First published in 1962.

Photoplethysmography: Technology, Signal Analysis, and Applications is the first comprehensive volume on the theory, principles, and technology (sensors and electronics) of photoplethysmography (PPG). It provides a detailed description of the current state-of-the-art technologies/optical components enabling the extreme miniaturization of such sensors, as well as comprehensive coverage of PPG signal analysis techniques including machine learning and artificial intelligence. The book also outlines the huge range of PPG applications in healthcare, with a strong focus on the contribution of PPG in wearable sensors and PPG for cardiovascular assessment. - Presents the underlying principles and technology surrounding PPG - Includes applications for healthcare and wellbeing - Focuses on PPG in wearable sensors and devices - Presents advanced signal analysis techniques - Includes cutting-edge research, applications and future directions

Practical Biomedical Signal Analysis Using MATLAB® presents a coherent treatment of various signal processing methods and applications. The book not only covers the current techniques of biomedical signal processing, but it also offers guidance on which methods are appropriate for a given task and different types of data. The first several chapters of the text describe signal analysis techniques—including the newest and most advanced methods—in an easy and accessible way. MATLAB routines are listed when available and freely available software is discussed where appropriate. The final chapter explores the application of the methods to a broad range of biomedical signals, highlighting problems encountered in practice. A unified overview of the field, this book explains how to properly use signal processing techniques for biomedical applications and avoid misinterpretations and pitfalls. It helps readers to choose the appropriate method as well as design their own methods.

The SEM Handbook of Experimental Structural Dynamics stands as a comprehensive overview and reference for its subject, applicable to workers in research, product design and manufacture, and practice. The Handbook is devoted primarily to the areas of structural mechanics served by the Society for Experimental Mechanics IMAC community, such as modal analysis, rotating machinery, structural health monitoring, shock and vibration, sensors and instrumentation, aeroelasticity, ground testing, finite element techniques, model updating, sensitivity analysis, verification and validation, experimental dynamics sub-structuring, quantification of margin and uncertainty, and testing of civil infrastructure. Chapters offer comprehensive, detailed coverage of decades of scientific and technologic advance and all demonstrate an experimental perspective. Several sections specifically discuss the various types of experimental testing and common practices utilized in the automotive, aerospace, and civil structures industries. · History of Experimental Structural Mechanics · DIC Methods - Dynamic Photogrammetry · LDV Methods · Applied Digital Signal Processing · Introduction to Spectral - Basic Measurements · Structural Measurements - FRF · Random and Shock Testing · Rotating System Analysis Methods · Sensors Signal Conditioning Instrumentation · Design of Modal Tests · Experimental Modal Methods · Experimental Modal Parameter Evaluation · Operating Modal Analysis Methods · Analytical Numerical Substructuring · Finite Element Model Correlation · Model Updating · Damping of Materials and Structures · Model Calibration and Validation in Structures · Uncertainty Quantification: UQ, QMU and Statistics · Nonlinear System Analysis Methods (Experimental) · Structural Health Monitoring and Damage Detection · Experimental Substructure Modeling · Modal Modeling · Response (Impedance) Modeling · Nonlinear Normal Mode Analysis Techniques (Analytical) · Modal Modeling with Nonlinear Connection Elements (Analytical) · Acoustics of Structural Systems (VibroAcoustics) · Automotive Structural Testing · Civil Structural Testing · Aerospace Perspective for Modeling and Validation · Sports Equipment Testing · Applied Math for Experimental Structural Mechanics Contributions present important theory behind relevant experimental methods as well as application and technology. Topical authors emphasize and dissect proven methods and offer detail beyond a simple review of the literature. Additionally, chapters cover practical needs of scientists and engineers who are new to the field. In most cases, neither the pertinent theory nor, in particular, the practical issues have been presented formally in current academic textbooks. Each chapter in the Handbook represents a ’must read’ for someone new to the subject or for someone returning to the field after an absence. Reference lists in each chapter consist of the seminal papers in the literature. This Handbook stands in parallel to the SEM Handbook of Experimental Solid Mechanics, where this Handbook focuses on experimental dynamics of structures at a macro-scale often involving multiple components and materials where the SEM Handbook of Experimental Solid Mechanics focuses on experimental mechanics of materials at a nano-scale and/or micro-scale.

The report presents two methods for the estimation of a periodic signal in additive noise. Both methods assume that only a finite time sample of the signal plus noise is available for processing. The estimate of the signal is chosen to minimize the mean square error between the estimate and the sample of signal plus noise. This is also a maximum likelihood estimate if the noise is white and Gaussian. The first method is frequency domain analysis. Estimates for the period of the signal and the complex amplitudes of its harmonics are derived. The second method is time domain analysis. Estimates for the period of the signal and for the waveform of one period are derived. Under the assumption of white noise and large signal-to-noise ratio, formulas for the expected values and variances of the period estimates are derived. The estimates for the period are found to be the same by both methods. The estimate is unbiased and has a variance inversely proportional to the signal-to-noise ratio, and inversely proportional to the cube of the number of periods in the given sample. The expected values of the estimates of the waveform itself are derived, and the estimates are found to be biased.