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signal-processing-algorithms
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A Minimization-Projection (MP) approach for blind separating convolutive mixtures
, Article Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, Que, 17 May 2004 through 21 May 2004 ; Volume 5 , 2004 , Pages V-533-V-536 ; 15206149 (ISSN) ; Jutten, C ; Nayebi, K ; Sharif University of Technology
2004
Abstract
In this paper, a new algorithm for blind source separation in convolutive mixtures, based on minimizing the mutual information of the outputs, is proposed. This minimization is done using a recently proposed Minimization-Projection (MP) approach for minimizing mutual information in a parametric model. Since the minimization step of the MP approach is proved to have no local minimum, it is expected that this new algorithm has good convergence behaviours
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) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2011
Abstract
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) ; Shamsollahi, M. B ; Clifford, G. D ; Sharif University of Technology
2010
Abstract
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) ; Shamsollahi, M. B ; Mozaffari, B ; Kharabian, S ; Sharif University of Technology
Abstract
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
A robust steganography algorithm based on texture similarity using Gabor filter
, Article 5th IEEE International Symposium on Signal Processing and Information Technology, Athens, 18 December 2005 through 21 December 2005 ; Volume 2005 , 2005 , Pages 578-582 ; Jamzad, M ; Sharif University of Technology
2005
Abstract
The main concern of steganography (image hiding) methods is to embed a secret image into a host image in such a way that the host should remain as similar as possible to its original version. In addition the host image should remain robust with respect to usual attacks. In this paper we present a method that tries to cover all above mentioned concerns. The secret and host images are divided into blocks of size 4 × 4. Each block in secret image is taken as a texture pattern for which the most similar block is found among the blocks of the host image. The embedding procedure is carried on by replacing these small blocks of the secret image with blocks in host image in such a way that least...
FEM enhanced signal processing approach for pattern recognition in the SQUID based NDE system
, Article Journal of Physics: Conference Series, 13 September 2009 through 17 September 2009 ; Volume 234, Issue PART 4 , 2010 ; 17426588 (ISSN) ; Jahed, N. M. S ; Hosseni, N ; Pourhashemi, A ; Banzet, M ; Schubert, J ; Fardmanesh, M ; Sharif University of Technology
Abstract
An efficient Non-Destructive Evaluation algorithm has been developed in order to extract the required information for pattern recognition of defects in the conductive samples. Using high-Tc gradiometer RF-SQUIDs in unshielded environments and incorporating an automated two dimensional non-magnetic scanning robot, samples with different intentional defects have been tested. We have used a developed noise cancellation approach for the improvement of the effectiveness of the used inverse-problem technique. In this approach we have used a well examined Finite Element Method (FEM) to apply a noise reduction filtering on the obtained raw magnetic image data before incorporating the signal...
Structural cost-optimal design of sensor networks for distributed estimation
, Article IEEE Signal Processing Letters ; Volume 25, Issue 6 , June , 2018 , Pages 793-797 ; 10709908 (ISSN) ; Rabiee, H. R ; Khan, U. A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
In this letter, we discuss cost optimization of sensor networks monitoring structurally full-rank systems under distributed observability constraint. Using structured systems theory, the problem is relaxed into two subproblems: first, sensing cost optimization; and second, networking cost optimization. Both problems are reformulated as combinatorial optimization problems. The sensing cost optimization is shown to have a polynomial-order solution. The networking cost optimization is shown to be NP-hard in general, but has a polynomial-order solution under specific conditions. A 2-approximation polynomial-order relaxation is provided for general networking cost optimization, which is...
Sparse signal recovery using iterative proximal projection
, Article IEEE Transactions on Signal Processing ; Volume 66, Issue 4 , 2018 , Pages 879-894 ; 1053587X (ISSN) ; Sadeghi, M ; Babaie Zadeh, M ; Chatterjee, S ; Skoglund, M ; Jutten, C ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
This paper is concerned with designing efficient algorithms for recovering sparse signals from noisy underdetermined measurements. More precisely, we consider minimization of a nonsmooth and nonconvex sparsity promoting function subject to an error constraint. To solve this problem, we use an alternating minimization penalty method, which ends up with an iterative proximal-projection approach. Furthermore, inspired by accelerated gradient schemes for solving convex problems, we equip the obtained algorithm with a so-called extrapolation step to boost its performance. Additionally, we prove its convergence to a critical point. Our extensive simulations on synthetic as well as real data verify...
