Research Titles – Engineering / Sciences / Mathematics

Master’s Degree

Faculty Title / Summary Supervisor
Faculty of Engineering and Technology (FET), Melaka Campus Microhydro Electricity Generation

Micro-hydro systems produce electric power from water. These small water-powered systems can produce up to 15 kW of electrical power, sufficient to provide electricity for a village of almost 100 houses to power lights and small motors. Obviously, this soft energy source, however, doesn’t provide enough power for industrial uses. Micro-hydro systems are important for villages that are near water and do not have electric power.

Perform a theoretical study of microhydro power in Malaysia, and come up with a design and setup of microhydro power system; finally, perform a feasibility study of microhydro power electricity generation in Malaysia.

Dr. Lim Wee Kuan

Faculty of Engineering and Technology (FET), Melaka Campus Solar Cookers

Today, people are using solar cookers in many countries around the world. People use solar ovens to cook food and to heat drinking water to kill bacteria and other harmful organisms. Also, solar ovens cook food at low temperatures over long periods of time; this permits people to leave food to cook while they do other things.

Perform a theoretical study of solar food dryers and come up with a prototype.

Dr. Lim Wee Kuan

Faculty of Engineering and Technology (FET), Melaka Campus Devulcanization of Rubber

The rubber industry has been researching the devulcanization of rubber for many years. The main difficulty in recycling rubber has been devulcanizing the rubber without compromising its desirable properties. The process of devulcanization involves treating rubber in granular form with heat and/or softening agents in order to restore its elastic qualities, in order to enable the rubber to be reused. Several experimental processes have achieved varying degrees of success in the laboratory, but have been less successful when scaled up to commercial production levels. Also, different processes result in different levels of devulcanization: for example, the use of a very fine granulate and a process that produces surface devulcanization will yield a product with some of the desired qualities of unrecycled rubber. In the rubber recycling process, devulcanization begins with the delinking of the sulfur molecules from the rubber molecules, thereby facilitating the formation of new cross-linkages. Two main rubber recycling processes have been developed: the modified oil process and the water-oil process. With each of these processes, oil, and a reclaiming agent are added to the reclaimed rubber powder, which is subjected to high temperature and pressure for a long period (5–12 hours) in special equipment and also requires extensive mechanical post-processing. The reclaimed rubber from these processes has altered properties and is unsuitable for use in many products, including tires. Typically, these various devulcanization processes have failed to result in significant devulcanization, have failed to achieve consistent quality, or have been prohibitively expensive.

Dr. Lim Wee Kuan

Faculty of Engineering and Technology (FET), Melaka Campus Electron, Photon, Neutron, & Positron Stochastic Transport in Semiconductors

Perform a computational study of the electron, photon, neutron, and positron quantum-mechanical stochastic transport in semiconductors. Design Monte Carlo simulations at various energies for various industrial semiconductor materials. Extrapolate the relevance of the findings to optoelectronics, biotechnology, and medical technology.

Dr. Lim Wee Kuan

Faculty of Engineering and Technology (FET), Melaka Campus Scaled-Up Osmotic Energy Design

Osmotic (or salinity-gradient) energy generation takes advantage of the osmotic pressure difference between salt and fresh water. If we place a semipermeable membrane (like that in a reverse-osmosis filter) between sealed bodies of salt water and fresh water, the fresh water will gradually travel through the filter by osmosis; exploiting the pressure difference between these two bodies of water, we can extract energy commensurate to the difference in pressure. Osmosis is the diffusion of a solvent through a semipermeable membrane from a region of low solute-concentration to a region of high solute-concentration. The semipermeable membrane is permeable to the solvent, but not to the solute, resulting in a chemical potential difference across the membrane that drives the diffusion; that is, the solvent flows from the side of the membrane where the solution is weaker to the side where it is stronger, until the solution on both sides of the membrane is of the same strength (that is, until the chemical potential is equal on both sides). Mixing of materials with different composition can be used, in principle, in energy production. In fact, the principle of osmotic energy is the exploitation of the entropy of mixing freshwater with saltwater. One of the most important components of osmotic power plant are osmotic membranes, which let water flow through, but does efficiently prevent penetration of saline ions. The membranes help unpressurized water to flow into pressurized seawater without any external mechanical equipment; the membranes work as natural pumps. The pressurized seawater mixed with river water will be conducted into turbine for electricity production. Some of the attractive features of the osmotic energy include the absence of carbon dioxide or other effluents that can adversely affect the environment, renewability, non-periodicity (unlike wind or wave power), and suitability for small or large plants (modularity). Its drawbacks include its current low efficiency, energy-cost sensitivity to membrane cost and efficiency, and vulnerability of membrane to fouling. In fact, one of the obstacles in the development has been, so far, the missing of proper membranes (good water permeability); therefore, pay particular attention to the work of membranes.

