Fourier Transform and Its Applications Using Microsoft EXCEL® (Second Edition) (eBook, ePUB)
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The Fourier transform (FT) converts a waveform into a representation characterized by sinusoidal function. It is one of the most powerful and useful mathematical tools and it has applications to numerous physics and engineering applications. There are many textbooks on FT focusing on its mathematical theory, however, in order to acquire the knowledge and skill to conduct FT analysis, students of science and engineering need to practice realistic examples through which they can confirm and understand FT theories.This book demonstrates Microsoft EXCEL®-based Fourier transform of selected physic...
The Fourier transform (FT) converts a waveform into a representation characterized by sinusoidal function. It is one of the most powerful and useful mathematical tools and it has applications to numerous physics and engineering applications. There are many textbooks on FT focusing on its mathematical theory, however, in order to acquire the knowledge and skill to conduct FT analysis, students of science and engineering need to practice realistic examples through which they can confirm and understand FT theories.
This book demonstrates Microsoft EXCEL®-based Fourier transform of selected physics examples, as well as describing spectral density of the auto-regression process in relation to Fourier transform. Rather than offering rigorous mathematics, the book provides readers with an opportunity to gain an understanding of Fourier transform through the examples. They will acquire and analyze their own data following the step-by-step procedure outlined, and a hands-on acoustic spectral analysis is suggested as the ideal long-term student project.
This new edition updates and greatly expands upon the first, with additional examples and exercises in various application domains as well as a new chapter on Quantum random walks and Fourier analysis.
This book demonstrates Microsoft EXCEL®-based Fourier transform of selected physics examples, as well as describing spectral density of the auto-regression process in relation to Fourier transform. Rather than offering rigorous mathematics, the book provides readers with an opportunity to gain an understanding of Fourier transform through the examples. They will acquire and analyze their own data following the step-by-step procedure outlined, and a hands-on acoustic spectral analysis is suggested as the ideal long-term student project.
This new edition updates and greatly expands upon the first, with additional examples and exercises in various application domains as well as a new chapter on Quantum random walks and Fourier analysis.
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