IEEE Seneca is offering an advanced OrCad Workshop. We will be reinforcing ETD555 concepts and learn about following topics: Transistor circuits (PNP, NPN, Darlington and MOSFETS) Components such as IRF840, IRF9510, TIP122, TIP127, 2N3904, and 2N3906 To amplify the experience, please have OrCad installed or using virual commons to follow through the instructions. Contact: IEEE Seneca Speakers: Gabriel Chen, Adi Malihi
Virtual platform will be delivered to registrants a couple of hours before starting the event. Contact: IEEE Montreal Young Professionals Abstract: Rate Splitting Multiple Access (RSMA), based on (linearly or nonlinearly) precoded Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers, has emerged as a novel, general and powerful framework for the design and optimization of non-orthogonal transmission, multiple access, and interference management strategies in future MIMO wireless networks. RSMA relies on the split of messages and the non-orthogonal transmission of common messages decoded by multiple users, and private messages decoded by their corresponding users. This enables RSMA to softly bridge and therefore reconcile the two extreme strategies of fully decode interference and treat interference as noise. RSMA has been shown to generalize, and subsume as special cases, four seemingly different strategies, namely Space Division Multiple Access (SDMA) based on linear precoding (currently used in 5G), Orthogonal Multiple Access (OMA), Non-Orthogonal Multiple Access (NOMA) based on linearly precoded superposition coding with SIC, and physical-layer multicasting. RSMA boils down to those strategies in some specific conditions, but outperforms them all in general. Through information and communication theoretic analysis, RSMA is shown to be optimal (from a Degrees-of-Freedom region perspective) in a number of scenarios and provides significant room for spectral efficiency, energy efficiency, fairness, reliability, QoS enhancements in a wide range of network loads and user deployments, robustness against imperfect Channel State Information at the Transmitter (CSIT), as well as feedback overhead and complexity reduction over conventional strategies used in 5G. The benefits of RSMA have been demonstrated in a wide range of scenarios (MU-MIMO, massive MIMO, multi-cell MIMO/CoMP, overloaded systems, NOMA, multigroup multicasting, mmwave communications, communications in the presence of RF impairments and superimposed unicast and multicast transmission, relay,…) and systems (terrestrial, cellular, satellite, …). Thanks to its versatility, RSMA has the potential to tackle challenges of modern communication systems and is a gold mine of research problems for academia and industry, spanning fundamental limits, optimization, PHY and MAC layers, and standardization. This lecture will share key principles of RSMA, recent developments, emerging applications and opportunities of RSMA for 6G networks and will cover many of the topics currently investigated as part of the new IEEE special interest group on RSMA https://sites.google.com/view/ieee-comsoc-wtc-sig-rsma/home. Speaker(s): Bruno Clerckx Biography: Bruno Clerckx is a (Full) Professor, the Head of the Wireless Communications and Signal Processing Lab, and the Deputy Head of the Communications and Signal Processing Group, within the Electrical and Electronic Engineering Department, Imperial College London, London, U.K. He received the M.S. and Ph.D. degrees in applied science from the Université Catholique de Louvain, Louvain-la-Neuve, Belgium, in 2000 and 2005, respectively. From 2006 to 2011, he was with Samsung Electronics, Suwon, South Korea, where he actively contributed to 4G (3GPP LTE/LTE-A and IEEE 802.16m) and acted as the Rapporteur for the 3GPP Coordinated Multi-Point (CoMP) Study Item. Since 2011, he has been with Imperial College London, first as a Lecturer from 2011 to 2015, Senior Lecturer from 2015 to 2017, Reader from 2017 to 2020, and now as a Full Professor. From 2014 to 2016, he also was an Associate Professor with Korea University, Seoul, South Korea. He also held various long or short-term visiting research appointments at Stanford University, EURECOM, National University of Singapore, The University of Hong Kong, Princeton University, The University of Edinburgh, The University of New South Wales, and Tsinghua University.
