In this project, new storage approaches for big data will be explored. Key point is the efficient use of modern hardware, especially modern storage technology such as SSDs. These new technologies have highly improved performance in comparison to traditional hardware. However, classical data structures and algorithms can not directly be applied due to the different characteristics of these devices. Also, the high cost of the new technology makes their exclusive use uneconomical in many cases.
In this project, I focus on low-delay error correction codes for streaming data at the application layer (c.f. Figure 1). Forward error correction codes designed for streaming sources require that (a) the channel input stream be produced sequentially from the source stream (b) the decoder sequentially reconsructs the source stream as it observes the channel output. Naturally both the optimal structure and the fundamental limits of streaming codes are expected to be different from classical error correction codes.
The goal of this project is to design a monitoring system which continuously observes the driver's cognitive state and provides a realtime feedback for the driver regarding his/her mental state and vigilance level, as described in the “Background Information” section. Studies in [5-9] have examined the changes in the theta, alpha, and beta EEG rhythms during different driving tasks and have shown a significant correlation between the changes in spectral power of EEG signals and the driver's workload and fatigue level.
Objectives: Manitoba Hydro Telecom (MHT) has proposed the development of a state-of-the-art optical communication network using Reconfigurable Optical Add Drop Multiplexers (ROADM) employing Wave Division Multiplexing (WDM). MHT will use ROADMs compliant with currently established industry standards such as ITU G.709 and G.8032. ROADMs with WDM methods with 50 GHz channel spacing enable up to 88 wavelengths to be carried on a single optical fiber.
New meters, sensors and other field data collection devices are providing a plethora of real time system operational data for building, industrial plants and production systems. At the same time, building owners, industrial plant owners and campus based operations are being challenged with managing their energy use both through conservation and demand management processes. In many jurisdictions the cost of energy is skyrocketing due to massive increases in demand.
A l’heure du Big Data et des données ouvertes, la valorisation et l’indexation des données produites par l’industrie et leur utilisation via des processus d’extraction et de traitement intelligents est un enjeu majeur. De nombreux besoins de recherche hautement spécialisés qui exploitent les nouveaux formats des données (RDF, NoSql) et dépassent le paradigme de la simple recherche documentaire voient le jour.
L'évaluation des lésions rétiniennes sur des images de fond d'oeil permet de diagnostiquer et de stadifier la rétinopathie diabétique, complication du diabète pouvant entrainer la cécité. Actuellement, ce processus est manuel, long et dispendieux. Le projet envisagé consiste à proposer un premier système complet de classification automatique de la rétinopathie diabétique. Nous développerons et validerons de nouveaux algorithmes de détection et classification de lésions rétiniennes en exploitant des techniques d’apprentissage-machine et la base d’images de Diagnos Inc.
In this project we intend to find an abstract understanding of heterogeneous networks (HetNets. Our main focus is the small cell networks based on LTE-A and WiFi systems. We will start by investigating simple and efficient resource management algorithms in such networks and attempt to find a simplified model for the quality of service (QoS) in such networks. This model will be interfaced with a software tool already developed at Siradel and used to clarify the expected performance of a HetNet in a given wireless environment.
Performance optimization and prototype demonstration of the low-cost high-efficiency Localized Inherently Thin (LIT) solar cell technology platform will be carried out during the two year Mitacs Elevate Post Doctoral Fellowship program. The candidate will also design and optimize the fabrication process, working alongside industry staff, for technology transfer and commercialization. A business study will also be carried out as part of the program in order to establish the framework for large scale manufacturing.
This project aims to develop a low cost, biologically inspired solar energy harvesting and storage device. This unique combination addresses the intermittent availability of sunlight during different times of the day by smoothing out variations in the output using stored energy. Solar energy will be captured by very efficient bacterial proteins that normally drive photosynthesis. The captured energy is harvested from the proteins and stored in a liquid salt solution that forms the bulk of the device.