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Photonic crystal fiber is a category of optical fibers, getting great attention by its promise to offer a range of optical characteristics that are not achievable in conventional optical fibers. Engineered dispersion and nonlinear characteristics of photonic crystal fiber (PCF) make it an attractive candidate for nonlinear optics and advanced optical networking in the all-optical domain. An optical network consists of different optical components such as laser sources, amplifiers, regenerators, and convertors for proper signal transmission over long distances. In recent years, the performance of the components has been improving by employing the appealing properties of PCF. The PCF’s application on such components is discussed, and the simulated results on gain amplification, regeneration, conversion, fiber laser are reviewed. These developments reveal that the enhanced performance provided by PCF makes it suitable for different optics applications.

Surface plasmon resonance (SPR)-based single-core photonic crystal fiber (PCF) biosensor is investigated with external gold coating. All the geometrical parameters such as a gold layer, an analyte layer, a lattice period and cladding air holes are optimized to enhance the sensing ability of the sensor by introducing the finite element method. The designed sensor is able to achieve the highest amplitude sensitivity (AS) of 2258.95 RIU[Formula: see text] with an acceptable refractive index sensitivity (RIS) of 6000 nm/RIU over the analyte refractive index (ARI) span of 1.31–1.40. This sensor can detect a slight index alteration in the sensing medium using a resolution of [Formula: see text] and a high figure of merit (FOM) of 79.01. With the enhanced modal behavior with simple geometry, the resulting sensor can be suitable for real-time monitoring in biological, biochemical and bio-imaging applications.

Abstract-Now a day's we are facing a problem regarding smoothing of the image.One of the very useful method to smoothing the image is 2-D Gaussian Filter, which is used in Image Processing. However, the heavy computational resources are required by 2-D Gaussian Filter, and it comes down to real-time applications. The vital efficiency is achieved in this implementation. We use floating-point representation, but there are certain obstacle for this implementation because it requires large computational power in order to achieve real-time image processing. On the other hand a fixed-point approach is much more suitable for the implementation of a 2-D Gaussian filter in FPGA.By using fixed-point arithmetic for the implementation, the efficiency is increases, size of area decreases, complexity decreases and the computational cost is also reduced.In this paper we reduced the LUT by 11.34 % by using Gaussian filter.