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Conference presentation text describing a Bayesian adaptive variational method for nonlinear chirp mode decomposition with simulation and real-signal experiments in time-frequency signal analysis.
Year: 2022
Conference: ICASSP
Title of Paper: Adaptive Variational Nonlinear Chirp Mode Decomposition
Authors: Xinghao Ding; Andreas Jakobsson; Xiaotong Tu; Yue Huang
Affiliations: Xiamen University; Lund University
Field: Signal Processing
Keywords: Nonlinear chirp signal; Non-stationary signal analysis; Bayesian inference; Mode decomposition; Time-frequency representation; Expectation-Maximization algorithm
Methodology: Bayesian adaptive variational framework with conjugate priors and iterative posterior estimation
Experiments: Simulated two-component nonlinear chirp signal; Real-world baiji whistle signal
Application Areas: Speech analysis; Fault diagnosis; Ocean signal analysis
Noise Assumption: Independent zero-mean Gaussian noise
Code Availability: Github Repositories
Price: 8 / 10 USD
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Estimation of River Discharges from SWOT Observations using Variational Data Assimilation and Saint-Venant Hydraulic Models
Year: 2026
Region / City: Montpellier, France
Subject: River Discharge Estimation
Document Type: Scientific Research Paper
Organization / Institution: INRAE
Author: Hind Oubanas, Pierre-Olivier Malaterre, Igor Gejadze
Target Audience: Researchers, Scientists, Engineers
Date Approved: 2026-02-26
Date Modified: Not specified
Period of Validity: Ongoing research, subject to updates post-SWOT mission launch
Keywords: Hydraulic Modeling, Data Assimilation, River Discharge, Satellite Observations, SWOT Mission
Note: Context
Variational autoencoder-based clustering for geophysical fluid circulations with small sample size
Year: 2026
Region: Coastal Japan
Topic: Geophysical fluid dynamics, extreme events
Document type: Research article
Institution: Meteorological Research Institute, Japan Meteorological Agency; Graduate School of Science, Kyoto University
Authors: Kunihiro Aoki, Hideyuki Nakano, Nariaki Hirose, Norihisa Usui, Kei Sakamoto, Takahiro Toyoda, Shogo Urakawa, Yuma Kawakami
Target audience: Atmospheric and oceanic scientists, data scientists
Methodology: Variational autoencoder with principal-component-scaled augmentation
Data scope: Approximately 150 samples of coastal ocean velocity fields during Kyucho events
Results: Identification of four distinct circulation modes
Application period: Kyucho event study period
Abstract type: Clustering and pattern analysis in small-sample geophysical datasets
Journal context: Geophysical research, extreme event prediction
Topics: Chirp ICASSP Variational