Electromagnetic disturbances in vehicle-mounted radios are mainly caused by conducted noise currents flowing through wiring-harnesses from vehicle-mounted printed circuit boards (PCBs) with common slitting ground patterns. To suppress these kinds of noise currents, we previously measured them for simple two-layer PCBs with two parallel signal traces and slitting or non-slitting ground patterns, and then investigated by the FDTD simulation the reduction characteristics of the FM-band cross-talk noise levels between two parallel signal traces on six simple PCB models having different slitting ground or different divided ground patterns parallel to the traces. As a result, we found that the contributory factor for the FM-band cross-talk reduction is the reduction of mutual inductance between the two parallel traces, and also the noise currents from PCBs can rather be suppressed even if the size of the return ground becomes small. In this study, to investigate this finding, we further simulated the frequency characteristics of cross-talk reduction for additional six simple PCB models with different dividing dimensions ground patterns parallel to the traces, which revealed an interesting phenomenon that cross-talk reduction characteristics do not always decrease with increasing the width between the divided ground patterns.

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We made in-vivo measurement of the complex relative permittivity of palm and sole in the frequency range from 100 MHz to 40 GHz with a network analyzer and an open-ended coaxial probe, which was compared with Gabriel in-vitro data for skin tissue to reveal that the in-vivo measurement results are mostly lower than the in-vitro data. For validation, we measured the dielectric constant of Teflon sheet with respect to its thickness from 0.05 mm to 5.00 mm, which showed that the open-ended coaxial probe provides sufficient measurement accuracy for Teflon with a thickness of over 0.5 mm, however, the probe data can be affected by the material beneath the Teflon with a thickness of less than 0.5 mm. This means that the in-vivo palm data derive from the epidermis and the dermis including blood, while the in-vivo sole data come from the epidermis. For further investigation, under the assumption that in-vitro data derive from a mixture of epidermis and blood, we calculated the complex relative permittivity for the compound from the Litchtenecker's law of exponent to show a possibility that due to the inclusion of blood, in-vitro measurement may provide a higher relative permittivity than in-vivo measurement.

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Before evaluating the quality of hemodialysis from the limited volume of human blood using a commercially available open-ended coaxial probe, we previously measured the complex relative permittivity of pure water from 200 MHz to 6 GHz with respect to its measured liquid volume, and revealed that 1.9 ml water in a beaker with a diameter of 24 mm and a depth of 2 mm gives a variation within ±0.5 % for the real part and ±7 % for the imaginary part. Based on the above finding, we measured the dielectric properties of 2.5 ml whole blood at 25°C for 10 normal healthy subjects and 9 hemodialysis patients. The measured results on healthy subjects show good agreement with the data reported by Gabriel for human blood at 37°C, while they provide different dispersion characteristics of straight lines for their Cole-Cole plots. The measured results on the patients give further different dispersion characteristics in comparison with the healthy subjects. In order to investigate the above differences statistically, the student t-test was conducted to reveal that permittivity at infinite frequency for the Cole-Cole plots is significantly different with a level of 1 % among its averaged values for normal healthy subjects and patients before dialysis.

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Most of the traffic accidents have been caused by inappropriate driver's mental state. Therefore, driver monitoring is one of the most important challenges to prevent traffic accidents. Some studies for evaluating the driver's mental state while driving have been reported; however driver's mental state should be estimated in real-time in the future. This paper proposes a way to estimate quantitatively driver's mental workload using heart rate variability. It is assumed that the tolerance to driver's mental workload is different depending on the individual. Therefore, we classify people based on their individual tolerance to mental workload. Our estimation method is multiple linear regression analysis, and we compare it to NASA-TLX which is used as the evaluation method of subjective mental workload. As a result, the coefficient of correlation improved from 0.83 to 0.91, and the standard deviation of error also improved. Therefore, our proposed method demonstrated the possibility to estimate mental workload.

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In this paper an evaluation method of signal efficacy for signal count correction methods on a self-coupling sensor with a Vertical-Cavity Surface-Emitting Laser (VCSEL) is reported on. In this study it is proposed that the method of quantifying the effectiveness of “signal count correction methods”. The determination coefficient R² of least square is used as a correlation coefficient between fluctuations by self-coupling effect and White Gaussian Noise (WGN). Binarization of the fluctuations and WGN as preprocessing keeps the small computational effort that is advantage of “signal count correction method”.

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An artificial ecosystem model was developed to study complex behavior of real organisms. The subject of the research is multi-generational migration of the Monarch Butterfly in North America, and we approached a problem from an evolutionary simulation. The ecosystem consists of artificial agents and five areas. The model is based on real organisms and the areas where they live simulated with a genetic algorithm. We then designed a model of agents that have genetic factors. These genetic factors determine the behavioral strategies, physical features, and transformational strategies of the agent. Each area is modeled on areas where we can see the migration of the Monarch, and the environmental factors of each area change periodically. Because of the genetic factor of the agent and the change of the environmental factors, the agent adapts to the environment and evolves gradually by using a genetic algorithm. Results of the evolutionary simulation show that multi-generational migration behavior of the agent emerges as its genetic factors adapt to periodic changes of the environmental factors in their evolution. The migration process of agents and their genetic factors are discussed, and the migration of the agent and that of Monarch Butterfly are compared.

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We suggest self motion evaluation method to adapt to environmental changes for service robots. Several motions such as walking, dancing, demonstration and so on are described with time series patterns. These motions are optimized with the architecture of the robot and under certain surrounding environment. Under unknown operating environment, robots cannot accomplish their tasks. We propose autonomous motion generation techniques based on heuristic search with histories of internal sensor values. New motion patterns are explored under unknown operating environment based on self-evaluation. Robot has some prepared motions which realize the tasks under the designed environment. Internal sensor values observed under the designed environment with prepared motions show the interaction results with the environment. Self-evaluation is composed of difference of internal sensor values between designed environment and unknown operating environment. Proposed method modifies the motions to synchronize the interaction results on both environment. New motion patterns are generated to maximize self-evaluation function without external information, such as run length, global position of robot, human observation and so on. Experimental results show that the possibility to adapt autonomously patterned motions to environmental changes.

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3D-Measuring is paid to attention because 3D-Display is making rapid spread. Especially, face and head are required to be measured because of necessary or contents production. However, it is a present problem that it is difficult to measure hair. Then, in this research, it is a purpose to measure face and hair with phase shift method. By using sine images arranged for hair measuring, the problems on hair measuring, dark color and reflection, are settled.

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Phase Shifting Method, measuring method of three-dimensional shapes, is able to measure by high accuracy in short time. However there are some difficulties to acquire three-dimensional data because of phase unwrapping problem which occurs phase unwrapping errors and makes accuracy worse. Some of techniques have been used for avoiding the problem in the traditional methods. One of the traditional methods combines with space encoding method. This method could be robust and high accurate but requires several gray-code patterns other than phase shifting patterns. This makes measuring time longer. In this paper, we propose a correcting phase unwrapping error method which requires only phase shifting patterns to measure objects. The proposal method corrects over 99 [%] phase unwrapping errors. This result is almost same as the result of the traditional method. Therefore we are able to unwrap phase values as same accuracy as the traditional method by our proposal method despite reducing projecting patterns.

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The data generated at a very high rate by sensors and RFIDs are required to be handled by continuous queries keeping real time response. Because of its purpose, DSMSs are used in several cases of these large scale systems. On the other hand, sensor terminal systems include light RDBMSs generally in many cases. So if light RDBMSs can handle the high rate data directly, it is convenient for several applications. This paper proposes a speed-up method of stream processing by using a light RDBMS SQLite without any special modifications. If DSMSs are categorized by performance such as large, medium and small scale, this method aims at a small or medium scale performance. The database performance mainly depends on storage access time, so this proposed method adopts a memory database, a bulk store records technique and parallel processing while taking advantage of multi-core CPU configurations of terminal systems.

