PhD Student, Faculty of Engineering
The world is experiencing the 4th industrial revolution which involves the use of digital technologies such as artificial intelligence, cloud computing, sensors and the industrial internet of things.… read more
The world is experiencing the 4th industrial revolution which involves the use of digital technologies such as artificial intelligence, cloud computing, sensors and the industrial internet of things. These digital manufacturing technologies have the potential to improve manufacturing productivity and efficiency whilst reducing the environmental impact it has. Food and drink is the largest manufacturing sector in the UK, contributing almost £30bn to the economy every year. One barrier preventing the widespread adoption of digital technologies within food and drink manufacturing is the lack of suitable online technologies capable of measuring the properties and therefore quality of the food. Ultrasonic techniques use mechanical waves to probe and therefore characterise the properties of multicomponent materials (e.g. food) and are an attractive sensing technology due to their low cost and size. However, for them to be a suitable online sensor, new signal and data processing algorithms are required to relate sensor measurements to the food's physical properties. Machine learning is a form of AI, which uses vast amounts of data to develop predictive algorithms. A key advantage of machine learning techniques is the variety of data they can process and their ability to improve as more or better data becomes available. I will focus on developing ultrasonic techniques, which utilise machine-learning algorithms to classify the structure and quality of food during my PhD.
The University of NottinghamUniversity Park
Nottingham, NG7 2RD
telephone: +44 (0) 115 95 14081
Connect with the University of Nottingham through social media and our blogs.
Campus maps | More contact information | Jobs
Browser does not support script.