Data-Driven Time-Frequency Analysis

In this paper, we introduce a new adaptive data analysis method to study trend and instantaneous frequency of nonlinear and non-stationary data. This method is inspired by the Empirical Mode Decomposition method (EMD) and the recently developed compressed (compressive) sensing theory. The main idea is to look for the sparsest representation of multiscale data within the largest possible dictionary consisting of intrinsic mode functions of the form $\{a(t) \cos(\theta(t))\}$, where $a \in V(\theta)$, $V(\theta)$ consists of the functions smoother than $\cos(\theta(t))$ and $\theta'\ge 0$. This problem can be formulated as a nonlinear $L^0$ optimization problem. In order to solve this optimization problem, we propose a nonlinear matching pursuit method by generalizing the classical matching pursuit for the $L^0$ optimization problem. One important advantage of this nonlinear matching pursuit method is it can be implemented very efficiently and is very stable to noise. Further, we provide a convergence analysis of our nonlinear matching pursuit method under certain scale separation assumptions. Extensive numerical examples will be given to demonstrate the robustness of our method and comparison will be made with the EMD/EEMD method. We also apply our method to study data without scale separation, data with intra-wave frequency modulation, and data with incomplete or under-sampled data

Time-frequency analysis of chaotic systems

We describe a method for analyzing the phase space structures of Hamiltonian systems. This method is based on a time-frequency decomposition of a trajectory using wavelets. The ridges of the time-frequency landscape of a trajectory, also called instantaneous frequencies, enable us to analyze the phase space structures. In particular, this method detects resonance trappings and transitions and allows a characterization of the notion of weak and strong chaos. We illustrate the method with the trajectories of the standard map and the hydrogen atom in crossed magnetic and elliptically polarized microwave fields.Comment: 36 pages, 18 figure

Time-Frequency Analysis of Fourier Integral Operators

We use time-frequency methods for the study of Fourier Integral operators (FIOs). In this paper we shall show that Gabor frames provide very efficient representations for a large class of FIOs. Indeed, similarly to the case of shearlets and curvelets frames, the matrix representation of a Fourier Integral Operator with respect to a Gabor frame is well-organized. This is used as a powerful tool to study the boundedness of FIOs on modulation spaces. As special cases, we recapture boundedness results on modulation spaces for pseudo-differential operators with symbols in $M^{\infty,1}$, for some unimodular Fourier multipliers and metaplectic operators

Time frequency analysis in terahertz pulsed imaging

Recent advances in laser and electro-optical technologies have made the previously under-utilized terahertz frequency band of the electromagnetic spectrum accessible for practical imaging. Applications are emerging, notably in the biomedical domain. In this chapter the technique of terahertz pulsed imaging is introduced in some detail. The need for special computer vision methods, which arises from the use of pulses of radiation and the acquisition of a time series at each pixel, is described. The nature of the data is a challenge since we are interested not only in the frequency composition of the pulses, but also how these differ for different parts of the pulse. Conventional and short-time Fourier transforms and wavelets were used in preliminary experiments on the analysis of terahertz pulsed imaging data. Measurements of refractive index and absorption coefficient were compared, wavelet compression assessed and image classification by multidimensional clustering techniques demonstrated. It is shown that the timefrequency methods perform as well as conventional analysis for determining material properties. Wavelet compression gave results that were robust through compressions that used only 20% of the wavelet coefficients. It is concluded that the time-frequency methods hold great promise for optimizing the extraction of the spectroscopic information contained in each terahertz pulse, for the analysis of more complex signals comprising multiple pulses or from recently introduced acquisition techniques

Semi-classical Time-frequency Analysis and Applications

This work represents a first systematic attempt to create a common ground for semi-classical and time-frequency analysis. These two different areas combined together provide interesting outcomes in terms of Schr\"odinger type equations. Indeed, continuity results of both Schr\"odinger propagators and their asymptotic solutions are obtained on $\hbar$-dependent Banach spaces, the semi-classical version of the well-known modulation spaces. Moreover, their operator norm is controlled by a constant independent of the Planck's constant $\hbar$. The main tool in our investigation is the joint application of standard approximation techniques from semi-classical analysis and a generalized version of Gabor frames, dependent of the parameter $\hbar$. Continuity properties of more general Fourier integral operators (FIOs) and their sparsity are also investigated.Comment: 23 page

Time-frequency analysis

Cílem tohoto bakalářské práce je zjistit možnosti získání analýzy časové, frekvenční a jejich vzájemné kombinace, časově-frekvenční, různými metodami například Fourierovou transformací a Vlnkovou transformací. Během práce se seznámíme s jednotlivými transformacemi a vysvětlíme postup jejich výpočtu a především jejich výhody a nevýhody z pohledu přesnosti určení velikosti frekvence signálu v čase.The aim of this bachelor`s thesis is to explore possibilities of solving time-, frequency analysis a their combination time-frequency analysis by different methods for example Fourier transform a Wavelet transform. Going through this project we will get know each transformation and we will make clear procedure of their solving and first of all their advantages and disadvantages in view of accuracy of frequention`s mark in time.