The objective of the internship is to better evaluate/quantify the quality of sources in a challenging weather environment and adapt the behavior of the sensor-fusion algorithm accordingly. For example, if two cameras are mounted on the car, and one is obstructed (or partially obstructed) by snow, second camera should become more reliable in this condition.
This project will address the production test needs of a silicon-based high-speed photonic transceiver solution for metro-reach terabit optical modems. For this project, the partnership will be between Ciena, DA Integrated (test development contractor) and Prof. Gordon W. Roberts from McGill University (academic expert in DFT and mixed-signal design and test).
In this project, Cardon Rehabilitation and Medical Equipment (CRME) and the University of Waterloo (UW) will test the Automated Rehabilitation System (ARS) for lower body physiotherapy in a clinical environment. ARS provides the ability to measure the human pose during the performance of rehabilitation exercises and provide real time feedback. CRME and UW are interested in developing algorithms to automatically assess the changes in patient recovery rate.
The goal is to create a conversation loop between 3D designers and artificial intelligence programs. This will help the AI provide suggestions to the designer, while the designer provides the AI with feedback. This can help make it easier for designing complicated objects as well as complicated textures that belong to the surface of 3D objects. Through this interaction, the hope that AI can extend the utility of design software.
Given a time series of returns for a portfolio of financial instruments, develop a model that accurately predicts returns which maximize profits. The objective function will take an input of financial indicators from the previous time interval and the returns from the current time interval. These indicators can explain relationships between financial instruments in the portfolio of interest, thus are important for explaining their returns and associated risk. A common challenge with these types of problems is how easy it can be to over-fit your model.
Conventional power systems rely on synchronous machines for generation of power and also for formation of an interconnected network of generation to which loads are connected via a transmission system (known as a grid). Increasingly renewable sources of energy are interconnected to a grid via power electronic converters. These converters have been traditionally operated with the assumption of an existing grid thanks to the presence of synchronous machines.
Highly accurate 3D object detectors require significant computational resources, and reducing computation and memory load while maintaining the same level of performance is a critical task for any safe and reliable autonomous vehicle. This research project investigates the deployment of an accurate 3D object detection model to a resource constrained architecture by changing the model structure, its parameters as well as its activity during operation.
SOTI MoblControl is an enterprise mobllity management solution that secures and manages, mobile devices and the mobile data across all endpomts. To ensure end-to-end security, MobiControl encrypts all
communication between the MobiControl Manager and the Deployment Server uslng Secure Socket layer (SSL). The SSL Is a cryptographic protocol that is use widely for secure communication. The process of the encryption and the decryption In the SSL require considerable computation power. Specially in a situation of handling a large number of devices.
Voltage instability is one of the major causes of many blackouts such as Canada-United State blackout (2003), Sweden-Denmark blackout (2003), India blackout (2012), and Turkey blackout (2015). If reliable methods are available for online voltage stability assessment, operators can be warned and automated corrective actions can be initiated to prevent voltage collapse. Although, a large number of Voltage Stability Indices (VSIs) are reported in literature, they are not practically applicable for real-time monitoring or not sufficiently reliable under all operating conditions.
This project will develop a hybrid framework by integrating AI and machine learning methods with tabular information extraction and semantic modeling to improve the state-of-the-art precision and recall in tabular detection while maximizing the value of extracted information for industrial applications.