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A highly sensitive Au-graphene structure D-type fiber surface plasmon resonance biosensor is presented in this study to specifically detect biomolecules. The method of growing graphene is employed directly on the copper, and then a gold film of optimum thickness is sputtered, and the copper foil is etched to obtain the structure. This method makes the contact closer between the gold layer and the graphene layer to improve surface plasmon resonance performance. The performance of this type of surface plasmon resonance (SPR) sensor has been previously verified both theoretically and experimentally. With the proposed Au-graphene structure D-type fiber biosensor, the SPR behaviors are obtained and discussed. In the detection of ethanol solution, a red shift of 40 nm is found between the refractive index of 1.3330 and 1.3657. By calculation, the sensitivity of the sensor we designed is 1223 nm/RIU. Besides, the proposed sensor can detect the nucleotide bonding between the double-stranded DNA helix structures. Thus, our sensors can distinguish between mismatched DNA sequences. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) image segmentation is an essential step in geographic object-based image analysis (GEOBIA) to ultimately derive “meaningful objects”. While many segmentation methods exist, most of them are not efficient for large data sets. Thus, the goal of this research is to develop an efficient parallel multi-scale segmentation method for RS imagery by combining graph theory and the fractal net evolution approach (FNEA). Specifically, a minimum spanning tree (MST) algorithm in graph theory is proposed to be combined with a minimum heterogeneity rule (MHR) algorithm that is used in FNEA. The MST algorithm is used for the initial segmentation while the MHR algorithm is used for object merging. An efficient implementation of the segmentation strategy is presented using data partition and the “reverse searching-forward processing” chain based on message passing interface (MPI) parallel technology. Segmentation results of the proposed method using images from multiple sensors (airborne, SPECIM AISA EAGLE II, WorldView-2, RADARSAT-2) and different selected landscapes (residential/industrial, residential/agriculture) covering four test sites indicated its efficiency in accuracy and speed. We conclude that the proposed method is applicable and efficient for the segmentation of a variety of RS imagery (airborne optical, satellite optical, SAR, high-spectral), while the accuracy is comparable with that of the FNEA method. [ABSTRACT FROM AUTHOR]