Professor Marimuthu Palaniswami
- Biomedical Instrumentation, Signal and Image Processing (Time Frequency Analysis, Machine Learning, Active Shape Models)
- Biomedical signal processing
- Cardiovascular signal processing and modelling, Human gait analysis, Medical Instrumentation (Machine learning, Biomedical Electronics)
- Control and Optimization of Communication Networks (Internet Traffic Control, Congestion Control, Quality of Service)
- Event Detection (Pattern Recognition, Machine Learning)
- Image Processing
- Machine learning (support vector machines)
- Signal Processing
- Signal and Image Processing, Computer Vision (Video Analysis, Surveillance)
- Sliding Mode Control (Nonlinear Systems)
- Smart City Research (Internet of Things)
- Smart City Research (Wireless Sensor Networks, Internet of Things)
- Smart City, Cyber-Physical System (Wireless Sensor Networks, Internet of Things)
- Smart grid (Security)
- Wireless Sensor Neteworks (Computer Network Monitoring, Environmental Monitoring)
- Wireless sensor networks (Communications)
- Wireless sensor networks (Security)
Marimuthu Palaniswami is a Fellow of the Institute of Electrical and Electronic Engineering (IEEE) and an internationally recognised expert in Internet of Things (IoT), Sensor Networks, Automated Learning, and Computational Intelligence in large-scale complex systems. He is a named Distinguished Lecturer of the IEEE Computational Intelligence Society over the period 2013-2015.
He obtained his Ph.D from the University of Newcastle, Australia and is currently the Professor in the Department of Electrical and Electronic Engineering. He has a demonstrated track record in leading large research initiatives. In particular, he has been the Founder and Director of the ARC Research Network ISSNIP (Information, Signals, Sensor Networks and Information Processing), which has become an internationally recognised constellation of researchers, partner universities and industry organisations in the area of sensor networks.
He has built many large-scale projects by bringing together teams of chief investigators in related areas such as the Distributed Sensor Networks Project funded by former Department of Education, Science and Technology (DEST), the SEMAT project of the Queensland government’s Smart State Program and IMOS-GBROOS for the Great Barrier Reef. He was the co-director of the Centre of Excellence for Networked Decision Systems (CENDS) funded by the Defence Science and Technology Organisation.
He is also a co-founder of the European Centred IoT forum. He served as a general Chair for over 10 IEEE Sponsored International conferences with a focus on Sensor Networks and Internet of Things (IoT). As a Chief Investigator, he was funded by various government agencies for a number of IoT projects covering Infrastructure, Smart City, Healthcare, Transport, BigData Analytics and Smart Governance. His international IoT funding for smart city covers projects such as European Union’s FP7 Smart Santander, SocioTal, and OrganiCity.
His research has also focused on translational aspects of his research and he has a fantastic track record in working with diverse industry sectors – from defence to environment, from telecom to biomedical and from health to local government domains. His extensive publication and citation record is a clear testament to his technical leadership spanning Internet flow control, cloud computing, computational tools for analytics, image processing, sensor network architectures, sensor data fusion, autonomous tracking, sensor network security and control engineering. He has published well over 400 scientific papers including books and edited volumes in related topics in IEEE Transactions on IoT Journal, Cybernetics, Fuzzy Systems, Neural Networks, Power Systems, Communications Magazine, Computational Intelligence Magazine, Information and Forensic Society, Mobile Computing, Automatica Control; ACM Transactions on Sensor Networks; Pattern Recognition, Computer Vision and Image Understanding (CVIU); Automatica; IEEE Journal of Biomedical and Health Informatics (JBHI), PLoS ONE, Frontiers in Physiology, Medical and Biological Engineering and Computing, and others.
He has been recognised and awarded (in 2007, 2008 and 2010) for his substantial knowledge transfer to industry and the community. Many of these knowledge transfers were using deployable technologies for research outcomes, e.g. DIISR-ISL Project on Distributed Sensor Networks. His research continues to deliver in Healthcare (e.g. CSIRO and Microsoft Research Diagnostic tools project) and on the environment (e.g. Great Barrier Reef monitoring) which is testimony to the capacity of his research in providing benefits to society.
He served as a member of the US NSF proposals panel, steering committee member for EU IoT projects and advisory panel member for Centre of Excellence on IoT projects. He has contributed immensely to creating IoT startups, keen to push the impact of IoT further on industry and society.
