ECE 699A/B - 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 transferable 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 2025 ECE 699A/B grades are to be submitted by April 17th, 2025 to the Faculty Coordinator (currently, Prof. Liang-Liang Xie) and the MASc/MEng Coordinator (currently, Amber Beaudoin).
Projects Available for Winter 2025 (the list will be updated as projects become available or unavailable).
Project: Optical image processing for visually impaired athletes
Visually impaired sport often requires support personnel, such as guide runners. This need for support personnel could be reduced through image analysis, e.g. through image processing and object tracking, to ensure that for example a runner or swimmer does not leave their lane. However, real-time image processing requires fast computation, which is not convenient to carry during sports activities. The aim of this project is to develop and characterise an optical computation setup that performs the image processing, e.g. contrast enhancement or edge detection and enhancement to in the future allow a low computation system, e.g. a mobile phone, to perform the remainder of the image processing and then provide feedback to the athlete.
Supervisor: Prof. Sebastian Schulz
Email: sebastian.schulz@uwaterloo.ca
Project: 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
Project: Application of neural network or deep learning algorithms in pattern recognition
This project aims to build deep machine learning models (e.g. recurrent neural networks, transformer models, etc.) to perform experimental data analysis where tasks of classification and pattern recognition are performed.
Required knowledge & skills: Python, machine learning algorithms.
Supervisor: Prof. Na Young Kim
Email: nayoung.kim@uwaterloo.ca
Project: Numerical Simulations of nanoelectronic devices
This project aims to perform numerical simulations of nanoelectronic devices based on nanomaterials, which will explain experimental device performance.
Required knowledge & skills: Semiconductor device physics
Supervisor: Prof. Na Young Kim
Email: nayoung.kim@uwaterloo.ca
Project: 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
Project: Optimizing Profiling Instrumentation Cost in the Cloud and AI Era
By collecting runtime information about how conditional branches, switches, virtual and interface calls execute, a compiler can optimize the code that it generates to a much higher degreeby staying focused on the code paths that truly matter. However, profiling instrumentation incurs a high overhead, which means we have to reduce the amount of information collected to avoid penalizing application performance too heavily. We plan to explore two alternatives to reduce profiling instrumentation cost : a) amortize the profiling activity over N running JVM instances connected to one cloud compiler instance and b) using AI to predict the profiling result, thus reducing the need to perform profiling instrument at runtime to obtain information for the compiler. This means that One the cloud compiler collects more profiling information than before so the compiler can improve performance, at a fraction of the run time cost that would normally be incurred.
Supervisor: Prof. Ladan Tahvildari
Email: ladan.tahvildari@uwaterloo.ca
Project: Optimizing Cold Compilation Cost in the Cloud and AI Era
The cloud compiler already recognizes a specific client JVM (typically a new application or at least a new version of an application) identified through its unique tag id. Whenever a container instance connects to the cloud compiler for the first time, it generates and caches optimized code, which it can reuse when other instances of the same container connect to the cloud compiler in the future. When a new container (application) connects to the cloud compiler for the first time (termed a “cold” run), however, the cloud compiler has no prior compiled code (or any other runtime information) and so must pay the compilation cost to optimize future instances of that same container. We plan to explore using new schemes (potentially based on AI techniques) to reuse compiled code previously generated for other containers (applications) even when a new container connects to the cloud compiler for the first time, thereby improving the performance of an application’s “cold” run.
