Review



matlab signal processing toolbox  (MathWorks Inc)


Bioz Verified Symbol MathWorks Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 98
      Buy from Supplier

    Structured Review

    MathWorks Inc matlab signal processing toolbox
    Matlab Signal Processing Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 98/100, based on 1101 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/saf+signal+processing+algorithm/Signal+Processing+Toolbox/pmc12014665-129-13-13
    Average 98 stars, based on 1101 article reviews
    matlab signal processing toolbox - by Bioz Stars, 2026-11
    98/100 stars

    Images

    Related Articles

    other:

    Article Title: Improved Myocardial Sodium Quantification at 7 T Using Interleaved 23 Na/ 1 H pTx MRI With Motion and Anatomy-Based B 1 Correction.
    Article Snippet: Within the expected cardiac frequency range (0.5–2 Hz), the dominant peak was identified using MATLAB’s findpeaks function (Signal Processing Toolbox), and its prominence (pcard), center frequency (fcard), and width (wcard) were extracted.

    Article Title: Improved Myocardial Sodium Quantification at 7 T Using Interleaved 23 Na/ 1 H pTx MRI With Motion and Anatomy-Based B 1 Correction.
    Article Snippet: For each 1H receive channel, the frequency power spectrum of the k0 navigator signal was computed and denoised using a Savitzky–Golay filter [25] (sgolayfilt in MATLAB’s Signal Processing Toolbox).

    Article Title: Reliable multimodal signals for in-vehicle heartbeat detection
    Article Snippet: We generate the ground truth with the STAPLE algorithm from Kashif et al.26, implemented in MATLAB (version R2023b, The MathWorks, Natick, United States) with the Signal Processing Toolbox.

    Article Title: Reduced central alpha power at rest is associated with the risk of alcohol-induced blackout and frequency of non-REM parasomnia episodes in adults.
    Article Snippet: 2.1.4 | Resting- state EEG data analysis Cleaned EEG datasets were analyzed using built- in MATLAB functions, the Fieldtrip toolbox for MATLAB (Oostenveld et al., 2011), the Signal Processing toolbox for MATLAB, and the fitting oscillations & 1/f (FOOOF) toolbox for python using a MATLAB wrapper (Donoghue et al., 2020).

    Article Title: Feature-Based Machine-Learning Spike Marking in Signal and Source Space EEG: A Proof-of-Principle Case Study of Focal Epilepsy.
    Article Snippet: All analyses were performed in MATLAB R2025b using FieldTrip (https://www.fieldtriptoolbox.org) [32], the Signal Processing Toolbox (accessed on 2 September 2026) (https://www.mathworks.com/products/ signal.html), the Statistics and Machine Learning Toolbox (accessed on 2 September 2026) (https://www.mathworks.com/products/statistics.html) and some custom scripts.

    Sampling:

    Article Title: Graph empirical mode decomposition and multiscale feature extraction for EEG-based classification of Alzheimer's disease and frontotemporal dementia.
    Article Snippet: Analyses were performed in MATLAB, using built-in functions from the Signal Processing, Wavelet, and Statistics and Machine Learning Toolboxes. .. The EEG signals were filtered at a 128 Hz sampling rate by MATLAB’s Signal Processing Toolbox with a 4th-order Butterworth band-pass filter (0.5–40 Hz) applied via the functions butter and filtfilt, followed by downsampling by a factor of 5 via decimate with the option ‘fir’. .. The channel graph was formed in MATLAB using corrcoef to calculate the inter-channel Pearson correlation coefficients as elements of a fully weighted adjacency matrix with the diagonal set to zero and without thresholding or sparsification.

    Software:

    Article Title: Htr3a receptors control attenuation of fear responses by modulating the corticolimbic activity and synchronization
    Article Snippet: .. Data were down sampled to 1000Hz, band pass filtered (1-100 Hz) and analyzed using custom-written script based on Matlab (R2017A, MathWorks) signal processing toolbox along with functions in Chronux open-source software package. ..

    Electroencephalography:

    Article Title: Patterns of Alpha-band Power Reflect Prioritization of the Most Recently Encoded Location in a Working Memory Sequence
    Article Snippet: .. Next, we applied a Hilbert transform (MATLAB Signal Processing Toolbox) to obtain the complex analytic signal of the EEG data for each trial. ..



