Results 31 to 40 of about 10,183 (167)
Frontiers in Neuroscience, 2022 As one of the most common neurological disorders, epilepsy causes great physical and psychological damage to the patients. The long-term recurrent and unprovoked seizures make the prediction necessary.
Xiao Wu +3 more
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Data-driven nonstationary signal decomposition approaches: a comparative analysis
Scientific Reports, 2023 Signal decomposition (SD) approaches aim to decompose non-stationary signals into their constituent amplitude- and frequency-modulated components. This represents an important preprocessing step in many practical signal processing pipelines, providing ...
Thomas Eriksen, Naveed ur Rehman
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HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference
, 2020 A large proportion of recent invertible neural architectures is based on a coupling block design. It operates by dividing incoming variables into two sub-spaces, one of which parameterizes an easily invertible (usually affine) transformation that is ...
Detommaso, Gianluca +3 more
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Hybrid Prediction Model Based on Decomposed and Synthesized COVID-19 Cumulative Confirmed Data
ISPRS International Journal of Geo-Information, 2023 Since 2020, COVID-19 has repeatedly arisen around the world, which has had a significant impact on the global economy and culture. The prediction of the COVID-19 epidemic will help to deal with the current epidemic and similar risks that may arise in the
Zongyou Xia, Gonghao Duan, Ting Xu
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D$^3$PO - Denoising, Deconvolving, and Decomposing Photon Observations
, 2015 The analysis of astronomical images is a non-trivial task. The D3PO algorithm addresses the inference problem of denoising, deconvolving, and decomposing photon observations.
Enßlin, Torsten, Selig, Marco
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Distributed Bayesian Matrix Factorization with Limited Communication
, 2019 Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and providing confidence intervals.
Blomstedt, Paul +4 more
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Applied SciencesFault location technology is crucial for enhancing the efficiency of fault maintenance and ensuring the safety of the power supply in small current grounding systems.
Jiyuan Cao +4 more
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Multiscale Bayesian State Space Model for Granger Causality Analysis of Brain Signal
, 2018 Modelling time-varying and frequency-specific relationships between two brain signals is becoming an essential methodological tool to answer heoretical questions in experimental neuroscience.
Cekic, Sezen +2 more
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Open Computer ScienceThe freezing of gait (FoG) presents a sudden challenge in sustaining movement which becomes a common gait issue in people with later stages of Parkinson’s disease (PD). FoG often results in falls that reduces the individual’s impact on life.
Rajendran Nancy +4 more
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Modeling Covariate Effects in Group Independent Component Analysis with Applications to Functional Magnetic Resonance Imaging [PDF]
, 2015 Independent component analysis (ICA) is a powerful computational tool for separating independent source signals from their linear mixtures. ICA has been widely applied in neuroimaging studies to identify and characterize underlying brain functional ...
Guo, Ying, Shi, Ran
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