The wavelet transform (WT) has recently emerged as a powerful tool for image compression. In this paper, a new image compression technique combining the genetic algorithm (GA) and grey-based competitive learning network (GCLN) in the wavelet transform domain is proposed. In the GCLN, the grey theory is applied to a two-layer modified competitive learning network in order to generate optimal solution for VQ. In accordance with the degree of similarity measure between training vectors and codevectors, the grey relational analysis is used to measure the relationship degree among them. The GA is used in an attempt to optimize a specified objective function related to vector quantizer design. The physical processes of competition, selection and reproduction operating in populations are adopted in combination with GCLN to produce a superior genetic grey-based competitive learning network (GGCLN) for codebook design in image compression. The experimental results show that a promising codebook can be obtained using the proposed GGCLN and GGCLN with wavelet decomposition.

Based on a Quantum-inspired Evolutionary Algorithm (QEA), a new disk allocation method is proposed for distributing buckets of a binary cartesian product file among unrestricted number of disks to maximize concurrent disk I/O. It manages the probability distribution matrix to represent the qualities of the genes. Determining the excellent genes quickly makes the proposed method have faster convergence than DAGA. It gives better solutions and 3.2 - 11.3 times faster convergence than DAGA.

The present paper deals with the blind deconvolution of a Multiple-Input Multiple-Output Finite Impulse Response (MIMO-FIR) system. To deal with the blind deconvolution problem using the second-order statistics (SOS) of the outputs, Hua and Tugnait considered it under the conditions that a) the FIR system is irreducible and b) the input signals are spatially uncorrelated and have distinct power spectra. In the present paper, the problem is considered under a weaker condition than the condition a). Namely, we assume that c) the FIR system is equalizable by means of the SOS of the outputs. Under b) and c), we show that the system can be blindly identified up to a permutation, a scaling, and a delay using the SOS of the outputs. Moreover, based on this identifiability, we show a novel necessary and sufficiently condition for solving the blind deconvolution problem, and then, based on the condition, we propose a new algorithm for finding an equalizer using the SOS of the outputs, while Hua and Tugnait have not proposed any algorithm for solving the blind deconvolution under the conditions a) and b).

RF noise in quarter-micron nMOSFETs is analysed on the device level based on Shockley's impedance field method. The impact of different transport models and physical parameters is discussed in detail. Well-calibrated drift-diffusion and energy-balance models give very similar results for noise current spectral densities and noise figures. We show by numerical simulations with the general-purpose device simulator DESSIS_ISE that the hot-electron effect on RF noise is unimportant under normal operating conditions and that thermal substrate noise is dominant below 0.5 GHz. The contribution of energy-current fluctuations to the terminal noise is found to be negligible. Application of noise sources generated in bulk full-band Monte Carlo simulations changes the noise figures considerably, which underlines the importance of proper noise source models for far-from-equilibrium conditions.

A brief review is given on a crossover in transport between quantum and classical regimes due to the presence of inelastic scattering destroying the phase coherence. In the integer quantum Hall effect, the quantum regime corresponds to the edge-current picture and the classical to the bulk Hall current picture. The crossover between two regimes occurs through inelastic scattering. In a metallic carbon nanotube, there is a perfectly transmitting channel independent of energy for conventional scatterers having potential range larger than the lattice constant, making the nanotube a perfect conductor. When several bands coexist at the Fermi level, such a perfect channel is destroyed by inelastic scattering.

The validity and capability of an iterative coupling scheme between single-particle frozen-field Monte Carlo simulations and nonlinear Poisson solutions for achieving self-consistency is investigated. For this purpose, a realistic 0.1 µm lightly-doped-drain (LDD) n-MOSFET with a maximum doping level of about 2.5 1020 cm-3 is simulated. It is found that taking the drift-diffusion (DD) or the hydrodynamic (HD) model as initial simulation leads to the same Monte Carlo result for the drain current. This shows that different electron densities taken either from a DD or a HD simulation in the bulk region, which is never visited by Monte Carlo electrons, have a negligible influence on the solution of the Poisson equation. For the device investigated about ten iterations are necessary to reach the stationary state after which gathering of cumulative averages can begin. Together with the absence of stability problems at high doping levels this makes the self-consistent single-particle approach (SPARTA) a robust and efficient method for the simulation of nanoscale MOSFETs where quasi-ballistic transport is crucial for the on-current.

