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A Distributed Group Signature Scheme with Traceable Signers for Mobile Ad Hoc Networks

A Distributed Group Signature Scheme with Traceable Signers ...

The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

作者： Hong Zhong Runhua Shi Key Laboratory of Intelligent Computing & Signal Processing Anhui University Ministry of Education Hefei China 230039 School of Computer Science and Technology Anhui University Hefei China 230039

ISBN: (纸本)9781424472352

In this paper, we propose a distributed group signature scheme with traceable signers for mobile Ad hoc networks. In such scheme, there isn't a trusted center, and all members of Ad hoc group cooperate to generate all system parameters and all private/public keys. Any ' members in Ad hoc group can collaboratively generate the group signature with the help of a designated clerk, where the signer respectively generates the partial signature by using his private key and the clerk can check the correctness of the partial signature. Furthermore, anyone can verify the validity of the group signature by the group public key and trace back to find the identities of signers from the warrant created by the clerk. What's more, it can dynamically increase the parameter values of ~t and ~n, according to the actual security needs of mobile Ad hoc networks, but the private and public keys of the whole Ad hoc group still are not changed for relatively long-term stability.

关键词： group signature shamir's secret sharing mobile ad hoc networks traceable distributed

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BSS method using exterior penalty function optimization

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Journal of Computational Information Systems 2010年第3期6卷 803-812页

作者： Liu, Jianting Li, Rui Wang, Fasong Department of Mathematics North China University of Water Resources and Electric Power Zhengzhou China School of Sciences Henan University of Technology Zhengzhou China China Electronics Technology Group Corporation the 27th Research Institute Zhengzhou China National Key Laboratory for Radar Signal Processing Xidian University Xi'an 710071 China

Blind Source Separation(BSS) algorithm for hybrid sources separation based on exterior penalty function optimization method is proposed. The proposed algorithm is under the framework of constrained BSS(cBSS) method which solves the constrained optimization problem by using the exterior penalty function method. In order to choose nonlinear functions as the probability density function(PDF) estimation of the source signals, the parameterized asymmetric generalized Gaussian density(AGGD) estimation is proposed which can separate the hybrid mixtures of source signals using only a flexible model. Moreover, AGGD based BSS method is completely blind to the source signals. Simulations confirm the effectiveness of the proposed algorithm. Copyright © 2010 Binary Information Press May, 2010.

关键词： Blind source separation

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On Dynamic Weighting of Data in Clustering with K-Alpha Means

On Dynamic Weighting of Data in Clustering with K-Alpha Mean...

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International Conference on Pattern Recognition

作者： Si-Bao Chen Hai-Xian Wang Bin Luo Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui China Key Laboratory of Child Development and Learning Science of Ministry of Education Research Center of Learning Science South-East University Nanjing Jiangsu China

ISBN: (纸本)9781424475421

Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an obstacle in applications. In this paper, we investigate a new class of clustering algorithm, K-Alpha Means (KAM), which is insensitive to the initial centers. With K-Harmonic Means as a special case, KAM dynamically weights data points during iteratively updating centers, which deemphasizes data points that are close to centers while emphasizes data points that are not close to any centers. Through replacing minimum operator in K-Means by alpha-mean operator, KAM significantly improves the clustering performances.

关键词： Clustering algorithms Heuristic algorithms Indexes Partitioning algorithms signal processing algorithms Euclidean distance Iris

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Performance evaluation of Independent Component Analysis in an iris recognition system

Performance evaluation of Independent Component Analysis in ...

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ACS/IEEE International Conference on Computer Systems and Applications

作者： Imen Bouraoui Salim Chitroub Ahmed Bouridane Signal and Image Processing Laboratory Electronics and Computer Science Faculty University of Sciences and Technology Houari Boumediene Algiers Algeria School of Computing Engineering and Information Sciences Northumbria University Newcastle-upon-Tyne UK

The overall performance of any iris recognition system relies on the performance of its components, which are preprocessing, feature extraction and matching. Feature extraction is the important step of such recognition system, but it is strongly dependent on the pre-processing step that is consisting of localising and normalising the iris. In this paper, Independent Component Analysis (ICA), which is a recently developed statistical method for data analysis, is applied for extracting the features for iris region of interest that are statistically independent. Based on some mathematical criteria, the performance of ICA is evaluated by using two different subsets of CASIA-V3 iris image database. The obtained results are convincing and some future improved research works are subsequently envisaged.

