Browsing by Author "Pandey, Rajeev"

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A fast digital chip implementing a real-time noise-resistant algorithm for estimating blood pressure using a non-invasive, cuffless PPG sensor
(1970-01-01)
Wang, Jeremy H. -S.
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Yeh, Ming-Hua
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Chao, Paul C. -P.
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Tu, Tse-Yi
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Kao, Yung-Hua
A fast digital chip is designed and fabricated successfully to implement the algorithm for estimating blood pressure (BP) in real time by a non-invasive cuffless photoplethysmogram (PPG) Sensor. The fast response accomplished by this digital computation chip is made possible by one-time calibration only and the Taiwan Semiconductor Manufacturing (TSMC) T18 process to realize the chip in an area of 3.97 mm(2). The experimental result shows that the overall power consumption of the chip is 15.62 mW. Finally, the blood pressure measurement platform has been developed with a user interface (UI) for long-term continuous cuffless BP measurement. The non-invasive blood pressure sensor is applied to the wrist arteries of 44 subjects for sensing the PPG pulsation of the blood vessel, and then estimating BPs (blood pressures). Measurement results shows that the maximum error in the BP measurement is +/- 6 mmHg. Which is less than 8 mmHg, defined by the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) standard.
A NEW ON-CHIP REAL-TIME ALGORITHM FOR NON-INVASIVE CUFFLESS BLOOD PRESSURE ESTIMATION USING PPG SENSOR
(2019-01-01)
Yeh, Ming Hua
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Chao, Paul C. -P.
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Pandey, Rajeev
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電子工程學系及電子研究所
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電機資訊國際學位學程
Blood pressure is a basic physiological quantity and prolonged abnormal blood pressure which will precipitate different kinds of cardiovascular diseases. The early detection of hypertension is extremely important for the prevention and cure of cardiovascular diseases. This study proposes a new on-chip real-time algorithm for non-invasive cuffless blood pressure estimation using PPG Sensor (PPG). The algorithm is implemented in the integrated chip with chip area of 3.97mm(2) and fabricated via TSMC T18 process. The experimental result shows that the overall power consumption of the chip is 15.62 mW. Finally, the blood pressure measurement platform has been developed with GUI for long-term continuous cuffless BP measurement. The non-invasive blood pressure sensor is applied to the wrist artery of 44 subjects for sensing the PPG pulsation of the blood vessel. Measurement results shows that the maximum error in the BP measurement is +/- 6 mmHg. Which is less than 8 mmHg, defined by the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) standard.
ACHIEVING SENSING PRECISION OF 0.5nA IN PIXEL WITH 7 mu s SETTLING TIME BY A NEW EXTERNAL CURRENT SENSING CIRCUIT FOR AMOLED DISPLAYS
(2019-01-01)
Pandey, Rajeev
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Huang, Tzu Hao
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Ho, Wei-Hsuan
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Chao, Paul C. -P.
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Pribadi, Eka Fitrah
This study proposes a new push-pull transient current feedforward (PPTCF) based pixel current sensing circuit for active matrix organic light emitting diode (AMOLED) displays with precision within 0.5nA. A finite state machine (FSM) based digital control circuit is designed and developed for the baseline compensation. In order to achieve high accuracy, the digital baseline compensation circuit also incorporates the current digital -to -analog converter (CDAC) and successive approximation current analog-to-digital converter (CADC). The minimum LSB current for the CADC is 10nA. The readout circuit is implemented in the integrated chip with chip area of 1251.1m x 46 m and fabricated via TSMC T18 process. With standard 3.3V supply, the experimental result shows that the overall power consumption of the chip is 990 W watt. Despite of all parasitic capacitances of the panel display, experimental results show that the proposed circuit can sense 0.5nA current within 7 s of settling time. The sensing precision of 0.5nA within 71 mu s are the best among all reported literature to date.
An efficient energy harvesting circuit for batteryless IoT devices
(2020-01-01)
Yang, Ching-Cheng
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Pandey, Rajeev
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Tu, Tse-Yi
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Cheng, Yuan-Po
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Chao, Paul C-P
A new energy harvesting circuit for battery-less IoT beacon tags is developed herein to maximize power conversion efficiency as well as high throughput power with a wide input-output range. This design energy harvest (EH) circuit incorporates a charge pump (CP) with shoot-through current suppression, a body selector circuit, a maximum power point tracking circuit (MPPT), a timing control circuit, a hysteresis control circuit and a low dropout regulator. Also in this MPPT circuit is a gated clock tuned in a self-adaptive fashion to match the input impedance of the EH circuit to the output impedance of the photovoltaic (PV) panel, thus achieving successfully maximum power point. The circuit is implemented in an integrated chip in an area of 1.2 mm(2) via the TSMC 0.18 process. Experiments on the chip are conducted and the results show that the input voltage range is allowed from 0.55 to 1.7 V to effectively harvest the solar power from a flexible dye-sensitized solar cell. The achieved peak power conversion efficiency (PCE) is 77% at the input power of 52 mu W. For a wide range of lighting luminance (300-1300 lx,) the achieved average PCE is more than 70%. The achieved wide input-output range and the maximum throughput power of 200 mu W is much larger than others reported, while the 77% of PCE is close to that best power conversion efficiency reported.
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
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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
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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.