Perform a study on osmotic energy, and identify some of its novel applications. Perform some demonstrations or simulations related to the study. Also, come up with a laboratory design and setup of osmotic energy. Perform a theoretical, technological, and feasibility study of osmotic energy implementation; pay particular attention to the membrane and chemical selections, energy storage technology, and power conversion and generation setup. Address the good, the bad, and the ugly aspects of the implementation.

Dr. Lim Wee Kuan

Faculty of Engineering and Technology (FET), Melaka Campus Using Municipal Waste to Generate Electricity

Municipal solid waste (MSW), also called urban solid waste, is a waste type that includes predominantly household waste (domestic waste) with sometimes the addition of commercial wastes collected by a municipality within a given area. The wastes are in either solid or semisolid form and generally exclude industrial hazardous wastes. Gasification of the waste can be widely used on industrial scales to generate electricity.

Since 1994 Majlis Bandaraya Melaka Bersejarah (MBMB) has been disposing its municipal waste directly to the MBMB sanitary landfill at Krubong; however, this sanitary landfill is scheduled to be terminated soon at the end of the first phase of Krubong Project. The MBMB sanitary landfill receives 750 tons of municipal solid waste daily. Even though, MBMB does not practice landfill gas collection in this sanitary landfill, this open lagoon is for the purpose of waste treatment. During the second phase, MBMB plans to use the collected municipal waste for fuel and energy recovery with gas engine generators, by installing equipment for landfill gas (LFG) collection; also, it plans to use the collected LFG to generate electricity and prevent the emission of methane from the landfill site. In final phase, any excess waste will be buried with water-proof clay, then sand, followed by topsoil; and grass will then be grown on the final layer.

Perform a theoretical, technological, and feasibility study of using municipal waste to generate electricity in Melaka; identify the waste and operation site, energy storage technology, and power conversion & generation setup; examine the gas generated and the related designs of gasifiers or reactors; perform some simulations or programming related to the study; and address the good, the bad, and the ugly aspects of the implementation.

Dr. Lim Wee Kuan

Faculty of Engineering and Technology (FET), Melaka Campus Cheerios Effect in Industry

In fluid mechanics, the cheerios effect is the tendency for small wettable floating objects to attract one another. It is due to surface tension and buoyancy. The same effect governs the behaviour of bubbles on the surface of fizzy drinks. Thus, small wettable objects floating on water tend to coalesce into rafts; in the case of bubbles, such rafts have many solid-like properties. Small wettable objects also tend to be attracted towards the edge of the meniscus. Writing in the American Journal of Physics, Dominic Vella and L. Mahadevan of the Harvard University discuss the cheerios effect and suggest that it may be useful in the study of self-assembly of small structures. The cheerios effect also refers to small objects that repel one another. Vella shows that two small floating items with identical wetting properties can repel one another if the relative density of them is of opposite sign.

Demonstrate the Cheerios effect, and identify its significance and application, and perform some demonstrations or simulations related to the study.

Dr. Lim Wee Kuan

Faculty of Engineering and Technology (FET), Melaka Campus Energy Storage & Retrieval Design Aspects of Atmospheric Electricity

Perform a theoretical, technological, and feasibility study of atmospheric electricity implementation in Melaka; identify the operation site, energy storage & retrieval technologies, and power conversion and generation setup; perform some simulations or programming related to the study; and address the good, the bad, and the ugly aspects of the implementation. Pay particular attention to the energy storage & retrieval designs and technologies.

Dr. Lim Wee Kuan

Faculty of Engineering and Technology (FET), Melaka Campus Money Detector for the Blind

This Master of Engineering Science project is to develop a money detector for the blind people.

Note: Only Bachelor of Electronics / Electrical Engineering  Degree holders are accepted.

Ir. Dr. Wong Wai Kit

Faculty of Engineering and Technology (FET), Melaka Campus Omnidirectional Car Burglar Detector

This Master of Engineering Science project is to develop an omnidirectional car burglar detector for carpark.

Note: Only Bachelor of Electronics/ Electrical Engineering Degree holders are accepted.

Ir. Dr. Wong Wai Kit

Faculty of Engineering and Technology (FET), Melaka Campus 3D Map Plotter Mobile Robot

This project is to develop a 3D Map Plotter Mobile Robot for indoor / tunnel inspection usage.

Note: Only Bachelor of Electrical/ Electrical Engineering degree graduates are accepted. Preferably from the area of robotics.

