Azura Che Soh

Infrared Heating in Food Drying: An Overview

This article aims to review and analyze the aspects and characteristics related to infrared food drying. Indeed, with a review of 100 relevant publications all dealing with infrared food drying, this article notes that infrared drying has several advantages over other common food drying methods. Shorter drying time, a better final dried product quality, and more energy savings in the process are revealed as the most important advantages of infrared drying over convective heat drying. Infrared dryers can also be easily combined with other drying methods such as hot air, microwave, vibration, and vacuum. This article clearly shows that using infrared heating for food drying purposes has become more popular in the last decade and its application in the industrial drying of different foodstuffs has been employed widely.

Real-Time Monitoring System for Parking Space Management Services

Real-Time Monitoring System for Parking Space Management Services is the evolution of traditional parking system that it does not only provide live information to users in order to make it easy for them to look for vacant parking lot, it also give authority to operators to monitor and perform simulations to illustrate the real parking system. The purpose of this project is to apply the principles of queue theory into parking system modeling. The queue system model enables the predictions of arrival and service time in the system through analysis and calculations. Aside of it, Graphical User Interface (GUI) is also designed and integrated into the parking system in order to allow parking zone operators to monitor the status of the parking lots and view the statistics of arrival rate, service time and so on. This project focuses on the system modeling and software development and implementation. It is expected that this project will be able to assist parking zone operators in designing their system that will work efficiently and generate high income. It will also provide convenience to the operators in managing the parking zone remotely. Users will also experience a time-saving and stress-free parking zone.

Modelling and Optimisation of a Traffic Intersection Based on Queue Theory and Markov Decision Control Methods

Traffic models play an important role in both todays traffic research and in many traffic applications such as traffic flow prediction, incident detection and traffic control. Modelling traffic dynamics and optimising the control signal are two interrelated problems. Modelling provides fundamental understanding of traffic dynamics and behaviour. In this paper, traffic signal is modelled as a M/M/l queueing theory. The validation of a simulation model (M/M/l queue) with different arrival rates is presented. From the result, a traffic light model was developed by applying M/M/l queue theory for single intersection. In the optimisation strategy, the Markov decision control is applied to minimize queue length and waiting time. Simulation results show the excellent potential of this approach

Traffic signal control based on Adaptive Neural-Fuzzy Inference System applied to intersection

Adaptive Neural-Fuzzy Inference System (ANFIS) that integrates the best features of fuzzy systems and neural networks has been widely applied in many areas. It can be applied to synthesize controllers, which are able to tune the fuzzy control system automatically, and models that learn from past data to predict future behavior. The aim of this research is to develop an ANFIS traffic signals controller for multilane intersection in order to ease traffic congestions at traffic intersections. The new concept to generate sample data for ANFIS training is introduced in this research. The sample data is generate based on fuzzy rules and can be analysed using tree diagram. This controller is simulated on multilane traffic intersection model developed using M/M/1 queuing theory and its performance in terms of average waiting time, queue length and delay time are compared with traditional controllers and fuzzy controller. Simulation result shows that the average waiting time, queue length, and delay time of ANFIS traffic signal controller are the lowest as compared to the other three controllers. In conclusion, the efficiency and performance of ANFIS controller are much better than that of fuzzy and traditional controllers in different traffic volumes.

A wireless flipper robot using interface free controller

Robots have been developed by human to do various things to make our life easier. There are many approaches, systems and technologies can be applied to develop different types of robots depended on the usage. This paper presents the new approach of interfacing a wireless robot using IFC (Interface Free Controller). This controller offers a new concept of microcontroller embedded system developed for robotic application. IFC is an interfacing hardware and it configures the peripherals. This flipper robot is a fighting robot which previously participated in the robot war game. The complete design, interfacing, robot constructions, specification of the robot and testing processes are detailed out.

