The satellite-based regression model provides the data model that identifies water quality for inland and coastal waters. However, the satellite regression usually depends on the selection of observation, satellite data, and model type. A resampling simulation technique, such as sequential simulation using geographically weighted regression (GWR simulation), can be applied in generating multiple realizations for water quality estimation to reduce the sampling effect and consider spatial heterogeneity. Traditional models often result in considerable underestimation in extreme observations. The GWR simulation provides the best goodness of fit and spatial varying relationship between observed water quality and remote sensing considering parameter outlier and noise removal for parameter stability. This simulation model can increase the sampling diversity from various observations and reduce the neighboring effects of observations using outlier and noise removal. The model that handles spatial uncertainty and heterogeneity is a novel tool for inferring the characteristics of water quality from a series of sample subsets.
为了增加单位增益频率与压摆率,并能够工作在低电源电压下,同时降低偏置电流,提出了一种改进的基于0.18μm CMOS工艺的AB类放大器,其采用多级放大器结构,第一级为具有电流镜负载的NMOS差分对,第二反相级由共源放大器实现,第三极为AB类放大器,其能够在±500 m V电源下工作.电路仿真结果显示该放大器相位裕度为87°;总补偿电容为5 p F,与传统放大器相比减少了50%;单位增益频率为21.17 MHz,比传统放大器增大约10倍;压摆率为7.5和8.57 V/μs,与传统电路相比,分别增加了2.8倍和2.6倍.此外,与其他文献相比,该放大器具有较大的单位增益带宽和压摆率以及较小的功耗. 相似文献