WIRLab is a quarter million dollar facility dedicated to signal processing, machine learning, and wireless and internet research at the Department of Electrical and Computer Engineering, University of Toronto. The current research direction in WIRLab is on IoT, Sensor Networks, Vehicular Networks, and 5G Cellular Networks, addressing the needs of the next generation wireless networks. Our research investigates both analytical approaches to network performance studies and implementation aspects. The researchers in WIRLab have strong analytical background and are involved in realization and engineering of novel networking solutions.
We have a vibrant group that concentrates on tackling difficult engineering problems. Our researchers are among the best with strong technical background and great programming skills. We use advanced signal processing and wireless networking techniques to solve challenging problems with a focus on mixing theory and practice. Our analytical strengths span a wide range that includes machine learning, compressive sensing, and network coding. We implement our machine learning algorithms on GPU hardware, and our localization algorithms on the Android planform.
Director - wirlab
University of Toronto
Post Doctoral Fellow
University of Toronto
Post Doctoral Fellow
University of Toronto
PhD Candidate
University of Toronto
PhD Candidate
University of Toronto
PhD Candidate
University of Toronto
PhD Student
University of Toronto
MASc Student
University of Toronto
MASc Student
University of Toronto
Visiting PhD Student
Isfahan Univ. of Sci. Tech.
Visiting PhD Student
Visitor
FutureBound Inc.
We have developed an accurate location estimation technique for indoor environment using compressive sensing. The proposed algorithm has been developed on HP PDA and Android devices and tested in Bahen Centre at the University of Toronto, Bayview Village shopping mall in North Toronto, and the Canadian National Institute for Blind (CNIB). Currently, we are using machine learning for location estimation.
Our research in vehicular networks focuses on interference management in wireless channel. The research includes both 802.11p Wireless Access in Vehicular Environment (WAVE), and the 3GPP C-V2X standards. We have shown that 802.11p suffers from significant interference that limits the number of vehicles that can participate in the network. Unfortunately the C-V2X standard does not provide any distributed scheduling. Using the concept of Pseudo Orthogonal Codes (POC), we have designed effective distributed schedulers that are applicable to both 802.11p and C-V2X. We have also shown that POC can be combined by opportunistic network coding to enhance the reliability of packet transmission in vehicular broadcast channel. We have further shown that Compressive Sensing can be used to reconstruct the vehicle data that is missed on lossy wireless channel.
In collaboration with researchers from the Department of Medical Imaging at St. Michael's Hospital in Toronto, we are working on the application of Deep Neural Networks for medical image classification. Our research on machine learning has allowed us to also explore other types of data (such as voice) on navigation. In our recent paper, we have developed an indoor navigation application based on voice-user interaction to help provide directions for users without assistance of a localization system. The performance studies show the superiority of our technique compared to the existing literature.