Udemy - Signal processing problems, solved in MATLAB and in Python

File Name

Size

1. Introductions/5. Writing code vs. using toolboxesprograms.mp4

53 MB

1. Introductions/3. Using Octave-online in this course.mp4

34 MB

1. Introductions/1. Signal processing = decision-making + tools.mp4

33 MB

1. Introductions/6. Using the Q&A forum.mp4

27 MB

1. Introductions/2. Using MATLAB in this course.mp4

24 MB

1. Introductions/4. Using Python in this course.mp4

24 MB

1. Introductions/5. Writing code vs. using toolboxesprograms.vtt

8.5 kB

1. Introductions/6. Using the Q&A forum.vtt

6.4 kB

1. Introductions/3. Using Octave-online in this course.vtt

6.3 kB

1. Introductions/1. Signal processing = decision-making + tools.vtt

5.1 kB

1. Introductions/2. Using MATLAB in this course.vtt

4.6 kB

1. Introductions/4. Using Python in this course.vtt

4.4 kB

1. Introductions/ReadMe.txt

241 B

10. Feature detection/6. Application Detect muscle movements from EMG recordings.mp4

152 MB

10. Feature detection/4. Wavelet convolution for feature extraction.mp4

136 MB

10. Feature detection/7. Full width at half-maximum.mp4

131 MB

10. Feature detection/2. Local maxima and minima.mp4

127 MB

10. Feature detection/3. Recover signal from noise amplitude.mp4

104 MB

10. Feature detection/5. Area under the curve.mp4

91 MB

10. Feature detection/8. Code challenge find the features!.mp4

24 MB

10. Feature detection/1.1 sigprocMXC_featuredet.zip.zip

1.7 MB

10. Feature detection/7. Full width at half-maximum.vtt

22 kB

10. Feature detection/6. Application Detect muscle movements from EMG recordings.vtt

21 kB

10. Feature detection/2. Local maxima and minima.vtt

19 kB

10. Feature detection/4. Wavelet convolution for feature extraction.vtt

17 kB

10. Feature detection/5. Area under the curve.vtt

15 kB

10. Feature detection/3. Recover signal from noise amplitude.vtt

15 kB

10. Feature detection/8. Code challenge find the features!.vtt

4.1 kB

10. Feature detection/1. MATLAB and Python code for this section.html

73 B

11. Variability/3. Signal-to-noise ratio (SNR).mp4

133 MB

11. Variability/5. Entropy.mp4

112 MB

11. Variability/2. Total and windowed variance and RMS.mp4

76 MB

11. Variability/4. Coefficient of variation (CV).mp4

29 MB

11. Variability/6. Code challenge.mp4

24 MB

11. Variability/1.1 sigprocMXC_variability.zip.zip

22 MB

11. Variability/5. Entropy.vtt

20 kB

11. Variability/3. Signal-to-noise ratio (SNR).vtt

18 kB

11. Variability/2. Total and windowed variance and RMS.vtt

13 kB

11. Variability/4. Coefficient of variation (CV).vtt

6.1 kB

11. Variability/6. Code challenge.vtt

3.7 kB

11. Variability/1. MATLAB and Python code for this section.html

47 B

12. Discounts on related courses/2. Bonus Coupons for related courses.html

2.5 kB

12. Discounts on related courses/1. Join the community!.html

553 B

2. Time series denoising/8. Remove nonlinear trend with polynomials.mp4

109 MB

2. Time series denoising/3. Gaussian-smooth a time series.mp4

96 MB

2. Time series denoising/10. Remove artifact via least-squares template-matching.mp4

85 MB

2. Time series denoising/6. Median filter to remove spike noise.mp4

77 MB

2. Time series denoising/2. Mean-smooth a time series.mp4

66 MB

2. Time series denoising/5. Denoising EMG signals via TKEO.mp4

57 MB

2. Time series denoising/9. Averaging multiple repetitions (time-synchronous averaging).mp4

50 MB

2. Time series denoising/4. Gaussian-smooth a spike time series.mp4

42 MB

2. Time series denoising/7. Remove linear trend (detrending).mp4

13 MB

2. Time series denoising/1.1 sigprocMXC_TimeSeriesDenoising.zip.zip

12 MB

2. Time series denoising/11. Code challenge Denoise these signals!.mp4

7.5 MB

2. Time series denoising/8. Remove nonlinear trend with polynomials.vtt

18 kB

2. Time series denoising/3. Gaussian-smooth a time series.vtt

16 kB

2. Time series denoising/10. Remove artifact via least-squares template-matching.vtt

12 kB

2. Time series denoising/6. Median filter to remove spike noise.vtt

12 kB

2. Time series denoising/2. Mean-smooth a time series.vtt

10 kB

2. Time series denoising/5. Denoising EMG signals via TKEO.vtt

9.7 kB

2. Time series denoising/9. Averaging multiple repetitions (time-synchronous averaging).vtt

6.5 kB

2. Time series denoising/4. Gaussian-smooth a spike time series.vtt

6.4 kB

2. Time series denoising/7. Remove linear trend (detrending).vtt

2.6 kB

2. Time series denoising/11. Code challenge Denoise these signals!.vtt

1.3 kB

2. Time series denoising/1. MATLAB and Python code for this section.html

84 B

3. Spectral and rhythmicity analyses/3. Fourier transform for spectral analyses.mp4

174 MB

3. Spectral and rhythmicity analyses/4. Welch's method and windowing.mp4

122 MB

3. Spectral and rhythmicity analyses/2. Crash course on the Fourier transform.mp4

117 MB

3. Spectral and rhythmicity analyses/5. Spectrogram of birdsong.mp4

76 MB

3. Spectral and rhythmicity analyses/6. Code challenge Compute a spectrogram!.mp4

15 MB

3. Spectral and rhythmicity analyses/1.1 sigprocMXC_spectral.zip.zip

2.3 MB

3. Spectral and rhythmicity analyses/3. Fourier transform for spectral analyses.vtt

23 kB

3. Spectral and rhythmicity analyses/2. Crash course on the Fourier transform.vtt

19 kB

3. Spectral and rhythmicity analyses/4. Welch's method and windowing.vtt

18 kB

3. Spectral and rhythmicity analyses/5. Spectrogram of birdsong.vtt

9.6 kB

3. Spectral and rhythmicity analyses/6. Code challenge Compute a spectrogram!.vtt

3.1 kB

3. Spectral and rhythmicity analyses/1. MATLAB and Python code for this section.html

99 B

4. Working with complex numbers/2. From the number line to the complex number plane.mp4

55 MB

4. Working with complex numbers/7. Magnitude and phase of complex numbers.mp4

48 MB

4. Working with complex numbers/4. Multiplication with complex numbers.mp4

39 MB

4. Working with complex numbers/5. The complex conjugate.mp4

23 MB

4. Working with complex numbers/3. Addition and subtraction with complex numbers.mp4

20 MB

4. Working with complex numbers/6. Division with complex numbers.mp4

19 MB

4. Working with complex numbers/1.1 sigprocMXC_complex.zip.zip

38 kB

4. Working with complex numbers/2. From the number line to the complex number plane.vtt

12 kB

4. Working with complex numbers/7. Magnitude and phase of complex numbers.vtt

9.4 kB

4. Working with complex numbers/4. Multiplication with complex numbers.vtt

8.0 kB

4. Working with complex numbers/5. The complex conjugate.vtt

5.4 kB

4. Working with complex numbers/6. Division with complex numbers.vtt

4.5 kB

4. Working with complex numbers/3. Addition and subtraction with complex numbers.vtt

4.5 kB

4. Working with complex numbers/1. MATLAB and Python code for this section.html

46 B

5. Filtering/3. FIR filters with firls.mp4

120 MB

5. Filtering/2. Filtering Intuition, goals, and types.mp4

115 MB

5. Filtering/7. Avoid edge effects with reflection.mp4

99 MB

5. Filtering/15. Remove electrical line noise and its harmonics.mp4

91 MB

5. Filtering/10. Windowed-sinc filters.mp4

88 MB

