1 code implementation • 9 Sep 2023 • Feng Zhou, Antonio Cicone, Haomin Zhou
Time-frequency analysis is an important and challenging task in many applications.
1 code implementation • 4 Jul 2023 • Feng Zhou, Antonio Cicone, Haomin Zhou
Inspired by the successful applications of deep learning in fields like image processing and natural language processing, and given the lack in the literature of works in which deep learning techniques are used directly to decompose non-stationary signals into simple oscillatory components, we use the convolutional neural network, residual structure and nonlinear activation function to compute in an innovative way the local average of the signal, and study a new non-stationary signal decomposition method under the framework of deep learning.
no code implementations • 28 Nov 2020 • Philippe Barbe, Antonio Cicone, Wing Suet Li, Haomin Zhou
Iterative filtering methods were introduced around 2010 to improve definitions and measurements of structural features in signal processing.
Numerical Analysis Numerical Analysis
2 code implementations • 13 Feb 2019 • Antonio Cicone
In this work, we present a new technique for the decomposition of multivariate data, which we call Multivariate Fast Iterative Filtering (MvFIF) algorithm.
Numerical Analysis Numerical Analysis
4 code implementations • 8 Nov 2018 • Antonio Cicone
The Iterative Filtering method is a technique developed recently for the decomposition and analysis of non-stationary and non-linear signals.
Numerical Analysis
6 code implementations • 5 Feb 2018 • Antonio Cicone, Haomin Zhou
Real life signals are in general non--stationary and non--linear.
Numerical Analysis
4 code implementations • 13 Oct 2017 • Antonio Cicone
How can I decompose a nonstationary signal?
History and Overview
no code implementations • 7 Dec 2015 • Antonio Cicone, Jingfang Liu, Haomin Zhou
Hyperspectral images can be used to identify chemical plumes, however the task can be extremely challenging.
5 code implementations • 21 Nov 2014 • Antonio Cicone, Jingfang Liu, Haomin Zhou
We provide sufficient conditions on the filters that ensure the convergence of IFs applied to any $L^2$ signal.
Numerical Analysis