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... 

For the target parameter estimation in MIMO radars in the presence of strong clutter, non-adaptive compressive sensing methods have a poor performance. On the other hand, the adaptive ones usually need higher data acquisition time. In this paper, we propose an adaptive compressive sensing method called L-BGT that has tolerable data acquisition time. furthermore, we have presented the essential changes in a distributed MIMO radar to exploit an adaptive group testing compressive sensing method  

One of the most relevant characteristics of social networks is community structure, in which network nodes are joined together in densely connected groups between which there are only sparser links. Uncovering these sparse links (i.e. intercommunity links) has a significant role in community detection problem which has been of great importance in sociology, biology, and computer science. In this paper, we propose a novel approach, called CS-ComDet, to efficiently detect the inter-community links based on a newly emerged paradigm in sparse signal recovery, called compressive sensing. We test our method on real-world networks of various kinds whose community structures are already known, and... 

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 MIMO 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 computational complexity. It... 

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... 

In this paper, we propose an information theoretic approach to fuse images compressed by compressed sensing (CS) techniques. The goal is to fuse multiple compressed images directly using measurements and reconstruct the final image only once. Since the reconstruction is the most expensive step, it would be a more economic method than separate reconstruction of each image. The proposed scheme is based on calculating the result using weighted average on the measurements of the inputs, where weights are calculated by information theoretic functions. The simulation results show that the final images produced by our method have higher quality than those produced by traditional methods, especially... 

This paper presents a novel technique to discover double JPEG compression traces. Existing detectors only operate in a scenario that the image under investigation is explicitly available in JPEG format. Consequently, if quantization information of JPEG files is unknown, their performance dramatically degrades. Our method addresses both forensic scenarios which results in a fresh perceptual detection pipeline. We suggest a dimensionality reduction algorithm to visualize behaviors of a big database including various single and double compressed images. Based on intuitions of visualization, three bottom-up, top-down and combined top-down/bottom-up learning strategies are proposed. Our tool... 

This paper presents a novel technique to discover double JPEG compression traces. Existing detectors only operate in a scenario that the image under investigation is explicitly available in JPEG format. Consequently, if quantization information of JPEG files is unknown, their performance dramatically degrades. Our method addresses both forensic scenarios which results in a fresh perceptual detection pipeline. We suggest a dimensionality reduction algorithm to visualize behaviors of a big database including various single and double compressed images. Based on intuitions of visualization, three bottom-up, top-down and combined top-down/bottom-up learning strategies are proposed. Our tool... 

Betweenness centrality is a prominent centrality measure expressing importance of a node within a network, in terms of the fraction of shortest paths passing through that node. Nodes with high betweenness centrality have significant impacts on the spread of influence and idea in social networks, the user activity in mobile phone networks, the contagion process in biological networks, and the bottlenecks in communication networks. Thus, identifying k-highest betweenness centrality nodes in networks will be of great interest in many applications. In this paper, we introduce CS-HiBet, a new method to efficiently detect top-k betweenness centrality nodes in networks, using compressive sensing.... 

The sensor selection is a technique to reduce the cost, energy consumption, and complexity of a system by discarding redundant or less useful sensors. In this technique, one attempts to select a subset of sensors within a larger set so as to optimise a performance criterion. In this work, the authors consider the detection of sources in the 3D space when the source space could be sparsely represented. The application of compressive sensing (CS) methods in this setting has been extensively studied. Their aim in this work is to carry out the task of sensor selection in a planar array without sacrificing the detection performance. Their approach is to adopt the equivalent CS model and reduce... 

Progressive image transmission enables the receivers to reconstruct a transmitted image at various bit rates. Most of the works in this field are based on the conventional Shannon-Nyquist sampling theory. In the present work, progressive image transmission is investigated using sparse recovery of random samples. The sparse recovery methods such as Iterative Method with Adaptive Thresholding (IMAT) and Iterative IKMAX Thresholding (IKMAX) are exploited in this framework since they have the ability for successive reconstruction. The simulation results indicate that the proposed method performs well in progressive recovery. The IKMAX has better final reconstruction than IMAT at the cost of... 

Since compressive sensing utilizes a random matrix to map the sparse signal space to a lower dimensional transform domain, it may be possible to apply this matrix at the same time for encrypting the signal opportunistically. In this paper, a compressed sensing based encryption method is considered and the secrecy of measurement matrix of compressive sensing is analysed from the detection theory perspective. Here, the detection probability of intended and unintended receivers are compared by applying the Neyman-Pearson test. We prove that the detection probability of eavesdropper will be reduced significantly because he does not know the transform domain subspace. Furthermore, in some... 

ABSTRACT Compressed sensing is a new theory that samples a signal below the Nyquist rate. While Gaussian and Bernoulli random measurements perform quite well on the average, structured matrices such as Toeplitz are mostly used in practice due to their simplicity. However, the signal compression performance may not be acceptable. In this paper, we propose to optimize the Toeplitz matrices to improve its compression performance to recover sparse signals. We establish the optimization on minimizing the coherence of the measurement matrix by an intelligent optimization method called Particle Swarm Optimization. Our simulation results show that the optimized Toeplitz matrix outperforms the... 

Reducing the time required for MRI, has taken a lot of attention since its inventions. Compressed sensing (CS) is a relatively new method used a lot to reduce the required time. Usage of ordinary compressed sensing in MRI imaging needs conversion of 2D MRI signal (image) to 1D signal by some techniques. This conversion of the signal from 2D to 1D results in heavy computational burden. In this paper, based on separable transforms, a method is proposed which enables the usage of CS in MRI directly in 2D case. By means of this method, imaging can be done faster and with less computational burden  

In analysing the structural organization of a social network, identifying important nodes has been a fundamental problem. The concept of network centrality deals with the assessment of the relative importance of a particular node within the network. Most of the traditional network centrality definitions have a high computational cost and require full knowledge of network topological structure. On the one hand, in many applications we are only interested in detecting the top-k central nodes of the network with the largest values considering a specific centrality metric. On the other hand, it is not feasible to efficiently identify central nodes in a large real-world social network via... 

Monitoring large-scale networks is a critical yet challenging task. Enormous number of nodes and links, limited power, and lack of direct access to the entire network are the most important difficulties. In applications such as network routing, where all nodes need to monitor the status of the entire network, the situation is even worse. In this letter, a collaborative model in which nodes pick up information from measurements generated by other nodes is proposed. Using this model, for the first time, an upper bound is derived for the number of measurements that each node must generate, such that the expected number of measurements observed by each node is sufficient to provide a global view...