A new method for object search is proposed. Conventional template matching schemes tend to fail in presence of irregularities and ill-conditions like background variations, illumination fluctuations resulting from shadowing or highlighting etc. The proposed scheme is robust against such irregularities in the real world scenes since it is based on matching gradient information around each pixel, computed in the form of orientation codes, rather than the gray levels directly. A probabilistic model for robust matching is given and verified by real image data. Experimental results for real world scenes demonstrate the effectiveness of the proposed method for object search in the presence of different potential causes of mismatches.

New equivalent characterizations are derived for Schur stability property of real polynomials. They involve a single scalar parameter, which can be regarded as a freedom incorporated in the given polynomials so long as the stability is concerned. Possible applications of the expressions are suggested to the latest results for stability robustness analysis in parameter space. Further, an extension of the characterizations is made to the matrix case, yielding one-parameter expressions of Schur matrices.

A robust moving object velocity estimation method in the mixed domain (MixeD) is proposed. By obtaining phase, that is, normalizing the 1-D complex sinusoid signals with their magnitudes, the velocity estimations of moving objects with conditions such as object rotation, shape and graylevel variation have been accomplished. Based on the appropriate spatial frequency selection, which choose the points where the signals are less influenced by the background and noise, the spectra of these 1-D temporal complex signals in selected points are estimated by FFT. The simulation results show that velocity vectors have been correctly estimated.

The hardware implementation of a proposed high dimensional discrete torus knot code was successfully realized on an ASIC chip. The code has been worked on for more than a decade since then at Aichi Prefectural University and Nagoya Institutes of Technology, both in Nagoya, Japan. The hardware operation showed the ability to correct the errors about five to ten times the burst length, compared to the conventional codes, as expected from the code configuration and theory. The result in random error correction was also excellent, especially at a severely degraded error rate range of one hundredth to one tenth, and also for high grade characteristic exceeding 10-6. The operation was quite stable at the worst bit error rate and realized a high speed up to 50 Mbps, since the coder-decoder configuration consisted merely of an assemblage of parity check code and hardware circuitry with no critical loop path. The hardware architecture has a unique configuration and is suitable for large scale ASIC design. The developed code can be utilized for wider applications such as mobile computing and qualified digital communications, since the code will be expected to work well in both degraded and high grade channel situations.

In this letter, we propose a robust adaptive filter with a Variable Forgetting Factor (VFF) for impulsive noise suppression. The proposed method can restrict the perturbation of the parameters using M-estimator and adaptively reduce the error propagation caused by the impulsive noise using VFF. Simulations show that the performance of the proposed algorithm is less vulnerable to the impulsive noise than those of the conventional Kalman filter based algorithms.

In this paper we develop a robust control theory to achieve fault-tolerant behaviors of timed discrete event systems (DESs) with model uncertainty represented as a set of some possible models. To demonstrate the effectiveness of the proposed theory, we provide a case study of a resistance spot welding process.

This paper addresses the L-gain filtering problem for continuous-time linear systems with time-varying structured uncertainties and non-zero initial conditions. We propose a full order linear filter that renders the L-gain from disturbance to filtering error within a prescribed level by solving a linear matrix inequality (LMI) feasibility problem. The filter gain is specified by the solution to a set of LMI's. A numerical example is given to illustrate the proposed method.

This paper describes a new method for detection and tracking of moving objects from a moving camera image sequence using robust estimation and active contour models. We assume that the apparent background motion between two consecutive image frames can be approximated by affine transformation. In order to register the static background, we estimate affine transformation parameters using LMedS (Least Median of Squares) method which is a kind of robust estimator. Split-and-merge contour models are employed for tracking multiple moving objects. Image energy of contour models is defined based on the image which is obtained by subtracting the previous frame transformed with estimated affine parameters from the current frame. We have implemented the method on an image processing system which consists of DSP boards for real-time tracking of moving objects from a moving camera image sequence.

