Detecting Cracks from Concrete Surface Images for Structural Health Monitoring
2019 - present
People: Aravinda Rao, Tuan Nguyen, Marimuthu Palaniswami and Tuan Ngo
Project: Routine assessment of structural conditions is necessary to ensure structural and operational safety of critical infrastructure. The current practice to detect structural damages such as cracks depends on human visual observation methods, which are prone to efficiency, cost and safety concern.
In this project, we present an automated localization of cracks in images based on Convolutional Neural Network (CNN) models. Our approach provides over 95% accuracy and 87% precision in detecting the cracks from images. Our proposed approach provides opportunities for real-time Structural Health Monitoring (SHM). The project also provides a dataset of crack and non-crack images for benchmarking purposes.
Funding: CRC-P for Advanced Manufacturing of High Performance Building Envelope project, funded by the CRC-P program of the Department of Industry, Innovation and Science, Australia; The Asia Pacific Research Network for Resilient and Affordable Housing (APRAH) grant, funded by the Australian Academy of Science, Australia.
Imputing Missing Data from IoT Sensors
2019 - 2020
People:Aurora González-Vidal, Punit Rathore, Aravinda Rao, José Mendoza-Bernal, Marimuthu Palaniswami and Antonio Skarmeta
Project: Internet of Things (IoT) enables seamless integration of sensors, actuators, and communication devices for real-time sensing, communication and the remote control of actuators. For applications involving real time decision-making, the quality of the spatio-temporal data is of the utmost importance. However, values are often missing from the collected sensor measurements. The research challenge is to impute the missing values.
This project proposes a new framework to impute missing values in IoT environments using Bayesian Maximum Entropy (BME). Our proposed scheme outperforms existing schemes as regards accuracy, execution time and robustness.
Collaborators:University of Murcia, Spain; Senseable City Lab, Massachusetts Institute of Technology, USA.
Funding: Australian Research Council (ARC) Discovery Project (DP190102828); MINECO through the PERSEIDES project; European Union (EU) H2020 IoTCrawler; DEMETER (grant agreement 857202) EU Projects; and Co-financed by the European Social Fund (ESF) and the Youth European Initiative (YEI) under the Spanish Seneca Foundation (CARM).[Paper - PDF]
IoT-Based Real-time Urban Micro-climate Monitoring
2012 - 2015
People:Jayavradhana Gubbi, Aravinda Rao, Punit Rathore, Sutharhsan Rajasegarar, Elena Vanz and Marimuthu Palaniswami
Project: Increase in human activities, modern urbanization and subsequent loss of vegetation in the urban landscape have been contributing to the increase of temperature in cities by several degrees higher than the surrounding suburbs, particularly at night. This phenomenon is known as Urban Heat Island (UHI) effect. The heat is stored in non-homogeneous proportions based on the characteristics of the surrounding environments (buildings, parks, public places). Monitoring UHI effect is essential for city councils and government agencies to plan and maintain a healthy Smart City environment.
Increasing the number of trees to reduce the UHI effect is a preferred solution, but achieving cost-effective solution and better environmental health benefits require analysis of how different trees, buildings, and parks affect their microclimate. In this regard, this project work introduces an integrated geo-visualization framework to collect data from IoT sensors and analyze complex patterns of urban microclimate variations using a novel spatiotemporal estimation model (Bayesian Maximum Entropy, BME).
Collaborators:City of Melbourne and Arup.
Funding: ARC Linkage Project (LP120100529) and ARC LIEF Project (LE120100129).[Paper - PDF]
Camera-based Assistive Technology for Vision-Impaired Mobility Needs
2015 - 2016
People: Aravinda S. Rao, Jayavardhana Gubbi, Marimuthu Palaniswami, Elaine Wong
Project: The World Health Organization (WHO) estimates that worldwide there are about 285 million vision-impaired people as of August 2014. WHO also estimates that around 39 million of the 285 million are blind, with 90% of the vision-impaired are from developing countries. Many people have vision problems due to birth defects, uncorrected errors, work nature, accidents, and aging. The white cane and guide dog are the most widely used means of navigation for vision-impaired needs. However, there is still a lack of devices to detect potholes and uneven pavements, which inhibits mobility after dark.
This pilot study proposes a computer vision based pothole and uneven surface detection approach to assist vision-impaired people in meeting their mobility needs. Our system uses laser patterns, monocular video and analyzes patterns for providing path cues. Our system provides over 90% accuracy in detecting potholes for assisting in real-time navigation.
Partners: Melbourne Networked Society Institute (MNSI), Vision Australia, Guide Dogs Australia.
Funding: Disability Research Initiative (DRI), University of Melbourne.[Pothole Detection] [Non-protruding Hazard Detection]
Video Analytics for Crowd Behavior Analysis
2011 - 2016
People: Aravinda S. Rao, Jayavardhana Gubbi, Slaven Marusic and Marimuthu Palaniswami
Project: Crowd behavior analysis is an important problem in video surveillance applications. Often times, security personnel manually scan each video feed from several sources to monitor safety and security of general public. However, this is a cumbersome process, can cause fatigue and prone to human errors.
Video analytics allows automated detection and monitoring of events. This project provides a suite of video surveillance for real time monitoring of crowd behavior.
- Detect suspicious people (loitering behavior) from videos. [Loitering Behavior], [Anomalous Behavior]
- Automatically count number of people in crowded videos. [Publisher Link].