Stochastic successive convex approximation for non-convex constrained stochastic optimization
, Article IEEE Transactions on Signal Processing ; Volume 67, Issue 16 , 2019 , Pages 4189-4203 ; 1053587X (ISSN) ; Lau, V. K. N ; Kananian, B ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
This paper proposes a constrained stochastic successive convex approximation (CSSCA) algorithm to find a stationary point for a general non-convex stochastic optimization problem, whose objective and constraint functions are non-convex and involve expectations over random states. Most existing methods for non-convex stochastic optimization, such as the stochastic (average) gradient and stochastic majorization-minimization, only consider minimizing a stochastic non-convex objective over a deterministic convex set. The proposed CSSCA algorithm can also handle stochastic non-convex constraints in optimization problems, and it opens the way to solving more challenging optimization problems that...
ECG denoising using parameters of ECG dynamical model as the states of an extended Kalman filter
, Article 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, Lyon, 23 August 2007 through 26 August 2007 ; 2007 , Pages 2548-2551 ; 05891019 (ISSN) ; 1424407885 (ISBN); 9781424407880 (ISBN) ; Sameni, R ; Shamsollahi, M. B ; Sharif University of Technology
2007
Abstract
In this paper an efficient Altering procedure based on the Extended Kalman Filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. We have suggested simple dynamics as the governing equations for the model parameters. Since we have not any observation for these new state variables, they are considered as hidden states. Quantitative evaluation of the proposed algorithm on the MIT-BIH signals shows that an average SNR improvement of 12 dB is achieved for a signal of -5 dB. The results show improved output SNRs compared to the EKF outputs in the absence of these new dynamics. © 2007 IEEE
A new approach for sparse decomposition and sparse source separation
, Article 14th European Signal Processing Conference, EUSIPCO 2006, Florence, 4 September 2006 through 8 September 2006 ; 2006 ; 22195491 (ISSN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2006
Abstract
We introduce a new approach for sparse decomposition, based on a geometrical interpretation of sparsity. By sparsedecomposition we mean finding sufficiently sparse solutions of underdetermined linear systems of equations. This will be discussed in the context of Blind Source Separation (BSS). Our problem is then underdetermined BSS where there are fewer mixtures than sources. The proposed algorithm is based on minimizing a family of quadratic forms, each measuring the distance of the solution set of the system to one of the coordinate subspaces (i.e. coordinate axes, planes, etc.). The performance of the method is then compared to the minimal 1-norm solution, obtained using the linear...
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) ; Sardouie, S. H ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2022
Abstract
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...
Noise cancelation of epileptic interictal EEG data based on generalized eigenvalue decomposition
, Article 2012 35th International Conference on Telecommunications and Signal Processing, TSP 2012 - Proceedings ; 2012 , Pages 591-595 ; 9781467311182 (ISBN) ; Shamsollahi, M. B ; Albera, L ; Merlet, I ; Sharif University of Technology
2012
Abstract
Denoising is an important preprocessing stage in some Electroencephalography (EEG) applications such as epileptic source localization. In this paper, we propose a new algorithm for denoising the interictal EEG data. The proposed algorithm is based on Generalized Eigenvalue Decomposition of two covariance matrices of the observations. Since one of these matrices is related to the spike durations, we should estimate the occurrence time of the spike peaks and the exact spike durations. To this end, we propose a spike detection algorithm which is based on the available spike detection methods. The comparison of the results of the proposed algorithm with the results of two well-known ICA...
