| 001 Bonus lecture.html | 3.6 KB | ||
| 001 Course materials (reader, MATLAB code, Python code).html | 0 B | ||
| 001 Course materials (reader, MATLAB code, Python code, exercises).html | 102.4 B | ||
| 002 Aliasing.mp4 | 48.8 MB | ||
| 002 Aliasing_en.srt | 12.6 KB | ||
| 002 Aliasing_en.vtt | 11.4 KB | ||
| 002 Complex numbers.mp4 | 45.5 MB | ||
| 002 Complex numbers_en.srt | 17.8 KB | ||
| 002 Complex numbers_en.vtt | 15.7 KB | ||
| 002 How and why it works.mp4 | 59.8 MB | ||
| 002 How and why it works_en.srt | 13.7 KB | ||
| 002 How and why it works_en.vtt | 12.5 KB | ||
| 002 How it works, speed tests.mp4 | 34.3 MB | ||
| 002 How it works, speed tests_en.srt | 9.4 KB | ||
| 002 How it works, speed tests_en.vtt | 8.5 KB | ||
| 002 How the 2D FFT works.mp4 | 59 MB | ||
| 002 How the 2D FFT works_en.srt | 14.1 KB | ||
| 002 How the 2D FFT works_en.vtt | 12.9 KB | ||
| 002 How the discrete Fourier transform works.mp4 | 63.7 MB | ||
| 002 How the discrete Fourier transform works_en.srt | 15.5 KB | ||
| 002 How the discrete Fourier transform works_en.vtt | 14.1 KB | ||
| 002 Nontechnical description of Fourier transform.mp4 | 49.2 MB | ||
| 002 Nontechnical description of Fourier transform_en.srt | 8.3 KB | ||
| 002 Nontechnical description of Fourier transform_en.vtt | 7.5 KB | ||
| 002 Rhythmicity in walking (gait).mp4 | 27.9 MB | ||
| 002 Rhythmicity in walking (gait)_en.srt | 8 KB | ||
| 002 Rhythmicity in walking (gait)_en.vtt | 7.2 KB | ||
| 002 Sampling and frequency resolution.mp4 | 55.8 MB | ||
| 002 Sampling and frequency resolution_en.srt | 21.4 KB | ||
| 002 Sampling and frequency resolution_en.vtt | 19.4 KB | ||
| 003 Converting indices to frequencies.mp4 | 38.9 MB | ||
| 003 Converting indices to frequencies_en.srt | 10.8 KB | ||
| 003 Converting indices to frequencies_en.vtt | 9.8 KB | ||
| 003 Examples of Fourier transform applications.mp4 | 71 MB | ||
| 003 Examples of Fourier transform applications_en.srt | 10.7 KB | ||
| 003 Examples of Fourier transform applications_en.vtt | 14.7 KB | ||
| 003 Inverse Fourier transform for bandstop filtering.mp4 | 40.9 MB | ||
| 003 Inverse Fourier transform for bandstop filtering_en.srt | 9.6 KB | ||
| 003 Inverse Fourier transform for bandstop filtering_en.vtt | 8.7 KB | ||
| 003 Rhythmicity in electrical brain waves.mp4 | 39.6 MB | ||
| 003 Rhythmicity in electrical brain waves_en.srt | 9.6 KB | ||
| 003 Rhythmicity in electrical brain waves_en.vtt | 8.7 KB | ||
| 003 Signal stationarity and non-stationarities.mp4 | 31.6 MB | ||
| 003 Signal stationarity and non-stationarities_en.srt | 7 KB | ||
| 003 Signal stationarity and non-stationarities_en.vtt | 6.4 KB | ||
| 003 The fast inverse Fourier transform.mp4 | 13.6 MB | ||
| 003 The fast inverse Fourier transform_en.srt | 2.9 KB | ||
| 003 The fast inverse Fourier transform_en.vtt | 2.7 KB | ||
| 003 Time-domain zero padding.mp4 | 56.9 MB | ||
| 003 Time-domain zero padding_en.srt | 14.5 KB | ||
| 003 Time-domain zero padding_en.vtt | 13.1 KB | ||
