A 1-V 2.6-mW Environmental Compensated Fully Integrated Nose-on-a-Chip

Chou, Ting-I; Chang, Kwuang-Han; Jhang, Jia-Yin; Chiu, Shih-Wen; Wang, Guoxing; Yang, Chia-Hsiang; Chiueh, Herming; Chen, Hsin; Hsieh, Chih-Cheng; Chang, Meng‐Fan; Tang, Kea-Tiong

doi:10.1109/tcsii.2018.2854588

Cited by 7 publications

(9 citation statements)

References 10 publications

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“…Tang, K.-T et al presented a following-up study [30] of an E-nose SoC with on-chip learning in 2018. Compared with their previous work with 8 sensors [32], this chip integrated 36 on-chip sensors including temperature and humidity sensors acquiring environmental information.…”

Section: B Hardware Implementationmentioning

confidence: 99%

Section: B Hardware Implementationmentioning

confidence: 99%

“…According to the analysis above, we compare the performance of E-nose using classical gas recognition circuits as shown in Table I, from which we find that recent studies focused on the integration of multiple on-chip sensors [30]- [33], [82]. Additionally, the more complex gas recognition algorithms are adopted, the higher accuracy is achieved [30], [32], [82]. Moreover, environment compensation circuits are important for interface readout circuits [26], [28] and gas recognition circuits [30], which help to alleviate the influence of temperature and humidity on the chip results to a great extent.…”

Section: B Hardware Implementationmentioning

confidence: 99%

“…The interface readout circuits convert sensor signals into electrical signals [26]. Generally, the sensor array with MEMS process and the readout interface circuit with complementary-metal-oxide semiconductor (CMOS) process can be integrated into an E-nose chip [27], which is discussed in [28]- [30]. After processing signals transmitted by interface readout circuits, the gas recognition circuits output final recognition results with various gas recognition algorithms.…”

mentioning

confidence: 99%

“…Each classical gas recognition method has a relatively fixed framework and a few parameters, which is easy-design and widely used, but it is difficult for them to recognize multiple odors accurately under noise. Moreover, they are often implemented by general microprocessors (such as MPU [30]- [33]), which consume too much power to be suitable for a portable E-nose system.…”

mentioning

confidence: 99%

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Gas Recognition in E-Nose System: A Review

Chen

Huo

Zhang

2022

IEEE Trans. Biomed. Circuits Syst.

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Gas recognition is essential in an electronic nose (Enose) system, which is responsible for recognizing multivariate responses obtained by gas sensors in various applications. Over the past decades, classical gas recognition approaches such as principal component analysis (PCA) have been widely applied in E-nose systems. In recent years, artificial neural network (ANN) has revolutionized the field of E-nose, especially spiking neural network (SNN). In this paper, we investigate recent gas recognition methods for E-nose, and compare and analyze them in terms of algorithms and hardware implementations. We find each classical gas recognition method has a relatively fixed framework and a few parameters, which makes it easy to be designed and perform well with limited gas samples, but weak in multi-gas recognition under noise. While ANN-based methods obtain better recognition accuracy with flexible architectures and lots of parameters. However, some ANNs are too complex to be implemented in portable E-nose systems, such as deep convolutional neural networks (CNNs). In contrast, SNN-based gas recognition methods achieve satisfying accuracy and recognize more types of gases, and could be implemented with energy-efficient hardware, which makes them a promising candidate in multi-gas identification.

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Section: B Hardware Implementationmentioning

confidence: 99%