Fast temporal path localization on graphs via multiscale viterbi decoding
, Article IEEE Transactions on Signal Processing ; Volume 66, Issue 21 , 2018 , Pages 5588-5603 ; 1053587X (ISSN) ; Chen, S ; Maddah Ali, M. A ; Grover, P ; Kar, S ; Kovacevic, J ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
We consider a problem of localizing a temporal path signal that evolves over time on a graph. A path signal represents the trajectory of a moving agent on a graph in a series of consecutive time stamps. Through combining dynamic programing and graph partitioning, we propose a path-localization algorithm with significantly reduced computational complexity. To analyze the localization performance, we use two evaluation metrics to quantify the localization error: The Hamming distance and the destination's distance between the ground-truth path and the estimated path. In random geometric graphs, we provide a closed-form expression for the localization error bound, and a tradeoff between...
Feedback acquisition and reconstruction of spectrum-sparse signals by predictive level comparisons
, Article IEEE Signal Processing Letters ; Volume 25, Issue 4 , 2018 , Pages 496-500 ; 10709908 (ISSN) ; Gazor, S ; Rahnavard, N ; Marvasti, F ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
In this letter, we propose a sparsity promoting feedback acquisition and reconstruction scheme for sensing, encoding and subsequent reconstruction of spectrally sparse signals. In the proposed scheme, the spectral components are estimated utilizing a sparsity-promoting, sliding-window algorithm in a feedback loop. Utilizing the estimated spectral components, a level signal is predicted and sign measurements of the prediction error are acquired. The sparsity promoting algorithm can then estimate the spectral components iteratively from the sign measurements. Unlike many batch-based compressive sensing algorithms, our proposed algorithm gradually estimates and follows slow changes in the...
Learning of tree-structured Gaussian graphical models on distributed data under communication constraints
, Article IEEE Transactions on Signal Processing ; Volume 67, Issue 1 , 2019 , Pages 17-28 ; 1053587X (ISSN) ; Motahari, S. A ; Manzuri Shalmani, M. T ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our...
Transabdominal fetal heart rate detection using NIR photopleythysmography: instrumentation and clinical results
, Article IEEE Transactions on Biomedical Engineering ; Volume 56, Issue 8 , 2009 , Pages 2075-2082 ; 00189294 (ISSN) ; Zahedi, E ; Mohd Ali, M. A ; Sharif University of Technology
2009
Abstract
In obstetrics, fetal heart rate (FHR) detection remains the standard for intrapartum assessment of fetal well-being. In this paper, a low-power (<55 mW) optical technique is proposed for transabdominal FHR detection using near-infrared photoplesthys-mography (PPG). A beam of IR-LED (890 nm) propagates through to the maternal abdomen and fetal tissues, resulting in a mixed signal detected by a low-noise detector situated at a distance of 4 cm. Low-noise amplification and 24-bit analog-to-digital converter resolution ensure minimum effect of quantization noise. After synchronous detection, the mixed signal is processed by an adaptive filter to extract the fetal signal, whereas the PPG from the...
Learning of tree-structured gaussian graphical models on distributed data under communication constraints
, Article IEEE Transactions on Signal Processing ; Volume 67, Issue 1 , 2019 , Pages 17-28 ; 1053587X (ISSN) ; Motahari, A ; Manzuri Shalmani, M. T ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our...
Learning of tree-structured Gaussian graphical models on distributed data under communication constraints
, Article IEEE Transactions on Signal Processing ; Volume 67, Issue 1 , 2019 , Pages 17-28 ; 1053587X (ISSN) ; Motahari, S. A ; Manzuri Shalmani, M. T ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our...
Utility of a nonlinear joint dynamical framework to model a pair of coupled cardiovascular signals
, Article IEEE Journal of Biomedical and Health Informatics ; Volume 17, Issue 4 , 2013 , Pages 881-890 ; 21682194 (ISSN) ; Shamsollahi, M. B ; Sharif University of Technology
2013
Abstract
We have recently proposed a correlated model to provide a Gaussian mixture representation of the cardiovascular signals, with promising results in identifying rhythm disturbances. The approach provides a transformation of the data into a set of integrable Gaussians distributed over time. Looking into the model from a new joint modeling perspective, it is capable of assembling a filtered estimation, and can be used to derive temporal information of the waveforms. In this paper, we present a step-by-step derivation of the joint model putting correlation assumptions together to conclude a minimal joint description for a pair of ECG-ABP signals. We then probe novel applications of this model,...