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Related Concept Videos

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...
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...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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...
Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...

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Related Experiment Video

Updated: Jun 7, 2026

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition
09:55

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition

Published on: January 5, 2024

Improved noise-immune phase-unwrapping algorithm.

R Cusack, J M Huntley, H T Goldrein

    Applied Optics
    |November 2, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study optimizes phase map unwrapping by improving branch cut placement. A modified nearest neighbor method reliably processes noisy speckle-interferometry data, even with high discontinuity source densities.

    Related Experiment Videos

    Last Updated: Jun 7, 2026

    Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition
    09:55

    Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition

    Published on: January 5, 2024

    Area of Science:

    • Optics and Photonics
    • Image Processing
    • Metrology

    Background:

    • Phase map unwrapping is crucial for interpreting interferometric data.
    • Noisy phase maps present challenges due to 2π discontinuities.
    • Existing algorithms rely on identifying discontinuity sources and branch cuts.

    Purpose of the Study:

    • To investigate and optimize methods for placing branch cuts in phase map unwrapping.
    • To enhance the reliability of phase unwrapping algorithms for noisy data.

    Main Methods:

    • Developed an algorithm identifying discontinuity sources in phase maps.
    • Investigated four distinct methods for optimizing branch cut placement.
    • Evaluated performance using unfiltered speckle-interferometry phase maps.

    Main Results:

    • A modified nearest neighbor approach demonstrated superior performance.
    • The optimized method reliably unwraps phase maps with high discontinuity source densities (0.05 sources/pixel).
    • The resulting phase maps are unique and independent of the unwrapping route.

    Conclusions:

    • Optimized branch cut placement significantly improves phase map unwrapping.
    • The modified nearest neighbor method offers a robust solution for noisy interferometric data.
    • This advancement enhances the accuracy and applicability of phase unwrapping techniques.