By Le-Wei Li

The flagship monograph addressing the spheroidal wave functionality and its pertinence to computational electromagnetics

*Spheroidal Wave capabilities in Electromagnetic Theory* offers intimately the speculation of spheroidal wave capabilities, its purposes to the research of electromagnetic fields in a variety of spheroidal constructions, and offers accomplished programming codes for these computations.

The themes lined during this monograph include:

- Spheroidal coordinates and wave functions
- Dyadic Green's services in spheroidal systems
- EM scattering by means of a undertaking spheroid
- EM scattering by means of a covered dielectric spheroid
- Spheroid antennas
- SAR distributions in a spheroidal head model

The programming codes and their functions are supplied on-line and are written in Mathematica 3.0 or 4.0. Readers may also boost their very own codes in accordance with the speculation or regimen defined within the e-book to discover next ideas of advanced constructions.

*Spheroidal Wave services in Electromagnetic Theory* is a basic reference for scientists, engineers, and graduate scholars working towards glossy computational electromagnetics or utilized physics.

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**Extra resources for Spheroidal wave functions in electromagnetic theory**

**Example text**

There are several definitions of ICA. In this book, depending on the problem, we use different definitions given below. 1 (Temporal ICA) The ICA of a noisy random vector x(k) ∈ IRm is obtained by finding an n × m, (with m ≥ n), a full rank separating matrix W such that the output signal vector y(k) = [y1 (k), y2 (k), . . 4) contains the estimated source components s(k) ∈ IRn that are as independent as possible, evaluated by an information-theoretic cost function such as the minimum Kullback-Leibler divergence.

For example, for MEG or EEG, we can use a phantom of the human head with known artificial source excitations located in specific places inside of the phantom. Similarly, for the cocktail party problem, we can record for short-time windows original test speech sources. These short-time window training sources enable us to determine, on the basis of a supervised algorithm, a suitable nonlinear demixing model and associated nonlinear basis functions of the neural network and their parameters. However, we assume that the mixing system is a slowly time-varying system for which some parameters fluctuate slightly over time, mainly due to the change in localization of source signals in space.

1 In this book, unless otherwise mentioned, we assume that the source signals (and consequently output signals) are zero-mean. Non zero-mean source can be modelled by zero-mean source with an additional constant source. This constant source can be usually detected but its amplitude cannot be recovered without some a priori knowledge. There are several definitions of ICA. In this book, depending on the problem, we use different definitions given below. 1 (Temporal ICA) The ICA of a noisy random vector x(k) ∈ IRm is obtained by finding an n × m, (with m ≥ n), a full rank separating matrix W such that the output signal vector y(k) = [y1 (k), y2 (k), .