Tutorialsplanet NET Udemy - Signal processing problems solved in MATLAB and in Python

1. 1. Introductions/1. Signal processing = decision-making + tools.mp433.2MB
2. 1. Introductions/2. Using MATLAB in this course.mp424.34MB
3. 1. Introductions/3. Using Octave-online in this course.mp433.55MB
4. 1. Introductions/4. Using Python in this course.mp423.7MB
5. 1. Introductions/5. Writing code vs. using toolboxesprograms.mp453.11MB
6. 1. Introductions/6. Using the Q&A forum.mp426.82MB
7. 10. Feature detection/2. Local maxima and minima.mp4126.65MB
8. 10. Feature detection/3. Recover signal from noise amplitude.mp4104.34MB
9. 10. Feature detection/4. Wavelet convolution for feature extraction.mp4135.76MB
10. 10. Feature detection/5. Area under the curve.mp491.16MB
11. 10. Feature detection/6. Application Detect muscle movements from EMG recordings.mp4151.47MB
12. 10. Feature detection/7. Full width at half-maximum.mp4131.28MB
13. 10. Feature detection/8. Code challenge find the features!.mp424.01MB
14. 11. Variability/2. Total and windowed variance and RMS.mp475.57MB
15. 11. Variability/3. Signal-to-noise ratio (SNR).mp4132.79MB
16. 11. Variability/4. Coefficient of variation (CV).mp428.8MB
17. 11. Variability/5. Entropy.mp4112.3MB
18. 11. Variability/6. Code challenge.mp423.53MB
19. 2. Time series denoising/10. Remove artifact via least-squares template-matching.mp484.98MB
20. 2. Time series denoising/11. Code challenge Denoise these signals!.mp47.5MB
21. 2. Time series denoising/2. Mean-smooth a time series.mp466.16MB
22. 2. Time series denoising/3. Gaussian-smooth a time series.mp496.15MB
23. 2. Time series denoising/4. Gaussian-smooth a spike time series.mp442.2MB
24. 2. Time series denoising/5. Denoising EMG signals via TKEO.mp457.17MB
25. 2. Time series denoising/6. Median filter to remove spike noise.mp477.1MB
26. 2. Time series denoising/7. Remove linear trend (detrending).mp412.85MB
27. 2. Time series denoising/8. Remove nonlinear trend with polynomials.mp4109.31MB
28. 2. Time series denoising/9. Averaging multiple repetitions (time-synchronous averaging).mp449.75MB
29. 3. Spectral and rhythmicity analyses/2. Crash course on the Fourier transform.mp4116.86MB
30. 3. Spectral and rhythmicity analyses/3. Fourier transform for spectral analyses.mp4173.98MB
31. 3. Spectral and rhythmicity analyses/4. Welch's method and windowing.mp4121.88MB
32. 3. Spectral and rhythmicity analyses/5. Spectrogram of birdsong.mp476.15MB
33. 3. Spectral and rhythmicity analyses/6. Code challenge Compute a spectrogram!.mp415.22MB
34. 4. Working with complex numbers/2. From the number line to the complex number plane.mp455.24MB
35. 4. Working with complex numbers/3. Addition and subtraction with complex numbers.mp419.89MB
36. 4. Working with complex numbers/4. Multiplication with complex numbers.mp438.96MB
37. 4. Working with complex numbers/5. The complex conjugate.mp423.08MB
38. 4. Working with complex numbers/6. Division with complex numbers.mp418.76MB
39. 4. Working with complex numbers/7. Magnitude and phase of complex numbers.mp448.31MB
40. 5. Filtering/10. Windowed-sinc filters.mp487.7MB
41. 5. Filtering/11. High-pass filters.mp452.42MB
42. 5. Filtering/12. Narrow-band filters.mp455.9MB
43. 5. Filtering/13. Two-stage wide-band filter.mp442.23MB
44. 5. Filtering/14. Quantifying roll-off characteristics.mp487.08MB
45. 5. Filtering/15. Remove electrical line noise and its harmonics.mp491.1MB
46. 5. Filtering/16. Use filtering to separate birds in a recording.mp474.66MB
47. 5. Filtering/17. Code challenge Filter these signals!.mp411.33MB
48. 5. Filtering/2. Filtering Intuition, goals, and types.mp4115.25MB
49. 5. Filtering/3. FIR filters with firls.mp4119.83MB
50. 5. Filtering/4. FIR filters with fir1.mp447.24MB
51. 5. Filtering/5. IIR Butterworth filters.mp480.32MB
52. 5. Filtering/6. Causal and zero-phase-shift filters.mp482.47MB
53. 5. Filtering/7. Avoid edge effects with reflection.mp499.3MB
54. 5. Filtering/8. Data length and filter kernel length.mp465.02MB
55. 5. Filtering/9. Low-pass filters.mp464.01MB
56. 6. Convolution/10. Code challenge Create a frequency-domain mean-smoothing filter.mp416.85MB
57. 6. Convolution/2. Time-domain convolution.mp471.11MB
58. 6. Convolution/3. Convolution in MATLAB.mp4100.74MB
59. 6. Convolution/4. Why is the kernel flipped backwards!!!.mp422.55MB
60. 6. Convolution/5. The convolution theorem.mp468.76MB
61. 6. Convolution/6. Thinking about convolution as spectral multiplication.mp487.65MB
62. 6. Convolution/6.1 TFtheory.mp4.mp418.18MB
63. 6. Convolution/7. Convolution with time-domain Gaussian (smoothing filter).mp449.48MB
64. 6. Convolution/8. Convolution with frequency-domain Gaussian (narrowband filter).mp451.82MB
65. 6. Convolution/9. Convolution with frequency-domain Planck taper (bandpass filter).mp446.06MB
66. 7. Wavelet analysis/10. Code challenge Compare wavelet convolution and FIR filter!.mp413.36MB
67. 7. Wavelet analysis/2. What are wavelets.mp493.01MB
68. 7. Wavelet analysis/3. Convolution with wavelets.mp448.17MB
69. 7. Wavelet analysis/5. Wavelet convolution for narrowband filtering.mp4135.88MB
70. 7. Wavelet analysis/6. Overview Time-frequency analysis with complex wavelets.mp448.65MB
71. 7. Wavelet analysis/8. MATLAB Time-frequency analysis with complex wavelets.mp4140.35MB
72. 7. Wavelet analysis/9. Time-frequency analysis of brain signals.mp463.48MB
73. 8. Resampling, interpolating, extrapolating/10. Code challenge denoise and downsample this signal!.mp425.17MB
74. 8. Resampling, interpolating, extrapolating/2. Upsampling.mp4100.91MB
75. 8. Resampling, interpolating, extrapolating/3. Downsampling.mp4110.76MB
76. 8. Resampling, interpolating, extrapolating/4. Strategies for multirate signals.mp444.17MB
77. 8. Resampling, interpolating, extrapolating/5. Interpolation.mp455.2MB
78. 8. Resampling, interpolating, extrapolating/6. Resample irregularly sampled data.mp493.92MB
79. 8. Resampling, interpolating, extrapolating/7. Extrapolation.mp436.67MB
80. 8. Resampling, interpolating, extrapolating/8. Spectral interpolation.mp477.28MB
81. 8. Resampling, interpolating, extrapolating/9. Dynamic time warping.mp4122.58MB
82. 9. Outlier detection/2. Outliers via standard deviation threshold.mp469.63MB
83. 9. Outlier detection/3. Outliers via local threshold exceedance.mp477.34MB
84. 9. Outlier detection/4. Outlier time windows via sliding RMS.mp446.09MB
85. 9. Outlier detection/5. Code challenge.mp439.06MB