Supervisor: Prof. Ladan Tahvildari
Email: ladan.tahvildari@uwaterloo.ca
Project: Exploring new avenues for accelerating vector similarity search
In data science and machine learning, a “vector” (also called an “embedding”) represents the “learned” latent space features of an item or entity in a collection of structured or unstructured data, e.g., image, document, audio and knowledge graph. Identifying similar vectors in a collection (e.g., a vector database) is a fundamental operation with wide applications to machine learning workflows, recommender systems and data mining. As the dimension of vectors become large (e.g., 10^2 – 10^3) or the size of the collection grows (e.g., 10^9 – 10^12), vector similarity search becomes computationally challenging, even more so when high accuracy is sought. This project will explore systems techniques to accelerate vector similarity search on parallel computing platforms towards improving performance at scale. More precisely, this project will focus on the top-k nearest neighbor search problem in a vector data collection. There exist several algorithmic techniques for nearest neighbor search in vector data collections: product quantization, local sensitivity hashing, clustering-based, e.g., DBSCAN and graph-based, e.g., HNSW; they offer different trade-offs in terms of throughput, scalability and accuracy. Additionally, past studies showed that it is possible to combine multiple algorithmic techniques in the same task to achieve further improvements. This project will capitalize on the existing algorithmic techniques and aims at engineering a parallel/distributed solution that offers high throughput and accuracy.
Required knowledge & skills: Object-oriented programming, data structures, familiarity with parallel/distributed computing concepts and implementation frameworks (e.g., CUDA, OpenMP, MPI, Hadoop/Spark), knowledge of machine learning concepts and tools (e.g., PyTorch, TensorFlow).
Supervisor: Prof. Ladan Tahvildari
Email: ladan.tahvildari@uwaterloo.ca
Supervisor: Prof. Tahsin Reza
Email: tahsin.reza@uwaterloo.ca
Project: 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
Project: 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
Project: 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
Project: EV Fast Charging
In order to encourage/accelerate the intake of EVs, a wide-spread fast-charging infrastructure would be necessary to reduce the charging time and alleviate range anxiety. To enable adoption of such infrastructure, various requirements in terms of available charging power from the grid, protection, and battery management system need to be satisfied. Also, the required equipment such as power electronic converter and BMS must be developed, keeping thermal management and cell balancing at high charging rates in mind. A feasible charging time has to be recognized as well. A few interesting topics that fall under EV Fast Charging are: (i) fast wireless charging, addressing feasibility, advantages/disadvantages, and issues/hurdles; (ii) the impact of fast charging on the grid and the resiliency of the grid in response to fast charging events; and (iii) optimal placement of fast charging stations.
Supervisor: Prof. Mehrdad Kazerani
Email: mkazerani@uwaterloo.ca
Project: State-of-Charge (SoC) Estimation Techniques for EV Battery Packs
The project consists of:
- A Critical Review of SoC estimation techniques for EV Battery Packs
- Design and design verification through simulation (based on a detailed model of a Li-ion battery cell).
Supervisor: Prof. Mehrdad Kazerani
Email: mkazerani@uwaterloo.ca
Project: State-of-Power (SoP) Estimation Techniques for EV Battery Packs
The project consists of:
- A Critical Review of SoP estimation techniques for EV Battery Packs
- Design and design verification through simulation (based on a detailed model of a Li-ion battery cell).
Supervisor: Prof. Mehrdad Kazerani
Email: mkazerani@uwaterloo.ca
Project: State-of-Health (SoH) Estimation Techniques for EV Battery Packs
The project consists of:
- A Critical Review of SoH estimation techniques for EV Battery Packs
- Design and design verification through simulation (based on a detailed model of a Li-ion battery cell).
Supervisor: Prof. Mehrdad Kazerani
Email: mkazerani@uwaterloo.ca
Project: Feasibility Analysis of Electrical Braking in More-Electric Airplanes
Electrification is the future of transportation. This project focuses on electrification of air transportation. As electric propulsion and actuation systems are being studied and tested for incorporation in more-electric airplanes, electric braking (both in the air and on the ground) must be considered as a viable option with great potential in energy saving. This project starts with a thorough and critical literature survey and analysis of the methods proposed/implemented for electric braking in more-electric airplanes. It will then continue with modeling and simulation of the most promising schemes and assessing their feasibility, as well as techno-economic and environmental benefits.
