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.
Citations
IRE Journals:
Chisom Onyenagubo , Odera Ohazurike
"Spectral Deconvolution and Its Advancements to Scientific Research" Iconic Research And Engineering Journals Volume 7 Issue 11 2024 Page 301-306
IEEE:
Chisom Onyenagubo , Odera Ohazurike
"Spectral Deconvolution and Its Advancements to Scientific Research" Iconic Research And Engineering Journals, 7(11)
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