A series of new mesomorphic block copolymers composedofdifferentmacroinitiators, including poly(ethylene oxide), polystyrene, and poly(ethylene oxide)‐b‐polystyrene,and polymethacrylate with a pendent cyanoterphenyl group were synthesized through atom transfer radical polymerization. The number‐average molecular weights of the three diblock copolymers, determined by gel permeation chromatography, were 10,254, 9,772, and 15,632 g mol−1, and their polydispersity indices were 1.17, 1.28, and 1.34. The mesomorphic and optical properties of all the block copolymers were investigated, and they possessed a smectic A phase with mesophasic ranges wider than 100 °C. Moreover, X‐ray diffraction patterns provided evidence of the smectic A phase and the corresponding interdigitated packing of all the polymers. © 2006 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 44: 4593–4602, 2006

A series of new liquid crystalline homopolymers, copolymers, and block copolymers were polymerized from styrene‐macroinitiator (SMi) and methacrylates with pendent 4,4′‐bis(biphenyl)fluorene (M1) and biphenyl‐4‐ylfluorene (M2) groups through atom transfer radical polymerization (ATRP). The number‐average molecular weights (Mn) of polymers P1‐P4 were 10,007, 14,852, 6,275, and 10,463 g mol−1 with polydispersity indices values of 1.21, 1.15, 1.31, and 1.22, respectively. All polymers exhibit the nematic phase. The thermal, mesogenic, and photoluminescent properties of all polymers were investigated. © 2007 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 45: 4564–4572, 2007

Recently, fall detection has become a popular research topic to take care of the increasing aging population. Many previous works used cameras, accelerometers and gyroscopes as sensor devices to collect motion data of human beings and then to distinguish falls from other normal behaviors of human beings. However, these techniques encountered some challenges such as privacy, accuracy, convenience and data-processing time. In this paper, a motion sensor which can compress motion data into skeleton points effectively meanwhile providing privacy and convenience are chosen as the sensor devices for detecting falls. Furthermore, to achieve high accuracy of fall detection, support vector machine (SVM) is employed in the proposed cloud system. Experimental results show that, under the best setting, the accuracy of our fall-detection SVM model can be greater than 99.90 %. In addition, the detection time of falls only takes less than 10 -3 s. Therefore, the proposed SVM-based cloud system with motion sensors successfully enables fall detection at real time with high accuracy.