Joule M&V AI

Buildings have a high share of energy consumption in Canada. Real-time monitoring and analysis of energy use data can assist in improving the performance of the building and help reduce operating costs, lower utility bills, increase equipment life, improve occupants’ comfort, and increase retention and leasing rates; all while lowering carbon emissions.

Free-Piston Direct-Drive Thermal Engine for Waste Heat Applications

The research project proposed here takes a radically new approach to on-vehicle electric power generation, with the potential for developing a scalable, flexible, and conceptually very efficient method without the need for complex mechanical off-take. The new method is called the Free-Piston Direct-Drive Linear Generator, a type of Rankine Cycle expansion engine. Excess thermal energy is used to heat a fluid via a heat exchanger, and the energy in this high-pressure and high-temperature fluid is then converted into work via the free-piston expansion engine.

Support cases resolution retrieval

Coveo provides search and recommendation software for customer support systems in which customers can ask for help by entering a case description and, on the other side, support agents must find the solution. In this context, queries to Coveo are in the form of long texts describing a problem and potential solutions are knowledge articles or help documents.
Support cases resolution retrieval (SCRR), at its very core, requires a mapping of casual English text, possibly with grammatical mistakes, to well-formed formal English documents.

Plotly + PyFR: Real-Time Visualization for Extreme-Scale Aerodynamics

Efficient and accurate visualization technologies can bring greater insight to the field of computational fluid dynamics (CFD), with implications for the design of aircraft with lighter environmental impacts. This project will utilize the open-source PyFR solver co-developed at Concordia University, and the open-source Dash analytic application framework developed by Plotly. The academic partner and their research group will explore the utility of Dash as a tool for 2D and 3D real-time visualization of complex data produced by simulations run with PyFR.

Radar Signal Processing and Machine Learning Methods for Human Activityand Fall Event Detection

Monitoring human activity and fall events is the cornerstone of medical applications. The rising costs of healthcare
and the aging of the population are factors that influence researches in the medical industry, mainly for the
development of assisted living and smart home. Several technologies have been proposed in the literature for
monitoring people and health care. Recently, radar technology for human activity monitoring, fall event and
presence detection is essential need of a patient, and this technology has attracted a lot of attention.

Using RTLS and Computer Vision to Extend Worksite Safety

The project aims to extend worksite safety of construction projects at Hydro-Quebec (HQ) using computer vision and a Real-Time Location System (RTLS). The case study is a substation construction project near Montreal. The main safety risks that will be targeted in the case study are related to equipment mobility (struck-by accidents) and not wearing Personal Protection Equipment. The concept of the method is to have a priori information about the types of expected risks in the planning phase, and then to monitor the site using video cameras and the RTLS.

Attribute-Driven Automatic Generation of Realistic Face Textures

When creating a video game, every digital character must be created by professional artists. Their work is very labor intensive because the number of created characters are in the thousands, each of which has multiple visual components that must be created for each one. “Scanning” real actors to create a digital version of themselves can help speed up this process, but each scan must be altered to preserve the actor’s anonymity.

Automating Configuration Management and Deployment in Large-scale Data Centers Augmented with Edge Data Centers - Year two

Data centers are now growing and expanding massively. They are large scale and heterogeneous. In addition, they rely more and more on emerging technologies such as Software Defined Networking (SDN) and Network Functions Virtualization (NFV) with “network softwarization” as their key feature. Moreover, they are now being augmented with edge data centers rooted in concepts such as cloudlets, ETSI Mobile Edge Computing (MEC), and fog computing. Such data centers bring a host of new challenges when it comes to the automation of configuration management and deployment.

Classifying Innovation Management Forms Using Ontology Reasoning

In this project, the goal is first to design a domain ontology that models the innovation management forms semantically. At this step, the ontology contains domain-specific background knowledge, which is expressed using terminological statements. Then every completed form and the value of its fields are asserted as instances of different concepts of the ontology. Afterwards, an ontology reasoning algorithm is deployed to classify every completed form into various categories defined in the ontology.

Adapting Human Performance Techniques, Illusion Aesthetics, and Specialized Apparatus from the World of Stage Conjuring to Contemporary Circus Disciplines - Year two

The vast majority of performance techniques, illusion aesthetics, and specialized apparatuses used by stage conjurors are still unknown and remain unexplored by the circus world. The tacit knowledge that magicians exchange amongst themselves is rarely transferred to members outside of their subculture, which leads to creative stagnation in their communities and beyond. The purpose of this project is to create an intensive and sustained collaboration between North American illusion experts and elite circus artists to produce new physical vocabulary for new equipment.