This paper proposes a receding horizon control scheme for a set of uncertain discrete-time linear systems with randomly jumping parameters described by a finite-state Markov process whose jumping transition probabilities are assumed to belong to some convex sets. The control scheme for the underlying systems is based on the minimization of an upper bound on the worst-case infinite horizon cost function at each time instant. It is shown that the mean square stability of the proposed control system is guaranteed under some matrix inequality conditions on the terminal weighting matrices. The proposed controller is obtained using semidefinite programming.

Single crystal MnZn Ferrites are used as core materials for the reader of inductive magnetic heads. Magnetic phase homogeneity of the material is one of the parameters, which affects the quality of the devise. We used static magnetic measurements above the apparent Curie temperature of the Ferrite materials to determine the presence of such phases. High performance samples are non-magnetic at high temperature. In low performance materials, a small but non-zero spontaneous magnetization at high temperature indicates the presence of the second phase.

In this paper, a novel adaptive digital watermarking approach based upon human visual system model and fuzzy clustering technique is proposed. The human visual system model is utilized to guarantee that the watermarked image is imperceptible. The fuzzy clustering approach has been employed to obtain the different strength of watermark by the local characters of image. In our experiments, this scheme allows us to provide a more robust and transparent watermark.

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.

The available bit rate (ABR) is an ATM service category that provides an economical support of connections having vague requirements. An ABR session may specify its peak cell rate (PCR) and minimum cell rate (MCR), and available bandwidth is allocated to competing sessions based on the max-min policy. In this paper, we investigate the ABR traffic control from a different point of view: Based on the decentralized bandwidth allocation model studied in [9], we prove that the max-min rate vector is the equilibrium of a certain system of noncooperative optimizations. This interpretation suggests a new framework for ABR traffic control that allows the max-min optimality to be achieved and maintained by end-systems, and not by network switches. Moreover, in the discussion, we consider the constrained version of max-min fairness and develop an efficient algorithm with theoretical justification to determine the optimal rate vector.

For low-complexity linear-phase FIR digital filters which have coefficients expressed as canonic signed digit (CSD) code, a design method to impose power-of-two DC gain is proposed. Output signal level can easily be compensated to that of input so that cascading many stages do not cause any gain errors, which are harmful in, for example, high precision measurement systems. The design is formulated as an optimization problem with magnitude response constraints. The integer linear programming modified for CSD codes is solved by the branch and bound method. The design example shows the effectiveness of the obtained filter in comparison with existing CSD filters. Also, an evaluation method for the area to implement the filter into field programmable gate array (FPGA) is proposed. The implementation example shows that the minimum critical path is obtained with only a little increase in the die area.

Technology scaling will become difficult due to power wall. On the other hand, future computer and communications technology will require further reduction in power dissipation. Since no new energy efficient device technology is on the horizon, low power CMOS design should be challenged. This paper discusses what and how much designers can do for CMOS power reduction.

A halftoning technique that uses a simple GA has proven to be very effective to generate high quality halftone images. Recently, the two major drawbacks of this conventional halftoning technique with GAs, i.e. it uses a substantial amount of computer memory and processing time, have been overcome by using an improved GA (GA-SRM) that applies genetic operators in parallel putting them in a cooperative-competitive stand with each other. The halftoning problem is a true multiobjective optimization problem. However, so far, the GA based halftoning techniques have treated the problem as a single objective optimization problem. In this work, the improved GA-SRM is extended to a multiobjective optimization GA to simultaneously generate halftone images with various combinations of gray level precision and spatial resolution. Simulation results verify that the proposed scheme can effectively generate several high quality images simultaneously in a single run reducing even further the overall processing time.

Digital signal processors (DSPs) usually employ indirect addressing using address registers (ARs) to indicate their memory addresses, which often introduces overhead codes in AR updates for next memory accesses. Reduction of such overhead code is one of the important issues in automatic generation of highly-efficient DSP codes. In this paper, a new automatic address allocation method incorpolated with computational order rearrangement at local commutative parts is proposed. The method formulates a given memory access sequence by a graph representation, where several strategies to handle freedom in memory access orders at the computational commutative parts are introduced and examined. A compiler scheme is also extended such that computational order at the commutative parts is rearranged according to the derived memory allocation. The proposed methods are applied to an existing DSP compiler for µPD77230(NEC), and codes generated for several examples are compared with memory allocations by the conventional methods.

Detection of nonlinearly distorted signals is an essential problem in telecommunications. Recently, neural network combined conventional equalizer has been used to improve the performance especially in compensating for nonlinear distortions. In this paper, the self-organizing map (SOM) combined with the conventional symbol-by-symbol detector is used as an adaptive detector after the output of the decision feedback equalizer (DFE), which updates the decision levels to follow up the nonlinear distortions. In the proposed scheme, we use the box distance to define the neighborhood of the winning neuron of the SOM algorithm. The error performance has been investigated in both 16 QAM and 64 QAM systems with nonlinear distortions. Simulation results have shown that the system performance is remarkably improved by using SOM detector compared with the conventional DFE scheme.

Although previous studies using artificial neural networks have been actively applied to object shape recognition, little attention has been paid to the recognition of spatial elements (e.g. position, rotation and size). In the present study, a rotation and size spreading associative neural network (RS-SAN net) is proposed and the efficacy of the RS-SAN net in object orientation (rotation), size and shape recognition is shown. The RS-SAN net pays attention to the fact that the spatial recognition system in the brain (parietal cortex) is involved in both the spatial (e.g. position, rotation and size) and shape recognition of an object. The RS-SAN net uses spatial spreading by spreading layers, generalized inverse learning and population vector methods for the recognition of the object. The information of the object orientation and size is spread by double spreading layers which have similar tuning characteristics to spatial discrimination neurons (e.g. axis orientation neurons and size discrimination neurons) in the parietal cortex. The RS-SAN net simultaneously recognizes the size of the object irrespective of its orientation and shape, the orientation irrespective of its size and shape, and the shape irrespective of its size and orientation.

