Jacobi–Fourier phase mask for wavefront coding

In this work we propose Jacobi–Fourier phase masks for wavefront coding-based imaging systems. The optical properties of the phase mask is study in detail and numerical simulation are shown. Pixel size and noise are taken into account for the deconvolution of images. Numerical simulations indicate that overall performance is better than of the well-known and commonly used trefoil phaseThis work was supported by the Spanish Ministry of Economía y Competitividad FIS2016-77319-C2-1-R, and FEDER, Xunta de Galicia/FEDER ED431E 2018/08. E. González Amador thanks to Consejo Nacional de Ciencia y Tecnología (CONACyT); with CVU no. 714742. Also, we thank by the support to PADES program; Award no. 2018-13-011-047S

Optical Fiber Sensor Based on Localized Surface Plasmon Resonance Using Silver Nanoparticles Photodeposited on the Optical Fiber End

This paper reports the implementation of an optical fiber sensor to measure the refractive index in aqueous media based on localized surface plasmon resonance (LSPR). We have used a novel technique known as photodeposition to immobilize silver nanoparticles on the optical fiber end. This technique has a simple instrumentation, involves laser light via an optical fiber and silver nanoparticles suspended in an aqueous medium. The optical sensor was assembled using a tungsten lamp as white light, a spectrometer, and an optical fiber with silver nanoparticles. The response of this sensor is such that the LSPR peak wavelength is linearly shifted to longer wavelengths as the refractive index is increased, showing a sensitivity of 67.6 nm/RIU. Experimental results are presented

Highly Discriminative Physiological Parameters for Thermal Pattern Classification

Infrared Thermography (IRT) is a non-contact, non-intrusive, and non-ionizing radiation tool used for detecting breast lesions. This paper analyzes the surface temperature distribution (STD) on an optimal Region of Interest (RoI) for extraction of suitable internal heat source parameters. The physiological parameters are estimated through the inverse solution of the bio-heat equation and the STD of suspicious areas related to the hottest spots of the RoI. To reach these values, the STD is analyzed by means: the Depth-Intensity-Radius (D-I-R) measurement model and the fitting method of Lorentz curve. A highly discriminative pattern vector composed of the extracted physiological parameters is proposed to classify normal and abnormal breast thermograms. A well-defined RoI is delimited at a radial distance, determined by the Support Vector Machines (SVM). Nevertheless, this distance is less than or equal to 1.8 cm due to the maximum temperature location close to the boundary image. The methodology is applied to 87 breast thermograms that belong to the Database for Mastology Research with Infrared Image (DMR-IR). This methodology does not apply any image enhancements or normalization of input data. At an optimal position, the three-dimensional scattergrams show a correct separation between normal and abnormal thermograms. In other cases, the feature vectors are highly correlated. According to our experimental results, the proposed pattern vector extracted at optimal position a=1.6 cm reaches the highest sensitivity, specificity, and accuracy. Even more, the proposed technique utilizes a reduced number of physiological parameters to obtain a Correct Rate Classification (CRC) of 100%. The precision assessment confirms the performance superiority of the proposed method compared with other techniques for the breast thermogram classification of the DMR-IR

Non-Binary Snow Index for Multi-Component Surfaces

A Non-Binary Snow Index for Multi-Component Surfaces (NBSI-MS) is proposed to map snow/ice cover. The NBSI-MS is based on the spectral characteristics of different Land Cover Types (LCTs) such as snow, water, vegetation, bare land, impervious, and shadow surfaces. This index can increase the separability between NBSI-MS values corresponding to snow from other LCTs and accurately delineate the snow/ice cover in non-binary maps. To test the robustness of the NBSI-MS, Greenland and France-Italy regions were examined where snow interacts with highly diversified geographical ecosystem. Data recorded by Landsat 5 TM, Landsat 8 OLI, and Sentinel-2A MSI satellites have been used. The NBSI-MS performance was also compared against the well-known NDSI, NDSII-1, S3, and SWI methods and evaluated based on Ground Reference Test Pixels (GRTPs) over non-binarized results. The results show that the NBSI-MS achieves overall accuracy (OA) ranging from 0.99 to 1 with kappa coefficient values in the same range as OA. The precision assessment confirms the performance superiority of the proposed NBSI-MS method for removing water and shadow surfaces over the compared relevant indices.Comment: 22 pages, 12 figure