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Ubiquitous computing analysis represents an emerging area that implements communication technologies in day-to-day life actions. Ubiquitous computing changes the methods in which people use computers, considering these are involved in everyday aspects. In ubiquitous computing, several types of processes operate automatically in the background and communicate on the user's account. The ubiquitous computing theory is to provide any information for everyone at anytime and anywhere instantly. Mobile ad-hoc Networks are currently a growing technology for the next generation of wireless communication networks. A mobile ad-hoc network can portray as a military or rescue operation network in which a set of mobile nodes are used to send out a mission operation in diplomatic terms. This paper presents common architecture principles of universal systems and analyses significant features in context-aware ubiquitous systems. The main purpose of this work is to define a principle for researchers who are new to ubiquitous computing and want to gain depth on analysis and implementation of a novel method for the ubiquitous computing system in military sectors, in order to contribute towards further research regulations expected into the quality-of-service pledge of ubiquitous computing. The ubiquitous computing is the future of technology related to the internet or smart devices. Moreover, the applicability of technologies found in smartphones, smart TVs and sensors can guide to an increase in strategic capabilities, like sensing and detecting, exchanging and sharing unique real-time data in the military field. This study aims to sketch particular methods by which the learning and teaching methods can be improved. The intelligence of technology advanced in the military sectors along with ubiquitous computing providing a rise in optimization, security, and defence. [ABSTRACT FROM AUTHOR]

The number of vehicles is constantly increasing and one of the challenges is efficient parking management. For this case, object recognition is required to detect and recognize cars, pedestrians, bicycles and obstacles in an indoor car parking. Cars must be recognized in order to indicate to drivers where to park their car, providing a semantic web of things. Vehicle recognition including car registration number must be followed by tracking, i.e., tracking the car in all subsequent image frames even if the image is partially obturated. Pedestrians should also be recognized for avoiding accidents, as well as being aware of different static objects/obstacles. This paper deals with object recognition through the Deep Learning (DL) method with Convolutional Neural Network (CNN), this being the newest and most efficient method based on our results in project VIRTUOSE (Virtualized Video Services). The paper briefly introduces CNN, image databases, and performance metrics. Then, several collaborative web methods/programs for object recognition are analyzed and compared, namely YOLO (You only look once), Google Cloud Vision, Advanced Convolutional Neural Networks in the Tensorflow framework and OpenCV. We analyze how programs detect and recognize multiple types of objects in an image or video, in real time. In case of video analysis on a PC / laptop, it is necessary that it has an NVIDIA module with several GPUs. The comparison refers especially to the precision of recognition, image resolution, the computation time (real-time response), viewpoint variations. The novelty of the work is the special structure of CNN suitable to the parking management application. [ABSTRACT FROM AUTHOR]