Molecular dynamics simulations and photoluminescence measurements of annealed ZnO surfaces

The effect of thermal annealing on wurtzite ZnO, terminated by two surfaces, (0 0 0 $\bar 1$) (which is oxygen-terminated) and (0 0 0 1) (which is Zn-terminated), is investigated via molecular dynamics simulation using reactive force field (ReaxFF). As a result of annealing at a threshold temperature range of 700~K < T_{\mbox{\small t}} \leq 800~K, surface oxygen atoms begin to sublimate from the (0 0 0 $\bar 1$) surface, while no atom leaves the (0 0 0 1) surface. The ratio of oxygen leaving the surface increases with temperature $T$ (for T \geq T_{\mbox{\small t}}). The relative luminescence intensity of the secondary peak in the photoluminescence (PL) spectra, interpreted as a measurement of amount of vacancies on the sample surfaces, qualitatively agrees with the threshold behavior as found in the MD simulations. Our simulations have also revealed the formation of oxygen dimers on the surface and evolution of partial charge distribution during the annealing process. Our MD simulation based on the ReaxFF is consistent with experimental observations.Comment: 26 pages, 11 figures. Manuscript submitted to Physica

Optimization of photovoltaic energy harvesting using artificial neural network

This paper proposes artificial neural network (ANN) based maximum power point tracking (MPPT) controller to maximize the energy harvested by a grid-connected photovoltaic (PV) system under various environmental conditions. Due to the non-linear characteristics, PV system will exhibit multiple peaks when the PV array receives non-uniform irradiance. As such, the conventional perturb and observe (P&O) MPPT controller will be trapped at local maximum power point (MPP). Therefore, this paper aims to integrate ANN into MPPT controller to improve the effectiveness of the MPPT controller in tracking the global MPP. The effectiveness of the proposed method is tested under uniform and non-uniform irradiance conditions, and the performances are compared with the conventional P&O. The simulation results show the proposed method able to track the global MPP even the PV system exhibits multiple peaks under non-uniform condition, whereas the conventional P&O is trapped at local MPP. Thus, the proposed algorithm is able to harvest much energy as compared to the conventional method

Adaptive route optimization for mobile robot navigation using evolutionary algorithm

As technologies are advancing, demand for an intelligent mobile robot also increases. In autonomous robot design, the main problem faced by researchers is the path planning of mobile robot. Various kind of path planning algorithm was introduced in the past, but no algorithm has absolute superior towards the others algorithm. Classical methods like artificial potential field, grid search, and visual method have been easily overtaken by artificial intelligence due to its adaptability and ability to learn from the past mistakes or experience. For example, Ant Colony Optimization (ACO) is an optimization algorithm based on swarm intelligence which is widely used to solve path planning problem. However, the performance of ACO is highly dependent on the selection of its parameters. In this paper, the proposed adaptive ACO introduced two different ants, namely abnormal ant and random ant into the normal ACO to increase its global search ability and reduce the high convergence rate of ACO. Conventional ACO and adaptive ACO are compared in this paper and the results showed that adaptive ACO has better performance than conventional ACO in path planning

Dynamic acceleration coefficient of particle swarm optimization in robotics motion coordination

This paper focuses on improving an optimization process for swarm robots using Particle Swarm Optimization (PSO) by altering the acceleration coefficient from static to dynamic. In swarm robotic, motion coordination addresses the issue of avoiding a group of robots interfere with each other in a limited workspace, while achieving the global motion objective. PSO is commonly suggested in the literature to optimize path trajectory in robotic field. However, the typical PSO tends to be trapped in local optima. Therefore, a dynamic acceleration coefficient is proposed to optimize the cognitive and social coefficients of PSO in order to improve its exploration ability in seeking the global optimum solution. With this novel feature, PSO becomes less dependent on the chain of its past experience that it had explored in a certain region within the solution space. The effectiveness of the proposed method is tested on a simulated swarm robotic platform. Results show the proposed PSO with Dynamic Coefficient (DCPSO) is 1.09 seconds and 3.58 seconds faster than the typical PSO under dynamic and extreme conditions respectively

SUMO ENHANCEMENT FOR VEHICULAR COMMUNICATION DEVELOPMENT

It is normal that every family is having at least one vehicle at their home as vehicles have become a daily needs for all of us. However, this also leads to the increased of road accidents where major causes are related to human errors which can be prevented. To tackle with this problem, vehicular ad hoc network (VANET) is introduced with the aim to make vehicles intelligent. In order to study the algorithm in VANET, a mobility simulator is needed for simulation purpose. In this case, SUMO is proved to be a good simulation tool in generating VANET environment while MATLAB is good for algorithm development. Yet, to develop a good simulation platform, modification on SUMO files are necessary. This paper discusses on the procedures in creating a left-hand traffic (LHT) simulation file that is suitable to be used in Malaysia. LHT simulation is not easy to achieve as modification on the road connection and traffic light files are required. This paper also showed the results of the simulation after SUMO files modification. Apart from that, this paper also showed the simulation of VANET environment using SUMO and MATLAB through a third party interfacing named TraCI4Matlab, which allows communication between MATLAB and SUMO simulator

