Thesis Title: An Efficient Multi-Destinations Trip Planning Protocol for Intelligent Transport System
Author: Hisham Jaber Naseir Siam, Supervisor: Maram Bani Younes, Year: 2018
Faculty: Information Technology, Department: Computer Science

Abstract: Road congestion results in a huge waste of time and productivity for millions of people. Selecting the optimal path for vehicles, based on the real-time traffic information, is a possible way to deal with this problem, which, in turn, can help drivers decide to route around congested areas. Several studies have been proposed in this field using the intelligent transport system technology (ITS). ITS describe technology applied to vehicles as well as infrastructure to transfer information between them for improving safety, productivity and minimizing traffic congestion. Information can be gathered by relying on transceivers placed at each vehicle and road side units (RSUs) placed at specific road locations to gather vehicles data which will include their location, speed, destination and travel time. Although this technology has been widely exploited, existing studies have never addressed the issue of multi-destinations trip. For this reason, in this work, we propose a Multi-Destination trip Planning Protocol (MDPP). Drivers intend to visit more than one destination at the same trip is a common scene over the road network. We aim to advice these drivers by considering the location of each destination in the trip and the real-time traffic information of the road network. MDPP will recommend the best sequence of visiting the targeted destinations. MDPP aims mainly to decrease the traveling time, fuel consumptions and gas emissions of each trip. To evaluate our work and the effectiveness of MDPP, we developed a simulation experiment that has been executed repeatedly 30 times using different traffic scenarios and congestion levels. Since previous studies have not tackled multi-destinations trip planning, we compared MDPP results with default used common sequences for visiting multiple destinations, they are Closest Destination First sequence and Random Destination First sequence. The results of the simulation experiment showed that MDPP is outperforming both default common sequences.

Keywords: ITS, Vehicular Network, Path Planer Protocol, Multi-destinations, Downtown

Thesis Title: A Bio-inspired Unified Modeling Language Class Diagram Variability Modeling
Author: Kifaya Ahmad Al Shalabi, Supervisor: Said Ghoul, Year: 2018
Faculty: Information Technology, Department: Computer Science

Abstract: Recently, Software Product Lines (SPLs) is a widely used approach to represent the variability modelling in variant software products. Feature Model (FM) is the most important technique used to define and manage the variability through products in SPLs. Software design variability model, simplifying the maintenance process, is an emerging approach in software engineering and is therefore important. One of ways to represent the software design is by using UML class diagram model. The design modelling using UML class diagram is still a challenge because it needs specific model for each system requirements, and it does not support variability meta modelling at several levels. Many researches dealt with class diagram variability modelling, but all of them are far from real world, and the feature models they are based on are not covering all class diagram concepts. In addition, the current approaches are not supported by a complete modelling methodology )domain variability modelling, application family modelling, specific application modelling). So, this thesis proposes a complete methodology (guiding from domain variability modelling in class diagrams to application specific class diagrams generation) and formal methodology dealing with the above shortages. Its process is carried out through Domain variable class diagram meta modelling, variable application family class diagram generating, and application specific class diagram generating. Bio-inspired, it decreases the gap between computing concepts and real world ones. It supports class diagram variability modelling by introducing versions and revisions features and related relations. A comparative evaluation with the closest recent works, upon some meaningful criteria in the domain, shows the conceptual value of the proposed methodology and leads to promising research perspectives.

Keywords: Bio-inspired, Unified Modeling Language, Class Diagram, Variability Modeling

Thesis Title: Path Planning in Nonholonomic System Using Hybridization of Voronoi and Q-Learning Algorithms
Author: Mustafa M. Hassan, Supervisor: Moayed A. Fadhil Al-athami, Year: 2018
Faculty: Information Technology, Department: Computer Science

Abstract: Robot movement in an environment may collide with obstacles. Path planning is an important part of a navigation system which is composed of four parts: presentation, localization, path planning and path execution. The path planning part is defined as a series of movement that leads the mobile robot to move from the starting point to the target without colliding with any obstacle. The mobile robot path planning is very important especially in the environment where it's very dangerous for humans to work in such as heavy workshops, power electric environment etc. The aim of this study is to solve path planning problems by using the hybridization of Voronoi and Q-learning algorithms. The Voronoi algorithm has the ability to obtain an obstacle position represented by multiple points in space. On the other hand, Q-learning has a good performance in navigation strategy. The simulation of the proposed work has the ability to use a different style of maps that represent the robot, the target, and the obstacle positions. The application areas of the proposed work are considered as mechatronics engineering, industrial, and robotics. The evaluation process is performed in two main scenarios: the first one in static environment whiles the second one in the dynamic environment. Finally, a comparison with other related works is performed and the result of this comparisons shows that our algorithm provides better performance in terms of wasting the space and the time needed to reach the target.

Keywords: Path Planning, Nonholonomic System, Hybridization, Voronoi, Q-Learning Algorithms

Thesis Title: Using the Software Resource Model Sub-Profile for Multiscale Coupling Library and Environment Simulation Platform Modeling
Author: Mujahed Ahmad Ibrahim, Supervisor: Mohamed Bettaz, Year: 2018
Faculty: Information Technology, Department: Computer Science

Abstract: This research work proposes a development methodology that aims to adopt the UML/MARTE through its SRM and bridging the gap between the e-science and Software Engineering communities. In recent years it has been noticed that the design practices are moving from traditional code-based engineering to Model-Driven Engineering (MDE) approaches. In UML, a profile can be defined to customize the standard model elements for specific purposes. A profile furthermore defines one or more UML stereotypes in order to mark a type as a representation of a particular kind of object. In the Real-time and Embedded Systems (RTES) domain, the UML/MARTE is used. MARTE has a Generic Resource Model (GRM) profile which offers the concepts that are necessary to model a general platform for executing real-time embedded applications. The GRM has two profiles (SRM and HRM) acting as API. SRM (Software Resource Model) is a UML profile used to describe API of software execution and allows users to describe RTE API and specific RTE libraries (middleware) as well; which are involved in the design cycle. As a motivation and after deeper study conducted on the e-science and Software Engineering fields, we have found that both UML/MARTE and Distributed Multiscale Modeling and Simulation (DMMS) modelers are using similar modeling methodology (Y structure design). Furthermore, the fact that SRM sub-profile can be used to model OS (for instance: RTOS) as well as modeling specific RTE libraries or Middleware (in DMMS; MUSCLE might be seen as a kind of middleware). This motivated us to go forward in reusing the SRM sub-profile in the context of the simulation domain. As a result, the proposed software development methodology is applicable for some of MUSCLE core elements, some other elements could not be modeled by SRM due to the incompatibility issues with SRM stereotypes. Due to the result, it has been concluded that using the second way of modeling (extending the SRM) would be better for modeling all the core elements of MUSCLE.

Keywords: Resource Model, Sub-Profile, Multiscale

Thesis Title: Wireless Sensor Network Based Monitoring and Scheduling of Automated Factory
Author: Mohammed Hazim AL-Obaidi, Supervisor: Kasim Mousa AL-Aubidy, Year: 2018
Faculty: Engineering, Department: Mechatronics Engineering

Abstract: The objective of this thesis is to design and implement a real-time monitoring, scheduling, and control system for a factory automation using the concepts of wireless sensor networks. The proposed factory automation consists of four load/unload stations for programmable machines, a conveyor belt, a manipulator, and a MATLAB Simulink model. Each unit in the proposed factory automation is considered as a node in a wireless sensor network. A fuzzy-based algorithm has been developed as an intelligent decision maker to obtain the destination load/unload station for the selected object. The MATLAB Simulink has been applied and integrated with available devices to complete the required loop of hardware parts. The feedback signals from IP camera, machines load/unload stations are used by the controllers to obtain the required control signals to manage the overall operation of the automated factory. A fuzzy-based algorithm has been used for decision making, scheduling, and routing purposes. The results from both experimental and simulated systems confirm that the implemented automated factory perform the required tasks with acceptable accuracy and speed.

Keywords: factory automation, wireless sensor network, MATLAB Simulink, fuzzy-based algorithm, scheduling, and routing