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hakem-zadeh--farimah
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A predictive loss minimisation direct torque control of permanent magnet synchronous motors
, Article Australian Journal of Electrical and Electronics Engineering ; Volume 9, Issue 1 , 2012 , Pages 89-98 ; 1448837X (ISSN) ; Vaez Zadeh, S ; Tahami, F ; Sharif University of Technology
2012
Abstract
Although permanent magnet synchronous motors (PMSMs) are inherently of high efficiency, their efficiency is enormously dependent on their control strategy. The purpose of this paper is to improve the efficiency of PMSMs under a direct torque control (DTC) method. The main idea behind the proposed method is to predict a required small change of the statorflux amplitude at each sampling period to reduce the machine electrical loss before the change is applied. Accordingly, at every sampling time, a voltage vector is predicted and applied to the machine to change the flux amplitude in a xvay that the electrical loss decreases. The results of simulation show significant improvement in the...
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...
High angular resolution diffusion image registration
, Article Iranian Conference on Machine Vision and Image Processing, MVIP ; Sept , 2013 , Pages 232-236 ; 21666776 (ISSN) ; 9781467361842 (ISBN) ; Fatemizadeh, E ; Soltanian Zadeh, H ; Sharif University of Technology
IEEE Computer Society
2013
Abstract
Diffusion Tensor Imaging (DTI) is a common method for the investigation of brain white matter. In this method, it is assumed that diffusion of water molecules is Gaussian and so, it fails in fiber crossings where this assumption does not hold. High Angular Resolution Diffusion Imaging (HARDI) allows more accurate investigation of microstructures of the brain white matter; it can present fiber crossing in each voxel. HARDI contains complex orientation information of the fibers. Therefore, registration of these images is more complicated than the scalar images. In this paper, we propose a HARDI registration algorithm based on the feature vectors that are extracted from the Orientation...
Watermarking based on independent component analysis in spatial domain
, Article Proceedings - 2011 UKSim 13th International Conference on Modelling and Simulation, UKSim 2011, 30 March 2011 through 1 April 2011, Cambridge ; 2011 , Pages 299-303 ; 9780769543765 (ISBN) ; Rahmati, A ; Babaie Zadeh, M ; Sharif University of Technology
2011
Abstract
This paper proposes an image watermarking scheme for copyright protection based on Independent Component Analysis (ICA). In the suggested scheme, embedding is carried out in cumulative form in spatial domain and ICA is used for watermark extraction. For extraction there is no need to access the original image or the watermark, and extraction is carried out only with two watermarked images. Experimental results show that the new method has better quality than famous methods [1], [2], [3] in spatial or frequency domain and is robust against various attacks. Noise addition, resizing, low pass filtering, multiple marks, gray-scale reduction, rotation, JPEG compression, and cropping are some...
On the error of estimating the sparsest solution of underdetermined linear systems
, Article IEEE Transactions on Information Theory ; Volume 57, Issue 12 , December , 2011 , Pages 7840-7855 ; 00189448 (ISSN) ; Jutten, C ; Mohimani, H ; Sharif University of Technology
2011
Abstract
Let A be an n × m matrix with m > n, and suppose that the underdetermined linear system As = x admits a sparse solution ∥s 0∥o < 1/2spark(A). Such a sparse solution is unique due to a well-known uniqueness theorem. Suppose now that we have somehow a solution ŝ as an estimation of s0, and suppose that ŝ is only "approximately sparse", that is, many of its components are very small and nearly zero, but not mathematically equal to zero. Is such a solution necessarily close to the true sparsest solution? More generally, is it possible to construct an upper bound on the estimation error ∥ŝ - s 0∥2 without knowing s0? The answer is positive, and in this paper, we construct such a bound based on...
Effect of different diffusion maps on registration results
, Article 2011 7th Iranian Conference on Machine Vision and Image Processing, MVIP 2011 - Proceedings ; 2011 ; 9781457715358 (ISBN) ; Fatemizadeh, E ; Soltanian Zadeh, H ; Sharif University of Technology
2011
Abstract
In this paper, we compare registration results obtained using different diffusion maps extracted from diffusion tensor imaging (DTI). Fractional Anisotropy (FA) and Ellipsoidal Area Ratio (EAR) are two diffusion maps (indices) that may be used for image registration. First, we use FA maps to find deformation matrix and register diffusion weighted images. Then, we use EAR maps and finally we use both of FA and EAR maps to register diffusion weighted images. The difference between FA values before deformation and after registration using the FA alone or EAR alone has a median of 0.57 and using both of them has a median of 0.29. Therefore, the results of registration using both of the FA and...
Regularized low-coherence overcomplete dictionary learning for sparse signal decomposition
, Article European Signal Processing Conference, 28 August 2016 through 2 September 2016 ; Volume 2016-November , 2016 , Pages 369-373 ; 22195491 (ISSN) ; 9780992862657 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
European Signal Processing Conference, EUSIPCO
2016
Abstract
This paper deals with learning an overcomplete set of atoms that have low mutual coherence. To this aim, we propose a new dictionary learning (DL) problem that enables a control on the amounts of the decomposition error and the mutual coherence of the atoms of the dictionary. Unlike existing methods, our new problem directly incorporates the mutual coherence term into the usual DL problem as a regularizer. We also propose an efficient algorithm to solve the new problem. Our new algorithm uses block coordinate descent, and updates the dictionary atom-by-atom, leading to closed-form solutions. We demonstrate the superiority of our new method over existing approaches in learning low-coherence...
An alternating minimization method for sparse channel estimation
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 27 September 2010 through 30 September 2010, St. Malo ; Volume 6365 LNCS , 2010 , Pages 319-327 ; 03029743 (ISSN) ; 364215994X (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2010
Abstract
The problem of estimating a sparse channel, i.e. a channel with a few non-zero taps, appears in many fields of communication including acoustic underwater or wireless transmissions. In this paper, we have developed an algorithm based on Iterative Alternating Minimization technique which iteratively detects the location and the value of the channel taps. In fact, at each iteration we use an approximate Maximum A posteriori Probability (MAP) scheme for detection of the taps, while a least square method is used for estimating the values of the taps at each iteration. For approximate MAP detection, we have proposed three different methods leading to three variants for our algorithm. Finally, we...
Image restoration using gaussian mixture models with spatially constrained patch clustering
, Article IEEE Transactions on Image Processing ; Volume 24, Issue 11 , June , 2015 , Pages 3624-3636 ; 10577149 (ISSN) ; Rabbani, H ; Babaie Zadeh, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2015
Abstract
In this paper, we address the problem of recovering degraded images using multivariate Gaussian mixture model (GMM) as a prior. The GMM framework in our method for image restoration is based on the assumption that the accumulation of similar patches in a neighborhood are derived from a multivariate Gaussian probability distribution with a specific covariance and mean. Previous methods of image restoration with GMM have not considered spatial (geometric) distance between patches in clustering. Our conducted experiments show that in the case of constraining Gaussian estimates into a finite-sized windows, the patch clusters are more likely to be derived from the estimated multivariate Gaussian...
Interpolation of orientation distribution functions in diffusion weighted imaging using multi-tensor model
, Article Journal of Neuroscience Methods ; Volume 253 , 2015 , Pages 28-37 ; 01650270 (ISSN) ; Fatemizadeh, E ; Soltanian Zadeh, H ; Sharif University of Technology
2015
Abstract
Background: Diffusion weighted imaging (DWI) is a non-invasive method for investigating the brain white matter structure and can be used to evaluate fiber bundles. However, due to practical constraints, DWI data acquired in clinics are low resolution. New method: This paper proposes a method for interpolation of orientation distribution functions (ODFs). To this end, fuzzy clustering is applied to segment ODFs based on the principal diffusion directions (PDDs). Next, a cluster is modeled by a tensor so that an ODF is represented by a mixture of tensors. For interpolation, each tensor is rotated separately. Results: The method is applied on the synthetic and real DWI data of control and...
