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WSAI 2021 : [Call For Participations] Workshop on Sensors, AI and IoT (Free, Virtual)


When Nov 20, 2021 - Dec 31, 2022
Where Virtual
Submission Deadline Dec 13, 2021
Categories    sensor   AI   IOT   security

Call For Papers

Theme: Recent Advances of Sensors, Artificial Intelligence and Internet-of-Things Technology
The proliferation of Artificial Intelligence (AI) and Internet-of-Things (IoT) opens up the possibility of integrating intelligence into various sensors. This creates many smart and efficient applications in various areas such as healthcare, biometrics, self-drive car, human activity recognition, transportation, robots in manufacturing, and risk management to name a few. Since the IoT sensor nodes are constrained in computational and storage resources, it is challenging to port an AI model into such resource-constrained devices. Moreover, the uncertainty issues arise from the sensor measurement in these systems are very important to ensure successful IoT applications. In this workshop, we will review the recent advancements and development of wearables and medical devices. Open set presentation attack, a recent trend in biometric, will be presented, followed by a discussion on the representative techniques to enable deep learning acceleration on constrained platforms. The uncertainty issues introduced by machine learning will be discussed, and the techniques to quantify it will be presented.

Organizer: IEEE Seoul Section Sensors Council Chapter, Korea
Sponsor: IEEE Sensors Council
Date: 16 and 17 December 2021, 09:00 am -11:00 am, Korea Standard Time (KST)
Venue: Virtual event

16 December, 2021
Talk 1
Speaker: Prof. Kevin W. Bowyer, University of Notre Dame, USA
Title: Recent Trends in Biometric: Open Set Presentation Attack Detection
Abstract: Biometric presentation attack detection is made especially difficult because of its open-set nature in the real world. The current highest-accuracy algorithms for iris presentation attack detection use deep learning approaches. We show a novel approach to achieve greater accuracy in deep learning from limited training data using human-aided saliency maps.
Bio: Kevin Bowyer is the Schubmehl-Prein Family Professor of Computer Science and Engineering at the University of Notre Dame. He has served as EIC of the IEEE Transactions on Biometrics, Behavior, and Identity Science and the IEEE Transactions on Pattern Analysis and Machine Intelligence, as well as General Chair or Program Chair of conferences such as Computer Vision and Pattern Recognition (CVPR), Winter Conference on Applications of Computer Vision (WACV), and Face and Gesture Recognition (FG), Biometrics Theory, Applications and Systems (BTAS) and International Joint Conference on Biometrics (IJCB). Professor Bowyer is a Fellow of the IAPR, IEEE and AAAS.

Talk 2
Speaker: Prof. Subhas Mukhopadhyay, Macquarie University, Australia
Title: Trends for Wearable and Medical Devices
Abstract: An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring of inhabitants is possible even without hospitalization. The advancement of sensing technologies, embedded systems, wireless communication technologies, nano-technologies, and miniaturization makes it possible to develop smart medical systems to monitor activities of human beings continuously. Wearable sensors monitor physiological parameters continuously along with detect other symptoms such as any abnormal and/or unforeseen situations which need immediate attention. Therefore, necessary help can be provided in times of dire need. This seminar reviews the latest reported systems and the trends on wearable and medical devices to monitor activities of humans and issues to be addressed to tackle the challenges.
Bio: Subhas Mukhopadhyay is working as a Professor of Mechanical/Electronics Engineering, Macquarie University, Australia and is the Discipline Leader of the Mechatronics Engineering Degree Programme. He has received various awards, most notably: the Australian Research Field Leader in Engineering and Computer Science 2020; Distinguished Lecturer, IEEE Sensors Council 2020-2022; Outstanding Volunteer by IEEE R10, 2019; World Famous Professor by Government of Indonesia, 2018; Certificate of Distinction from IEEE Sensors Council, 2017; IETE R.S. Khandpur Award – India, 2016; Best Performing Topical Editor of IEEE Sensors Journal from 2013 to 2018, six years consecutively. He is a Fellow of IEEE (USA), a Fellow of IET (UK), a Fellow of IETE (India), a Topical Editor of IEEE Sensors journal, an associate editor of IEEE Transactions on Instrumentation and Measurements, and IEEE Review of Biomedical Engineering. He is the Editor-in-Chief of the International Journal on Smart Sensing and Intelligent Systems and Springer Natura on Computer Science. He is a Distinguished Lecturer of the IEEE Sensors Council from 2017 to 2022. He is the Founding Chair of the IEEE Sensors Council New South Wales Chapter.

