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Analytics","2025-03-11T12:37:58.318Z","2025-03-11T12:37:59.626Z","2025-03-11T12:37:59.614Z","172",{"id":828,"name":829,"alternativeText":16,"caption":16,"width":830,"height":831,"formats":832,"hash":856,"ext":442,"mime":445,"size":857,"url":858,"previewUrl":16,"provider":23,"provider_metadata":16,"createdAt":859,"updatedAt":859},1659,"121212ifuLogo.jpg",1725,648,{"large":833,"small":838,"medium":844,"thumbnail":850},{"ext":442,"url":834,"hash":835,"mime":445,"name":836,"path":16,"size":545,"width":448,"height":837},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/i2mtc25/large_121212ifu_Logo_223b853490.jpg","large_121212ifu_Logo_223b853490","large_121212ifuLogo.jpg",376,{"ext":442,"url":839,"hash":840,"mime":445,"name":841,"path":16,"size":842,"width":455,"height":843},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/i2mtc25/small_121212ifu_Logo_223b853490.jpg","small_121212ifu_Logo_223b853490","small_121212ifuLogo.jpg",11.79,188,{"ext":442,"url":845,"hash":846,"mime":445,"name":847,"path":16,"size":848,"width":462,"height":849},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/i2mtc25/medium_121212ifu_Logo_223b853490.jpg","medium_121212ifu_Logo_223b853490","medium_121212ifuLogo.jpg",18.96,282,{"ext":442,"url":851,"hash":852,"mime":445,"name":853,"path":16,"size":854,"width":38,"height":855},"https://confcats-siteplex.s3.us-east-1.amazonaws.com/i2mtc25/thumbnail_121212ifu_Logo_223b853490.jpg","thumbnail_121212ifu_Logo_223b853490","thumbnail_121212ifuLogo.jpg",4.86,92,"121212ifu_Logo_223b853490",62.79,"https://confcats-siteplex.s3.us-east-1.amazonaws.com/i2mtc25/121212ifu_Logo_223b853490.jpg","2025-03-11T12:37:49.080Z","-138",{"pagination":862},{"page":5,"pageSize":327,"pageCount":5,"total":563},{"id":233,"heading":228,"pageHeader":864,"sections":865},{"id":233,"description":16,"showPageHeader":8,"backgroundColor":71,"image":16},[866],{"id":59,"__component":867,"componentVariation":868,"styles":869,"header":16,"disclosures":871},"content.disclosure","Disclosure Base",{"id":246,"edgeTop":70,"edgeBottom":70,"background":870,"containerWidth":16},"transparent",[872,875,878,881,884,887,890,893,896,900,904,908,911,915,919,922,925,928,931,934,937],{"id":98,"heading":873,"prose":874},"Non-Destructive Inspection Systems for Zero Defect Manufacturing ","\u003Cul>\u003Cli>Milena Martarelli , Università Politecnica delle Marche, Italy\u003C/li>\u003C/ul>\u003Cp>The Zero-Defect Manufacturing (ZDM) paradigm represents a modern approach to implementing the concept of digitalized factory environments in line with the Industry4.0 vision, especially in the manufacturing sector. Quality and process control relies on data, most of which is generated by measurement systems. Non-Destructive Inspection (NDI) systems, which encompass sensors, preferably non-contact, producing data and diagnostic methods producing information, can be safely stated to be among the most effective and minimally invasive approaches for product and process diagnosis, as well as conformity assessment. \u003Cbr>Contributions are expected with either conceptual or application characters. Focus on real industrial application is envisaged. \u003C/p>",{"id":117,"heading":876,"prose":877},"Instrumentation and measurement for reliable and safe applications to support Digital Transformation - Supported by TC-32","\u003Cul>\u003Cli>Lorenzo Ciani, University of Florence, Italy\u003C/li>\u003Cli>Ye Chow Kuang, University of Waikato, New Zealand\u003C/li>\u003Cli>Loredana Cristaldi , Politecnico di Milano, Italy\u003C/li>\u003Cli>Giulio D’Emilia, University of L’Aquila, Italy\u003C/li>\u003C/ul>\u003Cp>Nowadays in many contexts it is mandatory to improve quality, reliability, and safety by instrumentation and measurement. Such tasks play a fundamental role in different fields of application (energy, transportation, information and communication technology, logistics, etc.) and are considered as fundamental in high-tech industry and plant also involved in Digital Transformation. This Special Session represents an interesting opportunity for engineers and researchers who work in this area to meet and discuss about the state of the art, difficulties, innovations and improvements on instrumentation and measurement involved in Digital Transformation, components and system testing, fault diagnosis, condition monitoring, risk and safety assessment and management that allow to have reliable, safe and sustainable devices.\u003Cbr>\u003Cbr>Prospective authors can provide original contributions in this topic which can cover, but not only, the following aspects:\u003Cbr>\u003Cbr>• Instrumentation and measurement methods for Testing and Diagnostics\u003Cbr>• Condition monitoring and maintenance of industrial process, plants, and complex systems\u003Cbr>• Measurements and techniques for Fault detection and diagnosis\u003Cbr>• Measurements, methods, and instrumentation for evaluation of Reliability, Availability, Maintainability and Safety (RAMS), Risk assessment and management\u003Cbr>• Effects of measurement uncertainty on the estimation of the RAMS parameters\u003Cbr>• Standards definition, certification, and accreditation.