A localized data centre cooling solution based on fluid-submersion technology. The system is designed to use non-conductive re-engineered palm oil as the liquid coolant and function well in tropical climate.

Assess carbon emission baseline and establish measures for energy consumption and carbon emission reduction

Assess carbon emission baseline and establish measures for energy consumption and carbon emission reduction

Utilising solar and hydrogen fuel cell to provide full power supply required by a remote BTS site for rural areas without commercial power supply

Integrated system that can capture useful information to analyse, organise and improve the management of vehicle fleet through important vehicle data such as speed, location, driving pattern and route compliance.

Real time continuous water quality monitoring system (RTWQ) made up of multi probes sensors that measure water quality parameters at pre-defined intervals and store the information on digital data loggers without the need for any operator intervention or M2M communication.

A prototype platform on existing factory to measure, feedback and improve on their energy efficiency through placement of sensors across the factory and monitoring through an in-house dashboard.

A platform to measure PH, water level, temperature and humidity of paddy field through deployment of sensors for better paddy yield. Monitoring & notifications through mobile app utilising LORA for communication.

This project is an IoT ecosystem for elderly care consisting of wearable and other connected health devices, platform, application and analytics. The solution helps in relaying crucial information to their next of kin. The information will also be used to provide prediction on their health so that precautionary measures can be taken.

Development of new wireless device & protocol for vehicle tagging that has wireless charging capability. This solution was utilised for Smart-drive through takeaway system.

The remote environmental monitoring system aimed to develop and monitor various parameters at Pulau Bidong such as temperature, humidity, pH value of water, turbidity and etc. A long-range communication network using LPWAN technology was applied for the end node which is located at Pulau Bidong and the base station/gateway which is located at a top of building in UMT.

This project is a mobile application developed for allowing connection to recycle in a cleaner and more reliable system. It is also help to keep track of individual carbon footprint and help to reduce the nation’s carbon footprint by high quality recovery process for the valuable materials in electronic waste.

This project to develop autonomous wildlife intrusion detection system (ASIDS) by using recent machine learning technique and hybrid of computing optimization method for an optimal performance. It is also to architect a working prototype for the system in a real forest environment and to evaluate the performance of ASIDs with different classifiers in a real forest environment.

This project is aimed to utilise cloud-IoT platform in urban farming by analysing data. It is also to develop a decision support system (DSS) and smart scheduling system that automatically triggers urban farm sensors.
It also integrate the developed system into a mobile application.

This project to develop a speed monitoring feedback control system, a driver’s alert awareness feedback control system while driving on the road. It is also to implement driving assistance system using lane tracking method and to provide real-time information in vehicle activities through access portal.

This project aims to implement the Proof of Concept (PoC) of the Lightning Interference in 4G Wireless Communication Links. The duration of this PoC is for 12 months starting April 2021. The PoC is carried out in Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Jalan Hang Tuah Jaya, 76100 Durian Tunggal, Melaka.

The objective of this project is to quantify and analyse the effect of lightning electromagnetic interference to 4G wireless communication links. Comprehensive experimentation has been conducted to measure low frequency and microwave electric field radiations together with User Datagram Protocol (UDP) and Transmission Control Protocol (TCP) packets transmission over Fourth Generation (4G) wireless communication link during storm events.

This project aims to implement the Proof of Concept (PoC) of the Monitoring System and Localisation of COVID19 Patient. The duration of this PoC is for 12 months starting March 2021. The PoC is carried out in Pusat Kuarantin dan Rawatan (PKRC) at Kolej Jururawat Masyarakat Kangar (KJM).

Radio Frequency Identification (RFID) technology is an ideal data capture and sharing system in various of monitoring applications especially healthcare environment. From admissions and administration, through points-of-care to managing equipment and supplies, an RFID solution provides an efficient, cost-effective information system to support best practices in patient service.

Besides that, the secured parameter of facilities applying the RFID technology is becoming essential nowadays when the spreading of the virus COVID-19 become aggressive. Person Under Surveillance (PUS) and Person Under Investigation (PUI) were given special non-electronic wristbands to indicate that they are under quarantine. They were also monitored through the ‘MySejahtera’ mobile application. However, these methods cannot detect whether the PUS or PUI remained in their quarantine stations.

Thus, a localised monitoring system for PUS and PUI in the quarantine stations was developed. Frequency Selective Surface (FSS) RFID tag antenna was implemented for its capability to improve the antenna’s performance and better efficiency. Monitoring and localising the web-based system using RFID technology with the application of Internet of Things (loT) and cloud technology were integrated as a RFID full system application. This system can run and monitor in real-time on multiple devices concurrently and will alert the relevant parties on any case of PUS or PUI escaping the quarantine stations.

This project aims to develop a Proof of Concept (PoC) on a vision-based intelligent traffic monitoring system that classifies and counts vehicles as they move through a fixed detection zone in a CCTV field of view.

The system uses Artificial Intelligent (AI) to classify vehicles into one of three categories: car, motorcycle, and heavy vehicles (vans and trucks). With data trajectory, analysis, and prediction, traffic flow and anomalies condition can be determined in real-time to help authorities and road users make fast decisions.

In addition, historical data on traffic flow can help the city council enforce traffic diversion, revise road planning, develop better access to property and business areas and study the impact of vehicle carbon emissions.

This PoC uses deep learning Artificial Intelligence (AI) based on image classification and object detection to develop vocabulary and mathematics skills enhancement application with the integration of Internet of Things (IoT) platform. AI-IoT application which embedded into the PoC has the potential to revolutionize how the children learn especially in early education. This PoC allow the children need to physically move and interact with their environment to complete the tasks. All the activities done are recorder in the cloud which can be monitored through the database management application.

This PoC achieved more than 90% accuracy for each category. As a conclusion, the children are able to fully utilise the current technology with an interactive learning session instead of using other passive applications in the market. For future work, this PoC can be part of IoT use cases in standards development and more features can be added to cater for special children and other education levels.

This project was run based on proving the concept of medical drones and the delivery ability to rural area hospitals using a customized drone and green delivery network. The project also intends to explore the acceptance level of the medical health officers in rural areas for drones and medical delivery. In the plan, reducing carbon emission on deliveries is also one of the objectives thus comparison on current cost using van and drone was carried out.  The scope of the project was focused on rural area delivery. Other areas will be excluded. Around 200 health workers who work in the medical area in 3 rural area hospitals were interviewed using a stated preference survey and in-depth interviews were also conducted with the pharmacist to further understand the nature of the medical needs during the delivery process.

The PoC is successfully completed involving the development of appropriate system that is ready for medicine delivery using drone and some analysis on financial and environmental implications as well as supporting data on acceptance of drone as a delivery method for medicine at rural health centre. There is potential for commercialisation via the promotion of a green supply chain network for medical parcel delivery that can be proposed to logistics service providers as alternative to conventional methods.