دانلود مقاله انگلیسی رایگان:پردازش نویز به کمک هوش مصنوعی  مبتنی بر حسگر  اینترنت اشیا  بر Spintronic برای کاربرد مغناطیسی قلب - 2020
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  • AI Aided Noise Processing of Spintronic Based IoT Sensor for Magnetocardiography Application AI Aided Noise Processing of Spintronic Based IoT Sensor for Magnetocardiography Application
    AI Aided Noise Processing of Spintronic Based IoT Sensor for Magnetocardiography Application

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    AI Aided Noise Processing of Spintronic Based IoT Sensor for Magnetocardiography Application


    ترجمه فارسی عنوان مقاله:

    پردازش نویز به کمک هوش مصنوعی مبتنی بر حسگر اینترنت اشیا بر Spintronic برای کاربرد مغناطیسی قلب


    منبع:

    IEEE - ICC 2020 - 2020 IEEE International Conference on Communications (ICC);2020; ; ;


    نویسنده:

    Attayeb Mohsen∗1, Muftah Al-Mahdawi†2, Mostafa M. Fouda‡3, Mikihiko Oogane†§4, Yasuo Ando†§5, and Zubair Md Fadlullah¶6.


    چکیده انگلیسی:

    As we are about to embark upon the highly hyped “Society 5.0”, powered by the Internet of Things (IoT), traditional ways to monitor human heart signals for tracking cardio-vascular conditions are challenging, particularly in remote healthcare settings. On the merits of low power consumption, portability, and non-intrusiveness, there are no suitable IoT solutions that can provide information comparable to the conventional Electrocardiography (ECG). In this paper, we propose an IoT device utilizing a spintronic-technology-based ultra-sensitive Magnetic Tunnel Junction (MTJ) sensor that measures the magnetic fields produced by cardio-vascular electromagnetic activity, i.e. Magentocardiography (MCG). We treat the low-frequency noise generated by the sensor, which is also a challenge for most other sensors dealing with low-frequency bio-magnetic signals. Instead of relying on generic signal processing techniques such as moving average, we employ deep-learning training on biomagnetic signals. Using an existing dataset of ECG records, MCG signals are synthesized. A unique deep learning model, composed of a one-dimensional convolution layer, Gated Recurrent Unit (GRU) layer, and a fully-connected neural layer, is trained using the labeled data moving through a striding window, which is able to smartly capture and eliminate the noise features. Simulation results are reported to evaluate the effectiveness of the proposed method that demonstrates encouraging performance.
    Index Terms: Smart health | IoT | ECG | MCG | deep learning | noise | spintronic sensor | convolution | GRU | medical analytics


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 6
    حجم فایل: 199 کیلوبایت

    قیمت: رایگان


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