Blind source separation in nonlinear mixture for colored sources using signal derivatives
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 25 August 2015 through 28 August 2015 ; Volume 9237 , August , 2015 , Pages 193-200 ; 03029743 (ISSN) ; 9783319224817 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
Springer Verlag
2015
Abstract
While Blind Source Separation (BSS) for linear mixtures has been well studied, the problem for nonlinear mixtures is still thought not to have a general solution. Each of the techniques proposed for solving BSS in nonlinear mixtures works mainly on specific models and cannot be generalized for many other realistic applications. Our approach in this paper is quite different and targets the general form of the problem. In this advance, we transform the nonlinear problem to a time-variant linear mixtures of the source derivatives. The proposed algorithm is based on separating the derivatives of the sources by a modified novel technique that has been developed and specialized for the problem,...
A MAP-Based order estimation procedure for Sparse channel estimation
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 25 2015 through 28 August 2015 ; Volume 9237 , August , 2015 , Pages 344-351 ; 03029743 (ISSN) ; 9783319224817 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
Springer Verlag
2015
Abstract
Recently, there has been a growing interest in estimation of sparse channels as they are observed in underwater acoustic and ultrawideband channels. In this paper we present a new Bayesian sparse channel estimation (SCE) algorithm that, unlike traditional SCE methods, exploits noise statistical information to improve the estimates. The proposed method uses approximate maximum a posteriori probability (MAP) to detect the non-zero channel tap locations while least square estimation is used to determine the values of the channel taps. Computer simulations shows that the proposed algorithm outperforms the existing algorithms in terms of normalized mean squared error (NMSE) and approaches...
A geometric approach for separating post non-linear mixtures
, Article European Signal Processing Conference, 3 September 2002 through 6 September 2002 ; Volume 2015-March , September , 2015 ; 22195491 (ISSN) ; Jutten, C ; Nayebi, K ; Sharif University of Technology
European Signal Processing Conference, EUSIPCO
2015
Abstract
A geometric method for separating PNL mixtures, for the case of 2 sources and 2 sensors, has been presented. The main idea is to find compensating nonlinearities to transform the scatter plot of observations to a parallelogram. It then results in a linear mixture which can be separated by any linear source separation algorithm. An indirect result of the paper is another separability proof of PNL mixtures of bounded sources for 2 sources and 2 sensors
Axisymmetric Response of a Transversely Isotropic Half-space Stiffened by a Thin Plate Considering Refined Interaction Theory
, M.Sc. Thesis Sharif University of Technology ; Eskandari, Morteza (Supervisor)
Abstract
In this paper, the elastic response of a surface-stiffened transversely isotropic half-space subjected to a surface normal load is addressed. The half-space is reinforced by a Kirchhoff thin plate bonded to its surface. Two different boundary conditions are considered across the plate-half-space interface: (i) the classic approach in which the plate in-plane deformation is neglected by writing the interfacial boundary conditions across the plate mid-plane, and (ii) the refined approach that considers the in-plane deformation of plate due to bending by writing the boundary conditions across the bottom face of plate. By virtue of appropriate displacement potentials, the complete set of elastic...
Approximation Algorithms for Geometric Optimization on Sliding Windows
, M.Sc. Thesis Sharif University of Technology ; Zarrabi Zadeh, Hamid (Supervisor)
Abstract
In this thesis, we focus on a subset of geometric optimization problems (including k-center) in the Sliding Window model. The sliding window model is driven from the Data Stream model in which input points arrive one by one and the space is limited. The main diffrenece of these two models is that in the sliding window model we are interested in the N latest points not all of the arrived points. In this thesis, we study Minimum Enclosing Ball, 2-center, and Euclidean k-center in the Sliding Window model. We provide a (1 + ")-approximation algorithm for MEB in d-dimensions. To our knowledge there is no algorithm for MEB in d-dimensions where d >2. We also provide a (1 + ")-approximation...
