Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.

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A new concept dealing with digital analysis of loess terrain, slope spectrum, is presented and discussed in this paper, by introducing its characteristic, representation and extracting method from DEMs. Using 48 geomorphological units in different parts of the loess as test areas and 5 m-resolution DEMs as original test data, the quantitative depiction and spatial distribution of slope spectrum in China's Loess Plateau have been studied on the basis of a series of carefully-designed experiments. In addition, initial experiment indicates a strong relationship between the slope spectrum and the loess landform types, displaying a potential importance of the slope spectrum in geomorphological studies. Based on the slope spectrums derived from the 25 m-resolution DEM data in whole loess terrain in northern part of Shaanxi, 13 slope spectrum indices were extracted and integrated into a comprehensive layer with image integration method. Based on that, a series of unsupervised classifications was applied in order to make a landform classification in northern Shaanxi Loess Plateau. Experimental results show that the slope spectrum analysis is an effective method in revealing the macro landform features. A continuous change of slope spectrum from south to north in northern Shaanxi Loess Plateau shows an obvious spatial distribution of different loess landforms. This also proves the great significance of the slope spectrum method in describing the terrain roughness and landform evolution as well as a further understanding on landform genesis and spatial distribution rule of different landforms in the Loess Plateau.Loess Plateau, slope spectrum, slope, DEM, loess landform

Artificial nanomotors are nanoscale machines capable of converting surrounding other energy into mechanical motion and thus entering the tissues and cells of organisms. They hold great potential to revolutionize the diagnosis and treatment of diseases by actively targeting the lesion location, though there are many new challenges that arise with decreasing the size to nanoscale. This review summarizes and comments on the state-of-the-art artificial nanomotors with advantages and limitations. It starts with the fabrication methods, including common physical vapor deposition and colloidal chemistry methods, followed by the locomotion characterization and motion manipulation. Then, the in vitro and in vivo biomedical applications are introduced in detail. The challenges and future prospects are discussed at the end.

Urban vitality provides an important basis for evaluating urban development and spatial balance. In the era of big data, the quantitative analysis of urban vitality has become a research hotspot in the field of urban sustainability and planning research. However, time variation characteristics are often neglected, which leads to one-sidedness in the pattern analysis of urban vitality. In this paper, a method for extracting vitality areas and integrating spatiotemporal features clustering is proposed. The method is used to divide urban space into multiple vitality areas scientifically. The spatial and temporal distribution patterns of urban vitality areas are found, and the driving factors of various vitality patterns are analyzed by combining points of interest (POI)-based land use characteristics. To illustrate this method, this paper takes Nanjing city as an example. One week's worth of mobile phone data indicated that Nanjing has 10 and 8 vitality areas on weekdays and weekends, respectively. The spatial and temporal distribution patterns of the vitality areas and their correlation with land use were analyzed, which proved that POI density and entropy have strong correlations with urban vitality.