Let us introduce n ( 2) nonlinear mappings fi (i = 1,2,,n) defined on complete linear metric spaces (Xi-1,ρ) (i = 1,2,,n), respectively, and let fi:Xi-1 Xi be completely continuous on bounded convex closed subsets Xi-1, (i = 1,2,,n 0), such that fi() . Moreover, let us introduce n fuzzy-set-valued nonlinear mappings Fi:Xi-1Xi {a family of all non-empty closed compact fuzzy subsets of Xi}. Here, by introducing arbitrary constant βi (0,1], for every integer i (i = 1,2,,n 0), separately, we have a fixed point theorem on the recurrent system of βi -level fuzzy-set-valued mapping equations: xi Fiβi(xi-1, fi(xi-1)), (i = 1,2,,n 0), where the fuzzy set Fi is characterized by a membership function µFi(xi):Xi [0,1], and the βi -level set Fiβi of the fuzzy set Fi is defined as Fiβi {ξi Xi |µFi (ξi) βi}, for any constant βi (0,1]. This theorem can be applied immediately to discussion for characteristics of ring nonlinear network systems disturbed by undesirable uncertain fluctuations and to extremely fine estimation of available behaviors of those disturbed systems. In this paper, its mathematical situation and proof are discussed, in detail.

Moment matrix analysis (MMA) that can derive statistical properties of non-linear equations is presented in this paper. First, non-linear stochastic differential, or difference, equations are approximately expressed by simultaneous linear equations of moments defined at discrete events. Next, by eliminating higher order moments, the simultaneous linear equations are reduced to a linear vector equation of their coefficient matrix and a moment vector comprised of the moments of the system state. By computing the eigenvectors and eigenvalues of the coefficient matrix, we can analyze the moments, transient response, and spectrum of the system state. The behavior of Internet traffic was evaluated by using MMA and computer simulation, and it is shown that MMA is effective for evaluating simultaneous non-linear stochastic differential equations.

Learning algorithms for Vector Quantization (VQ) are categorized into two types: batch learning and incremental learning. Incremental learning is more useful than batch learning, because, unlike batch learning, incremental learning can be performed either on-line or off-line. In this paper, we develop effective incremental learning methods by using Stochastic Relaxation (SR) techniques, which have been developed for batch learning. It has been shown that, for batch learning, the SR techniques can provide good global optimization without greatly increasing the computational cost. We empirically investigates the effective implementation of SR for incremental learning. Specifically, we consider five types of SR methods: ISR1, ISR2, ISR3, WSR1 and WSR2. ISRs and WSRs add noise input and weight vectors, respectively. The difference among them is when the perturbed input or weight vectors are used in learning. These SR methods are applied to three types of incremental learning: K-means, Neural-Gas (NG) and Kohonen's Self-Organizing Mapping (SOM). We evaluate comprehensively these combinations in terms of accuracy and computation time. Our simulation results show that K-means with ISR3 is the most comprehensively effective among these combinations and is superior to the conventional NG method known as an excellent method.

We propose a method to determine accurate motion fields and illumination conditions such as non-uniform or non-stationary illuminations. The method extends a stabilization method using reliability indices of optical flow to combine with a gradient-based approach that determines a motion field and illumination conditions simultaneously. We applied the proposed method to two synthetic image sequences and a standard image sequence. The method is effective for image sequences including poorly textured areas, edges of brightness variation, and almost dark objects.

Malignant melanoma is a skin cancer that can be mistaken as a harmless mole in the early stages and is curable only in these early stages. Therefore, dermatologists use a microscope that shows the pigment structures of the skin to classify suspicious skin lesions as malignant or benign. This microscope is called "dermoscope." However, even when using a dermoscope a malignant skin lesion can be mistaken as benign or vice versa. Therefore, it seems desirable to analyze dermoscopic images by computer to classify the skin lesion. Before a dermoscopic image can be classified, it should be segmented into regions of the same color. For this purpose, we propose a segmentation method that automatically determines the number of colors by optimizing a cluster validity index. Cluster validity indices can be used to determine how accurately a partition represents the "natural" clusters of a data set. Therefore, cluster validity indices can also be applied to evaluate how accurately a color image is segmented. First the RGB image is transformed into the L*u*v* color space, in which Euclidean vector distances correspond to differences of visible colors. The pixels of the L*u*v* image are used to train a self-organizing map. After completion of the training a genetic algorithm groups the neurons of the self-organizing map into clusters using fuzzy c-means. The genetic algorithm searches for a partition that optimizes a fuzzy cluster validity index. The image is segmented by assigning each pixel of the L*u*v* image to the nearest neighbor among the cluster centers found by the genetic algorithm. A set of dermoscopic images is segmented using the method proposed in this research and the images are classified based on color statistics and textural features. The results indicate that the method proposed in this research is effective for the segmentation of dermoscopic images.

