SPRING SOURCE TECHNOLOGIES

A HARDWARE PLATFORM FOR EVALUATING LOW-ENERGY MULTIPROCESSOR EMBEDDED SYSTEMS BASED ON COTS DEVICES ABSTRACT Embedded systems are usually energy constrained. Moreover, in these systems increased productivity and reduced time-to-market are essential for product success. To design complex embedded systems while reducing the development time and cost, there is a great tendency to use “COTS” (commercial off-the-shelf) devices. At system level, dynamic voltage and frequency scaling (DVFS) is one of the most effective techniques for energy reduction. Nonetheless, many widely used COTS processors either do not have DVFS or apply DVFS only to processor cores. In this paper, an easy-to-implement COTS based evaluation platform for low-energy embedded systems is presented. To achieve energy saving DVFS is provided for the whole microcontroller (including core, PLL, memory and I/O). Also, facilities are provided for experimenting with fault tolerance techniques. The platform is equippe

STOCHASTIC DECISION MAKING FOR ADAPTIVE CROWDSOURCING IN MEDICAL BIG-DATA PLATFORMS ABSTRACT Two novel algorithms for adaptive crowdsourcing in medical imaging big-data platforms is considered, namely, a max-weight scheduling algorithm for medical cloud platforms and a stochastic decision-making algorithm for distributed power-and-latency-aware dynamic buffer management in medical devices. In the first algorithm, medical cloud platforms perform a joint queue-backlog and rate-aware scheduling decisions for matching deployed access points (APs) and medical users where APs are eventually connected to medical clouds. In the second algorithm, each scheduled medical device computes the amounts of power allocation to upload its own medical data to medical big-data clouds with stochastic decision making considering joint energy-efficiency and buffer stability optimization. INTRODUCTION In recent years, the volume of medical data generated by large hospitals is becoming in