Supervisor: Prof. Mehrdad Kazerani
Email: mkazerani@uwaterloo.ca
Project: Techno-Economic Analysis of Green Ammonia Production via Electrochemical Nitrogen Reduction Technique
Ammonia is a versatile chemical that has traditionally been used in the fertilizer industry for decades. However, ammonia’s inherent characteristics such as high hydrogen density, high volumetric density, ease of liquification, low flammability, and ease of transportation make it attractive as an energy storage medium in load-leveling applications. Renewable energy-based green ammonia is commonly produced using the electrified Haber Bosch (E/H-B) process; however, an alternative approach for green ammonia production using the “Electrochemical Nitrogen Reduction (ENR) Technique” is gaining attention. The E/H-B process is energy-intensive, requires expensive hydrogen electrolyzers, and a large, centralized HB plant (i.e., a high capital cost). The ENR technique, on the contrary, has low energy and capital costs, as it requires neither a hydrogen electrolyzer nor an HB plant, and it is decentralized which reduces transportation costs and enables deployment in remote locations. The objective of this project is to conduct and develop a techno-economic analysis of the ENR technique for ammonia production based on the Ontario electricity grid data. Conducting a sensitivity-analysis to study the economic impacts of ammonia production via the ENR technique, computing the Levelized Cost of Ammonia (LCOA) for the ENR technique, and comparing it with that of the E/H-B process will be used to show the economic competitiveness of the ENR approach.
Supervisor: Prof. Mehrdad Kazerani
Email: mkazerani@uwaterloo.ca
Project: 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
Project: 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
Project: STATCOMs and Beyond
In this project, an existing STATCOM, which is based on a full bridge modular multi-level converter, is used to provide power oscillation damping and other ancillary services to the power grid. The various steps in this project are:
1) Using the existing modular multi-level converter models in PSCAD to build a STATCOM
2) Develop the control and switching mechanism of the STATCOM to provide oscillation damping
3) Explore the STACOM capabilities to achieve other ancillary services such as frequency support
Supervisor: Prof. Sahar Azad
Email: sahar.azad@uwaterloo.ca
Project: 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
Project: 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
Project: 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
Project: 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
Project: 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
Project: 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
Project: 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
Project: 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
Project: Uncovering patterns of errors in modern language and vision models
While GenAI models can be quite impressive, they occasionally fail in surprising ways. For instance, using the prompt “which city is further north, London, England or Montreal, Quebec?” the obtained response (as of now) is “Montreal, Quebec is further north than London, England. Montreal is located at a latitude of approximately 45.5° N, while London is at a latitude of about 51.5° N.” Why do errors like this happen, and can we detect them automatically? This project will attempt to design algorithms to discover patterns in AI error cases, and group them in ways that are interpretable for human users.
Required knowledge & skills:Deep learning, probabilistic machine learning, python (torch, numpy, ...), LLMs
Supervisor: Prof. Elliot Creager
Email: creager@uwaterloo.ca
Project: 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
Project: 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
Project Title: Towards Developing More Affordable, Efficient, Interpretable, and Secure Artificial Intelligence Paradigms
While deep learning-based artificial intelligence (AI) has propelled the information age to unprecedented heights, its current trajectory—characterized by scaling up massive deep neural networks (DNNs) and leveraging vast datasets—faces significant limitations. These include enormous demands for energy, computing resources, and high-throughput networking, alongside persistent challenges in AI interpretability and security. Moreover, the high costs associated with this approach restrict accessibility to a few large players, limiting its societal impact and potential to create broader economic opportunities.
To address these challenges, this project aims to explore and develop alternative AI paradigms that prioritize efficiency, interpretability, security, and inclusivity. By leveraging concepts and techniques from information science and probability theory, the project will initially focus on enhancing the nonlinearity, structural properties, and training/inference efficiencies of DNNs, a critical step towards achieving the final goal.