\u003C/p>",{"id":164,"heading":879,"prose":880},"Machine learning algorithms in regulated measuring instruments","\u003Cul>\u003Cli>Marko Esche, Physikalisch-Technische Bundesanstalt, Germany\u003C/li>\u003C/ul>",{"id":403,"heading":882,"prose":883},"Impedance Bridges  ","\u003Cul>\u003Cli>Stephan Schlamminger, National Institute of Standards and Technology, USA\u003C/li>\u003C/ul>\u003Cp>Alternating current bridges have a rich history, starting with Maxwell and Wien. Wagner, Ogawa, and Thompson further developed them. They are used at National metrology institutes to disseminate the capacitance, resistance, and inductance units, the Farad, the Ohm, and the Henry. Significant developments occurred in the last decade, for example, Josephson bridges, digital bridges, and digitally assisted bridges. While there exist only a few bridges worldwide that work at the highest precision, the ideas and concepts employed in these bridges are useful for many instrumentation and measurement purposes.\u003C/p>",{"id":105,"heading":885,"prose":886},"TC-37 Special Session on Measurement Methods and Metrological Characterization for Time-Sensitive Networking (TSN) Systems and Applications","\u003Cul>\u003Cli>Alberto Morato, National Research Council (CNR), Italy\u003C/li>\u003Cli>Federico Tramarin, University of Modena and Reggio Emilia, Italy\u003C/li>\u003Cli>Gianfranco Miele, University of Cassino and Southern Lazio, Italy\u003C/li>\u003C/ul>\u003Cp>In recent years, the Internet of Things (IoT) paradigm has been a significant driver of transformation across numerous application domains, becoming an integral part of our everyday lives. The extent of adoption is such that it is also affecting highly specialized solutions. For example, emerging applications such as extended reality (XR), time-critical sensor networks, and real-time remote control in industrial, robotics, avionics, aerospace, and automotive devices necessitate ubiquitous time-critical connectivity to fulfill the increasingly rigorous quality of service (QoS) requirements.\u003Cbr>\u003Cbr>In particular, these applications necessitate communication networks that are distinguished by a number of specific attributes, including minimal latency, high throughput, high reliability, and availability, mobility, and, potentially, low power consumption. In this context, Time-Sensitive Networking (TSN) represents a crucial enabling technology, defining mechanisms to provide deterministic and low-latency communication across Ethernet (wired), WiFi (wireless), and 5G (mobile) networks.\u003Cbr>\u003Cbr>The extensive range of capabilities offered by TSN, coupled with its intrinsic complexity, presents novel challenges for the characterization of network performance. This, in turn, requires the development of innovative measurement methodologies for the assessment and optimization of its performance. The metrological characterization of devices and networks, as well as the possibility of modeling and tuning their digital twin, is essential to facilitate the adoption of these technologies. Moreover, the elevated complexity of the systems and the rigorous requirements present novel challenges, for example, due to the absence of widely accepted and well-defined metrics, standardized measurement procedures, and a dearth of real-world testbeds offering a substantial number of devices.\u003Cbr>\u003Cbr>This special session aims to bring together academic and industry professionals to a session in which the most recent studies, implementations, and proposals about applications IoT/IIoT technologies and their metrological characterization, such as the aforementioned TSN, wireless TSN, 5G, and other real-time communications systems can be presented.\u003C/p>",{"id":563,"heading":888,"prose":889},"Small Force Metrology and Applications ","\u003Cul>\u003Cli>Thomas Fröhlich, Technische Universität Ilmenau\u003C/li>\u003Cli>Suren Vasilyan, Technische Universität Ilmenau\u003C/li>\u003C/ul>\u003Cp>The redefinition of the International System of Units (SI) stimulated a new quest for developing ever improved methods supporting the calibration tasks in the fields of precision force metrology. In the calibration tasks, the traceability is key component for enabling uniformity and consistency of the measurement results across the globe independently from field of industry or science. Specifically, as of the recent advances in macroscopic realization of the quantum-electrical based kilogram, defined by a natural constant (Planck’s constant) and emerged from quantum mechanics, several guiding methods arose for improving the accuracy of quantification of other derived units and quantities (as much as it can be surprising, however, this includes also optical power measurements of CW or Pulsed Lasers, Watt). The capacitive, electromagnetic and some other type of hybrid electro- and magneto-mechanical systems show their growing capabilities to support fundamental physics experiments, engineering science as well as high-demand applications in industry. The list of concepts employed in small force metrology extends to variety of other applications that utilize the piezoresistive effect, either to sense microscopic forces as is the case with MEMS and cantilever technology or piezo actors used in precision driving mechanisms. The list of fields can be continued to including even those kind of developments that relate to recently emerged photon momentum (or radiation pressure) based small force metrology. The last field and the employed concept is very well-known from quantum- and atomic physics disciplines. However, in last decades it is already standing out as a candidate to include its capabilities and measurement methodology in common industry related measurement/calibration tasks where classical mechanics prevails. Its methodology greatly utilizes an established/traditional high-precision metrological concepts such as precision electrical (Josephson quantum voltage standard, quantum Hall resistance, etc) and optical length metrology disciplines. \u003C/p>\u003Cp>The contributions are expected from different fields where the precision force metrology serves as a unifying factor. Particularly, the main orientations will be those which (i) demonstrate continuous system developments and improvements exploring frontiers of measurement accuracy at the limits of traceable small force measurements, (ii) new conceptualizations and (iii) applications encompassing industry relevant developments.\u003C/p>",{"id":135,"heading":891,"prose":892},"TC10 Special Session on the Measurement, Modeling, and Prediction of Jitter in Space-grade Optical Transceivers ","\u003Cul>\u003Cli>Razvan Ciocan, The Charles Stark Draper Laboratory, Inc, Cambridge, MA, USA\u003C/li>\u003C/ul>\u003Cp> \u003C/p>",{"id":129,"heading":894,"prose":895},"Applications of Time-Frequency Analysis for Instrumentation and Measurement ","\u003Cul>\u003Cli>Yong-June Shin, School of Electrical and Electronic Engineering, Yonsei University, Korea\u003C/li>\u003C/ul>\u003Cp>Time-Frequency Analysis (TFA) is an effective method for post-processing signals with non-stationary characteristics, which is particularly valuable for modern instrumentation and measurement systems. The modern instrumentation and measurement systems are facing problems of monitoring and analyzing complex, dynamic processes in real-time and in real-world problems such as radar echoes, industrial vibrations, biomedical signals, and communication signals. \u003Cbr>\u003Cbr>The significance of this special session lies in its focus on how TFA methods can enhance the performance of instrumentation systems by offering improved accuracy, noise resilience, and real-time analysis capabilities. The session will highlight state-of-the-art developments and their varieties of applications of time-frequency analysis across various industries, including state estimation and industrial monitoring, which are the key parts of the I2MTC 2025. \u003Cbr>\u003Cbr>This session will contribute to advancing the I2MTC 2025 agenda by fostering discussions on the latest TFA advancements and their integration into modern measurement tools, addressing both theoretical and practical challenges. Recent advanced techniques in computing and artificial intelligent integrated with time-frequency analysis will be the highlights of the session. \u003C/p>",{"id":897,"heading":898,"prose":899},14,"Indirect Sensing in Harsh Technical Environments ","\u003Cul>\u003Cli>Markus Neumayer, Graz University of Technology, Austria\u003C/li>\u003C/ul>\u003Cp>In indirect sensing, the measurement result for the quantity of interest is inferred from measurements of related quantities. A key benefit of indirect sensing is the realization of non-invasive or less intrusive measurements, e.g. tomographic systems. Indirect sensing is also required if the measured variable is not directly accessible.  These properties have led indirect sensing principles to be well established among all relevant application fields, e.g. industrial, automotive, medical. From a scientific point of view, research into indirect sensing technology encompasses all elements of a measurement system (sensor, electronics, signal processing), which makes this field of measurement technology interesting for academia. However, the advantages of indirect sensing methods are often counteracted by application-specific cross-sensitivities. This makes their application challenging in harsh technical environments. It is common understanding among engineering practitioners and researchers that the transition of sophisticated measurement technology from lab systems to industrial applications requires additional development steps to countermeasure these influences, e.g. design measures, compensation techniques, etc. \u003Cbr>\u003Cbr>The aim of this special session is to draw together researchers from academia and industry involved in the development and application of indirect measurement technology in harsh environments to present their current research and solutions in order to push the further development of these techniques. \u003Cbr>\u003Cbr>Original contributions in this topic, which can cover, but not only, the following aspects are welcome:\u003Cbr>+ Application examples\u003Cbr>+ Instrumentation\u003Cbr>+ Measurement techniques\u003Cbr>+ Sensor modeling\u003Cbr>+ Signal processing\u003C/p>",{"id":901,"heading":902,"prose":903},15,"Inertial Measurement Units: From Testing and Characterization of MEMS Sensors to Advanced Position and Orientation Estimation Algorithms ","\u003Cul>\u003Cli>Gabriele Patrizi, University of Florence, Italy\u003C/li>\u003Cli>Marco Carratù, University of Salerno, Italy\u003C/li>\u003C/ul>\u003Cp>Inertial Measurement Units (IMUs) have become a critical component in various fields, including aerospace, robotics, automotive, and wearable devices. These systems, composed of accelerometers, gyroscopes, and sometimes magnetometers, are useful in providing precise measurements of motion, orientation, and acceleration. The accuracy and reliability of IMUs, especially those based on MEMS technology, are highly dependent on rigorous testing and thorough characterization. \u003Cbr>\u003Cbr>In this session, we aim to explore cutting-edge methodologies for the testing and calibration of MEMS-based IMUs, focusing on key performance parameters such as bias instability, scale factor, and noise. Additionally, the integration of sensor data for enhanced positioning and orientation estimation using advanced algorithms will be addressed. These topics are critical for improving the performance and reliability of