Knowledge Transfer Partnership
to embed DSP/FPGA design capability in support of a new range of image processing products.
This project will develop and validate an advanced robotics and remote monitoring platform to help dairy farms optimise their milk production efficiency and lower the carbon footprint of every litre of milk they produce. It will provide autonomous and objective accreditation of farm practices and animal welfare, exploiting latest developments in artificial intelligence to provide performance metrics for farmers, milk retailers and consumers. It will be sold internationally to support sustainable dairy farming in the UK and worldwide.
This project will create a mobile robot that will use ultraviolet light for rapid disinfection of items that are placed inside it. It will be able to autonomously self-navigate itself around a building and will be "on call" to arrive at a specific location on demand to perform disinfection services. Alternatively it could act as a delivery robot performing disinfection whilst en route. It capabilities will include regular and reliable disinfection of PPE such as face masks, face shields and goggles. The robot will be suitable for deployment in hospitals to enable staff to regularly disinfect their PPE to make sure their protective performance is maintained and to reduce the extent to which replacements are necessary. It will also be suitable for the disinfection of other equipment such as mobile phones, or could be deployed for the disinfection and delivery of medical equipment or documents.
Awaiting Public Project Summary
"The growth in the electrification of transport, including electric vehicles (EVs), has been driven by lithium-ion batteries. However, to make the next-generation of vehicles cheaper and more efficient, we need to be able to monitor, diagnose and respond to batteries in real-time. This project aims to combine new types of sensors to feed data into a battery management system (BMS) that will be able to react to the changing state of battery health and charge and improve operational safety. This could lead to an increase in battery life of up to 60%.
Crucially, we will look at producing sensors that are robust, sensitive and significantly cheaper than those commercially available. Our goal is that the sensors will be deployed into battery modules at low cost and adopted by industry. Eventually, they may become a requirement for new car certification and help to improve consumer safety, confidence and uptake of EVs.
To verify the feasibility of our approach, our consortium covers a range of commercial and academic expertise that will build sensors into a prototype battery pack."
"There is an urgent agronomic (reducing the amount of plant protection products applied to crops), environmental (pollution reduction), economic (lowering the cost of food production) and political (continuing public pressure for a reduction in ag-chem use) need to modernise and update agrochemical applications to crops from the traditional practice of applying a uniform rate across the whole crop to a much more targeted approach. SKAi aims to satisfy this need by building a smart camera and artificial intelligence platform for use by farmers, agronomists and agrochemical applicators. This platform will be integrated into the existing KORE ( [www.koresolution.com][0]) precision agricultural platform to extend its functionality to allow the support of in field smart cameras using image transfer and machine learning. Using this system, we hope to dramatically reduce the total amount of crop protection products applied to crops in the UK and worldwide.
[0]: http://www.koresolution.com"
The project will develop an experimental system which will demonstrate the feasibility and effectiveness of atomic magnetometer technology for monitoring the health and welfare of livestock. This is a new approach, made possible by recent advances in the sensitivity of atomic magnetometers. The feasibility of this application will involve tests in a real farm environment.
The Cloud-enabled Robust Intelligent Sensing Platform (CRISP) will enable rapid development and deployment of condition monitoring and alerting systems using wireless sensors in harsh envionments. The new platform will make full use of the capabilities of cloud-computing for data management and statistical algorithm deployment, with system access via fully customisable web portals and mobile device applications. A pilot demonstrator will validate the use of the system for health and welfare monitoring with dairy cattle, creating alerts for farmers and their vets, for instance in relation to problems of fertility and lameness. A further pilot demonstrator will be implemented for horse breeders to prove transferability into additional applications.
Awaiting Public Project Summary
Awaiting Public Project Summary