Spatial collocation patterns associate the co-existence of nonspatial features in a spatial neighborhood. An example of such a pattern can associate contaminated water reservoirs with certain deceases in their spatial neighborhood. Previous work on discovering collocation patterns converts neighborhoods of feature instances to itemsets and applies mining techniques for transactional data to discover the patterns. We propose a method that combines the discovery of spatial neighborhoods with the mining process. Our technique is an extension of a spatial join algorithm that operates on multiple inputs and counts long pattern instances. As demonstrated by experimentation, it yields significant performance improvements compared to previous approaches.

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This article presents a comprehensive survey of reconfigurable modular robots, which covers the origin, history, the state of the art, key technologies, challenges, and applications of reconfigurable modular robots. An elaborative classification of typical reconfigurable modular robots is proposed based on the characteristics of the modules and the reconfiguration mechanism. As the system characteristics of reconfigurable modular robots are mainly dependent on the functions of modules, the mechanical and electrical design features of modules of typical reconfigurable modular robots are discussed in detail. Furthermore, an in-depth comparison analysis is conducted, which encompasses discussions of module shape, module degrees of freedom, module attribute, connection mechanisms, interface autonomy, locomotion modes, and workspace. Meanwhile, many reconfigurable modular robot researches focus on the study of self-X capabilities (i.e. self-reconfiguration, self-assembly, self-adaption, etc.), which embodies autonomy performance of reconfigurable modular robots in certain extent. An evolutionary cobweb evaluation model is proposed in this article to evaluate the autonomy level of reconfigurable modular robots. Although various reconfigurable modular robots have been developed and some of them have been put into practical applications such as search and rescue missions, there still exist many open theoretical, technical, and practical challenges in this field. This work is hopefully to offer a reference for the further developments of reconfigurable modular robots.

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Automated electrocardiogram (ECG) diagnosis could be a useful aid for clinical use. We applied a deep learning method to build a system for automated detection and classification of ECG signals. We first trained a convolutional neural network (CNN) to detect cardiovascular disease in ECG signals using a training data set of 259,789 ECG signals collected from the cardiac function rooms of a tertiary care hospital. The CNN classification was validated using an independent test data set of 18,018 ECG signals.The labels used covered >90% of clinical diagnoses. The system grouped ECGs into 18 classifications-17 different types of abnormalities and normal ECG. The overall accuracy of the model was tested and found to be close to 95%; the accuracy for diagnosis of normal rhythm/atrial fibrillation was 99.15%. The proposed CNN model could help reduce misdiagnosis and missed diagnosis in primary care settings and also improve efficiency and save manpower cost for large general hospitals.

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