Efficient Design and Implementation of Concatenated Error-Correction Coding for High-Throughput Fiber-Optic Links

The project targets design and implementation of error-correction codes for high-throughput fiber-optic communication links. We focus on the error-correction encoding at the transmitter side as well as decoding at the receiver side considering the simplicity of implementation and low power consumption at both transmitter and receiver.

Mining Version Histories To Automate Merge-Conflict Resolutions

In current collaborative software development environments, developers usually work in parallel. They often share changes with other developers or incorporate changes from them, with the help of version control systems (VCSs) such as Git and Subversion. The parallel collaboration process improves the development speed on the one hand, but on the other hand, leads to possible code inconsistencies.
When multiple developers make inconsistent changes, textual, syntactic, or semantic merge conflicts may occur during integration.

Design and development of an intelligent measurement flight mode

Testing industrial facilities including pipelines and tanks are key to optimize operation and maintenance costs in heavy industries such as oil and gas. Hazardous industrial spaces (e.g. confined spaces) are of the most challenging and costly areas to inspect. WorksafeBC has reported about 18 people have been annually killed in confined spaces in BC, in the last decade. Avestec is focused on development and commercialization of a novel flying robot (Robotic UAV) for inspection of hazardous spaces such as interior of tanks for various industries.

A Customized CAD Tool for Automated CNC Program Code Generation

To develop parts from an initial design to the final product is a very tedious process in the mold manufacturing industry. Computer Numerical Control (CNC) plays a major role in the mold manufacturing industry to create products in a fast and efficient manner. The goal of this project is to automate the CAD to CNC program code generation. A customized CAD tool will be developed that reads a three-dimensional (3D) CAD file specification for a part, and automatically synthesize optimized G-code that will be used to program the CNC machine to manufacture the given part.

A nanoscale electrochemical sensor for measuring changes in blood-glucose levels

The proposed work main goal is to improve the overall human health quality through providing an online monitoring device acting as a data platform that represents a gateway to simple solutions that allow consumers and patients to better manage their health and disease risk. The broader mission includes leveraging the data platform to improve diagnostics
The technology is to be inserted in the human body under the skin layers without the need of any advance operational procedures.

Detection of Mental Health Conditions from Textual Device Communication

Research into child safety applications using Artificial Intelligence (AI) methods is a new area of investigation. SafeToNet is continuing to develop AI monitoring tools together with a team of researchers at the University of Ottawa. These tools, when used over time, will take advantage of outgoing text-based communications from devices to detect the early onset and progression of developmental and mental health issues in youth.

Jordan Shapes for Deep Learning

The proposed project aims to develop a systematic approach for improving deep-learning-based computer vision systems by augmenting the local pixel data with the global shape data (more specifically, Jordan curves) and by adjusting system architectures to accommodate the augmented input. Three canonical computer vision problems will be investigated in this project. They are respectively image dehazing, alpha-matting, and face detection. The potential roles of Jordan curves in these applications will be examined.

Deep-learning-based Fine-grained Furniture Classification and Winning Strategy Recommendation

The project aims to develop a novel deep learning based computer vision system to identify different categories and sub-categories of the furniture and the associated attributes (such as color, shape, style, and material). It will also develop an automated recommendation system that can learn from the massive historical data and the on-going stream of data to adaptively adjust the parameter combination for each product to maximize the chance of winning the competition against other companies.

A Machine Learning Approach for Digitalization of Engineering Specifications and Documents

Across industries, many engineering documents and drawings have accumulated over the past few decades. However, they are mostly archived in paper or rudimentary electronic form (typically in an image or PDF format), rendering information retrieval highly inconvenient. As such, a lot of valuable engineering data have been left unutilized or at very least, difficult to access. Unfortunately, the existing open source tools do not offer a simple remedy.

Advanced Battery Modelling in Electric Bus Platforms to Enable Next-Gen Low-Carbon Public Transit

With the ever-increasing growth of the consumer Electric Vehicle (EV) market and environmental awareness of federal and provincial governments, electrification of public transit systems has come under the spotlight in recent years. Currently, there is limited practical knowledge on how to efficiently deploy EV buses across different Canadian regions, which results in a wide gap between advanced EV technology and Canadian environmental parameters.
EV batteries are negatively affected by cold temperatures, bad road conditions, and aggressive driving behaviours.