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001 Bonus lecture.html
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001 MATLAB and Python code for this section.html
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001 Signal processing = decision-making + tools.mp4
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001 Signal processing = decision-making + tools_en.vtt
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002 Crash course on the Fourier transform.mp4
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002 Crash course on the Fourier transform_en.vtt
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002 Filtering Intuition, goals, and types.mp4
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002 Filtering Intuition, goals, and types_en.vtt
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002 From the number line to the complex number plane.mp4
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002 From the number line to the complex number plane_en.vtt
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002 Local maxima and minima.mp4
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002 Local maxima and minima_en.vtt
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002 Mean-smooth a time series.mp4
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002 Mean-smooth a time series_en.vtt
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002 Outliers via standard deviation threshold.mp4
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002 Outliers via standard deviation threshold_en.vtt
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002 Time-domain convolution.mp4
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002 Time-domain convolution_en.vtt
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002 Total and windowed variance and RMS.mp4
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002 Total and windowed variance and RMS_en.vtt
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002 Upsampling.mp4
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002 Upsampling_en.vtt
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002 Using MATLAB in this course.mp4
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002 Using MATLAB in this course_en.vtt
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002 What are wavelets.mp4
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002 What are wavelets_en.vtt
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003 Addition and subtraction with complex numbers.mp4
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003 Addition and subtraction with complex numbers_en.vtt
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003 Convolution in MATLAB.mp4
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003 Convolution in MATLAB_en.vtt
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003 Convolution with wavelets.mp4
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003 Convolution with wavelets_en.vtt
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003 Downsampling.mp4
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003 Downsampling_en.vtt
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003 FIR filters with firls.mp4
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003 FIR filters with firls_en.vtt
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003 Fourier transform for spectral analyses.mp4
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003 Fourier transform for spectral analyses_en.vtt
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003 Gaussian-smooth a time series.mp4
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003 Gaussian-smooth a time series_en.vtt
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003 Outliers via local threshold exceedance.mp4
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003 Outliers via local threshold exceedance_en.vtt
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003 Recover signal from noise amplitude.mp4
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003 Recover signal from noise amplitude_en.vtt
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003 Signal-to-noise ratio (SNR).mp4
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003 Signal-to-noise ratio (SNR)_en.vtt
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003 Using Octave-online in this course.mp4
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003 Using Octave-online in this course_en.vtt
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004 Coefficient of variation (CV).mp4
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004 Coefficient of variation (CV)_en.vtt
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004 FIR filters with fir1.mp4
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004 FIR filters with fir1_en.vtt
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004 Gaussian-smooth a spike time series.mp4
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004 Gaussian-smooth a spike time series_en.vtt
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004 Multiplication with complex numbers.mp4
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004 Multiplication with complex numbers_en.vtt
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004 Outlier time windows via sliding RMS.mp4
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004 Outlier time windows via sliding RMS_en.vtt
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004 Scientific publication about defining Morlet wavelets.html
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004 Strategies for multirate signals.mp4
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004 Strategies for multirate signals_en.vtt
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004 Using Python in this course.mp4
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004 Using Python in this course_en.vtt
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004 Wavelet convolution for feature extraction.mp4
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004 Wavelet convolution for feature extraction_en.vtt
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004 Welch's method and windowing.mp4
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004 Welch's method and windowing_en.vtt
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004 Why is the kernel flipped backwards!!!.mp4
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004 Why is the kernel flipped backwards!!!_en.vtt
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005 Area under the curve.mp4
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005 Area under the curve_en.vtt
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005 Code challenge.mp4
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005 Code challenge_en.vtt
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005 Denoising EMG signals via TKEO.mp4
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005 Denoising EMG signals via TKEO_en.vtt
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005 Entropy.mp4
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005 Entropy_en.vtt
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005 Having fun with filtered Glass dance.mp4
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005 Having fun with filtered Glass dance_en.vtt
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005 IIR Butterworth filters.mp4
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005 IIR Butterworth filters_en.vtt
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005 Interpolation.mp4
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005 Interpolation_en.vtt
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005 Spectrogram of birdsong.mp4
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005 Spectrogram of birdsong_en.vtt
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005 The complex conjugate.mp4
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005 The complex conjugate_en.vtt
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005 The convolution theorem.mp4
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005 The convolution theorem_en.vtt
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005 Wavelet convolution for narrowband filtering.mp4
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005 Wavelet convolution for narrowband filtering_en.vtt
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006 Application Detect muscle movements from EMG recordings.mp4
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64 MB
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006 Application Detect muscle movements from EMG recordings_en.vtt
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006 Causal and zero-phase-shift filters.mp4
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006 Causal and zero-phase-shift filters_en.vtt
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006 Code challenge Compute a spectrogram!.mp4
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006 Code challenge Compute a spectrogram!_en.vtt
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006 Code challenge.mp4
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006 Code challenge_en.vtt
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006 Division with complex numbers.mp4
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006 Division with complex numbers_en.vtt
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006 Median filter to remove spike noise.mp4
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006 Median filter to remove spike noise_en.vtt
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006 Overview Time-frequency analysis with complex wavelets.mp4
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006 Overview Time-frequency analysis with complex wavelets_en.vtt
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006 Resample irregularly sampled data.mp4
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006 Resample irregularly sampled data_en.vtt
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006 Thinking about convolution as spectral multiplication.mp4
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006 Thinking about convolution as spectral multiplication_en.vtt
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006 Writing code vs. using toolboxesprograms.mp4
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006 Writing code vs. using toolboxesprograms_en.vtt
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007 Avoid edge effects with reflection.mp4
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007 Avoid edge effects with reflection_en.vtt
