This book presents digital signal processing theories and methods and their applications in data analysis, error analysis and statistical signal processing. Algorithms and Matlab programming are included to guide readers step by step in dealing with practical difficulties. Designed in a self-contained way, the book is suitable for graduate students in electrical engineering, information science and engineering in general.
This book covers the basics of processing and spectral analysis of monovariate discrete-time signals. The approach is practical, the aim being to acquaint the reader with the indications for and drawbacks of the various methods and to highlight possible misuses. The book is rich in original ideas, visualized in new and illuminating ways, and is structured so that parts can be skipped without loss of continuity. Many examples are included, based on synthetic data and real measurements from the fields of physics, biology, medicine, macroeconomics etc., and a complete set of MATLAB exercises requiring no previous experience of programming is provided. Prior advanced mathematical skills are not needed in order to understand the contents: a good command of basic mathematical analysis is sufficient. Where more advanced mathematical tools are necessary, they are included in an Appendix and presented in an easy-to-follow way. With this book, digital signal processing leaves the domain of engineering to address the needs of scientists and scholars in traditionally less quantitative disciplines, now facing increasing amounts of data.
This book gives an overview of the numerical data analysis and signal treatment techniques that are used in chromatography and related separation techniques. Emphasis is given to the description of the symmetrical and asymmetrical chromatographic peak shape models. Both theoretical and empirical models are discussed.The fundamentals of data acquisition, types and effect of baseline noise, and methods of improving the signal-to-noise ratio (either in time or in frequency and wavelet domain) are thoroughly discussed. Resolution enhancement techniques, such as curve fitting, deconvolution by Fourier and wavelet transforms, iterative deconvolution, Kalman filtering and multivariate methods of curve resolution are all discussed with several chromatographic examples. Quantitative analysis by peak area of peak height measurement, the precision and accuracy of the quantitation of stand-alone or overlapping and symmetrical or asymmetrical peaks are treated. In a separate chapter, guidelines are given for the use of transform techniques for the analysis of chromatograms. A statistical description of peak overlap is given in the final chapters. Since the concept of resolution has to be reconsidered when one separates complex mixtures, the problem of resolution and overlap is quantitatively discussed by means of statistical methods, and by using Fourier analysis of the complex chromatogram.Features of this book• The ultimate source of numerical techniques to enhance chromatographic data• Gives a detailed description of signal and resolution enhancement techniques in a manner applicable for enhancing not only chromatography, but also spectroscopic and other analytical signals• The first book with a thorough overview of the statistics of peak overlap.This is the first volume to encompass both the simple and more sophisticated methods for the numerical treatment of chromatograms. It is, therefore, the fundamental resource of numerical analysis methods for every analyst.
Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest background in mathematics, physics, and computer programming. The focus of this text is on what can be considered the 'golden trio' in the signal processing field: averaging, Fourier analysis, and filtering. Techniques such as convolution, correlation, coherence, and wavelet analysis are considered in the context of time and frequency domain analysis. The whole spectrum of signal analysis is covered, ranging from data acquisition to data processing; and from the mathematical background of the analysis to the practical application of processing algorithms. Overall, the approach to the mathematics is informal with a focus on basic understanding of the methods and their interrelationships rather than detailed proofs or derivations. One of the principle goals is to provide the reader with the background required to understand the principles of commercially available analyses software, and to allow him/her to construct his/her own analysis tools in an environment such as MATLAB®. - Multiple color illustrations are integrated in the text - Includes an introduction to biomedical signals, noise characteristics, and recording techniques - Basics and background for more advanced topics can be found in extensive notes and appendices - A Companion Website hosts the MATLAB scripts and several data files: http://www.elsevierdirect.com/companion.jsp?ISBN=9780123708670
Signal processing may broadly be considered to involve the recovery of information from physical observations. The received signal is usually disturbed by thermal, electrical, atmospheric or intentional interferences. Due to the random nature of the signal, statistical techniques play an important role in analyzing the signal. Statistics is also used in the formulation of the appropriate models to describe the behavior of the system, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Statistical signal processing basically refers to the analysis of random signals using appropriate statistical techniques. The main aim of this book is to introduce different signal processing models which have been used in analyzing periodic data, and different statistical and computational issues involved in solving them. We discuss in detail the sinusoidal frequency model which has been used extensively in analyzing periodic data occuring in various fields. We have tried to introduce different associated models and higher dimensional statistical signal processing models which have been further discussed in the literature. Different real data sets have been analyzed to illustrate how different models can be used in practice. Several open problems have been indicated for future research.
Digital signal processing lies at the heart of the communications revolution and is an essential element of key technologies such as mobile phones and the Internet. This book covers all the major topics in digital signal processing (DSP) design and analysis, supported by MatLab examples and other modelling techniques. The authors explain clearly and concisely why and how to use digital signal processing systems; how to approximate a desired transfer function characteristic using polynomials and ratio of polynomials; why an appropriate mapping of a transfer function on to a suitable structure is important for practical applications; and how to analyse, represent and explore the trade-off between time and frequency representation of signals. An ideal textbook for students, it will also be a useful reference for engineers working on the development of signal processing systems.
This book presents digital signal processing theories and methods and their applications in data analysis, error analysis and statistical signal processing. Algorithms and Matlab programming are included to guide readers step by step in dealing with practical difficulties. Designed in a self-contained way, the book is suitable for graduate students in electrical engineering, information science and engineering in general.
This book describes the essential tools and techniques of statistical signal processing. At every stage theoretical ideas are linked to specific applications in communications and signal processing using a range of carefully chosen examples. The book begins with a development of basic probability, random objects, expectation, and second order moment theory followed by a wide variety of examples of the most popular random process models and their basic uses and properties. Specific applications to the analysis of random signals and systems for communicating, estimating, detecting, modulating, and other processing of signals are interspersed throughout the book. Hundreds of homework problems are included and the book is ideal for graduate students of electrical engineering and applied mathematics. It is also a useful reference for researchers in signal processing and communications.
This book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved. Signal processing can broadly be considered to be the recovery of information from physical observations. The received signals are usually disturbed by thermal, electrical, atmospheric or intentional interferences, and due to their random nature, statistical techniques play an important role in their analysis. Statistics is also used in the formulation of appropriate models to describe the behavior of systems, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Analyzing different real-world data sets to illustrate how different models can be used in practice, and highlighting open problems for future research, the book is a valuable resource for senior undergraduate and graduate students specializing in mathematics or statistics.
