In this paper, we propose a new design algorithm for nearly linear phase IIR digital filters with prescribed log magnitude response. The error function used here is the sum of the weighted log magnitude-squared error and phase -squared error, and so it is possible to control log magnitude and phase response directly. The gradient vector of the proposed error function is easily calculated as the closed form solution because of its nature, in which the real and imaginary part of the logarithm of a complex transfer transfer function corresponds to the log magnitude and phase response, respectively. This algorithm is simple and converges quickly. Finally, we show the validity of the proposed algorithm with some examples.

The robust statistics has recently been adopted by the computer vision community. Various robust approaches in the computer vision research have been proposed in the last decade for analyzing the image motion from the image sequence. Because of the frequent violation of the Gaussian assumption of the noise and the motion discontinuities due to multiple motions, the motion estimates based on the straightforward approaches such as the least squares estimator and the regularization often produces unsatisfactory result. Robust estimation is a promising approach to deal with these problems because it recovers the intrinsic characteristics of the original data with the reduced sensitivity to the contamination. Several previous works exist and report some isolated results, but there has been no comprehensive analysis. In this paper robust approaches to the optical flow estimation based on the maximum likelihood estimators are proposed. To evaluate the performance of the M-estimators for estimating the optical flow, comparative studies are conducted for every possible combinations of the parameters of three types of M-estimators, two types of residuals, two methods of scale estimate, and two types of starting values. Comparative studies on synthetic data show the superiority of the M-estimator of redescending ψ-function using the starting value of least absolute residuals estimator using Huber scale iteration, in comparison with the other M-estimators and least squares estimator. Experimental results from the real image experiments also confirm that the proposed combinations of the M-estimators handle the contaminated data effectively and produce the better estimates than the least squares estimator or the least absolute residuals estimator.

An active contour model which is called Snakes was proposed to extract the border line of an object from an image. This method presents the minimization problem of the energy function defined on the contour curve. The authors obtained an excellent result by applying genetic algorithm to the contour extraction. In this paper, the biphased genetic algorithm, which is a new type of genetic algorithm, is proposed to minimize the energy function of Snakes. The parameters of the genetic algorithm are examined to tune up its local and global search abilities. The biphased genetic algorithm composed of two phases of genetic search is constructed to use both abilities of the exploration and the exploitation properties of the genetic algorithm. The processing results of the biphased genetic algorithm are compared with those of the previous methods, and the advantages of the proposed algorithm are shown by several experiments.

A new steepest descent linear adaptive algorithm, called the proportion-sign algorithm (PSA), is introduced and its performance analysis is presented when the signals are from zero-mean jointly stationary Gaussian processes. The PSA improves the convergence speed over the least mean square (LMS) algorithm without overly degrading the steady-state error performance and has the robustness to impulsive interference occurring in the desired response by adding a minimal amount of computational complexity. Computer simulations are presented that show these advantages of the PSA over the LMS algorithm and demonstrate a close match between theoretical and empirical results to verify our analysis.

This paper uses both network analysis and experiments to confirm that the neural network learning algorithm that minimizes output variation (BPV) provides much more robustness than back-propagation (BP) or BP with noise-modified training samples (BPN). Network analysis clarifies the relationship between sample displacement and what and how the network learns. Sample displacement generates variation in the output of the output units in the output layer. The output variation model introduces two types of deformation error, both of which modify the mean square error. We propose a new error which combines the two types of deformation error. The network analysis using this new error considers that BPV learns two types of training samples where the modification is either towards or away from the category mean, which is defined as the center of sample distribution. The magnitude of modification depends on the position of the training sample in the sample distribution and the degree of leaning completion. The conclusions is that BPV learns samples modified towards to the category mean more stronger than those modified away from the category mean, namely it achieves nonuniform learning. Another conclusion is that BPN learns from uniformly modified samples. The conjecture that BPV is much more robust than the other two algorithms is made. Experiments that evaluate robustness are performed from two kinds of viewpoints: overall robustness and specific robustness. Benchmark studies using distorted handprinted Kanji character patterns examine overall robustness and two specifically modified samples (noise-modified samples and directionally-modified samples) examine specific robustness. Both sets of studies confirm the superiority of BPV and the accuracy of the conjecture.

This paper proposes new algorithms for adaptive FIR filters. The proposed algorithms provide both fast convergence and small final misadjustment with an adaptive step size even under an interference to the error. The basic algorithm pays special attention to the interference which contaminates the error. To enhance robustness to the interference, it imposes a special limit on the increment/decrement of the step-size. The limit itself is also varied according to the step-size. The basic algorithm is extended for application to nonstationary signals. Simulation results with white signals show that the final misadjustment is reduced by up to 22 dB under severe observation noise at a negligible expense of the convergence speed. An echo canceler simulation with a real speech signal exhibits its potential for a nonstationary signal.

