Sharif Digital Repository

One of the major problems in underdetermined Sparse Component Analysis (SCA) in the field of (semi) Blind Source Separation (BSS) is the appropriate estimation of the mixing matrix, A, in the linear model X = AS, especially where more than one source is active at each instant of time. Most existing algorithms require the restriction that at each instant (i.e. in each column of the source matrix S), there is at most one single dominant component. Moreover, these algorithms require that the number of sources must be determined in advance. In this paper, we proposed a new algorithm for estimating the matrix A, which does not require the restriction of single dominant source at each instant.... 

Recompression is a prevalent form of multimedia content manipulation. Different approaches have been developed to detect this kind of alteration for digital images of well-known JPEG format. However, they are either limited in performance or complex. These problems may arise from different quality level options of JPEG compression standard and their combinations after recompression. Inspired from semantic and perceptual analyses, in this paper, we suggest a part-level middle-out learning strategy to detect double compression via an architecturally efficient classifier. We first demonstrate that singly and doubly compressed data with different JPEG coder settings lie in a feature space... 

In this paper, based on a successively accuracy-increasing approximation of the ℓ0 norm, we propose a new algorithm for recovery of sparse vectors from underdetermined measurements. The approximations are realized with a certain class of concave functions that aggressively induce sparsity and their closeness to the ℓ0 norm can be controlled. We prove that the series of the approximations asymptotically coincides with the ℓ1 and ℓ0 norms when the approximation accuracy changes from the worst fitting to the best fitting. When measurements are noise-free, an optimization scheme is proposed that leads to a number of weighted ℓ1 minimization programs, whereas, in the presence of noise, we propose... 

We propose a new algorithm for finding sparse solution of a linear system of equations using `0 minimization. The proposed algorithm relies on approximating the non-smooth `0 (pseudo) norm with a differentiable function. Unlike other approaches, we utilize a particular definition of `0 norm which states that the `0 norm of a vector can be computed as the `1 norm of its sign vector. Then, using a smooth approximation of the sign function, the problem is converted to `1 minimization. This problem is solved via iterative proximal algorithms. Our simulations on both synthetic and real data demonstrate the promising performance of the proposed scheme. © 2019 IEEE  

Standard sparse decomposition (with applications in many different areas including compressive sampling) amounts to finding the minimum ℓ 0-norm solution of an underdetermined system of linear equations. In this decomposition, all atoms are treated 'uniformly' for being included or not in the decomposition. However, one may wish to weigh more or less certain atoms, or, assign higher costs to some other atoms to be included in the decomposition. This can happen for example when there is prior information available on each atom. This motivates generalizing the notion of minimal ℓ 0-norm solution to that of minimal weighted ℓ 0-norm solution. On the other hand, relaxing weighted ℓ 0-norm via... 

In this paper, an algorithm for ECG denoising and compression based on a sparse separable 2-dimensional transform for both complete and overcomplete dictionaries is studied. For overcomplete dictionary we have used the combination of two complete dictionaries. The experimental results obtained by the algorithm for both complete and overcomplete transforms are compared to soft thresholding (for denoising) and wavelet db9/7 (for compression). It is experimentally shown that the algorithm outperforms soft thresholding for about 4dB or more and also outperforms Extended Kalman Smoother filtering for about 2dB in higher input SNRs. The idea of the algorithm is also studied for ECG compression,... 

Hyperspectral image unmixing is a source separation problem whose goal is to identify the signatures of the materials present in the imaged scene (called endmembers), and to estimate their proportions (called abundances) in each pixel. Usually, the contributions of each material are assumed to be perfectly represented by a single spectral signature and to add up in a linear way. However, the main two limitations of this model have been identified as nonlinear mixing phenomena and spectral variability, i.e., the intraclass variability of the materials. The former limitation has been addressed by designing nonlinear mixture models, whereas the second can be dealt with by using (usually linear)... 

In this paper a new geometrical approach for separating speech signals is presented. This approach can be directly applied to separate more than two speech signals. It is based on clustering the observation points, and then fitting a line (hyper-plane) onto each cluster. The algorithm quality is shown to be improved by using DCT coefficients of speech signals, as opposed to using speech samples. © Springer-Verlag 2004  

In dictionary learning, a desirable property for the dictionary is to be of low mutual and average coherences. Mutual coherence is defined as the maximum absolute correlation between distinct atoms of the dictionary, whereas the average coherence is a measure of the average correlations. In this paper, we consider a dictionary learning problem regularized with the average coherence and constrained by an upper-bound on the mutual coherence of the dictionary. Our main contribution is then to propose an algorithm for solving the resulting problem based on convexly approximating the cost function over the dictionary. Experimental results demonstrate that the proposed approach has higher... 

Based on a modified secant equation proposed by Li and Fukushima, we derive a stepsize for the Barzilai-Borwein gradient method. Then, using the newly proposed stepsize and another effective stepsize proposed by Dai et al. in an adaptive scheme that is based on the objective function convexity, we suggest a modified two-point stepsize gradient algorithm. We also show that the limit point of the sequence generated by our algorithm is first-order critical. Finally, our numerical comparisons done on a set of unconstrained optimization test problems from the CUTEr collection are presented. At first, we compare the performance of our algorithm with two other two-point stepsize gradient algorithms... 

Sparse Decomposition (SD) of a signal on an overcomplete dictionary has recently attracted a lot of interest in signal processing and statistics, because of its potential application in many different areas including Compressive Sensing (CS). However, in the current literature, the dictionary matrix has generally been assumed to be of full-rank. In this paper, we consider non-full-rank dictionaries (which are not even necessarily overcomplete), and extend the definition of SD over these dictionaries. Moreover, we present an approach which enables to use previously developed SD algorithms for this non-full-rank case. Besides this general approach, for the special case of the Smoothed ℓ0 (SL0)... 

Solar heating and cooling technologies can have a vital role to play in understanding the targets in energy security, economic development, and mitigating climate change. This study aimed to investigate the performance of the combined solar cooling/heating system using a Photovoltaic Thermal collector (PVT) for residential applications. The main advantage of using PVT is the conversion of the maximum amount of solar energy into electricity and thermal energy. In this work, water is used to cool the panel and, consequently, increase the efficiency. The cooling cycle comprises a hybrid ejector-compression refrigeration cycle with two evaporator temperatures. To reduce the effect of the global... 

In this paper we show how the higher index theory can be used to prove results concerning the non-existence of a complete Riemannian metric with uniformly positive scalar curvature at infinity. By improving some classical results due to M. Gromov and B. Lawson we show the efficiency of these methods to prove such non-existence theorems  

In this paper we state and prove delocalized Morse type inequalities for Morse functions as well as for closed differential 1-forms. These inequalities involve delocalized Betti numbers. As an immediate consequence, we prove the vanishing of delocalized Betti numbers of manifolds fibering over the circle under a vanishing condition on the delocalizing conjugacy class  

Development of novel engineering techniques that can promote new clinical treatments requires implementing multidisciplinary in-vitro and in-vivo approaches. In this study, we have implemented microfluidic devices and in-vivorat model to study the mechanism of neural stem cell migration and differentiation.These studies can result in the treatment of damages to the neuronal system. In this research, we have shown that by applying appropriate ranges of biochemical and biomechanical factors as well as by exposing the cells to electromagnetic fields, it is possible to improve viability, proliferation, directional migration and differentiation of neural stem cells. The results of this study can...