Yu-Jin Lin’s scientific contributions

... The experiment has been performed to prove the efficiency of the proposed classification algorithm with the standard machine learning classifiers such as Support Vector Machine (SVM) [6], Naive Bayes (NB) [25], and Decision Tree (DT) [12]. All these experiments have been performed by 10-fold cross-validation. ...

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Classification of arrhythmia disease through electrocardiogram signals using sampling vector random forest classifier

... The goal of these attacks is to break encryption procedures. Encryption layer attacks include ciphertext-only attacks, known plaintext attacks, chosen plaintext or ciphertext attacks, and MitM attacks [35][36][37]. ...

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Attacks, Challenges, and Countermeasures for an Integrated IoT Framework

... This study can be compared with some previous studies [29][30][31] that similarly utilized wearable electrocardiographs, data collection for servers, application of noise filters, use of a convolutional neural network, learning with the public MIT-BIH databases, and their integration into an app service. Compared to these studies, this research incorporated methodological improvements. ...

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Development and Validation of a Real-Time Service Model for Noise Removal and Arrhythmia Classification Using Electrocardiogram Signals

... Phonocardiography, the graphic recording of heart sounds, offers a more analytical approach to cardiac auscultation [4]. Unlike the ephemeral nature of live listening, phonocardiograms provide a tangible, visual representation of cardiac acoustics, allowing for a detailed examination of heart sound waveforms. ...

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Digital Stethoscope for Early Detection of Heart Disease on Phonocardiography Data

... Hz [13]. Can be seen also on High Pass Filter data table that the frequency is below the cutoff has a small output as well as table data Low Pass Filter that the frequency above the cutoff has an output that is small [22][23] [24]. In this study, the researcher used an input of 4.4 volts [25], Cardiac signals that are captured and processed by the device This electric stethoscope is successfully displayed on the PC display and sending signals to the Bluetooth headset and the user can select the signal to be transmitted to the Bluetooth headset [27]. ...

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Analysis of the Effectiveness of Using Digital Filters in Electronic Stethoscopes

... These integral computing chips facilitate the functionality of a multitude of devices, ranging from everyday gadgets like smart watches 1) to more advanced systems pivotal in emerging applications like Internet of Things (IoT) edge and cloud computing, 2) Augmented Reality, 3) Virtual Reality, 4) autonomous vehicles, 5) and smart healthcare devices. 6) In the heart of the MCUs, there is a type of memory known as read only memory (ROM) mostly using an embedded NOR Flash (eFLASH), 7) which is responsible for storing the instructions that the MCUs need to function. However, with the rise of applications requiring quick responses and efficient energy use, such as IoT edge devices and cloud computing platforms, the eFLASH is hitting a wall due to its relatively high energy consumption and physical scaling limits. ...

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A 0.11pJ/bit read energy embedded NanoBridge non-volatile memory and its integration in a 28nm 32-bit RISC-V microcontroller units

... In the same year, they applied this device to rats and achieved good results [13]. At present, most of the stimulus signals are regular waveforms such as square waves [14][15][16]. And in each experiment, the stimulus waveform used by the researcher will not change. ...

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A Wireless Animal Robot Stimulation System Based on Neuronal Electrical Signal Characteristics