Browsing by Author "Fitrah, Eka P."

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Design and Implementation of OLED Driving and OPD Readout Circuitry for an Optical Vibration Sensor
(2018-01-01)
Wu, Yi-Cheng
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Kao, Yung-Hua
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Chao, Paul C. -P.
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Wey, Chin-Long
;
Sauter, Thilo
This study presents an organic light-emitting diode (OLED) drive circuit and an organic photo detector (OPD) readout circuits for a vibration sensor. Vibration sensor consist of micro-electro-mechanical systems (MEMS), which are OLED and OPD needs to be integrated for many applications in automation or consumer electronics. The light flux is generated by an OLED located on top of the structure and the modulated light flux is measured by an OPD. The OLED drive circuit contains pulse width modulation (PWM) driver to control PWM output for optimization of voltage operation is designed in order to minimize power consumption efficiency of device. The readout circuit includes a trans-impedance amplifier (TIA) to convert incident light of OPD to voltage output, a 14-bit analog-to-digital converter (ADC) to extract amplitude of vibration level, circuit to generate clock for time-to-digital converter (TDC) which detect frequency of vibration sensor. The designed circuit has been successfully fabricated by using TSMC 0.18 mu m CMOS process, the chip area is 1.012 mm(2). The power consumption of chip is about 6.2 mW. The measured frequency of PWM is about 200 kHz with 50% duty cycle. The frequency range of the vibration can be sensed well as about 10 Hz-1.5 kHz, and resulting in high SNR at frequencies under 60 Hz.
Estimating Blood Pressure via Artificial Neural Networks Based on Measured Photoplethysmography Waveforms
(2018-01-01)
Priyanka, K. N. G.
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Chao, Paul C. -P.
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Tu, Tse-Yi
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Kao, Yung-Hua
;
Yeh, Ming-Hua
A new approach for estimating blood pressure from photoplethysmography (PPG) signals is developed using artificial neural networks (ANNs). Blood Pressure is one of the most important parameters that can provide valuable information of personal healthcare. A reflective photoplethysmography (PPG) sensor module is developed for the cuffless, non-invasive blood pressure (BP) measurement based on PPG at wrist on radial artery. Blood Pressure is in a relation with the pulse duration of the PPG. In this paper, we propose to estimate blood pressure from PPG signal by using artificial neural networks approach. This is the first reported study to consider varied temporal periods of PPG waveforms as features for application of artificial neural networks (ANNs) to estimate blood pressure. We compared our results with those measured using a commercial cuff-based digital blood pressure measuring device and obtained encouraging results of overall SBP and DBP regression (R) as 0.99115.