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
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), .