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Akihiko Sugiura

Bio: Akihiko Sugiura is an academic researcher from Shizuoka University. The author has contributed to research in topics: Wireless & Bluetooth. The author has an hindex of 4, co-authored 41 publications receiving 77 citations. Previous affiliations of Akihiko Sugiura include Toyohashi University of Technology.

Papers
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Journal ArticleDOI
TL;DR: In this paper, in order to minimize the costs of equipment and simplify the design, equipment, structure of VC, IVC, and RVC for traffic jam area, the authors proposed to utilize a wireless Bluetooth technology system.
Abstract: At present, in the field of intelligent transport systems (ITSs), research on in-vehicle communications (VCs), intervehicle communications (IVCs), road-to-vehicle communications (RVCs), etc. continues. All information communication technology, especially radio communications technology, was applied. In this paper, in order to minimize the costs of equipment and simplify the design, equipment, structure of VC, IVC, and RVC for traffic jam area, the authors proposed to utilize a wireless Bluetooth technology system. The whole proposed system is connected to the Internet backbone, and provided some access point (AP) areas, the Internet can be accessed from inside the vehicle, and information, such as news and weather information, can be downloaded. It is also possible to know traffic information for each AP area by accessing a data center server.

35 citations

Proceedings ArticleDOI
01 Aug 2017
TL;DR: Results show that various patterns are classified as non-defective with high accuracy, demonstrating that the convolutional neural network method is effective for objects with greatly variable shape elements such as rubber products.
Abstract: This study examines a method of applying a convolutional neural network to classify defects occurring on various patterns on the inner surface of a tire by conducting three-dimensional (3D) shape measurement using the light-section method on the inner tire surface Classification objects are divided into “good parts,” “quasi-good parts,” and “defective parts” “Good parts” and “quasi-good parts” are classified as non-defective, but “defective parts” are classified as defects The experimentally obtained results revealed that a two-step classifier for classifying defects can improve classification accuracy Additionally, results show that various patterns are classified as non-defective with high accuracy, demonstrating that the convolutional neural network method is effective for objects with greatly variable shape elements such as rubber products This method is anticipated as a method that is suitable for defect inspection automation

8 citations

Journal ArticleDOI
TL;DR: In this article, the Enjoji method was used to detect mental retardation in children under age three by analyzing children's reactions while a feedback image is displayed for two minutes.
Abstract: Results have shown that in the first three years of human life, the brain undergoes most of its growth. If mentally retarded children could be detected before the age of three, correct treatment could be prescribed at an early stage before the brain completely develops. Therefore, the possibility for the brain’s recovery would be higher. In this study, we detect mentally retarded children at an early age merely by analyzing children’s reactions while a feedback image is displayed for two minutes. Results showed that by Social Reaction Test, we verified that our system renders the same evaluation as the Enjoji method. Furthermore, detection of mental retardation in children under age three was possible.

4 citations

Proceedings ArticleDOI
23 Jul 2000
TL;DR: If one can find a slight cerebral disease and rank evaluation, one can apply this to rehabilitation, and the load of both the medical doctor and the patient decreases, the authors say.
Abstract: Recently, cerebral disease has started to become a serious problem in an aging society. Rank evaluation of cerebral disease has not been developed and therefore rehabilitation is hard. In this study, the authors try to assess slight cerebral disease by using face recognition techniques and realizing face image synthesis using computer technology. If one can find a slight cerebral disease and rank evaluation, one can apply this to rehabilitation, and the load of both the medical doctor and the patient decreases. The authors have obtained results from experiments which they report here.

4 citations


Cited by
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Journal ArticleDOI
TL;DR: This article presents several major classes of applications and the types of services they require from an underlying network and analyzes existing networking protocols in a bottom-up fashion, from the physical to the transport layers, as well as security aspects related to IVC systems.
Abstract: Inter-vehicle communication (IVC) systems (i.e., systems not relying on roadside infrastructure) have the potential to radically improve the safety, efficiency, and comfort of everyday road travel. Their main advantage is that they bypass the need for expensive infrastructure; their major drawback is the comparatively complex networking protocols and the need for significant penetration before their applications can become effective. In this article we present several major classes of applications and the types of services they require from an underlying network. We then proceed to analyze existing networking protocols in a bottom-up fashion, from the physical to the transport layers, as well as security aspects related to IVC systems. We conclude the article by presenting several projects related to IVC as well as a review of common performance evaluation techniques for IVC systems.

