AI empowered Sensors and Measuring Devices for IoT, Robotics, Drones and Mechatronics

Prof. Dr. Subhas Mukhopadhyay

FIEEE (USA), FIEE (UK), FIETE (India)

School of Engineering, Macquarie University, NSW 2109

Email: Subhas.Mukhopadhyay@mq.edu.au

Abstract:

The advancement of sensing technologies, embedded systems, wireless communication technologies, nano-materials, miniaturization, vision sensing and processing speed makes it possible to develop smart mechatronics and machine systems. This seminar will discuss recent research and developmental activities on different sensors and sensing system, measurement devices, incorporating machine learning and artificial intelligent towards implementation of IoT, Mechatronics, robotics and drones along with machine visions at Macquarie University as applicable to medical science, biology and environmental monitoring.

Biography:

Subhas holds a B.E.E. (gold medalist), M.E.E., Ph.D. (India) and Doctor of Engineering (Japan). Currently he is working as a Professor of Mechanical/Electronics Engineering, Macquarie University, Australia and is the Discipline Leader of the Mechatronics Engineering Degree Programme. His fields of interest include Smart Sensors and sensing technology, instrumentation techniques, wireless sensors and network (WSN), Internet of Things (IoT), Robotics, Mechatronics and Drones etc. He has supervised over 60 postgraduate students and over 200 Honours students. He has examined over 85 postgraduate theses.

He has been co-inventor of 14 patents and published over 450 papers in different international journals and conference proceedings, written ten books and sixty two book chapters and edited twenty five conference proceedings. He has also edited fifty books with Springer-Verlag and thirty five journal special issues. He has organized over 20 international conferences as either General Chairs/co-chairs or Technical Programme Chair. He has delivered 456 presentations including keynote, invited, tutorial and special lectures. As per Scholargoogle, his total citation is 27769 and h-index is 86.

He is a Fellow of IEEE (USA), a Fellow of IET (UK), a Fellow of IETE (India). He is a Topical Editor of IEEE Sensors journal, an associate editor of IEEE Transactions on Instrumentation and Measurements and IEEE Reviews in Biomedical Engineering (RBME). He is EiC of the International Journal on Smart Sensing and Intelligent Systems. He was a Distinguished Lecturer of the IEEE Sensors Council from 2017 to 2022. He chairs the IEEE Instrumentation and Measurement Society NSW chapter.

AI-Enhanced Multi-Modal Sensing for Precision Biomolecular Diagnostics to Clinical Application

Assoc Prof. Kwoh Chee-Keong

Nanyang Technological University (NTU), Singapore

Abstract:

Recent advances in artificial intelligence and multi-modal sensing are transforming the way we detect, analyse, and respond to complex biomedical challenges. This keynote will explore AI-enhanced biomolecular diagnostics, focusing on the integration of heterogeneous data from genomics, proteomics, and clinical sensors into unified, predictive models. Drawing on our work in protein interaction network analysis, drug–target prediction, and pandemic response, we will demonstrate how smart sensors, data fusion, and big data analytics can yield clinically actionable insights. Case studies in infectious disease surveillance, single-cell sequencing and precision medicine will illustrate the path from algorithm development to bedside application.

Biography:

Kwoh Chee-Keong is an Associate Professor at the College of Computing and Data Science (CCDS), Nanyang Technological University (NTU), Singapore.

He earned his Bachelor’s degree in Electrical Engineering (First Class Honours) from the National University of Singapore in 1987, followed by a Master’s degree in Industrial Systems Engineering in 1991. He completed his PhD at Imperial College London in 1995.

Assoc. Prof. Kwoh's research spans data analytics, machine learning, soft computing, and bioinformatics. His work addresses challenges in high-dimensional data, ensemble learning, and the interpretation of complex datasets across domains such as engineering, education, life sciences, medicine, and manufacturing. He has authored over 400 publications, including high-impact journal papers and international conference presentations. According to Google Scholar, his works have garnered extensive recognition, with over 12,000 citations and an h-index of 54.

He is a Senior Member of IEEE and an active contributor to professional societies in Singapore and internationally. His awards include the Public Service Medal (2008) for his contributions to Singapore, the Ministry of Education Long Service Medal (2016) for his contributions to the education, and the COVID-19 Service Medal (2023) for his contributions during the pandemic. He also serves on advisory panels and committees for academic and industrial research initiatives.

Assoc. Prof. Kwoh is actively involved in interdisciplinary research and has led multiple projects that apply data science and Explainable AI to real-world problems in medicine, engineering, and manufacturing. He is also a mentor to numerous PhD students and young researchers, fostering innovation and knowledge transfer.

Keynote Speech 03——Prof. Hamid Reza Karimi

Exploring Learning Frameworks for Active Noise Control Systems

Prof. Hamid Reza Karimi,

MAE, MEAS, MEurASc, DFIIAV, FISCM, FAAIA

Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy,

Email: hamidreza.karimi@polimi.it

Abstract:

In active noise control (ANC), deterministic components are often mixed with broadband noise. Considering the difficulty of measuring acoustic pressure in advance for applying classical ANC methods, artificial intelligence tools such as deep learning algorithms offer an effective data-driven approach. This talk addresses new learning frameworks for computing and predicting acoustic pressure, leveraging their ability to capture spatially coherent patterns in radiated acoustic pressure fields. Additionally, deep learning-based ANC design can potentially play an important role in handling nonlinearities inherent in electro-acoustic systems, as well as in broadband noise removal. Since it is challenging to find accurate models for control design, direct data-driven approaches are introduced to compute controllers suitable for ANC by using only near-field acoustic holography—arranging microphones on the appliance fuselage and using the sound field produced by a loudspeaker array—without requiring detailed knowledge of the system model. The talk will conclude with technical and practical insights on the design and implementation of active control strategies for noise transmission through encapsulated structures.

Biography:

Hamid Reza Karimi is a Professor of Applied Mechanics in the Department of Mechanical Engineering at Politecnico di Milano, Milan, Italy. Prof. Karimi’s original research and development achievements span a broad spectrum within the topic of automation/control systems, and intelligence systems with applications to complex systems such as vehicles, robotics and mechatronics. Prof. Karimi is an ordinary Member of Academia Europaea (MAE), Member of European Academy of Sciences and Arts (EASA), Member of The European Academy of Sciences (MEurASc), Member of Agder Academy of Science and Letters in Norway, Member of National Academy of Artificial Intelligence (NAAI), Honorary Academic Member of National Academy of Sciences of Bolivia, Distinguished Fellow of the International Institute of Acoustics and Vibration (IIAV), Fellow of The International Society for Condition Monitoring (ISCM), Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA), and also a member of the IFAC Technical Committee on Mechatronic Systems, the IFAC Technical Committee on Robust Control, the IFAC Technical Committee on Automotive Control, member of the board of Directors of The International Institute of Acoustics and Vibration (IIAV) and member of Management Committee of The International Society for Condition Monitoring (ISCM). Prof. Karimi is the recipient of the 2025 NAAI Distinguished Artificial Intelligence Scholar Award, the 2021 BINDT CM Innovation Award, the Web of Science Highly Cited Researcher in Engineering, August-Wilhelm-Scheer Visiting Professorship Award, JSPS (Japan Society for the Promotion of Science) Research Award, and Alexander-von-Humboldt-Stiftung research Award, for instance. Prof. Karimi serves/served as Editor-in-Chief and Book Series Editor for Springer, CRC Press and Elsevier. He has also participated as General Chair, keynote/plenary speaker, distinguished speaker or program chair for several international conferences in the areas of Control Systems, Robotics and Mechatronics. Prof. Karimi has served as vice-president for International Prize "Lombardia è ricerca" and is also the conference chair for the 2026 World Congress on Condition Monitoring (WCCM2026) to be held in Milan, Italy. Additionally, he is an Honorary Visiting Professor in the School of Computing & Engineering at the University of Huddersfield, UK.