Manoranjan Paul received the BSc Eng (honours) degree in Computer Science and Engineering from Bangladesh University of Engineering and Technology (BUET), Bangladesh, in 1997 and the PhD degree from Monash University, Australia in 2005. He has also completed Graduate Certificate in University Leadership in 2017 and Graduate Certificate in Learning & Teaching in Higher Education in 2015 from Charles Sturt University.
Currently, Manoranjan Paul is an Associate Professor in Computer Science and the Head of E-Health Research at Charles Sturt University (CSU).
Previously A/Prof Paul was an Assistant Professor/Lecturer (1997-01) at Ahsanullah University, Assistant Lecturer (2001-05) at Monash University, Post-Doctoral Research Fellow at University of New South Wales (2005-06), Post-Doctoral Research Fellow (2006-09) at Monash University, Post-Doctoral Research Fellow (2009-11) at Nanyang Technological University (Singapore), Lecturer (2011-12) at CSU, and Senior Lecturer (2013-16) at CSU. He was an Associate Director of Centre for Research in Complex Systems (2013-17).
His major research interests are in the fields:
He has published more than 150 fully refereed international journals, book chapters, books, and conference articles. A/Prof Paul regularly published in the top ranked journals such as IEEE Transactions. A/Prof Paul is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and Australian Computer Society (ACS).
A/Prof Paul is the ICT Researcher of the Year 2017 awarded by Australian Computer Society. He obtained Faculty Research Excellent Award 2013 and Faculty Research Supervision Excellence Award 2015 at CSU. He obtained $15 million competitive grant money including Australian Research Council (ARC) Discovery Project Grant, Australia-China Science Research Grant, Cybersecurity CRC, etc.
A/Prof Paul was an invited Keynote speakers in DICTA 2017, CSWN 2017, DICTA 2013, WoWMoM 2014, and ICCIT 2010. He is an Associate Editor of Eurasip Journal on Advances in Signal Processing (JCR Rank Q2). He is also a Guest Editor of Journal of Multimedia and Journal of Computers. He has supervised 12 PhD in completion. For detailed information, please visit his personal website.
Project 1: EEG Signal Analysis
Researchers: A/Prof Manoranjan Paul (CSU), Dr. Michael Antolovich (CSU), and Dr. Zavid Parvez (ISI Foundation, Italy and A/Prof Paul’s previous PhD student at CSU)
An Electroencephalogram (EEG) is a non-invasive graphical record of ongoing electrical activity captured from the scalp which is produced by firing of neurons of the human brain due to internal and/or external stimuli. It has great potential for the diagnosis to treatment of mental disorder and brain diseases. We are focusing on detecting and predicting of epileptic seizure period by analysis EEG signals. We are looking for other applications of EEG signals.
Outcome: We are able to predict epileptic seizure by 15 minutes earlier with 90% accuracy when we use invasive EEG signals. I successfully supervised a PhD project. We published 14 papers in this area including two journals in IEEE Transactions on Biomedical Engineering and IEEE Transactions on Neural Systems and Rehabilitation Engineering.
Project 2: Video Coding and Compression
Researchers: A/Prof Manoranjan Paul (CSU), Professor Manzur Murshed (FedUni), A/Professor Weisi Lin (NTU), Dr. Mortuza Ali (FedUni), Dr. Tanmoy Debnath (CSU), Dr. Subrata Chakraborty (USQ), Shampa Shahriyar (PhD student, Monash University), Pallab Podder (PhD student, CSU) and Niras C.V. (PhD student, Macquarie University)
Video conferencing, video telephony, tele-teaching, tele-medicine and monitoring systems are some of the video coding/compression applications that have attracted considerable interest in recent years as 3D video is now reality. The burgeoning Internet has increased the need for transmitting (non-real-time) and/or streaming (real-time) video over a wide variety of different transmission channels connecting devices of varying storage and processing capacity. Stored movies or animations can now be downloaded and many reality-type interactive applications are also available via web-cams. In order to cater for devices with different storage and transmission bandwidth requirements, raw digital video data need to be coded at different bit rates with different timing complexity. Sometimes for realistic views of the scene of action, we also need to encode/ compress/ process videos with multiple cameras (multi-view) from different angles. We are focusing on video coding and compression in different scales suitable for different devices with accepted quality and computational time.
