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    Graph Signal Separation Based on Smoothness or Sparsity in the Frequency Domain

    , M.Sc. Thesis Sharif University of Technology Mohammadi, Sara (Author) ; Babaie-Zadeh, Massoud (Supervisor) ; Thanou, Dorina (Co-Supervisor)
    Blind separation of mixed graph signals is one of the new topics in the field of graph signal processing. However, similar to the most proposed methods for separating traditional signals, it is assumed that the number of observed signals is equal to or greater than the number of sources. In this thesis, we show that a signal can be uniquely decomposed into the summation of a set of smooth graph signals, up to the indeterminacy of their DC values. From the blind source separation point of view, this is like the separation of a set of graph signals from a single mixture, contrary to traditional blind source separation in which at least two observed mixtures are required. Moreover, we... 

    A dynamical model for generating synthetic phonocardiogram signals

    , Article Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; 2011 , Pages 5686-5689 ; 1557170X (ISSN) ; 9781424441211 (ISBN) Almasi, A ; Shamsollahi, M. B ; Senhadji, L ; Sharif University of Technology
    In this paper we introduce a dynamical model for Phonocardiogram (PCG) signal which is capable of generating realistic synthetic PCG signals. This model is based on PCG morphology and consists of three ordinary differential equations and can represent various morphologies of normal PCG signals. Beat-to-beat variation in PCG morphology is significant so model parameters vary from beat to beat. This model is inspired of Electrocardiogram (ECG) dynamical model proposed by McSharry et al. and can be employed to assess biomedical signal processing techniques  

    Fetal electrocardiogram R-peak detection using robust tensor decomposition and extended Kalman filtering

    , Article Computing in Cardiology ; Volume 40 , 2013 , Pages 189-192 ; 23258861 (ISSN) ; 9781479908844 (ISBN) Akhbari, M ; Niknazar, M ; Jutten, C ; Shamsollahi, M. B ; Rivet, B ; Sharif University of Technology
    In this paper, we propose an efficient method for R-peak detection in noninvasive fetal electrocardiogram (ECG) signals which are acquired from multiple electrodes on mother's abdomen. The proposed method is performed in two steps: first, we employ a robust tensor decomposition-based method for fetal ECG extraction, assuming different heart rates for mother and fetal ECG; then a method based on extended Kalman filter (EKF) in which the ECG beat is modeled by 3 state equations (P, QRS and T), is used for fetal R-peak detection. The results show that the proposed method is efficiently able to estimate the location of R-peaks of fetal ECG signals. The obtained average scores of event 4 and 5 on... 

    Steganography in silence intervals of speech

    , Article 2008 4th International Conference on Intelligent Information Hiding and Multiedia Signal Processing, IIH-MSP 2008, Harbin, 15 August 2008 through 17 August 2008 ; 2008 , Pages 605-607 ; 9780769532783 (ISBN) Shirali Shahreza, S ; Shirali Shahreza, M ; Sharif University of Technology
    This paper presents a new approach for hiding information in speech signals. In this method, the silence intervals of speech are found and the length (number of samples) of these intervals is changed to hide information. This method can be used simultaneously with other methods. © 2008 IEEE  

    A new synonym text steganography

    , Article 2008 4th International Conference on Intelligent Information Hiding and Multiedia Signal Processing, IIH-MSP 2008, Harbin, 15 August 2008 through 17 August 2008 ; 2008 , Pages 1524-1526 ; 9780769532783 (ISBN) Shirali Shahreza, M. H ; Shirali Shahreza, M ; Sharif University of Technology
    Steganography is a relatively new method for establishing hidden communication which gained attraction in recent years. Steganography is a method of hiding a secret message in a cover media such as image or text. In this paper a new method is proposed for steganography in English text by substituting the words which have different terms in British English and American English. © 2008 IEEE  

    Using and evaluating new confidence measures in word-based isolated word recognizers

    , Article 2007 9th International Symposium on Signal Processing and its Applications, ISSPA 2007, Sharjah, 12 February 2007 through 15 February 2007 ; 2007 ; 1424407796 (ISBN); 9781424407798 (ISBN) Vaisipour, S ; Babaali, B ; Sameti, H ; Sharif University of Technology
    In this paper a method for detecting out of vocabulary words in isolated word recognizers is introduced, our method utilized new kinds of confidence measure. After recognition task was completed and consequently confidence measure was extracted, a classifier would accept or reject result of recognition task using this CM. We used two different kinds of confidence measure where for extracting each one a different information source was used. Amount of competition between hypotheses through the recognition task was used for extracting first CM. The second one was extracted using information about manner of distribution of feature vectors in the states of winner HMM model. Both of these CMs... 

    Iterative least squares algorithm for inverse problem in MicroWave medical imaging

    , Article European Signal Processing Conference, 28 August 2016 through 2 September 2016 ; Volume 2016-November , 2016 , Pages 341-344 ; 22195491 (ISSN) ; 9780992862657 (ISBN) Azghani, M ; Marvasti, F ; Sharif University of Technology
    European Signal Processing Conference, EUSIPCO  2016
    The inverse problem in MicroWave Imaging (MWI) is an ill-posed one which can be solved with the aid of the sparsity prior of the solution. In this paper, an Iterative Least Squares Algorithm (ILSA) has been proposed as an inverse solver in MWI which seeks for the sparse vector satisfying the problem constraints. Minimizing a least squares cost function, we derive a relatively simple iterative algorithm which enforces the sparsity gradually with the aid of a reweighting operator. The simulation results confirm the superiority of the suggested method compared to the state-of-the-art schemes in the quality of the recovered breast tumors in the microwave images  

    Applications of sparse signal processing

    , Article 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016, 7 December 2016 through 9 December 2016 ; 2017 , Pages 1349-1353 ; 9781509045457 (ISBN) Azghani, M ; Marvasti, F ; IEEE Signal Processing Society; The Institute of Electrical and Electronics Engineers ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2017
    Sparse signal processing has found various applications in different research areas where the sparsity of the signal of interest plays a significant role in addressing their ill-posedness. In this invited paper, we give a brief review of a number of such applications in inverse scattering of microwave medical imaging, compressed video sensing, and missing sample recovery based on sparsity. Moreover, some of our recent results on these areas have been reported which confirms the fact that leveraging the sparsity prior of the underlying signal can improve different processing tasks in various problems. © 2016 IEEE  

    Compressed Video Sensing Using Deep Learning

    , M.Sc. Thesis Sharif University of Technology Ansarian Nezhad, Valiyeh (Author) ; Marvasti, Farokh (Supervisor) ; Azghani, Masoumeh (Co-Supervisor)
    Due to the ever-growing applications of video signals in day-to-day life and the large amount of information they contain, the compressing and processing of these signals is vital. In this thesis, a deep neural network called MC-ResNet is proposed which provides an approximation of non-reference frames based on reference ones. Next, three scenarios for compressed video sensing are presented. In all three scenarios, the reference frames are sampled and transmitted independently and reconstructed in the receiver by BCS-SPL method. In the first scenario, the difference between the non-reference frame and the approximation obtained from the MC-ResNet network is sampled and transmitted. In the... 

    Spectral distribution of the exponentially windowed sample covariance matrix

    , Article ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 25 March 2012 through 30 March 2012, Kyoto ; 2012 , Pages 3529-3532 ; 15206149 (ISSN) ; 9781467300469 (ISBN) Yazdian, E ; Bastani, M. H ; Gazor, S ; Sharif University of Technology
    IEEE  2012
    In this paper, we investigate the effect of applying an exponential window on the limiting spectral distribution (l.s.d.) of the exponentially windowed sample covariance matrix (SCM) of complex array data. We use recent advances in random matrix theory which describe the distribution of eigenvalues of the doubly correlated Wishart matrices. We derive an explicit expression for the l.s.d. of the noise-only data. Simulations are performed to support our theoretical claims  

