BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD DTSTART:19451014T230000 TZOFFSETFROM:+0630 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20241011T115831Z UID:DCB271BF-4E23-4CFF-B8DF-DD4748856629 DTSTART;TZID=Asia/Kolkata:20241007T190000 DTEND;TZID=Asia/Kolkata:20241007T200000 DESCRIPTION:Title: Biomedical Sensing with Digital Fingerprinting Tool Leve raging Microwave Imaging and Machine Learning\n\nAbstract: Microwaves are non-ionizing\, time-varying\, electromagnetic waves within a frequency spe ctrum of 300 MHz to 300 GHz. They are widely used in various industries\, including sensing\, telecommunications\, weather forecasting\, food indust ry\, defence\, manufacturing\, and precision agriculture. Microwave applic ations in biomedical fields are relatively still at a rudimentary stage\, with a growing interest in healthcare research and development. The first application of microwaves in medicine dates to the 1980s in the treatment of cancer via ablation therapy\; since then\, their applications have been expanded. Significant advances have been made in reconstructing microwave data for imaging and sensing applications in the field of healthcare. Rec ent advances in machine learning have enabled microwave systems to augment towards healthcare\, including clinical decision making\, guiding treatme nt\, and increasing resource-efficient facilities. Digitization of tissues allows examination of tissue morphologies in new ways enabling patient st ratification for effective treatments. Current slide-scanning techniques c apture the visible details of the tissue as whole-slide images and digital ly record them in the form of spatial and color relationships. Specialized experimental techniques like dielectric spectroscopy can also be used to investigate a tissue's response to an applied electric field. Material pro perties\, such as relative permittivity (εr) and conductivity (σ)\, can vary significantly between healthy and unhealthy tissue types at a given f requency. Understanding such differences through machine learning based cl assification models can be a key for identifying the disease state(s). Thi s technology pipeline\, thus\, shows great potentials for developing the n ext generation non-invasive diagnostic tools for therapeutic intervention. \n\nBiography: Dr. Sayan Roy is currently an Assistant Professor of Electr ical and Computer Engineering in the School of Science and Engineering at the University of Missouri-Kansas City (UMKC)\, USA. Dr. Roy received a B. Tech. degree in Electronics and Communications Engineering from West Benga l University of Technology\, Kolkata\, India\, in 2010\, and M.S. and Ph.D . degrees in Electrical and Computer Engineering from North Dakota State U niversity\, USA\, in 2012\, and 2017\, respectively. Before joining UMKC\, Dr. Roy served in various academic and research positions at the Universi ty of North Dakota\, Johns Hopkins University\, South Dakota School of Min es and Technology\, and Mayo Clinic. Dr. Roy's technical interests includi ng research and teaching are focused on applied electromagnetics. Dr. Roy has been awarded multiple federal and industrial grants of totaling over $ 1.5 million\, published more than 90 journal papers\, conference proceedin gs\, and book chapters\, delivered more than 30 keynotes\, invited talks/s eminars\, and advised 15 graduate level students. As a Senior Member of IE EE\, Dr. Roy is currently serving as the Regional Coordinator of Region 4 of IEEE MTT society\, technical program committee member of various IEEE c onferences\, and reviewer of multiple IEEE journals and magazines. Dr. Roy is also a member of IEEE HKN\, a Senior Member of URSI\, and a Full Membe r of Commission K\, USNC-URSI.\n\nCo-sponsored by: IEEE MTT-S Uttar Prades h Section Chapter\n\nSpeaker(s): Dr. Sayan Roy\, \n\nVirtual: https://even ts.vtools.ieee.org/m/436356 LOCATION:Virtual: https://events.vtools.ieee.org/m/436356 ORGANIZER:prakrati@iitk.ac.in SEQUENCE:18 SUMMARY:Biomedical Sensing with Digital Fingerprinting Tool Leveraging Micr owave Imaging and Machine Learning URL;VALUE=URI:https://events.vtools.ieee.org/m/436356 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="text-align: justi fy\;"><strong><span style="mso-bidi-font-size: 12.0pt\; line-height: 115%\ ; font-family: 'Times New Roman'\,serif\;">Title:</span></strong><span sty le="mso-bidi-font-size: 12.0pt\; line-height: 115%\; font-family: 'Times N ew Roman'\,serif\;"> Biomedical Sensing with Digital Fingerprinting Tool L everaging Microwave Imaging and Machine Learning</span></p>\n<p class="Mso Normal" style="text-align: justify\;"><strong><span style="mso-bidi-font-s ize: 12.0pt\; line-height: 115%\; font-family: 'Times New Roman'\,serif\;" >Abstract:</span></strong><span style="mso-bidi-font-size: 12.0pt\; line-h eight: 115%\; font-family: 'Times New Roman'\,serif\;"> Microwaves are non -ionizing\, time-varying\, electromagnetic waves within a frequency spectr um of 300 MHz to 300 GHz. They are widely used in various industries\, inc luding sensing\, telecommunications\, weather forecasting\, food industry\ , defence\, manufacturing\, and precision agriculture. Microwave applicati ons in biomedical fields are relatively still at a rudimentary stage\, wit h a growing interest in healthcare research and development. The first app lication of microwaves in medicine dates to the 1980s in the treatment of cancer via ablation therapy\; since then\, their applications have been ex panded. Significant advances have been made in reconstructing microwave da ta for imaging and sensing applications in the field of healthcare. Recent advances in machine learning have enabled microwave systems to augment to wards healthcare\, including clinical decision making\, guiding treatment\ , and increasing resource-efficient facilities. Digitization of tissues al lows examination of tissue morphologies in new ways enabling patient strat ification for effective treatments. Current slide-scanning techniques capt ure the visible details of the tissue as whole-slide images and digitally record them in the form of spatial and color relationships. Specialized ex perimental techniques like dielectric spectroscopy can also be used to inv estigate a tissue's response to an applied electric field. Material proper ties\, such as relative permittivity (&epsilon\;r) and conductivity (&sigm a\;)\, can vary significantly between healthy and unhealthy tissue types a t a given frequency. Understanding such differences through machine learni ng based classification models can be a key for identifying the disease st ate(s). This technology pipeline\, thus\, shows great potentials for devel oping the next generation non-invasive diagnostic tools for therapeutic in tervention.</span></p>\n<p class="MsoNormal" style="text-align: justify\;" ><strong><span style="mso-bidi-font-size: 12.0pt\; line-height: 115%\; fon t-family: 'Times New Roman'\,serif\;">Biography:</span></strong><span styl e="mso-bidi-font-size: 12.0pt\; line-height: 115%\; font-family: 'Times Ne w Roman'\,serif\;"> Dr. Sayan Roy is currently an Assistant Professor of E lectrical and Computer Engineering in the School of Science and Engineerin g at the University of Missouri-Kansas City (UMKC)\, USA. Dr. Roy received a B.Tech. degree in Electronics and Communications Engineering from West Bengal University of Technology\, Kolkata\, India\, in 2010\, and M.S. and Ph.D. degrees in Electrical and Computer Engineering from North Dakota St ate University\, USA\, in 2012\, and 2017\, respectively. Before joining U MKC\, Dr. Roy served in various academic and research positions at the Uni versity of North Dakota\, Johns Hopkins University\, South Dakota School o f Mines and Technology\, and Mayo Clinic. Dr. Roy's technical interests in cluding research and teaching are focused on applied electromagnetics. Dr. Roy has been awarded multiple federal and industrial grants of totaling o ver $1.5 million\, published more than 90 journal papers\, conference proc eedings\, and book chapters\, delivered more than 30 keynotes\, invited ta lks/seminars\, and advised 15 graduate level students. As a Senior Member of IEEE\, Dr. Roy is currently serving as the Regional Coordinator of Regi on 4 of IEEE MTT society\, technical program committee member of various I EEE conferences\, and reviewer of multiple IEEE journals and magazines. Dr . Roy is also a member of IEEE HKN\, a Senior Member of URSI\, and a Full Member of Commission K\, USNC-URSI.</span></p> END:VEVENT END:VCALENDAR