbs and identify the association rules among the herbs. Accordingly, we can gain new insights into Professor Guo's medication experience on insomnia therapy. Based on analysis of 3 084 formulae, we determined the frequencies for herbs in the formulae and identified the association rules among these herbs. At last, 41 core combinations and 7 new formulae were obtained. The identified medication experience conform with Professor Guo's views on the etiology and pathogenesis of insomnia: "pathogenic fire derived from stagnation of liver-QI (Gan Yu Hua Huo)" is the core pathogenesis of insomnia; "liver stagnation and spleen deficiency" and "chronic illness transferred to kidney" are the main features for insomnia. The TCM Inheritance Support Plat is of great practical value for mining clinical experience of famous TCM doctors. PMID:26975117

  19. Correlations for thermal conductivity and viscosity of water based nanofluids

    International Nuclear Information System (INIS)

    The thermo-physical properties of nanofluids such as thermal conductivity, viscosity, density and specific heat of nanofluids are required for the analysis of convection heat transfer coefficients. The density and specific heat of nanofluids can be estimated with the mixture relations in literature. Information regarding the properties at different volume concentration and temperature is required for the estimation of heat transfer coefficient. The two most fundamental properties which are, experimentally, determined, are viscosity and thermal conductivity. Investigators have been determining the properties of nanofluids at different temperatures and base liquids. The present work is an attempt to analyze the available data to develop a non-linear regression equation for the estimation of thermal conductivity and viscosity of water based nanofluids. In the present study, nanofluids are considered as a homogenous medium and the parameters influencing the thermo physical properties identified. Equations are developed for the analysis of thermo-physical properties of nanofluids as a function of parameters viz., material, concentration, temperature and particle size useful for designer. The opposing nature of thermal conductivity rise and viscosity decrease with temperature; dependence of nanofluid thermal conductivity on material properties alters the range of applicability of nanofluids for heat transfer applications. The thermal conductivity and viscosity of Al2O3, ZnO and TiO2 dispersed in water are measured to validate the proposed equations. The result shows that the equations are able to predict the thermal conductivity and viscosity of different types of nanofluids of different particle diameters closely.

  20. Efficient Detection of Sybil attack Based on Cryptography in Vanet

    Directory of Open Access Journals (Sweden)

    Mina Rahbari

    2011-12-01

    Full Text Available Vehicular communications play a substantial role in providing safety transportation by means of safetymessage exchange. Researchers have proposed several solutions for securing safety messages. Protocols based on a fixed key infrastructure are more efficient in implementation and maintain stronger security in comparison with dynamic structures. The purpose of this paper present a method based on a fixed keyinfrastructure for detection impersonation attack, in other words, Sybil attack, in the vehicular adhoc network. This attack, puts a great impact on performance of the network. The proposed method, using an cryptography mechanism to detection Sybil attack. Finally, using Mat lab simulator the results of this approach are reviewed, This method it has low delay for detection Sybil attack, because most operations are done in Certification Authority, so this proposed schema is a efficient method for detection Sybil attack.

  1. 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. PMID:27313603

  2. 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. PMID:27313603

  3. 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.

  4. a Uav-Based ROE Deer Fawn Detection System

    Science.gov (United States)

    Israel, M.

    2011-09-01

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

  5. Statistical detection of structural damage based on model reduction

    Institute of Scientific and Technical Information of China (English)

    Tao YIN; Heung-fai LAM; Hong-ping ZHU

    2009-01-01

    This paper proposes a statistical method for damage detection based on the finite element (FE) model reduction technique that utilizes measured modal data with a limited number of sensors.A deterministic damage detection process is formulated based on the model reduction technique.The probabilistic process is integrated into the deterministic damage detection process using a perturbation technique,resulting in a statistical structural damage detection method.This is achieved by deriving the firstand second-order partial derivatives of uncertain parameters,such as elasticity of the damaged member,with respect to the measurement noise,which allows expectation and covariance matrix of the uncertain parameters to be calculated.Besides the theoretical development,this paper reports numerical verification of the proposed method using a portal frame example and Monte Carlo simulation.

  6. The Unknown Computer Viruses Detection Based on Similarity

    Science.gov (United States)

    Liu, Zhongda; Nakaya, Naoshi; Koui, Yuuji

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

  7. Using Case-Based Reasoning for detecting computer virus

    Directory of Open Access Journals (Sweden)

    Abdellatif Berkat

    2011-07-01

    Full Text Available The typical antivirus approach consists of waiting for a number of computers to be infected, detecting the virus, designing a solution, delivering and deploying a solution. In such a situation, it is very difficult to prevent every machine from being compromised by viruses. In this paper, we propose a new method, for detecting computer viruses, that is based on the technique of Case-Based Reasoning (CBR. In this method: (1 even new viruses that do not exist in the database can be detected (2 The updating of the virus database is done automatically without connecting to the Internet. Whenever a new virus is detected, it will be automatically added to the database used by our application. This presents a major advantage

  8. Human Object Detection based on Context Awareness in the Surroundings

    Directory of Open Access Journals (Sweden)

    Nguyen Thanh Binh

    2015-08-01

    Full Text Available Surveillance system has been applied in providing public security for many complex places like railway stations, bus stops, etc. In most cases, human object detection is an important task in surveillance system. In the case that human objects are occlusion or outdoor environment, human objects detection is a challenging problem. In this paper, we propose a method to implement for human object detection based on context awareness in new wavelet generation domain in outdoor environment. We use curvelet transform based on context awareness combined with support vector machines as a classifier for human detection. The proposed method was tested on a standard dataset like PEST2001 dataset. For demonstrating the superiority of the proposed method, we have compared the results with the other recent methods available in literature.