| 003 xkcd explanation of why we need complex numbers.html | 102.4 B | ||
| 004 Converting indices to frequencies_ part 2.mp4 | 43.8 MB | ||
| 004 Converting indices to frequencies_ part 2_en.srt | 18.4 KB | ||
| 004 Converting indices to frequencies_ part 2_en.vtt | 16.6 KB | ||
| 004 Effects of non-stationarities on the power spectrum.mp4 | 109.3 MB | ||
| 004 Effects of non-stationarities on the power spectrum_en.srt | 20.9 KB | ||
| 004 Effects of non-stationarities on the power spectrum_en.vtt | 19 KB | ||
| 004 Euler's formula e^ik.mp4 | 32.4 MB | ||
| 004 Euler's formula e^ik_en.srt | 11.2 KB | ||
| 004 Euler's formula e^ik_en.vtt | 10.2 KB | ||
| 004 Frequency-domain zero padding.mp4 | 34 MB | ||
| 004 Frequency-domain zero padding_en.srt | 10.1 KB | ||
| 004 Frequency-domain zero padding_en.vtt | 9.2 KB | ||
| 004 MATLAB, Octave, Python, or just watch.mp4 | 9.1 MB | ||
| 004 MATLAB, Octave, Python, or just watch_en.srt | 3.8 KB | ||
| 004 MATLAB, Octave, Python, or just watch_en.vtt | 3.5 KB | ||
| 004 The perfection of the Fourier transform.mp4 | 47 MB | ||
| 004 The perfection of the Fourier transform_en.srt | 8.2 KB | ||
| 004 The perfection of the Fourier transform_en.vtt | 7.5 KB | ||
| 004 Time series convolution.mp4 | 11 MB | ||
| 004 Time series convolution_en.srt | 4.2 KB | ||
| 004 Time series convolution_en.vtt | 3.8 KB | ||
| 005 Narrowband temporal filtering.mp4 | 32.5 MB | ||
| 005 Narrowband temporal filtering_en.srt | 11 KB | ||
| 005 Narrowband temporal filtering_en.vtt | 10 KB | ||
| 005 Sampling rate vs. signal length.mp4 | 40.4 MB | ||
| 005 Sampling rate vs. signal length_en.srt | 12.2 KB | ||
| 005 Sampling rate vs. signal length_en.vtt | 11 KB | ||
| 005 Shortcut_ Converting indices to frequencies.mp4 | 83.6 MB | ||
| 005 Shortcut_ Converting indices to frequencies_en.srt | 14.5 KB | ||
| 005 Shortcut_ Converting indices to frequencies_en.vtt | 13.1 KB | ||
| 005 Sine waves and complex sine waves.mp4 | 65.1 MB | ||
| 005 Sine waves and complex sine waves_en.srt | 17 KB | ||
| 005 Sine waves and complex sine waves_en.vtt | 15.4 KB | ||
| 005 Solution to understanding nonstationary time series.mp4 | 105.6 MB | ||
| 005 Solution to understanding nonstationary time series_en.srt | 17.8 KB | ||
| 005 Solution to understanding nonstationary time series_en.vtt | 16.1 KB | ||
| 005 Using Udemy like a pro.mp4 | 38.1 MB | ||
| 005 Using Udemy like a pro_en.srt | 11.8 KB | ||
| 005 Using Udemy like a pro_en.vtt | 10.6 KB | ||
| 005 Using the fft on matrices.mp4 | 99.2 MB | ||
| 005 Using the fft on matrices_en.srt | 9.7 KB | ||
| 005 Using the fft on matrices_en.vtt | 8.8 KB | ||
| 006 2D image filtering.mp4 | 33.1 MB | ||
| 006 2D image filtering_en.srt | 9.6 KB | ||
| 006 2D image filtering_en.vtt | 8.7 KB | ||
| 006 Course tangent_ self-accountability in online learning.mp4 | 17.8 MB | ||
| 006 Course tangent_ self-accountability in online learning_en.srt | 4.1 KB | ||
| 006 Course tangent_ self-accountability in online learning_en.vtt | 3.7 KB | ||
| 006 Dot product.mp4 | 56.7 MB | ||
| 006 Dot product_en.srt | 21.1 KB | ||
| 006 Dot product_en.vtt | 19.2 KB | ||
| 006 Normalized time vector.mp4 | 54.5 MB | ||
| 006 Normalized time vector_en.srt | 13.1 KB | ||
| 006 Normalized time vector_en.vtt | 11.8 KB | ||
| 006 Windowing and Welch's method.mp4 | 49.5 MB | ||
| 006 Windowing and Welch's method_en.srt | 13.4 KB | ||
| 006 Windowing and Welch's method_en.vtt | 12 KB | ||
| 007 Complex dot product.mp4 | 51.5 MB | ||
| 007 Complex dot product_en.srt | 11.3 KB | ||
| 007 Complex dot product_en.vtt | 10.3 KB | ||
| 007 Image narrowband filtering.mp4 | 29.6 MB | ||
| 007 Image narrowband filtering_en.srt | 7.4 KB | ||
| 007 Image narrowband filtering_en.vtt | 6.7 KB | ||
| 007 Instantaneous frequency.mp4 | 60.2 MB | ||
| 007 Instantaneous frequency_en.srt | 15.5 KB | ||
| 007 Instantaneous frequency_en.vtt | 14 KB | ||
| 007 Positive and negative frequencies.mp4 | 12.2 MB | ||
| 007 Positive and negative frequencies_en.srt | 5.9 KB | ||
| 007 Positive and negative frequencies_en.vtt | 5.4 KB | ||
| 008 Accurate scaling of Fourier coefficients.mp4 | 30.9 MB | ||
| 008 Accurate scaling of Fourier coefficients_en.srt | 8.5 KB | ||
| 008 Accurate scaling of Fourier coefficients_en.vtt | 7.7 KB | ||
| 008 Real data from trends.google.com_.mp4 | 12.2 MB | ||
| 008 Real data from trends.google.com__en.srt | 4.8 KB | ||
| 008 Real data from trends.google.com__en.vtt | 4.4 KB | ||
| 009 Interpreting phase values.mp4 | 14.3 MB | ||
| 009 Interpreting phase values_en.srt | 6.2 KB | ||
| 009 Interpreting phase values_en.vtt | 5.7 KB | ||
| 010 Averaging Fourier coefficients.mp4 | 42.7 MB | ||
| 010 Averaging Fourier coefficients_en.srt | 11.8 KB | ||
| 010 Averaging Fourier coefficients_en.vtt | 10.7 KB | ||
| 011 The DC (zero frequency) component.mp4 | 27.7 MB | ||
| 011 The DC (zero frequency) component_en.srt | 9.8 KB | ||
| 011 The DC (zero frequency) component_en.vtt | 8.9 KB | ||
| 012 Amplitude spectrum vs. power spectrum.mp4 | 24.9 MB | ||
| 012 Amplitude spectrum vs. power spectrum_en.srt | 8.6 KB | ||
| 012 Amplitude spectrum vs. power spectrum_en.vtt | 7.8 KB | ||
| 013 A note about terminology of Fourier features.mp4 | 22.2 MB | ||
| 013 A note about terminology of Fourier features_en.srt | 6.6 KB | ||
| 013 A note about terminology of Fourier features_en.vtt | 6 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| EEGrestingState.mat | 335.8 KB | ||
| Fourier_2D.ipynb | 78.2 KB | ||
| Fourier_2D.m | 4 KB | ||
| Fourier_2D.pdf | 124.8 KB | ||
| Fourier_DTFT.ipynb | 20.3 KB | ||
| Fourier_DTFT.m | 14.3 KB | ||
| Fourier_DTFT.pdf | 227.8 KB | ||
| Fourier_FFT.ipynb | 6.8 KB | ||
| Fourier_FFT.m | 4.1 KB | ||
| Fourier_FFT.pdf | 182.3 KB | ||
| Fourier_aliasStation.ipynb | 18.4 KB | ||
| Fourier_aliasStation.m | 12.9 KB | ||
| Fourier_aliasStation.pdf | 172.7 KB | ||
| Fourier_apps.ipynb | 332 KB | ||
| Fourier_apps.m | 8.6 KB | ||
| Fourier_apps.pdf | 112.6 KB | ||
