Results 161 to 170 of about 6,448 (221)

A frequency-spatial dual perception network for efficient and accurate medical image segmentation. [PDF]

Sci Rep
Chen D, Wu J, Zhang XY, Wang DH.
europepmc   +1 more source

WaveST-Yield: a novel spatio-temporal deep learning framework with frequency-domain refinement for UAV-based maize yield prediction. [PDF]

Front Plant Sci
Li H, Hou P, Zhang R, Zhang X, Peng J, Cui X, Li F, Zhang X.   +7 more
europepmc   +1 more source

HFSUNet: A UNet architecture optimized for precise segmentation of pneumonia lesion boundaries. [PDF]

iScience
Zhang Z, Zhao Y, Wu X, Yao Z, Jia L, Yao C, Hua F.   +6 more
europepmc   +1 more source

SENet-SVWF: a spectral vegetation indices and wavelet features fusion method using squeeze and excitation network for predicting the SPAD value of maize leaves. [PDF]

BMC Plant Biol
Yan B, Zhang F, Zhang F, Zhang Y, Wang Y, Fu S.   +5 more
europepmc   +1 more source

Reconstruction of dance movements using reinforcement recurrent autoencoder and deep wavelet autoencoder. [PDF]

Sci Rep
Geng Y, Liu Y, Wang L, Khishe M.
europepmc   +1 more source

Constructing a "periodic table" of bacteria to map diversity in trait space. [PDF]

ISME J
Hoffert M, Gorman E, Lladser ME, Fierer N.   +3 more
europepmc   +1 more source

MS-YOLOv11: A Wavelet-Enhanced Multi-Scale Network for Small Object Detection in Remote Sensing Images. [PDF]

Sensors (Basel)
Liu H, Li X, Wang L, Zhang Y, Wang Z, Lu Q.   +5 more
europepmc   +1 more source

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Crystallographic Haar Wavelets

Journal of Fourier Analysis and Applications, 2011
Let \(\Gamma\) be a \(d\)-dimensional crystallographic group and let \(a:\,{\mathbb R}^d \to {\mathbb R}^d\) be an expanding affine map. By definition, \((\Gamma,a)\)-crystallographic multiwavelets form a finite set of functions \(\{\psi^1,\ldots, \psi^L\}\), which generate an orthonormal basis, a Riesz basis or a Parseval frame for \(L^1({\mathbb R}^d)
González, Alfredo L., Moure, María del Carmen   +1 more
openaire   +2 more sources

Haar Wavelet Splines

Journal of Interdisciplinary Mathematics, 2001
Abstract In this paper is discussed the numerical approximation of differential operators using Haar wavelet bases and their spline-derivatives. It is shown how to smooth the Haar family of wavelets using splines, and to compute the derivatives of the Haar function using the splines.
openaire   +3 more sources