Special Session 17

SPECIAL SESSION #17

Valentina Bello

University of Pavia, Italy

• valentina.bello@unipv.it

Simone Pasinetti

University of Brescia, Italy

• simone.pasinetti@unibs.it

Cristina Nuzzi

University of Brescia, Italy

• cristina.nuzzi@unibs.it

Sensors allow to understand and measure the world around us, they are crucial for advancing both the worlds of academia and industry, and they find application in a wide range of fields, from biomedicine to environmental monitoring, from food safety to space exploration. Moreover, the advent of the Internet of Things (IoT) paradigm has tremendously boosted the development of new sensing concepts and technologies. However, this poses new important challenges. In particular, the extraction of useful and meaningful information from big amounts of collected data requires smart processing of the outputs that come from one or many types of sensors, including sensor networks. Artificial intelligence (AI) can give a unique and novel contribution to enhance the operation and performances of sensors and measurement setups. The use of machine learning (ML) and deep learning (DL) techniques, among the others, can efficiently unveil the information, which are embedded in collected data and difficult to explore with traditional methodologies of signal and data processing, and can help to obtain a complete description of the studied phenomenon and improve the accuracy of the measurement.

This Special Session focuses on innovative and smart solutions to combine AI with sensors and measurement configurations.

Topics of interest for this Special Session include but are not limited to:

• AI combined with optical, electrical, thermal, mechanical, and chemical sensors;
• AI for biosensing;
• AI for environmental sensing;
• AI for measurement configurations;
• AI and MEMS sensors;
• AI and sensing for real world applications;
• AI, ML, and DL for sensory data processing and classification;
• AI-based fusion methods to integrate information from multiple sensors;
• AI and sensor networks;
• AI for data collection;
• AI and metrology;
• Explainable AI (XAI) and sensing.

Valentina Bello, (born in Tortona, Italy, in 1994) is Assistant Professor in the field of Electrical and Electronic Measurements (ING-INF/07) at the Laboratory of ElectroOptics of the University of Pavia (Pavia, Italy). She received the Bachelor Degree in Bioengineering and the Master Degree (cum Laude) in Electronic Engineering (track: photonics) from the University of Pavia, Italy, in 2016 and 2018, respectively. In 2018, she was visiting thesis student at KU Leuven (Leuven, Belgium) in the framework of the Erasmus+ Traineeship program, doing research for her master thesis on fiber-optic surface plasmon resonance-based biosensors. In 2019, she was visiting researcher at the Photonic Center of Boston University (Boston, MA, USA), working on the characterization of nano electro-mechanical systems (NEMS). She received her Ph.D. degree in 2022 discussing a thesis on the development of micro-opto-fluidic sensing platforms for contactless chemical and biological analyses. Despite her young age, Dr. Bello is co-author of several publications in international peer-reviewed journals. She is Member of IEEE Instrumentation & Measurement Society, IEEE Photonics Society, IEEE Women in Engineering and IEEE Young Professionals. Moreover, she is recipient of the “2021 Graduate Student Scholarship” awarded by the IEEE Photonics Society and of the “2022 Best Doctoral Thesis Award in the field of Applied Photonics” recognized by IEEE Photonics Society Italy Chapter. Dr. Bello’s research interests include micro-opto-fluidic (bio)sensors, optical interferometry, characterization of MEMS and micro-devices, speckle pattern imaging, and artificial intelligence for optical sensing.

Dr. Simone Pasinetti, (born 1985) received his B.S. degree and M.S. degree (cum laude) in Automation Engineering from the University of Brescia in 2009 and 2011 respectively. Since 2011 he works in the Mechanical and Thermal Measurement Laboratory (MMTLAB) at University of Brescia. Since 2020 he is an Assistant Professor in Mechanical and Thermal Measurements (ING-IND/12) at the University of Brescia, Italy. He has received the national scientific qualification in May 2021. He is currently the Head of the Vision Systems for Mechatronic and Agriculture division of the MMTLAB. He has a strong background in developing and characterizing vision systems and algorithms based both on deterministic and advanced (i.e. machine and deep learning vision processing) machine vision. During his research career, he worked mainly on the development of measurement systems based on 2D and 3D vision. In particular, he worked on human kinematic analysis, liquid lens objectives applications, and 3D vision systems development. He studied new measurement techniques based on vision and developed new systems for applications for different fields such as biomechanics, medicine, robotics, cultural heritage, and industry. In recent years, his main research interests include vision and measurement systems for the agriculture and food industries.

Dr. Cristina Nuzzi, (born 1993) is an assistant professor in Mechanical and Thermal Measurements (ING/IND-12) with a research topic on the detection of weeds and crops using visual systems and deep learning since 2022. Dr. Nuzzi holds both a Bachelor's and Master's Degree in Automation Engineering (2015 and 2017, respectively) received from the University of Brescia. She received her Ph. D. degree in Mechanical and Industrial Engineering (track Applied Mechanics) in 2020 from UNIBS, with a dissertation about the concept of Meta-Collaborative Workstations and software designed to communicate with robots developed using vision systems and deep learning models. She is a member of the Vision Systems for Mechatronics division of the Laboratory of Mechanical and Thermal Measurements (https://vis4mechs.unibs.it/) since her Ph. D. Despite her young age, she is the first author of several publications on this subject, exploiting the capabilities of intelligent algorithms for robotics and biomechanical measurements. Therefore, Dr. Nuzzi has wide experience in the development, training, and utilization of deep learning models for vision data and image processing techniques, which she also used to conduct research for industrial partners during her post-doctoral years. Her expertise also includes data management and dataset creation, since she authored a public dataset published during her Ph. D., 3D data elaboration and processing, and software deployment for target embedded hardware. She co-tutored several Master's Degree theses even during her Ph. D. on the topic of measurements and intelligent algorithms.