- Rathore P, Ghafoori Z, Bezdek JC, Palaniswami M, Leckie C. Approximating Dunn's Cluster Validity Indices for Partitions of Big Data. IEEE Transactions on Cybernetics. Institute of Electrical and Electronics Engineers. 2018, Vol. PP. DOI: 10.1109/TCYB.2018.2806886
- Li P, Karmakar C, Yearwood J, Venkatesh S, Palaniswami M, Liu C. Detection of epileptic seizure based on entropy analysis of short-term EEG. PLOS ONE. Public Library of Science. 2018, Vol. 13, Issue 3. DOI: 10.1371/journal.pone.0193691
- Lyu L, Nandakumar K, Rubinstein B, Jin J, Bedo J, Palaniswami M. PPFA: Privacy Preserving Fog-enabled Aggregation in Smart Grid. IEEE Transactions on Industrial Informatics. IEEE - Institute of Electrical and Electronic Engineers. 2018. DOI: 10.1109/TII.2018.2803782
- Rathore P, Sridhara Rao A, Rajasegarar S, Vanz E, Gubbi Lakshminarasimha J, Palaniswami M. Real-Time Urban Microclimate Analysis Using Internet of Things. IEEE Internet of Things Journal. 2018, Vol. 5, Issue 2. DOI: 10.1109/JIOT.2017.2731875
- K Udhayakumar R, Karmakar C, Palaniswami M. Understanding Irregularity Characteristics of Short-term HRV Signals using Sample Entropy Profile. IEEE Transactions on Biomedical Engineering. IEEE - Institute of Electrical and Electronic Engineers. 2018. DOI: 10.1109/TBME.2018.2808271
- Kumar D, Bezdek J, Rajasegarar S, Leckie C, Palaniswami M. A visual-numeric approach to clustering and anomaly detection for trajectory data. VISUAL COMPUTER. Springer. 2017, Vol. 33, Issue 3. DOI: 10.1007/s00371-015-1192-x
- Li J-W, Li S-N, Zhang Y, Gu T, Law YW, Yang Z, Zhou X, Palaniswami M. An Analytical Model for Coding-Based Reprogramming Protocols in Lossy Wireless Sensor Networks. IEEE TRANSACTIONS ON COMPUTERS. IEEE - Institute of Electrical and Electronic Engineers. 2017, Vol. 66, Issue 1. DOI: 10.1109/TC.2016.2560805
- Krishnavilas Udhayakumar R, Karmakar C, Palaniswami M. Approximate entropy profile: a novel approach to comprehend irregularity of short-term HRV signal. NONLINEAR DYNAMICS. Springer. 2017, Vol. 88, Issue 2. DOI: 10.1007/s11071-016-3278-z
- Desai N, Sridhara Rao A, Palaniswami P, Thyagarajan D, Palaniswami M. Arytenoid Cartilage Feature Point Detection Using Laryngeal 3D CT Images in Parkinson's Disease. 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC). IEEE. 2017. DOI: 10.1109/EMBC.2017.8037199
- Marzbanrad F, Khandoker A, Kimura Y, Palaniswami M, Clifford GD. Assessment of Fetal Development Using Cardiac Valve Intervals. FRONTIERS IN PHYSIOLOGY. Frontiers Research Foundation. 2017, Vol. 8, Issue MAY. DOI: 10.3389/fphys.2017.00313
- Rathore P, Bezdek J, Monazam Erfani S, Rajasegarar S, Palaniswami M. Ensemble Fuzzy Clustering using Cumulative Aggregation on Random Projections. IEEE Transactions on Fuzzy Systems. IEE Institute of Electronic Engineers. 2017. DOI: 10.1109/TFUZZ.2017.2729501
- Lyu L, Jin J, Rajasegarar S, He X, Palaniswami M. Fog-Empowered Anomaly Detection in IoT Using Hyperellipsoidal Clustering. IEEE INTERNET OF THINGS JOURNAL. IEEE - Institute of Electrical and Electronic Engineers. 2017, Vol. 4, Issue 5. DOI: 10.1109/JIOT.2017.2709942
- Fahiman F, Bezdek J, Monazam Erfani S, Palaniswami M, Leckie C. Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms. 2017 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE). Institute of Electrical and Electronics Engineers. 2017. DOI: 10.1109/FUZZ-IEEE.2017.8015525
- Fahiman F, Monazam Erfani S, Rajasegarar S, Palaniswami M, Leckie C. Improving Load Forecasting Based on Deep Learning and K-shape Clustering. 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN). IEEE. 2017, Vol. 2017-May. DOI: 10.1109/IJCNN.2017.7966378
- Philip B, Alpcan T, Jin J, Palaniswami M. Information Constrained and Finite-Time Distributed Optimisation Algorithms. 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). Institute of Electrical and Electronics Engineers. 2017.
View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile