We present theoretical foundations about error estimations of the global Krylov subspace techniques for multiple-inputs multiple-outputs (MIMO) Interconnect reductions. Analytical relationships between Lyapunov functions of the original interconnect network and those of the reduced system generated by the global Arnoldi algorithm will be developed. Under this framework, a new moment matching reduced network is proposed. Also, we will show that the reduced system can be expressed as the original network with some additive perturbations.

This paper deals with the singular behavior of the diffraction of transverse magnetic (TM) waves by a perfectly conductive triangular periodic surface at a low grazing limit of incidence. The wave field above the highest excursion of the surface is represented as a sum of Floquet modes with modified diffraction amplitudes, whereas the wave field inside a triangular groove is written as a sum of guided modes with unknown mode amplitudes. Then, two sets of equations are derived for such amplitudes. From the equation sets, all the amplitudes are analytically shown to vanish at a low grazing limit of incidence. From this fact, it is concluded analytically that no diffraction takes place and only reflection occurs at a low grazing limit of incidence for any period length and any triangle height. This theoretical result is verified by a numerical example.

Lightwave switching is discussed with a cascaded connection of optical couplers with light intensity control elements. By employing wavelength-selective amplifiers such as a waveguide-type Raman amplifier, all-optical wavelength-selective switching can be realized. We discuss analytically using coupled-mode theory that the lightwave switching is feasible by controlling the intensity of propagating lightwave. The switching operation is verified numerically using finite-difference beam-propagation method. As a result, the expected operation is realized and some characteristics involved with dependencies of wavelength and phase are also investigated. A preliminary experiment using attenuators, beam splitters and mirrors is also described to verify the switching operation with only light-intensity control in interferometers.

We consider an edge-weighted graph G with a designated vertex v0 such that weights of edges incident to v0 may increase or decrease. We show that, with an O(mn+n2log n) time preprocessing, a minimum cut of the current G can be computed in O(log n) time per update of weight of any edge {v0,u}.

We present a novel algorithm that can suppress impulsive noise in video colour sequences. It uses order statistics, and directional and adaptive processing techniques.

We propose a model for constructing a multilayered boundary in an information system to defend against intrusive anomalies by correlating a number of parametric anomaly detectors. The model formulation is based on two observations. First, anomaly detectors differ in their detection coverage or blind spots. Second, operating environments of the anomaly detectors reveal different information about system anomalies. The correlation among observation-specific anomaly detectors is first formulated as a Partially Observable Markov Decision Process, and then a policy-gradient reinforcement learning algorithm is developed for an optimal cooperation search, with the practical objectives being broader overall detection coverage and fewer false alerts. A host-based experimental scenario is developed to illustrate the principle of the model and to demonstrate its performance.

Sequences with good correlation properties are widely used in engineering applications, especially in the area of communications. Among the known sequences, cyclotomic families have the optimal autocorrelation property. In this paper, we decide the cross-correlation function of the known cyclotomic sequences completely. Moreover, to get our results, the relations between the multiplier group and the decimations of the characteristic sequence are also established for an arbitrary difference set.

In the past twenty years, there were only a few constructions for Boolean functions with nonlinearity exceeding the quadratic bound 2n-1-2(n-1)/2 when n is odd (we shall call them Boolean functions with very high nonlinearity). The first basic construction was by Patterson and Wiedemann in 1983, which produced unbalanced function with very high nonlinearity. But for cryptographic applications, we need balanced Boolean functions. Therefore in 1993, Seberry, Zhang and Zheng proposed a secondary construction for balanced functions with very high nonlinearity by taking the direct sum of a modified bent function with the Patterson-Wiedemann function. Later in 2000, Sarkar and Maitra constructed such functions by taking the direct sum of a bent function with a modified Patterson-Wiedemann function. In this paper, we propose a new secondary construction for balanced Boolean functions with very high nonlinearity by recursively composing balanced functions with very high nonlinearity with quadratic functions. This is the first construction for balanced function with very high nonlinearity not based on the direct sum approach. Our construction also have other desirable properties like high algebraic degree and large linear span.

