Quantify the Unquantifiable: Innovative Methods for Next-Gen Sensing

Abstract: Standard sensing technologies cannot be always suited for to quantify phenomena that cannot be directly measured due to the unavailability of dedicated sensors, or due to a general unfeasibility (e.g., practical constraints, expensiveness of infrastructures, etc.). Enhancing sensing with modern advanced methods can make non-quantitative techniques able to remedy this issue. The Special Session aims to collect papers from the community involved in this topic.

Organizers:

Sara Campanella

Università Politecnica delle Marche

Special Session Organizer

Sara Campanella received the bachelor's and master’s (cum laude) degree in biomedical engineering from the Università Politecnica delle Marche, Ancona, Italy, in July 2019 and 2022, respectively.

In November 2022 and 2023, she won the “O. Carlini” scholarship, funded by Consortium GARR. She ended her PhD in October 2025 and is currently a post-doc researcher at Information Engineering Department at Università Politecnica delle Marche. Her research focuses on innovative sensing and data-processing approaches for biomedical applications, with a particular interest in techniques that enhance and extend the quantitative information obtainable from wearable and embedded biomedical devices.

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Gianluca Ciattaglia

Università Politecnica delle Marche

Special Session Organizer

Gianluca Ciattaglia received the bachelor’s and master’s degrees in electronic engineering and the Ph.D. degree in information engineering from the Università Politecnica delle Marche, Ancona, Italy, in 2014, 2017, and 2021, respectively. In 2018, he joined Ferrari S.p.A. Gestione Sportiva, Maranello, Italy, as an Electronic Engineer. He is currently an Assistant Professor of Electrical and Electronic Measurements at the Università Politecnica delle Marche. His work focuses on radar signal processing techniques and measurements with radar sensors. He is a member of the IEEE Instrumentation and Measurement Society, GMEE, and CNIT.

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Marco Esposito

Università Politecnica delle Marche

Special Session Organizer

Marco Esposito received his B.Sc. and M.Sc. degrees in Electronics Engineering from Università Politecnica delle Marche, Ancona, Italy, in 2019 and 2021, respectively. He earned his Ph.D. in Information Engineering from the same university in 2025 and is currently a postdoctoral research fellow in its Department of Information Engineering.

His research interests include developing methods that enable quantitative extraction of precursor signals from low-cost sensing platforms for a range of early warning applications, including seismic monitoring. He is currently working on edge computing methods for transforming IoT and mobile sensors into effective measurement tools.

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Giacomo Peruzzi

University of Padova

Special Session Organizer

Giacomo Peruzzi received the B.Sc. degree in information engineering, the M.Sc. degree in computer and automation engineering, and the Ph.D. degree in information engineering and science from the University of Siena, Siena, Italy, in 2016, 2019, and 2023, respectively.

He is currently a Research Fellow with the Department of Information Engineering, University of Padova, Padua, Italy. His current research interests include fields of the Internet of Things (IoT) and distributed measurement systems. In particular, he deals with wireless sensor networks (WSNs) for monitoring systems that are enabled by low- power wide-area network (LPWAN) technologies, as well as embedded machine learning (ML) for measurement infrastructures.

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Energy-Efficient Edge AI in Resource-Constrained Critical Systems

Abstract: As sensor networks increase in density, transmitting raw data to the cloud becomes unsustainable, which requires a paradigm shift from “passive data collection” to “smart sensing”. Moreover, the deployment of learning models in critical environments requires strict constraints on power, bandwidth, and latency.

This Special Session bridges the gap between sensor hardware constraints and modern machine learning requirements, addressing the intersection of advanced data acquisition strategies and Edge AI to achieve energy autonomy in the Healthcare and Aerospace domains.

We invite contributions focused on innovations that optimize the entire chain, from physical sensing to final decision-making. Key topics include implementing smart data acquisition strategies to reduce sensor duty cycles, applying Edge AI compression techniques suitable for microcontrollers, and identifying trade-offs between model accuracy, energy consumption, and latency. These methodologies are contextualized, but not limited to, within two primary domains: Healthcare, where wearable devices require low-power operation to ensure continuous monitoring and data privacy; and Aerospace, where limited telemetry bandwidth necessitates autonomous onboard processing to guarantee mission reliability.

Organizers:

Luigi Capogrosso

Interdisciplinary Transformation University of Austria

Special Session Organizer

Luigi Capogrosso is a Postdoctoral Researcher at the Interdisciplinary Transformation University of Austria (IT:U), advised by Prof. Michele Magno. He earned his Ph.D. in Artificial Intelligence (Cum Laude) at the Polytechnic of Turin in collaboration with the University of Verona, under the supervision of Prof. Marco Cristani and Prof. Franco Fummi.

He received his B.Sc. (2019) and M.Sc. (2021) from the University of Verona in Computer Science and Computer Engineering for Robotics and Smart Industry, respectively. Additionally, he served as a visiting scholar at the Instituto Superior Técnico, working with Prof. Mário A. T. Figueiredo.

