10-15 July 2022 • Denver, Colorado, USA

IEEE AP-S/URSI 2022

10-15 July 2022 • Denver, Colorado, USA

MO-SP.1A.2

Enabling Scientific Research through Machine Learning Best Practices

Anna Otterstetter, Colorado School of Mines, United States; Donovan Quimby, Flatiron Analytics, United States; Jeanne Quimby, Jacob Rezac, National Institute of Standards and Technology, United States

Session:
Machine Learning and Advanced Statistical Methods for Millimeter-Wave Measurements and Modeling

Track:
Special Sessions

Location:
Centennial A/B/C (HR)

Presentation Time:
Mon, 11 Jul, 08:20 - 08:40 Denver Time (UTC -7)

Session Co-Chairs:
Jeanne Quimby, National Institute of Standards and Technology and David Michelson, University of British Columbia
Presentation
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Discussion
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Session MO-SP.1A
MO-SP.1A.1: Best Practices for Rigorous Millimeter-wave Channel Sounding
Sana Salous, Department of Engineering, United Kingdom; Sven Wittig, Fraunhofer Heinrich Hertz Institute, HHI, Germany; Jeanne Quimby, National Institute of Standards and Technology, United States
MO-SP.1A.2: Enabling Scientific Research through Machine Learning Best Practices
Anna Otterstetter, Colorado School of Mines, United States; Donovan Quimby, Flatiron Analytics, United States; Jeanne Quimby, Jacob Rezac, National Institute of Standards and Technology, United States
MO-SP.1A.3: Machine Learning Design of Printed Patch Antenna
Yiming Chen, Atef Elsherbeni, Colorado School of Mines, United States; Veysel Demir, Northern Illinois University, United States
MO-SP.1A.4: Impact of the Pre-Processing in AI-Based Classification at Mm-Waves.
Flora Zidane, Jérôme Lanteri, Claire Migliaccio, Université côte d'azur, France; Julien Marot, Aix-Marseille Université, France
MO-SP.1A.5: Extending Machine Learning Based RF Coverage Predictions to 3D
Muyao Chen, Mathieu Chateauvert, Jonathan Ethier, Communications Research Centre, Canada
MO-SP.1A.6: Advanced Statistical Methods for Assessing Wireless Coverage Predictions
Esther Xu, David Michelson, University of British Columbia, Canada
MO-SP.1A.7: Deep Learning Modeling of Power Delay Profile in Motherboard Desktop Environment
Jinbang Fu, Prateek Juyal, Erik Jorgensen, Alenka Zajic, Georgia Institute of Technology, United States
MO-SP.1A.8: Observations on the Angular Statistics of the Indoor Sub-THz Radio Channel at 158 GHz
Alper Schultze, Michael Peter, Fraunhofer HHI, Germany; Wilhelm Keusgen, Technische Universität Berlin, Germany; Taro Eichler, Rohde & Schwarz, Germany
MO-SP.1A.9: Deep Learning based Power Delay Profile Trend Generation: A 60 GHz Intra-Vehicle Case Study
Rajeev Shukla, Abhishek Narayan Sarkar, Aniruddha Chandra, National Institute of Technology, Durgapur, India., India; Tomas Mikulasek, Ales Prokes, Brno University of Technology, Brno, Czech Republic, Czech Republic; Jan M Kelner, Cezary Ziolkowski, Military University of Technology, Poland
MO-SP.1A.10: Millimeter Wave Channel Measurement and Analysis in Smart Warehouse Scenario
Hang Mi, Bo Ai, Ruisi He, Zhangfeng Ma, Mi Yang, Zhangdui Zhong, Beijing Jiaotong University, China; Xin Zhou, National Institute of Metrology of China, China
Resources
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