An experimental model of a redox microarray, which provides a foundation for constructing future massively parallel molecular computers, is proposed. The operation of a redox microarray is confirmed, using an experimental setup based on an array of microelectrodes with analog integrated circuits.

Scientific computations for diffusion equations and ANNs (Artificial Neural Networks) are data intensive tasks accompanied by heavy memory access; on the other hand, their computational complexities are relatively low. Thus, this type of tasks naturally maps onto SIMD (Single Instruction Multiple Data stream) parallel processing with distributed memory. This paper proposes a high performance acceleration processor of which architecture is optimized for scientific computing using diffusion equations and ANNs. The proposed architecture includes a customized instruction set and specific hardware resources which consist of a control unit (CU), 16 processing units (PUs), and a non-linear function unit (NFU) on chip. They are effectively connected with dedicated ring and global bus structure. Each PU is equipped with an address modifier (AM) and 16-bit 1.5 k-word local memory (LM). The proposed processor can be easily expanded by multi-chip expansion mode to accommodate to a large scale parallel computation. The prototype chip is implemented with FPGA. The total gate count is about 1 million with 530, 432-bit embedded memory cells and it operates at 15 MHz. The functionality and performance of the proposed processor is verified with simulation of oil reservoir problem using diffusion equations and character recognition application using ANNs. The execution times of two applications are compared with software realizations on 1.7 GHz Pentium IV personal computer. Though the proposed processor architecture and the instruction set are optimized for diffusion equations and ANNs, it provides flexibility to program for many other scientific computation algorithms.

Recent growth in computer communications has led to an increased requirement for high-speed backbone networks. In such high-speed networks, the principle adopted for a time-sensitive flow control mechanism should be that of autonomous decentralized control. In this mechanism, each node in a network manages its local traffic flow only on the basis of the local information directly available to it, although it is desirable that the individual decisions made at each node lead to high performance of the network as a whole. In our previous studies, we have investigated the behavior of local packet flows and the global performance achieved when a node is congested, and proposed the diffusion-type flow control model. However, since we used a simple and homogeneous network model in the evaluation, the results cannot be generalized. In this paper, we propose an extension of the diffusion-type flow control model in order to apply it to networks with inhomogeneous configurations. We show simulation results for two cases: different propagation delays and multiple bottlenecks. Both results show that the proposed diffusion-type flow control achieves high and stable performance even if the network is congested.

The paper discusses the spatio-temporal phenomena in autonomous two-layer Cellular Neural Networks (CNNs) with mutually coupled templates between two layers. By computer calculations, we show how pattern formations, autowaves and classical waves can be regenerated in the networks, and describe the properties of these phenomena in detail. In particular, we focus our discussion on the necessary conditions for generating these spatio-temporal phenomena. In addition, the influences of the template parameters and initial state conditions of CNNs on the spatio-temporal phenomena are investigated.

We propose binary-quantized and spatio-temporally discretized network models of linear diffusion systems and investigate their filtering effect on single-bit sigma-delta (ΣΔ) modulated signals. The network consists of only one kind of elements that add ΣΔ modulated signals and quantize the sum in the form of single-bit signal. A basic one-dimensional network is constructed first. Then, the network is extended into two dimensions. These networks have characteristics equivalent to those of linear diffusion systems in both time and frequency domains. In addition, network noise caused by the quantization in the elements contains low-level low-frequency components and high-level high-frequency components. Therefore, the proposed networks have possibility to be used as signal propagation and diffusion media of ΣΔ domain filters.

In face recognition, simple classifiers are frequently used. For a robust system, it is common to construct a multi-class classifier by combining the outputs of several binary classifiers; this is called output coding method. The two basic output coding methods for this purpose are known as OnePerClass (OPC) and PairWise Coupling (PWC). The performance of output coding methods depends on accuracy of base dichotomizers. Support Vector Machine (SVM) is suitable for this purpose. In this paper, we review output coding methods and introduce a new sequential fusion method using SVM as a base classifier based on OPC and PWC according to their properties. In the experiments, we compare our proposed method with others. The experimental results show that our proposed method can improve the performance significantly on the real dataset.

Edge detection has been an essential step in image processing, and there has been much work undertaken to date. This paper inspects a fuzzy mathematical morphology in order to reach a higher-level of edge-image processing. The proposed scheme uses a fuzzy morphological gradient to detect object boundaries, when the boundaries are roughly defined as a curve or a surface separating homogeneous regions. The automatic edge detection algorithm consists of two major steps. First, a new version of anisotropic diffusion is proposed for edge detection and image restoration. All improvements of the new version use fuzzy mathematical morphology to preserve the edge accuracy and to restore the images to homogeneity. Second, the fuzzy morphological gradient operation detects the step edges between the homogeneous regions as object boundaries. This operation uses geometrical characteristics contained in the structuring element in order to extract the edge features in the set of edgeness, a set consisting of the quality values of the edge pixels. This set is prepared with fuzzy logic for decision and selection of authentic edge pixels. For experimental results, the proposed method has been tested successfully with both synthetic and real pictures.