  9. A Detection Scheme for Cavity-based Dark Matter Searches

    CERN Document Server

    Bukhari, M H S

    2016-01-01

    We present here proposal of a scheme and some useful ideas for resonant cavity-based detection of cold dark matter axions with hope to improve the existing endeavors. The scheme is based upon our idea of a detector, which incorporates an integrated tunnel diode and a GaAs HEMT or HFET, High Electron Mobility Transistor or Heterogenous FET, for resonance detection and amplification from a resonant cavity (in a strong transverse magnetic field from a cylindrical array of halbach magnets). The idea of a TD-oscillator-amplifier combination could possibly serve as a more sensitive and viable resonance detection regime while maintaining an excellent performance with low noise temperature, whereas the halbach magnets array may offer a compact and permanent solution replacing the conventional electromagnets scheme. We believe that all these factors could possibly increase the sensitivity and accuracy of axion detection searches and reduce complications (and associated costs) in the experiments, in addition to help re...

  10. Efficient Detection of Sybil Attack Based on Cryptography in Vanet

    CERN Document Server

    Rahbari, Mina

    2011-01-01

    Vehicular communications play a substantial role in providing safety transportation by means of safety message exchange. Researchers have proposed several solutions for securing safety messages. Protocols based on a fixed key infrastructure are more efficient in implementation and maintain stronger security in comparison with dynamic structures. The purpose of this paper present a method based on a fixed key infrastructure for detection impersonation attack, in other words, Sybil attack, in the vehicular ad hoc network. This attack, puts a great impact on performance of the network. The proposed method, using an cryptography mechanism to detection Sybil attack. Finally, using Mat lab simulator the results of this approach are reviewed, This method it has low delay for detection Sybil attack, because most operations are done in Certification Authority, so this proposed schema is a efficient method for detection Sybil attack.

  11. Detection of non-classical space-time correlations with a novel type of single-photon camera

    CERN Document Server

    Just, Felix; Cavanna, Andrea; Michel, Thilo; Gleixner, Thomas; Taheri, Michael; Vallerga, John; Campbell, Michael; Tick, Timo; Anton, Gisela; Chekhova, Maria V; Leuchs, Gerd

    2014-01-01

    During the last decades, multi-pixel detectors have been developed capable of registering single photons. The newly developed Hybrid Photon Detector camera has a remarkable property that it has not only spatial but also temporal resolution. In this work, we use this device for the detection of non-classical light from spontaneous parametric down-conversion and use two-photon correlations for the absolute calibration of its quantum efficiency.

  12. Multi-Dimensional Time-Correlated Single Photon Counting (TCSPC) Fluorescence Lifetime Imaging Microscopy (FLIM) to Detect FRET in Cells

    OpenAIRE

    Duncan, R. R.; Bergmann, A; Cousin, M. A.; Apps, D. K.; Shipston, M. J.

    2004-01-01

    We present a novel, multi-dimensional, time-correlated single photon counting (TCSPC) technique to perform fluorescence lifetime imaging with a laser-scanning microscope operated at a pixel dwell-time in the microsecond range. The unsurpassed temporal accuracy of this approach combined with a high detection efficiency was applied to measure the fluorescent lifetimes of enhanced cyan fluorescent protein (ECFP) in isolation and in tandem with EYFP (enhanced yellow fluorescent protein). This tec...

  13. Vascularity in cutaneous melanoma detected by Doppler sonography and histology: correlation with tumour behaviour.

    OpenAIRE

    A Srivastava; Hughes, L E; Woodcock, J. P.; Laidler, P

    1989-01-01

    The blood flow in 71 primary skin melanomas was investigated by a 10MHz Doppler ultrasound flowmeter and flow signals were analysed on an Angioscan-II spectrum analyser. Doppler flow signals were detected in 44 tumours, with a close relationship to Breslow's tumour thickness. No blood flow signal was detected in 27 lesions and 25 of these had a tumour thickness of 0.8 mm or less. Ninety-seven per cent of tumours of thickness greater than 0.8 mm had detectable Doppler flow signals. Histologica...

  14. Abnormality Detection in Correlated Gaussian Molecular Nano-Networks: Design and Analysis

    OpenAIRE

    Ghavami, Siavash; Lahouti, Farshad

    2016-01-01

    A nano abnormality detection scheme (NADS) in molecular nano-networks is studied. This is motivated by the fact that early detection of diseases such as cancer play a crucial role in their successful treatment. The proposed NADS is in fact a two-tier network of sensor nano-machines (SNMs) in the first tier and a data-gathering node (DGN) at the sink. The SNMs detect the presence of competitor cells (abnormality) by variations in input and/or parameters of a nano-communications channel (NCC). ...

  15. Acoustic Event Detection Based on MRMR Selected Feature Vectors

    OpenAIRE

    VOZARIKOVA Eva; Juhar, Jozef; CIZMAR Anton

    2012-01-01

    This paper is focused on the detection of potentially dangerous acoustic events such as gun shots and breaking glass in the urban environment. Various feature extraction methods can be used forrepresenting the sound in the detection system based on Hidden Markov Models of acoustic events. Mel – frequency cepstral coefficients, low - level descriptors defined in MPEG-7 standard and another time andspectral features were considered in the system. For the selection of final subset of features Mi...

  16. Wavelet Based QRS Complex Detection of ECG Signal

    OpenAIRE

    Mukhopadhyay, Sayantan; Biswas, Shouvik; Roy, Anamitra Bardhan; Dey, Nilanjan

    2012-01-01

    The Electrocardiogram (ECG) is a sensitive diagnostic tool that is used to detect various cardiovascular diseases by measuring and recording the electrical activity of the heart in exquisite detail. A wide range of heart condition is determined by thorough examination of the features of the ECG report. Automatic extraction of time plane features is important for identification of vital cardiac diseases. This paper presents a multi-resolution wavelet transform based system for detection 'P', '...

  17. Edge Detection of Concrete Mesostructure Based on DIS Operator

    OpenAIRE

    Feng, Bin; Xu, Zicheng; Xia, Jin; Jin, Shijie; Jin, Weiliang

    2016-01-01

    Aggregate edge detection is the basis of creating concrete mesoscale model, which is applied to analyze concrete mesoscale characteristics. A concrete digital image edge detection method using DIS operator is presented in this paper. Mean filter, multi-scale filter, and Gaussian filter are compared on the effect of concrete image noise reduction. Based on the result, Gaussian filter is the most optimum method to reduce image noise and remain aggregate edge distinct. Sobel operator, Laplacian ...

