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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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.
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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, the...
Aliasing01:18

Aliasing

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 signal...
Coefficient of Correlation01:12

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

You might also read

Related Articles

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

Sort by
Same author

[Research progress in ecological prevention of dental caries].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology·2022
Same author

Roadmap on digital holography [Invited].

Optics express·2021
Same author

[An analysis of key points for root canal therapy technique].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology·2016
Same author

Immortalization and characterization of human dental papilla cells with odontoblastic differentiation.

International endodontic journal·2012
Same author

Flagellin-PAc fusion protein is a high-efficacy anti-caries mucosal vaccine.

Journal of dental research·2012
Same author

Validation of micro-CT against the section method regarding the assessment of marginal leakage of sealants.

Australian dental journal·2012
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: Jun 12, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Optimal maximum correlation filter for arbitrarily constrained devices.

M W Fan, J W Goodman

    Applied Optics
    |June 18, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new theory and fast algorithm for designing optimal optical correlation filters, considering physical realizability constraints imposed by different implementation media. This advances pattern recognition by enabling better filter design for various applications.

    Related Experiment Videos

    Last Updated: Jun 12, 2026

    Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
    11:54

    Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

    Published on: March 13, 2017

    Area of Science:

    • Optics and Photonics
    • Pattern Recognition
    • Information Processing

    Background:

    • Coherent pattern recognition heavily relies on optical correlation with designed filters.
    • Filter implementation media introduce unique physical realizability constraints.
    • Limited research exists on designing optimal filters for arbitrary realizability regions.

    Purpose of the Study:

    • To present a novel theory for optimal filter design under arbitrary realizability constraints.
    • To develop a fast algorithm for implementing this filter design theory.
    • To demonstrate the algorithm's effectiveness with practical examples.

    Main Methods:

    • Development of a theoretical framework for optimal filter design.
    • Formulation of a computationally efficient algorithm.
    • Application and validation of the algorithm using two distinct case studies.

    Main Results:

    • Successful theoretical formulation for optimal filter design.
    • A fast algorithm capable of implementing the designed filters.
    • Demonstration of the algorithm's practical utility through examples.

    Conclusions:

    • The presented theory and algorithm address a significant gap in optical filter design.
    • This work provides a practical method for designing optimal filters considering physical constraints.
    • The findings have implications for advancing coherent pattern recognition architectures.