We have 4 tutorials this year; details are shown as follows.

Tutorial 1: 803 (8F), June 29th, 14:45-16:45

The Internet of Bio-Nano Things -Smart Computing in the Human Body

Stefan Fischer, the University of Lübeck, Germany

The Internet of Bio-Nano Things (IoBNT) is an innovative field of research located at the intersection of nanotechnology, biotechnology and information and communication technologies. It aims to enable the seamless integration of biological and nanoscale systems into the Internet in order to develop advanced biomedical applications, environmental monitoring sensors and energy-efficient networks. At the core of IoBNT are biocompatible nanodevices that can function in living organisms to monitor or modify specific biological processes in real time. These devices communicate with each other and with the Internet to collect, process and transmit data, opening up entirely new possibilities for health monitoring, disease control, environmental protection and many other areas. By merging biology and nanotechnology, IoBNT promises to push the boundaries of what is technically possible while improving the efficiency and sustainability of technological solutions.

This tutorial first introduces the basics of IoBNT. It explains the basic requirements and existing technologies and looks at a number of example applications, primarily medical applications. In the main part, DNA-based nanonetworks are presented as a promising implementation technology. Before providing an outlook, further concepts are addressed, which focus in particular on gateways between communication inside and outside the body.

Prof. Stefan Fischer, the University of Lübeck, Germany
Stefan Fischer is a full professor in Computer Science at the University of Lübeck, Germany, and the director of the Institute for Telematics. He got his doctoral degree in Computer Science from the University of Mannheim, Germany, in 1996, respectively. He held positions at the International University in Germany as an assistant professor and at the Technical University of Braunschweig as an associate professor, until he joined Lübeck University in 2004. His research interest is focused on network and distributed system structures such as Internet of Things and nano communications in these fields. He has (co-)authored more than 200 scientific books and articles.

Tutorial 2: Conf. Hall (12F), June 29th, 14:45-16:45

Contactless Physiological Health Sensing: Challenges, Solutions & Opportunities

Nirmalya Roy, University of Maryland Baltimore County, USA
Zahid Hasan, University of Maryland Baltimore County, USA

 (*) We are very sorry, but due to unavoidable circumstances, Zahid will not be able to come to Japan. For this reason,  Nirmaly takes over and handles all parts.

Contactless health monitoring techniques, such as Remote Photoplethysmography (rPPG) and video-based Respiratory Rate (RR) estimation have emerged as promising methods utilizing regular camera sensors for capturing vital signs like Heart Rate (HR) and Respiratory Rate (RR). These approaches offer cost-effective, widely applicable, and safe solutions for regular and long-term health monitoring. However, the primary challenge lies in extracting accurate vital signals from the captured videos due to their low signal-to-noise ratios.

In this tutorial, we aim to provide a comprehensive background on rPPG and video-based heart rate estimation, covering signal acquisition and physics-inspired estimation principles. We will discuss signal-processing approaches and their limitations. We will delve into DL-based estimation systems, addressing the challenge of inherent aleatoric uncertainty (irreducible uncertainty from various stochastic factors like sensor variations and inter-subject differences) in the ground truth data annotation that hinders the development of generalized deep learning-based rPPG estimation system. To reinforce a robust DL model addressing these inherent uncertainties in rPPG data streams, we will discuss three novel deep-learning approaches – a multi-task learning method, a self-supervised learning method, and a generative adversarial network-based architecture for rPPG estimation. Furthermore, we will demonstrate a real-time heart rate estimation system, RhythmEdge, using a low-cost camera sensor and describe the rPPG-specific pruning techniques to reduce rPPG model size for efficient edge implementation. We will conclude the tutorial with existing research gaps and potential directions in contactless rPPG and respiratory rate estimation research.

Prof. Nirmalya Roy, University of Maryland Baltimore County, USA
Dr. Nirmalya Roy is a Professor in the Information Systems department and the director of the Mobile, Pervasive and Sensor Computing Lab at the University of Maryland, Baltimore County. He is also the associate director of the Center for Real-time Distributed Sensing and Autonomy (CARDS) at UMBC. His current research interests include use-inspired AI/ML and human-centric data science with applications to smart health, cyber-physical systems, IoT, robotics and autonomy. He received his B.E., M.S. and Ph.D. degrees in Computer Science and Engineering from Jadavpur University in 2001, and the University of Texas at Arlington in 2004 and 2008, respectively. More information about his research can be found at https://mpsc.umbc.edu/.

Ph.D. candidate Zahid Hasan, University of Maryland Baltimore County, USA
Mr. Zahid Hasan is a Ph.D. candidate under the supervision of Dr. Nirmalya Roy in the Information Systems department at the University of Maryland, Baltimore County. His research interests include video information retrieval and contactless physiological health monitoring using camera sensors. As the student lead of this project, he has pioneered the development of deep learning models for rPPG estimation, addressing the challenge of uncertainty associated with ground truth data annotation. More information about his research can be found at https://mxahan.github.io/digital-cv/.

Tutorial 3: 1101&1102 (11F), July 1st, 13:15-15:15

Advancing Smart Computing: A Comprehensive Tutorial to 3D Point Clouds

Tatsuya Amano, Osaka University, JAPAN
Hamada Rizk, Osaka University, JAPAN and Tanta University, Egypt

3D point clouds from LiDAR sensors have emerged as a powerful representation for understanding and interacting with the surrounding environment in smart computing systems. This tutorial aims to provide an overview of fundamental techniques, considerations, and applications of 3D point cloud recognition, with a focus on both deep learning and non-deep learning approaches. We will begin by introducing the unique characteristics and challenges of point cloud processing, highlighting the differences from traditional image-based approaches. Through the example of PointNet, a pioneering deep learning model for point cloud recognition, we will discuss the specific considerations and best practices for handling point cloud data. Recognizing the limitations of deep learning in resource-constrained and mobile environments, we will also explore alternative statistical and probabilistic techniques, such as Fisher Vector-based approaches, which enable efficient and lightweight point cloud processing and context recognition. These techniques are particularly relevant for smart computing applications that require real-time processing on edge devices and in Internet of Things scenarios.