This paper surveys two methods for designing numerically robust geometric algorithms. The first method is the exact-arithmetic method, in which numerical computations are done in sufficiently high precision so that all the topological judgements can be done correctly. This method is usually accompanied with lazy evaluation and symbolic perturbation in order to reduce the computational cost and the implementation cost. The second method is the topology-oriented method, in which the consistency of the topological structure is considered as higher-priority information than numerical computation, and thus inconsistency is avoided. Both of the methods are described with the implementation examples.

A convex hull is one of the most fundamental and interesting geometric constructs in computational geometry. Considerable research effort has focused on developing algorithms, both in serial and in parallel, for computing convex hulls. In particular, there are few problems whose parallel algorithms are so thoroughly studied as convex hull problems. In this paper, we review the convex hull parallel algorithms and their paradigm. We provide a summary of results and introduce several interesting topics including typical techniques, output-size sensitive methods, randomized approaches, and robust algorithms for convex hull problems, with which we may see the highlights of the whole research for parallel algorithms. Most of our discussion uses the PRAM (Parallel Random Access Machine) computational model, but still we give a glance at the results of the other parallel computational models such as mesh, mesh-of-trees, hypercube, recofigurable array, and models of coarse grained multicomputers like BSP and LogP.

This paper presents a new fuzzy dynamic output feedback controller design technique for the Takagi Sugeno (T-S) fuzzy model with unknown-but-bounded time-varying modeling error. It is shown that the quadratic stabilization problem of the T-S fuzzy modeled system can be converted into an H control problem of the scaled polytopic Linear Parameter Varying (LPV) system. Then, a controller satisfying a prescribed H performance is designed for the stabilization of the T-S fuzzy modeled system.

In this paper, we present the theoretical development to stabilize a class of uncertain time-delay system. The system under consideration is described in state space model containing distributed delay, uncertain parameters and disturbance. The main idea is to transform the system state into an equivalent one, which is easier to analyze its behavior and stability. Then, a computational method of robust controller design is presented in two parts. The first part is based on solving a Riccati equation arising in the optimal control theory. In the second part, the finite dimensional Lyapunov min-max approach is employed to cope with the uncertainties. Finally, we show how the resulting control law ensures asymptotic stability of the overall system.

The robustness of a template set for cellular neural networks (CNNs) is crucial for applications of VLSI CNN chips. Whereas the problem of designing any, possibly very sensitive, templates for a given task is fairly easy to solve, it is computationally expensive to find optimal solutions. For the class of bipolar CNNs, we propose an analytical approach to derive the optimally robust template set from any correctly operating template. Furthermore, our method yields a theoretical upper bound for the robustness of the CNN task.

A nonlinear time series predictor was proposed, in which a nonlinear sub-predictor (NSP) and a linear sub-predictor (LSP) are combined in a cascade form. This model is called "hybrid predictor" here. The nonlinearity analysis method of the input time series was also proposed to estimate the network size. We have considered the nonlinear prediction problem as a pattern mapping one. A multi-layer neural network, which consists of sigmoidal hidden neurons and a single linear output neuron, has been employed as a nonlinear sub-predictor. Since the NSP includes nonlinear functions, it can predict the nonlinearity of the input time series. However, the prediction is not complete in some cases. Therefore, the NSP prediction error is further compensated for by employing a linear sub-predictor after the NSP. In this paper, the prediction mechanism and a role of the NSP and the LSP are theoretically and experimentally analyzed. The role of the NSP is to predict the nonlinear and some part of the linear property of the time series. The LSP works to predict the NSP prediction error. Furthermore, predictability of the hybrid predictor for noisy time series is investigated. The sigmoidal functions used in the NSP can suppress the noise effects by using their saturation regions. Computer simulations, using several kinds of nonlinear time series and other conventional predictor models, are demonstrated. The theoretical analysis of the predictor mechanism is confirmed through these simulations. Furthermore, predictability is improved by slightly expanding or shifting the input potential of the hidden neurons toward the saturation regions in the learning process.

