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| 001 Bonus lecture.html |
3.82Кб |
| 001 MATLAB and Python code for this section.html |
80б |
| 001 MATLAB and Python code for this section.html |
97б |
| 001 MATLAB and Python code for this section.html |
44б |
| 001 MATLAB and Python code for this section.html |
85б |
| 001 MATLAB and Python code for this section.html |
70б |
| 001 MATLAB and Python code for this section.html |
84б |
| 001 MATLAB and Python code for this section.html |
67б |
| 001 MATLAB and Python code for this section.html |
72б |
| 001 MATLAB and Python code for this section.html |
71б |
| 001 MATLAB and Python code for this section.html |
47б |
| 001 Signal processing = decision-making + tools_en.vtt |
4.90Кб |
| 001 Signal processing = decision-making + tools.mp4 |
29.17Мб |
| 002 Crash course on the Fourier transform_en.vtt |
18.35Кб |
| 002 Crash course on the Fourier transform.mp4 |
54.62Мб |
| 002 Filtering Intuition, goals, and types_en.vtt |
18.85Кб |
| 002 Filtering Intuition, goals, and types.mp4 |
87.89Мб |
| 002 From the number line to the complex number plane_en.vtt |
12.07Кб |
| 002 From the number line to the complex number plane.mp4 |
21.26Мб |
| 002 Local maxima and minima_en.vtt |
18.66Кб |
| 002 Local maxima and minima.mp4 |
85.48Мб |
| 002 Mean-smooth a time series_en.vtt |
9.93Кб |
| 002 Mean-smooth a time series.mp4 |
56.97Мб |
| 002 Outliers via standard deviation threshold_en.vtt |
11.35Кб |
| 002 Outliers via standard deviation threshold.mp4 |
30.30Мб |
| 002 Time-domain convolution_en.vtt |
14.35Кб |
| 002 Time-domain convolution.mp4 |
34.96Мб |
| 002 Total and windowed variance and RMS_en.vtt |
12.91Кб |
| 002 Total and windowed variance and RMS.mp4 |
26.45Мб |
| 002 Upsampling_en.vtt |
15.52Кб |
| 002 Upsampling.mp4 |
43.30Мб |
| 002 Using MATLAB in this course_en.vtt |
4.56Кб |
| 002 Using MATLAB in this course.mp4 |
9.21Мб |
| 002 What are wavelets_en.vtt |
16.65Кб |
| 002 What are wavelets.mp4 |
72.67Мб |
| 003 Addition and subtraction with complex numbers_en.vtt |
4.23Кб |
| 003 Addition and subtraction with complex numbers.mp4 |
7.47Мб |
| 003 Convolution in MATLAB_en.vtt |
15.27Кб |
| 003 Convolution in MATLAB.mp4 |
41.59Мб |
| 003 Convolution with wavelets_en.vtt |
6.50Кб |
| 003 Convolution with wavelets.mp4 |
22.78Мб |
| 003 Downsampling_en.vtt |
14.37Кб |
| 003 Downsampling.mp4 |
51.47Мб |
| 003 FIR filters with firls_en.vtt |
17.72Кб |
| 003 FIR filters with firls.mp4 |
49.74Мб |
| 003 Fourier transform for spectral analyses_en.vtt |
22.40Кб |
| 003 Fourier transform for spectral analyses.mp4 |
69.98Мб |
| 003 Gaussian-smooth a time series_en.vtt |
15.87Кб |
| 003 Gaussian-smooth a time series.mp4 |
45.52Мб |
| 003 Outliers via local threshold exceedance_en.vtt |
10.36Кб |
| 003 Outliers via local threshold exceedance.mp4 |
25.10Мб |
