For the upcoming ubiquitous computing environments in the u-health areas involve the measurement of physiological signals in the daily life. However, the measurement of those signals, such as the photoplethysmorgraphy (PPG), and the electrocardiogram (ECG), requires being still tight during the measurement in order to get the accurate result preventing noises caused by the casual movement. In this paper, we propose a method to obtain the accurate physiological signals in the situation where the little movement is allowed. By measuring PPG and motion signals at the forehead during in motion, we calibrate the distorted PPG signal with the motion signals. We show that the calibrated PPG signal is accurate enough by comparing with the result that is measured at the finger without any movement.
Virtualization has the potential to dramatically increase the usability and reliability of high performance computing (HPC) systems. However, this potential will remain unrealized unless overheads can be minimized. This is particularly challenging on large scale machines that run carefully crafted HPC OSes supporting tightlycoupled, parallel applications. In this paper, we show how careful use of hardware and VMM features enables the virtualization of a large-scale HPC system, specifically a Cray XT4 machine, with ≤5% overhead on key HPC applications, microbenchmarks, and guests at scales of up to 4096 nodes. We describe three techniques essential for achieving such low overhead: passthrough I/O, workload-sensitive selection of paging mechanisms, and carefully controlled preemption. These techniques are forms of symbiotic virtualization, an approach on which we elaborate.
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