Now showing items 1-6 of 6
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., ...
How Quickly Can We Approach Channel Capacity?
Recent progress in code design has made it crucial to understand how quickly communication systems can approach their limits. To address this issue for the channel capacity C, we define the nonasymptotic capacity C/sub ...
Distributed Compressed Sensing of Jointly Sparse Signals
Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we expand our theory for ...
Variable-Rate Universal Slepian-Wolf Coding with Feedback
Traditional Slepian-Wolf coding assumes known statistics and relies on asymptotically long sequences. However, in practice the statistics are unknown, and the input sequences are of finite length. In this finite regime, ...
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 ...
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 ...