A quick evaluation method is proposed to obtain stability robustness measures in polynomial coefficient space based on knowledge of coefficients of a Hurwitz stable nominal polynomial. Two norms are employed: l- and l2-norm, which correspond to the stability hypercube and hyperball in the space, respectively. Just inverting Hurwitz matrix for the nominal polynomial immediately yields closed-form estimates for the size of the hypercube and hyperball.

Robust-fault tolerance is a property that a computational result becomes nearly equal to the correct one at the occurrence of faults in digital system. There are many cases where the safety of digital control systems can be maintained if the property is satisfied. In this paper, robust-fault-tolerant three-valued arithmetic modules such as an adder and a multiplier are proposed. The positive and negative integers are represented by the number of 1's and 1's, respectively. The design concept of the arithmetic modules is that a fault makes linearly additive effect with a small value to the final result. Each arithmetic module consists of identical submodules linearly connected, so that multi-stage structure is formed to generate the final output from the last submodule. Between the input and output digits in the submodule some simple functional relation is satisfied with respect to the number of 1's and 1's. Moreover, the output digit value depends on very small portion of the submodules including the input digits. These properties make the linearly additive effect with a small value to the final result in the arithmetic modules even if multiple faults are occurred at the input and output of any gates in the submodules. Not only direct three-valued representation but also the use of three-valued logic circuits is inherently suitable for efficient implementation of the arithmetic VLSI system. The evaluation of the robust-fault-tolerant three-valued arithmetic modules is done with regard to the chip size and the speed using the standard CMOS design rule. As a result, it is made clear that the chip size can be greatly reduced.

The robust finite settling time stabilization problem is considered for a multivariable discrete time plant with structured uncertainties. Finite settling time (FST) stability of a feedback system is a notion introduced recently for discrete time systems as a generalization of the dead-beat response. The uncertain plant treated in this paper is described by (E0+ΣKi=1qiEi)x(t+1)(A0+ΣKi=1qiAi)x(t)+(B0+ΣKi=1qiBi)u(t), and y(t)=(C0+ΣKi=1qiCi)x(t) where Ei, Ai, Bi and Ci (0iK) are prescribed real matrices and qi (1iK) are uncertain parameters restricted to prescribed intervals [qi, i]. It is shown in this paper that a controller robustly FST stabilizes such an uncertain plant, if, the uncertain plant satisfies some conditions, and the controller simultaneously FST stabilizes a finite set of plants. The result leads to a testable necessary and sufficient condition of the existence of a solution to the robust FST stabilization problem, and a systematic method of designing a robust FST stable feedback system, in the case where the plant contains only one uncertain parameter.

A simple inequality that guarantees stability of perturbed linear state space models is proposed. It is shown that the result is superior to some existing result in sharpness and possesses some advantageous aspects.

The single fault model is invalid in many cases. However, it is very difficult to generate tests for all multiple faults since an m-line circuit may have 3m --1 multiple faults. In this paper, we describe a method for generating tests for combinational circuits with multiple stuck-at faults. An input vector is a test for a fault on a target line, if it find the target line to be fault-free in the presence of undetected or undetectable lines. The test is called a robust test for fault on a target line. It is shown that the sensitizing input-pair for a completely single sensitized path can be a robust test-pair. The method described here consists of two procedures. We label these as SINGLE_SEN" procedure and DECISION" procedure. SINGLE_SEN generates a single sensitized path including a target line on it by using a PODEM-like method which uses a new seven-valued calculus. DECISION determines by utilizing the method proposed by H. Cox and J. Rajski whether the single sensitizing input-pair generated by the SINGLE_SEN is a robust test-pair. By using these two procedures the described method generates robust test-pairs for the combinational circuit with multiple stuck-at faults. Finally, we demonstrate by experimental results on the ISCAS85 benchmark circuits that SINGLE_SEN is effective for an algorithmic multiple fault test generation for circuits not including many XOR gates.

This paper presents a simple method for avoiding both numerical errors and degeneracy in an incremental-type algorithm for constructing the Voronoi diagram with respect to points on a plane. It is assumed that the coordinates of the given points are represented with a certain fixed number of bits. All the computations in the algorithm are carried out in four times higher precision, so that degeneracy can be discerned precisely. Every time degeneracy is found, the points are perturbed symbolically according to a very simple rule and thus are reduced to a nondegenerate case. The present technique makes a computer program simple in the sense that it avoids all numerical errors and requires no exceptional branches of processing for degenerate cases.