Excessive lighting or sunlight can make it difficult to judge visually. The same goes for cameras that function like the human eye. In the field of computer vision, object tasks have a significant impact on performance depending on how much object information is provided. Light presents difficulties in recognizing objects, and recognition is not easy in shadows or dark areas. In this paper, we propose a contrast enhancement-based preprocessing process to obtain improved results in object recognition tasks by solving problems that occur due to light or lighting conditions. The proposed preprocessing process involves the steps of extracting optimal values, generating optimal images, and evaluating quality and similarity, and it can be applied to the generation of training and input data. As a result of an experiment in which the preprocessing process was applied to an object task, the object task results for areas with shadows or low contrast were improved while the existing performance was maintained for datasets that require contrast enhancement technology.

There is a strong devotion in the automotive industry to be part of a wider progression towards the Fifth Generation (5G) era. In-vehicle integration costs between cellular and vehicle-to-vehicle networks using Dedicated Short Range Communication could be avoided by adopting Cellular Vehicle-to-Everything (C-V2X) technology with the possibility to re-use the existing mobile network infrastructure. More and more, with the emergence of Software Defined Networks, the flexibility and the programmability of the network have not only impacted the design of new vehicular network architectures but also the implementation of V2X services in future intelligent transportation systems. In this paper, we define the concepts that help evaluate software-defined-based vehicular network systems in the literature based on their modeling and implementation schemes. We first overview the current studies available in the literature on C-V2X technology in support of V2X applications. We then present the different architectures and their underlying system models for LTE-V2X communications. We later describe the key ideas of software-defined networks and their concepts for V2X services. Lastly, we provide a comparative analysis of existing SDN-based vehicular network system grouped according to their modeling and simulation concepts. We provide a discussion and highlight vehicular ad-hoc networks’ challenges handled by SDN-based vehicular networks.

Unmanned vehicles (Smart cars, drones, robots) need to understand and respond to the surrounding environment in order to perform their tasks. Therefore, they must be equipped with vision capabilities (vision sensors) to let them to detect the presence of objects in their paths or measure how far away objects are from the sensor depending of the type of application. Several technologies have been used to deal with this computer vision application such stereovision, LIDAR and RADAR but each of them still has advantages and disadvantages in term of limitations and price. In this paper, we developed one of the functions of unmanned vehicles to detect the presence of objects and measure how far they are using Kinect depth sensor, a low-cost range sensor along with its higher depth fidelity and attractive alternative in computer vision. The results showed that Kinect sensor can detect along with segmentation techniques the presence of objects in its field of view and measure how far they are from the sensor. Based on results, Kinect can be mounted on unmanned vehicles like a vision sensor for obstacle avoidance application or other applications which require a vision sensor to measure how far detected objects are from it as well for also manned vehicles to alert drivers but mostly computer vision has a crucial role in vehicles without human intervention.

A mobile ad-hoc network which does not use a wired network and base station system is composed of a group of mobile and wireless nodes. There are various types of restrictions. The biggest restriction is the confined energy of the batteries. If the network is divided into more than two, and one of the nodes consumes all the energy, that node can no longer participate in the network. In recent years, much research has been under taken to not only improve the energy storage but also to lengthen the networks lifetime. In this paper, we propose that the enhanced AODV (Ad-hoc On-demand Distance Vector) routing protocol which is modified to improve the networks lifetime in MANET (Mobile Ad-hoc NETwork). One improvement for the AODV protocol is to maximize the networks lifetime by applying an Energy Mean Value algorithm which considerate node energy-aware. We know the effectiveness of enhanced AODV compared with existing AODV by using NS-2. ..