Automotive electronic is full of challenges. Engine controller units, executing millions of line code & performing decision making influenced by several parameters, is one of the most complex embedded device being used. In this presentation we explore the possibility of how machine learning tools can enable the process of making ECU modeling and design validation easier and smarter. A data acquisition system is another essential tool at a race car engineer’s disposal that provides him with data necessary to make valid adjustments to achieve an overall better performance from the car. We explore how machine learning tools helps in deriving the meaning from the data and analyze them to interpret the performance of the car in conditions which are unmeasurable during the normal lap run. The presentation will conclude with a discussion on further possibilities of use of Machine Learning tools to help an automotive design & validation engineer.
Machine learning tools and their use in automotive electronic design & validation process: Case study of FM09 (Formula Manipal)
March 30, 2010
1:00 pm
Shadab Khan
Shadab is an undergraduate student at Dept of Electrical Engg, Manipal University, India. He is currently a visiting scholar at Institute for Systems & Robotics - Lisbon working on R & D of techniques of automatic karyotyping of chromosomes using advanced image processing algorithms. Shadab is Co-founder of ARRO, an organization that teaches various robotics related topics to college and school student in India & also guides them to carry out research projects. He is a Formula Manipal alumni, and worked on the electronics of the race car FM09.Instituto de Sistemas e RobóticaSeminários
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