IEEE AP-S/URSI 2023 || Portland, Oregon, USA || 23-28 July 2023

Institute of Electrical and Electronics Engineers (IEEE)

International Union of Radio Science

IEEE APS

U.S. NATIONAL COMMITTEE FOR URSI

National Academies Sciences Engineering Medicine

IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting

July 23–28, 2023 • Portland, Oregon, USA

23-28 July 2023 • Portland, Oregon, USA

IEEE AP-S/URSI 2023

23-28 July 2023 • Portland, Oregon, USA

FR-A5.2P.1

Deep Learning for Tumor Margin Identification in Electromagnetic Imaging

Amir Mirbeik, Radiosight LLC, United States; Negar Ebadi (Tavassolian), Stevens Institute of Technology, United States

Session:

Computational Electromagnetics and Artificial Intelligence for Biomedical Applications

Track:

AP-S: Antenna Applications & Emerging Technologies

Location:

A 107-109 (OCC)

Session Time:

Fri, 28 Jul, 15:20 - 17:00 PDT (UTC -7)

Presentation Time:

Fri, 28 Jul, 15:20 - 15:40 PDT (UTC -7)

Session Co-Chairs:

Negar Ebadi, Stevens Institute of Technology and Claire Migliaccio, Université Côte d'Azur

Session FR-A5.2P

FR-A5.2P.1: Deep Learning for Tumor Margin Identification in Electromagnetic Imaging

Amir Mirbeik, Radiosight LLC, United States; Negar Ebadi (Tavassolian), Stevens Institute of Technology, United States

FR-A5.2P.2: Full Wave Analysis of the Exposure of Implantable Medical Devices to Electromagnetic Fields

Jose Duque, Universidad Nacional de Colombia, Colombia; Robert Urbina, Manuel Perez, Pontificia Universidad Javeriana, Colombia; Javier Araque, Universidad Nacional de Colombia, Colombia

FR-A5.2P.3: An Unsupervised Learning Approach for Human Activity Recognition Based on Wave Propagation in Wireless Body Area Networks

Zhang Yanyang, Shao Yu, Xiong Lian, Chongqing University of Posts and Telecommunications China, China; Zhang Jie, University of Sheffield,Dept. of Electronic and Electrical Engineering,United Kingdom (Great Britain), United Kingdom

FR-A5.2P.4: Computerized Tomography with Radon Transform using Microwaves and Electrostatics

Omar Ramahi, Saleh Ba Raean, Hamid Akbari-Chelaresi, University of Waterloo, Canada; Seyed Mirjahanmardi, Stanford University, United States; Vahid Nayyeri, Iran University of Science and Technology, Iran

FR-A5.2P.5: High-Performance Numerical Modeling for Detection of Rotator Cuff Tear

Sahar Borzooei, Claire Migliaccio, Victorita Dolean, Côte d'Azur University, France; Pierre-Henri Tournier, Sorbonne University, France; Christian Pichot, Côte d'Azur University, France