Design of a 0.5 V 1.68mW nose-on-a-chip for rapid screen of chronic obstructive pulmonary disease

Chou, Ting-I; Chiu, Shih-Wen; Chang, Kwuang-Han; Chen, Yi–Ju; Tang, Chen-Ting; Shih, Chwen Ming; Hsieh, Chih-Cheng; Chang, Meng‐Fan; Yang, Chia-Hsiang; Chiueh, Herming; Tang, Kea-Tiong

doi:10.1109/biocas.2016.7833864

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…Albeit every traditional gas acknowledgment technique has a moderately fixed system and few boundaries, simplifying it to plan and broadly utilized, it is trying for them to distinguish numerous scents within the sight of clamor precisely. In addition, they are frequently carried out by large microchips (such as MPU [30]- [33]), which require a significant amount of power to function appropriately in a compact E-nose framework.…”

Section: Related Workmentioning

confidence: 99%

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Advancements in Gas Recognition Techniques for Electronic Nose Systems: A Comparative Review of Classical Methods and Spiking Neural Networks

Pandey¹

2023

IJSREM

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An electronic nose (E-nose) system's ability to recognize multivariate responses from gas sensors in a variety of applications necessitates gas recognition. Principal component analysis (PCA) and other traditional gas recognition methods have been widely used in E-nose systems for decades. ANNs have transformed the field of E- nose, particularly spiking neural networks (SNNs), significantly in recent years. In this paper, we compare and contrast recent E-nose gas recognition techniques in terms of algorithms and hardware implementations. Each classical gas recognition method has a relatively fixed framework and few parameters, making it easy to design. It works well with few gas samples but poorly with multiple gas recognition when noise is present. Keywords: Gas detection, electronic nose, artificial neural network, and spiking neural network.

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Section: Related Workmentioning

confidence: 99%

“…In addition, the E-nose chip's onchip learning function and standard interface protocol make it easy to connect additional devices. In 2016 [33], the developer released another of his E-Nose SoCs with 92° curacy to continue screening for obstructive pulmonary disease. It used a structure similar to [32].…”

Section: Related Workmentioning

confidence: 99%

Advancements in Gas Recognition Techniques for Electronic Nose Systems: A Comparative Review of Classical Methods and Spiking Neural Networks

Pandey¹

2023

IJSREM

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“…Moreover, the E-nose chip supported on-chip learning and common interface protocols for connections of other devices conveniently. With the similar framework in [32], the authors presented another E-nose SoC consuming 1.68 mW at 0.5V for chronic obstructive pulmonary disease screening with 92% accuracy in 2016 [33].…”

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].…”

Section: B Hardware Implementationmentioning

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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An Energy-Efficient and Reconfigurable CNN Accelerator Applied To Lung Cancer Detection

Liao

Chen

Tang

et al. 2023

2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)

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Design of a 0.5 V 1.68mW nose-on-a-chip for rapid screen of chronic obstructive pulmonary disease

Cited by 4 publications

References 5 publications

Advancements in Gas Recognition Techniques for Electronic Nose Systems: A Comparative Review of Classical Methods and Spiking Neural Networks

Advancements in Gas Recognition Techniques for Electronic Nose Systems: A Comparative Review of Classical Methods and Spiking Neural Networks

Gas Recognition in E-Nose System: A Review

An Energy-Efficient and Reconfigurable CNN Accelerator Applied To Lung Cancer Detection

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