Recent years have witnessed the rapid development of human activity recognition (HAR) based on wearable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such
as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in HAR. Although these methods are fast and easy for implementation, they still have some limitations due to poor performance in a number of situations. In this paper, we propose a novel method based on the ensemble learning to boost the performance of these machine learning methods for HAR.
Half-Heusler (HH) phases (space group F43m, Clb) are increasingly gaining attention as promising thermoelectric materials in view of their thermal stability, scalability, and environmental benignity as well as efficient power output. Until recently,
the verifiable dimensionless figure of merit (ZT) of HH phases has remained moderate near 1, which limits the power conversion efficiency of these materials. We report herein ZT~1.3 in n-type (Hf,Zr)NiSn alloys near 850 K developed through elemental substitution and simultaneously embedment of nanoparticles in the HH matrix, obtained by annealing the samples close to their melting temperatures. Introduction of mass fluctuation and scattering centers play a key role in the high ZT measured, as shown by the reduction of thermal conductivity and increase of thermopower. Based on computation, the power conversion efficiency of a n-p couple module based on the new n-type (Hf,Zr,Ti)NiSn particles-in-matrix composite and recently reported high-ZT p-type HH phases is expected to reach 13%, comparable to that of state-of-the-art materials, but with the mentioned additional materials and environmental attributes. Since the high efficiency is obtained without tuning the microstructure of the Half-Heusler phases, it leaves room for further optimization.
Half-Heusler alloys (MgAgSb structure) are promising thermoelectric materials. RNiSn half-Heusler phases (R=Hf, Zr, Ti) are the most studied in view of their thermal stability. The highest dimensionless figure of merit (ZT) obtained is ~1 in the temp
erature range ~450-900oC, primarily achieved in nanostructured alloys. Through proper annealing, ZT~1.2 has been obtained in a previous ZT~1 n-type (Hf,Zr)NiSn phase without the nanostructure. There is an appreciable increase in the power factor, decrease in charge carrier density, and increase in carrier mobility. The findings are attributed to the improvement of structural order. Present approach may be applied to optimize the functional properties of Heusler-type alloys.
We report superfluorescent (SF) emission in electrically pumped InGaN/InGaN QW lasers with saturable absorber. In particular, we observe a superlinear growth of the peak power of SF pulses with increasing amplitude of injected current pulses and attr
ibute it to cooperative pairing of electron-hole (e-h) radiative recombinations. The phase transitions from amplified spontaneous emission to superfluorescence and then to lasing regime is confirmed by observing (i) abrupt peak power growth accompanied by spectral broadening, (ii) spectral shape with hyperbolic secant envelope and (iii) red shift of central wavelength of SF emission pulse. The observed red shift of SF emission is shown to be caused by the pairing of e-h pairs in an indirect cooperative X-transition.