E-nose herbs recognition system based on Artificial Neural Network technique

Electronic sensing technology intervention was intended to overcome humans physical limitation. It has developed and greatly advanced over the decade. This project emphasizes on characterizing herbs species based on unique of herbs odor. E-nose system in this project consist an array of commercial gas sensor which detects gas through an increase in electrical conductivity when reducing gases are absorbed on the sensors surface. Data obtained from sensors array are classified using Artificial Neural Network (ANN) technique. The E-nose system with five sensors has the highest capability in classifying herbs sample. Accuracy in classifying the correct herbs increases with number of the sensors used. Results show that sensitivity of E-nose towards herbs classification increases with higher number of sensors.

Smart monitoring fault detection system for malfunction traffic light operation

Normally, signboards are used to remind road users to call contractor and authority department, when malfunction of traffic light operation occurs. As a result, the action that taken for repairing the traffic light system is depend on initiative of the road users to call and report the failure of traffic light operation. The delay of report to local authority will cause the repairing work delayed. To overcome this problem, a smart monitoring fault detection system has been developed to monitor the traffic light operation in rural area or small city. The Fuzzy-Fault Identification and FuzzyFault Classifications are two modules which employed as the subsystems in fault detection system. Both of the modules are used to classify the level of seriousness for traffic light failure based on two factors of failures which are electrical fault and mechanical fault. The seriousness of the traffic light failures are classified to four conditions such as normal, non-critical 1, non-critical 2 and critical and a signal will be sent to the contractor and authority department via message service for further action. Thus, it is a more effective system and has a bright prospect to be implemented in Malaysia.

A discrete-event traffic simulation model for multilane-multiple intersection

In this paper, the model is simplified by proposing a decomposed queuing theory to multilane-multiple intersection. The concept of queuing theory looks promising due to its simplicity and its combination with standard approach techniques. This can expand the model to be more reliable in dealing with problem associated to multilane-multiple intersections. The proposed model is simple, flexible and fit to any real case study with different structure of topology. The traffic model is developed using discrete-event simulation (DES) framework for computation and analysis the model. Simulation results have shown good correlation between the proposed traffic model and data taken from real traffic situations, and have confirmed that the proposed modeling technique based on queuing theory and standard decomposition method can be realized. In addition, simulation results give a confirmation of the model capability to correctly predict traffic performance measures.

Improved ADALINE Harmonics Extraction Algorithm for Boosting Performance of Photovoltaic Shunt Active Power Filter under Dynamic Operations

This paper presents improved harmonics extraction based on Adaptive Linear Neuron (ADALINE) algorithm for single phase photovoltaic (PV) shunt active power filter (SAPF). The proposed algorithm, named later as Improved ADALINE, contributes to better performance by removing cosine factor and sum of element that are considered as unnecessary features inside the existing algorithm, known as Modified Widrow-Hoff (W-H) ADALINE. A new updating technique, named as Fundamental Active Current, is introduced to replace the role of the weight factor inside the previous updating technique. For evaluation and comparison purposes, both proposed and existing algorithms have been developed. The PV SAPF with both algorithms was simulated in MATLABSimulink respectively, with and without operation or connection of PV. For hardware implementation, laboratory prototype has been developed and the proposed algorithm was programmed in TMS320F28335 DSP board. Steady state operation and three critical dynamic operations, which involve change of nonlinear loads, off-on operation between PV and SAPF, and change of irradiances, were carried out for performance evaluation. From the results and analysis, the Improved ADALINE algorithm shows the best performances with low total harmonic distortion, fast response time and high source power reduction. It performs well in both steady state and dynamic operations as compared to the Modified W-H ADALINE algorithm.

Optimizing effect of frying cycles on cooking oil properties using particle swarm optimization

In this paper, Particle swarm optimization (PSO) technique has been proposed to evaluate the quality of recycled cooking palm oil. This technique proved to be the most powerful and robust technique in the modern era. The sensitivity of the threat by used cooking oil on public health and the complex nature and laborious approaches of evaluating the oil will be overcome through the proposed algorithm. The findings in this study have revealed that the appropriate temperature in degree centigrade (°C), of grading cooking oil is higher than 200°C for both the TPC and FFA. The study also portrays none significant differences between PSO and GA in terms of optimum estimated value, fitness and temperature. The findings conclude that it is better to evaluate TPC and FFA of used oil at higher temperatures than at low temperatures.