The relatively unused millimeter-wave (mmWave) spectrum offers excellent opportunities to increase mobile capacity due to the enormous amount of available raw bandwidth. This paper presents experimental measurements and empirically-based propagation channel models for the 28, 38, 60, and 73 GHz mmWave bands, using a wideband sliding correlator channel sounder with steerable directional horn antennas at both the transmitter and receiver from 2011 to 2013. More than 15,000 power delay profiles were measured across the mmWave bands to yield directional and omnidirectional path loss models, temporal and spatial channel models, and outage probabilities. Models presented here offer side-by-side comparisons of propagation characteristics over a wide range of mmWave bands, and the results and models are useful for the research and standardization process of future mmWave systems. Directional and omnidirectional path loss models with respect to a 1 m close-in free space reference distance over a wide range of mmWave frequencies and scenarios using directional antennas in real-world environments are provided herein, and are shown to simplify mmWave path loss models, while allowing researchers to globally compare and standardize path loss parameters for emerging mmWave wireless networks. A new channel impulse response modeling framework, shown to agree with extensive mmWave measurements over several bands, is presented for use in link-layer simulations, using the observed fact that spatial lobes contain multipath energy that arrives at many different propagation time intervals. The results presented here may assist researchers in analyzing and simulating the performance of next-generation mmWave wireless networks that will rely on adaptive antennas and multiple-input and multiple-output (MIMO) antenna systems.

Wideband Millimeter-Wave Propagation Measurements and Channel Models for Future Wireless Communication System Design

The human startle response is a sensitive, noninvasive measure of central nervous system activity that is currently used in a wide variety of research and clinical settings. In this article, we raise methodological issues and present recommendations for optimal methods of startle blink electromyographic (EMG) response elicitation, recording, quantification, and reporting. It is hoped that this report will foster more methodological validity and reliability in research using the startle response, as well as increase the detail with which relevant methodology is reported in publications using this measure.

/pdf/committee-report-guidelines-for-human-startle-eyeblink-3wopiiqcru.pdf

Committee report: Guidelines for human startle eyeblink electromyographic studies.

Past, present and future of nonlinear system identification in structural dynamics

Fluctuating lift on a circular cylinder: review and new measurements

We consider the frequency domain form of proper orthogonal decomposition (POD), called spectral proper orthogonal decomposition (SPOD). Spectral POD is derived from a space–time POD problem for statistically stationary flows and leads to modes that each oscillate at a single frequency. This form of POD goes back to the original work of Lumley (Stochastic Tools in Turbulence, Academic Press, 1970), but has been overshadowed by a space-only form of POD since the 1990s. We clarify the relationship between these two forms of POD and show that SPOD modes represent structures that evolve coherently in space and time, while space-only POD modes in general do not. We also establish a relationship between SPOD and dynamic mode decomposition (DMD); we show that SPOD modes are in fact optimally averaged DMD modes obtained from an ensemble DMD problem for stationary flows. Accordingly, SPOD modes represent structures that are dynamic in the same sense as DMD modes but also optimally account for the statistical variability of turbulent flows. Finally, we establish a connection between SPOD and resolvent analysis. The key observation is that the resolvent-mode expansion coefficients must be regarded as statistical quantities to ensure convergent approximations of the flow statistics. When the expansion coefficients are uncorrelated, we show that SPOD and resolvent modes are identical. Our theoretical results and the overall utility of SPOD are demonstrated using two example problems: the complex Ginzburg–Landau equation and a turbulent jet.

/pdf/spectral-proper-orthogonal-decomposition-and-its-422122q6iv.pdf

Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis

This is a new edition of a book on random data analysis which has been on the market since 1966 and which was extensively revised in 1971. The book has been a bestseller since. It has been fully updated to cover new procedures developed in the last 15 years and extends the discussion to a broad range of applied fields, such as aerospace, automotive industries or biomedical research. The primary purpose of this book is to provide a practical reference and tool for working engineers and scientists investigating dynamic data or using statistical methods to solve engineering problems. It is comprehensive and self-contained and expands the coverage of the theory, including derivations of the key relationships in probability and random-process theory not usually found to such extent in a book of this kind. It could well be used as a teaching textbook for advanced courses on the analysis of random processes. The first four chapters present the background material on descriptions of data, properties of linear systems and statistical principles. They also include probability distribution formulas for one-, two- and higher-order changes of variables. Chapter five gives a comprehensive discussion of stationary random-process theory, including material on wave-number spectra, level crossings and peak values of normally distributed random data. Chapters six and seven develop mathematical relationships for the detailed analysis of single input/output and multiple input/output linear systems including algorithms. In chapters eight and nine important practical formulas to determine statistical errors in estimates of random data parameters and linear system properties from measured data are derived. Chapter ten deals with data aquisition and processing, including data qualification. Chapter eleven describes methods of data analysis such as data preparation, Fourier transforms, probability density functions, auto- and cross-correlation, spectral functions, joint record functions and multiple input/output functions. Chapter twelve shows how to handle nonstationary data analysis, classification of nonstationary data, probability structure of nonstationary data, calculation of nonstationary mean values or mean square values, correlation structures of nonstationary data and spectral structures of nonstationary data. The last chapter deals with the Hilbert transform including applications for both nondispersive and dispersive propagation problems. All chapters include many illustrations and references as well as examples and problem sets. This allows the reader to use the book for private study purposes. Altogether the book can be recommended for practical working engineers and scientists to support their daily work, as well as for university readers as a teaching textbook in advanced courses. M Krystek

J S Bendat and A G Piersol

Papers

Random Data Analysis and Measurement Procedures