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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220606T160000
DTEND;TZID=America/New_York:20220606T180000
DTSTAMP:20260417T050953
CREATED:20220606T205930Z
LAST-MODIFIED:20220606T205930Z
UID:10000541-1654531200-1654538400@www.ieeetoronto.ca
SUMMARY:Python Development 101 (05 out of 06)
DESCRIPTION:Python Development 101 continues with the IEEE Toronto Magnetics chapter and WIE. \nSpeaker(s): Reza Dibaj \nRegister: https://events.vtools.ieee.org/m/314668
URL:https://www.ieeetoronto.ca/event/python-development-101-05-out-of-06/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/314668
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220603T160000
DTEND;TZID=America/New_York:20220603T180000
DTSTAMP:20260417T050953
CREATED:20220606T205843Z
LAST-MODIFIED:20220606T205843Z
UID:10000540-1654272000-1654279200@www.ieeetoronto.ca
SUMMARY:C# Development 101 (04 out of 06)
DESCRIPTION:C# Development 101 continues with the Magnetics chapter and WIE. \nSpeaker(s): Reza Dibaj \nRegister: https://events.vtools.ieee.org/m/314672
URL:https://www.ieeetoronto.ca/event/c-development-101-04-out-of-06/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/314672
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220603T160000
DTEND;TZID=America/New_York:20220603T170000
DTSTAMP:20260417T050953
CREATED:20220526T190131Z
LAST-MODIFIED:20220603T104757Z
UID:10000534-1654272000-1654275600@www.ieeetoronto.ca
SUMMARY:C/ID: A Design Methodology for Implementing Nanoscale Analog FET Circuits.
DESCRIPTION:Most of the existing circuit design methodologies are based on iterative methods\, which are very time consuming and sometimes far from being optimal. The process of analog circuit design is generally so complex that most designers rely mainly on their own intuition to design and move toward an acceptable design point\, which in many cases is based on a long process of trial-and-errors. There are two dominant circuit design methodologies used in academic institutions and industry: (1) Inversion-Coefficient (IC) method\, and (2) Gm/IDS (GmID) approach. While IC method is more analytical\, GmID require extensive device characterizations in order to create a comprehensive data-base describing device behavior in all modes of operations for different device sizes. Meanwhile\, designers need to develop their own optimization scripts to search through all possible design points and select the best fit for their application\, as these methodologies are not supported by the common EDA Tools. \nIn this seminar\, an improved design methodology will be introduced\, which lies somewhere between the two approaches. Called C/IDS\, the proposed design methodology requires prior knowledge on only few technology-dependent parameters\, which are very easy to extract. Due to its analytical nature\, this approach provides comprehensive design insight\, while the flow of design can be automatized easily. Several examples will be provided to show effectiveness of the proposed algorithm for implementing energy and power efficient circuits. A set of data points demonstrating how performance of analog circuits evolve with technology scaling will be provided. \nSpeaker(s): Armin Tajalli \nRegister: https://events.vtools.ieee.org/m/314527 \nBiography:Armin Tajalli received his B.S. from Sharif University of Technology\, Tehran\, Iran\, and the Ph.D. from Swiss Federal Institute of Technology (EPFL)\, Lausanne\, Switzerland. He was part of the initiating team and a Senior Analog Architect with Kandou Bus\, Lausanne\, Switzerland\, where he is currently the lead of the R&D Department. Since December 2017\, he has joined as an Assistant Professor to the University of Utah\, Salt Lake City\, USA. He has published more than 90 articles in peer reviewed journals and conferences and holds 40 patents. He has received several awards\, including The Best Paper Award in DesignCon (2016)\, PhD Prime Award at EPFL\, Switzerland (2010)\, and IEEE AMD/CICC Scholarship (2009). He is currently serving as a Technical Program Committee (TPC) Member in IEEE CICC and ESSCIRC\, and an Associate Editor of the IEEE Transactions on VLSI Systems.
