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

Parking lot monitoring system using an autonomous quadrotor UAV

The main goal of this thesis is to develop a drone-based parking lot monitoring system using low-cost hardware and open-source software. Similar to wall-mounted surveillance cameras, a drone-based system can monitor parking lots without affecting the flow of traffic while also offering the mobility of patrol vehicles. The Parrot AR Drone 2.0 is the quadrotor drone used in this work due to its modularity and cost efficiency. Video and navigation data (including GPS) are communicated to a host computer using a Wi-Fi connection. The host computer analyzes navigation data using a custom flight control loop to determine control commands to be sent to the drone. A new license plate recognition pipeline is used to identify license plates of vehicles from video received from the drone

Car make and model recognition under limited lighting conditions at night

Car make and model recognition (CMMR) has become an important part of intelligent transport systems. Information provided by CMMR can be utilized when license plate numbers cannot be identified or fake number plates are used. CMMR can also be used when a certain model of a vehicle is required to be automatically identified by cameras. The majority of existing CMMR methods are designed to be used only in daytime when most of the car features can be easily seen. Few methods have been developed to cope with limited lighting conditions at night where many vehicle features cannot be detected. The aim of this work was to identify car make and model at night by using available rear view features. This paper presents a one-class classifier ensemble designed to identify a particular car model of interest from other models. The combination of salient geographical and shape features of taillights and license plates from the rear view is extracted and used in the recognition process. The majority vote from support vector machine, decision tree, and k-nearest neighbors is applied to verify a target model in the classification process. The experiments on 421 car makes and models captured under limited lighting conditions at night show the classification accuracy rate at about 93 %

Vision-based Detection of Mobile Device Use While Driving

The aim of this study was to explore the feasibility of an automatic vision-based solution to detect drivers using mobile devices while operating their vehicles. The proposed system comprises of modules for vehicle license plate localisation, driver’s face detection and mobile phone interaction. The system were then implemented and systematically evaluated using suitable image datasets. The strengths and weaknesses of individual modules were analysed and further recommendations made to improve the overall system’s performance

Recent Developments in Video Surveillance

With surveillance cameras installed everywhere and continuously streaming thousands of hours of video, how can that huge amount of data be analyzed or even be useful? Is it possible to search those countless hours of videos for subjects or events of interest? Shouldn’t the presence of a car stopped at a railroad crossing trigger an alarm system to prevent a potential accident? In the chapters selected for this book, experts in video surveillance provide answers to these questions and other interesting problems, skillfully blending research experience with practical real life applications. Academic researchers will find a reliable compilation of relevant literature in addition to pointers to current advances in the field. Industry practitioners will find useful hints about state-of-the-art applications. The book also provides directions for open problems where further advances can be pursued