A thorough guide to the classical and contemporary mathematical methods of modern signal and image processing Discrete Fourier Analysis and Wavelets presents a thorough introduction to the mathematical foundations of signal and image processing. Key concepts and applications are addressed in a thought-provoking manner and are implemented using vector, matrix, and linear algebra methods. With a balanced focus on mathematical theory and computational techniques, this self-contained book equips readers with the essential knowledge needed to transition smoothly from mathematical models to practical digital data applications. The book first establishes a complete vector space and matrix framework for analyzing signals and images. Classical methods such as the discrete Fourier transform, the discrete cosine transform, and their application to JPEG compression are outlined followed by coverage of the Fourier series and the general theory of inner product spaces and orthogonal bases. The book then addresses convolution, filtering, and windowing techniques for signals and images. Finally, modern approaches are introduced, including wavelets and the theory of filter banks as a means of understanding the multiscale localized analysis underlying the JPEG 2000 compression standard. Throughout the book, examples using image compression demonstrate how mathematical theory translates into application. Additional applications such as progressive transmission of images, image denoising, spectrographic analysis, and edge detection are discussed. Each chapter provides a series of exercises as well as a MATLAB project that allows readers to apply mathematical concepts to solving real problems. Additional MATLAB routines are available via the book's related Web site. With its insightful treatment of the underlying mathematics in image compression and signal processing, Discrete Fourier Analysis and Wavelets is an ideal book for mathematics, engineering, and computer science courses at the upper-undergraduate and beginning graduate levels. It is also a valuable resource for mathematicians, engineers, and other practitioners who would like to learn more about the relevance of mathematics in digital data processing.
Color Image Processing Techniques using Quaternion Fourier Transforms is an ideal book for researchers in the area of color image processing. This book uses plain and simple English to explain the applications of Quaternion Fourier Transforms(QFT) for color image processing. The chapters cover the use of QFT for color image processing applications such as frequency domain filtering, color image registration, color texture segmentation, color image denoising and watermarking.
Colored digital images are extensively used in computer applications. Uncompressed digital images require considerable storage capacity and transmission bandwidth. Efficient image compression solutions are becoming more critical with the recent growth of data intensive, multimedia-based web applications. This book studies color image compression with discrete Cosine, Fourier and Wavelets (Haar and Daubechies) transforms. As a necessary background, the basic concepts of color images, Wavelets and recently used compression algorithms are discussed. In the end, comparison of different transforms using different color image has been given.
The auscultation method is an important diagnostic indicator for hemodynamic anomalies. Heart sound classification and analysis play an important role in the auscultative diagnosis. The term phonocardiography refers to the tracing technique of heart sounds and the recording of cardiac acoustics vibration by means of a microphone-transducer. Therefore, understanding the nature and source of this signal is important to give us a tendency for developing a competent tool for further analysis and processing, in order to enhance and optimize cardiac clinical diagnostic approach. This book gives the reader an inclusive view of the main aspects in phonocardiography signal processing. Table of Contents: Introduction to Phonocardiography Signal Processing / Phonocardiography Acoustics Measurement / PCG Signal Processing Framework / Phonocardiography Wavelets Analysis / Phonocardiography Spectral Analysis / PCG Pattern Classification / Special Application of Phonocardiography / Phonocardiography Acoustic Imaging and Mapping
Digital Image Processing and Analysis techniques are widely used in the field of Medical Science. This book presents application of digital image processing and analysis in the field of medical science through the medical palmistry. Medical palmistry is scientific study of human palm and nails for their color and texture to identify and predict diseases. The sample codes are also given wherever required. The book is very useful to the student of computer science, especially those who do programming for image processing applications.
Miller and Childers provides the reader with the tools and skills needed to solve problems. It provides enough depth to equip graduate students and professionals with the necessary tools to study modern communication systems, control systems, signal processing techniques, and many other applications, hut the concepts are explained in a clear and simple manner that makes the text accessible to undergraduates as well. The book also contains an entire chapter devoted to simulations which have become an integral part of both academic and industrial research and development. Features: Features real-world applications in digital communications, information theory, coding theory, image processing, speech analysis, synthesis and recognition, and others. Clearly presents concepts through exceptional exposition and numerous worked-out problems. Includes MATLAB exercises and applications throughout the text; other software.
This book provides a balanced account of analog, digital and mixed-mode signal processing with applications in telecommunications. Part I Perspective gives an overview of the areas of Systems on a Chip (Soc) and mobile communication which are used to demonstrate the complementary relationship between analog and digital systems. Part II Analog (continuous-time) and Digital Signal Processing contains both fundamental and advanced analysis, and design techniques, of analog and digital systems. This includes analog and digital filter design; fast Fourier transform (FFT) algorithms; stochastic signals; linear estimation and adaptive filters. Part III Analog MOS Integrated Circuits for Signal Processing covers basic MOS transistor operation and fabrication through to the design of complex integrated circuits such as high performance Op Amps, Operational Transconductance Amplifiers (OTA's) and Gm-C circuits. Part IV Switched-capacitor and Mixed-mode Signal Processing outlines the design of switched-capacitor filters, and concludes with sigma-delta data converters as an extensive application of analog and digital signal processing Contains the fundamentals and advanced techniques of continuous-time and discrete-time signal processing. Presents in detail the design of analog MOS integrated circuits for signal processing, with application to the design of switched-capacitor filters. Uses the comprehensive design of integrated sigma-delta data converters to illustrate and unify the techniques of signal processing. Includes solved examples, end of chapter problems and MATLAB® throughout the book, to help readers understand the mathematical complexities of signal processing. The treatment of the topic is at the senior undergraduate to graduate and professional levels, with sufficient introductory material for the book to be used as a self-contained reference.
