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Related Concept Videos

Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Fast Fourier Transform01:10

Fast Fourier Transform

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...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

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Related Experiment Video

Updated: Jul 3, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Adaptive asynchronous algorithm for fringe pattern demodulation.

J A Gómez-Pedrero1, J A Quiroga, M Servín

  • 1Optics Department, Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Ciudad Universitaria, s/n 28040 Madrid, Spain. jagomezp@fis.ucm.es

Applied Optics
|July 22, 2008
PubMed
Summary
This summary is machine-generated.

We developed a new spatial adaptive asynchronous algorithm for fringe pattern demodulation. This method improves frequency response by selecting optimal sample spacing for each point, enhancing accuracy in optical metrology.

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Area of Science:

  • Optical Metrology
  • Signal Processing

Background:

  • Fringe pattern analysis is crucial for optical measurement techniques.
  • Standard asynchronous methods for fringe demodulation have limitations in frequency response.

Purpose of the Study:

  • To introduce a novel spatial adaptive asynchronous algorithm for fringe pattern demodulation.
  • To enhance the frequency response of fringe analysis methods.

Main Methods:

  • The algorithm builds upon the standard five-step asynchronous method.
  • It adaptively selects the optimal sample spacing for each fringe pattern point.
  • Frequency response is maximized by choosing the best sample spacing per location.

Main Results:

  • The proposed algorithm demonstrates an improved frequency response compared to existing methods.
  • Theoretical analysis confirms the algorithm's effectiveness.
  • Experimental validation shows the practical feasibility of the spatial adaptive approach.

Conclusions:

  • The spatial adaptive asynchronous algorithm offers superior performance in fringe pattern demodulation.
  • This method provides a more robust and accurate approach for optical measurements.