Correlation measurement and evaluation of stochastic electromagnetic fields

Russer, Johannes A.; Haider, Michael; Baharuddin, Mohd Hafiz; Smartt, Christopher; Baev, Andrey; Wane, Sidina; Kuznetsov, Yu.V.; Thomas, David William; Russer, P.

doi:10.1109/emceurope.2016.7739292

Cited by 10 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where the diagonal and off-diagonal entries in (35) denote the self-impedance and the mutual-impedance respectively and are defined by the resistance and admittance parameter as,…”

Section: B Equally Strong Parallel Multi-channel Communicationmentioning

confidence: 99%

Near-Field MIMO Communication Links

Phang

Ivrlač

Gradoni

et al. 2018

IEEE Trans. Circuits Syst. I

View full text Add to dashboard Cite

A procedure to achieve near-field multiple input multiple output (MIMO) communication with equally strong channels is demonstrated in this paper. This has applications in near-field wireless communications, such as Chip-to-Chip (C2C) communication or wireless links between printed circuit boards. Designing the architecture of these wireless C2C networks is, however, based on standard engineering design tools. To attain this goal, a network optimization procedure is proposed, which introduces decoupling and matching networks. As a demonstration, this optimization procedure is applied to a 2-by-2 MIMO with dipole antennas. The potential benefits and design tradeoffs are discussed for implementation of wireless radio-frequency interconnects in chip-to-chip or device-to-device communication such as in an Internet-of-Things scenario.

show abstract

“…where the diagonal and off-diagonal entries in (35) denote the self-impedance and the mutual-impedance respectively and are defined by the resistance and admittance parameter as,…”

Section: B Equally Strong Parallel Multi-channel Communicationmentioning

confidence: 99%

Near-Field MIMO Communication Links

Phang

Ivrlač

Gradoni

et al. 2018

IEEE Trans. Circuits Syst. I

View full text Add to dashboard Cite

show abstract

“…The measurement methods are compared in terms of their RF, accessible resolution, reliability (including mechanical stress) performances and test time for industrial deployment. The amount of data recorded in two-point measurements required for the characterization of stochastic EM near fields can be reduced considerably by principal component analysis [55][56][57].…”

Section: (B) Transport Of Stochastic Fieldsmentioning

confidence: 99%

Stochastic electromagnetic field propagation— measurement and modelling

Gradoni

Russer

Baharuddin

et al. 2018

Phil. Trans. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

This paper reviews recent progress in the measurement and modelling of stochastic electromagnetic fields, focusing on propagation approaches based on Wigner functions and the method of moments technique. The respective propagation methods are exemplified by application to measurements of electromagnetic emissions from a stirred, cavity-backed aperture. We discuss early elements of statistical electromagnetics in Heaviside's papers, driven mainly by an analogy of electromagnetic wave propagation with heat transfer. These ideas include concepts of momentum and directionality in the realm of propagation through confined media with irregular boundaries. We then review and extend concepts using Wigner functions to propagate the statistical properties of electromagnetic fields. We discuss in particular how to include polarization in this formalism leading to a Wigner tensor formulation and a relation to an averaged Poynting vector.This article is part of the theme issue ‘Celebrating 125 years of Oliver Heaviside's ‘Electromagnetic Theory’’.

show abstract

“…Principal component analysis (PCA) is well suited for this purpose. [31][32][33] In the following, we develop an algorithm based on PCA for reducing the dimensionality of a given set of correlation matrices, while retaining most of the information present in the original data. The algorithm will be flexible enough to allow for different qualities of the resulting approximations.…”

Section: Introductionmentioning

confidence: 99%

“…To make further numerical treatment possible, eg, for equivalent source localization in EMI scenarios or for computationally propagating correlation spectra, we need to reduce the amount of data considerably, without loosing the relevant contained information. Principal component analysis (PCA) is well suited for this purpose . In the following, we develop an algorithm based on PCA for reducing the dimensionality of a given set of correlation matrices, while retaining most of the information present in the original data.…”

Section: Introductionmentioning

confidence: 99%

Principal component analysis for efficient characterization of stochastic electromagnetic fields

Haider

Russer

2017

Int J Numerical Modelling

Self Cite

View full text Add to dashboard Cite

The propagation of stochastic electromagnetic fields can be accurately modeled using the auto-and cross-correlation spectra of the field components. In this work, we introduce the framework of principal component analysis for reducing the computational cost of handling stochastic electromagnetic fields described by correlation matrices. We consider noisy electromagnetic fields originating from stationary random processes with Gaussian probability distribution. The amount of data obtained by 2-dimensional near-field measurements and by determining the autoand cross-correlation information of electromagnetic interference can become burdensome for further processing even for problems of moderate size. For obtaining the correlation data, 2 measurement probes have to scan a defined grid of measurement points. For each pair of points, the spatial correlations need to be calculated, and hence, the data obtained scales quadratically with the number of sampling points.To reduce the amount of data, we project the given data obtained by measurement or simulation onto directions of maximum variation, the so called principal components, and keep only those principal components which contribute most to the total variance. In this way, the memory demand for storage and further computation of stochastic electromagnetic fields can be reduced significantly. KEYWORDS electromagnetic interference, noisy electromagnetic fields, principal component analysis, stochastic electromagnetic fields Int J Numer Model. 2018;31:e2246.wileyonlinelibrary.com/journal/jnm

show abstract

Correlation measurement and evaluation of stochastic electromagnetic fields

Cited by 10 publications

References 17 publications

Near-Field MIMO Communication Links

Near-Field MIMO Communication Links

Stochastic electromagnetic field propagation— measurement and modelling

Principal component analysis for efficient characterization of stochastic electromagnetic fields

Contact Info

Product

Resources

About