Computer

Monday December 7, 2015 at 12:30 p.m. Shahrokh Valaee, Professor and Associate Chair for Undergraduate Studies at the Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, will be presenting “Connected Cars for Smart Cities”. Speaker: Shahrokh Valaee Professor, Associate Chair for Undergraduate Studies Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Day & Time: Monday, December 7, 2015 12:30 p.m. – 1:30 p.m. Location: Room ENG 288, Ryerson University George Vari Center for Engineering & Computing 245 Church Street, Toronto, ON Organizer: IEEE Toronto Computer, Magnetics and Instrument-Measurement Chapters Contact: Dr. Maryam Davoudpour Abstract: Recently we are witnessing the emergence of situation-aware vehicles, equipped with plurality of sensors that can help driver with vehicle control and maneuvering. Cars that can park themselves, provide lane-departure warning, and monitor the driver alertness are marketed with affordable prices. The sensing and processing power of cars are increasing, enabling various safety-enhancing features, such as blind-spot warning, adaptive headlights, adaptive cruise control, and so on. In this talk, we will discuss the next steps for autonomous vehicles. In particular, we will project the path forward by transitioning from autonomous cars to cognitive and intelligent vehicles. Future cars will be enabled with car-to-car and car-to-infrastructure communication capabilities. We will review such enhancement and will focus on two recent research directives that will make future cars intelligent. The two enablers are compressive sensing and network coding. We will show that cooperative compressive sensing can reduce the wireless channel congestion, which is the main challenge in dense vehicular networks. To discuss the communications aspects of vehicular networks, we will introduce a repetition-based medium access control method using positive orthogonal codes, and then propose an opportunistic network-coding scheme to enhance the reliability of communication. We will finally discuss some open research issues. Biography: Shahrokh Valaee is with the Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, where he is a Professor and the Associate Chair for Undergraduate Studies. He is the Founder and the Director of the Wireless and Internet Research Laboratory (WIRLab) at the University of Toronto. Professor Valaee recently served as the TPC Co-Chair of ICT 2015. He was the Track Chair of the IEEE Wireless Communications and Networking Conference (WCNC) 2014, the TPC Co-Chair and the Local Organization Chair of IEEE Personal Mobile Indoor Radio Communication (PIMRC) Symposium 2011, and the Co-Chair for Wireless Communications Symposium of IEEE GLOBECOM 2006. From December 2010 to December 2012, he was the Associate Editor of the IEEE Signal Processing Letters. Currently, he serves as an Editor of IEEE Transactions on Wireless Communications. Since Feb 2015 he has been an Editor of the Elsevier Journal of Computer and System Science. Professor Valaee is a Fellow of the Engineering Institute of Canada.

Thursday November 19, 2015 at 1:00 p.m. Mohammad Norouzi, PhD candidate in computer science at the University of Toronto, will be presenting “Compact Discrete Representations for Scalable Similarity Search”. Speaker: Mohammad Norouzi PhD Candidate Day & Time: Thursday, November 19, 2015 1:00 p.m. – 2:00 p.m. Location: Room ENG 106 George Vari Engineering and Computing Centre Ryerson University 245 Church Street Toronto Organizer: IEEE Toronto Computer, Magnetics and Instrument-Measurement Chapters Contact: Maryam Davoudpour, Email:maryam.davoudpour@ieee.org Abstract: Scalable similarity search on images, documents, and user activities benefits generic search, data visualization, and recommendation systems. This talk concerns the design of algorithms and machine learning tools for faster and more accurate similarity search. The proposed techniques advocate the use of discrete codes for representing the similarity structure of data in a compact way. In particular, I will discuss how one can learn to map high-dimensional data onto binary codes with a metric learning approach. Then, I will describe a simple algorithm for fast exact nearest neighbour search in Hamming distance, which exhibits sub-linear query time performance. Going beyond binary codes, I will highlight a compositional generalization of k-means clustering which maps data points onto integer codes with storage and search costs that grow sub-linearly in the number of cluster centers. This representation improves upon binary codes, and provides an even more precise approximation of Euclidean distance. Experimental results are reported on multiple datasets including a dataset of SIFT descriptors with 1B entries. Biography: Mohammad Norouzi is a PhD candidate in computer science at the University of Toronto. His research lies at the intersection of machine learning and computer vision. He is a recipient of a Google US/Canada PhD fellowship in machine learning. He is going to join Google as a research scientist in January 2016.