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The Internet of Things plays an indispensable role in the development of connected vehicles, which will pave the way for road safety applications. In recent years, the concept of a cooperative collision warning system (CCWS) has been introduced and developed to enhance road safety, and it has been seen as a typical Internet-of-Vehicles application. In most CCWSs, it is vital to have a detection mechanism based on trajectory predictions where the uncertainties associated with vehicular state and motion are complex. However, most available approaches in this regard did not consider these uncertainties. Hence, this article proposes a new collision risk assessment (CRA) method where sigma trajectories that include multiple possible trajectories considering multiple aspects of vehicular motion are designed to cope with vehicular uncertainties. Our method is implemented in a novel server-based architecture, which is different from the commonly used vehicle-based controlled CCWSs. The CRA is provided as a service by a cloud server. The proposed method and architecture are validated and evaluated through extensive real-world experiments. Experimental results show that our method outperforms a referenced method in terms of CRA and achieves better robustness in tolerating communication delays and dropouts. Latencies in CRA service were analyzed, and it was found that powerful computing resources provided by cloud servers can significantly decrease computational cost, which will indirectly compensate for communication costs in the future. Based on our high-performance CRA method, the proposed architecture can be regarded as a novel option for CCWS design.

Vehicle state estimation and path prediction, which usually involve Kalman filter and motion model, are critical tasks for intelligent driving. In vehicle state estimation, the comparative performance assessment, regarding accuracy and efficiency, of the unscented Kalman filter (UKF) and the extended Kalman filter (EKF) is rarely discussed. This paper is devoted to empirically evaluating the performance of UKF and EKF incorporating different motion models and investigating the models’ properties and the affecting factors in path prediction. Extensive real world experiments have been carried out and the results show that EKF and UKF have roughly identical accuracy in state estimation; however, EKF is faster than UKF generally; the fastest filter is about 2⋅6 times faster than the slowest. The path prediction experiments reveal that the velocity estimate and the used motion model affect path prediction; the more realistically the model reflects the vehicle's driving status, the more reliable its predictions.

Pedestrian detection sensors in road infrastructure and GPS sensors mounted in smartphones have been used in driving safety support systems for detecting pedestrians. However, although pedestrian detection sensors in road infrastructure can detect pedestrians' high-precision position, they cannot acquire the attribute information of pedestrians. Conversely, a smartphone's built-in sensor can send GPS location information, user identifier, and the attribute information of a user, but it has a margin of error from meters to more than tens of meters in GPS location information. This renders acquiring a pedestrian's high-precision location and attribute information simultaneously impossible. To date, no research on the simultaneous acquisition of pedestrian high-precision position and attribute information has been conducted. To fill this gap, this paper proposes an approach to extract both pedestrian high-precision position and attribute information in real-time based on multi-sensor fusion. Moreover, an experiment is conducted to evaluate the proposed approach.

A point cloud is a data set including a number of 3-dimensional point data. Mobile robots such as autonomous vehicles require point clouds for localization, which is a technique for estimating self-position. Localization is a kind of similarity searches between the current point cloud from on-board sensors and historical point clouds observed in advance. Thus, how to manage historical point clouds is quite important. But, some experimental systems for mobile robots do not care about data management of historical point clouds. They are usually stored in text files and are fully loaded on memory at the time of startup. We believe that historical point clouds should be managed by DBMS and loaded by queries if need arises. Before challenging this research theme, in this paper, we investigate performance of querying point clouds stored in RDBMS (PostgreSQL and PostGIS). We measure execution time for range query and rectangular query, and performance of B-tree index and R-tree index.

Liquid lithium (Li) jet is planned as a beam target in fusion neutron sources (FNSs), such as IFMIF in Japan and EU, A-FNS in Japan and IFMIF-DONES in EU. For the safety and the efficiency of such FNSs, it is desirable to keep the high-speed Li jet stable. In addition to many experimental researches, numerical approaches using CFD simulation have been also required to evaluate the detailed flow pattern inner the Li jet because of the opacity. In our previous simulation, it was found that the vortex structure under the free surface of the Li jet had the strong influence on the surface fluctuation using LES simulation. In this study, for evaluating the interaction between the Li surface behavior and the flow pattern inner the Li jet, two simulations are conducted: one is the LES simulation considering wall roughness, and another one is the RANS simulation using full-scale model with the nozzle and the Li jet. The influence of wall roughness on the vortex structure becomes an issue in long-term operation of actual FNSs, and the influence of the secondary flow generated due to the side wall on the surface shape is evaluated in the latter simulation.

Mammalian circadian rhythms are governed by an endogenous circadian clock system, including the molecular clock works in each cell and tissue. Adaptation of the circadian clock to different environmental stimuli such as light, food, and stress is essential for homeostasis maintenance. However, the influence of oxidative stress on the circadian clock phase is not fully understood 'in vitro' and 'in vivo'. Here, we examined the effects of hydrogen peroxide (H2O2)-induced oxidative stress on the PERIOD2::LUCIFERASE bioluminescence rhythm in mouse embryonic fibroblasts 'in vitro' and in mouse peripheral tissues 'in vivo'. The circadian clock phase changed with the dose of H2O2 and time of day 'in vitro'; similar phase changes were observed 'in vivo' in the circadian clocks of the peripheral tissues. In addition, mice treated with hemin-induced oxidative stress also showed phase changes of peripheral clocks, similarly as H2O2 treatment. Thus, oxidative stress can entrain circadian clock systems.

We discuss the principle and biomedical utility of dispersive Fourier transformation - a method that maps the spectrum of a pulse into a temporal waveform whose intensity mimics the spectrum for fast continuous single-shot spectroscopic measurements.

In situ ellipsometry was carried out as well as ex situ measurements by scanning electron microscopy and Raman spectroscopy for the analyses of substrate surface in the first stage of graphene growth in plasma-enhanced chemical vapor deposition. Evolutions of the ellipsometric parameters Ψ and Δ were precisely measured during the growth of graphene with the sensitivity far less than 1 nm in film thickness. By the fitting of the experimentally obtained trajectory of ellipsometric parameters on the Ψ–Δ coordinate plane to that of the calculated ones, we confirmed that the graphite volume fraction decreased with growth after a dense graphite material initially formed. This suggests that carbon nanowalls grew on a thin graphitic layer.