Ir. Dr. Wong Wai Kit

Faculty of Engineering and Technology (FET), Melaka Campus Vision-Based Patrol Robot

This project is to develop a vision based patrol robot for security purposes during off time.

Note: Only Bachelor of Electrical/ Electrical Engineering degree graduates are accepted. Preferably from the area of robotics.

Ir. Dr. Wong Wai Kit

Faculty of Engineering and Technology (FET), Melaka Campus Sustainable Water Supply System for Household Usage

This project is to develop a Sustainable Water Supply System for Household Usage using Electronics, Sensor and AI.

Note: Only graduates from Bachelor’s degree of Electronics/Electrical Engineering are accepted. Preferably good in Electronics Sensor and Control field.

Ir. Dr. Wong Wai Kit

Faculty of Engineering and Technology (FET), Melaka Campus Development of Driver Drowsiness Detection System using a Combination of Vehicle Diagnostics, Physiology and Remote Sensing Information

Drowsiness is one of the most critical factors in contributing to a high number of crashes in Malaysia. Several types of driver drowsiness detection (DDD) system have been developed to tackle this problem. They are based on vehicle diagnostics, physiology or facial recognition. However, these systems have several limitations in terms of reliability and intrusiveness. Therefore, in order to tackle this problem, hybrid approach based on vehicle diagnostics, physiology and remote sensing information is proposed. In this system, the vehicle diagnostics information is obtained from the steering angle sensor and wheel speed sensor through on-board diagnostics (OBD) socket. For the physiological information, it is acquired through the photodiode sensor built inside the health sensor band. The remote sensing information such as road curvature and vehicle position are obtained through the LiDAR sensor. In order to ensure correct signals are received from the sensors, the filters are applied to remove the noises presented in the signals. After that, the training and test data are collected from the test subjects by driving the car on North-South Expressway at 4 different time periods (morning, afternoon, evening and night). The training data is then used to train the deep learning model in classifying the drowsiness of the driver. Recurrent neural network is used in the system because it has temporal characteristic which can be utilised to make prediction on drowsiness of the driver. It can also incrementally learn the features through backpropagation. Once the DDD system is developed, the test data is fed into the deep learning model to determine the accuracy of the model in drowsiness detection. Lastly, the test subjects are required to drive the car with the DDD system at 4 different time periods. A survey is conducted to investigate the possibility of promoting the system to other drivers in Malaysia.

Note: Only for full-time candidates.

Dr. Em Poh Ping

Faculty of Engineering and Technology (FET), Melaka Campus Driver Drowsiness Detection based on Non-intrusive Metrics considering Individual Specifics

Objectives: Drowsy driving is a serious highway safety problem. If drivers could be warned before they became too drowsy to drive safely, some drowsiness-related crashes could be prevented. The presentation of timely warnings, however, depends on reliable detection. To date, the effectiveness of drowsiness detection methods has been limited by their failure to consider individual differences. The present study sought to develop a drowsiness detection model that accommodates the varying individual effects of drowsiness on driving performance.

Methods: Nineteen driving behavior variables and four eye feature variables were measured as participants drove a fixed road course in a high fidelity motion-based driving simulator after having worked an 8-h night shift. During the test, participants were asked to report their drowsiness level using the Karolinska Sleepiness Scale at the midpoint of each of the six rounds through the road course. A multilevel ordered logit (MOL) model, an ordered logit model, and an artificial neural network model were used to determine drowsiness.

Results: The MOL had the highest drowsiness detection accuracy, which shows that consideration of individual differences improves the models’ ability to detect drowsiness. According to the results, percentage of eyelid closure, average pupil diameter, standard deviation of lateral position and steering wheel reversals was the most important of the 23 variables.

Conclusion: The consideration of individual differences on a drowsiness detection model would increase the accuracy of the model’s detection accuracy.

Keywords: Driving behavior; Driving simulator; Drowsiness detection; Eye feature; Multilevel ordered logit model; Non-intrusive.

Note: Only for full-time candidates.

Dr. Em Poh Ping

Faculty of Engineering and Technology (FET), Melaka Campus Design and Development of Miniature Synthetic Aperture Radar

To design and develop a drone based miniaturize Synthetic Aperture Radar

Assoc. Prof. Ir. Dr. Chan Yee Kit

Faculty of Engineering and Technology (FET), Melaka Campus Classification of Drone Using Radar Imaging

To identify types of drone via synthetic aperture radar (SAR) images. Doppler signature of the drone in SAR images will be used as main feature to differential types of drone.

