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Language:
English
Total Size:
5.7 GB
Info Hash:
C9D0738D42AEE1239F7610AB1D510B1CC0FC7910
Added By:
Added:
Oct. 29, 2023, 2:29 a.m.
Stats:
|
(Last updated: May 19, 2025, 1:47 a.m.)
| File | Size |
|---|---|
| 5. Writing code vs. using toolboxesprograms.mp4 | 53.1 MB |
| 3. Using Octave-online in this course.mp4 | 33.5 MB |
| 1. Signal processing = decision-making + tools.mp4 | 33.2 MB |
| 6. Using the Q&A forum.mp4 | 26.8 MB |
| 2. Using MATLAB in this course.mp4 | 24.3 MB |
| 4. Using Python in this course.mp4 | 23.7 MB |
| 5. Writing code vs. using toolboxesprograms.vtt | 8.5 KB |
| 6. Using the Q&A forum.vtt | 6.4 KB |
| 3. Using Octave-online in this course.vtt | 6.3 KB |
| 1. Signal processing = decision-making + tools.vtt | 5.1 KB |
| 2. Using MATLAB in this course.vtt | 4.6 KB |
| 4. Using Python in this course.vtt | 4.4 KB |
| ReadMe.txt | 241 bytes |
| 6. Application Detect muscle movements from EMG recordings.mp4 | 151.5 MB |
| 4. Wavelet convolution for feature extraction.mp4 | 135.8 MB |
| 7. Full width at half-maximum.mp4 | 131.3 MB |
| 2. Local maxima and minima.mp4 | 126.6 MB |
| 3. Recover signal from noise amplitude.mp4 | 104.3 MB |
| 5. Area under the curve.mp4 | 91.2 MB |
| 8. Code challenge find the features!.mp4 | 24.0 MB |
| 1.1 sigprocMXC_featuredet.zip.zip | 1.7 MB |
| 7. Full width at half-maximum.vtt | 21.5 KB |
| 6. Application Detect muscle movements from EMG recordings.vtt | 21.4 KB |
| 2. Local maxima and minima.vtt | 18.7 KB |
| 4. Wavelet convolution for feature extraction.vtt | 17.3 KB |
| 5. Area under the curve.vtt | 15.3 KB |
| 3. Recover signal from noise amplitude.vtt | 14.7 KB |
| 8. Code challenge find the features!.vtt | 4.1 KB |
| 1. MATLAB and Python code for this section.html | 73 bytes |
| 3. Signal-to-noise ratio (SNR).mp4 | 132.8 MB |
| 5. Entropy.mp4 | 112.3 MB |
| 2. Total and windowed variance and RMS.mp4 | 75.6 MB |
| 4. Coefficient of variation (CV).mp4 | 28.8 MB |
| 6. Code challenge.mp4 | 23.5 MB |
| 1.1 sigprocMXC_variability.zip.zip | 22.2 MB |
| 5. Entropy.vtt | 19.8 KB |
| 3. Signal-to-noise ratio (SNR).vtt | 17.8 KB |
| 2. Total and windowed variance and RMS.vtt | 12.9 KB |
| 4. Coefficient of variation (CV).vtt | 6.1 KB |
| 6. Code challenge.vtt | 3.7 KB |
| 1. MATLAB and Python code for this section.html | 47 bytes |
| 2. Bonus Coupons for related courses.html | 2.5 KB |
| 1. Join the community!.html | 553 bytes |
| 8. Remove nonlinear trend with polynomials.mp4 | 109.3 MB |
| 3. Gaussian-smooth a time series.mp4 | 96.2 MB |
| 10. Remove artifact via least-squares template-matching.mp4 | 85.0 MB |
| 6. Median filter to remove spike noise.mp4 | 77.1 MB |
| 2. Mean-smooth a time series.mp4 | 66.2 MB |
| 5. Denoising EMG signals via TKEO.mp4 | 57.2 MB |
| 9. Averaging multiple repetitions (time-synchronous averaging).mp4 | 49.7 MB |
