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    Dictionary learning for blind one bit compressed sensing

    , Article IEEE Signal Processing Letters ; Volume 23, Issue 2 , 2016 , Pages 187-191 ; 10709908 (ISSN) Zayyani, H ; Korki, M ; Marvasti, F ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc 
    This letter proposes a dictionary learning algorithm for blind one bit compressed sensing. In the blind one bit compressed sensing framework, the original signal to be reconstructed from one bit linear random measurements is sparse in an unknown domain. In this context, the multiplication of measurement matrix A and sparse domain matrix φ, i.e., D = Aφ, should be learned. Hence, we use dictionary learning to train this matrix. Towards that end, an appropriate continuous convex cost function is suggested for one bit compressed sensing and a simple steepest-descent method is exploited to learn the rows of the matrix D. Experimental results show the effectiveness of the proposed algorithm... 

    Semi-reversible quantization based data hiding using missing samples recovery technique

    , Article 16th International Conference on Telecommunications, ICT 2009, 25 May 2009 through 27 May 2009 ; 2009 , Pages 298-302 ; 9781424429370 (ISBN) Ameri, A ; Saberian, M. J ; Akhaee, M. A ; Marvasti, F ; Sharif University of Technology
    A blind semi-invertible quantization based data hiding scheme, which reconstructs the original signal with high precision has been proposed. In order to produce correlated quantization yielding reversibility of the quantization based approach, a new transform domain has been introduced. In decoder, by compensating the quantization error and using the iterative technique, the original signal is recovered; then the watermarked signal is compared with the reconstructed original signal and hidden data is retrieved. Simulation results show that the proposed method in comparison with other reversible methods imposes less distortion and thus a higher Signal to Noise Ratio (SNR) is achieved. © 2009... 

    A new framework based on recurrence quantification analysis for epileptic seizure detection

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 17, Issue 3 , 2013 , Pages 572-578 ; 21682194 (ISSN) Niknazar, M ; Mousavi, S. R ; Vosoughi Vahdat, B ; Sayyah, M ; Sharif University of Technology
    This study presents applying recurrence quantification analysis (RQA) on EEG recordings and their subbands: delta, theta, alpha, beta, and gamma for epileptic seizure detection. RQA is adopted since it does not require assumptions about stationarity, length of signal, and noise. The decomposition of the original EEG into its five constituent subbands helps better identification of the dynamical system of EEG signal. This leads to better classification of the database into three groups: Healthy subjects, epileptic subjects during a seizure-free interval (Interictal) and epileptic subjects during a seizure course (Ictal). The proposed algorithm is applied to an epileptic EEG dataset provided... 

    Glottal Pulse Shape Optimization using Simulated Annealing

    , Article AISP 2012 - 16th CSI International Symposium on Artificial Intelligence and Signal Processing ; 2012 , Pages 112-115 ; 9781467314794 (ISBN) Bahaadini, S ; Sameti, H ; Jabbari, F ; Mohammadi, S. H ; Sharif University of Technology
    Excitation signal has essential role in speech synthesis filters to produce natural speech. In this study, a new method is proposed for modeling the glottal pulse shape of a speaker. A search is done on the glottal pulse shape space using simulated annealing method. The PESQ measure and Cepstral distance between the original signal and the synthesized signal are used as the cost function. An LPC filter with 10 coefficients is used as the synthesis filter. The PESQ value between the original and synthesized speech using traditional impulse is 2.402. Here, the glottal pulse for a certain speaker is modeled in three different experiments. In the first experiment, the negative PESQ measure is...