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 DNA to the stock market. Synthesis algorithms play a key role by providing the feedstock of data necessary for running complex ...
    • 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 scenarios, these sensors acquire very high-dimensional data such as audio signals, images, and video. To cope with such high-dimensional ...
    • 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 SVM. In practice, however, because these criteria require especially accurate error estimation, standard techniques for tuning ...
    • Single-pixel imaging via compressive sampling 

      Duarte, Marco F.; Davenport, Mark A.; Takhar, Dharmpal; Laska, Jason N.; Sun, Ting; (2008-03-01)
    • Multiscale random projections for compressive classification 

      Duarte, Marco F.; Davenport, Mark A.; Wakin, Michael B.; Laska, Jason N.; Takhar, Dharmpal; (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, it exploits the fact that a set of images of a fixed scene under varying articulation parameters forms a low-dimensional, nonlinear ...
    • 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 the relative costs of the two types of errors or true frequency of the two classes in nature. Examining two approaches – one ...
    • 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 for questions posed in these more common frameworks. This note explains how estimating the level set of a regression function ...
    • 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 inequalities for random inner products that have recently provided algorithmically simple proofs of the Johnson–Lindenstrauss lemma; ...
    • 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 recovered by a convex optimization. Random CS measurements are universal in the sense that the same acquisition system is ...
    • The smashed filter for compressive classification and target recognition 

      Davenport, Mark A.; Duarte, Marco F.; Wakin, Michael B.; Laska, Jason N.; Takhar, Dharmpal; (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 object and target recognition, we are ultimately interested in making a decision about an image rather than computing a reconstruction. ...
    • 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 be much smaller than the number of Nyquist rate samples. Interestingly, it has been shown that random projections are a satisfactory ...
    • 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 dimensionality reduction techniques for data processing that attempt to reduce or eliminate the impact of the ambient dimension N on ...
    • 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 theoretical concept of the critical time scale and provides practical approximations for the tail queue probability. These approximations ...
    • 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: two phases encode local image shifts while the third contains image texture information. The QWT is based on an alternative theory ...
    • 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 of on-line arithmetic to provide truncated computations with communication systems as one of the applications. In contrast to ...
    • 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 foundation of Compressed sensing is built on the availability of noise-free measurements. However, measurement noise is unavoidable ...
    • Learning minimum volume sets with support vector machines 

      Baraniuk, Richard G.; Scott, Clayton D.; Davenport, Mark A. (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 of support vector machines (SVMs) for estimating an MV-set from a collection of data points drawn from P, a problem with applications ...
    • 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. Specifically, this work describes the implementation of a custom prototype DSP data acquisition/system controller based on 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 number of linked embedded subgraphs and applies the classical parallel block Jacobi iteration comprising local LMMSE estimation in ...