507 citations

Journal ArticleDOI
TL;DR: This tutorial survey collates research across a number of topics in V2X, from historical developments to standardization activities and a high-level view of research in anumber of important fields to provide a useful reference for the state of V2x research and development for newcomers and veterans alike.
Abstract: As we edge closer to the broad implementation of intelligent transportation systems, the need to extend the perceptual bounds of sensor-equipped vehicles beyond the individual vehicle is more pressing than ever. Research and standardization efforts toward vehicle to everything (V2X), technology is intended to enable the communication of individual vehicles with both one another and supporting road infrastructure. The topic has drawn interest from a large number of stakeholders, from governmental authorities to automotive manufacturers and mobile network operators. With interest sourced from many disparate parties and a wealth of research on a large number of topics, trying to grasp the bigger picture of V2X development can be a daunting task. In this tutorial survey, to the best of our knowledge, we collate research across a number of topics in V2X, from historical developments to standardization activities and a high-level view of research in a number of important fields. In so doing, we hope to provide a useful reference for the state of V2X research and development for newcomers and veterans alike.

290 citations

Journal ArticleDOI
TL;DR: A new Bayesian combination method (BCM) is developed here that is more sensitive to the perturbed performance of the component predictors and can adjust their credits more rapidly, and better predictions are generated as a result.
Abstract: The Bayesian combination method (BCM) proposed by Petridis et al. (2001) is an integrated method that can effectively improve the predictions of single predictors. However, research has found that it considers redundant prediction errors of component predictors when calculating their credits, which makes it quite impervious to the fluctuated accuracy of the component predictors. To address this problem, a new BCM has been developed here to improve the performance of the traditional BCM. It assumes that at one prediction interval, the traffic flow is correlated with the traffic flows of only a few previous intervals. With this assumption, the credits of the component predictors in the BCM are only accounted for by their prediction performance for a few intervals rather than for all intervals. Therefore, compared with the traditional BCM, the new BCM is more sensitive to the perturbed performance of the component predictors and can adjust their credits more rapidly, and better predictions are generated as a result. To analyze the relevancy between the historical traffic flows and the traffic flow at the current interval, the entropy-based grey relation analysis method is proposed in detail. Three single predictors, namely the autoregressive integrated moving average (ARIMA), Kalman filter (KF) and back propagation neural network (BPNN) are designed and incorporated linearly into the BCM to take advantage of each method. A numerical application demonstrates that the new BCM considerably outperforms the traditional BCM both in terms of accuracy and stability.

141 citations

Journal ArticleDOI
TL;DR: A data analytic methodology to extract critical information from raw BSM data available from SPMD, and a framework for generating alerts/warnings, and control assistance from extreme events, transmittable through V2V and V2I applications.
Abstract: When vehicles share their status information with other vehicles or the infrastructure, driving actions can be planned better, hazards can be identified sooner, and safer responses to hazards are possible. The Safety Pilot Model Deployment (SPMD) is underway in Ann Arbor, Michigan; the purpose is to demonstrate connected technologies in a real-world environment. The core data transmitted through Vehicle-to-Vehicle and Vehicle-to-Infrastructure (or V2V and V2I) applications are called Basic Safety Messages (BSMs), which are transmitted typically at a frequency of 10 Hz. BSMs describe a vehicle’s position (latitude, longitude, and elevation) and motion (heading, speed, and acceleration). This study proposes a data analytic methodology to extract critical information from raw BSM data available from SPMD. A total of 968,522 records of basic safety messages, gathered from 155 trips made by 49 vehicles, was analyzed. The information extracted from BSM data captured extreme driving events such as hard accelerations and braking. This information can be provided to drivers, giving them instantaneous feedback about dangers in surrounding roadway environments; it can also provide control assistance. While extracting critical information from BSMs, this study offers a fundamental understanding of instantaneous driving decisions. Longitudinal and lateral accelerations included in BSMs were specifically investigated. Varying distributions of instantaneous longitudinal and lateral accelerations are quantified. Based on the distributions, the study created a framework for generating alerts/warnings, and control assistance from extreme events, transmittable through V2V and V2I applications. Models were estimated to untangle the correlates of extreme events. The implications of the findings and applications to connected vehicles are discussed in this paper.

99 citations

Journal ArticleDOI
TL;DR: Simulation results demonstrate that the proposed ART-GAS algorithm significantly outperforms the existing GTS mechanism specified in IEEE 802.15.4 in terms of success probability, average delay, average waiting time, and CFP bandwidth utilization.

71 citations