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custom matlab graphical user interface (gui) control software  (MathWorks Inc)


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    MathWorks Inc custom matlab graphical user interface (gui) control software
    Custom Matlab Graphical User Interface (Gui) Control Software, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/control+software+and+graphical+user+interface+(gui)/us10802255-279-7-2
    Average 90 stars, based on 1 article reviews
    custom matlab graphical user interface (gui) control software - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    Control:

    Article Title: SMT-Based Verification of Safety-Critical Embedded Control Software
    Article Snippet: A large fraction of bugs discovered in the design flow of Embedded Control Software (ECS) arises from the control software’s interaction with the plant it controls.. Traditional formal analysis approaches using interleaved controller-plant reach-set analysis grossly over-approximate the reachable states and does not scale.. In this article, we examine a verification approach that considers a control system with the (possibly nonlinear) plant dynamics and mode switches specified along with the actual control software implementation.

    Article Title: Tile-based massively scalable MIMO and phased arrays for 5G/B5G-enabled smart skins and reconfigurable intelligent surfaces
    Article Snippet: .. The control software was written in MATLAB to control the SPI using a NI USB-8452 I \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{2}$$\end{document} 2 C/SPI interface. ..

    Article Title: Development of a Low-Profile, Piezoelectric Robot for MR-Guided Abdominal Needle Interventions.
    Article Snippet: Purpose Minimally invasive needle-based interventions are commonly used in cancer diagnosis and treatment, including procedures, such as biopsy, brachytherapy, and microwave ablation.. Although MR-guided needle placement offers several distinct advantages, such as high-resolution target visualization and accurate device tracking, one of the primary limitations that affect its widespread adoption is the ergonomic constraints of the closed-bore MRI environment, requiring the patients to be frequently moved in and out to perform the needle-based procedures.. This paper introduces a low-profile, body-mounted, MR-guided robot designed to address this limitation by streamlining the operation workflow and enabling accurate needle placement within the MRI scanner.

    Article Title: Video-rate remote refocusing through continuous oscillation of a membrane deformable mirror
    Article Snippet: .. The control software (MATLAB), was implemented to ensure that the DM was continually oscillating at 26.3 Hz throughout the entirety of the optimisation procedure and afterwards in order to ensure that thermal creep effects settled out and remained constant. ..

    Article Title: A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution.
    Article Snippet: .. Control software was custom written in MATLAB R2018a using PsychToolbox 3.0.14 and for the Teensy. ..

    Article Title: An automated, low-latency environment for studying the neural basis of behavior in freely moving rats.
    Article Snippet: .. It could be used to replace some of the proprietary control software we used, and potentially replace Matlab in the main loop of the RIFF. ..

    Article Title: Raman-based dynamic feeding strategies using real-time glucose concentration monitoring system during adalimumab producing CHO cell cultivation.
    Article Snippet: .. From the iC Raman software the acquired Raman spectra were transferred via TCP/IP network to the data analysis and control software developed in Matlab environment. ..

    Software:

    Article Title: SMT-Based Verification of Safety-Critical Embedded Control Software
    Article Snippet: A large fraction of bugs discovered in the design flow of Embedded Control Software (ECS) arises from the control software’s interaction with the plant it controls.. Traditional formal analysis approaches using interleaved controller-plant reach-set analysis grossly over-approximate the reachable states and does not scale.. In this article, we examine a verification approach that considers a control system with the (possibly nonlinear) plant dynamics and mode switches specified along with the actual control software implementation.

    Article Title: Tile-based massively scalable MIMO and phased arrays for 5G/B5G-enabled smart skins and reconfigurable intelligent surfaces
    Article Snippet: .. The control software was written in MATLAB to control the SPI using a NI USB-8452 I \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{2}$$\end{document} 2 C/SPI interface. ..

    Article Title: Development of a Low-Profile, Piezoelectric Robot for MR-Guided Abdominal Needle Interventions.
    Article Snippet: Purpose Minimally invasive needle-based interventions are commonly used in cancer diagnosis and treatment, including procedures, such as biopsy, brachytherapy, and microwave ablation.. Although MR-guided needle placement offers several distinct advantages, such as high-resolution target visualization and accurate device tracking, one of the primary limitations that affect its widespread adoption is the ergonomic constraints of the closed-bore MRI environment, requiring the patients to be frequently moved in and out to perform the needle-based procedures.. This paper introduces a low-profile, body-mounted, MR-guided robot designed to address this limitation by streamlining the operation workflow and enabling accurate needle placement within the MRI scanner.

    Article Title: Video-rate remote refocusing through continuous oscillation of a membrane deformable mirror
    Article Snippet: .. The control software (MATLAB), was implemented to ensure that the DM was continually oscillating at 26.3 Hz throughout the entirety of the optimisation procedure and afterwards in order to ensure that thermal creep effects settled out and remained constant. ..

    Article Title: A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution.
    Article Snippet: .. Control software was custom written in MATLAB R2018a using PsychToolbox 3.0.14 and for the Teensy. ..

    Article Title: An automated, low-latency environment for studying the neural basis of behavior in freely moving rats.
    Article Snippet: .. It could be used to replace some of the proprietary control software we used, and potentially replace Matlab in the main loop of the RIFF. ..

    Article Title: Raman-based dynamic feeding strategies using real-time glucose concentration monitoring system during adalimumab producing CHO cell cultivation.
    Article Snippet: .. From the iC Raman software the acquired Raman spectra were transferred via TCP/IP network to the data analysis and control software developed in Matlab environment. ..

