Sharif Digital Repository

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... 

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)... 

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... 

This paper presents the scheduling optimisation of cargo trains by hybrid computer simulation (CS) and genetic algorithm. Scheduling cargo trains is based on the timetable of passenger trains that have priority in relation to cargo trains. System modelling is accomplished by Visual SLAM by considering time limitations, queue priority and limited station lines. Time limitations define that a cargo train is permitted to travel from station i to j if scheduled passenger trains have completed the travel from station i to j. Queue priority means that passenger trains have priority over cargo trains. In addition, each station has a limited storage track. In addition, all repair, maintenance,... 

In the present work, several new thermodynamic linear isotherm regularities for the dense fluids have been derived for the first time. For this purpose, the thermodynamic perturbation theory (TPT) employing only the attractive effective pair potential (AEPP) as u(r) = - ϵeff (σeff / r)m was used, where σeff which is the effective hard core diameter, is temperature dependent and m > 0. Based on the derived regularities, the isotherm (Z - Z(0))v2 is a linear function of ρ2, ρ and 1/ρ, depends on the values of m = 12, 9 and 6, respectively where Z - Z(0) is the difference between the experimental compressibility factor of the real fluid (Z) and that of the reference fluid (Z(0)). Also, Z - Z(0)... 

In this letter, we propose a new accurate approach for target localization in multiple-input multiple-output (MIMO) radars, which exploits the sparse spatial distribution of targets to reduce the sampling rate. We express the received signal of a MIMO radar in terms of the deviations of target parameters from the grid points in the form of a block sparse signal using the expansion around all the neighbor points. Applying a block sparse recovery method, we can estimate both the grid-point locations of targets and these deviations. The proposed approach can yield more accurate localization with higher detection probability compared with its counterparts. Moreover, the proposed approach can... 

In this paper, we propose a Bayesian Pursuit algorithm for sparse representation. It uses both the simplicity of the pursuit algorithms and optimal Bayesian framework to determine active atoms in sparse representation of a signal. We show that using Bayesian Hypothesis testing to determine the active atoms from the correlations leads to an efficient activity measure. Simulation results show that our suggested algorithm has better performance among the algorithms which have been implemented in our simulations in most of the cases. ©2009 IEEE  

In this paper, a Block Iterative Method with Adaptive Thresholding for Sparse Recovery (BIMATSR) is proposed to recover the received signal in an under-sampled distributed multiple-input multiple-output radar. The BIMATSR scheme induces block sparsity with the aid of a signal-dependent thresholding operator which increases the accuracy of the target parameter estimation task. We have proved that under some sufficient conditions, the suggested scheme converges to a stable solution. Moreover, different simulation scenarios confirm that the BIMATSR algorithm outperforms its counterparts in terms of the target parameter estimation. This superiority is achieved at the expense of slightly more... 

Uptake rate of 133Cs, at three different concentrations of CsCl, by Calendula alata, Amaranthus chlorostachys and Chenopodium album plants grown outdoors was studied. These plants grow abundantly in semi-arid regions and their varieties exist in many parts of the world. When exposed to lowest Cs concentration 68 percent Cs was remediated by Chenopodium album. 133Cs accumulation in shoots of Amaranthus chlorostachys reached its highest value of 2146.2mgkg -1 at a 133Cs supply level of 3.95mgl -1 of feed solution. The highest concentration ratio value was 4.89 for Amaranthus chlorostachys, whereas for the other tests it ranged from 0.74 to 3.33. Furthermore uptake of 133Cs by all three species... 

To explore the role of hemodynamic in the initiation and progression of stenosis in carotid artery bifurcation, a Computational Fluid Dynamics (CFD) technique is applied. The effect of four rheology models is investigated as well as various mechanical phenomena. In this study, a Finite Element Method (FEM) was applied to simulate the physiologic Circumferential Strain/Stress (CS) Meanwhile, to investigate the role of vessel wall flexibility, a Fluid-Structure Interaction (FSI) analysis was applied. It was concluded that velocity profiles and WSS show sensitivity to arterial wall stiffening while shear thinning models do not have a dominant effect on the flow field  

In this paper, a sparsity-aware target localization method in multiple-input-multiple-output (MIMO) radars by utilizing time difference of arrival (TDOA) measurements is proposed. This method provides a maximum likelihood (ML) estimator for target position by employing compressive sensing techniques. Also, for fast convergence and mitigating the mismatch problem due to grid discretization, we address a block-based search coupled with an adaptive dictionary learning technique. The Cramer-Rao lower bound for this model is derived as a benchmark. Simulations results are included to verify the localization performance  

