Results 251 to 260 of about 430,171 (297)

A Mathematical Model of Metformin Action on COVID-19 Risk Infection in Cardiovascular Diabetic Patients Studied by FTIR Spectroscopy. [PDF]

Int J Mol Sci
Mylonas E, Mamareli C, Filippakis M, Mamarelis I, Anastassopoulou J, Theophanides T.   +5 more
europepmc   +1 more source

Nonstandard power grid frequency statistics across continents. [PDF]

Sci Rep
Wen X, Anvari M, Rydin Gorjão L, Yalcin GC, Hagenmeyer V, Schäfer B.   +5 more
europepmc   +1 more source

Fundamental solution of the time-space bi-fractional diffusion equation with a kinetic source term for anomalous transport. [PDF]

Sci Rep
Allagui A, Paradezhenko G, Pervishko A, Yudin D, Benaoum H.   +4 more
europepmc   +1 more source

Rotation Invariant Texture Analysis Using Radon and Fractional Fourier Transform

, 2014
Fakhordin Mohammadi, Junbin Gao, Daming Shi   +2 more
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Synchrosqueezing-Based Short-Time Fractional Fourier Transform

IEEE Transactions on Signal Processing, 2023
A novel synchrosqueezing transform to the time-frequency representation based on short-time fractional Fourier transform for strong time-varying signals is proposed in this paper.
Zhichun Zhao, Gang Li
semanticscholar   +1 more source

Fractional Fourier Transform Meets Transformer Encoder

IEEE Signal Processing Letters, 2022
Utilizing signal processing tools in deep learning models has been drawing increasing attention. Fourier transform (FT), one of the most popular signal processing tools, is employed in many deep learning models.
Furkan Şahinuç, Aykut Koç
semanticscholar   +1 more source

Fractional Fourier Transform in Time Series Prediction

IEEE Signal Processing Letters, 2022
Several signal processing tools are integrated into machine learning models for performance and computational cost improvements. Fourier transform (FT) and its variants, which are powerful tools for spectral analysis, are employed in the prediction of ...
E. Koç, Aykut Koç
semanticscholar   +1 more source

Fractional Fourier Transform and Transferred CNN Based on Tensor for Hyperspectral Anomaly Detection

IEEE Geoscience and Remote Sensing Letters, 2022
Most of the algorithms for hyperspectral anomaly detection (AD) are based on the original spectral signatures which may suffer noise contamination. In recent years, some AD algorithms based on deep learning (DL) and tensor have achieved satisfactory ...
Lili Zhang, Baozhi Cheng
semanticscholar   +1 more source