关键词： Iris recognition Feature extraction Entropy Iris Transforms Vectors Random variables

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Gaussian modeling and Discrete Cosine Transform for efficient and automatic palmprint identification

Gaussian modeling and Discrete Cosine Transform for efficien...

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International Conference on Machine and Web Intelligence (ICMWI)

作者： Abdallah Meraoumia Salim Chitroub Ahmed Bouridane Signal and Image Processing Laboratory Electronics and Computer Science Faculty University of Sciences and Technology Houari Boumediene Bab-Ezzouar Algeria School of Computing Engineering and Information Sciences Northumbria University Newcastle-upon-Tyne UK

Automatic personal identification using biometric information is playing a more and more important role in applications such as public security, access control, banking, etc. Palmprint identification is a subcategory of biometrics identification, which can efficiently used to identify the people. It is for this reason that palmprint-based identification is becoming increasingly popularity in recent years. In this paper, we present a novel scheme for palmprint identification using the multi-variate Gaussian Probability Density Function (GPDF) and two-dimensional Block based Discrete Cosine Transform (2D-BDCT). In this method, a palmprint is firstly divided into overlapping and equal-sized blocks, and then, applies the discrete cosine transform over each block. By using zigzag scan order (starting at the top-left) each transform block is reordered to produce the observation vector. Subsequently, we use the Gaussian probability density function for modeling the feature vector of each palmprint. Finally, Log-likelihood scores are used for palmprint matching. The proposed scheme is validated for their efficacy on PolyU palmprint database of 100 users. Our experimental results show the effectiveness and reliability of the proposed approach, which brings both high identification accuracy rate.

关键词： Feature extraction Discrete cosine transforms Databases Accuracy Hidden Markov models Probability density function

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Volume-based magnetic resonance brain image classification

Volume-based magnetic resonance brain image classification

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International Conference on Genetic and Evolutionary Computing

作者： Chiou, Yaw-Jiunn Chai, Jyh-Wen Chen, Clayton Chi-Chang Chen, Shih-Yu Chen, Hsian-Min Ouyang, Yen-Chieh Su, Wu-Chung Tsai, Ching Tsorng Yang, Ching-Wen Lee, San-Kan Chang, Chein-I Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Biomedical Engineering Hungkuang University Taichung Taiwan Computer Science Department Tunghai University 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 County Baltimore MD 21250 United States

ISBN: (纸本)9780769542812

This paper develops a volume-based technique, called Volume Sphering Analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices-formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices. © 2010 IEEE.

关键词： Support vector machines

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Rhythmic component extraction considering phase alignment and the application to motor imagery-based brain computer interfacing

Rhythmic component extraction considering phase alignment an...

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International Joint Conference on Neural Networks (IJCNN)

作者： Hiroshi Higashi Toshihisa Tanaka Yasue Mitsukura Department of Electrical and Electronic Engineering Tokyo University of Agriculture슠and슠Technology Japan Laboratory for Advanced Brain Signal Processing RIKEN Brain Science Institute Japan Graduate School of Bio-Applications and System Engineering Tokyo University of Agriculture슠and슠Technology Japan

We propose a novel method for extracting a rhythmically oscillating signal from EEG recordings including multiple source signals which have similar frequencies. The main application of this method is brain computer interfaces (BCI), which use rhythmically oscillating signals such as alpha, mu, and beta waves, as feature signals. It is difficult to separate those components and/or extract an command-related component when these feature signals span the same frequency band. The main idea is to assume that signals generated in different brain parts have different phases, even though they have the same frequency. This hypothesis is effectively incorporated with the previously proposed rhythmic component extraction (RCE) method, which successfully extracts a signal oscillating at a certain frequency from multi-channel sensor signals in the BCI application. The signal model is firstly given and then this novel extraction method is formulated as an optimization problem. We apply the proposed method for the classification of multi-channel EEG signals between imaginary left/right hand movement. Our experiment suggests that the proposed method is effective in feature extraction for motor-imagery based BCI.

关键词： Feature extraction Electroencephalography Electrodes Brain modeling Cost function Eigenvalues and eigenfunctions Rhythm

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Techniques for automatic magnetic resonance image classification

Techniques for automatic magnetic resonance image classifica...

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International Conference on Genetic and Evolutionary Computing

作者： Chen, Hsian-Min Chen, Shih-Yu Chai, Jyh Wen Chen, Clayton Chi-Chang Wu, Chao-Cheng Ouyang, Yen-Chieh Tsai, Ching Tsorng Yang, Ching-Wen Lee, San-Kan Chang, Chein-I Department of Biomedical Engineering Hungkuang University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Computer Science Department Tunghai University 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 County Baltimore MD 21250 United States

ISBN: (纸本)9780769542812

Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely used for automatic brain tissue segmentation for this purpose. This paper investigates limitations of this software algorithm on implementation and further develops a new approach to automatic MR brain tissue classification. The proposed new technique first implements an unsupervised training sample generation process (UTSGP) which includes a Pixel Purity Index (PPI) to generate an initial set of training samples that are further refined by a Support Vector Machine. The resulting training samples are then as a set of training samples for an Iterative Fisher's Linear Discriminant Analysis (IFLDA) which implements FLDA iteratively to improve classification. In order to conduct a fair comparison synthetic images are used for performance evaluation. Experimental results show that our proposed technique is superior in practical implementation to this software algorithm in several aspects of generalization ability, flexibility of choosing number of classes to be classified, avoidance of inconsistent results caused by different initial conditions. © 2010 IEEE.

关键词： Support vector machines

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An image co-registration method for wide-swath and high-resolution spaceborne InSAR

An image co-registration method for wide-swath and high-reso...

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2009 Asia-Pacific Conference on Synthetic Aperture radar, APSAR 2009

作者： Wang, Ping Li, Zhenfang Suo, Zhiyong Liu, Yanyang National Key Laboratory of Science and Technology on Radar Signal Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

In this paper, we analyse the issue of SAR image co-registration for wide-swath and high-resolution spaceborne InSAR. A solution to this problem is proposed in which three processing procedures are adopted. First, image co-registration pre-processing is performed. There are two methods for this step: one is based on InSAR orbit and radar system parameters, and the other is based on the cross-correlation information of the master and the slave image. Second, coarse co-registration is implemented by block processing. Finally, fine co-registration is executed in order to obtain sub-pixel co-registration accuracy. Data simulation and qualitative analysis of coherence show that this method can provide accurate co-registration for wide-swath and high-resolution spaceborne InSAR, and meet the practical engineering requirement. ©2009 IEEE.

关键词： Synthetic aperture radar

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Iterative Design of MIMO radar Transmit Waveforms and Receive Filter Bank

Iterative Design of MIMO Radar Transmit Waveforms and Receiv...

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IEEE International Conference on Acoustics, Speech, and signal processing

作者： Y.-C. Wang Hongwei Liu Z.-Q. Luo State Key Laboratory on Integrated Service Networks Xidian University Xi'an 710071 China. Currently Y.-C Wang is a post doctorial fellow at the University of Minnesota National Lab of Radar Signal Processing Xidian University Xi'an 710071 China Department of Electrical Computer Science Engineering University of Minnesota Minneapolis 55455 USA

ISBN: (纸本)9781424442959

In this paper, we propose an iterative design approach to jointly optimize probing signal waveforms and a receive filter bank for a multiple-input multiple-output (MIMO) radar under a constant modulus constraint. The design goals are to approximate a desired beampattern and to minimize the auto/cross- correlation levels of the probing signal waveforms for different time lags and between different spatial angles. Since the overall design problem is nonconvex, we propose to optimize the transmit probing signals and receive filter bank separately and alternately. The optimization of receive filter bank is a standard least squares problem, while the optimization of the constant modulus transmit signal waveforms is a norm-constrained least squares problem which can be approximately solved using a low-rank semidefinite relaxation procedure. We demonstrate the effectiveness of our proposed approach through a simulation example.

关键词： Multiple-input multiple-output (MIMO) radar Beampattern Mismatched filter bank Constant modulus probing signal Auto correlation peak sidelobes Cross correlation Level signal waveform Filter banks design target Banks MIMO radar TRANSMITTING Exploration with a probe Iterative design problems

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