This paper focuses on flow control in high-speed networks. Each node in a network handles its local traffic flow on the basis of only the information it is aware of, but it is preferable that the decision-making of each node leads to high performance of the whole network. To this end, we investigate the relationship between the flow control mechanism of each node and network performance. We consider the situation in which the capacity of a link in the network is changed but individual nodes are not aware of this. Then we investigate the stability and adaptability of the network performance, and discuss an appropriate flow control model on the basis of simulation results.

This paper presents an algorithm for fingerprint image restoration using Digital Reaction-Diffusion System (DRDS). The DRDS is a model of a discrete-time discrete-space nonlinear reaction-diffusion dynamical system, which is useful for generating biological textures, patterns and structures. This paper focuses on the design of a fingerprint restoration algorithm that combines (i) a ridge orientation estimation technique using an iterative coarse-to-fine processing strategy and (ii) an adaptive DRDS having a capability of enhancing low-quality fingerprint images using the estimated ridge orientation. The phase-only image matching technique is employed for evaluating the similarity between an original fingerprint image and a restored image. The proposed algorithm may be useful for person identification applications using fingerprint images.

In this work, we review our recent efforts to make effective use of atomistic calculations for the advancement of VLSI process simulation. We focus on three example applications: the behavior of implanted fluorine, arsenic diffusion and activation, and the impact of charge interactions on doping fluctuations.

In this work it is shown for the first time how to calculate in advance by momentum-based noise simulation for stationary Monte Carlo (MC) device simulations the CPU time, which is necessary to achieve a predefined error level. In addition, analytical expressions for the simulation-time factor of terminal current estimation are given. Without further improvements of the MC algorithm MC simulations of small terminal currents are found to be often prohibitively CPU intensive.

The implant-anneal cycle for B doping during Si device fabrication causes transient enhanced diffusion (TED) of B and the formation of small immobile B-interstitial clusters (BICs) which deactivate the B. Additionally, since modern ultrashallow devices put most of the B in immediate proximity of the Si/SiO2 interface, interface-dopant interactions like segregation become increasingly important. In this work, we use density-functional theory calculations to study TED, clustering, and segregation of B during annealing and discuss a continuum model which combines the TED and clustering results.

This paper describes the behavior of voids that were formed due to electromigration and diffusion in the interconnections of gold during a DC bias tests of GaAs ICs to current densities in the interconnections of 0.67 106 A/cm2 to 1.27 106 A/cm2 in the high temperature range of 230 to 260. We have found that the voids were formed at the centers in the cross sections of the interconnections and that gold is left around the voids, which means current still flows after the void formation. We have carefully observed the movement of the anode and cathode side edge of the voids during the tests and found that edges moved toward the cathode, in the direction opposite to the electron flow. This direction is constant. Also, the voids are extended, which means that the velocity of the cathode side edge is greater than that of the anode side edge. The velocity of the edges almost proportionally increased with the current density. The constant edge movement direction and the velocity of the edge dependence on the current density suggest that one of the causes of the edge movement is electromigration. The velocity of the edge depends on the distance between the anode side edge of the void and the through hole. The velocity increases in accordance with a decrease in the distance. This means that one of the causes of the edge movement is the diffusion of gold atoms by a concentration and pressure gradient. The GaAs IC failed at almost the same time as the voids appeared. It is important for reliability to prevent the formation of voids caused by electromigration and diffusion.

The Belousov-Zhabotinsky (BZ) reaction provides us important clues in controlling 2D phase-lagged stable synchronous patterns in an excitable medium. Because of the difficulty in computing reaction-diffusion systems in large systems using conventional digital processors, we here propose a cellular-automaton (CA) circuit that emulates the BZ reaction. In the circuit, a two-dimensional array of parallel processing cells is responsible for fast emulation, and its operation rate is independent of the system size. The operations of the proposed CA circuit were demonstrated by using a simulation program with integrated circuit emphasis (SPICE).

In Vivo Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) can now be used to elucidate and investigate major nerve pathways in the brain. Nerve pathways are constructed by a) calculating a continuous diffusion tensor field from the discrete, noisy, measured DT-MRI data and then b) solving an equation describing the evolution of a fiber tract, in which the local direction vector of the trajectory is identified with the direction of maximum apparent diffusivity. This approach has been validated previously using synthesized, noisy DT-MRI data. Presently, it is possible to reconstruct large white matter structures in the brain, such as the corpus callosum and the pyramidal tracts. Several problems, however, still affect the method's reliability. Its accuracy degrades where the fiber-tract directional distribution is non-uniform, and background noise in diffusion weighted MRIs can cause computed trajectories to jump to different tracts. Nonetheless, this method can provide quantitative information with which to visualize and study connectivity and continuity of neural pathways in the central and peripheral nervous systems in vivo, and holds promise for elucidating architectural features in other fibrous tissues and ordered media.

Generalized Pulse Spectrum Technique (GPST) is a method to solve the inverse problems of wave-propagation and diffusion-dominated phenomena, and therefore has been popularly applied in image reconstruction of time-resolved diffuse optical tomography. With a standard GPST for simultaneous reconstruction of absorption and scattering coefficients, the products of the gradients of the Green's function and the photon-density flux, based on the photon-diffusion equation, are required to calculate the diffusion-related Jacobian matrix. The adversities are of two-folds: time-consuming and singular in the field near the source. The latter causes a severe insensitivity of the algorithm to the scattering changes deep inside tissue. To cope with the above difficulties, we propose in this paper a modified GPST algorithm that only involves the Green's function and the photon-density flux themselves in the scattering-related matrix. Our simulated and experimental reconstructions show that the modified algorithm can significantly improve the quality of scattering image and accelerate the reconstruction process, without an evident degradation in absorption image.

In order to assess the possible impacts of meteors with spacecraft, which is among major hazard in the space environment, it is essential to establish an accurate statistics of their mass and velocity. We developed a radar-optical combined system for detecting faint meteors consisting of a powerful VHF Doppler radar and an ICCD video camera. The Doppler pulse compression scheme is used to enhance the S/N ratio of the radar echoes with very large Doppler shifts, as well as to determine their range with a resolution of 200 m. A very high sensitivity of more than 14 magnitude and 9 magnitude for radar and optical sensors, respectively, has been obtained. Instantaneous direction of meteor body observed by the radar is determined with the interferometry technique. We examined the optimum way of the receiving antenna arrangements, and also of the signal processing. Its absolute accuracy was confirmed by the optical observations with background stars as a reference. By combining the impinging velocity of meteor bodies derived by the radar with the absolute visual magnitude determined by the video camera simultaneously, the mass of each meteor body was estimated. The developed observation system will be used to create a valuable data base of the mass and velocity information of faint meteors, on which very little is known so far. The data base is expected to play a vital role in our understanding of the space environment needed for designing large space structures.

This paper presents a digital reaction-diffusion system (DRDS)--a model of a discrete-time discrete-space reaction-diffusion dynamical system--for designing new image processing algorithms inspired by biological pattern formation phenomena. The original idea is based on the Turing's model of pattern formation which is widely known in mathematical biology. We first show that the Turing's morphogenesis can be understood by analyzing the pattern forming property of the DRDS within the framework of multidimensional digital signal processing theory. This paper also describes the design of an adaptive DRDS for image processing tasks, such as enhancement and restoration of fingerprint images.

Although a number of car-traffic simulators have been developed for various purposes, none of the existing simulators enhance the simulation accuracy using sensor data or allow the system structure to re-configure the system structure depending on the application. Our goal was to develop a highly accurate, highly modular, flexible, and scalable micro-model car-traffic simulation system. The HLA (High Level Architecture) was applied to every system module as a standard interface between each module. This allows an efficient means for evaluating and validating a variety of micro-model simulation schemes. Our ongoing projects consist of running several identical simulations concurrently, with different parameter sets. By sending the results of these simulations to a manager module, which analyzes both the parameter sets and the simulated results, the manager module can evaluate the best-simulated results and determine the next action by comparing these results with the sensor data. In this system, the sensor data or the statistical data on the flow of traffic, obtained by monitoring real roads, is used to improve the simulation accuracy. Future systems are being planned to employ real time sensor data, where the input of the data occurs at almost real time speed. In this paper, we discuss the design of a HLA-based car-traffic simulation system and the construction of a sensor-data fusion algorithm. We also discuss our preliminary evaluation of the results obtained with this system. The results show that the proposed fusion algorithm can adjust the simulation accuracy to the logged sensor data within a difference of 5% (minimum 1.5%) in a specific time period. We also found that simulations with 500 different parameter sets can be executed within 5 minutes using 8 simulator modules.

Post annealing treatment for CoCrPt magnetic thin films were tried in different thermal conditions, by changing the time of annealing procedure. Coercivity (Hc) improvement was achieved in annealed sample compared with those as deposited, in which as high as 5.2 kOe has been attained. To clarify the mechanism of annealing treatment on the magnetic properties, X-ray diffraction (XRD) spectrums of those samples and their magnetic properties were carefully studied. Co and Cr lattice parameters were separately calculated from different crystal lattice plane. It was found that a axis lattice spacing of Co hexagonal structure increases monotonically with increased annealing time. Variation of Co hcp peaks significance may due to Cr or Pt redistribution in the crystal grains and its boundaries. Combined with the grain size analysis of Co-rich area by X-ray diffraction peak broaden width, which was not very consistent with the result obtained from other's TEM and AFM studies, Cr diffusion was suggested to be the governing factor at short annealing time region. Co-rich grain growth should also be applied to explain the variation of magnetic properties in longer post annealing.