Carbon nanotubes (CNTs) are nanoscale cylinders of graphene with exceptional properties such as high mechanical strength, high aspect ratio and large specific surface area. To exploit these properties for membranes, macroscopic structures need to be designed with controlled porosity and pore size. This manuscript reviews recent progress on two such structures: (i) CNT Bucky-papers, a non-woven, paper like structure of randomly entangled CNTs, and (ii) isoporous CNT membranes, where the hollow CNT interior acts as a membrane pore. The construction of these two types of membranes will be discussed, characterization and permeance results compared, and some promising applications presented.

Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. Also, a review of the available public gait datasets is presented. The concluding discussions outline a number of research challenges and provide promising future directions for the field.

Automatic analysis of biomedical time series such as electroencephalogram (EEG) and electrocardiographic (ECG) signals has attracted great interest in the community of biomedical engineering due to its important applications in medicine. In this work, a simple yet effective bag-of-words representation that is able to capture both local and global structure similarity information is proposed for biomedical time series representation. In particular, similar to the bag-of-words model used in text document domain, the proposed method treats a time series as a text document and extracts local segments from the time series as words. The biomedical time series is then represented as a histogram of codewords, each entry of which is the count of a codeword appeared in the time series. Although the temporal order of the local segments is ignored, the bag-of-words representation is able to capture high-level structural information because both local and global structural information are well utilized. The performance of the bag-of-words model is validated on three datasets extracted from real EEG and ECG signals. The experimental results demonstrate that the proposed method is not only insensitive to parameters of the bag-of-words model such as local segment length and codebook size, but also robust to noise.

Because of highly frozen macromolecule chains, polyvinyl alcohol (PVA) hydrogels have never been used for dye removal. This work focuses on improving the adsorption capacity of the PVA hydrogel by using amphiphilic graphene oxide to improve its macromolecular chain mobility in crystal domain and introduce new functional groups. To evaluate its effectiveness, crystal structure, swelling kinetics, and model dye methylene blue (MB) adsorption of the as-prepared PVA hybrid hydrogels were systematically investigated. The results indicate that the hybrid PVA hydrogels have lower crystallinity and less crystal stability, demonstrating the improved macromolecular chain mobility. Moreover, improved swelling ratios of PVA/GO hydrogels also illustrate the enhanced macromolecular chain mobility. MB adsorption experiment indicates that GO introduced can result in great improvement in MB adsorption. And the adsorption process follows the second-order kinetic model and Morris-Weber model, which is determined by the intraparticle diffusion. Furthermore, MB adsorption isotherm follows Freundlich model and the adsorption is heterogeneous. Desorption studies indicate that the interaction between PVA hydrogels and MB consists of both physisorption and chemisorption.

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Human action recognition has been attracted lots of interest from computer vision researchers due to its various promising applications. In this paper, we employ Pyramid Histogram of Orientation Gradient (PHOG) to characterize human figures for action recognition. Comparing to silhouette-based features, the PHOG descriptor does not require extraction of human silhouettes or contours. Two state-space models, i.e., Hidden Markov Model (HMM) and Conditional Random Field (CRF), are adopted to model the dynamic human movement. The proposed PHOG descriptor and the state-space models with respect to different parameters are tested using a standard dataset. We also testify the robustness of the method with respect to various unconstrained conditions and viewpoints. Promising experimental result demonstrates the effectiveness and robustness of our proposed method.

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