Mobile machine vision for railway surveillance system using deep learning algorithm

Trains have been a popular transportation in our daily life. However, there is no proper surveillance system for obstacle detection at the railway, leading to the happen of unwanted accidents. In order to overcome this issue, machine vision embedded with deep learning algorithm can be implemented. Obstacle detection can be achieved through vision-based object detection, where the object classification model computes the images similarity to its respective classes, classifying its potential as an obstacle. In this paper, object detection model is developed and implemented with deep learning algorithm. Object classification model is produced through the model training with Deep Neural Networks (DNN). The detection model used in this paper is Single-Shot multibox Detection (SSD) MobileNet detection model. This model can be implemented with Raspberry Pi to simulate the object detection algorithm virtually. During simulation, the object recognition algorithm is able to detect and classify various objects into its respective classes. By applying past research approaches, the developed object detection model is able to analyze image as well as real-time video feed to identify multiple objects. Any object that has been detected at the Region of Interest (ROI) can be characterized as an obstacle

A genetic algorithm for management of coding resources in VANET

This project aims to improve the throughput, energy consumption and overhead of vehicular ad hoc network (VANET) by optimising the network coding (NC) using Genetic Algorithm (GA). VANET shows a promising technology as it could enhance the traffic efficiency and promote traffic safety on the road systems. The conventional store-and-forward transmission protocol used in the intermediate node(s) simply stores the received packet and then send at a later time to the destination. However, the rapid changing in VANET topology has made the conventional store-and-forward approach inefficient to meet the throughput and reliability demand posed by VANET. Hence, NC is proposed to perform additional functions on the packet in the source or intermediate node(s). However, the chances to perform NC in wireless network is highly unlikely if the packet is not transmit to the potential NC node. Therefore, GA based network routing (GANeR) is embedded into network to search for shortest path from the source to the destination. It showed that the developed GANER in this work provides a better route with coding opportunities and reduces energy consumption in the network. The total energy consumed by GANER is 5.6% fewer than NC in wireless network transmission and forwarding structure (COPE)

Genetic Algorithm based Chain Leader Election in Wireless Sensor Network for Precision Farming

Wireless Sensor Network (WSN) is one of the commonly used technologies in Precision Farming (PF). It provides farmers with accurate real-time information on their farms. In practice, WSN consists of numerous wireless sensor nodes, where each node relies on limited energy sources such as battery to maintain its operation. The energy management issue in WSN has gained attention of scholars, leading to new protocols or schemes developed over the years. Conventionally, PEGASIS protocol selects chain leader without considering the distance and residual energy level of each sensor node. As such, it might increase the energy consumption rate to transmit collected data from sensor node to sink. Inadequate energy management leads to rapid energy drain and eventually shorten the lifespan of WSN. Therefore, this study aims to prolong the lifespan of WSN while minimizing the energy consumption. Genetic Algorithm (GA) is proposed to enhance the chain leader selection scheme of the conventional PEGASIS. The proposed GA will select optimum chain leader by considering the energy consumption rate of each node. As such, the proposed algorithm is able to increase node's transmission as well as improve the lifespan of WSN by 50% as compared to the conventional approach

Exploration of genetic algorithm in network coding for wireless sensor networks

Wireless network comprises of multiples nodes that work together to form a network. Each node in a wireless network communicates with one another by disseminating information packet among them. Source node and destination node are often far apart from each other, thus the information packet has to be transmitted to intermediate node(s) before it is able to be relayed to its destination. Network coding is introduced to combine several packets from different sources and broadcast the combined packet to several destinations in single transmission time slot. Each destination is capable to extract the intended information by decoding from a common packet. In short, network coding improves the throughput for wireless and wired networks but also causes side effects such as complexity of packets management and increases delay for coding opportunity. Hence, genetic algorithm is used to optimize the resources for network coding. Genetic algorithm will search for optimum routes to the destination according to the desired throughput with a desired multicast rate. In this paper, genetic algorithm is further enhanced in searching of optimum route for a packet. The simulation results show the enhanced genetic algorithm can adapt to various situations with different topologies with a better throughput and energy consumption compared to the store-and-forward method used in conventional wireless sensor network

HYBRID SIMULATION NETWORK FOR VEHICULAR AD HOC NETWORK (VANET)

Intelligent Transportation Systems (ITS) plays a vital role in providing different means of traffic management and enables users to be better informed of traffic condition, promoting safer, coordinated and efficient use of transport network. Vehicular Ad Hoc Network (VANET) shows promising reliability and validity in ITS. But, it poses challenges to researchers in designing protocol specifically for VANET as the deployment of VANET in real world will incur high cost. Therefore, simulation and non-physical testbed implementation have been widely adopted by the VANET research community in the development and assessment of the new or improved system and protocol of VANET. This paper presents a viable simulation platform for network development. Besides, a code cast or better known as network coding, a data packet transmission method has been developed and introduced into VANET protocol using the presented platform to assess and determine the potential of the introduced simulation platform