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A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data. SENSORS 2019; 19:s19224831. [PMID: 31698785 PMCID: PMC6891685 DOI: 10.3390/s19224831] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 10/26/2019] [Accepted: 10/31/2019] [Indexed: 11/17/2022]
Abstract
In several application domains, electronic nose systems employing conventional data processing approaches incur substantial power and computational costs and limitations, such as significant latency and poor accuracy for classification. Recent developments in spike-based bio-inspired approaches have delivered solutions for the highly accurate classification of multivariate sensor data with minimized computational and power requirements. Although these methods have addressed issues related to efficient data processing and classification accuracy, other areas, such as reducing the processing latency to support real-time application and deploying spike-based solutions on supported hardware, have yet to be studied in detail. Through this investigation, we proposed a spiking neural network (SNN)-based classifier, implemented in a chip-emulation-based development environment, that can be seamlessly deployed on a neuromorphic system-on-a-chip (NSoC). Under three different scenarios of increasing complexity, the SNN was determined to be able to classify real-valued sensor data with greater than 90% accuracy and with a maximum latency of 3 s on the software-based platform. Highlights of this work included the design and implementation of a novel encoder for artificial olfactory systems, implementation of unsupervised spike-timing-dependent plasticity (STDP) for learning, and a foundational study on early classification capability using the SNN-based classifier.
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Schroeder V, Evans ED, Wu YCM, Voll CCA, McDonald BR, Savagatrup S, Swager TM. Chemiresistive Sensor Array and Machine Learning Classification of Food. ACS Sens 2019; 4:2101-2108. [PMID: 31339035 DOI: 10.1021/acssensors.9b00825] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Successful identification of complex odors by sensor arrays remains a challenging problem. Herein, we report robust, category-specific multiclass-time series classification using an array of 20 carbon nanotube-based chemical sensors. We differentiate between samples of cheese, liquor, and edible oil based on their odor. In a two-stage machine-learning approach, we first obtain an optimal subset of sensors specific to each category and then validate this subset using an independent and expanded data set. We determined the optimal selectors via independent selector classification accuracy, as well as a combinatorial scan of all 4845 possible four selector combinations. We performed sample classification using two models-a k-nearest neighbors model and a random forest model trained on extracted features. This protocol led to high classification accuracy in the independent test sets for five cheese and five liquor samples (accuracies of 91% and 78%, respectively) and only a slightly lower (73%) accuracy on a five edible oil data set.
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Affiliation(s)
- Vera Schroeder
- Department of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge Massachusetts 02139, United States
| | - Ethan D. Evans
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge Massachusetts 02139, United States
| | - You-Chi Mason Wu
- Department of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge Massachusetts 02139, United States
| | - Constantin-Christian A. Voll
- Department of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge Massachusetts 02139, United States
| | - Benjamin R. McDonald
- Department of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge Massachusetts 02139, United States
| | - Suchol Savagatrup
- Department of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge Massachusetts 02139, United States
| | - Timothy M. Swager
- Department of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge Massachusetts 02139, United States
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Abstract
Carbon nanotubes (CNTs) promise to advance a number of real-world technologies. Of these applications, they are particularly attractive for uses in chemical sensors for environmental and health monitoring. However, chemical sensors based on CNTs are often lacking in selectivity, and the elucidation of their sensing mechanisms remains challenging. This review is a comprehensive description of the parameters that give rise to the sensing capabilities of CNT-based sensors and the application of CNT-based devices in chemical sensing. This review begins with the discussion of the sensing mechanisms in CNT-based devices, the chemical methods of CNT functionalization, architectures of sensors, performance parameters, and theoretical models used to describe CNT sensors. It then discusses the expansive applications of CNT-based sensors to multiple areas including environmental monitoring, food and agriculture applications, biological sensors, and national security. The discussion of each analyte focuses on the strategies used to impart selectivity and the molecular interactions between the selector and the analyte. Finally, the review concludes with a brief outlook over future developments in the field of chemical sensors and their prospects for commercialization.
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Affiliation(s)
- Vera Schroeder
- Department of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge Massachusetts 02139, United States
| | - Suchol Savagatrup
- Department of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge Massachusetts 02139, United States
| | - Maggie He
- Department of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge Massachusetts 02139, United States
| | - Sibo Lin
- Department of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge Massachusetts 02139, United States
| | - Timothy M. Swager
- Department of Chemistry and Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge Massachusetts 02139, United States
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An Investigation into Spike-Based Neuromorphic Approaches for Artificial Olfactory Systems. SENSORS 2017; 17:s17112591. [PMID: 29125586 PMCID: PMC5713038 DOI: 10.3390/s17112591] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 11/06/2017] [Accepted: 11/07/2017] [Indexed: 02/07/2023]
Abstract
The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the application of neuromorphic engineering concepts for electronic noses has provided an impetus for research focusing on improving these instruments. While conventional e-noses apply computationally expensive and power-consuming data-processing strategies, neuromorphic olfactory sensors implement the biological olfaction principles found in humans and insects to simplify the handling of multivariate sensory data by generating and processing spike-based information. Over the last decade, research on neuromorphic olfaction has established the capability of these sensors to tackle problems that plague the current e-nose implementations such as drift, response time, portability, power consumption and size. This article brings together the key contributions in neuromorphic olfaction and identifies future research directions to develop near-real-time olfactory sensors that can be implemented for a range of applications such as biosecurity and environmental monitoring. Furthermore, we aim to expose the computational parallels between neuromorphic olfaction and gustation for future research focusing on the correlation of these senses.
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Qi PF, Zeng M, Li ZH, Sun B, Meng QH. Design of a portable electronic nose for real-fake detection of liquors. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2017; 88:095001. [PMID: 28964212 DOI: 10.1063/1.5001314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2017] [Accepted: 08/22/2017] [Indexed: 06/07/2023]
Abstract
Portability is a major issue that influences the practical application of electronic noses (e-noses). For liquors detection, an e-nose must preprocess the liquid samples (e.g., using evaporation and thermal desorption), which makes the portable design even more difficult. To realize convenient and rapid detection of liquors, we designed a portable e-nose platform that consists of hardware and software systems. The hardware system contains an evaporation/sampling module, a reaction module, a control/data acquisition and analysis module, and a power module. The software system provides a user-friendly interface and can achieve automatic sampling and data processing. This e-nose platform has been applied to the real-fake recognition of Chinese liquors. Through parameter optimization of a one-class support vector machine classifier, the error rate of the negative samples is greatly reduced, and the overall recognition accuracy is improved. The results validated the feasibility of the designed portable e-nose platform.
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Affiliation(s)
- Pei-Feng Qi
- Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Ming Zeng
- Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Zhi-Hua Li
- Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Biao Sun
- Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
| | - Qing-Hao Meng
- Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
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Wang JH, Tang CT, Chen H. An Adaptable Continuous Restricted Boltzmann Machine in VLSI for Fusing the Sensory Data of an Electronic Nose. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2017; 28:961-974. [PMID: 26863678 DOI: 10.1109/tnnls.2016.2517078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
An embedded system capable of fusing sensory data is demanded for many portable or implantable microsystems. The continuous restricted Boltzmann machine (CRBM) is a probabilistic neural network not only capable of classifying data reliably but also amenable to very-large-scale-integration (VLSI) implementation. Although the embedded system based on the CRBM has been demonstrated with analog VLSI, the precision required by the learning algorithm is hardly achievable with analog circuits. Therefore, this paper investigates the feasibility of realizing the CRBM as a digital embedded system for fusing the sensory data of an electronic nose (eNose). The fusion here refers to data clustering and dimensional reduction that facilitates reliable classification. The capability of the CRBM to model different types of eNose data is first examined by MATLAB simulation. Afterward, the CRBM algorithm is customdesigned as a digital embedded system within an eNose microsystem. The functionality of the embedded CRBM system is then tested and discussed. With on-chip learning ability, the CRBM-embedded eNose is able to adapt its parameters in response to new data inputs or environmental changes.
