RESUMEN
Microelectronic gas-sensor devices were developed for the detection of carbon monoxide (CO), nitrogen dioxides (NO2), ammonia (NH3) and formaldehyde (HCHO), and their gas-sensing characteristics in six different binary gas systems were examined using pattern-recognition methods. Four nanosized gas-sensing materials for these target gases, i.e., Pd-SnO2 for CO, In2O3 for NOX, Ru-WO3 for NH3, and SnO2-ZnO for HCHO, were synthesized using a sol-gel method, and sensor devices were fabricated using a microsensor platform. Principal component analysis of the experimental data from the microelectromechanical systems gas-sensor arrays under exposure to single gases and their mixtures indicated that identification of each individual gas in the mixture was successful. Additionally, the gas-sensing behavior toward the mixed gas indicated that the traditional adsorption and desorption mechanism of the n-type metal oxide semiconductor (MOS) governs the sensing mechanism of the mixed gas systems.
RESUMEN
In practice, a high-dynamic vibration sensor is often plagued by the problem of drift, which is caused by thermal effects. Conversely, low-drift sensors typically have a limited sample rate range. This paper presents a system combining different types of sensors to address general drift problems that occur in measurements for high-dynamic vibration signals. In this paper, the hardware structure and algorithms for fusing high-dynamic and low-drift sensors are described. The algorithms include a drift state estimation and a Kalman filter based on a linear prediction model. Key issues such as the dimension of the drift state vector, the order of the linear prediction model, are analyzed in the design of algorithm. The performance of the algorithm is illustrated by a simulation example and experiments. The simulation and experimental results show that the drift can be removed while the high-dynamic measuring ability is retained. A high-dynamic vibration measuring system with the frequency range starting from 0 Hz is achieved. Meanwhile, measurement noise was improved 9.3 dB through using the linear-prediction-based Kalman filter.