OBJECTIVE: To develop a secure, efficient, and easy-to-use data collection platform to measure the prevalence of sepsis in Wales over 24 hours. MATERIALS AND METHODS: Open Data Kit was used on Android devices with Google App Engine and a digital data collection form. RESULTS: A total of 184 students participated in the study using 59 devices across 16 hospitals, 1198 datasets were submitted, and 97% of participants found the Open Data Kit form easy to use. DISCUSSION: We successfully demonstrated that by combining a reliable Android device, a free open-source data collection framework, a scalable cloud-based server, and a team of 184 medical students, we can deliver a low-cost, highly reliable platform that requires little training or maintenance, providing results immediately on completion of data collection. CONCLUSION: Our platform allowed us to measure, for the first time, the prevalence of sepsis in Wales over 24 hours.

Application-specific VLIW processors provide an energy and area efficient solution for high-performance embedded applications. One significant design issue is that the long instruction word required to express the instruction parallelism represents a significant cause of energy dissipation. We present an application-tailored instruction encoding solution that modifies the instruction architecture to minimize the instruction word width. We demonstrate the effectiveness of our solution across a range of benchmarks, resulting in average energy savings of 20% and an average area reduction of 18%, with no performance penalty.