| 003 Recover signal from noise amplitude_en.vtt |
14.18Кб |
| 003 Recover signal from noise amplitude.mp4 |
42.37Мб |
| 003 Signal-to-noise ratio (SNR)_en.vtt |
17.56Кб |
| 003 Signal-to-noise ratio (SNR).mp4 |
54.36Мб |
| 003 Using Octave-online in this course_en.vtt |
6.29Кб |
| 003 Using Octave-online in this course.mp4 |
16.92Мб |
| 004 Coefficient of variation (CV)_en.vtt |
5.95Кб |
| 004 Coefficient of variation (CV).mp4 |
10.54Мб |
| 004 FIR filters with fir1_en.vtt |
6.83Кб |
| 004 FIR filters with fir1.mp4 |
22.74Мб |
| 004 Gaussian-smooth a spike time series_en.vtt |
6.25Кб |
| 004 Gaussian-smooth a spike time series.mp4 |
17.96Мб |
| 004 Multiplication with complex numbers_en.vtt |
7.84Кб |
| 004 Multiplication with complex numbers.mp4 |
17.14Мб |
| 004 Outlier time windows via sliding RMS_en.vtt |
6.86Кб |
| 004 Outlier time windows via sliding RMS.mp4 |
15.97Мб |
| 004 Scientific publication about defining Morlet wavelets.html |
465б |
| 004 Strategies for multirate signals_en.vtt |
7.91Кб |
| 004 Strategies for multirate signals.mp4 |
38.35Мб |
| 004 Using Python in this course_en.vtt |
4.31Кб |
| 004 Using Python in this course.mp4 |
10.62Мб |
| 004 Wavelet convolution for feature extraction_en.vtt |
16.89Кб |
| 004 Wavelet convolution for feature extraction.mp4 |
104.65Мб |
| 004 Welch's method and windowing_en.vtt |
18.02Кб |
| 004 Welch's method and windowing.mp4 |
40.71Мб |
| 004 Why is the kernel flipped backwards!!!_en.vtt |
5.80Кб |
| 004 Why is the kernel flipped backwards!!!.mp4 |
8.97Мб |
| 005 Area under the curve_en.vtt |
15.16Кб |
| 005 Area under the curve.mp4 |
39.52Мб |
| 005 Code challenge_en.vtt |
4.51Кб |
| 005 Code challenge.mp4 |
15.23Мб |
| 005 Denoising EMG signals via TKEO_en.vtt |
9.75Кб |
| 005 Denoising EMG signals via TKEO.mp4 |
47.67Мб |
| 005 Entropy_en.vtt |
19.15Кб |
| 005 Entropy.mp4 |
55.89Мб |
| 005 Having fun with filtered Glass dance_en.vtt |
8.98Кб |
| 005 Having fun with filtered Glass dance.mp4 |
48.38Мб |
| 005 IIR Butterworth filters_en.vtt |
12.22Кб |
| 005 IIR Butterworth filters.mp4 |
34.26Мб |
| 005 Interpolation_en.vtt |
9.26Кб |
| 005 Interpolation.mp4 |
27.25Мб |
| 005 Spectrogram of birdsong_en.vtt |
9.41Кб |
| 005 Spectrogram of birdsong.mp4 |
31.20Мб |
| 005 The complex conjugate_en.vtt |
5.13Кб |
| 005 The complex conjugate.mp4 |
10.52Мб |
| 005 The convolution theorem_en.vtt |
11.75Кб |
| 005 The convolution theorem.mp4 |
29.26Мб |
| 005 Wavelet convolution for narrowband filtering_en.vtt |
17.15Кб |
| 005 Wavelet convolution for narrowband filtering.mp4 |
55.43Мб |
| 006 Application Detect muscle movements from EMG recordings_en.vtt |
20.84Кб |
| 006 Application Detect muscle movements from EMG recordings.mp4 |
64.01Мб |
| 006 Causal and zero-phase-shift filters_en.vtt |
11.62Кб |
| 006 Causal and zero-phase-shift filters.mp4 |