Supervisor: Prof. En-Hui Yang
Email: ehyang@uwaterloo.ca
Project: 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
Project: Electrical Characterization Studies on Power Cables used in Nuclear Power Plants
Research Description: The work will consist of electrical characterization studies on long samples (i.e. 3 to 10 meters) prepared from medium voltage (MV) polymeric cables utilized in power generation and distribution applications. The samples will be subjected to varying degrees of thermal aging. The work will be conducted as a part of an overall industry-partnered R&D study with Kinectrics Inc. to localize the thermal aging related defects in polymeric cables used in nuclear power plants.
Research Objectives and Methodolgies: You will be taught different, efficient, and practical characterization methods of power cables which are used in industry. These methods include but not limited to Dielectric Spectroscopy (DS), Polarization and Depolarization Current (PDC) measurements, Time- and Frequency- Domain Reflectometry (TDR and FDR) tests, high-frequency dielectric characterizations, and polymer tests such as tensile tests.
Supervisor: Prof. Shesha Jayaram
Email: shesha.jayaram@uwaterloo.ca
Project: Investigating the degradation of windfarm connected transformer insulation under repetitive transient voltages
The step-up transformers in the windfarms are prone to premature failure due to high-frequency repetitive transients generated by the operation of power electronics switching and frequent circuit breaker operations. In this work, transformer insulation degradation under repetitive transient voltages will be evaluated. Partial discharge inception voltage, intensity of discharges, time to failure and the rate of hydrogen gas generation are the parameters considered to evaluate the level of degradation under different operating conditions.
Supervisor: Prof. Shesha Jayaram
Email: shesha.jayaram@uwaterloo.ca
Project: Hardware Architecture for Machine Learning Algorithms Based on Stochastic Computing
In this project, you will implement an RTL hardware architecture for a convolutional neural network (CNN) based on stochastic computing, which offers compact arithmetic node implementation, to be applied to problems in medical imaging. The target will be a field-programmable gate array (FPGA) platform.
Supervisor: Prof. Vincent Gaudet
Email: vcgaudet@uwaterloo.ca
Project: Robot design for long-term environmental monitoring
The deployment of robots in the wild to monitor the environment over long time horizons poses a number of challenges which should be addressed at the design stage. This project aims at developing novel mechanical, electronic, and control systems design principles which will allow robots to be deployed for sustained amounts of time with the objective of monitoring environmental phenomena of interest.
Required knowledge & skills: Prior experience with either (i) CAD of mechanical and electronic components or (ii) embedded control systems is required.
Supervisor: Prof. Gennaro Notomista
Email: gennaro.notomista@uwaterloo.ca
Project: Safety in human-robot interaction
This project aims at advancing the state of the art in understanding the concept of safety in human-robot interaction. Recently developed theoretical tools in dynamical systems have allowed us to formalize the idea of safety. These will serve as building blocks for defining and enforcing safety in dynamical systems comprised of robotic platforms interacting with humans.
Required knowledge & skills: Good knowledge of dynamical systems and control theory.
Supervisor: Prof. Gennaro Notomista
Email: gennaro.notomista@uwaterloo.ca
Project: Control of teams of quadcopters for long-term environmental monitoring
This project explores coordination strategies for teams of quadcopters deployed in the field and tasked with monitoring the environment for long time horizons. Novel strategies to combine team-level coordination and energy management will be developed with the goal of defining a new control framework suitable for long-term monitoring of environmental phenomena using teams of autonomous mobile robots.
Required knowledge & skills: Good knowledge of control theory and mobile robots. Experience with robot programming (Python, C++, ROS).
Supervisor: Prof. Gennaro Notomista
Email: gennaro.notomista@uwaterloo.ca
Project: Robots painting music
“Dark sounds” and “Dissonant colors” are just two of the many expressions highlighting the tight relationship between music and painting. The goal of this project is to connect these forms of art and human creativity via robot motion. This will be attempted by designing algorithms for a team of brushbots to transform musical inputs into paintings by leaving trails of color while moving on a canvas, controlled by music.
Required knowledge & skills: Experience with control of mobile robots and robot programming (Python and ROS).
Supervisor: Prof. Gennaro Notomista
Email: gennaro.notomista@uwaterloo.ca
Project: 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, noise immunity, and data throughput.
