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

Name: Digital Signal Processing (DSP) From Ground Up™ in C
Rating: 4.2 (873 reviews)

Practical DSP in C : FFT, Filter Design, Convolution, IIR, FIR, Hamming Window, Linear Systems, Chebyshev filters etc

Created byIsrael Gbati, BHM Engineering Academy

Last updated 2/2023

English

What you'll learn

Be able to develop the Convolution Kernel algorithm in C
Be able able to develop the Discrete Fourier Transform (DFT) algorithm in C
Be able to develop the Inverse Discrete Fourier Transform (IDFT) algorithm in C
Be able to develop the Fast Fourier Transform (FFT) algorithm in C
Be able to perform spectral analysis on ECG signals in C
Be able to design and develop Windowed-Sinc filters in C
Be able to design and develop Finite Impulse Response (FIR) filters in C
Be able to design and develop Infinite Impulse Response (IIR) filters in C
Be able to develop the FFT Convolution algorithm in C
Be able to develop the First Difference algorithm in C
Be able to develop the Running Sum algorithm in C
Be able to develop the Moving Average filter algorithm in C
Be able to develop the Recursive Moving Average filter algorithm in C
Be able to develop signal statistical algorithms in C
Be able to build passive Low-pass and High-pass filters
Be able to build Modified Sallen-Key filters
Be able to build Bessel, Chebyshev and Butterworth filters
Understand all about Linear Systems and their characteristics
Understand how to synthesize and decompose signals
Understand the relationship between the delta function and the Impulse response
Be able to plot signals with gnuplot
Be able to give a lecture on Digital Signal Processing (DSP)
Be able to suppress noise in signals

Course content

14 sections • 92 lectures • 7h 47m total length

Setting up an Integrated Development Environment (IDE)2:37
Download and install code blocks as your C integrated development environment, verify the GNU GCC compiler is detected, and you may use another C IDE or compiler if already available.
Overview of CodeBlocks4:01
Explore a quick overview of CodeBlocks, create a new C console project, and set a dedicated folder. Build and run to test installation by seeing Hello World and Hello DSP.
Downloading gnuplot2:39
Installing gnuplot2:21
Overview of gnuplot4:00
Plot signals with gnuplot from dot dat files exported by your C code, and set the data folder to plot accurately, including signals with lines and multiple signals.

Signal Statistics and Noise4:07
Define signal as a relationship between parameters and distinguish continuous from discrete signals. Explain ADC digitization, independent and dependent variables, and how samples form the digitized signal.
Mean and Standard Deviation4:06
Compute the signal mean by averaging all samples. Understand the standard deviation as the measure of fluctuation from the mean, with variance representing the power of that fluctuation.
Coding : Developing the Signal Mean algorithm7:43
Coding : Computing the Signal Mean3:36
Coding : Developing the Signal Variance algorithm9:36
Develop a variance algorithm for a signal in c by computing the mean, summing squared deviations with pow, and dividing by length. The lesson covers project setup and debugging.
Coding : Developing the Signal Standard Deviation algorithm4:06
Develop the standard deviation algorithm by implementing a double function that takes signal variance and returns the standard deviation via its square root in the signal variants project in C.

Introduction to Convolution3:22
Define convolution as a math operation that combines an input signal and an impulse response to yield output. Represent impulse responses as shifted and scaled delta functions, i.e., the filter kernel or PSF.
The Convolution Operation6:44
Explore the convolution operation, using a low pass filter example, by combining an input signal with an impulse response to produce an output, illustrating length and the commutative property.
Examinging the Output of Convolution4:45
The Convolution Sum Equation2:05
A Closer look at the Delta function5:41
Explore how the impulse response defines a linear system and guides digital low-pass filter design, with the delta function as the convolution identity enabling amplification, attenuation, and delays.
Coding : Developing the Convolution algorithm (Part I )10:46
Develop a convolution algorithm by applying a 29-element impulse response to an input signal, producing a low-pass output that preserves 1 kHz and attenuates 15 kHz.
Coding : Developing the Convolution algorithm (Part I I)5:19
Coding : Developing the Convolution algorithm (Part III)6:24
Coding : Developing the Convolution algorithm (Part IV)9:45
Develop the convolution algorithm (part iv) to apply an impulse response to an input signal and produce a filtered output. Implement a low-pass filter with arrays and simple plotting.
The Running Sum and First Difference2:02
Coding : Developing the Running Sum algorithm12:16

Introduction to Fourier Analysis5:29
The DFT Engine4:11
Understanding Forward and Inverse DFT4:32
Code : Developing the DFT algorithm (Part I)9:09
Develop and implement a discrete Fourier transform algorithm in C, building a console project from scratch, initialize inputs, and compute real and imaginary frequency components.
Code : Developing the DFT algorithm (Part II)10:06
Code : Developing the DFT algorithm (Part III)4:23
Develops the DFT algorithm by guiding you through plotting input signals, setting origins and colors, and comparing time-domain and frequency-domain representations in a C DSP project.
Coding : Developing the Inverse DFT algorithm (Part I)5:25
Coding : Developing the Inverse DFT algorithm (Part II)8:21
Coding : Developing the Inverse DFT algorithm (Part III)5:16
Develop and validate the inverse DFT algorithm by plotting time-domain inputs and frequency-domain real and imaginary parts, then compare the IDFT output with the forward DFT results to verify consistency.
Coding : Computing the DFT and IDFT of an ECG signal (Part I)5:11
Coding : Computing the DFT and IDFT of an ECG signal (Part II)6:34
Coding : Identifying the frequencies present in the DFT plot9:23
Symmetry between Time domain and frequency domain -Duality0:55
Polar Notation2:50
Coding : Rectangular notation to the polar notation ( Part I)12:21
Coding : Rectangular notation to the polar notation ( Part II)9:48

Introduction to Complex Fourier Transform1:43
Learn how real DFT extends to complex numbers, decomposing a time-domain signal into cosine and sine components with amplitudes in X(K) and iX(K) via forward transform to the frequency domain.
Mathematical Equivalence1:38
The Complex DFT Equation0:36
Comparing Real DFT and Complex DFT3:17
Coding : Developing the Complex DFT equation (Part I)10:34
Develops the complex discrete Fourier transform equation (part i) to handle time-domain signals with real and imaginary parts in C, extending the real-valued DFT in C.
Coding : Developing the Complex DFT equation (Part II )7:57

Requirements

Having basic C programming skills is a plus

Description

With a programming based approach, this course is designed to give you a solid foundation in the most useful aspects of Digital Signal Processing (DSP) in an engaging and easy to follow way. The goal of this course is to present practical techniques while avoiding obstacles of abstract mathematical theories. To achieve this goal, the DSP techniques are explained in plain language, not simply proven to be true through mathematical derivations.

Still keeping it simple, this course comes in different programming languages and hardware architectures so that students can put the techniques to practice using a programming language or hardware architecture of their choice. This version of the course uses the C programming language.

By the end of this course you should be able develop the Convolution Kernel algorithm in C, develop the Discrete Fourier Transform (DFT) algorithm in C, develop the Inverse Discrete Fourier Transform (IDFT) algorithm in C, design and develop Finite Impulse Response (FIR) filters in C, design and develop Infinite Impulse Response (IIR) filters in C, develop Windowed-Sinc filters in C, build Modified Sallen-Key filters, build Bessel, Chebyshev and Butterworth filters, develop the Fast Fourier Transform (FFT) algorithm in C , even give a lecture on DSP and so much more. Please take a look at the full course curriculum.

Who this course is for:

Engineering Students
C Developers
Embedded Systems Engineers
Computer Engineering students
Hobbyists
Embedded Systems Instructors

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

What you'll learn

Explore related topics

Course content

Set up5 lectures • 16min

Getting started with gnuplot2 lectures • 12min

Signal Statistics and Noise6 lectures • 33min

Quantization and The Sampling Theorem5 lectures • 34min

Linear Systems and Superposition4 lectures • 14min

Convolution11 lectures • 1hr 9min

Fourier Transsform16 lectures • 1hr 44min

Complex Numbers5 lectures • 9min

Complex Fourier Transform6 lectures • 26min

Fast Fourier Transform (FFT)3 lectures • 20min

Requirements

Description

Who this course is for: