A direct numerical simulation ͑DNS͒ of turbulent channel flow with high Reynolds number has been carried out to show the effects of the magnetic field. In this study, the Reynolds number for channel flow based on bulk velocity U b , viscosity , and channel width 2␦ was set to be constant; Re b =2␦U b / = 45818. A uniform magnetic field was applied in the direction of the wall normal. The value of the Hartmann number, Ha were 32.5 and 65, where Ha= 2␦B 0 ͱ / . The turbulent quantities such as the mean flow, turbulent stress, and turbulent statistics were obtained by DNS. Although the influence of the magnetohydrodynamic dissipation terms in the turbulent kinetic energy budget was small, large-scale turbulent structures, e.g., vertical structures, low-speed streaks, ejection, and sweep, were found to decrease at the central region of the channel. Consequently, the difference between production and dissipation in the turbulent kinetic energy decreased with increasing Hartmann number at the central region and large-scale structures at this region were reduced.

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This paper presents an energy-aware method for recognizing time series acceleration data containing both activities and gestures using a wearable device coupled with a smartphone. In our method, we use a small wearable device to collect accelerometer data from a user's wrist, recognizing each data segment using a minimal feature set chosen automatically for that segment. For each collected data segment, if our model finds that recognizing the segment requires high-cost features that the wearable device cannot extract, such as dynamic time warping for gesture recognition, then the segment is transmitted to the smartphone where the high-cost features are extracted and recognition is performed. Otherwise, only the minimum required set of low-cost features are extracted from the segment on the wearable device and only the recognition result, i.e., label, is transmitted to the smartphone in place of the raw data, reducing transmission costs. Our method automatically constructs this adaptive processing pipeline solely from training data.

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