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

Poisson Probability Distribution01:09

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
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...
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
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...

You might also read

Related Articles

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

Sort by
Same author

Noise sources in laser radar systems.

Applied optics·2010
Same author

Heterodyne mixing efficiency for detector arrays.

Applied optics·2010
Same author

Optical autodyne detection: theory and experiment.

Applied optics·2010
Same author

Precise comparison of experimental and theoretical SNRs in CO(2) laser heterodyne systems: comments.

Applied optics·2010
Same author

Turbulence effects on coherent laser radar target statistics.

Applied optics·2010
Same author

Target-reflectivity theory for coherent laser radars.

Applied optics·2010

Related Experiment Video

Updated: Jun 16, 2026

Optical Trapping of Nanoparticles
13:39

Optical Trapping of Nanoparticles

Published on: January 15, 2013

Signal processing for a signal with poisson noise: comments

J H Shapiro

    Applied Optics
    |February 6, 2010
    PubMed
    Summary

    No abstract available in PubMed .

    More Related Videos

    Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
    15:06

    Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

    Published on: January 3, 2016

    Pupillometry to Assess Auditory Sensation in Guinea Pigs
    09:25

    Pupillometry to Assess Auditory Sensation in Guinea Pigs

    Published on: January 6, 2023

    Related Experiment Videos

    Last Updated: Jun 16, 2026

    Optical Trapping of Nanoparticles
    13:39

    Optical Trapping of Nanoparticles

    Published on: January 15, 2013

    Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
    15:06

    Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

    Published on: January 3, 2016

    Pupillometry to Assess Auditory Sensation in Guinea Pigs
    09:25

    Pupillometry to Assess Auditory Sensation in Guinea Pigs

    Published on: January 6, 2023