Identity verification using computer vision for automatic garage door opening

We present a novel system for automatic identification of vehicles as part of an intelligent access control system for a garage entrance. Using a camera in the door, cars are detected and matched to the database of authenticated cars. Once a car is detected, License Plate Recognition (LPR) is applied using character detection and recognition. The found license plate number is matched with the database of authenticated plates. If the car is allowed access, the door will open automatically. The recognition of both cars and characters (LPR) is performed using state-ofthe- art shape descriptors and a linear classifier. Experiments have revealed that 90% of all cars are correctly authenticated from a single image only. Analysis of the computational complexity shows that an embedded implementation allows user authentication within approximately 300ms, which is well within the application constraints

High dimensionality carrierless amplitude phase modulation technique for radio over fiber system

Advanced modulation formats such as carrierless amplitude phase (CAP) modulation technique is one of the solutions to increase flexibility and high bit rates to support multi-level and multi-dimensional modulations with the absence of sinusoidal carrier. Recent work are focussing on the 2D CAP-64 QAM Radio-over-Fiber (RoF) system but no extension of higher dimensions is reported. This thesis expands the area of CAP modulation technique and RoF system. The work described in this thesis is devoted to the investigation of 1.25 GSa/s sampling rate for multi-level and multi-dimensional CAP in point-to-point (P2P) and RoF system at 3 km single-mode fiber (SMF). Another advanced modulation format which is known as discrete multitone (DMT) is compared with CAP modulation in order to observe the performance in different modulation schemes. The 4QAM-DMT and 16QAM-DMT at different number of subcarriers are carried out in this propagation. Based on the results, the transmission performance in terms of BER and received optical power for RoF transmission are degraded to almost 3 dB when comparing to 3 km SMF transmission. These are caused by the wireless power loss and impairment effects. The bit rate and spectral efficiency can be increased with the increasing number of levels, and may decreased once the number of dimensions is increased due to the higher up-sampling factor. However, the additional dimensions can be used to support multiple service applications. Therefore, it can be concluded that CAP has better performance as compared to DMT in terms of higher spectral efficiency and data rate. To conclude, the results presented in this thesis exhibit high feasibility of CAP modulation in the increasing number of dimensions and levels. Thus, CAP has the potential to be utilized in multiple service allocations for different number of users

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving