Scanning the Issue

Azim Eskandarian
2020 IEEE transactions on intelligent transportation systems (Print)  
Traditional industry is experiencing a worldwide evolution with Industry 4.0. Wireless sensor networks (WSNs) have a main role in this evolution as an essential part of data acquisition and the way in which WSNs are powered is one of the main challenges to face, if the industry wants to achieve digital transformation. Energy harvesting technologies are one of the possible solutions to this challenge. The main purpose of this article is to present a novel method to taxonomize knowledge in the
more » ... ld of mechanical energy harvesting to enhance the use of energy harvesting technologies in industrial applications. The taxonomy is applied to rail axles in order to select the energy harvesting technology that is more appropriate for a specific location, demonstrating the potential of mechanical energy harvesting technologies (MEHTs) for the railway industry as a use case of the industrial environment. Detection efficiency in vehicular ad hoc networks (VANETs) is critical to human safety. Thus it is important for Intrusion Detection Systems (IDSs) to quickly ascertain the reliability of exchanging messages among vehicles. In this article, the authors propose a novel filter model based on a hidden generalized mixture transition distribution (HgMTD) model for IDSs in VANETs, called FM-HgMTD. The proposed filter model combines a well-known multiobjective optimization algorithm, namely NSGA-II, with an expectation-maximization (EM) algorithm to build up a tailormade HgMTD model for all individual neighboring vehicles, so as to forecast the future states of neighboring vehicles to quickly filter out malicious messages. Moreover, a timeliness method is further designed in FM-HgMTD to maintain the accuracy of the forecast. The experiments show that the IDS with FM-HgMTD has a better performance than the other available IDSs for VANETs in terms of detection rate, detection time, and overhead. Ground Vehicle Navigation in GNSS-Challenged Environments Using Signals of Opportunity and a Closed-Loop Map-Matching Approach M. Maaref and Z. M. Kassas A ground vehicle navigation approach in a global navigation satellite system (GNSS)-challenged environments is Digital Object Identifier 10.1109/TITS.2020.3001822 developed, which uses signals of opportunity (SOPs) in a closed-loop map-matching fashion. The proposed approach employs a particle filter that estimates the ground vehicle's states as it navigates without GNSS signals, by fusing pseudoranges drawn from ambient SOP transmitters with road data from commercial maps. The simulation and experimental results with cellular long-term evolution (LTE) SOPs are presented, evaluating the efficacy and accuracy of the proposed framework in different driving environments. The experimental results demonstrate a position root-mean-squared error of 1.6 m over an 825 m trajectory in an urban environment with five cellular LTE SOPs, 3.9 m over a 1.5 km trajectory in a suburban environment with two cellular LTE SOPs, and 3.6 m over a 345 m trajectory in a challenging urban environment with two cellular LTE SOPs. Although the 3D reconstruction of dynamic road environment by moving cameras has been broadly applied in recognition and navigation systems, this task is still considered challenging, especially under circumstances with moving objects, where the reconstruction precision is strongly harassed by the ghosting problem. To address this issue, in this article, the authors propose a novel approach for reconstructing 3D maps of complete static scenes, based on a combination of an elaborately designed moving-object filtering mechanism and a map repairing and blank refilling procedure, where both plausible color and depth information from stereo image pairs are utilized. In this approach, first, the authors employ the planarity knowledge into the initial depth map based on the simple linear iterative cluster (SLIC) superpixel segmentation. The dynamic area in the image is determined under the supervision of odometry calculation. After wiping off moving objects, by collaboratively repairing color and depth information, the final 3D map containing only static scene is obtained. The experimental results on extensive challenging real-world scenarios demonstrate the effectiveness and robustness of their approach. In many parts of the world, passengers traveling on underground metro systems do not enjoy uninterrupted Internet connectivity. This results in passenger frustration since during such trips the access of online social media services is a highly popular activity. Being the world's oldest underground
doi:10.1109/tits.2020.3001822 fatcat:yk66ziu2kbgsfbit6mabxfwrdu