33.72Мб |
| 006 Code challenge_en.vtt |
3.72Кб |
| 006 Code challenge.mp4 |
10.42Мб |
| 006 Code challenge Compute a spectrogram!_en.vtt |
3.12Кб |
| 006 Code challenge Compute a spectrogram!.mp4 |
5.58Мб |
| 006 Division with complex numbers_en.vtt |
4.62Кб |
| 006 Division with complex numbers.mp4 |
7.33Мб |
| 006 Median filter to remove spike noise_en.vtt |
12.03Кб |
| 006 Median filter to remove spike noise.mp4 |
25.82Мб |
| 006 Overview Time-frequency analysis with complex wavelets_en.vtt |
9.54Кб |
| 006 Overview Time-frequency analysis with complex wavelets.mp4 |
20.56Мб |
| 006 Resample irregularly sampled data_en.vtt |
13.20Кб |
| 006 Resample irregularly sampled data.mp4 |
39.01Мб |
| 006 Thinking about convolution as spectral multiplication_en.vtt |
14.98Кб |
| 006 Thinking about convolution as spectral multiplication.mp4 |
34.69Мб |
| 006 Writing code vs. using toolboxesprograms_en.vtt |
8.51Кб |
| 006 Writing code vs. using toolboxesprograms.mp4 |
24.76Мб |
| 007 Avoid edge effects with reflection_en.vtt |
13.68Кб |
| 007 Avoid edge effects with reflection.mp4 |
85.29Мб |
| 007 Convolution with time-domain Gaussian (smoothing filter)_en.vtt |
7.07Кб |
| 007 Convolution with time-domain Gaussian (smoothing filter).mp4 |
21.00Мб |
| 007 Extrapolation_en.vtt |
7.13Кб |
| 007 Extrapolation.mp4 |
18.41Мб |
| 007 Full width at half-maximum_en.vtt |
21.08Кб |
| 007 Full width at half-maximum.mp4 |
64.79Мб |
| 007 Link to youtube channel with 3 hours of relevant material.html |
621б |
| 007 Magnitude and phase of complex numbers_en.vtt |
9.41Кб |
| 007 Magnitude and phase of complex numbers.mp4 |
21.30Мб |
| 007 Remove linear trend (detrending)_en.vtt |
2.62Кб |
| 007 Remove linear trend (detrending).mp4 |
4.66Мб |
| 007 Using Udemy like a pro_en.vtt |
10.27Кб |
| 007 Using Udemy like a pro.mp4 |
25.67Мб |
| 008 Code challenge find the features!_en.vtt |
3.97Кб |
| 008 Code challenge find the features!.mp4 |
10.72Мб |
| 008 Convolution with frequency-domain Gaussian (narrowband filter)_en.vtt |
8.04Кб |
| 008 Convolution with frequency-domain Gaussian (narrowband filter).mp4 |
25.74Мб |
| 008 Data length and filter kernel length_en.vtt |
9.82Кб |
| 008 Data length and filter kernel length.mp4 |
22.57Мб |
| 008 MATLAB Time-frequency analysis with complex wavelets_en.vtt |
17.26Кб |
| 008 MATLAB Time-frequency analysis with complex wavelets.mp4 |
113.55Мб |
| 008 Remove nonlinear trend with polynomials_en.vtt |
17.76Кб |
| 008 Remove nonlinear trend with polynomials.mp4 |
53.55Мб |
| 008 Spectral interpolation_en.vtt |
12.02Кб |
| 008 Spectral interpolation.mp4 |
26.20Мб |
| 009 Averaging multiple repetitions (time-synchronous averaging)_en.vtt |
6.28Кб |
| 009 Averaging multiple repetitions (time-synchronous averaging).mp4 |
23.24Мб |
| 009 Convolution with frequency-domain Planck taper (bandpass filter)_en.vtt |
