Thermo-optical characterization of fluorescent rhodamine B based temperature-sensitive nanosensors using a CMOS MEMS micro-hotplate

A custom designed microelectromechanical systems (MEMS) micro-hotplate, capable of operating at high temperatures (up to 700 C), was used to thermo-optically characterize fluorescent temperature-sensitive nanosensors. The nanosensors, 550 nm in diameter, are composed of temperature-sensitive rhodamine B (RhB) fluorophore which was conjugated to an inert silica sol-gel matrix. Temperature-sensitive nanosensors were dispersed and dried across the surface of the MEMS micro-hotplate, which was mounted in the slide holder of a fluorescence confocal microscope. Through electrical control of the MEMS micro-hotplate, temperature induced changes in fluorescence intensity of the nanosensors was measured over a wide temperature range. The fluorescence response of all nanosensors dispersed across the surface of the MEMS device was found to decrease in an exponential manner by 94%, when the temperature was increased from 25 C to 145 C. The fluorescence response of all dispersed nanosensors across the whole surface of the MEMS device and individual nanosensors, using line profile analysis, were not statistically different (p < 0.05). The MEMS device used for this study could prove to be a reliable, low cost, low power and high temperature micro-hotplate for the thermo-optical characterisation of sub-micron sized particles. The temperature-sensitive nanosensors could find potential application in the measurement of temperature in biological and micro-electrical systems. The Authors. © 2013 Published by Elsevier B.V. All rights reserved.

Fourier analysis and gabor filtering for texture analysis and local reconstruction of general shapes

Since the pioneering work of Gibson in 1950, Shape- From-Texture has been considered by researchers as a hard problem, mainly due to restrictive assumptions which often limit its applicability. We assume a very general stochastic homogeneity and perspective camera model, for both deterministic and stochastic textures. A multi-scale distortion is efficiently estimated with a previously presented method based on Fourier analysis and Gabor filters. The novel 3D reconstruction method that we propose applies to general shapes, and includes non-developable and extensive surfaces. Our algorithm is accurate, robust and compares favorably to the present state of the art of Shape-From- Texture. Results show its application to non-invasively study shape changes with laid-on textures, while rendering and retexturing of cloth is suggested for future work. © 2009 IEEE.