Neural networks application to determine the types and magnitude of aberrations from the pattern of the point spread function out of the focal plane

Abstract Recognition of the types of aberrations corresponding to individual Zernike functions were carried out from the pattern of the intensity of the point spread function (PSF) outside the focal plane using convolutional neural networks. The PSF intensity patterns outside the focal plane are more informative in comparison with the focal plane even for small values/magnitudes of aberrations. The mean prediction errors of the neural network for each type of aberration were obtained for a set of 8 Zernike functions from a dataset of 2 thousand pictures of out-of-focal PSFs. As a result of training, for the considered types of aberrations, the obtained averaged absolute errors do not exceed 0.0053, which corresponds to an almost threefold decrease in the error in comparison with the same result for focal PSFs.

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Recognition of wavefront aberrations types corresponding to single Zernike functions from the pattern of the point spread function in the focal plane using neural networks

Computer Optics ◽

10.18287/2412-6179-co-810 ◽

2020 ◽

Vol 44 (6) ◽

pp. 923-930

Author(s):

I.A. Rodin ◽

S.N. Khonina ◽

P.G. Serafimovich ◽

S.B. Popov

Keyword(s):

Neural Network ◽

Neural Networks ◽

Point Spread Function ◽

Focal Plane ◽

Prediction Errors ◽

Intensity Pattern ◽

Data Set ◽

Point Spread ◽

The Neural Network ◽

Spread Function

In this work, we carried out training and recognition of the types of aberrations corresponding to single Zernike functions, based on the intensity pattern of the point spread function (PSF) using convolutional neural networks. PSF intensity patterns in the focal plane were modeled using a fast Fourier transform algorithm. When training a neural network, the learning coefficient and the number of epochs for a dataset of a given size were selected empirically. The average prediction errors of the neural network for each type of aberration were obtained for a set of 15 Zernike functions from a data set of 15 thousand PSF pictures. As a result of training, for most types of aberrations, averaged absolute errors were obtained in the range of 0.012 – 0.015. However, determining the aberration coefficient (magnitude) requires additional research and data, for example, calculating the PSF in the extrafocal plane.

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Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation by Use of Convolutional Neural Networks

2018 25th IEEE International Conference on Image Processing (ICIP) ◽

10.1109/icip.2018.8451736 ◽

2018 ◽

Cited By ~ 7

Author(s):

Adrian Shajkofci ◽

Michael Liebling

Keyword(s):

Neural Networks ◽

Optical Microscopy ◽

Convolutional Neural Networks ◽

Point Spread Function ◽

Function Estimation ◽

Point Spread ◽

Local Point ◽

Spread Function ◽

Spatially Variant

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Estimation of Site Amplification from Geotechnical Array Data Using Neural Networks

Bulletin of the Seismological Society of America ◽

10.1785/0120200346 ◽

2021 ◽

Author(s):

Daniel Roten ◽

Kim B. Olsen

Keyword(s):

Neural Network ◽

Neural Networks ◽

Site Response ◽

Site Amplification ◽

Data Driven ◽

Vertical Array ◽

The Neural Network ◽

Simplifying Assumptions ◽

The Mean ◽

Fully Connected

ABSTRACT We use deep learning to predict surface-to-borehole Fourier amplification functions (AFs) from discretized shear-wave velocity profiles. Specifically, we train a fully connected neural network and a convolutional neural network using mean AFs observed at ∼600 KiK-net vertical array sites. Compared with predictions based on theoretical SH 1D amplifications, the neural network (NN) results in up to 50% reduction of the mean squared log error between predictions and observations at sites not used for training. In the future, NNs may lead to a purely data-driven prediction of site response that is independent of proxies or simplifying assumptions.

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A Genetic Algorithm to Improve a Neural Network to Predict a Patient’s Response to Warfarin

Methods of Information in Medicine ◽

10.1055/s-0038-1634888 ◽

1993 ◽

Vol 32 (01) ◽

pp. 55-58 ◽

Cited By ~ 23

Author(s):

M. N. Narayanan ◽

S. B. Lucas

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Neural Networks ◽

Predictive Ability ◽

Predictor Variables ◽

Optimal Subset ◽

The Neural Network ◽

International Normalised Ratio ◽

The Mean ◽

Paired T Test

Abstract:The ability of neural networks to predict the international normalised ratio (INR) for patients treated with Warfarin was investigated. Neural networks were obtained by using all the predictor variables in the neural network, or by using a genetic algorithm to select an optimal subset of predictor variables in a neural network. The use of a genetic algorithm gave a marked and significant improvement in the prediction of the INR in two of the three cases investigated. The mean error in these cases, typically, reduced from 1.02 ± 0.29 to 0.28 ± 0.25 (paired t-test, t = −4.71, p <0.001, n = 30). The use of a genetic algorithm with Warfarin data offers a significant enhancement of the predictive ability of a neural network with Warfarin data, identifies significant predictor variables, reduces the size of the neural network and thus the speed at which the reduced network can be trained, and reduces the sensitivity of a network to over-training.

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Hand gesture recognition by means of region-based convolutional neural networks

Contemporary Engineering Sciences ◽

10.12988/ces.2017.710154 ◽

2017 ◽

Vol 10 (27) ◽

pp. 1329-1342 ◽

Cited By ~ 2

Author(s):

Javier O. Pinzon Arenas ◽

Robinson Jimenez Moreno ◽

Paula C. Useche Murillo

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Real Time ◽

Convolutional Neural Networks ◽

Gesture Recognition ◽

Hand Gesture ◽

Hand Gestures ◽

The Neural Network ◽

Average Accuracy

This paper presents the implementation of a Region-based Convolutional Neural Network focused on the recognition and localization of hand gestures, in this case 2 types of gestures: open and closed hand, in order to achieve the recognition of such gestures in dynamic backgrounds. The neural network is trained and validated, achieving a 99.4% validation accuracy in gesture recognition and a 25% average accuracy in RoI localization, which is then tested in real time, where its operation is verified through times taken for recognition, execution behavior through trained and untrained gestures, and complex backgrounds.

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