On research to determine reliable acoustic indicators for the type of stress present in speech, the majority of systems have concentrated on the statistics extracted from pitch contour, energy contour, wavelet based subband features and Teager-Energy-Operator (TEO) based feature parameters. These systems work mostly on pair-wise distinction between stress and neutral speech. Their performance decreases substantially when tested in multi-style detection among many stress categories. In this paper, a novel system is proposed using linear short time Log Frequency Power Coefficients (LFPC) and TEO based nonlinear LFPC features in both time and frequency domain. Five-state Hidden Markov Model (HMM) with continuous Gaussian mixture distribution is used. The stress classification ability of the system is tested using data from the SUSAS (Speech Under Simulated and Actual Stress) database to categorize five stress conditions individually. It is found that the performance of linear acoustic features LFPC is better than that of nonlinear TEO based LFPC feature parameters. Results show that with linear acoustic feature LFPC, average accuracy of 84% and the best accuracy of 95% can be achieved in the classification of the five categories. Results of test of the system under different signal-to-noise conditions show that the performance of the system does not degrade drastically with increase in noise. It is also observed that classification using nonlinear frequency domain LFPC features gives relatively higher accuracy than that using nonlinear time domain LFPC features.

A fast nearest neighbor codeword search algorithm for vector quantization (VQ) is introduced. The algorithm uses three significant features of a vector, that is, the mean, the variance and the norm, to reduce the search space. It saves a great deal of computational time while introducing no more memory units than the equal-average equal-variance codeword search algorithm. With two extra elimination criteria based on the mean and the variance, the proposed algorithm is also more efficient than so-called norm-ordered search algorithm. Experimental results confirm the effectiveness of the proposed algorithm.

The problem of separating blindly independent sources from a convolutive mixture cannot be addressed in its widest generality without resorting to statistics of order higher than two. The core of the problem is in fact to identify the paraunitary part of the mixture, which is addressed in this paper. With this goal, a family of statistical contrast is first defined. Then it is shown that the problem reduces to a Partial Approximate Joint Diagonalization (PAJOD) of several cumulant matrices. Then, a numerical algorithm is devised, which works block-wise, and sweeps all the output pairs. Computer simulations show the good behavior of the algorithm in terms of Symbol Error Rates, even on very short data blocks.

Beaulieu has proposed four signal-to-noise ratio (SNR) estimators for quadrature phase shift keying (QPSK) signaling in time domain. In this letter, we propose SNR estimators for QPSK signaling in frequency domain. A discrete Fourier transform (DFT) algorithm is used for the frequency domain analysis of the received signal. The frequency spectrum enables biased SNR estimation in the frequency domain. Circular convolution is used for robust and fast SNR estimation when the received signal exhibits a frequency offset. Simulation results show that the new estimators present good performance even when the received signal exhibits a large frequency offset.

We describe progress we have achieved in the development of our quantum transport modeling for nano-scale devices. Our simulation is based upon either the non-equilibrium Green's function method (NEGF) or the quantum correction (QC) associated with density gradient method (DG) and/or effective potential method (EP). We show the results of our modeling methods applied to several devices and discuss issues faced with regards to computational time, open boundary conditions, and their relationship to self-consistent solution of the Poisson-NEGF equations. We also discuss those for efficiently tailored QC Monte Carlo techniques.

This paper presents a baseband filter for multi-mode applications with a new automatic tuning method. 5th-order Chebyshev low pass filter is designed for implementing the baseband channel-select filter. Capacitors and resistors were shared between modes to minimize the area. The new corner frequency tuning method is proposed to compensate the process variation. This method can reduce the noise level due to MOS switches.

The differentiated services (DiffServ) architecture is proposed to provide a service differentiation between traffic classes or behavior aggregates in a scalable manner. A key functional element to deploy DiffServ is traffic conditioning, more specifically traffic marker. This paper proposes an adaptive and aggregated traffic marker embodying the functions: (1) inter-connect two-rate three color markers (trTCMs), (2) estimate the aggregate rate of Assured Forwarding (AF) classes, and (3) re-mark the some parts of excessive portion of assigned link-rate per AF class queue with some down-grading probability to lower AF class. Both analysis and simulations are used to evaluate the performance of the proposed aggregate-traffic marker. The analysis of the proposed marker shows its clear service differentiation among behavior aggregates (BAs) under different traffic load conditions. Also a performance evaluation is performed through network simulation with more realistic traffics as unbalanced intensities among different BAs. Shortly, our proposed aggregate-traffic marker enables to keep the priority orders in terms of loss rate and delay/jitter among BAs in spites of varying and unbalanced traffic intensities.

In this paper, we propose a nonlinear control model to characterize the AQM algorithm-GREEN. Based on this model, we analyze its performance and prove that there exists a stable oscillation when in equilibrium. Furthermore, we also investigate the effects of the factors such as bandwidth, round trip time, and load level on the amplitude and frequency of the oscillation. Theoretical analysis and simulation results indicate that GREEN algorithm is insensitive to the network conditions when the link rate and the round trip time are relatively small and becomes more sensitive to the change of network conditions when the bandwidth delay product is relatively high. For GREEN the adaptability to a wide range of network conditions is based on the compromising of the efficiency.

As the Internet role changes from the experimental environment to the social infrastructure, end-to-end quality for data transfer of various types of traffic has been required as well as its connectivity. So we should precisely measure some performance such as packet loss probability and delay at routers on some source-destination path. By using so-called passive measurement technique, we can get the queue length distribution from some routers individually and estimate the end-to-end transmission delay. However, there may be some correlation between queue lengths of two or more routers packets go through in sequence, which would lead to inaccurate estimation of end-to-end delay performance. Thus in this paper, we model two tandem routers as queueing system, and analyze the queue length distributions and their correlation. Through some numerical results, we will investigate the impact of traffic parameters on the degree of correlation and how it affects the estimation of delay performance.

In recent years, requests for new advanced features, decentralization, and improvements in the forwarding speed, have been made for IX. A new IX architecture using MPLS (Multiple Label Switching) technology (MPLS-IX) is proposed to solve these requirements and rectify the problems. In this paper, a comparison between current IX and MPLS-IX based on router's packet forwarding performance is examined, and the feasibility of MPLS-IX is discussed. Moreover, the QoS requirements to provide IX service are discussed.

One purpose of Quality-of-Service (QoS) routing is to develop polynomial-time heuristic algorithms to tackle the MCP (multi-constrained-path) problem, which is NP-complete. In this paper, we introduce a new QoS routing heuristic framework, which focuses on how to increase the success ratio for finding a feasible path subject to multiple additive constraints. The key issue of this framework is to transform the single source single destination QoS routing problem to a single source multi-destination problem by expanding the destination vertex to its neighboring vertices. After that, the modified problem can be solved by existing source routing heuristic algorithms. The analysis and simulation results demonstrate that the framework can achieve a higher success ratio of finding a feasible path without increasing the computational complexity by setting the expansion operation properly.

Evolutionary learning methods have been applied to a variety of different problems. In this paper, a new algorithm for active queue management based on an evolutionary learning model is proposed. This novel algorithm generates packet marks for the purpose of improving robustness and responsiveness of congestion control in the Internet routers, while maintaining a reasonable degree of queueing performance such as utilization, delay, and packet drops due to buffer overflow. Simulation results demonstrate the effectiveness of the proposed algorithm and compare the performance of various algorithms.

To support video streaming over the current Internet, a media server usually needs to perform adaptive streaming in combination with congestion control. While existing streaming schemes are mainly designed for particular congestion algorithms and rate shaping techniques, this paper proposes a general buffer-driven adaptive streaming scheme based on control theory. Our scheme can be applied in combination with different congestion control algorithms and different rate shaping (and source coding) techniques. It is also amenable to analysis and easy to implement. Analytical and simulation results show that the proposed scheme gives satisfactory video quality under various Internet conditions while fully utilizing the available network bandwidth.

In wireless access system, we need to use a limited frequency and electric power efficiently. And so we propose the fixed wireless access network using 5 GHz frequency which band has a good propagation performance in line of sight (LOS). In the proposed network, the several multi-level modulation methods are combined and identical frequency is reused by considering on the antenna directivity. As constructing this network, we can efficiently use frequency in 5 GHz band and enlarge system capacity. In this paper, it is assumed that user terminals are distributed nonuniformly over the service area. We analyze accommodation number of user terminals and the optimum combination of modulation methods. Numerical results show that most effective method is the combination of 16QAM and 256QAM, which can accommodate up to about 1.4 times as many users as only QPSK modulation method.