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

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

398
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...
398
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

6.3K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
6.3K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

146
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....
146
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

449
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
449
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

133
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
133
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.2K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Influence of Source Parameters on the Polarization Properties of Beams for Practical Free-Space Quantum Key Distribution.

Entropy (Basel, Switzerland)·2021
Same author

Targeting lysophospholipid acid receptor 1 and ROCK kinases promotes antiviral innate immunity.

Science advances·2021
Same author

RORα is critical for mTORC1 activity in T cell-mediated colitis.

Cell reports·2021
Same author

Oestradiol promotes the intrahepatic bile duct development of C57BL/6CrSlc mice during embryonic period via Notch signalling pathway.

Journal of cellular and molecular medicine·2021
Same author

IGSF11 is required for pericentric heterochromatin dissociation during meiotic diplotene.

PLoS genetics·2021
Same author

Health-related quality of life in osteoarthritis patients: a systematic review and meta-analysis.

Psychology, health & medicine·2021
Same journal

Denoising algorithm of Φ-OTDR systems based on adaptive fractional wavelet transform denoising.

Optics express·2026
Same journal

Millisecond photon-to-photon latency and high-speed volumetric projection system for optogenetics.

Optics express·2026
Same journal

Polarization-encoded coaxial structured light for high-precision 3D surface profilometry.

Optics express·2026
Same journal

Discrete freeform optical design based on collaborative optimization of point cloud and local normals.

Optics express·2026
Same journal

Ultrafast ghost imaging with 25 GHz speckle switching and wavelength-division multiplexing.

Optics express·2026
Same journal

Atomic vapor cells fabricated by femtosecond laser welding of standard-optical-quality glass.

Optics express·2026
See all related articles

Related Experiment Video

Updated: Sep 30, 2025

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

14.7K

Compressive sensing based parameter estimation for free-space continuous-variable quantum key distribution.

Feng Jing, Xiaowen Liu, Xingyu Wang

    Optics Express
    |March 18, 2022
    PubMed
    Summary
    This summary is machine-generated.

    Compressive sensing (CS) enhances free-space continuous-variable quantum key distribution (CV-QKD) by enabling efficient channel parameter estimation. This method reduces key data sacrifice, improving secret key rates and performance in turbulent atmospheric conditions.

    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

    9.1K
    Quasi-light Storage for Optical Data Packets
    07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    11.0K

    Related Experiment Videos

    Last Updated: Sep 30, 2025

    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

    14.7K
    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

    9.1K
    Quasi-light Storage for Optical Data Packets
    07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    11.0K

    Area of Science:

    • Quantum Information Science
    • Optical Communication Systems
    • Signal Processing

    Background:

    • Satellite-based continuous-variable quantum key distribution (CV-QKD) relies on accurate atmospheric channel parameter estimation.
    • Turbulence and attenuation in free-space channels necessitate robust estimation techniques.
    • Current methods often sacrifice significant key data, reducing secret key rates and potentially compromising security.

    Purpose of the Study:

    • To apply compressive sensing (CS) theory for efficient channel parameter estimation in free-space CV-QKD.
    • To minimize the sacrifice of key data required for parameter estimation.
    • To enhance the secret key rate and overall performance of CV-QKD systems.

    Main Methods:

    • Analysis of sparse representation possibility for free-space channels based on CS theory.
    • Construction of two sparse reconstruction models for channel parameters, considering sub-channel stability.
    • Utilization of quantum signal variables and second-order statistics for model construction.

    Main Results:

    • Developed CS-based models that significantly reduce key data sacrifice for parameter estimation.
    • Achieved higher secret key generation and improved secret key rates compared to conventional methods.
    • Demonstrated adaptability for scenarios with limited communication time due to minimal or no key data loss.

    Conclusions:

    • Compressive sensing offers an effective solution for parameter estimation in free-space CV-QKD.
    • The proposed methods enhance secret key generation efficiency and security.
    • CS-based approaches are well-suited for practical implementation in satellite-based QKD.