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Faculty of Engineering and Applied Science Project Summaries

Supervisors

Aaron Yurkewich | Akramul Azim | Atef Mohany | Ghaus Rizvi | Horia Hangan | Hossam Gaber | Hossam Gaber | Martin Agelin-Chaab | Matthew Kaye & Wendy Barber | Scott Nokleby | Shabnam Pejhan |  Tao Liu | Zia Saadatnia

 

Supervisor name: Akramul Azim

Project title: Automated Quality Assurance for Cyber Physical Systems in Continuous Integration Environments

Summary of research project: The complexity of cyber-physical system (CPS) software development is increasing. Developers of CPSs are constantly searching for time-efficient and cost-effective methods to maintain the quality and reliability of their products. Many organizations have adopted the agile paradigm, resulting in growing interest in continuous integration (CI) environments. The goals of the proposed research is to increase CPS software quality by facilitating dynamic interaction between developers and large language models (LLMs) for recommendations on code refactoring. The proposed research starts with setting up a CI pipeline and then extracting CPS code metrics continuously to analyze using LLMs.

Student responsibilities/tasks: 

  • Setting up a CI pipeline.
  • Extracting CPS code metrics.
  • LLMs integration and developer tools integration.
  • Reports writing.

Student qualifications required:

  • Machine Learning.

Expected training/skills to be received by the Student:

  • Familiarization with DevOps and programming tools.
  • Familiarization with Large Language Model (LLM) tools.

Length of award: 14 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Atef Mohany

Project title: Active Flow Control Techniques for Noise Reduction in Industrial Systems

Summary of research project: This project explores the use of active flow control techniques to mitigate aerodynamic and flow-induced noise in critical engineering systems found in aerospace and nuclear applications. By manipulating flow behavior in real time through actuators such as synthetic jets, plasma actuators, shape morphing, or piezoelectric devices, these methods can reduce flow separation and vortex shedding, which are the primary sources of unwanted noise and vibration problems. The successful candidate will work with a large team to use both experimental and computational tools to investigate how noise and vibration problems can be mitigated using active flow control techniques.

Student responsibilities/tasks:

  • Assist in designing and building experimental setups required for the work.
  • Work closely with senior group members to run simulations and/or experiments as needed.
  • Make progress presentations in the research group meetings.
  • Write a report that summarizes the results and assist in writing conference/journal papers as needed.

Student qualifications required:

  • Good GPA (3.7 and above).
  • Good communication skills.
  • Good teamwork ability.
  • Experience using power tools is an asset.
  • A strong background in fluid mechanics and mechanical vibration is an asset.

Expected training/skills to be received by the Student:

  • Training in data acquisition and analysis.
  • Training in wind tunnel testing.
  • Training in acoustics and noise control.
  • Training in flow simulation software.
  • Training in presentation skills.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Ghaus Rizvi

Project title: Design and development of a Volumetric Additive Manufacturing (VAM) System

Summary of research project: The VAM method is a revolutionary additive manufacturing process that reduces the product built-time by orders of magnitude - from hours to minutes. VAM uses principles similar to Computed Tomography, where X-ray images projected at different angles are recorded as Radon Transforms (RT) of the sliced images. The RT represents the cumulative intensity attenuation as the light pass through the object at each angle. The original image is reconstructed by using inverse RT and image processing techniques. With help of a digital light processing DLP projector this image is used to build a physical object in a photo-curable resin using a process somewhat similar to stereo-lithography method.

Student responsibilities/tasks: 

  • Improve the design of the VAM setup.
  • Prepare CAD images and convert them into RTs.
  • Prepare resin formulations and use it for producing parts by projecting the RTs with a DLP onto a rotating vial filled with resin. A number of parameters that affect the image formation will be studied using the design of experiments methodology and statistical software will used to analyze the results obtained.

Student qualifications required:

  • The student should have completed two years of engineering education with good grades.

Expected training/skills to be received by the Student:

  • Design capability will be developed by working on improvement of the existing setup.
  • Gain knowledge about Image processing techniques.
  • Get to know about issues involved in volumetric additive manufacturing.
  • Learn design of experiment (DOE) methodology.
  • Hands on experience with a number of lab equipment.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Horia Hangan

Project title: Effect of Adverse Weather Condition on Autonomous Vehicles

Summary of research project: The research investigates the effect of harsh weather conditions on Autonomous Vehicles (AVs), focusing on how environmental factors such as snow, rain, fog and strong winds impact the performance of perception sensors and overall vehicle safety.

Research Objectives: 

  • Sensor Performance Analysis  
  • Quantification of Degradation 
  • Experimental Testing Framework 
  • Impact on Perception and Safety 
  • Mitigation Strategies

Student responsibilities/tasks: 

  • Data Collection and Preprocessing.
  • Assist with gathering experimental data from Light Detection and Ranging (LiDAR), RADAR (Radio Detection and Ranging) and camera sensors.
  • Organize and preprocess large datasets for further analysis.
  • Experimental Setup and Test

Student qualifications required:

  • Students must finish 2nd year with a GPA of 3.5 or better.

Expected training/skills to be received by the Student:

  • The applicant will work closely with a Ph.D. student who is working on this project, as well as students in the researcher's team. 
  • The methods to be employed for the proposed research are wind tunnel testing and computational fluid dynamics (CFD). 

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Hossam Gaber

Project title: Micro Energy Grid Control and Integration

Summary of research project: Design of micro energy grid with lab scale. Control design and optimization of micro energy grids. Develop diagnostic and testing system of micro energy grid, with user interface and real time monitoring and integrate with physical system. Develop resiliency controller and integrate with the micro energy grid control system. Testing of micro energy grid, with lab setup and analysis of performance.

Student responsibilities/tasks: 

  • Configuration of micro energy grid with lab scale.
  • Control design of micro energy grids.
  • Develop diagnostic and testing system with real time monitoring.
  • Develop user interface for integrated energy management and battery management.
  • Develop resiliency controller and integrate with the micro energy grid control system.
  • Testing of micro energy grid, with lab setup and analysis of performance.

Student qualifications required:

  • Energy system modeling.
  • Power electronics.
  • Battery system management.
  • Control design.
  • Python programming.

Expected training/skills to be received by the Student:

  • Energy modeling and simulation with HOMER.
  • Energy simulation with TRANSYS.
  • Engineering design with SOLIDWORKS.
  • Energy management system.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Hossam Gaber

Project title: Plasma-Based Waste Treatment

Summary of research project: Design plasma-based waste treatment, design plasma system, engineering design, power supply system, control system and experiments in the lab setting. Study different types of wastes, modeling and simulation of the integrated system with Multiphysics modeling, development of control system to control plasma torch, chamber and the integrated process.

Student responsibilities/tasks: 

  • Waste analysis and characterization, with literature review.
  • Engineering design of plasma system.
  • Modeling and simulation of the integrated system.
  • Experimental tests and data analysis.
  • Reporting and presentation.

Student qualifications required:

  • Knowledge of engineering design process.
  • Modeling and simulation with some of these tools: Aspen/COMSOL/ANSYS.
  • Programming with Python.
  • Control design.
  • Experimental and lab test.

Expected training/skills to be received by the Student:

  • Plasma system testing and operation.
  • Modeling and simulation.

Length of award: 14 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Martin Agelin-Chaab

Project title: Soiling Mitigation in Autonomous Vehicle Sensors

Summary of research project: Emerging vehicles, such as self-driving vehicles, entirely depend on a network of sophisticated sensors to provide environmental data for navigation. However, adverse weather, such as rain and snow, can contaminate the external surfaces of these sensors, which can impair their performance, leading to accidents. The proposed research will focus on developing rigorous techniques for characterizing and providing physical insight into these complex processes of surface contamination of optical sensors. This will lay the groundwork for developing fundamental concepts and novel mitigation strategies and devices.

Student responsibilities/tasks: 

  • Perform experiments with simulated rain on model vehicles and optical sensors.
  • Develop concepts of passive aerodynamic devices and strategies to minimize surface contamination of the sensors.
  • Use CFD to evaluate the effectiveness of all concepts and strategies.
  • Conduct 3D printing of the most effective concepts for future wind tunnel testing and validation.

Student qualifications required:

  • Students must finish 2nd year with a GPA of 3.5 or better.

Expected training/skills to be received by the Student:

  • The applicant will work closely with a Ph.D. student who is working on this project, as well as students in the researcher's team. So the student will learn teamwork and technical communication skills.
  • The methods to be employed for the proposed research are wind tunnel testing and computational fluid dynamics (CFD). Therefore the student will develop skills in wind tunnel testing and CFD.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Matthew Kaye & Wendy Barber (FEd)

Project title: Improving Access to Nuclear Engineering/Science in the Indigenous Community

Summary of research project: Systemic barriers to educational and technical resources limit Indigenous engagement with large scale nuclear projects. These barriers limit the potential economic benefits. This project will:

  • Determine the current situation in terms of what educational and technical resources are available as compared to what might be necessary.
  • Understand and identify areas of concern of the community at large and for students in particular.
  • Propose alternatives to the current educational model.
  • Determine what success would look like from the perspective of the Indigenous Community, the nuclear industry and the university.
  • Ultimately, the project will propose paths to overcome all identified barriers.

Student responsibilities/tasks: 

  • Considerable focus will be to gather data and information on what barriers exist to prevent equal Indigenous engagement on large scale nuclear projects.
  • Other aspects will be to determine how approaches to the Indigenous community can be made to facilitate a positive dialogue between researchers and the Indigenous community.

Student qualifications required:

  • Students in education and/or engineering.
  • Nuclear background an asset but not necessary.
  • Preferably completed second year.
  • Good writing skills an asset.
  • Good communication skills an asset.
  • An ability to think outside the box.

Expected training/skills to be received by the Student:

  • Data gathering and organization.
  • Research skills.
  • An ability to self-regulate and self-manage a project. 

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Scott Nokleby

Project title: Design and Development of Multi-Modal Robots

Summary of research project:The goal of this project is to design and develop a proof-of-concept multi-modal robot that is equally effective in operating on the water and in the air. Such a robot must easily transition between aquatic mode and flying mode. To achieve stable flight, some form of multi-rotor design will be utilized. However, this multi-rotor system will need to be stowed in as a compact manner as possible when in aquatic mode to ensure that it does not interfere in the robot’s ability to navigate on the water. In addition, the system must also be completely waterproof.

Student responsibilities/tasks: 

  • The student will work on research tasks related to the design and development of advanced robotic systems.
  • Duties include, but are not limited to: designing, developing, programming and building prototypes; designing and conducting experiments; analyzing experimental results; writing reports and peer-reviewed publications. In addition, the candidate is expected to collaborate with other personnel working in the MARS Lab.

Student qualifications required:

  • The successful candidate must have strong engineering, design, mathematics, programming and written/oral English communication skills.
  • Robotics experience, particularly with ROS2 (Robot Operating System 2), is an asset.
  • Minimum GPA B+ and completion of second year of their engineering program is preferred.

Expected training/skills to be received by the Student:

  • Learning Robot Operating System 2 (ROS2).
  • Designing, programming, building and testing/debugging robotic systems.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Shabnam Pejhan

Project title: Machine Learning for Sit-to-Stand Phase and Stability Prediction

Summary of research project: This project develops a data-driven framework to predict movement phases and postural stability during the sit-to-stand (STS) transition based on wearable sensors data. Using open datasets such as CeTI-Age-Kinematics with full-body inertial measurement unit (IMU) recordings, multi-segment IMU signals will be analyzed to model human motion dynamics during STS. The primary objectives are to: (1) classify STS phases with machine-learning (ML) models and (2) predict stability indices from IMU-derived metrics (e.g., acceleration variance and angular velocity). The results will support intelligent assistive technologies that adapt to user movement and balance in real time.

Student responsibilities/tasks: 

  • Literature Review: Study sit-to-stand (STS) biomechanics, wearable-sensor use, and machine-learning (ML) methods.
  • Data Processing: Extract and filter IMU signals and label movement phases.
  • Feature Analysis: Compute kinematic metrics (e.g., angular velocity, sway index).
  • Modeling: Train and evaluate ML models (e.g., Random Forest, LSTM) for phase and stability prediction.
  • Reporting: Analyze results and compare age groups in a report.

