High-Resolution Power Spectral Estimation Method Using Deconvolution

Guo, Wei; Piao, Shilong; Yang, T. C.; Guo, Jing; Iqbal, Kashif

doi:10.1109/joe.2018.2882275

Cited by 15 publications

(12 citation statements)

References 26 publications

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“…Due to their high complexity connected with overlapping or broadened signals from different simultaneously absorbing cellular components, sometimes, it becomes a problem to ascribe variations in absorbance at a particular wavenumber to a specific molecule [35,81]. To overcome this difficulty, advanced mathematical methodologies for spectral data analyses [82,83], such as second-order derivative determination, Gaussian and/or Lorentzian curve fitting, and Voight deconvolution, can be applied [84]. In our case, the second-order derivative function proved to be sufficient because we compared the composition of the biofilms produced by various streptococci strains, not the changes in biofilm created by one bacterial strain during different conditions (such as drug treatment or alternating ion composition, pH, or temperature).…”

Section: Discussionmentioning

confidence: 99%

The FT-IR and Raman Spectroscopies as Tools for Biofilm Characterization Created by Cariogenic Streptococci

Gieroba

Krysa

Wojtowicz

et al. 2020

IJMS

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Fourier transform infrared (FT-IR) and Raman spectroscopy and mapping were applied to the analysis of biofilms produced by bacteria of the genus Streptococcus. Bacterial biofilm, also called dental plaque, is the main cause of periodontal disease and tooth decay. It consists of a complex microbial community embedded in an extracellular matrix composed of highly hydrated extracellular polymeric substances and is a combination of salivary and bacterial proteins, lipids, polysaccharides, nucleic acids, and inorganic ions. This study confirms the value of Raman and FT-IR spectroscopies in biology, medicine, and pharmacy as effective tools for bacterial product characterization.

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Section: Discussionmentioning

confidence: 99%

The FT-IR and Raman Spectroscopies as Tools for Biofilm Characterization Created by Cariogenic Streptococci

Gieroba

Krysa

Wojtowicz

et al. 2020

IJMS

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“…The spectrum of the time series indicates the fluctuations contributions of innumerable wavelengths to its variable. Classic texts covered the mathematics of the methods (Guo et al, 2020; Stoica & Moses, 2005; Tan & Jiang, 2019).…”

Section: Methodsmentioning

confidence: 99%

Trends and periodicities of annual rainfall over Ibadan region, Nigeria

Adejuwon

2022

World Water Policy

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Discovering trends and periodicities in the rainfall of a region provides essential information for understanding the climate especially the cyclic, periodic, persistent, or oscillatory patterns in rainfall. However, inadequate literature, lack of detailed research work, and data restriction to the 20th century necessitate the research. The research examines the trends and periodicities of rainfall over the Ibadan region from 1972 to 2020, Nigeria, using the Kendall tau correlation, the Spearman (rho) rank correlation, and the smoothed power spectral analysis. The results revealed upward trends in rainfall in all the stations, but only Idi‐Ayunre showed a significant trend (r = 0.034, 0.045) at p ≤ 0.05. Rainfall periodicities show that prominent short‐term periodicity of between 2.8 and 7.0 years and less prominent periodicity of 14.0 years were discovered in the rainfall series. The result provides a climatic shift toward increased rainfall and a wetter period. Thus, this study explains the trend and cyclic state of rainfall to water management for the Ibadan region and is important for agriculture and water resource planning.

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“…where P x N ð f Þ, P( f ), and P w ( f ) are the power spectrum estimation results of x N (n), x(n), and g(n) respectively. Because P ( f ) is derived from an infinite data set, it can be regarded as a combination of pulses, with frequencies and amplitudes corresponding to components of the signal x(n), especially as x(n) is composed of narrow-band signals [21].…”

Section: Signal Modelmentioning

confidence: 99%

Enhancing time‐frequency resolution via deep‐learning framework

Wang

Chen

Xiao

et al. 2023

IET Signal Processing

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The fixed window function used in the short‐time Fourier transform (STFT) does not guarantee both time and frequency resolution, exerting a negative impact on the subsequent study of time‐frequency analysis (TFA). To avoid these limitations, a post‐processing method that enhances the time‐frequency resolution using a deep‐learning (DL) framework is proposed. Initially, the deconvolution theoretical formula is derived and a post‐processing operation is performed on the time‐frequency representation (TFR) of the STFT via deconvolution, a theoretical calculation to obtain the ideal time‐frequency representation (ITFR). Then, aiming at the adverse influence of the window function, a novel fully‐convolutional encoder‐decoder network is trained to preserve effective features and acquire the optimal time‐frequency kernel. In essence, the generation of the optimal time‐frequency kernel can be regarded as a deconvolution process. The authors conducted the qualitative and quantitative analyses of numerical simulations, with experimental results demonstrate that the proposed method achieves satisfactory TFR, possesses strong anti‐noise capabilities, and exhibits high steady‐state generalisation capability. Furthermore, results of a comparative experiment with several TFA methods indicate that the proposed method yields significantly improved performance in terms of time‐frequency resolution, energy concentration, and computational load.

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High-Resolution Power Spectral Estimation Method Using Deconvolution

Cited by 15 publications

References 26 publications

The FT-IR and Raman Spectroscopies as Tools for Biofilm Characterization Created by Cariogenic Streptococci

The FT-IR and Raman Spectroscopies as Tools for Biofilm Characterization Created by Cariogenic Streptococci

Trends and periodicities of annual rainfall over Ibadan region, Nigeria

Enhancing time‐frequency resolution via deep‐learning framework

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