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Base","Card Contact Base",[728],{"id":50,"groupTitle":16,"sessions":729},[730,755,779,812,837],{"id":175,"session":731},{"id":155,"title":732,"teaser":733,"body":734,"createdAt":735,"updatedAt":736,"publishedAt":737,"url_path_id":738,"contacts":739,"url_path":754},"Intelligent Sensor Systems as a Pathway to the Next Generation of Maintenance Operations","\u003Cp style=\"text-align:justify;\">\u003Cspan style=\"background-color:oklch(100);color:oklch(0.200);\">The session focuses on sensor networks and intelligent systems driving next-generation monitoring across diverse fields, such as structural health inspection, predictive maintenance, condition monitoring, and automotive. Topics might include sensor node design, data acquisition, digital twins, edge processing and tiny Machine Learning.\u003C/span>\u003C/p>","\u003Cp style=\"margin-left:0px;text-align:justify;\">Topics of Interest\u003C/p>\u003Cp style=\"margin-left:0px;text-align:justify;\">We welcome original contributions, including but not limited to:\u003C/p>\u003Cul>\u003Cli style=\"text-align:justify;\">Advanced Sensor Node Design – Development of innovative sensor nodes with integrated processing capabilities for real-time applications.\u003C/li>\u003Cli style=\"text-align:justify;\">Data Acquisition and Management – Efficient data acquisition methodologies, data fusion techniques, and strategies to ensure data integrity and quality.\u003C/li>\u003Cli style=\"text-align:justify;\">Digital Twins – Implementation of digital twins for simulation and predictive monitoring of complex systems, enabling proactive and optimized maintenance.\u003C/li>\u003Cli style=\"text-align:justify;\">Edge Processing and Tiny Machine Learning – Application of lightweight machine learning algorithms and edge computing techniques for real-time data analytics at the sensor level, reducing latency and bandwidth usage.\u003C/li>\u003Cli style=\"text-align:justify;\">Practical Applications – Case studies and real-world implementations in fields such as smart and sustainable industries, automotive, aerospace, showcasing the benefits of intelligent sensor systems in maintenance operations.\u003C/li>\u003C/ul>","2025-02-18T16:54:14.339Z","2025-02-18T16:54:16.664Z","2025-02-18T16:54:16.659Z","103",[740,747],{"id":489,"name":741,"committee":16,"position":16,"affiliation":16,"email":16,"biography":742,"createdAt":743,"updatedAt":743,"url_path_id":744,"contactPhoto":16,"socialLinks":745,"url_path":746},"Federica Zonzini","\u003Cp style=\"text-align:justify;\">Federica Zonzini received the B.S. and M.S. degrees in Electronic Engineering and the Ph.D. degree in Structural and Environmental Health Monitoring and Management (SEHM2) from the University of Bologna, Bologna, Italy, in 2016, 2018, and 2022, respectively. She is currently Junior Assistant Professor in Electronics with the Intelligent Sensor System Laboratory of the University of Bologna. Her research interests include advanced signal processing techniques, tiny machine learning and edge computing in general, with a primary focus on Structural Health Monitoring applications. In this pursuit, she combines instrumental aspects related to signal acquisition, conditioning and digitalization, and those pertaining to data management, data analytics and information sharing.\u003C/p>","2025-01-28T00:30:51.810Z","86",[],"-56",{"id":292,"name":748,"committee":16,"position":16,"affiliation":16,"email":16,"biography":749,"createdAt":750,"updatedAt":750,"url_path_id":751,"contactPhoto":16,"socialLinks":752,"url_path":753},"Matteo Zauli","\u003Cp style=\"text-align:justify;\">Matteo Zauli is Adjunct Professor and Research Fellow at the Intelligent Sensor System Laboratory of the University of Bologna. He completed his Ph.D. in Structural and Environmental Health Monitoring and Management (SEHM2) at the University of Bologna in 2023. In March 2019, he graduated with honours in Electronic Engineering from the University of Bologna. His research interests focus on embedded systems and edge computing for monitoring applications in fields such as structural inspection, bioengineering, precision agriculture, and automotive.\u003C/p>","2025-01-28T00:31:53.225Z","87",[],"-57","-65",{"id":193,"session":756},{"id":175,"title":757,"teaser":758,"body":52,"createdAt":759,"updatedAt":760,"publishedAt":761,"url_path_id":762,"contacts":763,"url_path":778},"Next-Generation Microsystems for Sensors and Actuators: Emerging Design and Innovative Applications","\u003Cp style=\"text-align:justify;\">\u003Cspan style=\"background-color:oklch(100);color:oklch(0.200);\">The continuous evolution of microsystems has led to a significant impact in the fields of sensors and actuators. The possibility to integrate non-standard materials, e.g. piezoelectric or magnetic materials, with silicon represents a cornerstone for next-generation miniaturized chemical, physical, biological or optical sensors and microactuators. This progress is leading to new challenges and opportunities for modern applications in several research fields, such as physics, electronics, chemistry, biology, biomedical sciences, and microfluidics. In this context, we invite researchers and scientists to submit contributions on scientific and technical aspects of emerging designs and innovative employments of microsystems for sensors and actuators.\u003C/span>\u003C/p>","2025-02-18T16:48:15.071Z","2025-02-18T16:48:16.670Z","2025-02-18T16:48:16.664Z","99",[764,771],{"id":719,"name":765,"committee":16,"position":16,"affiliation":16,"email":16,"biography":766,"createdAt":767,"updatedAt":767,"url_path_id":768,"contactPhoto":16,"socialLinks":769,"url_path":770},"Alessandro Nastro","\u003Cp style=\"text-align:justify;\">Alessandro Nastro was born in Romano di Lombardia, Italy, in 1991. He received the Master Degree cum laude in Electronics Engineering and the europaeus PhD in Information Engineering at Università degli Studi di Brescia in 2016 and 2020, respectively. From 2019 he has been a research fellow in electronics and since May 2022 he is assistant professor with the Department of\u003Cbr>Information Engineering and part of the Sensors, Microsystems and Electronics (SME) research group at Università degli Studi di Brescia, Italy. His research activity deals with the development of electronic techniques and circuits coupled to Micro Electro-Mechanical Systems (MEMS) for static and dynamic micromechanical sensing and actuation. He has co/authored more then 50 publications in international peer-reviewed journals and international and national conference proceedings on piezoelectric transducers and resonant microsensors, MEMS and microsystems, sensors with contactless interrogation and energy harvesting for autonomous sensors. He was chair of the biomedical sensing session for the conference IEEE Sensors 2024 (Kobe, Japan) and he has been assigned as guest-editor of a special issue in Sensors regarding innovative MEMS devices. He is an IEEE member since 2020, a member of CNR-INO (National Institute of Optics) since 2023 and a member of SIE (Italian Society of Electronics) since 2018.\u003C/p>","2025-01-28T00:04:19.227Z","75",[],"-45",{"id":622,"name":772,"committee":16,"position":16,"affiliation":16,"email":16,"biography":773,"createdAt":774,"updatedAt":774,"url_path_id":775,"contactPhoto":16,"socialLinks":776,"url_path":777},"Marco Baù","\u003Cp style=\"text-align:justify;\">Marco Baù was born in 1981 in Castiglione delle Stiviere (Italy). He received the Laurea Degree cum laude in Electronic Engineering and the Research Doctorate degree in Electronic Instrumentation from the University of Brescia in 2005 and 2009, respectively. Since October 2024 is Associate Professor in electronics at the Department of Information Engineering at University of\u003Cbr>Brescia where he is part of the Sensors, Microsystems and Electronics (SME) research group. His research activity deals with the investigation of techniques for the contactless interrogation of autonomous passive sensors based on electrical and electro-mechanical resonators with particular interest towards piezoelectric MEMS resonators. Additional research topics comprise front-end electronic circuits for sensors and the investigation of principles and techniques for energy\u003Cbr>harvesting from vibrations and thermal gradients. He has co/authored more then 60 publications in international peer-reviewed journals and international and national conference proceedings. He is an IEEE member since 2019.\u003C/p>","2025-01-28T00:05:51.204Z","76",[],"-46","-61",{"id":136,"session":780},{"id":5,"title":781,"teaser":782,"body":783,"createdAt":784,"updatedAt":785,"publishedAt":786,"url_path_id":787,"contacts":788,"url_path":811},"Sensors for Precision Agriculture Applications","\u003Cp style=\"margin-left:0px;text-align:justify;\">The world's growing population, changing climate, and shrinking agricultural land pose immense challenges for meeting global food security needs. Crop yields must increase dramatically on existing farmland to feed up to 10 billion people by 2050. Precision agriculture aims to optimize crop production and minimize environmental impact by using technologies like sensors, robotics, and data analytics. \u003C/p>","\u003Cp style=\"margin-left:0px;text-align:justify;\">In this framework, emerging areas are:\u003C/p>\u003Cul>\u003Cli style=\"text-align:justify;\">Developments of lab-on-a-chip devices for quick and accurate plant pathogen detection. Indeed, the deterioration of plant products, which is estimated to cause a loss of between 10 and 30 percent overall, is one of the main constraints on food resources. Since one of the major reasons for this deterioration is the presence of pathogens and their associated toxic metabolites and parasites, food safety is an issue to be considered.\u003C/li>\u003Cli style=\"text-align:justify;\">Reduction of waste and environmental contamination. Indeed, agriculture must reduce its massive environmental impacts which include 20% of anthropogenic greenhouse gas emissions and 70% of freshwater withdrawals. Precision agriculture addresses these issues by using data collected from specialized sensors to tailor the application of water, fertilizers, and pesticides to specific localized needs. Therefore, in contrast to uniform treatment of large areas, this targeted approach allows more efficient resource application.\u003C/li>\u003Cli style=\"text-align:justify;\">Development of wireless sensor networks for efficient data processing and transmission. These networks provide detailed spatiotemporal data at high resolution, facilitating a deeper understanding of the agricultural landscape. The real-time information they offer enables rapid decision-making, reducing the need for labor-intensive field monitoring. \u003C/li>\u003C/ul>\u003Cp style=\"margin-left:0px;text-align:justify;\">This session focuses on the most suitable Key Enabling Technologies (KET) that can allow a profitable advance in productivity, sustainability, ecological and digital transition in the agricultural sector. Therefore, the main topics covered by this session are:\u003C/p>\u003Cul>\u003Cli style=\"text-align:justify;\">sensors for detection of plant pathogens and their harmful metabolic products in food, as well as plant parasites;\u003C/li>\u003Cli style=\"text-align:justify;\">lab-on-chip devices and microfluidics for in-field analysis;\u003C/li>\u003Cli style=\"text-align:justify;\">sensors and actuators for controlled irrigation and pesticide treatments in agriculture;\u003C/li>\u003Cli style=\"text-align:justify;\">sensor networks for smart data-driven agriculture.\u003C/li>\u003C/ul>","2025-02-18T16:43:52.077Z","2025-02-18T16:43:57.054Z","2025-02-18T16:43:57.045Z","97",[789,796,804],{"id":147,"name":790,"committee":16,"position":16,"affiliation":16,"email":16,"biography":791,"createdAt":792,"updatedAt":792,"url_path_id":793,"contactPhoto":16,"socialLinks":794,"url_path":795},"Domenico Caputo","\u003Cp style=\"text-align:justify;\">Domenico Caputo (Member, IEEE) received the cum laude Electronic Engineering degree in Electronic Engineering from Sapienza University of Rome, Italy. He was a Visiting Staff at the University of Princeton, New Jersey, USA, in 1994.He is an Associate Professor at the Department of Information Engineering, Electronics and Telecommunications of the same University. He is referee of several scientific journals an author of more than 200 articles in international journals and proceedings. He is involved in several Program Committee of international conferences. He has been principal investigator of numerous national and international research projects. His main research fields concerned the development of amorphous silicon photodetectors for detection of radiation from the UV to the near infrared range and of innovative electronic devices based on amorphous silicon. The present research interests are focused on the development of thin film photodetector and lab-on-chip systems for virus detection in agriculture and mycotoxin detection in food.