17 December, 2021
Talk 1
Speaker: Prof. Shervin Shirmohammadi, Ottwa University, Canada
Title: Quantifying Uncertainty in Machine Learning Based Sensing
Abstract: Like any science and engineering field, Instrumentation and Measurement (I&M) including sensors are currently experiencing the impact of the recent rise of Applied AI and in particular Machine Learning (ML). In fact the relationship between I&M and ML has reached new levels: sensors are used to measure and collect data, which is used to train an ML model, which is then used in a sensor system or application. Uncertainty is accumulated at every stage, and quantifying it is crucial. But I&M and ML use terminology that sometimes sound or look similar, though they might only have a marginal relationship or even be false friends. Therefore, understanding the terminology used by both communities is of crucial importance to understand the influences of ML and I&M in each other. In this talk, we will give an overview of ML’s contribution to a sensor’s measurement error, and how to avoid confusion with the said terminology in order to better understand the application of ML in sensor measurements. We then use that understanding and terminology to show how to quantify the uncertainty introduced by ML, specifically Deep learning (DL), in DL-based sensor systems and applications.
Bio: Shervin Shirmohammadi is the Editor-in-Chief of the IEEE Transactions on Instrumentation and Measurement, and was also the Associate Editor-in-Chief of IEEE Instrumentation and Measurement Magazine in 2014 and 2015, and is currently on its editorial board. He has been an IEEE Instrumentation and Measurement Society AdCom member since 2014, served as the Vice President of its Membership Development Committee from 2014 to 2017, and was a member of the IEEE I2MTC Board of Directors from 2014 to 2016. He is an IEEE Fellow for contributions to multimedia systems and network measurements, winner of the 2019 George S. Glinski Award for Excellence in Research, a Senior Member of the ACM, a University of Ottawa Gold Medalist, and a licensed Professional Engineer in Ontario.

Talk 2
Speaker: Prof. Yiran Chen, Duke University, USA
Title: Efficient Deep Learning at Scale: Hardware and Software
Abstract: The rapid growth of modern neural network models’ scale generates ever-increasing demands for high computing power of Artificial Intelligence (AI) systems. Many specialized computing devices have been also deployed in the AI systems, forming a truly application-driven heterogeneous computing platform. This talk discusses the importance of hardware/software co-design in the development of AI computing systems. We first use resistive memory based Neural Network (NN) accelerators to illustrate the design philosophy of heterogeneous AI computing systems, and then present several hardware-friendly neural network model compression techniques. We also extend our discussions to distributed systems and briefly introduce the automation of the co-design flow, e.g., neural architecture search. A research roadmap of our relevant research is given at the end of the talk.
Bio: Yiran Chen is now the Professor of the Department of Electrical and Computer Engineering at Duke University and serving as the director of the NSF AI Institute for Edge Computing Leveraging the Next-generation Networks (Athena) and the NSF Industry–University Cooperative Research Center (IUCRC) for Alternative Sustainable and Intelligent Computing (ASIC), and the co-director of Duke Center for Computational Evolutionary Intelligence (CEI). He is now serving as the Editor-in-Chief of the IEEE Circuits and Systems Magazine. He received many professional awards and is the distinguished lecturer of IEEE CEDA (2018-2021). He is a Fellow of the ACM and IEEE and now serves as the chair of ACM SIGDA.

Program Committee Chair:
Prof. Seong Oun Hwang (Gachon University, Korea)

Program Committee:
Prof. Hyung Jin Chang (University of Birmingham, United Kingdom)
Prof. Cheon-won Choi (Dankook University, Korea)
Prof. Markus Westner (OTH Regensburg, Germany)
Prof. Martin Schubert (OTH Regensburg, Germany)
Prof. Ramachandra Achar (Carleton University, Canada)
Prof. Bok-Min Goi (UTAR, Malaysia)
Prof. Haider Abbas (NUST, Pakistan)
Prof. Chin-Chen Chang (Feng Chia University, Taiwan)
Prof. Jin-Min Lin (Feng Chia University, Taiwan)
Dr. Ayesha Khalid (Queen's University Belfast, UK)
Dr. Boon-Yaik Ooi (UTAR, Malaysia)
Dr. Wai Kong Lee (Gachon University, Korea)

Registration (Free of Charge):
All participants need to pre-register no later than 13 December 2021 by filling up the following form:
Zoom sign-in details will be shared with the registered participants using the email address provided in the registration form.
For further enquiries, please contact Prof. Seong Oun Hwang (,

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