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Related Concept Videos

Transfer Function in Control Systems01:21

Transfer Function in Control Systems

The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
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...
Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
State Space to Transfer Function01:21

State Space to Transfer Function

The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
Transfer Function to State Space01:23

Transfer Function to State Space

State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

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Related Experiment Video

Updated: May 30, 2026

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
09:04

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

Realistic 3D coherent transfer function inverse filtering of complex fields.

Yann Cotte, Fatih M Toy, Cristian Arfire

    Biomedical Optics Express
    |August 12, 2011
    PubMed
    Summary
    This summary is machine-generated.

    We developed a new 3D image processing technique using a realistic 3D coherent transfer function (CTF) to reconstruct complex scattering object fields from holographic microscopy data.

    Keywords:
    (090.1995) Digital holography(100.1830) Deconvolution(100.5070) Phase retrieval(100.6890) Three-dimensional image processing(110.0180) Microscopy(180.6900) Three-dimensional microscopy

    More Related Videos

    Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator
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    Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

    Published on: January 28, 2019

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    Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
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    Published on: February 23, 2018

    Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator
    08:39

    Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

    Published on: January 28, 2019

    Area of Science:

    • Optics and Photonics
    • Image Processing
    • Holography

    Background:

    • Three-dimensional (3D) imaging of complex fields is challenging.
    • Digital holographic microscopy (DHM) offers potential for high-resolution 3D reconstruction.
    • Accurate modeling of the imaging system's transfer function is crucial for precise reconstruction.

    Purpose of the Study:

    • To introduce a novel technique for 3D image processing of complex fields.
    • To demonstrate the reconstruction of scattering object fields using DHM.
    • To validate the technique under non-ideal imaging conditions.

    Main Methods:

    • Inverting coherent image formation by filtering the complex spectrum.
    • Utilizing a realistic 3D coherent transfer function (CTF) specific to high-NA DHM.
    • Combining principles of scattering theory and advanced signal processing.

    Main Results:

    • Successful reconstruction of a scattering object field in both phase and amplitude.
    • Demonstration of the technique's efficacy under non-design imaging conditions.
    • Validation of the proposed method for complex field reconstruction.

    Conclusions:

    • The developed technique provides accurate 3D reconstructions of scattering objects.
    • The method is robust and effective even under non-ideal imaging parameters.
    • This approach is highly suitable for high-resolution diffraction tomography applications.