AR-based quadratic modeling for GOP MPEG-encoded video traffic in ATM networks

Statistical analysis and performance modeling of compressed video traffic streams are efficient tools to estimate network resources and to predict network behavior under various conditions. Among current video traffic models, Autoregressive (AR) processes have been extensively used as good representation of variable bit rate video services, due basically to their simplicity and ease of computation. Recently, an elegant approach to the modeling of teleconferencing video has been proposed by Zhang in IEEE Trans. Circuits Syst. Video Technol. 9 (1999) p. 1130. This approach decomposes the traffic data into a linear combination of a number of chi-square sequences, each of which is obtained by passing a Gaussian AR process through a simple non-linearity. In this article, we first show that the model presented by Zhang is a special case of the general form of models represented by quadratic filters that are widely used in modern digital signal processing. Second, we extend Zhang's approach to model MPEG video traffic; an important full motion video source that exhibits dynamic and complex pattern. Specifically, we propose modeling MPEG video at GOP layer in ATM packet switching networks. This layer has an important feature in that it reflects the behavior of video scene activities. Finally, by using a wide variety of real MPEG video sequences, we experimentally demonstrate that the proposed GOP-based model approximates the real video traffic extremely well in terms of first and second order statistics as well as queuing performance. © 2003 Elsevier B.V. All rights reserved

Blind identification of nonminimum phase FIR systems: Cumulants matching via genetic algorithms

In this paper, a new method for estimating the parameters of a nonminimum phase, linear, time-invariant (NMP-LTI) system, from only its output measurements, is presented. The method makes use of genetic algorithms (GA) to minimize a cost function defined in terms of system's output cumulants. It is demonstrated by computer simulations that the new method is insensitive to model order mismatch and is capable of identifying the parameters of an NMP system using output data which may be corrupted by additive Gaussian noise. © 1998 Elsevier Science B.V. All rights reserved

A high-speed systolic array for computing third-order cumulants

Abstract The computational complexity involved in the estimation of higher-order statistics far exceeds that of conventional second-order statistics. This paper presents a parallel-processing system that speeds up the computational process of third-order cumulants. An algorithm for estimating third-order cumulants based on matrix multiplications is presented. Then, a special structure of systolic array system for matrix multiplications is developed. The structure and complexity of the array as well as the internal structure and the delay of the processing elements are discussed. The system is suitable for VLSI implementation

Identification of Nonminimum Phase MA Systems Using Cepstral Operations on Slices of Higher Order Spectra

In this paper, recursive and least squares methods for reconstructing the impulse response of a nonminimum phase, linear, time-invariant (NMP-LTI) MA system from its (k + l)st-order spectrum (k > 1) are presented. The methods are nonparametric. They are based on computing the complex cepstrum of the impulse response sequence of the unknown system from the diagonal slice of the (k + 1)st-order spectrum. The computational complexity of the system identification problem can be considerably reduced by using slices of higher order spectra. © 1992 IEE

Blind channel estimation and data recovery in DS spread spectrum systems

Recently, a new approach to the problem of data detection for communications over band-limited channels with unknown channel-impulse response is proposed (Vaidis and Weber, IEEE Trans. Commun. 46 (February 1998) 232). This approach utilizes the Viterbi algorithm (VA) for maximum-likelihood sequence estimation (MLSE) in a block adaptive technique for simultaneous channel and data estimation. In this paper, a novel computationally efficient modified VA is developed for MLSE in direct sequence (DS) spread spectrum system. The new modified VA is employed in the approach of Vaidis and Weber (1998) for recovering DS spread spectrum signals in the presence of channel distortion and additive Gaussian noise. The simulation results show that the approach of Vaidis and Weber (1998) implemented with the new modified VA achieves lower probability of error and higher speed of convergence. © 2001 Elsevier Science B.V. All rights reserved

An Adaptive System Identification Method Based on Bispectrum

This paper presents a new adaptive technique for the identification of a linear system driven by white non-Gaussian noise. The system can be a non-minimum phase system. The adaptive identification technique is a least mean-square (LMS) type algorithm and it is obtained by using the higher order correlations of the system output. © 1991 IEE

Cumulants and genetic algorithm for parameters estimation of noncausal autoregressive models

The authors introduce a new method for estimating the coefficients of a noncausal autoregressive (AR) model. This method is based on a new formulation that relates the unknown AR parameters to both second- and third-order cumulants. The new formulation facilitates the use of linear and nonlinear least-square estimation techniques, and includes some published works as a special case. The nonlinear least-square estimation techniques presented in this work make use of a genetic algorithm (GA) to minimize a cost function that is defined in terms of the model's output cumulants. We also introduce a new method for estimating the coefficients of a noncausal AR model using the power spectrum and a one-dimensional (1-D) slice of the bispectrum. To illustrate the effectiveness of the proposed AR modelling approaches, extensive simulation examples are presented

A new approach for computing higher-order moments on linear systolic arrays

The last few years have witnessed significant advances in the use of higher-order moments in various signal processing applications. As a result, the demand for efficient architectural designs for these functions is on the rise. In this paper we present a new formulation for implementing third- and fourth-order moments on linear systolic arrays. In the proposed approach the moments are computed as elements of a matrix which are obtained after a series of matrix multiplication operations. The resultant architecture is a simple linear array that can be implemented efficiently in VLSI

A fast VLSI system for computing third order cumulants

A fast concurrent system for computing third order cumulants is presented. The system consists of (q + 1)(q + 2) processing elements (PEs), where q is the maximum lag of third order cumulant sequence. A huge saving in computation time compared to sequential computation is realized. The system performance in terms of the speedup and efficiency is evaluated. The system is suitable for VLSI implementation. © 1995

Concurrent system for the computation of higher-order moments

The cumulants defined in terms of moments are basic to the study of higher-order statistics (HOS) of a stationary stochastic process. This paper presents a concurrent systolic array system for the computation of higher-order moments. The system allows for the simultaneous computation of the second-, third-, and fourth-order moments. The architecture achieves good speedup through its excellent exploitation of parallelism, pipelining, and reusability of some intermediate results. The computational complexity and system performance issues related to the architecture are discussed. The concurrent system is designed with the CMOS VLSI technology and is capable of operating at 3.9 MHz