Theses & Dissertations

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https://hdl.handle.net/20.500.14170/206

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  • Publication
    A surface roughness based visual and analysis system for surface quality improvement in fused deposition modeling rapid prototype machine
    ( 2006)
    Khairul Fauzi Karim
    In rapid prototyping (RP), part deposition orientation and surface finish are two significant concerns, but they are contradicting with each other. In model building in RP, a concession is commonly made between these two features to get good quality surface roughness at a short build time. A concession among these two contradicting concerns can be achieved via an adaptive slicing method; on the other hand, selection of an appropriate part deposition orientation will further provide an improved solution. In this thesis, an effort towards determining an optimum part deposition orientation and adaptive slicing method for Fused Deposition Modeling (FDM) process for enhancing part surface finish, and hence, reducing build time (repeating process in RP cycle) is proposed. The quality of the surface roughness is determined by using visual and analysis. This Surface Roughness Based Visual and Analysis (SRVA) system is obtained based on the calculation of surface roughness (Ra). In this present work, the Region Based Adaptive Slicing method is applied in building the model in FDM. The proposed methodology allows the RP user to observe and analyze the prototype model before fabricating the prototype model in the FDM. A program based on fuzzy logic is also used to verify the input and output parameters obtained from the proposed method. The developed SRVA system has successfully improved the surface finish and minimized the build time in fabricating the prototype model in FDM. The result showed that increasing part deposition orientation would decrease the Ra value of the model. For 00 and 900 part deposition orientation, the Ra from measurement are closed to the Ra output from fuzzy logic with percentage differences 1.78% and 1.52% respectively. Therefore, the Ra values calculated from the SRVA system are acceptable for these orientations. However, for 450 part deposition orientation, it is 2.26% higher than the Ra output from fuzzy logic because during fabrication process, the surrounding support model affects the surface finish of the prototype model. However, this value is also acceptable because the effect of surrounding support model to the surface finish has not been the focus of the present work. The result also shows that the adaptive slicing method has improved the surface roughness of the prototype model. The inspected Ra obtained by this method is 1.22% lower than that obtained without adaptive slicing method, but 0.56% higher than that obtained by fuzzy logic. This result is obtained without the necessity to repeatedly fabricate the model or piecework in FDM for good quality surface roughness as the proposed method in this thesis successfully managed to optimize RP cycle; hence the build time in RP is reduced.
  • Publication
    Design and development of autonomous omni-directional mobile robot with mecanum wheel
    ( 2007)
    Jefri Efendi Mohd Salih
    A variety of designs of mobile robot have been developed in recent years in order to improve their omni-directional maneuver and practical applications. Omni-directional mobile robot has vast advantages over conventional design likes differential drive in term of mobility in congested environments. The main purpose of this research is to design, develop and implement an omni-directional mobile robot with custom made mecanum wheel for autonomous navigation. Using these mecanum wheels, the mobile robot is provided with three degree of freedom (DOF) mobility. Attention also paid for the development of kinematics and dynamics model to analyze the mobile platform motion performances. Motor driver and robot controller were also developed. Motion control algorithm using BasicStamp Editor software was developed to test the capabilities of the system’s mobility performance. Experiments were performed to analyze the motion characteristic of the mobile robot motion in Y axis, X axis and differential drive capabilities. Using ultrasonic and line follower sensors, basic autonomous navigation had been develop and tested. Based on the experiment conducted, the autonomous navigation gave satisfactory result. The developed mobile robot will provide a test bed for advanced path planning and navigation projects in the future.