    Error correction via smoothed L0-norm recovery

    , Article IEEE Workshop on Statistical Signal Processing Proceedings, 28 June 2011 through 30 June 2011 ; June , 2011 , Pages 289-292 ; 9781457705700 (ISBN) Ashkiani, S ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
    Channel coding has been considered as a classical approach to overcome corruptions occurring in some elements of input signal which may lead to loss of some information. Proper redundancies are added to the input signal to improve the capability of detecting or even correcting the corrupted signal. A similar scenario may happen dealing with real-field numbers rather than finite-fields. This paper considers a way to reconstruct an exact version of a corrupted signal by using an encoded signal with proper number of redundancies. The proposed algorithm uses Graduated Non-Convexity method beside using a smoothed function instead of 0-norm to correct all the corrupted elements. Simulations show... 

    Robust detection of premature ventricular contractions using a wave-based Bayesian framework

    , Article IEEE transactions on bio-medical engineering ; Volume 57, Issue 2 , September , 2010 , Pages 353-362 ; 15582531 (ISSN) Sayadi, O ; Shamsollahi, M. B ; Clifford, G. D ; Sharif University of Technology
    Detection and classification of ventricular complexes from the ECG is of considerable importance in Holter and critical care patient monitoring, being essential for the timely diagnosis of dangerous heart conditions. Accurate detection of premature ventricular contractions (PVCs) is particularly important in relation to life-threatening arrhythmias. In this paper, we introduce a model-based dynamic algorithm for tracking the ECG characteristic waveforms using an extended Kalman filter. The algorithm can work on single or multiple leads. A "polargram"--a polar representation of the signal--is introduced, which is constructed using the Bayesian estimations of the state variables. The polargram... 

    ECG denoising using modulus maxima of wavelet transform

    , Article Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 ; 2009 , Pages 416-419 ; 1557170X (ISSN) Ayat, M ; Shamsollahi, M. B ; Mozaffari, B ; Kharabian, S ; Sharif University of Technology
    ECG denoising has always been an important issue in medical engineering. The purposes of denoising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal. This paper proposes a method for removing white Gaussian noise from ECG signals. The concepts of singularity and local maxima of the wavelet transform modulus were used for analyzing singularity and reconstructing the ECG signal. Adaptive thresholding was used to remove white Gaussian noise modulus maximum of wavelet transform and then reconstruct the signal  

    Analog optical computing by half-wavelength slabs

    , Article Optics Communications ; Volume 407 , 2018 , Pages 338-343 ; 00304018 (ISSN) Zangeneh Nejad, F ; Khavasi, A ; Rejaei, B ; Sharif University of Technology
    Elsevier B.V  2018
    A new approach to perform analog optical differentiation is presented using half-wavelength slabs. First, a half-wavelength dielectric slab is used to design a first order differentiator. The latter works properly for both major polarizations, in contrast to our previously design based on Brewster effect (Youssefi et al., 2016). Inspired by the proposed dielectric differentiator, and by exploiting the unique features of graphene, we further design and demonstrate a reconfigurable and highly miniaturized differentiator using a half-wavelength plasmonic graphene film. To the best of our knowledge, our proposed graphene-based differentiator is even smaller than the most compact differentiator... 

    Speech accent profiles: Modeling and synthesis

    , Article IEEE Signal Processing Magazine ; Volume 26, Issue 3 , 2009 , Pages 69-74 ; 10535888 (ISSN) Vaseghi, S ; Yan, Q ; Ghorshi, A ; Sharif University of Technology
    A discussion regarding speech accents will be given while describing a set of statistical signal processing methods for the modeling, analysis, synthesis, and morphing of English language accents. Accent morphing deals with the changing of the accent of a speech to a different accent. Accent itself is a distinctive pattern of pronunciation of speech within a community of people who belong to a national, geographic, or socioeconomic grouping. Then, the signal processing methodology for speech accent processing will be reviewed while the concept of an accent profile has been presented  

    Submodularity in action: from machine learning to signal processing applications

    , Article IEEE Signal Processing Magazine ; Volume 37, Issue 5 , 2020 , Pages 120-133 Tohidi, E ; Amiri, R ; Coutino, M ; Gesbert, D ; Leus, G ; Karbasi, A ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    Submodularity is a discrete domain functional property that can be interpreted as mimicking the role of well-known convexity/concavity properties in the continuous domain. Submodular functions exhibit strong structure that lead to efficient optimization algorithms with provable near-optimality guarantees. These characteristics, namely, efficiency and provable performance bounds, are of particular interest for signal processing (SP) and machine learning (ML) practitioners, as a variety of discrete optimization problems are encountered in a wide range of applications. Conventionally, two general approaches exist to solve discrete problems: 1) relaxation into the continuous domain to obtain an... 

