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DTSTART;TZID=America/Toronto:20210510T130000
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DTSTAMP:20261108T012812
CREATED:20210430T023731Z
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SUMMARY:The Role of AI in 5G/6G and IoT-Enabled Smart Grids
DESCRIPTION:IEEE Young Professionals brings speakers from Canadian research groups to give the community technical talks in AI-Powered Wireless Communications and Intelligent Cyber Physical Analysis in IoT-Enabled Smart Grids. \nThe virtual platform information will be sent to registrants a couple of hours ahead of starting the event. \nContact: IEEE Young Professionals Montreal \nSpeakers: \nDr. Melike Erol-Kantarci of University of Ottawa\nTopic: AI-Enabled Wireless Networks: A Bridge from 5G to 6G \nAbstract: \nFuture wireless networks are expected to support a multitude of services demanded by Enhanced Mobile Broadband (eMBB)\, Ultra-Reliable and Low-latency Communications (uRLLC)\, and massive Machine Type Communications (mMTC) users. Heterogeneous devices with different quality of service (QoS) demands will require intelligent and flexible allocation of network resources in response to network dynamics. For instance\, a highly reliable and low-latency network is needed to enable rapid transfer of messages between connected autonomous vehicles. At the same time\, the same physical infrastructure is expected to serve users with high-quality video demand or even mobile Augmented/Virtual Reality entertainment applications. Next-generation wireless networks are expected to accommodate such diverse use cases. In addition\, resource efficiency\, reliability\, and robustness are becoming more stringent for 5G and beyond networks. To meet this\, future wireless networks must incorporate a paradigm shift in network resource optimization\, in which efficient and intelligent resource management techniques are employed. Artificial intelligence\, or more specifically machine learning algorithms stand as promising tools to intelligently manage the networks such that network efficiency\, reliability\, robustness goals are achieved and quality of service demands are satisfied. The opportunities that arise from learning the environment parameters under varying behavior of the wireless channel\, positions AI-enabled 5G and 6G\, superior to preceding generations of wireless networks. In this keynote\, we will provide an overview of the state-of-art in machine learning algorithms and their applications to wireless networks\, in addition to their challenges and the open issues in terms of their applicability to various functions of future wireless networks. \nBiography: \nMelike Erol-Kantarci is Canada Research Chair in AI-enabled Next-Generation Wireless Networks and Associate Professor at the School of Electrical Engineering and Computer Science at the University of Ottawa. She is the founding director of the Networked Systems and Communications Research (NETCORE) laboratory. She is a Faculty Affiliate at the Vector Institute\, Toronto\, and the Institute for Science\, Society and Policy at University of Ottawa. She has over 150 peer-reviewed publications which have been cited over 5500 times and she has an h-index of 38. She has received numerous awards and recognitions. Recently\, she received the 2020 Distinguished Service Award of the IEEE ComSoc Technical Committee on Green Communications and Computing. She was named as N2Women Stars in Computer Networking and Communications in 2019. Dr. Erol-Kantarci has delivered 50+ keynotes\, tutorials and panels around the globe and has acted as the general chair and technical program chair for many international conferences and workshops. Her main research interests are AI-enabled wireless networks\, 5G and 6G wireless communications\, smart grid and Internet of things. She is an IEEE ComSoc Distinguished Lecturer\, IEEE Senior member and ACM Senior Member \nHadis Karimipour \nTopic: Intelligent Cyber Security Analysis in IoT-Enabled Smart Grids \nAbstract: \nToday’s smart grids are complex Cyber-physical Systems (CPSs) that integrate computational and physical capabilities for controlling and managing the ever-growing number of cyber-connected devices. \nAside from a fault in the physical domain\, CPS also suffer from cyber-attacks in both cyber and physical domain e.g.