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Archive for the ‘Signals & Computational Intelligence’ Category

IEEE Ryerson Python Workshop 7

Saturday, May 5th, 2018

IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE WIE, IEEE Computational Intelligence Chapter, and Robotics/ Automation Chapter are please to announce the start of their 7 and final workshop of their series of python workshops. At theend of this workshop the participantwill be awarded thecertificates forcompletion their attendance of the workshop.

Day & Time: Monday, April 2, 2018

Location: Ryerson University (Victoria Building, Room VIC 301)

Contact: ieee.ryersonu@gmail.com

Organizer: IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE Computational Intelligence Chapter, WIE IEEE Toronto, Instrumentation-Measurement/Robotics-Automation

IEEE Ryerson Python Workshop 6

Saturday, May 5th, 2018

IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE WIE, IEEE Computational Intelligence Chapter, and Robotics/ Automation Chapter are pleased to announce the start of their series of python workshops. A series of 7 workshops will give the participants the ability to use the basics of python as well as Machine learning to help them in their study or workplace. At the end of these workshops there will be a certificate given to participants who attended these workshops.

Day & Time: Monday, March 26, 2018

Location: Ryerson University (Victoria Building, Room VIC 301)

Contact: ieee.ryersonu@gmail.com

Organizer: IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE Computational Intelligence Chapter, WIE IEEE Toronto, Instrumentation-Measurement/Robotics-Automation

IEEE Ryerson Python Workshop 5

Monday, March 19th, 2018

IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE WIE, IEEE Computational Intelligence Chapter, and Robotics/ Automation Chapter are pleased to announce the start of their series of python workshops. A series of 6 workshops will give the participants the ability to use the basics of python as well as Machine learning to help them in their study or workplace. At the end of these workshops there will be a certificate given to participants who attended these workshops.

Day & Time: Monday, March 19, 2018

Location: Ryerson University (Victoria Building, Room VIC 301)

Contact: ieee.ryersonu@gmail.com

Organizer: IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE Computational Intelligence Chapter, WIE IEEE Toronto, Instrumentation-Measurement/Robotics-Automation

IEEE Ryerson Python Workshop 4

Monday, March 12th, 2018

IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, WIE IEEE Toronto, IEEE Computational Intelligence Chapter, and Robotics/ Automation Chapter are Please to announce the fourth workshop of their series of python workshops. A series of 6 workshops will give the participants the ability to use the basics of python to help them in their study or workplace. At the end of these workshops there will be a certificate given to participants who attended these workshops.

Day & Time: Monday, March 12, 2018
6:00 p.m. ‐ 8:00 p.m.

Location: Ryerson University (Victoria Building, Room VIC 301)

Contact: ieee.ryersonu@gmail.com

Organizer: IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE Computational Intelligence Chapter, WIE IEEE Toronto, Instrumentation-Measurement/Robotics-Automation

IEEE Ryerson Python Workshop 3

Friday, March 2nd, 2018

IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, WIE IEEE Toronto, IEEE Computational Intelligence Chapter, and Robotics/ Automation Chapter are Please to announce the third workshop of their series of python workshops. A series of 6 workshops will give the participants the ability to use the basics of python to help them in their study or workplace. At the end of these workshops there will be a certificate given to participants who attended these workshops.

Day & Time: Monday, March 5, 2018
6:00 p.m. ‐ 8:00 p.m.

Location: Ryerson University (Victoria Building, Room VIC 301)

Contact: ieee.ryersonu@gmail.com

Organizer: IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE Computational Intelligence Chapter, WIE IEEE Toronto, Instrumentation-Measurement/Robotics-Automation

Register at: https://www.eventbrite.com/e/ieee-ryerson-python-workshop-3-tickets-43189931247

IEEE Ryerson Python Workshop 2

Saturday, February 10th, 2018

IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, WIE IEEE Toronto, IEEE Computational Intelligence Chapter, and Robotics/ Automation Chapter are Please to announce the second workshop of their series of python workshops. A series of 6 workshops will give the participants the ability to use the basics of python to help them in their study or workplace. At the end of these workshops there will be a certificate given to participants who attended these workshops.

Day & Time: Monday, February 12, 2018
6:00 p.m. ‐ 8:00 p.m.

Location: Ryerson University (Victoria Building, Room VIC 301)

Contact: ieee.ryersonu@gmail.com

Organizer: IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE Computational Intelligence Chapter, WIE IEEE Toronto, Instrumentation-Measurement/Robotics-Automation

Register at: https://www.eventbrite.com/e/ieee-ryerson-python-workshop-2-tickets-42931234478

Introduction to Python Workshop

Thursday, February 1st, 2018

IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE WIE, IEEE Computational Intelligence Chapter, and Robotics/ Automation Chapter are Please to announce the start of their series of python workshops. A series of 6 workshops will give the participants the ability to use the basics of python as well as Machine learning to help them in their study or workplace. At the end of these workshops there will be a certificate given to participants who attended these workshops.

Day & Time: Monday, February 5, 2018
6:00 p.m. ‐ 8:00 p.m.

Location: Ryerson University (Victoria Building, Room VIC 301)

Contact: ieee.ryersonu@gmail.com

Organizer: IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE Computational Intelligence Chapter, WIE IEEE Toronto, Instrumentation-Measurement/Robotics-Automation

RVSP: https://www.eventbrite.com/e/ieee-ryerson-intro-to-python-workshop-tickets-42588313793

Why Deep Learning Works So Well?

Friday, November 17th, 2017

Monday, November 27th at 10:30 a.m., Prof. C.-C. Jay Kuo, Fellow of IEEE and Dean’s Professor in Electrical Engineering-Systems, University of Southern California, will be presenting “Why Deep Learning Works So Well?”.

Day & Time: Monday, November 27, 2017
10:30 a.m. ‐ 11:30 a.m.

Speaker: Prof. C.-C. Jay Kuo, Fellow of IEEE, AAAS, SPIE
Dean’s Professor in Electrical Engineering-Systems, University of Southern California

Location: Room ENG 358
George Vari Engineering Building (Intersection of Church & Gould)
Ryerson University
245 Church St, Toronto, M5B 1Z4

Contact: Xiao-Ping Zhang, Alireza Sadeghian, Alex Dela Cruz

Organizer: Electrical and Computer Engineering and CASPAL Ryerson
Signals & Computational Intelligence Chapter

Abstract: Deep learning networks, including convolution and recurrent neural networks (CNN and RNN), provide a powerful tool for image, video and speech processing and understanding nowadays. However, their superior performance has not been well understood. In this talk, I will unveil the myth of the superior performance of CNNs. To begin with, I will describe network architectural evolution in three generations: first, the McClulloch and Pitts (M-P) neuron model and simple networks (1940-1980); second, the artificial neural network (ANN) (1980-2000); and, third, the modern CNN (2000-Present). The differences between these three generations will be clearly explained. Next, theoretical foundations of CNNs have been studied from the approximation, the optimization and the signal representation viewpoints, and I will present main results from the signal processing viewpoints. I will use an intuitive way to explain the complicated operations of the CNN systems.

Biography: Dr. C.-C. Jay Kuo received his Ph.D. degree from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as Director of the Media Communications Laboratory and Dean’s Professor in Electrical Engineering-Systems. His research interests are in the areas of digital media processing, compression, communication and networking technologies. Dr. Kuo was the Editor-in-Chief for the IEEE Trans. on Information Forensics and Security in 2012-2014. He was the Editor-in-Chief for the Journal of Visual Communication and Image Representation in 1997-2011, and served as Editor for 10 other international journals. Dr. Kuo received the 1992 National Science Foundation Young Investigator (NYI) Award, the 1993 National Science Foundation Presidential Faculty Fellow (PFF) Award, the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2011 Pan Wen-Yuan Outstanding Research Award, the 2014 USC Northrop Grumman Excellence in Teaching Award, the 2016 USC Associates Award for Excellence in Teaching, the 2016 IEEE Computer Society Taylor L. Booth Education Award, the 2016 IEEE Circuits and Systems Society John Choma Education Award, the 2016 IS&T Raymond C. Bowman Award, and the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award. Dr. Kuo is a Fellow of AAAS, IEEE and SPIE. He has guided 140 students to their Ph.D. degrees and supervised 25 postdoctoral research fellows. Dr. Kuo is a co-author of about 250 journal papers, 900 conference papers and 14 books.

Data-Driven Care: Enabling Science and Technologies

Friday, November 10th, 2017

Tuesday, November 21st at 5:00 p.m., Dr. Philip Asare, Assistant Professor of Electrical and Computer Engineering at Bucknell University, will be presenting “Data-Driven Care: Enabling Science and Technologies”.

Day & Time: Tuesday November 21st, 2017
5:00 p.m. – 6:00 p.m.

Speaker: Dr. Philip Asare
Assistant Professor of Electrical and Computer Engineering
Swanson Fellow in Sciences and Engineering
Multicultural Student Services Faculty Fellow (Fall 2015)
Bucknell University

Location: Room ENG-LG 12
George Vari Engineering Building (Intersection of Church & Gould)
Ryerson University
245 Church St, Toronto, M5B 1Z4

Contact: Alireza Sadeghian, Alex Dela Cruz

Organizer: Signals & Computational Intelligence Chapter

Abstract: Recent advances in medical technologies provide an opportunity to collect and use a variety of data to assist in the delivery of care to patients in and out of the clinic. In the clinic, tools can be developed that provide insights into patient state that were not previously possible. In some cases various actions can be automated to assist clinicians in delivering care. Outside the clinic, patients can be empowered to manage their own care as they go about their daily lives without being confined to the hospital. Quite a number of impressive technologies have been demonstrated in the research space with a few emerging as commercial projects on the market; however, there are a number of challenges to overcome in order to realize the full potential of these technological advances. This talk will describe past and on-going work in this area by the speaker and others to ensure that the data are trustworthy, the tools that depend on the data are robust and safe, and the technologies are more likely to be adopted by the healthcare ecosystem. These would hopefully lead to the greatest possible impact for patients and their care providers.

Biography: Philip Asare is an Assistant Professor of Electrical and Computer Engineering and Swanson Fellow in the Sciences and Engineering at Bucknell University, in Lewisburg, Pennsylvania, in the USA. He is currently a Visiting Scholar/Professor in Electrical and Computer Engineering at Ryerson University during his leave from Bucknell for the 2017-18 academic year. His research interests are in the general are of cyber-physical systems with medicine being one of his primary application areas. He was a Scholar-in-Residence at the U.S. Food and Drug Administration for the 2012-13 academic year working with researchers in the Office of Science and Engineering Laboratories on regulatory approaches for emerging mobile connected medical devices. His work in this area has received a best student paper and best paper award at the Interncation Conference on Body Area Networks (BodyNets). He most recently co-organize the Prototype to Patient Treatment workshop as part of the 2016 Annual Wireless Health Conference through the National Science Foundation Nanosystems Engineering Research Center (NERC) for Advanced Self-Powered Systems of Integrated Sensors and Technologies (ASSIST). Asare is a member of the IEEE and its Computer Society and Engineering in Medicine and Biology Society (EMBS). He is also a member of the ACM and its Special Interest Group on Embedded Systems (SIGBED).

On System-Level Analysis & Design of Cellular Networks: The Magic of Stochastic Geometry

Thursday, August 31st, 2017

Friday September 8, 2017 at 10:00 a.m. Professor Marco Di Renzo from Paris-Saclay University/CNRS, will be presenting “On System-Level Analysis & Design of Cellular Networks: The Magic of Stochastic Geometry”.

Day & Time: Friday September 8, 2017
10:00 a.m. – 11:00 a.m.

Speaker: Professor Marco Di Renzo
Paris-Saclay University/CNRS, France

Location: Room ENG288
George Vari Engineering Building (Intersection of Church & Gould)
Ryerson University
245 Church St, Toronto, M5B 1Z4

Contact: Alireza Sadeghian, Alex Dela Cruz

Organizers: Signals & Computational Intelligence Chapter

Abstract: This talk is aimed to provide a comprehensive crash course on the critical and essential importance of spatial models for an accurate system-level analysis and optimization of emerging 5G ultra-dense and heterogeneous cellular networks. Due to the increased heterogeneity and deployment density, new flexible and scalable approaches for modeling, simulating, analyzing and optimizing cellular networks are needed. Recently, a new approach has been proposed: it is based on the theory of point processes and it leverages tools from stochastic geometry for tractable system-level modeling, performance evaluation and optimization. The potential of stochastic geometry for modeling and analyzing cellular networks will be investigated for application to several emerging case studies, including massive MIMO, mmWave communication, and wireless power transfer. In addition, the accuracy of this emerging abstraction for modeling cellular networks will be experimentally validated by using base station locations and building footprints from two publicly available databases in the United Kingdom (OFCOM and Ordnance Survey). This topic is highly relevant to graduate students and researchers from academia and industry, who are highly interested in understanding the potential of a variety of candidate communication technologies for 5G networks.

Biography: Marco Di Renzo received the “Laurea” and Ph.D. degrees in Electrical and Information Engineering from the University of L’Aquila, Italy, in 2003 and 2007, respectively. In October 2013, he received the Doctor of Science degree from the University Paris-Sud, France. Since 2010, he has been a “Chargé de Recherche Titulaire” CNRS (CNRS Associate Professor) in the Laboratory of Signals and Systems of Paris-Saclay University – CNRS, CentraleSupélec, Univ Paris Sud, France. He is an Adjunct Professor at the University of Technology Sydney, Australia, a Visiting Professor at the University of L’Aquila, Italy, and a co-founder of the university spin-off company WEST Aquila s.r.l., Italy. He serves as the Associate Editor-in-Chief of IEEE COMMUNICATIONS LETTERS, and as an Editor of IEEE TRANSACTIONS ON COMMUNICATIONS and IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS. He is a Distinguished Lecturer of the IEEE Vehicular Technology Society and IEEE Communications Society. He is a recipient of several awards, and a frequent tutorial and invited speaker at IEEE conferences.