The tutorial will also showcase various applications of point cloud recognition in smart computing contexts. We will showcase a case study of Osaka University smart campus, where we have deployed a large-scale pedestrian tracking system using a network of approximately 70 LiDAR sensors. Additionally, we will discuss the use of point clouds for remote space sharing in extended reality (XR) applications, demonstrating how point cloud recognition enables immersive and interactive experiences.We will also present a case study of our LiDAR mobile device (hitonavi-μ).

Asst. Prof. Tatsuya Amano, Osaka University, JAPAN
Tatsuya Amano is an Assistant Professor at Osaka University, Japan. He received M.E. Degree and Ph.D. in Information and Computer Science from Osaka University in 2018 and 2021, respectively. He was a young research fellow (DC1) of the Japan Society for the Promotion of Science (JSPS) from2018 to 2021. His research interests include spatial computing, smart city, and mobile computing.Since 2023, he has been an PRESTO Researcher, Japan Science and Technology Agency (JST). He is a member of IEEE, IPSJ (Information Processing Society of Japan), and IEICE (Institute of Electronics, Information and Communication Engineers).

Assoc. Prof. Hamada Rizk, Osaka University, JAPAN and Tanta University, Egypt
Hamada Rizk (Associate Professor, IEEE Senior Member) received the M.E. and Ph.D. degrees in computer science and engineering from Tanta University and E-JUST in 2016 and 2020, respectively. He is with Osaka University, Japan and Tanta University, Egypt. He has been working in mobile and pervasive computing, spatial intelligence, and AI research areas. He has been involved in several projects funded by many academic and industrial organizations such as NTRA Egypt, Uber, USA, ASTEP JST, Kakenhi  JSPS, NVIDIA, Japan, etc.  He has authored several publications in top journals and conferences and holds a number of patents. Hamada is the recipient of the silver medal in the 2019th ACM SigSpatial competition held in Chicago and was honored as an outstanding young researcher by the HLF foundation, Germany (2019), and by Google (2019 & 2020), among other awards.

Tutorial 4: Conf. Hall (12F), July 1st, 13:15-15:15

Science of Cyber Physical Security in Smart Living Applications

Sajal K. Das, Missouri University of Science and Technology, USA
Shameek Bhattacharjee, Western Michigan University, USA

The vision behind community-scale smart living applications is to use sensor-actuator devices, the so-called Internet of Things (IoT), to generate sensing data that provide situational awareness of the   physical world to improve the quality of human life at the city scale. Examples applications include smart transportation, customer and distribution layers of the smart grid metering, smart water networks, etc. The effects of threats such cyber-attacks, device/network faults and malfunctions, unsafe events, typically manifest themselves as anomalies that need to be promptly detected. However, there are unique challenges in anomaly detection for smart living : (1) behavioral randomness of humans creates dynamic spatiotemporal variations in data patterns making it difficult to learn the profile of benign behavior leading to unusable false alarm frequencies; (2) High non-linearity, non-IID data, random evolving patterns, cause traditional anomaly detection/learning methods to lose detection sensitivity; (3) smart living sensing data often have privacy and individual device profiling concerns; (4) unlabeled threats present while learning benign profile.
To address such challenges, we present a unified framework to detect various threats in a light-weight, timely, and privacy preserving manner. The tutorial covers the following topics: (1) decentralized graph representations of the wide area CPS; (2) anomaly detection metric learning; (3) resilient machine learning for finding detection thresholds; (4) bio-inspired classification methods to identify individual devices affected by the threats.  We show key results achieved by the overarching framework in smart grid, smart transportation, smart water networks, etc.

Prof. Sajal K. Das, Missouri University of Science and Technology, USA
Sajal K. Das is a Curators’ Distinguished Professor of Computer Science and Daniel St. Clair Endowed Chair at Missouri University of Science and Technology, USA. His interdisciplinary research expertise includes machine learning, data science, security of cyber-physical systems, IoT, smart environments, UAVs wireless and sensor networks, mobile and pervasive computing, edge/ and cloud computing. He has made fundamental contributions to these areas, published more than 600 papers in high quality journals and conference proceedings, coauthored 4 books, and 5 US patents. A recipient of 12 Best Paper Awards at prestigious IEEE and ACM conferences, he received the IEEE Computer Society’s Technical Achievement Award for pioneering contributions to sensor networks, and University of Missouri System President’s Award for Sustained Career Excellence. His h-index is 99 with more than 41,800 citations. He is the founding Editor-in-Chief of Elsevier’s Pervasive and Mobile Computing journal, and Associate Editor of IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Mobile Computing, IEEE Transactions on Sustainable Computing, ACM/IEEE Transactions on Networking, and ACM Transactions on Sensor Networks. He is a Distinguished Alumnus of the Indian Institute of Science, Bangalore, and a Fellow of the IEEE, National Academy Inventors (NAI), Asia-Pacific Artificial Intelligence Association (AAIA).

Asst. Prof. Shameek Bhattacharjee, Western Michigan University, USA
Dr. SHAMEEK BHATTACHARJEE is an assistant professor of Computer Science at Western Michigan University, Kalamazoo, USA. His research interests include the theory of anomaly detection, explainable artificial intelligence and data science for cyber security. He is a recipient of IEEE PIMRC best paper award and Top 3 paper award in IEEE/ACM ICCPS.