In this paper, belief propagation (BP) multiple-input multiple-output (MIMO) detection with minimum mean square error (MMSE) pre-cancellation matrix switching in overloaded MIMO systems is proposed. Loops in a factor graph degrades the demodulation performance of the BP scheme. Therefore, several schemes that suppresses the negative impact of loopy BP have been proposed. Among them, the application of MMSE pre-cancellation prior to BP has been proposed. In the scheme, the effects of loops in the factor graph is suppressed by MMSE pre-cancellation. However, many loops with large coefficients remain in the factor graph even after MMSE pre-cancellation. Therefore, the proposed scheme applies MMSE pre-cancellation matrix switching every after a preset number of BP iterations in order to suppress the occurrence of large beliefs in the loops. Numerical results obtained through computer simulation show that the proposed scheme achieves better BER performance than BP with MMSE pre-cancellation without matrix switching. The proposed scheme outperforms the conventional without switching by 0.7 dB at BER= 10-3.

In this paper, two-stage BP detection is proposed for overloaded MIMO. In the two-stage BP detection scheme, the BP with MMSE pre-cancellation algorithm followed by the normal BP detection is applied. In overloaded MIMO systems, the loops in a factor graph degrade the demodulation performance of BP detection. MMSE pre-cancellation reduces the number of connections or coefficient values in the factor graph and improves the convergence characteristics of posteriori probabilities. Numerical results obtained through computer simulation have shown that the BERs of the proposed two-stage BP detection are about 1.0-1.5dB better than those of the conventional BP with MMSE pre-cancellation when the MMSE block size is four and the number of MMSE blocks is one.

In this paper, the application of minimum mean square error (MMSE) pre-cancellation prior to belief propagation (BP) is proposed as a detection scheme for overloaded multiple-input multiple-output (MIMO). In overloaded MIMO, the loops in a factor graph deteriorate the demodulation performance with BP. Therefore, the proposed scheme applies the MMSE pre-cancellation prior to BP and reduces the number of the loops. Furthermore, it is applied to the selected transmit and receive nodes so that the condition number of an inverse matrix in the MMSE weight matrix is minimized in order to suppress the residual interference and the noise after the MMSE precancellation. It is shown by numerical results obtained through computer simulation that the proposed scheme achieves better bit error rate (BER) performance than BP without the MMSE pre-cancellation. The proposed scheme improves the BER performance by 4.6-5.0 dB at a BER of 5.0 × 10-3as compared with conventional BP. Numerical results also shows that the MMSE pre-cancellation reduces the complexity in BP by a factor of 1/896 in terms of the number of multiplication operations.

The Japanese society has labor shortage problems as Japan is facing a declining birth rate and an increasing aged population. In this situation, automation can be one of the major solutions. Recently, automation for mobility has been actively researched. In particular, self-driving cars have received enormous attention from the scientific community and many research groups have focused on their testing and development. At the same time, for autonomous vehicles, a simple and easy interface is required for setting the destination, which will help them become popular. In this scenario, we aim to develop a learning interface based on speech recognition to input destinations for autonomous vehicles. The interface is inspired by the directions that people use to explain a route in order to make others understand the way to a destination. In this paper, the experimental results for collecting speech voice directions are discussed. The route directions are reported in Japanese.

Visual and auditory communication over the Internet has enabled communication unlike any humankind has experienced before. However, Internet communication based on haptic interfaces is not yet widespread. This study seeks to allow thousands of people to share the sense of force and enjoy Internet games such as tug-of-war by providing a stabilizing system able to overcome Internet communication delay. First, we derive non-positivity for the multi-coupling physical model. Second, we prove that a closed loop using a phase control filter is stable despite constant communication delay using Integral Quadratic Constraints (IQC). Finally, we validate our proposed system through a series of experiments with 8 clients.

In our vehicle driving, it is important to take appropriate evasive action based on the recognition of the hazardous events as one of driver's ability. There is a concept of "Risk perception" that makes effect to both driver's ability sides of the behavioral and acceptance with their controllability. In the previous study, the relation of Risk perception with the number of traffic accidents have been shown, however, the quantification of the Risk perception have not been performed in addition to driving knowledge and behavior. In this study, driving knowledge and driving behavior are quantified, and an analysis method for it is constructed. In order to obtain the knowledge and ability, the experiments with subject driver were designed and conducted. By using obtained data, validation for proposed analysis method was also carried out. As a result of correlated with driving tendency, classification possibility of hazard sensitivity based on the individual characteristics had been confirmed.

