Signal Processing Solutions With Python

Applied Signal Processing With Python

This course will bridge the gap between the theory of signal processing and implementation in Python. All the lecture slides and python codes are provided.

What you’ll learn

  • Fundamentals of Signal Processing..
  • Sampling and Reconstruction.
  • Nyquist Theorem.
  • Convolution.
  • Signal Denoising.
  • Fourier Transform and its Application.
  • Designing of FIR and IIR Filters.
  • Implementation of all above algorithms with Python.

Course Content

  • Introduction of the course –> 3 lectures • 7min.
  • Python Crash Course –> 17 lectures • 3hr 29min.
  • Analog to Digital Conversion –> 11 lectures • 2hr 7min.
  • The Convolution –> 9 lectures • 1hr 38min.
  • Signal Denoising –> 10 lectures • 1hr 41min.
  • Complex Numbers –> 9 lectures • 31min.
  • Fourier Transform –> 11 lectures • 1hr 59min.
  • FIR Filter Design –> 13 lectures • 1hr 57min.
  • IIR Filter Design –> 8 lectures • 45min.

Signal Processing Solutions With Python

Requirements

  • Some fundamental knowledge of programming may be helpful but not necessary..

This course will bridge the gap between the theory of signal processing and implementation in Python. All the lecture slides and python codes are provided.

Why Signal Processing?

Since the availability of digital computers in the 1970s, digital signal processing has found its way in all sections of engineering and sciences.

Signal processing is the manipulation of the basic nature of a signal to get the desired shaping of the signal at the output. It is concerned with the

representation of signals by a sequence of numbers or symbols and the processing of these signals.

Following areas of sciences and engineering are specially benefitted by rapid growth and advancement in signal processing techniques.

1. Machine Learning.

2. Data Analysis.

3. Computer Vision.

4. Image Processing and Medical Imaging.

5. Communication Systems.

6. Power Electronics.

7. Probability and Statistics.

8. Numerical Analysis.

9. Decision Theory.

10. Integrated Circuit design.

 

What you will learn from the course

1. Fundamentals of signals and signal Processing.

2. Analog to digital conversion.

3. Sampling and Reconstruction.

4. Nyquist Theorem.

5. The Convolution.

6. Signal denoising.

7. Fourier transform.

8. Signal filtering by FIR and IIR filters.

9. Implementing all signal processing techniques with python.

 

Course Outline

Section 01 : Introduction of the course

Section 02 : Python crash course

Section 03 : Fundamentals of Signal Processing

Section 04 : Convolution

Section 05 : Signal Denoising

Section 06: Complex Numbers

Section 07 : Fourier Transform

Section 08 : FIR Filter Design

Section 09 : IIR Filter Design