High frequency (HF) diffraction is known as local phenomena, and only parts of the scatterer contribute to the field such as the edge, corner and specular reflection point etc. Many HF diffraction techniques such as Geometrical Theory of Diffraction (GTD), Uniform Theory of Diffraction (UTD) and Physical Theory of Diffraction (PTD) utilize these assumptions explicitly. Physical Optics (PO), on the other hand, expresses the diffraction in terms of radiation integral or the sum total of contributions from all the illuminated parts of scatterers, while the PO currents are locally defined at the point of integration. This paper presents PO-based visualization of the scattering and diffraction phenomena and tries to provide the intuitive understanding of local property of HF diffraction as well as the relations between PO and the ray techniques such as GTD, UTD etc. A weighting named "eye function" is introduced in PO radiation integrals to take into account of local cancellation between rapidly oscillating contributions from adjacent currents; this extracts important areas of current distribution, whose location moves not only with the source but also with the observation point. PO visualization illustrates both local property of HF scattering and defects associated with ray techniques. Furthermore, careful examination of visualized image reminds us of the error factor in PO as applied for curved surfaces, named fictitious penetrating rays. They have been scarcely recognized if not for visualization, though they disturb the geometrical shadow behind the opaque scatterer and can be the leading error factors of PO in shadow regions. Finally, visualization is extended to slot antennas with finite ground planes by hybrid use of modified edge representation (MER) to assess the significance of edge diffraction.

This paper presents an algorithm to analyze the stability and detect an upper-bound of every possible overload of a ΣΔ modulator for a set of input signals that are characterized by specified peak amplitudes and auto-correlations. The approach is to introduce a hyper cube in which all possible state vectors are recursively mapped into a subset of the hyper cube itself for the specified inputs and detect such a hyper cube by iteratively solving linear programming problems. Then the proposed algorithm may not identify every stable ΣΔ modulator but cannot evaluate any unstable ΣΔ modulator as a stable one. In numerical examples, two 1-bit ΣΔ modulators are analyzed, and it is revealed that a band-limitation of inputs to OSR 256 guarantees the rigorous stability even with an extension of input range to at least 240% of conventional range.

The Camera sensor network is a new advent of technology in which each sensor node can capture video signal, process and communicate with other nodes. We have investigated a dense node configuration. The requested processing task in this network is arbitrary view generation among nodes view. To avoid unnecessary communication between nodes in this network and to speed up the processing time, we propose a distributed processing architecture where the number of nodes sharing image data are optimized. Therefore, each sensor node processes part of the interpolation algorithm with local communication between sensor nodes. Two processing methods are used based on the image size shared. These two methods are F-DP (Fully image shared Distributed Processing) and P-DP (Partially image shared Distributed Processing). In this research, the network processing time has been theoretically analyzed for one user. The theoretical results are compatible with the experimental results. In addition, the performance of proposed DP methods were compared with Centralized Processing (CP). As a result, the best processing method for optimum number of nodes can be chosen based on (i) communication delay of the network, (ii) whether the network has one or more channels for communication among nodes and (iii) the processing ability of nodes.

In this paper, we propose a design method of filters by successive projection (SP) method using multiple extreme frequency points based on Fritz John's theorem. In conventional SP method, only one extreme frequency point at which the deviation from the given specification is maximized is used in the update of the filter coefficients. Therefore, enormous amount of iteration numbers are necessary for research the solution which satisfies the given specification. In the proposed method, the updating coefficient using multiple extreme frequency points is possible by Fritz John's theorem. As a result, the solution converges less iteration number than the conventional SP method.