Assoc. Prof. Ir. Dr. Chan Yee Kit

Faculty of Engineering and Technology (FET), Melaka Campus Development of Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR)

The student is required to design and develop a MIMO antenna for ground based SAR system.

Assoc. Prof. Ir. Dr. Chan Yee Kit

Faculty of Engineering and Technology (FET), Melaka Campus Parameter in PVT

Various types of parameter will study in new PVT system.

Assoc. Prof. Ts. Dr. Ervina Efzan Mhd Noor

Faculty of Engineering and Technology (FET), Melaka Campus Hybrid Nanocomposite for Crush Absorption

In this project, fibre reinforced hybrid composite crush tubes loaded with nanoparticles will be fabricated. The hybrid composite has a functionally graded structure, and is made of carbon and kevlar fibres. The optimum volume fraction of the nanoparticles, and the effective functionalization of the nanoparticles will be sought after. Optimized and functionalize nanoparticles are expected to enhance their bond with the epoxy matrix, hence maximizing the crush tubes energy absorption in low-speed impact.

Ir. Dr. Kok Chee Kuang

Faculty of Engineering and Technology (FET), Melaka Campus Micro Stir Friction Welding to Join Aluminium and Copper

Micro stir friction welding in the form of butt and/or lap joint to join aluminium and copper is a much sought-after green welding technology. However, due to the inherent incompatibility between the two materials, and the technical challenges related to downscaling of the friction stir welding to mesoscale material thickness (i.e. less than 1mm), the technique has not been proven to be feasible. This project seeks to optimize the welding parameters for a sound stir friction welding of the two materials.

Ir. Dr. Kok Chee Kuang

Faculty of Engineering and Technology (FET), Melaka Campus Intelligent Autonomous Mobile Robot System

One of the main challenges in developing an intelligent and autonomous robot is to be able to plan its navigation path without any human intervention. Both classical path finding algorithms and AI-based approaches will be studied. A mobile robot equipped with LIDAR and camera-based vision system will be used to conduct field test. A new path finding algorithm will be proposed for autonomous navigation in an unknown indoor environment.

Prof. Ir. Dr. Koo Voon Chet

Faculty of Engineering and Technology (FET), Melaka Campus A Prediction Framework of Energy Consumption based on IoT Time Series Missing Value Datasets

Electricity consumption and load forecasting have been the most important area of research in recent years. Researchers utilized, machine learning, and deep learning techniques for forecasting electricity consumption data. These studies are done to efficiently and accurately predict future electricity production for supply management in the government and private sector. The electricity forecasting methods could be used to predict a large number of unobserved values found in IoT time-series data sources. These observations could be missed due to network failure,ordevice failure, etc. In this proposal, our aim to develop a framework that will use to predict a large number of missing values in IoT energy consumption time-series data. The framework performs data preprocessing before utilizing the data for forecasting. In the forecasting process, the framework will utilize deep learning long-short term memory LSTM neural network model. This model is frequently used in time-series forecasting in recent years using past observation to predict future values. The LSTM model, using complex time series and showed reduced Mean Absolute Error (MAE) and Root Mean Square Error ( RMSE) for medium to long-range forecasting. The major drawback of the LSTM model needs to learn the scalar weights and biases through the back-propagation learning algorithm to update the neural/cell for the next layer. This process requires large computational time which is not sufficient for real-time monitoring systems. we aim to cover this issue by modifying or maybe use some different learning algorithms that minimize the processing time without sacrificing accuracy. This study also compares the results of our framework with statistical ARIMA and machine learning LS-SVM models. These models were extensively used in past studies for electricity consumption.

Ts. Dr. Md. Jakir Hossen

Faculty of Engineering and Technology (FET), Melaka Campus Design and Development of an Autonomous LibBox

In Malaysia, Pos Laju introduced EziBoxes for parcel pick-ups at the convenience of the recipients. If you’re not home to receive your Pos Laju parcel when it arrives, you won’t get a note on your gate anymore. You will receive an SMS informing you that your parcel is currently in an EziBox. All you have to do is head over and collect your parcel from their boxes that are accessible 24/7, within 48 hours.

With the same concept, we will extend it to collection and also returning of books from these ‘Eziboxes’ designed especially for library users. Let us call it LibBox (library box).

