ECE 699 Project Course - Fall 2021 | Electrical and Computer Engineering

ECE 699 - Master of Engineering Project

This is a project course, designed exclusively for MEng students. Students will carry out a research project over one academic term, under the direct supervision of an ECE faculty member. At the end of the term, a written Project Report has to be submitted, which will be evaluated and marked by the Supervisor.

Eligibility and Guidelines:

MEng students from ECE Department only (MASc and PhD students are NOT eligible).
Coursework average ≥ 80%, after at least 3 courses.
No RA or GRS is paid.
The course is not transferrable to the ECE MASc program.

Course enrolment process is as follows:

Projects that are available with ECE faculty members are listed below.
Students should contact the faculty member, and the faculty member shall confirm allocating the project to the student.
Faculty member will notify the MASc/MEng Coordinator, who will issue a Permission Number to the student for registering in the course.

Information for course supervisors:

Fall 2021 ECE 699 grades are to be submitted by December 17th, 2021 to the Faculty Coordinator (currently, Prof. Andrew Heunis) and the MASc/MEng Coordinator.

Projects Available for Fall 2021 (the list will be updated as projects become available or unavailable).

Project #1: Perceptually motivated and deep learning approaches for image and video processing

The objective of this project is to develop novel methodologies for image and video processing, optimization, compression, transmission, and streaming based on advanced technologies including perceptually motivated and deep learning approaches. Working with a group of experienced researchers and fellow students, the student will carry out research in the forms of algorithm and software development, experiment design and setup, perceptual testing, and data processing and analysis

Supervisor: Prof. Zhou Wang
Email: zhou.wang@uwaterloo.ca
Phone: 519-888-4567 x35301
Location: E5-5113

Project #2: Hardware Automation protocols and Image Processing Analysis using Machine-learning algorithms

This project consists of (1) development of python-based hard automation graphic user interfaces with classical control protocols and troubleshooting and (2) analysis of experimental and simulated images and data using various machine learning algorithms. Required skills: Python and classical control theory.

Supervisor: Prof. Na Young Kim
Email: nayoung.kim@uwaterloo.ca
Phone: 519-888-4567 x30481
Location: RAC 2101

Project #3: Classical and Quantum Simulations of Computer Optimization Problems

This project investigates quantitative analysis of classical and quantum simulations on a few classes of computer optimization problems (e.g. MAX-CUT, 3-SAT, Travelling salesman problem, phase transition etc.), with which we aim to establish the hybrid classical and quantum algorithms as a smart protocol to those problems. The tasks are to formulate mathematical equations with generated data or available datasets (e.g. MNIST, MIS etc.) for classical and quantum simulations and to compare their performance by setting up fair criterion. If these are done successfully, the final stage is to propose an efficient hybrid classical/quantum protocol. Required skills: Python, computer science basics on algorithms and complexity.

Supervisor: Prof. Na Young Kim
Email: nayoung.kim@uwaterloo.ca
Phone: 519-888-4567 x30481
Location: RAC 2101

Project #4 Application of sensor fusion for outdoor insulator assessment

Development of non-contact and robust inspection techniques is vital to detect any possible damage in outdoor insulators. Currently, certain sensors like IR camera and acoustic sensors are used by utility engineers during patrol inspection of overhead lines. Each available sensor can detect certain number of defects but not all of them. In this project sensor fusion will be applied between both vision based (IR and regular camera) and field emission based (acoustic and RF antenna) sensors to detect all types of defects in either ceramic and/or non-ceramic insulators. The work will be conducted at the high voltage lab at University of Waterloo.

Supervisor: Prof. Ayman El-Hag
Email: ahalhaj@uwaterloo.ca
Phone: 519-888-4567 x31431
Location: EIT 4016

Project #5 Design and implementation of RF Antenna for power asset assessment

Different types of power system assets like transformers, circuit breaker, cables and outdoor insulators are approaching their end of life. Since it is not financially feasible to change all these assets at once, priority replacement strategy is implemented by several utility companies. Hence, it is required to develop non-intrusive techniques to assess the current status of the aged assets. One of the promising techniques is the deployment of RF Antenna in the detection of partial discharge (PD) inside different insulation systems. In this project, the student will learn to design, build, test and characterize an RF antenna on a PCB to analyze the behavior of PD. Moreover, Machine learning techniques will be implemented to identify the root cause for the PD initiation in the insulation system. The work will be conducted on real case scenarios setup that are available in the High voltage lab.

Supervisor: Prof. Ayman El-Hag and Prof. Maher Bakri-Kassem (Systems Design)
Email: ahalhaj@uwaterloo.ca or mbakrikassem@uwaterloo.ca
Phone: 519-888-4567 x31431
Location: EIT 4016

Project #6 Accelerating real-time AI on SoC FPGAs

In this project, students will work on design and implementation of an open-source, VTA-based accelerator framework for real-time AI. Prior knowledge of compilers, FPGA design, and kernel programming is necessary.

Supervisor: Prof. Seyed Majid Zahedi
Email: smzahedi@uwaterloo.ca
Phone: 519-888-4567 x35761
Location: DC 2524

Project #7 Detection of Anomalous Behavior of Wireless Devices

Wireless devices, namely, smartphones, IoT (Internet of Things) devices, and wireless sensors, are finding widespread applications in personal communication, monitoring of critical infrastructure, and even human bodies for healthcare applications. The devices may report unexpected behavior or even behave abnormally because of various reasons: hardware malfunction, a device being compromised, and changes in the communication environment of a device, to name a few. The objective of this project is to design non-intrusive (aka touchless) anomaly detection techniques by using thermal images of the devices and applying machine learning techniques. Anomaly detection algorithms will be applied on actual data obtained in a lab environment, and the students will closely work with a PhD student and a Master’s student doing their theses on anomaly detection.

Supervisor: Sagar Naik
Email: snaik@uwaterloo.ca
Phone: 519-888-4567 x35313
Location: EIT 4174

Project #8 Antenna Measurements

The goal of this course project is to familiarize students with antenna concepts that affect the performance of wireless devices. The students will learn how to perform basic antenna measurements in an anechoic chamber. The students will then need to propose a setup that characterizes/demonstrates the properties of an antenna system and provide detailed material to explain these properties. These properties include (but are not limited to) impedance, efficiency, frequency of operation, bandwidth, gain, polarization, beam width, RCS, MIMO performance. Students will be encouraged to use their report towards an international competition organized by the IEEE Antennas and Propagation Society (note: a team from UW won the competition in 2016).