  18. Pulse Onset Detection using Neighbor Pulse-Based Signal Enhancement

    OpenAIRE

    Xu, Peng; Bergsneider, Marvin; Hu, Xiao

    2008-01-01

    Detecting onsets of cardiovascular pulse wave signals is an important prerequisite for successfully conducting various analysis tasks involving the concept of pulse wave velocity. However, pulse onsets are frequently influenced by inherent noise and artifacts in signals continuously acquired in a clinical environment. The present work proposed and validated a neighbor pulse-based signal enhancement algorithm for reducing error in the detected pulse onset locations from noise-contaminated puls...

  19. An algorithm for UWB radar-based human detection

    OpenAIRE

    Chang, SangHyun; Mitsumoto, Naoki; Burdick, Joel W.

    2009-01-01

    This paper presents an algorithm for human presence detection in urban environments using an ultra-wide-band (UWB) impulse-based mono-static radar. A specular multi-path model (SMPM) is used to characterize human body scattered UWB waveforms. The SMPM parameters are used within a classical likelihood ratio detector framework to detect the presence of humans via gait, with the aid of a multi-target tracking technique (MTT). Experimental results on a simple human gait detec...

  20. Development of a Wearable-Sensor-Based Fall Detection System

    OpenAIRE

    Falin Wu; Hengyang Zhao; Yan Zhao; Haibo Zhong

    2015-01-01

    Fall detection is a major challenge in the public healthcare domain, especially for the elderly as the decline of their physical fitness, and timely and reliable surveillance is necessary to mitigate the negative effects of falls. This paper develops a novel fall detection system based on a wearable device. The system monitors the movements of human body, recognizes a fall from normal daily activities by an effective quaternion algorithm, and automatically sends request for help to the caregi...

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

    OpenAIRE

    Jensen, Thomas B. S.; Gill, Rashpal S.; Overgaard, Søren; Hansen, Lars Kai; Nielsen, Allan Aasbjerg

    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 expect...

  2. On the Use of Cross-Correlation between Volume Scattering and Helix Scattering from Polarimetric SAR Data for the Improvement of Ship Detection

    Directory of Open Access Journals (Sweden)

    Jujie Wei

    2016-01-01

    Full Text Available Synthetic Aperture Radar (SAR ship detection is an important maritime application. However, azimuth ambiguities caused by the finite sampling of the Doppler spectrum are often visible in SAR images and are always mistaken as ships by classic detection techniques, like the Constant False Alarm Rate (CFAR. It is known that radar targets and azimuth ambiguities have different characteristics in polarimetric SAR (PolSAR data, i.e., first ambiguities usually have strong odd- or double-bounce scattering and the maximum amplitude of the first ambiguity in SHV is always considerably smaller than that of the corresponding target for zero or high velocity. On the basis of this characteristics, this paper finds that first ambiguities usually have low volume scattering power relative to ships and almost have no helix scattering by Yamaguchi decomposition. But some residual ambiguities still exit in the volume scattering power and have similar scattering intensity to small ships, and some parts of a ship also have zero helix scattering owing to some physical factors (e.g., ship structure, radar incidence angle, etc.. Thus, for high-precision ship detection, a new ship detection method based on cross-correlation between the volume and helix scattering mechanisms derived from Yamaguchi decomposition is proposed to avoid false alarms caused by azimuth ambiguities and enhance Target-to-Clutter Ratio (TCR for improving the miss detection rate of small ships. By experiments, it is proved that our method can work effectively and has high detection accuracy.

  3. QRS Detection Based on an Advanced Multilevel Algorithm

    Directory of Open Access Journals (Sweden)

    Wissam Jenkal

    2016-01-01

    Full Text Available This paper presents an advanced multilevel algorithm used for the QRS complex detection. This method is based on three levels. The first permits the extraction of higher peaks using an adaptive thresholding technique. The second allows the QRS region detection. The last level permits the detection of Q, R and S waves. The proposed algorithm shows interesting results compared to recently published methods. The perspective of this work is the implementation of this method on an embedded system for a real time ECG monitoring system.

  4. Research and Implementation of Unsupervised Clustering-Based Intrusion Detection

    Institute of Scientific and Technical Information of China (English)

    LuoMin; ZhangHuan-guo; WangLi-na

    2003-01-01

    An unsupervised clustering-based intrusion detection algorithm is discussed in this paper. The basic idea of the algorithm is to produce the cluster by comparing the distances of unlabeled training data sets. With the classified data instances, anomaly data clusters can be easily identified by normal duster ratio and the identified cluster can be used in real data detection. The benefit of the algorithm is that it doesn't need labeled training data sets. The experiment coneludes that this approach can detect unknown intrusions efficiently in the real network connections via using the data sets of KDD99.

  5. Sonoclot(®)-based method to detect iron enhanced coagulation.

    Science.gov (United States)

    Nielsen, Vance G; Henderson, Jon

    2016-07-01

    Thrombelastographic methods have been recently introduced to detect iron mediated hypercoagulability in settings such as sickle cell disease, hemodialysis, mechanical circulatory support, and neuroinflammation. However, these inflammatory situations may have heme oxygenase-derived, coexistent carbon monoxide present, which also enhances coagulation as assessed by the same thrombelastographic variables that are affected by iron. This brief report presents a novel, Sonoclot-based method to detect iron enhanced coagulation that is independent of carbon monoxide influence. Future investigation will be required to assess the sensitivity of this new method to detect iron mediated hypercoagulability in clinical settings compared to results obtained with thrombelastographic techniques. PMID:26497986

  6. Inverse Problem Solution in Landmines Detection Based on Active Thermography

    Directory of Open Access Journals (Sweden)

    B. Szymanik

    2014-12-01

    Full Text Available Landmines still affect numerous territories in the whole world and pose a serious threat, mostly to civilians. Widely used non-metallic landmines are undetectable using metal detector. Therefore, there is an urging need to improve methods of detecting such objects. In the present study we introduce relatively new method of landmines' detection: active infrared thermography with microwave excitation. In this paper we present the optimization based method of solving inverse problem for microwave heating. This technique will be used in the reconstruction of detected landmines geometric and material properties.

