Now showing items 282-301 of 508

  • Near Best Tree Approximation 

    Baraniuk, Richard G.; DeVore, Ronald A.; Kyriazis, George; Yu, Xiang Ming (2002-01-15)
    Tree approximation is a form of nonlinear wavelet approximation that appears naturally in applications such as image compression and entropy encoding. The distinction between tree approximation and the more familiar ...
  • Network and User Driven Alpha-Beta Onâ Off Source Model for Network Traffic 

    Sarvotham, Shriram; Riedi, Rudolf H.; Baraniuk, Richard G. (2005-06-01)
    We shed light on the effect of network resources and user behavior on network traffic through a physically motivated model. The classical onâ off model successfully captures the long-range, second-order correlations of ...
  • Network Loss Inference Using Unicast End-to-End Measurement 

    Coates, Mark J.; Nowak, Robert David (2000-09-20)
    The fundamental objective of this work is to determine the extent to which unicast, end-to-end network measurement is capable of determining internal network losses. The major contributions of this paper are two-fold: we ...
  • Network Traffic Modeling using a Multifractal Wavelet Model 

    Riedi, Rudolf H.; Ribeiro, Vinay Joseph; Crouse, Matthew; Baraniuk, Richard G. (2000-07-01)
    In this paper, we develop a simple and powerful multiscale model for syntheizing nonFaussian, long-range dependent (LRD) network traffic. Although wavelets effectively decorrelate LRD data, wavelet-based models have generally ...
  • Network Traffic Modeling using a Multifractal Wavelet Model 

    Riedi, Rudolf H.; Crouse, Matthew; Ribeiro, Vinay Joseph; Baraniuk, Richard G. (1999-02-01)
    In this paper, we describe a new multiscale model for characterizing positive-valued and long-range dependent data. The model uses the Haar wavelet transform and puts a constraint on the wavelet coefficients to guarantee ...
  • Neural population structures and consequences for neural coding 

    Johnson, Don (2002-09-20)
    Researchers studying neural coding have speculated that populations of neurons would more effectively represent the stimulus if the neurons "cooperated:" by interacting through lateral connections, the neurons would process ...
  • A New and Efficient Program for Finding All Polynomial Roots 

    Lang, Markus; Frenzel, Bernhard-Christian (1993-01-15)
    Finding polynomial roots rapidly and accurately is an important problem in many areas of signal processing. We present a new program which is a combination of Muller's and Newton's method. We use the former for computing ...
  • New Bayesian Model Averaging Framework for Wavelet-Based Signal Processing 

    Wan, Yi; Nowak, Robert David (2000-06-20)
    This paper develops a new signal modeling framework using Bayesian model averaging formulation and the redundant or translation-invariant wavelet transform. The aim of this framework is to provide a paradigm general enough ...
  • New class of wavelets for signal approximation 

    Odegard, Jan E.; Burrus, C. Sidney (1996-05-20)
    This paper develops a new class of wavelets for which the classical Daubechies zero moment property has been relaxed. The advantages of relaxing higher order wavelet moment constraints is that within the framework of ...
  • New Dimensions In Wavelet Analysis 

    Baraniuk, Richard G.; Jones, Douglas L. (1992-03-01)
    In this paper we propose a new class of signal analysis tools that generalizes the popular wavelet and short-time Fourier transforms. The class allows skews and rotations of the analyzing wavelet in the time-frequency ...
  • A New Framework for Complex Wavelet Transforms 

    Fernandes, Felix; van Spaendonck, Rutger; Burrus, C. Sidney (2003-06-20)
    Although the Discrete Wavelet Transform (DWT) is a powerful tool for signal and image processing, it has three serious disadvantages: shift sensitivity, poor directionality and lack of phase information. To overcome these ...
  • A new look at the informational gain of soft decisions 

    Lexa, Michael; Johnson, Don (2003-04-20)
    This paper develops a new systematic method of studying the benefits of 2-bit soft decisions by applying the concepts of information processing theory. We quantify performance in terms of the information transfer ratio and ...
  • New multivariate dependence measures and applications to neural ensembles 

    Goodman, Ilan; Johnson, Don (2003-09-20)
    We develop two new multivariate statistical dependence measures. The first, based on the Kullback-Leibler distance, results in a single value that indicates the general level of dependence among the random variables. The ...
  • New Signal-Space Orthonormal Bases via the Metaplectic Transform 

    Baraniuk, Richard G.; Jones, Douglas L. (1992-10-01)
    The discretization of the metaplectic transform (MT) is considered, and it is shown that it can lead to completely new orthonormal bases (ONBs) for the signal space of square integrable functions. Two new classes of bases, ...
  • Noise Reduction Using an Undecimated Discrete Wavelet Transform 

    Lang, Markus; Guo, Haitao; Odegard, Jan E.; Burrus, C. Sidney; Wells, R.O. (1995-01-15)
    A new nonlinear noise reduction method is presented that uses the discrete wavelet transform. Similar to Donoho and Johnstone, we employ thresholding in the wavelet transform domain but, following a suggestion by Coifman, ...
  • Non-Redundant, Linear-Phase, Semi-Orthogonal, Directional Complex Wavelets 

    Fernandes, Felix; Wakin, Michael; Baraniuk, Richard G. (2004-05-01)
    The directionality and phase information provided by non-redundant complex wavelet transforms (NCWTs) provide significant potential benefits for image/video processing and compression applications. However, because existing ...
  • Nonlinear phase FIR filter design with minimum LS error and additional constraints 

    Lang, Markus; Bamberger, Joachim (1994-07-01)
    We examine the problem of approximating a complex frequency response by a real-valued FIR filter according to the <i>L<sub>2</sub></i> norm subject to additional inequality constraints for the complex error function. ...
  • Nonlinear Processing of a Shift Invariant DWT for Noise Reduction 

    Lang, Markus; Guo, Haitao; Odegard, Jan E.; Burrus, C. Sidney; Wells, R.O. (1995-04-20)
    A novel approach for noise reduction is presented. Similar to Donoho, we employ thresholding in some wavelet transform domain but use a nondecimated and consequently redundant wavelet transform instead of the usual orthogonal ...
  • Nonlinear Processing of a Shift Invariant DWT for Noise Reduction 

    Lang, Markus; Guo, Haitao; Odegard, Jan E.; Burrus, C. Sidney; Wells, R.O. (1995-03-20)
    A novel approach for noise reduction is presented. Similar to Donoho, we employ thresholding in some wavelet transform domain but use a nondecimated and consequently redundant wavelet transform instead of the usual orthogonal ...
  • Nonlinear Wavelet Processing for Enhancement of Images 

    Odegard, Jan E.; Lang, Markus; Guo, Haitao; Gopinath, Ramesh A.; Burrus, C. Sidney (1994-05-20)
    In this note we apply some recent results on nonlinear wavelet analysis to image processing. In particular we illustrate how the (soft) thresholding algorithm due to Donoho and Johnstone can successfully be used to remove ...