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Fast Fourier Transform01:10

Fast Fourier Transform

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The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
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Trigonometric Fourier series01:17

Trigonometric Fourier series

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Fourier series is a foundational mathematical technique that decomposes periodic functions into an infinite series of sinusoidal harmonics. This method enables the representation of complex periodic signals as sums of simple sine and cosine functions, facilitating their analysis and interpretation in various fields, including signal processing, acoustics, and electrical engineering.
The trigonometric Fourier series specifically expresses a periodic function with a defined period T using sine...
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Parseval's Theorem for Fourier transform01:15

Parseval's Theorem for Fourier transform

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Parseval's theorem is a fundamental principle in signal processing that enables the calculation of a signal's energy in either the time domain or the frequency domain. This theorem is pivotal in demonstrating energy conservation between these two domains, ensuring that the computed energy value remains consistent regardless of the domain of analysis.
To understand Parseval's theorem, it is essential to first comprehend how signal energy is typically calculated. When considering a...
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Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

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Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...
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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

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The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
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Related Experiment Video

Updated: Nov 27, 2025

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

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A New Surrogating Algorithm by the Complex Graph Fourier Transform (CGFT).

Jordi Belda1, Luis Vergara1, Gonzalo Safont1

  • 1Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, 46022 València, Spain.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel graph Fourier transform method for creating surrogate graph signals. This approach offers greater flexibility in preserving signal properties, aiding classifier training with limited data.

Keywords:
Hermitian Laplacian matrixgraph Fourier transformsurrogates

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Area of Science:

  • Graph Signal Processing
  • Machine Learning
  • Data Augmentation

Background:

  • Surrogating algorithms typically involve phase randomization of the Fourier transform while maintaining spectrum amplitude.
  • This process is crucial for generating synthetic data that preserves essential signal characteristics.

Purpose of the Study:

  • To propose a new method for surrogating graph signals using the graph Fourier transform.
  • To enhance flexibility in defining and preserving properties of original graph signals in their surrogates.
  • To address the challenge of limited data instances in classifier training.

Main Methods:

  • Development of a complex graph Fourier transform based on a Hermitian Laplacian matrix.
  • Utilizing eigenvectors of the Hermitian Laplacian matrix as the basis for the transform.
  • Allowing unconstrained phase randomization in the transformed domain for the complex case.

Main Results:

  • Demonstration that the Hermitian Laplacian matrix can exhibit negative eigenvalues.
  • Showing that preserving graph spectrum amplitude leads to controllable invariances defined by the Hermitian Laplacian matrix.
  • Validation of the method's utility in scenarios with scarce data for classifier training.

Conclusions:

  • The proposed graph Fourier transform method provides a flexible framework for generating graph signal surrogates.
  • This technique is particularly valuable for data augmentation in machine learning, especially when dealing with limited datasets.
  • The control over invariances through the Hermitian Laplacian matrix offers new possibilities in signal processing and analysis.