  • Publication
    Face emotion recognition using artificial intelligence techniques
    ( 2008)
    Karthigayan Muthukaruppan
    Recently, there has been tremendous growth in the area of Human Computer Interaction (HCI). Many HCI applications were documented, and among them, the Face Emotion Recognition(FER) is one of the well known areas. Seven face emotions are considered universally in FER research: they are happy, sad, angry, fear, surprise, disgust and neutral. The FER can find applications in hospital and in home (for senior citizens, bed ridden persons and severely injured patients) and in analyzing the personal emotion psychology. The FER comes with various approaches and methods in the way to have a good recognition package. However, there are various reasons for the failures in the packages and one of them is due to face features that change with age, color, mental state and individual face expressions. In this research, the problem is focused on the personalized face emotion and some studies are extended for better emotion recognition. FER is achieved in two parts, they are image processing part and classification part. The first part investigates a set of image processing methods suitable for recognizing the face emotion. The acquired images have gone through few preprocessing methods. The edge detection has to be successful even when the intensity of light is uneven. So, to overcome the difficulty of uneven lighting, the histogram equalized image is split into two regions of interest (ROI) – eye and lip regions. These two regions have been applied with the same preprocessing methods but with different threshold values. The human eye and lip configurations are found to be more of towards ellipses. With the objective of finding the changes in eye and lip areas, a set of new forms for ellipse fitness function is proposed. The fitness functions find changes in the minor axes of both eye and lip images. The fitness functions are utilized by genetic algorithm (GA) to find the optimized values of minor axes. Three fitness functions are developed, one for the eye and two for the lip (top and bottom lip). These fitness functions are applied on eye and lip images of South East Asian, Japanese and Chinese subjects. Observation of various emotions of the three subjects leads to a unique characteristic of eye and lip. Outcome of optimized values indicate the ratios of the minor axes with respect to neutral emotion for the SEA, Japanese and Chinese subjects. It is found, from the optimized data, that there is no common pattern to recognize emotions within among the three subjects. The absence of common patterns leads to studies on emotion personalized to an ethnic. In order to understand the personalized face emotion recognition, the developed fitness functions are applied on two SEA subjects. However, it is found that some emotion range overlaps with other emotion ranges. In order to circumvent this problem in recognizing the emotions, two Artificial Intelligence (AI) classification techniques such as neural network and fuzzy clustering are employed. Various forms of neural networks have been applied and one of them is found to perform well in achieving a success rate of 91.42% for SEA1 and 89.76% for SEA2. In the case of second classification technique, two forms of fuzzy c-mean clustering are considered and their performances are compared. One of them performs better by achieving a 90% success rate for both SEA1 and SEA2. It is concluded that the analysis of personalized emotion through facial features of two subjects indicate higher rate of success compared to a general form of analysis that is applied to varieties of faces of several ethnic personalities.
  • Publication
    Development of background subtraction algorithm for biometric identification
    ( 2008)
    Akbah A. Khalifa
    This thesis presents an improved approach for an automatic face detection system. Segmentation of novel or dynamic objects in a scene can be achieved using background subtraction or foreground segmentation. This is a critical early step in most computer vision applications in domains such as surveillance and human-computer interaction. The proposed system consists of three parts. In the first part, the use of background subtraction algorithm to deal with the problem of lighting changes, shadows and repetitive motions. All previous implementations fail to handle properly one or more common phenomena, such as global illumination changes, shadows, inter-reflections, similarity of foreground color to background and non-static backgrounds (e.g. active video displays or trees waving in the wind). The proposed method is a background model that uses per-pixel, time-adaptive and Gaussian mixtures in the combined input space of pixel neighborhood and luminance invariant color. This combination in itself is novel. In the second part, another technique known as morphological erosion and dilation operators are used to remove the noise in the resulting binary image to improve the accuracy. The third part is accomplished by using a new technique to locate the face position in the image and extract ilfor recognition and identification purposes. The algorithm has been tested in several different lighting conditions and environments. The experimental results show that the method possesses much greater robustness to problematic phenomena than the prior state of the art methods, without sacrificing real-time performance, making it well-suited for a wide range of practical applications in video events which requiring detection in real-time. The experimental results in real time applications show the robustness, reliability and efficiency in fhe proposed approach; they can accurately detect and extract human face 98% of the time, with the ability to detect the face of different types of people gender, skin color and head attire. The proposed algorithm can be executed at 30 to 35 FPS for an image size of 320 x 240 pixel, which is much better when compared with any other real time applications.
  • Publication
    Design and measurement of losses in AC induction motor with different rotor bar material
    ( 2009)
    Gomesh Nair Shasidharan
    In this thesis, the three phase AC induction motor have been thoroughly investigated and analyzed in terms of the induction motor parameter, efficiency, power factor and loss segregation. Through out this project, a copper rotor bar is fabricated and compared with the existing aluminium rotor bar. The first part of comparison is done with software simulation using Opera 2D between aluminium rotor bar and copper rotor bar for the same 0.5HP stator slot design and winding configuration. The Opera 2D is compared in terms of power loss, magnetic flux density, magnetic field intensity, eddy current density, torque vs. speed, torque vs. slip, power loss vs. speed and power loss vs. slip. The second part is the hardware comparison between the fabricated copper rotor bars with the existing aluminium rotor bar. In this part, the copper rotor bar and aluminium rotor bar are tested using no load, blocked rotor, and DC resistance test to obtain the difference of efficiency, losses and power factor improvement. The load test is also performed to investigate the efficiency of the induction motor at low load factor and result shows that at lower load, the induction motors lose its efficiency and power factor. From the overall experiment of software and hardware, results shows that copper rotor bar does increase the efficiency and power factor to 1% and reduce losses to 5 watts compare to aluminium rotor bar. An economical aspect is presented to show the amount of energy and money that can be saved from replacing the aluminium rotor bar with a copper rotor bar. As for the annual energy saving (AES) and total cost saving (TCS), the copper rotor manage to save 40.32kWh per year and utility billing by RM13.54 per year per motor. Finally a rough estimation of 100,000 pieces induction motor that have been replaced with the copper rotor bars is assumed and shows that it will save approximately RM1.3 million.
