• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.433-438
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• 2013

Interference reduction method of vehicle collision avoidance sensor which is used for the low speed electric vehicle has been investigated. Various methods were attempted for the vehicle collision avoidance distance sensor, which received a transmitted signal from a front driving vehicle to measure the distance between two vehicles, to avoid interference by the own transmitter signal toward the rear following vehicle. In this study, -12dB of interference cancellation ratio was realized by using the phase cancellation method to the transmitted signal from the own vehicle. Proposed phase cancellation method is regarded to have the advantage of continuous monitoring in comparison to the conventional time sharing transmitting and receiving method.

• Current Optics and Photonics
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• v.5 no.3
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• pp.213-219
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• 2021

We describe a single-channel rubidium (Rb) radio-frequency atomic magnetometer (RFAM) as a receiver that takes magnetic signal resonating with Zeeman splitting of the ground state of Rb. We optimize the performance of the RFAM by recording the response signal and signal-to-noise ratio (SNR) in various parameters and obtain a noise level of 159 $fT{\sqrt{Hz}}$ around 30 kHz. When a resonant radiofrequency magnetic field with a peak amplitude of 8.0 nT is applied, the bandwidth and signal-to-noise ratio are about 650 Hz and 88 dB, respectively. It is a good agreement that RFAM using alkali atoms is suitable for receiving signals in the very low frequency (VLF) carrier band, ranging from 3 kHz to 30 kHz. This study shows the new capabilities of the RFAM in communications applications based on magnetic signals with the VLF carrier band. Such communication can be expected to expand the communication space by overcoming obstacles through the high magnetic sensitive RFAM.

• Science of Emotion and Sensibility
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• v.18 no.3
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• pp.63-70
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• 2015

This study identified heart response of participants while listening to sounds which have 1/f fluctuations with exponent ${\alpha}$ gradient. The participants were engaged in emotional stress work. Prior studies related to 1/f fluctuation sound have reported that sound source can alleviate psychological and physiological state of users. Subjects of this study were exposed to sound with three levels of ${\alpha}$ gradient. Heart response of subjects were measured with Photoplethysmography(PPG) sensor simultaneously. The dependent variables of this study were beat per minute(BPM), very low frequency percent of pulse rate variability (VLF percent), the standard deviation of all normal RR intervals (SDNN), and high frequency power(HF power). Subject showed arousal response when exposed to sound with exponent ${\alpha}$ gradient of 3 whereas the sound with exponent ${\alpha}$ gradient of 1 and 2 resulted in relax effect. The characteristic of 1/f fluctuation sounds can be applied to alleviate stress for employers under emotional labor.

• Journal of Digital Policy
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• v.1 no.2
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• pp.61-69
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• 2022

The paper is a study to develop and verify a blood glucose level prediction model based on biosignals obtained from photoplethysmography (PPG) sensors, ICT technology and data. Blood glucose prediction used the MLP architecture of machine learning. The input layer of the machine learning model consists of 10 input nodes and 5 hidden layers: heart rate, heart rate variability, age, gender, VLF, LF, HF, SDNN, RMSSD, and PNN50. The results of the predictive model are MSE=0.0724, MAE=1.1022 and RMSE=1.0285, and the coefficient of determination (R²) is 0.9985. A blood glucose prediction model using bio-signal data collected from digital devices and machine learning was established and verified. If research to standardize and increase accuracy of machine learning datasets for various digital devices continues, it could be an alternative method for individual blood glucose management.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.3
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• pp.136-142
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• 2014

Distinct features in heart rate signals during the driver's wake and sleep states could provide an initiative for the development of a safe driving systems such as drowsiness detecting sensor in a smart wheel. We measured ECG from health subjects ($23.5{\pm}2.5$ in age) during the wake and drowsiness states. The proposed method is able to detect R waves and R-R interval calculation in the ECG even when the signal includes in abnormal signals. Heart rate variability(HRV) was investigated for the time domain and frequency domains. The STD HR(0.029), NN50(0.044) and VLF power(0.0018) of the RR interval series of the subjects were significantly different from those of the control group (p < 0.05). In conclusion, there are changes in heart rate from wake to drowsiness that are potentially to be detected. The results in our study could be useful for the development of drowsiness detection sensors for effective real-time monitoring.