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Award Abstract #1319799

CIF: Small: Sampling Rate Distortion

NSF Org: CCF
Division of Computing and Communication Foundations
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Initial Amendment Date: June 25, 2013
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Latest Amendment Date: June 25, 2013
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Award Number: 1319799
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Award Instrument: Standard Grant
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Program Manager: Richard Brown
CCF Division of Computing and Communication Foundations
CSE Direct For Computer & Info Scie & Enginr
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Start Date: September 1, 2013
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End Date: August 31, 2016 (Estimated)
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Awarded Amount to Date: $499,670.00
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Investigator(s): Prakash Narayan prakash@eng.umd.edu (Principal Investigator)
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Sponsor: University of Maryland College Park
3112 LEE BLDG 7809 Regents Drive
COLLEGE PARK, MD 20742-5141 (301)405-6269
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NSF Program(s): COMM & INFORMATION THEORY
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Program Reference Code(s): 7923, 7937
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Program Element Code(s): 7935

ABSTRACT

This research takes an information theoretic approach to understanding principles that govern a coordinated rate-efficient sampling of multiple signals and centralized compression of the sampled subset. The goal is to reconstruct the entirety of the signals within acceptable distortion levels. The research has three main components: an integrated analysis of sampling and rate distortion behavior, and their tradeoffs; sampling rate distortion theory for Markov random fields; and sampling rate distortion performance for signals with memory. Expected outcomes are a characterization of fundamental performance limits of optimum sampling rate and lossy compression rate and their interplay, together with the best choice of sampling mechanisms and attendant processing for reconstruction.

The investigators' technical approach involves the development of a principle of "sampling rate distortion" which lies at the intersection of specialities in information theory and signal processing, and has the larger objective of elucidating material connections between sampling and rate distortion performance. The performance of specific sampling and rate distortion processing schemes will be investigated. Specific groups of open problems chosen for investigation address a general class of multisignal models for sampling and lossy data compression. These are motivated by potential applications including dynamic thermal management for on-chip temperature control during runtime; network function computation; and image restoration, surface reconstruction and visual integration in computer vision.

 

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