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

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

251
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
251
Properties of the z-Transform I01:17

Properties of the z-Transform I

196
The z-transform is a fundamental tool in digital signal processing, enabling the analysis of discrete-time systems through its various properties. It is an invaluable tool for analyzing discrete-time systems, offering a range of properties that simplify complex signal manipulations. One fundamental property is linearity. For any two discrete-time signals, the z-transform of their linear combination equals the same linear combination of their individual z-transforms. This property is essential...
196
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

264
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
264
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

399
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
399
Sampling Theorem01:15

Sampling Theorem

345
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
345
Classification of Systems-II01:31

Classification of Systems-II

149
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,
149

You might also read

Related Articles

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

Sort by
Same author

Entropy-stabilized quinary sulfide nanozyme for complementary dual-mode detection of sulfonamide antibiotics via electrochemiluminescence and colorimetric sensing.

Biosensors & bioelectronics·2026
Same author

Nutritional risk and cancer pain as determinants of radiotherapy-induced severe lymphocytopenia: development and validation of a nutrition-integrated predictive nomogram.

Frontiers in nutrition·2026
Same author

Development and validation of a multimodal clinical-radiomics-deep learning nomogram based on automated chest CT segmentation for classifying COPD severity: a multicenter study.

Frontiers in medicine·2026
Same author

UBE2M-mediated neddylation modification stabilizes VEGFR2 to delay pulmonary vascular endothelial cell senescence.

Cell death & disease·2026
Same author

A Novel Role of Ume6 in <i>Candida albicans</i> in Regulation of Oxidative Stress Tolerance.

Journal of fungi (Basel, Switzerland)·2026
Same author

Oxidized lipids as molecular biomarkers in carotid in-stent restenosis: mechanisms and clinical implications.

Frontiers in neurology·2026

Related Experiment Video

Updated: Jul 8, 2025

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

14.6K

Optical design algorithm utilizing continuous-discrete variables grounded on stochastic processes.

Qiao Chen, Xiongxin Tang, Feijun Song

    Optics Express
    |December 13, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel optical design optimization algorithm using ergodic and stochastic processes. The method significantly accelerates finding optimal solutions for complex optical systems, achieving high-quality results efficiently.

    More Related Videos

    Patterning via Optical Saturable Transitions - Fabrication and Characterization
    08:19

    Patterning via Optical Saturable Transitions - Fabrication and Characterization

    Published on: December 11, 2014

    6.9K
    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

    9.9K

    Related Experiment Videos

    Last Updated: Jul 8, 2025

    Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
    09:23

    Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

    Published on: May 30, 2014

    14.6K
    Patterning via Optical Saturable Transitions - Fabrication and Characterization
    08:19

    Patterning via Optical Saturable Transitions - Fabrication and Characterization

    Published on: December 11, 2014

    6.9K
    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

    9.9K

    Area of Science:

    • Optical Design
    • Statistical Mechanics
    • Optimization Algorithms

    Background:

    • Mixed-variable optimization problems in optical design present significant computational challenges.
    • Traditional methods may struggle with efficiency and speed in complex optical system design.

    Purpose of the Study:

    • To propose a novel optimization algorithm for optical design.
    • To enhance the speed and efficiency of solving mixed-variable optimization problems.
    • To leverage concepts from ergodic and stochastic processes in statistical mechanics for optical design.

    Main Methods:

    • The algorithm utilizes pseudo-random numbers for selecting glass combinations and replacing glass parameters.
    • It incorporates discrete-to-continuous variable conversion to speed up the optimization process.
    • Two series of stochastic processes are applied to rapidly decrease the merit function value.

    Main Results:

    • The proposed method dramatically increases the speed of solving for optimal solutions in optical design.
    • The optimization algorithm successfully optimized a plan apochromatic objective with a long working distance.
    • A high-quality optical system was achieved in a very short duration.

    Conclusions:

    • The developed optimization algorithm offers a significant advancement in optical design.
    • The integration of ergodic and stochastic processes provides an efficient approach to mixed-variable optimization.
    • This method enables the rapid design of high-performance optical systems.