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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

509
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
509
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

2.4K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
2.4K
Classification of Signals01:30

Classification of Signals

1.7K
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...
1.7K
Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

7.7K
When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
7.7K
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

12.3K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
12.3K
UV–Vis Spectroscopy: Molecular Electronic Transitions01:16

UV–Vis Spectroscopy: Molecular Electronic Transitions

3.6K
In Ultraviolet–Visible (UV–Vis) spectroscopy, the absorption of electromagnetic radiation is used to probe the electronic structure of molecules. This technique provides insights into molecular electronic transitions, particularly the movement of electrons between different molecular orbitals. Radiation is absorbed if the energy of the electromagnetic radiation passing through the molecule is precisely equal to the energy difference between the excited and ground states. During this...
3.6K

You might also read

Related Articles

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

Sort by
Same author

TMEM184A-mediated autophagy in MHC-I degradation promotes tumor immune evasion.

Autophagy·2026
Same author

Endovascular therapy versus standard medical treatment for vertebrobasilar artery occlusion: a systematic review and meta-analysis.

Frontiers in neurology·2026
Same author

Experimental demonstration of high space compression by optical spaceplates.

Nature communications·2026
Same author

Parametric Amplification of Optical Pulses through Synthetic Motion in a Time-Varying Medium.

Nano letters·2026
Same author

Engineering walk-off-induced orbital angular momentum spectrum in spontaneous parametric downconversion.

Optics letters·2026
Same author

Epsilon-near-zero time-gate for high-fidelity spatial information transfer through dynamic scattering media.

Nature communications·2026
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: Apr 20, 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

10.5K

Multiple-output multivariate optical computing for spectrum recognition.

Joseph E Vornehm, Ava Jingwen Dong, Robert W Boyd

    Optics Express
    |November 18, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel multivariate optical computer for rapid spectrum recognition. The device simultaneously identifies multiple spectra, significantly accelerating analysis with high fidelity.

    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

    9.8K
    Multimodal Optical Imaging Platform for Studying Cellular Metabolism
    04:47

    Multimodal Optical Imaging Platform for Studying Cellular Metabolism

    Published on: June 6, 2025

    1.4K

    Related Experiment Videos

    Last Updated: Apr 20, 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

    10.5K
    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

    9.8K
    Multimodal Optical Imaging Platform for Studying Cellular Metabolism
    04:47

    Multimodal Optical Imaging Platform for Studying Cellular Metabolism

    Published on: June 6, 2025

    1.4K

    Area of Science:

    • Optics and Photonics
    • Computer Science
    • Materials Science

    Background:

    • Spectrum recognition is crucial in various scientific fields.
    • Traditional methods can be time-consuming.
    • Optical computing offers potential for faster data processing.

    Purpose of the Study:

    • To develop a multivariate optical computer for simultaneous spectral filter implementation.
    • To enhance the speed of spectrum recognition using optical computing.
    • To demonstrate the capability of identifying multiple spectra concurrently.

    Main Methods:

    • Implementing multiple spectral filters within a single optical system.
    • Utilizing parallel detection for multiple outputs.
    • Developing an optical computer architecture for spectral analysis.

    Main Results:

    • The system successfully implemented multiple spectral filters simultaneously.
    • More than two spectra were identified concurrently.
    • Fidelity of at least 0.83 was achieved in recognizing rare-earth-doped glass and white light spectra.

    Conclusions:

    • The multivariate optical computer significantly speeds up spectrum recognition.
    • Parallel detection in optical computing enables simultaneous multi-spectrum identification.
    • The demonstrated approach shows high potential for advanced spectral analysis applications.