Now showing items 21-40 of 508

    • JPEG Compression History Estimation for Color Images 

      Neelamani, Ramesh; de Queiroz, Ricardo; Fan, Zhigang; Baraniuk, Richard G. (2006-06-01)
      We routinely encounter digital color images that were previously compressed using the Joint Photographic Experts Group (JPEG) standard. En route to the image's current representation, the previous JPEG compression's various settingsâ termed its JPEG compression history (CH)â are often discarded after the JPEG decompression step. Given a JPEG-decompressed ...
    • Sparse Signal Detection from Incoherent Projections 

      Duarte, Marco F.; Baraniuk, Richard G.; Wakin, Michael B.; Davenport, Mark A. (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 much smaller than the number of Nyquist rate samples. In this paper, we show that the CS framework is information scalable to a wide ...
    • Random Filters for Compressive Sampling and Reconstruction 

      Baron, Dror; Baraniuk, Richard G.; Duarte, Marco; Wakin, Michael; Tropp, Joel A. (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., ones that are well approximated by a short linear combination of vectors from an orthonormal basis. Signal reconstruction involves ...
    • Controlling False Alarms with Support Vector Machines 

      Davenport, Mark A.; Baraniuk, Richard G.; Scott, Clayton D.; Digital Signal Processing (http://dsp.rice.edu/) (2006-05-01)
      We study the problem of designing support vector classifiers with respect to a Neyman-Pearson criterion. Specifically, given a user-specified level alpha, 0 < alpha < 1, how can we ensure a false alarm rate no greater than a while minimizing the miss rate? We examine two approaches, one based on shifting the offset of a conventionally trained SVM and ...
    • Wavelet-Domain Approximation and Compression of Piecewise Smooth Images 

      Wakin, Michael; Romberg, Justin; Choi, Hyeokho; Baraniuk, Richard G. (2006-05-01)
      The wavelet transform provides a sparse representation for smooth images, enabling efficient approximation and compression using techniques such as zerotrees. Unfortunately, this sparsity does not extend to piecewise smooth images, where edge discontinuities separating smooth regions persist along smooth contours. This lack of sparsity hampers the ...
    • Random Projections of Signal Manifolds 

      Wakin, Michael; Baraniuk, Richard G. (2006-05-01)
      Random projections have recently found a surprising niche in signal processing. The key revelation is that the relevant structure in a signal can be preserved when that signal is projected onto a small number of random basis functions. Recent work has exploited this fact under the rubric of Compressed Sensing (CS): signals that are sparse in some ...
    • 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 fastest possible rate of 1/2log(n) bits per unknown parameter. We show that the complexity of this algorithm is /spl Omega/(nlog(n)), ...
    • An Architecture for Distributed Wavelet Analysis and Processing in Sensor Networks 

      Wagner, Raymond; Baraniuk, Richard G.; Du, Shu; Johnson, David B.; Cohen, Albert (2006-04-01)
      Distributed wavelet processing within sensor networks holds promise for reducing communication energy and wireless bandwidth usage at sensor nodes. Local collaboration among nodes de-correlates measurements, yielding a sparser data set with significant values at far fewer nodes. Sparsity can then be leveraged for subsequent processing such as ...
    • Universal Distributed Sensing via Random Projections 

      Duarte, Marco F.; Wakin, Michael B.; Baron, Dror; Baraniuk, Richard G. (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 of joint sparsity; just a few measurements of a jointly sparse signal ensemble contain enough information for reconstruction. DCS ...
    • 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 edges, video sequences of moving objects, and seismic data containing geological horizons. For both function classes, we derive ...
    • Broadcast Detection Structures with Applications to Sensor Networks 

      Lexa, Michael; Johnson, Don (2006-03-01)
      Data broadcasting is potentially an effective and efficient way to share information in wireless sensor networks. Broadcasts offer energy savings over multiple, directed transmissions, and they provide a vehicle to exploit the statistical dependencies often present in distributed data. In this paper, we examine two broadcast structures in the context ...
    • Optimal Sampling Strategies for Multiscale Stochastic Processes 

      Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Baraniuk, Richard G. (2006-01-15)
      In this paper, we determine which non-random sampling of fixed size gives the best linear predictor of the sum of a finite spatial population. We employ different multiscale superpopulation models and use the minimum mean-squared error as our optimality criterion. In a multiscale superpopulation tree models, the leaves represent the units of the ...
    • Wavelet-domain Approximation and Compression of Piecewise Smooth Images 

      Wakin, Michael; Romberg, Justin; Choi, Hyeokho; Baraniuk, Richard G. (2006)
      The wavelet transform provides a sparse representation for smooth images, enabling efficient approximation and compression using techniques such as zerotrees. Unfortunately, this sparsity does not extend to <i>piecewise smooth</i> images, where edge discontinuities separating smooth regions persist along smooth contours. This lack of sparsity hampers ...
    • JPEG Compression History Estimation for Color Images 

      Neelamani, Ramesh; de Queiroz, Ricardo; Fan, Zhigang; Dash, Sanjeeb; Baraniuk, Richard G. (2006)
      We routinely encounter digital color images that were previously JPEG-compressed. En route to the image's current representation, the previous JPEG compression's various settings&mdash;termed its JPEG compression history (CH)&mdash;are often discarded after the JPEG decompression step. Given a JPEG-decompressed color image, this paper aims to estimate ...
    • The 2nu-SVM: A Cost-Sensitive Extension of the nu-SVM 

      Davenport, Mark A. (2005-12-01)
      Standard classification algorithms aim to minimize the probability of making an incorrect classification. In many important applications, however, some kinds of errors are more important than others. In this report we review cost-sensitive extensions of standard support vector machines (SVMs). In particular, we describe cost-sensitive extensions of ...
    • Analysis of the DCS one-stage Greedy Algorothm for Common Sparse Supports 

      Sarvotham, Shriram; Baraniuk, Richard G.; Duarte, Marco; Baron, Dror; Wakin, Michael (2005-11-01)
      Analysis of the DCS one-stage Greedy Algorothm for Common Sparse Supports
    • Modeling wireless sensor and actuator networks using frame theory 

      Rozell, Chris; Johnson, Don (2005-11-01)
      Wireless sensor networks are often studied with the goal of removing information from the network as efficiently as possible. However, when the application also includes an actuator network, it is advantageous to determine actions in-network. In such settings, optimizing the sensor node behavior with respect to sensor information fidelity will not ...
    • 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, we must allow a non-zero probability of codeword error and also pay a penalty by adding redundant bits in the encoding process. In ...
    • Distributed Compressed Sensing of Jointly Sparse Signals 

      Duarte, Marco; Baraniuk, Richard G.; Baron, Dror; Sarvotham, Shriram; Wakin, Michael (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 distributed compressed sensing (DCS) that enables new distributed coding algorithms for multi-signal ensembles that exploit both intra- ...
    • The Dual-Tree Complex Wavelet Transform 

      Selesnick, Ivan W.; Baraniuk, Richard G.; Kingsbury, Nicholas G. (2005-11-01)
      The paper discusses the theory behind the dual-tree transform, shows how complex wavelets with good properties can be designed, and illustrates a range of applications in signal and image processing. The authors use the complex number symbol C in CWT to avoid confusion with the often-used acronym CWT for the (different) continuous wavelet transform. ...