    Generated:

    Article Title: SMT-Based Verification of Safety-Critical Embedded Control Software
    Article Snippet: A large fraction of bugs discovered in the design flow of Embedded Control Software (ECS) arises from the control software’s interaction with the plant it controls.. Traditional formal analysis approaches using interleaved controller-plant reach-set analysis grossly over-approximate the reachable states and does not scale.. In this article, we examine a verification approach that considers a control system with the (possibly nonlinear) plant dynamics and mode switches specified along with the actual control software implementation.

    other:

    Article Title: Quantitative detection of benzoyl peroxide in wheat flour using line-scan short-wave infrared hyperspectral imaging
    Article Snippet: The addition of benzol peroxide (BPO) to wheat flour as a bleaching agent has been widely recognized as an important food safety issue due to its negative effects on human health.. To address this issue, various nondestructive optical-based techniques have been developed to screen for BPO, such as Raman spectroscopy and hyperspectral imaging (HSI).. In this study, a shortwave infrared (SWIR) HSI system was developed for the rapid detection of BPO particles in wheat flour.



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    A schematic of the light-sheet excitation-detection module and the proposed deep neural network-based autofocus workflow. (a) An illustration of the geometry of light-sheet fluorescence microscopy <t>(LSFM),</t> and the drift in the relative position of the light-sheet and the focal plane of the detection objective (Δz), when imaging deep into the tissue. (b) Fluorescence images of in focus ( Δz=0μm ) and out-of-focus ( Δz=20μm ) neurons (left) and hair cells (right). The images were captured from a whole mouse brain and a pig cochlea that were tissue cleared for 3D volume imaging. The red boxes mark the locations of the zoom-in images at the bottom. The degradation in the quality of out-of-focus images can be observed. (c) Overview of the integration of the deep learning-based autofocus method with a custom-built LSFM. During image acquisition, two defocused images will be collected and sent to a classification network to estimate the defocus level. The color of the borders of each individual patch in the right image indicates the predicted defocus distance. In the color bar, the red and purple colors represent the extreme cases, in which the defocus distance is −36 µm and 36 µm respectively. The borders’ dominant color is green, which indicates that this image is in focus.
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    A schematic of the light-sheet excitation-detection module and the proposed deep neural network-based autofocus workflow. (a) An illustration of the geometry of light-sheet fluorescence microscopy <t>(LSFM),</t> and the drift in the relative position of the light-sheet and the focal plane of the detection objective (Δz), when imaging deep into the tissue. (b) Fluorescence images of in focus ( Δz=0μm ) and out-of-focus ( Δz=20μm ) neurons (left) and hair cells (right). The images were captured from a whole mouse brain and a pig cochlea that were tissue cleared for 3D volume imaging. The red boxes mark the locations of the zoom-in images at the bottom. The degradation in the quality of out-of-focus images can be observed. (c) Overview of the integration of the deep learning-based autofocus method with a custom-built LSFM. During image acquisition, two defocused images will be collected and sent to a classification network to estimate the defocus level. The color of the borders of each individual patch in the right image indicates the predicted defocus distance. In the color bar, the red and purple colors represent the extreme cases, in which the defocus distance is −36 µm and 36 µm respectively. The borders’ dominant color is green, which indicates that this image is in focus.
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    Image Search Results


    A schematic of the light-sheet excitation-detection module and the proposed deep neural network-based autofocus workflow. (a) An illustration of the geometry of light-sheet fluorescence microscopy (LSFM), and the drift in the relative position of the light-sheet and the focal plane of the detection objective (Δz), when imaging deep into the tissue. (b) Fluorescence images of in focus ( Δz=0μm ) and out-of-focus ( Δz=20μm ) neurons (left) and hair cells (right). The images were captured from a whole mouse brain and a pig cochlea that were tissue cleared for 3D volume imaging. The red boxes mark the locations of the zoom-in images at the bottom. The degradation in the quality of out-of-focus images can be observed. (c) Overview of the integration of the deep learning-based autofocus method with a custom-built LSFM. During image acquisition, two defocused images will be collected and sent to a classification network to estimate the defocus level. The color of the borders of each individual patch in the right image indicates the predicted defocus distance. In the color bar, the red and purple colors represent the extreme cases, in which the defocus distance is −36 µm and 36 µm respectively. The borders’ dominant color is green, which indicates that this image is in focus.

    Journal: Biomedical Optics Express

    Article Title: Deep learning-based autofocus method enhances image quality in light-sheet fluorescence microscopy

    doi: 10.1364/BOE.427099

    Figure Lengend Snippet: A schematic of the light-sheet excitation-detection module and the proposed deep neural network-based autofocus workflow. (a) An illustration of the geometry of light-sheet fluorescence microscopy (LSFM), and the drift in the relative position of the light-sheet and the focal plane of the detection objective (Δz), when imaging deep into the tissue. (b) Fluorescence images of in focus ( Δz=0μm ) and out-of-focus ( Δz=20μm ) neurons (left) and hair cells (right). The images were captured from a whole mouse brain and a pig cochlea that were tissue cleared for 3D volume imaging. The red boxes mark the locations of the zoom-in images at the bottom. The degradation in the quality of out-of-focus images can be observed. (c) Overview of the integration of the deep learning-based autofocus method with a custom-built LSFM. During image acquisition, two defocused images will be collected and sent to a classification network to estimate the defocus level. The color of the borders of each individual patch in the right image indicates the predicted defocus distance. In the color bar, the red and purple colors represent the extreme cases, in which the defocus distance is −36 µm and 36 µm respectively. The borders’ dominant color is green, which indicates that this image is in focus.

    Article Snippet: The control software and graphical user interface (GUI) of the LSFM were implemented in MATLAB R2019b environment.

    Techniques: Fluorescence, Microscopy, Imaging