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Intelligent Transportation System ( ITS ) with Internet of Things ( IoT ) and Artificial Intelligence ( AI )
ELECTRICAL AND COMPUTER SYSTEMS ENGINEERING
Ir Ts Dr Joanne Lim Mun Yee Senior Lecturer PhD , PEng , MIEM
Research expertise : Internet of Things ( IoT ), Artificial Intelligence , Intelligent Transportation System ( ITS ), Unmanned Aerial Vehicle ( UAV ) / Drones , Sustainable Systems and Applications
E : joanne . lim @ monash . edu T : + 603 5514 6081
Intelligent Transportation System ( ITS ) aims to provide innovative transportation and traffic management , enabling users to coordinate the use of transportation networks in making transportation safer and efficient . Our research focuses on enabling artificial intelligence , AI to improve data analytics with the use of communication to provide sustainable transportation applications . Unmanned aerial vehicles ( UAVs ), better known as drones are the next intelligent transportation system advancement which is one of the major technological developments of today .
Our research focuses on developing UAV communications systems to help drones and their operators achieve promising results in applications . By designing efficient Internet of Things ( IoT ) technologies , different objects which are embedded with sensors can be used to connect and exchange data for system analytics . Currently we are working with transportation and IoT industries to create future technologies for the Intelligent Transportation System ( ITS ).
Internet of Things , IoT and Drones Technologies
Quantitative Image Analysis of Medical Images : Assisting Radiologists in Their Medical Decision-Making Processes
Dr Maxine Tan Senior Lecturer PhD , CPEng
Research expertise : Quantitative Medical Image Analysis , Deep Learning , Radiomics , Biomedical Image Analysis , Computer-Aided Diagnosis ( CAD ), Machine Learning , Feature Classification and Modeling , Evolutionary Algorithms
E : maxine . tan @ monash . edu T : + 603 5515 9702
Quantitative image analysis and Computer-Aided Diagnosis ( CAD ) are broad concepts that integrate image processing , computer vision and artificial intelligence ( AI ) -based methods into computerized techniques that assist radiologists in their medical decision-making processes . These techniques include the detection of disease and anatomic structures of interest , classification of lesions , and risk assessment . We , along with colleagues and collaborators in other institutions , are conceptualising and developing novel deep learning , CAD , radiomics and quantitative image analysis-based methodologies . Active research projects span different imaging modalities
( including mammography , computed tomography , and digital breast tomosynthesis ) across a range of anatomic systems ( including lungs , breast , and brain ). The techniques we develop produce objective , quantitative computations of the numeric imaging data . These objective computations serve to augment the radiologists ’ subjective , qualitative interpretation of the displayed images . Unlike the radiologists ’ interpretation that may be prone to human error and fatigue as well as to inter-or intraobserver variabilities , deep learning and CAD schemes produce an objective and consistent reading / result to complement the radiologists ’ readings .
Intelligent Transportation System ( ITS )
Quantitative image and texturebased analysis for the detection of breast lesions and for cancer risk assessment
Computer-Aided Diagnosis ( CAD ) schemes for lung cancer detection and classification
R E S E A R C H E R P R O F I L E 2022 / 2023