Compressed sensing block MAP-LMS adaptive filter for sparse channel estimation and a Bayesian Cramer-Rao bound
, Article Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009, 2 September 2009 through 4 September 2009 ; 2009 ; 9781424449484 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2009
Abstract
This paper suggests to use a Block MAP-LMS (BMAP-LMS) adaptive filter instead of an Adaptive Filter called MAP-LMS for estimating the sparse channels. Moreover to faster convergence than MAP-LMS, this block-based adaptive filter enables us to use a compressed sensing version of it which exploits the sparsity of the channel outputs to reduce the sampling rate of the received signal and to alleviate the complexity of the BMAP-LMS. Our simulations show that our proposed algorithm has faster convergence and less final MSE than MAP-LMS, while it is more complex than MAP-LMS. Moreover, some lower bounds for sparse channel estimation is discussed. Specially, a Cramer-Rao bound and a Bayesian...
Blind separation of bilinear mixtures using mutual information minimization
, Article Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009, 2 September 2009 through 4 September 2009, Grenoble ; 2009 ; 9781424449484 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2009
Abstract
In this paper an approach for blind source separation in bilinear (or linear quadratic) mixtures is presented. The proposed algorithm employs the same recurrent structure as [Hosseini and Deville, 2003) for separating these mixtures . However, instead of maximal likelihood, our algorithm is based on minimizing the mutual information of the outputs for recovering the independent components. Simulation results show the efficiency of the proposed algorithm. © 2009 IEEE
An adaptive thresholding approach for image denoising using redundant representations
, Article Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009, 2 September 2009 through 4 September 2009, Grenoble ; 2009 ; 9781424449484 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2009
Abstract
A frequently used approach for denoising is the shrinkage of coefficients of the noisy signal representation in a transform domain. Although the use of shrinkage is optimal for Gaussian white noise with complete and unitary transforms, it has already been shown that shrinkage has promising results even with redundant transforms. In this paper, we propose using adaptive thresholding of redundant representations of the noisy image for image denoising. In the proposed thresholding scheme, a different threshold is used for each representation coefficient of the noisy image in an overcomplete transform. In this method, each threshold is automatically set based on statistical properties of the...
A new loss minimization approach with flux and torque ripples reduction of direct torque controlled permanent magnet synchronous motors
, Article 2009 13th European Conference on Power Electronics and Applications, EPE '09, 8 September 2009 through 10 September 2009, Barcelona ; 2009 ; 9781424444328 (ISBN) ; Vaez Zadeh, S ; Tahami, F ; Sharif University of Technology
2009
Abstract
The main purpose of this paper is to minimize the loss while reducing the flux and torque ripples in permanent magnet synchronous motors (PMSMs) under direct torque control (DTC). Offline method is used to minimize the loss and obtain the optimal flux. To reduce the amount of computation, the optimal flux table is produced according to the conditions in which the machine works. To reduce the flux and torque ripples and to control the switching frequency, SVM-DTC method is applied. Simulation results depict a noticeable increase in efficiency of the motor and a reduction in flux and torque ripples
A fast approach for overcomplete sparse decomposition based on smoothed ℓ0 norm
, Article IEEE Transactions on Signal Processing ; Volume 57, Issue 1 , 2009 , Pages 289-301 ; 1053587X (ISSN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2009
Abstract
In this paper, a fast algorithm for overcomplete sparse decomposition, called SL0, is proposed. The algorithm is essentially a method for obtaining sparse solutions of underdetermined systems of linear equations, and its applications include underdetermined sparse component analysis (SCA), atomic decomposition on overcomplete dictionaries, compressed sensing, and decoding real field codes. Contrary to previous methods, which usually solve this problem by minimizing the ℓ1 norm using linear programming (LP) techniques, our algorithm tries to directly minimize the ℓ0 norm. It is experimentally shown that the proposed algorithm is about two to three orders of magnitude faster than the...