In the more general framework "shift invariant subspace," the paper obtains a different estimate of sampling in function subspace to our former work, by using the Frame Theory. The derived formula is easy to be calculated, and the estimate is relaxed in some shift invariant subspaces. The former work is now, however, a special case of the present.

The adaptive system design by 16-bit fixed point processing enables to employ an inexpensive digital signal processor (DSP). The narrow dynamic range of such 16 bits, however, does not guarantee the same performance that is confirmed beforehand by computer simulations. A cause of degrading the performance originates in the operation halving the word length doubled by multiplication. This operation rounds off small signals staying in the lower half of the doubled word length to zero. This problem can be solved by limiting the multiplier to only its sign () like the signed regressor algorithm, named 'bi-quantized-x' algorithm in this paper, for the convenience mentioned below. This paper first derives the equation describing the convergence property provided by a type of signed regressor algorithms, the bi-quantized-x normalized least mean square (NLMS) algorithm, and then formulates its convergence condition and the step size maximizing the convergence rate. This paper second presents a technique to improve the convergence property. The bi-qiantized-x NLMS algorithm quantizes the reference signal to 1 according to the sign of the reference signal, whereas the technique moreover assigns zero to the reference signal whose amplitude is less than a predetermined level. This paper explains the principle that the 'tri-qunatized-x' NLMS algorithm employing the technique can improve the convergence property, and confirms the improvement effect by computer simulations.

This paper proposes a design method of variable IIR digital filters based on balanced realizations and minimum round-off noise realizations of digital filters. Highly accurate variable digital filters are easily derived by the proposed method. The coefficient matrices of both realizations of second-order digital filters are obtained directly from prototype realizations. The filter coefficients of variable digital filters can be obtained by frequency transformations to the realizations. The filter coefficients are presented as truncated Taylor series for the purpose of reducing a number of calculations to tune the coefficients. However the proposed filters have highly accurate variable characteristics against the coefficient truncation since balanced realizations and minimum round-off noise realizations have very low coefficient sensitivities, which are invariant under the frequency transformations. Moreover, the dynamic ranges of the proposed filters are almost constant against the frequency transformations. Numerical examples show the effectiveness of the variable digital filters designed by the proposed method.

We propose a method for compensating distortion of image by calibrating intrinsic camera parameters by image registration which does not need point-to-point correspondence. The proposed method divides the registration between a calibration pattern and a distorted image observed by a camera into two steps. The first step is the straightforward registration from the pattern in order to correct the displacement due to projection. The second step is the backward registration from the observed image for compensating the distortion of the image. Both of the steps use Gauss-Newton method, a nonlinear optimization technique, to minimize residuals of intensities so that the pattern and the observed image become the same. Experimental results show the usefulness of the proposed method. Finally we discuss the convergence of the proposed method which consists of the two registration steps.

Recognition of specified wave patterns in one-dimensional signals is an important task in many application areas such as computer science, medical science, and geophysics. Many researchers have tried to automate this task with various techniques, recently the soft computing algorithms. This paper proposes a new neuro-fuzzy recognition system for detecting one-dimensional wave patterns using wavelet coefficients as features of the signals and evolution strategy as the training algorithm of the system. The neuro-fuzzy recognition system first trains the wavelet coefficients of the training wave patterns and then evaluates the degree of matching between test wave patterns and the training wave patterns. This system was applied to picking first arrival events in seismic data. Experimental results with three seismic data showed that the system was very successful in terms of learning speed and performances.

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.

This paper presents the characterization and validation of a time-domain physical model for illuminated high-frequency active devices and shows the possibility of use of the electromagnetic analysis of FDTD not only for electromagnetic interaction and scattering but also for the device simulation as a good candidate for a microwave simulator. The model is based on Boltzmann's Transport Equation, which accurately accounts for carrier transport in microwave and millimeter wave devices with sub-micrometer gate lengths. Illumination effects are accommodated in the model to represent carrier density changes inside the illuminated device. The simulation results are compared to available experimental records for a typical MESFET for validation purposes. Simulation results show that the microscopic as well as the macroscopic characteristics of the active device are altered by the light energy. This fact makes the model an important tool for the active device design method under illumination control.

Personal authentication technologies will be necessary to ensure security of electronic transactions over open networks. Although biometric authentication is one of the most efficient approaches, accuracy of the biometric authentication is affected by the environment of the data acquisition, procedural parameters, and so on. There is as yet no means of giving a fair comparison of the accuracy of products of biometric vendors. Therefore, a standardization of the accuracy evaluation is necessary. This paper gives a standardization of the accuracy evaluation for the fingerprint verification system.

A gradient ascent learning algorithm of the Hopfield neural networks for graph planarization is presented. This learning algorithm uses the Hopfield neural network to get a near-maximal planar subgraph, and increases the energy by modifying parameters in a gradient ascent direction to help the network escape from the state of the near-maximal planar subgraph to the state of the maximal planar subgraph or better one. The proposed algorithm is applied to several graphs up to 150 vertices and 1064 edges. The performance of our algorithm is compared with that of Takefuji/Lee's method. Simulation results show that the proposed algorithm is much better than Takefuji/Lee's method in terms of the solution quality for every tested graph.