A smart temperature sensor is performed based on the high birefringence (HB) fiber loop mirror. The sensing can be achieved by simply detecting the light intensity, utilizing a single mode laser diode (LD) as the source. In the range of 25°C-35°C, the resolution and linear regression are 0.05°C and 0.9986, respectively. The sensor can be employed to precise temperature measurement and the range can expand further when selecting shorter HB fiber in the loop mirror.
A new reconstruction algorithm of computer tomography (CT) from a few views based on a neural network of Gaussian Machine (GM) is presented. The problem of image reconstruction is formulated as optimization under the criterion of maximum entropy, and a GM is then constructed to solve the optimization problem using simulated annealing technique with hyperbolic temperature adjustment. We demonstrate both the Simultaneous Algebraic Reconstruction Technique (SART) reconstruction of this image and the GM reconstruction using the same measured input data. The effect of noise in the projection data, projection angles and sample intervals are addressed. The results of numerical simulation show that this technique using the projection data obtained from four views with the projection angles 45°apart has fairly high accuracy (the average relative error is 0.03%) and good stability against noise.
A new algorithm for the reconstruction of tomographic images from a few views is presented. A variable metric method is used to solve the unconstrained optimization problem that resulted from the analysis by the use of the maximum entropy formalism. The numerical simulations study the reconstruction effect on the different asymmetric functions and the stability against noise. The results show that the reconstruction accuracy is adequate.
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