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Activity recognition is mainly used in health care, authentication and many other fields. Activity recognition based on a smart watch usually performs much better than any other means of activity recognition in these areas. With its small size, high integration and multi-function, smart watch holds more advantages over the devices used in image based activity recognition, for example, a monitor, which...
In this paper, we propose a novel approach based on deep belief networks for features learning of ECG arrhythmias. The method consists of four steps: ECG signals preprocessing, segmentation and resampling, features learning, and validation. In the resampling stage all segmented ECG beats are changed into the same periodic length for the deep learning model. For extracting features of ECG data, we...
Many crowd abnormal motion detection methods in video surveillance have been proposed in resent years. However, most of them are based on low semantic features, such gray value, velocity and gradient. Usually, low semantic features contain weak discriminative information of the scene. In addition, these methods often ignore important information in time and space dimension. In this work, a high semantic...
In the past few years, Bag of Word (BOW) plays an important role in the field of image retrieval and automatic annotation especially in large scale image databases. However, in practice, the spatial location information of visual word is often ignored. Despite much previous work, it remains a challenging task to extract the spatial location information of visual word. Recently, salient object detection...
This paper proposed a fuzzy logic based approach to label speech segments from media files for content production in e-Learning systems. A fuzzy inference system (FIS) was introduced to fuzzify the extracted features extracted from different dimensions. apply predefined rules, and generate an output that represents the degree a clip belonging to speech. We show that our methodology improves the accuracy...
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