To make the best use of the resources in a shared grid environment, an application scheduler must make a prediction of available performance on each resource. In this paper, we examine the problem of predicting available CPU performance in time-shared grid system. We present and evaluate a new and innovative method to predict the one-step-ahead CPU load in a grid. Our prediction strategy forecasts the future CPU load based on the variety tendency in several past steps and in previous similar patterns, and uses a polynomial fitting method. Our experimental results on large load traces collected from four different kinds of machines demonstrate that this new prediction strategy achieves average prediction errors which are between 22% and 86% less than those incurred by four previous methods.

The estimation of the point-spread function (PSF) is one of very important and indispensable tasks for the practical image restoration. Especially, for the motion blur, various PSF estimation algorithms have been developed so far. However, a majority of them becomes useless in the low blurred signal-to-noise ratio (BSNR) environment. This paper describes a new robust PSF estimation algorithm based on Hough transform concerning gradient vectors, which can accurately and robustly estimate the motion blur PSF even in low BSNR case. The effectiveness and validity of the proposed algorithm are verified by applying it to the PSF estimation and the image restoration for noisy and motion blurred images.

In this paper, we propose a new region extraction method using chroma key with a two-tone checker pattern background. The method solves the problem in conventional chroma key techniques that foreground objects become transparent if their colors are the same as the background color. The method utilizes the adjacency condition between two-tone regions of the background and the geometrical information of the background grid line. The procedure of the proposed method consists of four steps: 1) background color extraction, 2) background grid line extraction, 3) foreground extraction, and 4) image composition. As to background color extraction, a color space approach is used. As to background grid line extraction, it is difficult to extract background grid line by a color space approach because the color of this region may be a composite of two background colors and different from them. Therefore, the background grid line is extracted from adjacency conditions between two background colors. As to foreground extraction, the boundary between the foreground and the background is detected to recheck the foreground region whose color is same as the background, and the background region whose color is same as the foreground. To detect regions whose colors are same as the background, the adjacency conditions with the background grid line are utilized. As to image composition, the process that smoothes the color of the foreground's boundary against the new background is carried out to create natural images. Experimental results show that the foreground objects can be segmented exactly from the background regardless of the colors of the foreground objects.

A semi-supervised classification method is presented. A robust unsupervised spectral mapping method is extended to a semi-supervised situation. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis for the iris and wine data and is also more accurate than a semi-supervised algorithm of the logistic GRF for the ionosphere dataset.

The coexistence of MPEG-2 and its powerful successor H.264/AVC has created a huge need for MPEG-2/H.264 video transcoding. However, a traditional transcoder where an MPEG-2 decoder is simply cascaded to an H.264 encoder requires huge computational power due to the adoption of a complicated rate-distortion based mode decision process in H.264. This paper proposes a 2-D Sobel filter based motion vector domain method and a DCT domain method to measure macroblock complexity and realize content-based H.264 candidate mode decision. A new local edge based fast INTRA prediction mode decision method is also adopted to boost the encoding efficiency. Simulation results confirm that with the proposed methods the computational burden of a traditional transcoder can be reduced by 20%30% with only a negligible bit-rate increase for a wide range of video sequences.

The relationship between the topology and collective function of a nonlinear oscillator network was investigated using nonlinear electrochemical oscillators. The constitutive experiments showed that the physiological robustness in the living system is due to their topological redundancy and asymmetry in the nonlinear network.

Conditional random fields (CRFs) have been successfully applied to various applications of predicting and labeling structured data, such as natural language tagging & parsing, image segmentation & object recognition, and protein secondary structure prediction. The key advantages of CRFs are the ability to encode a variety of overlapping, non-independent features from empirical data as well as the capability of reaching the global normalization and optimization. However, estimating parameters for CRFs is very time-consuming due to an intensive forward-backward computation needed to estimate the likelihood function and its gradient during training. This paper presents a high-performance training of CRFs on massively parallel processing systems that allows us to handle huge datasets with hundreds of thousand data sequences and millions of features. We performed the experiments on an important natural language processing task (text chunking) on large-scale corpora and achieved significant results in terms of both the reduction of computational time and the improvement of prediction accuracy.