IMUs, especially in complex environments where accuracy and precision are crucial. The session will provide a comprehensive view of both the theoretical and practical aspects of IMU characterization, making it relevant to researchers and engineers focused on instrumentation, measurement systems, and algorithm development. The topics of the session include, but are not limited to: \u003C/p>\u003Cp>• Characterization of Inertial Measurement Units\u003Cbr>• Testing and experimental platform for IMU-based solutions, including gyroscopes and accelerometers\u003Cbr>• Positioning estimation of objects based on IMU data\u003Cbr>• Calibration of MEMS-based Inertial Measurement Units\u003Cbr>• Orientation estimation algorithms based on IMU data, including AI-based algorithms\u003Cbr>• Uncertainty evaluation in IMU-based systems and algorithms\u003Cbr>• Reliability of Inertial Measurement Units\u003Cbr>• IMU systems for automotive and aerospace applications\u003C/p>",{"id":905,"heading":906,"prose":907},16,"Artificial Intelligence in Instrumentation and Measurement: theoretical fundamentals and applications ","\u003Cul>\u003Cli>Antonio Pietrosanto, University of Salerno, Italy\u003C/li>\u003Cli>Marco Carratù, University of Salerno, Italy\u003C/li>\u003C/ul>\u003Cp>The widespread adoption of Artificial Intelligence (AI) techniques has infiltrated nearly every research domain, thanks to their exceptional ability to handle tasks traditionally managed by humans. These tasks range from speech recognition and decision-making to pattern recognition, data classification, prediction, and various computer vision activities such as object detection, classification, and segmentation. Nevertheless, the impact of data uncertainty on the reliability of AI-generated results is often overlooked. This omission invalidates any evaluation of the quality and trustworthiness of AI outputs. On the contrary, identifying and mitigating the effects of input data uncertainty could prove crucial. To this aim, research efforts should focus on analyzing how uncertainty propagates through AI algorithms. Moreover, interest in the fields of instrumentation and measurements could grow with the development of applications involving real-time responses and the integration of edge and fog computing. This special session will focus primarily on the application of measurement theory to AI algorithms, with a particular emphasis on broadening the understanding and characterization of uncertainty. The aim is to establish a forum for researchers and professionals to explore these pressing challenges and share the latest developments in this emerging field of science It is expected that the contributions proposed for the special session will cover the whole variety of issues relating the quality of measured data with the results produced by AI, such as: the evaluation of epistemic and aleatoric uncertainty in Deep Learning models, the impact of dataset measurement uncertainty on training and testing results, and the development of new metrics for evaluating the quality of AI results in compliance with the ISO GUM standard. \u003Cbr>\u003Cbr>Topics in this session include, but are not limited to: \u003Cbr>- Applications of AI algorithms to Instrumentation and Measurement\u003Cbr>- Uncertainty propagation through AI algorithms\u003Cbr>- Epistemic and Aleatoric uncertainty in AI algorithms.\u003Cbr>- New metrics for assessing the quality of AI results that comply with the ISO GUM standard.\u003Cbr>- Metrological characterization of AI-based systems.\u003Cbr>- New guidelines and rules for uncertainty evaluation in AI algorithms\u003Cbr>- Impact of dataset uncertainty on AI results\u003Cbr>- AI-based measurement systems for Industry 4.0\u003C/p>",{"id":123,"heading":909,"prose":910},"Ultra-low power and energy-autonomous wireless sensor systems ","\u003Cul>\u003Cli>Sebastian Bader, Mid Sweden University, Sweden\u003C/li>\u003Cli>Alessandro Pozzebon, University of Padova, Italy\u003C/li>\u003C/ul>\u003Cp>In the last few years, there has been an incredible growth in interconnected sensing devices within the Internet of Things framework: recent statistics assert that the global quantity of IoT devices by 2025 will reach the astonishing number of 41 billion. A large part of these devices will be composed of low complexity wireless sensing tools whose main task will be data acquisition according to the deploy-and-forget philosophy. For these devices, the main challenge is still represented by their limited lifetime: indeed, these platforms are expected to be able to acquire and transmit data from sensors for years without any direct intervention of operators for battery replacement or recharging.\u003Cbr>\u003Cbr>To this aim, significant research efforts are being put in the study and design of ultra-low power architectures as well as micro-energy harvesting techniques for IoT devices. This Special Session is specifically targeted at all those contributions which propose novel techniques to minimize the energy consumption of wireless sensing devices, tackling this aspect both from the data transmission, sensing and computing point of view. Similarly, contributions dealing with novel techniques for energy provisioning are welcomed, focusing on the identification of novel energy sources as well as on innovative energy management techniques.