Supervisor: Prof. G. Shaker (Adjunct)
Email: gshaker@uwaterloo.ca
Project: Antenna Design for the Internet of Things
The goal of this course project is to empower students with antenna design skills to meet the increasing demand for custom wireless internet of things (IoT) devices. The students will decide upon a given IoT application. The students will then use a conceptual CAD model for the IoT device and utilize numerical computer aided design tools (Ansys HFSS & Keysight ADS) to design a suitable antenna solution.
Supervisor: Prof. G. Shaker (Adjunct)
Email: gshaker@uwaterloo.ca
Project: Building a Radar System
The goal of this course project is to help students understand basic radar concepts. The course spans topics of applied electromagnetics, antennas, RF design, analog circuits, digital signal processing, machine learning, and artificial intelligence. Students will decide upon a radar application (whether for autonomous drones/robots/vehicles or in the general theme of sensing for healthcare). Students will then get to work towards building a simulation model of their own radar system.
Supervisor: Prof. G. Shaker (Adjunct)
Email: gshaker@uwaterloo.ca
Project: An AXI-compliant Platform Cache for RISC-V Embedded SoCs
The goal of this project is to design a platform-level cache IP for integration in a RISC-V System-on-a-Chip (SoC) based on an ARM AXI interconnection. Specifically, the student will design and implement advanced cache replacement, locking and partitioning policies suitable for safety-critical embedded systems, and work with the rest of the team to integrate and test them in a full stack system.
Required knowledge & skills: computer architecture, RTL design
Supervisor: Prof. Rodolfo Pellizzoni
Email: rpellizz@uwaterloo.ca
Project: Gem5 Evaluation Platform for High-Performance Real-Time Systems
The goal of this project is to create a suitable simulation platform to evaluate the effects of QoS regulation on traffic flows in an heterogeneous system-on-chip platform used for real-time computation. Specifically, following a case study proposed by Arm, the student will first determine how to connect specific traffic injectors and memory simulators to Gem5, and then study how QoS parameters affect the behaviour of a mixed CPU/GPU workload.
Required knowledge & skills: software engineering, computer architecture, familiarity with Gem5 a plus
Supervisor: Prof. Rodolfo Pellizzoni
Email: rpellizz@uwaterloo.ca
Project: Application of machine learning to assess outdoor insulators using ultrasonic sensor
Outdoor insulators are crucial components to the power system. As they age in the field, they suffer from different defects that need to be detected at an early stage to avoid any sudden failure. Different sensors have been implemented to detect these defects and among them is the ultrasonic sensor. In this project, the ultrasonic signals captured from different defects in ceramic insulators will utilized to extract distinctive features for defect classification. The focus of the project will be in using state-of-the-art machine learning algorithms, feature selection and reduction techniques to classify these defects.
Supervisor: Prof. Ayman El-Hag
Email: ahalhaj@uwaterloo.ca
Project: Robot learning to adapt to user preferences
Robots are being deployed in human-centric environments, working alongside and with humans. However, different people have different preferences on how a robot should act -- how fast it should move, how close it can come, and how it should interact. These preferences are user specific, and so the robot should learn them online, and adapt in real-time. This project will build on our recent work to develop learning algorithms for robots to adapt to human preferences and improve robot performance.
