Sharif Digital Repository

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  

This paper presents a new framework for the optimal placement of actuators in uncertain dynamic networks. The objective is to select a subset of nodes from a set of potential actuator placements, such that the controllability of the network with norm-bounded perturbation is preserved over the entire uncertain region. Evaluation of robust controllability is known to be computationally intractable. The recent results of Babazadeh and Nobakhti (2016) are utilized to establish equivalent conditions for robust controllability of uncertain networks. It is shown that for a large class of dynamic systems, including undirected networks, the exact distance to uncontrollability is evaluated by solving... 

Recovering low-rank and sparse components from missing observations is an essential problem in various fields. In this paper, we have proposed a method to address the missing low-rank and sparse decomposition problem. We have used the smoothed nuclear norm and the L1 norm to impose the low-rankness and sparsity constraints on the components, respectively. Furthermore, we have suggested a linear modeling for the corrupted observations. The problem has been solved with the aid of alternating minimization. Moreover, some simplifications have been applied to the relations to reduce the computational complexity, which makes the algorithm suitable for large-scale problems. To evaluate the proposed... 

This letter presents a novel method based on sparsity, to solve the problem of deinterleaving pulse trains. The proposed method models the problem of deinterleaving pulse trains as an underdetermined system of linear equations. After determining the mixing matrix, we find sparsest solution of an underdetermined system of linear equations using basis pursuit denoising. This method is superior to previous ones in a number of aspects. First, spurious and missing pulses would not cause any performance reduction in the algorithm. Second, the algorithm works well despite the type of pulse repetition interval modulation that is used. Third, the proposed method is able to separate similar... 

Functional behavior of the brain can be captured using functional Magnetic Resonance Imaging (fMRI). Even though fMRI signals have temporal and spatial structures, most studies have neglected the temporal structure when inferring mental states (brain decoding). This has two main side effects: 1. Degradation in brain decoding performance due to lack of temporal information in the model, 2. Inability to provide temporal interpretability. Few studies have targeted this issue but have had less success due to the burdening challenges related to high feature-to-instance ratio. In this study, a novel model for incorporating temporal information while maintaining a low feature-to-instance ratio, is... 

We present a modified examplar-based inpainting method in the framework of patch sparsity. In the examplar-based algorithms, the unknown blocks of target region are inpainted by the most similar blocks extracted from the source region, with the available information. Defining a priority term to decide the filling order of missing pixels ensures the connectivity of object boundaries. In the exemplar-based patch sparsity approaches, a sparse representation of missing pixels was considered to define a new priority term. Here, we modify this representation of the priority term and take measures to compute the similarities between fill-front and candidate patches. Comparative reconstructed test... 

In this paper, the task is to determine whether an HTTP session buys an item, or not, and if so, which items will be purchased. An HTTP session is a series of item clicks. A session has type buy, if it buys at least one item, or non-buy otherwise. Accordingly, data is in (session, item, time) format, which tells us when an item is clicked or purchased during an HTTP session. The main challenge comes from the fact that (1) user information is not available for clicked or purchased items, which are merely tagged with anony-mous sessions, and (2) suggestions are highly temporal as they are suggested to sessions instead of users. In other words, users which are stable and identified are replaced... 

In this paper, we propose a new method for impulsive noise removal from images. It uses the sparsity of natural images when they are expanded by mean of a good learned dictionary. The zeros in sparse domain give us an idea to reconstruct the pixels that are corrupted by random-value impulse noises. This idea comes from this reality that noisy image in sparse domain of original image will not have a sparse representation as much as original image sparsity. In this method we assume that the proper dictionary to achieve image in sparse domain is available  

The sampling process in electrical devices includes non-linear distortion which needs to be compensated to boost up the system efficiency. In this paper, a blind method is suggested for non-linear distortion compensation. The core idea is to leverage the sparsity of the signal to cope with the ill-posedness of the distortion compensation task. The proposed scheme is an iterative method based on out of support energy minmization where the support information is not available. An adaptive thresholding operator is used to give a rough approximation of the support according to the estimated signal at each iteration. Various simulation scenarios have validated the capability of the suggested... 

The sampling process in electrical devices includes nonlinear distortion that needs to be compensated to boost up the system efficiency. In this brief, a blind method is suggested for nonlinear distortion compensation. The core idea is to leverage the sparsity of the signal to cope with the ill-posedness of the distortion compensation task. The proposed scheme is an iterative method based on out of support energy minimization, in which the support information is not available. An adaptive thresholding operator is used to give a rough approximation of the support according to the estimated signal at each iteration. Various simulation scenarios have validated the capability of the suggested... 

This brief presents a systematic approach for the design of sparse dynamic output feedback control structures. A supplementary complexity cost function term is used to promote sparsity in the structure while optimizing an H2 performance cost simultaneously. Optimization problems in which a combinatorial sparsity measure is combined with a nonlinear performance cost function are NP-hard. NP-hard problems do not have tractable solutions, requiring either a numerical solution or a relaxation into a solvable form. Relaxations will introduce conservatism, but at the same time retain stability and performance guarantees. In this brief, a new relaxation methodology is proposed, which allows the...