\u003C/p>",{"id":912,"heading":913,"prose":914},18,"Advanced Hyperspectral Imaging and Its Applications ","\u003Cul>\u003Cli>Chi-Hung Hwang, Taiwan Instrument Technology Research Center, NARLabs, Taiwan\u003C/li>\u003Cli>Chun-Jen Weng, Taiwan Instrument Technology Research Center, NARLabs, Taiwan\u003C/li>\u003Cli>Lijuan Wang , School of Engineering, University of Kent, UK\u003C/li>\u003C/ul>\u003Cp style=\"text-align:justify;\">The purpose of this special session is to provide a platform for researchers and professionals working on the development and application of advanced hyperspectral imaging (HSI) technologies. Hyperspectral imaging, with its ability to capture detailed spectral information across a wide range of wavelengths, has become a crucial tool in numerous scientific and industrial fields. This session aims to explore both the technical innovations in HSI and its broad range of applications, from environmental monitoring and agriculture to medical diagnostics and material analysis. Additionally, the integration of artificial intelligence (AI) in hyperspectral data analysis opens new avenues for automating feature extraction, enhancing image interpretation, and creating predictive models. These AI-driven approaches significantly improve the accuracy and efficiency of hyperspectral imaging in various domains, further expanding its potential applications. This session will cover key topics such as:\u003C/p>\u003Cp style=\"text-align:justify;\">\u003Cspan lang=\"ZH-TW\" dir=\"ltr\">－\u003C/span> The creative development of HSI module;\u003Cbr>\u003Cspan lang=\"ZH-TW\" dir=\"ltr\">－\u003C/span> Integration of hyperspectral imaging systems with existing instrumentation;\u003Cbr>\u003Cspan lang=\"ZH-TW\" dir=\"ltr\">－ \u003C/span>Hyperspectral data collection, analysis, and visualization techniques;\u003Cbr>\u003Cspan lang=\"ZH-TW\" dir=\"ltr\">－ \u003C/span>Leveraging AI and machine learning for hyperspectral image analysis, classification, and feature extraction;\u003Cbr>\u003Cspan lang=\"ZH-TW\" dir=\"ltr\">－\u003C/span> Applications of hyperspectral imaging in remote sensing and precision agriculture;\u003Cspan lang=\"ZH-TW\" dir=\"ltr\">－ \u003C/span>HSI potential for forest carbon sequestration and carbon credits\u003Cbr>\u003Cspan lang=\"ZH-TW\" dir=\"ltr\">－\u003C/span>Medical applications of HSI, including non-invasive diagnostics and tissue characterization;\u003Cbr>\u003Cspan lang=\"ZH-TW\" dir=\"ltr\">－ \u003C/span>HSI industrial applications, such as quality control and material inspection;\u003C/p>\u003Cp style=\"text-align:justify;\">We encourage the submission of research that not only showcases new advancements in hyperspectral imaging but also highlights ongoing projects and real-world applications. This session will serve as a valuable opportunity to exchange knowledge, foster collaborations, and explore the future potential of hyperspectral imaging across diverse disciplines.\u003C/p>",{"id":916,"heading":917,"prose":918},19,"From Rigid to Flexible: Advances in Flexible Conformal Sensing","\u003Cul>\u003Cli>Nan Li, School of Mechano-Eletronic Engineering, Xidian University, Xi’an, China\u003C/li>\u003Cli>Yunjie Yang, School of Engineering, University of Edinburgh, UK\u003C/li>\u003Cli>Haotian Chen, HIMEX lab, James Watt School of Engineering, University of Glasgow, UK\u003C/li>\u003C/ul>\u003Cp>Flexible conformal sensing is transforming the way we monitor and interact with complex, non-standard surfaces, such as robotics, mechanical systems, and building structures. Unlike traditional rigid sensors, flexible sensors are uniquely capable of adapting to varying contours and maintaining consistent contact, even under dynamic conditions. This adaptability makes them ideal for high-precision applications in fields ranging from wearable health monitoring to soft robotics and environmental sensing.\u003Cbr>\u003Cbr>In contrast to rigid sensing technologies, flexible conformal sensors exhibit remarkable resilience, maintaining functionality and accuracy after repeated deformation. Their high sensitivity and durability are pushing the boundaries of what is possible in real-time monitoring and control, making them essential for addressing the evolving demands of modern industries for instrumentation and measurement.\u003Cbr>\u003Cbr>Conventional instrumentation and measurement sessions primarily focus on rigid, traditional sensors and instrumentation platforms. This special session will highlight the recent advancements in flexible conformal sensing, exploring the integration of advanced materials, and innovations in sensors, instrumentation and signal processing algorithms. These emerging technologies hold the potential to revolutionize industries that require adaptable, high-precision sensing in dynamic environments.\u003Cbr>\u003Cbr>This special session on \"From Rigid to Flexible: Advances in Flexible Conformal Sensing\" is dedicated to presenting recent developments in the realm of a) soft sensors and instrumentation designed for complex or non-standard surfaces, b) novel signal processing algorithms and sensing techniques optimized for flexible platforms, and c) pioneering applications that leverage flexible conformal sensing. We invite prospective authors to enrich this session with their research spanning all nuances of the stated domains. Submissions across a spectrum of topics are encouraged, including but not limited to:\u003Cbr> \u003C/p>\u003Cul>\u003Cli>Novel design and manufacturing processes for flexible sensors\u003C/li>\u003Cli>Sensing methods that exploit flexibility and conformal capabilities\u003C/li>\u003Cli>Flexible electronics and instrumentation for integrated sensing and signal processing\u003C/li>\u003Cli>Advanced signal processing and machine learning algorithms for flexible and dynamic sensing environments\u003C/li>\u003Cli>Emerging applications