Search

Handover from WLAN to a cellular network in loosely coupled WLAN/Cellular systems is considered as a new call and therefore is not given any priority in the cellular network. Hence, it induces a remarkable delay that makes loosely coupled WLAN/Cellular systems inappropriate for online services. Due to this fact, during a handover from a WLAN to a cellular network, a mobile station (MS) may be trapped in a low bit rate region before completing the handover. This paper suggests using relay stations (RS's) during a handover to extend WLAN coverage until the handover is completed. When RS's are not available, especially in sparse hotspots, allocating a large amount of the WLAN bandwidth to the MS can assist the handover process. In contrast, in a dense hotspot, the probability of finding an appropriate RS is high. Thus, the handover could be completed by using the potential RS's inside the WLAN coverage. It is confirmed, by analytical modeling and simulation, that the proposed method can reduce the call dropping probability and improve the handover performance. [ABSTRACT FROM AUTHOR]

Driver behaviour is an important factor in road safety. Computer vision techniques have been widely used to monitor the driver behaviour. The violation of privacy and the possibility of spoofing are two continuing challenges in camera‐based systems. To address these challenges, we propose an efficient approach to monitor and detect driver behaviour based on movement characteristics of the vehicle rather than the visual features of the driver. The main goal of this paper is to classify the driver behaviour into five classes: safe, distracted, aggressive, drunk, and drowsy driving. A lightweight 1D Convolutional Neural Network with high efficiency and low computational complexity is suggested to classify the driver behaviour. Experimental results confirm that our method could successfully classify behaviours of a driver with accuracy of 99.999%. [ABSTRACT FROM AUTHOR]