Manhattan Cutset Sampling and Sensor Networks.

image sampling;sensor networks;Electrical Engineering;Engineering;Electrical Engineering: Systems

Prelee, Matthew A.Pappas, Thrasyvoulos N ;

University of Michigan

关键词: image sampling;    sensor networks;    Electrical Engineering;    Engineering;    Electrical Engineering: Systems;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/120876/mprelee_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Cutset sampling is a new approach to acquiring two-dimensional data, i.e., images, where values are recorded densely along straight lines.This type of sampling is motivated by physical scenarios where data must be taken along straight paths, such as a boat taking water samples.Additionally, it may be possible to better reconstruct image edges using the dense amount of data collected on lines.Finally, an advantage of cutset sampling is in the design of wireless sensor networks.If battery-powered sensors are placed densely along straight lines, then the transmission energy required for communication between sensors can be reduced, thereby extending the network lifetime.A special case of cutset sampling is Manhattan sampling, where data is recorded along evenly-spaced rows and columns.This thesis examines Manhattan sampling in three contexts.First, we prove a sampling theorem demonstrating an image can be perfectly reconstructed from Manhattan samples when its spectrum is bandlimited to the union of two Nyquist regions corresponding to the two lattices forming the Manhattan grid.An efficient ``onion peeling;;;; reconstruction method is provided, and we show that the Landau bound is achieved.This theorem is generalized to dimensions higher than two, where again signals are reconstructable from a Manhattan set if they are bandlimited to a union of Nyquist regions.Second, for non-bandlimited images, we present several algorithms for reconstructing natural images from Manhattan samples.The Locally Orthogonal Orientation Penalization (LOOP) algorithm is the best of the proposed algorithms in both subjective quality and mean-squared error.The LOOP algorithm reconstructs images well in general, and outperforms competing algorithms for reconstruction from non-lattice samples.Finally, we study cutset networks, which are new placement topologies for wireless sensor networks. Assuming a power-law model for communication energy, we show that cutset networks offer reduced communication energy costs over lattice and random topologies.Additionally, when solving centralized and decentralized source localization problems, cutset networks offer reduced energy costs over other topologies for fixed sensor densities and localization accuracies.Finally, with the eventual goal of analyzing different cutset topologies, we analyze the energy per distance required for efficient long-distance communication in lattice networks.