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matlab functions islocalmax  (MathWorks Inc)


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    MathWorks Inc matlab functions islocalmax
    Matlab Functions Islocalmax, 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/matlab+function+'islocalmax/pm37378045-140-3-1
    Average 90 stars, based on 1 article reviews
    matlab functions islocalmax - by Bioz Stars, 2026-09
    90/100 stars

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    Software:

    Article Title: Tropical forests are mainly unstratified especially in Amazonia and regions with lower fertility or higher temperatures
    Article Snippet: In figure 1, we show an example GEDI footprint and then classify it using a flow diagram on the right. (1) We first classified each footprint by the number of local maxima (change in first derivative—hereafter: peaks= P) using the Matlab (Mathworks) function ‘islocalmax’ on each PAVD profile.

    Article Title: Structured barycentric forms for interpolation-based data-driven reduced modeling of second-order systems
    Article Snippet: As interpolation points, we have chosen the local minima and maxima of the given data samples on the positive part of the imaginary axis using the MATLAB functions islocalmin and islocalmax supplemented by the limits of the considered frequency intervals of interest [iωmin, iωmax] and, if necessary, some additional intermediate points.

    Article Title: Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors
    Article Snippet: The MATLAB functions islocalmin and islocalmax have been used for the detection of local extrema [ , ].

    Article Title: Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors.
    Article Snippet: The MATLAB functions islocalmin and islocalmax have been used for the detection of local extrema [51,52].

    Article Title: I-Spin live, an open-source software based on blind-source separation for real-time decoding of motor unit activity in humans
    Article Snippet: Local peaks were identified for each motor unit using the MATLAB function ‘islocalmax’ with a minimal separation of 25 ms between peaks to limit the number of false positives ( ).

    Article Title: PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points.
    Article Snippet: The MATLAB functions islocalmax and islocalmin are also applied on the VPG waveform in order to identify four additional fiducial points, i.e., w which is the first maxima of VPG, x which is the corresponding systolic point of the PPG waveform, y which is the first minima of the VPG, and z which is the second maxima of the VPG or the zero-crossing point of the APG after the e point.

    Article Title: A predictive propensity measure to enter REM sleep
    Article Snippet: To do so, we computed the propensity for | N | = 1, 2, 3, ..., 3, 000 s. Using MATLAB's built-in “islocalmax” function, we computed all values of | N | at which the propensity has a local maximum, and found the largest of the resulting values of | N |.

    Article Title: Accurate Quantification and Imaging of Cellular Uptake Using Single-Particle Surface-Enhanced Raman Scattering
    Article Snippet: The spectrum with brightest signal for each particle was identified with ‘islocalmax’ in MATLAB for time series and cell data.

    Plasmid Preparation:

    Article Title: Tropical forests are mainly unstratified especially in Amazonia and regions with lower fertility or higher temperatures
    Article Snippet: In figure 1, we show an example GEDI footprint and then classify it using a flow diagram on the right. (1) We first classified each footprint by the number of local maxima (change in first derivative—hereafter: peaks= P) using the Matlab (Mathworks) function ‘islocalmax’ on each PAVD profile.

    Article Title: Structured barycentric forms for interpolation-based data-driven reduced modeling of second-order systems
    Article Snippet: As interpolation points, we have chosen the local minima and maxima of the given data samples on the positive part of the imaginary axis using the MATLAB functions islocalmin and islocalmax supplemented by the limits of the considered frequency intervals of interest [iωmin, iωmax] and, if necessary, some additional intermediate points.

    Article Title: Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors
    Article Snippet: The MATLAB functions islocalmin and islocalmax have been used for the detection of local extrema [ , ].

    Article Title: Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors.
    Article Snippet: The MATLAB functions islocalmin and islocalmax have been used for the detection of local extrema [51,52].

    Article Title: I-Spin live, an open-source software based on blind-source separation for real-time decoding of motor unit activity in humans
    Article Snippet: Local peaks were identified for each motor unit using the MATLAB function ‘islocalmax’ with a minimal separation of 25 ms between peaks to limit the number of false positives ( ).

    Article Title: PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points.
    Article Snippet: The MATLAB functions islocalmax and islocalmin are also applied on the VPG waveform in order to identify four additional fiducial points, i.e., w which is the first maxima of VPG, x which is the corresponding systolic point of the PPG waveform, y which is the first minima of the VPG, and z which is the second maxima of the VPG or the zero-crossing point of the APG after the e point.

    Article Title: A predictive propensity measure to enter REM sleep
    Article Snippet: To do so, we computed the propensity for | N | = 1, 2, 3, ..., 3, 000 s. Using MATLAB's built-in “islocalmax” function, we computed all values of | N | at which the propensity has a local maximum, and found the largest of the resulting values of | N |.

    Article Title: Accurate Quantification and Imaging of Cellular Uptake Using Single-Particle Surface-Enhanced Raman Scattering
    Article Snippet: The spectrum with brightest signal for each particle was identified with ‘islocalmax’ in MATLAB for time series and cell data.

    Activity Assay:

    Article Title: Tropical forests are mainly unstratified especially in Amazonia and regions with lower fertility or higher temperatures
    Article Snippet: In figure 1, we show an example GEDI footprint and then classify it using a flow diagram on the right. (1) We first classified each footprint by the number of local maxima (change in first derivative—hereafter: peaks= P) using the Matlab (Mathworks) function ‘islocalmax’ on each PAVD profile.

    Article Title: Structured barycentric forms for interpolation-based data-driven reduced modeling of second-order systems
    Article Snippet: As interpolation points, we have chosen the local minima and maxima of the given data samples on the positive part of the imaginary axis using the MATLAB functions islocalmin and islocalmax supplemented by the limits of the considered frequency intervals of interest [iωmin, iωmax] and, if necessary, some additional intermediate points.

    Article Title: Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors
    Article Snippet: The MATLAB functions islocalmin and islocalmax have been used for the detection of local extrema [ , ].

    Article Title: Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors.
    Article Snippet: The MATLAB functions islocalmin and islocalmax have been used for the detection of local extrema [51,52].

    Article Title: I-Spin live, an open-source software based on blind-source separation for real-time decoding of motor unit activity in humans
    Article Snippet: Local peaks were identified for each motor unit using the MATLAB function ‘islocalmax’ with a minimal separation of 25 ms between peaks to limit the number of false positives ( ).

    Article Title: PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points.
    Article Snippet: The MATLAB functions islocalmax and islocalmin are also applied on the VPG waveform in order to identify four additional fiducial points, i.e., w which is the first maxima of VPG, x which is the corresponding systolic point of the PPG waveform, y which is the first minima of the VPG, and z which is the second maxima of the VPG or the zero-crossing point of the APG after the e point.

    Article Title: A predictive propensity measure to enter REM sleep
    Article Snippet: To do so, we computed the propensity for | N | = 1, 2, 3, ..., 3, 000 s. Using MATLAB's built-in “islocalmax” function, we computed all values of | N | at which the propensity has a local maximum, and found the largest of the resulting values of | N |.

    Article Title: Accurate Quantification and Imaging of Cellular Uptake Using Single-Particle Surface-Enhanced Raman Scattering
    Article Snippet: The spectrum with brightest signal for each particle was identified with ‘islocalmax’ in MATLAB for time series and cell data.



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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
    Islocalmax (Signal, Minimal Distance, And Minimal Peak Prominence) Matlab Function, 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
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    islocalmax (signal, minimal distance) matlab function  (MathWorks Inc)
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    MathWorks Inc islocalmax (signal, minimal distance) matlab function
    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function <t>‘islocalmax’.</t> Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.
    Islocalmax (Signal, Minimal Distance) Matlab Function, 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/matlab+function+'islocalmax/pmc08359364-111-43-47
    Average 90 stars, based on 1 article reviews
    islocalmax (signal, minimal distance) matlab function - by Bioz Stars, 2026-09
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    ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function ‘islocalmax’. Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.

    Journal: eLife

    Article Title: I-Spin live, an open-source software based on blind-source separation for real-time decoding of motor unit activity in humans

    doi: 10.7554/eLife.88670

    Figure Lengend Snippet: ( A ) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. ( B ) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). ( C ) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function ‘islocalmax’. Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.

    Article Snippet: Local peaks were identified for each motor unit using the MATLAB function ‘islocalmax’ with a minimal separation of 25 ms between peaks to limit the number of false positives ( ).

    Techniques: Software, Plasmid Preparation, Activity Assay