This user guide serves as a companion to Digital Spectral Analysis, Second Edition (Dover Publications, 2019), illustrating all the text's techniques and algorithms, plus time versus frequency analysis. The spectral demonstrations use MATLAB software that encompasses the full experience from inputting signal sources, interactively setting technique parameters and processing with those parameters, and choosing from a variety of plotting techniques to display the results. The processing functions and scripts have been coded to automatically handle sample data that is either real-valued or complex-valued, permitting the user to simply modify the demonstration scripts to input their own data for analysis. Four integrated software categories support the demonstrations. These are the main MATLAB spectral demonstration scripts, supporting MATLAB plotting scripts, MATLAB processing functions listed in this guide, and signal sample data sources. Scripts and demonstration data files can be found on the Dover website for free downloading; see the Introduction for details.

Digital Spectral Analysis offers a broad perspective of spectral estimation techniques and their implementation. Coverage includes spectral estimation of discrete-time or discrete-space sequences derived by sampling continuous-time or continuous-space signals. The treatment emphasizes the behavior of each spectral estimator for short data records and provides over 40 techniques described and available as implemented MATLAB functions. In addition to summarizing classical spectral estimation, this text provides theoretical background and review material in linear systems, Fourier transforms, matrix algebra, random processes, and statistics. Topics include Prony's method, parametric methods, the minimum variance method, eigenanalysis-based estimators, multichannel methods, and two-dimensional methods. Suitable for advanced undergraduates and graduate students of electrical engineering — and for scientific use in the signal processing application community outside of universities — the treatment's prerequisites include some knowledge of discrete-time linear system and transform theory, introductory probability and statistics, and linear algebra. 1987 edition.

Advanced imaging spectral technology and hyperspectral analysis techniques for multiple applications are the key features of the book. This book will present in one volume complete solutions from concepts, fundamentals, and methods of acquisition of hyperspectral data to analyses and applications of the data in a very coherent manner. It will help readers to fully understand basic theories of HRS, how to utilize various field spectrometers and bioinstruments, the importance of radiometric correction and atmospheric correction, the use of analysis, tools and software, and determine what to do with HRS technology and data.

Based on fundamental principles from mathematics, linear systems, and signal analysis, digital signal processing (DSP) algorithms are useful for extracting information from signals collected all around us. Combined with today's powerful computing capabilities, they can be used in a wide range of application areas, including engineering, communicati

Quickly Engages in Applying Algorithmic Techniques to Solve Practical Signal Processing Problems With its active, hands-on learning approach, this text enables readers to master the underlying principles of digital signal processing and its many applications in industries such as digital television, mobile and broadband communications, and medical/scientific devices. Carefully developed MATLAB® examples throughout the text illustrate the mathematical concepts and use of digital signal processing algorithms. Readers will develop a deeper understanding of how to apply the algorithms by manipulating the codes in the examples to see their effect. Moreover, plenty of exercises help to put knowledge into practice solving real-world signal processing challenges. Following an introductory chapter, the text explores: Sampled signals and digital processing Random signals Representing signals and systems Temporal and spatial signal processing Frequency analysis of signals Discrete-time filters and recursive filters Each chapter begins with chapter objectives and an introduction. A summary at the end of each chapter ensures that one has mastered all the key concepts and techniques before progressing in the text. Lastly, appendices listing selected web resources, research papers, and related textbooks enable the investigation of individual topics in greater depth. Upon completion of this text, readers will understand how to apply key algorithmic techniques to address practical signal processing problems as well as develop their own signal processing algorithms. Moreover, the text provides a solid foundation for evaluating and applying new digital processing signal techniques as they are developed.

Signals and Systems Using MATLAB, Third Edition, features a pedagogically rich and accessible approach to what can commonly be a mathematically dry subject. Historical notes and common mistakes combined with applications in controls, communications and signal processing help students understand and appreciate the usefulness of the techniques described in the text. This new edition features more end-of-chapter problems, new content on two-dimensional signal processing, and discussions on the state-of-the-art in signal processing. - Introduces both continuous and discrete systems early, then studies each (separately) in-depth - Contains an extensive set of worked examples and homework assignments, with applications for controls, communications, and signal processing - Begins with a review on all the background math necessary to study the subject - Includes MATLAB® applications in every chapter

This supplement to any standard DSP text is one of the first books to successfully integrate the use of MATLAB® in the study of DSP concepts. In this book, MATLAB® is used as a computing tool to explore traditional DSP topics, and solve problems to gain insight. This greatly expands the range and complexity of problems that students can effectively study in the course. Since DSP applications are primarily algorithms implemented on a DSP processor or software, a fair amount of programming is required. Using interactive software such as MATLAB® makes it possible to place more emphasis on learning new and difficult concepts than on programming algorithms. Interesting practical examples are discussed and useful problems are explored. This updated second edition includes new homework problems and revises the scripts in the book, available functions, and m-files to MATLAB® V7.

This is the second volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs. This second book focuses on recent developments in response to the demands of new digital technologies. It is divided into two parts: the first part includes four chapters on the decomposition and recovery of signals, with special emphasis on images. In turn, the second part includes three chapters and addresses important data-based actions, such as adaptive filtering, experimental modeling, and classification.

Relying heavily on MATLAB® problems and examples, as well as simulated data, this text/reference surveys a vast array of signal and image processing tools for biomedical applications, providing a working knowledge of the technologies addressed while showcasing valuable implementation procedures, common pitfalls, and essential application concepts. The first and only textbook to supply a hands-on tutorial in biomedical signal and image processing, it offers a unique and proven approach to signal processing instruction, unlike any other competing source on the topic. The text is accompanied by a CD with support data files and software including all MATLAB examples and figures found in the text.