WE-A6.2P.4

Convolutional Neural Networks for Chipless RFID Classification in the Time-Frequency Domain

Jafait Junior Fodop Sokoudjou, Pablo Garcia Cardarelli, Ainhoa Rezola Garcíandia, Javier Díaz Dorronsoro, Idoia Ochoa Álvarez, University of Navarra, Spain
Session:
AI for Antenna Appications Oral
Track:
AP-S: Antenna Applications and Emerging Technologies
Location:
Spadolini: 1-07
Session Time:
Wed, 17 Jul, 13:40 - 17:20
Presentation Time:
Wed, 17 Jul, 14:40 - 15:00
Session Co-Chairs:
Christos G. Christodoulou, University of New Mexico and Dan Jiao, Purdue University and Ahmed Kishk, Concordia University
Presentation
Discussion
Session WE-A6.2P
WE-A6.2P.1: Machine Learning Based Radio Environment Map Construction for Cellular Networks
Vasileios P. Rekkas, Sotirios P. Sotiroudis, Zaharias D. Zaharis, George Koudouridis, Aristotle University of Thessaloniki, Greece; Panagiotis Sarigiannids, University of Western Macedonia, Greece; George K. Karagiannidis, Aristotle University of Thessaloniki, Greece; Christos G. Christodoulou, The University of New Mexico, United States; Sotirios K. Goudos, Aristotle University of Thessaloniki, Greece
WE-A6.2P.2: Deep-Learning-Based Antenna Impedance Modeling With Output Nonlinear Normalization
Abdullah Muhammad Mahfouz Abdelrahman, Ahmed Kishk, Concordia University, Canada
WE-A6.2P.3: Explainable Planar Multiband Antenna Designer with Wasserstein Generative Adversarial Network
Hoin Jung, Vinicius Cabral Do Nascimento, Hongyang Liu, Xiaoqian Wang, Cheng-Kok Koh, Dan Jiao, Purdue University, United States
WE-A6.2P.4: Convolutional Neural Networks for Chipless RFID Classification in the Time-Frequency Domain
Jafait Junior Fodop Sokoudjou, Pablo Garcia Cardarelli, Ainhoa Rezola Garcíandia, Javier Díaz Dorronsoro, Idoia Ochoa Álvarez, University of Navarra, Spain
WE-A6.2P.5: An Efficient Approach to Predicting Performance Metrics of Reconfigurable Intelligent Surfaces
You-Cheng Chen, Chun-Wei Tseng, Pei-Chen Lin, Alan Liu, Shih-Cheng Lin, Sheng-Fuh Chang, National Chung Cheng University, Taiwan
WE-A6.2P.6: A Progressive Sampling Method for 3-D Reconstruction of DPO-SDF
Ruoming Zhang, Yuhao Shen, Qianyan Shen, Lizhen Yang, Hai Lin, Zhejiang University, China
WE-A6.2P.7: Employing Deep Learning to Enhance Angle Resolution of Directional-of-Arrival (DoA) Systems using Rotman Lens
Donggeun An, Myeonggin Hwang, Suho Chang, Youngno Youn, Daehyeon Kim, Wonhyung Heo, Wonbin Hong, Pohang University of Science and Technology, Korea (South)
WE-A6.2P.8: Machine Learning-based Frequency Selection to Improve Breast Cancer Detection in MammoWave Device
Mehran Taghipour-Gorjikolaie, London South Bank University (LSBU), United Kingdom; Navid Ghavami, UBT - Umbria Bioengineering Technologies, Italy; Gianluigi Tiberi, London South Bank University (LSBU), United Kingdom; Mario Badia, Lorenzo Papini, Arianna Fracassini, Alessandra Bigotti, Gianmarco Palomba, UBT - Umbria Bioengineering Technologies, Italy; Mohammad Ghavami, London South Bank University (LSBU), United Kingdom
WE-A6.2P.9: Active Learning Based on Scattering Center Model for Target Recognition
Shaoran Wang, Mengmeng Li, Dazhi Ding, Nanjing University of Science and Technology, China
WE-A6.2P.10: An Efficient Approach for a Digital Twin model of Harsh EM Environments
Elisa Augello, Francesca Benassi, Diego Masotti, Alessandra Costanzo, Università di Bologna, Italy
Resources
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