IEEE Winnipeg Section


Archive for the ‘Section Event’ Category

IEEE Women in Engineering Networking Event – August 18, 2017

Tuesday, August 15th, 2017

IEEE Women In Engineering (WIE) Winnipeg section would like to invite all students and staffs in the University of Manitoba ECE department, as well as IEEE Winnipeg Section members for a networking event. IEEE WIE is the largest international professional organization dedicated to promoting women engineers and scientists and inspiring girls around the world to follow their academic interests to a career in engineering.

Date: 18 August 2017

Time: 10:30 AM to 11:30 AM

Location: University of Manitoba, E2-350 EITC 

Refreshments: Coffee and Donuts

Registration: Not Required

Join us on Friday for a casual networking event before the new school term.

To view updates, future events, pictures, and videos from WIE Winnipeg section please visit:

IEEE Women in Engineering BBQ – July 5, 2017

Wednesday, June 21st, 2017

This year the annual BBQ will be hosted by the WIE group on Wednesday, July 5th. This is a good opportunity to enjoy a summer evening outdoors and meet some new people from the section in a fun and relaxing setting. Food and drinks will be provided (burgers, hotdogs, drinks, etc.). Halal meat and Vegetarian options will be available if you choose the vegetarian when you register. There is a sand volleyball court next to the picnic site for anyone that wishes to play.

When: Wednesday, July 5, 2017, 6pm-9pm

Where: Assiniboine Part, picnic site 4

Registration: Registration is required in advance and is available at: The registration fee is $5.50 for IEEE members and $10.50 for non-members. Registration includes entry into a draw for a door prize.

For more information please email the event contact.

IEEE Communications Society Seminar – A Probabilistic Theory of Deep Learning – May 18, 2017

Thursday, May 11th, 2017
Event Title: Technical Seminar – A Probabilistic Theory of Deep Learning
Speaker: Dr. Richard Baraniuk
Date: Thursday May 18, 2017
Time: 2:00 pm
Location: Room E3-262 , EITC, University of Manitoba, Fort Garry Campus
Abstract: A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation. For instance, visual object recognition involves the unknown object position, orientation, and scale in object recognition while speech recognition involves the unknown voice pronunciation, pitch, and speed. Recently, a new breed of deep learning algorithms have emerged for high-nuisance inference tasks that routinely yield pattern recognition systems with near- or super-human capabilities. But a fundamental question remains: Why do they work? Intuitions abound, but a coherent framework for understanding, analyzing, and synthesizing deep learning architectures has remained elusive. We answer this question by developing a new probabilistic framework for deep learning based on the Deep Rendering Model: a generative probabilistic model that explicitly captures latent nuisance variation. By relaxing the generative model to a discriminative one, we can recover two of the current leading deep learning systems, deep convolutional neural networks and random decision forests, providing insights into their successes and shortcomings, a principled route to their improvement, and new avenues for exploration.
Biography of the Speaker: Richard G. Baraniuk is the Victor E. Cameron Professor of Electrical and Computer Engineering at Rice University.  He received the B.Sc. degree in 1987 from the University of Manitoba, the M.Sc. degree in 1988 from the University of Wisconsin-Madison, and the Ph.D. degree in 1992 from the University of Illinois at Urbana-Champaign, all in Electrical Engineering.  His research interests lie in  new theory, algorithms, and hardware for sensing, signal processing, and machine learning.  He is a Fellow of the American Academy of Arts and Sciences, National Academy of Inventors, American Association for the Advancement of Science, and IEEE.  He has received the DOD Vannevar Bush Faculty Fellow Award (National Security Science and Engineering Faculty Fellow), the IEEE Signal Processing Society Technical Achievement Award, and the IEEE James H. Mulligan, Jr. Education Medal.  He holds 28 US and 4 foreign patents that have been licensed to 2 companies.
Other information: The seminar is free and open to all who wish to attend. For more information please contact Dr. Jun Cai.

IEEE PES Seminar – Linear Analysis of Power Systems in the Presence of Black-Boxed Simulation Models – March 21, 2017

Tuesday, March 7th, 2017

IEEE PES Winnipeg is proud to announce the upcoming Technical Meeting scheduled for Tuesday March 21, 2017 at Holiday Inn South, 1330 Pembina Highway, featuring a presentation on “Linear analysis of power systems in the presence of black-boxed simulation models” by Akbo Rupasinghe. Please review the upcoming event on the chapter website for detailed information and to register for this event. 


The IEEE PES Winnipeg Chapter must provide Holiday Inn with the number of attendees. Our best estimate of walk-in registrants will be submitted but we cannot guarantee all walk-in registrants will be served lunch. Please register early to avoid any unwanted inconvenience. No refund after registration closes. Registration closes at end of Sunday, March 17, 2017. If you have any questions, please contact Kang Liu at 204-360-6419.

Waves Chapter Seminar – LTE on Unlicensed Band – February 22, 2017

Saturday, February 11th, 2017

IEEE Winnipeg Waves Chapter (APS/MTTS/VTS) is pleased to present:


Seminar Title: LTE on Unlicensed Band


Speaker: Prof. Geoffrey Ye Li

IEEE Distinguished Lecturer—Vehicular Technology Society (VTS)

Professor, ECE Department, Georgia Tech.


Date: Wednesday, Feb 22, 2017 at 11.15 AM


Location:  EITC E1-270 (Fort Garry Campus; Engineering Building)


Abstract of the Presentation: Future 5G cellular networks are facing the challenging task on increasing their capacity dramatically. Despite some cutting-edge capacity-approaching techniques, the limited licensed spectrum is still a major bottleneck for capacity improvement. To tackle this issue, a new standard has been developed within 3GPP for LTE systems, currently on the licensed bands, to operate on the unlicensed bands, which is called LTE on unlicensed bands (LTE-U). LTE has many advanced techniques, which can be exploited in the unlicensed bands to achieve a high spectral efficiency. However, the unlicensed bands are currently occupied by the widely-deployed WiFi networks.  The major challenge on LTE-U is how to design fair and efficient coexistence mechanisms between LTE and WiFi networks. In this talk, we will first discuss traditional traffic offloading, resource sharing, and hybrid of the both for LTE users to optimally exploit unlicensed bands while guaranteeing of the QoS of the existing WiFi users. To improve both the LTE and WiFi users simultaneously and achieve a win-win situation, we then provide a novel traffic offloading strategy, which is just opposite to the traditional one and offloads some WiFi users to the LTE networks and at the same time relinquishes some unlicensed bands to LTE-U. Since the unlicensed bands are usually operated less energy-efficiently than the licensed ones due to the higher carrier frequency of the unlicensed bands and larger pass-loss, we also present a framework for energy-efficiency (EE) optimization in LTE-U. We establish a criterion to determine whether the EE of the LTE system can be improved with the help of the unlicensed bands. Based on the criterion, we then develop a joint licensed and unlicensed resource allocation algorithm to maximize the EE of each LTE small cell base station.


Biography of the Speaker: Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He is also holding a Cheung Kong Scholar title at the University of Electronic Science and Technology of China since 2006. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include wireless communications and statistical signal processing. In these areas, he has published over 300 referred journal and conference papers in addition to 26 granted patents. His publications have been cited by over 24,000 times and he has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters. He has been an IEEE Fellow since 2006. He received the Stephen O. Rice Prize Paper Award in 2010 and the WTC Wireless Recognition Award in 2013 from the IEEE Communications Society and the James Evans Avant Garde Award in 2013 and the Jack Neubauer Memorial Award in 2014 from the IEEE Vehicular Technology Society. Recently, he won 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech.