Sharif Digital Repository

Analog-to-digital conversion and signal processing has been increasing due to its many advantages. So that mostly we prefer to convert signal from analog area to digital samples, then they are processed and finaly put the result signal at the system output. How ever because the restriction of the sampling rate, Prevent the spread of digital processing for the high-frequency signal (RF). In recent years, ADCs sampling rate rise up to several GHz (for example ADC with 4 GSPS and 12 bits for TI) that output of the these ADCs by powerful and fast FPGAs are processed but According to Shannon theorem band width of these ADCs is not desirable.the goal of this thesis uses of the compressed sensing... 

Computerized tomography (CT) imaging is a powerful tool among the existing bio-imaging techniques for capturing bio-images. In a CT scan procedure, linear sensors receive x-ray radiations, passed through the patient’s body and the reconstruction algorithms provide the required image for physicians from the captured data. In spite of its great advantages, due to the use of x-ray radiations and its ionization effect, it will raise the risk of cancer in long times. Therefore, to take benefit from several advantages of CT such as low-cost and high speed, solving this problem is aimed by many physicians and scientists. A new compressed sensing-based algorithm in order to reduce the number of... 

Channel estimation is one of the fundamental challenges in every communication system and different algorithms have been proposed to deal with it. Obviously, type of a communication channel is an important factor in choosing the appropriate method for channel estimation. Sparse channels are one kind of them that occur in many real-world applications such as wireless communication systems. In addition, emergence of a new means in signal processing to deal with sparse signals, known as Compressed Sensing(CS), paved the way for their extensive usage in many applications including sparse channel estimation.On the other hand, one of the most fundamental problems in sparse signal recovery using CS... 

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 of soil moisture plays an essential role in correct decision making and implementation of any closed loop control system in precision agriculture. On the one hand, continuous using of a lot of moisture sensors and monitoring of soil moisture via direct measurement is restricted by practical prohibitions. On the grounds that activation of a lot of sensors continuously requires communication of a large amount of information and causes high power consumption for a moisture monitoring system. On the other hand, using remote sensing methods such as satellite methods, for moisture monitoring in arbitrary spatial and time resolution for control purposes, is not practically possible. The... 

Magnetic Resonance Imaging ,MRI, although as one of the most advanced medical imaging equipment is well known but compared to other medical imaging systems needs more time to imaging, so it has caused the patient dissatisfaction. According to the recently researches, filling the image data in a space of information that called ‘k-space’, in the process of image reconstruction, is the main reason of slow MRI. Image reconstruction methods for accelerating like half scan based on image reconstruction by using only a part of k-space is used in MRI systems today, but this method is associated with lower SNR image. Recent research on the theory of compressed sensing has been opened a new approach... 

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... 

The emerging field of compressed sensing deals with the techniques of combining the two blocks of sampling and compression into a single unit without compromising the performance. Clearly, this is not feasible for any general signal; however, if we restrict the signal to be sparse, it becomes possible. There are two main challenges in compressed sensing, namely the sampling process and the reconstruction methods. In this thesis, we will focus only on the deterministic sampling process as opposed to the random sampling. The sampling methods discussed in the literature are mainly linear, i.e., a matrix is used as the sampling operator. Here, we first consider linear sampling methods and... 

In recent years, the Cognitive Radio Network has received significant attentions due to its high potential for better employment of the spectrum. One of the most important parts of this technology is the spectrum sensing that requires being fast and accurate. The conventional algorithms proposed so far encounter some fundamental challenges at low SNR regimes and in wideband sensing. On the other hand, the compressed sensing algorithms, which take advantages of the sparsity of the signal of interest and utilize measurements instead of the samples, can reduce the sampling rate and thus decrease the complexity associated with the wideband sensing. In this thesis, by exploiting the... 

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... 

In this thesis, we deal with the problem of direction of arrival (DOA) using single-snapshot samples taken from multiple source signals by an array of sensors. The array we are looking for in this work is a uniform linear array. There are many ways to solve this problem, including MUSIC and Prony. But one of the few methods that are able to estimate correctly with single-snapshot samples with incomplete uniform linear arrays in compressed sensing base algorithms. In the first step, we can estimate the data of a incomplete uniform linear array by arranging them in the form of low-rank Hankel matrices and using leverage scores and minimizing the weight nuclear norm. If the weight matrices are... 

