Wavelets: Theory and Applications for Manufacturing (original) (raw)
Introduction to Wavelets in Engineering
The aim of this paper is to provide an introduction to the subject of wavelet analysis for engineering applications. The paper selects from the recent mathematical literature on wavelets the results necessary to develop wavelet based numerical algorithms. In particular, we p r o vide extensive details of the derivation of Mallat's transform and Daubechies' wavelet coe cients, since these are fundamental to gaining an insight i n to the properties of wavelets. The potential bene ts of using wavelets are highlighted by presenting results of our research in one and two dimensional data analysis and in wavelet solutions of partial di erential equations.
Wavelets in industrial applications: a review
Wavelet Applications in Industrial Processing II, 2004
This paper aims at reviewing the recent published works dealing with industrial applications of wavelet and, more generally speaking, multiresolution analysis. After a quick recall in a simple overview of the basics of wavelet transform and of its main variations, some of its applications are reviewed domain by domain, beginning with signal processing, continuous and discrete wavelet transform proceeding with image processing and applications. More than 150 recent papers are presented in these two sections.
Wavelets and Wavelet Transform Systems and Their Applications
2022
Dedicated to my family: My Wife Caroline and my Children Obinna and Chijioke. I also dedicate this book to all my students, particularly all of my many master's and doctoral students from all over the world. Preface Overview This book is all about wavelets, wavelet transforms, and how they can be applied to solve problems in different fields of study. The question asked often is, what are wavelets? The answer is that wavelets are waveforms of limited duration that have average values of zero. In comparison to sinusoids, wavelets do have a beginning and an end, while sinusoids theoretically extend from minus to plus infinity. Sinusoids are smooth and predictable and are good at describing constant frequency which otherwise can be called stationary signals. In the case of wavelets, they are irregular, of limited duration, and often non-symmetrical. They are better at describing anomalies, pulses, and other events that start and stop within the signal. This book on wavelets and wavelet transform systems and their applications has grown out of my teaching "Wavelets and Their Applications" graduate course and my research activities in the fields of digital signal processing and communication systems for many decades. The notes on which this book is based on have been used for a one-semester graduate course entitled "Wavelets and Their Applications" that I have taught for several decades at Prairie View A & M University. The book chapters have increased to 21 because of additional new materials considered and therefore can be used for a two-semester course as well. The materials have been updated continuously because of active research in the application of wavelets and wavelet transforms to several areas of science and engineering and lots of research with my graduate students in the areas of wavelet applications. We live in the Information Age where information is analyzed, synthetized, and stored at a much faster rate using different techniques such as different wavelets and wavelet transforms. For many decades, wavelets and wavelet transforms have received much attention in the literature of many communities in the areas of science and engineering. There are different types of wavelets. These wavelets are used as analyzing tools by pure mathematicians (in harmonic analysis, for the study of vii Calderon-Zygmund operators), by statisticians (in nonparametric estimation), and by electrical engineers (in signal analysis). In physics, wavelets are used because of their applications to time-frequency or phase-space analysis and their renormalization concepts. In computer vision research, wavelets are used for "scale-space" methods. In stochastic processes, they are used in application of self-similar processes. Because of wavelets and wavelet transforms' connections with multirate filtering, quadrature mirror filters, and sub-band coding, they have found home in the digital signal processing community. The image processing community uses wavelets because of their applications in pyramidal image representation and compression. In harmonic analysis, wavelets are used because of the special properties of wavelet bases, while the speech processing community uses wavelets because of their efficient signal representation, event extraction, and the mimicking of the human auditory system. Most importantly, wavelet analysis tools can be used as an adaptable exchange to Fourier transform analysis and representation. While there may be many books written over the past decades in the area of wavelets, it is hard to find a wavelet book that is not heavily into rigorous mathematical equations, and in most cases, little or no real applications. In addition, most of the books are not written as textbooks for classroom teaching and to make students understand what wavelets are and how to apply them to solving societal problems. In this book, it is very simplified to real applications in solving societal problems. It is not buried into rigorous mathematical formulas. The book is very suitable as a textbook for upper-level undergraduate study and graduate studies. The practicing engineers in industry will find the book very useful. Not only can the book be used for training of future digital signal processing engineers, it can also be used in research, developing efficient and faster computational algorithms for different multidisciplinary