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:
- Winter 2022 ECE 699 grades are to be submitted by April 18th, 2022 to the Faculty Coordinator (currently, Prof. Andrew Heunis) and the MASc/MEng Coordinator.
Projects Available for Winter 2022 (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 and Image Processing Analysis using Machine- or Deep-learning algorithms
This project consists of (1) development of python-based hardware automation setup and graphic user interfaces with classical control protocols and troubleshooting and (2) analysis of experimental and simulated images and data using various machine learning algorithms. There is a chance to learn firmware programming and computer vision system. Required skills: Python, classical control theory. Machine-learning and Deep-learning knowledge are a plus.
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: Real-time wide-band FPGA-based Hardware Development, Experiments and Analysis
This project develops a wide-band FPGA-based real-time hardware measurement setup, whose target frequency range is ~ GHz. Time and frequency experimental data are collected and analyzed for modelling.
Required skills: Python, classical control theory. FPGA, Machine-learning and Deep-learning knowledge are a plus.
Supervisor:
Prof.
Na
Young
Kim
Email: nayoung.kim@uwaterloo.ca
Phone:
519-888-4567
x30481
Location:
RAC
2101
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 Developing Self-Adaptive Systems Using IBM Run-Time Technologies
The complexity of information systems is increasing in recent years. A consequence of this continuous evolution is that systems must become more customizable by adapting to changing contexts and environments. One of the most promising approaches to achieving such properties is to equip systems with self-adaptation mechanisms. The goal of this project is to build “Self-Adaptive Software Systems (SAS)” using open source runtime technologies and IBM Cloud Private. The project will provide student with a great opportunity to gain hands-on experience of run-time technologies and state-of-the-art self-adaptation mechanisms.
Supervisor
#1:
Prof.
Ladan
Tahvildari
Email: ladan.tahvildari@uwaterloo.ca
Phone:
519-888-4567
x36093
Location:
EIT
4136
Project #9 Performance Comparison of Rule and Integrity Checkers
Modern safety-critical systems require runtime monitoring to ensure integrity and safety. At the same time, these systems remain energy efficient to support small device size and operate without fans. The goal of this project is to evaluate runtime monitoring frameworks and perform a gap analysis which can then lead to subsequent research.
You will learn about: runtime verification, stream processing, embedded software, safety-critical systems, data analysis, performance evaluation
Supervisor:
Prof.
Sebastian
Fischmeister
Email: sebastian.fischmeister@uwaterloo.ca
Phone:
519-888-4567
x33694
Location:
E5
4112
Project #10 Root-Cause Analysis for Safety and Security Incidents
Security and safety are paramount for modern systems like autonomous vehicles, airplanes, and medical devices. The challenge is to reason about incidents in such systems. The goal of the project is to review open-source reasoning frameworks and build a prototype for incident response for embedded systems.
You will learn about: root-cause analysis, data analysis, reasoning and AI, embedded systems, safety-critical systems
Supervisor:
Prof.
Sebastian
Fischmeister
Email: sebastian.fischmeister@uwaterloo.ca
Phone:
519-888-4567
x33694
Location:
E5
4112
Project #11 Pwn-a-Truck: Cybersecurity of Heavy Vehicles
Security of autonomous vehicles is crucial to eventually deploy them at scale. We own a truck that we use for cybersecurity. The goal of the project is to identify exploitable vulnerabilities in electronic control units of an actual truck on campus. Pwn a truck!
You will learn: embedded systems security, low level programming, CAN, cybersecurity attack tools
Supervisor:
Prof.
