Cloud Based Small Cell Networks: System Model, Performance Analysis and Resource Allocation

In cloud-based small cell networks (C-SCNs), radio resource allocation at the base station (BS) is moved to a cloud data centre for centralised optimisation. In the centre, multiple processors referred to as the cloud computational unit (CCU), is used for the optimisation. As the cell size and networks become respectively smaller and denser, the number of BSs to be optimised grows exponentially, resulting in high computational complexity and latency at CCUs. This thesis propose belief propagation (BP) based power allocation schemes for C-SCNs that can be used for any network optimisation objectives such as energy efficiency at the centre and BSs; and spectral efficiency (SE). The computation for the schemes is done in parallel, leading to very low latency and computational complexity with increasing number of BSs. The transmission-latency depends on the number of bits used to quantise the received signal from terminals at the remote radio head (RRH). The computational-latency depend on the speed of resource allocation procedure at the CCU. BP based joint SE and latency optimisation scheme that compute the optimum terminal’s uplink power and number of quantisation bits for each RRHs. The results indicate a significant reduction in transmission and computational-latencies compared to other schemes. This thesis further investigates a user association (UA) to the BS and subcarrier allocation (SCA) where a BS allocates different number of SC to different users associated to it. In jointly optimising the UA and SCA, the Sharpe Ratio (SR) is used as the utility function, which is defined as the ratio between the mean of user achievable rates to its standard deviation. Thus, the achieved user rates will be closer to each other, leading to a fair network access. By using binary BP algorithm, the results show that the achievable user rates are doubled in comparison with other schemes

The Integration of Fuzzy Logic System for Obstacle Avoidance Behavior of Mobile Robot

A mobile robot has a capability of sensing its location under uncertain environment, planning a real-time path as well as controlling its steering angle and speed to reach the target location. A robust controller is embedded in mobile robot whilst analyzing the input and output that help it to navigate without colliding with any obstacles. Meanwhile, Fuzzy Logic Controllers (FLC) is an intelligent technique that proves to be the one of the most reliable controllers that suits well for nonlinear system like robot due to the simple control based on user input without any prior knowledge to the mathematical model. In this paper, the Mamdani and Sugeno FLC are developed for a mobile robot. The smoothness and efficiency that generated from these FLC is analyzed based on simulation of Pioneer P3-DX robot in virtual robotic software for single and multirobot environments under static obstacles environment. Simulation results for the Pioneer P3-DX robot shows the Sugeno FLC able to produce smoother path and reach the goal faster than Mamdani FLC

The Integration of Fuzzy Logic System for Obstacle Avoidance Behavior of Mobile Robot

A mobile robot has a capability of sensing its location under uncertain environment,planning a real-time path as well as controlling its steering angle and speed to reach the target location. A robust controller is embedded in mobile robot whilst analyzing the input and output that help it to navigate without colliding with any obstacles. Meanwhile, Fuzzy Logic Controllers (FLC) is an intelligent technique that proves to be the one of the most reliable controllers that suits well for nonlinear system like robot due to the simple control based on user input without any prior knowledge to the mathematical model. In this paper, the Mamdani and Sugeno FLC are developed for a mobile robot. The smoothness and efficiency that generated from these FLC isanalyzed based on simulation of Pioneer P3-DX robot in virtual robotic software for single and multirobot environments under static obstacles environment. Simulation results for the Pioneer P3-DX robot shows the Sugeno FLC able to produce smoother path and reach the goal faster than Mamdani FLC

A comparative study of bachelor of electrical engineering programmes in terms of students’ performance

This study was aimed at measuring and comparing the effectiveness of two curricular structures in the Faculty of Electrical Engineering at the Technical University of Malaysia of Malacca (Universiti Teknikal Malaysia Melaka - UTeM). The comparison involved two programmes for a Bachelor of Engineering degree. One of them was a conventional academic programme and was offered to potential candidates as conventional electrical engineering (EE); and the other one was a broad programme referred to as BEKG. Data generated from students’ performances for the conventional EE and the BEKG programme were compared to assess how the EE curriculum is delivered at the University. The study targeted five categories of subjects, such as mathematics and basic science, laboratory, electrical, elective subjects and the final year project. The results indicate that the students’ mean performance in BEKG was better than that in the conventional EE across most of the categories. The outcome of this study could be useful to researchers and policymakers to improve EE curricula and teaching approaches

Industry 4.0 With Intelligent Manufacturing 5G Mobile Robot Based On Genetic Algorithm