of flexible conformal sensing technologies\u003C/li>\u003C/ul>",{"id":246,"heading":920,"prose":921},"Sensing and Measurement for Smart Transportation Systems","\u003Cul>\u003Cli>Hongrui Wang, Delft University of Technology, The Netherlands\u003C/li>\u003Cli>Zhigang Liu, Southwest Jiaotong University, China\u003C/li>\u003Cli>Daniel Watzenig, Graz University of Technology, Austria\u003C/li>\u003C/ul>\u003Cp>As the global demand for more efficient, safe, and sustainable transportation systems grows, the role of sensing and measurement technologies has become central to achieving these goals. Smart transportation systems utilize advanced sensors and measurement solutions to gather real-time data, enabling better decision-making, reducing accidents, and improving overall traffic management. These technologies are fundamental to the development of autonomous vehicles, smart infrastructures, and intelligent transportation networks. \u003Cbr>\u003Cbr>The significance of this session lies in its focus on the advanced sensing technologies that provide the foundation for innovations like autonomous vehicles, smart roads, high-speed railways, and environmental monitoring within transportation ecosystems. Precision measurement is critical for ensuring the accuracy of data on vehicle performance, road and railway conditions, and traffic patterns. This data is used for optimizing routes, predicting maintenance needs, and enhancing vehicle safety systems. \u003Cbr>\u003Cbr>In addition, smart transportation systems are essential in addressing challenges such as urban congestion, carbon emissions, and energy efficiency. The integration of IoT sensors, real-time data analytics, and wireless communication in these systems creates opportunities for more resilient, responsive, and adaptive transportation solutions. \u003Cbr>\u003Cbr>This special session will bring together experts from academia, industry, and government to discuss the latest advances in sensing and measurement technologies. Participants will explore how innovations in this field can help build smarter, more connected, and sustainable transportation networks, driving the future of mobility. Topics in this special session include but not limited to: \u003C/p>\u003Cp>• Sensor Fusion Techniques for Enhanced Vehicle Perception and Control\u003Cbr>• Health Monitoring of Transportation Infrastructure\u003Cbr>• Precision Positioning and Navigation Systems\u003Cbr>• Cybersecurity in Sensing and Measurement Systems\u003Cbr>• Energy-efficient Sensing for Electric and Hybrid Vehicles\u003Cbr>• Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) Communication\u003Cbr>• IoT-enabled Sensing for Connected Vehicles and Smart Infrastructure\u003Cbr>• Real-time Data Acquisition and Analytics in Transportation Systems\u003Cbr>• Sensing for Traffic and Environmental Monitoring\u003C/p>",{"id":233,"heading":923,"prose":924},"Advances in Gas Sensing: emerging technologies and metrological challenges ","\u003Cul>\u003Cli>Giovanni Gugliandolo, University of Messina, Italy\u003C/li>\u003Cli>Mariangela Latino, National Research Council (CNR-IPCF), Italy\u003C/li>\u003Cli>Antonino Quattrocchi, University of Messina, Italy\u003C/li>\u003C/ul>\u003Cp>Gas sensing technologies have advanced rapidly in recent years, driven by the growing demand for accurate, reliable and fast detection in a wide range of application fields. As a result, various technological solutions have been explored to develop innovative gas sensors with enhanced performance. Among these, chemical gas sensors have received particular attention due to their high sensitivity, fast response/recovery times, and relatively low cost. In particular, electrochemical and metal oxide semiconductor (MOX) gas sensors are among the most commercially available and represent a major focus of research. Recent advances in materials science, such as the use of nanostructured materials, have significantly improved their performance, resulting in more accurate and faster gas concentration measurements. Despite their widespread use, these technologies still face significant challenges, such as long-term stability and cross-sensitivity, and offer opportunities for further improvement. More recently, resonant sensors have emerged as a powerful tool for gas detection. Operating over a wide range of frequencies, including microwave frequencies, these sensors use frequency shifts to measure gas concentrations with relatively high accuracy. However, this technology is still in its infancy and requires further research to develop gas sensors that can compete with more traditional technologies. \u003Cbr>\u003Cbr>This special session aims to highlight the state-of-the-art in gas sensing technologies and promote discussion of recent studies and future trends. Gas sensors have a significant impact in a variety of fields of interest, this special session is focused on potential applications in environmental monitoring, safety assessment and healthcare integration. We welcome papers covering various aspects of gas sensors and related measurements. In particular, we invite contributions that explore innovative gas sensor designs involving new technologies and transduction methods, as well as their characterization, calibration, and real-world applications.  