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particle image velocimetry algorithm  (MathWorks Inc)


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    MathWorks Inc particle image velocimetry algorithm
    Figure 5. a) Superimposed time-lapse images showing the acoustic streaming pattern around microrobots with 6 and 18 oscillating microbubbles. b) Computation of the flow analysis using particle image <t>velocimetry</t> algorithm (PIVlab, MATLAB).[38] A sectional line A–A 0 is selected at the inlet side of the microrobot to analyze the flow velocity magnitude profile of a selected frame. c) The flow velocity magnitude profile along the line A–A 0. In the n = 6 case, the maximal flow velocity is 0.99 mm s1, whereas in the n = 18 case, the maximal flow velocity is 1.77 mm s1. This result shows that the acoustic streaming intensity can be strengthened in multibubble systems with a larger number of simultaneously oscillating bubbles. The x-axis from left to right corresponds to the section line from top to bottom in (b). Scale bars are 500 μm.
    Particle Image Velocimetry Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2633 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/particle image velocimetry algorithm/product/MathWorks Inc
    Average 96 stars, based on 2633 article reviews
    particle image velocimetry algorithm - by Bioz Stars, 2026-03
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    1) Product Images from "Acoustically Actuated Flow in Microrobots Powered by Axisymmetric Resonant Bubbles"

    Article Title: Acoustically Actuated Flow in Microrobots Powered by Axisymmetric Resonant Bubbles

    Journal: Advanced Intelligent Systems

    doi: 10.1002/aisy.202300465

    Figure 5. a) Superimposed time-lapse images showing the acoustic streaming pattern around microrobots with 6 and 18 oscillating microbubbles. b) Computation of the flow analysis using particle image velocimetry algorithm (PIVlab, MATLAB).[38] A sectional line A–A 0 is selected at the inlet side of the microrobot to analyze the flow velocity magnitude profile of a selected frame. c) The flow velocity magnitude profile along the line A–A 0. In the n = 6 case, the maximal flow velocity is 0.99 mm s1, whereas in the n = 18 case, the maximal flow velocity is 1.77 mm s1. This result shows that the acoustic streaming intensity can be strengthened in multibubble systems with a larger number of simultaneously oscillating bubbles. The x-axis from left to right corresponds to the section line from top to bottom in (b). Scale bars are 500 μm.
    Figure Legend Snippet: Figure 5. a) Superimposed time-lapse images showing the acoustic streaming pattern around microrobots with 6 and 18 oscillating microbubbles. b) Computation of the flow analysis using particle image velocimetry algorithm (PIVlab, MATLAB).[38] A sectional line A–A 0 is selected at the inlet side of the microrobot to analyze the flow velocity magnitude profile of a selected frame. c) The flow velocity magnitude profile along the line A–A 0. In the n = 6 case, the maximal flow velocity is 0.99 mm s1, whereas in the n = 18 case, the maximal flow velocity is 1.77 mm s1. This result shows that the acoustic streaming intensity can be strengthened in multibubble systems with a larger number of simultaneously oscillating bubbles. The x-axis from left to right corresponds to the section line from top to bottom in (b). Scale bars are 500 μm.