Student qualifications required:

  • Upper-year student in Engineering related field with minimum B+ average in relevant technical courses.
  • Skilled in Python programming, signal processing and time-series analysis.
  • Knowledge of ML methods, data visualization and kinematic analysis.
  • Strong data visualization, documentation and communication skills.
  • Detail-oriented.

Expected training/skills to be received by the Student:

  • Hands-on experience in IMU signal processing and human-motion data analysis.
  • Practical experience in machine-learning (ML) models development, application and evaluation.
  • Experience interpreting biomechanical movement patterns and assessing postural stability.
  • Skills in Python-based data analysis workflows.
  • Development of technical reporting and presentation skills for research communication.

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: Ontario Tech STAR

 

Supervisor name: Tao Liu

Project title: Design and evaluation of a compliant material coated hip implant to improve contact characteristics: a finite element study

Summary of research project: Hip fractures are a major health issue among older adults, with cases projected to exceed 6 million globally by 2050. Roughly 50% of hip fractures occur at the femoral neck, which are typically treated using unipolar prostheses. However, these implants often lead to acetabular erosion due to unnatural contact characteristics (e.g., contact pattern, contact area), a major postoperative complication that compromises joint longevity and patient quality of life.

This project aims to develop an innovative hip implant design that incorporates a layer of compliant material to mimic the protective effects of natural cartilage, thereby reducing joint wear and improving its effectiveness.

Student responsibilities/tasks: 

  • Design a hip implant incorporating a compliant material layer.
  • Reconstruct patient-specific hip joint geometries from CT scans.
  • Develop finite element (FE) models of the hip joint using ABAQUS.
  • Evaluate the biomechanical performance and effectiveness of the proposed implant design.

Student qualifications required:

  • Strong motivation and interest in computational biomechanics.
  • Solid background in Solid Mechanics (MECE 2420U) and Computer-Aided Design (MECE 3030).
  • Proficiency with SolidWorks and MATLAB.
  • Excellent oral and written communication skills, with fluency in English.

Expected training/skills to be received by the Student:

  • Develop practical skills in computational modeling of the hip joint.
  • Obtain hands-on experience with ABAQUS and HyperMesh for FE simulation and preprocessing.
  • Gain an understanding of hip joint biomechanics and implant design principles.
  • Strengthen scientific communication skills through presentations and conference participation.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

 

Supervisor name: Zia Saadatnia 

Project title: Smart Wearable Mouthpiece for Real-Time Tongue Tracking and Sleep Apnea Treatment

Summary of research project: This research focuses on creating an intelligent, custom-fitted mouthpiece that functions both as a monitoring and therapeutic device for sleep apnea. The system, resembling a mouthguard or denture, will integrate miniature pressure sensors and electrodes to capture tongue dynamics and deliver precise electrical stimulation to support diagnosis and therapy. The activities of this project are outlined below:

  • Creation of the mouthpiece geometry using CAD.
  • Prototyping by 3D printing.
  • Design of the control and data acquisition architecture.
  • Integration of sensors, electrodes and microcontroller-based electronics.
  • Implementation of wireless communication or SD-based data storage.

Student responsibilities/tasks:

  • CAD modeling and design iteration.
  • Structural simulation and performance analysis.
  • Prototyping using 3D printing technologies.
  • Developing embedded hardware and sensor interfaces.
  • Programming microcontrollers and data acquisition modules.
  • Applying AI/ML algorithms for data interpretation and performance prediction.

Student qualifications required:

  • Strong foundation in mechanical or mechatronic design.
  • Experience with sensors, electronics and embedded programming.
  • Proficiency in programming languages such as Python or MATLAB.
  • Experience with CAD and finite element tools.
  • Interest in applying AI/ML in engineering systems.

Expected training/skills to be received by the Student:

  • Hands-on experience in developing integrated mechatronic devices and sensor networks for biomedical applications.
  • Advanced skills in CAD modeling, FEM analysis and 3D fabrication for biomedical devices.
  • Practical understanding of sensor data acquisition and analysis and AI-assisted prediction.
  • Improved interdisciplinary teamwork, communication and problem-solving abilities in a research environment.

Length of award: 16 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR