Workplace of the Future

Applying advances in ambient intelligence technologies, this research aims to design and develop a smart workplace to optimize not only physical comfort in employees but also participant happiness. Through ubiquitous monitoring of ambient factors and affective states a number of important research questions associated with designing and developing a smart workplace will be tackled.

An Integrated Co-Simulation-Data Analytics Platform for Smart Grid Modeling and Cybersecurity Analysis

The smart grid represents a marriage between power systems and information technology to provide increased and reliable access to power. The greater dependence on information systems however makes it more vulnerable to cyberattack. Modeling these systems accurately is a significant challenge due to their complexity and connected nature. In this work, we focus on the open research problem of developing a modeling platform that combines co-simulation, real equipment and data analytics.

Wide-baseline Novel Scene Synthesis from a Single Image

Novel view synthesis is the process of generating new images from an unseen perspective, given at least one image of a scene. There may be more than one probable novel view associated with each unseen perspective, an assumption made by existing methods. This simplifying assumption prevents these methods from being applied to more difficult novel views where the set of probable novel views is highly varied. This project proposes to investigate a new approach to generate a wide variety novel views from a single image, and can produce multiple probable outputs.

TEDS - Train Early Detection System

In Canada there are just over 17,000 public rail crossings—17% have gates, 22% just have bells and lights, and the remaining 61% have a white, reflective X crossing sign which at times is accompanied by a stop sign. In the U.S., the Federal Highway Administration’s (FHWA) latest figures (2009) indicate there are 136,041 public rail crossings—31% have gates, 16% have flashing lights and 1% have highway traffic signals, wigwags and bells. The remaining 52% have a yellow and black crossbuck. Unsecured rail crossings, combined with distracted drivers, can lead to fatal accidents.

An Automatic Tool for Developing Transactive Energy Smart-Contracts: Development, Validation and Integration with the IEMS Blockchain Platform

Energy consumers and prosumers are currently dealing with each other via utility companies, which is a slow, costly and indirect mechanism. With the aim of moving toward a free market, the goal of this project is to provide a suitable platform for automatic development and evolution of smart contracts in distributed transactive energy markets. This platform will make the blockchain technology, underlying smart contracts, applicable to direct transactions between energy consumers and prosumers, enabling additional steps towards a free market.

A Machine Learning based approach for Portfolio Allocation

The goal of this project is to create new algorithms and state-of-the-art methods for resource allocation in a financial context. This model can be applied to other domains, such as fleet and personnel management, scheduling of computer programs, manufacturing production control or controlling a mobile telecommunication network. Alpine Macro provides market insights, investment strategy and asset allocation recommendations supported by proprietary models, charts and data. This project will enhance Alpine’s repertoire of tools and techniques for supporting investment decisions.

Intelligent control of space cameras

The intelligent control of space cameras project is concerned with development of the next generation of space cameras. Currently, there is a large gap between the onboard capabilities of standard commercial cameras and those currently in space (examples include image resolution, onboard storage, advanced scene understanding and exposure control).

Behavioral Clustering in Big Data with Application in Super Customer Networks

Analyzing customer behavioral patterns and attrition aids organizations in understanding its core customers and improve its decision-making processes in regards to customer attrition and targeted marketing. In this research project, we will develop behavioural analytics super network models and algorithms for behavioural customer segmentation and attrition prediction in the presence of big data. Our models will help our partner organization develop better understanding of its customers and how to better manage their business relationships with them, thus solidifying its market position.

A methodological approach to the use of data-supported environmental factors in support of the introduction of autonomous air vehicles in a shared airspace

The evolution of aerospace technologies and automated systems has been accompanied by the phenomenon of “de-crewing”. A large body of current research focuses on how to move to single-pilot operations (SPO), but a major barrier to the implementation of SPO and other autonomous commercial aircraft operations is that advances in human-machine interactions and human factors have not kept pace with technological change. The objective of the research project that is the subject of this proposal is to develop a methodology to simulate autonomous flight in a real-time, virtual environment.

Transparent and Trustworthy Deep Feature Learning for Cyber-Physical System Security

The latest artificial intelligence (AI) technologies have effectively leveraged the wealth of data from cyber-physical systems (CPSs) to automate intelligent decisions. However, for safety-critical CPS like smart grids and smart cities, the conversion of massive data into actionable information by the AI must be not only effective but also reliable.

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