Supervisor: Prof. Stephen L. Smith
Email: stephen.smith@uwaterloo.ca
Project:
Self-driving vehicles are becoming reality. Thanks to technological advances in communication and in artificial intelligence, self-driving vehicles are becoming a much closer reality than we think. Indeed, recognizing the undisputed promise of self-driving to improve safety and road efficiency, several cities around the globe have started to conduct self-driving trials in order to ensure their readiness for deploying this revolutionary technology. Nonetheless, self-driving vehicles currently face several challenges: They are limited in their speed performance, crash prevention, sensing, cooperation, and coordination capabilities. Their decision making, and hence their actions, rely merely on their on-board sensory and internal control models. To avoid crashes and prevent traffic congestion, self-driving vehicles must anticipate the behavior of other vehicles in their environment (self-driving and human-operated vehicles), share their internal state with these vehicles, and cooperatively operate to choose joint safe and efficient control policies. A primitive example of self-driving cooperative behavior can be seen in platooning, where a small group of trucks assemble in a linear structure, using wireless connectivity to maintain a prescribed distance between each other. However, more research is needed to investigate and develop techniques that enable full autonomy performance in irregular vehicle arrangements such as convoys, and in unstructured driving situations. Moving from structured small-scale working environments to large-scale unstructured working environments presents a major challenge to the future of self- driving technology due to the lack of proper understanding and modeling of the interaction dynamics in such situations; as well as due to the lack of collective sensing and cooperating methodologies that can facilitate cooperation in complex driving situations. Such collaboration is potentially feasible, given the emerging communication technology (e.g., IEEE 802.11p standard) and V2X communications services under the future 5G wireless standard. In this context, the goal of this research program is the development of a framework for coordinating interaction and cooperation among a convoy of self-driving vehicles, operating in complex driving conditions in the presence of human-operated vehicles.
1. Behaviour Modeling of Human-Driven Vehicles
Develop a class of models that can mimic the behavior of human-operated vehicles under various driving conditions- environmental, infrastructure, and traffic, and for mitigating the impact of human-operated vehicles on self-driving performance, in general, and safety in particular.
2. Situation Assessment in Human/Machine Driven Environments
Develop strategies for situational assessment, both at the vehicle level and the convoy level.
3. Self-Driving Cooperation Strategies
Develop tactical self-driving strategies that can enable interaction and cooperation between self-driving vehicles to facilitate joint mobility control and coordination, in the presence of human-operated vehicles.
4. Cooperative Resource Sharing in Self-Driving Applications
Develop self-driving strategies that can enable cooperation between convoys (coalitions) of self-driving vehicles to facilitate optimal trip planning and infrastructure resources sharing.
Note: Various group/collective behavior use-case studies should be conducted to validate the outcome of each project, both theoretically and using simulations. Examples of use cases of interest include speed harmonization, emergency response, intersection negotiation, crash avoidance, platoons and Convoys.
Project: Software implementation of FRI protocol in zkSNARK systems
The blockchain privacy is implemented by a zero knowledge succinct noninteractive argument of knowledge (zkSNARK) proof system. FRI (fast Reed Solomon code Proximity) protocol is a popular protocol employed in a number of efficient and practical zkSNARK systems. The project is to implement this protocol and test the performance when it is embedded into the existing zero knowledge proof systems for blockchain privacy.
Supervisor: Prof. Guang Gong
Email: ggong@uwaterloo.ca
Project: Development of high performance nanomaterial-based energy harvesting devices for self-powered applications
The objective of this project is to develop high performance energy harvesting devices based on novel nanomaterials, which can convert mechanical energy to electrical energy, for self-powered electronic and sensing applications. Working with a group of experienced postdoctoral fellow and graduate students, the student will be trained on nanomaterial synthesis, nanomaterial characterization, device microfabrication and characterization, data analysis and technical writing.
Required knowledge & skills: hands-on capability
Supervisor: Prof. Dayan Ban
Email: dban@uwaterloo.ca
Project: Development and validation of Data-driven Biomass Prediction Models with LiDAR and Satellite images (University-Industry collaborative project)
Accurate biomass estimation is essential for monitoring forest health, assessing carbon stocks, and informing sustainable land management practices. Traditional biomass measurement methods are often labor-intensive and limited in spatial scope, making it challenging to assess large, forested areas efficiently. This project aims to develop data-driven machine learning models for biomass prediction that leverage Lidar and satellite imagery to provide accurate, large-scale biomass estimates.
