Artificial Skin Prototype developed at IIET MCAST

November 1, 2019 nocilla_d3oqqe

Artificial Skin Prototype developed

at the Institute of Engineering and Transport, Electrical and Electronics MCAST

Students and academic staff from the Malta College of Arts, Science and Technology had their research work published at the IEEE 45th Annual Conference of Industrial Electronics Society (IECON 2019) which was held in Lisbon, Portugal between October 14th – 17th. The conference focuses on contemporary industry topics ranging from electronics, controls, manufacturing, to communications and computational intelligence. It is an international event where industry experts, researchers, and academics share ideas and experiences surrounding frontier technologies, breakthrough and innovative solutions and applications. All research work presented at the conference is subject to an in-depth peer review process by the international scientific community in the relevant fields.

Senior Lecturer Ing. Jeremy Scerri and former undergraduate students Ms. Tiziana Borg and Ms. Shelly Cardona Mills attended the conference and presented the results of their work. Two research papers were accepted for publication and presented in the conference proceedings:

“Integrated Position and Force Sensing for Optical Skin using Machine Learning Methods” by Ms. Tiziana Borg (MCAST B. Eng. Hons graduate), Ms. Shelley Cardona Mills (MCAST B. Eng. Hons graduate), Ing. Jeremy Scerri, Ing. Clive Seguna, Ing. Kris Scicluna (Lecturers).

and “A Low-Cost Real-Time Monitoring System for an Industrial Mini-Climatic Chamber” by Mr. Luke Tanti (MCAST B. Eng. Hons graduate), Ing. Clive Seguna, Ing. Jeremy Scerri, Ing. Kris Scicluna (Lecturers).

Both research publications are based on the dissertation work carried by Ms. Borg, Ms. Cardona Mills, and Mr. Tanti who at the time were undergraduate students at the Institute of Engineering and Transport – Electrical and Electronics. These research projects are co-ordinated within the recently setup Optimization and Machine Learning Research Group (OptiMaL) within the Institute of Engineering and Transport which is aimed at researching emerging technologies such as Machine Learning, Adaptive Control, and Intelligent Industrial Systems.