In this paper, a novel independent component analysis (ICA) approach is proposed, which is robust against the interference of impulse noise. To implement ICA in a noisy environment is a difficult problem, in which traditional ICA may lead to poor results. We propose a method that consists of noise detection and image signal recovery. The proposed approach includes two procedures. In the first procedure, we introduce a self-organizing map (SOM) network to determine if the observed image pixels are corrupted by noise. We will mark each pixel to distinguish normal and corrupted ones. In the second procedure, we use one of two traditional ICA algorithms (fixed-point algorithm and Gaussian moments-based fixed-point algorithm) to separate the images. The fixed-point algorithm is proposed for general ICA model in which there is no noise interference. The Gaussian moments-based fixed-point algorithm is robust to noise interference. Therefore, according to the mark of image pixel, we choose the fixed-point or the Gaussian moments-based fixed-point algorithm to update the separation matrix. The proposed approach has the capacity not only to recover the mixed images, but also to reduce noise from observed images. The simulation results and analysis show that the proposed approach is suitable for practical unsupervised separation problem.

The block based motion estimation technique is adopted by various video coding standards to reduce the temporal redundancy in video sequences. The core of that technique is the search algorithm implemented to find the location of the best matched block. Indeed, the full search algorithm is the most straightforward and optimal but computationally demanding search algorithm. Consequently, many fast and suboptimal search algorithms have been proposed. Reduction of the number of location being searched is the approach used to decrease the computational load of full search. In this paper, hybridization between an adaptive search algorithm and the full search algorithm is proposed. The adaptive search algorithm benefits from the correlation within spatial and temporal adjacent blocks. At the same time, a feature domain based matching criteria is used to reduce the complexity resulting from applying the pixel based conventional criteria. It is shown that the proposed algorithm produces good quality performance and requires less computational time compared with popular block matching algorithms.

We present a new method for the identification of time-invariant multichannel autoregressive (AR) processes corrupted by additive white observation noise. The method is based on the Yule-Walker equations and identifies the autoregressive parameters from a finite set of measured data. The input signals to the underlying process are assumed to be unknown. An inverse filtering technique is used to estimate the AR parameters and the observation noise variance, simultaneously. The procedure is iterative. Computer simulation results that demonstrate the performance of the identification method are presented.

This paper is devoted to a new design method for infinite impulse response approximate inverse system of a nonminimum phase system. The design is carried out such that the convolution of the nonminimum phase polynomial and its approximate inverse system can be represented by an approximately linear phase all-pass filter. A method for estimating the time delay and order of an approximate inverse system is also presented. Using infinite impulse response approximate inverse systems better accuracy is achieved with reduced computational complexity. Numerical examples are included to show the effectiveness of the proposed method.

This paper presents a new method for the selftuning control of nonminimum phase discrete-time stochastic systems using approximate inverse systems obtained from the leastsquares approximation. Using this approximate inverse system the gain response of the system can be made approximately unit and phase response exactly zero. We show how unstable polezero cancellations can be avoided. This approximate inverse system can be used in the same manner for both minimum and nonminimum phase systems. Moreover, the degrees of the controller polynomials do not depend on the approximate inverse system. We just need an extra FIR filter in the feedforward path.

We present a new method for the adaptive control of nonminimum phase continuous-time systems based on the pole-zero placement using approximate inverse systems to avoid the unstable pole-zero cancellations. Using this method effect of the unstable zeros cab be compensated approximately. We show how unstable pole-zoro cancellations can be avoided, and that this method has the advantage of being able to determine an approximate inverse system independently of the plant zeros. The proposed scheme uses only the available input and output data and the stability using approximate inverse systems is analyzed. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.

This paper deals with the problem of autoregressive (AR) spectral estimation from a finite set of noisy observations without a priori knowledge of additive noise power. A joint technique is proposed based on the high-order and true-order AR model fitting to the observed noisy process. The first approach utilizes the uncompensated lattice filter algorithm to estimate the parameters of the over-fitted AR model and is one-pass. The latter uses the noise compensated low-order Yule-Walker (LOYW) equations to estimate the true-order AR model parameters and is iterative. The desired AR parameters, equivalently the roots, are extracted from the over-fitted model roots using a root matching technique that utilizes the results obtained from the second approach. This method is highly accurate and is particularly suitable for cases where the system of unknown equations are strongly nonlinear at low SNR and uniqueness of solution from the LOYW equations cannot be guaranteed. In addition, fuzzy logic is adopted for calculating the step size adaptively with the cost function to reduce the computational time of the iterative total search technique. Several numerical examples are presented to evaluate the performance of the proposed scheme in this paper.

The performance of a twisted-pair channel under ADSL environment is assumed to be dominated by far end crosstalk (FEXT) and additive white Gaussian noise (AWGN). In this paper, we study the channel capacity of the copper twisted pair and the optimum input power spectral density distribution at this channel capacity in the presence of ADSL environment. The channel capacity under different loop length and different input power will also be given. The simulation results show that the distribution of the optimum input power spectral density in the presence of AWGN and FEXT is not uniform. This is different from the situation where AWGN is the only interference, where the input power distribution is approximately uniform.

This paper presents a method of simple adaptive control (SAC) using neural networks for a class of nonlinear systems with bounded-input bounded-output (BIBO) and bounded nonlinearity. The control input is given by the sum of the output of the simple adaptive controller and the output of the neural network. The neural network is used to compensate for the nonlinearity of the plant dynamics that is not taken into consideration in the usual SAC. The role of the neural network is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems. Furthermore, convergence and stability analysis of the proposed method is performed. Finally, the effectiveness of the proposed method is confirmed through computer simulation.

