Effect of yttrium doping on structural and electrical properties of Bi2Sr1.9Ca0.1−xYxCu2O7+δ (Bi-2202) cuprate ceramics

In this work, we report on the effect of Y3+ doping on structural, mechanical and electrical properties of Bi-2202 phase. Samples of Bi2Sr1.9Ca0.1−xYxCu2O7+δ with x = 0, 0.025, 0.05, 0.075 and 0.10 are elaborated in air by conventional solid state reaction and characterized by X-ray diffraction (XRD), scanning electronic microscopy (SEM) combined with EDS spectroscopy, density, Vickers microhardness and resistivity measurements. A good correlation between the variations of the bulk density and the Vickers microhardness with doping is obtained. The SEM photograph shows that the samples are composed of grains with a flat shape that characterizes the Bi-based cuprates. Quantitative EDS analysis confirms the reduction of Ca content and the increase of Y content when x is increased. The variation of resistivity with temperature shows that only samples with x = 0, 0.025 and 0.05 present an onset transition to the superconducting state. The higher onset transition temperature is obtained for x = 0.025 and is about 93.62 K. The transition is wide and is realized in two steps confirming then the presence of the low Tc Bi-2201 phase in the samples. For x = 0.075 and 0.10, a transition to a semiconducting state is seen at low temperatures. Some physical parameters are extracted from these curves and discussed

Structural and Magneto-Electrical Properties of Bi2-xSmxSr2CaCu2O8 + δ High Tc Superconductor Prepared by Pechini Method

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Automatic adaptation of SIFT for robust facial recognition in uncontrolled lighting conditions

The scale invariant feature transform (SIFT), which was proposed by David Lowe, is a powerful method that extracts and describes local features called keypoints from images. These keypoints are invariant to scale, translation, and rotation, and partially invariant to image illumination variation. Despite their robustness against these variations, strong lighting variation is a difficult challenge for SIFT‐based facial recognition systems, where significant degradation of performance has been reported. To develop a robust system under these conditions, variation in lighting must be first eliminated. Additionally, SIFT parameter default values that remove unstable keypoints and inadequately matched keypoints are not well‐suited to images with illumination variation. SIFT keypoints can also be incorrectly matched when using the original SIFT matching method. To overcome this issue, the authors propose propose a method for removing the illumination variation in images and correctly setting SIFT's main parameter values (contrast threshold, curvature threshold, and match threshold) to enhance SIFT feature extraction and matching. The proposed method is based on an estimation of comparative image lighting quality, which is evaluated through an automatic estimation of gamma correction value. Through facial recognition experiments, the authors find significant results that clearly illustrate the importance of the proposed robust recognition system