  1. You choose the books you want to borrow. The librarian will find the books and put it inside the LibBox. You get to choose the location of the LibBox. You get an SMS once it is ready for collection.
  2. You can also return books through a LibBox.
  3. The LibBox is autonomous. It can automatically move on its own from library to its designated location in campus. Locations such as in front of faculty offices or lecture halls.
Dr. Chua Shing Chyi

Faculty of Engineering and Technology (FET), Melaka Campus Low Excess Noise of AlxIn1-xAsySb1-y Multilayers Avalanche Photodiodes

Avalanche photodiodes (APDs) are widely used in optical receivers for high-speed communication systems. The material AlGaAsSb have been measured for evaluating its potential as an alternative avalanche material for APDs. The limited data in previous measurements and simulation results for AlInAsSb is the motivation of this research. Since the impact ionization coefficients determine the breakdown voltage and the safe-operating area of APDs, the accuracy in the determination of the impact ionization coefficients in AlInAsSb has been an important issue to design high gain, low noise and high speed device. AlInAsSb APDs has been measured by many researchers. The current issues to be solved
are the presence of multilayers structure, hole injection mechanism and polarization induced charge at the hetero-interface arising from the lattice mismatch and difference in spontaneous polarization between the multiplication regions in APDs.

Assoc Prof Dr. You Ah Heng

Faculty of Engineering and Technology (FET), Melaka Campus Thin PEO-based Polymer Lithium Ion Battery

Lithium ion (Li-ion) batteries are widely used in electric and hybrid vehicles, smartphones, computers etc in recent years. With the current challenge, solid polymer electrolytes (SPEs) are chosen as one of the most considerable solutions to solve the security issues for Li-ion batteries because they possess several advantages such as fire resistance, no-leakage, and outstanding stability in electrochemistry. Among all the polymer electrolytes, poly(ethylene oxide) (PEO)-based SPEs have attracted extensive interest for real practical applications.
The main objective of this project is to develop and test a completely working prototype of
LiFeO4/PEO-EC-LiCF3SO3-SiO2/Li cell for commercialization in near future. In this study, PEO-based (PEO-EC-LiCF3SO3-SiO2) SPEs with 2 wt% of SiO2 nanofillers will be prepared and characterized.

Assoc Prof Dr. You Ah Heng

Faculty of Engineering and Technology (FET), Melaka Campus 3D printed Functionally Graded Hollow Structures for Crush Energy Absorption

In this project, functionally graded PLA hollow structures will be 3D printed and subjected to quasi-static axial loading or impact. The aim is to find important aspect of functional gradation that maximizes impact energy absorption, and to relate the fracture modes with the amount of absorbed energy.

Ir. Dr. Kok Chee Kuang

Faculty of Engineering and Technology (FET), Melaka Campus Predictive Maintenance by Audible Sound using Adaptive Signal Processing and Deep Neural Network

The objective of this project is to design a powerful tool for early detection of machinery breakdown. It can be achieved through acoustic condition monitoring via airborne sound analysis in conjunction with advanced signal processing and machine learning methods. The Hilbert Huang Transform (HHT) is proposed, as it is an adaptive signal processing technique emerged recently. A deep neural network is proposed for machine learning activity as it is one of the most sophisticated mathematical models to process data in complex ways. The methodology involves collecting extensive data from the real factories involved rotating and moving machineries. This data includes recorded sound signals of various normal and abnormal conditions of the machineries. Few microphones will be used for this purpose. These multicomponent sound signals because of noise in it, will be decomposed into mono component signals using empirical mode decomposition technique (EMD) of HHT. All the necessary features required to train the deep neural network (DNN) will be extracted from these decomposed sound signals using Hilbert transform. These features will be used to train the deep neural network. The trained neural networks will be tested for its classification accuracy for the sound data collected from the same factories. After fine tuning the parameters, the system can be recommended for implementation of factory use.

Dr. Joseph Emerson Raja

Faculty of Engineering  (FOE), Cyberjaya Campus Realization of Electrochromic Smart Window Technology based on Solid-State Electrolyte

This project will involve the fabrication of electrochromic smart window devices based on solid-state electrolytes, which is expected to be deployed as intelligent windows for smart building applications.

Assoc. Prof. Ir. Dr. Chan Kah Yoong

Faculty of Engineering  (FOE), Cyberjaya Campus IOT-based Pneumatic Bed for Bed-ridden Patients

The IOT pneumatic bed can adjust the position of the bed according to the patients reactions while sleeping to enhance the quality of sleeping.

