WeChat Mini Program
Old Version Features

    • Analysis and Performance of Non-Circular Polynomials in the Wavefront Modelling

    Petr Janout,Petr Pata

    OPTICAL MODELING AND PERFORMANCE PREDICTIONS X（2018）

    Czech Tech Univ

    Cited 0|Views5
    Abstract
    Imaging system design is not limited to circular aperture shapes. However, non-circular apertures require a different set of polynomials, because broadly used Zernike polynomials are not orthogonal over non-circular shapes. Applying the Gram-Schmidt orthogonalization process provide the adopted set of orthogonal polynomials over selected non-circular aperture shape. However, when the aperture shape is complicated, non-symmetrical, the resulting set of polynomials can be very complex. In the case of odd-sided polygons is the analytical form of the polynomials inappropriate due to their complexity and these polynomials have to be expressed in their numerical form. Concerning the laborious complexity of some non-circular polynomials, we analyze the desired accuracy of such polynomials and their performance of the wavefront modeling according to classical circular Zernike polynomials.
    More
    Translated text
    Key words
    Numerical approximation and analysis,imaging systems,apertures,Zernike polynomials,polygon aperture
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined