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Energy efficiency is of steadily growing importance in virtually all areas from mobile to high performance computing. Therefore, lots of research projects focus on this topic and strongly rely on power measurements from their test platforms. The need for finer grained measurement data-both in terms of temporal and spatial resolution (component breakdown)-often collides with very rudimentary measurement setups that rely e.g., on non-professional power meters, IMPI based platform data or model-based interfaces such as RAPL or APM. This paper presents an in-depth study of several different AC and DC measurement methodologies as well as model approaches on test systems with the latest processor generations from both Intel and AMD. We analyze most important aspects such as signal quality, time resolution, accuracy, and measurement overhead and use a calibrated, professional power analyzer as our reference.

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Historically, healthcare has been mainly provided in a reactive manner that limits its usefulness. With progress in sensor technologies, the instrumentation of the world has offered unique opportunities to better observe patients physiological signals in order to provide healthcare in a more proactive manner. To reach this goal, it is essential to be able to analyze patient data and turn it into actionable information using data mining. This chapter surveys existing applications of sensor data mining technologies in healthcare. It starts with a description of healthcare data mining challenges before presenting an overview of applications of data mining in both clinical and non clinical settings.

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