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Related Concept Videos

Signal Flow Graphs01:18

Signal Flow Graphs

Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
Modeling and Similitude01:12

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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 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...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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...

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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Modeling imager deterministic and statistical modulation transfer functions.

J F Johnson

    Applied Optics
    |September 22, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a unified method to evaluate modulation transfer functions (MTF

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    Area of Science:

    • Optical Engineering
    • Image Sensors
    • Signal Processing

    Background:

    • Charge-coupled imagers (CCIs) are crucial for image acquisition.
    • Evaluating their performance requires understanding modulation transfer functions (MTFs).
    • Existing MTF evaluation methods may not capture all performance aspects.

    Purpose of the Study:

    • To present a unified two-step approach for evaluating deterministic and statistical MTFs.
    • To apply this method to a time-delay-and-integration charge-coupled imager.
    • To derive and analyze novel interpixel and intrapixel synchronism MTFs.

    Main Methods:

    • A two-step evaluation framework was developed.
    • Deterministic MTFs, including spatial, temporal aperture, interpixel, and intrapixel synchronism MTFs, were analyzed.
    • Statistical MTFs, specifically phase and jitter MTFs, were derived.

    Main Results:

    • The unified approach successfully evaluated various deterministic MTFs.
    • Novel interpixel and intrapixel synchronism MTFs were derived, linked to velocity mismatch.
    • Statistical phase and jitter MTFs were obtained, providing further performance insights.

    Conclusions:

    • The unified method offers a comprehensive evaluation of CCI MTFs.
    • Understanding synchronism MTFs is vital for mitigating motion-related image degradation.
    • This approach enhances the characterization of time-delay-and-integration charge-coupled imagers.