| Fourier_foundations.ipynb | 14.4 KB | ||
| Fourier_foundations.m | 10.3 KB | ||
| Fourier_foundations.pdf | 1.3 MB | ||
| Fourier_freqres0pad.pdf | 171.1 KB | ||
| Fourier_intro.ipynb | 6.5 KB | ||
| Fourier_inverse.ipynb | 8 KB | ||
| Fourier_inverse.m | 4.8 KB | ||
| Fourier_inverse.pdf | 169.2 KB | ||
| Fourier_resolution0.ipynb | 9.9 KB | ||
| Fourier_resolution0.m | 8 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| LFPdata.mat | 1.1 MB | ||
| Lenna.png | 462.7 KB | ||
| braindata.mat | 17.1 KB | ||
| fourier_intro.m | 4.1 KB | ||
| fourier_nontech_intro.pdf | 227.8 KB | ||
| gait.mat | 3.8 KB | ||
| sinewave_from_params.fig | 29.1 KB | ||
| sinewave_from_params.m | 5.4 KB | ||
| ▲ 186 total files | |||
Master the Fourier transform and its applications (updated 11/2021)
https://CourseMega.com
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.98 GB | Duration: 6h 42m
Learn the Fourier transform in MATLAB and Python, and its applications in digital signal processing and image processing
What you'll learn
Learn about one of the single most important equations in all of modern technology and therefore human civilization.
The fundamental concepts underlying the Fourier transform
Sine waves, complex numbers, dot products, sampling theorem, aliasing, and more!
Interpret the results of the Fourier transform
Apply the Fourier transform in MATLAB and Python!
Use the fast Fourier transform in signal processing applications
Improve your MATLAB and/or Python programming skills
Know the limitations of interpreting the Fourier transform.
Requirements
A curious mind!
Some MATLAB or Python experience is useful but not required
High-school math (calculus is not necessary)
Previous knowledge of the Fourier transform is NOT necessary!
* Manually correct English captions *
Description
The Fourier transform is one of the most important operations in signal processing and modern technology, and therefore in modern human civilization. But how does it work, and why does it work?
What you will learn in this course:
You will learn the theoretical and computational bases of the Fourier transform, with a strong focus on how the Fourier transform is used in modern applications in signal processing, data analysis, and image filtering. The course covers not only the basics, but also advanced topics including effects of non-stationarities, spectral resolution, normalization, filtering. All videos come with MATLAB and Python code for you to learn from and adapt!
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 3.2 GB | freecoursewb | 5 days | 1 | 17 | |
| 1.8 GB | freecoursewb | 1 week | 30 | 9 | |
| 1.3 GB | freecoursewb | 1 week | 14 | 10 | |
|
Udemy - LoadRunner Essentials - Master Performance Testing and Analysis Posted by
freecoursewb in Other
|
942.4 MB | freecoursewb | 2 weeks | 0 | 0 |
|
Udemy - Master AI Prompt Writing - Content, Branding and Productivity Posted by
freecoursewb in Other
|
1.2 GB | freecoursewb | 2 weeks | 23 | 9 |
All Comments