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Andrea Massa

Andrea Massa

Prof. Andrea Massa
ELEctromagnetic DIAgnostics Research Center
DISI ‐ Università di Trento
Trento, Italy
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Digiteo Chair@Laboratoire des Signaux et Systèmes
Gif‐sur‐Yvette, France
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Prof. Massa received the “laurea” degree in Electronic Engineering from the University of Genoa, Genoa, Italy, in 1992 and Ph.D. degree in EECS from the same university in 1996. From 1997 to 1999, he was an Assistant Professor of Electromagnetic Fields at the Department of Biophysical and Electronic Engineering (University of Genoa). From 2001 to 2004, he was an Associate Professor at the University of Trento. Since 2005, he has been a Full Professor of Electromagnetic Fields at the University of Trento, where he currently teaches electromagnetic fields, inverse scattering techniques, antennas and wireless communications, wireless services and devices, and optimization techniques.

At present, Prof. Massa is the director of the ELEDIA Research Center with a staff of more than 30 researchers located in the headquarter at the University of Trento and in the offshore labs (ELEDIA@L2S within the L2S‐CentraleSupélec (Paris), ELEDIA@UniNAGA at the University of Nagasaki). Moreover, he is Adjunct Professor at Penn State University (USA) and holder of a Senior DIGITEO Chair developed in co‐operation between the Laboratoire des Signaux et Systèmes in Gif‐sur‐Yvette and the Department "Imagerie et Simulation for the Contrôle" of CEA LIST in Saclay (France) from December 2014, and he has been Visiting Professor at the Missouri University of Science and Technology (USA), the Nagasaki University (Japan), the University of Paris Sud (France), the Kumamoto University (Japan), and the National University of Singapore (Singapore).

Prof. Massa serves as Associate Editor of the "IEEE Transaction on Antennas and Propagation" and Associate Editor of the "International Journal of Microwave and Wireless Technologies" and he is member of the Editorial Board of the "Journal of Electromagnetic Waves and Applications", a permanent member of the "PIERS Technical Committee” and of the “EuMW Technical Committee”, and a ESoA member. He has been appointed in the Scientific Board of the "Società Italiana di Elettromagnetismo (SIEm)" and elected in the Scientific Board of the Interuniversity National Center for Telecommunications (CNIT). Recently Prof. Massa has been appointed by the National Agency for the Evaluation of the University System and National Research (ANVUR) as a member of the Recognized Expert Evaluation Group (Area 09, 'Industrial and Information Engineering') for the evaluation of the researches at the Italian University and Research Center. Moreover, he has been appointed as the Italian Member of the Management Committee of the COST Action TU1208 "Civil Engineering Applications of Ground Penetrating Radar".

His research activities are mainly concerned with direct and inverse scattering problems, propagation in complex and random media, analysis/synthesis of antenna systems and large arrays, design/applications of WSNs, cross‐layer optimization and planning of wireless/RF systems, semantic wireless technologies, material‐by‐design (metamaterials and reconfigurable‐materials), and theory/applications of optimization techniques to engineering problems (telecommunications, medicine, and biology).

Prof. Massa published more than 500 scientific publications among which about 270 on international journals and more than 270 in international conferences where he presented more than 50 invited contributions. He has organized 45 scientific sessions in international conferences and has participated to several technological projects in the European framework (20 EU Projects) as well as at the national and local level with national agencies (75 Projects/Grants).

Inverse Problems in Electromagnetics ‐ Challenges and New Frontiers

Inverse problems arise when formulating and addressing many synthesis and sensing applications in modern electromagnetic engineering. Indeed, the objective of antenna design, microwave imaging, and radar remote sensing can be seen as that of retrieving a physical quantity (the shape of the radiating system, the dielectric profile of a device under test, the reflectivity of an area) starting from (either measured or “desired”) electromagnetic field data. Nevertheless, the solution of the well‐known theoretical features (including ill‐posedness, non‐uniqueness, ill‐conditioning, etc.) of electromagnetic inverse problems still represents a major challenge from the practical viewpoint. Indeed, developing and implementing robust, fast, effective, and general‐purpose techniques able to solve arbitrary electromagnetic inverse problem still represent a holy grail from the academic and industrial viewpoint. Accordingly, several ad‐hoc solutions (i.e., effective only for specific application domains) have been developed in the recent years.

In this framework, one of the most important research frontiers is the development of inversion techniques which enable the exploitation of both the information coming from the electromagnetic data and of that which is provided by prior knowledge of the scenario, application, or device of interest. Indeed, exploiting a‐priori information to regularize the problem formulation is known to be a key asset to reduce the drawbacks of inversion processes (i.e., the its ill‐posedness). However, properly introducing prior knowledge within an inversion technique is an extremely complex task, and suitable solutions are available only for specific classes of scenarios (e.g., comprising sparseness regularization terms).The aim of this talk is to provide a broad review of the current trends and objectives in the development of innovative inversion methodologies and algorithms. Towards this end, after a review of the literature on the topic, different classes of methodologies aimed at combining prior and acquired information (possibly in an iterative fashion) will be discussed, and guidelines on how to apply the arising strategies to different domains will be provided, along with numerical/experimental results. The open challenges and future trends of the research in this area will be discussed as well.

Evolutionary Optimization for Next Generation Electromagnetic Engineering

In the last decades, thanks to the growing computational capabilities, optimization techniques based on evolutionary algorithms (EAs) have received great attention and they have been successfully applied to a wide number of problems in engineering and science. As a matter of fact, EAs have shown many attractive features suitable for dealing with large, complex, and nonlinear problems. More specifically, they are hill‐climbing algorithms which not require the differentiation of the cost function, which is a “must” for gradient‐based methods. Moreover, a‐priori information can be easily introduced, usually in terms of additional constraints on the actual solution, and they can directly deal with real values as well as with a coded representation of the unknowns (e.g., binary coding). As regards to the architecture of their implementation, EAs can be effectively hybridized with deterministic procedures and are suitable for parallel computing.

Despite several positive advantages, many times EAs are used as "black‐box" tools without an adequate knowledge of their peculiarities and functionalities. Unfortunately, neglecting the features and properties of each EA can be extremely dangerous, as it is theoretically predicted by the "No free lunch theorem". Indeed, such a theorem states that any optimization methodology works on the average as a "random search", if applied to all optimization problems. Accordingly, the knowledge of the specific class of optimization problem to be handled is mandatory in order to choose and configure the correct EA, and thus to avoid sub‐optimal solutions/performance.

In this talk, a review of EA‐based approaches for electromagnetic engineering is presented. Starting from the theoretical framework of EAs and the state‐of‐the‐art techniques, some meaningful examples of EA‐based approaches for electromagnetics are reported to show the capabilities, but also current limitations, of such techniques. Finally, some indications on future trends of EA‐based techniques are envisaged.

Unconventional Array Design ‐ Fundamental and Advances

Antenna arrays are a key‐technology in several Electromagnetics applicative scenarios, including satellite and ground wireless communications, MIMO systems, remote sensing, biomedical imaging, radar, and radio‐astronomy. Because of their wide range of application, the large number of degrees of freedom at hand (e.g., type, position, and excitation of each radiating element), the available architectures (fully populated, thinned, clustered, etc.), and the possible objectives (maximum directivity, minimum sidelobes, maximum beam efficiency, etc.), the synthesis of arrays turns out to be a complex task which cannot be tackled by a single methodology.

Despite this wide heterogeneity, most of the synthesis approaches share a common theoretical framework which is of paramount importance for all engineers and students interested in such a topic. Moreover, this is also true for innovative methodologies aimed at the design of "unconventional arrays" (i.e., based sparse, thinned, conformal, clustered, overlapped, interleaved architectures, both in the frequency and in the time domain), which are currently receiving a great attention from the academic and industrial viewpoint.

The objective of the talk is therefore firstly to provide the attendees the fundamentals of Antenna Array synthesis, starting from intuitive explanations to rigorous mathematical and methodological insights about their behavior and design. Recent synthesis methodologies aimed at "unconventional architectures" (i.e., architectures close to the real‐applications and operative non‐ideal constraints/guidelines) will be then discussed in detail, with particular emphasis on innovative layouts for very large arrays.

Compressive Sensing – Basics, State of the Art, and Advances in Electromagnetic Engineering

The widely known Shannon/Nyquist theorem relates the number of samples required to reliably retrieve a "signal" to its (spatial and temporal) bandwidth. This fundamental criterion yields to both theoretical and experimental constraints in several Electromagnetic Engineering applications. Indeed, there is a relation between the number of measurements/data (complexity of the acquisition/ processing), the degrees of freedom of the field/signal (temporal/spatial bandwidth), and the retrievable information regarding the phenomena at hand (e.g., dielectric features of an unknown object, presence/position of damages in an array, location of an unknown incoming signal).

The new paradigm of Compressive Sensing (CS) is enabling to completely revisit these concepts by distinguishing the "informative content" of signals from their bandwidth. Indeed, CS theory asserts that one can recover certain signal/phenomena exactly from far fewer measurements than it is indicated by Nyquist sampling rate. To achieve this goal, CS relies on the fact that many natural phenomena are sparse (i.e., they can be represented by few non‐zero coefficients in suitable expansion bases), and on the use of aperiodic sampling strategies, which can guarantee, under suitable conditions, a perfect recovery of the information content of the signal.

Despite its recent introduction, the application of CS methodologies Electromagnetics has already enabled several innovative design/synthesis methodologies and retrieval/diagnosis methods to be developed.

In this framework, this talk is aimed at reviewing the fundamentals of the CS paradigm, specifically focusing on the applicability conditions, requirements, and guidelines for EM applications. Moreover, it is aimed at illustrating the state‐of‐the‐art and the most recent advances in Electromagnetic Engineering (including application of CS to antenna synthesis and diagnosis, direction‐of‐arrival estimation, inverse scattering, and radar imaging), as well as at envisaging possible future research trends and challenges within CS as applied to Electromagnetics.