Dr. Cham Chin Leei

Faculty of Engineering  (FOE), Cyberjaya Campus QoS Provisioning of 5G Vehicular Network Resource Management

The current cellular technology and vehicular networks cannot satisfy the mighty strides of vehicular network demands. Resource management has become a complex and challenging objective to gain expected outcomes in a vehicular environment. The 5G cellular network promises to provide ultra-high-speed, reduced delay, and reliable communications. The development of new technologies such as network function virtualization (NFV) and software defined networking (SDN) are critical enabling technologies leveraging 5G. The SDN provides flexibility in communication administration and resource management, which are of critical importance when considering the ad-hoc nature of vehicular network infrastructures, in terms of safety, privacy, and security, in vehicular network environments. However, the frequent handover activities in the vehicular network remain a challenge to provide the Quality of Service (QoS) to the users. Artificial Intelligence enabled architecture can be used to cater to the sophisticated demands of modern vehicular Internet infrastructures. The inclination towards robust communications in 5G-enabled networks has made it somewhat tricky to manage network slicing efficiently.

Ir. Dr. Pang Wai Leong

Faculty of Engineering  (FOE), Cyberjaya Campus Real-time Cell Detection and Segmentation on Whole-slide Breast Carcinoma Images

The project aims to develop a reliable real-time cell detection and segmentation system for various histological stains of breast carcinoma (H&E) and evaluate its performance in terms of accuracy and speed.

Assoc. Prof. Dr. Mohammad Faizal Bin Ahmad Fauzi


Faculty of Engineering  (FOE), Cyberjaya Campus Efficient GPU Ray Tracing for Radio Propagation Modelling Using NVIDIA Optix

Efficient Optix implementation of GPU propagation ray tracing is desired. However, most previous works focused more on the application aspects and gave little account on the challenges or problems of efficient Optix implementation. Consequently, the learning curve is steep and researchers could unknowingly reinventing the wheel or adopting an inefficient solution. In this project, we will develop a GPU ray tracer for propagation modelling using Optix. In doing so, we strive to lay the ground work for efficient Optix implementation of GPU propagation ray tracing, by uncovering and spelling out the challenges, proposing possible solutions to the challenges, and evaluating their performance in terms of speed, memory, and accuracy.

Note: Full-time only; before 31/12/2021

Dr. Teh Chin Hui

Faculty of Engineering  (FOE), Cyberjaya Campus A Novel Design of MEMS based Energy Harvester Processor for Electric Vehicle Application

The advent of Electric Vehicle (EV) accelerates the trend for designing more and more new sensors and actuators by global automotive sensors manufacturers. On-board automotive sensors powered by means of wires from battery increase car design complexity, adding significant to the car weight and higher cost leading to very complicated sensing architectures and many other problems related to the maintenance and reliability. A new approach for autonomous on-board sensing systems based on the self-powering by means of micro Energy Harvesters (EH) is emerging. While bulk piezoelectric, EH can produce enough power for a few tens of mW, the insufficient power is still a major issue during miniaturizing into micro scale. Contrary to the traditional designs based on cantilever beams which use the bending strain, a novel design is needed to be investigated particularly for the EV application. Because in EV application the EH device will be in direct contact with the driving force and the ambient acceleration amplitudes will be too large for the previously examined cantilever devices. Ubiquitous mechanical vibration in vehicles environment is a key promising source for the MEMS based micro generator for the conversion of the mechanical power into electrical power for the self-powering of vehicles on-board sensing systems. With the development of Micro-Electro-Mechanical-System (MEMS) technology miniaturization feasibility promotes the integration of MEMS based micro Energy Harvesters and ultra-low-power Very Large Scale Integration (VLSI) circuits to form self-powering sensor nodes. In future thrusts of research, it is believed that new geometries and device structures that is apart from cantilever based design, will allow for significant improvements in EH efficiency, eventually granting the realization of truly autonomous MEMS devices. This study will focus to design and fabricate a novel cymbal-type EH device vibration-based MEMS micro EH device that will use tensile stress/strain on the piezoelectric film in order to generate energy together with interface power conversion circuitry. Issues related to the geometric optimization, robustness, reliability of the device, fabrication process including the integration of piezoelectric thin film, structure design for high power density, and CMOS power management circuitry of device will be focused. This is expected to provide the optimal desired dc output power characteristics with high efficiency satisfying all the desired parameters and also maintain an output power that can be used to power EVs sensor networks instead of the conventional batteries.

Note: Full-time only. Candidate needs own financial support.

Prof. Dr. Md. Shabiul Islam



Faculty Title / Summary Supervisor
Faculty of Engineering and Technology (FET), Melaka Campus Early Drowsiness Detection of Drivers using Artificial Neural Network Focusing on Behavioral, Driving, and Physiological Features

One of the leading causes of traffic accidents is the drowsiness of the drivers. Around 20% (Guede-Fernández et al. 2019) of car crashes are occurred due to drowsiness. Accurate early identification of drowsy driving is required to minimize such accidents.