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Recently, demands for shorter development periods through minimization of new source code by reusing web applications are required increasingly in the new development using features of web applications. In this paper, we propose a method for virtualization of framework to aim to re-use web applications and explain its implementation. Using the proposed virtualization of a framework, web applications don't inherit from template classes provided by the framework directly. Instead, web applications inherit from customized template classes that are extended template classes provided by the framework.

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We present a method for improving the accuracy of detecting the duplicated regions, accompanied with rotation and/or reversal, in an image. In judgment of reversal and estimation of rotation angle, we add the following processes, 1) paring pixels by principle component values, 2) calculating gradation centroid vectors for each of paired pixels, 3) evaluating the difference in angles and magnitudes between the centroid vectors. We conducted an experiment by using 100 duplicated images. The proposed method gave the correct estimation for 90 images, while the conventional one for 68 images.

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A single point feeded circularly polarized coplanar antenna is proposed. The antenna shape composes rectangular slot loop antenna of 1λ (Wavelength) and the earth conductor. The antenna shows orthogonal surface currents and appropriate phase difference to generate the circular polarization wave. Antenna design method is studied and the design parameters table is obtained for several applications. Moreover, antenna performance are measured and examined with the numerical simulation results. Finally, the application for XMSR was examined with the car body model.

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In recent times, considerable research has been conducted on the development of brain-computer interfaces (BCIs). Although there have been several reports on BCIs that assist motor functions by measurement of brain activity in the motor cortex, only a few studies have reported on BCI that assist motor functions by measurement of activity in areas other than the motor cortex. In this study, we experimentally develop a BCI that assists motor functions on the basis of brain activity in the prefrontal cortex. In this BCI system, subjects are shown the labyrinth problem. Concretely, brain activity is measured using fNIRS and the data are acquired in real time. The signal processing module implements low pass filtering of these signals. Further, the pattern classification module used in this system currently is a support vector machine. 22 subjects, both male and female, volunteered to participate in this experiment. 8 of these 22 subjects were able to solve the labyrinth problem. In this experiment, we could not obtain a high distinction. However, these results show that it is possible to develop BCI systems that assist motor functions using information from the prefrontal cortex.

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As a cardiac pacemaker, sinoatrial node spontaneously generates periodic electrical signals (action potentials) in its cells. The action potential generation is deeply related to various ion channels in cell membranes, and the abnormalities of ion channels cause sinus arrhythmia. We use the Zhang model of sinoatrial node cells to investigate the relation between pacemaker rhythm (frequency of action potential generation) and ion channels. The Zhang model is described by the Hodgkin-Huxley-type nonlinear ordinary differential equations, and its parameter values vary between periphery and center cells of sinoatrial node. We analyze the bifurcation structure of the Zhang model, and investigate the variability of pacemaker rhythm and its sensitivity on ion channel conductance changes for both periphery and center cells. Moreover, these results are compared with the previous results of another sinoatrial node cell model: Yanagihara-Noma-Irisawa model.

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This paper shows the basic experiment and simulation about peripheral visual field characteristics as visual factor, two major factors for rural districts accident ((1)collision course relationship, (2)within peripheral visual field), from the stand of view that detects recognizing quantitative threshold for shape change (stimulus) in peripheral visual field. It is understood that critical such dimension as diameter for circle, side length for square, triangle, and oval are important to recognize the right angle direction vehicle. These showed that a similar thing applied even if the aircrafts that moved three-dimensional spaces not only the vehicles in two dimensional planes.
This paper made clear the limit of the peripheral visual field characteristics that is one of the human visual characteristics and also showed no dependency on figure shape as long as the maximum side size is the same as the diameter of circle.