- Estimate density of people in densely crowded scenarios. [Estimate Density]
- Track people movements and their behavior. [Tracking People], [Crowd Movement]
- Identify and detect crowd activities (events) [Crowd Event Detection], [Crowd Activities], [Dimensionality Reduction], [Anomalous Event], [Non-linear Dimensionality Reduction]
Funding: Australian Research Council (ARC) Linkage Project (LP100200430)
Partners: Melbourne Cricket Club, Arup and SenSen Networks.
Automated Scoring of Motor Weakness in Acute Stroke Using Wearable Devices
2015 - present
People: Shreyasi Datta, Aravinda S. Rao, Chandan Karmakar, Bernard Yan and Marimuthu Palaniswami
Project: Stroke affects nearly 15 million people worldwide every year, leading to death, disability and huge medical expenses in patient monitoring and treatment. Stroke often leads to paralysis in one half of the body i.e., hemiparesis which severely affects the upper limbs by limiting movements and coordination.
The project focuses on quantifying the movement differences of two arms by accelerometer measurements that can discriminate between different levels of hemiparetic severity using wearable accelerometer devices.
Funding: Australian Research Council (ARC) Discovery Project (DP19010248)
Collaborator: Melbourne Brain Centre (MBC), Royal Melbourne Hospital, Australia.[Paper - PDF]
Use of Wearable Devices for Monitoring Motor Recovery in Stroke Care
2010 - 2015
People: Jayavardhana Gubbi, Aravinda S. Rao, Bernard Yan, Marimuthu Palaniswami
Project: Stroke is a major cause of morbidity and mortality in Australia. There is an annual incidence of 48,000 new strokes and the risk of death is 25 to 30%. Of those who survive, stroke contributes to 25% of all chronic disabilities in Australia. Acute stroke is caused by a blockage of one of the arteries in the brain resulting in interrupted blood supply. The National Institute of Health Stroke Scale (NIHSS) is an international initiative to systematically assess stroke and provide a quantitative measure.
This project focuses on monitoring stroke survivors during the "hot" hours which is usually the first 24 hours after the onset of stroke. We propose a wireless accelerometer-based system to monitor the motor recovery in acute-stroke patients.
Collaborator: Melbourne Brain Centre (MBC), Royal Melbourne Hospital, Australia.[Paper - Online]
IoT-based Low-cost Water Quality Monitoring System
2013 - 2014
People: Aravinda S. Rao, Jayavardhana Gubbi, Steven Marshall, Marimuthu Palaniswami
Project: With increasing human population and consequently, because of the radical urbanization, our precious water resources are being polluted at alarming rates. In Victoria, 80% of the waterways are in poor to moderate condition. A biological early-warning system can aid in forestalling the impact of the immediate pollution. A prototype sensor system as a part of the Autonomous Live Animal Response Monitor (ALARM) system, CAPIM, Victoria. This system is designed to measure a suite of biologically relevant physiochemical parameters in freshwater.
The system measures temperature, light intensity, pH, electrical conductivity (EC), total dissolved solids (TDS), salinity (SAL), dissolved oxygen (DO) and oxidation reduction potential (ORP). The project focuses on identifying possible pollutants by monitoring physical properties in water streams, improve the health of water-dependent aquatic and terrestrial lives, and provide environmental benefits.
Partner: Centre for Anthropogenic Pollution Impact and Management (CAPIM), Victorian Government.[Paper - PDF]
Internet of Things for Low-cost Urban Noise Monitoring System
2011 - 2013
People: Jayavardhana Gubbi, Yee Wei Law, Aravinda S. Rao, Slaven Marusic and Marimuthu Palaniswami
Project: Exposure to excessive noise levels is known to negatively impact quality of life. Exposure to excessive noise levels is known to have detrimental health impacts (at sound pressure levels above 65 dBA). Some of these effects include (a) stress, anxiety contributing to mental illness; (b) pain (at 120 dB); hearing damage (at 85dB); (c) sleep disorders, hypertension; heart diseases. A 2007 social survey by Australian state Victoria’s EPA found that almost half of its people (49 per cent) were disturbed or annoyed by environmental noise and one-quarter (24 per cent) of respondents reported sleep disturbance at some stage in the previous 12 months.
A three dimensional urban noise map is an ideal solution. This requires continuous monitoring. Armed with wireless sensor networks and IoT, this pilot project proposes a new architecture and reports development of necessary infrastructure for this objective. The framework also allows collection of temperature, humidity and light in addition to noise intensity levels in urban environments.
Partner: Melbourne City Council, Arup
Funding:ARC LIEF Project (LE120100129), ARC Linkage Project (LP120100529) and Institute of Broadband Enabled Society (IBES) grant on Participatory Sensing; EU SmartSantander and Internet of Things - Initiative.[Paper- PDF]
Real-time Monitoring: Sensing, Tracking and Visualization of Asset Locations
2008 - 2009
People: Aravinda S. Rao, Davood Izadi, Reuben F. Tellis, Samitha W. Ekanayake and Pubudu N. Pathirana
Project: Knowledge of vehicle locations is of utmost importance for taxi services, car rentals, postal/courier services and emergency services that provide services based on locations or that needs to keep track of its vehicle fleet. However, the astounding cost of the system and the lack of ability to incorporate other sensor devices to the same platform restrict the applicability of such system for wide range of applications.
This project develops an embedded system to acquire, process and send GPS locations to a centralized server through text messages or Short Message Service (SMS). The platform is scalable to generic sensor networking platform having numerous applications ranging from medical to military and from aerospace to underwater applications.
Funding: ARC LIEF Project (LE00883073)[Paper - PDF]