"Written for vibration analysts, predictive maintenance specialists, field mechanics, and a wide variety of engineers, Vibration Spectrum Analysis assumes no prior knowledge of advanced mathematics or mechanical engineering. It carefully guides the reader through sophisticated analysis techniques in a logical, easy-to-understand manner."--BOOK JACKET.

This state-of-the-art survey serves as a complete overview of the subject. Besides the principles and theoretical foundations, emphasis is laid on practical applicability -- describing not only classical methods, but also modern developments and their applications. Students, researchers and practitioners, especially in the fields of data registration, treatment and evaluation, will find this a wealth of information.

Provides an introduction to those needing to use infrared spectroscopy for the first time, explaining the fundamental aspects of this technique, how to obtain a spectrum and how to analyse infrared data covering a wide range of applications. Includes instrumental and sampling techniques Covers biological and industrial applications Includes suitable questions and problems in each chapter to assist in the analysis and interpretation of representative infrared spectra Part of the ANTS (Analytical Techniques in the Sciences) Series.

Fundamentals of Energy Dispersive X-ray Analysis provides an introduction to the fundamental principles of dispersive X-ray analysis. It presents descriptions, equations, and graphs to enable the users of these techniques to develop an intuitive and conceptual image of the physical processes involved in the generation and detection of X-rays. The book begins with a discussion of X-ray detection and measurement, which is accomplished by one of two types of X-ray spectrometer: energy dispersive or wavelength dispersive. The emphasis is on energy dispersive spectrometers, given their rather widespread use compared to the wavelength dispersive type. This is followed by separate chapters on techniques such as X-ray absorption; spectrum processing; and elimination of spectrum background produced by electron excitation. Subsequent chapters cover X-ray fluorescence; the use of regression models; hardware for X-ray fluorescence analysis; scattering, background, and trace element analysis; and methods for producing inner shell excitation of atoms in a sample of interest. The final chapter deals with applications of X-ray analysis.

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

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.