Now showing items 1-7 of 7

  • Detection and estimation with compressive measurements 

    Baraniuk, Richard G.; Davenport, Mark A.; Wakin, Michael B. (2006-11-01)
    The recently introduced theory of compressed sensing enables the reconstruction of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can ...
  • 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, ...
  • A simple proof of the restricted isometry property for random matrices 

    Baraniuk, Richard G.; Davenport, Mark A.; DeVore, Ronald A.; Wakin, Michael B. (2007-01-18)
    We give a simple technique for verifying the Restricted Isometry Property (as introduced by Candès and Tao) for random matrices that underlies Compressed Sensing. Our approach has two main ingredients: (i) concentration ...
  • 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 ...
  • The geometry of low-dimensional signal models 

    Wakin, Michael B. (2007)
    Models in signal processing often deal with some notion of structure or conciseness suggesting that a signal really has "few degrees of freedom" relative to its actual size. Examples include: bandlimited signals, images ...
  • 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 ...