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Martinelli E, Magna G, Polese D, Vergara A, Schild D, Di Natale C. Stable odor recognition by a neuro-adaptive electronic nose. Sci Rep 2015; 5:10960. [PMID: 26043043 PMCID: PMC4455291 DOI: 10.1038/srep10960] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Accepted: 04/07/2015] [Indexed: 11/20/2022] Open
Abstract
Sensitivity, selectivity and stability are decisive properties of sensors. In chemical gas sensors odor recognition can be severely compromised by poor signal stability, particularly in real life applications where the sensors are exposed to unpredictable sequences of odors under changing external conditions. Although olfactory receptor neurons in the nose face similar stimulus sequences under likewise changing conditions, odor recognition is very stable and odorants can be reliably identified independently from past odor perception. We postulate that appropriate pre-processing of the output signals of chemical sensors substantially contributes to the stability of odor recognition, in spite of marked sensor instabilities. To investigate this hypothesis, we use an adaptive, unsupervised neural network inspired by the glomerular input circuitry of the olfactory bulb. Essentially the model reduces the effect of the sensors’ instabilities by utilizing them via an adaptive multicompartment feed-forward inhibition. We collected and analyzed responses of a 4 × 4 gas sensor array to a number of volatile compounds applied over a period of 18 months, whereby every sensor was sampled episodically. The network conferred excellent stability to the compounds’ identification and was clearly superior over standard classifiers, even when one of the sensors exhibited random fluctuations or stopped working at all.
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Affiliation(s)
- Eugenio Martinelli
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome 00133, Italy
| | - Gabriele Magna
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome 00133, Italy
| | - Davide Polese
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome 00133, Italy
| | - Alexander Vergara
- BioCircuits Institute, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093-0402, USA
| | - Detlev Schild
- 1] Inst. of Neurophysiology and Cellular Biophysics, University of Göttingen, Humboldtallee 23, 37077 Göttingen, Germany [2] DFG Excellence Cluster 171 and Bernstein Forum of Neurotechnology, Univ. Göttingen
| | - Corrado Di Natale
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome 00133, Italy
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Wang LC, Su TH, Ho CL, Yang SR, Chiu SW, Kuo HW, Tang KT. A bio-inspired two-layer sensing structure of polypeptide and multiple-walled carbon nanotube to sense small molecular gases. SENSORS 2015; 15:5390-401. [PMID: 25751078 PMCID: PMC4435137 DOI: 10.3390/s150305390] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2014] [Revised: 02/10/2015] [Accepted: 02/15/2015] [Indexed: 12/01/2022]
Abstract
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.
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Affiliation(s)
- Li-Chun Wang
- Analytical Chemistry Section, Chung-Shan Institute of Science & Technology, Hsinchu 30325, Taiwan.
| | - Tseng-Hsiung Su
- Analytical Chemistry Section, Chung-Shan Institute of Science & Technology, Hsinchu 30325, Taiwan.
| | - Cheng-Long Ho
- Analytical Chemistry Section, Chung-Shan Institute of Science & Technology, Hsinchu 30325, Taiwan.
| | - Shang-Ren Yang
- Analytical Chemistry Section, Chung-Shan Institute of Science & Technology, Hsinchu 30325, Taiwan.
| | - Shih-Wen Chiu
- Department of Electrical Engineering, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu 30013, Taiwan.
| | - Han-Wen Kuo
- Department of Chemistry, National Central University, Taoyuan 32001, Taiwan.
| | - Kea-Tiong Tang
- Department of Electrical Engineering, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Road, Hsinchu 30013, Taiwan.
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Chiu SW, Wang JH, Chang KH, Chang TH, Wang CM, Chang CL, Tang CT, Chen CF, Shih CH, Kuo HW, Wang LC, Chen H, Hsieh CC, Chang MF, Liu YW, Chen TJ, Yang CH, Chiueh H, Shyu JM, Tang KT. A fully integrated nose-on-a-chip for rapid diagnosis of ventilator-associated pneumonia. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2014; 8:765-778. [PMID: 25576573 DOI: 10.1109/tbcas.2014.2377754] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
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.
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Authentication and Discrimination of Whiskies of High Commercial Value by Pattern Recognition. FOOD ANAL METHOD 2014. [DOI: 10.1007/s12161-014-9958-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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11
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Electronic nose and electronic mucosa as innovative instruments for real-time monitoring of food dryers. Trends Food Sci Technol 2014. [DOI: 10.1016/j.tifs.2014.05.004] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Chiu SW, Wu HC, Chou TI, Chen H, Tang KT. A miniature electronic nose system based on an MWNT-polymer microsensor array and a low-power signal-processing chip. Anal Bioanal Chem 2014; 406:3985-94. [PMID: 24385138 DOI: 10.1007/s00216-013-7547-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2013] [Revised: 11/08/2013] [Accepted: 12/02/2013] [Indexed: 11/29/2022]
Abstract
This article introduces a power-efficient, miniature electronic nose (e-nose) system. The e-nose system primarily comprises two self-developed chips, a multiple-walled carbon nanotube (MWNT)-polymer based microsensor array, and a low-power signal-processing chip. The microsensor array was fabricated on a silicon wafer by using standard photolithography technology. The microsensor array comprised eight interdigitated electrodes surrounded by SU-8 "walls," which restrained the material-solvent liquid in a defined area of 650 × 760 μm(2). To achieve a reliable sensor-manufacturing process, we used a two-layer deposition method, coating the MWNTs and polymer film as the first and second layers, respectively. The low-power signal-processing chip included array data acquisition circuits and a signal-processing core. The MWNT-polymer microsensor array can directly connect with array data acquisition circuits, which comprise sensor interface circuitry and an analog-to-digital converter; the signal-processing core consists of memory and a microprocessor. The core executes the program, classifying the odor data received from the array data acquisition circuits. The low-power signal-processing chip was designed and fabricated using the Taiwan Semiconductor Manufacturing Company 0.18-μm 1P6M standard complementary metal oxide semiconductor process. The chip consumes only 1.05 mW of power at supply voltages of 1 and 1.8 V for the array data acquisition circuits and the signal-processing core, respectively. The miniature e-nose system, which used a microsensor array, a low-power signal-processing chip, and an embedded k-nearest-neighbor-based pattern recognition algorithm, was developed as a prototype that successfully recognized the complex odors of tincture, sorghum wine, sake, whisky, and vodka.
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Affiliation(s)
- Shih-Wen Chiu
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan
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Chiu SW, Tang KT. Towards a chemiresistive sensor-integrated electronic nose: a review. SENSORS (BASEL, SWITZERLAND) 2013; 13:14214-47. [PMID: 24152879 PMCID: PMC3859118 DOI: 10.3390/s131014214] [Citation(s) in RCA: 141] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Revised: 09/28/2013] [Accepted: 10/09/2013] [Indexed: 01/17/2023]
Abstract
Electronic noses have potential applications in daily life, but are restricted by their bulky size and high price. This review focuses on the use of chemiresistive gas sensors, metal-oxide semiconductor gas sensors and conductive polymer gas sensors in an electronic nose for system integration to reduce size and cost. The review covers the system design considerations and the complementary metal-oxide-semiconductor integrated technology for a chemiresistive gas sensor electronic nose, including the integrated sensor array, its readout interface, and pattern recognition hardware. In addition, the state-of-the-art technology integrated in the electronic nose is also presented, such as the sensing front-end chip, electronic nose signal processing chip, and the electronic nose system-on-chip.
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Affiliation(s)
- Shih-Wen Chiu
- Department of Electrical Engineering, National Tsing Hua University/No. 101, Sec. 2, Kuang-Fu Road, Hsinchu 30013, Taiwan; E-Mail:
| | - Kea-Tiong Tang
- Department of Electrical Engineering, National Tsing Hua University/No. 101, Sec. 2, Kuang-Fu Road, Hsinchu 30013, Taiwan; E-Mail:
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