\u003C/p>","2025-01-27T23:55:16.287Z","68",[],"-38",{"id":797,"name":798,"committee":16,"position":16,"affiliation":16,"email":16,"biography":799,"createdAt":800,"updatedAt":800,"url_path_id":801,"contactPhoto":16,"socialLinks":802,"url_path":803},19,"Alessandro Sanginario","\u003Cp style=\"text-align:justify;\">Alessandro Sanginario (Member, IEEE) received his degree in Biomedical Engineering and a Ph.D. in Physics from Politecnico di Torino, Italy, in 2006 and 2010, respectively. He was a Visiting Scholar at the University of South-Eastern Norway in 2009. After his Ph.D., he held postdoctoral positions at the Chilab Laboratory and the IIT @ PoliTo Center for Space Human Robotics until 2017. Currently, he is a researcher at the eLIONS Laboratory, Department of Electronics and Telecommunications, Politecnico di Torino.\u003Cbr>In 2022, he completed an executive master’s in Project Management and earned the \"Certified Project Management Associate - IPMA Level D\" qualification. He teaches “Design of Microsystems” for Electronic Engineering and Nanotechnologies for ICT. Co-founder of a startup in the AgriFood sector, his research focuses on MEMS, advanced sensors, and micro- and nano-system integration for biomedical and AgriFood applications. He is the author/co-author of two patents and over 100 publications in international journals and conferences.\u003C/p>","2025-01-27T23:56:31.753Z","69",[],"-39",{"id":274,"name":805,"committee":16,"position":16,"affiliation":16,"email":16,"biography":806,"createdAt":807,"updatedAt":807,"url_path_id":808,"contactPhoto":16,"socialLinks":809,"url_path":810},"Francesca Costantini","\u003Cp style=\"text-align:justify;\">Francesca Costantini earned her master's degree in Pharmaceutical Chemistry and Technology (CTF) from the University of Bologna in October 2003. In 2004, she undertook a research internship at Pepscan System B.V. in Lelystad, The Netherlands, from July to December. From February 2005, Francesca pursued her Ph.D. as a candidate within the Supramolecular Chemistry and Technology and Molecular Nanofabrication groups at the University of Twente, Enschede, The Netherlands. She was awarded her Ph.D. in Chemistry in December 2009, with research centered on the immobilization of organic, nanometallic, and enzymatic catalysts in microfluidic systems.\u003C/p>\u003Cp style=\"text-align:justify;\">From December 2010 to November 2019, Francesca served as a researcher at Sapienza University of Rome, specializing in the development of Lab-on-Chip systems for healthcare and agrifood. Since 2019, she has been a researcher at the Research Centre for Plant Protection and Certification (CREA-DC) in Rome, where her work primarily involves developing diagnostic protocols for plant pathogens (bacteria, fungi, viruses) and their harmful metabolic products in food, as well as parasites, utilizing Lab-on-Chip technologies. Additionally, Francesca teaches courses on Macromolecular Structures and Environmental Sensors at Sapienza University of Rome. She is author/co-author of one patent and over 50 publications in international journals and conferences.\u003C/p>","2025-01-27T23:57:53.704Z","70",[],"-40","-59",{"id":155,"session":813},{"id":28,"title":814,"teaser":815,"body":816,"createdAt":817,"updatedAt":818,"publishedAt":819,"url_path_id":820,"contacts":821,"url_path":836},"Sensors in Extreme or Demanding Environments","\u003Cp style=\"text-align:justify;\">\u003Cspan style=\"background-color:oklch(100);color:oklch(0.200);\">The conference highlights the ubiquity of sensors in everyday products, industrial processes, and specialist research applications. This can often be attributed to the transferability of sensor systems between applications. In recent years this has been facilitated by factors such as the rapid expansion of IoT and mobile electronics. Systems can often be tested in lab conditions, which are consistent with many indoor applications.\u003C/span>\u003C/p>","\u003Cp style=\"margin-left:0px;text-align:justify;\">However, the sometimes considerable technical and logistical challenges of translating lab-based prototypes to practical trials and real-world measurement are underestimated or overlooked by the research community. These challenges are acute in cases where sensors are deployed in extreme or demanding environments. This could include severe conditions that impose constraints on the sensor’s design or require innovation to be able achieve measurement; sensors applied under conditions that are uncontrollable, inaccessible, or inhospitable to measurement or electronic prototypes in general; or where the sensor system, other assets, or human operators are exposed to risks or hazards.