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As a major research field in computer vision, automatic generation of Labanotation has been attracting the attention of many researchers. These researchers use various methods to engage in dance education and protection, but the existing methods do not consider the spatial-temporal dependence modeling of dance movements. Therefore, they do not represent complex dance movements optimally. We propose an automatic generation network of Labanotation based on the long and short spatial-temporal relations. It includes local spatial-temporal feature extraction network, global spatial-temporal feature extraction network, and local and global feature fusion network. It can align the input and output sequences while modeling the skeletal spatial-temporal relation. The local spatial-temporal features among short interval skeleton sequence relationship are obtained through the multi-scale convolution of time and space. The global spatial-temporal features are learned through the transformer network to obtain the relationship among skeleton sequences with long interval. For the output of the two networks, we use the pyramid squeeze attention network to exchange information in long and short spatial-temporal information, achieving complementarity to improve the accuracy of action recognition. Experimental results show that the proposed method outperforms the state-of-the-art methods on laban16 and laban48, which are common datasets for the research of Labanotation. [ABSTRACT FROM AUTHOR]

Image matting is the extraction of the foreground from an image through the use of provided information. It has been an important technique in the image and video editing field. Current image matting methods estimate the foreground and background, based on information provided regarding the nearby pixels. Color sampling has been an effective means for matting directly, and quite a few methods have achieved high quality matting results based on color sampling. However, there are some drawbacks; for example it is easy to overlook important candidate pixels for matting, and even if the candidate pixels are effectively selected, similar foregrounds and backgrounds will reduce the accuracy of the matting. In this paper’s work, a Weighted-Least Squares (WLS) filter was utilized to sharpen the boundaries between the foregrounds and backgrounds, which facilitated the matting process; and an innovative sampling criterion based on random searching for the matting was then presented. This innovative method could effectively prevent valid samples being overlooked, and could manage the relationships of the nearby and distant pixels. In this process, a new cost function was utilized to evaluate the candidate samples. Experiments utilizing an image database demonstrated that this method significantly improved the matting results. [ABSTRACT FROM AUTHOR]

Image compositing techniques are primarily utilized to achieve realistic composite results. Some existing image compositing methods, such as gradient domain and alpha matting, are widely used in the field of computer vision, and can typically achieve realistic results, especially for seamless boundaries. However, when the candidate composite images and the target images have obvious differences, such as color, texture and brightness, the composite results are unrealistic and inconsistent. At the same time, traditional compositing methods focus on basic feature matching, ignoring semantic rationality in composition processing. Quite a few compositing methods thus generate composite results without semantic rationality. In this paper, a new multi-scale image composition method has been presented. In the composition process, wavelet pyramid and basic feature handling were used to achieve multi-scale compositions. More importantly, a new criterion was established, based on the semantic rationality of images, which could ensure that the composite images are semantically valid. A large database was created to facilitate experimentation. The experiments showed that the methodology introduced in this paper produced superior results compared to traditional composition methods; the composite results were not only consistent and seamless, but were also semantically valid. [ABSTRACT FROM AUTHOR]

Abstract: In this work, mesoporous CeO2 crystalline film was prepared via calcining the solution of Ce(NO3)3 in ionic liquid (1-allyl-3-methylimidazolium chloride) containing cellulose. The as-prepared CeO2 film was characterized by means of different techniques including X-ray diffraction, transmission electron microscopy equipped with energy dispersive X-ray spectroscopy, scanning electron microscopy and nitrogen sorption analysis. It was indicated that the as-prepared CeO2 film was composed of numerous CeO2 crystals with size of ∼9nm, which assembled with each other to form interconnected channels within the film. The BET specific surface area and the pore volume of the CeO2 film were estimated to be 62m2/g and 0.15cm3/g, respectively, based on the N2 sorption isotherms. Being used as a support, the CeO2 film was loaded with Au nanoparticles of ∼5nm in size via the reduction of HAuCl4 with NaBH4 in aqueous solution. The as-synthesized Au/CeO2 nanocomposite demonstrated a good catalytic activity for aerobic oxidation of benzyl alcohol. [Copyright &y& Elsevier]

Abstract: SnO2 nanofibers were obtained by electrospinning of a poly(vinyl alcohol) (PVA)/SnCl4·5H2O solution. A novel micro gas sensor based on the SnO2 nanofibers was successfully fabricated. The morphology and crystal structure of the fibers were analyzed by scanning electron microscope (SEM), transmission electron microscope (TEM) and X-ray diffraction (XRD), and the ethanol-sensing properties of the sensor were tested. The results showed that the SnO2 nanofibers with an average diameter of ∼100nm could be directly deposited on a microhotplate by “near-field” electrospinning. The micro gas sensor exhibited large response, low detection limit, fast response/recovery and good reproducibility. The detection limit was <10ppb and the response/recovery time towards 10ppm ethanol was <14s. [Copyright &y& Elsevier]

Abstract: A facile method to decorate the polymeric hollow spheres with ZnS nanoparticles has been presented. In this method, the precursors, Zn(Ac)2⋅H2O and CH3CSNH2, were first adsorbed by the polymer substrate in supercritical CO2–ethanol solution at 35 °C. Followed by heating the mixture at 100 °C for 2 h, ZnS/polymer composites were obtained. The as-produced ZnS/polymer composites were characterized by means of IR spectra, X-ray diffraction, transmission electron microscopy, high-resolution transmission electron microscopy. It was demonstrated that crystalline ZnS nanoparticles with size of 3–5 nm were uniformly decorated on the polymer spheres under suitable conditions. The resultant ZnS/polymer composites exhibited high efficiency for degrading eosin B, methyl orange and methylene blue under UV light irradiation. [Copyright &y& Elsevier]

