| 001 Bonus lecture.html | 3.8 KB | ||
| 001 MATLAB and Python code for this section.html | 0 B | ||
| 001 Signal processing = decision-making + tools.mp4 | 29.2 MB | ||
| 001 Signal processing = decision-making + tools_en.vtt | 4.9 KB | ||
| 002 Crash course on the Fourier transform.mp4 | 54.6 MB | ||
| 002 Crash course on the Fourier transform_en.vtt | 18.3 KB | ||
| 002 Filtering Intuition, goals, and types.mp4 | 87.9 MB | ||
| 002 Filtering Intuition, goals, and types_en.vtt | 18.8 KB | ||
| 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 | ||
| 002 Local maxima and minima.mp4 | 85.5 MB | ||
| 002 Local maxima and minima_en.vtt | 18.7 KB | ||
| 002 Mean-smooth a time series.mp4 | 57 MB | ||
| 002 Mean-smooth a time series_en.vtt | 9.9 KB | ||
| 002 Outliers via standard deviation threshold.mp4 | 30.3 MB | ||
| 002 Outliers via standard deviation threshold_en.vtt | 11.3 KB | ||
| 002 Time-domain convolution.mp4 | 35 MB | ||
| 002 Time-domain convolution_en.vtt | 14.3 KB | ||
| 002 Total and windowed variance and RMS.mp4 | 26.5 MB | ||
| 002 Total and windowed variance and RMS_en.vtt | 12.9 KB | ||
| 002 Upsampling.mp4 | 43.3 MB | ||
| 002 Upsampling_en.vtt | 15.5 KB | ||
| 002 Using MATLAB in this course.mp4 | 9.2 MB | ||
| 002 Using MATLAB in this course_en.vtt | 4.6 KB | ||
| 002 What are wavelets.mp4 | 72.7 MB | ||
| 002 What are wavelets_en.vtt | 16.6 KB | ||
| 003 Addition and subtraction with complex numbers.mp4 | 7.5 MB | ||
| 003 Addition and subtraction with complex numbers_en.vtt | 4.2 KB | ||
| 003 Convolution in MATLAB.mp4 | 41.6 MB | ||
| 003 Convolution in MATLAB_en.vtt | 15.3 KB | ||
| 003 Convolution with wavelets.mp4 | 22.8 MB | ||
| 003 Convolution with wavelets_en.vtt | 6.5 KB | ||
| 003 Downsampling.mp4 | 51.5 MB | ||
| 003 Downsampling_en.vtt | 14.4 KB | ||
| 003 FIR filters with firls.mp4 | 49.7 MB | ||
| 003 FIR filters with firls_en.vtt | 17.7 KB | ||
| 003 Fourier transform for spectral analyses.mp4 | 70 MB | ||
| 003 Fourier transform for spectral analyses_en.vtt | 22.4 KB | ||
| 003 Gaussian-smooth a time series.mp4 | 45.5 MB | ||
| 003 Gaussian-smooth a time series_en.vtt | 15.9 KB | ||
| 003 Outliers via local threshold exceedance.mp4 | 25.1 MB | ||
| 003 Outliers via local threshold exceedance_en.vtt | 10.4 KB | ||
| 003 Recover signal from noise amplitude.mp4 | 42.4 MB | ||
| 003 Recover signal from noise amplitude_en.vtt | 14.2 KB | ||
| 003 Signal-to-noise ratio (SNR).mp4 | 54.4 MB | ||
| 003 Signal-to-noise ratio (SNR)_en.vtt | 17.6 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 Coefficient of variation (CV).mp4 | 10.5 MB | ||
| 004 Coefficient of variation (CV)_en.vtt | 6 KB | ||
| 004 FIR filters with fir1.mp4 | 22.7 MB | ||
| 004 FIR filters with fir1_en.vtt | 6.8 KB | ||
| 004 Gaussian-smooth a spike time series.mp4 | 18 MB | ||
| 004 Gaussian-smooth a spike time series_en.vtt | 6.3 KB | ||
| 004 Multiplication with complex numbers.mp4 | 17.1 MB | ||
| 004 Multiplication with complex numbers_en.vtt | 7.8 KB | ||
| 004 Outlier time windows via sliding RMS.mp4 | 16 MB | ||
| 004 Outlier time windows via sliding RMS_en.vtt | 6.9 KB | ||
| 004 Scientific publication about defining Morlet wavelets.html | 512 B | ||
| 004 Strategies for multirate signals.mp4 | 38.4 MB | ||
| 004 Strategies for multirate signals_en.vtt | 7.9 KB | ||
| 004 Using Python in this course.mp4 | 10.6 MB | ||
| 004 Using Python in this course_en.vtt | 4.3 KB | ||
