Use of adaptive window wavelet neural networks to solve inverse problems of spectroscopy

Alexander O. Efitorov; Sergey A. Dolenko

doi:10.1117/12.2590936

4 May 2021 Use of adaptive window wavelet neural networks to solve inverse problems of spectroscopy

Alexander O. Efitorov, Sergey A. Dolenko

Proceedings Volume 11847, Saratov Fall Meeting 2020: Computations and Data Analysis: from Molecular Processes to Brain Functions; 118470C (2021) https://doi.org/10.1117/12.2590936
Event: Saratov Fall Meeting 2020, 2020, Saratov, Russian Federation

Abstract

Wavelet transformation uses a special basis widely known for its unique properties, the most important of which are its compactness and multi-resolution analysis of original signal. However, for a standard discrete and continuous wavelet transform (CWT), the extracted set of feature may be not optimal for solving given inverse problem. If no inverse transformation is needed, the values of transition and dilation coefficients may be determined during network training, and the windows corresponding to various wavelet functions may overlap. In this study, we suggest Adaptive Window Wavelet Neural Network (AWWNN) with bottom to top strategy of iterative neighboring windows merging, designed primarily for signal processing. The efficiency of proposed algorithm was compared on the example of the inverse problem (IP) of Raman spectroscopy of complex solutions of inorganic salts. The IP was solved using a dense neural network based on features generated using the proposed approach and a standard CWT.

Citation Download Citation

Alexander O. Efitorov and Sergey A. Dolenko "Use of adaptive window wavelet neural networks to solve inverse problems of spectroscopy", Proc. SPIE 11847, Saratov Fall Meeting 2020: Computations and Data Analysis: from Molecular Processes to Brain Functions, 118470C (4 May 2021); https://doi.org/10.1117/12.2590936

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available