\u003C/p>\u003Cp style=\"margin-left:0px;text-align:justify;\">This special session aims to redress the balance in the research community by offering a forum to examine research on the design, deployment and application of sensing technologies out-of-the-lab in extreme or demanding environments. Authors will be encouraged to submit to the session original research, new sensors designs or implementation, and case studies of sensor application.\u003C/p>\u003Cp style=\"margin-left:0px;text-align:justify;\">Examples include but are not limited to the following:\u003C/p>\u003Cul>\u003Cli style=\"text-align:justify;\">Sensing in difficult, inhospitable or extreme natural environments.\u003C/li>\u003Cli style=\"text-align:justify;\">Sensing in dangerous environments, e.g. where the sensor supports or mitigates harm to people or property.\u003C/li>\u003Cli style=\"text-align:justify;\">Sensing in controlled environments where the sensor system is at risk of damage or destruction, e.g., explosive atmospheres.\u003C/li>\u003Cli style=\"text-align:justify;\">Sub-merged or airborne sensing imposing unique design requirements on sensors.\u003C/li>\u003Cli style=\"text-align:justify;\">Measurement under extremes of temperature, pressure, noise, radioactivity, corrosive media, etc.\u003C/li>\u003Cli style=\"text-align:justify;\">Measurement of difficult to handle work- or test- pieces in manufacturing or metrology.\u003C/li>\u003C/ul>","2025-02-18T16:50:15.148Z","2025-02-18T16:50:17.260Z","2025-02-18T16:50:17.255Z","100",[822,829],{"id":470,"name":823,"committee":16,"position":16,"affiliation":16,"email":16,"biography":824,"createdAt":825,"updatedAt":825,"url_path_id":826,"contactPhoto":16,"socialLinks":827,"url_path":828},"Liam Marsh","\u003Cp style=\"text-align:justify;\">Dr Liam Marsh is a Lecturer in Embedded Systems based in the Department of Electrical and Electronic\u003Cbr>Engineering at the University of Manchester. Liam gained a first-class MEng degree (Hons) in Electrical Engineering and Electronics from the University of Manchester Institute of Science and Technology (UMIST) in 2007. In 2011 he completed his PhD titled\u003Cbr>‘Electromagnetic Tomography and People Screening’ at the University of Manchester. Liam’s research activities are in the areas of security, the design of novel sensing systems for extreme natural climates, humanitarian landmine and ERW detection, and non-destructive testing. He has worked on several high-profile research projects including: EMBODY (2007-2011) – the design of intelligent walk-through metal detectors for aviation security purposes; SEMIS (2011-2023) – the design of a new generation of multi-modal landmine detection systems; FRUITGRADING (2012-2015) & Q-MEAT (2012-2014) – the development of non-destructive sensing technologies to minimise food waste through the determination of the internal quality of fruit and meat samples; The Lost Meteorites of Antarctica (2016-\u003Cbr>2020) – the design and deployment of a novel, polar-hardened meteorite detection system to prove the\u003Cbr>existence of sub-surface iron-rich meteorites in the Antarctic. Liam is currently the Principal Investigator for the EPSRC-funded project TRANSMIT - A towable RF\u003Cbr>system for non-invasive sensing and measurement of Arctic sea ice thickness (2024 - 2027). This project\u003Cbr>aims to develop novel sensing technology and methods capable of non-destructively measuring the\u003Cbr>thickness of sea ice. Through these research activities Liam has collaborated with a range of external organisations, universities, and institutes, both nationally and internationally. Liam has a history of interdisciplinary collaboration with the Departments of Mathematics, and Earth and Environmental Sciences at the University of Manchester.\u003C/p>","2025-01-28T00:07:47.177Z","77",[],"-47",{"id":350,"name":830,"committee":16,"position":16,"affiliation":16,"email":16,"biography":831,"createdAt":832,"updatedAt":832,"url_path_id":833,"contactPhoto":16,"socialLinks":834,"url_path":835},"Adam Fletcher","\u003Cp style=\"text-align:justify;\">Dr Adam Fletcher is a Research Associate in Electromagnetic Inspection in the Department of Electrical and Electronic Engineering at the University of Manchester. Adam received a first-class MSc degree in Mathematics and Physics from the University of Durham, U.K., in 2008. In 2014 he completed his EngD degree at the University of Manchester with his thesis titled Non-Destructive Testing of the Graphite Core Within an Advanced Gas-Cooled Reactor. He joined EDF Energy in 2014 as a Graphite Core Inspection Engineer working on implementing new techniques for AGR core\u003Cbr>inspection, the analysis of graphite core inspections and weight loss data.