In the faculty of Electrical Engineering, research is organized in three Research Groups. These groups bring together Faculty staff and students, with similar interests, to focus on particular areas of theoretical research and project development. Here, they undertake joint activities and share expertise relevant to the research. These groups are shared below. 

Team Leader: Mr. Muneeb Abrar (Assistant Professor EE)

The Internet of Things (IoT) offers great potential to design new or upgrade existing embedded systems for a wide range of applications. Not only commercial products will benefit from new IoT innovations, but also industrial applications and systems. Embedded IoT systems consist of a network of smart devices, sensors, and actuators interconnecting with each other over the internet. It is rapidly evolving throughout the related industry and it is projected that there will be about 50 billion IoT devices connected to the internet by 2030.

This research group focuses on design methodology and hardware/software co-design of distributed embedded systems for use in the Internet of Things. We aim to transfer knowledge and research results to our cooperation partners, supporters, and students. 

Recent Projects:

1. Driver Assistance System 
2. RFID and IoT Based Door Lock.
3. Smart Socket with Energy Meter.

Team Leader: Dr. Qasim Awais (HoD EE)

The group focuses on fundamental and applied research in all the aspects of power and energy production, distribution, industrial aspects, and control. The key application sectors currently include power estimation, electric vehicles, smart grids, and renewable energy.

Here, we are putting effort into the research & development of innovative technologies on the energy demand side that improve energy efficiency, energy distribution system, and diversification of energy sources.

Recent Projects:

1. Electric Vehicle.
2. SVM Based Invertor using FPGA

Team Lead: Mr. Ali Arshad (Lecturer EE)

The MLCV Research Group emerged as a result of cross-disciplinary interests in research and applications related to Artificial Intelligence, Machine Learning, and Computer Vision.

MLCV research group addresses a wide range of problems including Real-Time Object Detection, Semantic Segmentation, and Anomaly Detection. The group has been successfully exploring and implementing novel techniques using computer vision and machine learning algorithms. MLCV is committed to design and apply intelligent real-time algorithms that efficiently automate computer vision tasks.

Recent Projects:

1. Real-time object tracking system on FPGA
2. CV based PCB Machine