Threshold current temperature dependence of quantum-dot photonic crystal surface-emitting lasers with respect to gain-cavity detuning.

We investigate threshold current temperature dependence of electrically injected quantum-dot (QD) photonic crystal (PC) surface-emitting lasers (SELs) with respect to wavelength detuning between QD gain peak and PC cavity resonance. The lasing emissions cover wavelengths from 1283 nm to 1318 nm. Almost infinite characteristic temperature is realized at certain temperature range for PCSEL with large negative gain-cavity detuning. Moreover, band-edge lasing mode is identified in our "PC slab-on-substrate" structure, and its far-field distribution is characterized as doughnut-shaped beam with azimuthal polarization.

A Machine Learning Method for Power Prediction on the Mobile Devices.

Energy profiling and estimation have been popular areas of research in multicore mobile architectures. While short sequences of system calls have been recognized by machine learning as pattern descriptions for anomalous detection, power consumption of running processes with respect to system-call patterns are not well studied. In this paper, we propose a fuzzy neural network (FNN) for training and analyzing process execution behaviour with respect to series of system calls, parameters and their power consumptions. On the basis of the patterns of a series of system calls, we develop a power estimation daemon (PED) to analyze and predict the energy consumption of the running process. In the initial stage, PED categorizes sequences of system calls as functional groups and predicts their energy consumptions by FNN. In the operational stage, PED is applied to identify the predefined sequences of system calls invoked by running processes and estimates their energy consumption.

A wireless gait analysis system by digital textile sensors.

This paper studies the feasibility of spatio-temporal gait analysis based upon digital textile sensors. Digitized legs and feet patterns of healthy subjects and their relations with spatio-temporal gait parameters were analyzed. In the first experiment, spatio-temporal gait parameters were determined during over ground walking. In the second experiment, predicted running, backward walking, walking up stairs and walking down stairs parameters were determined. From the results of the experiments, it is concluded that, for healthy subjects, the duration of subsequent stride cycles and left/right steps, the estimations of step length, cadence, walking speed, central of pressure and central of mass trajectory, can be obtained by analyzing the digital signals from the textile sensors on pants and socks. These parameters are easily displayed in several different graphs allowing the user to view the parameters during gait. Finally, the digital data are easily to analyze the feature of activity recognition.