The project will involve using Lidar data, which provides precise structural information about vegetation, and multispectral satellite images that capture broader spatial and spectral information, to develop models capable of estimating biomass at various scales. Machine learning algorithms will be trained and validated on these remote sensing datasets to create models that can accurately predict biomass across diverse landscapes and environmental conditions. This will result in a scalable, efficient biomass estimation tool for applications in environmental monitoring, carbon budgeting, and conservation.
Must have skills/courses by a candidate to conduct the project:
Experience with Python programming is required. Candidates with experience in data processing, machine learning, deep learning (e.g., CNN, RNN, U-Net) would be given higher preference.
Interested students may send the following documents in the form of a single PDF file to the two supervisors at snaik@uwaterloo.ca and marzia.zaman@uwaterloo.ca:
- CV
- Undergraduate Transcript
- Graduate Transcript
Supervisor: Prof. Kshirasagar Naik
Email: snaik@uwaterloo.ca
Co-Supervisor: Dr. Marzia Zaman
Email: marzia.zaman@uwaterloo.ca
Project: Development of Machine Learning Method to Perform Multi-class Classification with Imbalanced Data for Fault Diagnosis of Aero-engine Components (University-Industry collaborative project)
In the aviation industry, accurately diagnosing faults in aero-engine components is critical for ensuring safety, reducing downtime, and minimizing maintenance costs. However, identifying faults is challenging due to the imbalanced nature of the data, where some fault types are rare but highly consequential. This project aims to develop a machine learning method specifically designed for multi-class classification with imbalanced data to reliably diagnose faults in aero-engine components.
The project will involve building a model capable of accurately classifying faults across multiple categories, addressing data imbalance by employing techniques like synthetic data generation, cost-sensitive learning, and specialized algorithms tailored for imbalance. The final deliverable will be a fault diagnosis tool that can distinguish between multiple types of faults and non-faulty states, supporting preventive maintenance and enhancing operational safety.
Must have skills/courses by a candidate to conduct the project:
Experience with Python programming is required. Candidates with experience in machine learning, deep learning (e.g. LSTM, CNN, RNN, U-Net) would be given higher preference.
Interested students may send the following documents in the form of a single PDF file to the two supervisors at snaik@uwaterloo.ca and marzia.zaman@uwaterloo.ca:
- CV
- Undergraduate Transcript
- Graduate Transcript
Supervisor: Prof. Kshirasagar Naik
Email: snaik@uwaterloo.ca
Co-Supervisor: Dr. Marzia Zaman
Email: marzia.zaman@uwaterloo.ca
Project: Development of Diverse Model Generation, Selection and Ensemble Tool (University-Industry collaborative project)
In data science and machine learning, leveraging a single model often limits predictive accuracy and robustness, especially in complex, high-dimensional datasets. Model diversity through ensemble techniques—such as bagging, boosting, and stacking—can significantly enhance performance by combining the strengths of multiple models. This project focuses on developing a Diverse Model Generation, Selection, and Ensemble Modeling Tool to automate and optimize the process of model creation, evaluation, and ensembling for a wide range of applications.
The tool will generate a pool of diverse machine learning models, evaluate their performance on specified metrics, and select the best-performing models. Through ensemble techniques, it will then combine these models to achieve improved accuracy, stability, and generalization across datasets. Built with flexibility and scalability in mind, the tool will be suitable for various types of machine learning tasks, from regression and classification to time series forecasting and beyond.
Must have skills/courses by a candidate to conduct the project:
Experience with Python programming is required. Candidates with experience in machine learning (e.g. Random Forest, Gradient Boosting, eXtreme Gradient Boosting, LightGBM, CatBoost) would be given higher preference.
Interested students may send the following documents in the form of a single PDF file to the two supervisors at snaik@uwaterloo.ca and marzia.zaman@uwaterloo.ca:
- CV
- Undergraduate Transcript
- Graduate Transcript
Supervisor: Prof. Kshirasagar Naik
Email: snaik@uwaterloo.ca
Co-Supervisor: Dr. Marzia Zaman
Email: marzia.zaman@uwaterloo.ca