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EN
In this work, we proposed a highly sensitive design of a plasmonic sensor which is formed by embedding a periodic array of nanoblocks in L-shaped cavity formed by the metal–insulator–metal waveguide. The nanoblocks are placed in the strong electric field confinement region to further enhance its strength by confining it to a small area. To validate the study, the spectral characteristics of the proposed sensor design is compared to the spectral characteristics of a standard design having the same geometric parameters excluding nanoblocks in the cavity. The study shows that the incorporation of 5 nanoblocks of length 25 nm in the cavity can provide best performance indicators in the form of sensitivity, figure of merit and Q-factor. The sensitivity, figure of merit and Q-factor of the proposed sensor design is 1065 nm/RIU, 251.17 and 343.4 which is significantly higher than the standard L-shape resonator design. The sensor design can be developed with a single fabrication step. Due to the ease of fabrication and the highly responsive nature of the design, it can be used in biomedical applications.
EN
In differential morphology, multiscale dilations and erosions are modeled via nonlinear partial differential equations (PDEs) in scale-space. Curve evolution employs methods of differential geometry to study the differential equations governing the propagation of time-evolving curves, under velocities dependent on global information or on local geometric properties of the curve. The PDEs governing multiscale morphology, and most cases of curve evolution, are of the Hamilton-Jacobi type and are related to the eikonal PDE of optics. In this paper, we explore the common theoretical concepts, tools, and numerical algorithms used in differential morphology and curve evolution, by emphasizing level set methods. Morphological operator representations of various curve evolution cases are discussed, as well as evolution laws for various morphological curve operations. We also focus on distance transforms, as the major route to connect differential morphology and curve evolution to the eikonal PDE. Furthermore, we discuss applications of differential morphology and curve evolution to various multiscale and/or eikonal problems, such as distance transform computation, ray tracing in optics, eikonal image halftoning, and watershed-based image segmentation.
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