A manufacturing fifth-generation (5G) mobile robot is a new development of industry 4.0 application, deploying an unmanned system. This study aims to implement a robot system for industrial applications in real-time with remote sensors to enable humans. Moreover, there is still some obstacle to cope with the better optimization solution for manufacturing 5G robot. This paper proposed a latency network algorithm for the manufacturing 5G mobile robot. An improved genetic algorithm (GA) by restructuring the genes is applied to plan a mobile robot path. The process of the robot path in a complex workspace is proposed, considering the node's collision-free constraint in the moving phase of a robot. The proposed scheme improves the robot path and delivery efficiency of the robot on average at 68% by moving on the industrial environment's shortest path and time average of the mobile robot reach its destination

Individual student key performance using mobile web apps based on knowledge profile and cumulative grade point average

This study investigated the performance of individual students toward the knowledge profile and cumulative grade point average or known as CGPA. The proposal of this paper involves two major components. The first component is investigating the individual student performance based on the knowledge profile and CGPA. The performance of students from cohort 2017 for the program Bachelor of Electrical Engineering (BEKG) courses in the Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM) was used as the dataset. The case study separates into various groups (excellent, honors and pass). Secondly, the study proposed a responsive web application leveraging a Google Visualization Feature to self-check individual academic performance. The goal of this app is to assist users in evaluating individual student performance and assist management in planning for continual quality improvement using the web and mobile apps. The marketing segments include students, academics, university, and school administration for analysis and evaluate the individual student’s performance

Task scheduling based on genetic algorithm for robotic system in 5G manufacturing industry

With the development of 5G technology, the robotic system has been bought into industrials. Even manufacturers plan the task flow by using project management. An error may occur and make the tasks overlap because they use the traditional scheduling method. It may waste much time between the tasks, and robots will get into standby mode to wait for the next tasks if the scheduling is failed. An algorithm with flexible scheduling is needed to arrange the tasks accordingly with the shortest total completion time. Genetic Algorithm (GA) is applied to task scheduling, and it provides a better solution from previous results or arrangements due to iteration. In this study, an analysis involves multi robots to complete various industrial operations, consisting of multi-tasks. To save time during processing and costs in production, GA may help it have the optimal value about total complete time to avoid any wastage. In short, the manufacturer will have higher productivity and better performance among the robots when applied a suitable Task Scheduling in the industry or workplace

Modelling Daily Load Profiles In The Utility Of Malaysia

This paper presents a statistical framework for the modelling of daily load profiles. Data of short term load profiles in the utility of Malaysia with the reading recorded every half an hour was used for this study. The daily plot and analysis of average maximum demand and minimum demand was done in order to choose the best statistical distribution to be fit this data. Analysis shows that the average maximum load, minimum load and average load consumption are vary for every seven days. The frequency distribution plots of the data reveal different pattern for different days. Based on these, the normal distribution, log-normal distribution and Weibull distribution was fit to the data. Maximum likelihood method was used to estimate parameters in these distributions. The assessments of the best distribution are by using numerical method, namely the mean square error (MSE) and normal absolute error (NAE). Result shows that the load consumption for each seven day can be model using different statistical distributions. Hence results from this study can be used to forecast the daily load consumption using confidence interval method

Introduction to linear Algebra

One of the most challenging aspects of mathematics learning is to give students suitable examples and exercises which can improve their understanding. This book is designed to familiarize the student with the form of questions asked in Linear Algebra. The topics are based on the syllabus of Linear Algebra teaching in UTeM. The material in this book will cover questions and answers for: Functions and Graphs Matrices and System of Linear Equations Trigonometry Analytic Geometry Complex Numbers An excellent student must have an initiative to learn before being taught by lecturers. By using this book, students can be more prepared before attending a tutorial session. The examples are presented in a sequence of steps with full details so that students can follow systematically

Relay node placement in wireless sensor network for manufacturing industry

elay nodes are necessary to maintain scalability and increase longevity as the number of manufacturing industrial sensors grows. In a fixed-budget circumstance, however, the cost of purchasing the bare minimum of relay nodes to connect the network may exceed the budget. Although it is hard to establish a network that connects all sensor nodes, in this case, a network with a high level of connection is still desirable. This paper proposes two metrics for determining the connectedness of a disconnected graph of sensor nodes and determining the optimum deployment method for relay nodes in a network with the highest connectedness while staying within a budget restriction. The metrics are the number of connected graph components and the size of the most significant connected graph component. Prim's algorithm and the approximation minimum spanning tree algorithm are applied to construct a disconnected graph and discover the best relay node placement to solve these two criteria. Compared to the other metrics, simulation findings suggest that prioritizing the most significant connected components in the disconnected graph can yield superior outcomes by deploying the fewest number of relay nodes while retaining the connectedness of the graph