\u003Cbr>He rejoined the University of Manchester in 2019 as a Research Associate initially developing high sensitivity electromagnetic techniques for landmine detection as part of the SEMIS project. Since then, Adam has also worked on the High-Temp and Online Microstructural Analysis projects applying eddy current inspection with AI data processing to hot-strip steel manufacturing in close collaboration with the University of Warwick. He is also a researcher on the MetalID project aiming to apply electromagnetic sensing to scrap metal identification and sorting. Since the conclusion of Adam’s EngD he has been actively involved with subsequent work on graphite core inspection via collaboration with EDF Energy and the EU funded Inno4Graph progamme. His main research interests are in electromagnetic sensor systems, automation, inverse problems and computational electromagnetics.\u003C/p>","2025-01-28T00:09:06.455Z","78",[],"-48","-62",{"id":267,"session":838},{"id":161,"title":839,"teaser":840,"body":841,"createdAt":842,"updatedAt":843,"publishedAt":844,"url_path_id":845,"contacts":846,"url_path":891},"Pushing Intelligence to the Edge: Advances in Low-Power AI and Near-Sensor Computing for Smart Systems","\u003Cp style=\"text-align:justify;\">The rapid proliferation of Internet of Things (IoT) devices has opened transformative possibilities across several sectors such as healthcare, sports, industrial monitoring, agriculture, and human-computer interaction. However, the practical deployment of intelligent IoT and wearable systems still faces significant challenges related to energy efficiency, real-time performance, sensor heterogeneity, and scalable artificial intelligence (AI) integration. These constraints are especially pronounced in resource-constrained environments, where bandwidth, latency, and power are at a premium.\u003C/p>","\u003Cp style=\"text-align:justify;\">Edge AI, and more specifically near-/in-sensor computing, are meeting these challenges by enabling intelligence at or close to the point of sensing. These approaches bring computation directly to the edge of the network or even into the sensor itself, reducing the need for continuous cloud connectivity while improving responsiveness, privacy, and energy usage. By processing and analyzing data closer to the source, this new paradigm offers faster data throughput, reduced or even zero bandwidth requirements, enhanced data privacy, security, and the potential for autonomous operation.\u003C/p>\u003Cp style=\"text-align:justify;\">This session seeks to explore cutting-edge research and practical breakthroughs in the design, development, and deployment of intelligent low-power systems. It aims to emphasize the synergy between advanced AI techniques, hardware-software co-design, and sensor-level innovation, positioning near-sensor computing as a core enabler within the broader Edge AI ecosystem.\u003C/p>\u003Cp style=\"text-align:justify;\">Contributions can be application-agnostic, but practical problems tackling areas within human activity recognition, smart agriculture, structural health monitoring, personalized health management, are highly encouraged.\u003C/p>\u003Cp style=\"text-align:justify;\">Topics of interest include, but are not limited to:\u003C/p>\u003Cul>\u003Cli style=\"text-align:justify;\">Efficient Edge Learning Architectures: Development of lightweight and energy-efficient machine learning models deployable on MCUs, FPGAs, and SoCs, including federated learning, knowledge distillation, and on-device training.\u003C/li>\u003Cli style=\"text-align:justify;\">Near-Sensor and In-Sensor Computation: Architectures and algorithms that embed computation within or close to the sensing hardware to minimize latency, reduce data transmission, and enable autonomous operation.\u003C/li>\u003Cli style=\"text-align:justify;\">Multimodal Sensor Fusion and Context Awareness: Integration of heterogeneous sensor data (e.g., inertial, visual, acoustic, bioelectrical) for robust perception and adaptive inference in noisy or dynamic environments.