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Related Concept Videos

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...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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...

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Quasi-light Storage for Optical Data Packets
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Beating Nyquist with light: a compressively sampled photonic link.

J M Nichols1, F Bucholtz

  • 1Naval Research Laboratory, Washington, DC 20375, USA. jonathan.nichols@nrl.navy.mil

Optics Express
|April 20, 2011
PubMed
Summary

We demonstrate a photonic link using compressive sampling to recover signals beyond digitizer limits. This enables high-frequency signal reconstruction below the Nyquist rate.

Area of Science:

  • Photonics
  • Signal Processing
  • Electrical Engineering

Background:

  • Traditional signal acquisition adheres to the Nyquist-Shannon sampling theorem, requiring digitizers to sample at twice the highest frequency.
  • Digital signal processing capabilities are often limited by the sampling rate of analog-to-digital converters (ADCs).
  • Compressive sampling (CS) offers a potential pathway to overcome these limitations by exploiting signal sparsity.

Purpose of the Study:

  • To demonstrate the feasibility of a compressively sampled photonic link.
  • To enable signal recovery beyond the Nyquist limit using photonic systems.
  • To validate the reconstruction of high-frequency signals digitized below the Nyquist rate.

Main Methods:

  • Development of an all-photonic system architecture tailored for compressive sampling.

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  • Ensuring the signal possesses a sparse representation for effective CS.
  • Digitizing the photonic signal with samples incoherent to the signal's sparse representation.

    • Main Results:

    • Successful demonstration of a compressively sampled photonic link.
    • Faithful reconstruction of 1 GHz harmonic signals.
    • Achieved signal recovery significantly below the Nyquist rate (digitizing at 500 MS/s).

    Conclusions:

    • Compressive sampling can be effectively implemented in photonic links.
    • This approach allows for signal acquisition and recovery beyond traditional digitizer bandwidth limitations.
    • The demonstrated system offers a promising method for high-speed signal processing in photonic systems.