Elements of information theory
Elements of information theory
A histogram-based model for video traffic behavior in an ATM multiplexer
IEEE/ACM Transactions on Networking (TON)
Wide-area traffic: the failure of Poisson modeling
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
Analysis, modeling and generation of self-similar VBR video traffic
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Source models for VBR broadcast-video traffic
IEEE/ACM Transactions on Networking (TON)
Conference proceedings on Applications, technologies, architectures, and protocols for computer communications
IEEE/ACM Transactions on Networking (TON)
The GBAR source model for VBR videoconferences
IEEE/ACM Transactions on Networking (TON)
A fast fixed-point algorithm for independent component analysis
Neural Computation
Information-theoretic approach to blind separation of sources in non-linear mixture
Signal Processing - Special issue on neural networks
Natural gradient works efficiently in learning
Neural Computation
Independent component analysis for identification of artifacts in magnetoencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Blind identification of MIMO-FIR systems: a generalized linear prediction approach
Signal Processing - Special issue on blind source separation and multichannel deconvolution
Blind separation of filtered sources using state-space approach
Proceedings of the 1998 conference on Advances in neural information processing systems II
Hidden Image Separation from Incomplete Image Mixtures by Independent Component Analysis
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
IEEE Transactions on Signal Processing
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
Fast maximum likelihood for blind identification of multiple FIRchannels
IEEE Transactions on Signal Processing
On subspace methods for blind identification of single-inputmultiple-output FIR systems
IEEE Transactions on Signal Processing
Serial updating rule for blind separation derived from the methodof scoring
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
Modeling full-length VBR video using Markov-renewal-modulated TES models
IEEE Journal on Selected Areas in Communications
Modeling video traffic using M/G/∞ input processes: a compromise between Markovian and LRD models
IEEE Journal on Selected Areas in Communications
Blind deconvolution in nonminimum phase systems using cascade structure
EURASIP Journal on Applied Signal Processing
Temporal and spatial features of single-trial EEG for brain-computer interface
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Multichannel Blind Deconvolution Using the Conjugate Gradient
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Stability analysis of multichannel blind deconvolution
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Hi-index | 0.00 |
Markov modulated self-similar processes are proposed to model MPEG video sequences that can capture the LRD (Long Range Dependency) characteristics of video ACF (Auto-Correlation Function). The basic idea is to decompose an MPEG compressed video sequence into three parts according to different motion/content complexity such that each part can individually be described by a self-similar process. Beta distribution is used to characterize the marginal cumulative distribution (CDF) of the self-similar processes. To model the whole data set, Markov chain is used to govern the transitions among these three self-similar processes. In addition to the analytical derivation, initial simulations have demonstrated that our new model can capture the LRD of ACF and the marginal CDF very well. Network cell loss rate using our proposed synthesized traffic is found to be comparable with that using empirical data as the source traffic.