applications. Engineering and scientific professionals can use this book in their research and work-related activities. In actuality, wavelets provide a common link between mathematicians and engineers. Topics such as decomposition and reconstruction algorithms, subdivision algorithms, fast numerical computations, frames, time-frequency localizations, and continuous-and discrete-wavelet transforms are covered for their use of wavelets and wavelet transforms. In addition, topics such as fractals and fractal transforms, mixed signal systems, sub-band coding, image compression, real-time filtering, radar applications, transient analysis, medical imaging, segmentation, blockchain systems, information security, and vibration in aeroelastic systems are some of the areas covered in the book. Applications of many of these wavelets and wavelet transform analyses are developed across disciplines in the book. This book, entitled Wavelets and Wavelet Transform Analysis and Applications: A Signal Processing Approach is a unique book because of its in-depth treatment of the applications of wavelets and wavelet transforms in many areas across many disciplines. The book does this in a very simplified and understandable manner without the mathematical rigor that scares many people away from the field. It uses lots of diagrams to illustrate points being discussed. In addition, the concepts introduced in the book are reinforced with review questions and problems. MATLAB codes and algorithms viii Preface Preface ix x Preface I thank Prairie View A&M University (PVAMU) and The Texas A&M University Board of Regents that approved the Center of Excellence for Communication Systems Technology Research (CECSTR) as one of the Board of Regents Approved Centers on the Campus of PVAMU and within the entire Texas A&M System. I thank all the student researchers and faculty colleagues at CECSTR and the College of Engineering, especially my colleagues in the Department of Electrical and Computer Engineering for all their support. My special thanks go to our former president, Dr. George Wright; former provost, Dr. Thomas-Smith; and former vice president for research and a great friend Dr. Willie Trotty. Special thanks to my former dean of the College of Engineering, Dean Bryant, for all his support. Without the help and support of these giants, there would not be CECSTR, and therefore there would not have been the kind of research work that may have resulted in some of the information in this book. Finally, I thank my wife Caroline Chioma Akujuobi and my two sons Obinna and Chijioke Akujuobi for their patience, encouragement, and support throughout the time I was preparing the manuscript for this book.
IEEE Instrumentation & Measurement Magazine, 2000
S ignal transformation is a mathematical tool commonly applied in signal processing. It maps a mathematical function or a physical signal from one domain into another to reveal information embedded within the original function or signal that may not be obvious otherwise. The Fourier transform introduced in part 11 of this tutorial series [1] is probably the most widely used signal transformation technique in measurement science and technology. H o w e v e r, t h e F o u r i e r t r a n s f o r m t e c h n i q u e w o r k s o n l y w h e n t h e statistical properties of a signal (e.g., the mean value a n d t h e a u t o -c o r re l a t i o n function) do not vary with time [2]. Because the basis functions (i.e., a series of sine and cosine functions) used for the Fourier transform extend over an infinite time interval, they are not well suited for non-stationary signals (the frequency content of which varies with time), which are often the signals encountered in real-world applications. The time domain representation of a signal does not provide quantitative information on frequency content of the signal, while the Fourier transformed frequency representation provides frequency content without any indication of time localization of the frequency components. Consequently, analyzing a nonstationary signal requires a transformation technique that can provide a t w o -d i m e n s i o n a l t i m efrequency representation. Of the various timefrequency techniques, the wavelet transform provides information about a signal in the t i m e -s c a l e d o m a i n simultaneously through a series of convolution operations between the signal being analyzed and the base wavelet [3]
Wavelet Applications in Industrial Processing VI (Proceedings Volume)
2009
The Joint Photographic Experts Group (JPEG) committee is a joint working group of the International Standardization Organization (ISO) and the International Electrotechnical Commission (IEC). The word" Joint" in JPEG however does not refer to the joint efforts of ISO and IEC, but to the fact that the JPEG activities are the result of an additional collaboration with the International Telecommunication Union (ITU). Inspired by technology and market evolutions, ie the advent of wavelet technology and need for additional functionality such ...
Wavelet analyses and applications
European Journal of …, 2009
It is shown how a modern extension of Fourier analysis known as wavelet analysis is applied to signals containing multiscale information. First, a continuous wavelet transform is used to analyse the spectrum of a nonstationary signal (one whose form changes in time). The spectral analysis of such a signal gives the strength of the signal in each frequency as a function of time. Next, the theory is specialized to discrete values of time and frequency, and the resulting discrete wavelet transform is shown to be useful for data compression. This paper is addressed to a broad community, from undergraduate to graduate students to general physicists and to specialists in other fields than wavelets.