Sharif Digital Repository

In this correspondence, a non-linear method of convergence accelerating and improving for iteration based algorithms is introduced. After convergence analysis, some enough conditions are proposed to guarantee convergence of the algorithm.For the sake of low complexity implementation of the proposed algorithm, some simple stabilizing methods are suggested. Simulation results show desirable performance of the proposed method and its capability to stabilize the iteration based algorithms. In the literature of missing samples recovery, the proposed method is applied to an Iterative Method (IM) as a general signal reconstruction method,then it is extended to the image recovery problem where... 

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... 

In this research, we propose analog to digital (A/D) converters based on Level Crossing (LC) sampling and the corresponding signal processing techniques for effecient acquisition of spectrum-sparse signals. Spectrum-sparse signals arise in many applications such as cognitive radio networks, frequency hopping communications, radar/sonar imaging systems, musical audio signals and many more. In such cases, the signal components maybe sparsely spread over a wide spectrum and need to be acquired at a reasonable cost without prior knowledge of their frequencies. Compared with the literature, the proposed scheme not only enables efficient acquisition of spectrum-sparse signals with a less complex... 

Level crossing sampling is a sampling method in which a sample is taken whenever signal crosses predefined and specific levels. In this dissertation, some recommendations are made in order to increase the sampler’s performance in the sampling, data compression, and reconstruction stages.The IMATMirror algorithm is introduced in the sample reconstruction stage. This algorithm is derived from the IMAT reconstruction method. However, additional data processing is done in each iteration, causing the reconstructed signal to satisfy some properties of the Level-Crossing samples.In order to solve the problem of level crossing sampling of extremely bursty signals (ECG signals for example), a sampler...