  • Publication
    Development of voice controlled drive interface for car like mobile platform
    ( 2009)
    Khairulnizam Othman
    Intelligent Transportation Systems (ITS) involve projects that aim to integrate modern communication and information technology into existing transportation management systems. In this project, one approach to ITS for a prototype elementary vehicle is presented. One of the most important research topics in this field is the design and development of locomotion methods and intelligent communication systems for ITS. Voice is one of the convenient methods for interaction between humans and vehicles. Therefore, we explore the design and development of a prototype Car-Like Mobile Platform (CLMP) that mimics a real car. The design is rear wheels drive because it creates stable motion for calculation. The drive system for this mobile platform was developed using bipolar stepper motors for accurate lateral and horizontal movements. For the voice control, we developed an application that is able to learn and recognize voice commands. Freely available libraries on voice recognition using Hidden Markov Models (HMM) were integrated into the application. From the tested voice commands, 8 words were chosen to represent all possible types of motion for the CLMP. Communication between the interface and the drive system was achieved through serial communication. In the testing phase, it was found that the CLMP responded well to all given voice commands and conformed to standard non-holonomic behavior. The idea behind this application is to create an alternative form of interaction between car and user. Such an interaction can be very useful in the framework of an ITS designed for the handicapped.
  • Publication
    Modeling and control of a Pico-satellite attitude using Fuzzy Logic controller
    ( 2009)
    Zaridah Mat Zain
    Fuzzy logic concept was first conceived by Lotfi Zadeh in 1965 by incorporating rule based approach to solve control problems. The advantage of Fuzzy Logic Controller (FLC) is that the control process can be controlled without knowing much knowledge of their dynamics. FLC is applied as the controller to most of commercial mercantile products in past 25 years. Since that, many applications of the FLC in controlling the Pico-satellite’s attitude have been proposed successfully. In this regards, a new method of Pico-satellite attitude control using Mamdani Fuzzy Logic Principles is introduced. The design of the APFLC is initially started with the designation of Basic FLC with two input and single output system. Then, a Predictive FLC is designed to compensate the effects of delay time which occurs in the Pico-satellite control system. The predictor is a one step-ahead predictor which estimates the required control at the next sampling time and applies to the system at current sampling time. Finally the adaptive portion of FLC is applied in order to compensate the effect of unknown parameter variations in the Pico-satellite system by using an adaptable gain which is connected in the forward path of the FLC. The response of the Pico-satellite is compared with a model reference adaptive system, derived on the basis of deviation in the responses and updates the adaptive gain. The adaptation continues until the Pico-satellite attitude reaches the set-reference attitude. The design schemes of modeling adaptive and predictive FLC (APFLC) is described as follow: Basic FLC, Predictive FLC (PFLC) and APFLC. The APFLC is compared with a conventional Proportional Integral Derivative (PID) controller. The simulation results are presented and the output responses indicate that this approach of FLC is acceptable even in the case of a Pico-satellite subjected to input noise, measurement noises, intermittent disturbances and also with sensor nonlinearity. It is observed that the APFLC showed convincing performance over the entire simulation of the Pico-satellite. Genetic Algorithm (GA) is a computational model inspired by evaluation. This algorithm encode a potential solution to a specific problem on a simple chromosome like data structure and apply recombination operators to this structure to preserve critical information. The contribution of this work is to optimize the base of Fuzzy membership function of the APFLC by using GA technique. The optimization technique involved from two points to four points and end with six points. The performances obtained show that the optimized APFLC is better than the non-optimize APFLC in terms of RMSE and the settling time.
