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Browsing by Author Ma, S.

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  • Magnetocardiography-Based Ischemic Heart Disease Detection and Localization Using Machine Learning Methods

    • Authors: Tao, R.;  Advisor: -;  Participants: Zhang, S.; Huang, X.; Tao, M.; Ma, J.; Ma, S.; Zhang, C.; Zhang, T.; Tang, F.; Lu, J.; Shen, C.; Xie, X. (2019)

  • Objective: This study focused on developing a fast and accurate automatic ischemic heart disease detection/localization methodology. Methods: Twavewas segmented from averaged Magnetocardiography (MCG) recordings and 164 features were subsequently extracted. These features were categorized into three groups: time domain features, frequency domain features, and informa-tion theory features. Next, we compared different machine learning classifiers including: k-nearest neighbor, decision tree, support vector machine (SVM), and XGBoost. To identify ischemia heart disease (IHD) case, we selected three classifiers with best performance and applied model ensemble to average results. All 164 f...
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  • Signal Demodulation With Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset, and Algorithms

    • Authors: Ma, S.;  Advisor: -;  Participants: Dai, J.; Lu, S.; Li, H.; Zhang, H.; Du, C.; Li, S. (2019)

  • In this paper, we investigate the design and implementation of machine learning (ML)-based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a exible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), the deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Speci cally, the CNN-based demodulator converts the modulated signals to images and recognizes the signals by the image classi cation. Th...