Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer)

Overview

Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer)

Introduction

By applying the principles of geometric optics, imaging performances of lenses were investigated via examining the propagation of optical rays through various optical systems. The optical system and its elements were modelled with an object-oriented approach using the Python programming language. Through utilising a ray bundle with specific parameters, the performances of a planoconvex lens with different orientations were analysed. The orientation with the convex surface facing the incident beam was found to be more effective at minimising the spherical aberration. This was evident from the value of the geometric RMS spot radius of 1.85 x 10^-5} m at the paraxial focus compared to 7.04 x 10^-5 m for the plano-convex orientation. This was further supported by the relatively slow rate of increase in the RMS spot radius with the beam size for the convex-plano orientation. Furthermore, by optimising the curvatures of a singlet lens with a image distance of 100 mm, the best form curvatures were approximated as 0.01417 mm^-1 and -0.00532 mm^-1 with the RMS spot radius of 6.07 x 10^-8 m, leading to a conclusion that the system was diffraction limited and the effect of diffraction was substantial when using a beam radius smaller than 13.60 mm.

Requirements

Python 2.x is required to run the scripts (except for those with name beginning with 'ODE_').

Create an environment using conda as follows:

  conda create -n python2 python=2.x

Then activate the new environment by:

  conda activate python2

Results

In an ideal case, optical rays refracting through a spherical lens can be made to converge at a single point known as the focal point. However, in practice, rays fail to converge at a single point and a blurring effect occurs. This optical effect, known as the spherical aberration, is a result of the rays propagating parallel to the optical axis through a spherical lens at different distances from the axis.$^{1, 3}$ The rays further away from the optical axis experience greater refraction and thus they intersect the optical axis slightly behind the paraxial focus before diverging (FIG. 1).

For a single lens, spherical aberration can be minimised either by changing the orientation of the lens or by carefully choosing the curvatures of the spherical surfaces into the best form. In this investigation, both cases are examined using collimated ray bundles with uniformly distributed rays of various diameters with the aim to minimise this effect.

SA Figure 1: A lens displaying spherical aberration - the marginal and paraxial rays focus at the points F_1 and F_2 respectively.


single

Figure 2: A ray bundle of radius 5 mm propagating through a single spherical surface with a curvature of 0.03 mm^-1 and refracting towards the optical axis.


spotplot2

Figure 3: The non-uniform ring pattern that is shown in the figure is symbolic of the spherical aberration effect. The aberration is significantly reduced using the convex-plano orientation.


RMSPC

Figure 4: A graph depicting the change in the RMS spot radius at the paraxial focus with increasing beam size.


RMSDL

Figure 5: A graph showing the relationships of the diffraction limit and the RMS spot radius with increasing beam size.

🔗 Links

linkedin

License

MIT License

Owner
Son Gyo Jung
Son Gyo Jung
Official PyTorch implementation of Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval.

Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval PyTorch This is the PyTorch implementation of Retrieve in Style: Unsupervised Fa

60 Oct 12, 2022
Unofficial pytorch implementation of the paper "Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution"

DFSA Unofficial pytorch implementation of the ICCV 2021 paper "Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution" (p

2 Nov 15, 2021
Supplemental learning materials for "Fourier Feature Networks and Neural Volume Rendering"

Fourier Feature Networks and Neural Volume Rendering This repository is a companion to a lecture given at the University of Cambridge Engineering Depa

Matthew A Johnson 133 Dec 26, 2022
Speech Recognition using DeepSpeech2.

deepspeech.pytorch Implementation of DeepSpeech2 for PyTorch using PyTorch Lightning. The repo supports training/testing and inference using the DeepS

Sean Naren 2k Jan 04, 2023
Face-Recognition-Attendence-System - This face recognition Attendence system using Python

Face-Recognition-Attendence-System I have developed this face recognition Attend

Riya Gupta 4 May 10, 2022
Library for machine learning stacking generalization.

stacked_generalization Implemented machine learning *stacking technic[1]* as handy library in Python. Feature weighted linear stacking is also availab

114 Jul 19, 2022
BEAMetrics: Benchmark to Evaluate Automatic Metrics in Natural Language Generation

BEAMetrics: Benchmark to Evaluate Automatic Metrics in Natural Language Generation Installing The Dependencies $ conda create --name beametrics python

7 Jul 04, 2022
WarpRNNT loss ported in Numba CPU/CUDA for Pytorch

RNNT loss in Pytorch - Numba JIT compiled (warprnnt_numba) Warp RNN Transducer Loss for ASR in Pytorch, ported from HawkAaron/warp-transducer and a re

Somshubra Majumdar 15 Oct 22, 2022
Download and preprocess popular sequential recommendation datasets

Sequential Recommendation Datasets This repository collects some commonly used sequential recommendation datasets in recent research papers and provid

125 Dec 06, 2022
Official repository for "PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation"

pair-emnlp2020 Official repository for the paper: Xinyu Hua and Lu Wang: PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long

Xinyu Hua 31 Oct 13, 2022
paper list in the area of reinforcenment learning for recommendation systems

paper list in the area of reinforcenment learning for recommendation systems

HenryZhao 23 Jun 09, 2022
Code, Data and Demo for Paper: Controllable Generation from Pre-trained Language Models via Inverse Prompting

InversePrompting Paper: Controllable Generation from Pre-trained Language Models via Inverse Prompting Code: The code is provided in the "chinese_ip"

THUDM 101 Dec 16, 2022
SPEAR: Semi suPErvised dAta progRamming

Semi-Supervised Data Programming for Data Efficient Machine Learning SPEAR is a library for data programming with semi-supervision. The package implem

decile-team 91 Dec 06, 2022
Data labels and scripts for fastMRI.org

fastMRI+: Clinical pathology annotations for the fastMRI dataset The fastMRI dataset is a publicly available MRI raw (k-space) dataset. It has been us

Microsoft 51 Dec 22, 2022
Pytorch implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion"

MOSNet pytorch implementation of "MOSNet: Deep Learning based Objective Assessment for Voice Conversion" https://arxiv.org/abs/1904.08352 Dependency L

9 Nov 18, 2022
This code provides various models combining dilated convolutions with residual networks

Overview This code provides various models combining dilated convolutions with residual networks. Our models can achieve better performance with less

Fisher Yu 1.1k Dec 30, 2022
This is the research repository for Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition.

Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition This is the research repository for Vid2

Future Interfaces Group (CMU) 26 Dec 24, 2022
A TikTok-like recommender system for GitHub repositories based on Gorse

GitRec GitRec is the missing recommender system for GitHub repositories based on Gorse. Architecture The trending crawler crawls trending repositories

337 Jan 04, 2023
For holding anime-related object classification and detection models

Animesion An end-to-end framework for anime-related object classification, detection, segmentation, and other models. Update: 01/22/2020. Due to time-

Edwin Arkel Rios 72 Nov 30, 2022
Learning the Beauty in Songs: Neural Singing Voice Beautifier; ACL 2022 (Main conference); Official code

Learning the Beauty in Songs: Neural Singing Voice Beautifier Jinglin Liu, Chengxi Li, Yi Ren, Zhiying Zhu, Zhou Zhao Zhejiang University ACL 2022 Mai

Jinglin Liu 257 Dec 30, 2022