  7. Single-base mismatch detection based on charge transduction through DNA.

    OpenAIRE

    Kelley, S O; Boon, E M; Barton, J K; Jackson, N M; Hill, M. G.

    1999-01-01

    High-throughput DNA sensors capable of detecting single-base mismatches are required for the routine screening of genetic mutations and disease. A new strategy for the electrochemical detection of single-base mismatches in DNA has been developed based upon charge transport through DNA films. Double-helical DNA films on gold surfaces have been prepared and used to detect DNA mismatches electrochemically. The signals obtained from redox-active intercalators bound to DNA-modified gold surfaces d...

  8. A new phase-correlation-based Iris matching for degraded images

    OpenAIRE

    Krichen, Emine; Garcia-Salicetti, Sonia; Dorizzi, Bernadette

    2009-01-01

    In this paper, we present a new phase correlation-based iris matching approach in order to deal with degradations in iris images due to unconstrained acquisition procedures. Our matching system is a fusion of global and local Gabor phase correlation schemes. The main originality of our local approach is that we do not only consider the correlation peak amplitudes but also their locations in different regions of the images. Results on several degraded databases namely CASIA-BIOSECURE and Iris ...

  9. Joint transform correlator based on CIELAB model with encoding technique for color pattern recognition

    Science.gov (United States)

    Lin, Tiengsheng; Chen, Chulung; Liu, Chengyu; Chen, Yuming

    2010-10-01

    The CIELAB standard color vision model instead of the traditional RGB color model is utilized for polychromatic pattern recognition. The image encoding technique is introduced. The joint transform correlator is set to be the optical configuration. To achieve the distortion invariance in discrimination processes, we have used the minimum average correlation energy approach to yield sharp correlation peak. From the numerical results, it is found that the recognition ability based on CIELAB color specification system is accepted.

  10. Hough Forest-based Corner Detection for Cervical Spine Radiographs

    OpenAIRE

    Al-Arif, S. M.; Asad, M; Knapp, K.; Gundry, M.; Slabaugh, G. G.

    2015-01-01

    The cervical spine (neck region) is highly sensitive to trauma related injuries, which must be analysed carefully by emergency physicians. In this work, we propose a Hough Forest-based corner detection method for cervical spine radiographs, as a first step towards a computer-aided diagnostic tool. We propose a novel patch-based model based on two-stage supervised learning (classification and regression) to estimate the corners of cervical vertebral bodies. Our method is evaluated using 106 ce...

  11. Using LBG quantization for particle-based collision detection algorithm

    Institute of Scientific and Technical Information of China (English)

    SAENGHAENGTHAM Nida; KANONGCHAIYOS Pizzanu

    2006-01-01

    Most collision detection algorithms can be efficiently used only with solid and rigid objects, for instance, Hierarchical methods which must have their bounding representation recalculated every time deformation occurs. An alternative algorithm using particle-based method is then proposed which can detect the collision among non-rigid deformable polygonal models.However, the original particle-based collision detection algorithm might not be sufficient enough in some situations due to the improper particle dispersion. Therefore, this research presents an improved algorithm which provides a particle to detect in each separated area so that particles always covered all over the object. The surface partitioning can be efficiently performed by using LBG quantization since it can classify object vertices into several groups base on a number of factors as required. A particle is then assigned to move between vertices in a group by the attractive forces received from other particles on neighbouring objects.Collision is detected when the distance between a pair of corresponding particles becomes very small. Lastly, the proposed algorithm has been implemented to show that collision detection can be conducted in real-time.

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

    Directory of Open Access Journals (Sweden)

    Vincent Fremont

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

    This paper presents a vision-based people detection system for improving safety in heavy machines. We propose a perception system composed of a monocular fisheye camera and a LiDAR. Fisheye cameras have the advantage of a wide field-of-view, but the strong distortions that they create must be handled at the detection stage. Since people detection in fisheye images has not been well studied, we focus on investigating and quantifying the impact that strong radial distortions have on the appearance of people, and we propose approaches for handling this specificity, adapted from state-of-the-art people detection approaches. These adaptive approaches nevertheless have the drawback of high computational cost and complexity. Consequently, we also present a framework for harnessing the LiDAR modality in order to enhance the detection algorithm for different camera positions. A sequential LiDAR-based fusion architecture is used, which addresses directly the problem of reducing false detections and computational cost in an exclusively vision-based system. A heavy machine dataset was built, and different experiments were carried out to evaluate the performance of the system. The results are promising, in terms of both processing speed and performance. PMID:26805838

  14. Correlation of Doppler-detected tumor vascular signals with vascular morphology

    International Nuclear Information System (INIS)

    Abnormal vascular morphology exists in many malignant tumors, including arterio-venous anastomoses, thin-walled vessels, and tumor lakes. These emit abnormal Doppler signals that allow tissue characterization. Doppler signals and angiographic and histologic findings were compared in 19 liver, kidney, adrenal, and pancreatic tumors. Abnormal signals were obtained in 18 (95%). These consisted of high frequencies exceeding 4 kHz (using a 3-MHz insonating frequency) which correlated with arterio-venous shunts on angiography. In 11 patients there were low impedance signals with little systolic/diastolic variation (pulsatility index 1-3). These correlated with thin-walled, sinusoidal vessels with endothelial lining but no muscular wall, and also correlated with dense tumor staining on angiography

  15. A meta-analysis of kidney microarray datasets: investigation of cytokine gene detection and correlation with rt-PCR and detection thresholds

    Directory of Open Access Journals (Sweden)