Orthogonal Frequency Division Multiplexing (OFDM) is a digital transmission method developed to meet the increasing demand for higher data rates in communications which can be used in both wired and wireless environments. This thesis describes the issue of the Peak to Average Power Ratio (PAPR) in OFDM system, based on compressed sensing at the receiver of a peak-reducing sparse clipper applied to an OFDM signal at the transmitter. By choosing proper clipping threshold, clipping signal will be sparse so locations, magnitudes, and phases of the clipping signal can be recovered by compressed sensing method. We demonstrate that in the absence of optimization algorithms at the transmitter,... 

Channel equalization is a crucial part of the OFDM communications protocols, which in turn requires channel estimation. Pilot-based methods are one the most popular channel estimation approaches in OFDM systems. The pilot signals are generally classified as orthogonal and nonorthogonal pilots. Orthogonal pilots can better estimate the channels and are widely used in communications systems. The number of orthogonal pilots is proportional to the number of transmitters. Therefore, utilizing orthogonal pilots are not amenable for the increasing number of users in communications systems and the emerging of new technologies such as Massive-MIMO employing a large number of antennas. Consequently,... 

Increasing need to high rates of transmission through radio channels is a challenging problem in wireless communications. Simple implementation and high spectral efficiency of OFDM system turned it into a suitable choice to meet this need. Accurate estimation of the communication channel has a significant impact on performance of Orthogonal Frequency Division Multiplexing (OFDM) systems. Conventional methods of channel estimation are not able to exploit the inherent sparsity of the channel that is due to sparse distribution of scaterrers. On the other hand, Compressed Sensing (CS) is getting attention in variety of aspects such as communications, recently.CS-based channel estimation exploits... 

In this dissertation, Data analysis and information retrieval from incomplete observations are investigated in different applications. Incomplete observations may be induced by lack of observations or part of data affected by specific noise (quantization noise). Data-driven algorithms are among important hot topics. Our goal is to process the lost information inducing certain assumption on big data structures. Then, the approach is to mathematically model the problem of interest as an optimization problem. Next, the designed algorithms for the optimization problems are proposed trying to cut down on the computational complexity of as well as enhancing recovery accuracy for big data... 

The design of deterministic sensing matrices is an important issue in compressive sensing in sparse signal processing. Various designs using finite field structures, combinatorics, and coding theory have been presented. The contribution of this thesis is designing many codes with large minimum distance using algebraic curves. Here, we initially design a algebraic-geometric code over a maximal curve in a Galoi field Fpm Afterwards, we map the code to the field Fp using trace map. This code has a large minimum distance. Using this code, we design a matrix with low coherence. One of the main issues in presented designs is that the number of matrix rows is considered to specific integers near... 

This thesis proposes a novel method for enhancing the speech signal based on compressed sensing. Compressed sensing, as a new rapidly growing research field, promises to effectively recover a sparse signal at the rate of below Nyquist rate. This revolutionary technology strongly relies on the sparsity of the signal and incoherency between sensing basis and representation basis. Exact recovery of a sparse signal will be occurred in a situation that the signal of interest sensed randomly and the measurements are also taken based on sparsity level and log factor of the signal dimension.
In this research, compressed sensing method is proposed to reconstruct speech signal and for noise... 

Sensor Networks are set of devices which are distributed throughout an environment and are connected to each other, usually wirelessly, to collect environmental information including temperature, aire pressure, moist, pollution and physiological functions of the human body. Each device consists of a microprocessor, converter and power supply, transmitter and a receiver. In this study we intend to investigate such setup and the measured signals assuming they are sparse. A sparse signal is a discrete time signal most of indices of which are equal to zero. With this assumption at hand, we will be able to reduce the sampling rate and take advantage of sparse signal processing techniques. This... 

Sparse representation and compressed sensing have been widely used in various fields, one of the most popular of which is ISAR imaging. Inverse Synthetic Aperture Radar (ISAR) provides an electromagnetic image of the target, which is mainly used to identify and classify targets. In some applications, recognizing targets from a 2D image can be difficult and error-prone. One idea to deal with this problem is 3D ISAR imaging. The most widely used method of ISAR imaging is direct method based on Fourier transform. This method requires the measurement of radar data with high measurement density in 3 directions, which increases the data collection time and volume, which is the main problem of...