\, an industrial controller can be manipulated to launch various attacks such as the device state inference attack\, leading to system instability. Therefore\, any effort to secure the emerging critical CPSs is of paramount importance.  Nowadays\, a cyber-security specialist must detect\, analyze\, and defend against many cyber threats in almost real-time conditions. Without the employment of artificial intelligence and machine learning techniques\, dealing with a huge number of attacks in a timely manner is not possible. Intelligent\, big-data analytical techniques are necessary to mine\, interpret and extract knowledge of data when there is a significant amount collected from or generated by different security monitoring solutions. This talk will go through the CPS security challenges and AI-enabled state of the art solution in the literature. \nBiography: \nDr. Hadis Karimipour is the director of the Smart Cyber-physical System (SCPS) Lab and an Assistant Professor in the School of Engineering at the University of Guelph. She is among the pioneers of using Machine Learning (ML) for security analysis of critical infrastructure. She has published more than 80 journal articles\, conference papers and book chapters in top IEEE journals and conferences. She has been a keynote/invited speaker for more than 20 different IEEE/International Conference. She was the chair of the IEEE workshop on AI for Securing Cyber-Physical System (AI4SCPS) at IEEE CCECE 2019 and IEEE CyberSciTech 2020 conferences and chair of the special session on the AI for Security of IoT-Enabled Critical Infrastructures at the IEEE SMC 2020 conference. She was the technical committee member of numerous IEEE conferences\, including IEEE SEGE 2018\, 2019\, 2020\, IEEE DSAA 2020\, PST 2020\, IEEE EPEC 2018\, 2020\, and IEEE SMC 2020.\nDr. Karimipour is the Associate Editor of the Frontiers in Communications and Networks Journal\, Editor of American Journal of Electrical and Electronic Engineering\, and Editor of Journal of Electrical Engineering. She has also served as Guest Editor for Elsevier Journal of Computer and Electrical Engineering. She was the Editor of the Springer book on Security of Cyber-physical System. Dr. Karimipour is a Senior Member of IEEE member\, chair of IEEE Women in Engineering and chapter chair of the IEEE Information Theory Kitchener-Waterloo Section\, and an active member of Society for Canadian Women in Science and Technology.
URL:https://www.ieeetoronto.ca/event/the-role-of-ai-in-5g-6g-and-iot-enabled-smart-grids/
LOCATION:Montreal\, Quebec Canada
CATEGORIES:Women in Engineering,Young Professionals
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BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20210517T010000
DTEND;TZID=America/Toronto:20210517T143000
DTSTAMP:20261108T012812
CREATED:20210430T023731Z
LAST-MODIFIED:20210809T204646Z
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SUMMARY:Reconfigurable Intelligent Surfaces: A Signal Processing Perspective
DESCRIPTION:Wireless connectivity is becoming as essential as electricity in our modern world. Although we would like to deliver wireless broadband services everywhere\, the underlying physics makes it inherently complicated: the signal power vanishes very quickly with the propagation distance and is absorbed or scattered when interacting with objects in the way. Even when we have a “strong” signal\, only one in a million parts of the signal energy is being received\, thus\, there is a huge room for improvements! \nWhat if we could tune the propagation environment to our needs? This is the main goal of reconfigurable intelligent surfaces\, which is an emerging concept for beyond-5G communications. The idea is to support the transmission from a source to a destination by deploying so-called metasurfaces that can reconfigure how incident signal waves are scattered. These surfaces can be electronically configured to interact with the wireless signals as if they had different shapes. For example\, it can be configured to behave as a parabolic reflector that is rotated to gather signal energy and re-radiates it as a beam focused on the receiver. This feature makes use of a new design dimension: we can not only optimize the transmitter and receiver but also control the channel. This might be a game-changer when communicating at mmWave and THz frequencies\, where the traditional propagation conditions are particularly cumbersome. \nThis might sound like science fiction but is theoretically possible. In this talk\, Dr. Emil will explain the fundamentals of this new technology from a signal processing perspective. By deriving a signals-and-systems description\, we can look beyond the initial hype and understand what is actually happening when using reconfigurable intelligent surfaces. Dr. Emil will also describe recent experimental validations of the fundamentals. The talk will culminate in a description of the main research challenges that need to be tackled in the coming years. \nThe virtual platform information will be sent to registrants a couple of hours ahead of starting the event. \nContact: IEEE Young Professionals Montreal \nSpeaker: Emil Björnson \nBiography: \nEmil Björnson received the M.S. degree in engineering mathematics from Lund University\, Sweden\, in 2007\, and the Ph.D. degree in telecommunications from the KTH Royal Institute of Technology\, Sweden\, in 2011. From 2012 to 2014\, he held a joint post-doctoral position at the Alcatel-Lucent Chair on Flexible Radio\, SUPELEC\, France\, and the KTH Royal Institute of Technology. He joined Linköping University\, Sweden\, in 2014\, where he is currently an Associate Professor. In September 2020\, he became a part-time Visiting Full Professor at the KTH Royal Institute of Technology. \nHe has authored the textbooks Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013)\, Massive MIMO Networks: Spectral\, Energy\, and Hardware Efficiency (2017)\, and Foundations of User-Centric Cell-Free Massive MIMO (2021). He is dedicated to reproducible research and has made a large amount of simulation code publicly available. He performs research on MIMO communications\, radio resource allocation\, machine learning for communications\, and energy efficiency. He has been on the Editorial Board of the IEEE Transactions on Communications since 2017. He has been a member of the Online Editorial Team of the IEEE Transactions on Wireless Communications since 2020. He has been an Area Editor in IEEE Signal Processing Magazine since 2021. \nHe has performed MIMO research for over 14 years\, his papers have received more than 12000 citations\, and he has filed more than twenty patent applications. He is a host of the podcast Wireless Future and has a popular YouTube channel. He has received the 2014 Outstanding Young Researcher Award from IEEE ComSoc EMEA\, the 2015 Ingvar Carlsson Award\, the 2016 Best Ph.D. Award from EURASIP\, the 2018 IEEE Marconi Prize Paper Award in Wireless Communications\, the 2019 EURASIP Early Career Award\, the 2019 IEEE Communications Society Fred W. Ellersick Prize\, the 2019 IEEE Signal Processing Magazine Best Column Award\, the 2020 Pierre-Simon Laplace Early Career Technical Achievement Award\, and the 2020 CTTC Early Achievement Award. He also co-authored papers that received Best Paper Awards at the conferences\, including WCSP 2009\, the IEEE CAMSAP 2011\, the IEEE SAM 2014\, the IEEE WCNC 2014\, the IEEE ICC 2015\, and WCSP 2017.
URL:https://www.ieeetoronto.ca/event/reconfigurable-intelligent-surfaces-a-signal-processing-perspective/
LOCATION:Montreal\, Quebec Canada
CATEGORIES:Young Professionals
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BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20210531T000000
DTEND;TZID=America/Toronto:20210531T235500
DTSTAMP:20261108T012812
CREATED:20210520T162352Z
LAST-MODIFIED:20210809T204824Z
UID:10000415-1622419200-1622505300@www.ieeetoronto.ca
SUMMARY:AI against COVID-19: Screening X-ray Images for COVID-19 Infections
DESCRIPTION:Join the virtual competition on AI for COVID diagnosis\, thanks to Microsoft Canada\, the exclusive technology and cloud platform sponsor! \n\n\n\nThe coronavirus disease 2019 (COVID-19) pandemic\, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus\, has generated an unprecedented global health crisis\, with more than 2.7 million deaths worldwide. Do you want to contribute to the fight against this pandemic? \nIEEE SIGHT (Special Interest Group on Humanitarian Technology) of Montreal Section\, Vision and Image Processing Research Group of the University of Waterloo and DarwinAI Corp. invite data scientists\, students and professionals working on Artificial Intelligence (AI) to participate in a virtual competition to help medical researchers diagnose COVID-19 with chest X-ray (CXR) images. The ultimate goal is to contribute to the development of highly accurate yet practical AI solutions for detecting COVID-19 cases and\, hopefully\, accelerating the treatment of those who need it the most. Moreover\, this AI for Good initiative will also allow us to take action on at least one of the United Nations Sustainable Development Goals (SDGs)\, Good Health and Well-being. \nIn the First Phase of the competition\, the challenge consists of designing robust machine learning algorithms to predict if the subjects of study are either COVID-19 positive or COVID-19 negative. The dataset for this competition is the dataset curated by COVID-Net\, a global open-source initiative launched by DarwinAI Corp.\, Canada\, and Vision and Image Processing Research Group\, University of Waterloo\, Canada\, for accelerating advancements in machine learning to aid healthcare workers around the world in the fight against the COVID-19 pandemic. More about the COVID-Net initiative and available open-source resources are available here. In the Second Phase\, the 10 top teams of the first phase will have the opportunity to refine their solution and submit a proposal for a follow-up project to positively impact society or the academic community. \nThis competition is organized in collaboration with the National Research Council Canada and is co-hosted by the IEEE Young Professionals Affinity Groups of Montreal\, Ottawa\, Toronto and Vancouver Sections\, Vancouver Circuit and Systems (CAS) Technical Chapter\, the Student Branches of INRS (Institut National de la Recherche Scientifique)\, University of Toronto and Vancouver Simon Fraser University\, WIE (Women In Engineering) Ottawa. It is largely sponsored by Microsoft\, and partially by the IEEE Canada Humanitarian Initiatives Committee and the IEEE Montreal Section. \nHow to participate \nNote: This competition only accepts participants living in Canada\, due to restrictions on funds transfer. \nNO PURCHASE NECESSARY TO ENTER OR WIN. \nThe competition is hosted on the Eval.ai online platform. To participate\, you or your team will need to perform the following steps: \n\nRegister individually at the link provided below in the current webpage (vTools).\nRegister yourself or your team at the link on Eval.ai: https://eval.ai/web/challenges/challenge-page/925/participate. Follow the instructions here: https://evalai.readthedocs.io/en/latest/participate.html#.\nDownload the dataset from https://www.kaggle.com/andyczhao/covidx-cxr2.\nDesign an AI algorithm that gets CXR images as inputs and predicts the labels of the images in the output (COVID or non-COVID).\nTrain your AI algorithm using the training dataset.\nSubmit your AI algorithm through Eval.ai for evaluation against the test dataset for the competition.\n\nPrizes \nFor the First Phase\, the first five best solutions will be awarded monetary prizes and Azure credits: \n\nFirst place: 1\,000 CAD + 500 CAD in Azure.\nSecond place: 800 CAD + 300 CAD in Azure.\nThird place: 600 CAD + 300 CAD in Azure.\nFourth place: 400 CAD + 300 CAD in Azure.\nFifth place: 300 CAD + 300 CAD in Azure.\n\nThe top 10 teams on the leaderboard will also have the following opportunities: \n\nParticipate in the 2nd phase to refine their solution and receive funding for a project.\nWrite a scientific paper with the Vision and Image Processing Research Group\, from the University of Waterloo\, to explain their approach.\n\nFor the Second Phase\, the best three projects can receive funds up to the following amounts: \n\nProject 1: 5\,000 CAD.\nProject 2: 5\,000 CAD.\nProject 3: 4\,000 CAD.\n\nTerm of funding: Up to 4 months following the announcement of the selected teams. The deadline is December 31st\, 2021. \n\nFor more information\, visit IEEE SIGHT Montreal website. \n 
URL:https://www.ieeetoronto.ca/event/ai-against-covid-19-screening-x-ray-images-for-covid-19-infections/
LOCATION:Virtual
CATEGORIES:SIGHT,Young Professionals
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