Prof. Yue Rong
Curtin University, Australia
Application of signal processing and machine learning in cardiac health monitoring
The advancement of artificial intelligence (AI) has significantly changed the paradigm of biomedical engineering in recent years. In this talk, we present a non-invasive technique for cardiovascular diseases pre-screening using AI-augmented multi-channel phonocardiography (PCG) and electrocardiography (ECG). After a brief overview of the state-of-the-art in PCG, we discuss key steps of the multichannel PCG system including heart sound signal acquisition, PCG signal processing, machine learning and deep learning based PCG signal classification. Our recent results will be demonstrated. Hope this talk can attract researchers and engineers to work in this interdisciplinary area.
Biography
Yue Rong received the Ph.D. degree (summa cum laude) in electrical engineering from Darmstadt University of Technology, Darmstadt, Germany, in 2005. He was a Postdoctoral Researcher with the Department of Electrical Engineering, University of California at Riverside, Riverside, CA, USA, from February 2006 to November 2007. Since December 2007, he has been with Curtin University, Bentley, WA, Australia, where he is currently a Professor. His research interests include signal processing for communications, machine learning, speech recognition, and biomedical engineering. He has published over 200 journal and conference papers in these areas. Prof. Rong was a Senior Area Editor of the IEEE Transactions on Signal Processing from 2020 to 2024, and an Associate Editor of this journal from 2014 to 2018. He was an Editor of the IEEE Wireless Communications Letters from 2012 to 2014 and a Guest Editor of the IEEE Journal on Selected Areas in Communications.
Ngakan Nyoman Kutha Krisnawijaya, PhD
Universitas Pendidikan Nasional, Indonesia
Machine Learning–Based Detection of Foot and Mouth Disease in Dairy Farms with Suboptimal Veterinary Infrastructure
Foot and Mouth Disease (FMD) is one of the most devastating diseases of livestock production, causing economic losses and socioeconomic effects. In this talk, we present a machine learning-based predictive model for FMD incidence using key predictors of outbreaks. We also discuss the methods to maintain the quality of data input to enhance the performance of Machine Learning model, which are the imbalanced data management and noise detection algorithm. The results show that our noise detection algorithm, together with imbalanced data management, significantly improves the performance of the ML models, leading to remarkable results.
Biography
Ngakan Nyoman Kutha Krisnawijaya is currently working as a lecturer in the Faculty of Engineering and Informatics at Undiknas University, Bali, Indonesia. He was awarded a scholarship for a bridging program in 2018 at The University of Western Australia and received a research grant in 2019, both from The Ministry of Research, Technology and Higher Education Indonesia.
Ngakan received funding from the Interdisciplinary Research and Education Fund (INREF) programme to pursue his PhD in the Information Technology Group at Wageningen University, which he completed in 2025. He became part of the Smart Indonesian Agriculture (Smart-in-Ag) project, which is a collaboration project between Wageningen University, IPB University and other Indonesian Universities. In this project, he focuses on designing system infrastructure for dairy and fish farming in Indonesia. His research interests are parallel computing, data analytics, data management, and data infrastructure.