Now showing items 1-5 of 5
Measurements vs. Bits: Compressed Sensing meets Information Theory
Compressed sensing is a new framework for acquiring sparse signals based on the revelation that a small number of linear projections (measurements) of the signal contain enough information for its reconstruction. The ...
Random Filters for Compressive Sampling and Reconstruction
We propose and study a new technique for efficiently acquiring and reconstructing signals based on convolution with a fixed FIR filter having random taps. The method is designed for sparse and compressible signals, i.e., ...
Representation and Compression of Multi-Dimensional Piecewise Functions Using Surflets
We study the representation, approximation, and compression of functions in M dimensions that consist of constant or smooth regions separated by smooth (M-1)-dimensional discontinuities. Examples include images containing ...
Faster Sequential Universal Coding via Block Partitioning
Rissanen provided a sequential universal coding algorithm based on a block partitioning scheme, where the source model is estimated at the beginning of each block. This approach asymptotically approaches the entropy at the ...
Universal Distributed Sensing via Random Projections
This paper develops a new framework for distributed coding and compression in sensor networks based on distributed compressed sensing (DCS). DCS exploits both intra-signal and inter-signal correlations through the concept ...