Издательство InTech, 2012. — 646 p.Wavelets are functions fulfilling certain mathematical requirements and used in representing data or other functions. The basic idea behind wavelets is to analyze according to scale. Wavelets received considerable attention in the last years because they are very appropriate for application in practical problems in areas of Engineering, Physics and Technology.
The book is organized in five main sections denoted as Signal Processing, Electrical Systems, Fault Diagnosis and Monitoring, Image Processing and Applications in Engineering.
The wavelet method is used in this book to extract more information than the standard techniques from a given complex signal and it has capabilities for the deconvolution framework. Applications of wavelet transform to the image processing, audio compression and communication systems are also reported.
The applications of wavelet transform in the field of power system dynamics and stability, in fault diagnosis of analogue electronic circuits as well as for practical condition monitoring issues are covered by this book. In addition the application of wavelet analysis combined with artificial neural networks as automatic rolling bearing fault detection and diagnosis is illustrated. The use of the wavelet transform to the denoising process is an important chapter of this book. The reader can see how the wavelet transform was used as a classification criterion applied to improve the compression of the hyper-spectral images.
The last chapter of the book presents some specific applications of the wavelet transform in engineering, e.g. to robust lossless data hiding by feature-based bit embedding algorithm, for the understanding of vortex-induced vibration, in rotorcraft UAV's integrated navigation system. Also, a constructive design methodology for multi-resolution- scalable mesh compression systems is presented.
The chapters of this book present the problems for which wavelet transform is best well-suited, indicates how to implement the corresponding algorithms efficiently, and finally show how to assign the appropriate wavelets for a specified application. Researchers, working in the field of the wavelet transform, will find several open problems being mentioned within this book. Both theoretical considerations as well as the corresponding applications are clearly presently in such a way to be understandable by a large variety of readers.Signal Processing.
Real-Time DSP-Based License Plate Character Segmentation Algorithm Using 2D Haar Wavelet Transform.
Wavelet Transform Based Motion Estimation and Compensation for Video Coding.
Speech Scrambling Based on Wavelet Transform.
Wavelet Denoising.
Oesophageal Speech’s Formants Measurement Using Wavelet Transform.
The Use of the Wavelet Transform to Extract Additional Information on Surface Quality from Optical Profilometers.
Multi-Scale Deconvolution of Mass Spectrometry Signals.
Electrical Systems.
Wavelet Theory and Applications for Estimation of Active Power Unbalance in Power System.
Application of Wavelet Transform and Artificial Neural Network to Extract Power Quality Information from Voltage Oscillographic Signals in Electric Power Systems.
Wavelet Transform in Fault Diagnosis of Analogue Electronic Circuits.
Application of Wavelet Analysis in Power Systems.
Discrete Wavelet Transform Application to the Protection of Electrical Power System: A Solution Approach for Detecting and Locating Faults in FACTS Environment.
Fault Diagnosis and Monitoring.
Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications.
Wavelet Analysis and Neural Networks for Bearing Fault Diagnosis.
On the Use of Wavelet Transform for Practical Condition Monitoring Issues.
Image Processing.
Information Extraction and Despeckling of SAR Images with Second Generation of Wavelet Transform.
The Wavelet Transform for Image Processing Applications.
Wavelet Based Image Compression Techniques.
mage Denoising Based on Wavelet Analysis for Satellite Imagery.
mage Watermarking in Higher-Order Gradient Domain.
Signal and Image Denoising Using Wavelet Transform.
A DFT-DWT Domain Invisible Blind Watermarking Techniques for Copyright Protection of Digital Images.
The Wavelet Transform as a Classification Criterion Applied to Improve Compression of Hyperspectral Images.
Applications in Engineering.
Robust Lossless Data Hiding by Feature-Based Bit Embedding Algorithm.
Time-Varying Discrete-Time Wavelet Transforms.
Optimized Scalable Wavelet-Based Codec Designs for Semi-Regular 3D Meshes.
Application of Wavelet Analysis for the Understanding of Vortex-Induced Vibration.
Application of Wavelets Transform in Rotorcraft UAV’s Integrated Navigation System.
