Keynote Speaker 1:
Prof Ts. Dr. Massila Kamalrudin
A native of Banda Hilir, Melaka, Prof Massila started her academic career as a tutor at the Faculty of Information and Communication Technology (FTMK) in 2004 at Kolej Universiti Teknikal Kebangsaan Malaysia (KUTKM) before it was transformed into Universiti Teknikal Malaysia Melaka (UTeM). Currently, she is the Vice Chancellor of UTeM since March 2022. She graduated from Universiti Putra Malaysia (UPM) in 2003 with a Bachelor of Computer Science (Software Engineering), a Master of Science in Computing and Software Technology from University of Wales, Swansea, United Kingdom in 2006, a PhD in Electrical and Electronic Engineering from The University of Auckland, New Zealand in 2011 and a post-doctorate at the Faculty of ICT, Swinburne University of Technology, Australia in 2012. She is an active academic and Professor in Software Engineering at FTMK and the Leader of the multidisciplinary research group called the Innovative Software System & Services (IS3). She has an extensive industry exposure that is relevant to the Digital Economy and the Industrial Revolution 4.0. She is also actively promoting the Research, Innovation, Commercialization and Entrepreneurship (RICE) value chain. Professor Massila is recognized as a professional technologist by the Malaysian Board of Technology (MBOT) and a Certified Requirements Engineer by the International Requirements Engineering Body (IREB) since 2013. Her research specialization is in the field of Requirements Engineering focusing on the consistency management of requirements.
Requirements Engineering for Quality Instrumentation and Control Systems
Regardless, whether the system is an optical, ultrasonic, biological, electrical, or electronic instrumentation or control system for medical, disaster mitigation or a retail outlet, the engineers who are responsible for designing, developing, installing, managing and maintaining the equipment, need to first start with the requirements engineering analysis of the system. Requirements engineering is the process of eliciting, understanding, specifying and validating customers’ and users’ requirements. It also identifies the technological constraints under which the system should be constructed and operated. Unfortunately, in practice, many designers under pressure to deliver the promised system in the shortest time, bypass this stage and use many assumptions and guesstimates in their design. As can be expected, this action will ultimately lead to inaccurate systems that does not fulfil stakeholders expectations and satisfaction. The time and cost to rectify the system deficiencies are more when in fact, the requirements engineering phase would have reduced them significantly and also save the organization the embarrassment of delivering a failed system. This keynote address, champions the cause to ensure that requirements engineering is adequately conducted to deliver quality instrumentation and control systems.
Keynote Speaker 2:
Dr. Hairi Zamzuri
Dr Hairi Zamzuri currently a CEO of eMooVit Technology which is business focus on commercializing autonomous
Autonomous Vehicle Landscape in Malaysia
Keynote Speaker 3:
Dr. Daniel Watzenig
Daniel Watzenig was born in Austria. He received his doctoral degree in electrical engineering from Graz University of Technology, Austria, in 2006. In 2009 he received the venia docendi for Electrical Measurement and Signal Processing. Since 2008 he is Divisional Director of the Automotive Electronics Department at the Virtual Vehicle Research Center Graz. In 2017 he was appointed as Full Professor of Autonomous Driving at the Institute of Automation and Control, Graz University of Technology, Austria. His research interests focus on sense & control of automated vehicles, sensor fusion, and uncertainty estimation. He is author or co-author of over 180 peer-reviewed papers, book chapters, patents, and articles. He is Editor-in-Chief of the SAE Int. Journal on Connected and Automated Vehicles (SAE JCAV, launched in 2018). Since 2019 he is invited guest lecturer at Stanford University, USA, teaching multi-sensor perception, data fusion, and software for autonomous systems (Principles of Robot Autonomy I). He is founder of the Autonomous Racing Graz Team, one of currently six teams of the global Roborace race series.
• A basic introduction to the sense-plan-act challenges of autonomous vehicles
• Introduction to the most common state-of-the-art sensors used in autonomous driving (radar, camera, lidar, GPS, odometry, vehicle-2-x) in terms of benefits and disadvantages along with mathematical models of these sensors
Autonomous driving is seen as one of the pivotal technologies that considerably will shape our society and will influence future transportation modes and quality of life, altering the face of mobility as we experience it by today. Many benefits are expected ranging from reduced accidents, optimized traffic, improved comfort, social inclusion, lower emissions, and better road utilization due to efficient integration of private and public transport. Autonomous driving is a highly complex sensing and control problem. State-of-the-art vehicles include many different compositions of sensors including radar, cameras, and lidar. Each sensor provides specific information about the environment at varying levels and has an inherent uncertainty and accuracy measure. Sensors are the key to the perception of the outside world in an autonomous driving system and whose cooperation performance directly determines the safety of such vehicles. The ability of one isolated sensor to provide accurate reliable data of its environment is extremely limited as the environment is usually not very well defined. Beyond the sensors needed for perception, the control system needs some basic measure of its position in space and its surrounding reality. Real-time capable sensor processing techniques used to integrate this information have to manage the propagation of their inaccuracies, fuse information to reduce the uncertainties, and, ultimately, offer levels of confidence in the produced representations that can be then used for safe navigation decisions and actions.