Results 41 to 50 of about 71,463 (232)

Weak peak identification of gamma spectrum based on singular value decomposition

He jishu
BackgroundWhen performing gamma-ray spectroscopy analysis of samples with low levels of radioactive nuclide content, the weak peaks are difficult to be identified.PurposeThis study aims to propose a new method for identifying peaks in γ spectra by ...
CHEN Feng, ZHOU Jianbin, LIU Yi
doaj   +1 more source

Structural Eigenmodes of the Brain to Improve the Source Localization of EEG: Application to Epileptiform Activity

Advanced Science, EarlyView.
Geometry and connectivity are complementary structures, which have demonstrated their ability to represent the brain's functional activity. This study evaluates geometric and connectome eigenmodes as biologically informed constraints for EEG source localization.
Pok Him Siu, Philippa J. Karoly, Sina Mansour L., Artemio Soto‐Breceda, Levin Kuhlmann, Mark J. Cook, David B. Grayden   +6 more
wiley   +1 more source

Objects features extraction by singular projections of data tensor to matrices

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
The problem of multidimensional tensor objects features extraction in a manner of matrices is considered. The tensor’ elements Higher Order Singular Value Decomposition (SVD) is presented as the d-SVD which includes SVD of the tensor reshaped as a ...
Yuriy Bunyak, Roman Kvуetnyy, Olga Sofina, Volodymyr Kotsiubynskyi   +3 more
doaj   +1 more source

Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain

Advanced Science, EarlyView.
A new approach uses simple neural networks to create digital twins of brain activity, capturing how different patterns unfold over time. The method generates and recovers key dynamics even from noisy data. When applied to fMRI, it predicts brain signals and reveals distinctive activity patterns across regions and individuals, opening possibilities for ...
Gabriele Di Antonio, Tommaso Gili, Andrea Gabrielli, Maurizio Mattia   +3 more
wiley   +1 more source

Cross‐Modal Denoising and Integration of Spatial Multi‐Omics Data with CANDIES

Advanced Science, EarlyView.
In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. We conduct extensive evaluations on diverse synthetic and real datasets, CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain ...
Ye Liu, Wanpeng Zou, Yuekai Li, Jiayi Wang, Mingxuan Cai, Hongmin Cai   +5 more
wiley   +1 more source

SVD-based IWFA for next generation wireless MIMO communication

ICT Express, 2018
Point-to-point MIMO systems without channel state information at the transmitter (No CSIT) and with perfect channel state information at the transmitter (Perfect CSIT) were considered for the application of a novel singular-value-decomposition-based (SVD-
Vinodh Kumar Minchula, G. Sasibhushana Rao   +1 more
doaj   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Condition of a linear system [PDF]

, 2009
U ovom članku analizira se uvjetovanost matrice linearnog sustava i njena povezanost sa singularnom dekompozicijom matrice. Pokazuje se da su loše uvjetovane matrice "gotovo singularne". Singularna dekompozicija također ukazuje na tu vezu, a koristi se i
Jasenka Augustinović
core   +1 more source

Modified Truncated Randomized Singular Value Decomposition (MTRSVD) Algorithms for Large Scale Discrete Ill-posed Problems with General-Form Regularization

, 2018
In this paper, we propose new randomization based algorithms for large scale linear discrete ill-posed problems with general-form regularization: ${\min} \|Lx\|$ subject to ${\min} \|Ax - b\|$, where $L$ is a regularization matrix.
Jia, Zhongxiao, Yang, Yanfei
core   +1 more source

Two dimensional Singular Value Decomposition (2D-SVD) based video coding [PDF]

2010 IEEE International Conference on Image Processing, 2010
In this paper, we propose a low-complexity video codec based on two-dimensional Singular Value Decomposition (2D-SVD). We exploit the common temporal characteristics of video without resorting to motion estimation. It has been demonstrated that this codec has higher coding efficiency than the relevant existing low complexity codecs.
Zhouye Gu, Weisi Lin, Bu-sung Lee, Chiew Tong Lau, Manoranjan Paul   +4 more
openaire   +1 more source