Noise removal in ionograms by neural network

被引：0
|
作者：
M. Hagenbuchner
J. Fulcher
机构：
[1] University of Wollongong,Department of Computer Science
来源：
Neural Computing & Applications | 1997年 / 6卷
关键词：
Back propagation; Image processing; Ionogram; Noise filtering;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A neural network approach to the extraction of 1hop F-layer traces from oblique-incidence ionograms is shown to offer performance at least comparable with conventional filtering techniques. Preprocessing in the form of background noise and vertical (horizontal) line removal is utilised prior to training a 110∶7∶100 MLP using backpropagation with momentum. It is further demonstrated that such successful trace extraction can be achieved with just 50 ionogram training exemplars.
引用
收藏
页码：165 / 172
页数：7
相关论文
共 50 条
  • [21] Speckle Noise Removal Model Based on Diffusion Equation and Convolutional Neural Network
    Nao, Siwei
    Wang, Yan
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [22] Noise Removal on Batak Toba Handwritten Script using Artificial Neural Network
    Pasaribu, Novie Theresia Br
    Hasugian, M. Jimmy
    2016 3RD INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY, COMPUTER, AND ELECTRICAL ENGINEERING (ICITACEE), 2016, : 373 - 376
  • [23] High Impulse Noise Intensity Removal in Natural Images Using Convolutional Neural Network
    Mafi, Mehdi
    Izquierdo, Walter
    Adjouadi, Malek
    2020 10TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), 2020, : 673 - 677
  • [24] Noise Removal from Electrocardiogram Signal Employing an Artificial Neural Network in Wavelet Domain
    Farahabadi, E.
    Farahabadi, A.
    Rabbani, H.
    Mahjoob, M. Parsa
    Dehnavi, A. Mehri
    2009 9TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND APPLICATIONS IN BIOMEDICINE, 2009, : 205 - +
  • [25] Single Infrared Image Optical Noise Removal Using a Deep Convolutional Neural Network
    Kuang, Xiaodong
    Sui, Xiubao
    Liu, Yuan
    Chen, Qian
    Gu, Guohua
    IEEE PHOTONICS JOURNAL, 2018, 10 (02):
  • [26] Refinement of spectra using a deep neural network: Fully automated removal of noise and background
    Gebrekidan, Medhanie Tesfay
    Knipfer, Christian
    Braeuer, Andreas Siegfried
    JOURNAL OF RAMAN SPECTROSCOPY, 2021, 52 (03) : 723 - 736
  • [27] Automatic detection and classification of Spread-F in ionograms using convolutional neural network
    Yacoub, Moheb
    Pacheco, Edgardo E.
    Abdelwahab, Moataz
    de la Jara, Cesar
    Mahrous, Ayman
    JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS, 2025, 270
  • [28] Noise removal in electroencephalogram signals using an artificial neural network based on the simultaneous perturbation method
    Mateo, J.
    Torres, A. M.
    Garcia, M. A.
    Santos, J. L.
    NEURAL COMPUTING & APPLICATIONS, 2016, 27 (07): : 1941 - 1957
  • [29] Two-Stage Convolutional Neural Network forMedical Noise Removal via Image Decomposition
    Chang, Yi
    Yan, Luxin
    Chen, Meiya
    Fang, Houzhang
    Zhong, Sheng
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2020, 69 (06) : 2707 - 2721
  • [30] Noise removal in electroencephalogram signals using an artificial neural network based on the simultaneous perturbation method
    J. Mateo
    A. M. Torres
    M. A. García
    J. L. Santos
    Neural Computing and Applications, 2016, 27 : 1941 - 1957
12345