This paper proposed an over sampling successive approximation (OSSA) technique to build switched-capacitor capacitance-to-voltage convertor (SC-CVC) for readout circuit of MEMS differential capacitive sensor. The readout circuit employing the OSSA technique has significantly improved resistance to common-mode parasitic capacitance of the input terminal of the readout circuit. In the OSSA readout circuit, there are 5 main nonideal characteristics: holding error, recovery degradation, increment degradation, rise-edge degradation and charge injection which reduce the accuracy and the settling time of the circuit. These problems are explained in detail and their solutions are given in the paper. The OSSA readout circuit is fabricated in a commercial 0.18um BCD process. To show the improvement evidently, a reported traditional readout circuit is also reproduced and fabricated using the same process. Compared with the traditional readout circuit, the proposed readout circuit reduces the affect of common-mode parasitic capacitance on the accuracy of SC-CVC by more than 23.8 dB, reduces power dissipation by 69.3%, and reduces die area by 50%.
Studies have shown that there is a certain correlation between air pollution and various human diseases, especially lung diseases, so it is very meaningful to monitor the concentration of pollutants in the air. Compared with the national air quality monitoring station (national control point), the micro air quality detector has the advantage that it can monitor the concentration of pollutants in real time and grid, but its measurement accuracy needs to be improved. This paper proposes a model combining the least absolute selection and shrinkage operator (LASSO) regression and nonlinear autoregressive models with exogenous inputs (NARX) to calibrate the data measured by the micro air quality detector. Before establishing the LASSO-NARX model, correlation analysis is used to test whether the correlation between the concentration of air pollutants and its influencing factors is significant, and to find out the main factors that affect the concentration of pollutants. Due to the multicollinearity between various influencing factors, LASSO regression is used to further screen the influencing factors and give the quantitative relationship between the pollutant concentration and various influencing factors. In order to improve the prediction accuracy of pollutant concentration, the predicted value of each pollutant concentration in the LASSO regression model and the measurement data of the micro air quality detector are used as input variables, and the LASSO-NARX model is constructed using the NARX neural network. Several indicators such as goodness of fit, root mean square error, mean absolute error and relative mean absolute percent error are used to compare various air quality models. The results show that the prediction results of the LASSO-NARX model are not only better than the LASSO model alone and the NARX model alone, but also better than the commonly used multilayer perceptron and radial basis function neural network. Using this model to calibrate the measurement data of the micro air quality detector can increase the accuracy by 61.3–91.7%.
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