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Urban intelligence is an emerging concept which guides a series of infrastructure developments in modern smart cities. Human-computer interaction (HCI) is the interface between residents and the smart cities, it plays a key role in bridging the gap in applicating information technologies in modern cities. Hand gestures have been widely acknowledged as a promising HCI method, recognition human hand gestures using surface electromyogram (sEMG) is an important research topic in the application of sEMG. However, state-of-the-art signal processing technologies are not robust in feature extraction and pattern recognition with sEMG signals, several technical problems are still yet to be solved. For example, how to maintain the availability of myoelectric control in intermittent use, since pattern recognition qualities are greatly affected by time variability, but it is unavoidable during daily use. How to ensure the reliability and effectiveness of myoelectric control system also important in developing a good human-machine interface. In this paper, linear discriminant analysis (LDA) and extreme learning machine (ELM) are implemented in hand gesture recognition system, which is able to reduce the redundant information in sEMG signals and improve recognition efficiency and accuracy. The characteristic map slope (CMS) is extracted by using the feature re-extraction method because CMS can strengthen the relationship of features cross time domain and enhance the feasibility of cross-time identification. This study is focusing on optimizing the time differences in sEMG pattern recognition, the experimental results are beneficial to reducing the time differences in gesture recognition based on sEMG. The recognition framework proposed in this paper can enhance the generalization ability of HCI in the long term use and it also simplifies the data collection stage before training the device ready for daily use, which is of great significance to improve the time generalization performance of an HCI system.

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The channel numbers and electrode layouts are usually determined empirically that would reduce robustness when acquiring surface electromyography (EMG) signals for prosthetic hand systems. It is necessary to study how they can be exploited effectively for a more accurate extraction. In response to the problem, an experiment is designed that establishes the relationship between sEMG signals and forearm muscles based on signal-to-noise ratio (SNR). The SNR of sEMG signals in different sampling channels can be calculated and compared, and then the potential contribution of each channel during different hand motions will be evaluated comprehensively. The active muscle regions can be obtained from the established relationship that is a useful reference for feature extraction. Finally, the relations between the computational cost, channel numbers and electrode layouts are explored. The findings of this paper support the idea that the accuracy of pattern recognition will not be affected when reducing the redundant electrodes.

In order to solve the problem of uneven distribution of picture features and stitching of images, an improved SURF feature extraction method is proposed. Image feature extraction and image registration are the core of image stitching, which is directly related to stitching quality. In this paper, a comprehensive and in-depth study of feature-based image registration is carried out, and an improved algorithm is proposed. Firstly, the Heisen detection operator in the SURF algorithm is introduced to realize feature detection, and the features are extracted as much as possible. Secondly, the characteristics are described by BRIEF operator in the ORB algorithm to realize the invariance of the rotation change. Then, the European pull distance is used to complete the similarity calculation, and the KNN algorithm is used to realize the feature rough matching. Finally, the distance threshold is used to remove the matching pair with larger distance, and then the RANSAC algorithm is used to complete the purification. Experiments show that the proposed algorithm has good real-time performance, strong robustness and high accuracy.