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007 Convolution with time-domain Gaussian (smoothing filter).mp4
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21 MB
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007 Convolution with time-domain Gaussian (smoothing filter)_en.vtt
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007 Extrapolation.mp4
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007 Extrapolation_en.vtt
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007 Full width at half-maximum.mp4
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007 Full width at half-maximum_en.vtt
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007 Link to youtube channel with 3 hours of relevant material.html
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007 Magnitude and phase of complex numbers.mp4
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007 Magnitude and phase of complex numbers_en.vtt
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007 Remove linear trend (detrending).mp4
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007 Remove linear trend (detrending)_en.vtt
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007 Using Udemy like a pro.mp4
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007 Using Udemy like a pro_en.vtt
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008 Code challenge find the features!.mp4
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008 Code challenge find the features!_en.vtt
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008 Convolution with frequency-domain Gaussian (narrowband filter).mp4
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008 Convolution with frequency-domain Gaussian (narrowband filter)_en.vtt
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008 Data length and filter kernel length.mp4
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008 Data length and filter kernel length_en.vtt
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008 MATLAB Time-frequency analysis with complex wavelets.mp4
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008 MATLAB Time-frequency analysis with complex wavelets_en.vtt
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008 Remove nonlinear trend with polynomials.mp4
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008 Remove nonlinear trend with polynomials_en.vtt
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008 Spectral interpolation.mp4
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008 Spectral interpolation_en.vtt
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009 Averaging multiple repetitions (time-synchronous averaging).mp4
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009 Averaging multiple repetitions (time-synchronous averaging)_en.vtt
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009 Convolution with frequency-domain Planck taper (bandpass filter).mp4
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009 Convolution with frequency-domain Planck taper (bandpass filter)_en.vtt
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009 Dynamic time warping.mp4
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009 Dynamic time warping_en.vtt
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009 Low-pass filters.mp4
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009 Low-pass filters_en.vtt
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009 Time-frequency analysis of brain signals.mp4
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009 Time-frequency analysis of brain signals_en.vtt
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010 Code challenge Compare wavelet convolution and FIR filter!.mp4
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010 Code challenge Compare wavelet convolution and FIR filter!_en.vtt
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010 Code challenge Create a frequency-domain mean-smoothing filter.mp4
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5.1 MB
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010 Code challenge Create a frequency-domain mean-smoothing filter_en.vtt
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010 Code challenge denoise and downsample this signal!.mp4
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010 Code challenge denoise and downsample this signal!_en.vtt
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010 Remove artifact via least-squares template-matching.mp4
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39.8 MB
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010 Remove artifact via least-squares template-matching_en.vtt
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12 KB
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010 Windowed-sinc filters.mp4
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010 Windowed-sinc filters_en.vtt
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011 Code challenge Denoise these signals!.mp4
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011 Code challenge Denoise these signals!_en.vtt
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011 High-pass filters.mp4
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21.6 MB
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011 High-pass filters_en.vtt
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012 Narrow-band filters.mp4
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012 Narrow-band filters_en.vtt
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013 Two-stage wide-band filter.mp4
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013 Two-stage wide-band filter_en.vtt
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014 Quantifying roll-off characteristics.mp4
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014 Quantifying roll-off characteristics_en.vtt
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015 Remove electrical line noise and its harmonics.mp4
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015 Remove electrical line noise and its harmonics_en.vtt
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016 Use filtering to separate birds in a recording.mp4
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016 Use filtering to separate birds in a recording_en.vtt
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017 Code challenge Filter these signals!.mp4
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5 MB
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017 Code challenge Filter these signals!_en.vtt
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Bonus Resources.txt
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TXT
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409.6 B
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EEGrestingState.mat
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MAT
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335.8 KB
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EMGRT.mat
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Get Bonus Downloads Here.url
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URL
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204.8 B
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SNRdata.mat
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MAT
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4.5 MB
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XC403881.mp3
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244.3 KB
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XC403881.wav
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WAV
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1.7 MB
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data4TF.mat
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denoising_codeChallenge.mat
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emg4TKEO.mat
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eyedat.mat
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3.8 MB
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filtering_codeChallenge.mat
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150.5 KB
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forex.mat
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172.7 KB
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glassDance.mat
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MAT
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3.3 MB
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lineNoiseData.mat
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MAT
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2.2 MB
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resample_codeChallenge.mat
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52.2 KB
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signprocMXC_complexNumbers.ipynb
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IPYNB
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53.3 KB
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sigprocMXC_2stageWide.m
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sigprocMXC_AUC.m
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sigprocMXC_CV.m
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sigprocMXC_EMGonsets.m
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sigprocMXC_FWHM.m
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sigprocMXC_FourierTransform.m
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sigprocMXC_FreqDomainGaus.m
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sigprocMXC_GauSmoothSpikes.m
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sigprocMXC_Gaussian_smooth.m
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sigprocMXC_RMSoutlierWindows.m
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sigprocMXC_SNR.m
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sigprocMXC_SpectBirdcall.m
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sigprocMXC_TKEO.m
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sigprocMXC_TimeDomainGaus.m
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sigprocMXC_Welch.m
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sigprocMXC_averaging.m
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sigprocMXC_butter.m
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sigprocMXC_causal0phase.m
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sigprocMXC_complexAddSub.m
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sigprocMXC_complexConj.m
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sigprocMXC_complexDivision.m
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sigprocMXC_complexIntro.m