Previous studies mostly focused on facial features and applied conventional machine learning algorithms like SVM, KNN, etc., to detect the driver’s drowsiness. These studies also missed the driver’s slight drowsiness stage and failed to detect the early drowsiness. However, only facial features are not sufficient to detect drowsiness. Drowsiness depends on multiple features like driving performance, behavioral features, and physiological response. Although, previous researches did not utilize these features simultaneously.

Therefore, the study aims to propose a noble artificial intelligence system to predict the driver’s early drowsiness. The system will emphasize all the major features related to the drowsiness of the driver. It will also consider the slight drowsiness stage of the driver to make the model more efficient.

All the fractures will be categorized into behavioral, driving, and physiological features. The behavioral feature includes eye blink, yawning, duration of eye-opening. Vehicle velocity, vehicle acceleration, steering wheel acceleration, and distance from lane center will be considered driving performance. And, the features obtained from the EEG and ECG signal will be contemplated as a physiological response. All the extracted features will be arranged according to the drowsiness level after the pre-processing stages. A noble Artificial Neural Network (ANN) will be implemented to classify and predict the early drowsiness of the driver.

As the proposed system would have the ability to detect drowsiness at the early stage considering all the possible features, it would play a significant role in the driver drowsiness detection system. Moreover, the system would reduce the probability of sudden death of a human being due to drowsiness.

Dr. Em Poh Ping

Faculty of Engineering and Technology (FET), Melaka Campus Development of PV System

To develop novel PV system

Assoc. Prof. Ts. Dr. Ervina Efzan Mhd Noor

Faculty of Engineering and Technology (FET), Melaka Campus Failure Analysis on Interconnect Solder for Electronic Application

Study on the failure analysis of interconnect solder for electronic application

Assoc. Prof. Ts. Dr. Ervina Efzan Mhd Noor

Faculty of Engineering and Technology (FET), Melaka Campus Single Image Signal to Noise Ratio Estimation Techniques for Images

This project applies Single image estimation techniques to estimate Signal to noise ratio value for images. The images can be material science images, medical images as well as general images.

Note: The research is open for full-time and part-time candidates. For more information, please refer to Prof. Ir. Dr. Sim Kok Swee’s Google scholar page.


Prof. Ir. Dr. Sim Kok Swee

Faculty of Engineering and Technology (FET), Melaka Campus Analysis of Protein Level in Medical Latex Glove Images using Regression Methods

In healthcare environment, some of medical gloves contain high protein level which increases possibility of healthcare professional to have a risk for latex allergy. The objective of this project is to develop regression methods to estimate protein level through analyses of color difference in glove images. To estimate protein level, latex gloves initially go through chemical test for protein detection. After that, it will be digitally converted into a sample image to perform image processing and calculate color difference values. Those values are used to plot a standard curve for protein level determination.

Note: The research is open for full-time and part-time candidates. For more information, please refer to Prof. Ir. Dr. Sim Kok Swee’s Google scholar page.

Prof. Ir. Dr. Sim Kok Swee

Faculty of Engineering and Technology (FET), Melaka Campus Deformation Monitoring and Analysis using Ground-based Synthetic Aperture Radar

In this work, the effectiveness of ground-based synthetic aperture radar (GBSAR) as an early warning system for surface deformation monitoring will be investigated. In the first part of this study, a 3D model will be derived and simulated to determine optimum working conditions of the GBSAR for different types of natural terrains and man-made structures. Next, field experiments will be carried out to collect measurement data for further analysis and validation. An early warning framework will be proposed to perform real-time monitoring using GBSAR at selected test sites.

Prof. Ir. Dr. Koo Voon Chet

Faculty of Engineering and Technology (FET), Melaka Campus Plant Disease Detection and Classification using Hyperspectral Imaging

An accurate estimate of plant disease, its severity and effects is crucial in sustainable crop production. In this work, a new framework of plant disease detection and classification using hyperspectral imaging will be developed. A drone-based hyperspectral imaging system will be used to conduct field measurements at selected plantations / vegetation fields. The dataset will be preprocessed, labeled and trained using state-of-the-art machine learning techniques. The trained model will be applied on a large scale farm to evaluate its effectiveness for early disease detection.

Prof. Ir. Dr. Koo Voon Chet

Faculty of Engineering and Technology (FET), Melaka Campus Fabrication and Characterization of Porosity in Polyaniline Supercapacitor for High Specific Power in Electric Vehicles

Supercapacitor is functioning like conventional capacitor by possessing the potential energy from the opposite charges on the two parallel plates. The current research interest is seeking the maximum effective surface area and the small separation distance of capacitor electrodes in order to increase the capacitance. We intend to prepare the electrode consisting of PANI doped with LiPF6 deposited on the surface of stainless steel plate using chemical deposition technique in this work. A feasible and low cost technique for PANI electrode preparation will be recommended for mass production of supercapacitor in future.

