This work is the first demonstration of a monolithic multiband RF front-end module (RF-FEM), integrating MEMS Lamb-wave filters and switches on 200mm RF silicon-on-insulator (RFSOI) foundry technology. Multiple MEMS filters with photolithography-defined frequencies coexist with RF components in the same wafer. This technology enables vertical integration of RF-FEM components for more compact System-on-Chip (SoC) architectures. The resulting RF-FEMs will then integrate in the same process multi-frequency filter banks, low noise amplifiers (LNAs), and switches, with a footprint reduction up to 50% compared to system-in-package (SiP) modules. The SoC architecture also simplifies the design of interconnection lines and impedance matching networks.
Currently, the generation of alternative energy from solar radiation with photovoltaic systems is growing, its efficiency depends on internal variables such as powers, voltages, currents; as well as external variables such as temperatures, irradiance, and load. To maximize performance, this research focused on the application of regularization techniques in a multiparametric linear regression model to predict the active power levels of a photovoltaic system from 14 variables that model the system under study. These variables affect the prediction to some degree, but some of them do not have so much preponderance in the final forecast, so it is convenient to eliminate them so that the processing cost and time are reduced. For this, we propose a hybrid selection method: first we apply the elimination of Recursive Feature Elimination (RFE) within the selection of subsets and then to the obtained results we apply the following contraction regularization methods: Lasso, Ridge and Bayesian Ridge; then the results were validated demonstrating linearity, normality of the error terms, without autocorrelation and homoscedasticity. All four prediction models had an accuracy greater than 99.97%. Training time was reduced by 71% and 36% for RFE-Ridge and RFE-OLS respectively. The variables eliminated with RFE were Total Energy, Daily Energy, and Irradiance, while the variable eliminated by Lasso was: "Frequency". In all cases we see that the root mean square errors were reduced for RFE. Lasso by 0.15% while for RFE-Bayesian Ridge by 0.06%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.