Abstract: A simple and efficient route to decorate carbon nanotubes (CNTs) with nanocrystalline RuO2 has been developed. In this method, RuCl3 ·3H2O was oxidized into RuO2 by oxygen in supercritical diethyl amine, and the produced RuO2 deposited on CNTs, resulting in RuO2/CNT nanocomposites. The as-prepared composites were structurally and morphologically characterized by X-ray diffraction, X-ray photoelectron spectroscopy, and transmission electron microscopy (TEM). TEM images showed that RuO2 nanoparticles attached on CNTs had uniform shape and a narrow particle size distribution. The loading content and the size of RuO2 particles on CNTs could be tuned by changing the mass ratio of RuCl3 ·3H2O/CNT. Electrochemical measurements by cyclic voltammetry demonstrated a substantial increment of the specific capacitance of CNTs due to a pseudocapacitance originated from the deposited RuO2 nanoparticles. [Copyright &y& Elsevier]

Human activity localization aims at recognizing contents and detecting locations of activities in video sequences. With an increasing number of untrimmed video data, traditional activity localization methods always suffer from two major limitations. First, detailed annotations are needed in most existing methods, i.e., bounding-box annotations in every frame, which are both expensive and time consuming. Second, the search space is too large for 3D activity localization, which requires generating a large number of proposals. In this paper, we propose a unified deep Q-network with weak reward and weak loss (DWRLQN) to address the two problems. Certain weak knowledge and weak constraints involving the temporal dynamics of human activity are incorporated into a deep reinforcement learning framework under sparse spatial supervision, where we assume that only a portion of frames are annotated in each video sequence. Experiments on UCF-Sports, UCF-101 and sub-JHMDB demonstrate that our proposed model achieves promising performance by only utilizing a very small number of proposals. More importantly, our DWRLQN trained with partial annotations and weak information even outperforms fully supervised methods.

We report an ionic liquid (IL) assisted hydrothermal method to synthesize anatase TiO(2) nanocrystals (NCs), in which TiCl(4) was used as precursor, 1-butyl-3-methylimidazolium chloride (bmim(+)Cl(-)) as IL, and F(-) or SO(4)(2-) ions as phase transformation inhibitor. The surfactant-like nature of IL was found to play a key role in controlling the crystallization process via controlling the aggregation manner of the NCs. The fine-tuning abilities of the operating parameters of the bmim(+)Cl(-)/TiCl(4)/H(2)O system facilitated the controlling over the shape and size of TiO(2) NCs. Phase-pure anatase monodisperse NCs with various shape and size were controllably obtained. Moreover, the aggregation manners of anatase NCs were also studied, and it was demonstrated that the high concentration of HF or H(2)SO(4) could result in aggregation of anatase NCs to form pseudo single crystals.

A low-temperature, efficient and one-step deposition method, in which Mn(CH(3)COO)(2)·4H(2)O serves as precursor and O(2) as oxidant, was employed to deposit Mn(3)O(4) nanoparticles on multi-walled carbon nanotubes (MWCNTs) in ethanol solution at 150 and 200 °C. The resulting Mn(3)O(4)/MWCNT composites were characterized by means of different techniques including x-ray diffraction, x-ray photoelectron spectroscopy and transmission electron microscopy. It was indicated that the Mn(3)O(4) nanoparticles were attached uniformly on MWCNTs with sizes less than 10 nm, and the loading amount of Mn(3)O(4) could be tuned by changing the initial weight ratio of Mn(CH(3)COO)(2)·4H(2)O/MWCNT. The electrochemical behavior of the Mn(3)O(4)/MWCNT composites was examined by cyclic voltammetry, and the result indicated the specific capacitance of the composite electrode was 330 F g(-1), nearly 18 times higher than that of the pure MWCNT electrode. The good performance of the as-prepared composites as electrode material may be attributed to the synergistic effects of the Mn(3)O(4) nanoparticles and the MWCNTs.

Chromium oxide nanotubes were successfully prepared using multi-walled carbon nanotubes (MWCNTs) as a template via a supercritical fluid-mediated route. In this method, with chromium (III) nitrate nonahydrate as precursor, chromium oxide was first deposited on MWCNTs in supercritical ethanol in the presence of NH(4)HCO(3). The as-prepared chromium oxide/MWCNT nanocomposites were characterized by transmission electron microscopy, x-ray diffraction, infrared spectroscopy and thermogravimetric analysis. It was demonstrated that the MWCNTs were coated with a layer of amorphous Cr(2)O(3)·xH(2)O. The thickness of the Cr(2)O(3)·xH(2)O sheath on MWCNTs could be tuned by manipulating the ratio of precursor to MWCNTs. Calcining the composites at 550 °C, the MWCNTs were removed, producing polycrystalline α-Cr(2)O(3) nanotubes. The as-prepared α-Cr(2)O(3) sample was used as a sensor material to detect ethanol vapor, and it was demonstrated that the α-Cr(2)O(3) nanotubes exhibited good performance even at 400 °C.

This paper discusses a pervasive computing based multimodal tele-home healthcare system in terms of a Human-Centered Pervasive Computing System Model.