  • Publication
    Design of a portable continuous systolic blood pressure monitoring kit with built-in low and high blood pressure early warnings
    ( 2009)
    Muhamad Khairul Ali Hassan
    About one in three adults in the United States have high blood pressure but high blood pressure itself usually has no symptoms. The prevalence of hypertension in Malaysians aged 30 years and above was 42.6%. The majority of cases (64%) in this country remain undiagnosed. Only 26% of Malaysian patients achieved blood pressure control (<140/90 mmHg). Now days, many people have high blood pressure for years without knowing it. Uncontrolled high blood pressure can lead to stroke, heart attack, heart failure or kidney failure. This is why high blood pressure is often called the "silent killer." The only way to tell if you have high blood pressure is to have your blood pressure checked. Blood pressure is often measured using a device called a sphygmomanometer, a stethoscope and a blood pressure cuff. Almost all the existing manual or automatic measuring techniques of blood pressure are based on this principle, which is not convenient for continuous monitoring of blood pressure. The objective of this study is to develop a portable continuous blood pressure monitoring system using an electrocardiography (ECG) sensor and a pulse sensor. Two methods were used to measure blood pressure continuously. The first method measures blood pressure continuously based on Heart Rate (HR) and the second method is based on Pulse Wave Transit Time (PWTT). Both methods were separately implemented for different techniques to measure systolic blood pressure (SBP). The techniques that were used to model the relationship between the PWTT or HR data to systolic blood pressure are linear regression model, non- linear regression model and neural network model. Neural network model gave the smallest value of mean of error and standard deviation of error for measuring blood pressure based on PWTT or HR. These errors are acceptable and relatively small compared to the standard accuracy, which should have a minimum mean of error value of 6 mmHg with a standard deviation of error of ±10mmHg. The subjects that were involved in portable BP monitoring kit testing are normal blood pressure subjects, low blood pressure subjects and high blood pressure subjects. All the data were taken about five minutes for each subject and the results were monitored by medical cardiologist/doctor or nurses. The accuracy of the SBP data from portable continuous BP monitoring kit was validated using sphygmomanometer. The results indicate that the developed portable BP system is adequate to be used for monitoring or measuring systolic blood pressure continuously. Warning system was developed in this portable BP monitoring kit. The warning system is generated based on blood pressure value and trend of increasing or decreasing of systolic blood pressure values. The warning is given in form of alarm. The alarm will be “on” when the systolic blood pressure value goes more than 140mmHg (High Blood Pressure) or less than 100mmHg (Low Blood Pressure) or if the SBP increasing or decreasing trend in more than 5mmHg for each 30 seconds.
  • Publication
    Development of solar water pump for small scale paddy field irrigation
    ( 2010)
    Noor Syafawati Ahmad
    In this thesis, the solar powered water pump have been thoroughly investigated and analyzed in terms of the Photovoltaic (PV) module electrical output, performance, field observation, and modeling of solar radiation. The objective of this research is to obtain the solar radiation using Hargreaves method, determine the PV electrical output through some installation and specification required to apply for paddy fields irrigation. The field installation involved a 50 W PV module, direct connected to a 12 VDC water pump, solar tracking system and other installation include a 12 V 100 Ah battery, power inverter, AC water pump and battery charge controller. The parameters observed and monitored are voltage, current, power of PV module and water pump. While water flow for water pump and ambient temperature also include in data collecting. Earth orientation and solar geometry help to obtain suitable tilt angle to mount the PV module for fixed position otherwise tracking device as an alternative of development. This research had covered the theory and technical in solar water pump system design and installation. From the overall of this thesis, 30° tilt angle is most suitable to apply in equator latitude for fixed position to mount the solar panel. Otherwise using tracking system is better to increase the system output compared to fixed position in term of cost and technical performance. The solar water pump system able to support the small scale of paddy field from quarter acre up to seven acre. This system is completed with water level control for water reservoir and paddy field because paddy plantation behavior which is sensitive to water level. This research able to helps the farmer to improve the paddy production for outside the granary of paddy field.
