---
abstract: |
    The EMD algorithm, first proposed in [@Huang:98], made more robust as well as more versatile in [@HuangWuLong:09], is a technique that aims to decompose into their building blocks functions that are the superposition of a (reasonably) small number of components, well separated in the time-frequency plane, each of which can be viewed as approximately harmonic locally, with slowly varying amplitudes and frequencies.  The EMD has already shown its usefulness in a wide range of applications including meteorology, structural stability analysis, medical studies – see, e.g. [@HuangWu:08].  On the other hand, the EMD algorithm contains heuristic and ad-hoc elements that make it hard to analyze mathematically. 

    In this paper we describe a method that captures the flavor and philosophy of the EMD approach, albeit using a different approach in constructing the components.  We introduce a precise mathematical definition for a class of functions that can be viewed as a superposition of a reasonably small number of approximately harmonic components, and we prove that our method does indeed succeed in decomposing arbitrary functions in this class.  We provide several examples, for simulated as well as real data. 
author:
- |
    Ingrid Daubechies, Jianfeng Lu and Hau-Tieng Wu\
    Department of Mathematics and\
    Program in Applied and Computational Mathematics\
    Princeton University, 08544\
    ingrid@math.princeton.edu, jianfeng@math.princeton.edu, hauwu@math.princeton.edu
bibliography:
- 'main.bib'
title: 'Synchrosqueezed Wavelet Transforms: a Tool for Empirical Mode Decomposition'
---

Introduction {#intro}
============

Synchrosqueezing Wavelet Transforms {#synchro}
===================================

Main Result {#main_result}
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A Variational Approach {#variational}
======================

Numerical Results {#numerical}
=================

Acknowledgments
===============

The authors are grateful to the Federal Highway Administration, which supported this research via FHWA grant DTFH61-08-C-00028.  They also thank Prof. Norden Huang and Prof. Zhaohua Wu for many stimulating discussions and their generosity in sharing their code and insights.  They also thank MD.  Shu-Shya Hseu and Prof. Yu-Te Wu for providing the real medical signal.