Convolution Gaussian filtering is a technique that can be used for a wide array of image processing tasks, some of which may include smoothing, edge detection, line parameter estimation and texture analysis. The greatest advantage of the Gaussian filters is that they are separable. Our main goal in this paper is to develop an efficient architecture for Separable Gaussian smoothing filters using intermediate controller based on Finite State Machine simulated in VHDL and prototyped on device technology of XILINX VirtexV FPGA platform. The proposed separable filtering approach is significantly faster and occupied few resources in terms of number of clock cycles and number of multipliers compared to the general two-dimensional convolution implementation. This difference is very important for larger mask sizes because hardware complexity is widely reduced.

Unidimensional photonic crystal-based biosensors have gained much attention in the area of blood glucose measurement. In this paper, we propose two novel designs based on two-dimensional (2D) Van der Waals materials. The first 1D photonic crystal design consists of multilayers of 2D gallium sulfide and 2D muscovite mica [GaS/Mica]^ND[GaS/Mica]^N, and the second design consists of multilayers of 2D gallium sulfide [GaS/G]^ND[GaS/G]^N. We conducted a numerical analysis using the transfer matrix method to investigate the properties of photonic crystals, both with and without defect layers, in order to assess their suitability for biosensing applications. The biosensors’ performances were investigated as a function of glucose concentration, revealing a high sensitivity of 832 nm/RIU, a notable figure-of-merit of 1.46 × 10⁵ RIU⁻¹, a Q-factor exceeding 10⁵, and a minimum limit of detection of 3.4 × 10⁻⁷ RIU. Finally, we modified the [GaS/G]^ND[GaS/G]^Nstructure in order to enhance the sensitivity nearly 5-fold. The proposed biosensors offer the advantage of being label-free, making them promising platforms for the sensitive and reliable detection of blood glucose levels.

In this paper, we propose a computer aided diagnosis system for brain MRI. This system is based on region-growing segmentation and KNN classification. The co-occurrence matrix and the discrete wavelet decomposition methods are used to extract the parameters of suspect region. At the last part, we applied our system to other subject to estimate its accuracy.

This paper is motivated by present a new segmentation method that integrates a novel feature, which is able to enhance the dissimilarity between regions. This feature is integrated to formulate a new level set based active contour model, which addresses the segmentation of regions with highly similar intensities, which do not have clear boundaries between them. The power of wavelet transform is adapted to formulate the new feature, named as wavelet energy. In this formulation, the two terms that guide the contour are the wavelet energy incorporated region term and the contour smoothness term. With this formulation, the equations for evolving the contour are derived and implemented in MATLAB. This segmentation method is named Wavelet energy Embedded into a level set method. The experimental results show that the proposed method is able to segment the region of interest that have high similarity in intensities with their background.

In recent years, unidimensional photonic crystal-based biosensors have attracted significant attention due to their potential for label-free bio-detection of cells, proteins, and other organic molecules. These biosensors are based on alternating materials with different refractive indices and a cavity region in which biomolecules can be injected. In this study, we investigated numerically the optical properties of unidimensional photonic crystals based on [LiTaO₃/MgF₂], [LiTaO₃/SiO₂], [PbTiO₃/MgF₂], and [PbTiO₃/SiO₂] heterostructures, and focused our discussion on the detection of four kinds of extracellular vesicles. Our results demonstrated that the [PbTiO₃/MgF₂] photonic crystal biosensor exhibited the best biosensing performance, with a maximum value of the sensitivity of 511.3 nm/RIU. This study provides valuable insights into the use of perovskites materials, in particular, LiTaO₃ and PbTiO₃ for photonic crystal-based biosensors for various applications, including disease diagnosis and monitoring therapy responses.

This paper presents the implementation of curve on FPGA circuit (Field Programmable Gate Array) Spartan 3 of Xilinx, it based on Taylor approximation by segment, used third order polynomial in order to reduce the maximum error. First of all, we have stocked all coefficients, computing with Mat lab tool, in the block memory. After that, an architecture is proposed and implemented under ISE9.2i environment (Integrated Software Environment) of Xilinx for a description, and Mentor Graphics Model Sim XEIII 6.3c for a simulation.