Papers on the metrological evaluation of device processing and gas sensor measurement systems are strongly welcome. Studies that focus on innovative materials used in gas sensors, including their synthesis, characterization and their improvements on measurement performance are also encouraged. \u003Cbr>Areas of interest include, but are not limited to: \u003Cbr>• Gas sensing technologies\u003Cbr>• Electrochemical gas sensors\u003Cbr>• Metal-oxide-semiconductor (MOX) gas sensors\u003Cbr>• Resonant gas sensors\u003Cbr>• Gas sensors calibration\u003Cbr>• Metrological evaluation of the gas sensor processing\u003Cbr>• Sensor-based measurement systems\u003Cbr>• Future trends in gas sensing\u003C/p>",{"id":215,"heading":926,"prose":927},"Advanced Techniques for Predictive Maintenance ","\u003Cul>\u003Cli>J. M. Dias Pereira, Polytechnic Institute of Setúbal, Portugal\u003C/li>\u003C/ul>\u003Cp>In the recent past, it was common for industrial process maintenance management systems to not have sufficient data to predict critical equipment failures, and the proper functioning of systems and equipment depended only on preventive and corrective maintenance. Nowadays, with the significant increase in digitalization and in the reduction of costs associated with the implementation of intelligent instrumentation systems, based on the Industrial Internet of Things (IIoT), large amounts of data are available, being possible to extract crucial information and to implement predictive maintenance. The development of advanced processing techniques makes it possible to predict failures and to avoid operational breakdowns, in equipment or systems, providing, in real time, data to manage maintenance activity in an efficient way. In this context, advanced processing techniques and data analytics of large volumes of data, known as Bigdata, enable predictions of failures and improves equipment and system performance.  \u003C/p>",{"id":209,"heading":929,"prose":930},"Rydberg Arrays for Enhanced RF Direction Finding - Supported by TC-10","\u003Cul>\u003Cli>Charles L.A. Cerny, Air Force Research Lab, Sensors Directorate, USA\u003C/li>\u003C/ul>\u003Cp>Extend existing Rydberg Atom model for the purposes Radio Frequency (RF) direction finding and designs for E-field vector sensors. For the purpose of direction finding, the detection elements of the Rydberg atom cephase coherent, and the differential output can be used to directly determine the angle of arrival for a signal of interest. research performed will adapt the Physics-based behavioral model of the Rydberg atom cell to provide angle of arrival estimates via E-field patterns. This complements well with electrically small array designs that go beyond classical computational electromagnetics and identify enhancements to RF sensing through Quantum effects.\u003Cbr> \u003C/p>",{"id":308,"heading":932,"prose":933},"Sensors Related Technologies for AIoT Applications ","\u003Cul>\u003Cli>Cheng-Tang Pan, Taiwan Instrument Research Institute, NARLabs, Taiwan\u003C/li>\u003Cli>Kuo-Cheng Huang, Taiwan Instrument Research Institute, NARLabs, Taiwan\u003C/li>\u003Cli>Wen-Tse Hsiao, Taiwan Instrument Research Institute, NARLabs, Taiwan \u003C/li>\u003Cli>Chi-Hung Hwang, Taiwan Instrument Research Institute, NARLabs, Taiwan \u003C/li>\u003C/ul>\u003Cp>Artificial intelligence (AI) has brought up a huge wave of evolution and innovation across various industries. As AI continues to transform the world, the integration of AI technologies with Internet of things (IoT) is becoming increasingly essential.\u003Cbr>\u003Cbr>The special session on “Sensors Related Technologies for AIoT Applications” features on providing a platform that brings together all experts in the multidisciplinary field of IoT sensing technologies and their applications on AI.  Aiming to make thorough discussion on this significant scientific revolution, this session is devoted to R&D works in the broader discussion on the future of AIoT technology and its applications across various fields.\u003Cbr>\u003Cbr>Papers reporting about research related to the following topics are planned:\u003Cbr>• Advanced semiconductors and heterogeneous integration for sensing systems and applications\u003Cbr>• Advances in intelligent sensors and robots for AI-enhanced application\u003Cbr>• AI sensors applied to smart machinery\u003Cbr>• AIoT’s impact on manufacturing workforce and required skillsets.\u003Cbr>• Big data and AIoT manufacturing system\u003Cbr>• Energy harvesting technology for self-sustained AIoT system\u003Cbr>• Harnessing sensor data for manufacturing insight\u003Cbr>• Interoperability and security – devices, standards and architectures\u003Cbr>• IoT sensors and system integration\u003Cbr>• Self-powered sensors and sensing systems\u003Cbr>• Sensors and IC sensors using CMOS, MEMS and CMOS compatible materials\u003Cbr>• Sensors for harsh environment\u003Cbr>• Bioimaging for Artificial Intelligence Analysis\u003Cbr>• Biosensors and Environmental Sensors Applied in AIoT\u003Cbr>• Other-related topics\u003C/p>",{"id":327,"heading":935,"prose":936},"Calibration and Blind Calibration using Machine Learning ","\u003Cul>\u003Cli>Amit Kumar Mishra, Visiting Professor in Sensors and AI, University West, Sweden\u003C/li>\u003Cli>Sebastian Meyer, Fraunhofer, Cottbus, Germany\u003C/li>\u003C/ul>\u003Cp>Calibration is a major component of any metrological system, especially for sensors that are installed in remote places. Without a thorough investigation and methodology around sensor calibration, the data collected from the sensors are usually not reliable. In one of the very few honest papers in the open literature, Bittner et al [1] discussed how they got their sensors calibrated to a high standard and then those were installed in Malawi. However, they observed how quickly the quality of the data from the sensor network became almost nonusable. This is a major pain point in the current day of ubiquitous sensing. This is also a pain point in industrial settings where the sensors need to be re-calibrated regularly. Because calibration is a costly process, it often may need the unit's closure.