Now showing items 1-12 of 12

  • Analysis of the DCS one-stage Greedy Algorothm for Common Sparse Supports 

    Baron, Dror; Duarte, Marco F.; Wakin, Michael; Sarvotham, Shriram; Baraniuk, Richard G. (2005-11-01)
    Analysis of the DCS one-stage Greedy Algorothm for Common Sparse Supports
  • Compressing Piecewise Smooth Multidimensional Functions Using Surflets: Rate-Distortion Analysis 

    Chandrasekaran, Venkat; Wakin, Michael; Baron, Dror; Baraniuk, Richard G. (2004-03-01)
    Discontinuities in data often represent the key information of interest. Efficient representations for such discontinuities are important for many signal processing applications, including compression, but standard Fourier ...
  • Compression of Higher Dimensional Functions Containing Smooth Discontinuities 

    Chandrasekaran, Venkat; Wakin, Michael; Baron, Dror; Baraniuk, Richard G. (2004-03-01)
    Discontinuities in data often represent the key information of interest. Efficient representations for such discontinuities are important for many signal processing applications, including compression, but standard Fourier ...
  • Distributed Compressed Sensing of Jointly Sparse Signals 

    Sarvotham, Shriram; Baron, Dror; Wakin, Michael; Duarte, Marco F.; Baraniuk, Richard G. (2005-11-01)
    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 ...
  • Faster Sequential Universal Coding via Block Partitioning 

    Baron, Dror; Baraniuk, Richard G. (2006-04-01)
    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 ...
  • How Quickly Can We Approach Channel Capacity? 

    Baron, Dror; Khojastepour, Mohammad; Baraniuk, Richard G. (2004-11-01)
    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 ...
  • Measurements vs. Bits: Compressed Sensing meets Information Theory 

    Sarvotham, Shriram; Baron, Dror; Baraniuk, Richard G. (2006-09-01)
    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 

    Baraniuk, Richard G.; Wakin, Michael; Duarte, Marco F.; Tropp, Joel A.; Baron, Dror (2006-05-01)
    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 

    Chandrasekaran, Venkat; Wakin, Michael; Baron, Dror; Baraniuk, Richard G. (2006-03-01)
    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 ...
  • Surflets: A Sparse Representation for Multidimensional Functions Containing Smooth Discontinuities 

    Chandrasekaran, Venkat; Wakin, Michael; Baron, Dror; Baraniuk, Richard G. (2004-07-01)
    Discontinuities in data often provide vital information, and representing these discontinuities sparsely is an important goal for approximation and compression algorithms. Little work has been done on efficient representations ...
  • Universal Distributed Sensing via Random Projections 

    Wakin, Michael; Duarte, Marco F.; Baraniuk, Richard G.; Baron, Dror (2006-04-01)
    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 ...
  • Variable-Rate Universal Slepian-Wolf Coding with Feedback 

    Sarvotham, Shriram; Baron, Dror; Baraniuk, Richard G. (2005-11-01)
    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, ...