    Content-based video coding for distance learning

    , Article ISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology, Cairo, 15 December 2007 through 18 December 2007 ; 2007 , Pages 1005-1010 ; 9781424418350 (ISBN) Bagheri, M ; Lotfi, T ; Darabi, A. A ; Kasaei, S ; Sharif University of Technology
    This paper presents a novel video encoding method for cooperative Educational Dissemination Systems. Taking into consideration the inherent characteristics of distance learning video streams, existing a few moving objects in the scene and objects having slow motions, we propose a novel content-based video encoding method which is very efficient on low bandwidth channels. In the encoding process, we apply a background subtraction algorithm for motion segmentation with a novel statistical background modeling. In each frame, the moving objects are extrapolated with rectangular bounding boxes which are the only data send over the low bandwidth channel. In the decoding process, we propose a new... 

    High rate data hiding in speech signal

    , Article SIGMAP 2007 - 2nd International Conference on Signal Processing and Multimedia Applications, Barcelona, 28 July 2007 through 31 July 2007 ; 2007 , Pages 287-292 ; 9789898111135 (ISBN) Jahangiri, E ; Ghaemmaghami, S ; Sharif University of Technology
    One of the main issues with data hiding algorithms is capacity of data embedding. Most of data hiding methods suffer from low capacity that could make them inappropriate in certain hiding applications. This paper presents a high capacity data hiding method that uses encryption and the multi-band speech synthesis paradigm. In this method, an encrypted covert message is embedded in the unvoiced bands of the speech signal that leads to a high data hiding capacity of tens of kbps in a typical digital voice file transmission scheme. The proposed method yields a new standpoint in design of data hiding systems in the sense of three major, basically conflicting requirements in steganography, i.e.... 

    Source estimation in noisy sparse component analysis

    , Article 2007 15th International Conference onDigital Signal Processing, DSP 2007, Wales, 1 July 2007 through 4 July 2007 ; July , 2007 , Pages 219-222 ; 1424408822 (ISBN); 9781424408825 (ISBN) Zayyani, H ; Babaiezadeh, M ; Jutten, C ; Sharif University of Technology
    In this paper, a new algorithm for Sparse Component Analysis (SCA) in the noisy underdetermined case (i.e., with more sources than sensors) is presented. The solution obtained by the proposed algorithm is compared to the minimum l1 -norm solution achieved by Linear Programming (LP). Simulation results show that the proposed algorithm is approximately 10 dB better than the LP method with respect to the quality of the estimated sources. It is due to optimality of our solution (in the MAP sense) for source recovery in noisy underdetermined sparse component analysis in the case of spiky model for sparse sources and Gaussian noise. © 2007 IEEE  

    A new blind source separation approach based on dynamical similarity and its application on epileptic seizure prediction

    , Article Signal Processing ; Volume 183 , 2021 ; 01651684 (ISSN) Niknazar, H ; Nasrabadi, A. M ; Shamsollahi, M. B ; Sharif University of Technology
    Elsevier B.V  2021
    Blind source separation is an important field of study in signal processing, in which the goal is to estimate source signals by having mixed observations. There are some conventional methods in this field that aim to estimate source signals by considering certain assumptions on sources. One of the most popular assumptions is the non-Gaussianity of sources which is the basis of many popular blind source separation methods. These methods may fail to estimate sources when the distribution of two or more sources is Gaussian. Hence, this study aims to introduce a new approach in blind source separation for nonlinear and chaotic signals by using a dynamical similarity measure and relaxing...