Outcome: An ARC DP13 which partially solve interactive problem for 3D video call. Our contributions have been adapted in the latest video coding standards e.g. HEVC, VP9, AVS. Published 4 IEEE Transactions. Supervising three PhD projects on it.
Project 3: Object Detection and Background Modeling
Researchers: A/Prof Manoranjan Paul (CSU), Professor Manzur Murshed (FedUni), A/Professor Weisi Lin (NTU), and Dr. Subrata Chakraborty (USQ)
Object detection from the complex and dynamically changing background is still a challenging task in the field of computer vision. Adaptive background modeling based object detection techniques are widely used in machine vision applications for handling the challenges of real-world multimodal background. But they are constrained to specific environment due to relying on environment specific parameters, and their performances also fluctuate across different operating speeds. Camera motions of a scene makes dynamic background modeling more complicated. We are focusing on dynamic background modeling in challenging environments and their applications in different fields.
Outcome: We are able to extract background from a challenging environment. We have successfully applied background modeling in video coding and video summarization. Published 2 IEEE Transactions. Supervised one PhD project to completion.
Project 4: Eye tracking Technology
Researchers: A/Prof Manoranjan Paul (CSU), Dr. Tanmoy Debnath (CSU), and Pallab Podder (PhD student, CSU)
An eye tracker is a device to capture eye movement and its duration. In my lab, we have an eye tracker. We are focusing on various research issues e.g., Abnormal event detection and prediction, speech/alcohol/drug impairment detection, retail/advertising effectiveness, video communication for deaf people, eye can be used as an input device, improving electronic document design, increasing user acceptability of learning technologies, telemedicine, and remote surgery.
Outcome: We are able to determine human engagement using eye tracker. We are able to find some facts of marker discrepancy in multiple markers scenarios. We are also able to assess quality of a video when there is no reference. We have published three papers in this area.
Project 5: Hyperspectral Imaging
Researchers: A/Prof Manoranjan Paul (CSU), Professor Junbin Gao (University of Sydney), Professor Terry Bossomaier (CSU), and Rui Xiao (PhD student, CSU)
Hyperspectral imaging sensor divides images into many bands which can be extended beyond visible. The applications of hyperspectral imaging are in the fields of military to detect chemical weapon, geological survey, agriculture to quantify crops, mineralogy to identify different minerals, physics to identify different properties of materials, surveillance to detect different objects and reflections, measurement of surface CO2 emissions, mapping hydrological formations, tracking pollution levels, and more. We are investigating different applications of hyperspectral imaging for example Fruit-life span prediction.
Outcome: We are able to model a spectral predictor and develop a context-adaptive entropy coding technique which give us up to 7 compression ratio in some images whereas the current state-of-the-art methods only can provide around 3 compression ratio. We published 4 papers in area including PLoS ONE 2016 journal.
Project 6: Video Summarising
Researchers: A/Prof Manoranjan Paul (CSU) and MD Salehin (PhD student, CSU)
In our daily life we capture video for various purposes such as security, entertainment, monitoring, investigating and so on. It requires huge memory space to store as well as enormous time to retrieve or replay important information manually from this high volume of videos. In this project, we explore the various features based on scene content, scene transition, human visual systems to extract important importation for particular application and make a shorter version of the video.
Outcome: We are able to summarize different type videos including surveillance, sports, movie, etc. We published 10 papers in this area including PLOS ONE 2017, JOSA A 2017.
Project 7: Collagen Defect Detection
Researchers: A/Prof Manoranjan Paul (CSU) and Chris Williams (Honors student, CSU)
Machine vision is now being extensively used for defect detection in the manufacturing process of collagen-based products such as sausage skins which is a multimillion dollar industry worldwide. At present the industry standard is to use a LabView software environment, whereby a graphical interface is used to manage and detect any defects in the collagen skins. Available data corroborates that this method allows for false positives to appear in the results where creases or folds are resolved to be defects in the product instead of being a by-product in the inspection process. This is directly responsible for reducing the overall system performance and resulting wastage of resources. Hence novel criteria were added to enhance the current techniques used with defect detection as elaborated in this paper. The proposed improvements aim to achieve a higher accuracy in detecting both true and false positives by utilizing a function that probes for the color deviation and fluctuation in the collagen skins. From the operating point of view, this method has a more flexible approach with a higher accuracy than the original graphical LabView program and could be incorporated into any programming environment. After implementation of the method in a well-known Australian company, investigational results demonstrate an average 26% increase in the ability to detect false positives with a corresponding substantial reduction in operating cost.
Project 8: View Synthesis for 3D video and Free Viewpoint Video
In recent years, free viewpoint video (FVV) or Free Viewpoint Television (FVT) technology is increasing popularity for rendering intermediate views from existing adjacent views to avoid the large volume of video data transmission. For generating an intermediate view on a remote display, Depth Image Based Rendering (DIBR) techniques are normally used, however, sometimes it has some missing pixels due to inaccurate depth, low precision rounding error and occlusion problem. To address the issue, we are working on hole filling technique, view rendering, and depth coding, 3D video formation.
Outcome: I can reconstruct a better virtual view compared to the existing one. I have published 12 papers on this area including IEEE Transactions on Image Processing (Rank 1 in Signal Processing, CORE A*).
Project 9: Medical Imaging
Outcome: Our unsupervised vessel extraction method for detecting vessel provides comparable results compared to the popular supervised methods. I have published 5 papers on this area including journals in Signal, Image and Video Processing 2017 and Pattern Analysis and Application 2017.
Analysis of output images:
(a) ground truth (right eye image), (b) output image (right eye image),
(c) ground truth (left eye image), and (d) Output image (left eye image).
|Falling from a chair|
Falling towards camera
|Falling sideways||Picking up object|
|Sitting on sofa||Sweeping floor|
Fall Detection: Some challenging scenario where the existing method fails as it considers as fall for sweeping floor, picking object but the proposed method is successful.
|Upper front teeth image registration||Lower front when patient moves||Image overlaying if patient moves||If surgical instrument moves|
|When surgical instrument moves||Mandibular reconstruction||If surgical instrument moves||Image overlaying if patient moves|
AR on Oral and Maxillofacial Surgeries and AR for Visualising the Blood Vessels in Breast Implant Surgeries
Project 10: Early Diagnosis of Alzheimer’s Disease
Successful, timely diagnosis of neuropsychiatric diseases is key to management. Research efforts in the area of diagnosis of Alzheimer’s disease have used various aspects of computer-aided multi-class diagnosis approaches with varied degrees of success. However, there is still need for more efficient and reliable approaches to successful diagnosis of the disease. We are conducting research to diagnose the Alzheimer’s disease so that we are able to detect problem earlier.
Outcome: We use k-sparse autoencoder method with deep learning framework for this purpose. The results show promising results. I am supervising a Professional Doctorate project on it. I have published 1 paper on this area.
Project 11: Cardiopulmonary measurement using the smartphone
The aim of the project is to develop a non-contact cardiopulmonary detection method in natural disaster rescue relief, based on the use of frequencies and antennas that are similar to current smartphone-like devices. Current focuses are in particular on the current Wi-Fi 2.4 GHz and 5 GHz radio frequencies wavelength, Doppler effect radar, signal processes, and a signal noise cleansing technique that detects and measures vital signs. This approach could be used as an alternative to the commercially designed, devices currently used for heartbeat and respiration detection during rescue relief with the aim of reducing device transit time and cost. This project further evaluates a new type of multi-directional antenna that could be used to detect the human physiology pulsatile from 360o degree view.
Outcome: I am supervising a DIT project on it.
Detection theory of heart and breathing using continued wave
Associate Director (2013-2017), Centre for Research in Complex Systems (CRiCS)
ARC (Australian Research Council) Assessor Since 2013:
Current PhD Supervision
Editorial (from 2009)
Session Chair/PC member:
International Journal Reviewer
Seminar presentation at different International Conferences such as
The detailed publication list with preprint copy is available at http://csusap.csu.edu.au/~rpaul/.
Book (from 2009):
International Conference Publications (from 2009):