This paper describes the use of a hybrid evolutionary optimization algorithm (HEOA) for computing the wavefront aberration from real interferometric data. By finding the near-optimal solution to an optimization problem, this algorithm calculates the Zernike polynomial expansion coefficients from a Fizeau interferogram, showing the validity for the reconstruction of the wavefront aberration. The proposed HEOA incorporates the advantages of both a multimember evolution strategy and locally weighted linear regression in order to minimize an objective function while avoiding premature convergence to a local minimum. The numerical results demonstrate that our HEOA is robust for analyzing real interferograms degraded by noise.
When strong Jaundice is presented, babies or adults should be subject to clinical exam like “serum bilirubin” which can cause traumas in patients. Often jaundice is presented in liver disease such as hepatitis or liver cancer. In order to avoid additional traumas we propose to detect jaundice (icterus) in newborns or adults by using a not pain method. By acquiring digital images in color, in palm, soles and forehead, we analyze RGB attributes and diffuse reflectance spectra as the parameter to characterize patients with either jaundice or not, and we correlate that parameters with the level of bilirubin. By applying support vector machine we distinguish between healthy and sick patients.
This article [Opt. Eng.. 51, , 073607 (2012)] was originally published on 17 July 2012 with errors on p. 2 and in Tables 2 and 4. On p. 2, the last sentence has been changed from "Therefore, taking the value of the distance point to point from z rta+ trans to z rta we get:" to "Therefore, taking the value of the distance point to point from z rot+ trans to z rta we get:"
.
Also, on the left side of Table 2, row 6, the value of A 3 should be negative. On the left side of Table 4, row 5, mm −7 should be mm −5 . In the right side of row 6, mm −9 should be mm −7 . The corrected tables appear below.
This paper describes a method based on bi-objective evolutionary algorithms to obtain the profile of a convex aspherical surface, which is defined by a set of synthetic points placed on an xyz coordinate system. The set of points to be analyzed is constructed considering the sources of measurement error in a coordinate measuring machine (CMM), such as machine, probe, and positioning errors. The proposed method is applied to solve a bi-objective optimization problem by minimizing two objective functions. By minimizing the first objective function the positioning error is removed from the coordinates of each affected point. Once the first goal is achieved, the second objective function is minimized to determine from the resulting data all parameters related to the test surface, such as paraxial radius of curvature, the conic constant and the deformation constants. Hence, this method can obtain the correct surface profile even when the positioning error tends to increase the CMM measurement error in the set of analyzed points. The bi-objective evolutionary algorithm (BEA) was tested against a single-objective evolutionary algorithm, and illustrative numerical examples demonstrate that the BEA performs better.
We present a new method for testing an optical surface. It uses the Ronchi test with variable-frequency rulings and a liquid-crystal display. The rulings can be formed by substructuring the spacing of a Ronchi ruling or combining several classical Ronchi rulings in a single variable-frequency ruling. This change allows us to observe smaller defects on the surface, because it enlarges the spatial-frequency domain of the ruling, and a larger dynamic range of detection of the Ronchi test can be obtained instead of increasing the resolution of the detection of the Ronchi test by iteratively changing classical Ronchi rulings with higher line density. As a result, we have found that it is possible to measure defects on a optical surface that are of size 57 nm (/11).
We show an optimization method based on an evolutive algorithm to obtain the profile of a simulated machined aspherical surface starting from a set of noisy discrete Cartesian coordinates (x,y,z), where the experimental coordinates x and y are used to simulate the sagitta z of the analyzed surface. By minimizing an objective function, the proposed method fits the sagitta function to the set of noisy discrete coordinates; thus the geometrical parameters of the simulated surface under test, such as the paraxial radius of curvature, the conic constant, and the aspheric deformation constants, can be obtained. Numerical results show that our method can be successfully applied to retrieve the simulated machined surface profile.
A method based on a hybrid genetic algorithm is proposed to obtain the wavefront aberrations of a real interferogram. By solving an optimization problem, the proposed method fits a set of Zernike polynomials to the experimental data. The results show that, compared to a conventional genetic algorithm, the hybrid genetic algorithm not only improves the searching ability but also accelerates the convergence. The proposed method is robust to spatial variation of the illumination.
We report on the experimental verification of an alternative method to the conventional least-squares fit method to obtain the phase of real interferograms. The proposed method performs an automatic polynomial fitting by solving an optimization problem, where an objective function is minimized and the solution to the irradiance equation is considered as an inverse problem. In this work, we consider fundamental concepts by comparing the performance of the least-squares fit method and the evolutionary algorithm. It is important to point out that the experimental verification of interferograms with the proposed method is applied to confirm the quality of some manufactured optical surfaces at the Instituto Nacional de Astrofísica, Optica y Electrónica.
In this work a hybrid algorithm genetic (GA) is proposed in order to obtain the wavefront OPD of experimental interferograms. The method takes as input an experimental interferograms, and it is by solving of an optimization problem in an accurate form that the evolutionary algorithm (GA) performs an automatic polynomial fitting under experimental data. In this way the method gives as output the vector of Zernike aberration coefficients to 5th order that best matches to the analyzed interferograms. It is important to point out that the experimental interferograms analysis was applied to confirm the quality of a manufactured optical surface.
We report the experimental verification of an alternative method to the conventional least squares fit method for obtaining the phase of real interferograms, making an automatical polynomial fitting on exprimental data in accurate form. The process can be realized solving an optimization problem using an evolutionary algorithm, where the solution to the irradiance equation should be considered like an inverse problem and an objective function should be minimized. In this work we review the fundamental concepts comparing the performance of the least squares fit method and the evolutionary algorithm used in the experimental interferogram analysis.
We present a method for obtaining the phase of a noisy synthetic interferogram. We find the wave-front aberrations by transforming the problem of fitting a polynomial in an optimization problem, which is then solved using an evolutionary algorithm. Our experimental results show that with our method more accurate results are obtained than with other methods commonly used to solve this problem.
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