Search for: signal-processing
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    A new efficient PNLMS based algorithm for adaptive line echo cancellation

    , Article 7th International Symposium on Signal Processing and Its Applications, ISSPA 2003, Paris, 1 July 2003 through 4 July 2003 ; Volume 2 , 2003 , Pages 555-558 ; 0780379462 (ISBN); 9780780379466 (ISBN) Maleki, A ; Nayebi, K ; Sharif University of Technology
    IEEE Computer Society  2003
    Echo cancellers with long impulse responses are usually used in networks with long echoes. Since long adaptive filters with sparse target filters usually converge very slowly, adaptation algorithms with fast convergence rate are needed. PNLMS [2] is one of the algorithms that is designed for fast convergence on sparse impulse responses. But there are some disadvantages to this algorithm. The computational complexity of this algorithm is prohibitive, especially for long echo tails. Another disadvantage is that its convergence rate slows down significantly after the adaptation of large taps. In this paper we propose new algorithms to solve both these problems. In another part of this paper, 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  

    Traffic improvements in wireless communication networks using antenna arrays

    , Article IEEE Journal on Selected Areas in Communications, Piscataway, NJ, United States ; Volume 18, Issue 3 , 2000 , Pages 458-471 ; 07338716 (ISSN) Razavilar, J ; Rashid Farrokhi, F ; Liu, K. J. R ; Sharif University of Technology
    IEEE  2000
    A wireless network with beamforming capabilities at the receiver is considered that allows two or more transmitters to share the same channel to communicate with the base station. A novel approach is introduced, which combines the effects of the digital signal processing (adaptive beamforming) at the physical layer with the traffic policies at the network layer on the overall queuing model of a cell. The effect of signal processing on the queuing model of the cell is represented by a parameter in the final cell model. Each cell is modeled by a multiuser/multiserver service facility, where each server is a beamformed channel formed by the cell's base station. From this effective cell model,... 

    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... 

    A unified approach for simultaneous graph learning and blind separation of graph signal sources

    , Article IEEE Transactions on Signal and Information Processing over Networks ; Volume 8 , 2022 , Pages 543-555 ; 2373776X (ISSN) Einizade, A ; Sardouie, S. H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2022
    In the nascent and challenging problem of the blind separation of the sources (BSS) supported by graphs, i.e., graph signals, along with the statistical independence of the sources, additional dependency information can be interpreted from their graph structure. To the best of our knowledge, in these cases, only GraDe and GraphJADE methods have been proposed to exploit the graph dependencies and/or Graph Signal Processing (GSP) techniques to improve the separation quality. Despite the significant advantages of these graph-based methods, they assume that the underlying graphs are known, which is a serious drawback, especially in many real-world applications. To address this issue, in this... 

    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  

    Robust blind separation of smooth graph signals using minimization of graph regularized mutual information

    , Article Digital Signal Processing: A Review Journal ; Volume 132 , 2022 ; 10512004 (ISSN) Einizade, A ; Hajipour Sardouie, S ; Sharif University of Technology
    Elsevier Inc  2022
    The smoothness of the graph signals on predefined/constructed graphs appears in many natural applications of processing unstructured (i.e., graph-based) data. In the case of latent sources being smooth graph signals, blind source separation (BSS) quality can be significantly improved by exploiting graph signal smoothness along with the classic measures of statistical independence. In this paper, we propose a BSS method benefiting from the minimization of mutual information as a well-known independence criterion and also graph signal smoothness term of the estimated latent sources, and show that its performance is superior and fairly robust to the state-of-the-art classic and Graph Signal... 

    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....