Proceedings of the 2021 4th International Conference on Sensors, Signal and Image Processing最新文献

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Indoor Seat Occupancy Classification with Wi-Fi Channel State Information and Machine Learning Methods 基于Wi-Fi信道状态信息和机器学习方法的室内座位占用分类
Yichuan Zhang, Jiefeng Li, Han Wang
{"title":"Indoor Seat Occupancy Classification with Wi-Fi Channel State Information and Machine Learning Methods","authors":"Yichuan Zhang, Jiefeng Li, Han Wang","doi":"10.1145/3502814.3502826","DOIUrl":"https://doi.org/10.1145/3502814.3502826","url":null,"abstract":"Keeping a distance by monitoring the seat occupancy is an essential way to prevent the spread of virus inside a room. However, most current human sensing methods need customized devices, so a cheaper way of indoor seat occupancy classification is in need. Recent researches indicate that Wi-Fi channel state information (CSI) can be utilized for indoor human sensing without wearable sensors. This paper proposes a multi-person seat occupancy classification method based on machine learning and Wi-Fi CSI received by commercial network interface card. We designed an experimental scenario of 5 seats and 2 individuals, and use commercial Wi-Fi devices to build a multi-input multi-output (MIMO) system indoors to acquire an adequate dataset. Then a pipeline consists of phase calibration, linear interpolation, outlier removal and threshold de-noising was applied to preprocess the raw CSI amplitude and phase data. After sliding window feature extraction, convolutional neural network (CNN) and some conventional machine learning methods, such as naive Bayes (NB), decision tree (DT), support vector machine (SVM) and K-nearest neighbors (KNN), are used to classify seat occupancy, among which CNN performs the best, with a classification accuracy of 95%.","PeriodicalId":115172,"journal":{"name":"Proceedings of the 2021 4th International Conference on Sensors, Signal and Image Processing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133985986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
End-to-end Speech Recognition Based on BGRU-CTC 基于BGRU-CTC的端到端语音识别
Yu Yan, Xizhong Shen
{"title":"End-to-end Speech Recognition Based on BGRU-CTC","authors":"Yu Yan, Xizhong Shen","doi":"10.1145/3502814.3502822","DOIUrl":"https://doi.org/10.1145/3502814.3502822","url":null,"abstract":"In recent years, the end-to-end speech recognition model has gradually become the development trend of large-scale continuous speech recognition because of its simplicity and easy training characteristics. In this paper, we use the good performance of bidirectional gated recurrent unit (BGRU), a variant of long short term memory (LSTM), in the field of speech recognition. At the same time, we use connectionist temporal classification (CTC) algorithm to train the model, build an end-to-end speech recognition system, and carry out speech recognition experiments on TIMIT. The results show that, compared with the traditional recognition model, the accuracy of the improved end-to-end model is improved by about 2.4%.","PeriodicalId":115172,"journal":{"name":"Proceedings of the 2021 4th International Conference on Sensors, Signal and Image Processing","volume":"243 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132624580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Proceedings of the 2021 4th International Conference on Sensors, Signal and Image Processing 第四届传感器、信号和图像处理国际会议论文集
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引用次数: 0
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