7.17Кб |
| 009 Convolution with frequency-domain Planck taper (bandpass filter).mp4 |
22.22Мб |
| 009 Dynamic time warping_en.vtt |
19.09Кб |
| 009 Dynamic time warping.mp4 |
50.28Мб |
| 009 Low-pass filters_en.vtt |
8.55Кб |
| 009 Low-pass filters.mp4 |
30.22Мб |
| 009 Time-frequency analysis of brain signals_en.vtt |
9.83Кб |
| 009 Time-frequency analysis of brain signals.mp4 |
27.78Мб |
| 010 Code challenge Compare wavelet convolution and FIR filter!_en.vtt |
2.51Кб |
| 010 Code challenge Compare wavelet convolution and FIR filter!.mp4 |
5.09Мб |
| 010 Code challenge Create a frequency-domain mean-smoothing filter_en.vtt |
2.05Кб |
| 010 Code challenge Create a frequency-domain mean-smoothing filter.mp4 |
5.09Мб |
| 010 Code challenge denoise and downsample this signal!_en.vtt |
5.13Кб |
| 010 Code challenge denoise and downsample this signal!.mp4 |
9.46Мб |
| 010 Remove artifact via least-squares template-matching_en.vtt |
11.97Кб |
| 010 Remove artifact via least-squares template-matching.mp4 |
39.75Мб |
| 010 Windowed-sinc filters_en.vtt |
13.85Кб |
| 010 Windowed-sinc filters.mp4 |
37.09Мб |
| 011 Code challenge Denoise these signals!_en.vtt |
1.28Кб |
| 011 Code challenge Denoise these signals!.mp4 |
3.36Мб |
| 011 High-pass filters_en.vtt |
6.94Кб |
| 011 High-pass filters.mp4 |
21.63Мб |
| 012 Narrow-band filters_en.vtt |
7.84Кб |
| 012 Narrow-band filters.mp4 |
23.25Мб |
| 013 Two-stage wide-band filter_en.vtt |
5.49Кб |
| 013 Two-stage wide-band filter.mp4 |
37.34Мб |
| 014 Quantifying roll-off characteristics_en.vtt |
12.98Кб |
| 014 Quantifying roll-off characteristics.mp4 |
36.29Мб |
| 015 Remove electrical line noise and its harmonics_en.vtt |
12.47Кб |
| 015 Remove electrical line noise and its harmonics.mp4 |
37.84Мб |
| 016 Use filtering to separate birds in a recording_en.vtt |
7.57Кб |
| 016 Use filtering to separate birds in a recording.mp4 |
35.44Мб |
| 017 Code challenge Filter these signals!_en.vtt |
1.66Кб |
| 017 Code challenge Filter these signals!.mp4 |
5.03Мб |
| Bonus Resources.txt |
386б |
| data4TF.mat |
17.13Кб |
| denoising_codeChallenge.mat |
60.40Кб |
| EEGrestingState.mat |
335.83Кб |
| emg4TKEO.mat |
8.07Кб |
| EMGRT.mat |
1.14Мб |
| eyedat.mat |
3.77Мб |
| filtering_codeChallenge.mat |
150.49Кб |
| forex.mat |
172.65Кб |
| Get Bonus Downloads Here.url |
182б |
| glassDance.mat |
3.31Мб |
| lineNoiseData.mat |
2.23Мб |
| resample_codeChallenge.mat |
52.19Кб |
| signprocMXC_complexNumbers.ipynb |
53.32Кб |
| sigprocMXC_2stageWide.m |
3.43Кб |
| sigprocMXC_AUC.m |
1.29Кб |
| sigprocMXC_averaging.m |
1.31Кб |
| sigprocMXC_butter.m |
3.16Кб |
| sigprocMXC_causal0phase.m |
1.71Кб |
| sigprocMXC_complexAddSub.m |
572б |
| sigprocMXC_complexConj.m |
484б |
| sigprocMXC_complexDivision.m |
387б |
| sigprocMXC_complexIntro.m |
1010б |
| sigprocMXC_complexMult.m |
612б |
| sigprocMXC_complexPolar.m |
1.01Кб |
| sigprocMXC_convolution.ipynb |
352.56Кб |
| sigprocMXC_convolutionTheorem.m |
1.53Кб |
| sigprocMXC_CV.m |
779б |
| sigprocMXC_detrend.m |
543б |
| sigprocMXC_downsample.m |
2.76Кб |
| sigprocMXC_dtw.m |
1.60Кб |
| sigprocMXC_EMGonsets.m |
2.33Кб |
| sigprocMXC_entropy.m |
2.95Кб |
| sigprocMXC_extrap.m |
1.00Кб |
| sigprocMXC_featuredetection.ipynb |
22.52Кб |
| sigprocMXC_filterGlass.ipynb |
3.97Кб |
| sigprocMXC_filterGlass.m |
1.67Кб |
| sigprocMXC_filtering_part1.ipynb |
1.35Мб |
| sigprocMXC_filtering_part2.ipynb |
1.74Мб |
| sigprocMXC_filterTheBirds.m |
1.99Кб |
| sigprocMXC_fir1.m |
2.55Кб |
| sigprocMXC_firls.m |
3.60Кб |
| sigprocMXC_FourierTransform.m |
2.50Кб |
| sigprocMXC_FreqDomainGaus.m |
1.83Кб |
| sigprocMXC_FWHM.m |
3.00Кб |
| sigprocMXC_GauSmoothSpikes.m |
1.30Кб |
| sigprocMXC_Gaussian_smooth.m |
2.30Кб |
| sigprocMXC_highpass.m |
2.53Кб |
| sigprocMXC_interp.m |
1.90Кб |
| sigprocMXC_irregular.m |
1.76Кб |
| sigprocMXC_linenoise.m |
2.06Кб |
| sigprocMXC_localMinMax.m |
1.48Кб |
| sigprocMXC_localOutliers.m |
1.84Кб |
| sigprocMXC_lowpass.m |
2.00Кб |
| sigprocMXC_mean_smooth.m |
1.40Кб |
| sigprocMXC_median_filter.m |
1.22Кб |
| sigprocMXC_multirate.m |
1.93Кб |
| sigprocMXC_narrowband.m |
1.74Кб |
| sigprocMXC_outliers.ipynb |
129.24Кб |
| sigprocMXC_outZ.m |
999б |
| sigprocMXC_planckBandPass.m |
2.33Кб |
| sigprocMXC_polynomialDetrend.m |
2.45Кб |
| sigprocMXC_reflection.m |
2.30Кб |
| sigprocMXC_resample.ipynb |
19.91Кб |
| sigprocMXC_RMSoutlierWindows.m |
1.56Кб |
| sigprocMXC_rolloff.m |
2.35Кб |
| sigprocMXC_signalFromNoise.m |
2.38Кб |
| sigprocMXC_signalLength.m |
709б |
| sigprocMXC_SNR.m |
2.67Кб |
| sigprocMXC_SpectBirdcall.m |
1.44Кб |
| sigprocMXC_spectral.ipynb |
348.68Кб |
| sigprocMXC_spectralInterp.m |
1.17Кб |
| sigprocMXC_template_projection.m |
1.33Кб |
| sigprocMXC_timeConvolution.m |
2.95Кб |
| sigprocMXC_TimeDomainGaus.m |
2.29Кб |
| sigprocMXC_timefreq.m |
2.23Кб |
| sigprocMXC_timefreqBrain.m |
2.40Кб |
| sigprocMXC_timeSeriesDenoising.ipynb |
19.52Кб |
| sigprocMXC_TKEO.m |
1.26Кб |
| sigprocMXC_upsample.m |
1.90Кб |
| sigprocMXC_variability.ipynb |
13.00Кб |
| sigprocMXC_wavelet.ipynb |
21.58Кб |
| sigprocMXC_waveletConv.m |
2.07Кб |
| sigprocMXC_waveletFeatureEx.m |
2.59Кб |
| sigprocMXC_wavelets.m |
3.33Кб |
| sigprocMXC_wavelets4narrowband.m |
2.72Кб |
| sigprocMXC_waveletTF.m |
3.40Кб |
| sigprocMXC_Welch.m |
1.95Кб |
| sigprocMXC_windowedVar.m |
1.15Кб |
| sigprocMXC_windowSinc.m |
3.09Кб |
| SNRdata.mat |
4.54Мб |
| spectral_codeChallenge.mat |
61.45Кб |
| templateProjection.mat |
7.55Мб |
| v1_laminar.mat |
17.39Мб |
| wavelet_codeChallenge.mat |
276.71Кб |
| XC403881.mp3 |
244.29Кб |
| XC403881.mp3 |
244.29Кб |
| XC403881.wav |
1.72Мб |
| XC403881.wav |
1.72Мб |