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sigprocMXC_complexMult.m
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sigprocMXC_complexPolar.m
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sigprocMXC_convolution.ipynb
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352.6 KB
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sigprocMXC_convolutionTheorem.m
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sigprocMXC_detrend.m
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sigprocMXC_downsample.m
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sigprocMXC_dtw.m
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M
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1.6 KB
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sigprocMXC_entropy.m
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M
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2.9 KB
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sigprocMXC_extrap.m
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M
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1 KB
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sigprocMXC_featuredetection.ipynb
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IPYNB
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22.5 KB
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sigprocMXC_filterGlass.ipynb
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IPYNB
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4 KB
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sigprocMXC_filterGlass.m
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M
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1.7 KB
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sigprocMXC_filterTheBirds.m
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M
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2 KB
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sigprocMXC_filtering_part1.ipynb
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IPYNB
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1.4 MB
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sigprocMXC_filtering_part2.ipynb
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IPYNB
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1.7 MB
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sigprocMXC_fir1.m
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M
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2.6 KB
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sigprocMXC_firls.m
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M
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3.6 KB
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sigprocMXC_highpass.m
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M
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2.5 KB
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sigprocMXC_interp.m
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M
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1.9 KB
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sigprocMXC_irregular.m
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M
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1.8 KB
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sigprocMXC_linenoise.m
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M
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2.1 KB
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sigprocMXC_localMinMax.m
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M
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1.5 KB
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sigprocMXC_localOutliers.m
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M
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1.8 KB
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sigprocMXC_lowpass.m
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M
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2 KB
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sigprocMXC_mean_smooth.m
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M
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1.4 KB
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sigprocMXC_median_filter.m
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M
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1.2 KB
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sigprocMXC_multirate.m
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M
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1.9 KB
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sigprocMXC_narrowband.m
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M
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1.7 KB
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sigprocMXC_outZ.m
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M
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1 KB
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sigprocMXC_outliers.ipynb
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IPYNB
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129.2 KB
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sigprocMXC_planckBandPass.m
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M
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2.3 KB
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sigprocMXC_polynomialDetrend.m
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M
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2.5 KB
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sigprocMXC_reflection.m
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M
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2.3 KB
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sigprocMXC_resample.ipynb
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IPYNB
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19.9 KB
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sigprocMXC_rolloff.m
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M
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2.3 KB
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sigprocMXC_signalFromNoise.m
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M
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2.4 KB
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sigprocMXC_signalLength.m
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M
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716.8 B
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sigprocMXC_spectral.ipynb
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IPYNB
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348.7 KB
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sigprocMXC_spectralInterp.m
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M
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1.2 KB
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sigprocMXC_template_projection.m
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M
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1.3 KB
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sigprocMXC_timeConvolution.m
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M
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2.9 KB
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sigprocMXC_timeSeriesDenoising.ipynb
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IPYNB
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19.5 KB
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sigprocMXC_timefreq.m
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M
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2.2 KB
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sigprocMXC_timefreqBrain.m
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M
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2.4 KB
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sigprocMXC_upsample.m
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M
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1.9 KB
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sigprocMXC_variability.ipynb
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IPYNB
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13 KB
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sigprocMXC_wavelet.ipynb
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IPYNB
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21.6 KB
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sigprocMXC_waveletConv.m
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M
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2.1 KB
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sigprocMXC_waveletFeatureEx.m
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M
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2.6 KB
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sigprocMXC_waveletTF.m
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M
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3.4 KB
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sigprocMXC_wavelets.m
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M
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3.3 KB
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sigprocMXC_wavelets4narrowband.m
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M
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2.7 KB
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sigprocMXC_windowSinc.m
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M
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3.1 KB
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sigprocMXC_windowedVar.m
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M
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1.1 KB
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spectral_codeChallenge.mat
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MAT
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61.5 KB
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templateProjection.mat
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MAT
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7.5 MB
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v1_laminar.mat
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MAT
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17.4 MB
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wavelet_codeChallenge.mat
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MAT
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276.7 KB
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