Now showing items 1-8 of 8

    • 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
    • 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 ...
    • Multiscale random projections for compressive classification 

      Duarte, Marco F.; Davenport, Mark A.; Wakin, Michael B.; Laska, Jason N.; Takhar, Dharmpal; Kelly, Kevin F.; Baraniuk, Richard G. (2007-09-01)
      We propose a framework for exploiting dimension-reducing random projections in detection and classification problems. Our approach is based on the generalized likelihood ratio test; in the case of image classification, ...
    • 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., ...
    • Single-pixel imaging via compressive sampling 

      Duarte, Marco F.; Davenport, Mark A.; Takhar, Dharmpal; Laska, Jason N.; Sun, Ting; Kelly, Kevin F.; Baraniuk, Richard G. (2008-03-01)
    • The smashed filter for compressive classification and target recognition 

      Davenport, Mark A.; Duarte, Marco F.; Wakin, Michael B.; Laska, Jason N.; Takhar, Dharmpal; Kelly, Kevin F.; Baraniuk, Richard G. (2007-01-01)
      The theory of compressive sensing (CS) enables the reconstruction of a sparse or compressible image or signal from a small set of linear, non-adaptive (even random) projections. However, in many applications, including ...
    • Sparse Signal Detection from Incoherent Projections 

      Davenport, Mark A.; Wakin, Michael B.; Duarte, Marco F.; Baraniuk, Richard G. (2006-05-01)
      The recently introduced theory of Compressed Sensing (CS) enables the reconstruction or approximation of sparse or compressible signals from a small set of incoherent projections; often the number of projections can be ...
    • 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 ...