His research interests span the broad area of learning, with a current focus on efficient machine learning, learning-enabled cyber-physical systems, and representation learning.

He is a member of the Istituto Nazionale di Alta Matematica within the Gruppo Nazionale per il Calcolo Scientifico (GNCS), as well as a member of IEEE and the Computer Vision Foundation (CVF).

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Florenc Demrozi

University of Stavanger

Special Session Organizer

Florenc Demrozi is currently an Associate Professor in Medical Technology at the Department of Electrical Engineering and Computer Science, University of Stavanger, Norway. His research focuses on Human Activity Recognition (HAR), Active and Assisted Living (AAL), Sensors and Measurements, and Artificial Intelligence of Things (AIoT).

He obtained his Ph.D. in Computer Science in May 2020, his M.Sc. in Computer Science and Engineering (2016) and B.Sc. in Computer Science (2014) all from the University of Verona, Italy. Since April 2021, he co-founded of the IoT for Care (IoT4Care) research group at the Department of Computer Science, University of Verona, Italy. The group concentrates on developing Virtual Coaching Systems grounded in IoT infrastructure and the Extended Mind concept, with the primary objective of assisting people with special needs in carrying out Activities of Daily Life (ADLs). He is an editorial board member of journals such as the IEEE Sensors Journal and is actively involved in the program committees of premier conferences, including COINS, DATE, EMBC, and COMPSAC.

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Farshad Firouzi

Arizona State University

Special Session Organizer

Farshad Firouzi is a technology entrepreneur, a faculty member at Arizona State University, as well as Adjunct Faculty at Duke University. He has authored over 100 publications, edited and authored three books, and serves as PI/Co-PI on research projects funded by agencies such as NSF and SRC. He has successfully led proposals with a combined funding portfolio exceeding $10M.

His professional background includes research roles at leading technology organizations such as imec. He is an editorial board member of journals such as the IEEE Internet of Things Journal and is actively involved in the program committees of premier conferences, including COINS, DATE, DAC, ICCAD, HOST, and VTS.

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Hyperconnected Human Bodies –Distributed and Smart Onlife Sensing

Abstract: The convergence of sensor and embedded computing technologies is enabling sensor units to process digital workloads directly at the point of data collection. This development facilitates the integration of heterogeneous sensor nodes distributed across the human body, offering a more comprehensive understanding of physiology and movement in real-world conditions. The resulting data creates new opportunities for the detection of biomarkers, continuous monitoring, personalized medicine, early intervention, and human-computer interaction.

Building on this shift toward localized and distributed intelligence, emerging RF bands and protocols such as

next generation WiFi (ie. WiFiHaLow, WiFi 8) and new BLE generations deliver higher throughput with lower energy requirements, while human body communication offers a private and ultra-low power alternative in proximity scenarios. Together, these technological developments create an unprecedented opportunity to rethink how we sense, compute, and communicate around the body in a cloud connected world.

The motivation for this special session is grounded in the scientific relevance of these innovations, as they enable new applications in health, fitness, human performance, and interactive systems.

Organizers:

Christian Kupsch

TU Bergakademie Freiberg

Special Session Organizer

Professior Christian Kupsch; Acoustic Measurement Systems (TU Bergakademie Freiberg)

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Christoph Leitner

ETH Zürich

Special Session Organizer

Dr. Christoph Leitner; Integrated Systems (ETH Zürich)

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Richard Nauber

TU Dresden

Special Session Organizer

Dr. Richard Nauber; Mobile Communications Systems (TU Dresden)

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Bruno Sgambato

Special Session Organizer

Dr. Bruno Sgambato; Human Computer Interfaces (Imperial College London)

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Advanced Sensing for Intelligent Transportation Systems

Abstract: The proposal emphasizes the strategic importance of advanced sensing in intelligent transportation systems, highlighting that the deployment of reliable, distributed, and heterogeneous sensor networks (including cameras, LiDAR, radar, and connected vehicles) enables the accurate estimation of critical variables to support real-time adaptive control algorithms, anticipate congestion, adjust traffic operations within milliseconds, and mitigate incidents before they propagate. The integration of these technologies fosters information fusion and the development of digital twins, which are crucial for ensuring safety, resilience, and energy efficiency under highly variable demand, extreme weather conditions, and the increasing presence of automated vehicles.

The Special Session aims to address key challenges in sensing architectures explicitly designed for real-time adaptive control, including temporal synchronization, interoperability, cybersecurity, calibration, and large-scale maintenance, by promoting a holistic approach to the joint design of sensing, communication, and control strategies. Structured for 90 minutes, the session will feature six oral presentations focused on sensing architecture and its implications for real-time adaptive control, followed by a joint discussion exploring common challenges, future research directions, and opportunities for collaboration. It will conclude with a summary of the session’s main technical contributions.

