Investigations and Analysis of Industrial Internet of Things Ecosystems

Rimot monitors, controls and provides insights into remote critical infrastructure through the use of enabling technologies such as data analytics, security and advanced networking.  Critical research areas in data analytics, security and advanced networking are beneficial for the creation of unique intellectual property and commercialization opportunities for Rimot.  This project will focus on the investigations and analysis of industrial Internet of Things ecosystems including, exploring the impacts of IOT security and threat assessment using port and naval systems; determining the optima

The Future of Learning

Today's technological advancements are changing job requirements and skills expectations at a rapid rate. Businesses are consistently striving to investigate these trends and prepare for upcoming "disruptions." In terms of job automation, there are many barriers that people experience when trying to access learning platforms such as online learning tools and micro learning sessions, as the majority of these platforms are aimed at individuals who already have a post-secondary education, and other credentials.

A Unified Framework for Remote Monitoring the State and Performance of Photovoltaic Power Plants

Energy produced using the solar radiation as the source is one of the most prominent parts of the clean energy mix. More than 300 GW of photovoltaic systems (PV systems) of different size supply the daily needs of millions of families and industries around the world. Photovoltaic panels are installed on the roof of houses and buildings, or they constitute large-scale photovoltaic power stations. Monitoring the energy production of the PV systems has a crucial role in both predictability and maintenance.

Developing the use of UAV Imagery Systems for Agriculture Applications

During the last decade, Unmanned Aerial Vehicles (UAVs) drew the attention to be used as platform for imagery systems for different applications including agriculture applications. Therefore, the proposed research aims to develop the use of UAVs for specific important agriculture applications as weed management. These developments include the use of low-cost imagery sensors for these applications. On the other hand, Cansel is fully supportive organization for cooperative research with universities.

Developing Intelligent BI Applications using Machine Learning

SAP is a multinational software corporation that makes enterprise software to manage business operations and customer relations. SAP Labs Montreal focuses on supporting the consumer products industry. It provides these industries with the software infrastructure that allows its customers to run end-to-end processes including the capturing and analysis of sales transactions. Sales transactions include for example purchases made by each and every customer of the retail industry.

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.

Regime Switch Analysis on Time-series Data for Financial Prediction

In recent years, the emergence of massive temporal data has become a reality in almost all aspects of social life, economic activity, security and defense, and poses a big challenge for existing methods. This project focuses on prediction from temporal data that arise ubiquitously in healthcare, social, industrial and financial fields. Events typically include changes in health status such as hospital readmission or death, evolution in social networks such that communities arise or vanish, modifications in energy consumption (e.g., wattage changes) and regime changes in stock markets.

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.

Texture Synthesis for Visual Effects: Improving Quality and Decreasing Computation Times

This proposal focuses on the automatic creation of color textures for 3D objects found in virtual content for movies, television, and advertisements. Such color details could correspond to the color variations seen at the surface of fabric or concrete. Another example of the problems we want to address consists of automatically creating color details for an animation of liquids such as mud. The solutions will reduce the computation times, increase the realism, and enable some methods to synthesize a broader variety of color textures.