Browsing by Author "Coates, Mark J."

Now showing 1 - 5 of 5
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    Hybrid Linear/Quadratic Time-Frequency Attributes
    (2000-06-01) Baraniuk, Richard G.; Coates, Mark J.; Steeghs, Philippe; Digital Signal Processing (http://dsp.rice.edu/)
    We present an efficient method for robustly calculating time-frequency attributes of a signal, including instantaneous mean frequency, bandwidth, and kurtosis. Most current approaches involve a costly intermediate step of computing a (highly oversampled) 2-D bilinear time-frequency representation (TFR), which is then collapsed to the 1-D attribute. Using the principles of hybrid linear/bilinear time-frequency analysis, we propose computing attributes as nonlinear combinations of the (barely oversampled) linear Gabor transform of the signal. The method is both computationally efficient and accurate-it performs as well as the best bilinear techniques based on adaptive TFRs. To illustrate, we calculate an attribute of a seismic cross-section.
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    Hybrid Linear/Quadratic Time-Frequency Attributes
    (2001-04-01) Baraniuk, Richard G.; Coates, Mark J.; Steeghs, Philippe; Digital Signal Processing (http://dsp.rice.edu/)
    We present an efficient method for robustly calculating time-frequency attributes of a signal, including instantaneous mean frequency, bandwidth, kurtosis, and other moments. Most current attribute estimation techniques involve a costly intermediate step of computing a (highly oversampled) two-dimensonal (2-D) quadratic time-frequency representation (TFR), which is then collapsed to the one-dimensonal (1-D) attribute. Using the principles of hybrid linear/quadratic time-frequency analysis (time-frequency distribution series), we propose computing attributes as nonlinear combinations of the (slightly oversampled) linear Gabor coefficients of the signal. The method is both computationally efficient and accurate; it performs as well as the best techniques based on adaptive TFRs. To illustrate, we calculate an attribute of a seismic cross section.
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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.
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    Network Loss Inference Using Unicast End-to-End Measurement
    (2000-09-20) Coates, Mark J.; Nowak, Robert David; Digital Signal Processing (http://dsp.rice.edu/)
    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 formulate a measurement procedure for network loss inference based on end-to-end packet pair measurements,and we develop a statistical modeling and computation framework for inferring internal network loss characteristics. Simulation experiments demonstrate the potential of our new framework.
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    Network tomography using closely-spaced unicast packets
    (2005-01-04) Nowak, Robert D.; Coates, Mark J.; Rice University; United States Patent and Trademark Office
    This work discloses a unicast, end-to-end network performance measurement process which is capable of determining internal network losses, delays, and probability mass functions for these characteristics. The process is based on using groups of closely-spaced communications packets to determine the information necessary for inferring the performance characteristics of communications links internal to the network. Computationally efficient estimation algorithms are provided.