    Stegall Mark D

    2007-03-01

    Full Text Available Abstract Background Microarrays provide a means to simultaneously examine the gene expression of the entire transcriptome in a single sample. Many studies have highlighted the need for novel software and statistical approaches to assess the measured gene expression. Less attention has been directed toward whether genes considered undetectable by microarray can be detected by other strategies or whether these genes can provide accurate gene expression determinations. In the kidney this is a concern for genes such as cytokines which dramatically influence the immune response but are often considered low abundance genes produced by a small number of cells. Results Using both publicly available and our own microarray datasets we analyzed the detection p-value and detection call values for 81 human kidney samples run on the U133A or U133Plus2.0 Affymetrix microarrays (Affymetrix, Santa Clara, CA. For the cytokine genes, the frequency of detection in each sample group (normal, transplant and renal cell carcinoma was examined and revealed that a majority of cytokine related genes are not detectable in human kidney by microarray. Using a subset of 29 Mayo transplant samples, a group of seven transplant-related cytokines and eight non-cytokine genes were evaluated by real-time PCR (rt-PCR. For these 15 genes we compared the impact of decreasing microarray detection frequency with the changes in gene expression observed by both microarray and rt-PCR. We found that as microarray detection frequency decreased the correlation between microarray and rt-PCR data also decreased. Conclusion We conclude that, when analyzing microarray data from human kidney samples, genes generally expressed at low abundance (i.e. cytokines should be evaluated with more sensitive approaches such as rt-PCR. In addition, our data suggest that the use of detection frequency cutoffs for inclusion or exclusion of microarray data may be appropriate when comparing microarray and rt

  16. Ion Beam Analysis Of Silicon-Based Surfaces And Correlation With Surface Energy Measurements

    International Nuclear Information System (INIS)

    The water affinity of Si-based surfaces is quantified by contact angle measurement and surface free energy to explain hydrophobic or hydrophilic behavior of silicone, silicates, and silicon surfaces. Surface defects such as dangling bonds, surface free energy including Lewis acid-base and Lifshitz-van der Waals components are discussed. Water nucleation and condensation is further explained by surface topography. Tapping mode atomic force microscopy (TMAFM) provides statistical analysis of the topography of these Si-based surfaces. The correlation of the above two characteristics describes the behavior of water condensation at Si-based surfaces. Surface root mean square roughness increasing from several A ring to several nm is found to provide nucleation sites that expedite water condensation visibly for silica and silicone. Hydrophilic surfaces have a condensation pattern that forms puddles of water while hydrophobic surfaces form water beads. Polymer adsorption on these surfaces alters the water affinity as well as the surface topography, and therefore controls condensation on Si-based surfaces including silicone intraocular lens (IOL). The polymer film is characterized by Rutherford backscattering spectrometry (RBS) in conjunction with 4.265 MeV 12C(α, α)12C, 3.045 MeV 16O(α,α)16O nuclear resonance scattering (NRS), and 2.8 MeV elastic recoil detection (ERD) of hydrogen for high resolution composition and areal density measurements. The areal density of hydroxypropyl methylcellulose (HPMC) film ranges from 1018 atom/cm2 to 1019 atom/cm2 gives the silica or silicone surface a roughness of several A ring and a wavelength of 0.16±0.02 μm, and prevents fogging by forming a complete wetting layer during water condensation.

  17. Ion Beam Analysis Of Silicon-Based Surfaces And Correlation With Surface Energy Measurements

    Science.gov (United States)

    Xing, Qian; Herbots, N.; Hart, M.; Bradley, J. D.; Wilkens, B. J.; Sell, D. A.; Sell, Clive H.; Kwong, Henry Mark; Culbertson, R. J.; Whaley, S. D.

    2011-06-01

    The water affinity of Si-based surfaces is quantified by contact angle measurement and surface free energy to explain hydrophobic or hydrophilic behavior of silicone, silicates, and silicon surfaces. Surface defects such as dangling bonds, surface free energy including Lewis acid-base and Lifshitz-van der Waals components are discussed. Water nucleation and condensation is further explained by surface topography. Tapping mode atomic force microscopy (TMAFM) provides statistical analysis of the topography of these Si-based surfaces. The correlation of the above two characteristics describes the behavior of water condensation at Si-based surfaces. Surface root mean square roughness increasing from several Å to several nm is found to provide nucleation sites that expedite water condensation visibly for silica and silicone. Hydrophilic surfaces have a condensation pattern that forms puddles of water while hydrophobic surfaces form water beads. Polymer adsorption on these surfaces alters the water affinity as well as the surface topography, and therefore controls condensation on Si-based surfaces including silicone intraocular lens (IOL). The polymer film is characterized by Rutherford backscattering spectrometry (RBS) in conjunction with 4.265 MeV 12C(α, α)12C, 3.045 MeV 16O(α,α)16O nuclear resonance scattering (NRS), and 2.8 MeV elastic recoil detection (ERD) of hydrogen for high resolution composition and areal density measurements. The areal density of hydroxypropyl methylcellulose (HPMC) film ranges from 1018 atom/cm2 to 1019 atom/cm2 gives the silica or silicone surface a roughness of several Å and a wavelength of 0.16±0.02 μm, and prevents fogging by forming a complete wetting layer during water condensation.

  18. [A Hyperspectral Imagery Anomaly Detection Algorithm Based on Gauss-Markov Model].

    Science.gov (United States)

    Gao, Kun; Liu, Ying; Wang, Li-jing; Zhu, Zhen-yu; Cheng, Hao-bo

    2015-10-01

    With the development of spectral imaging technology, hyperspectral anomaly detection is getting more and more widely used in remote sensing imagery processing. The traditional RX anomaly detection algorithm neglects spatial correlation of images. Besides, it does not validly reduce the data dimension, which costs too much processing time and shows low validity on hyperspectral data. The hyperspectral images follow Gauss-Markov Random Field (GMRF) in space and spectral dimensions. The inverse matrix of covariance matrix is able to be directly calculated by building the Gauss-Markov parameters, which avoids the huge calculation of hyperspectral data. This paper proposes an improved RX anomaly detection algorithm based on three-dimensional GMRF. The hyperspectral imagery data is simulated with GMRF model, and the GMRF parameters are estimated with the Approximated Maximum Likelihood method. The detection operator is constructed with GMRF estimation parameters. The detecting pixel is considered as the centre in a local optimization window, which calls GMRF detecting window. The abnormal degree is calculated with mean vector and covariance inverse matrix, and the mean vector and covariance inverse matrix are calculated within the window. The image is detected pixel by pixel with the moving of GMRF window. The traditional RX detection algorithm, the regional hypothesis detection algorithm based on GMRF and the algorithm proposed in this paper are simulated with AVIRIS hyperspectral data. Simulation results show that the proposed anomaly detection method is able to improve the detection efficiency and reduce false alarm rate. We get the operation time statistics of the three algorithms in the same computer environment. The results show that the proposed algorithm improves the operation time by 45.2%, which shows good computing efficiency. PMID:26904830

  19. Tentative Detection of Quasar Feedback from WMAP and SDSS Cross-Correlation

    OpenAIRE

    Chatterjee, Suchetana; Ho, Shirley; Newman, Jeffrey A.; Kosowsky, Arthur

    2009-01-01

    We perform a cross-correlation analysis of microwave data from Wilkinson Microwave Anisotropy Probe and photometric quasars from the Sloan Digital Sky Survey, testing for Sunyaev-Zeldovich (SZ) effect from quasars. A statistically significant (2.5 $\\sigma$) temperature decrement exists in the 41 GHz microwave band. A two-component fit to the cross-correlation spectrum incorporating both dust emission and SZ yields a best-fit $y$ parameter of $(7.0 \\pm 3.4)\\times 10^{-7}$. A similar cross-corr...

  20. The Virgo - MiniGRAIL cross correlation for the detection of scalar gravitational waves

    OpenAIRE

    Corda, Christian

    2007-01-01

    After a review of the frequency - dependent angular pattern of interferometers in the TT gauge for scalar gravitational waves (SGWs), which has been recently analysed by Capozziello and Corda, in this letter the result is used to study the cross correlation between the Virgo interferometer and the MiniGRAIL resonant sphere. It is shown that the overlap reduction function for the cross correlation bewteen Virgo and the monopole mode of MiniGRAIL is very small, but a maximum is also found in th...

  1. Aptamer-based microcantilever biosensor for ultrasensitive detection of tumor marker nucleolin.

    Science.gov (United States)

    Li, Huiyan; Bai, Xiaojing; Wang, Nan; Chen, Xuejuan; Li, Jing; Zhang, Zhe; Tang, Jilin

    2016-01-01

    We present an aptamer-based microcantilever biosensor for label-free detection of nucleolin. The sensor cantilevers in the microcantilever array were functionalized with nucleolin aptamer (AS1411) while the reference cantilevers were modified by 6-mercapto-1-hexanol (MCH) to eliminate environmental disturbances. The interaction between nucleolin and AS1411 induced surface stress changes, resulting in a differential deflection between sensor and reference cantilevers. The amplitude of differential cantilever deflection had a good linear relationship with the nucleolin concentration ranging from 10 nM to 250 nM with a correlation coefficient of 0.999. The detection limit was about 1.0 nM, at a signal-to-noise ratio of 3. The aptamer-based microcantilever sensor demonstrated good selectivity and was facile, rapid, and reagentless. Our results show the potential for the application of microcantilever biosensor system as a powerful tool to detect tumor markers with high sensitivity and specificity. PMID:26695322

  2. The detection of bulk explosives using nuclear-based techniques

    Energy Technology Data Exchange (ETDEWEB)

    Morgado, R.E.; Gozani, T.; Seher, C.C.

    1988-01-01

    In 1986 we presented a rationale for the detection of bulk explosives based on nuclear techniques that addressed the requirements of civil aviation security in the airport environment. Since then, efforts have intensified to implement a system based on thermal neutron activation (TNA), with new work developing in fast neutron and energetic photon reactions. In this paper we will describe these techniques and present new results from laboratory and airport testing. Based on preliminary results, we contended in our earlier paper that nuclear-based techniques did provide sufficiently penetrating probes and distinguishable detectable reaction products to achieve the FAA operational goals; new data have supported this contention. The status of nuclear-based techniques for the detection of bulk explosives presently under investigation by the US Federal Aviation Administration (FAA) is reviewed. These include thermal neutron activation (TNA), fast neutron activation (FNA), the associated particle technique, nuclear resonance absorption, and photoneutron activation. The results of comprehensive airport testing of the TNA system performed during 1987-88 are summarized. From a technical point of view, nuclear-based techniques now represent the most comprehensive and feasible approach for meeting the operational criteria of detection, false alarms, and throughput. 9 refs., 5 figs., 2 tabs.

  3. DNA methylation detection based on difference of base content

    Science.gov (United States)

    Sato, Shinobu; Ohtsuka, Keiichi; Honda, Satoshi; Sato, Yusuke; Takenaka, Shigeori

    2016-04-01

    Methylation frequently occurs in cytosines of CpG sites to regulate gene expression. The identification of aberrant methylation of certain genes is important for cancer marker analysis. The aim of this study was to determine the methylation frequency in DNA samples of unknown length and/or concentration. Unmethylated cytosine is known to be converted to thymine following bisulfite treatment and subsequent PCR. For this reason, the AT content in DNA increases with an increasing number of methylation sites. In this study, the fluorescein-carrying bis-acridinyl peptide (FKA) molecule was used for the detection of methylation frequency. FKA contains fluorescein and two acridine moieties, which together allow for the determination of the AT content of double-stranded DNA fragments. Methylated and unmethylated human genomes were subjected to bisulfide treatment and subsequent PCR using primers specific for the CFTR, CDH4, DBC1, and NPY genes. The AT content in the resulting PCR products was estimated by FKA, and AT content estimations were found to be in good agreement with those determined by DNA sequencing. This newly developed method may be useful for determining methylation frequencies of many PCR products by measuring the fluorescence in samples excited at two different wavelengths.

