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The practical use of electronic nose (e-nose) systems suffers from drift issue, which alters data distribution and reduces the accuracy of classification. This work proposes a transfer learning method called concentration-based drift calibration (CDC) for calibrating the sensor drift. Based on the sensor characteristic that sensor response is correspondent to gas concentration, transfer samples were collected in the target domain for certain gas concentrations, and then used for calibration with a designed concentration-based (CB) model and CDC transfer process. This method was evaluated on a complex time-varying drift dataset. The experimental results show that the proposed method for drift calibration is effective, and can be used for real-world applications. Moreover, the CDC transfer process can be applied over time with data that has been previously collected to yield a more generalized model.
Yu-Chieh Cheng; Ting-I. Chou; Shih-Wen Chiu; Kea-Tiong Tang. A Concentration-Based Drift Calibration Transfer Learning Method for Gas Sensor Array Data. IEEE Sensors Letters 2020, 4, 1 -4.
AMA StyleYu-Chieh Cheng, Ting-I. Chou, Shih-Wen Chiu, Kea-Tiong Tang. A Concentration-Based Drift Calibration Transfer Learning Method for Gas Sensor Array Data. IEEE Sensors Letters. 2020; 4 (10):1-4.
Chicago/Turabian StyleYu-Chieh Cheng; Ting-I. Chou; Shih-Wen Chiu; Kea-Tiong Tang. 2020. "A Concentration-Based Drift Calibration Transfer Learning Method for Gas Sensor Array Data." IEEE Sensors Letters 4, no. 10: 1-4.
Electronic nose (E-nose) systems have become popular in food and fruit quality evaluation because of their rapid and repeatable availability and robustness. In this paper, we propose an E-nose system that has potential as a non-destructive system for monitoring variation in the volatile organic compounds produced by fruit during the maturing process. In addition to the E-nose system, we also propose a camera system to monitor the peel color of fruit as another feature for identification. By incorporating E-nose and camera systems together, we propose a non-destructive solution for fruit maturity monitoring. The dual E-nose/camera system presents the best Fisher class separability measure and shows a perfect classification of the four maturity stages of a banana: Unripe, half-ripe, fully ripe, and overripe.
Li-Ying Chen; Cheng-Chun Wu; Ting-I. Chou; Shih-Wen Chiu; Kea-Tiong Tang. Development of a Dual MOS Electronic Nose/Camera System for Improving Fruit Ripeness Classification. Sensors 2018, 18, 3256 .
AMA StyleLi-Ying Chen, Cheng-Chun Wu, Ting-I. Chou, Shih-Wen Chiu, Kea-Tiong Tang. Development of a Dual MOS Electronic Nose/Camera System for Improving Fruit Ripeness Classification. Sensors. 2018; 18 (10):3256.
Chicago/Turabian StyleLi-Ying Chen; Cheng-Chun Wu; Ting-I. Chou; Shih-Wen Chiu; Kea-Tiong Tang. 2018. "Development of a Dual MOS Electronic Nose/Camera System for Improving Fruit Ripeness Classification." Sensors 18, no. 10: 3256.
An electronic nose (E-Nose) is one of the applications for surface acoustic wave (SAW) sensors. In this paper, we present a low-noise complementary metal–oxide–semiconductor (CMOS) readout application-specific integrated circuit (ASIC) based on an SAW sensor array for achieving a miniature E-Nose. The center frequency of the SAW sensors was measured to be approximately 114 MHz. Because of interference between the sensors, we designed a low-noise CMOS frequency readout circuit to enable the SAW sensor to obtain frequency variation. The proposed circuit was fabricated in Taiwan Semiconductor Manufacturing Company (TSMC) 0.18 μm 1P6M CMOS process technology. The total chip size was nearly 1203 × 1203 μm2. The chip was operated at a supply voltage of 1 V for a digital circuit and 1.8 V for an analog circuit. The least measurable difference between frequencies was 4 Hz. The detection limit of the system, when estimated using methanol and ethanol, was 0.1 ppm. Their linearity was in the range of 0.1 to 26,000 ppm. The power consumption levels of the analog and digital circuits were 1.742 mW and 761 μW, respectively.
Cheng-Chun Wu; Szu-Chieh Liu; Shih-Wen Chiu; Kea-Tiong Tang. A Low Noise CMOS Readout Based on a Polymer-Coated SAW Array for Miniature Electronic Nose. Sensors 2016, 16, 1777 .
AMA StyleCheng-Chun Wu, Szu-Chieh Liu, Shih-Wen Chiu, Kea-Tiong Tang. A Low Noise CMOS Readout Based on a Polymer-Coated SAW Array for Miniature Electronic Nose. Sensors. 2016; 16 (11):1777.
Chicago/Turabian StyleCheng-Chun Wu; Szu-Chieh Liu; Shih-Wen Chiu; Kea-Tiong Tang. 2016. "A Low Noise CMOS Readout Based on a Polymer-Coated SAW Array for Miniature Electronic Nose." Sensors 16, no. 11: 1777.
In this paper, we propose a bio-inspired, two-layer, multiple-walled carbon nanotube (MWCNT)-polypeptide composite sensing device. The MWCNT serves as a responsive and conductive layer, and the nonselective polypeptide (40 mer) coating the top of the MWCNT acts as a filter into which small molecular gases pass. Instead of using selective peptides to sense specific odorants, we propose using nonselective, peptide-based sensors to monitor various types of volatile organic compounds. In this study, depending on gas interaction and molecular sizes, the randomly selected polypeptide enabled the recognition of certain polar volatile chemical vapors, such as amines, and the improved discernment of low-concentration gases. The results of our investigation demonstrated that the polypeptide-coated sensors can detect ammonia at a level of several hundred ppm and barely responded to triethylamine.
Li-Chun Wang; Tseng-Hsiung Su; Cheng-Long Ho; Shang-Ren Yang; Shih-Wen Chiu; Han-Wen Kuo; Kea-Tiong Tang. A Bio-Inspired Two-Layer Sensing Structure of Polypeptide and Multiple-Walled Carbon Nanotube to Sense Small Molecular Gases. Sensors 2015, 15, 5390 -5401.
AMA StyleLi-Chun Wang, Tseng-Hsiung Su, Cheng-Long Ho, Shang-Ren Yang, Shih-Wen Chiu, Han-Wen Kuo, Kea-Tiong Tang. A Bio-Inspired Two-Layer Sensing Structure of Polypeptide and Multiple-Walled Carbon Nanotube to Sense Small Molecular Gases. Sensors. 2015; 15 (3):5390-5401.
Chicago/Turabian StyleLi-Chun Wang; Tseng-Hsiung Su; Cheng-Long Ho; Shang-Ren Yang; Shih-Wen Chiu; Han-Wen Kuo; Kea-Tiong Tang. 2015. "A Bio-Inspired Two-Layer Sensing Structure of Polypeptide and Multiple-Walled Carbon Nanotube to Sense Small Molecular Gases." Sensors 15, no. 3: 5390-5401.