Compressed sensing has been a very active area of research and several elegant algorithms have been developed for the recovery of sparse signals in the past few years. However, most of these algorithms are either computationally expensive or make some assumptions that are not suitable for all real world problems. Recently, focus has shifted to Bayesian-based approaches that are able to perform sparse signal recovery at much lower complexity while invoking constraint and/or a priori information about the data. While Bayesian approaches have their advantages, these methods must have access to a priori statistics. Usually, these statistics are unknown and are often difficult or even impossible to predict. An effective workaround is to assume a distribution which is typically considered to be Gaussian, as it makes many signal processing problems mathematically tractable. Seemingly attractive, this assumption necessitates the estimation of the associated parameters; which could be hard if not impossible. In the thesis, we focus on this aspect of Bayesian recovery and present a framework to address the challenges mentioned above. The proposed framework allows Bayesian recovery of sparse signals but at the same time is agnostic to the distribution of the unknown sparse signal components. The algorithms based on this framework are agnostic to signal statistics and utilize a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. In the thesis, we propose several algorithms based on this framework which utilize the structure present in signals for improved recovery. In addition to the algorithm that considers just the sparsity structure of sparse signals, tools that target additional structure of the sparsity recovery problem are proposed. These include several algorithms for a) block-sparse signal estimation, b) joint reconstruction of several common support sparse signals, and c) distributed estimation of sparse signals. Extensive experiments are conducted to demonstrate the power and robustness of our proposed sparse signal estimation algorithms. Specifically, we target the problems of a) channel estimation in massive-MIMO, and b) Narrowband interference mitigation in SC-FDMA. We model these problems as sparse recovery problems and demonstrate how these could be solved naturally using the proposed algorithms.

<p>Recovery is a fundamental service in database systems. In this work, we present a new mechanism for diskless real-time recovery in fully replicated distributed real-time database systems. Traditionally, recovery has relied on disk-resident redundant data. Unfortunately, disks cannot always be used in real-time systems since these systems are sometimes used in environments which do not allow the use of disks. Also, minimizing the amount of hardware can save money, especially in mass-produced products. Instead of loading the database from disk, our recovery mechanism enables a restarted node to retrieve a copy of the database from an arbitrary remote node. The recovery mechanism does not violate timeliness during normal processing and, during recovery, all nodes except for the recovering node can guarantee the timeliness of critical transactions. The mechanism uses fuzzy checkpointing to copy the database to the recovering node. Fuzzy checkpointing has been chosen since it copies the database without regard to concurrency control and, thus, does not increase data contention in the database. We conclude that the suggested recovery mechanism is a feasible option for fully replicated distributed real-time database systems.</p>

Recovery is a fundamental service in database systems. In this work, we present a new mechanism for diskless real-time recovery in fully replicated distributed real-time database systems. Traditionally, recovery has relied on disk-resident redundant data. Unfortunately, disks cannot always be used in real-time systems since these systems are sometimes used in environments which do not allow the use of disks. Also, minimizing the amount of hardware can save money, especially in mass-produced products. Instead of loading the database from disk, our recovery mechanism enables a restarted node to retrieve a copy of the database from an arbitrary remote node. The recovery mechanism does not violate timeliness during normal processing and, during recovery, all nodes except for the recovering node can guarantee the timeliness of critical transactions. The mechanism uses fuzzy checkpointing to copy the database to the recovering node. Fuzzy checkpointing has been chosen since it copies the database without regard to concurrency control and, thus, does not increase data contention in the database. We conclude that the suggested recovery mechanism is a feasible option for fully replicated distributed real-time database systems.