| 004 Wavelet convolution for feature extraction.mp4 | 104.6 MB | ||
| 004 Wavelet convolution for feature extraction_en.vtt | 16.9 KB | ||
| 004 Welch's method and windowing.mp4 | 40.7 MB | ||
| 004 Welch's method and windowing_en.vtt | 18 KB | ||
| 004 Why is the kernel flipped backwards!!!.mp4 | 9 MB | ||
| 004 Why is the kernel flipped backwards!!!_en.vtt | 5.8 KB | ||
| 005 Area under the curve.mp4 | 39.5 MB | ||
| 005 Area under the curve_en.vtt | 15.2 KB | ||
| 005 Code challenge.mp4 | 15.2 MB | ||
| 005 Code challenge_en.vtt | 4.5 KB | ||
| 005 Denoising EMG signals via TKEO.mp4 | 47.7 MB | ||
| 005 Denoising EMG signals via TKEO_en.vtt | 9.7 KB | ||
| 005 Entropy.mp4 | 55.9 MB | ||
| 005 Entropy_en.vtt | 19.2 KB | ||
| 005 Having fun with filtered Glass dance.mp4 | 48.4 MB | ||
| 005 Having fun with filtered Glass dance_en.vtt | 9 KB | ||
| 005 IIR Butterworth filters.mp4 | 34.3 MB | ||
| 005 IIR Butterworth filters_en.vtt | 12.2 KB | ||
| 005 Interpolation.mp4 | 27.2 MB | ||
| 005 Interpolation_en.vtt | 9.3 KB | ||
| 005 Spectrogram of birdsong.mp4 | 31.2 MB | ||
| 005 Spectrogram of birdsong_en.vtt | 9.4 KB | ||
| 005 The complex conjugate.mp4 | 10.5 MB | ||
| 005 The complex conjugate_en.vtt | 5.1 KB | ||
| 005 The convolution theorem.mp4 | 29.3 MB | ||
| 005 The convolution theorem_en.vtt | 11.8 KB | ||
| 005 Wavelet convolution for narrowband filtering.mp4 | 55.4 MB | ||
| 005 Wavelet convolution for narrowband filtering_en.vtt | 17.2 KB | ||
| 006 Application Detect muscle movements from EMG recordings.mp4 | 64 MB | ||
| 006 Application Detect muscle movements from EMG recordings_en.vtt | 20.8 KB | ||
| 006 Causal and zero-phase-shift filters.mp4 | 33.7 MB | ||
| 006 Causal and zero-phase-shift filters_en.vtt | 11.6 KB | ||
| 006 Code challenge Compute a spectrogram!.mp4 | 5.6 MB | ||
| 006 Code challenge Compute a spectrogram!_en.vtt | 3.1 KB | ||
| 006 Code challenge.mp4 | 10.4 MB | ||
| 006 Code challenge_en.vtt | 3.7 KB | ||
| 006 Division with complex numbers.mp4 | 7.3 MB | ||
| 006 Division with complex numbers_en.vtt | 4.6 KB | ||
| 006 Median filter to remove spike noise.mp4 | 25.8 MB | ||
| 006 Median filter to remove spike noise_en.vtt | 12 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 | ||
| 006 Resample irregularly sampled data.mp4 | 39 MB | ||
| 006 Resample irregularly sampled data_en.vtt | 13.2 KB | ||
| 006 Thinking about convolution as spectral multiplication.mp4 | 34.7 MB | ||
| 006 Thinking about convolution as spectral multiplication_en.vtt | 15 KB | ||
| 006 Writing code vs. using toolboxesprograms.mp4 | 24.8 MB | ||
| 006 Writing code vs. using toolboxesprograms_en.vtt | 8.5 KB | ||
| 007 Avoid edge effects with reflection.mp4 | 85.3 MB | ||
| 007 Avoid edge effects with reflection_en.vtt | 13.7 KB | ||
| 007 Convolution with time-domain Gaussian (smoothing filter).mp4 | 21 MB | ||
| 007 Convolution with time-domain Gaussian (smoothing filter)_en.vtt | 7.1 KB | ||
| 007 Extrapolation.mp4 | 18.4 MB | ||
| 007 Extrapolation_en.vtt | 7.1 KB | ||
| 007 Full width at half-maximum.mp4 | 64.8 MB | ||
| 007 Full width at half-maximum_en.vtt | 21.1 KB | ||
| 007 Link to youtube channel with 3 hours of relevant material.html | 614.4 B | ||
| 007 Magnitude and phase of complex numbers.mp4 | 21.3 MB | ||
| 007 Magnitude and phase of complex numbers_en.vtt | 9.4 KB | ||
| 007 Remove linear trend (detrending).mp4 | 4.7 MB | ||
