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  • Intelligent and Secure Integration of Electric Vehicles into the Smart Grid

    Virtual: https://events.vtools.ieee.org/m/309875

    The transition to electric vehicles (EVs) is gaining momentum around the world and the major drivers for this acceleration are the rising awareness by the public for maintaining a clean environment, reducing pollutant emissions, breaking dependencies on oil, as well as tapping into cleaner sources of energies. EVs acceptance however is hindered by several challenges; among them is their shorter driving range, slower charging rates, and the ubiquitous availability of charging locations, collectively contributing to higher anxieties for EVs drivers. To mitigate this anxiety, a naïve approach is to expand the charging network, while an unplanned expansion may challenge the generation, transmission and distribution sector of the grid along with being a potential cyber-physical attack platform. As a consequence, to attain a graceful EV penetration for curtailing GHG emission, along with the socioeconomic initiatives, an extensive research is required, especially to mitigate the range anxiety and ameliorate the load congestion on the grid. Fortunately, the IoT enabled charging ecosystem (i.e., EVs, charging stations, the grid etc.) enables smart and informed charging schemes to exploit the benefit of different distributed energy sources (e.g., renewable energy based standalone chargers, vehicle to grid or vehicle to vehicle energy transfer technology, etc.) to minimize the load burden of the grid. But, on the other hand, this IoT enabled charging ecosystem unveils a new cyber-physical attack surface and hence, new challenges also need to be addressed to make this charging ecosystem secure as well. Virtual: https://events.vtools.ieee.org/m/309875 Speaker: Dr. Mohammad Ekramul Kabir Biography: Dr. Mohammad Ekramul Kabir is currently working as a Horizon postdoctoral research fellow in CIISE at Concordia University, Montreal, Canada. He obtained his PhD on Information and Systems Engineering from Concordia University in May 2021. He has received the B.Sc. and M.S. degree in Applied Physics, Electronics and Communication engineering from University of Dhaka, Bangladesh. His research interests include green, smart, and secure charging of electric vehicle, cloud/edge computing security and applications of artificial intelligence. He is a coauthor of a number of peer-reviewed journal and conference papers. He also serves/served as a reviewer for IEEE Transactions on Transportation Electrification, IEEE Transactions on Vehicular Technology, IEEE Transactions on Mobile Computing, IEEE Transactions on Network and Service Management, IEEE Intelligent Transportation Systems Magazine, IEEE PES General Meeting, etc.

  • Distributed Phased Arrays: Challenges and Recent Progress

    Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/311733

    There has been significant research devoted to the development of distributed microwave wireless systems in recent years. The progression from large, single-platform wireless systems to collections of smaller, coordinated systems on separate platforms enables significant benefits for radar, remote sensing, communications, and other applications. The ultimate level of coordination between platforms is at the wavelength level, where separate platforms operate as a coherent distributed system. Wireless coherent distributed systems operate in essence as distributed phased arrays, and the signal gains that can be achieved scale proportionally to the number of transmitters squared multiplied by the number of receivers, providing potentially dramatic increases in wireless system capabilities. Distributed array coordination requires accurate control of the relative electrical states of the nodes. Generally, such control entails wireless frequency synchronization, phase calibration, and time alignment, but for remote sensing operations, phase control also requires high-accuracy knowledge of the relative positions of the nodes in the array to support beamforming. This lecture presents an overview of the challenges involved in distributed phased array coordination, and describes recent progress on microwave technologies that address these challenges. Requirements for achieving distributed phase coherence at microwave frequencies are discussed, including the impact of component non-idealities such as oscillator drift on beamforming performance. Architectures for enabling distributed beamforming are reviewed, along with the relative challenges between transmit and receive beamforming. Microwave and millimeter-wave technologies enabling wireless phase-coherent synchronization are discussed, focusing on technologies for high-accuracy internode ranging, wireless frequency transfer, and high-accuracy time alignment. The lecture concludes with a discussion of open challenges in distributed phased arrays, and where microwave technologies may play a role. Speaker(s): Prof. Jeffrey Nanzer Register: https://events.vtools.ieee.org/m/311733 Biography: Jeffrey Nanzer (S’02-M’08-SM’14) received the B.S. degree in electrical engineering and computer engineering from Michigan State University, East Lansing, MI, USA, in 2003, and the M.S. and Ph.D. degrees in electrical engineering from The University of Texas at Austin, Austin, TX, USA, in 2005 and 2008, respectively. From 2008 to 2009, he was a Postdoctoral Fellow with Applied Research Laboratories, The University of Texas at Austin, where he was involved in designing electrically small HF antennas and communication systems. From 2009 to 2016, he was with The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA, where he created and led the Advanced Microwave and Millimeter-Wave Technology Section. In 2016, he joined the Department of Electrical and Computer Engineering, Michigan State University, where he is currently the Dennis P. Nyquist Associate Professor. He has authored or co-authored more than 150 refereed journal and conference papers, authored the book Microwave and Millimeter-Wave Remote Sensing for Security Applications (Artech House, 2012), and co-authored chapters in the books Wireless Transceiver Circuits (Taylor and Francis, 2015) and Short-Range Micro-Motion Sensing: Hardware, signal processing and machine learning (IET, 2019). His current research interests include distributed arrays, radar and remote sensing, antennas, electromagnetics, and microwave photonics. Dr. Nanzer was a founding member and the First Treasurer of the IEEE APS/MTT-S Central Texas Chapter. He is also a member of the IEEE Antennas and Propagation Society Education Committee and the USNC/URSI Commission B. He was a recipient of the Outstanding Young Engineer Award from the IEEE Microwave Theory and Techniques Society in 2019, the DARPA Director’s Fellowship in 2019, the National Science Foundation (NSF) CAREER Award in 2018, the DARPA Young Faculty Award in 2017, and the JHU/APL Outstanding Professional Book Award in 2012. He has served as the Vice-Chair for the IEEE Antenna Standards Committee from 2013 to 2015 and the Chair of the Microwave Systems Technical Committee (MTT-16) of the IEEE Microwave Theory and Techniques Society from 2016 to 2018. He is also an Associate Editor of the IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION.

