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    Deterministic randomness extraction from generalized and distributed Santha-Vazirani sources

    , Article SIAM Journal on Computing ; Volume 46, Issue 1 , 2017 , Pages 1-36 ; 00975397 (ISSN) Beigi, S ; Etesami, O ; Gohari, A ; Sharif University of Technology
    Society for Industrial and Applied Mathematics Publications  2017
    Abstract
    A Santha-Vazirani (SV) source is a sequence of random bits where the conditional distribution of each bit, given the previous bits, can be partially controlled by an adversary. Santha and Vazirani show that deterministic randomness extraction from these sources is impossible. In this paper, we study the generalization of SV sources for nonbinary sequences. We show that unlike the binary setup of Santha and Vazirani, deterministic randomness extraction in the generalized case is sometimes possible. In particular, if the adversary has access to s "nondegenerate" dice that are c-sided and can choose one die to throw based on the previous realizations of the dice, then deterministic randomness... 

    Improving data protection in BSS based secure communication: mixing matrix design

    , Article Wireless Networks ; Volume 27, Issue 7 , 2021 , Pages 4747-4758 ; 10220038 (ISSN) Aslani, M. R ; Shamsollahi, M. B ; Nouri, A ; Sharif University of Technology
    Springer  2021
    Abstract
    Abstract: In this paper, a secure and efficient Blind Source Separation (BSS) based cryptosystem is presented. The use of BSS in audio and image cryptography in wireless networks has attracted more attention. A BSS based cryptosystem consists of three main parts: secret data, secret keys, and mixing matrix. In this paper, we propose a new design to create a proper mixing matrix in BSS based cryptosystem. We offer a mathematical criterion to select mixing matrix elements before encryption. The proposed criterion gives a simple way to attach the secret sources to keys, which makes source separation very hard for the adversary. Versus, we show that using the random mixing matrix can lead to... 

    Separation of speech sources in under-determined case using SCA and time-frequency methods

    , Article 2008 International Symposium on Telecommunications, IST 2008, Tehran, 27 August 2008 through 28 August 2008 ; 2008 , Pages 533-538 ; 9781424427512 (ISBN) Mahdian, R ; Babaiezadeh, M ; Jutten, C ; Sharif University of Technology
    2008
    Abstract
    This paper presents a new algorithm for Blind Source Separation (BSS) of Instantaneous speech mixtures in under-determined case. A demixing algorithm which exploits the sparsity of speech signals in the short time Fourier transform (STFT) domain is proposed. This algorithm combines the modified k-means clustering procedure involved in the Line Orientation Separation Technique (LOST) with Smoothed l0-norm minimization (SL0) method. First procedure along with a transformation into a sparse domain tries to estimate the mixing matrix, and the second method tries to extract the sources from the mixtures. Simulation results are presented and compared to the Degenerate Unmixing Estimation Technique... 

    Extraction and automatic grouping of joint and individual sources in multi-subject fMRI data using higher order cumulants

    , Article IEEE Journal of Biomedical and Health Informatics ; 24 May , 2018 ; 21682194 (ISSN) Pakravan, M ; Shamsollahi, M. B ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    Abstract
    The joint analysis of multiple datasets to extract their interdependency information has wide applications in biomedical and health informatics. In this paper, we propose an algorithm to extract joint and individual sources of multi-subject datasets by using a deflation based procedure, which is referred to as joint/individual thin independent component analysis (JI-ThICA). The proposed algorithm is based on two cost functions utilizing higher order cumulants to extract joint and individual sources. Joint sources are discriminated by fusing signals of all subjects, whereas individual sources are extracted separately for each subject. Furthermore, JI-ThICA algorithm estimates the number of... 

    Sequential blind source extraction for quasi-periodic signals with time-varying period

    , Article IEEE Transactions on Biomedical Engineering ; Volume 56, Issue 3 , 2009 , Pages 646-655 ; 00189294 (ISSN) Tsalaile, T ; Sameni, R ; Sanei, S ; Jutten, C ; Chambers, J ; Sharif University of Technology
    2009
    Abstract
    A novel second-order-statistics-based sequential blind extraction algorithm for blind extraction of quasi-periodic signals, with time-varying period, is introduced in this paper. Source extraction is performed by sequentially converging to a solution that effectively diagonalizes autocorrelation matrices at lags corresponding to the time-varying period, which thereby explicitly exploits a key statistical nonstationary characteristic of the desired source. The algorithm is shown to have fast convergence and yields significant improvement in signal-to-interference ratio as compared to when the algorithm assumes a fixed period. The algorithm is further evaluated on the problem of separation of... 

    Extraction and automatic grouping of joint and individual sources in multisubject fMRI data using higher order cumulants

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 23, Issue 2 , 2019 , Pages 744-757 ; 21682194 (ISSN) Pakravan, M ; Shamsollahi, M. B ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Abstract
    The joint analysis of multiple data sets to extract their interdependency information has wide applications in biomedical and health informatics. In this paper, we propose an algorithm to extract joint and individual sources of multisubject data sets by using a deflation-based procedure, which is referred to as joint/individual thin independent component analysis (JI-ThICA). The proposed algorithm is based on two cost functions utilizing higher order cumulants to extract joint and individual sources. Joint sources are discriminated by fusing signals of all subjects, whereas individual sources are extracted separately for each subject. Furthermore, JI-ThICA algorithm estimates the number of... 

    Joint, partially-joint, and individual independent component analysis in multi-subject fMRI data

    , Article IEEE Transactions on Biomedical Engineering ; Volume 67, Issue 7 , 2020 , Pages 1969-1981 Pakravan, M ; Shamsollahi, M. B ; Sharif University of Technology
    IEEE Computer Society  2020
    Abstract
    Objective: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-joint/individual multiple datasets unidimensional (JpJI-MDU), and accordingly, a source extraction method is developed. Method: We present a deflation-based algorithm utilizing higher order cumulants to analyze the JpJI-MDU source model. The algorithm maximizes a cost function which leads to an eigenvalue problem solved with thin-SVD (singular value decomposition)...