Image and video analysis in unmanned aerial vehicle (UAV) systems have been a recent interest in many applications since the images taken by UAV systems can provide useful information in many domains including maintenance, surveillance and entertainment. However, a constraint on UAVs is having limited battery power and recent developments in the artificial intelligence (AI) domain encourages many applications to run computationally heavy algorithms on the taken UAV images. Such applications drain the power from the on-board battery rapidly, while requiring strong computationally strong resources. An alternative to that approach is offloading heavy tasks such as object segmentation to a remote (edge) server and perform the heavy computation on that server. However, the effect of the communication system and the used channel introduce noise on the transferred data and the effect of the noise due to the use of such LTE communication system on pre-trained deep networks has not been previously studied in the literature. In this paper, we study one such scenario where the images taken by UAVs and (the same images) transferred to an edge server via an LTE communication system under different scenarios. In our case, the edge server runs an off-the-shelf pretrained deep learning algorithm to segment the transmitted image. We provide an analysis of the effect of the wireless channel and the communication system on the final segmentation of the transmitted image on such a scenario.

Electric Vehicles (EVs) are rapidly becoming the forerunners of vehicle technology. First electric vehicles were overlooked because of not having adequate battery capacity and because of low efficiency of their electric motors. Developing semiconductor and battery technologies increased the interest in the EVs. Nevertheless, current batteries still have insufficient capacity. As a result of this, vehicles must be recharged at short distances (approximately 150 km). Due to scheduled departure and arrival times EVs appear to be more suitable for city buses rather than regular automobiles. Thanks to correct charging technology and the availability of renewable energy for electric buses, the cities have less noise and CO2 emissions. The energy consumption of internal combustion engines is higher than of the electric motors. In this paper, studies on the commercial electric vehicle charging methods will be reviewed and the plug-in charging processes will be described in detail. This study strives to answer the questions of how plug-in charging process communication has performed between the EV and Electric Vehicle Supply Equipment (EVSE).