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

Force Classification01:22

Force Classification

2.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.8K
Classification of Signals01:30

Classification of Signals

1.6K
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...
1.6K
Classification of Systems-I01:26

Classification of Systems-I

749
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
749
Classification of Systems-II01:31

Classification of Systems-II

657
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
657
Methods of Classification and Identification01:28

Methods of Classification and Identification

2.4K
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
2.4K

You might also read

Related Articles

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

Sort by
Same author

Theranostic <sup>99m</sup>Tc-labeled vancomycin-functionalized cerium oxide nanoparticles: Antibacterial efficacy, in vitro cytotoxicity, and in vivo biodistribution studies.

Journal of pharmaceutical sciences·2026
Same author

Simultaneous demonstration of multiple optical tapped delay line functions on multiple data channels.

Optics letters·2026
Same author

Promising purine-silver nanoparticles for tumor theranostic: Synthesis, radiolabeling, in vitro cytotoxicity, structure-activity relationship, and in vivo biodistribution studies.

Journal of pharmaceutical sciences·2026
Same author

Demonstration of optical pattern matching between two QPSK data channels using nonlinear wave mixing.

Optics letters·2025
Same author

Formulation of synergistic theranostic nanoradiopharmaceutical for bacterial infections inflammation using a novel fenticonazole platform: In vitro biological assessment, radioiodination and in vivo biodistribution.

Journal of pharmaceutical sciences·2025
Same author

Intranasal propranolol hydrochloride-loaded PLGA-lipid hybrid nanoparticles for brain targeting: Optimization and biodistribution study by radiobiological evaluation.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences·2025
Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026
Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026
Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026
Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026
Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026
Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026
See all related articles

Related Experiment Video

Updated: May 7, 2026

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

12.2K

Machine learning-based classification of structured light modes under turbulence and eavesdropping effects.

Ahmed B Ibrahim, Faisal J Aljasser, Saud A Alowais

    Applied Optics
    |June 10, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study classifies multiplexed structured light modes for reliable free-space optic (FSO) communications. Machine learning models achieved over 92% accuracy, enhancing data transfer rates even in turbulent conditions.

    More Related Videos

    A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
    11:15

    A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

    Published on: May 30, 2016

    25.2K
    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
    06:25

    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

    Published on: February 23, 2024

    586

    Related Experiment Videos

    Last Updated: May 7, 2026

    Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
    13:02

    Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

    Published on: February 27, 2016

    12.2K
    A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
    11:15

    A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

    Published on: May 30, 2016

    25.2K
    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
    06:25

    Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

    Published on: February 23, 2024

    586

    Area of Science:

    • Optical communications
    • Machine learning
    • Free-space optics

    Background:

    • Multiplexed structured light modes offer potential for enhanced data transmission.
    • Free-space optic (FSO) systems face challenges from turbulence and eavesdropping.
    • Classifying these modes is crucial for reliable FSO communication.

    Purpose of the Study:

    • To classify multiplexed structured light modes in FSO systems.
    • To evaluate the impact of turbulence and interception threats on mode classification.
    • To develop and compare machine/deep learning algorithms for this classification task.

    Main Methods:

    • An experimental 3-m FSO system was used to transmit 16 modes (8-ary Laguerre Gaussian and 8-ary superposition LG).
    • Four machine/deep learning algorithms were employed: artificial neural network, support vector machine, 1D CNN, and 2D CNN.
    • A fusion approach combining outputs from these algorithms was utilized.

    Main Results:

    • Classification accuracy exceeded 92% in weak turbulence, 81% in moderate turbulence, and 69% in strong turbulence.
    • The study is the first to concurrently address turbulence and interception threats in structured light mode classification.
    • The fused model demonstrated robust performance across varying turbulence levels.

    Conclusions:

    • Multiplexed structured light modes show significant potential for reliable, high-capacity data transmission in FSO systems.
    • Machine and deep learning algorithms are effective for classifying these modes under challenging conditions.
    • The proposed classification method enhances communication reliability in turbulent FSO channels.