    Similar Products

    saf signal processing algorithm  (Decon Laboratories)
    90
    Decon Laboratories saf signal processing algorithm
    Simulated waveforms at four different levels of local distortion parameter, σq, before (left column) and after (right column) accounting for <t>spatial</t> <t>averaging</t> effects due to reception by a membrane hydrophone with a geometrical sensitive element diameter of 500 μm (by application of the <t>SAF).</t> The source has a center frequency of 5 mHz, diameter of 19 mm, and a focal length of 38 mm. The nonlinearity index values were σq = 0.6, σm = 0.5, SI = 0.06 (top row), σq = 1.0, σm = 0.8, SI = 0.11 (second row), σq = 2.3, σm = 1.9, SI = 0.32 (third row), and σq = 4.6, σm = 3.9, SI = 0.45 (fourth row).
    Saf Signal Processing Algorithm, supplied by Decon Laboratories, 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/saf+signal+processing+algorithm/saf+signal+processing+algorithm/pmc08325168-367-4-5
    Average 90 stars, based on 1 article reviews
    saf signal processing algorithm - by Bioz Stars, 2026-11
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Simulated waveforms at four different levels of local distortion parameter, σq, before (left column) and after (right column) accounting for spatial averaging effects due to reception by a membrane hydrophone with a geometrical sensitive element diameter of 500 μm (by application of the SAF). The source has a center frequency of 5 mHz, diameter of 19 mm, and a focal length of 38 mm. The nonlinearity index values were σq = 0.6, σm = 0.5, SI = 0.06 (top row), σq = 1.0, σm = 0.8, SI = 0.11 (second row), σq = 2.3, σm = 1.9, SI = 0.32 (third row), and σq = 4.6, σm = 3.9, SI = 0.45 (fourth row).

    Journal: IEEE transactions on ultrasonics, ferroelectrics, and frequency control

    Article Title: Correction for Hydrophone Spatial Averaging Artifacts for Circular Sources

    doi: 10.1109/TUFFC.2020.3007808

    Figure Lengend Snippet: Simulated waveforms at four different levels of local distortion parameter, σq, before (left column) and after (right column) accounting for spatial averaging effects due to reception by a membrane hydrophone with a geometrical sensitive element diameter of 500 μm (by application of the SAF). The source has a center frequency of 5 mHz, diameter of 19 mm, and a focal length of 38 mm. The nonlinearity index values were σq = 0.6, σm = 0.5, SI = 0.06 (top row), σq = 1.0, σm = 0.8, SI = 0.11 (second row), σq = 2.3, σm = 1.9, SI = 0.32 (third row), and σq = 4.6, σm = 3.9, SI = 0.45 (fourth row).

    Article Snippet: Inverse filtering with the SAF (“Decon Sens + SAF”) significantly suppresses spatial averaging effects. shows that measurements made with the high-resolution HGL-0085 hydrophone ( d g = 85 μ m) that were not corrected for spatial averaging (“Raw RF” or “Decon Sens”) were consistent with the trends of measurements made with the membrane hydrophones that were corrected for spatial averaging (“Decon Sens + SAF”).

    Techniques: Membrane

    Effects of spatial averaging on measurements of peak compressional pressure (pc), peak rarefactional pressure (pr), and pulse intensity integral for the 25 transducer/hydrophone combinations. The plots show: 1) raw data (“Raw RF”); 2) data deconvolved for hydrophone sensitivity only (“Decon Sens”); and 3) data deconvolved for hydrophone sensitivity and inverse filtered using Sp(nf1) over all harmonics with sufficient SNR (see Fig. 8) (“Decons Sens + SAF”). Data are plotted as a function of hydrophone nominal geometric sensitive element diameter, dg. Error bars (which are so small they are difficult to discern) denote standard deviations. Linear and exponential fits to data are also shown.

    Journal: IEEE transactions on ultrasonics, ferroelectrics, and frequency control

    Article Title: Correction for Hydrophone Spatial Averaging Artifacts for Circular Sources

    doi: 10.1109/TUFFC.2020.3007808

    Figure Lengend Snippet: Effects of spatial averaging on measurements of peak compressional pressure (pc), peak rarefactional pressure (pr), and pulse intensity integral for the 25 transducer/hydrophone combinations. The plots show: 1) raw data (“Raw RF”); 2) data deconvolved for hydrophone sensitivity only (“Decon Sens”); and 3) data deconvolved for hydrophone sensitivity and inverse filtered using Sp(nf1) over all harmonics with sufficient SNR (see Fig. 8) (“Decons Sens + SAF”). Data are plotted as a function of hydrophone nominal geometric sensitive element diameter, dg. Error bars (which are so small they are difficult to discern) denote standard deviations. Linear and exponential fits to data are also shown.