| 007 Remove linear trend (detrending)_en.vtt | 2.6 KB | ||
| 007 Using Udemy like a pro.mp4 | 25.7 MB | ||
| 007 Using Udemy like a pro_en.vtt | 10.3 KB | ||
| 008 Code challenge find the features!.mp4 | 10.7 MB | ||
| 008 Code challenge find the features!_en.vtt | 4 KB | ||
| 008 Convolution with frequency-domain Gaussian (narrowband filter).mp4 | 25.7 MB | ||
| 008 Convolution with frequency-domain Gaussian (narrowband filter)_en.vtt | 8 KB | ||
| 008 Data length and filter kernel length.mp4 | 22.6 MB | ||
| 008 Data length and filter kernel length_en.vtt | 9.8 KB | ||
| 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 | ||
| 008 Remove nonlinear trend with polynomials.mp4 | 53.5 MB | ||
| 008 Remove nonlinear trend with polynomials_en.vtt | 17.8 KB | ||
| 008 Spectral interpolation.mp4 | 26.2 MB | ||
| 008 Spectral interpolation_en.vtt | 12 KB | ||
| 009 Averaging multiple repetitions (time-synchronous averaging).mp4 | 23.2 MB | ||
| 009 Averaging multiple repetitions (time-synchronous averaging)_en.vtt | 6.3 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 | ||
| 009 Dynamic time warping.mp4 | 50.3 MB | ||
| 009 Dynamic time warping_en.vtt | 19.1 KB | ||
| 009 Low-pass filters.mp4 | 30.2 MB | ||
| 009 Low-pass filters_en.vtt | 8.6 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 | ||
| 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 | ||
| 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 | ||
| 010 Remove artifact via least-squares template-matching.mp4 | 39.8 MB | ||
| 010 Remove artifact via least-squares template-matching_en.vtt | 12 KB | ||
| 010 Windowed-sinc filters.mp4 | 37.1 MB | ||
| 010 Windowed-sinc filters_en.vtt | 13.9 KB | ||
| 011 Code challenge Denoise these signals!.mp4 | 3.4 MB | ||
| 011 Code challenge Denoise these signals!_en.vtt | 1.3 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 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 MB | ||
| 017 Code challenge Filter these signals!_en.vtt | 1.7 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| EEGrestingState.mat | 335.8 KB | ||
| EMGRT.mat | 1.1 MB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| SNRdata.mat | 4.5 MB | ||
| XC403881.mp3 | 244.3 KB | ||
| XC403881.wav | 1.7 MB | ||
| data4TF.mat | 17.1 KB | ||
| denoising_codeChallenge.mat | 60.4 KB | ||
| emg4TKEO.mat | 8.1 KB | ||
| eyedat.mat | 3.8 MB | ||
| filtering_codeChallenge.mat | 150.5 KB | ||
| forex.mat | 172.7 KB | ||
| glassDance.mat | 3.3 MB | ||
| lineNoiseData.mat | 2.2 MB | ||
| resample_codeChallenge.mat | 52.2 KB | ||
| signprocMXC_complexNumbers.ipynb | 53.3 KB | ||
| sigprocMXC_2stageWide.m | 3.4 KB | ||
| sigprocMXC_AUC.m | 1.3 KB | ||
| sigprocMXC_CV.m | 819.2 B | ||
| sigprocMXC_EMGonsets.m | 2.3 KB | ||
| sigprocMXC_FWHM.m | 3 KB | ||
| sigprocMXC_FourierTransform.m | 2.5 KB | ||
| sigprocMXC_FreqDomainGaus.m | 1.8 KB | ||
| sigprocMXC_GauSmoothSpikes.m | 1.3 KB | ||
| sigprocMXC_Gaussian_smooth.m | 2.3 KB | ||
| sigprocMXC_RMSoutlierWindows.m | 1.6 KB | ||
| sigprocMXC_SNR.m | 2.7 KB | ||
| sigprocMXC_SpectBirdcall.m | 1.4 KB | ||
| sigprocMXC_TKEO.m | 1.3 KB | ||
| sigprocMXC_TimeDomainGaus.m | 2.3 KB | ||
| sigprocMXC_Welch.m | 2 KB | ||
| sigprocMXC_averaging.m | 1.3 KB | ||
| sigprocMXC_butter.m | 3.2 KB | ||
| sigprocMXC_causal0phase.m | 1.7 KB | ||
| sigprocMXC_complexAddSub.m | 614.4 B | ||
| sigprocMXC_complexConj.m | 512 B | ||
| sigprocMXC_complexDivision.m | 409.6 B | ||
| sigprocMXC_complexIntro.m | 1 KB | ||
| sigprocMXC_complexMult.m | 614.4 B | ||
| sigprocMXC_complexPolar.m | 1 KB | ||
| sigprocMXC_convolution.ipynb | 352.6 KB | ||
| sigprocMXC_convolutionTheorem.m | 1.5 KB | ||
| sigprocMXC_detrend.m | 512 B | ||
| sigprocMXC_downsample.m | 2.8 KB | ||
| sigprocMXC_dtw.m | 1.6 KB | ||
| sigprocMXC_entropy.m | 2.9 KB | ||
| sigprocMXC_extrap.m | 1 KB | ||
| sigprocMXC_featuredetection.ipynb | 22.5 KB | ||
| sigprocMXC_filterGlass.ipynb | 4 KB | ||
| sigprocMXC_filterGlass.m | 1.7 KB | ||
| sigprocMXC_filterTheBirds.m | 2 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_interp.m | 1.9 KB | ||
| sigprocMXC_irregular.m | 1.8 KB | ||
| sigprocMXC_linenoise.m | 2.1 KB | ||
| sigprocMXC_localMinMax.m | 1.5 KB | ||
| sigprocMXC_localOutliers.m | 1.8 KB | ||
| sigprocMXC_lowpass.m | 2 KB | ||
| sigprocMXC_mean_smooth.m | 1.4 KB | ||
| sigprocMXC_median_filter.m | 1.2 KB | ||
| sigprocMXC_multirate.m | 1.9 KB | ||
| sigprocMXC_narrowband.m | 1.7 KB | ||
| sigprocMXC_outZ.m | 1 KB | ||
| sigprocMXC_outliers.ipynb | 129.2 KB | ||
| sigprocMXC_planckBandPass.m | 2.3 KB | ||
| sigprocMXC_polynomialDetrend.m | 2.5 KB | ||
| sigprocMXC_reflection.m | 2.3 KB | ||
| sigprocMXC_resample.ipynb | 19.9 KB | ||
| sigprocMXC_rolloff.m | 2.3 KB | ||
| sigprocMXC_signalFromNoise.m | 2.4 KB | ||
| sigprocMXC_signalLength.m | 716.8 B | ||
| sigprocMXC_spectral.ipynb | 348.7 KB | ||
| sigprocMXC_spectralInterp.m | 1.2 KB | ||
| sigprocMXC_template_projection.m | 1.3 KB | ||
| sigprocMXC_timeConvolution.m | 2.9 KB | ||
| sigprocMXC_timeSeriesDenoising.ipynb | 19.5 KB | ||
| sigprocMXC_timefreq.m | 2.2 KB | ||
| sigprocMXC_timefreqBrain.m | 2.4 KB | ||
| sigprocMXC_upsample.m | 1.9 KB | ||
| sigprocMXC_variability.ipynb | 13 KB | ||
| sigprocMXC_wavelet.ipynb | 21.6 KB | ||
| sigprocMXC_waveletConv.m | 2.1 KB | ||
| sigprocMXC_waveletFeatureEx.m | 2.6 KB | ||
| sigprocMXC_waveletTF.m | 3.4 KB | ||
| sigprocMXC_wavelets.m | 3.3 KB | ||
| sigprocMXC_wavelets4narrowband.m | 2.7 KB | ||
| sigprocMXC_windowSinc.m | 3.1 KB | ||
| sigprocMXC_windowedVar.m | 1.1 KB | ||
| spectral_codeChallenge.mat | 61.5 KB | ||
| templateProjection.mat | 7.5 MB | ||
| v1_laminar.mat | 17.4 MB | ||
| wavelet_codeChallenge.mat | 276.7 KB | ||
| ▲ 282 total files | |||
Signal processing problems, solved in MATLAB and in Python
https://DevCourseWeb.com
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.85 GB | Duration: 98 lectures • 12h 32m
Applications-oriented instruction on signal processing and digital signal processing (DSP) using MATLAB and Python codes
What you'll learn
Understand commonly used signal processing tools
Design, evaluate, and apply digital filters
Clean and denoise data
Know what to look for when something isn't right with the data or the code
Improve MATLAB or Python programming skills
Know how to generate test signals for signal processing methods
*Fully manually corrected English captions!
Requirements
Basic programming experience in MATLAB or Python
High-school math
Description
Why you need to learn digital signal processing.
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 2.3 GB | freecoursewb | 3 weeks | 2 | 3 | |
| 1.2 GB | freecoursewb | 3 months | 13 | 6 | |
| 809 MB | freecoursewb | 5 months | 0 | 0 | |
| 4.1 GB | freecoursewb | 6 months | 0 | 0 | |
| 806.1 MB | freecoursewb | 1 year | 1 | 19 |
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