Now showing items 1-14 of 14

  • Additive and Multiplicative Mixture Trees for Network Traffic Modeling 

    Sarvotham, Shriram; Wang, Xuguang; Riedi, Rudolf H.; Baraniuk, Richard G. (2002-05-01)
    Network traffic exhibits drastically different statistics, ranging from nearly Gaussian marginals and long range dependence at very large time scales to highly non-Gaussian marginals and multifractal scaling on small scales. ...
  • Analysis and modeling of bursty long-range-dependent network traffic 

    Sarvotham, Shriram (2001)
    In this thesis, we study the cause and impact of burstiness in computer network traffic. A connection-level analysis of traffic at coarse time scales (time scales greater than a round-trip-time) reveals that a single ...
  • Analysis of the DCS one-stage Greedy Algorothm for Common Sparse Supports 

    Baron, Dror; Duarte, Marco F.; Wakin, Michael; Sarvotham, Shriram; Baraniuk, Richard G. (2005-11-01)
    Analysis of the DCS one-stage Greedy Algorothm for Common Sparse Supports
  • Bounds for optimal compressed sensing matrices and practical reconstruction schemes 

    Sarvotham, Shriram (2008)
    Compressed Sensing (CS) is an emerging field that enables reconstruction of a sparse signal x ∈ Rn that has only k << n non-zero coefficients from a small number m << n of linear projections. The ...
  • Connection-level Analysis and Modeling of Network Traffic 

    Sarvotham, Shriram; Riedi, Rudolf H.; Baraniuk, Richard G. (2001-11-01)
    Most network traffic analysis and modeling studies lump all connections together into a single flow. Such aggregate traffic typically exhibits long-range-dependent (LRD) correlations and non-Gaussian marginal distributions. ...
  • Distributed Compressed Sensing of Jointly Sparse Signals 

    Sarvotham, Shriram; Baron, Dror; Wakin, Michael; Duarte, Marco F.; Baraniuk, Richard G. (2005-11-01)
    Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we expand our theory for ...
  • Distributed Multiscale Data Analysis and Processing for Sensor Networks 

    Wagner, Raymond; Sarvotham, Shriram; Choi, Hyeokho; Baraniuk, Richard G. (2005-02-01)
    While multiresolution data analysis, processing, and compression hold considerable promise for sensor network applications, progress has been confounded by two factors. First, typical sensor data are irregularly spaced, ...
  • 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 ...
  • Multifractal Cross-Traffic Estimation 

    Ribeiro, Vinay Joseph; Coates, Mark J.; Riedi, Rudolf H.; Sarvotham, Shriram; Hendricks, Brent; Baraniuk, Richard G. (2000-09-01)
    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 ...
  • Multiscale Connection-Level Analysis of Network Traffic 

    Sarvotham, Shriram; Riedi, Rudolf H.; Baraniuk, Richard G. (2002-11-01)
    Network traffic exhibits drastically different statistics, ranging from nearly Gaussian marginals and long range dependence at very large time scales to highly non-Gaussian marginals and multifractal scaling on small scales. ...
  • A Multiscale Data Representation for Distributed Sensor Networks 

    Wagner, Raymond; Sarvotham, Shriram; Baraniuk, Richard G. (2005-03-01)
    Though several wavelet-based compression solutions for wireless sensor network measurements have been proposed, no such technique has yet appreciated the need to couple a wavelet transform tolerant of irregularly sampled ...
  • A Multiscale Data Representation for Distributed Sensor Networks: Proofs of Basis Characteristics and Error Bounds 

    Sarvotham, Shriram; Wagner, Raymond; Baraniuk, Richard G. (2004-09-01)
    Provides proofs of Parseval tight-frame membership and approximation properties for the basis proposed in "A Multiscale Data Representation for Distributed Sensor Networks" by R. Wagner, S. Sarvotham, and R. Baraniuk (ICASSP ...
  • 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 ...
  • Variable-Rate Universal Slepian-Wolf Coding with Feedback 

    Sarvotham, Shriram; Baron, Dror; Baraniuk, Richard G. (2005-11-01)
    Traditional Slepian-Wolf coding assumes known statistics and relies on asymptotically long sequences. However, in practice the statistics are unknown, and the input sequences are of finite length. In this finite regime, ...