In recent years, in order to reduce traffic accidents, several kind of support systems for driving safety have been put to practical use. Almost the support systems avoid the dangerous situation based on the recognition results of outside environment of the car. On the other hand, more than 90 % of traffic accidents are caused by human errors. In the research field of human - machine interaction, to estimate and recognize of human psychological of emotional state is needed as one of important fuction for machine side. It is thought that these system are effective as fundamental instrumentation to realize the driving support system, and to avoid the human errors and overconfidence for these support system. The authors have been researched about driver modeling based on the Hidden Markov Model, and its application for the estimation of driver's intention. In the previous paper, decision methodology of reasonable true value of driver's intention in the experiment had been proposed. In this paper, implementation of real-time estimation for driver's intention on the driving simulator system with higher realistic has been introduced, and their validity have been discussed with experimental results.

In this paper, we propose the novel design method of directional selection for three-dimensional complex discrete wavelet transform (3D-CDWT) (hereafter, all abbreviations will be written with capital letters). Previously, the complex discrete wavelet transform has been able to extract directional edges from images using the directional selection property. This can be applied to shape and texture analysis. However the angular range of each directional edge is still unclear and the angular ranges are fixed. Thus, it is difficult to be applied to image processing. Therefore, we firstly clarify the angular range of directional edges by using frequency characteristics. Secondly, we propose a design method for the angular range of directional edges with a desirable angular range. As a result, we can clarify the angular ranges of directional edges and it is possible to obtain the directional edges with arbitrary angular range.

This paper presents a tele-control system comprised of a robot hand/arm and an operator. In our system, the angle of the finger of the robot hand is controlled according to the angle of the operator's finger, and the position of the robot arm is controlled according to the position of the operator's arm. Simultaneously, the operator feels the environmental force as detected by the touch sensor attached to the robot hand, resulting in so-called bilateral master/slave control. To date, there have been a few studies of bilateral master/slave systems that use a multi-fingered humanoid robot hand in communication networks with delay caused by physical distance. The purpose of our study was to achieve tele-operation with bilateral master/slave control between the operator's hand/arm and the multi-fingered humanoid robot hand/arm using the Internet with delay caused by distance. For our experiment, the operator was in the USA, and the multi-fingered humanoid robot hand/arm and object were in Japan. Using our system, the operator could successfully grasp and move an object while he felt the reaction force from the object. This technology can applicable to tele-operation such as feeling the hardness and weight of the stone which exist in the moon, on the ground etc.

In recent years, senior driver-related traffic accidents have been increasing while the number of deaths in these accidents has been decreasing. This also shows the complexity of the composition ratio of drivers to pedestrians in the traffic environment. In order to ensure traffic safety under this environment, not only is advanced technology for vehicle safety necessary but also traffic safety education of vulnerable road users. As one education method, the use of driving simulators is proposed in this paper, and its validity is evaluated through the application of the proposed method in a special course for high school students in our university. Finally, a questionnaire survey for students was conducted to confirm its effectiveness and the validity of the educational content.

In recent years, in order to reduce traffic accidents, support systems for driving safety have been put to practical use, such as ITS. However, a support system which considers the driver's intentions has not yet been developed. If the system reflects the intentions of the driver in system behavior, important information will be provided at suitable timing. In a previous study, the authors have studied an estimation method for driver intentions, and a reasonable definition method for labeling driver intentions has been proposed based on the assumption of compliance with traffic rules. In this paper, the validity of our proposed method by using a driving simulator is discussed. The system is specially designed to induce driver intention changes by using traffic flow and environmental control. Moreover, in order increase the effectiveness in practical use, a real-time estimation system has also been constructed.

