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Discrete Fourier Analysis And Wavelets: Applica...

Topics will include: Fourier series: pointwise convergence, summability methods, mean-square convergence. Discrete Fourier Transform (including Fast Fourier Transform), and Discrete Haar Transform (including Fast Haar Transform) Fourier transform on the line. Time-frequency diccionary. Heisenberg's Uncertainty Principle, Sampling theorems and other applications. Including excursions into Lp spaces and distributions. Time/frequency analysis, windowed Fourier Transform, Gabor basis, Wavelets. Multiresolution analysis on the line. Prime example: the Haar basis. Basic wavelets examples:Shannon's and Daubechies' compactly supported wavelets. Time permiting we will explore variations over the themeof wavelets: Biorthogonalwavelets, and two-dimentional wavelets for image processing.

Discrete Fourier Analysis and Wavelets: Applica...

Delivers an appropriate mix of theory and applications to help readers understand the process and problems of image and signal analysisMaintaining a comprehensive and accessible treatment of the concepts, methods, and applications of signal and image data transformation, this Second Edition of Discrete Fourier Analysis and Wavelets: Applications to Signal and Image Processing features updated and revised coverage throughout with an emphasis on key and recent developments in the field of signal and image processing. Topical coverage includes: vector spaces, signals, and images; the discrete Fourier transform; the discrete cosine transform; convolution and filtering; windowing and localization; spectrograms; frames; filter banks; lifting schemes; and wavelets.Discrete Fourier Analysis and Wavelets introduces a new chapter on frames--a new technology in which signals, images, and other data are redundantly measured. This redundancy allows for more sophisticated signal analysis. The new coverage also expands upon the discussion on spectrograms using a frames approach. In addition, the book includes a new chapter on lifting schemes for wavelets and provides a variation on the original low-pass/high-pass filter bank approach to the design and implementation of wavelets. These new chapters also include appropriate exercises and MATLAB projects for further experimentation and practice.* Features updated and revised content throughout, continues to emphasize discrete and digital methods, and utilizes MATLAB to illustrate these concepts* Contains two new chapters on frames and lifting schemes, which take into account crucial new advances in the field of signal and image processing* Expands the discussion on spectrograms using a frames approach, which is an ideal method for reconstructing signals after information has been lost or corrupted (packet erasure)* Maintains a comprehensive treatment of linear signal processing for audio and image signals with a well-balanced and accessible selection of topics that appeal to a diverse audience within mathematics and engineering* Focuses on the underlying mathematics, especially the concepts of finite-dimensional vector spaces and matrix methods, and provides a rigorous model for signals and images based on vector spaces and linear algebra methods* Supplemented with a companion website containing solution sets and software exploration support for MATLAB and SciPy (Scientific Python)Thoroughly class-tested over the past fifteen years, Discrete Fourier Analysis and Wavelets: Applications to Signal and Image Processing is an appropriately self-contained book ideal for a one-semester course on the subject.

The DFT is the most important discrete transform, used to perform Fourier analysis in many practical applications.[1] In digital signal processing, the function is any quantity or signal that varies over time, such as the pressure of a sound wave, a radio signal, or daily temperature readings, sampled over a finite time interval (often defined by a window function[2]). In image processing, the samples can be the values of pixels along a row or column of a raster image. The DFT is also used to efficiently solve partial differential equations, and to perform other operations such as convolutions or multiplying large integers.

When the DFT is used for signal spectral analysis, the x n \displaystyle \x_n\ sequence usually represents a finite set of uniformly spaced time-samples of some signal x ( t ) \displaystyle x(t)\, , where t \displaystyle t represents time. The conversion from continuous time to samples (discrete-time) changes the underlying Fourier transform of x ( t ) \displaystyle x(t) into a discrete-time Fourier transform (DTFT), which generally entails a type of distortion called aliasing. Choice of an appropriate sample-rate (see Nyquist rate) is the key to minimizing that distortion. Similarly, the conversion from a very long (or infinite) sequence to a manageable size entails a type of distortion called leakage, which is manifested as a loss of detail (a.k.a. resolution) in the DTFT. Choice of an appropriate sub-sequence length is the primary key to minimizing that effect. When the available data (and time to process it) is more than the amount needed to attain the desired frequency resolution, a standard technique is to perform multiple DFTs, for example to create a spectrogram. If the desired result is a power spectrum and noise or randomness is present in the data, averaging the magnitude components of the multiple DFTs is a useful procedure to reduce the variance of the spectrum (also called a periodogram in this context); two examples of such techniques are the Welch method and the Bartlett method; the general subject of estimating the power spectrum of a noisy signal is called spectral estimation.

In numerical analysis and functional analysis, a discrete wavelet transform (DWT) is any wavelet transform for which the wavelets are discretely sampled. As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and location information (location in time).

The discrete wavelet transform has a huge number of applications in science, engineering, mathematics and computer science. Most notably, it is used for signal coding, to represent a discrete signal in a more redundant form, often as a preconditioning for data compression. Practical applications can also be found in signal processing of accelerations for gait analysis,[13][14] image processing,[15][16] in digital communications and many others.[17][18][19]

This course is an introduction to the mathematics of image and data analysis. The course will cover the discrete Fourier and Wavelet transforms, with applications to image and audio processing. We will also cover mathematics of common data analysis algorithms, including principal component analysis (PCA), data ranking (e.g., Google's PageRank for ranking webpages), and clustering algorithms such as k-means and spectral clustering. Time-permitting, we will give an introduction to machine learning (ML), and cover basic ML classifiers, neural networks (in particular, convolutional neural networks for image classification), and graph-based learning.

The second part of this book provides the foundations of least-squares approximation, the discrete Fourier transform, and Fourier series. The third part explains the Fourier transform and then demonstrates how to apply basic Fourier analysis to designing and analyzing mathematical wavelets. Particular attention is paid to Daubechies wavelets.

MATH 515 - Dynamical SystemsHours: 3Topics can be chosen from discrete or/and continuous dynamical systems such as linear systems and linear algebra, local theory for nonlinear systems, local existence-uniqueness theorem, the Hartman-Grobman theorem, Liapunov functions, the stable manifold theorem, limit sets of trajectories, the Poincare-Bendixson theorem, bifurcation theory, center manifold and normal form, chaotic dynamics, iteration of functions, graphical analysis, the linear, quadratic and logistic families, fixed points, symbolic dynamics, topological conjugacy, complex iteration, Julia and Mandelbrot sets. Prerequisites: MATH 2414 and MATH 2318.

The title may suggest that this book is only about wavelets and all the successful applications.However, the 'introduction' is very throughout and after some historical survey,you will find an extensive discussion of Fourier analysis(Fourier transform, Fourier series, DFT, FFT,...) and Hilbert spaces.Of course the latter are important to introduce (bi)orthogonal bases, frames,etc., which are important in wavelet analysis.Time-frequency analysis comes into the picture with Gabor (both continuous and discrete) and Zak transformsand the Wigner-Ville and the ambiguity distributions (again continuous as well as discrete transforms are analysed).So it is only on page 337 that the wavelets as such show up.The continuous (CWT) and discrete (DWT) wavelet transforms in chapter 6 andmultiresolution analysis (MRA) in chapter 7 form the core wavelet chapters.The remaining chapters treat more advanced or recent topics likea p-MRA on the positive real halfline, nonuniform MRA andthe Newland harmonic wavelets combining the short-time Fourier transform and the CWT.

The exponential development and success of wavelet analysis is due toa simultaneous alertness and collaboration between mathematicians, engineers,and theoretical physicists. So there are several approaches to the topic.On avarage, the continuous transforms are the favorites of mathematicians and physicists,the engineering applications in (digital) signal and image analysis andthe numerical solution of functional equations are favoring the discretetransforms. The former often emphasize the analysis aspect, the latteroften find that they are better off with a computational (linear) algebraic approach.This book tries to keep a good balance between both, but I believe theauthors have a reasonable bias towards the former camp of the CWT.Although the discrete transforms are all presented and given proper attention,the continuous transforms come up first to pave the way for the discrete versions.The applications are more the applications in a mathematician's vision:functional equations, analysis of turbulence (emphasis on analysis), while engineers would callsubjects like for example image and signal processing and compression or thecomputational and numerical aspects of the equation solvers,as true applications.Those engineering kind of applications is not exactly what you should look for in this book.On the other hand, all the elements for practical(also engineering) applications are introduced.However as much as the text keeps far away from unnecessary abstraction like'harmonic analysis on locally compact groups', it keeps away fromcomputer programming too. 041b061a72


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