The goal of this paper is to reduce the antenna count in a millimeter (mm)-wave imaging system by proposing both hardware and software solutions. The concept of image sparsity in the transform domain is utilized to present the compressive sensing (CS) formulation for both mono-static and multistatic imaging at mm-wave frequencies. To reduce the complexity of the imaging system and reconstruction process, we introduce 1.5-D array structure, which is a random sparse array with orthogonal element locations. It is shown that the peak signal-to-noise ratio (PSNR) of the reconstructed image obtained by a 1.5-D array with 65% sparsity is very close to the PSNR of a uniform 2-D array for mono-static... 

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... 

Compressive sensing (CS) has been widely used in multiple-input-multiple-output (MIMO) radar in recent years. Unlike traditional MIMO radar, detection/estimation of targets in a CS-based MIMO radar is accomplished via sparse recovery. In this article, for a CS-based colocated MIMO radar with linear arrays, we attempt to improve the target detection performance by reducing the coherence of the associated sensing matrix. Our tool in reducing the coherence is the placement of the antennas across the array aperture. In particular, we choose antenna positions within a given grid. Initially, we formalize the position selection problem as finding binary weights for each of the locations. This... 

The current statistical model and adaptive filtering (CSMAF) algorithm is one of the most effective methods for tracking the maneuvering targets. However, it is still worthy to investigate the characteristics of the CSMAF algorithm, which has a higher precision in tracking the maneuvering targets with larger accelerations while it has a lower precision in tracking the maneuvering targets with smaller acceleration. In this paper a novel adaptive tracking algorithm for maneuvering targets is proposed. To overcome the disadvantage of the CSMAF algorithm, a simple multi-layer feedforward neural network (NN) is used By introducing NN, two sources of information of the filter are fused while its... 

Main objective of this article is to present the implementation and simulation of a Conjugate Structure Algebraic Code Excited Linear Prediction speech coder (CSACELP) based upon ITU-T's G.729 recommendation and to optimize it for real-time implementation on an FPGA. The suggested architecture is characterized by pipelining and parallel operation of functional units; using fixed point two's complement representation for integers. The design was functionally verified by utilizing the ModelSim software package from Mentor Graphics Corporation Company and then synthesized by Xilinx Integrated Software Environment (ISE) 6.1 software. Preliminary results show that the overall system delay is less... 

Supply Chain (SC) coordination, quality requirements, and environmental issues are the main areas of interest within the field of inventory management. Few studies have integrated these concepts into an inventory problem despite its overwhelming necessity. In this direction, this paper deals with a vendor-buyer SC under the Vendor-Managed Inventory with Consignment Stock (VMI-CS) agreement. According to the agreement, the vendor bears the buyer's financial holding cost in return for the storage of products in the buyer's warehouse. Further, each shipment involves a random fraction of repairable items, which need to be withdrawn from the inventory system. Unlike the existing literature, when... 

Using the Lyapunov stability theory an adaptive control is proposed for chaos synchronization between two different systems which have stochastically time varying unknown coefficients. The stochastic variations of the coefficients about their unknown mean values are modeled through white Gaussian noise produced by the Weiner process. It is shown that using the proposed adaptive control the mean square of synchronization error converges to an arbitrarily small bound around zero. To demonstrate the effectiveness of the proposed technique, it is applied to the Lorenz-Chen and the Chen-Rossler dynamical systems, as some case studies. Simulation results indicate that the proposed adaptive... 

Today's software systems are more frequently composed from preexisting commercial or non-commercial components and connectors. These components provide complex and independent functionality and are engaged in complex interactions. Component-Based Software Engineering (CBSE) is concerned with composing, selecting and designing such components. As the popularity of this approach and hence number of commercially available software components grows, selecting a set of components to satisfy a set of requirements while minimizing cost is becoming more difficult. This problem necessitates the design of efficient algorithms to automate component selection for software developing organizations. We... 

The spatial sparsity of targets in the radar scene is widely used in multiple-input multiple-output (MIMO) radar signal processing, either to improve the detection/estimation performance of the radar or to reduce the cost of the conventional MIMO radars (e.g., by reducing the number of antennas). While sparse target estimation is the main challenge in such an approach, here, we address the design of a compressive-sensing-based MIMO radar, which facilitates such estimations. In particular, we propose an efficient solution for the problem of joint power allocation and antenna placement based on minimizing the number of transmit antennas while constraining the coherence of the sensing matrix....