Results 51 to 60 of about 590,374 (288)
IEEE Open Access Journal of Power and Energy, 2022 Day-ahead energy forecasting systems struggle to provide accurate demand predictions due to pandemic mitigation measures. Decomposition-Residuals Deep Neural Networks (DR-DNN) are hybrid point-forecasting models that can provide more accurate electricity
Konstantinos Theodorakos +3 more
doaj +1 more source
On the Uniqueness of Sparse Time-Frequency Representation of Multiscale Data [PDF]
, 2015 In this paper, we analyze the uniqueness of the sparse time frequency decomposition and investigate the efficiency of the nonlinear matching pursuit method. Under the assumption of scale separation, we show that the sparse time frequency decomposition is
Hou, Thomas Y. +2 more
core +2 more sources
FEBS Open Bio, EarlyView.Time‐resolved X‐ray solution scattering captures how proteins change shape in real time under near‐native conditions. This article presents a practical workflow for light‐triggered TR‐XSS experiments, from data collection to structural refinement. Using a calcium‐transporting membrane protein as an example, the approach can be broadly applied to study ...
Fatemeh Sabzian‐Molaei +3 more
wiley +1 more source
Multi-band network fusion for Alzheimer’s disease identification with functional MRI
Frontiers in Psychiatry, 2022 IntroductionThe analysis of functional brain networks (FBNs) has become a promising and powerful tool for auxiliary diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its prodromal stage.
Lingyun Guo +4 more
doaj +1 more source
Frequency-Selective Vandermonde Decomposition of Toeplitz Matrices with Applications
, 2017 The classical result of Vandermonde decomposition of positive semidefinite Toeplitz matrices, which dates back to the early twentieth century, forms the basis of modern subspace and recent atomic norm methods for frequency estimation.
Xie, Lihua, Yang, Zai
core +1 more source
Single channel speech music separation using nonnegative matrix factorization and spectral masks [PDF]
, 2011 A single channel speech-music separation algorithm based on nonnegative matrix factorization (NMF) with spectral masks is proposed in this work. The proposed algorithm uses training data of speech and music signals with nonnegative matrix factorization ...
Erdogan, Hakan +2 more
core +1 more source
FEBS Open Bio, EarlyView.This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill +4 more
wiley +1 more source
Screen Content Image Segmentation Using Sparse-Smooth Decomposition
, 2015 Sparse decomposition has been extensively used for different applications including signal compression and denoising and document analysis. In this paper, sparse decomposition is used for image segmentation.
Abdolrashidi, Amirali +2 more
core +1 more source
Single channel speech-music separation using matching pursuit and spectral masks [PDF]
, 2011 A single-channel speech music separation algorithm based on matching pursuit (MP) with multiple dictionaries and spectral masks is proposed in this work. A training data for speech and music signals is used to build two sets of magnitude spectral vectors
Erdogan, Hakan +2 more
core +1 more source
Spectral Decomposition of Signaling Networks [PDF]
2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, 2007 Many dynamical processes can be represented as directed attributed graphs or Petri nets where relationships between various entities are explicitly expressed. Signaling networks modeled as Petri nets are one class of such graphical modeling and representations. These networks encode how different protein in specific compartments, interact to create new
Parvin, Bahram +7 more
openaire +2 more sources