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Compressive Sensing Based Imaging Via Belief Propagation, Preethi Modur Ramachandra
Compressive Sensing Based Imaging Via Belief Propagation, Preethi Modur Ramachandra
Masters Theses and Doctoral Dissertations
Multiple description coding (MDC) using Compressive Sensing (CS) mainly aims at restoring an image from a small subset of samples with reasonable accuracy using an iterative message passing decoding algorithm commonly known as Belief Propagation (BP). The CS technique can accurately recover any compressible or sparse signal from a lesser number of non-adaptive, randomized linear projection samples than that specified by the Nyquist rate. In this work, we demonstrate how CS-based encoding generates measurements from the sparse image signal and the measurement matrix. Then we demonstrate how a BP decoding algorithm reconstructs the image from the measurements generated. In our …
Distributed Biogeography Based Optimization For Mobile Robots, Arpit Shah
Distributed Biogeography Based Optimization For Mobile Robots, Arpit Shah
ETD Archive
I present hardware testing of an evolutionary algorithm (EA) known as distributed biogeography based optimization (DBBO). DBBO is an extended version of biogeography based optimization (BBO). Typically, EAs require a central computer to control the evaluation of candidate solutions to some optimization problem, and to control the sharing of information between those candidate solutions. DBBO, however, does not require a centralized unit to control individuals. Individuals independently run the EA and find a solution to a given optimization problem. Both BBO and DBBO are based on the theory of biogeography, which describes how organisms are distributed geographically in nature. I …