Sharif Digital Repository

In this thesis, we aim to explore the recovery of sparse signals from their compressive or random samples. At first, the Compressed Sensing (CS) recovery is considered and an iterative method with adaptive thresholding has been suggested which has superior performance compared to its counterparts in both reconstruction quality and simplicity. Then, random sampling, a special kind of compressive sensing, is investigated which is practically more efficient to be implemented than the compressive sampling scheme. A number of random sampling recovery techniques are offered based on sparsity which has very low computational complexity in a way that largedimensional signals can efficiently be... 

Direction Of Arrival (DOA) estimation or direction finding refers to determining the arrival angle of a planar wave impinging on the array of sensors or antennas. The DOA information can be used by the smart antenna system for beam-forming and reliable data transmission. The problem of DOA estimation in propagating plane waves played a fundamental role in many applications including acoustic, wireless communication systems, navigation, biomedical imaging, radar/sonar systems, seismic sensing, and wireless 911 emergency call locating. In the conventional DOA estimating systems, an array of elements (antennas or sensors) is used that are colocated in a uniform pattern called, Uniform Linear... 

The emerging field of compressed sensing has found wide-spread applications in signal processing. Exploiting the sparsity of natural image signals on basis of a set of atoms called dictionary, one can find numerous examples for applications of compressed sensing in the field of image processing. One of these interesting applications is to help recover missing samples of a damaged or lossy image signal which is also known as image inpainting. There are dozens of reasons why an image may get damaged, for instance, during data transmission, some blocks of an image (or frames of a video ) may get lost due to error in the telecommunication channel (this is known as block-loss). In this case image... 

In analyzing the structural organization of many real-world networks, identifying important nodes has been a fundamental problem. The network centrality concept deals with the assessment of the relative importance of network nodes based on specific criteria. Central nodes can play significant roles 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. High computational cost and the requirement of full knowledge about the network topology are the most significant obstacles for applying the general concept of network centrality to large real-world social... 

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 thesis, a collaborative model in which nodes pick up information from measurements generated by other nodes, is proposed. Considering the fact that in most cases the networked data is sufficiently sparse, we used the Compreßive Sensing theory in the recovery phase of the proposed method. Using this model, for the first time, an upper bound is derived... 

Recovery of low-rank matrices from compressed linear measurements is an extension for the more well-known topic of recovery of sprse vectors from underdetermined measurements.Since the natural approach (i.e., rank minimization) for recovery of low-rank matrices is generally NP-hard, several alternatives have been proposed. However, there is a large gap between what can be achieved from these alternatives and the natural approach in terms of maximum rank of the unique solutions and the error of recovery. To narrow this gap, two novel algorithms are proposed. The main idea of both algorithms is to closely approximate the rank with a smooth function of singular values and then minimize the... 

Due to its higher degrees of freedom in comparison with a Single-Input Single-Output (SISO) radar , a Multiple-Input Multiple-Output (MIMO) radar has superior resolution , higher accuracy in detection and estimation , and more flexibility in beamforming . As there are multiple receivers in a MIMO radar system , if we can reduce the sampling rate and send fewer samples to the common processing center , the cost can significantly be reduced . Sometimes , the problem is not even the cost . It is the technology issues of high sampling rates . The reduction in sampling rate can be achieved using Compressive Sensing (CS) or in a much simpler form Random Sampling (RS) . In CS , we take...