Events

Mar
13
Wed
Carleton Workshop on AI, Machine Learning and Data Analytics For Communication Networks @ 4359 ME (Mackenzie Building)
Mar 13 @ 09:30 – 16:30

Systems and Computer Engineering Carleton University

Workshop Chair: Dr. Halim Yanikomeroglu, Professor, Carleton

Co-Organized and Sponsored by IEEE Ottawa Young Professionals Affinity Group.

https://carleton.ca/campus/map

Arrival: 9:30 – 10:00 am
Morning Session: 10:00 am – 12:00 noon

Keynote Speech (abstract and bio at the end)

Exploration Strategy in Wireless Systems with Reinforcement Learning
Dr. Haris Gacanin, Department Head, Nokia Bell Labs, Belgium

AI/ML-based Security and Trust in Mobile Services
Dr. Burak Kantarci, Professor, uOttawa

Lunch: 12:15 – 1:45 pm

The Caf – Carleton University, Residence Commons, 3rd floor

https://dining.carleton.ca/locations/the-caf

Afternoon Session I: 2:00 – 3:00 pm

Threat of Adversarial Attacks on Machine Learning in Network Security
Kunle Ibitoye, PhD candidate, Carleton, and Rana Abou Khamis, MASc candidate, Carleton
(Supervisors: Professors Ashraf Matrawy and Omair Shafiq, Carleton)

Resource Allocation with Deep Reinforcement Learning for Microgrid Communications
Dr. Melike Erol-Kantarci, Professor, uOttawa, and Medhat Alsayed, PhD candidate, uOttawa

Coffee Break: 3:00 – 3:30 pm

Afternoon Session II: 3:30 – 4:30 pm

Wireless Network Personalization: Why it Matters and How to Approach It
Rawan Alkurd, PhD candidate, Carleton
(Supervisors: Professors Ibrahim Abualhaol and Halim Yanikomeroglu, Carleton)

Machine Learning for Wireless Networks: Applications to Routing and Resource Management
Dr. Thomas Kunz, Professor, Carleton

Keynote Abstract

We are now several years into explosion of machine learning (ML) in wireless networks, used to enrich decision-making by finding structures in data – knowledge discovery – as means to describe the user behavior and network performance. With new designs of wireless networks, complexity and dynamicity rises, network resources are scattered and diversity of network elements increases. Consider these examples with interesting challenges: 1) massive number of Internet-of-Things devices, sensors and actuators give rise to the problem of dynamic network planning; 2) broadband wireless leads to problems with real-time radio resource management; 3) ultra-reliable communications require support of real-time adjustments on latency and reliability in the orders of 99,99999%. For such designs artificial intelligence (AI) is expected to support high adaptability with respect to wireless environment and its services (e.g. virtual reality).

This talk discusses a paradigm shift from contemporary data-driven wireless with ML toward autonomous wireless with AI. We explore motivation, opportunities and methodology to adopt training-free AI methods for self-organization of wireless systems. We point out specific properties of wireless environment and classify future directions on training-free vs training-based systems. We start from popular data-driven ML techniques and briefly elaborate their benefits and shortcomings for wireless application mentioned above. The main focus is on reinforcement learning as a major (training-free) representative of AI. We briefly discuss learning principles of intelligent agent with problem of random exploration for wireless-specific environment. We discuss principles of self-organization by synthesizing reasoning and learning with knowledge management. Finally, we end with a case study using wireless AI prototype for self-deployment and self-optimization. The talk provokes new coming challenges and unveil interesting future directions across multi-disciplinary research areas.

Keynote Biography:

Haris Gačanin received his Dipl.-Ing. degree in Electrical engineering from University of Sarajevo in 2000. In 2005 and 2008, respectively, he received MSc and PhD from Tohoku University in Japan. He worked at Tohoku University until 2010 as Assistant Professor and joined Alcatel-Lucent (now Nokia) in 2010, where he established research on data-driven analysis of communication systems at physical and media access layers. Currently, he is department head at Bell Labs and adjunct teaching professor at KU Leuven. His professional interests relate to research confluence between artificial intelligence and physical-layer communications to establish autonomous wireless systems. He has 200+ scientific publications (journals, conferences and patens) and invited/tutorial talks. He is senior member of IEEE and IEICE and recipient of IEICE Communication Systems Best Paper Award (joint 2014, 2015, 2017), The 2013 Alcatel-Lucent Award of Excellence, the 2012 KDDI Foundation Research Award, the 2009 KDDI Foundation Research Grant Award, the 2008 JSPS Postdoctoral Fellowships for Foreign Researchers, the 2005 Active Research Award in Radio Communications, 2005 Vehicular Technology Conference (VTC 2005-Fall) Student Paper Award from IEEE VTS Japan Chapter and the 2004 Institute of IEICE Society Young Researcher Award. He was awarded by Japanese Government (MEXT) Research Scholarship in 2002.

Mar
14
Thu
Opportunities and Design Considerations for GaN HEMTs in Industrial and Automotive Applications @ Algonquin College, T-Building, Room T129
Mar 14 @ 18:00 – 20:00

Admission: Free. Registration required.
Please register by e-mail contacting: ottawapels@gmail.com

Parking: Parking in Lots 8 and 9 after 5 p.m. is $5 flat rate, pay at a machine and display the ticket on
your dashboard.

Download the official PDF here.

Abstract

GaN HEMT has been a focus in both academia and industry, due to the extremely low figure of
merits (RDS(on) x QG) compared with conventional Silicon counterparts. The opportunities, challenges and design considerations for GaN HEMTs in industrial and automotive applications will be presented in the device/packaging and system perspectives. Design examples are detailed to show how the system
performance maximization is enabled by GaN HEMTs with minimum cost in the selected applications.

The key design procedures will be thoroughly discussed, i.e., topology selection, loss analysis, cost reduction, power stage layout, thermal design, etc.

This presentation is aimed at covering the fundamentals as well as the latest research and updates of GaN HEMTs applications. The target audience is the design engineers, researchers, graduate/undergraduate
students interested in industrial/automotive applications or just GaN technology.

Speaker’s Bio

Juncheng (Lucas) Lu received B.S. degree from Zhejiang University, Hangzhou, China, and M.S. degree from Kettering University, Michigan, USA. He was a research engineer with Delta Power Electronics Center, Shanghai, China. Since 2016, he has been with GaN Systems, Inc., Ottawa, Canada. He manages the head office applications and is responsible for Americas and EMEA application support. His research interest is wide bandgap devices application, power electronics packaging, high-efficiency high power
density power supply, and electric vehicle battery charger. He published more than 20 IEEE/SAE transaction and conference papers and holds 9 U.S. Patents.

Mar
19
Tue
Series of Risk and Project Managements Seminars by IEEE WIE: #1 Risk Management (RM)
Mar 19 @ 18:00 – 19:30
Risk Management (RM) is a process that provides
confidence that planned objectives will be achieved.

The focus of this seminar will be (1) Defining the risk management and
its importance (2) Illustrating ISO:31000 framework: steps and deliverables (3)
Presenting the one of the highly utilized risk assessment tools; the Failure
Model Effect Criticality Analysis (FEMCA) with an applied example. (4) Sharing
the best practices of Key Risk Indicators (KRIs).

Speaker: Dr. Ola Abdrabou

Visiting Professor, the Infrastructure Protection and Security (IPIS) Program-
Faculty of Engineering, Carleton University.

Mar
26
Tue
Managements Seminars by IEEE WIE: #2 Business Continuity Management (BCM)
Mar 26 @ 18:00 – 19:30

Business
Continuity (BC) 
is the capability of the organization to continue delivery of products
or services at acceptable predefined levels following disruptive incidents
(disaster).

The focus of
this seminar
 will be (1)
Defining the Business Continuity and its importance (2) Illustrating Canada
Public Safety BC framework: steps and deliverables (3) Presenting the Business
Impact Analysis (BIA) Process in details with an applied example.

Speaker: Dr. Ola Abdrabou

Visiting Professor, the Infrastructure Protection and Security (IPIS) Program-
Faculty of Engineering, Carleton University.

Apr
2
Tue
Machine Learning Seminar: “Machine Learning for Time Series Prediction in Smart Grids: Summerside Electric Use Case @ Carelton University ME4463
Apr 2 @ 18:00 – 19:30
Machine Learning Seminar: "Machine Learning for Time Series Prediction in Smart Grids: Summerside Electric Use Case @ Carelton University ME4463 | Ottawa | Ontario | Canada

This presentation discusses BluWave-ai’s findings on applying machine learning techniques for time series prediction in smart grids. In particular, the Summerside Electric grid is used as an example to demonstrate the effectiveness of more accurate artificial intelligence (AI)-enabled predictors on reducing the overall cost of energy for grid operators.

Located in Prince Edward Island (PEI), Canada, Summerside Electric grid has a 12 MW local wind farm, introducing Canada’s first municipally owned and operated wind farm. In 2018, around 25% of the 137.5 TWh electrical demand was met by the wind farm, while the rest was mostly imported from New Brunswick (NB) Power by scheduling an hourly power import; the city pays a commitment rate for scheduled power. Wind farm stochastic output yields costly inaccuracy in import scheduling, as power surplus is exported back to NB Power at a lower rate and power deficit is imported in real-time at a much higher rate than the commitment rate.

To address the scheduling inaccuracy, Summerside grid data from 2016 to 2018 is leveraged to design more accurate AI-enabled time series predictors, revealing the significant potential for reducing energy cost in renewable-penetrated grids through state-of-the-art predictors.

Machine learning flyer BluWave

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