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signal processing for neural spike trains

Computational intelligence and neuroscience 2010年第1期2010卷 698751页

作者： Theodore W Berger Zhe Sage Chen Andrzej Cichocki Karim G Oweiss Rodrigo Quian Quiroga Nitish V Thakor Department of Biomedical Engineering Viterbi School of Engineering University of Southern California Los Angeles CA 90089 USA Neuroscience Statistics Research Laboratory Massachusetts Institute of Technology Cambridge MA 02139 USA Laboratory for Advanced Brain Signal Processing RIKEN Brain Science Institute Wako-shi Saitama 351-0198 Japan Department of Electrical and Computer Engineering Michigan State University MI 48824 USA Department of Engineering University of Leicester Leicester LE1 7RH UK School of Medicine Johns Hopkins University Baltimore MD 21205 USA

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A threshold RSA signature scheme with traceable signers

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Journal of Computational Information Systems 2009年第4期5卷 1143-1146页

作者： Shi, Runhua Zhong, Hong Huang, Liusheng Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei 230039 China School of Computer Science and Technology Anhui University Hefei 230039 China NHPCC Depart. of CS. and Tech. USTC Hefei 230027 China

This paper proposes a threshold RSA signature scheme with traceable signers. In such scheme, there is a trusted center (TC), which generates all system parameters, the secret keys and the corresponding public keys of the group and all members, and exists a designated clerk, who creates a warrant recording the identities of the signers and other import information. Any t members can collaboratively generate the group signature with the help of the clerk, who verifies the validity of the partial signatures and combines them to the group signature. Any one can verify the correctness of the group signature by the public information and trace out to find the identities of the signers by the warrant. © 2009 Binary Information Press August, 2009.

关键词： Authentication

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Real-time processing of simplex growing algorithm

Real-time processing of simplex growing algorithm

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2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009

作者： Wu, Chao-Cheng Chang, Chein-I. Ren, Husen Chang, Yang Lang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Center for Space and Remote Sensing Research National Central University Jhong-Li Tao-Yuan Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781424433957

Simplex growing algorithm (SGA) was recently developed as an alternative to the N-finder algorithm (N-FINDR) which is shown to be a promising endmember extraction technique. This paper further extends the SGA to a real-time processing algorithm, referred to as real-time SGA (RT SGA) that can effectively address four major issues arising in practical implementation for N-FINDR, (1) use of random initial endmembers which causes inconsistent final results, (2) very high computational complexity which results from an exhaustive search for finding all endmembers simultaneously, (3) requirement of dimensionality reduction because of enormous data volumes to be processed and (4) lack of real-time capability. ©2009 IEEE.

关键词： Extraction

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Achieving Close-Capacity Performance with Simple Concatenation Scheme on Multiple-Antenna Channels

Achieving Close-Capacity Performance with Simple Concatenati...

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GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

作者： Nghi H. Tran Tho Le-Ngoc Tad Matsumoto Ha H. Nguyen Department of Electrical & Computer Engineering McGill University Montreal QUE Canada Information Theory and Signal Processing Laboratory School of Information Science Japan Advanced Institute of Science and Technology Japan Department of Electrical & Computer Engineering University of Saskatchewan Saskatoon SAS Canada

This paper proposes a simple yet capacity-approaching concatenation of a mixture of short memory length convolutional codes and simple rate-1 block code followed by either complex 1-dimensional (1-D) anti-Gray or Gray mapping over multiple antenna channels with quadrature phase-shift keying (QPSK). By interpreting rate-1 code together with 1-D mapping as a multi-D mapping employed over multiple transmit antennas, the error performance is analyzed in two regions, the error-floor and turbo pinch-off regions. In the former one, a tight union bound and design criterion on the asymptotic performance are first derived, which provide an useful tool to predict the error performance. Based on the design criterion, an optimal rate-1 code for each 1-D mapping is then constructed to achieve the best asymptotic performance. In the turbo pinch-off area, by using extrinsic information transfer (EXIT) chart, the most suitable mixed codes are selected for both symmetric and asymmetric antenna setups. It is demonstrated that the simple concatenation scheme can achieve near-capacity. Furthermore, its error performance is comparable to that obtained by using well-designed irregular low-density parity-check (LDPC) and repeat accumulate (RA) codes, and thereby, outperforms a scheme employing a parallel concatenated turbo code.

关键词： Parity check codes Convolutional codes Block codes Phase shift keying Quadrature phase shift keying Transmitting antennas Error analysis Performance analysis Concatenated codes Turbo codes

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An automatic computer-aided detection system for meniscal tears on magnetic resonance images

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IEEE Transactions on Medical Imaging 2009年第8期28卷 1308-1316页

作者： Ramakrishna, Bharath Liu, Weimin Saiprasad, Ganesh Safdar, Nabile Chang, Chein-I Siddiqui, Khan Kim, W. Siegel, Eliot Chai, Jyh-Wen Chen, Clayton Chi-Chang Lee, San-Kan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD 21250 United States University of Maryland School of Medicine Baltimore MD 21201 United States Department of Electrical Engineering and Environmental Restoration Disaster Reduction Research Center National Chung Hsing University Taichung 402 Taiwan VA Maryland Health Care System Baltimore MD 21201 United States Hospital of the University of Pennsylvania Philadelphia PA 19104 United States Department of Radiology College of Medicine China Medical University Taichung 404 Taiwan Department of Radiology Taichung Veterans General Hospital Taichung 40705 Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung 406 Taiwan Department of Radiology Taichung Veterans General Hospital Taichung 407 Taiwan

Knee-related injuries including meniscal tears are common in both young athletes and the aging population, and require accurate diagnosis and surgical intervention when appropriate. With proper techniques and radiologists' experienced skills, confidence in detection of meniscal tears can be quite high. This paper develops a novel computer-aided detection (CAD) diagnostic system for automatic detection of meniscal tears in the knee. Evaluation of this CAD system using an archived database of images from 40 individuals with suspected knee injuries indicates that the sensitivity and specificity of the proposed CAD system are 83.87% and 75.19%, respectively, compared to the mean sensitivity and specificity of 77.41% and 81.39%, respectively, obtained by experienced radiologists in routine diagnosis without using the CAD. The experimental results suggest that the developed CAD system has great potential and promise in automatic detection of both simple and complex meniscal tears of the knee. © 2009 IEEE.

关键词： Magnetic resonance imaging

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Landmark Extraction from Echocardiography Sequence Based on Corner Detection Algorithms Using Product Moment Coefficient Matcher

Landmark Extraction from Echocardiography Sequence Based on ...

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International Conference on signal processing Systems, ICSPS

作者： Meysam Siyah Mansoory Hamid Behnam Emad Fatemi Zadeh Bio Medical Engineering Department Science and Research Branch Islamic Azad University슠 Tehran Iran Bio Medical Engineering Group Electrical Engineering Department Iran University of Science and Technology Tehran Iran Bio Medical Signal and Image Processing Laboratory School of Electrical Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9781424442133

Landmark extraction is used as the first step of many vision tasks such as tracking, image registration, localization, image matching and recognition. Furthermore, landmarks are used to reduce the data flow and consequently the computational costs. In this paper we extracted landmarks from echocardiography sequence, our algorithm is based on corner extraction, and then we evaluate our algorithm with applying some test. For this purpose, we evaluated the detectors according to their repeatability, stability and landmark localization under changes in noise.

关键词： Echocardiography Detection algorithms Data mining Image registration Image matching Image recognition Computational efficiency Testing Detectors Stability

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Brain tissue classification using independent vector analysis (IVA) for magnetic resonance image

Brain tissue classification using independent vector analysi...

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2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009

作者： Chiou, Yaw-Jiunn Chen, Hsiang-Min Chai, Jyh Wen Chen, Clayton Chi-Chang Ouyang, Yen-Chieh Su, Wu-Chung Yang, Ching-Wen Lee, San-Kan Chang, Chein-I. Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology College of Medicine China Medical University Taichung Taiwan School of Medicine National Yang-Ming University Taipei Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung 406 Taiwan Computer Center Taichung Veterans General Hospital Taichung 407 Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769536569

The purpose of this study is to present a new method, independent vector analysis (IVA), by extending independent component analysis (ICA) of univariate source signals to multivariate source signals on Magnetic Resonance Imaging (MRI). IVA is utilized to relief the limitation of the conventional ICA approach. The proposed method can resolve the permutation problem during individual ICA runs for group brain MR images. The proposed IVA method in conjunction with support vector machine (SVM), we can effectively separate the different part of gray, white matter and cerebrospinal fluid (CSF) from brain soft tissues. In order to demonstrate the proposed IVA-SVM method, experiments are conducted for performance analysis and evaluation. Simulation results show that using IVA can greatly release from the problem cause from traditional ICA to the situation of analyzing inconsistent results of MR image. © 2009 IEEE.