Generally, inattentive driving is driving while in a state where attention has been weakened by a psychological factor. As a result, either the driver does not realize this condition or realization is delayed. In order to evaluate inattentive driving, the authors constructed a neural network-based NARX model for each driver using operation signals of neutral driving without secondary tasks and inattentive driving without secondary tasks and made the difference between the neutral driving and inattentive driving clear. However, the influence of the secondary task for inattentive driving is different for different drivers. Therefore, the problem is how to examine drivers' characteristics. In this study, in order to examine drivers' characteristics, we designed an experiment to measure a driver's reaction time, which is an important driver characteristic, by using a driving simulator, and obtained encouraging results.

Today, Virtual Reality (VR) technology has been applied in various fields, and the effects of inducing VR contents in humans have been studied. Generally, VR contents mainly consist of visual stimulation and it is considered that it influences the recognition mechanisms in the human brain, the behaviors in the human body and so on. In this research, in order to construct a methodology of VR content production with higher realism, an analysis method for behaviors in the human body induced by visual stimulation is studied. It is known that the sensation induced by visual stimulation affects the human behavior with a specific frequency component. If this kind of behavior can be modeled by using simple model parameters, it has the possibility to control the human behavior using visual stimulation or to increase its realism. In previous research, a parameterized first-order human behavior model has been proposed. The validity of the proposed model and its parameters has been confirmed through the analysis of verification experimental results. In this research, long-term experiments have been conducted to check the validity of the proposed analysis method. In addition, the possibility of applying the method as a time series modeling method to postural sway analysis is also considered.

A vehicle's steering wheel is a special interface because it needs to be grasped by the driver's hand while driving. This is very useful from the viewpoint of biological instrumentation. This may achieve steady instrumentation for biological information, and by using this information, an estimate of the driver's state can also be made. In past research, a steering wheel sensor system was proposed by the authors. It has 28 pressure sensor points, and several of its characteristics have been analyzed. In this paper, an estimation method of grasp behavior is proposed based on the distribution of grasp pressure data. Four kinds of steering wheel grasp states are defined. Finally, the estimation method and its results are evaluated experimentally.

This paper presents an evaluation of driving attentiveness by using Fuzzy Inference based on the driver's gaze when driving. Two cameras are used respectively to capture images in front of the driving car and to capture images of the driver's face in order to calculate the coordinates of the objects in front of the car and the driver's eye sight angle by using image processing. There are many kinds of inattentive driving such as loss of concentration on the objects in front of the vehicle, looking aside or idly thinking of some other things while driving, where we are able to detect the inattentive driving by evaluating the movement of the driver's gaze. Driving data with and without task were taken through offline mode experiment by using driving simulator. The tasks are to influence the concentration of the driver when driving. As a result, with the Fuzzy Inference that we proposed, inattentive driving can be detected due to the significant difference evaluation result between attentive driving and inattentive driving.

This paper presents a tele-control system constructed by a robot hand/arm and operator. The angle of the robot hand is controlled by the angle of the operator's finger, and the operator feels the environmental force, as detected by the touch sensor of the robot hand, constituting so-called bilateral master/slave control. The position of the robot arm is controlled by the position of the operator's arm.

Today, Virtual Reality (VR) technology has been widely applied in various fields. In past research, the influence of inducing VR in humans through the human senses or virtual effects has been studied. However the methods used to analyze human sensation have only involved measuring the center of gravity and questionnaire surveys. On the other hand, it is known that the sensation induced by VR affects the userfs behavior with specific frequency component. If this kind of behavior can be modeled by using simple model parameters, it has possibility to control the humanfs behavior using visual stimulation or to increase their reality. And its application field has widely for the VR application with higher reality or medical rehabilitation supports and so on. In this papar, the human behavior model using first order system with parameters has been proposed. And identification method for their parameters by using frequency analysis of human behavior with measurement system is also proposed. In addition, various experiments are performed using the proposed system in order to evaluate its effectiveness. As a result, validity of proposed model and its parameters are confirmed through the analysis of experimental results.

In order to prevent the traffic accident, several kind of safe driving support systems have been developed. And, consideration for the intention of driver's operation will be needed for prevalence of these systems with higher safety and availability. In this paper, estimation method for driver's intention considering using HMM has been proposed considering with the driver's individuality to its symbolizing process. The validity of the method has been examined by the experimental driving data on the driving simulator system. And the results make clear the possibility that the proposed method can be classified the driver's intention into the driving in hurry or normal driving.

