IEEE IMAS 2026 Microwave Imaging and Inversion Workshop

Francesca Vipiana received the Laurea and Ph.D. degrees in electronic engineering from the Politecnico di Torino, Torino, Italy, in 2000 and 2004, respectively, with doctoral research carried out partly at the European Space Research Technology Center, Noordwijk, The Netherlands.

From 2005 to 2008, she was a Research Fellow with the Department of Electronics, Politecnico di Torino. From 2009 to 2012, she was the Head of the Antenna and EMC Laboratory, Istituto Superiore Mario Boella, Torino.

Since 2012, she has been an Assistant Professor with the Department of Electronics and Telecommunications, Politecnico di Torino, where she has been an Associate Professor since 2014 and a Full Professor since 2021.

Her main research activities concern numerical techniques based on integral equations and method of moments, with a focus on multiresolution and hierarchical schemes, domain decomposition, preconditioning and fast solution methods, advanced quadrature integration schemes, and analysis of glide-periodic structures.

Her research interests also include the modeling, design, realization and testing of microwave imaging and sensing systems for medical and industrial applications.

She received the Lot Shafai Mid-Career Distinguished Award from the IEEE Antennas and Propagation Society (AP-S) in 2017, the Best Paper Award at the Spanish URSI Symposium in 2023, and the Best Electromagnetics Paper Award at the 20th European Conference on Antennas and Propagation (EuCAP) in 2026.

She was an Associate Editor of the IEEE Transactions on Antennas and Propagation (2018–2024) and the founder and responsible of the Women in Engineering Column in the IEEE Antennas and Propagation Magazine (2019–2024).

She is a member of the EurAAP Board of Directors and the Vice-Chair of the IEEE AP-S Expanding Collaboration & Engagement (ECE) committee.

Microwave sensing and imaging is a technology able to exploit differences in dielectric properties (i.e., permittivity and conductivity) of objects illuminated with an array of antennas/sensors at low-power microwave frequencies in order to identify unknown targets in a known scenario.

This technology has several attractive characteristics such as it is non-destructive, contactless, totally safe for operators, potentially real-time, cost-efficient and easy to operate.

On the other side there are some intrinsic constraints: the dielectric contrast between the targets and the surrounding media, and the needed wave spatial resolution and penetration depth within the considered media.

In this presentation, the key ingredients to model, design, realize and test microwave imaging and sensing systems will be presented, including an ad-hoc problem-based design procedure, a reliable and effective forward solver, model-based imaging techniques and specialized hardware.

The proposed microwave systems are designed for two real-world applications: the quality control in food industry in order to detect foreign bodies contamination in packaged products, and the medical diagnostic for the continuous monitoring of patients after the stroke onset.

In both cases systems prototypes will be presented and discussed highlighting their capabilities with respect to the current state of the art.

Moreover, both deterministic and machine learning microwave imaging and sensing algorithms will be analyzed with focus on the systems experimental testing.

Abdulla Desmal (Senior Member, IEEE) received the B.S. degree in Electronics Engineering from the University of Bahrain (UOB), Isa Town, Bahrain, in 2009, and the M.S. and Ph.D. degrees in Electrical Engineering from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2010 and 2016, respectively.

From 2016 to 2017, he was a Postdoctoral Research Associate at Tufts University, Medford, MA, USA, and from 2017 to 2018, a Postdoctoral Research Fellow at Duke University, Durham, NC, USA.

He then served as an Assistant Professor at the Higher Colleges of Technology, UAE, from 2018 to 2024. Since 2024, he has been an Assistant Professor of Electrical Engineering at Bahrain Polytechnic, Isa Town, Bahrain, and in 2025, he was appointed Head of the Systems Engineering School (Power and Automation).

His research interests include efficient machine learning models, forward and inverse electromagnetic solvers, X-ray modeling for luggage screening and radiation therapy, and nonlinear optimization.

Since 2020, Dr. Desmal chaired the International Bioengineering Conference under the ASET Multi-Conference in Dubai.

He received the Rasheed Al-Deen Bin Al-Soori Award from UOB in 2015 for his outstanding academic achievements and was recognized as a Fellow of the Higher Education Academy (FHEA) in 2021.

He also authored two finalist papers in the student paper competitions at the 2014 IEEE Antennas and Propagation Society International Symposium and the 2014 Applied Computational Electromagnetics Society Conference.

Inverse scattering problems (ISPs) involve determining medium characteristics from noisy scattered field measurements.

Over the past decade, significant progress has been made in this area, with a new wave of advances driven by machine learning (ML) approaches.

This workshop will classify ML algorithms for ISPs into direct and indirect training approaches.

Within the indirect category, two approaches will be introduced: on-system and off-system training.

Examples from each category will be presented and discussed.

These examples will cover a range of ML models, from multilayer perceptrons and convolutional neural networks (CNNs) to U-Nets, deep residual neural networks, and recently developed transformer architectures.