Browsing by Author "Tang, Kea-Tiong"

Now showing 1 - 9 of 9

A 0.5V 1.27mW Nose-on-a-Chip for Rapid Diagnosis of Ventilator-Associated Pneumonia
(2014-01-01)
Tang, Kea-Tiong
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Chiu, Shih-Wen
;
Shih, Chung-Hung
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Chang, Chia-Ling
;
Yang, Chia-Min
A 1-V 2.6-mW Environmental Compensated Fully Integrated Nose-on-a-Chip
(2018-10-01)
Chou, Ting-I
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Chang, Kwuang-Han
;
Jhang, Jia-Yin
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Chiu, Shih-Wen
;
Wang, Guoxing
Miniature gas detection system is desirable for many applications. In this brief, we propose a fully integrated nose-on-a-chip for mobile phones and wearable devices. The chip has 36 on-chip sensors, a 36-channel adaptive interface with an integrated programmable amplifier, a four-channel frequency readout interface, one on-chip temperature sensor, a successive approximation analog-to-digital converter, a scalable learning kernel cluster, and a reduced instruction set computing core with low-voltage static random-access memory. This chip is fabricated using the TSMC 180-nm CMOS process and consumes 2.6 mW at 1 V. on-chip temperature and humidity sensors are included in this system for acquiring environmental information. An environmental compensation algorithm based on common principal components analysis is also implemented for high-accuracy odor classification. This fully integrated nose-on-a-chip provides promising results for odor detection and classification for use in mobile and wearable device applications.
A Fully Integrated Nose-on-a-Chip for Rapid Diagnosis of Ventilator-Associated Pneumonia
(2014-12-01)
Chiu, Shih-Wen
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Wang, Jen-Huo
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Chang, Kwuang-Han
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Chang, Ting-Hau
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Wang, Chia-Min
Ventilator-associated pneumonia (VAP) still lacks a rapid diagnostic strategy. This study proposes installing a nose-on-a-chip at the proximal end of an expiratory circuit of a ventilator to monitor and to detect metabolite of pneumonia in the early stage. The nose-on-a-chip was designed and fabricated in a 90-nm 1P9M CMOS technology in order to downsize the gas detection system. The chip has eight on-chip sensors, an adaptive interface, a successive approximation analog-to-digital converter (SAR ADC), a learning kernel of continuous restricted Boltzmann machine (CRBM), and a RISC-core with low-voltage SRAM. The functionality of VAP identification was verified using clinical data. In total, 76 samples infected with pneumonia (19 Klebsiella, 25 Pseudomonas aeruginosa, 16 Staphylococcus aureus, and 16 Candida) and 41 uninfected samples were collected as the experimental group and the control group, respectively. The results revealed a very high VAP identification rate at 94.06% for identifying healthy and infected patients. A 100% accuracy to identify the microorganisms of Klebsiella, Pseudomonas aeruginosa, Staphylococcus aureus, and Candida from VAP infected patients was achieved. This chip only consumes 1.27 mW at a 0.5 V supply voltage. This work provides a promising solution for the long-term unresolved rapid VAP diagnostic problem.
A Physiological Valence/Arousal Model from Musical Rhythm to Heart Rhythm
(2011)
Wang, Hui-Min
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Lee, Yaw-Chern
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Yen, Brad S.
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Wang, Chun-Yen
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Huang, Sheng-Chieh
This paper studies how the musical-rhythmic features affect the performance of heart rate variability (HRV). A physiological valence/arousal model of such a relationship is proposed. A systematic experiment was performed, subjecting human subjects to four different drum loops, in which was tried to define a set of rhythmic features (and 'factors') that could be correlated with the observed HRV readings. Twenty-two healthy subjects and these four testing rhythm patterns were studied in this paper. The results show that dedicated rhythm can be synthesized for relaxing and exciting, and such an understanding between music and autonomic nervous system can improve life and health
A Single-Walled Carbon Nanotube Network Gas Sensing Device
(2011-08-01)
Wang, Li-Chun
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Tang, Kea-Tiong
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Teng, I-Ju
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Kuo, Cheng-Tzu
;
Ho, Cheng-Long
The goal of this research was to develop a chemical gas sensing device based on single-walled carbon nanotube (SWCNT) networks. The SWCNT networks are synthesized on Al(2)O(3)-deposted SiO(2)/Si substrates with 10 nm-thick Fe as the catalyst precursor layer using microwave plasma chemical vapor deposition (MPCVD). The development of interconnected SWCNT networks can be exploited to recognize the identities of different chemical gases by the strength of their particular surface adsorptive and desorptive responses to various types of chemical vapors. The physical responses on the surface of the SWCNT networks cause superficial changes in the electric charge that can be converted into electronic signals for identification. In this study, we tested NO(2) and NH(3) vapors at ppm levels at room temperature with our self-made gas sensing device, which was able to obtain responses to sensitivity changes with a concentration of 10 ppm for NO(2) and 24 ppm for NH(3).
An 8 mu W 100kS/s Successive Approximation ADC for Biomedical Applications
(2009)
Chen, Yung-Jui
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Tang, Kea-Tiong
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Fang, Wai-Chi
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
A successive approximation analog-to-digital converter (SA-ADC) for biomedical application is presented. It is based on 0.18-mu m standard CMOS technology and operates at low supply voltage at 1V. Boosted switch for sample-and-hold stage, split capacitor array for DAC, and clocked rail-to-rail comparator are used to achieve low-power consumption. The ADC has signal-to-noise-and-distortion ratio of 53dB for supply voltage of 1V, at sampling rate of 100kS/s and power consumption of 8 mu W.
Design of a 0.5V 1.68mW Nose-on-a-Chip for Rapid Screen of Chronic Obstructive Pulmonary Disease
(2016-01-01)
Chou, Ting-I
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Chiu, Shih-Wen
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Chang, Kwuang-Han
;
Chen, Yi-Ju
;
Tang, Chen-Ting
Chronic obstructive pulmonary disease (COPD) still lacks a rapid diagnosis strategy. In this paper, we propose a low-power nose-on-a-chip for rapid COPD screening. This chip is designed for implementation in a personal handheld device that detects patient breath for COPD diagnosis. The chip has 36 on-chip sensors, a 36-channel adaptive interface with an integrated programmable amplifier, a four-channel frequency readout interface, one on-chip temperature sensor, a two-channel successive approximation analog-to-digital converter, a scalable learning kernel cluster, and a reduced instruction set computing core with low-voltage static random-access memory. This chip is fabricated in 90 nm CMOS and consumes 1.68 mW at 0.5 V. In simulation, the system distinguished between undiseased and diseased patients with 90.82% accuracy for a set of diseases including COPD and asthma and exhibited 92.31% accuracy for identifying patients with COPD or asthma. The system classified severity levels of COPD under four labels (normal, mild, moderate, and severe) with 92.00% accuracy. Accordingly, this work provides a promising solution for the unmet medical need of rapid COPD screening.
Development of a Multisite, Closed-loop Neuromodulator for the Theranosis of Neural Degenerative Diseases
(2018-01-01)
Chen, Hsin
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Chang, Yen-Chung
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Yeh, Shih-Rung
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Hsieh, Chih-Cheng
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Tang, Kea-Tiong
Stimulating specific brain regions has been found useful for treating neural disorders, such as the Parkinson's disease, epilepsy, depression, etc. However, how electrical stimulation modulates neural activities remains not fully understood. As animal models provide the advantage of recording and stimulating different disease-related regions simultaneously, this paper introduces the latest development of a multisite, closed-loop-controlled microsystem for investigating novel treatments on neural degenerative diseases with freely-moving rats. The algorithms for recognizing pathological neural activities automatically are also developed and realized in hardware, so as to control the stimulation in a closed loop and in real time. The pilot studies on the efficacy of treating the Parkinson's disease with closed-loop-controlled stimulation will be presented and discussed. Finally, the feasibility of modelling and probing how neural dynamics and connectivity are modulated by stimulation will be an important topic for future research.
Multi-input Silicon Neuron with Weighting Adaptation
(2009)
Li, Ming-Ze
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Ping-Wang, Po
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Tang, Kea-Tiong
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Fang, Wai-Chi
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電子工程學系及電子研究所
This paper presents a biologically inspired "integrate-and-fire (I&F) neuron" which has multiple input dendrites for adaptive weight storage. By using a capacitor-free integrator, longer time constant and smaller chip area can be achieved. A low-power Schmitt Trigger is used to implement the feedback loop to achieve smaller power consumption. Weights are stored by using floating gate MOS transistors as nonvolatile analog memory. Simulation results show that this I&F neuron can be utilized in an analog VLSI neural network system.