A micro-accelerometer is accomplished by use of a bridge-type structure with a flexible fixed-end beam. A PtFe layer is attached on the beam center surface as a magnetic field source. Giant Magneto-Resistance (GMR) material is used to detect magnetic field variation at different heights arising from the motion of the beam. An inverse numerical scheme is proposed to inversely obtain beam fixed-end boundary conditions, which are also the displacement of the detected object. This scheme employs the Laplace transform and applies numerical techniques to its inversion. Numerical verifications show the proposed scheme is accurate, stable, and tolerates errors arising from measurement, calibration and manufacture. Performance of this sensor is analyzed and sensors of other dimensions satisfying the prescribed performance requirements are designed by the proposed inverse analysis scheme.

The deployment of an automatic network performance measurement system is crucial to the early detection and analysis of network quality degradations and failures. Once the overall network health status can be summarized and visualized in an easily accessible graphic user interface, the difficulty of network maintenance and troubleshooting can be significantly reduced. This study provided a detailed introduction to how the perfSONAR is implemented in TWAREN backbone and how the individual data are integrated and eventually visualized as a handy weathermap for network operators to use.