This paper proposes a learning classifier based on Support Vector Domain Description (SVDD) for two-class problem. First, by the description of the training samples from one class, a sphere boundary containing these samples is obtained; then, this boundary is used to classify the test samples. In addition, instead of the traditional quadratic programming, multiplicative updates is used to solve the Lagrange multiplier in optimizing the solution of the sphere boundary. The experiment on CBCL face database illustrates the effectiveness of this learning algorithm in comparison with Support Vector Machine (SVM) and Sequential Minimal Optimization (SMO).

In terms of the formulation of the optimality, image restoration filters can be divided into two streams. One is formulated as an optimization problem in which the fidelity of a restored image is indirectly evaluated, and the other is formulated as an optimization problem based on a direct evaluation. Originally, the formulation of the optimality and the solutions derived from the formulation are identical each other. However in many studies adopting the former stream, an arbitrary choice of a solution without a mathematical ground passes unremarked. In this paper, we discuss the relation between the formulation of the optimality and the solution derived from the formulation from a mathematical point of view, and investigate the relation between a direct style formulation and an indirect one. Through these analyses, we show that the both formulations yield the identical filter in practical situations.

Software rejuvenation is a preventive and proactive solution that is particularly useful for counteracting the phenomenon of software aging. In this paper, we consider both the periodic and non-periodic software rejuvenation policies under different dependability measures. As is well known, the steady-state system availability is the probability that the software system is operating in the steady state and, at the same time, is often regarded as the mean up rate in the system operation period. We show that the mean up rate should be defined as the mean value of up rate, but not as the mean up time per mean operation time. We derive numerically the optimal software rejuvenation policies which maximize the steady-state system availability and the mean up rate, respectively, for each periodic or non-periodic model. Numerical examples show that the real mean up rate is always smaller than the system availability in the steady state and that the availability overestimates the ratio of operative time of the software system.

This paper discusses the orbital motion and elevation properties of a quasi-zenith satellite system using circular orbits. The satellites are deployed on inclined geosynchronous orbits with identical sub-satellite loci on earth. The satellites trace the locus at even intervals. This satellite system can provide mobile satellite communications and navigation services at very high elevations to middle-latitude regions. In general, the orbital parameters of the satellite system are determined by numerical simulation to maximize the minimum elevation angle in areas where satellite services are to be provided. However, an understanding of the properties of the orbit and consequent elevation properties are important for efficient constellation design. This paper formulates the orbital motion of inclined geosynchronous circular orbits, including the relative motion to the rotating earth. Although elliptical orbit constellations are also possible and can gain higher elevation, only circular orbits, which can be accurately formulated without using an analytically unsolvable Kepler's equation, are discussed in this paper. Elevation properties are evaluated using the geocentric angle between the sub-satellite point and an arbitrary point in the intended service area. This angle is a typical parameter that can be derived as a single-valued function of the elevation at a specific point. Optimum orbital parameters for an intended service area can be easily estimated without numerical simulation using the results of the evaluation described in this paper. These results can also be used to infer whether a circular-orbit constellation is applicable to an intended service area.

We propose a new output arbitration method for an input buffered switch with a buffered crossbar. In the proposed method, each output selects the first nonempty buffer from the starting point. The starting points of output are determined to minimize the synchronization phenomenon that more than one input module sends cells destined for a same output. Using an approximate analysis of the synchronization phenomenon, we show the uniqueness of the starting points improves the switch performance. Finally, using computer simulations, we verify the proposed method outperforms the previous methods under the uniform and burst traffic.

By modelling the quantization error as additive white noise in the transform domain, Wiener filter is used to reduce quantization noise for DCT coded images in DCT domain. Instead of deriving the spectrum of the transform coefficient, a DPCM loop is used to whiten the quantized DCT coefficients. The DPCM loop predicts the mean for each coefficient. By subtracting the mean, the quantized DCT coefficient is converted into the sum of prediction error and quantization noise. After the DPCM loop, the prediction error can be assumed uncorrelated to make the design of the subsequent Wiener filter easy. The Wiener filter is applied to remove the quantization noise to restore the prediction error. The original coefficient is reconstructed by adding the DPCM predicted mean with the restored prediction error. To increase the prediction accuracy, the decimated DCT coefficients in each subband are interpolated from the overlapped blocks.