\u003Cbr>\u003Cbr>One can find a summary review of in situ calibration methods in [2]. Calibration efforts for individual sensor types are extensive. Most of these processes need a reference sensor or some ground-truths. For example in a work on low-cost air pollution sensors in Norway [3], the researchers used reference-based calibration. Running a reference-based calibration for remote sensors is a costly task. Also, it does not scale up. I.e. when the number of sensors is in the hundreds the task becomes impossible to be carried out regularly.\u003Cbr>\u003Cbr>One of the solutions to this challenge has been to treat the battery of sensors as a single system. This system can, then, be calibrated as a whole rather than focusing on individual sensors in this network. In their pioneering work, Whitehouse et al [4] used a physics-based model. The data from the sensor network is expected to fit the model as closely as possible. Hence, the individual sensor calibration parameters are fine-tuned to force this fit. Many following works have used this approach and modified it as well. Though efficient, this approach is not fully blind.\u003Cbr>\u003Cbr>The second important piece of work in the domain of blind calibration was presented by Balzano and Nowak [5] in their work on blind sensor network calibration methodology. Their assumption of the existence of spatial oversampling gave an elegant solution which has been leveraged by many other works since then. For example, in a recent work [6] machine learning has been used to learn the sub-space projection part of Balzano’s method. Though elegant, this method does not work when the number of sensors is not too many. Unfortunately, in most real-life cases, the number of sensors available is usually limited. However, when it comes to the methodologies of blind calibration processes for a single sensor or a limited number of sensors (negating the oversampling assumption) there are not many reports in the open literature.\u003Cbr>\u003Cbr>For the last few years, the proposers have been working on machine learning based blind calibration methodologies [7] that may alleviate many of the current challenges of sensor-calibration on the field. The first proposer also gave a tutorial in I2MTC 2024 on this topic.\u003Cbr>\u003Cbr>In the proposed special session, we would like to create a platform for innovators to discuss their work in this increasingly important area of blind calibration. Through this session, we would also endeavour to create the momentum to start working on a new standard for blind and semi-blind sensor-calibration. \u003Cbr> \u003C/p>",{"id":938,"heading":939,"prose":940},26,"Sensors, Instrumentation, and Networks Technologies for Environmental Measurement and Intelligent Forecasting","\u003Cul>\u003Cli>Chi-Hung Hwang, Taiwan Instrument Technology Research Center, NARLabs, Taiwan \u003C/li>\u003Cli>Tuan Guo, Jinan University, China\u003C/li>\u003Cli>Faouzi Derbel, Leipzig University of Applied Sciences, Germany\u003C/li>\u003Cli>Huan Liu, China University of Geosciences, China\u003C/li>\u003Cli>Alexander Knut, Leipzig University of Applied Sciences, Germany\u003C/li>\u003Cli>Der-Chen Huang, National Chung Hsing University, Taiwan \u003C/li>\u003C/ul>\u003Cp style=\"text-align:justify;\">The purpose of this special session is to provide a discussion platform for all colleagues who are working on the development of sensors, devices, instruments, and network systems for environmental monitoring and intelligent forecasting; this particular session also expects contributions from the Envi-IoTs' development and applications aspects; development of the algorithm for analyzing the collected field data, and the potential application of the collected data for the environmental change investigation, disaster monitoring, modeling and predicting with many other applications are including into this special session. We also expect authors to share the new outcomes from ongoing projects to catalyze international cooperation on environmental monitoring, field testing/ verification, and establishing global databases.  The main topics of the special session are   \u003C/p>\u003Cp style=\"text-align:justify;\">－The development of sensors or devices for the field data collection\u003C/p>\u003Cp style=\"text-align:justify;\">－Environmental monitoring system integrating and validations \u003C/p>\u003Cp style=\"text-align:justify;\">－Volunteer-based distributing system for Envi-IoTs applications\u003C/p>\u003Cp style=\"text-align:justify;\">－Applications of robots and unmanned systems for field data collecting \u003C/p>\u003Cp style=\"text-align:justify;\">－Field data collecting, management, and fusion \u003C/p>\u003Cp style=\"text-align:justify;\">－Applying artificial intelligent technologies for data analysis, classifications, modeling, and forecasting\u003C/p>\u003Cp style=\"text-align:justify;\">－Impacts on environmental change due to global warming \u003C/p>\u003Cp>The topics not limited to the items mentioned previously; any subject and application associated with collecting and the application of the collected spatial/ time-domain data from the field which relate to human safety and sustainability are welcome. \u003C/p>",{"data":942,"meta":943},{"id":233,"heading":228,"createdAt":234,"updatedAt":235,"publishedAt":236,"url_path_id":237,"url_path":229,"contentType":103},{},1778852464885]