Sebastian
Fischmeister
Email: sebastian.fischmeister@uwaterloo.ca
Phone:
519-888-4567
x33694
Location:
E5
4112
Project #12 Modeling Techniques, End of Life Estimation and Battery Management Systems for EV Battery Packs
This project has been inspired by the expected high volume of EV battery packs that will become available after the end of their first lives in the vehicles and their potential application in stationary energy storage systems before their end of second life and being recycled. The project consists of:
-
A
Critical
Review
of
Methods
for
Modeling
and
Estimation
of
EV
Batteries’
End
of
First
(In-Vehicle)
Life
and
Second
(Used/Repurposed
in
Stationary
Energy
Storage
System)
Life,
and
- A Critical Review of Battery Management Systems (BMSs) for EV Battery Packs – Requirements, Design Specifications, Performance Parameters, Relation to Type and Age of Battery Cells, Relation to Charging System
Supervisor:
Prof.
Mehrdad
Kazerani
Email: mkazerani@uwaterloo.ca
Phone:
519-888-4567
x33737
Location:
EIT
4171
Project #13 Critical Review of Energy Access Projects for Off-Grid Communities
About 600 million people in Sub-Saharan Africa have no access to electricity at all. One of the important goals of the United Nations 2030 Agenda for Sustainable Development is to not leave anyone without electricity by 2030. This is a very aggressive target and needs tremendous global effort. A lot of projects in different regions of the world, including Sub-Saharan Africa and Bangladesh, have targeted to put an end to energy poverty. These projects have been mainly initiated by start-up tech companies, with specific business plans, supported by local governments and non-governmental organizations (NGOs). Some of these projects have been successful, but some have failed to continue with the initial agenda due to different reasons. The aim of this project is to make a critical review of the energy access projects on the ground, especially in Sub-Saharan Africa and Bangladesh, and make an analysis of the reasons behind successes and failures, and hopefully make practical recommendations useful for future projects.
Supervisor:
Prof.
Mehrdad
Kazerani
Email: mkazerani@uwaterloo.ca
Phone:
519-888-4567
x33737
Location:
EIT
4171
Project #14 Cellular Data Analysis
This project is about analyzing data that has been collected on the cellular network. The student(s) will work closely with the PhD student in charge of the project. A knowledge of networking is a plus.
Supervisor:
Prof.
Catherine
Rosenberg
Email: cath@uwaterloo.ca
Phone:
519-888-4510
Location:
EIT
4008
Project #15 Geometric nonlinear control of underactuated mechanical systems
We will design and implement a nonlinear feedback controller for motion control of a rotational inverted pendulum. We will use the tools of nonlinear control and differential geometry to motivate our design and mathematically prove its effectiveness. The task includes (1) modelling the system (2) analyzing the resulting model (3) design and simulate a path following controller to move the pendulum in a desired manner (4) implement the controller on a Quanser designed hardware platform.m.
Supervisor:
Prof.
Chris
Nielsen
Email: cnielsen@uwaterloo.ca
Phone:
519-888-4567
x32241
Location:
EIT
4106
Project #16 Fault Detection in Hybrid HVDC Grids
High voltage direct current (HVDC) grids, where a number of point-to-point HVDC links are connected together in a meshed configuration, have recently gained substantial attention in Europe, China and Canada. These HVDC grids enable the bulk and low-loss transfer of power and allow for large integration of renewable resources. As future HVDC grids will be built by different manufacturers, various types of converters will be operating in the same grid. The creation of such hybrid HVDC grids will bring forth significant technical challenges. One significant challenge is the hybrid HVDC grid protection. This project revolves around developing a relaying algorithm for hybrid HVDC grids.
Supervisor:
Prof.
Sahar
Azad
Email:
sahar.azad@uwaterloo.ca
Phone:
519-888-4567
x33974
Location:
EIT
4017
Project #17 Protection of Modernized Distribution Systems
The conventional protection strategies and protective relays in the electric power distribution systems have been developed based on the characteristics of large centralized generation systems, i.e., synchronous generators. The existing protection systems are not designed taking into account the different behaviour of electronically‐interfaced Distributed Energy Resources (DERs), e.g., renewables and energy storage systems. This project aims to enable reliable protection of the modernized distribution systems with increased penetration of electronically‐interfaced DERs, especially the large‐scale wind and solar power plants.
