Results 71 to 80 of about 62,622 (323)
Deep Underground Science and Engineering, EarlyView.The flowchart illustrates rock specimen testing, vibration signal acquisition, and feature extraction with Gaborlet and sparse filtering for classification. Abstract Traditional lithology identification methods mainly rely on core sampling and well‐logging data.
Jian Hao +5 more
wiley +1 more source
Transformation of Non-Euclidean Space to Euclidean Space for Efficient Learning of Singular Vectors
IEEE Access, 2020 Singular value decomposition (SVD) is a popular technique to extract essential information by reducing the dimension of a feature set. SVD is able to analyze a vast matrix in spite of a relatively low computational cost.
Seunghyun Lee, Byung Cheol Song
doaj +1 more source
Image Compression using Singular Value Decomposition (SVD)
, 2023 Abstract Images, integral to numerous applications, are encoded as matrices where each element represents a pixel's grayscale intensity. In grayscale images, values range from 0 (representing black) to 1 (indicating white). As image dimensions increase, so does the demand for storage space.
openaire +1 more source
KOMPRESI GAMBAR MENGGUNAKAN SINGULAR VALUE DECOMPOSITION (SVD) DENGAN PYTHON
. Image Compression Using Singular Value Decomposition (SVD) with Python is a powerful technique for dimension reduction and data compression. SVD decomposes a matrix into three separate matrices: , and .
Rahmadi, Deddy +3 more
core +1 more source
Shortening the order of paraunitary matrices in SBR2 algorithm [PDF]
, 2007 The second order sequential best rotation (SBR2) algorithm has recently been proposed as a very effective tool in decomposing a para-Hermitian polynomial matrix R(z) into a diagonal polynomial matrix T(z) and a paraunitary matrix B(,z), extending the ...
Weiss, Stephan +5 more
core +1 more source
Subspace Acceleration for Efficient Nonlinear Water Wave Simulation
International Journal for Numerical Methods in Fluids, EarlyView.We introduce an exponentially weighted subspace acceleration technique to reduce GMRES iterations for solving the Poisson equation with time‐dependent coefficients in nonlinear, dispersive free‐surface flows governed by the incompressible Navier‐Stokes equations. The method significantly reduces memory requirements and computational complexity compared
Rasmus Kleist Hørlyck Sørensen +3 more
wiley +1 more source
Unraveling complexity: Singular value decomposition in complex experimental data analysis
SciPost Physics CoreAnalyzing complex experimental data with multiple parameters is challenging. We propose using Singular Value Decomposition (SVD) as an effective solution.
Judith F. Stein, Aviad Frydman, Richard Berkovits
doaj +1 more source
Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery
Shock and Vibration, 2009 Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is ...
Junsheng Cheng +3 more
doaj +1 more source
Adaptive template-updating strategy based on singular value decomposition
, 2007 The problem of image matching and target tracking based on singular value decomposition (SVD) is discussed. The SVD has robust performance that is invariant to image disturbance and it makes the singular value credible to represent the image as an ...
Shi ZL(史泽林) +2 more
core
Journal of Magnetic Resonance Imaging, EarlyView.ABSTRACT Background The demand for cardiac MRI is increasing with the growing burden of cardiovascular disease. However, conventional protocols require sequential acquisitions for multi‐breath‐hold 2D cine and 3D MR angiography (MRA), which is time‐consuming.
Ruixin Chen +7 more
wiley +1 more source