Digital Signal Processing Essentials: From Signals To Filter

signals classifications, system classifications, DTFT, Discrete Fourier Transform, FFT, Convolutions & filter designing

What you'll learn

Represent signals mathematically in discrete-time and sketch its frequency response

Compute DFT using Radix-2 FFT algorithms.

Design different types of digital filters in various domains.

Design the realizations of circuits

Requirements

signals & systems

Description

Signal processing is a fundamental area in electronics and communication engineering, focusing on the analysis and manipulation of signals. Signals can be broadly classified into continuous-time and discrete-time signals, as well as periodic, aperiodic, deterministic, and random types. Similarly, systems are categorized based on their properties such as linear or nonlinear, time-invariant or time-variant, causal or non-causal, and stable or unstable. Understanding these classifications is essential for predicting system behavior.In the frequency domain, the Discrete-Time Fourier Transform (DTFT) provides a representation of discrete signals, showing how energy is distributed across frequencies. Its computational counterpart, the Discrete Fourier Transform (DFT), is widely used in digital applications, although it can be computationally intensive for large datasets. To address this, the Fast Fourier Transform (FFT) was developed, offering a highly efficient way to compute the DFT, making real-time applications like communications, audio, and image processing possible.Another important concept is convolution, which describes how an input signal interacts with a system’s impulse response to produce an output. Convolution is closely linked with filter design, where digital filters (FIR or IIR) are created to modify signals—whether for noise reduction, signal enhancement, or extracting useful information. Together, these concepts form the backbone of digital signal processing.

Overview

Section 1: Introduction

Lecture 1 Introduction to MATLAB

Lecture 2 MATLAB

Lecture 3 Generation of Discrete time signal

Lecture 4 Discrete time signals

Lecture 5 Generation of ramp and impulse signal

Lecture 6 Introduction to system

Lecture 7 Introduction to DSP

Lecture 8 DSP

Section 2: Parabolic signal generation

Lecture 9 parabolic signal

Lecture 10 exponential and rectangular signals

Section 3: Discrete time system Classification

Lecture 11 Linear and Non linear systems

Lecture 12 Time Invariance and Time variance

Section 4: Discrete Time Fourier transform

Lecture 13 frequency response finding of a first order system

Lecture 14 Linear Convolution

Lecture 15 linear convolution-2

Lecture 16 Convolution using Tabular method

Lecture 17 DTFT MATLAB Program

This course is designed for engineering students, aspiring signal processing professionals, and educators who want to build a strong foundation in Digital Signal Processing and its practical applications.,beginners to signal processing learners