URL:https://www.ieeetoronto.ca/event/c-id-a-design-methodology-for-implementing-nanoscale-analog-fet-circuits/
LOCATION:Virtual: https://events.vtools.ieee.org/m/314527
CATEGORIES:Circuits & Devices
ORGANIZER;CN="Wagih Ismail":MAILTO:wagih.ismail@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220530T160000
DTEND;TZID=America/New_York:20220530T180000
DTSTAMP:20260417T050953
CREATED:20220606T205746Z
LAST-MODIFIED:20220606T205746Z
UID:10000539-1653926400-1653933600@www.ieeetoronto.ca
SUMMARY:Python Development 101 (04 out of 06)
DESCRIPTION:Python Development 101 continues with the IEEE Toronto Magnetics chapter and WIE. \nSpeaker(s): Reza Dibaj \nRegister: https://events.vtools.ieee.org/m/314667
URL:https://www.ieeetoronto.ca/event/python-development-101-04-out-of-06/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/314667
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220527T180000
DTEND;TZID=America/New_York:20220527T200000
DTSTAMP:20260417T050953
CREATED:20220526T185938Z
LAST-MODIFIED:20220528T100255Z
UID:10000533-1653674400-1653681600@www.ieeetoronto.ca
SUMMARY:Data Visualization using Tableau
DESCRIPTION:Join the IEEE Toronto Magnetics Chapter and Women In Engineering for this two-hour data visualization workshop! \nVisualization is an indispensable part of today’s data science\, and Tableau is one of the most common tools for visualization. In a two-hour workshop technical presentation\, we will quickly go through the fundamentals of Tableau visualization. \nSpeaker(s): Dr. Reza Dibaj \nVirtual: https://events.vtools.ieee.org/m/315202
URL:https://www.ieeetoronto.ca/event/data-visualization-using-tableau/
LOCATION:Virtual: https://events.vtools.ieee.org/m/315202
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220527T160000
DTEND;TZID=America/New_York:20220527T180000
DTSTAMP:20260417T050953
CREATED:20220606T204156Z
LAST-MODIFIED:20220606T204156Z
UID:10000537-1653667200-1653674400@www.ieeetoronto.ca
SUMMARY:C# Development 101 (03 out of 06)
DESCRIPTION:IEEE Toronto’s Magnetic Chapter and WIE C# Development 101 continues. \nSpeaker(s): Reza Dibaj \nRegister: https://events.vtools.ieee.org/m/314671
URL:https://www.ieeetoronto.ca/event/c-development-101-03-out-of-06/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/314671
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220526T200000
DTEND;TZID=America/New_York:20220526T213000
DTSTAMP:20260417T050953
CREATED:20220526T185754Z
LAST-MODIFIED:20220526T185803Z
UID:10000532-1653595200-1653600600@www.ieeetoronto.ca
SUMMARY:MATLAB Deep learning Seminar
DESCRIPTION:Join MathWorks and IEEE to learn Deep-Learning Seminar. Dr. Aycan Hacioglu (MathWorks) will demonstrate how to manage\, automated labelling and augment large data sets. We will also show you how to leverage pre-trained models such as GoogLeNet\, ResNet for transfer learning and more! \nCo-sponsored by: Vancouver Section Affinity Group\,YP \nVirtual: https://events.vtools.ieee.org/m/314598
URL:https://www.ieeetoronto.ca/event/matlab-deep-learning-seminar/
LOCATION:Virtual: https://events.vtools.ieee.org/m/314598
CATEGORIES:Young Professionals
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220519T140000
DTEND;TZID=America/New_York:20220519T150000
DTSTAMP:20260417T050953
CREATED:20220518T190956Z
LAST-MODIFIED:20220524T100554Z
UID:10000364-1652968800-1652972400@www.ieeetoronto.ca
SUMMARY:Navigating the Tech Transfer Process for Researchers
DESCRIPTION:IEEE Canada Industry Relations together with the Toronto section present you the following talk. \nThis session will be presented by Jim Banting\, Assistant Vice-Principal (Partnerships and Innovation) at Queen’s University. Topics will include understanding your University’s IP policy\, writing an invention disclosure\, working with the technology your transfer office\, the importance of a ‘title’ check (checking to ensure IP rights were not given away in a research contract\, for example)\, market and patentability assessment\, drafting and filing a patent application\, terms for licensing a technology from University to NewCo\, sources of funding (dilutive/non-dilutive)\, and an overview of the innovation ecosystem (e.g. accelerators\, incubators and other programs to support startup growth). \nSpeaker(s): Jim Banting \nRegister: https://events.vtools.ieee.org/m/311734 \nBiography: Jim Banting is the Assistant Vice-Principal (Partnerships and Innovation) at Queen’s University. His career began as a co-founder of the Queen’s spin-off company named Vaxis Therapeutics. Vaxis was venture capital-funded\, grown\, and sold to a U.S. specialty pharmaceutical company.  A large portion of Jim’s career has entailed a focus on partnerships\, licensing\, and M&\, in the biotech sector in the United States.  He returned to Canada in 2014 to serve as President & CEO of PARTEQ Innovations\, the commercialization unit for Queen’s\, which has since been incorporated into Queen’s University Partnerships and Innovation unit with the office of the VP Research. He holds Ph.D. and B.Sc.H. degrees from Queen’s University.
URL:https://www.ieeetoronto.ca/event/navigating-the-tech-transfer-process-for-researchers/
LOCATION:Virtual: https://events.vtools.ieee.org/m/311734
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220514T180000
DTEND;TZID=America/New_York:20220514T190000
DTSTAMP:20260417T050953
CREATED:20220518T191134Z
LAST-MODIFIED:20220524T100553Z
UID:10000526-1652551200-1652554800@www.ieeetoronto.ca
SUMMARY:Visualization Techniques in Cancer Level Detection System – Students’ Research in ML and DL at Durham College
DESCRIPTION:Cancer is one of the leading causes of death in the world. To tackle this menace\, pathologists need a faster and better way to diagnose their patients. This led the team to work on evaluating different machine learning models to find out which model works best in accurately predicting the level of cancer development in a patient. In the course of the project\, we explored different features of our datasets with the help of visualization tools like tableau and python data visualization libraries to enable us to see the relationship between each feature and the level of cancer in a patient. We also\, in the end\, evaluated the performance of each algorithm using python visualization tools to better understand which algorithms performed the best. \nSpeaker(s): Rakesh Pattanayak\, Chisom Nnabuisi\, Dhruv Mistry\, Kar Chun Kan\, Shanuka Rathnayake \nRegister: https://events.vtools.ieee.org/m/313212
URL:https://www.ieeetoronto.ca/event/visualization-techniques-in-cancer-level-detection-system-students-research-in-ml-and-dl-at-durham-college/
LOCATION:toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/313212
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220513T190000
DTEND;TZID=America/New_York:20220513T210000
DTSTAMP:20260417T050953
CREATED:20220518T191548Z
LAST-MODIFIED:20220524T100553Z
UID:10000528-1652468400-1652475600@www.ieeetoronto.ca
SUMMARY:C# Development 101 - Introduction (01 out of 06)
DESCRIPTION:Join the IEEE Toronto Magnetics Chapter and Women in Engineering for a C# Development workshop. \nSpeaker(s): Reza Dibaj \nRegister: https://events.vtools.ieee.org/m/314229
URL:https://www.ieeetoronto.ca/event/c-development-101-introduction-01-out-of-06/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/314229
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220513T180000
DTEND;TZID=America/New_York:20220513T190000
DTSTAMP:20260417T050953
CREATED:20220518T191404Z
LAST-MODIFIED:20220524T100553Z
UID:10000527-1652464800-1652468400@www.ieeetoronto.ca
SUMMARY:Visualization Techniques to Demonstrate the Cause of Climate Changes – Students’ Research in ML and DL at Durham College
DESCRIPTION:We might always be confused about climate and weather\, and what is the difference between each other? Weather refers to the day-to-day temperature and atmospheric conditions\, whereas climate is the average weather in a specific region over a long period. The simplest way to describe climate is to analyze the average temperature and precipitation over time. Climate change relates to the shift in the average conditions such as average temperature and rainfall in a region over a period. Global climate change describes the average long-term changes over the entire Earth. Global warming\, Rise in sea level\, and Shrinking Mountain glaciers are a few of the adverse effects of climatic changes. Greenhouse gases are the prominent factors for the rising temperature\, which is the main factor contributing to global warming. Among the greenhouse gases\, carbon dioxide is the main factor that traps the heat in the atmosphere\, which makes an increase in the overall temperature that can affect lives on Earth. Earth’s temperature has risen by 0.14° F (0.08° C) per decade since 1880\, and the rate of warming over the past 40 years is more than twice that: 0.32° F (0.18° C) per decade since 1981. We will try to find the possible reasons for climatic changes and the factors that contributed to the current situation. Moreover\, we will consider greenhouse gas emissions and their harmful effects on climatic changes\, different countries’ contributions to this global problem\, and measures taken by officials to reduce its impact. \nSpeaker(s): Neenu Markose\, Akhil Mathew \nRegister: https://events.vtools.ieee.org/m/313211
URL:https://www.ieeetoronto.ca/event/visualization-techniques-to-demonstrate-the-cause-of-climate-changes-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/313211
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Maryam Davoudpour":MAILTO:maryam.davoudpour@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220513T160000
DTEND;TZID=America/New_York:20220513T170000
DTSTAMP:20260417T050953
CREATED:20220518T191706Z
LAST-MODIFIED:20220524T100553Z
UID:10000529-1652457600-1652461200@www.ieeetoronto.ca
SUMMARY:Data Analysis and Visualization Techniques in Supermarket Sales – Students’ Research in ML and DL at Durham College
DESCRIPTION:We will explain the significance of visualization charts in narratives and presentations with a brief explanation of chart appropriateness\, noise reduction\, and decluttering aspects. We will continue by shedding light on the necessity of good communication tactics\, criteria and approaches for improving visuals and narrative techniques. Moreover\, applying the above concepts\, we will explain how to use tableau as a software application to produce visuals to perform the superstore sales data analysis. Furthermore\, we will analyze supermarket sales data\, using appropriate charts for six product lines\, customer types\, and payment methods. We will use six categories of products\, i.e. Electronic accessories\, Food & Beverages\, Health & Beauty\, Home & Lifestyle\, and Sports & travelling products\, to carry out the analysis. We will emphasize the research’s target audience by providing pertinent insights and making recommendations. \nSpeaker(s): Minu Ahlawat\, Megha Garg\, Dwij Dua & Taxil Savani \nRegister: https://events.vtools.ieee.org/m/313209
URL:https://www.ieeetoronto.ca/event/data-analysis-and-visualization-techniques-in-supermarket-sales-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/313209
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220512T160000
DTEND;TZID=America/New_York:20220512T170000
DTSTAMP:20260417T050953
CREATED:20220518T191802Z
LAST-MODIFIED:20220524T100552Z
UID:10000530-1652371200-1652374800@www.ieeetoronto.ca
SUMMARY:Visualization Techniques in Text Summarization of Online Transcripts – Students’ Research in ML and DL at Durham College
DESCRIPTION:Text summarization is a method for generating a summary of long texts by focusing on the sections that contain essential information while keeping the overall meaning intact. Its goal is to reduce the size of long documents\, which would be difficult and expensive to process manually. With the current explosion of data circulating in digital space\, particularly unstructured textual data\, there is a need to build tools that allow people to extract insights from it. Taking notes is a popular practice for many people employed in situations where it is essential to keep track of what is said\, such as during an online lecture. The art of note-taking does not entail taking down every single word stated but rather broad summaries of what is covered. Making succinct yet informative summaries is the key to successful note-taking. In this seminar\, we will be discussing how we have used visualization and data storytelling techniques in our project to make informed decisions. This project aims to address the difficulties of note-taking by building an application that produces notes based on the transcripts generated by the Automatic Speech Recognition (ASR) technology of the meeting platforms. We relied on visualization concepts for three major decisions that would define our project as a whole. These decisions are the choice of online meeting platform\, preference of text summarization model and the messaging platform choice. \nSpeaker(s): Manoj Varma Alluri\, Navaneeth Jawahar\, Sharath Kumar Prabhu\, Jeel Jani \nRegister: https://events.vtools.ieee.org/m/313207
URL:https://www.ieeetoronto.ca/event/visualization-techniques-in-text-summarization-of-online-transcripts-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/313207
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220512T140000
DTEND;TZID=America/New_York:20220512T150000
DTSTAMP:20260417T050953
CREATED:20220518T191856Z
LAST-MODIFIED:20220524T100552Z
UID:10000531-1652364000-1652367600@www.ieeetoronto.ca
SUMMARY:Fraud Data Analysis & Exploration using Interactive Tableau Dashboard – Students’ Research in ML and DL at Durham College
DESCRIPTION:Credit card fraud detection is an ever-growing problem in today’s financial market with a rapid increase in plastic card usage worldwide. According to the Nelson report\, by 2027\, financial service providers are expected to take a $40 billion hit globally in credit card losses\, a significant increase compared to previous years. Hence\, data-driven decisions can largely help in mitigating that risk. We chose this topic to deep dive into different aspects of Fraud Data Analysis & Exploration using Interactive Tableau Dashboard. Tableau dashboards can be very powerful in driving data-driven decisions. We created an interactive dashboard to help stakeholders or less technical people to drive insights\, understand data better\, and help in business decisions making. The interactive feature helps users to add filters as per their needs and understand the data in a way they want to analyze. Our dashboard is dynamic and would be updated when several filters are applied together. Multiple filters can be added to multiple charts at the same time. These charts are intuitive which makes even new users easily interact and understand the data. \nSpeaker(s): Priyanka Singh & Devy Ratnasari \nRegister: https://events.vtools.ieee.org/m/313201
URL:https://www.ieeetoronto.ca/event/fraud-data-analysis-exploration-using-interactive-tableau-dashboard-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/313201
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220510T110000
DTEND;TZID=America/New_York:20220510T120000
DTSTAMP:20260417T050953
CREATED:20220505T201243Z
LAST-MODIFIED:20220511T081818Z
UID:10000362-1652180400-1652184000@www.ieeetoronto.ca
SUMMARY:High Order Adaptive Mesh Refinement (AMR) for Divergence Constraint-Preserving Schemes (Prof. Dinshaw Balsara\, U. of Notre Dame)
DESCRIPTION:Join the IEEE Toronto Electromagnetics & Radiation Society Chapter for a talk on High Order Adaptive Mesh Refinement\, presented by Professor Dinshaw S. Balsara. \nAbstract: 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. \nIn this talk\, 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. \nCo-sponsored by: Center for Computational Science and Engineering\, University of Toronto \nSpeaker(s): Prof. D. S. Balsara\, \nRegister: https://events.vtools.ieee.org/m/312557 \nBiography: 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. 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.
URL:https://www.ieeetoronto.ca/event/high-order-adaptive-mesh-refinement-amr-for-divergence-constraint-preserving-schemes-prof-dinshaw-balsara-u-of-notre-dame/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312557
CATEGORIES:Electromagnetics & Radiation
ORGANIZER;CN="Costas Sarris":MAILTO:costas.sarris@utoronto.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220507T180000
DTEND;TZID=America/New_York:20220507T190000
DTSTAMP:20260417T050953
CREATED:20220505T200912Z
LAST-MODIFIED:20220507T074848Z
UID:10000361-1651946400-1651950000@www.ieeetoronto.ca
SUMMARY:Alert on Mask Detection System – Students Research in ML and DL at Durham College
DESCRIPTION:As a result of the fast development and spread of the COVID-19 pandemic throughout the world\, people’s everyday lives have been severely disrupted in recent times. One proposal for controlling the epidemic is to make individuals wear face masks in public. As a result\, we require face detection systems that are both automated and efficient for such enforcement. We propose a face mask identification model for static and real-time videos in this research\, and the pictures are classified as “with mask” or “without a mask.” The model uses a Kaggle dataset to train and test. The collected data set contains over 10\,000 images (considering 5\,000 with mask and similarly 5\,000 without) and has a 98 percent performance accuracy rate. The proposed model is computationally efficient and precise compared to Haar-Cascade & ANN. The application of this research are various\, including digitized scanning tool in schools\, hospitals\, banks\, airports\, and many other public or commercial locations. \nSpeaker(s): Henil Shah\, Neenu Markose \nRegister: https://events.vtools.ieee.org/m/312341
URL:https://www.ieeetoronto.ca/event/alert-on-mask-detection-system-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312341
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220506T183000
DTEND;TZID=America/New_York:20220506T203000
DTSTAMP:20260417T050953
CREATED:20220526T190240Z
LAST-MODIFIED:20220526T190240Z
UID:10000535-1651861800-1651869000@www.ieeetoronto.ca
SUMMARY:HUMBER IN PERSON COMPETITIVE PROGRAMMING WORKSHOPS
DESCRIPTION:Dr. Andrew Rudder will be teaching programming concepts with a focus on competitive programming. Various languages may be used. You should be familiar with any of the following programming languages Java\, C#\, C\, C++ or python. A basic knowledge of selection logic (such as if statements)\, loops and functions are sufficient. \nThis is a prerequisite for Humber IEEE Students attending IEEExtreme 16.0 in October 2022. Course continues depending on registration. Course is free. Available to any current Humber students. \nCourse will probably last until October 2022. Breaks for Humber Midterm exams\, final exams and reading weeks. \nLocation: Building F\, 205 Humber College Blvd\, Etobicoke\, Ontario\, Canada\, M9W5L7 \nRegister: https://events.vtools.ieee.org/m/312262
URL:https://www.ieeetoronto.ca/event/humber-in-person-competitive-programming-workshops/
LOCATION:Bldg: F\, 205 Humber College Blvd\, Etobicoke\, Ontario\, Canada\, M9W5L7\, Virtual: https://events.vtools.ieee.org/m/312262
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220506T180000
DTEND;TZID=America/New_York:20220506T190000
DTSTAMP:20260417T050953
CREATED:20220502T172656Z
LAST-MODIFIED:20220507T074847Z
UID:10000357-1651860000-1651863600@www.ieeetoronto.ca
SUMMARY:Cancer Level Detection System – Students Research in ML and DL at Durham College
DESCRIPTION:Cancer ranks as a leading cause of death and an important barrier to increasing life expectancy everywhere. According to available data\, lung cancer contributes the most to cancer deaths. Also\, according to available data\, those diagnosed early have a 50 percent chance of survival over those diagnosed with late-stage cancer. It means that early detection is paramount to the survival of a lung cancer patient\, leading to a reduction in the number of cancer deaths. We\, therefore\, evaluated six different machine learning algorithms to see which one performed optimally in accurately predicting the level of lung cancer development in a patient. We considered various parameters when choosing the dataset for this evaluation as the pathogenesis of lung cancer involves a combination of intrinsic factors and exposure to environmental carcinogens. We also considered varying the features in our data\, categorizing them under diagnostic risk factors (age\, gender\, alcohol use\, air pollution\, balanced diet\, obesity\, smoking\, passive smoker) and symptoms (fatigue\, weight loss\, shortness of breath\, swallowing difficulty\, frequent cold\, dry cough) and the inferences we drew from this indicated that those that have the symptom features prior to diagnosis had the highest chance of being diagnosed with a high level of cancer. The final results of our evaluation showed that the best levels of predictions on new data were achieved by optimized Random Forest\, KNN\, and SVM models. \nSpeakers: Rakesh Pattanayak\, Chisom Nnabuisi\, Dhruv Mistry\, Kar Chun Kan\, Shanuka Rathnayake \nRegister: https://events.vtools.ieee.org/m/312340
URL:https://www.ieeetoronto.ca/event/cancer-level-detection-system-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312340
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220505T183000
DTEND;TZID=America/New_York:20220505T203000
DTSTAMP:20260417T050953
CREATED:20220502T172442Z
LAST-MODIFIED:20220506T073312Z
UID:10000355-1651775400-1651782600@www.ieeetoronto.ca
SUMMARY:Humber Computer Hardware/Software Thursday evening
DESCRIPTION:Computer Hardware/Software course comparing Arduinos/ESP32/STM32/Raspberry Pi with simple to advanced programming in Arduino IDE in C++. Hardware includes Radios\, LED Displays\, LCD displays\, Servos\, I2c\, Clock chips\, Analog/Digital\, Touch Screens\, Flash Memory\, TSOP infrared\, TCP/IP\, Bluetooth and BLE Mesh. Do you want to learn beyond what Humber can offer? \nCourse is free. In-Person. Available to Humber Students on Thursday Evenings from 6:30 to 8:30. Also available Saturdays from 2-4. \nCourse is dependent on registrants and availability of lab space at that time. \nLocation: J232 Pending Approval\, Bldg: J\, 205 Humber College Blvd\,\nEtobicoke\, Ontario\, Canada\, M9W 5L7 \nRegister: https://events.vtools.ieee.org/m/312266
URL:https://www.ieeetoronto.ca/event/humber-computer-hardware-software-thursday-evening/
LOCATION:Room: J232 Pending Approval\, Bldg: J\, 205 Humber College Blvd\, Etobicoke\, Ontario\, Canada\, M9W 5L7\, Virtual: https://events.vtools.ieee.org/m/312266
ORGANIZER;CN="Humber College Inst of Tech & Advanced Learning":MAILTO:mdc_on_ca@hotmail.com
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220504T193000
DTEND;TZID=America/New_York:20220504T213000
DTSTAMP:20260417T050953
CREATED:20220505T200750Z
LAST-MODIFIED:20220505T200750Z
UID:10000359-1651692600-1651699800@www.ieeetoronto.ca
SUMMARY:Amateur Radio Morse Code Study
DESCRIPTION:Wednesday night Online Morse Code Study for the Canadian Amateur Radio certification exam. 2hrs/week \nCourse continues depending on registration. Course is free. Available to anyone. \nCourse will probably last until December 2022. Breaks for Humber Midterm exams\, final exams and reading weeks \nRegister: https://events.vtools.ieee.org/m/313669
URL:https://www.ieeetoronto.ca/event/amateur-radio-morse-code-study/
LOCATION:Etobicoke\, Ontario\, Canada\, M9V4A9\, Virtual: https://events.vtools.ieee.org/m/313669
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220504T160000
DTEND;TZID=America/New_York:20220504T173000
DTSTAMP:20260417T050953
CREATED:20220502T172244Z
LAST-MODIFIED:20220505T073259Z
UID:10000352-1651680000-1651685400@www.ieeetoronto.ca
SUMMARY:Conceiving Noise: Transformation from Disturbing Sounds to Informational Errors\, 1900-1955
DESCRIPTION: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. \nIn 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. \nSpeaker(s): Prof. Chen-Pang Yeang \nRegister: https://events.vtools.ieee.org/m/313075 \nBiography: 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.
URL:https://www.ieeetoronto.ca/event/conceiving-noise-transformation-from-disturbing-sounds-to-informational-errors-1900-1955/
LOCATION:Virtual: https://events.vtools.ieee.org/m/313075
CATEGORIES:Communications
ORGANIZER;CN="IEEE Toronto Communications Chapter":MAILTO:Toronto_Chapter@comsoc.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220503T193000
DTEND;TZID=America/New_York:20220503T213000
DTSTAMP:20260417T050953
CREATED:20220502T171800Z
LAST-MODIFIED:20220504T073258Z
UID:10000348-1651606200-1651613400@www.ieeetoronto.ca
SUMMARY:Humber Amateur Radio certification study Tuesday online
DESCRIPTION:Tuesday night Online Study Group preparing for the Canadian Amateur Radio certification exam. 2hrs/week \nCourse based on the certification study guide from https://www.coaxpublications.ca/ord0001.php Purchase the book if you are serious about learning this. \nCourse continues depending on registration. Course is free. Available to anyone. \nCourse will probably last until December 2022. Breaks for Humber Midterm exams\, final exams and reading weeks \nEtobicoke\, Quebec\, Canada\, M9V4A9\, Virtual: https://events.vtools.ieee.org/m/312260
URL:https://www.ieeetoronto.ca/event/humber-amateur-radio-certification-study-tuesday-online/
LOCATION:Etobicoke\, Quebec\, Canada\, M9V4A9\, Virtual: https://events.vtools.ieee.org/m/312260
ORGANIZER;CN="Humber College Inst of Tech & Advanced Learning":MAILTO:mdc_on_ca@hotmail.com
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220503T180000
DTEND;TZID=America/New_York:20220503T190000
DTSTAMP:20260417T050953
CREATED:20220502T171646Z
LAST-MODIFIED:20220504T073258Z
UID:10000346-1651600800-1651604400@www.ieeetoronto.ca
SUMMARY:DDoS Detection System – Students Research in ML and DL at Durham College
DESCRIPTION: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. \nWe have used the UNSW-15 dataset for AI-based DDOS detection systems. \nThe 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. \nSpeaker(s): Minu Ahlawat\, Dwij Dua\, Megha Garg\, Taxil Savani \nRegister: https://events.vtools.ieee.org/m/312339
URL:https://www.ieeetoronto.ca/event/ddos-detection-system-students-research-in-ml-and-dl-at-durham-college/
LOCATION:toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312339
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220503T110000
DTEND;TZID=America/New_York:20220503T120000
DTSTAMP:20260417T050953
CREATED:20220502T172040Z
LAST-MODIFIED:20220504T073258Z
UID:10000350-1651575600-1651579200@www.ieeetoronto.ca
SUMMARY:Higher Order Globally Constraint-Preserving FVTD and DGTD Schemes for Time-Dependent Computational Electrodynamics (Prof. Dinshaw Balsara\, U. of Notre-Dame)
DESCRIPTION: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. \nIn 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. \nCo-sponsored by: Center for Computational Science and Engineering (CCSE)\, University of Toronto \nSpeaker(s): Prof. Dinshaw Balsara\, \nRegister: https://events.vtools.ieee.org/m/312555 \nBiography: 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.
URL:https://www.ieeetoronto.ca/event/high-order-adaptive-mesh-refinement-amr-for-divergence-constraint-preserving-schemes-focus-on-mhd-and-ced-prof-dinshaw-balsara-university-of-notre-dame/
LOCATION:Virtual: https://events.vtools.ieee.org/m/312555
CATEGORIES:Electromagnetics & Radiation
ORGANIZER;CN="Costas Sarris":MAILTO:costas.sarris@utoronto.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220501T180000
DTEND;TZID=America/New_York:20220501T190000
DTSTAMP:20260417T050953
CREATED:20220502T171452Z
LAST-MODIFIED:20220502T171549Z
UID:10000525-1651428000-1651431600@www.ieeetoronto.ca
SUMMARY:Sentiment Analysis on Twitter Data – Students Research in ML and DL at Durham College
DESCRIPTION: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. \nSpeaker(s): Akhil Mathew\, Anmol Wadera\, Deepan Ellenti Padmanabhan\, Saketh Vemula\, Sivaramakrishna Malakalapalli \nRegister: https://events.vtools.ieee.org/m/312338
URL:https://www.ieeetoronto.ca/event/sentiment-analysis-on-twitter-data-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312338
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220428T180000
DTEND;TZID=America/New_York:20220428T190000
DTSTAMP:20260417T050953
CREATED:20220425T202839Z
LAST-MODIFIED:20220429T070737Z
UID:10000524-1651168800-1651172400@www.ieeetoronto.ca
SUMMARY:Text Summarization of Transcripts from Online Meetings – Students Research in ML and DL at Durham College
DESCRIPTION: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. \nSpeaker(s): Manoj Varma Alluri\, Navaneeth Jawahar\, Sharath Kumar Prabhu\, Jeel Jani\, Shravya Sandupata\nToronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312337
URL:https://www.ieeetoronto.ca/event/text-summarization-of-transcripts-from-online-meetings-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312337
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220428T173000
DTEND;TZID=America/New_York:20220428T190000
DTSTAMP:20260417T050953
CREATED:20220425T202739Z
LAST-MODIFIED:20220429T070736Z
UID:10000523-1651167000-1651172400@www.ieeetoronto.ca
SUMMARY:Electrification: Extraordinary Opportunities\, Extreme Challenges
DESCRIPTION:Despite a head start over 100 years ago\, Electrification is only receiving widespread interest recently.\nSee attached poster for more details. \n\nThis is a hybrid meeting with in-person event shared on the web. http://meet.google.com/xdn-kpji-eyk \nSIRC Building is on the corner of Conlin Road and Simcoe Rd North. There is parking in the rear of the Building. \nCo-sponsored by: Ontario Tech University \nSpeaker(s): Rick Szymczyk P.Eng.\, MBA\, \nLocation:\n2060\, Bldg: Software and Informatics Research Centre\, Ontario Tech University\,\n2000 Simcoe Street North\, Oshawa\, Ontario\, Canada\, L1G 0C5
URL:https://www.ieeetoronto.ca/event/electrification-extraordinary-opportunities-extreme-challenges/
LOCATION:Room: 2060\, Bldg: Software and Informatics Research Centre\, Ontario Tech University\, 2000 Simcoe Street North\, Oshawa\, Ontario\, Canada\, L1G 0C5
CATEGORIES:Life Members
ORGANIZER;CN="Vijay Sood":MAILTO:vijay.sood@ontariotechu.ca
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220427T180000
DTEND;TZID=America/New_York:20220427T190000
DTSTAMP:20260417T050953
CREATED:20220425T202533Z
LAST-MODIFIED:20220429T070736Z
UID:10000522-1651082400-1651086000@www.ieeetoronto.ca
SUMMARY:Credit Card Fraud Detection – Students Research in ML and DL at Durham College
DESCRIPTION: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. \nSpeaker(s): Priyanka Singh\, Devy Ratnasari\, Gopika Shaji\, Oluwole Ayodele\, Saurav Bisht\,\nToronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312336
URL:https://www.ieeetoronto.ca/event/credit-card-fraud-detection-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312336
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220426T180000
DTEND;TZID=America/New_York:20220426T190000
DTSTAMP:20260417T050953
CREATED:20220425T202413Z
LAST-MODIFIED:20220426T234759Z
UID:10000521-1650996000-1650999600@www.ieeetoronto.ca
SUMMARY:Fake News Detection – Students Research in ML and DL at Durham College
DESCRIPTION: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. \nIn 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.\nFake 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. \nSpeaker(s): Roshna Babu\, Abraham Mathew\, Neha Joseph\nToronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312334
URL:https://www.ieeetoronto.ca/event/fake-news-detection-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312334
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220421T161000
DTEND;TZID=America/New_York:20220421T170000
DTSTAMP:20260417T050953
CREATED:20220330T181903Z
LAST-MODIFIED:20220421T231238Z
UID:10000514-1650557400-1650560400@www.ieeetoronto.ca
SUMMARY:Wideband Digital-to-Analog Converters for mmWave Transmitter
DESCRIPTION: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. \nSpeaker(s): Dr. Xilin Liu \nVirtual: https://events.vtools.ieee.org/m/309574 \nBiography: 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.
URL:https://www.ieeetoronto.ca/event/wideband-digital-to-analog-converters-for-mmwave-transmitter/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/309574
CATEGORIES:Solid-State Circuits
ORGANIZER;CN="Dustin Dunwell":MAILTO:dustin.dunwell@gmail.com
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