Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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...
Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
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...
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

On the link between Fourier transformation and passive amplification in temporal Talbot array illuminators.

Optics letters·2026
Same author

Quantum state revival via coherent energy redistribution.

Science advances·2026
Same author

Linear-optics time-frequency analysis of complex multi-THz-bandwidth waveforms from a single optical spectrum.

Optics letters·2025
Same author

Ultra-low loss optical delay lines based on silicon nitride SWG technology.

Optics express·2025
Same author

On-chip high-speed and multilevel passive NOT gate on a silicon photonic platform.

Optics letters·2025
Same author

Exploiting Nonlocal Correlations for Dispersion-Resilient Quantum Communications.

Physical review letters·2025
Same journal

Gaussian-modulated continuous-variable quantum key distribution over 60 km fiber using an integrated silicon photonic receiver.

Optics letters·2026
Same journal

E2E-OCT: end-to-end joint learning model using optical coherence tomography images for vocal cord leukoplakia diagnosis.

Optics letters·2026
Same journal

Holographic generation of panoramic 3D scenes by concave ellipsoidal mirror reflection.

Optics letters·2026
Same journal

Dual-pilot phase recovery with pair-wise maximum-ratio combining for coherent PONs.

Optics letters·2026
Same journal

Mapping the whispering gallery modes of a CaF<sub>2</sub> disk resonator with half-tapered fibers to estimate the fundamental mode volume.

Optics letters·2026
Same journal

Quantitative estimation of deep-subwavelength scale via dark-field scattering axial energy concentration decay profiles.

Optics letters·2026
See all related articles

Related Experiment Video

Updated: Jun 15, 2026

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Optical signal processors based on a time-spectrum convolution.

Yongwoo Park1, José Azaña

  • 1Institut National de la Recherche Scientifique-Energie, Matériaux et Télécommunications (INRS-EMT), Varennes, Québec, J3X, 1S2, Canada. park@emt.inrs.ca

Optics Letters
|March 19, 2010
PubMed
Summary
This summary is machine-generated.

A novel fiber-optics approach enables time-spectrum convolution (TSC) for optical signal processing. This method uses modulated incoherent light and dispersive media for advanced analog operations on temporal and spectral waveforms.

More Related Videos

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Related Experiment Videos

Last Updated: Jun 15, 2026

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Area of Science:

  • Photonics
  • Optical Signal Processing
  • Fiber Optics

Background:

  • Conventional microwave photonic filtering architectures are explored.
  • The need for versatile optical signal processing operations is identified.

Purpose of the Study:

  • To demonstrate a fiber-optics architecture for time-spectrum convolution (TSC).
  • To showcase the application of TSC in fundamental analog processing operations.

Main Methods:

  • Utilizing a fiber-optics architecture for microwave photonic filtering.
  • Implementing TSC by temporally modulating a filtered broadband incoherent light source.
  • Propagating the modulated light through a linear dispersive medium.

Main Results:

  • Successfully implemented the time-spectrum convolution (TSC) process.
  • Demonstrated TSC's capability for analog processing of temporal and spectral intensity waveforms.
  • Experimentally validated three specific operations: time-integration, spectrum-integration, and time-frequency correlation.

Conclusions:

  • The proposed fiber-optics architecture offers a powerful platform for optical signal processing.
  • The time-spectrum convolution (TSC) concept has broad applicability in fundamental optical signal processing and analysis.
  • Experimental demonstrations confirm the potential of TSC for various signal processing tasks.