[Udemy] - Signal processing problems, solved in MATLAB and in Python [Getnewcourses]

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Udemy - Signal processing problems, solved in MATLAB and in Python 1. Introductions
  • 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 (0.2 KB)
10. Feature detection
  • 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 (0.1 KB)
11. Variability
  • 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 (0.0 KB)
12. Discounts on related courses
  • 2. Bonus Coupons for related courses.html (2.5 KB)
  • 1. Join the community!.html (0.5 KB)
2. Time series denoising
  • 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 (0.1 KB)
3. Spectral and rhythmicity analyses
  • 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 (0.1 KB)
4. Working with complex numbers
  • 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 (0.0 KB)
5. Filtering
  • 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)

  • Description

    Signal processing problems, solved in MATLAB and in Python




    What you'll learn
    Understand commonly used signal processing tools
    Design, evaluate, and apply digital filters
    Clean and denoise data
    Know what to look for when something isn't right with the data or the code
    Improve MATLAB or Python programming skills
    Know how to generate test signals for signal processing methods
    *Fully manually corrected English captions!

    Download For More Latest Courses Visit >>> Getnewcourses

    Requirements
    Basic programming experience in MATLAB or Python
    High-school math

    Description
    Why you need to learn digital signal processing.

    Nature is mysterious, beautiful, and complex. Trying to understand nature is deeply rewarding, but also deeply challenging. One of the big challenges in studying nature is data analysis. Nature likes to mix many sources of signals and many sources of noise into the same recordings, and this makes your job difficult.

    Download Udemy Courses For Free

    Therefore, one of the most important goals of time series analysis and signal processing is to denoise: to separate the signals and noises that are mixed into the same data channels.

    The big idea of DSP (digital signal processing) is to discover the mysteries that are hidden inside time series data, and this course will teach you the most commonly used discovery strategies.



    What's special about this course?

    The main focus of this course is on implementing signal processing techniques in MATLAB and in Python. Some theory and equations are shown, but I'm guessing you are reading this because you want to implement DSP techniques on real signals, not just brush up on abstract theory.

    Udemy courses free download

    The course comes with over 10,000 lines of MATLAB and Python code, plus sample data sets, which you can use to learn from and to adapt to your own coursework or applications.

    In this course, you will also learn how to simulate signals in order to test and learn more about your signal processing and analysis methods.

    freetutorials

    Are there prerequisites?

    You need some programming experience. I go through the videos in MATLAB, and you can also follow along using Octave (a free, cross-platform program that emulates MATLAB). I provide corresponding Python code if you prefer Python. You can use any other language, but you would need to do the translation yourself.

    I recommend taking my Fourier Transform course before or alongside this course. However, this is not a requirement, and you can succeed in this course without taking the Fourier transform course.



    What should you do now?

    Watch the sample videos, and check out the reviews of my other courses -- many of them are "best-seller" or "top-rated" and have lots of positive reviews. If you are unsure whether this course is right for you, then feel free to send me a message. I hope you to see you in class!

    Who this course is for:
    Students in a signal processing or digital signal processing (DSP) course
    Scientific or industry researchers who analyze data
    Developers who work with time series data
    Someone who wants to refresh their knowledge about filtering
    Engineers who learned the math of DSP and want to learn about implementations in software

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[Udemy] - Signal processing problems, solved in MATLAB and in Python [Getnewcourses]

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Download torrent
5.7 GB
seeders:9
leechers:12
[Udemy] - Signal processing problems, solved in MATLAB and in Python [Getnewcourses]

Torrent hash: C9D0738D42AEE1239F7610AB1D510B1CC0FC7910