Project Descriptions – Summer 2023

**This Page is Updated Regularly – Please check back often**

How To Select a Project: First, have a look at the projects listed here. Contact the supervisors for more information. When you have found a project, and the supervisor is willing/able to give you the project, you MUST complete a contract on OnQ and sign it physically. The OnQ page for the course will be available early in the Fall term for students registered in the course only. Your supervisor should also sign the form and notify the coordinator, so that we can create an OnQ group for your team that allows you to submit the contract and proposal electronically. ONLY when the contract is received is the project officially yours. If you cannot register in the course due to conflicts or not meeting the eligibility criteria, please contact the undergraduate advisor or the course coordinator as soon as possible. You need to complete your contract in the Fall term.

Each project entry lists the maximum number of students who can participate in the project, and the number of students whom the supervisor has already agreed to supervise (conditional on receipt of the contract). If they are equal, no more slots remain for that project, and you should pursue a different one.
I strongly recommend that you try to find a project from the list below. However, if you can’t find a project here that fits your interests and abilities, you can propose a project yourself. In this case you will need to find a School of Computing faculty member who is willing to supervise your project. As a rough guideline to project design, the amount of work involved should be the same as a typical 400-level CISC course.

SupervisorProject TitleAvailability
Christian Muise & Furkan Alaca Research Ready Smart Watch 0 / 2 spots taken
Christian Muise & Ting Hu Autonomous Agriculture 2 / 3 spots taken
James Stewart Goodwin Elevator Tracker 0 / 2 spots taken
Race Car Telemetry Modelling 0 / 2 spots taken
Video Enhancement of Lightboard Lectures 0 / 1 spots taken
Salimur Choudhury Resource Allocation and Scheduling in the supply chain management 0 / 1 spots taken
Use of Artificial Intelligence in the Family Medicine Practice 0 / 2 spots taken
Ting Hu Neuroevolution 0 / 2 spots taken


  • Resource Allocation and Scheduling in the supply chain management
    Supervisor: Salimur Choudhury
    Since the Covid-19 pandemic, supply chain optimization issues have become more evident. The objective of this project involves scheduling and resource allocation issues (considering uncertainty) in supply chain management. The project members will collaborate with current members (graduate students) of the newly established GOAL research lab to design and implement various scheduling/resource allocation algorithms. The students should have good expertise in data structures and algorithms. Excellent skills in at least one programming language (C/C++, Java, or Python) is mandatory.

    Intended for CISC 499, but can be expanded/adapted for CISC 500
  • Use of Artificial Intelligence in the Family Medicine Practice
    Supervisor: Salimur Choudhury
    In this project, the student will do a comprehensive survey (and a few new use cases will be presented) on the use of AI in family medicine practice. The student should have strong programming expertise (preferably in Python). The student should have taken AI and Data Analytics courses as well.

    Intended for CISC 499, but can be expanded/adapted for CISC 500
  • Goodwin Elevator Tracker
    Supervisor: James Stewart
    On most floors of Goodwin, there's no indicator to show the elevator's current floor. This may lead hopeful people to wait a long time for the elevator.

    The project will develop a barometric tracking device that remains inside the elevator and communicates with another device on the 5th-floor Goodwin lobby. The lobby device will show the current floor, direction, and expected arrival time. The project will use two Adafruit BMP388 barometric pressure boards for altitude measurement, two SparkFun Pro RF boards for communication, and a Raspberry Pi or similar for overall management.
  • Race Car Telemetry Modelling
    Supervisor: James Stewart
    Race car driver analyse telemetry from many sensors to improve their lap times. This telemetry is usually presented as 2D graphs that are functions of time or distance and include position, orientation, acceleration in various directions and around various axes, brake pressures, steering angle, and so on.

    The project will develop an application to display a 3D model of a real car travelling around a real track: Canadian Tire Motorsport Park. 3D models for the car and track can be found online. Telemetry from the car will be provided from a CSV file and will be displayed on the moving car with 3D widgets, such as arrows, heatmaps, percentage bars, spinners, and so on.
  • Video Enhancement of Lightboard Lectures
    Supervisor: James Stewart
    In a lightboard lecture, the instructor writes with a dry-erase pen on a glass surface. But the ink deposition is uneven and sometimes fades, making it difficult to read. See below for examples circled in yellow (open the image in a new tab to see the details).

    This project will take a video of a lightboard lecture and will enhance the faded writing. This will involve: detecting pen strokes by analysing the frame-to-frame differences (but excluding differences due to the motion of the instructor); determining the base colour, width, and direction of each pen stroke; and replacing the pen-stroke pixels in each frame with the base colour. Frame-to-frame coherence will be exploited and video cuts must be detected.

  • Autonomous Agriculture
    Supervisor: Christian Muise & Ting Hu
    The MuLab, along with the Machine Intelligence & Biocomputing (MIB) Laboratory, are building a lab-scale platform for the exploration of autonomous agriculture. This will include sensors such as video feeds, nutrient detection, etc., and actuators such as lighting, watering, etc. This project will involve working with the MIB lab and MuLab to prototype the initial software for the first iteration of the system being built in the winter'23 term.

    Vertical Farm
  • Research Ready Smart Watch
    Supervisor: Christian Muise & Furkan Alaca
    To conduct ongoing research in both continuous authentication and smart office analytics, this project aims to build a custom Android Wear application that will let us make use of all the sensors in modern smartwatches. Students located in Kingston will optionally have access to modern wearables in order to test the developed application, and the resulting prototype will contribute directly to research in both the MuLab and CSRL research labs.

    To apply, please contact both Prof. Muise and Prof. Alaca (,

    Smart Watch
  • Neuroevolution
    Supervisor: Ting Hu
    Neuroevolution is a promising research direction that employs evolutionary algorithms to create or optimize neural networks. In this project, students will explore various ways of combining the powerful ideas of evolution and deep learning. Students with experience and interests in evolutionary computing and deep learning are welcome to apply.