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

303
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...
303
Aliasing01:18

Aliasing

200
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...
200
Carrier Generation and Recombination01:22

Carrier Generation and Recombination

688
Carrier generation is the process by which electron-hole pairs (EHPs) are created within the semiconductor. In direct-bandgap semiconductors, such as gallium arsenide (GaAs), this occurs efficiently when energy absorption prompts valence electrons to leap into the conduction band, leaving behind holes.
This process is given by the generation rate G and is efficient due to the conservation of momentum between the valence band maximum and conduction band minimum.
Indirect generation involves an...
688

You might also read

Related Articles

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

Sort by
Same author

Study on the correlation between lifestyle and negative conversion time in patients diagnosed with coronavirus disease (COVID-19): a retrospective cohort study.

BMC public health·2023
Same author

Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learning.

EBioMedicine·2023
Same author

The burden of HEV-related acute liver failure in Bangladesh, China and India: a systematic review and meta-analysis.

BMC public health·2023
Same author

Establishment of a predictive nomogram for differentiated thyroid cancer: an inpatient-based retrospective study.

Endokrynologia Polska·2023
Same author

Remnant cholesterol and severity of nonalcoholic fatty liver disease.

Diabetology & metabolic syndrome·2023
Same author

Black phosphorus quantum dots induce myocardial inflammatory responses and metabolic disorders in mice.

Journal of environmental sciences (China)·2023

Related Experiment Video

Updated: Aug 23, 2025

Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

10.9K

Signal recovery in optical wireless communication using photonic convolutional processor.

Qiuyi Lu, Zwei Li, Guoqiang Li

    Optics Express
    |October 27, 2022
    PubMed
    Summary

    This study introduces a photonic convolutional processor for faster, more power-efficient optical communication signal recovery. The optoelectronic convolutional neural network (OECNN) effectively mitigates distortions, achieving below-threshold bit error rates.

    More Related Videos

    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

    10.0K
    Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
    09:23

    Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

    Published on: May 30, 2014

    14.6K

    Related Experiment Videos

    Last Updated: Aug 23, 2025

    Quasi-light Storage for Optical Data Packets
    07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    10.9K
    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

    10.0K
    Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
    09:23

    Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

    Published on: May 30, 2014

    14.6K

    Area of Science:

    • Photonics and Optical Communications
    • Artificial Intelligence and Machine Learning
    • Signal Processing

    Background:

    • Deep neural networks (DNNs) show promise in mitigating distortions in optical communication systems.
    • High data throughput demands rapid and power-efficient signal processing, which current DNNs struggle to provide due to computational costs.

    Purpose of the Study:

    • To propose and demonstrate a novel optical communication signal recovery technology using a photonic convolutional processor.
    • To implement an optoelectronic convolutional neural network (OECNN) for signal post-equalization and nonlinear compensation.
    • To evaluate the performance of the OECNN in terms of bit error ratio (BER) and quality (Q) factor.

    Main Methods:

    • Development of a photonic convolutional processor utilizing dispersion delay units and wavelength division multiplexing.
    • Implementation of an optoelectronic convolutional neural network (OECNN) based on the photonic processor.
    • Experimental demonstration on 16QAM and 32QAM signals in an optical wireless communication system.

    Main Results:

    • The OECNN achieved accurate signal recovery with a bit error ratio (BER) below the 7% forward error correction threshold (3.8×10-3) at 2Gbps.
    • Nonlinear compensation using OECNN improved the quality (Q) factor by 3.35 dB for 16QAM and 3.30 dB for 32QAM compared to linear compensation alone.
    • Performance was comparable to electronic neural network counterparts.

    Conclusions:

    • The photonic implementation of DNNs, specifically the OECNN, offers a promising solution for fast and power-efficient optical communication signal processing.
    • This technology effectively mitigates both linear and nonlinear distortions, enhancing signal quality and enabling higher data rates.