  4. Sequential Bayesian Detection: A Model-Based Approach

    Energy Technology Data Exchange (ETDEWEB)

    Candy, J V

    2008-12-08

    Sequential detection theory has been known for a long time evolving in the late 1940's by Wald and followed by Middleton's classic exposition in the 1960's coupled with the concurrent enabling technology of digital computer systems and the development of sequential processors. Its development, when coupled to modern sequential model-based processors, offers a reasonable way to attack physics-based problems. In this chapter, the fundamentals of the sequential detection are reviewed from the Neyman-Pearson theoretical perspective and formulated for both linear and nonlinear (approximate) Gauss-Markov, state-space representations. We review the development of modern sequential detectors and incorporate the sequential model-based processors as an integral part of their solution. Motivated by a wealth of physics-based detection problems, we show how both linear and nonlinear processors can seamlessly be embedded into the sequential detection framework to provide a powerful approach to solving non-stationary detection problems.

  5. Land-based infrared imagery for marine mammal detection

    Science.gov (United States)

    Graber, Joseph; Thomson, Jim; Polagye, Brian; Jessup, Andrew

    2011-09-01

    A land-based infrared (IR) camera is used to detect endangered Southern Resident killer whales in Puget Sound, Washington, USA. The observations are motivated by a proposed tidal energy pilot project, which will be required to monitor for environmental effects. Potential monitoring methods also include visual observation, passive acoustics, and active acoustics. The effectiveness of observations in the infrared spectrum is compared to observations in the visible spectrum to assess the viability of infrared imagery for cetacean detection and classification. Imagery was obtained at Lime Kiln Park, Washington from 7/6/10-7/9/10 using a FLIR Thermovision A40M infrared camera (7.5-14μm, 37°HFOV, 320x240 pixels) under ideal atmospheric conditions (clear skies, calm seas, and wind speed 0-4 m/s). Whales were detected during both day (9 detections) and night (75 detections) at distances ranging from 42 to 162 m. The temperature contrast between dorsal fins and the sea surface ranged from 0.5 to 4.6 °C. Differences in emissivity from sea surface to dorsal fin are shown to aid detection at high incidence angles (near grazing). A comparison to theory is presented, and observed deviations from theory are investigated. A guide for infrared camera selection based on site geometry and desired target size is presented, with specific considerations regarding marine mammal detection. Atmospheric conditions required to use visible and infrared cameras for marine mammal detection are established and compared with 2008 meteorological data for the proposed tidal energy site. Using conservative assumptions, infrared observations are predicted to provide a 74% increase in hours of possible detection, compared with visual observations.

  6. Obstacle detection of mobile robot based on data fusion

    Institute of Scientific and Technical Information of China (English)

    YUAN Xin; SU Li; SUN Li-ning

    2009-01-01

    To study the problem of obstacle detection based on multi-sensors data fusion, the multi-target track-ing theory and techniques are introduced into obstacle detection systems, and the exact position of obstacle can be determined. Data fusion problems are discussed directly based on achievable data from some sensors without considering the specific structure of each individual sensor. With respect to normal linear systems and nonlinear systems, the corresponding algorithms are proposed. The validity of the method is confirmed by simulation re-suits.

  7. An Improved Retransmission-based Network Steganography: Design and Detection

    Directory of Open Access Journals (Sweden)

    Jiangtao Zhai

    2013-01-01

    Full Text Available Network steganography is a covert communication technique that uses redundancies in network protocols to transfer secret information. The retransmission-based steganography (RSTEG embeds covert messages into the payload field of the intentionally retransmission packets. So its capacity is higher than most of the existing methods. Because TCP checksum field of the original packet is different from that of the retransmitted packet, RSTEG is not stealthy in fact. An improved method named IRSTEG is presented to resolve the flaw by introducing the payload compensation. Further, a method is proposed to detect IRSTEG based the payload segment comparison. Experiments show that the method can detect IRSTEG well.

  8. Real-time fault detection method based on belief rule base for aircraft navigation system

    Institute of Scientific and Technical Information of China (English)

    Zhao Xin; Wang Shicheng; Zhang Jinsheng; Fan Zhiliang; Min Haibo

    2013-01-01

    Real-time and accurate fault detection is essential to enhance the aircraft navigation system's reliability and safety.The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults.On account of this reason,we propose an online detection solution based on non-analytical model.In this article,the navigation system fault detection model is established based on belief rule base (BRB),where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output.To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update,a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization (EM) algorithm.Furthermore,the proposed method is verified by navigation experiment.Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model.The output of the detection model can track the fault state very well,and the faults can be diagnosed in real time and accurately.In addition,the detection ability,especially in the probability of false detection,is superior to offline optimization method,and thus the system reliability has great improvement.

  9. 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.

  10. Analog Signal Correlating Using an Analog-Based Signal Conditioning Front End

    Science.gov (United States)

    Prokop, Norman; Krasowski, Michael

    2013-01-01

    This innovation is capable of correlating two analog signals by using an analog-based signal conditioning front end to hard-limit the analog signals through adaptive thresholding into a binary bit stream, then performing the correlation using a Hamming "similarity" calculator function embedded in a one-bit digital correlator (OBDC). By converting the analog signal into a bit stream, the calculation of the correlation function is simplified, and less hardware resources are needed. This binary representation allows the hardware to move from a DSP where instructions are performed serially, into digital logic where calculations can be performed in parallel, greatly speeding up calculations.

  11. THE CONSTRUCTIONS OF ALMOST BINARY SEQUENCE PAIRS WITH THREE-LEVEL CORRELATION BASED ON CYCLOTOMY

    Institute of Scientific and Technical Information of China (English)

    Peng Xiuping; Xu Chengqian

    2012-01-01

    In this paper,a new class of almost binary sequence pair with a single zero element is presented.The almost binary sequence pairs with three-level correlation are constructed based on cyclotomic numbers of order 2,4,and 6.Most of them have good correlation and balance property,whose maximum nontrivial correlation magnitudes are 2 and the difference between the numbers of occurrence of +1's and -1's are 0 or 1.In addition,the corresponding binary sequence pairs are investigated as well and we can also get some kinds of binary sequence pairs with optimum balance and good correlation.

