Results 51 to 60 of about 13,343 (226)

A blind watermarkingalgorithm based on DWT and SVD

Journal of Measurement Science and Instrumentation, 2014
This paper presents a new digital image blind watermarking algorithm based on combination of discrete wavelet transform（DWT）and singular value decomposition（SVD）.First of all,we make wavelet decomposition for the original image and divide the acquired ...
XUAN Chun-qing, XUAN Zhi-wei, ZHANG Xia, CHEN Bao-li   +3 more
doaj  

An Aggregative High-Order Singular Value Decomposition Method in Edge Computing

IEEE Access, 2020
In edge computing, for dimensionality reduction and core data extraction, both edge computing node (ECN) and cloud server may implement a high-order singular value decomposition (HOSVD) algorithm before data are passed to local computing models. However,
Junhua Chen, Ping Wang, Chenggen Pu, Qingqing Huang   +3 more
doaj   +1 more source

Optimal subsampling for regression with mixed‐type predictors

Canadian Journal of Statistics, EarlyView.
Abstract Subsampling has emerged as an appealing strategy to mitigate the computational and storage challenges imposed by large datasets. Recent subsampling techniques have shown notable computational gains for data dominated by numerical predictors. However, real‐world datasets frequently contain both numerical and categorical predictors.
Jiaqing Zhu, Lin Wang, Fasheng Sun
wiley   +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, Dejie Yu, Jiashi Tang, Yu Yang   +3 more
doaj   +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

Gaborlet‐guided sparse filtering: A novel intelligent method for lithology identification by vibration signals while drilling

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, Danqi Zhang, Zhenqi Song, Jinrui Wang, Qi Wang, Heqing Liu   +5 more
wiley   +1 more source

Unraveling complexity: Singular value decomposition in complex experimental data analysis

SciPost Physics Core
Analyzing 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

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, Margherita Guido, Allan Peter Engsig‐Karup, Daniel Kressner   +3 more
wiley   +1 more source

An Enhanced Incremental SVD Algorithm for Change Point Detection in Dynamic Networks

IEEE Access, 2018
Change point detection is essential to understand the time-evolving structure of dynamic networks. Recent research shows that a latent semantic indexing (LSI)-based algorithm effectively detects the change points of a dynamic network.
Yongsheng Cheng, Jiang Zhu, Xiaokang Lin
doaj   +1 more source

Common‐Mode Rejection Shifted‐Excitation Raman Difference Spectroscopy (CMR‐SERDS) Preserves Broad Structure Predictive of Soil Organic Carbon

Journal of Raman Spectroscopy, EarlyView.
Common‐mode rejection (CMR) is introduced as a physics‐motivated preprocessing method for shifted excitation Raman difference spectroscopy (SERDS) that removes the shared background of paired measurements while preserving the noncommon excitation‐dependent component. Applied to more than 900 North American soil samples, CMR improves soil organic carbon
Mahsa Zarei, Miayan Larose, Natalia V. Solomatova, Sadegh Shokatian, Edward Grant   +4 more
wiley   +1 more source