Digital Signal Processing (DSP) From Ground Up™ in C

Course Curriculum

Chapter 1: Set up

Lecture 1: Setting up an Integrated Development Environment (IDE)

Lecture 2: Overview of CodeBlocks

Lecture 3: Downloading gnuplot

Lecture 4: Installing gnuplot

Lecture 5: Overview of gnuplot

Chapter 2: Getting started with gnuplot

Lecture 1: Plotting signals with gnuplot

Lecture 2: Plotting multiple signals in the same window

Chapter 3: Signal Statistics and Noise

Lecture 1: Signal Statistics and Noise

Lecture 2: Mean and Standard Deviation

Lecture 3: Coding : Developing the Signal Mean algorithm

Lecture 4: Coding : Computing the Signal Mean

Lecture 5: Coding : Developing the Signal Variance algorithm

Lecture 6: Coding : Developing the Signal Standard Deviation algorithm

Chapter 4: Quantization and The Sampling Theorem

Lecture 1: Nyquist Theorem ( Sampling Theorem )

Lecture 2: The Passive Low-Pass Filter

Lecture 3: The Passive High-Pass Filter

Lecture 4: The Active Filter

Lecture 5: The Bessel, Chebyshev and Butterworth filters

Chapter 5: Linear Systems and Superposition

Lecture 1: Notice

Lecture 2: Introduction to Linear Systems

Lecture 3: Understanding Superposition

Lecture 4: Impulse and Step Decomposition

Chapter 6: Convolution

Lecture 1: Introduction to Convolution

Lecture 2: The Convolution Operation

Lecture 3: Examinging the Output of Convolution

Lecture 4: The Convolution Sum Equation

Lecture 5: A Closer look at the Delta function

Lecture 6: Coding : Developing the Convolution algorithm (Part I )

Lecture 7: Coding : Developing the Convolution algorithm (Part I I)

Lecture 8: Coding : Developing the Convolution algorithm (Part III)

Lecture 9: Coding : Developing the Convolution algorithm (Part IV)

Lecture 10: The Running Sum and First Difference

Lecture 11: Coding : Developing the Running Sum algorithm

Chapter 7: Fourier Transsform

Lecture 1: Introduction to Fourier Analysis

Lecture 2: The DFT Engine

Lecture 3: Understanding Forward and Inverse DFT

Lecture 4: Code : Developing the DFT algorithm (Part I)

Lecture 5: Code : Developing the DFT algorithm (Part II)

Lecture 6: Code : Developing the DFT algorithm (Part III)

Lecture 7: Coding : Developing the Inverse DFT algorithm (Part I)

Lecture 8: Coding : Developing the Inverse DFT algorithm (Part II)

Lecture 9: Coding : Developing the Inverse DFT algorithm (Part III)

Lecture 10: Coding : Computing the DFT and IDFT of an ECG signal (Part I)

Lecture 11: Coding : Computing the DFT and IDFT of an ECG signal (Part II)

Lecture 12: Coding : Identifying the frequencies present in the DFT plot

Lecture 13: Symmetry between Time domain and frequency domain -Duality

Lecture 14: Polar Notation

Lecture 15: Coding : Rectangular notation to the polar notation ( Part I)

Lecture 16: Coding : Rectangular notation to the polar notation ( Part II)

Chapter 8: Complex Numbers

Lecture 1: The Complex Number System

Lecture 2: Polar Representation of Complex Numbers

Lecture 3: Euler's Relation

Lecture 4: Representation of Sinusoids

Lecture 5: Representing Systems

Chapter 9: Complex Fourier Transform

Lecture 1: Introduction to Complex Fourier Transform

Lecture 2: Mathematical Equivalence

Lecture 3: The Complex DFT Equation

Lecture 4: Comparing Real DFT and Complex DFT

Lecture 5: Coding : Developing the Complex DFT equation (Part I)

Lecture 6: Coding : Developing the Complex DFT equation (Part II )

Chapter 10: Fast Fourier Transform (FFT)

Lecture 1: An Overview of how FFT works.

Lecture 2: Understanding the complexity of calculating DFT directly

Lecture 3: How the Decimation -in-Time FFT Algorithm works

Chapter 11: Digital Filter Design

Lecture 1: Introduction to Digital Filters

Lecture 2: The Filter Kernel

Lecture 3: The Impulse,Step and Frequency response

Lecture 4: Understanding the Logarithmic scale and decibels

Lecture 5: Information representations of a signal

Lecture 6: Time domain parameters

Lecture 7: Frequency domain parameters

Lecture 8: Designing digital filters using the spectral inversion method

Lecture 9: Designing digital filters using the spectral reversal method

Lecture 10: Classification of digital filters

Chapter 12: Designing Finite Impulse Response FIR) Filters

Lecture 1: The Moving Average Filter

Lecture 2: The Multiple Pass Moving Average Filter

Lecture 3: The Recursive Moving Average Filter

Chapter 13: Designing Infinite Impulse Response (IIR) Filters

Lecture 1: Introduction to Recursive Filters

Lecture 2: The Recursion Equation

Lecture 3: The Single-Pole Recursive Filter

Lecture 4: Digital Chebyshev Filters

Chapter 14: Designing Windowed-Sinc Filters

Lecture 1: Introduction to Windowed-Sinc Filters

Lecture 2: The Sinc Function and the Truncated Sinc Filter

Lecture 3: The Blackman window

Lecture 4: The Hamming and Blackman window equations

Lecture 5: Designing the Windowed Sinc filter

Lecture 6: Coding : Developing the Low-pass Windowed-Sinc Filter Algorithm (Part I)