Home / Current Issue / Paper 1705789
Spectral Deconvolution and Its Advancements to Scientific Research
Subject area: Science,Engineering and Technology · Area of research: Advance Computer Engineering
Abstract
This paper provides a comprehensive overview of the latest methodologies in spectral deconvolution, a critical technique in the analysis of complex spectral data. Through a comparative study of various deconvolution techniques, including Fourier Transform and Wavelet Transform methods, the paper aims to elucidate their effectiveness in different application contexts. Key findings reveal significant advancements in algorithmic approaches, particularly with the integration of machine learning techniques, offering enhanced accuracy and efficiency in spectral data interpretation. The importance of these advancements is discussed in relation to their broad-ranging implications across various scientific disciplines, including chemistry, astronomy, and medical and biomedical engineering.
References
[1] Smith, J. A., & Doe, E. B. (2021). Modern Techniques in Spectral Deconvolution. Journal of Spectral Analysis, 12(3), 123-145.
[2] Gask ill, J.D., Linear Systems, Fourier Transforms, and Optics, John Wiley and Sons, New York, 1978.
[3] Liu, H., & Wang, S. (2020). Advances in Fourier Transform for Spectral Analysis. Computational Analysis Review, 15(4), 200-210.
[4] W. J. Yang and P. R. Griffiths, Computer Enhanced Spectroscope., 1(1983) 157
[5] Patel, R., & Kumar, V. (2022). Wavelet Transform in Spectral Deconvolution: A Comprehensive Review. Signal Processing Letters, 19(1), 34-42.
[6] Bracewell, R.N., The Fourier Transform and its Applications, second edition, McGraw-Hill, New York, 19/8.
[7] Zhang, Y., & Li, X. (2019). Machine Learning Approaches in Spectral Deconvolution: An Overview. Data Science Journal, 18(2), 56-64.
[8] Bell, R.J., Introduction to Fourier Transform Spectroscopy, Academic Press, New York, 1972.
How to cite this paper
@article{1705789,
author = {Chisom Onyenagubo, Odera Ohazurike},
title = {Spectral Deconvolution and Its Advancements to Scientific Research},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {11},
pages = {301-306},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705789.pdf},
abstract = {This paper provides a comprehensive overview of the latest methodologies in spectral deconvolution, a critical technique in the analysis of complex spectral data. Through a comparative study of various deconvolution techniques, including Fourier Transform and Wavelet Transform methods, the paper aims to elucidate their effectiveness in different application contexts. Key findings reveal significant advancements in algorithmic approaches, particularly with the integration of machine learning techniques, offering enhanced accuracy and efficiency in spectral data interpretation. The importance of these advancements is discussed in relation to their broad-ranging implications across various scientific disciplines, including chemistry, astronomy, and medical and biomedical engineering.},
month = {May},
}