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This paper presents a novel wearable hand exoskeleton (WHE) system with light weight and adjustable character. The mechanical structure of the WHE is based on articulated linkages. The multi-mode signal acquisition module consists of two wearable sensors for detecting the electromyography (EMG) signals and the fingertip force signals. EMG based control is applied to the WHE system. The performance of the WHE system was tested through the free motion task and the grasping object task. Results showed that the WHE could assist hand function in the daily grasping tasks. In the future work, training experiments will be performed on patients.

This paper presents a novel method to model urban land use conversion using support vector machines (SVMs), a new generation of machine learning algorithms used in the classification and regression domains. This method derives the relationship between rural-urban land use change and various factors, such as population, distance to road and facilities, and surrounding land use. Our study showed that SVMs are an effective approach to estimating the land use conversion model, owing to their ability to model non-linear relationships, good generalization performance, and achievement of a global and unique optimum. The rural-urban land use conversions of New Castle County, Delaware between 1984---1992, 1992---1997, and 1997---2002 were used as a case study to demonstrate the applicability of SVMs to urban expansion modeling. The performance of SVMs was also compared with a commonly used binomial logistic regression (BLR) model, and the results, in terms of the overall modeling accuracy and McNamara's test, consistently corroborated the better performance of SVMs.

Modeling land-use change is a prerequisite to understanding the complexity of land-use-change patterns. This paper presents a novel method to model urban land-use change using support-vector machines (SVMs), a new generation of machine learning algorithms used in classification and regression domains. An SVM modeling framework has been developed to analyze land-use change in relation to various factors such as population, distance to roads and facilities, and surrounding land use. As land-use data are generally unbalanced, in the sense that the unchanged data overwhelm the changed data, traditional methods are incapable of classifying relatively minor land-use changes with high accuracy. To circumvent this problem, an unbalanced SVM has been adopted by enhancing the standard SVMs. A case study of Calgary land-use change demonstrates that the unbalanced SVMs can achieve high and reliable performance for land-use-change modeling.