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In the synthesis of tracking control systems, the compensation signal, which is applied in the finite-horizon time, is effective for improving the performance of controlled system. In the past researches, a calculation method of finite-horizon compensation signal and optimal internal state of controller is discussed for the servo-mechanism. In this paper, a design method of a target signal is derived for a tracking problem with some numerical examples based on the our past researches.

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We propose a probabilistic face recognition algorithm for Access Control System(ACS)s. Comparing with existing ACSs using low cost IC-cards, face recognition has advantages in usability and security that it doesn't require people to hold cards over scanners and doesn't accept imposters with authorized cards. Therefore face recognition attracts more interests in security markets than IC-cards. But in security markets where low cost ACSs exist, price competition is important, and there is a limitation on the quality of available cameras and image control. Therefore ACSs using face recognition are required to handle much lower quality images, such as defocused and poor gain-controlled images than high security systems, such as immigration control.
To tackle with such image quality problems we developed a face recognition algorithm based on a probabilistic model which combines a variety of image-difference features trained by Real AdaBoost with their prior probability distributions. It enables to evaluate and utilize only reliable features among trained ones during each authentication, and achieve high recognition performance rates.
The field evaluation using a pseudo Access Control System installed in our office shows that the proposed system achieves a constant high recognition performance rate independent on face image qualities, that is about four times lower EER (Equal Error Rate) under a variety of image conditions than one without any prior probability distributions. On the other hand using image difference features without any prior probabilities are sensitive to image qualities. We also evaluated PCA, and it has worse, but constant performance rates because of its general optimization on overall data. Comparing with PCA, Real AdaBoost without any prior distribution performs twice better under good image conditions, but degrades to a performance as good as PCA under poor image conditions.

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The problem of adaptive robust stabilization is considered for a class of uncertain nonlinear time-delay dynamical systems. It is assumed that the upper bound of the nonlinear delayed state perturbations is a linear function of some parameters which are assumed to be unknown. It is also assumed that the time delays are time-varying, and can be any nonnegative continuous and bounded functions. In this paper, it is not required that the derivatives of the time-varying delays have to be less than one. For such a class of uncertain nonlinear time-delay systems, a new method is presented whereby a class of memoryless continuous adaptive robust state feedback controllers with a rather simpler structure is proposed. That is, being completely different from the related works reported in the control literature, the nonlinear perturbations are not included in the proposed control schemes. By employing a quasi-Lyapunov function, it is shown that the solutions of uncertain nonlinear time-delay systems can be guaranteed to be uniformly exponentially convergent towards a ball which can be as small as desired. Finally, as an application of the results, the problem of water pollution control is considered for uncertain river time-delay systems due to industrial waste treatment facility, and the corresponding simulations are given.

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The goal of our study is to develop sensing and control systems for walking on a step using a wearable robot. Our system consists of (1) sensing of a bump from a movement of a walker, (2) detecting a foot placement state related to the bump and (3) generating gait patterns of stepping up and down for the bump. In the generation of gait patterns for the bump, toe trajectories are generated according to the height of the bump to avoid the collision of the swing leg and the bump. A hip trajectory is generated by the optimization technique to minimize the sum total of joint angular jerk of the robot subject to the constrained condition of the hip position and velocity at toe-off. Each joint angle trajectory is calculated from the generated trajectories using inverse kinematics equations. We examined the feasibility of the proposed sensor and control systems for two kinds of bumps with different height.

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A fisheye camera system is usually used for eliminating the blind spot around a vehicle. In this paper we propose a method of estimating vehicle pose relative to current lane from the side fisheye cameras of such a fisheye camera system. The side fisheye camera with hemispherical field of view can observe the side boundary of the vehicle and the lane markings simultaneously. An algorithm of estimating the distance and the relative orientation between the vehicle and the current lane is presented based on the side boundary of the vehicle and the nearest lane marking. The experimental results are also presented to show the effectiveness of the proposed method.