| 4. Gaussian-smooth a spike time series.mp4 | 42.2 MB |
| 7. Remove linear trend (detrending).mp4 | 12.9 MB |
| 1.1 sigprocMXC_TimeSeriesDenoising.zip.zip | 11.8 MB |
| 11. Code challenge Denoise these signals!.mp4 | 7.5 MB |
| 8. Remove nonlinear trend with polynomials.vtt | 18.2 KB |
| 3. Gaussian-smooth a time series.vtt | 16.4 KB |
| 10. Remove artifact via least-squares template-matching.vtt | 12.3 KB |
| 6. Median filter to remove spike noise.vtt | 12.2 KB |
| 2. Mean-smooth a time series.vtt | 10.2 KB |
| 5. Denoising EMG signals via TKEO.vtt | 9.7 KB |
| 9. Averaging multiple repetitions (time-synchronous averaging).vtt | 6.5 KB |
| 4. Gaussian-smooth a spike time series.vtt | 6.4 KB |
| 7. Remove linear trend (detrending).vtt | 2.6 KB |
| 11. Code challenge Denoise these signals!.vtt | 1.3 KB |
| 1. MATLAB and Python code for this section.html | 84 bytes |
| 3. Fourier transform for spectral analyses.mp4 | 174.0 MB |
| 4. Welch's method and windowing.mp4 | 121.9 MB |
| 2. Crash course on the Fourier transform.mp4 | 116.9 MB |
| 5. Spectrogram of birdsong.mp4 | 76.1 MB |
| 6. Code challenge Compute a spectrogram!.mp4 | 15.2 MB |
| 1.1 sigprocMXC_spectral.zip.zip | 2.3 MB |
| 3. Fourier transform for spectral analyses.vtt | 23.0 KB |
| 2. Crash course on the Fourier transform.vtt | 18.6 KB |
| 4. Welch's method and windowing.vtt | 18.5 KB |
| 5. Spectrogram of birdsong.vtt | 9.6 KB |
| 6. Code challenge Compute a spectrogram!.vtt | 3.1 KB |
| 1. MATLAB and Python code for this section.html | 99 bytes |
| 2. From the number line to the complex number plane.mp4 | 55.2 MB |
| 7. Magnitude and phase of complex numbers.mp4 | 48.3 MB |
| 4. Multiplication with complex numbers.mp4 | 39.0 MB |
| 5. The complex conjugate.mp4 | 23.1 MB |
| 3. Addition and subtraction with complex numbers.mp4 | 19.9 MB |
| 6. Division with complex numbers.mp4 | 18.8 MB |
| 1.1 sigprocMXC_complex.zip.zip | 38.1 KB |
| 2. From the number line to the complex number plane.vtt | 12.4 KB |
| 7. Magnitude and phase of complex numbers.vtt | 9.4 KB |
| 4. Multiplication with complex numbers.vtt | 8.0 KB |
| 5. The complex conjugate.vtt | 5.4 KB |
| 6. Division with complex numbers.vtt | 4.5 KB |
| 3. Addition and subtraction with complex numbers.vtt | 4.5 KB |
| 1. MATLAB and Python code for this section.html | 46 bytes |
| 3. FIR filters with firls.mp4 | 119.8 MB |
| 2. Filtering Intuition, goals, and types.mp4 | 115.2 MB |
| 7. Avoid edge effects with reflection.mp4 | 99.3 MB |
| 15. Remove electrical line noise and its harmonics.mp4 | 91.1 MB |
| 10. Windowed-sinc filters.mp4 | 87.7 MB |
| 14. Quantifying roll-off characteristics.mp4 | 87.1 MB |
| 6. Causal and zero-phase-shift filters.mp4 | 82.5 MB |
| 5. IIR Butterworth filters.mp4 | 80.3 MB |
| 16. Use filtering to separate birds in a recording.mp4 | 74.7 MB |
| 8. Data length and filter kernel length.mp4 | 65.0 MB |