Organizers:

Víctor Manuel García Martínez

Federal University of Espírito Santo

Special Session Organizer

Víctor Manuel García Martínez received his B.Sc. in Telecommunications Engineering from the Universidad Tecnológica de La Habana (CUJAE), Cuba, in 2011, and his M.Sc. in Electrical Engineering from the Universidade Federal do Espírito Santo (UFES), Brazil, in 2019. He is currently pursuing a Ph.D. in Electrical Engineering at UFES. His research interests include software-defined networking, wireless communications, network performance analysis, and cyber-physical systems. He serves as R&D Manager at Atman Systems, where he focuses on applying operations research to the real-time adaptive control of urban traffic systems.

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Rodolfo Vieira Valentim

Politecnico di Torino

Special Session Organizer

Rodolfo Vieira Valentim received his B.Sc. degree from the Universidade Federal do Espírito Santo (UFES), Brazil, in 2018, his M.Sc. in Computer Science from UFES in 2020, and his Ph.D. in Computer Science from the Politecnico di Torino (PoliTo), Italy, in 2024.

In 2015, he was awarded a scholarship to spend one year at the Hanze Institute of Technology in the Netherlands as an exchange student. His research interests include network security, artificial intelligence, and anomaly detection. He is currently collaborating with Atman Systems on the development of intelligent control solutions for urban traffic systems.

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Fernando Luiz Sossai Martinelli

Atman Systems

Special Session Organizer

Fernando Luiz Sossai Martinelli is the CEO of Atman Systems, a company specializing in the development of hardware and software for smart city solutions, and of Sinales, a leading firm in traffic engineering and urban mobility projects. He holds a degree in Electrical Engineering from the Federal University of Espírito Santo (UFES), with an emphasis on Electronics and Computing; participated in the PET-UFES program; and was a recipient of a “Science Without Borders” scholarship, completing an academic exchange at Politecnico di Torino (Italy) focused on embedded systems. He has extensive experience in traffic signaling, traffic engineering, electronic product and embedded software development, production planning and control, management of public tenders and technical commercial proposals, funding for technological innovation, institutional and governmental relations, and strategic management. He operates at the intersection of engineering, technology, and public policy, driving innovative solutions for urban mobility and smart infrastructure with a focus on efficiency, sustainability, and improving quality of life in cities.

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Imaging Sensors in Intelligent Systems

Abstract: Cameras have become one of the predominant sensors inintelligent systems due to their rich data output, non-invasive nature and rapid algorithmic advances. This session addresses the critical challenge of advancing computer vision algorithms—such as detection, classification, tracking, semantic segmentation and 3D reconstruction for direct application in robust, real-world systems.

We seek to showcase and discuss works that simultaneously advances the state-of-the-art in computer vision tasks and demonstrates their important role within applications and integrated intelligent systems.

Our goal is to bring together researchers pushing the boundaries of fundamental vision models with those engineering novel sensor-based solutions. The session will highlight work where the camera is not just a component, but the central source of perceptual intelligence.

The session will allow the symposium's audience to explore how vision centric approaches are providing smart sensor solutions for current and future applications. We hope the attendees will gain insights into vision algorithms that are more efficient, accurate, and capable of operating under real-world constraints.

Organizers:

Raquel Frizera Vassallo

Federal University of Espírito Santo

Special Session Organizer

Prof. Raquel Frizera Vassallo is an Associate Professor in Electrical Engineering at the Federal University of Espírito Santo (UFES), Brazil. She holds B.Sc. (1995), M.Sc. (1998), and Ph.D. (2004) degrees in Electrical Engineering from UFES, with research periods at the Institute for Systems and Robotics in Lisbon, Portugal, and a post-doctoral fellowship at the University of Bristol, UK (2017-2018).

With over two decades of experience in Computer Vision and Robotics, her research focuses on mobile robotics, intelligent spaces, human-robot-environment interaction, aerial robotics, mapping, and navigation. She has led the development of practical vision-based systems, including robotic guides for assisted navigation and automated industrial measurement solutions. Her work emphasizes intelligent spaces based on computer vision, mobile robotics, and the implementation of robust vision systems in real-world environments.

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Thais Pedruzzi do Nascimento

Federal University of Espírito Santo

Special Session Organizer

Prof. Thais Pedruzzi do Nascimento is an Assistant Professor in the Department of Electrical Engineering at the Federal University of Espírito Santo (UFES). She holds B.Sc. (2013), M.Sc. (2015), and Ph.D. (2021) degrees in Electrical Engineering from UFES, with a research period at Concordia University, in Montreal, Canada. Her research primarily focuses on digital image processing, image super-resolution and artificial intelligence, with a dedicated interest in social technology. Complementing her academic work, she applied her expertise as a Researcher at the SENAI Institute of Innovation in Embedded Systems (ISI-SE) in 2021, where she conducted applied R&D, focusing on practical implementations of computer vision and embedded systems.

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