Correlation between Sensing Accuracy and Read Margin of a Memristor-Based NO Gas Sensor Array Estimated by Neural Network Analysis

From materials to applications: a review of research on artificial olfactory memory

Olfactory memory forms the basis for biological perception and environmental adaptation. Advancing artificial intelligence to replicate this biological perception as artificial olfactory memory is essential. The widespread use of various robotic systems, intelligent wearable devices, and artificial olfactory memories modeled after biological olfactory memory is anticipated. This review paper highlights current developments in the design and application of artificial olfactory memory, using examples from materials science, gas sensing, and storage systems. These innovations in gas sensing and neuromorphic technology represent the cutting edge of the field. They provide a robust scientific foundation for the study of intelligent bionic devices and the development of hardware architectures for artificial intelligence. Artificial olfaction will pave the way for future advancements in intelligent recognition by progressively enhancing the level of integration, understanding of mechanisms, and application techniques of machine learning algorithms.

Detection of Breath Nitric Oxide at Ppb Level Based on Multiperiodic Spectral Reconstruction Neural Network

As breath nitric oxide (NO) is a biomarker of respiratory inflammation, reliable techniques for the online detection of ppb-level NO in exhaled breath are essential for the noninvasive diagnosis of respiratory inflammation. Here, we report a breath NO sensor based on the multiperiodic spectral reconstruction neural network. First, a spectral reconstruction method that transforms a spectrum from the wavelength domain to the intensity domain is proposed to remove noise and interference signals from the spectrum. Different from the traditional spectral processing method based on the wavelength domain, the method enhances the absorption characteristics of a target gas in the intensity domain, while discretizing noise and interference signals. This facilitates the extraction of the target gas spectrum. Then, a neural network is built to detect the concentration of breath NO. Laboratory-based results show that the sensor enables online detection of NO (1.63-846.68 ppb) with mean absolute error (MAE), mean absolute percentage error (MAPE), and detection accuracy of 0.31 ppb, 0.96% and 0.63%, respectively. Furthermore, an actual exhalation experiment proved that the sensor is capable of distinguishing breath NO of healthy people from that of simulated patients, which provides a reliable way to realize exhaled breath detection based on optical methods in the medical field.

A Novel Calibration Scheme of Gas Sensor Array for a More Accurate Measurement Model of Mixed Gases

Gas sensor arrays (GSAs) usually encounter challenges due to the cross-contamination of mixed gases, leading to reduced accuracy in measuring gas mixtures. However, with the advent of artificial intelligence, there is a promising avenue for addressing this issue effectively. In pursuit of more accurate mixed gas measurements, we proposed a measurement model leveraging neural networks. Our approach involved employing the encoder of an autoencoder network (AEN) to extract features from experimental data, while fully connected layers were utilized for predicting concentrations of mixed gases. To refine the neural network parameters, we employed a variational autoencoder to generate additional data resembling the distribution of experimental data. Subsequently, we designed a domain difference maximum entropy technique to identify optimal concentration points for the calibration data. These calibration points were instrumental in training the fully connected layers, enhancing the model's accuracy. During practical usage, with the AEN configuration fixed, the model can be fine-tuned by using a small subset of test points across large-scale GSA deployments. Simulation and practical measurement results demonstrated the efficacy of our proposed measurement model, boasting high accuracy, with confidence intervals for relative errors of the four gas measurements below 3% at the 95% confidence level. Besides, the calibration scheme reduced the number of test points compared with traditional methods, reducing the cost of labor and equipment.

Ultrasensitive Online NO Sensor Based on a Distributed Parallel Self-Regulating Neural Network and Ultraviolet Differential Optical Absorption Spectroscopy for Exhaled Breath Diagnosis

The concentration of fractional exhaled nitric oxide (FeNO) is closely related to human respiratory inflammation, and the detection of its concentration plays a key role in aiding diagnosing inflammatory airway diseases. In this paper, we report a gas sensor system based on a distributed parallel self-regulating neural network (DPSRNN) model combined with ultraviolet differential optical absorption spectroscopy for detecting ppb-level FeNO concentrations. The noise signals in the spectrum are eliminated by discrete wavelet transform. The DPSRNN model is then built based on the separated multipeak characteristic absorption structure of the UV absorption spectrum of NO. Furthermore, a distributed parallel network structure is built based on each absorption feature region, which is given self-regulating weights and finally trained by a unified model structure. The final self-regulating weights obtained by the model indicate that each absorption feature region contributes a different weight to the concentration prediction. Compared with the regular convolutional neural network model structure, the proposed model has better performance by considering the effect of separated characteristic absorptions in the spectrum on the concentration and breaking the habit of bringing the spectrum as a whole into the model training in previous related studies. Lab-based results show that the sensor system can stably achieve high-precision detection of NO (2.59-750.66 ppb) with a mean absolute error of 0.17 ppb and a measurement accuracy of 0.84%, which is the best result to date. More interestingly, the proposed sensor system is capable of achieving high-precision online detection of FeNO, as confirmed by the exhaled breath analysis.