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As the basic spatial unit of urban planning and management, it is necessary to know the distribution status of urban functional areas in time. Due to the complexity of urban land use, it is difficult to identify the urban functional areas using only remote sensing images. Social perception data can provide additional information for the identification of urban functional areas. However, the sources of remote sensing data and social perception data differ, with some differences in data forms. Existing methods cannot comprehensively consider the characteristics of these data for functional area identification. Therefore, in this study, we propose a multimodal deep learning method with an attention mechanism to fully utilize the data features of these two modalities and apply it to the recognition of urban functional areas. First, the pre-processed remote sensing images, points of interest, and building footprint data are divided into block-based target units of features by the road network. Next, the remote sensing image features and social perception data features of the target unit are extracted separately using a two-branch convolutional network. Finally, features are extracted sequentially along two separate dimensions, being channel and spatial, to generate an attention weight map for the identification and classification mapping of urban functional areas. The model framework was finally applied to the Ningbo dataset for testing, and the recognition accuracy was above 93%. The experimental results deduce, as a whole, that the prediction performance of the deep multimodal fusion model framework with an attention mechanism is comparatively superior to other traditional methods. It can provide a reference for the classification of urban land use and provide data support for urban planning and management. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) is an important technique for heart disease diagnosis. This paper proposes a novel method for ECG beat classification. Several important issues exist in the ECG beat classification, which, if suitably addressed, may lead to development of more robust and efficient recognizers. Some of these issues include feature extraction, choice of classification approach and optimization. A new method for non-linear feature extraction of ECG signals based on empirical mode decomposition (EMD), approximate entropy (ApEn) and wavelet packet entropy is presented. The proposed method first uses EMD to decompose ECG signals into a finite number of intrinsic mode functions (IMFs), calculates the ApEn of IMFs as one feature and then obtains the wavelet packet entropy of wavelet packet coefficients as another feature. The two features are regarded as a feature vector. The support vector machine (SVM) and probabilistic neural network (PNN) are used for beat classification. The particle swarm optimization algorithm is used to optimize parameters of the PNN and SVM. The performance of the SVM classifier is slightly superior to that of the PNN classifier with 98.6% accuracy. [ABSTRACT FROM AUTHOR]