    Article Snippet: Inverse filtering with the SAF (“Decon Sens + SAF”) significantly suppresses spatial averaging effects. shows that measurements made with the high-resolution HGL-0085 hydrophone ( d g = 85 μ m) that were not corrected for spatial averaging (“Raw RF” or “Decon Sens”) were consistent with the trends of measurements made with the membrane hydrophones that were corrected for spatial averaging (“Decon Sens + SAF”).

    Techniques:

    Membrane hydrophone simulation results (open circles and squares) for percent error in estimates of peak compressional pressure (pc), peak rarefactional pressure (pr), and pulse intensity integral, when: 1) no spatial averaging correction is applied (“Without SAF”) and 2) the full SAF inverse filter correction is applied (“With SAF”). Measurements are shown in asterisks and x’s. All simulation and experimental data were inverse filtered to deconvolve the effects of hydrophone frequency-dependent sensitivity. Simulation data without SAF are fit to functions of the form – 100% × [1 − exp(−αx)], where x = dg/(λ1F#) and α is a fitting parameter. Standard deviations for measurements are less than 1%.

    Journal: IEEE transactions on ultrasonics, ferroelectrics, and frequency control

    Article Title: Correction for Hydrophone Spatial Averaging Artifacts for Circular Sources

    doi: 10.1109/TUFFC.2020.3007808

    Figure Lengend Snippet: Membrane hydrophone simulation results (open circles and squares) for percent error in estimates of peak compressional pressure (pc), peak rarefactional pressure (pr), and pulse intensity integral, when: 1) no spatial averaging correction is applied (“Without SAF”) and 2) the full SAF inverse filter correction is applied (“With SAF”). Measurements are shown in asterisks and x’s. All simulation and experimental data were inverse filtered to deconvolve the effects of hydrophone frequency-dependent sensitivity. Simulation data without SAF are fit to functions of the form – 100% × [1 − exp(−αx)], where x = dg/(λ1F#) and α is a fitting parameter. Standard deviations for measurements are less than 1%.

    Article Snippet: Inverse filtering with the SAF (“Decon Sens + SAF”) significantly suppresses spatial averaging effects. shows that measurements made with the high-resolution HGL-0085 hydrophone ( d g = 85 μ m) that were not corrected for spatial averaging (“Raw RF” or “Decon Sens”) were consistent with the trends of measurements made with the membrane hydrophones that were corrected for spatial averaging (“Decon Sens + SAF”).

    Techniques: Membrane

    Needle/fiber optic hydrophone simulation results (open circles and squares) for percent error in estimates of peak compressional pressure (pc), peak rarefactional pressure (pr), and pulse intensity integral, when: 1) no spatial averaging correction is applied (“Without SAF”) and 2) the full SAF inverse filter correction is applied (“With SAF”). All simulation data were inverse filtered to deconvolve the effects of hydrophone frequency-dependent sensitivity. Simulation data without SAF are fit to functions of the form −100% × [1 − exp(−βx2)], where x = dg/(λ1F#) and β is a fitting parameter.

    Journal: IEEE transactions on ultrasonics, ferroelectrics, and frequency control

    Article Title: Correction for Hydrophone Spatial Averaging Artifacts for Circular Sources

    doi: 10.1109/TUFFC.2020.3007808

    Figure Lengend Snippet: Needle/fiber optic hydrophone simulation results (open circles and squares) for percent error in estimates of peak compressional pressure (pc), peak rarefactional pressure (pr), and pulse intensity integral, when: 1) no spatial averaging correction is applied (“Without SAF”) and 2) the full SAF inverse filter correction is applied (“With SAF”). All simulation data were inverse filtered to deconvolve the effects of hydrophone frequency-dependent sensitivity. Simulation data without SAF are fit to functions of the form −100% × [1 − exp(−βx2)], where x = dg/(λ1F#) and β is a fitting parameter.

    Article Snippet: Inverse filtering with the SAF (“Decon Sens + SAF”) significantly suppresses spatial averaging effects. shows that measurements made with the high-resolution HGL-0085 hydrophone ( d g = 85 μ m) that were not corrected for spatial averaging (“Raw RF” or “Decon Sens”) were consistent with the trends of measurements made with the membrane hydrophones that were corrected for spatial averaging (“Decon Sens + SAF”).

    Techniques: