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matrix laboratory matlab based vessel analysis software program rapid analysis  (MathWorks Inc)


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    Structured Review

    MathWorks Inc matrix laboratory matlab based vessel analysis software program rapid analysis
    Matrix Laboratory Matlab Based Vessel Analysis Software Program Rapid Analysis, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 93/100, based on 76 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/matlab-based+data+analysis+tool/MATLAB+Parallel+Server/us12059429-527-1-3
    Average 93 stars, based on 76 article reviews
    matrix laboratory matlab based vessel analysis software program rapid analysis - by Bioz Stars, 2026-09
    93/100 stars

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    Related Articles

    other:

    Article Title: Application of weighted centroid algorithm based on weight correction in node localization of wireless sensor networks
    Article Snippet: The computer operating system was Windows 10 Pro 64 bit, the CPU was Intel Core i7-10700 K @ 3.8 GHz, and supported MATLAB parallel computing.

    Article Title: Application of weighted centroid algorithm based on weight correction in node localization of wireless sensor networks.
    Article Snippet: The computer operating system was Windows 10 Pro 64 bit, the CPU was Intel Core i7-10700 K @ 3.8 GHz, and supported MATLAB parallel computing.

    Blocking Assay:

    Article Title: Remote Real-Time Monitoring and Control of Small Wind Turbines Using Open-Source Hardware and Software
    Article Snippet: .. Algorithm 1: Pseudocode—Process Management and Communication Logic maestro.py (Process Manager) ---------------------- Define list of subprocesses: [process1.py, process2.py] For each process in list: -Create a new worker process -Assign target: run process as Python script Start all worker processes in parallel Wait for processes to run continuously (blocking) process1.py (Measurement Data Server) ------------------------------- Initialize UART port (9600 bps, timeout 2.5 s) Start WebSocket server on port 8081 On client connection: Loop indefinitely: -Read up to 32 bytes from UART buffer -If more data is available: -Read remaining bytes (in waiting) -Concatenate full message -Send raw data to client via WebSocket -Wait for client acknowledgment before next cycle process2.py (Control Command Server) ------------------------------- Start WebSocket server on port 8080 On client connection: Loop indefinitely: -Wait for command from client via WebSocket -Convert string command to ASCII bytes -Open UART port (9600 bps) -Send command to Arduino® -Send confirmation back to client Under normal conditions, the total system latency—from signal acquisition on the Arduino® to data visualization on the MATLAB® app—remains under 200 ms, supporting effective real-time control. ..

    Control:

    Article Title: Remote Real-Time Monitoring and Control of Small Wind Turbines Using Open-Source Hardware and Software
    Article Snippet: .. Algorithm 1: Pseudocode—Process Management and Communication Logic maestro.py (Process Manager) ---------------------- Define list of subprocesses: [process1.py, process2.py] For each process in list: -Create a new worker process -Assign target: run process as Python script Start all worker processes in parallel Wait for processes to run continuously (blocking) process1.py (Measurement Data Server) ------------------------------- Initialize UART port (9600 bps, timeout 2.5 s) Start WebSocket server on port 8081 On client connection: Loop indefinitely: -Read up to 32 bytes from UART buffer -If more data is available: -Read remaining bytes (in waiting) -Concatenate full message -Send raw data to client via WebSocket -Wait for client acknowledgment before next cycle process2.py (Control Command Server) ------------------------------- Start WebSocket server on port 8080 On client connection: Loop indefinitely: -Wait for command from client via WebSocket -Convert string command to ASCII bytes -Open UART port (9600 bps) -Send command to Arduino® -Send confirmation back to client Under normal conditions, the total system latency—from signal acquisition on the Arduino® to data visualization on the MATLAB® app—remains under 200 ms, supporting effective real-time control. ..

    Software:

    Article Title: A Proxy-guided Workflow for Virtual Population Development.
    Article Snippet: .. Our software runs on a cluster using MATLAB Parallel Server (MPS) on MATLAB R2024a. ..



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    Image Search Results


    Multimodal setup in MEG environment. In the pictures, the black cables with fibre optics based sensors attached on the body and head are optical fibres for measurements of cNIBP and NIRS. On the right, shown is performing of simultaneous recording with the setup. During imaging, the display is used to instruct the subject to perform the given task.

    Journal: Scientific Reports

    Article Title: Multimodal brain imaging with magnetoencephalography: A method for measuring blood pressure and cardiorespiratory oscillations

    doi: 10.1038/s41598-017-00293-7

    Figure Lengend Snippet: Multimodal setup in MEG environment. In the pictures, the black cables with fibre optics based sensors attached on the body and head are optical fibres for measurements of cNIBP and NIRS. On the right, shown is performing of simultaneous recording with the setup. During imaging, the display is used to instruct the subject to perform the given task.

    Article Snippet: Measured raw NIRS time courses were converted into two time courses representing temporal changes in HbO and Hb concentrations using Matlab-based NIRS data analysis tool HoMer2.

    Techniques: Imaging

    Comparison of mean power values (black dots, whiskers = SD) measured with magnetometers ( a ) and planar gradiometers ( b and c ), with (x axis) and without (y axis) the multimodal setup. For better visualisation the plots show an extract of all data, which were shown in each inset separately. In all cases the data can be well explained by a linear fit (red line). Note the slight positive intercept of the linear fits due to smaller power values when the multimodal setup is applied. The external sensors for NIRS are attached on back of the head and on forehead above left eye. Sensors for cNIBP are attached on chest and neck (see Figure ).

    Journal: Scientific Reports

    Article Title: Multimodal brain imaging with magnetoencephalography: A method for measuring blood pressure and cardiorespiratory oscillations

    doi: 10.1038/s41598-017-00293-7

    Figure Lengend Snippet: Comparison of mean power values (black dots, whiskers = SD) measured with magnetometers ( a ) and planar gradiometers ( b and c ), with (x axis) and without (y axis) the multimodal setup. For better visualisation the plots show an extract of all data, which were shown in each inset separately. In all cases the data can be well explained by a linear fit (red line). Note the slight positive intercept of the linear fits due to smaller power values when the multimodal setup is applied. The external sensors for NIRS are attached on back of the head and on forehead above left eye. Sensors for cNIBP are attached on chest and neck (see Figure ).

    Article Snippet: Measured raw NIRS time courses were converted into two time courses representing temporal changes in HbO and Hb concentrations using Matlab-based NIRS data analysis tool HoMer2.

    Techniques: Comparison

    Averaged responses for PTT ( a ), BP ( b ), HR ( c ) measured with the cNIBP device; HbO/Hb ( d ) measured with the NIRS device from back of the head and four scouts (MEG) covering visual (green line), auditory (brown line), motor (dark yellow line) and temporal pole (blue line) brain areas ( e ). Average for NIRS and cNIBP data was calculated using values obtained during 4 breath holding sequences. Vertical bars represent standard deviation of the values. In addition, ( f ) shows an averaged response of 8 breath holds using the same breath hold task but recorded independently with our MREG multimodal setup . In all subfigures t = 0 marks the beginning of breath holding for 30 s, highlighted with grey area.

    Journal: Scientific Reports

    Article Title: Multimodal brain imaging with magnetoencephalography: A method for measuring blood pressure and cardiorespiratory oscillations

    doi: 10.1038/s41598-017-00293-7

    Figure Lengend Snippet: Averaged responses for PTT ( a ), BP ( b ), HR ( c ) measured with the cNIBP device; HbO/Hb ( d ) measured with the NIRS device from back of the head and four scouts (MEG) covering visual (green line), auditory (brown line), motor (dark yellow line) and temporal pole (blue line) brain areas ( e ). Average for NIRS and cNIBP data was calculated using values obtained during 4 breath holding sequences. Vertical bars represent standard deviation of the values. In addition, ( f ) shows an averaged response of 8 breath holds using the same breath hold task but recorded independently with our MREG multimodal setup . In all subfigures t = 0 marks the beginning of breath holding for 30 s, highlighted with grey area.

    Article Snippet: Measured raw NIRS time courses were converted into two time courses representing temporal changes in HbO and Hb concentrations using Matlab-based NIRS data analysis tool HoMer2.

    Techniques: Standard Deviation