Electrostatic actuated mems for precision AC voltage references
The purpose of this paper is to present results on the design of electrostatic actuated micro- electromechanical systems (MEMS) aiming at developing AC voltage references through mechanical-electrical transduction for electrical metrology and more generally for miniaturized instrumentation. Several test MEMS architectures (targeted AC voltages from 10 V up to 100 V) for the vertical motion have been designed and are under fabrication by an epitaxial silicon on insulator (SOI) surface micromachining process with the aim to investigate the potentialities of the different structures with respect to the geometrical dispersion, the strain sensitivity and the electrical stability.
Dielectric charging in radio frequency microelectromechanical system capacitive switches: A study of material properties and device performance
The paper analyze the direct relation between the dielectric polarization mechanisms and the radio frequency microelectromechanical system capacitive switches performance. The dielectric polarization mechanisms are investigated in metal-insulator-metal capacitors with the aid of the thermally stimulated depolarization current method. The performance of microelectromechanical system is studied through the temperature dependence of the bias for the minimum capacitance. The simultaneous assessment allows the discrimination of dipolar and space charge polarization that allows the prediction of the shift of capacitance minimum with temperature.
Multivariate Image Texture by Multivariate Variogram for Multispectral Image Classification
Traditional image texture measure usually allows a texture description of a single band of the spectrum, characterizing the spatial variability of gray-level values within the singleband image. A problem with the approach while applied to multispectral images is that it only uses the texture information from selected bands. In this paper, we propose a new multivariate texture measure based on the multivariate variogram. The multivariate texture is computed from all bands of a multispectral image, which characterizes the multivariate spatial autocorrelation among those bands. In order to evaluate the performance of the proposed texture measure, the derived multivariate texture image is combined with the spectral data in image classification. The result is compared to classifications using spectral data alone and plus traditional texture images. A machine learning classifier based on Support Vector Machines (SVMs) is used for image classification. The experimental results demonstrate that the inclusion of multivariate texture information in multispectral image classification significantly improves the overall accuracy, with 5 to 13.5 percent of improvement, compared to the classification with spectral information alone. The results also show that when incorporated in image classification as an additional band, the multivariate texture results in high overall accuracy, which is comparable with or higher than the best results from the existing single-band and two-band texture measures, such as the variogram, cross variogram and Gray-Level Co-occurrence Matrix (GLCM) based texture. Overall, the multivariate texture provides the useful spatial information for land-cover classification, which is different from the traditional single band texture. Moreover, it avoids the band selection procedure which is prerequisite to traditional texture computation and would help to achieve high accuracy in the most classification tasks.