Mai Said Mabrouk received her B.Sc. degree in Systems and Biomedical Engineering Department, Cairo University, Giza, Egypt, in 2000. She completed her M.Sc. and Ph.D. degrees in Biomedical Engineering from the same school in 2004 and 2008 respectively. She is an assistant professor of Biomedical Engineering, Misr University for Science and Technology (MUST) since August 2008. She has several research articles in the area of image processing, Digital Signal processing and bioinformatics. Her research interests include biomedical image processing, bioinformatics, and digital signal processing in addition to genomic signal processing.
Denoising of any type of signal is a vital part of communication and signal processing system. A signal in the communication system is the information containing part which needs to be processed, but during the process some noise is added in the signal and signal become noisy. The source of noise like noisy engine, pump etc introduces noise over telephone channel or in radio communication device. This is now necessary to denoise that signal or to remove that noise from that signal. Denoising of a signal can be done by using a low pass Butterworth filter, statistically matched wavelet filter and wavelet thresholding method. Wavelet transform is a very helpful method of speech signal analysis and it can be used in many applications for e.g. image processing and signal de-noising. Wavelet transform breaks a speech signal into multi-scale representation. It is also called wavelet thresholding. This technique replaces the coefficients by zero below and above a threshold value. This technique is very useful to minimize the mean square error. This report is based on wavelet denoising algorithm. Number of wavelets is applied on different speech signal and performance is evaluated.
Digital signal processing plays a central role in the development of modern communication and information processing systems. The theory and application of signal processing is concerned with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and therefore noise reduction, the removal of channel distortion, and replacement of lost samples are important parts of a signal processing system. The fourth edition of Advanced Digital Signal Processing and Noise Reduction updates and extends the chapters in the previous edition and includes two new chapters on MIMO systems, Correlation and Eigen analysis and independent component analysis. The wide range of topics covered in this book include Wiener filters, echo cancellation, channel equalisation, spectral estimation, detection and removal of impulsive and transient noise, interpolation of missing data segments, speech enhancement and noise/interference in mobile communication environments. This book provides a coherent and structured presentation of the theory and applications of statistical signal processing and noise reduction methods. Two new chapters on MIMO systems, correlation and Eigen analysis and independent component analysis Comprehensive coverage of advanced digital signal processing and noise reduction methods for communication and information processing systems Examples and applications in signal and information extraction from noisy data Comprehensive but accessible coverage of signal processing theory including probability models, Bayesian inference, hidden Markov models, adaptive filters and Linear prediction models Advanced Digital Signal Processing and Noise Reduction is an invaluable text for postgraduates, senior undergraduates and researchers in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be of interest to professional engineers in telecommunications and audio and signal processing industries and network planners and implementers in mobile and wireless communication communities.
Digital image processing and analysis covers large topics such as image acquisition, image pre-processing, enhancement, segmentation and classification. Medical image segmentation and analysis algorithms differ from traditional images due to their basic nature of image characteristics. In this work the authors discussed the elementary image segmentation concepts and used the elementary and advanced techniques to segment and analyze the medical images. Different image segmentation techniques like color characteristic based segmentation and analysis, watershed segmentation techniques, active contours and graph based methods are applied for segmentation and results are presented here.
An ideal resource for students, industrial engineers, and researchers, Signal Processing with Free Software Practical Experiments presents practical experiments in signal processing using free software. The text introduces elementary signals through elementary waveform, signal storage files and elementary operations on signals and then presents the first tools to signal analysis such as temporal and frequency characteristics leading to Time-frequency analysis. Non-parametric spectral analysis is also discussed as well as signal processing through sampling, resampling, quantification, and analog and digital filtering. Table of Contents: 1. Generation of Elementary Signals. Generation of Elementary Waveform. – Elementary Operations on the Signals. – Format of Signal Storage Files. 2. First tools of Signal Analysis. Measurement of Temporal and Frequency Characteristics of a Signal. Time-Frequency Analysis of a Signal. 3. Non-parametric Spectral Analysis. 4. Signal Processing. Sampling. – Resampling. – Quantification. – “Analog” Filtering. Digital Filtering
The book is aimed on the development of a cheap and universal image processing camera system (IPCAM), for the possibility of further arbitrary modifications and simple reconfiguration for image processing applications or vision and motion systems. The system should be also powerful enough to be able to obtain and process images from the digital camera sensor in a real time. Further part of the book is design and implementation of the functions for image processing applications and communication with other superior system/s.
Emerging applications such as high definition television (HDTV), streaming video, image processing in embedded applications and signal processing in high-speed wireless communications are driving a need for high performance digital signal processors (DSPs) with real-time processing. This class of applications demonstrates significant data parallelism, finite precision,need for power-efficiency and the need for 100's of arithmetic units in the DSP to meet real-time requirements. Data-parallel DSPs meet these requirements by employing clusters of functional units, enabling 100's of computations every clock cycle. These DSPs exploit instruction level parallelism and subword parallelism within clusters, similar to atraditional VLIW (Very Long Instruction Word) DSP, and exploit data parallelism across clusters, similar to vector processors.
The usual method for time-frequency representation using Short Time Fourier Transform or Spectrogram does not give precise time frequency information; hence the analysis of signal using them is not much easy. The Wigner Ville Distribution (WVD) can be used for a time-frequency representation of signals with a very good resolution in time and frequency domain. WVD has many features which make them suitable for applications in transient signal detection. This book talks about different time frequency signal representations including Wigner Ville Distribution and presents some methods to reduce the cross terms in WVD. The book also describes some important applications of the variants of WVD in the world of signal processing. This book will help a student or a researcher to learn about time frequency representations and to understand how to use them in a variety of applications.