\u003C/li>\u003Cli style=\"text-align:justify;\">Adaptive and Self-Optimizing Edge AI: Dynamic reconfiguration of models or system parameters to balance energy, accuracy, and workload in real-time applications.\u003C/li>\u003Cli style=\"text-align:justify;\">Embedded Analytics: Edge-based time series analysis and real-time decision-making for applications in health monitoring, ambient intelligence, and robotics.\u003C/li>\u003Cli style=\"text-align:justify;\">Energy-Aware Hardware-Software Co-Design: Co-optimization strategies for building ultra-low-power devices through hardware accelerators, power-aware communication protocols, and AI model compression techniques. \u003C/li>\u003Cli style=\"text-align:justify;\">On-Device Learning and AutoML at the Edge: Frameworks for incremental learning, online adaptation, and automated model generation under compute and memory constraints.\u003C/li>\u003Cli style=\"text-align:justify;\">Deployment Tools and Ecosystems: Software stacks, tool chains, and benchmarking environments that support the end-to-end deployment of AI models in constrained edge environments.\u003C/li>\u003Cli style=\"text-align:justify;\">Scalability, Generalization, and Validation: Techniques to ensure robust performance across different deployment settings and validation against real-world datasets, with a focus on interpretability and reliability.\u003C/li>\u003C/ul>","2025-05-27T19:14:14.875Z","2025-05-27T19:15:50.904Z","2025-05-27T19:15:50.891Z","120",[847,854,861,868,875,883],{"id":298,"name":848,"committee":16,"position":16,"affiliation":16,"email":16,"biography":849,"createdAt":850,"updatedAt":850,"url_path_id":851,"contactPhoto":16,"socialLinks":852,"url_path":853},"Salvatore Tedesco","\u003Cp style=\"text-align:justify;\">Salvatore Tedesco (Member, IEEE) received the B.Sc. degree (Hons.) in information technology engineering and the M.Sc. degree (Hons.) in telecommunications engineering from the University of Salento, Lecce, Italy, and the Ph.D. degree in electrical and electronic engineering from University College Cork (UCC), Cork, Ireland. Since April 2012, he has been with the Wireless\u003Cbr>Sensor Networks Group, Tyndall National Institute, UCC, where he is currently a Senior Researcher II and a Team Leader responsible for leading a research team in the data analytics area. He has authored more than 100 articles in international journals and conference proceedings. He has received over 1.5 million euro in grant funding as a Principal Investigator and a Co-Principal Investigator. Since joining the Tyndall National Institute, he has managed and successfully led over 30 industrial and research-oriented projects focused on his main research interests on wearable technologies for health and well-being, motion analysis in sports and\u003Cbr>clinical populations, digital health, physiological monitoring, image and signal processing, edge analytics, and machine learning. Further contributions deal with radio frequency identification (RFID) technology, ultrawideband localization systems for indoor applications, sensor calibration, animal monitoring, and industry 4.0.\u003C/p>","2025-01-28T00:13:14.879Z","79",[],"-49",{"id":433,"name":855,"committee":16,"position":16,"affiliation":16,"email":16,"biography":856,"createdAt":857,"updatedAt":857,"url_path_id":858,"contactPhoto":16,"socialLinks":859,"url_path":860},"Ian Cleland","\u003Cp style=\"text-align:justify;\">Dr Ian Cleland is a Senior Lecturer within the School of Computing at Ulster University, Belfast Northern Ireland, where he leads the research theme of Human Computer Interaction within the Pervasive Computing Research Centre. He received his BSc in Biomedical Engineering in 2009 and a PhD in Computer Science in 2012, both from Ulster University. Ian been the Research Director for the School of Computing since February 2024. He was previously Postgraduate Tutor\u003Cbr>for the School from 2018-2024 where he overseen the academic processes for around 55 PhD Researchers each year. His research combines wearable, pervasive, and mobile computing with data science and artificial intelligence to produce innovative digital solutions, mainly in the healthcare domain. Areas of particular interest include sensor-based activity recognition and the use of synthetic data