  • Publication
    Brain machine interface controlled robot chair
    ( 2010)
    Hema Chengalvarayan Radhakrishnamurthy
    Brain Machine Interface Controlled Robot Chair: Brain Machine Interface is a device that links the human brain directly to devices such as computer, wheelchairs and prosthetic arms. Such interfaces provide a digital channel for communication and control in the absence of the biological channels and thus help in the rehabilitation of mobility and speech impaired individuals. In this thesis, a novel four-class brain machine interface (BMI) is designed for a robot chair using neural networks. Simple and novel protocols for acquiring brain EEG signals from two non-invasive scalp electrodes are presented. Four tasks based on motor imagery of left and right hand movements are proposed to control the directions of the robot chair. A novel algorithm for acquisition of motor imagery signals using only hand movements is proposed. Simple preprocessing algorithms are presented to remove noise from the raw signals. Mu, Beta and Gamma frequency bands related to the motor actions are extracted using customised filters. New features based on time and frequency components of the EEG signals are proposed and tested with classifiers. Classification of the four hand motor imagery signals is presented using static and dynamic neural networks. A particle swarm optimization based algorithm is proposed to train the neural networks. Combinations of the features proposed and the static and dynamic classifiers are analysed. Signals collected from 10 trained subjects are used in the analysis of synchronous and asynchronous BMI designs. A max-one algorithm for translation of the hand motor imagery signals into robot chair movements is presented. A prototype robot chair is designed and interfaced with the developed asynchronous BMI. Safety features are integrated through a collision avoidance system to enhance the performance of the robot chair. The BMI controls the joystick of the robot chair using a shared control algorithm. Real-time experiments are also presented using 10 trained and 5 untrained subjects to validate the applicability of the brain machine interface. Experiments were carried out at two expositions (out-of-lab environments) with 25 untrained subjects to assess its feasibility in real life environments.
  • Publication
    Non-invasive pathological voice classifications using linear and non-linear classifiers
    ( 2010)
    Hariharan Muthusamy
    In this research work, a non-invasive method is conducted to diagnose the voice diseases through acoustic analysis of voice signal. Three feature extraction methods are proposed based on the time-domain energy variations, Mel frequency cepstral coefficients combined with singular value decomposition and wavelet packet and entropy features. Linear classifier namely LDA based classifier and non-linear classifiers such as k-NN classifier, MLP network, PNN, and GRNN are suggested to discriminate pathological voices from normal voices. In this research work, three databases such as MEEI voice disorders database, MAPACI Speech Pathology database, and dataset-III (collected at Hospital Tuanku Fauziah, Kangar, Perlis) are used to test the independence of the algorithms to the databases and the proposed feature extraction algorithms are also tested in noisy condition at 30dB signal-to-noise ratio. Two types of experiments are conducted using the proposed feature extraction and classification algorithms. In the first experiment, classification of normal and pathological voice has been investigated. In the second experiment, the detection of the specific type of voice disorders has been carried out through twoclass pattern classification problems. The different kind of voice disorders are selected such as AP squeezing, vocal fold edema and vocal fold paralysis based on the previous research works. The experiment investigations elucidate that the proposed feature extraction algorithms give very promising classification accuracy for the classification of normal and pathological voices under controlled and noisy environment. In the case of detection of specific disorders, wavelet packet and entropy features perform well compared to time-domain energy variations based features and MFCCs and SVD based features. The following performance measures such as positive predictivity, specificity, sensitivity, and overall accuracy have been considered, in order to test the reliability and effectiveness of the linear and non-linear classifiers. For the MEEI voice disorders database, the success rate of the classifiers is above 98% for the classification of normal and pathological voices and for the detection of specific disorders the best classification accuracy of 100% is achieved. The experiments have also been repeated for the MAPACI speech pathology database and dataset- III under controlled and noisy environment. The results indicate that the wavelet packet and entropy based features provides better classification accuracy compared to time-domain energy based features and MFCCs and SVD based features for the two more databases. It is concluded that proposed feature extraction and classification algorithms can be employed to help the medical professionals for early investigation of voice disorders.
  • Publication
    Flux density and power loss distribution in 100kVA Distribution Transformer core assembled with different cutting angle of T-joint
    ( 2010)
    Shuhaimi Zakaria
    The power losses occurring under magnetising condition have received a great deal of attention for a long time. The transformer designs with Butt Lap joint cause the highest power loss at the T-joint. These due to the flux need to rotate 90º into the hard direction and transfer up and down to the adjacent layers causes high rotational power loss occurred at the T-joint. To overcome this problem, the different cutting angle such as 23º, 45 º and 60º at the T-joint was introduces in order to find out the most efficient design. The development of the 100kVA Distribution Transformer model core with the four difference types of the T-joint for the power loss and building factor measurement has been tested in order to find which T-joint has minimum power loss and better building factor. The localised flux density was measured using the search coil in order to find out their distribution at the corner joint and T-joint. The fundamental, third and fifth harmonic in the normal and inplane flux density were measured at the corner joint and T-joint. The localised power loss was measured using the thermistor at the similar location of the search coil. The results show that the minimum power loss, better building factor, the minimum fundamental, third and fifth harmonic the normal and inplane flux density and also minimum localised power loss are occurred at the transformer model core assembled with the 60° T-joint.
  • Publication
    Feature extraction and classification of Malay speech vowels
    ( 2010)
    Shahrul Azmi Mohd Yusof
    In human language, a phoneme is the smallest structural unit that distinguishes meaning. Normally, language like English commonly combines phonemes to form a word. In many languages, the Consonant-Vowel (CV) units have the highest frequency of occurrence among different forms of subword units. Therefore, recognition of CV units with a good accuracy is crucial for development of a speech recognition system. There are also many applications that use vowels phonemes. Among them are speech therapy systems that improve utterances of word pronunciation especially to children. There are also systems that teach hearing impaired person to speak properly by pronouncing words with a good degree of intelligibility. All of these systems require high degree of vowel recognition capability in which this study focuses on. This thesis contributes five modified feature extraction methods for vowel recognition based on intensities of the Frequency Filter Bands. They are First Formant Bandwidth (F1BW), Fixed Formant Frequency Band (FFB), Spectral Delta (SpD), Bark Intensity (BrKI) and Formant Frequency Difference (FFD). The performance of these five proposed methods are compared with performance of three conventional feature extraction methods of single frame Mel-frequency cepstrum coefficients (MFCCs), multiple frame Mel-frequency cepstrum coefficients (MFCCf) and the first three formant features. The classifiers analysed in this study were Multinomial Logistic Regression (MLR), Levenberg-Marquardt (LM) network, k-Nearest Neighbors (KNN) and Linear Discriminant Analysis (LDA). There are four main contributions of this thesis. First is the new vowel corpus consisting of more than 1300 recorded vowels from 100 Malaysian speakers. Second are the five improved feature extraction methods which perform better than MFCC on single frame analysis. The third is the performance and robustness analysis using different classifiers and different Gaussian noise level. The fourth contribution is the frame analysis criteria for isolated vowel analysis.
  • Publication
    Development of a microcontroller-based inverter for photovoltaic application
    ( 2011)
    Sharulnizam Mohd Mukhtar
    Presented in this thesis is the development of a microcontroller-based inverter for photovoltaic systems. A single 8-bit microcontroller generated switching pulses for both stages of the inverter’s dc-dc converter and dc-ac inverter. A simple, effective algorithm suitable for implementation in a single 8051 microcontroller was developed, for control of the inverter’s power stage. The thesis explained details on the PWM and the SPWM switching strategies including calculation to presents their switching pulse widths. An inverter of 500W capacity was built to test the switching pulse generated by the microcontroller. A high frequency (HF) planar transformer raised input voltage to the inverter’s required level and provided isolation and solving difficulties in producing a handmade conventional HF transformer. The experiment were conducted in laboratory environment, where input voltage was taken from DC power supply and the inverter load was purely resistive. The prototype’s performance analysis results were found to be similar to those calculated. The prototype inverter performed well that able to produce a stable 240V output voltage at 50Hz, able to handle loads up to 500W, and had a low THD.
  • Publication
    Design & development of microorganism activity monitoring transducer using ultrasonic sensors
    ( 2011)
    Aini Salwa Hasan Nudin
    This thesis presents the design and development of a microorganism activity monitoring transducer by using ultrasonic sensors as the main part in detecting the signal of microorganism activity. Three types of samples which are Saccharum Officinarum in tap water, Saccharomyces Cerevisiae and Aspergillus Niger in distilled water are used in the experiments and are found to give differents voltage signals. This indicates that the transducer is able to detect changes of concentrations and the number of cells in the samples tested. The proposed design consists of two parts. In the first part, the selection of electronic components that is able to produce an appropriate output signal has been made and tested. The design of the circuit is done by combining the application circuit for each component based on the detail that is supplied by each component manufacturer. In this stage, a Computer-Aided Design (CAD) software is used to design a Printed Circuit Board (PCB). The second part is the design of a container that is used to place the sample and also the sensor. A material with properties such as glass, light weight, temperature resistant and chemically stable has been chosen as the main material to build the container. A CAD software is used to design the shape of the container and fabricate it. The functionality of the transducer is tested by using pseudo bacteria liquids, real microorganisms and the results obtained shows that the output signal of the transducer is dependent on the density and concentration of the tested liquid. During the testing of the microorganisms, a proposed method of cell counting is done to show the relationship between the signal magnitude and the number of cells. The increment or decrement of live cells has been used as indicator of microbe activity. The experiment results show that the use of ultrasonic sensors for microorganism monitoring has a potential to be further investigated and developed towards a reliable and fast monitoring system required in certain industries.
  • Publication
    Adaptive neural controller for attitude stabilization of quadrotor unmanned aerial vehicle (UAV)
    ( 2011)
    Shaiful Zairi Ahmad Subhi
    In line with the rapid development of the world science and technology, our country has matured in the exploration of new knowledge. Our country has given the very bright opportunity in an engineering field. The researchers intend to produce a useful technology to the community. Now on, air to space technology become famous for them, especially in the unmanned aerial vehicle (UAV) area. Therefore, this thesis also will present the development of an UAV that uses a new concept named quadrotor which are a combination of helicopter and airplane. This quadrotor capable of takeoff and landing vertically like a helicopter and can maneuver like an airplane. Quadrotor built with a fourrotor placed symmetrically like an added shape, Electronic Speed Controller (ESC), Inertial Measurement Unit (IMU) sensor and a Lithium Polymer (Li-Po) battery. Simple configuration causes quadrotor preferred used as unmanned vehicles. Quadrotor movement is controlled by the thrust produced by the four-rotor which means to move quadrotor the four rotor speed must be controlled independently. The rotor can be controlled separately through the programming provided in the control system. The main problem faced is to control the quadrotor attitude during flight time. Adaptive Neural Controller (ANC) is used to control the quadrotor attitude. This system was adopted because it can respond quickly and accurately following the reference model. Before that, the quadrotor kinematic and dynamic analysis must be done. Through the analysis, we can determine quadrotor modeling. Modeling quadrotor used to find the quadrotor plant so that the system will be able to identify the quadrotor characteristics. Constant parameters are getting from calculation and experimental test used in quadrotor modeling. Simulation tests carried out using Matlab software to find quadrotor stability in roll, pitch and yaw axis. After the controller gives a good response in simulation, the code converts into C programming and implement in the microcontroller and should synchronize with the hardware configuration. The actual test flights conducted in an indoor to test for stability. Disturbance test also will be conducted. For the future development several of sensors will be added to increase the ability quadrotor to fly autonomously without any guidance from the ground station and also human.
  • Publication
    Development of ultrasonic tomography system for liquid/gas flow measurement in a vertical column
    ( 2011)
    Nor Muzakkir Nor Ayob
    Liquid/gas two-phase flow widely exists in many applications including chemical and petroleum industries. Measurement of this two-phase flow is an essential parameter for these applications where accurate flow measurement is required. Two-phase flow, a phenomenon of critical importance to oil and gas sector where compared with singlephase flow, its flow characteristic is much more complex. Thus, the improvement of the instrumentation and measurement technology for the two-phase flow such as the development of two-phase flowmeters will have a growing demand since it will bring significant benefits to many industries. Based on the advantage of tomographic imaging technique, non-invasive ultrasonic sensing technique is realized by using electronic measurement circuits for transmitting and receiving the analog signals, data acquisition system for transferring the data to the PC and most importantly the suitable image reconstruction algorithm for providing visual access to the two-phase liquid/gas flow and estimating the component distribution for real-time measurement. The important characteristic for sensor selection is noted and balanced between high operating frequency for increased spatial sensitivity and the cross-sectional area of the vertical column to avoid undetected measurement signal caused by complete attenuation. The enhancement of liquid and gas component distribution imaging over the common Linear Back Projection (LBP) algorithm has been successful by implementing the new Eminent Pixel Reconstruction (EPR) algorithm. Simulative study on liquid/gas two-phase velocity measurement for dual-plane ultrasonic tomography system also has been made. Two different methods based on the use of cross-correlation technique have shown the usability of the technique for estimating the flow velocity. The image reconstructions quality of the two-phase flow is seen to have a significant increase by combining the EPR algorithm with Median Filtering technique that eliminated pixel values which are unrepresentative of their surroundings. Another advantage of such combination is the smoothen effect on the reconstructed images, resulting in better visualization of the twophase liquid and components because the outcome have approximating shape and size if compared with the actual flow condition. Linear regression method are also introduced for configuring the appropriate threshold values for imaging different size of gas component inside the investigated column especially on the successful detection of small gas bubbles. The results obtained can be useful for the online monitoring of liquid/gas flow in many industrial processes such as chemical mixing process, fluid transportation or process monitoring.
  • Publication
    Optimal location and sizing of distributed generation using particle swarm optimization with mutation strategy
    ( 2011)
    Wong Lye Yee
    The current energy crisis has led to the increasing demand of environmental-friendly and high efficient energy. On top of all the solutions, distributed generation (DG) is one of the solutions that is capable to overcome this problem. The impact of DG towards the distribution system is significant where it can be used to improve the system reliability and efficiency such as improving the voltage profile, reducing the total power losses, etc. The optimal location and size of DG is very important in order to obtain the maximum output from the DG allocation. Many researchers found out that solutions using metaheuristic methods yield a better result compared to the conventional analytical method. In this thesis, the Particle Swarm Optimization (PSO) combined with the mutation strategy (PSO-MS) method is proposed in solving the DG allocation problem with the purpose of minimizing the total real power loss and improving the voltage profile of the system. This is to prevent the stagnancy of the particles’ population that usually happens in PSO algorithm. A set of comprehensive simulations have been carried out to validate the performance of the proposed method where they are categorized into small system (24-bus distribution system), medium system (33-bus distribution system), and large system (69-bus distribution system) for single DG and 2 DGs installation. The simulation results of the PSO-MS method are then compared with PSO and Genetic Algorithm (GA) method in order to validate the performance of the proposed method. From the results, it is shown that the proposed method has successfully obtained the optimal DG location and size. As for the comparative study with PSO and GA, the PSO-MS method also yields a better performance in terms of total real power loss, voltage profile and simulation time.
  • Publication
    Markerless human motion tracking for golf swing application
    ( 2011)
    Sim Kwoh Fung
    Sports video tracking is one of the emerging topics that have grabbed a lot of attention due to its high commercial potential. A variety of sports, including tennis, soccer, gymnastics, running, and golf have been utilized to demonstrate new ideas in sports motion tracking. The main challenge for this research work concerns the extraction of a highly complex articulated motion of a golf player performing sports action from a video scene. This research work focuses on developing a markerless human motion tracking system that tracks major body parts of an athlete directly from a sports broadcast video. A hybrid tracking method is proposed in this research work which consists of a combination of three algorithms namely the pyramidal Lucas-Kanade optical flow, normalized correlation based template matching and background subtraction. These algorithms are used to track the head, body, hands, shoulders, knees and the feet of a golfer while the individual is performing a full swing. Finally, the output results are tracked and mapped onto a 2D articulated human stick model to represent the pose of the golfer. The research work has been tested on a broadcast video of a golfer on various background complexities.
  • Publication
    Determination and classification of stress level using EEG signal and audio modalities
    ( 2011)
    Syahrull Hi-Fi Syam Ahmad Jamil
    Stress is defined as the disruption of homeostasis by physical or psychological stimuli. It can occur in two different approaches either positive way or negative way. Positive stress is called eustress and negative stress is called distress. Eustress is a positive form of stress, usually related to desirable event in person life, while distress will bring negative implication towards health on life. Thus it is essential to comprehend and come out with stress index. By knowing this, it will lead towards effective stress management and the efficiencies way of suppressing stress. This research work intends to determine the stress level (normal, very low stress, low stress, very moderate stress, moderate stress, high stress and very high stress) at 3 different sound pressure levels (60 dB, 70 dB and 80 dB) through physiological signal measurement which is Electroencephalogram signal (EEG). For stress state inducement audio clip modalities is being used. 36 sound clips which are mixed with noise selected from pilot test result, played at 3 different sound pressure levels and associated with the subjective evaluation obtained from the 30 participating subjects. EEG signal was simultaneously recorded while subjects were exposed to the played sound clips. The recorded EEG signal were analyzed and processed where features were extracted through time domain analysis (Band Energy and Approximate Entropy feature) and frequency domain analysis (Power Spectral Density feature). Theses extracted features classified through linear classifier (Linear Discriminated Analysis classifier) and non linear classifier (Neural Network and k-Nearest Neighbor classifier). The classification results by this classifier on the extracted features show the classification accuracy of the developed stress level at 3 different sound pressure levels. The classification accuracy results dwell within the range of 88.29% to 99.87%. These promising results show that the stress level were successfully developed using audio clip modalities through physiological signal measurement.