The MIDAS consortium is a partnership involving health authorities, and technical big data experts from universities, research institutions, MNCs and SMEs across six EU countries (UK (NI and England), Ireland, Belgium, Finland, Spain and Slovenia), and the USA. The management of big data for 'health in all' poses a significant challenge for health policy makers. This challenge is addressed by the MIDAS project through the development of an integrated solution enabling knowledge liberation from data silos and unification of heterogeneous big data sources that can provide evidence-based actionable information and transform the way care is provided.

Road sign recognition is a key component in autonomous vehicles and also has applications in driver assistance systems and road sign maintenance. Here an algorithm is presented using the Xilinx Zynq-7020 chip on a Zedboard to scan 1920×1080 images taken by an ON Semiconductor VITA-2000 sensor attached via the FMC slot. The PL section of the Zynq is used to perform essential image pre-processing functions and color based filtering of the image. Software classifies the shapes in the filtered image, and OpenCV's template matching function is used to identify the signs from a database of UK road signs. The system was designed in six weeks, and can process one frame in approximately 5 seconds. This is a promising start for a real-time System on Chip based approach to the problem of road sign recognition and also for using the Zynq platform for rapid deployment of these types of applications.

There is considerable interest in creating embedded, speech recognition hardware using the weighted finite state transducer (WFST) technique but there are performance and memory usage challenges. Two system optimization techniques are presented to address this; one approach improves token propagation by removing the WFST epsilon input arcs; another one-pass, adaptive pruning algorithm gives a dramatic reduction in active nodes to be computed. Results for memory and bandwidth are given for a 5,000 word vocabulary giving a better practical performance than conventional WFST; this is then exploited in an adaptive pruning algorithm that reduces the active nodes from 30,000 down to 4,000 with only a 2 percent sacrifice in speech recognition accuracy; these optimizations lead to a more simplified design with deterministic performance.