Supervisor:
Prof.
Sahar
Azad
Email:
sahar.azad@uwaterloo.ca
Phone:
519-888-4567
x33974
Location:
EIT
4017
Project #18 Distance relays for protection of systems with wind farms
The protection of power systems with renewable energy resources against large fault currents and voltage transients are one of the main technical challenges hindering the large integration of renewable resources to the electric grid. This research project aims to address the protection challenges by developing and experimentally validating innovative relaying strategies for grids with wind farms.
Supervisor:
Prof.
Sahar
Azad
Email:
sahar.azad@uwaterloo.ca
Phone:
519-888-4567
x33974
Location:
EIT
4017
Project #19 Reinforcement Learning in Video Games
The student will utilize concepts from Machine Learning and Reinforcement Learning to implement a basic game playing agent for Minecraft. This will require strong familiarity with API programming in python.
It
will
also
be
beneficial
if
the
student
has
some
knowledge
of
image
processing
and
Machine
Learning
algorithms.
The
student
will
conduct
literature
review
on
the
related
topics,
create
an
installation
of
the
MinceRL
packages,
implement
some
ML
and
RL
solutions,
depending
on
their
experience
level,
and
write
a
report
describing
their
achievements,
results
and
outline
of
next
steps
to
be
taken
in
future
research
on
this
domain.
Supervisor:
Prof.
Mark
Crowley
Email:
mcrowley@uwaterloo.ca
Phone:
519-888-4567
x31464
Location:
E5
4114
Project #20 Combining Image Processing and Natural Language Processing for Medical Data
This project will centre around a large dataset for Digital Pathology including scanned images of patient tissue samples and corresponding medical reports from doctors in partially structured text documents.
The student will perform a literature review of related topics in this field, particularly the use of CNNs, for such images and Word2Vec methods on text data. Depending on the student's level of background knowledge, they will perform analysis of these datasets and use CNN a Word2Vec algorithms to build a first draft combined model of the data.
The student will meet regularly with Prof Crowley to report their progress and to get guidance on next steps. At the end the term, the student will write up a short report on their achievements and findings. Existing familiarity with API programming in Python is preferred.
Supervisor:
Prof.
Mark
Crowley
Email:
mcrowley@uwaterloo.ca
Phone:
519-888-4567
x31464
Location:
E5
4114
Project #21 Learning Human Driving Behaviour from Car Sensor Data
Use concepts from Data Analysis and Machine Learning on a large, multi-model, time-series dataset collected by UWaterloo researchers in partnership with a large automaker. The data comes from a vehicle with multi-directional radar, roof-mounted LiDAR, GoPro cameras, GPS/Map data and internal automobile CanBus data. The target of the project will be to develop, train and evaluate some Machine Learning models for predicting and classifying various predefined driver behaviours from this data. The student will write a report on achievements, results, methods used and experimental analysis. Familiarity with python, scikitlearn, tensorflow packages is necessary. Note, dataset is subject to a research privacy agreement.
Supervisor:
Prof.
Mark
Crowley
Email:
mcrowley@uwaterloo.ca
Phone:
519-888-4567
x31464
Location:
E5
4114
Project #22 Developing a microfluidic biosensor for profiling cancer biomarkers
During cancer progression, many tumors shed cancer biomarkers, including circulating tumor cell (CTC), exosomes and cell-free circulating tumor DNA (ctDNA) into the bloodstream. In this project, the candidate will be working on developing a platform for in-line detection of exosomes as a biomarker for early cancer diagnosis. They will fabricate a specially designed microfluidic device and integrate a bead-based assays for in-line capture of exosomes from whole blood sample.
For further information please visit: https://uwaterloo.ca/integrated-devices-early-awareness-lab/
Supervisor:
Prof.