  • Wideband Digital-to-Analog Converters for mmWave Transmitter

    Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/309574

    Over the past ten years, the data rate of cellular communication networks has increased by 100x. The next-generation software-defined-radio based wireless transmission in mmWave bands demands multi-GHz bandwidth digital-to-analog conversion with medium to high resolution (e.g., 14-16 bit) and sampling rates beyond 10GS/s. The rate of bandwidth increase and the required improvements in energy efficiency have exceeded the benefits of CMOS process scaling alone. There are compelling needs for novel architecture and circuit design techniques. In this talk, I will review recent development and present emerging parallel-path DAC architectures for extending the bandwidth with higher power and area efficiency than conventional interleaving designs. I will discuss the practical challenges along with several key analog design techniques. I will conclude with some future directions. At the end of the talk, I will briefly introduce some other research activities in my group, such as low power bioelectronics, neural interfacing and modulation circuits, and machine-learning accelerators. Speaker(s): Dr. Xilin Liu Virtual: https://events.vtools.ieee.org/m/309574 Biography: Dr. Xilin Liu (Senior Member, IEEE) is currently an Assistant Professor at the University of Toronto. He obtained his Ph.D. degree from the University of Pennsylvania. Before joining the University of Toronto in 2021, he held industrial positions at Qualcomm Inc., where he conducted R&D of high-performance mixed-signal circuits for cellular communication. He led and contributed to the IPs that have been integrated into products in high-volume production, including the industry’s first 5G chipset. He was a visiting scholar at Princeton University in 2014. He has co-authored two books along with over 30 peer-reviewed articles. He was the first author of the papers that have received the Best Student Paper Award at the 2017 ISCAS, the Best Paper Award at the 2015 BioCAS, the Best Track Award at the 2014 ISCAS, and the student research preview (SRP) award at 2014 ISSCC. He also received the SSCS predoctoral achievement award at the 2016 ISSCC.