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PF-mCRL method is a rapid and robust information extraction method for non-Gaussian probability distribution by combination of a particle filter (PF) and an adaptive vector quantization algorithm mCRL (modified Competitive Re-initialization Learning). In this research, a novel method for tracking and shape estimation of easily deformable object in dynamic scene by using the PF-mCRL is proposed. Moreover, several feature value extraction methods from output of PF-mCRL useful for the robot handling are proposed. Further, effectiveness of this proposed method is shown by a real image experiments.

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This paper addresses a problem to decide the combination of risk-reducing plans quickly. The combinatorial problem is formulated as one of the 0-1 integer programming, and Branch and Bound is used. However, Simplex method that is executed on Branch and Bound takes much time. Our proposed method decides the optimal combination based on approximation algorithms, greedy algorithm and single constraint selection in addition to Simplex method. Only if bounding by approximate algorithms leads to incorrect optimal solutions, Simplex method is executed to verify the bounding. As a result of evaluation experiments, the proposed method can reduce the computational time by 71% in comparison with the existing method.

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Due to advent of powerful and easily available Multi core PC clusters, the computing power per node has been increasing significantly. On the other hand, installation and maintenance costs of powerful interconnection networks (Myrinet, Infiniband, etc.) are still expensive. Moreover, because they use nonstandard protocols and special device drivers, they tend to increase the specializations and complexities both in programming and in operability, and degrade portability. This paper proposes the portable method for improving the performance of bandwidth-oriented parallel applications by increasing the bandwidth without dedicated hardware, drivers, protocols, libraries and IEEE802.3ad (LACP). Since proposed method is introduced only by loading the proposed driver without any modifications to the TCP/IP protocol stacks and to existing applications, it has advantages in both high portability and stability. Proposed method also performs better than LACP, which is the most similar in comparison to proposal, without LACP supported switches and drivers. In addition, LACP performance is influenced both by the distribution algorithms implemented both in switches and in NIC drivers, and by the network parameters such as MAC addresses, IP addresses, VLAN id, etc. used in distribution algorithms. On the other hand, proposed method shows a stable effect regardless of them.

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In this paper stacked NOR type PRAM with phase change channel transistor has been newly proposed. Fast access time competitive to DRAM can be realized with stacked NOR type PRAM. The proposed scheme is a promissing candidate for realizing hight- performance, low cost non-volatile semiconductor memory.

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Recent fMRI studies of human motor function and learning have reported that the magnitude of brain activity involves a decreasing trend over repeated tasks in the absence of improvements in task performance, probably suggesting the effect of habituation. Here we show that similar effect can be detected by NIRS. In experiments, oxygenated hemoglobin (HbO) changes were monitored during a finger tapping task over repeated sessions. Results showed that task-related brain activity exhibited a decreasing trend on motor-related areas over the sessions. These suggest that measurements of NIRS may exhibit the brain-induced trends over repetition of simple motor tasks.

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We have developed a three-dimensional shape measurement technique using the optimal intensity-modulation pattern projection method that provides three-dimensional information from a single pattern projection. The practical use of the technique is expected in the near future. However, when the color distribution and surface reflections of the target are complex, to cancel their influence, it is necessary to use another observation image as a reference to correct the intensity of the observed pattern. In this study, we propose an analysis method with an original color system and image correction technology to realize three-dimensional measurement using only one observation image.

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To improve prediction accuracy in pattern recognition, many approaches using multiple classifiers are being presented nowadays. On the other hand, pattern recognition to time-series data such as video sequences are still challenging due to the real-time requirement. In this paper, we present a novel method for pattern recognition of video sequences using prediction probability calculated by a pattern classifier. Generally, in applying pattern classifier to video sequences, predicted classes are often partially fragmented. From the idea that prediction probabilities of the video sequences which have same recognition pattern would be similar to each other, the proposed method corrects the fragmented classes to correct one using the similarity of prediction probabilities. Evaluation experiments have shown that the proposed method works well to the system which estimates handlings for flexible cystoscope.

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