We propose and demonstrate wavelength-selectable filters available for 32 WDM channels using a micro-mechanically movable mechanism with miniaturized voice-coil motors (VCMs). A simple straight geometry with a staggered configuration is used to densely pack 32 in/out moving elements into a small space of 452411 mm. The elements are precisely arranged along a collimated beam between fiber facets to provide flat-top passbands centered at ITU-T grids while maintaining small total insertion losses of less than 2.5 dB for all elements. The driving condition of the VCMs is also optimized for quick dynamic response with typical settling time of less than 10 ms. A repetition test 106 repetitions per element showed good wavelength reproducibility to an accuracy of below 0.1 nm, indicating the switches are feasible for practical system equipped with reconfigurable functionality for the next generation of optical networks.

The UWB (ultra-wideband) pulse radar is a promising candidate as an environment measurement method for rescue robots. Radar imaging to locate a nearby target is known as an ill-posed inverse problem, on which various studies have been done. However, conventional algorithms require long computational time, which makes it difficult to apply them to real-time operations of robots. We have proposed a fast radar imaging algorithm, the SEABED algorithm, for UWB pulse radars. This algorithm is based on a reversible transform, BST (Boundary Scattering Transform), between the target shape and the observed data. This transform enables us to estimate target shapes quickly and accurately in a noiseless environment. However, in a noisy environment the image estimated by the SEABED algorithm is degraded because BST utilizes differential operations. We have also proposed an image stabilization method, which utilizes the upper bound of the smoothness of received data. This method can be applied only to convex objects, not to concave ones. In this paper, we propose a fractional BST, which is obtained by expanding the conventional BST, and an image stabilization method by using the fractional BST. We show that the estimated image can be stabilized regardless of the shape of target.

A typical watermarking scheme consists of an embedding scheme and a detection scheme. In detecting a watermark, there are two kinds of detection errors, a false positive error (FPE) and a false negative error (FNE). A detection scheme is said to be optimum if the FNE probability is minimized for a given FPE probability. In this paper, we present an optimum watermark detection scheme for an additive embedding scheme with a spatial domain. The key idea of the proposed scheme is to use the differences between two brightnesses for detecting a watermark. We prove that under the same FPE probability the FNE probability of the proposed optimum detection scheme is no more than that of the previous optimum detection scheme for the additive embedding scheme with the spatial domain. Then, it is confirmed that for an actual image, the FNE probability of the proposed optimum detection scheme is much lower than that of the previous optimum detection scheme. Moreover, it is confirmed experimentally that the proposed optimum detection scheme can control the FPE probability strictly so that the FPE probability is close to a given probability.

The element-by-element finite element method (EBE-FEM) combined with the preconditioned conjugate gradient (PCG) technique is employed in this paper to calculate the coupling capacitances of multi-level high-density three-dimensional interconnects (3DIs). All capacitive coupling 3DIs can be captured, with the effects of all geometric and physical parameters taken into account. It is numerically demonstrated that with this hybrid method in the extraction of capacitances, an effective and accurate convergent solution to the Laplace equation can be obtained, with less memory and CPU time required, as compared to the results obtained by using the commercial FEM software of either MAXWELL 3D or ANSYS.

We determine the annealing dynamics of AsGa antisite defects in As ion-implanted GaAs. An Arrhenius plot of the carrier decay rate or the defect density vs. the annealing temperature in the high temperature regime gives an energy EPA, which is different from true activation energy. The annealing time dependence of EPA obtained by the two diffusion models (self diffusion of AsGa antisite defects and VGa vacancy assisted diffusion of AsGa antisite defects) are compared with EPA's obtained from already published works. The results prove that the diffusion of AsGa antisite defects is assisted by the VGa vacancy defects that exist in a high density.