Signals And Systems With Python: A Practical Approach

A Practical Approach using Python

What you'll learn

Critically evaluate different types of signals and system properties using mathematical models.

Design and implement signal processing operations (e.g., convolution, filtering) in Python.

Analyze and interpret signals in time and frequency domains using Fourier, Laplace, and Z-transforms.

Synthesize real-world solutions by applying systems theory and Python-based simulations.

Requirements

Basic understanding of mathematics, especially calculus and linear algebra

Familiarity with Python programming (variables, loops, functions, basic libraries)

Description

This course provides an in-depth exploration of the fundamental principles of Signals and Systems, with an emphasis on practical implementation using Python. Designed for students, professionals, and researchers, it offers a comprehensive understanding of both the theoretical concepts and computational techniques required to analyze and process signals and systems.The course begins with an introduction to the core concepts of signals and systems, including classifications, properties, and operations on continuous and discrete signals. Through hands-on coding in Python, learners will apply these concepts to solve real-world signal processing problems. The course covers key topics such as convolution, Fourier analysis, Laplace transforms, and Z-transforms, ensuring a thorough understanding of both time and frequency domain analysis.Learners will gain proficiency in using Python libraries such as NumPy, SciPy, and Matplotlib to simulate, analyze, and visualize signals and systems. The course progresses to advanced topics, such as system stability, filtering techniques, and real-time signal processing applications. By the end of the course, participants will have developed both the theoretical knowledge and practical coding skills necessary to tackle complex signal processing challenges in diverse fields, including communications, control systems, biomedical engineering, and data science.This course is ideal for individuals with a basic understanding of Python programming and a keen interest in learning about signals and systems.

Overview

Section 1: Fundamentals of Signals and classification of signals

Lecture 1 Introduction to Course

Lecture 2 Understanding the Basics: Types and Classifications

Lecture 3 Classification of Systems

Lecture 4 Standard Signal Generation in Python-Part1

Lecture 5 Standard Signal Generation in python- Part2

Lecture 6 Operations on Signals Using Python

Lecture 7 System Classification in Signal Processing using Python

Lecture 8 Convolution between two continuous time signals

Lecture 9 Convolution between continuous time signals using Python

Lecture 10 Fundamentals of Signals and classification of signals notes

Section 2: Fourier Series Representation of Periodic Signals

Lecture 11 Trigonometric & Exponential Forms of Fourier Series

Lecture 12 Introduction to Signals and Systems

Lecture 13 Dirichlet Conditions for Fourier Series Existence

Lecture 14 Symmetry Conditions in Fourier Series (Even and Odd)

Lecture 15 Trigonometric Forms of Fourier Series Example

Lecture 16 Exponential Fourier Series Example

Lecture 17 Fourier Series Approximation of a Square Wave using python

Lecture 18 Fourier Series Notes

Section 3: Frequency Domain Representation: Fourier Transform

Lecture 19 Introduction to Fourier Transform

Lecture 20 Applications of Fourier Transform

Lecture 21 Fourier Transform of Standard Signals

Lecture 22 Fourier Transform of Unit step signal using Python

Lecture 23 Fourier Transform of Rectangular pulse using python

Lecture 24 Fourier Transform of sinusoidal signal using python

Lecture 25 Fourier Transform of Gaussian signal Using python

Lecture 26 Fourier Transform Notes

Section 4: Laplace Transform: Definition and region of convergence

Lecture 27 Introduction to Laplace Transform

Lecture 28 Laplace Transform for standard signals

Lecture 29 Solution of Differential Equations using LaPlace Transform

Lecture 30 Laplace Transform of Unit impulse using python

Lecture 31 Laplace Transform of Unit step using python

Lecture 32 Laplace Transform of Unit Ramp using python

Lecture 33 Laplace Transform of Unit exponential using python

Lecture 34 Laplace Transform of Sinusoidal using python

Section 5: Z-Transform

Lecture 35 Introduction to Z Transform

Lecture 36 Z Transform of standard signals

Lecture 37 Z Transform of Unit Impulse using python

Lecture 38 Z Transform of Unit step using Python

Lecture 39 Z Transform of exponential signal using Python

Lecture 40 Z Transform of Sinusoidal signal using Python

Section 6: Sampling and Reconstruction Sampling theorem and its significance

Lecture 41 Sampling Theorem

Lecture 42 Notes: Sampling Theorem

Lecture 43 Problems on Sampling Theorem

Lecture 44 Sampling Theorem using Python

Section 7: Applications and Case Studies

Lecture 45 Signal Filtering and Noise Reduction Techniques

Lecture 46 Biomedical Signal Processing (ECG/EEG)

Lecture 47 Audio and Speech Signal Applications

Lecture 48 Mini Project – End-to-End System

Engineering students (ECE, EE, CS) who want to master Signals and Systems with practical Python applications,Python learners looking to apply their skills in real-world signal processing scenarios