TeejLab’s mission: Protect the privacy, security, and integrity of APIs at a global scale by building Data Science and Artificial Intelligence driven API management solutions to help enterprises with API Governance. Learn more about TeejLab: https://apidiscovery.teejlab.com. Contact: Mehrdad Tirandazian Abstract: Software development is becoming increasingly reliant on using third-party services accessed through APIs. These APIs connect various IT systems and processes with people to offer useful services that help us run our businesses and personal lives. API integration may be simple, but APIs may directly or indirectly expose your IT assets and Databases to unofficial or illegitimate use. This talk aims to help students understand the overall implications of API, including information security, data management, legal risk management, and licensing costs. Speaker(s): Dr. Baljeet Baljeet of TeejLab Biography: Dr. Malhotra is an award-winning researcher known for his work in Open Source and API data management. He conceptualized the world's first "API Composition Analysis" based on source code static analysis. He founded TeejLab in 2017 and steered the team to build, API Discovery™, world's first comprehensive end-to-end API Management platform. He also established R&D unit of Black Duck Software in 2016 (acquired for US $565M by Synopsys). Previously, he was Research Director at SAP (2011-2016), Computational Scientist at the EOS Lab (2009) and Software Engineer at Satyam Computers (1999). He received a PhD in Computing Science from the University of Alberta. He was awarded NSERC (Canada) scholar in 2005, and Global Young Scientist (Singapore) in 2011. He concurrently holds Adjunct Professor positions at the University of British Columbia, University of Victoria and University of Northern BC.
Join the IEEE Kingston Communications Society Chapter for the Virtual Distinguished Lecture: Localization in Drone Assisted and Vehicular Networks, presented by Shahrokh Valaee. Contact: IEEE Kingston ComSoc Abstract: The next generation of wireless systems will employ networking equipment mounted on mobile platforms, unmanned air vehicles (UAV), and low orbit satellites. As a result, the topology of 6G wireless technology will extend to 3D vertical networking. With its extended service, 6G will also give rise to new challenges which include, the introduction of intelligent reflective surfaces (IRS), the mmWave spectrum, the employment of massive MIMO systems, and the agility of networks. Along with the advancement in networking technology, user devices are also evolving rapidly, with the emergence of highly capable cellphones, smart IoT equipment, and wearable devices. One of the key elements of 6G technology is the need for accurate positioning information. The accuracy of today’s positioning systems is not acceptable for many applications of future, especially in smart environments. In this talk, we will discuss how positioning can be a key enabler of 6G, and what challenges the next generation of localization technology will face when integrated within the new wireless networks. Speaker(s): Shahrokh Valaee Biography: Shahrokh Valaee is a Professor with the Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, and the holder of Nortel Chair of Network Architectures and Services. He is the Founder and the Director of the Wireless and Internet Research Laboratory (WIRLab) at the University of Toronto. Professor Valaee was the TPC Co-Chair and the Local Organization Chair of the IEEE Personal Mobile Indoor Radio Communication (PIMRC) Symposium 2011. He was the TCP Chair of PIMRC2017, the Track Co-Chair of WCNC 2014, the TPC Co-Chair of ICT 2015. He has been the guest editor for various journals. He was a Track Co-chair for PIMRC 2020 and VTC Fall 2020. From December 2010 to December 2012, he was the Associate Editor of the IEEE Signal Processing Letters. From 2010 to 2015, he served as an Editor of IEEE Transactions on Wireless Communications. Currently, he is an Editor of Journal of Computer and System Science. Professor Valaee is a Fellow of the Engineering Institute of Canada, and a Fellow of IEEE.
IEEE 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 you to the closing ceremony of the virtual competition on AI for COVID-19 diagnosis with chest X-ray images. In the First Phase, the challenge consisted of designing robust machine learning algorithms to predict if the subjects of study are either COVID-19 positive or COVID-19 negative. Join us to celebrate the amazing work done by all the teams and know who will be participating in the Second Phase. Then, you are also invited to a networking session with everybody! All the information will be sent to the registrants.
Join the IEEE Kingston ComSoc and IEEE Toronto ComSoc for the Virtual Distinguished Lecture "Intelligent Reflected Surfaces for Future Wireless Systems", presented by Dr. Shahid Mumtaz. Contact: IEEE Kingston ComSoc Abstract: As we have finalized the research for 5G, now there is a race for technologies that will conquer 6G. The 6G technologies will achieve much better latency and computation efficiency as compared to 5G. From 1G to 5G, almost all research and standardization randomly model the wireless channel between transmitter and receiver. There is no control of humans over a wireless medium, as it is given by nature. In 6G, we will break this assumption and go from random wireless channels to controllable wireless. Thanks to Intelligent Reflected Surfaces for Future Wireless System(IRS). This talk will explain in detail the physics of metasurface and the progress of IRS till today. This talk will also present different use case, study cases, signal processing and communication techniques for IRS, standardization, Prototype and testbed, and the open research challenges. Speaker(s): Dr. Shahid Mumtaz Biography: Shahid Mumtaz is an IET Fellow, IEEE ComSoc and ACM Distinguished speaker, recipient of IEEE ComSoC Young Researcher Award (2020), IEEE Senior member, founder and EiC of IET “Journal of Quantum communication”, Vice-Chair: Europe/Africa Region- IEEE ComSoc: Green Communications & Computing society and Vice-chair for IEEE standard on P1932.1: Standard for Licensed/Unlicensed Spectrum Interoperability in Wireless Mobile Networks. He has more than 15 years of wireless industry/academic experience. He has received his Master's and Ph.D. degrees in Electrical & Electronic Engineering from Blekinge Institute of Technology, Sweden, and University of Aveiro, Portugal in 2006 and 2011, respectively. From 2002 to 2003, he worked for Pak Telecom as System Engineer and from 2005 to 2006 for Ericsson and Huawei at Research Labs in Sweden. He has been with Instituto de Telecomunicações since 2011 where he currently holds the position of Auxiliary Researcher and adjunct positions with several universities across the Europe-Asian Region. He is the author of 4 technical books, 12 book chapters, 250+ technical papers (170+ Journal/transaction, 90+ conference, 2 IEEE best paper award- in the area of mobile communications. He had/has supervised/co-supervising several Ph.D. and Master Students. He uses mathematical and system-level tools to model and analyze emerging wireless communication architectures, leading to innovative theoretically optimal new communication techniques. He is working closely with leading R&D groups in the industry to transition these ideas to practice. He secures the funding of around 2M Euro.