| 9. Low-pass filters.mp4 | 64.0 MB |
| 12. Narrow-band filters.mp4 | 55.9 MB |
| 11. High-pass filters.mp4 | 52.4 MB |
| 4. FIR filters with fir1.mp4 | 47.2 MB |
| 13. Two-stage wide-band filter.mp4 | 42.2 MB |
| 17. Code challenge Filter these signals!.mp4 | 11.3 MB |
| 1.1 sigprocMXC_filtering.zip.zip | 4.6 MB |
| 2. Filtering Intuition, goals, and types.vtt | 19.1 KB |
| 3. FIR filters with firls.vtt | 17.7 KB |
| 10. Windowed-sinc filters.vtt | 14.2 KB |
| 7. Avoid edge effects with reflection.vtt | 14.0 KB |
| 14. Quantifying roll-off characteristics.vtt | 13.3 KB |
| 5. IIR Butterworth filters.vtt | 12.4 KB |
| 15. Remove electrical line noise and its harmonics.vtt | 12.0 KB |
| 6. Causal and zero-phase-shift filters.vtt | 11.9 KB |
| 8. Data length and filter kernel length.vtt | 9.8 KB |
| 9. Low-pass filters.vtt | 8.9 KB |
| 12. Narrow-band filters.vtt | 7.9 KB |
| 16. Use filtering to separate birds in a recording.vtt | 7.7 KB |
| 11. High-pass filters.vtt | 7.2 KB |
| 4. FIR filters with fir1.vtt | 7.0 KB |
| 13. Two-stage wide-band filter.vtt | 5.4 KB |
| 17. Code challenge Filter these signals!.vtt | 1.5 KB |
| 1. MATLAB and Python code for this section.html | 85 bytes |
| 3. Convolution in MATLAB.mp4 | 100.7 MB |
| 6. Thinking about convolution as spectral multiplication.mp4 | 87.6 MB |
| 2. Time-domain convolution.mp4 | 71.1 MB |
| 5. The convolution theorem.mp4 | 68.8 MB |
| 8. Convolution with frequency-domain Gaussian (narrowband filter).mp4 | 51.8 MB |
| 7. Convolution with time-domain Gaussian (smoothing filter).mp4 | 49.5 MB |
| 9. Convolution with frequency-domain Planck taper (bandpass filter).mp4 | 46.1 MB |
| 4. Why is the kernel flipped backwards!!!.mp4 | 22.5 MB |
| 6.1 TFtheory.mp4.mp4 | 18.2 MB |
| 10. Code challenge Create a frequency-domain mean-smoothing filter.mp4 | 16.9 MB |
| 1.1 sigprocMXC_convolution.zip.zip | 250.1 KB |
| 3. Convolution in MATLAB.vtt | 15.6 KB |
| 6. Thinking about convolution as spectral multiplication.vtt | 15.2 KB |
| 2. Time-domain convolution.vtt | 14.7 KB |
| 5. The convolution theorem.vtt | 12.0 KB |
| 8. Convolution with frequency-domain Gaussian (narrowband filter).vtt | 8.1 KB |
| 9. Convolution with frequency-domain Planck taper (bandpass filter).vtt | 7.5 KB |
| 7. Convolution with time-domain Gaussian (smoothing filter).vtt | 7.3 KB |
| 4. Why is the kernel flipped backwards!!!.vtt | 5.8 KB |
| 10. Code challenge Create a frequency-domain mean-smoothing filter.vtt | 2.1 KB |
| 1. MATLAB and Python code for this section.html | 72 bytes |
| 8. MATLAB Time-frequency analysis with complex wavelets.mp4 | 140.3 MB |
| 5. Wavelet convolution for narrowband filtering.mp4 | 135.9 MB |
| 2. What are wavelets.mp4 | 93.0 MB |
| 9. Time-frequency analysis of brain signals.mp4 | 63.5 MB |
| 6. Overview Time-frequency analysis with complex wavelets.mp4 | 48.7 MB |
| 3. Convolution with wavelets.mp4 | 48.2 MB |
| 10. Code challenge Compare wavelet convolution and FIR filter!.mp4 | 13.4 MB |
| 1.1 sigprocMXC_wavelets.zip.zip | 769.7 KB |
| 8. MATLAB Time-frequency analysis with complex wavelets.vtt | 17.8 KB |
| 2. What are wavelets.vtt | 17.4 KB |
| 5. Wavelet convolution for narrowband filtering.vtt | 17.4 KB |
| 9. Time-frequency analysis of brain signals.vtt | 9.9 KB |
| 6. Overview Time-frequency analysis with complex wavelets.vtt | 9.5 KB |
| 3. Convolution with wavelets.vtt | 6.6 KB |
| 10. Code challenge Compare wavelet convolution and FIR filter!.vtt | 2.5 KB |
| 7. Link to youtube channel with 3 hours of relevant material.html | 621 bytes |
| 4. Scientific publication about defining Morlet wavelets.html | 465 bytes |
| 1. MATLAB and Python code for this section.html | 84 bytes |
| 9. Dynamic time warping.mp4 | 122.6 MB |
| 3. Downsampling.mp4 | 110.8 MB |
| 2. Upsampling.mp4 | 100.9 MB |
| 6. Resample irregularly sampled data.mp4 | 93.9 MB |
| 8. Spectral interpolation.mp4 | 77.3 MB |
| 5. Interpolation.mp4 | 55.2 MB |
| 4. Strategies for multirate signals.mp4 | 44.2 MB |
| 7. Extrapolation.mp4 | 36.7 MB |
| 10. Code challenge denoise and downsample this signal!.mp4 | 25.2 MB |
| 1.1 sigprocMXC_resampling.zip.zip | 411.2 KB |
| 9. Dynamic time warping.vtt | 19.7 KB |
| 2. Upsampling.vtt | 15.8 KB |
| 3. Downsampling.vtt | 14.8 KB |
| 6. Resample irregularly sampled data.vtt | 13.2 KB |
| 8. Spectral interpolation.vtt | 12.5 KB |
| 5. Interpolation.vtt | 9.4 KB |
| 4. Strategies for multirate signals.vtt | 8.0 KB |
| 7. Extrapolation.vtt | 7.1 KB |
| 10. Code challenge denoise and downsample this signal!.vtt | 5.0 KB |
| 1. MATLAB and Python code for this section.html | 67 bytes |
| 3. Outliers via local threshold exceedance.mp4 | 77.3 MB |
| 2. Outliers via standard deviation threshold.mp4 | 69.6 MB |
| 4. Outlier time windows via sliding RMS.mp4 | 46.1 MB |
| 5. Code challenge.mp4 | 39.1 MB |
| 1.1 sigprocMXC_outliers.zip.zip | 268.3 KB |
| 2. Outliers via standard deviation threshold.vtt | 11.5 KB |
| 3. Outliers via local threshold exceedance.vtt | 10.7 KB |
| 4. Outlier time windows via sliding RMS.vtt | 7.1 KB |
| 5. Code challenge.vtt | 4.6 KB |
| 1. MATLAB and Python code for this section.html | 72 bytes |
| Visit Getnewcourses.com.url | 343 bytes |
| Visit Freecourseit.com.url | 342 bytes |
| ReadMe.txt | 241 bytes |
Name
DL
Uploader
Size
S/L
Added
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510.9 MB
[0
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0]
2023-10-26
| Uploaded by freecoursewb | Size 510.9 MB | Health [ 0 /0 ] | Added 2023-10-26 |
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406.1 MB
[0
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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 Getnewcourses
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