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Category:
Language:
English
Total Size:
2.9 GB
Info Hash:
21ED18711529A4ADC5C29C14D6F2447499EC51DB
Added By:
Added:
Oct. 23, 2023, 5:30 p.m.
Stats:
|
(Last updated: May 14, 2025, 10:06 p.m.)
| File | Size |
|---|---|
| Get Bonus Downloads Here.url | 182 bytes |
| 001 Signal processing = decision-making + tools.mp4 | 29.2 MB |
| 001 Signal processing = decision-making + tools_en.vtt | 4.9 KB |
| 002 Using MATLAB in this course.mp4 | 9.2 MB |
| 002 Using MATLAB in this course_en.vtt | 4.6 KB |
| 003 Using Octave-online in this course.mp4 | 16.9 MB |
| 003 Using Octave-online in this course_en.vtt | 6.3 KB |
| 004 Using Python in this course.mp4 | 10.6 MB |
| 004 Using Python in this course_en.vtt | 4.3 KB |
| 005 Having fun with filtered Glass dance.mp4 | 48.4 MB |
| 005 Having fun with filtered Glass dance_en.vtt | 9.0 KB |
| 006 Writing code vs. using toolboxesprograms.mp4 | 24.8 MB |
| 006 Writing code vs. using toolboxesprograms_en.vtt | 8.5 KB |
| 007 Using Udemy like a pro.mp4 | 25.7 MB |
| 007 Using Udemy like a pro_en.vtt | 10.3 KB |
| glassDance.mat | 3.3 MB |
| sigprocMXC_filterGlass.ipynb | 4.0 KB |
| sigprocMXC_filterGlass.m | 1.7 KB |
| 001 MATLAB and Python code for this section.html | 80 bytes |
| 002 Mean-smooth a time series.mp4 | 57.0 MB |
| 002 Mean-smooth a time series_en.vtt | 9.9 KB |
| 003 Gaussian-smooth a time series.mp4 | 45.5 MB |
| 003 Gaussian-smooth a time series_en.vtt | 15.9 KB |
| 004 Gaussian-smooth a spike time series.mp4 | 18.0 MB |
| 004 Gaussian-smooth a spike time series_en.vtt | 6.3 KB |
| 005 Denoising EMG signals via TKEO.mp4 | 47.7 MB |
| 005 Denoising EMG signals via TKEO_en.vtt | 9.7 KB |
| 006 Median filter to remove spike noise.mp4 | 25.8 MB |
| 006 Median filter to remove spike noise_en.vtt | 12.0 KB |
| 007 Remove linear trend (detrending).mp4 | 4.7 MB |
| 007 Remove linear trend (detrending)_en.vtt | 2.6 KB |
| 008 Remove nonlinear trend with polynomials.mp4 | 53.5 MB |
| 008 Remove nonlinear trend with polynomials_en.vtt | 17.8 KB |
| 009 Averaging multiple repetitions (time-synchronous averaging).mp4 | 23.2 MB |
| 009 Averaging multiple repetitions (time-synchronous averaging)_en.vtt | 6.3 KB |
| 010 Remove artifact via least-squares template-matching.mp4 | 39.8 MB |
| 010 Remove artifact via least-squares template-matching_en.vtt | 12.0 KB |
| 011 Code challenge Denoise these signals!.mp4 | 3.4 MB |
| 011 Code challenge Denoise these signals!_en.vtt | 1.3 KB |
| denoising_codeChallenge.mat | 60.4 KB |
| emg4TKEO.mat | 8.1 KB |
| eyedat.mat | 3.8 MB |
| sigprocMXC_GauSmoothSpikes.m | 1.3 KB |
| sigprocMXC_Gaussian_smooth.m | 2.3 KB |
| sigprocMXC_TKEO.m | 1.3 KB |
| sigprocMXC_averaging.m | 1.3 KB |
| sigprocMXC_detrend.m | 543 bytes |
| sigprocMXC_mean_smooth.m | 1.4 KB |
| sigprocMXC_median_filter.m | 1.2 KB |
| sigprocMXC_polynomialDetrend.m | 2.5 KB |
| sigprocMXC_template_projection.m | 1.3 KB |
| sigprocMXC_timeSeriesDenoising.ipynb | 19.5 KB |
| templateProjection.mat | 7.5 MB |
| 001 MATLAB and Python code for this section.html | 97 bytes |
| 002 Crash course on the Fourier transform.mp4 | 54.6 MB |
| 002 Crash course on the Fourier transform_en.vtt | 18.3 KB |
| 003 Fourier transform for spectral analyses.mp4 | 70.0 MB |
| 003 Fourier transform for spectral analyses_en.vtt | 22.4 KB |
| 004 Welch's method and windowing.mp4 | 40.7 MB |
| 004 Welch's method and windowing_en.vtt | 18.0 KB |
| 005 Spectrogram of birdsong.mp4 | 31.2 MB |
| 005 Spectrogram of birdsong_en.vtt | 9.4 KB |
| 006 Code challenge Compute a spectrogram!.mp4 | 5.6 MB |
| 006 Code challenge Compute a spectrogram!_en.vtt | 3.1 KB |
| EEGrestingState.mat | 335.8 KB |
| XC403881.mp3 | 244.3 KB |
| XC403881.wav | 1.7 MB |
| sigprocMXC_FourierTransform.m | 2.5 KB |
| sigprocMXC_SpectBirdcall.m | 1.4 KB |
| sigprocMXC_Welch.m | 2.0 KB |
| sigprocMXC_spectral.ipynb | 348.7 KB |
| spectral_codeChallenge.mat | 61.5 KB |
| 001 MATLAB and Python code for this section.html | 44 bytes |
| 002 From the number line to the complex number plane.mp4 | 21.3 MB |
| 002 From the number line to the complex number plane_en.vtt | 12.1 KB |
| 003 Addition and subtraction with complex numbers.mp4 | 7.5 MB |
| 003 Addition and subtraction with complex numbers_en.vtt | 4.2 KB |
| 004 Multiplication with complex numbers.mp4 | 17.1 MB |
| 004 Multiplication with complex numbers_en.vtt | 7.8 KB |
| 005 The complex conjugate.mp4 | 10.5 MB |
| 005 The complex conjugate_en.vtt | 5.1 KB |
| 006 Division with complex numbers.mp4 | 7.3 MB |
| 006 Division with complex numbers_en.vtt | 4.6 KB |
| 007 Magnitude and phase of complex numbers.mp4 | 21.3 MB |
| 007 Magnitude and phase of complex numbers_en.vtt | 9.4 KB |
| signprocMXC_complexNumbers.ipynb | 53.3 KB |
| sigprocMXC_complexAddSub.m | 572 bytes |
| sigprocMXC_complexConj.m | 484 bytes |
| sigprocMXC_complexDivision.m | 387 bytes |
| sigprocMXC_complexIntro.m | 1010 bytes |
| sigprocMXC_complexMult.m | 612 bytes |
| sigprocMXC_complexPolar.m | 1.0 KB |
| 001 MATLAB and Python code for this section.html | 85 bytes |
| 002 Filtering Intuition, goals, and types.mp4 | 87.9 MB |
| 002 Filtering Intuition, goals, and types_en.vtt | 18.8 KB |
| 003 FIR filters with firls.mp4 | 49.7 MB |
| 003 FIR filters with firls_en.vtt | 17.7 KB |
| 004 FIR filters with fir1.mp4 | 22.7 MB |
| 004 FIR filters with fir1_en.vtt | 6.8 KB |
| 005 IIR Butterworth filters.mp4 | 34.3 MB |
| 005 IIR Butterworth filters_en.vtt | 12.2 KB |
| 006 Causal and zero-phase-shift filters.mp4 | 33.7 MB |
| 006 Causal and zero-phase-shift filters_en.vtt | 11.6 KB |
| 007 Avoid edge effects with reflection.mp4 | 85.3 MB |
| 007 Avoid edge effects with reflection_en.vtt | 13.7 KB |
| 008 Data length and filter kernel length.mp4 | 22.6 MB |
| 008 Data length and filter kernel length_en.vtt | 9.8 KB |
| 009 Low-pass filters.mp4 | 30.2 MB |
| 009 Low-pass filters_en.vtt | 8.6 KB |
| 010 Windowed-sinc filters.mp4 | 37.1 MB |
| 010 Windowed-sinc filters_en.vtt | 13.9 KB |
| 011 High-pass filters.mp4 | 21.6 MB |
| 011 High-pass filters_en.vtt | 6.9 KB |
| 012 Narrow-band filters.mp4 | 23.3 MB |
| 012 Narrow-band filters_en.vtt | 7.8 KB |
| 013 Two-stage wide-band filter.mp4 | 37.3 MB |
| 013 Two-stage wide-band filter_en.vtt | 5.5 KB |
| 014 Quantifying roll-off characteristics.mp4 | 36.3 MB |
| 014 Quantifying roll-off characteristics_en.vtt | 13.0 KB |
| 015 Remove electrical line noise and its harmonics.mp4 | 37.8 MB |
| 015 Remove electrical line noise and its harmonics_en.vtt | 12.5 KB |
| 016 Use filtering to separate birds in a recording.mp4 | 35.4 MB |
| 016 Use filtering to separate birds in a recording_en.vtt | 7.6 KB |
| 017 Code challenge Filter these signals!.mp4 | 5.0 MB |
| 017 Code challenge Filter these signals!_en.vtt | 1.7 KB |
| XC403881.mp3 | 244.3 KB |
| XC403881.wav | 1.7 MB |
| filtering_codeChallenge.mat | 150.5 KB |
| lineNoiseData.mat | 2.2 MB |
| sigprocMXC_2stageWide.m | 3.4 KB |
| sigprocMXC_butter.m | 3.2 KB |
| sigprocMXC_causal0phase.m | 1.7 KB |
| sigprocMXC_filterTheBirds.m | 2.0 KB |
| sigprocMXC_filtering_part1.ipynb | 1.4 MB |
| sigprocMXC_filtering_part2.ipynb | 1.7 MB |
| sigprocMXC_fir1.m | 2.6 KB |
| sigprocMXC_firls.m | 3.6 KB |
| sigprocMXC_highpass.m | 2.5 KB |
| sigprocMXC_linenoise.m | 2.1 KB |
| sigprocMXC_lowpass.m | 2.0 KB |
| sigprocMXC_narrowband.m | 1.7 KB |
| sigprocMXC_reflection.m | 2.3 KB |
| sigprocMXC_rolloff.m | 2.3 KB |
| sigprocMXC_signalLength.m | 709 bytes |
| sigprocMXC_windowSinc.m | 3.1 KB |
| 001 MATLAB and Python code for this section.html | 70 bytes |
| 002 Time-domain convolution.mp4 | 35.0 MB |
| 002 Time-domain convolution_en.vtt | 14.3 KB |
| 003 Convolution in MATLAB.mp4 | 41.6 MB |
| 003 Convolution in MATLAB_en.vtt | 15.3 KB |
| 004 Why is the kernel flipped backwards!!!.mp4 | 9.0 MB |
| 004 Why is the kernel flipped backwards!!!_en.vtt | 5.8 KB |
| 005 The convolution theorem.mp4 | 29.3 MB |
| 005 The convolution theorem_en.vtt | 11.8 KB |
| 006 Thinking about convolution as spectral multiplication.mp4 | 34.7 MB |
| 006 Thinking about convolution as spectral multiplication_en.vtt | 15.0 KB |
| 007 Convolution with time-domain Gaussian (smoothing filter).mp4 | 21.0 MB |
| 007 Convolution with time-domain Gaussian (smoothing filter)_en.vtt | 7.1 KB |
| 008 Convolution with frequency-domain Gaussian (narrowband filter).mp4 | 25.7 MB |
| 008 Convolution with frequency-domain Gaussian (narrowband filter)_en.vtt | 8.0 KB |
| 009 Convolution with frequency-domain Planck taper (bandpass filter).mp4 | 22.2 MB |
| 009 Convolution with frequency-domain Planck taper (bandpass filter)_en.vtt | 7.2 KB |
| 010 Code challenge Create a frequency-domain mean-smoothing filter.mp4 | 5.1 MB |
| 010 Code challenge Create a frequency-domain mean-smoothing filter_en.vtt | 2.1 KB |
| sigprocMXC_FreqDomainGaus.m | 1.8 KB |
| sigprocMXC_TimeDomainGaus.m | 2.3 KB |
| sigprocMXC_convolution.ipynb | 352.6 KB |
| sigprocMXC_convolutionTheorem.m | 1.5 KB |
| sigprocMXC_planckBandPass.m | 2.3 KB |
| sigprocMXC_timeConvolution.m | 2.9 KB |
| 001 MATLAB and Python code for this section.html | 84 bytes |
| 002 What are wavelets.mp4 | 72.7 MB |
| 002 What are wavelets_en.vtt | 16.6 KB |
| 003 Convolution with wavelets.mp4 | 22.8 MB |
| 003 Convolution with wavelets_en.vtt | 6.5 KB |
| 004 Scientific publication about defining Morlet wavelets.html | 465 bytes |
| 005 Wavelet convolution for narrowband filtering.mp4 | 55.4 MB |
| 005 Wavelet convolution for narrowband filtering_en.vtt | 17.2 KB |
| 006 Overview Time-frequency analysis with complex wavelets.mp4 | 20.6 MB |
| 006 Overview Time-frequency analysis with complex wavelets_en.vtt | 9.5 KB |
| 007 Link to youtube channel with 3 hours of relevant material.html | 621 bytes |
| 008 MATLAB Time-frequency analysis with complex wavelets.mp4 | 113.6 MB |
| 008 MATLAB Time-frequency analysis with complex wavelets_en.vtt | 17.3 KB |
| 009 Time-frequency analysis of brain signals.mp4 | 27.8 MB |
| 009 Time-frequency analysis of brain signals_en.vtt | 9.8 KB |
| 010 Code challenge Compare wavelet convolution and FIR filter!.mp4 | 5.1 MB |
| 010 Code challenge Compare wavelet convolution and FIR filter!_en.vtt | 2.5 KB |
| data4TF.mat | 17.1 KB |
| sigprocMXC_timefreq.m | 2.2 KB |
| sigprocMXC_timefreqBrain.m | 2.4 KB |
| sigprocMXC_wavelet.ipynb | 21.6 KB |
| sigprocMXC_waveletConv.m | 2.1 KB |
| sigprocMXC_waveletTF.m | 3.4 KB |
| sigprocMXC_wavelets.m | 3.3 KB |
| sigprocMXC_wavelets4narrowband.m | 2.7 KB |
| wavelet_codeChallenge.mat | 276.7 KB |
| 001 MATLAB and Python code for this section.html | 67 bytes |
| 002 Upsampling.mp4 | 43.3 MB |
| 002 Upsampling_en.vtt | 15.5 KB |
| 003 Downsampling.mp4 | 51.5 MB |
| 003 Downsampling_en.vtt | 14.4 KB |
| 004 Strategies for multirate signals.mp4 | 38.4 MB |
| 004 Strategies for multirate signals_en.vtt | 7.9 KB |
| 005 Interpolation.mp4 | 27.2 MB |
| 005 Interpolation_en.vtt | 9.3 KB |
| 006 Resample irregularly sampled data.mp4 | 39.0 MB |
| 006 Resample irregularly sampled data_en.vtt | 13.2 KB |
| 007 Extrapolation.mp4 | 18.4 MB |
| 007 Extrapolation_en.vtt | 7.1 KB |
| 008 Spectral interpolation.mp4 | 26.2 MB |
| 008 Spectral interpolation_en.vtt | 12.0 KB |
| 009 Dynamic time warping.mp4 | 50.3 MB |
| 009 Dynamic time warping_en.vtt | 19.1 KB |
| 010 Code challenge denoise and downsample this signal!.mp4 | 9.5 MB |
| 010 Code challenge denoise and downsample this signal!_en.vtt | 5.1 KB |
| resample_codeChallenge.mat | 52.2 KB |
| sigprocMXC_downsample.m | 2.8 KB |
| sigprocMXC_dtw.m | 1.6 KB |
| sigprocMXC_extrap.m | 1.0 KB |
| sigprocMXC_interp.m | 1.9 KB |
| sigprocMXC_irregular.m | 1.8 KB |
| sigprocMXC_multirate.m | 1.9 KB |
| sigprocMXC_resample.ipynb | 19.9 KB |
| sigprocMXC_spectralInterp.m | 1.2 KB |
| sigprocMXC_upsample.m | 1.9 KB |
| 001 MATLAB and Python code for this section.html | 72 bytes |
| 002 Outliers via standard deviation threshold.mp4 | 30.3 MB |
| 002 Outliers via standard deviation threshold_en.vtt | 11.3 KB |
| 003 Outliers via local threshold exceedance.mp4 | 25.1 MB |
| 003 Outliers via local threshold exceedance_en.vtt | 10.4 KB |
| 004 Outlier time windows via sliding RMS.mp4 | 16.0 MB |
| 004 Outlier time windows via sliding RMS_en.vtt | 6.9 KB |
| 005 Code challenge.mp4 | 15.2 MB |
| 005 Code challenge_en.vtt | 4.5 KB |
| forex.mat | 172.7 KB |
| sigprocMXC_RMSoutlierWindows.m | 1.6 KB |
| sigprocMXC_localOutliers.m | 1.8 KB |
| sigprocMXC_outZ.m | 999 bytes |
| sigprocMXC_outliers.ipynb | 129.2 KB |
| 001 MATLAB and Python code for this section.html | 71 bytes |
| 002 Local maxima and minima.mp4 | 85.5 MB |
| 002 Local maxima and minima_en.vtt | 18.7 KB |
| 003 Recover signal from noise amplitude.mp4 | 42.4 MB |
| 003 Recover signal from noise amplitude_en.vtt | 14.2 KB |
| 004 Wavelet convolution for feature extraction.mp4 | 104.6 MB |
| 004 Wavelet convolution for feature extraction_en.vtt | 16.9 KB |
| 005 Area under the curve.mp4 | 39.5 MB |
| 005 Area under the curve_en.vtt | 15.2 KB |
| 006 Application Detect muscle movements from EMG recordings.mp4 | 64.0 MB |
| 006 Application Detect muscle movements from EMG recordings_en.vtt | 20.8 KB |
| 007 Full width at half-maximum.mp4 | 64.8 MB |
| 007 Full width at half-maximum_en.vtt | 21.1 KB |
| 008 Code challenge find the features!.mp4 | 10.7 MB |
| 008 Code challenge find the features!_en.vtt | 4.0 KB |
| EMGRT.mat | 1.1 MB |
| sigprocMXC_AUC.m | 1.3 KB |
| sigprocMXC_EMGonsets.m | 2.3 KB |
| sigprocMXC_FWHM.m | 3.0 KB |
| sigprocMXC_featuredetection.ipynb | 22.5 KB |
| sigprocMXC_localMinMax.m | 1.5 KB |
| sigprocMXC_signalFromNoise.m | 2.4 KB |
| sigprocMXC_waveletFeatureEx.m | 2.6 KB |
| 001 MATLAB and Python code for this section.html | 47 bytes |
| 002 Total and windowed variance and RMS.mp4 | 26.5 MB |
| 002 Total and windowed variance and RMS_en.vtt | 12.9 KB |
| 003 Signal-to-noise ratio (SNR).mp4 | 54.4 MB |
| 003 Signal-to-noise ratio (SNR)_en.vtt | 17.6 KB |
| 004 Coefficient of variation (CV).mp4 | 10.5 MB |
| 004 Coefficient of variation (CV)_en.vtt | 6.0 KB |
| 005 Entropy.mp4 | 55.9 MB |
| 005 Entropy_en.vtt | 19.2 KB |
| 006 Code challenge.mp4 | 10.4 MB |
| 006 Code challenge_en.vtt | 3.7 KB |
| SNRdata.mat | 4.5 MB |
| sigprocMXC_CV.m | 779 bytes |
| sigprocMXC_SNR.m | 2.7 KB |
| sigprocMXC_entropy.m | 2.9 KB |
| sigprocMXC_variability.ipynb | 13.0 KB |
| sigprocMXC_windowedVar.m | 1.1 KB |
| v1_laminar.mat | 17.4 MB |
| 001 Bonus lecture.html | 3.8 KB |
| Bonus Resources.txt | 386 bytes |
Name
DL
Uploader
Size
S/L
Added
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510.9 MB
[0
/
0]
2023-10-26
| Uploaded by freecoursewb | Size 510.9 MB | Health [ 0 /0 ] | Added 2023-10-26 |
-
406.1 MB
[0
/
1]
2023-10-23
| Uploaded by freecoursewb | Size 406.1 MB | Health [ 0 /1 ] | Added 2023-10-23 |
NOTE
SOURCE: Udemy Signal processing problems solved in MATLAB and in Python
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