The lecture is composed of two one-hour parts. In Part I a general overview of SSR/IFF is presented which includes a review of terms and definitions. From there, a historical timeline of SSR/IFF is summarized beginning with early implementations and ending with modern day systems. Then system architectures are reviewed starting with block diagrams and the challenges of scanning airspace. System-level features discussed include sidelobe suppression, antenna dwell time, azimuth determination and RF link budgets. In addition, the trade-offs between 2-channel and 3-channel systems are reviewed. Link to virtual event will be provided after registration. Contact: IEEE Long Island CAS Society Speaker(s): Frank Messina Biography: Frank Messina is the Chief Engineer of the SSR and IFF products for Telephonics. Frank has 50 years of experience in the design, development, and fielding of innovative IFF and SSR products for Military and Civil use. Frank is the lead IFF Interrogator Systems Engineer for the world’s fleet of AWACS aircraft, US Navy P8-A Multi-Mission Aircraft (MMA), US Navy MH-60R aircraft, Canadian Maritime Patrol Aircraft (CP140), Canadian Maritime Helicopter (MHP), Canadian Frigate Upgrade, USMC G/ATOR, USAF D-RAPCON, Mode 5 Operational Autonomous Surveillance (M5 OAS), SAAB Giraffe Mobile Platforms and other ground, shipboard and airborne based products at Telephonics. Earlier in his career, Frank was the lead engineer for the FAA Common Digitizer 2 (CD-2) SSR Beacon Extractor System. Frank was also instrumental in adding full Mode S interrogator capability to the NATO AWACS, which represents the first military IFF interrogator system to integrate the high-priority AEW Military IFF Modes with Mode S. He was also the IFF team leader for the design and development of the AN/APS-147 and AN/APS-153 IFF interrogator system – the first integrated and tightly-coupled Multi-Mode Radar and IFF interrogator fusion system. More recently, Frank lead the design and development of the AN/UPR-4(V) Passive Detection and Reporting System (PDRS) and Small Form Factor SFF-44 All-Mode Active and Passive IFF system.
Join us on Thursday, June 24, 2021 for the IEEE VDL: Learning to Learn to Communicate, presented by Prof. Osvaldo Simeone. Contact: IEEE Kingston ComSoc Abstract: The application of supervised learning techniques for the design of the physical layer of a communication link is often impaired by the limited amount of pilot data available for each device; while the use of unsupervised learning is typically limited by the need to carry out a large number of training iterations. In this talk, meta-learning, or learning-to-learn, is introduced as a tool to alleviate these problems. The talk will consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols over a fading channel. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the transmission-side distortion. To tackle this problem, pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Various state-of-the-art meta-learning schemes are adapted to the problem at hand and evaluated, including MAML, FOMAML, REPTILE, and CAVIA. Both offline and online solutions are developed. Speaker(s): Prof. Osvaldo Simeone Biography: Osvaldo Simeone is a Professor of Information Engineering with the Centre for Telecommunications Research at the Department of Engineering of King's College London, where he directs the King's Communications, Learning and Information Processing lab. He received an M.Sc. degree (with honors) and a Ph.D. degree in information engineering from Politecnico di Milano, Milan, Italy, in 2001 and 2005, respectively. From 2006 to 2017, he was a faculty member of the Electrical and Computer Engineering (ECE) Department at New Jersey Institute of Technology (NJIT), where he was affiliated with the Center for Wireless Information Processing (CWiP). His research interests include information theory, machine learning, wireless communications, and neuromorphic computing. Dr Simeone is a co-recipient of the 2019 IEEE Communication Society Best Tutorial Paper Award, the 2018 IEEE Signal Processing Best Paper Award, the 2017 JCN Best Paper Award, the 2015 IEEE Communication Society Best Tutorial Paper Award and of the Best Paper Awards of IEEE SPAWC 2007 and IEEE WRECOM 2007. He was awarded a Consolidator grant by the European Research Council (ERC) in 2016. His research has been supported by the U.S. NSF, the ERC, the Vienna Science and Technology Fund, as well as by a number of industrial collaborations. He currently serves in the editorial board of the IEEE Signal Processing Magazine and is the vice-chair of the Signal Processing for Communications and Networking Technical Committee of the IEEE Signal Processing Society. He was a Distinguished Lecturer of the IEEE Information Theory Society in 2017 and 2018. Dr Simeone is a co-author of two monographs, two edited books published by Cambridge University Press, and more than one hundred research journal papers. He is a Fellow of the IET and of the IEEE.
On Monday, June 28, 2021, come listen to Dr. Shiwen Mao present the IEEE ComSoc VDL: Machine Learning for Wireless Communications and Networking: Motivations, Case Studies, and Open Problems. ZOOM link will be provided to attendees. Contact: IEEE Denver ComSoc Abstract: While 5G deployment is being carried out in many places of the world, there has been great interest in the prospects of 5G beyond and the next generation. Among the various visions, a common theme is that artificial intelligence will play a key role, as evidenced by the great interest and advances in machine learning enabled wireless communications and networking. In this talk, we will discuss the motivation, potential, and challenges of incorporating machine learning in wireless communications and networking for 5G and beyond systems. We will start with two motivating examples, i.e., channel estimation and mobile edge computing, to show why machine learning could be helpful. We will share our experience of several case studies, including (i) a hybrid approach to the classical energy efficiency maximization problem, where traditional models could be used to train a deep learning model; (ii) data augmentation for convolutional neural network (CNN) based automatic modulation classification (AMC), where a conditional generative adversarial network (CGAN) is utilized to generate synthesized training data; and (iii) and an adaptive model for RFID-based 3D human skeleton tracking, which utilizes meta-learning and few-shot fine-tuning to achieve high adaptability to new environments. We will conclude this talk with a discussion of challenges and open problems. Speaker(s): Dr. Shiwen Mao Biography: Shiwen Mao received his Ph.D. in electrical engineering from Polytechnic University, Brooklyn, NY in 2004. He was a postdoc at Virginia Tech from 2004 to 2006, and joined Auburn University, Auburn, AL as an assistant professor of Electrical and Computer Engineering in 2006. He held the McWane Endowed Professorship from 2012 to 2015 and the Samuel Ginn Endowed Professorship from 2015 to 2020. Currently, he is a professor and Earle C. Williams Eminent Scholar Chair, and Director of the Wireless Engineering Research and Education Center at Auburn University. His research interest includes wireless networks, multimedia communications, and smart grid. He is on the editorial board of several IEEE and ACM journals. He is a Distinguished Lecturer of IEEE Communications Society and IEEE Council of RFID, and a Distinguished Speaker of IEEE Vehicular Technology Society. He received the IEEE ComSoc TC-CSR Distinguished Technical Achievement Award in 2019 and NSF CAREER Award in 2010. He is a co-recipient of the 2021 IEEE Communications Society Outstanding Paper Award and the IEEE Vehicular Technology Society 2020 Jack Neubauer Memorial Award. Agenda: 6pm (MT) - Introductions 6:10-7:15 - VDL Presentation 7:15-7:30 - Q&A
We are living in the age of innovation. Every day, innovators are solving many problems that people are facing in life. In the process of innovation, there are many questions about how we can find problems. What is innovation exactly? How can we find solutions? And how can we learn the innovation process? I am Masoud Valinejad, CEO-Director of technology in NovoSolTech Company, and innovation mentor with more than five-year experience, with 10 USA patents, and more than five national and international special prizes in innovation competitions. In this webinar, I want to show you how you can become an innovator and entrepreneur through some steps and practices. Contact: Ayda Naserialiabadi
This is an introduction to Python programming for students without any prior programming knowledge or experience. The proposed 5-day course covers the fundamental aspects of programming, which include data types, various operators, input/output, conditions, control flow, functions, and algorithms. The learning experience is enhanced by a number of examples and problem sets (data, strings, file processing and simple graphics) that will be solved interactively during the lecture with the participation of the students. The course format includes 3 hours of daily lectures (2 hours of lecture and 1 hour of lab). A certificate of completions will be given to the student who successfully complete the course and pass a short exam at the end of the course to evaluate their knowledge. Electronic copies of the course materials will be provided to the students. The students will also be provided with career advice, and skills development. The course is delivered online and limited space (25 spots) is available. Please register by July 11. After the registration, applicants will be contacted with the virtual meeting information and course material prior to the start of the course. Fees: - $250 CAD (IEEE or OSPE Members) - $350 CAD (Non-members) Please follow IEEE on Social Media: https://twitter.com/ieeetoronto https://www.ieeetoronto.ca/ Course Objective: Attendees will gain a solid understanding of principles of programing using Python; they can progress to more advanced programming topics and explores algorithms that are integral parts of more sophisticated methodologies, e.g., Artificial Intelligence. Attendees will have the knowledge to write various Python programs, and to design algorithms manipulating files and different types of data including numbers, and text. Note: This course is designed to be offered online, and it requires the attendees to use their personal computers/laptops. Details to Join in will be forwarded to Registered Attendees Who should attend: Students, second career trainees, engineers, scientists, clinicians, and in general specialists in variety of non-STEM fields. What will you receive after completion: A certificate of completion will be given to the students who successfully complete the course and pass a short exam. Electronic copies of the course materials. Attendees will also be provided with career, and skills development advice. Speaker Dr. Alireza Sadeghian Dr. Alireza Sadeghian has been with the Department of Computer Science at Ryerson University since 1999, where he holds the position of the Professor. He is also an Affiliate Scientist at the Li Ka Shing Knowledge Institute, St. Michael’s Hospital, and serves as the AI research Theme Lead in Healthcare and Analytics at the Institute for Biomedical Engineering, Science, and Technology. Dr. Sadeghian was the Chair of the Department of Computer Science from 2005 to 2015. He is the founding Director of the Advanced Artificial Intelligence Initiative (AI2) Laboratory and has extensive expertise in the areas of AI, machine learning, and Deep Learning particularly related to industrial and medical applications. He has supervised 9 postdoctoral fellows, 8 PhD, and 24 Master’s students, as well as 60 research assistants. He has published over 150 journal manuscripts, refereed conference papers, and book chapters, as well as two edited books. He has 2 invention disclosures and 2 patents. Dr. Sadeghian has been actively involved with a number of international professional and academic boards including IEEE Education Activity Board. Presently, he is the Chair of IEEE Computational Intelligence Technical Society Chapter, Toronto Section. Dr. Sadeghian is also on the Editorial Board of Applied Soft Computing Journal and serves as an Associate Editor of IEEE Access, Information Sciences, and Expert Systems Journal. Email: dr.alireza.sadeghian@ieee.org Agenda Day 1 – June 7, 2021, 6:00-9:00 pm: Introduction to computer systems, hardware architecture, CPU, memory, compilation, high level vs. low-level programming language, data representation, Python and PyCharm interactive IDE installation, writing/editing/saving/retrieving and running a simple program, basic data types, variables, assignments, comments, and expressions. The material learned will be reinforced through examples provided during the lecture. Day 2 – June 8, 2021, 6:00-9:00 pm: The following topics will be discussed: conditions, operators (arithmetic, logic, and comparison), control statements (if and if-else), and loops (for and while). The material learned will be reinforced through examples provided during the lecture. Day 3 – June 9, 2021, 6:00-9:00 pm: Students will be introduced to Strings and text files in Python. They will learn how to work with files, reading/writing text and numbers from/to a file, string manipulation, indexing, and string slicing. The material learned will be reinforced through examples provided during the lecture. Day 4 – June 10, 2021, 6:00-9:00 pm: Functions, arguments, and return values will be discussed. The material learned will be reinforced through examples provided during the lecture. Day 5 – June 11, 2021, 6:00-9:00 pm: The topics of lists and dictionaries will be discussed. Students will learn about the basic operators, creating, accessing, slicing, adding, removing, replacing, and iteration methods for lists and dictionaries. The material learned will be reinforced through examples provided during the lecture.
The FRDM-K64 board is a development board designed around the Kinetis K64 MCU. This IEEE Seneca workshop is intended to demonstrate some of the basic functionality of the ARM Mbed platform and to offer assistance with Seneca course content related to microcontrollers and embedded systems. Bring your code, design ideas, or technical problems and discuss the best path forward with the workshop host and your fellow students. Speaker(s): John Hooper, Adi Malihi Virtual: https://events.vtools.ieee.org/m/276197