This paper investigates the design curves of the class DE amplifier with the nonlinear shunt capacitances for any output Q and any grading coefficient m of the diode junction in the MOSFET. The design curves are derived by the numerical calculation using Spice. The results of this paper have two important meanings. Firstly, it is clarified that the nonlinearities of the shunt capacitances affect the design curves of the class DE amplifier, especially, for low output Q. Moreover, the supply voltage is a quite important parameter to design the class DE amplifier with the nonlinear shunt capacitances. Secondly, it is also clarified that the numerical design tool using Spice, which is proposed by authors, can be applied to the derivation of the design curves. This shows the possibility of the algorithm to be a powerful tool for the analysis of the class E switching circuits. The waveforms from Spice simulations denote the validity of the design curves.

In this paper, we study the problem of perfect tracking control of nonminimum phase systems in magnetic levitation system. Generally, perfect tracking control schemes cannot be applied to nonminimum phase plants because of unstable pole-zero cancellations. Although the method of state matching using multirate feedforward control to realize perfect tracking control have been proposed, the oscillation restraint and the feasibility in nonminimum phase system cannot be satisfied at same time. We propose a method using the difference of state variables to generate a smooth desired state variable trajectory in the discrete-time systems. The techniques we proposed are applicable to nonminimum phase discrete-time systems and the oscillations between the sampling points are well restrained. We will show that the structure of the proposed perfect tracking controller is very simple and clear. Finally, computer simulations and experiment results based on magnetic levitation apparatus are presented.

Chen et al., have proposed a new estimation method for the membership values in fuzzy sets. The proposed scheme takes input from empirical/experimental data, which reflect the expert's knowledge on the relative degree of belonging of the members, and then searches for the best fit membership values of the element. Through the estimation of the practical case (Sect. 3 in [1]) the algorithm suggests to normalize the estimated membership values if there is any among them more than one and change some condition to guarantee its positiveness. In this paper, we show how to use the same imposed condition to guarantee that the estimated membership values will be within the unit interval without normalization.

This paper presents a new method for estimating lattice parameters of a system with additive white noise. A new lattice structure filter is used to reduce the effect of additive white noise, and then, an overfitting lattice filter is proposed to obtain the ARMA parameters by using the estimated lattice parameters with additive white noise.

In two- or more-dimensional systems where the components of the sample data are strongly correlated, it is not proper to divide the input space into several subspaces without considering the correlation. In this paper, we propose the usage of the method of principal component in order to uncorrelate and remove any redundancy from the input space of the adaptive neuro-fuzzy inference system (ANFIS). This leads to an effective partition of the input space to the fuzzy model and significantly reduces the modeling error. A computer simulation for two frequently used benchmark problems shows that ANFIS with the uncorrelation process performs better than the original ANFIS under the same conditions.

Finite-state vector quantization (FSVQ) is a well-known block encoding technique for digital image compression at low bit rate application. In this paper, an improved feature map finite-state vector quantization (IFMFSVQ) algorithm using three-sided side-match prediction is proposed for image coding. The new three-sided side-match improves the prediction quality of input blocks. Precoded blocks are used to alleviate the error propagation of side-match. An edge threshold is used to classify the blocks into nonedge or edge blocks to improve bit rate performance. Furthermore, an adaptive method is also obtained. Experimental results reveal that the new IFMFSVQ reduces bit rate significantly maintaining the same subjective quality, as compared to the basic FMFSVQ method.

In most of the methods proposed so far to design approximately linear phase IIR digital filters (IIR DFs), the design takes place only in the time or in the frequency domain. However, when both magnitude and phase responses are considered, IIR DFs with better frequency responses can be obtained if their characteristics in both domains are taken into account. This paper proposes a design method for approximately linear phase IIR DFs, which is based on parameter estimation techniques in the time domain followed by a nonlinear optimization algorithm in the frequency domain. Several examples are presented, illustrating the proposed method.

This paper presents a novel design procedure for class E oscillator. It is the characteristic of the proposed design procedure that a free-running oscillator is considered as a forced oscillator and the feedback waveform is tuned to the timing of the switching. By using the proposed design procedure, it is possible to design class E oscillator that cannot be designed by the conventional one. By carrying out two circuit experiments, we find that the experimental results agree with the calculated ones quantitatively, and show the validity of the proposed design procedure. One experimental measured power conversion efficiency is 90.7% under 6.8 W output power at an operating frequency 2.02 MHz, the other is 89.7% under 2.8 W output power at an operating frequency 1.97 MHz.

Image restoration using estimated parameters of image model and noise statistics is presented. The image is modeled as the output of a 2-D noncausal autoregressive (NCAR) model. The parameter estimation process is done by using the autocorrelation function and a biased term to a conventional least-squares (LS) method for the noncausal modeling. It is shown that the proposed method gives better results than the other parameter estimation methods which ignore the presence of the noise in the observation data. An appropriate image model selection process is also presented. A genetic algorithm (GA) for solving a multiobjective function with single constraint is discussed.

This letter presents a new method for adaptive control of nonminimum phase discrete-time systems with disturbances based on the technique of pole-zero placement. The long division method is used to decompose apolynomial into a stable polynomial and an unstable one. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.

Iterative decoding is a key feature of turbo code and each decoding results in additional power consumption of the decoder and decoding delay. In this letter, we propose an effective stop criterion based on the Gaussian assumption at the decoder output. Simulation results show that the proposed method can dynamically stop the iterative process with a negligible degradation of the error performance.