Browsing by Author "Hendricks, Brent"

Now showing 1 - 4 of 4
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    Analysis of Multiscale Texture Segmentation using Wavelet-Domain Hidden Markov Trees
    (1999-10-01) Choi, Hyeokho; Hendricks, Brent; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    This paper describes a technique for estimating the Kullback-Leibler (KL) distance between two Hidden Markov Models (HMMs), and for measuring the quality of the estimator. It also provides some results based on applying the technique to wavelet domain Hidden Markov Tree (HMT) models used in image segmentation. The technique is easily applied, because in most situations the necessary tools (data generation and likelihood calculation) are already in place.
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    CNXML Tutorial
    (Rice University, 2011-03-18) Connexions; Hendricks, Brent
    A brief tutorial on CNXML - an XML language by Connexions
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    Connexions Tutorial and Reference
    (Rice University, 2013-03-18) Connexions; Galvan, Adan; Hendricks, Brent; Husband, Mark
    Tutorial and reference materials for getting started with Connexions
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    Multifractal Cross-Traffic Estimation
    (2000-09-01) Ribeiro, Vinay Joseph; Coates, Mark J.; Riedi, Rudolf H.; Sarvotham, Shriram; Hendricks, Brent; Baraniuk, Richard G.; Center for Multimedia Communications (http://cmc.rice.edu/)
    In this paper we develop a novel model-based technique, the Delphi algorithm, for inferring the instantaneous volume of competing cross-traffic across an end-to-end path. By using only end-to-end measurements, Delphi avoids the need for data collection within the Internet. Unique to the algorithm is an efficient exponentially spaced probing packet train and a parsimonious multifractal parametric model for the cross-traffic that captures its multiscale statistical properties (including long-range dependence) and queuing behavior. The algorithm is adaptive; it requires no a priori traffic statistics and effectively tracks changes in network conditions. NS (network simulator) experiments reveal that Delphi gives accurate ross-traffic estimates for higher link utilization levels while at lower utilizations it over-estimates the cross-traffic. Also, when Delphi's single bottleneck assumption does not hold it over-estimates the cross-traffic.