On chrome-plated surfaces, specular reflection is very strong while reflection is very weak. This makes the automatic inspection on a chrome-plated very difficult. Based on analysis of chrome-plated reflection properties, this paper propose and develop a method to reduce specular reflection. Using the median of gradients of all input images taken with various lighting direction, we reconstruct a syntheses image that contains only diffuse reflection, and the useful information is remained. Experimental results on chrome-plated ABS resin show that the method is efficient in reduction of specular reflections. The work is the basis of create visual inspection system for chrome-plated surfaces.

Cellular neural networks (CNNs) are one type of interconnected neural network and differ from the well-known Hopfield model in that each cell has a piecewise linear output characteristic. In this paper, we present a multi-valued CNN model in which each nonlinear element consists of a multi-valued output function. The function is defined by a linear combination of piecewise linear functions. We conduct computer experiments of auto-associative recall to verify our multi-valued CNN's ability as an associative memory. In addition, we also apply our multivalued CNN to a disease diagnosis problem. The results obtained show that the multi-valued CNN improves classification accuracy by selecting the output level q properly. Moreover, these results also show that the multi-valued associative memory can expand both the flexibility of designing the memory pattern and its applicability.

The prevention of car accidents is one of the main issues in any car-oriented society. A method for this kind of prevention system is the estimation of the driver's conditions based on human sensing. This research's aim is the development of a measurement system for human behavior by using contact sensing on a steering wheel. In this paper, a sensing system using an array of small pressure sensors is proposed, and a design method for the sensor part on the steering wheel based on a fundamental analysis of the driver's operation of the steering wheel and its validity are discussed. Finally, characteristics of the proposed sensor system are evaluated through implementation on a real steering wheel device.

This paper presents a new method for modeling driver operation behavior. The proposed method is based on using the predictor coefficients as feature vectors extracted from driving operation signal by linear prediction analysis (LPA). The distribution of the feature vectors is captured by employing auto associative neural networks (AANN) model. The performance of the model was evaluated through driver identification process and the results obtained demonstrate that the model can grasp the individual characteristics of the driver.

Prevention for car accident is one of main issue in the car-oriented society. This research aims at the development of fundamental solution for decrease the car accidents. For this purpose, driver model for evaluation of abnormal driver's operations through the handling behavior are proposed. In this paper, analysis method for driver's operations of left-turning in a intersection and sensing method using steering wheel with pressure sensors have been discussed. Through the experimental results and questionnaire survey with 11 subjects, validity of the proposed analysis method has been confirmed. And the basic design of steering wheel for sensing also defined based on the grabing style survey.

Nowadays, research and development in vehicle safety system has been intensively conducted. However, research focusing on detection unsafe signal from driving behavior is much less defined and explored. Abnormal driving behavior is a result of instability of internal state of the driver that could lead to uncomfortable and dangerous situation in driving task. In this study we described a method for abnormal driving behavior detection and classification using statistical analysis. Two parts of driving data called as start and stop period were used in the analysis. Preliminary result from analysis shows that the driver control behavior can be characterize into normal and abnormal using the proposed method based on jerk information. Through questionnaire survey, the relationship between driving behavior and internal state of driver is verified.

Prevention of traffic accidents is one of the main issues in car-oriented societies. In this paper, we aim at a fundamental solution for decreasing car accidents. For this purpose, models for the evaluation of a driver’s ability and a database of handling ability are proposed. Methodologies of construction of the model and database using a driving simulator are discussed. Through results of the experiments performed with five participants, the validity of the proposed methods has been confirmed.

In this paper, a design and implementation method of cellular neural networks with a multi-valued saturation function is discussed. The multi-valued saturation function is defined as a linear combination of piecewise linear functions that consist of a saturation range and a linear-slope range. The length of these ranges determine the output characteristics of each neuron, and is expressed by two parameters. We investigate the relation between these output characteristics and the ability of the multi-valued associative memory with computer simulation. Finally, we propose a design guideline for deciding its parameters from these results.