5. Filtering/14. Quantifying roll-off characteristics.mp4

87 MB

5. Filtering/6. Causal and zero-phase-shift filters.mp4

82 MB

5. Filtering/5. IIR Butterworth filters.mp4

80 MB

5. Filtering/16. Use filtering to separate birds in a recording.mp4

75 MB

5. Filtering/8. Data length and filter kernel length.mp4

65 MB

5. Filtering/9. Low-pass filters.mp4

64 MB

5. Filtering/12. Narrow-band filters.mp4

56 MB

5. Filtering/11. High-pass filters.mp4

52 MB

5. Filtering/4. FIR filters with fir1.mp4

47 MB

5. Filtering/13. Two-stage wide-band filter.mp4

42 MB

5. Filtering/17. Code challenge Filter these signals!.mp4

11 MB

5. Filtering/1.1 sigprocMXC_filtering.zip.zip

4.6 MB

5. Filtering/2. Filtering Intuition, goals, and types.vtt

19 kB

5. Filtering/3. FIR filters with firls.vtt

18 kB

5. Filtering/10. Windowed-sinc filters.vtt

14 kB

5. Filtering/7. Avoid edge effects with reflection.vtt

14 kB

5. Filtering/14. Quantifying roll-off characteristics.vtt

13 kB

5. Filtering/5. IIR Butterworth filters.vtt

12 kB

5. Filtering/15. Remove electrical line noise and its harmonics.vtt

12 kB

5. Filtering/6. Causal and zero-phase-shift filters.vtt

12 kB

5. Filtering/8. Data length and filter kernel length.vtt

9.8 kB

5. Filtering/9. Low-pass filters.vtt

8.9 kB

5. Filtering/12. Narrow-band filters.vtt

7.9 kB

5. Filtering/16. Use filtering to separate birds in a recording.vtt

7.7 kB

5. Filtering/11. High-pass filters.vtt

7.2 kB

5. Filtering/4. FIR filters with fir1.vtt

7.0 kB

5. Filtering/13. Two-stage wide-band filter.vtt

5.4 kB

5. Filtering/17. Code challenge Filter these signals!.vtt

1.5 kB

5. Filtering/1. MATLAB and Python code for this section.html

85 B

6. Convolution/3. Convolution in MATLAB.mp4

101 MB

6. Convolution/6. Thinking about convolution as spectral multiplication.mp4

88 MB

6. Convolution/2. Time-domain convolution.mp4

71 MB

6. Convolution/5. The convolution theorem.mp4

69 MB

6. Convolution/8. Convolution with frequency-domain Gaussian (narrowband filter).mp4

52 MB

6. Convolution/7. Convolution with time-domain Gaussian (smoothing filter).mp4

50 MB

6. Convolution/9. Convolution with frequency-domain Planck taper (bandpass filter).mp4

46 MB

6. Convolution/4. Why is the kernel flipped backwards!!!.mp4

22 MB

6. Convolution/6.1 TFtheory.mp4.mp4

18 MB

6. Convolution/10. Code challenge Create a frequency-domain mean-smoothing filter.mp4

17 MB

6. Convolution/1.1 sigprocMXC_convolution.zip.zip

250 kB

6. Convolution/3. Convolution in MATLAB.vtt

16 kB

6. Convolution/6. Thinking about convolution as spectral multiplication.vtt

15 kB

6. Convolution/2. Time-domain convolution.vtt

15 kB

6. Convolution/5. The convolution theorem.vtt

12 kB

6. Convolution/8. Convolution with frequency-domain Gaussian (narrowband filter).vtt

8.1 kB

6. Convolution/9. Convolution with frequency-domain Planck taper (bandpass filter).vtt

7.5 kB

6. Convolution/7. Convolution with time-domain Gaussian (smoothing filter).vtt

7.3 kB

6. Convolution/4. Why is the kernel flipped backwards!!!.vtt

5.8 kB

6. Convolution/10. Code challenge Create a frequency-domain mean-smoothing filter.vtt

2.1 kB

6. Convolution/1. MATLAB and Python code for this section.html

72 B

7. Wavelet analysis/8. MATLAB Time-frequency analysis with complex wavelets.mp4

140 MB

7. Wavelet analysis/5. Wavelet convolution for narrowband filtering.mp4

136 MB

7. Wavelet analysis/2. What are wavelets.mp4

93 MB

7. Wavelet analysis/9. Time-frequency analysis of brain signals.mp4

64 MB