Mahla
Poudineh
Email:
mahla.poudineh@uwaterloo.ca
Phone:
519-888-4567
x33319
Location:
QNC
3622
Project #23 Developing a real-time, electrochemical biosensor for glucose and insulin detection
In this project, an electrochemical biosensor will be developed for multiplexed and real-time detection of glucose and insulin. The detection is based on aptamer switching probes where a redox agent is conjugated to the aptamer probe and upon binding of the target, a change in conformation of aptamer happens and this will allow target detection.
For further information please visit: https://uwaterloo.ca/integrated-devices-early-awareness-lab/
Supervisor:
Prof.
Mahla
Poudineh
Email:
mahla.poudineh@uwaterloo.ca
Phone:
519-888-4567
x33319
Location:
QNC
3622
Project #24 Path Planning/Controls for Autonomous Racing
In this project you will be designing, implementing, validating, and iterating upon various path planning and control algorithms to drive a modified Dallara IL-15 Indy Lights vehicle around the Indianapolis Motor Speedway in simulation. This project is a part of Waterloo Autonomous Racing (WATORACE)’s stack for competing in the Indy Autonomous Challenge.
Supervisor:
Prof.
Derek
Rayside
Email:
drayside@uwaterloo.ca
Phone:
519-888-4567
x40248
Location:
E7
5426
Project #25 Action Detection in Road Scenes
Leveraging the ROAD Dataset, WATonomous is developing an action classifier for road participants from video streams. The system will be deployed on-vehicle and validated in lead vehicle overtaking and pedestrian interaction scenarios. The team is looking for experience in computer vision research, development with PyTorch and Docker, and deploying neural networks to on-vehicle GPUs.
Supervisor:
Prof.
Derek
Rayside
Email:
drayside@uwaterloo.ca
Location:
E7
5426
Project #26 Urban Decision Making for Autonomous Vehicles
Developing decision-making frameworks for safe, yet efficient, urban autonomous driving under environment uncertainties is a challenging topic for modern learning-based algorithms. Therefore, in this project, reinforcement learning-based decision-making schemes are developed to learn optimal policies for driving in multi-agent environments where intents of road users and other dynamic states are unforeseeable. Candidates with programming experience with PyTorch/ TensorFlow and familiarity with the CARLA simulator are preferable.
Supervisor:
Prof.
Derek
Rayside
Email:
drayside@uwaterloo.ca
Location:
E7
5426
Project #27 Sim-to-Real Applications for Autonomous Driving
As driving simulators become more photorealistic and simulation platforms become more flexible in terms of sensor configuration and environmental setting, synthetic data has a greater chance of filling gaps in existing real-world datasets. Nonetheless, there is a domain difference between the appearance of simulation and real-world data, which can be bridged using transfer learning techniques. Watonomous researchers have been working on developing such schemes for autonomous driving applications. Candidates with programming experience with PyTorch/ TensorFlow and familiarity with the CARLA simulator are preferable.
Supervisor:
Prof.
Derek
Rayside
Email:
drayside@uwaterloo.ca
Location:
E7
5426
Project #28 Robust Deep Learning of Deep Neural Networks
Deep neural networks (DNNs) are vulnerable to adversarial examples, maliciously modified raw input data which is imperceptible to human vision, but once fed into DNNs, can lead DNNs to produce incorrect outputs. The existence and easy construction of adversarial examples pose significant security risks to DNNs, especially in safety-critical applications, including visual object recognition and autonomous driving. One way to partially mitigate this problem is to formulate deep learning as a type of minimax problem instead of the standard minimization problem. The objective of this project is to explore and implement effective methods for solving such a minimax problem, yielding robust deep learning.
Supervisor:
Prof.
En-Hui
Yang
Email:
ehyang@uwaterloo.ca
Phone:
519-888-4567
x32873
Location:
EIT
4157
Project #29 Modelling Adversarial Perturbations in Deep Neural Networks
Deep neural networks (DNNs) are vulnerable to adversarial examples, maliciously modified raw input data which is imperceptible to human vision, but once fed into DNNs, can lead DNNs to produce incorrect outputs. The existence and easy construction of adversarial examples pose significant security risks to DNNs, especially in safety-critical applications, including visual object recognition and autonomous driving. The objective of this project is to model adversarial perturbations in DNNs through statistical analysis and DNN visualization. The established model will provide a basis for developing a radically different approach for detecting adversarial examples.
Supervisor:
Prof.
En-Hui
Yang
Email:
ehyang@uwaterloo.ca
Phone:
519-888-4567
x32873
Location:
EIT
4157
Project #30 Comparing the accuracy of bone density measurements obtained from a dual energy chest radiograph to a state-of-the-art DEXA scan
DEXA is the established gold standard when it comes to bone density measurements. However, it is a test that requires specialized equipment and is prescribed for a very targeted population e.g. seniors. In contrast, X-ray imaging is a commonly prescribed imaging test in outpatient clinics and emergency departments across Canada. This project will investigate the difference in error between a bone density measurement extrapolated from a dual energy X-ray image and compare it to the measurement obtained from a Dual Energy X-ray Absorptiometry (DEXA) scan.
Supervisor:
Prof.
Karim
S.
Karim
Email:
kkarim@uwaterloo.ca
Location:
E7
1326A
Project #31 Memory Visualization for an Assistive Robot
The student will develop a basic software tool for a robot to store and view memory of salient objects in a lab environment. This will require strong familiarity with Python or C++. It will also be beneficial if the student has some knowledge of object tracking and basic GUI design. The task includes: 1) Implement an off-the-shelf object detector and object tracking mechanism from OpenCV to detect and track some salient objects, 2) Store videos of salient objects in memory, 3) Develop a simple GUI to search and visualize videos of salient objects stored in memory.
Supervisor:
Prof.
Kerstin
Dautenhahn
Email:
kerstin.dautenhahn@uwaterloo.ca
Location:
E5
5027
Project #32 Admittance control for safer control of a mobile manipulator robot
The student will implement an admittance controller for safer motion control of our Fetch robot. The student should have a good mathematics knowledge and some understanding of electronics and hardware. The task includes (1) reviewing the state of art on admittance control and choosing an appropriate method (2) implementing the chosen controller on our robot, which will entail getting the appropriate information at the motor level and Python or C++ programming for the control algorithm.
Supervisor:
Prof.
Kerstin
Dautenhahn
Email:
kerstin.dautenhahn@uwaterloo.ca
Location:
E5
5027
Project #33 Deep Learning to speed-up and augment Autonomous Vehicle simulators
Project description: Autonomous driving is a safety-critical application where it is near impossible to safely and efficiently test autonomous vehicles (AVs) in their target environments. Simulation-based testing has emerged as a promising approach to test AVs and discover issues prior to on-road deployment. AV simulators however are computationally intensive and cannot simulate behaviors faster than real-time (in general), slowing down the testing process. This project aims to find strategies to speed up AV simulators by developing neural network architectures that can accurately approximate simulated vehicle (and environmental) behaviors while reducing or entirely eliminating the use of AV simulators in simulation-based testing.
Required skills: Python programming (including familiarity with Tensorflow/PyTorch), basic knowledge of deep learning, familiarity with working in a Unix environment.
What you will learn: State-of-the-art autonomous vehicle simulators (CARLA and LGSVL); vehicle dynamics; basics of perception, planning and control in autonomous vehicles; implementing and training different deep learning architectures (such as GNNs and GANs).
Supervisor:
Prof.
Yash
Vardhan
Pant
Email: yash.pant@uwaterloo.ca
Location:
E5
5114
Supervisor:
Prof.
Yash
Vardhan
Pant
Email: vganesh@uwaterloo.ca
Location:
DC
2530