Assoc Prof Dr. You Ah Heng

Faculty of Engineering  (FOE), Cyberjaya Campus A Novel Architecture of Maximum Power Point Tracking Algorithm for Ultra-Low-Power based Hybrid Energy Harvester in Ubiquitous Devices

This research work presents a novel architecture of an Ultra-Low-Power (ULP) based Hybrid Energy Harvester (HEH) consisting of multiple input sources such as kinetic, thermal and solar energy, harvested from passive human power. Having multiple ambient sources mitigates limitations caused by single sources especially for bodily-worn applications; however, this results in impedance mismatch among the different integrated sources. To overcome this limitation, the proposed ULP based HEH will use one power management unit with Maximum Power Point Tracking (MPPT) algorithm and impedance matching considerations to efficiently manage and combine the power harvested from all three sources to achieve ULP consumptions. Among other crucial sub-modules of the proposed HEH are its Asynchronous Finite State Machine (AFSM) cum resource sharing arbiter to prioritize and share energy sources for overall power reduction, an efficient rectification scheme for the piezoelectric input, an adaptive feedback for low power conditioning, Zero-Current Switching (ZCS) for semi-lossless switching in its on-chip energy storage, a self-start circuit for low ambient startup of 20 mV, a Boost converter to pump-up voltage levels, a Buck regulator to stabilize fluctuating voltage levels, a fuzzy-based micro-battery charger and a multiplexer to switch between harvesting or charging capabilities. All of which is implemented for maximum output extraction and minimal losses. The approach of developing this ULP-HEH begins with a literature review on past HEH architectures. Next, all ULP-HEH sub-modules will be modeled, designed and simulated in PSPICE software with MPPT algorithm; and charging technique in MATLAB. After achieving optimum results, the developed HEH system will then be written in Verilog coding under Mentor Graphics environment and later the designed codes are to be downloaded on the Field Programmable Gate Array (FPGA) board for verification purposes. Once verified, the ULP-HEH will finally be implemented into the layout level using the CMOS 0.13 µm process technology. This ULP based HEH system is expected to operate at 4kHz and deliver up to 3.0-5.0V of regulated voltage output from ambient sources with a minimum input voltage of 35 mV to startup without batteries to aid its proposed (end-to-end) peak efficiency of 90% or above with an output power of about 650 µW when all sources are summed. Also, the proposed HEH is aimed at reducing power consumption to at least 2 times (<70 µW) of the conventional HEH due to multi-sourcing impedance mismatch. The proposed ULP based HEH system can be widely used for low power bodily-worn electrical gadgets, wearable biomedical devices or to power or charge micro-batteries for portable electronic devices.

Note: Full-time only. Candidate needs own financial support.

Prof. Dr. Md. Shabiul Islam

Faculty of Engineering  (FOE), Cyberjaya Campus Focal Point Calibration of Antenna to Minimize Background Noise of Reflected Scattering Objects.

The focal point calibration of antenna is a prime parameter to reduce the background noise of reflected scattering objects for tumor detection in microwave imaging technique. Very small size tumors or any unwanted cell can be detected with high accuracy bycalibrating the focal point of the antenna with minimizing the background noise. However, due to low spectral power density and weak scattering behavior of the body tissue, reflected scattering objects must rely on the very low power reflections, which are often drowned out by the background noise and reflections from the nearby scattering objects. Thus, it is essential to overcome the drawbacks in terms of susceptibility to background noise and scattering objects, false detections, low-resolution scanning, and uncomfortable processing. It is necessary to determine the antenna phase center for accurate ranging, signal processing, and image reconstruction since it makes the antenna simple as a point source. The main aim of this project is to introduce and explore a new approach of antenna focal point calibration by utilizing a new optimization technique. The new parallel-start variant of iterative optimization (PSVI) approach will be assessed to heuristically optimize the focal point of the antenna with respect to the imaging domain. The difference between the two data sets will be calculated and measured due to the generated noise from reflected scattering objects. After that, the antennas will be used to reduce the background noise of reflected scattering objects that will detect the tumor or any unwanted cell inside the body phantom. The noise and artifact reduction of the images using the optimized antenna will be evaluated by comparing with the antenna without the optimal focal point. The expected outcome of this project has the potential to generate noiseless images that significantly paves the detection of tumor or any unwanted cell.

Note: FRGS-related topic.

Prof. Dr. Md. Shabiul Islam