We propose a real-time low-rate video compression algorithm using fixed-rate multi-stage hierarchical vector quantization. Vector quantization is suitable for mobile computing, since it demands small computation on decoding. The proposed algorithm enables transmission of 10 QCIF frames per second over a low-rate 29.2 kbps mobile channel. A frame is hierarchically divided by sub-blocks. A frame of images is compressed in a fixed rate at any video activity. For active frames, large sub-blocks for low resolution are mainly transmitted. For inactive frames, smaller sub-blocks for high resolution can be transmitted successively after a motion-compensated frame. We develop a compression system which consists of a host computer and a memory-based processor for the nearest neighbor search on VQ. Our algorithm guarantees real-time decoding on a poor CPU.

This paper describes subband-crosscorrelation analysis (SBXCOR) using two input channel signals. SBXCOR is an extended signal processing technique of subband-autocorrelation analysis (SBCOR) that extracts periodicities associated with the inverse of center frequencies present in speech signals. In addition, to extract more periodicity information associated with the inverse of center frequencies, the multi-delay weighting (MDW) processing is applied to SBXCOR. In experiments, the noise robustness of SBXCOR is evaluated using a DTW word recognizer under (1) a simulated acoustic condition with white noise and (2) a real acoustic condition in a sound proof room with human speech-like noise. As the results, under the simulated acoustic condition, it is shown that SBXCOR is more robust than the conventional one-channel SBCOR, but less robust than SBCOR extracted from the two-channel-summed signal. Furthermore, by applying MDW processing, the performance of SBXCOR improved about 2% at SNR 0 dB. The resultant performance of SBXCOR with MDW processing was much better than those of smoothed group delay spectrum (SGDS) and mel-filterbank cepstral coefficient (MFCC) below SNR 10 dB. The results under the real acoustic condition were almost the same as the simulated acoustic condition.

A new traffic engineering and operation of ATM networks is described, which features adaptive virtual path (VP) bandwidth control and VP network reconfiguration capabilities. We call this operation system resilient self-sizing operation. By making full use of self-sizing network (SSN) capabilities, we can operate an ATM network efficiently and keep high robustness against traffic demand fluctuation and network failures, while reducing operating costs. In a multimedia environment, the multimedia services and unpredictability of traffic demand make network traffic management a very challenging problem. SSNs, which are defined as ATM networks with self-sizing traffic engineering and operation capability are expected to overcome these difficulties. This paper proposes VP network operation methods of self-sizing networks for high flexibility and survivability. The VP network operation is composed of adaptive VP bandwidth control to absorb changes in traffic demand, VP rerouting control to recover from failures, and VP network reconfiguration control to optimize the network. The combination of these controls can achieve good performance in flexibility and survivability.

We here consider an extension of the validity of classical criteria ensuring the robustness of the statistical features of discrete time dynamical systems with respect to implementation inaccuracies and noise. The result is achieved by proving that, whenever a discrete time dynamical system is robust, all the discrete time dynamical systems topologically conjugate with it are also robust. In particular, this result offer an explanation for the stochastic robustness of the logistic map, which is confirmed by the reported experimental measurements.

We propose a robust detection scheme by employing an order statistic filter as a preprocessor of the input signal. For ease of design, the variance of the order statistic filtered output is modeled by proposing an approximate upper bound. The detector is analytically designed using a fixed sample size (FSS) test scheme. The performance of the proposed detector is compared to that of other robust detectors in terms of the sample size required for given false alarm and miss detection probabilities. Finally, analytical results are verified by computer simulation.

The TLS method is an unbiased estimator for solving the overdetermined set of linear equations when errors occur in all data. However it doesn't show robustness while the errors have a heavy tailed pdf. In this letter we derive a robust method of TLS (ROTLS) based on the characteristics of TLS solution, where the performance of ROTLS is verified by applying it to the system identification problem.