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.
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
Who this course is for:
- University students taking signal processing course.
- Engineers and scientists working in the signal processing area.
- Engineers and scientists who know the Maths of signal processing and want to learn the implementations in Python.
- People who want to know about data and time series filtering.
- People who know implementation of signal processing algorithms in Matlab and want to switch to the Python.