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    particle image velocimetry algorithm  (MathWorks Inc)
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    MathWorks Inc particle image velocimetry algorithm
    Figure 5. a) Superimposed time-lapse images showing the acoustic streaming pattern around microrobots with 6 and 18 oscillating microbubbles. b) Computation of the flow analysis using particle image <t>velocimetry</t> algorithm (PIVlab, MATLAB).[38] A sectional line A–A 0 is selected at the inlet side of the microrobot to analyze the flow velocity magnitude profile of a selected frame. c) The flow velocity magnitude profile along the line A–A 0. In the n = 6 case, the maximal flow velocity is 0.99 mm s1, whereas in the n = 18 case, the maximal flow velocity is 1.77 mm s1. This result shows that the acoustic streaming intensity can be strengthened in multibubble systems with a larger number of simultaneously oscillating bubbles. The x-axis from left to right corresponds to the section line from top to bottom in (b). Scale bars are 500 μm.
    Particle Image Velocimetry Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Figure 5. a) Superimposed time-lapse images showing the acoustic streaming pattern around microrobots with 6 and 18 oscillating microbubbles. b) Computation of the flow analysis using particle image <t>velocimetry</t> algorithm (PIVlab, MATLAB).[38] A sectional line A–A 0 is selected at the inlet side of the microrobot to analyze the flow velocity magnitude profile of a selected frame. c) The flow velocity magnitude profile along the line A–A 0. In the n = 6 case, the maximal flow velocity is 0.99 mm s1, whereas in the n = 18 case, the maximal flow velocity is 1.77 mm s1. This result shows that the acoustic streaming intensity can be strengthened in multibubble systems with a larger number of simultaneously oscillating bubbles. The x-axis from left to right corresponds to the section line from top to bottom in (b). Scale bars are 500 μm.
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    Figure 5. a) Superimposed time-lapse images showing the acoustic streaming pattern around microrobots with 6 and 18 oscillating microbubbles. b) Computation of the flow analysis using particle image <t>velocimetry</t> algorithm (PIVlab, MATLAB).[38] A sectional line A–A 0 is selected at the inlet side of the microrobot to analyze the flow velocity magnitude profile of a selected frame. c) The flow velocity magnitude profile along the line A–A 0. In the n = 6 case, the maximal flow velocity is 0.99 mm s1, whereas in the n = 18 case, the maximal flow velocity is 1.77 mm s1. This result shows that the acoustic streaming intensity can be strengthened in multibubble systems with a larger number of simultaneously oscillating bubbles. The x-axis from left to right corresponds to the section line from top to bottom in (b). Scale bars are 500 μm.
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    Figure 5. a) Superimposed time-lapse images showing the acoustic streaming pattern around microrobots with 6 and 18 oscillating microbubbles. b) Computation of the flow analysis using particle image <t>velocimetry</t> algorithm (PIVlab, MATLAB).[38] A sectional line A–A 0 is selected at the inlet side of the microrobot to analyze the flow velocity magnitude profile of a selected frame. c) The flow velocity magnitude profile along the line A–A 0. In the n = 6 case, the maximal flow velocity is 0.99 mm s1, whereas in the n = 18 case, the maximal flow velocity is 1.77 mm s1. This result shows that the acoustic streaming intensity can be strengthened in multibubble systems with a larger number of simultaneously oscillating bubbles. The x-axis from left to right corresponds to the section line from top to bottom in (b). Scale bars are 500 μm.
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    Figure 5. a) Superimposed time-lapse images showing the acoustic streaming pattern around microrobots with 6 and 18 oscillating microbubbles. b) Computation of the flow analysis using particle image <t>velocimetry</t> algorithm (PIVlab, MATLAB).[38] A sectional line A–A 0 is selected at the inlet side of the microrobot to analyze the flow velocity magnitude profile of a selected frame. c) The flow velocity magnitude profile along the line A–A 0. In the n = 6 case, the maximal flow velocity is 0.99 mm s1, whereas in the n = 18 case, the maximal flow velocity is 1.77 mm s1. This result shows that the acoustic streaming intensity can be strengthened in multibubble systems with a larger number of simultaneously oscillating bubbles. The x-axis from left to right corresponds to the section line from top to bottom in (b). Scale bars are 500 μm.
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    Figure 5. a) Superimposed time-lapse images showing the acoustic streaming pattern around microrobots with 6 and 18 oscillating microbubbles. b) Computation of the flow analysis using particle image <t>velocimetry</t> algorithm (PIVlab, MATLAB).[38] A sectional line A–A 0 is selected at the inlet side of the microrobot to analyze the flow velocity magnitude profile of a selected frame. c) The flow velocity magnitude profile along the line A–A 0. In the n = 6 case, the maximal flow velocity is 0.99 mm s1, whereas in the n = 18 case, the maximal flow velocity is 1.77 mm s1. This result shows that the acoustic streaming intensity can be strengthened in multibubble systems with a larger number of simultaneously oscillating bubbles. The x-axis from left to right corresponds to the section line from top to bottom in (b). Scale bars are 500 μm.
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    Figure 5. a) Superimposed time-lapse images showing the acoustic streaming pattern around microrobots with 6 and 18 oscillating microbubbles. b) Computation of the flow analysis using particle image <t>velocimetry</t> algorithm (PIVlab, MATLAB).[38] A sectional line A–A 0 is selected at the inlet side of the microrobot to analyze the flow velocity magnitude profile of a selected frame. c) The flow velocity magnitude profile along the line A–A 0. In the n = 6 case, the maximal flow velocity is 0.99 mm s1, whereas in the n = 18 case, the maximal flow velocity is 1.77 mm s1. This result shows that the acoustic streaming intensity can be strengthened in multibubble systems with a larger number of simultaneously oscillating bubbles. The x-axis from left to right corresponds to the section line from top to bottom in (b). Scale bars are 500 μm.
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    Figure 5. a) Superimposed time-lapse images showing the acoustic streaming pattern around microrobots with 6 and 18 oscillating microbubbles. b) Computation of the flow analysis using particle image velocimetry algorithm (PIVlab, MATLAB).[38] A sectional line A–A 0 is selected at the inlet side of the microrobot to analyze the flow velocity magnitude profile of a selected frame. c) The flow velocity magnitude profile along the line A–A 0. In the n = 6 case, the maximal flow velocity is 0.99 mm s1, whereas in the n = 18 case, the maximal flow velocity is 1.77 mm s1. This result shows that the acoustic streaming intensity can be strengthened in multibubble systems with a larger number of simultaneously oscillating bubbles. The x-axis from left to right corresponds to the section line from top to bottom in (b). Scale bars are 500 μm.

    Journal: Advanced Intelligent Systems

    Article Title: Acoustically Actuated Flow in Microrobots Powered by Axisymmetric Resonant Bubbles

    doi: 10.1002/aisy.202300465

    Figure Lengend Snippet: Figure 5. a) Superimposed time-lapse images showing the acoustic streaming pattern around microrobots with 6 and 18 oscillating microbubbles. b) Computation of the flow analysis using particle image velocimetry algorithm (PIVlab, MATLAB).[38] A sectional line A–A 0 is selected at the inlet side of the microrobot to analyze the flow velocity magnitude profile of a selected frame. c) The flow velocity magnitude profile along the line A–A 0. In the n = 6 case, the maximal flow velocity is 0.99 mm s1, whereas in the n = 18 case, the maximal flow velocity is 1.77 mm s1. This result shows that the acoustic streaming intensity can be strengthened in multibubble systems with a larger number of simultaneously oscillating bubbles. The x-axis from left to right corresponds to the section line from top to bottom in (b). Scale bars are 500 μm.

    Article Snippet: We analyze the flow velocity at the inlet side (Figure 5a) using the particle image velocimetry algorithm (PIVlab toolbox, MATLAB)[38] and determine the peak velocity by analyzing the particle velocity along the sectional line perpendicular to the flow direction (Figure 5b, Movie S3, Supporting Information).

    Techniques: