The 13 C NMR chemical shifts of six kinds of substituted benzylidene anilines, with different backbone conjugation length, have been used as a probe to investigate the long-range transmission of substituent effects. In this context, it was found that for substituents Y at the aniline unit, the transmission of the inductive and conjugative effects depend on the chemical bond numbers n (Y) between Y and the imine carbon, and the parameters n (Y) À2 s F (Y) and n (Y) À2 s R (Y) are suitable to scale the corrected inductive and conjugative effects, respectively. However, for substituents X, the chemical bond numbers n (X) between X and the imine carbon influences only the transmission of inductive effects of X, and the n (X) À2 s F (X) item is appropriate to evaluate the modified inductive effects of X. Similarly, Δs (cor) 2 was proposed to describe the transmitted effect of the cross-interaction effect. With the parameters n (X), and d C (parent), the d C (C = N) values of 181 samples can be well correlated. The correlation coefficient is 0.9957, and the standard derivation is only 0.23 ppm. Moreover, the multi-parameter correlation equation is predicted well the d C (C = N) of other 25 samples of designed conjugated benzylidene anilines.

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Anthropometry is widely applied to the research in skeleton extraction from surface meshes of human body. Especially the anatomical proportion can be employed as a benchmark in model segmentation and joint extraction. Unfortunately, the anatomical proportion is usually measured with the Euclidean distance, which makes it difficult to correlate it with the surface mesh. To bridge this gap, we take advantage of the property of the geodesic metrics that is invariance to rotation, translation, scaling and model pose, and propose an original geodesic model in which the length of each part of human body is measured by geodesic metrics, by which the anatomic proportions can be directly mapped to the contours of the mesh surface of human body in arbitrary pose. Combining the geodesic model with automatic extraction of feature points, we can determine the candidate scopes of joint positions and boundaries between the parts on meshes, and then refine the joint positions in the scopes using existing methods. And finally, we illustrate the utility of the geodesic model with an application to joint extraction.

Logical classification of motion data is the precondition of motion editing and behaviour recognition. The typical distance metrics of sequences can not identify logical relation between motions well. Based on the traditional DTW distance metrics, this paper proposes strategies bidirectional DTW and segment DTW, both of which could improve the robustness of identifying logically related motions, and then proposes a DTW-Curve method which is used to compare the logical similarity between the motions. The generation of DTW-Curve includes three steps. Firstly, motions should be normalized to remove the global translation and align the global orientation. Secondly, motions are resampled to cluster local frames and remove redundant frames. Finally, DTW-Curve is generated under the control of different thresholds. DTW-Curve may produce many statistical properties, which could be used to unsupervised logical classification of motions. We propose two types of statistical properties, and classify motion data by using hierarchical clustering procedure. The experiment results demonstrate that the logical classification based on DTW-Curve has better classification performance and robustness.