7. Wavelet analysis/6. Overview Time-frequency analysis with complex wavelets.mp4

49 MB

7. Wavelet analysis/3. Convolution with wavelets.mp4

48 MB

7. Wavelet analysis/10. Code challenge Compare wavelet convolution and FIR filter!.mp4

13 MB

7. Wavelet analysis/1.1 sigprocMXC_wavelets.zip.zip

770 kB

7. Wavelet analysis/8. MATLAB Time-frequency analysis with complex wavelets.vtt

18 kB

7. Wavelet analysis/2. What are wavelets.vtt

17 kB

7. Wavelet analysis/5. Wavelet convolution for narrowband filtering.vtt

17 kB

7. Wavelet analysis/9. Time-frequency analysis of brain signals.vtt

9.9 kB

7. Wavelet analysis/6. Overview Time-frequency analysis with complex wavelets.vtt

9.5 kB

7. Wavelet analysis/3. Convolution with wavelets.vtt

6.6 kB

7. Wavelet analysis/10. Code challenge Compare wavelet convolution and FIR filter!.vtt

2.5 kB

7. Wavelet analysis/7. Link to youtube channel with 3 hours of relevant material.html

621 B

7. Wavelet analysis/4. Scientific publication about defining Morlet wavelets.html

465 B

7. Wavelet analysis/1. MATLAB and Python code for this section.html

84 B

8. Resampling, interpolating, extrapolating/9. Dynamic time warping.mp4

123 MB

8. Resampling, interpolating, extrapolating/3. Downsampling.mp4

111 MB

8. Resampling, interpolating, extrapolating/2. Upsampling.mp4

101 MB

8. Resampling, interpolating, extrapolating/6. Resample irregularly sampled data.mp4

94 MB

8. Resampling, interpolating, extrapolating/8. Spectral interpolation.mp4

77 MB

8. Resampling, interpolating, extrapolating/5. Interpolation.mp4

55 MB

8. Resampling, interpolating, extrapolating/4. Strategies for multirate signals.mp4

44 MB

8. Resampling, interpolating, extrapolating/7. Extrapolation.mp4

37 MB

8. Resampling, interpolating, extrapolating/10. Code challenge denoise and downsample this signal!.mp4

25 MB

8. Resampling, interpolating, extrapolating/1.1 sigprocMXC_resampling.zip.zip

411 kB

8. Resampling, interpolating, extrapolating/9. Dynamic time warping.vtt

20 kB

8. Resampling, interpolating, extrapolating/2. Upsampling.vtt

16 kB

8. Resampling, interpolating, extrapolating/3. Downsampling.vtt

15 kB

8. Resampling, interpolating, extrapolating/6. Resample irregularly sampled data.vtt

13 kB

8. Resampling, interpolating, extrapolating/8. Spectral interpolation.vtt

12 kB

8. Resampling, interpolating, extrapolating/5. Interpolation.vtt

9.4 kB

8. Resampling, interpolating, extrapolating/4. Strategies for multirate signals.vtt

8.0 kB

8. Resampling, interpolating, extrapolating/7. Extrapolation.vtt

7.1 kB

8. Resampling, interpolating, extrapolating/10. Code challenge denoise and downsample this signal!.vtt

5.0 kB

8. Resampling, interpolating, extrapolating/1. MATLAB and Python code for this section.html

67 B

9. Outlier detection/3. Outliers via local threshold exceedance.mp4

77 MB

9. Outlier detection/2. Outliers via standard deviation threshold.mp4

70 MB

9. Outlier detection/4. Outlier time windows via sliding RMS.mp4

46 MB

9. Outlier detection/5. Code challenge.mp4

39 MB

9. Outlier detection/1.1 sigprocMXC_outliers.zip.zip

268 kB

9. Outlier detection/2. Outliers via standard deviation threshold.vtt

12 kB

9. Outlier detection/3. Outliers via local threshold exceedance.vtt

11 kB

9. Outlier detection/4. Outlier time windows via sliding RMS.vtt

7.1 kB

9. Outlier detection/5. Code challenge.vtt

4.6 kB

9. Outlier detection/1. MATLAB and Python code for this section.html

72 B

Visit Getnewcourses.com.url

343 B

Visit Freecourseit.com.url

342 B

ReadMe.txt

241 B