Now showing items 1-20 of 508

    • Sparse Coding with Population Sketches 

      Dyer, Eva L.; Baraniuk, Richard G.; Johnson, Don H. (2009-07-13)
    • Fast, Exact Synthesis of Gaussian and nonGaussian Long-Range-Dependent Processes 

      Baraniuk, Richard; Crouse, Matthew (2009-04-15)
      1/f noise and statistically self-similar random processes such as fractional Brownian motion (fBm) and fractional Gaussian noise (fGn) are fundamental models for a host of real-world phenomena, from network traffic to ...
    • A Theoretical Analysis of Joint Manifolds 

      Davenport, Mark A.; Hegde, Chinmay; Duarte, Marco; Baraniuk, Richard G. (2009-01)
      The emergence of low-cost sensor architectures for diverse modalities has made it possible to deploy sensor arrays that capture a single event from a large number of vantage points and using multiple modalities. In many ...
    • Tuning support vector machines for minimax and Neyman-Pearson classification 

      Scott, Clayton D.; Baraniuk, Richard G.; Davenport, Mark A. (2008-08-19)
      This paper studies the training of support vector machine (SVM) classifiers with respect to the minimax and Neyman-Pearson criteria. In principle, these criteria can be optimized in a straightforward way using a cost-sensitive ...
    • 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)
    • 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, ...
    • Minimax support vector machines 

      Davenport, Mark A.; Baraniuk, Richard G.; Scott, Clayton D. (2007-08-01)
      We study the problem of designing support vector machine (SVM) classifiers that minimize the maximum of the false alarm and miss rates. This is a natural classification setting in the absence of prior information regarding ...
    • Regression level set estimation via cost-sensitive classification 

      Scott, Clayton D.; Davenport, Mark A. (2007-06-01)
      Regression level set estimation is an important yet understudied learning task. It lies somewhere between regression function estimation and traditional binary classification, and in many cases is a more appropriate setting ...
    • 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 ...
    • Quantization of Sparse Representations 

      Boufounos, Petros T.; Baraniuk, Richard G. (2007-01-16)
      Compressive sensing (CS) is a new signal acquisition technique for sparse and compressible signals. Rather than uniformly sampling the signal, CS computes inner products with randomized basis functions; the signal is then ...
    • 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 ...
    • 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 Queuing Analysis 

      Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Baraniuk, Richard G. (2006-10-01)
      This paper introduces a new multiscale framework for estimating the tail probability of a queue fed by an arbitrary traffic process. Using traffic statistics at a small number of time scales, our analysis extends the ...
    • Coherent Multiscale Image Processing using Quaternion Wavelets 

      Chan, Wai Lam; Choi, Hyeokho; Baraniuk, Richard G. (2006-10-01)
      The quaternion wavelet transform (QWT) is a new multiscale analysis tool for geometric image features. The QWT is a near shift-invariant tight frame representation whose coefficients sport a magnitude and three phases: ...
    • Random Projections of Smooth Manifolds 

      Baraniuk, Richard G.; Wakin, Michael (2006-10-01)
      Many types of data and information can be described by concise models that suggest each data vector (or signal) actually has â few degrees of freedomâ relative to its size N. This is the motivation for a variety of ...
    • Truncated on-line arithmetic with applications to communication systems 

      Rajagopal, Sridhar; Cavallaro, Joseph R. (2006-09-01)
      Truncation and saturation in digit-precision are very important and common operations in embedded system design for bounding the required finite precision and for area-time-power savings. In this paper, we present the use ...
    • Learning minimum volume sets with support vector machines 

      Davenport, Mark A.; Baraniuk, Richard G.; Scott, Clayton D. (2006-09-01)
      Given a probability law P on d-dimensional Euclidean space, the minimum volume set (MV-set) with mass beta , 0 < beta < 1, is the set with smallest volume enclosing a probability mass of at least beta. We examine the use ...
    • 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 ...
    • Robust Distributed Estimation Using the Embedded Subgraphs Algorithm 

      Delouille, Veronique; Neelamani, Ramesh; Baraniuk, Richard G. (2006-08-01)
      We propose a new iterative, distributed approach for linear minimum mean-square-error (LMMSE) estimation in graphical models with cycles. The embedded subgraphs algorithm (ESA) decomposes a loopy graphical model into a ...
    • Development of Digital Signal Processor controlled Quantum Cascade Laser based Trace Gas Sensor Technology 

      So, Stephen; Wysocki, Gerard; Frantz, Patrick; Tittel, Frank K. (2006-08-01)
      This work reports the design and integration of a custom digital signal processor (DSP) system into a pulsed quantum cascade laser (QCL) based trace gas sensor to improve its portability, robustness and operating performance. ...