  12. Video Shot Boundary Detection based on Multifractal Analisys

    Directory of Open Access Journals (Sweden)

    B. D. Reljin

    2011-11-01

    Full Text Available Extracting video shots is an essential preprocessing step to almost all video analysis, indexing, and other content-based operations. This process is equivalent to detecting the shot boundaries in a video. In this paper we presents video Shot Boundary Detection (SBD based on Multifractal Analysis (MA. Low-level features (color and texture features are extracted from each frame in video sequence. Features are concatenated in feature vectors (FVs and stored in feature matrix. Matrix rows correspond to FVs of frames from video sequence, while columns are time series of particular FV component. Multifractal analysis is applied to FV component time series, and shot boundaries are detected as high singularities of time series above pre defined treshold. Proposed SBD method is tested on real video sequence with 64 shots, with manually labeled shot boundaries. Detection accuracy depends on number FV components used. For only one FV component detection accuracy lies in the range 76-92% (depending on selected threshold, while by combining two FV components all shots are detected completely (accuracy of 100%.

  13. 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

  14. 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.

  15. Semantic-based technique for thai documents plagiarism detection

    Directory of Open Access Journals (Sweden)

    Sorawat Prapanitisatian

    2014-03-01

    Full Text Available Plagiarism is the act of taking another person's writing or idea without referring to the source of information. This is one of major problems in educational institutes. There is a number of plagiarism detection software available on the Internet. However, a few numbers of them works. Typically, they use a simple method for plagiarism detection e.g. string matching. The main weakness of this method is it cannot detect the plagiarism when the author replaces some words using synonyms. As such, this paper presents a new technique for a semantic-based plagiarism detection using Semantic Role Labeling (SRL and term weighting. SRL is deployed in order to calculate the semantic-based similarity. The main different from the existing framework is terms in a sentence are weighted dynamically depending on their roles in the sentence e.g. subject, verb or object. This technique enhances the plagiarism detection mechanism more efficiently than existing system although positions of terms in a sentence are reordered. The experimental results show that the proposed method can detect the plagiarism document more effective than the existing methods, Anti-kobpae, Turnit-in and Traditional Semantic Role Labeling.

  16. 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.

  17. aTrunk—An ALS-Based Trunk Detection Algorithm

    Directory of Open Access Journals (Sweden)

    Sebastian Lamprecht

    2015-08-01

    Full Text Available This paper presents a rapid multi-return ALS-based (Airborne Laser Scanning tree trunk detection approach. The multi-core Divide & Conquer algorithm uses a CBH (Crown Base Height estimation and 3D-clustering approach to isolate points associated with single trunks. For each trunk, a principal-component-based linear model is fitted, while a deterministic modification of LO-RANSAC is used to identify an optimal model. The algorithm returns a vector-based model for each identified trunk while parameters like the ground position, zenith orientation, azimuth orientation and length of the trunk are provided. The algorithm performed well for a study area of 109 trees (about 2/3 Norway Spruce and 1/3 European Beech, with a point density of 7.6 points per m2, while a detection rate of about 75% and an overall accuracy of 84% were reached. Compared to crown-based tree detection methods, the aTrunk approach has the advantages of a high reliability (5% commission error and its high tree positioning accuracy (0.59m average difference and 0.78m RMSE. The usage of overlapping segments with parametrizable size allows a seamless detection of the tree trunks.

  18. An EEG-Based Fatigue Detection and Mitigation System.

    Science.gov (United States)

    Huang, Kuan-Chih; Huang, Teng-Yi; Chuang, Chun-Hsiang; King, Jung-Tai; Wang, Yu-Kai; Lin, Chin-Teng; Jung, Tzyy-Ping

    2016-06-01

    Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual's internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant's EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha- and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject's cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments. PMID:27121994

  19. R-peaks detection based on stationary wavelet transform.

    Science.gov (United States)

    Merah, M; Abdelmalik, T A; Larbi, B H

    2015-10-01

    Automatic detection of the QRS complexes/R-peaks in an electrocardiogram (ECG) signal is the most important step preceding any kind of ECG processing and analysis. The performance of these systems heavily relies on the accuracy of the QRS detector. The objective of present work is to drive a new robust method based on stationary wavelet transform (SWT) for R-peaks detection. The decimation of the coefficients at each level of the transformation algorithm is omitted, more samples in the coefficient sequences are available and hence a better outlier detection can be performed. Using the information of local maxima, minima and zero crossings of the fourth SWT coefficient detail, the proposed algorithm identifies the significant points for detection and delineation of the QRS complexes, as well as detection and identification of the QRS individual waves peaks of the pre-processed ECG signal. Various experimental results show that the proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, achieving excellent performance on different databases, on the MIT-BIH database (Se=99.84%, P=99.88%), on the QT Database (Se=99.94%, P=99.89%) and on MIT-BIH Noise Stress Test Database, (Se=95.30%, P=93.98%). Reliability and accuracy are close to the highest among the ones obtained in other studies. Experiments results being satisfactory, the SWT may represent a novel QRS detection tool, for a robust ECG signal analysis. PMID:26105724

  20. An FPGA-Based Rapid Wheezing Detection System

    Directory of Open Access Journals (Sweden)

    Bor-Shing Lin

    2014-01-01

    Full Text Available Wheezing is often treated as a crucial indicator in the diagnosis of obstructive pulmonary diseases. A rapid wheezing detection system may help physicians to monitor patients over the long-term. In this study, a portable wheezing detection system based on a field-programmable gate array (FPGA is proposed. This system accelerates wheezing detection, and can be used as either a single-process system, or as an integrated part of another biomedical signal detection system. The system segments sound signals into 2-second units. A short-time Fourier transform was used to determine the relationship between the time and frequency components of wheezing sound data. A spectrogram was processed using 2D bilateral filtering, edge detection, multithreshold image segmentation, morphological image processing, and image labeling, to extract wheezing features according to computerized respiratory sound analysis (CORSA standards. These features were then used to train the support vector machine (SVM and build the classification models. The trained model was used to analyze sound data to detect wheezing. The system runs on a Xilinx Virtex-6 FPGA ML605 platform. The experimental results revealed that the system offered excellent wheezing recognition performance (0.912. The detection process can be used with a clock frequency of 51.97 MHz, and is able to perform rapid wheezing classification.