Taking calcium supplements can reduce the risk of developing osteoporosis, but they are not readily absorbed in the gastrointestinal tract. Nanotechnology is expected to resolve this problem. In the present study, we examined whether the bioavailability of calcium carbonate and calcium citrate can be improved by reducing the particle size. The morphology of nano calcium carbonate and nano calcium citrate was characterized by dynamic laser-light scattering (DLS), field-emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM). The measurements obtained from DLS, FE-SEM and TEM were comparable. Acute and sub-chronic toxicity tests were performed to establish the safety of these products after oral administration. The no-observed-adverse-effect levels of nano calcium carbonate and nano calcium citrate were 1.3 and 2.3 g kg(-1) body weight, respectively. The results of our in vivo studies indicate that administering nano calcium carbonate and nano calcium citrate can enhance the serum calcium concentration and maintain the whole-body bone mineral density in ovariectomized mice. These data suggest that nano calcium carbonate and nano calcium citrate are more bioavailable than micro calcium carbonate and micro calcium citrate, respectively.

Based on the rolling shutter effect of the complementary metal-oxide-semiconductor (CMOS) image sensor, bright and dark fringes can be observed in each received frame. By demodulating the bright and dark fringes, the visible light communication (VLC) data logic can be retrieved. However, demodulating the bright and dark fringes is challenging as there is a high data fluctuation and large extinction ratio (ER) variation in each frame due. Hence proper thresholding scheme is needed. In this work, we propose and compare experimentally three thresholding schemes; including third-order polynomial curve fitting, iterative scheme and quick adaptive scheme. The evaluation of these three thresholding schemes is performed.

In recent years, the fall detection system has become an important topic in the homecare system. Compared with the traditional fall detection algorithm, the method used by neural network is more robust and has higher accuracy. However neural network consumes a large amount of energy due to a huge number of computations, and needs more memory to store parameters as compared to traditional algorithms. In this paper, we propose a fall detection system in combination of the traditional algorithm with the neural network. First, we propose a skeleton information extraction algorithm, which transforms depth information into skeleton information and extracts the important joints related to fall activity. Also we have modified the skeleton-based method with seven highlight feature points. Second, we propose a highly robust deep convolution neural network architecture, which uses a pruning method to reduce parameters and calculations in the network. The low number of parameters and calculations makes the system suitable for the implementation on an embedded system. The experiment results show the high accuracy and robustness on the popular benchmark dataset NTU RGB+D. The proposed system has been implemented on NVIDIA Jetson Tx2 platform with real-time processing.

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