Rating 4.65 out of 5 (80 ratings in Udemy)
What you'll learn- Signal processing with analog filters
- Anaysis of analog filters
- The concept of discrete time systems in comparison to continuous time systems
- Analog to digital conversion theory
- Laplace transforms and it's applicaton in analog filters
- Laplace transforms in the digital domain
- Continuous to discrete time conversion in the frequency domain
- Installing and setting up Python, Numpy and Matplotlib
- Generating and plotting signals …
Rating 4.65 out of 5 (80 ratings in Udemy)
What you'll learn- Signal processing with analog filters
- Anaysis of analog filters
- The concept of discrete time systems in comparison to continuous time systems
- Analog to digital conversion theory
- Laplace transforms and it's applicaton in analog filters
- Laplace transforms in the digital domain
- Continuous to discrete time conversion in the frequency domain
- Installing and setting up Python, Numpy and Matplotlib
- Generating and plotting signals
- Sampling signals and simulating discrete time systems
- Simulating the capacitor as a digital filter
- Simulating the inductor as a digital filter
- Simulating non-ideal capacitors and inductors as digital filters
- Simulating an LC filter digitally
- Using the signal package in Scipy
- Synthesizing transfer functions in Python with signal
- Generating Bode plots
- Using frequency response characteristics to design filters
- Designing and implementing a low pass and a notch filter
DescriptionThis course introduces signal processing to a power engineer with the objective of fulfilling one of the most pressing needs faced in power engineering - filter design. The course begins with a basic introduction to the concept of signal processing, discrete time systems and basic hardware applications. The course dives into the mathematics behind signal processing in order to translate many of the obscure concepts into plain English with the final objective of implementation in hardware. The course will then have code-along sessions where students will learn how filters are designed, analyzed and implemented using Python, Numpy, Scipy and Matplotlib. The course has a section on how to install and setup software on different operating systems and used only free and open source software, making the course and the materials accessible to students irrespective of their background.
