Improved quasi-invariance result for the periodic Benjamin-Ono-BBM equation. [PDF]
Forlano J.
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Parameter estimation in blood flow models from highly undersampled k-space magnetic resonance imaging data. [PDF]
Löcke M, van Ooij P, Bertoglio C.
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Brain tumor classification using fractional Laplacian image enhancement and vision transformer. [PDF]
Darcus B J, Ghosh S.
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Hadamard gate realization based on quantum Fourier transform for the creation of quantum numbers in the integrated optical domain. [PDF]
Armaghani S, Rostami A.
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The Association of Apparent Myelin Water Fraction With Diffusional Kurtosis and Biophysical Modeling Parameters. [PDF]
Moss HG +5 more
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High-Sensitivity Defect Inspection for Unpatterned Wafers via Integrating Dark-Field Scattering and Diffraction Phase Microscopy. [PDF]
Zhang X, Zheng Q, Li D, Ye R.
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Fractional Fourier Transform Meets Transformer Encoder
IEEE Signal Processing Letters, 2022Utilizing 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. Transformer-based sequential input processing models have also started to make use of FT.
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The fractional Fourier transform on graphs
2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2017The emerging field of signal processing on graphs merges algebraic or spectral graph theory with discrete signal processing techniques to process signals on graphs. In this paper, a definition of the fractional Fourier transform on graphs (GFRFT) is proposed and consolidated, which extends the discrete fractional Fourier transform (DFRFT) in the same ...
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