to support data driven modelling. He has published his research in over 100\u003Cbr>peer reviewed scientific publications in top forums in digital health and health informatics. Ian has secured externally funded research, as both Principal Investigator and Co-Investigator, to the value of over £7.89M. Most notably he is a Co-I on the Connected Health Innovation Centre (CHIC-Phase 2 £3.36M) and the PwC Advanced Engineering and Research Centre (ARC £3.13M). Ian was Co-Chair for the International Conference of Ambient Intelligence (UCAmI2024). He is\u003Cbr>currently Vice Chair of the Executive Committee for the Alzheimer’s Association Technology and Dementia Professional Interest Area.\u003C/p>","2025-01-28T00:22:07.223Z","80",[],"-50",{"id":327,"name":862,"committee":16,"position":16,"affiliation":16,"email":16,"biography":863,"createdAt":864,"updatedAt":864,"url_path_id":865,"contactPhoto":16,"socialLinks":866,"url_path":867},"Luciano Sebastián Martínez-Rau","\u003Cp style=\"text-align:justify;\">Dr. Luciano Sebastián Martínez-Rau received the Ph.D. degree in Engineering Science from National University of the Littoral, Argentina in 2022. Since 2023, he is a Postdoctoral Researcher at Mid Sweden University, Department of Computer and Electrical Engineering, Sweden. He is also an Adjunct Professor at the Universidad Nacional del Litoral in Argentina. His research interests include low-power embedded systems, machine learning, signal processing and on-device training.\u003C/p>","2025-01-27T23:59:20.459Z","71",[],"-41",{"id":250,"name":869,"committee":16,"position":16,"affiliation":16,"email":16,"biography":870,"createdAt":871,"updatedAt":871,"url_path_id":872,"contactPhoto":16,"socialLinks":873,"url_path":874},"Yuxuan Zhang","\u003Cp style=\"text-align:justify;\">Mr. Yuxuan Zhang received the M.Sc. degree in Embedded Systems Engineering from the University of Leeds, UK in 2019. He is currently a Ph.D. candidate of Electronics Engineering at Mid Sweden University, Department of Computer and Electrical, Sweden since 2021. His research interests include machine learning and signal processing on resource-constrained embedded devices, especially in structural health monitoring (SHM) and fault diagnosis applications. He has chaired one session in IEEE SAS 2022 international conference.\u003C/p>","2025-01-28T00:00:33.819Z","72",[],"-42",{"id":876,"name":877,"committee":16,"position":16,"affiliation":16,"email":16,"biography":878,"createdAt":879,"updatedAt":879,"url_path_id":880,"contactPhoto":16,"socialLinks":881,"url_path":882},23,"Leonardo Luis Giovanini","\u003Cp style=\"text-align:justify;\">Assoc. Prof. Leonardo Luis Giovanini received the Ph.D. degree in Engineering Science from Universidad Nacional del Littoral, Argentina in 2000. He was Senior Research Fellow and Technical Manager of the Industrial Control Center, University of Strathclyde, U. K., from 2001 until 2007. In 2008 he joined the Institute for Signals, Systems and Computational Intelligence, at the National Council of Technological and Scientific Research. His research interests are robust and nonlinear control theory, signal processing and applied machine learning. Application fields are robotics, autonomous vehicles and multi-agent systems.\u003C/p>","2025-01-28T00:01:36.325Z","73",[],"-43",{"id":884,"name":885,"committee":16,"position":16,"affiliation":16,"email":16,"biography":886,"createdAt":887,"updatedAt":887,"url_path_id":888,"contactPhoto":16,"socialLinks":889,"url_path":890},24,"Zenghua Liu","\u003Cp style=\"text-align:justify;\">Prof. Zenghua Liu received the Ph.D. from Beijing University of Technology (BJUT), China in 2006. He has been a lecturer, associate professor and professor at BJUT since 2006, 2008 and 2014, respectively. He was a visiting scholar at City University of Hong Kong in 2006 and at University College London, from 2010 to 2011. His research interests include ultrasonic and electromagnetic nondestructive testing techniques, advanced sensing and intelligent inspection, SHM, and signal processing methods. He has authored and co-authored over 240 papers, 40 patents and 4 books. He was recognized among the world’s top 2% of scientists for 2024.\u003C/p>","2025-01-28T00:02:47.957Z","74",[],"-44","-76",{"data":893,"meta":894},{"id":470,"heading":465,"createdAt":471,"updatedAt":472,"publishedAt":473,"url_path_id":474,"url_path":466,"contentType":118},{},1778853381645]