关键词： Magnetic resonance imaging

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Eye movement data modeling using a genetic algorithm

Eye movement data modeling using a genetic algorithm

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Congress on Evolutionary Computation

作者： Yun Zhang Hong Fu Zhen Liang Xiaoyu Zhao Zheru Chi Dagan Feng Xinbo Zhao School of Computer Science Northwestern University Evanston IL USA Center of Multimedia Signal Processing Hong Kong Polytechnic University Hong Kong China School of Information Technologies University of Sydney NSW Australia Key Laboratory of Contemporary Design & Integrated Manufacturing Technology Northwestern Polytechnical University Xi'an Shanxi China

We present a computational model of human eye movements based on a genetic algorithm (GA). The model can generate elemental raw eye movement data in a four-second eye viewing window with a 25 Hz sampling rate. Based on the physiology and psychology characters of human vision system, the fitness function of the GA model is constructed by taking into consideration of five factors including the saliency map, short time memory, saccades distribution, Region of Interest (ROI) map, and a retina model. Our model can produce the scan path of a subject viewing an image, not just several fixations points or artificial ROI's as in the other models. We have also developed both subjective and objective methods to evaluate the model by comparing its behavior with the real eye movement data collected from an eye tracker. Tested on 18 (9 times 2) images from both an obvious-object image group and a non-obvious-object image group, the subjective evaluations shows very close scores between the scan paths generated by the GA model and those real scan paths; for the objective evaluation, experimental results show that the distance between GA's scan paths and human scan paths of the same image has no significant difference by a probability of 78.9% on average.

关键词： Genetic algorithms Humans Computational modeling Sampling methods Physiology Psychology Machine vision Retina Tracking Testing

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Component analysis-based unsupervised linear spectral mixture analysis for hyperspectral imagery

Component analysis-based unsupervised linear spectral mixtur...

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Workshop on Hyperspectral Image and signal processing: Evolution in Remote Sensing, WHISPERS

作者： Xiaoli Jiao Yingzi Du Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science & Electrical Engineering University of Maryland Baltimore MD USA Department of Electrical and Computer Engineering Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA Department of Electrical Engineering National Chung Hsing University Taichung Taiwan

One of the most challenging issues in unsupervised linear spectral mixture analysis (LSMA) is how to obtain unknown knowledge of target signatures referred to as virtual endmembers (VEs) directly from the data to be processed. This issue has never arisen in supervised LSMA where the VEs are either assumed to be known a priori or can be provided by visual inspection. With the recent advent of hyperspectral sensor technology many unknown and subtle signal sources can be uncovered and revealed without prior knowledge. This paper addresses this issue and develops a component analysis-based unsupervised LSMA where the desired VEs can be extracted by component analysis-based transforms directly from the data to be processed without appealing for prior knowledge. In order to substantiate the utility of the proposed approach extensive experiments are conducted for demonstration.

关键词： Image analysis Spectral analysis Hyperspectral imaging Independent component analysis Personal communication networks Inspection Statistics signal analysis Hyperspectral sensors Principal component analysis

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Brain Tissue Classification Using Independent Vector Analysis (IVA) for Magnetic Resonance Image

Brain Tissue Classification Using Independent Vector Analysi...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yaw-Jiunn Chiou Hsiang-Min Chen Jyh Wen Chai Clayton Chi-Chang Chen Yen-Chieh Ouyang Wu-Chung Su Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology College of Medicine China Medical University and Hospital Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and technology Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

关键词： Brain Image analysis Magnetic analysis Magnetic resonance Independent component analysis Magnetic resonance imaging Support vector machines signal analysis Image resolution signal resolution

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