Related Experiment Video

Updated: Aug 25, 2025

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods
09:17

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods

Published on: April 23, 2018

10.8K

Coherent Doppler wind lidar signal denoising adopting variational mode decomposition based on honey badger algorithm

Yilun Zhou, Lang Li, Kaixin Wang

    Optics Express
    |October 14, 2022
    PubMed

    More Related Videos

    Echo Particle Image Velocimetry16:31

    Echo Particle Image Velocimetry

    Published on: December 27, 2012

    14.8K
    Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters15:25

    Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

    Published on: February 4, 2018

    6.2K

    Related Concept Videos

    Doppler Effect - II01:05

    Doppler Effect - II

    The Doppler effect has several practical, real-world applications. For instance, meteorologists use Doppler radars to interpret weather events based on the Doppler effect. Typically, a transmitter emits radio waves at a specific frequency toward the sky from a weather station. The radio waves bounce off the clouds and precipitation and travel back to the weather station. The radio frequency of the waves reflected back to the station appears to decrease if the clouds or precipitation are moving...
    3.5K
    Doppler Effect - I00:56

    Doppler Effect - I

    The Doppler effect and Doppler shift were named after the Austrian physicist and mathematician Christian Johann Doppler in 1842, who conducted experiments with both moving sources and moving observers. Consider an observer standing on a street corner, observing an ambulance with a siren sound passing by at a constant speed. The observer experiences two characteristic changes in the sound of the siren. Initially, the sound increases in loudness as the ambulance approaches and decreases in...
    3.7K
    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...
    233
    Boundary Layer Characteristics01:18

    Boundary Layer Characteristics

    When a fluid encounters a solid surface, a boundary layer forms due to the interaction between the fluid's motion and the stationary surface. This phenomenon is characterized by a thin region adjacent to the surface where viscous forces dominate, influencing the fluid's velocity profile. The development of the boundary layer begins at the leading edge of the surface and evolves as the fluid moves downstream.As the fluid flows over the surface, friction between the fluid and the wall slows down...
    204
    Classification of Signals01:30

    Classification of Signals

    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...
    719
    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....
    127