This paper examines the effect of segmentation mismatch on audio-video transmission by Bluetooth. We focus on the segmentation mismatch caused by the difference between the RFCOMM Maximum Frame Size and the baseband packet payload size. By experiment, we assessed the maximum throughput and media synchronization quality for various types of ACL packets. In the experiment, a media server transferred stored video and audio streams to a single terminal with point-to-point communication; we supposed no fading environment and added white noise by which interference from DSSS systems is modeled. The experiment showed that the effect of segmentation mismatch is large especially when the total bit rate of the two streams is near the channel transmission rate. We also observed that the media synchronization control is effective in compensating for the disturbance by the segmentation mismatch in noisy environments.

This paper proposes the tile size conversion method for the wavelet image transcoding gateway and a set of methods to reduce the tile boundary artifacts caused by the conversion. In the wavelet image coding system represented by JPEG 2000, pictures are usually divided into one or more tiles and each tile is then transformed separately. On low memory terminals such as mobile terminals, some decoders are likely to have limits on what tile sizes they can decode. Assuming a system using these limited decoders, methods were investigated for converting the tile size quickly and automatically at the gateway when image data with a non-decodable tile size is received at the gateway from another system. Furthermore, tile boundary artifacts reduction methods are investigated. This paper verifies the validity of the proposed scheme by implementing it with a (5, 3) reversible filter and a (9, 7) irreversible filter. In addition, we implemented the tile size conversion gateway and evaluated the performance of the processing time. The results show the validity of the conversion gateway.

This paper addresses the utility-based radio resource allocation problem in DS-CDMA systems carrying multimedia traffic. The proposed scheme, aiming at achieving optimal resource allocation, considers the joint power and data rate allocation. To avoid high computational complexity of nonlinear optimization, we reformulate the radio resource allocation problem as a market model, where resource is regarded as a commodity. Since the market model satisfies the incentive-compatible constraint, the optimal resource allocation can be obtained at the market equilibrium in a distributed manner. According to whether to allocate a minimal transmission data rate to each user, two algorithms, UCA and FCA, are proposed. UCA emphasize on maximizing system overall utilities, while FCA guarantees fairness to users. Simulation results show that the proposed radio resource allocation scheme and algorithms are flexible and efficient for multimedia DS-CDMA systems.

In a high-rate indoor wireless personal communication system, the delay spread due to multi-path propagation results in intersymbol interference which can significantly increase the transmission bit error rate (BER). The technique most commonly used for combating the intersymbol interference and frequency-selective fading found in communications channels is the adaptive equalization. In this paper, we propose a novel neural detector based on self-organizing map (SOM) to improve the system performance of the receiver. In the proposed scheme, the SOM is used as an adaptive detector of equalizer, which updates the decision levels to follow the received faded signal. To adapt the proposed scheme to the time-varying channel, we use the Euclidean distance, which will be updated automatically according to the received faded signal, as an adaptive radius to define the neighborhood of the winning neuron of the SOM algorithm. Simulations on a 16 QAM system show that the receiver using the proposed neural detector has a significantly better BER performance than the traditional receiver.

Multi-Input Multi-Output (MIMO) systems, which utilize multiple antennas at both the receiver and transmitter, promise very high data rates in a rich scattering environment. It was proven in literature that with optimal power allocation, MIMO eigenmode transmission system (EMTS) is optimal because MIMO capacity is maximized. However, the performance of MIMO EMTS is very sensitive to the accuracy of channel state information and thus it is of practical importance to analyze its performance when channel state information is corrupted under realistic system and propagation conditions. In this paper, we lower bound the mutual information of MIMO EMTS with imperfect channel estimation and delayed quantized feedback in a spatially correlated continuous fading channel. Our results showed that this lower bound is tight and can serve as a comprehensive guide to the actual performance of MIMO EMTS under practical operating conditions.