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Titlebook: Communications, Signal Processing, and Systems; Proceedings of the 1 Qilian Liang,Wei Wang,Baoju Zhang Conference proceedings 2023 The Edit

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https://doi.org/10.1007/978-3-540-68853-2dient information is repeatedly used to update the weights of different dense layers in the Darknet53 network in the process of back propagation. Firstly, the residual block in the original network is replaced by THE CSP module, referring to the idea of cutting off the gradient flow in CSPnet to pre
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https://doi.org/10.1007/978-3-540-68853-2isfying commercial demands and better understanding user urban lifestyles. Current researches mainly resort to the traditional POI-level (point-of-interest) or neural network-based recommendation methods for ROI recommendation, in disregard of the rich heterogeneous graph information, such as user-r
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https://doi.org/10.1007/978-3-540-68853-2rnal interference and is not easily disguised. By classifying the emotions of EEG signals, it can provide an aid for future treatment of depression, epilepsy and other diseases. Therefore, this article uses the publicly available EEG emotion dataset and uses a ratio of 80%–20% to divide the training
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https://doi.org/10.1007/978-3-540-68853-2 The common solution is parallel receiving echo data by multi-channel. According to the character of synthetic aperture radar, this paper proposes the technology of the multi-channel receiver and sub-band pulse compression. Finally, the computer simulation confirms the validity of the method success
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https://doi.org/10.1007/978-3-540-68853-2) is proposed in this paper. This algorithm determines decoding path expansion parameters based on the reliability of each polarization splitting sub-channel. At the same time, it dynamically modifies the size of decoding list according to the current CRC decoding results and the previous decoding l
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https://doi.org/10.1007/978-3-540-68853-2ime to rescue, causing severe injury or even death. This paper proposes a video-based fall detection method to solve this problem. This method first performs motion detection of inter-frame difference on the video, performs feature extraction through deep learning, and finally completes classificati
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https://doi.org/10.1007/978-3-540-68853-2um. In this paper, an improved Grey Wolf Optimizer (IGWO) is proposed. In this algorithm, a new update equation is used to improve the search speed of the algorithm. Meanwhile, the forgetting mechanism is introduced, which improves the diversification of the population and enables the algorithm to j
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