  • Fake News Detection – Students Research in ML and DL at Durham College

    Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/312334

    The term "fake news" was pretty much unknown and unpopular a few decades ago, but it has emerged as a massive monster in the digital era of social media. Fake news is spreading like wildfire these days, and people share it without confirming it. Often, it is to promote or enforce specific views, and it is carried out through political agendas. Fake news refers to news that may or may not be correct and is widely disseminated via social media and other internet platforms. In this digital age, it is not easy to tackle the spread of fake news, where thousands of information-sharing sites via fake news or misinformation can be shared. It has become a greater issue as AI advances, bringing with it artificial bots that may be used to create and propagate fake news. The problem is critical because many individuals believe anything they read on the internet, and those who are inexperienced or new to digital technologies are vulnerable to being misled. Fraud is another issue that can arise as a result of spam or harmful emails and communications. Fake news has grown in popularity and spread as a result of recent political events. Humans are inconsistent, if not outright terrible detectors of fake news, as evidenced by the pervasive effects of the widespread onset of fake news. As a result, efforts have been made to automate detecting fake news. The most prominent of these attempts are "blacklists" of unreliable sources and authors. While these technologies are useful we need to account for more complex instances when trusted sources and authors leak fake news in order to provide a complete end-to-end solution. As a result, the goal of this project was to develop a tool that used machine learning and natural language processing techniques to recognize the language patterns that distinguish fake and true news. The outcomes of this project show that machine learning can be effective in this situation. We developed a model that detects a variety of intuitive indicators of real and fake news and an application to aid in the visual representation of the classification decision. We aim to give users the ability to classify news as fake or real and verify the website legitimacy that published it. Speaker(s): Roshna Babu, Abraham Mathew, Neha Joseph Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/312334

  • Credit Card Fraud Detection – Students Research in ML and DL at Durham College

    Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/312336

    With the new trend of Online Shopping and Online Platforms for transactions, the number of Credit Card based transactions increased tremendously. However, there have been a lot of cases where illegal use of Debit/Credit Cards for making Fraudulent Transactions. Credit card companies have been paying a lot of attention to providing the best service for their customers by having process enhancements and pro-actively looking into transactions before making them through. Global financial losses related to payment cards are estimated to reach $34.66 billion in 2022, according to The Nilson Report, a newsletter that tracks the payment industry. Related to the negative impacts of credit card fraud activities, and financial and product losses, it’s easy for merchants and users to feel victimized and helpless. Machine Learning Models can work well in detecting such Fraudulent actions when they are trained on a large quantity of historical data and then fine-tuned depending on validation and evaluation metrics. Speaker(s): Priyanka Singh, Devy Ratnasari, Gopika Shaji, Oluwole Ayodele, Saurav Bisht, Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/312336

  • Electrification: Extraordinary Opportunities, Extreme Challenges

    Room: 2060, Bldg: Software and Informatics Research Centre, Ontario Tech University, 2000 Simcoe Street North, Oshawa, Ontario, Canada, L1G 0C5

    Despite a head start over 100 years ago, Electrification is only receiving widespread interest recently. See attached poster for more details. This is a hybrid meeting with in-person event shared on the web. http://meet.google.com/xdn-kpji-eyk SIRC Building is on the corner of Conlin Road and Simcoe Rd North. There is parking in the rear of the Building. Co-sponsored by: Ontario Tech University Speaker(s): Rick Szymczyk P.Eng., MBA, Location: 2060, Bldg: Software and Informatics Research Centre, Ontario Tech University, 2000 Simcoe Street North, Oshawa, Ontario, Canada, L1G 0C5

  • Text Summarization of Transcripts from Online Meetings – Students Research in ML and DL at Durham College

    Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/312337

    Text Summarization is a technique for generating a concise and precise summary of voluminous texts while focusing on the sections that convey useful information without losing the overall meaning. It aims to transform lengthy documents into shortened versions, which could be difficult and costly to undertake if done manually. With the current explosion of data circulating in digital space, primarily unstructured textual data, there is a need to develop tools that allow people to get insights from them quickly. In situations where it is essential to keep track of what is being spoken, such as during an online lecture, taking notes is a popular activity used by many. The art of notetaking does not involve making notes of every single word that is spoken but comprehensive outlines of what is discussed. The key to good notetaking lies in making concise yet informative summaries. In this seminar, we will be discussing how we have tried to address the difficulties of notetaking by building an application that produces notes based on transcripts generated by the Automatic Speech Recognition (ASR) technology of the meeting platforms. We experimented with six summarization models for this application, including transformer-based models pre-trained on large corpora. The datasets used for this application are the transcripts dataset acquired from online meeting platforms and the Extreme Summarization (XSum) dataset. We evaluated the models using Rouge metrics (Rouge-1, Rouge-2, and Rouge-L) and selected the best-performing model as the final model. We have built a bot that utilizes Telegram’s API and shares the generated summaries via group chat with the users. Speaker(s): Manoj Varma Alluri, Navaneeth Jawahar, Sharath Kumar Prabhu, Jeel Jani, Shravya Sandupata Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/312337

  • Sentiment Analysis on Twitter Data – Students Research in ML and DL at Durham College

    Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/312338

    The rise of digitalization and the advent of social media and e-commerce have generated an abundance of data than before. Natural Language Processing (NLP) is a significant branch of artificial intelligence that helps the machine interpret human languages and perform the desired task by analyzing the semantics, content, and pattern. Sentiment analysis is the most common technique in Natural Language Processing used to determine the underlying sentiments of a text. This technique is currently in place for different Business Organizations to analyze their brand’s market value, brand reputation, and customer perception of new brand/new change. Businesses use social media channels to cater to their customer service, and people use social media to express/share their wide range of opinions or experiences about a product/brand. These opinions and experiences reflect the real-time sentiments of a customer. Sentiment analysis will help businesses designing an effective marketing campaign, better customer satisfaction, boost sales, help improve customer experience, understand customer perception to change and the brand’s market reputation. The customer views expressed on Twitter, Facebook, and other online forums are forming the base of customer strategy for brands worldwide. Businesses are opting to shift their traditional customer feedback analysis method to text classification since people prefer to post the genuine reviews on the internet. Analyzing the underlying sentiments in the text will help the business to understand their customers' voices and their brand reputation in the market in real-time. Sentiment analysis will help the businesses designing an effective marketing campaign, better customer satisfaction, boost sales, help improve customer experience, understand customer perception to change and the brand’s market reputation. Twitter sentiment analysis aims to classify text into positive/negative based on its underlying semantics. Speaker(s): Akhil Mathew, Anmol Wadera, Deepan Ellenti Padmanabhan, Saketh Vemula, Sivaramakrishna Malakalapalli Register: https://events.vtools.ieee.org/m/312338

  • Higher Order Globally Constraint-Preserving FVTD and DGTD Schemes for Time-Dependent Computational Electrodynamics (Prof. Dinshaw Balsara, U. of Notre-Dame)

    Virtual: https://events.vtools.ieee.org/m/312555

    Adaptive mesh refinement (AMR) is the art of solving PDEs on a mesh hierarchy with increasing mesh refinement at each level of the hierarchy. Accurate treatment on AMR hierarchies requires accurate prolongation of the solution from a coarse mesh to a newly-defined finer mesh. For scalar variables, suitably high order finite volume WENO methods can carry out such a prolongation. However, classes of PDEs, like computational electrodynamics (CED) and magnetohydrodynamics (MHD), require that vector fields preserve a divergence constraint. The primal variables in such schemes consist of normal components of the vector field that are collocated at the faces of the mesh. As a result, the reconstruction and prolongation strategies for divergence constraint-preserving vector fields are necessarily more intricate. In this seminar, we present a fourth order divergence constraint-preserving prolongation strategy that is analytically exact. Extension to higher orders using analytically exact methods is very challenging. To overcome that challenge, a novel WENO-like reconstruction strategy is invented that matches the moments of the vector field in the faces where the vector field components are collocated. This approach is almost divergence constraint-preserving; so we call it WENO-ADP. To make it exactly divergence constraint-preserving, a touch-up procedure is developed that is based on a constrained least squares (CLSQ) based method for restoring the divergence constraint up to machine accuracy. With the touch-up, it is called WENO-ADPT. It is shown that refinement ratios of two and higher can be accommodated. An item of broader interest in this work is that we have also been able to invent very efficient finite volume WENO methods where the coefficients are very easily obtained and the multidimensional smoothness indicators can be expressed as perfect squares. We demonstrate that the divergence constraint-preserving strategy works at several high orders for divergence-free vector fields as well as vector fields where the divergence of the vector field has to match a charge density and its higher moments. We also show that our methods overcome the late time instability that has been known to plague adaptive computations in Computational Electrodynamics. Co-sponsored by: Center for Computational Science and Engineering (CCSE), University of Toronto Speaker(s): Prof. Dinshaw Balsara, Register: https://events.vtools.ieee.org/m/312555 Biography: Dinshaw S. Balsara received the Ph.D. degree in computational physics and astrophysics from the University of Illinois at Urbana-Champaign, Champaign, IL, USA, in 1990. He is currently a Professor with the Department of Physics and the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He has developed computational algorithms and applications in the areas of interstellar medium, turbulence, star formation, planet formation, the physics of accretion disks, compact objects, and relativistic astrophysics. Many of the algorithms developed by him for higher order methods have seen extensive use and have been copiously cited.,Dr. Balsara was the recipient of the 2014 Department of Energy Award of Excellence for significant contributions to the Stockpile Stewardship Program and the 2017 Global Initiative on Academic Networks Award from the Government of India. He serves the community as an Associate Editor of Journal of Computational Physics and Computational Astrophysics and Cosmology.

  • DDoS Detection System – Students Research in ML and DL at Durham College

    toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/312339

    The research goal is to implement different machine learning algorithms to detect any DDoS (Distributed Denial of Service) attacks using the UNSW-NB15 dataset. We started by going through the data description and finding null values in our features. After that we dropped the ‘id’ column. We have used the UNSW-15 dataset for AI-based DDOS detection systems. The UNSW-15 dataset has a hybrid of the real modern normal and the contemporary synthesized attack activities of the network traffic. It contains different attacks, including DoS, worms, Backdoors etc. The raw network packets of the UNSW-NB 15 datasets are created by the IXIA Perfect Storm tool in the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS) for generating a hybrid of real modern normal activities and synthetic contemporary attack behaviours. We incorporated different feature selection methods for dropping insignificant features followed by the implementation of 6 classification algorithms, namely Naive Bayes, Random Forest, Decision Tree, KNN, Logistic Regression and SVM. Speaker(s): Minu Ahlawat, Dwij Dua, Megha Garg, Taxil Savani Register: https://events.vtools.ieee.org/m/312339

  • Humber Amateur Radio certification study Tuesday online

    Etobicoke, Quebec, Canada, M9V4A9, Virtual: https://events.vtools.ieee.org/m/312260

    Tuesday night Online Study Group preparing for the Canadian Amateur Radio certification exam. 2hrs/week Course based on the certification study guide from https://www.coaxpublications.ca/ord0001.php Purchase the book if you are serious about learning this. Course continues depending on registration. Course is free. Available to anyone. Course will probably last until December 2022. Breaks for Humber Midterm exams, final exams and reading weeks Etobicoke, Quebec, Canada, M9V4A9, Virtual: https://events.vtools.ieee.org/m/312260

  • Conceiving Noise: Transformation from Disturbing Sounds to Informational Errors, 1900-1955

    Virtual: https://events.vtools.ieee.org/m/313075

    The Communications Group at the University of Toronto, in collaboration with the IEEE Communications Society, Toronto Chapter are happy to host the seminar titled "Conceiving Noise: Transformation from Disturbing Sounds to Informational Errors, 1900-1955" given by Prof. Chen-Pang Yeang, from the Institute for the History and Philosophy of Science and Technology, University of Toronto. In this talk, Prof. Yeang examine the historical origin of the attempts to understand, control, and use noise at modern times.  Today, the concept of noise is employed to characterize random fluctuations in general.  Before the twentieth century, however, noise only meant disturbing sounds.  In the 1900s-50s, noise underwent a conceptual transformation from unwanted sounds that needed to be domesticated into a synonym for errors and deviations on all kinds of signals and information. Prof. Yeang argue that this transformation proceeded in four stages.  The rise of sound reproduction technologies—phonograph, telephone, and radio—in the 1900s-20s prompted engineers to tackle unwanted sounds as physical effects of media through quantitative representations and measurements.  Around the same time, physicists developed a theory of Brownian motions for random fluctuations and applied it to electronic noise in thermionic tubes of telecommunication systems.  These technological and scientific backgrounds led to three distinct theoretical treatments of noise in the 1920s-30s: statistical physicists’ studies of Brownian fluctuations’ temporal evolution, radio engineers’ spectral analysis of atmospheric disturbances, and mathematicians’ measure-theoretic formulation.  Finally, during and after World War II, researchers working on the military projects of radar, gunfire control, and secret communications converted the interwar theoretical studies of noise into tools for statistical detection, estimation, prediction, and information transmission.  In so doing, they turned noise into an informational concept.  Since the grappling of noise involved multiple disciplines, its history sheds light on the interactions between physics, mathematics, mechanical technology, electrical engineering, and information and data sciences in the twentieth century. Speaker(s): Prof. Chen-Pang Yeang Register: https://events.vtools.ieee.org/m/313075 Biography: Prof. Chen-Pang Yeang is an associate professor at the Institute for the History and Philosophy of Science and Technology, University of Toronto.  Trained both in electrical engineering and the history of science and technology, he does research and teaching in the history of physics, electrical engineering, information and computer science and technology in the 20th and 21st centuries.  He published Probing the Sky with Radio Waves: From Wireless Technology to the Development of Atmospheric Science (University of Chicago Press, 2013).  He is completing a book on the history of noise.  In addition, he is undertaking a research project that uses the material replication of Heinrich Hertz’s radio-wave experiment as a means of historical inquiry, and another project on the grassroots innovation in information and computing technology in the US and China.