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Digital Signal Processing (DSP) From Ground Up™ on Arm Processors

Practical Signal Processing  : DFT, Filter Design, Convolution, IIR, FIR, CMSIS-DSP, Linear Systems

 15+ hours | Complete Source Code Included

 Do you want to learn practical digital signal processing (dsp) without confusion?
Here’s an overview of what you’re getting in this dsp on Arm processors course...

Understanding the foundations of signal processing without complications

Before going on to implement practical dsp algorithms from scratch, this course teaches you the foundation of signal processing step-by-step. We shall look at key topics in signal processing including:
  • Signal statistics and noise
  • ​Quantization and sampling theorem
  • ​Analog filter design
  • ​Performance metrics of the Chebyshev, Butterworth, and Bessel filters
  • ​Linear systems and their properties
  • ​Finite Impulse Response Filters (FIR)
  • ​Infinite Impulse Response Filters (IIR)
  • ​Superposition, synthesis, and decomposition
  • ​Convolution and its properties
  • ​Discrete Fourier Transform (DFT) and IDFT

Developing Digital Signal Processing Algorithms

We shall practically develop the signal processing algorithms we discussed in the theory class. Over here rather than use live signals we shall use some already acquired and generated signals to test our algorithms, to keep the focus on developing the algorithms and testing them, rather than signal acquisition.

We shall develop the following algorithms:
  • Signal statistics algorithms: signal mean, signal standard deviation, signal variance
  • ​The Convolution algorithm
  • ​The Running Sum algorithm
  • ​The Discrete Fourier Transform (DFT) algorithm
  • ​The Inverse Discrete Fourier Transform (IDFT) algorithm
We shall also implement some of these algorithms using the CMSIS-DSP library and then compare the dynamic performance     of our algorithm to that of the ones provided by CMSIS-DSP

Developing Drivers and Data Structures for Signal Acquisition

To be able to properly acquire signals from the external world and then apply our signal processing algorithms, we first need to develop analog-to-digital converter (ADC) drivers for acquiring the signals and appropriate data structures more storing and managing the signal. Over here we shall develop :
  • A bare-Metal ADC driver for acquiring the signal
  • ​A First-In-First-Out data structure for storing and managing the signal

Digital Filter Design and Implementations

We shall learn about the various types of digital filters available and then go on to implement them from scratch. We shall implement:
  • The Moving Average Filter
  • ​The Finite Impulse Response (FIR) filter
  • ​The Infinite Impulse Response (IIR) Filter
We shall also see how to design the filter kernel of the finite impulse response filters using Matlab.

Practical DSP Application on Live Signal

Over here, we shall apply all that we have learnt to process live signals from our microcontroller’s ADC
This course is more than just getting the code to work. It will teach you how to ….

Write Practical DSP Algorithms WITHOUT a fancy Engineering Degree

You will be able to understand the foundations of signal processing without the hassle of complex mathematical derivations.

Taken by 3000+ Students with 290+ Reviews

This course is the fully updated version of the 1st edition of the course. The first edition has been taken by over 3000 students with over 290 reviews. 
 Here is what one student had to say about the course :
"The information covered in this course is exactly what I needed to learn for a new assignment. Both general information about DSP as well as how to implement things on the ARM Cortex M4."
Here is what another student had to say:
"It is exciting to see how MATLAB is used in embedded systems for signal generation and filter design. The explanation here is simple and to the point. Keeps the viewer's interest captured and avoids unnecessary details."
In summary, you really have nothing to lose. Give it a try, it comes with a full money back guarantee. Hope to see you in the course.


 Lesson : Programming : Developing the Convolution Algorithm

Table of Contents

  • Setting Up
  • ​Downloading CubeIDE
  • ​Installing CubeIDE
  • ​Getting the required documentation
  • ​Getting the required package for bare-metal development
  • ​Testing the project setup
  • ​Getting Stasrted
  • ​Programming : Enabling the Floating Point Unit (FPU)
  • ​Programming : Plotting Signals using the Internal Logic Analyzer
  • ​Programming : UART Driver - Analyzing the Documentation
  • ​Programming : UART Driver - GPIO Pin Configuration
  • ​Programming : UART Driver - Protocol Paramters Configuration
  • ​Programming : UART Driver - Transmission Function
  • ​Programming : UART Driver - Testing the Driver
  • ​Programming : UART Driver - Plotting Signals
  • ​Programming : Integrating the CMSIS-DSP Library
  • ​Programming : Testing the CMSIS-DSP float32_t
  • ​Signal Statistics and Noise
  • ​​Introduction to Signals
  • ​The Signal Mean and Standard Deviation
  • ​Programming : Developing the Signal Mean Algorithm
  • ​Programming : Developing the Signal Variance Algortihm
  • ​Programming : Developing the Signal Standard Deviation Algorithm
  • ​Programming : Computing the Signal Standard Deviation using CMSIS-DSP
  • ​Quantization and The Sampling Theorem
  • ​Understanding the Sampling Theorem
  • ​The Passive Low-Pass Filter
  • ​The Passive High-Pass Filter
  • ​The Active Filter
  • ​Chebyshev, Butterworth and Bessel Filters
  • ARM Cortex-M DSP Support Features
  • ​Overview of Arm Cortex-M DSP Support Features
  • Linear Systems and Superposition
  • ​Introduction to Linear Systems
  • ​Understanding Superposition
  • ​Impulse and Step Decomposition
  • ​Convolution
  • Introduction to Convolution
  • ​The Convolution Operation
  • ​Examining the Output of Convolution
  • ​The Convolution Sum Equation
  • ​Programming : Analyzing the Input Signals of Convolution
  • ​Programming : Developing the Convolution Algorithm
  • ​Programming : Analyzing the Output Signal of Convolution
  • ​Programming : Computing Convolution using CMSIS-DSP
  • ​Programming : Developing a SysTick Driver to Measure Dynamic Efficiency
  • ​Programming : Measuring the Dynamic Performance of CMSIS-DSP (Part I)
  • ​Programming : Measuring the Dynamic Performance of CMSIS-DSP (Part II)
  • ​A closer look at the Delta function
  • ​The First Difference and Running Sum
  • ​Programming : Implementing the Running Sum Algorithm
  • Discrete Fourier Transform (DFT)
  • ​Introduction to Fourier Transform
  • ​The Discrete Fourier Transform (DFT) Engine
  • ​The Inverse Discrete Fourier Transform (IDFT)
  • ​Programming : Developing the Discrete Fourier Transform (DFT) Algorithm
  • ​Programming : Analyzing the ECG Signal for Inverse DFT
  • ​Programming : Developing the Inverse DFT Algorithm (Part I)
  • ​Programming : Developing the Inverse DFT Algorithm (Part II)
A little about me : Israel Ninsaw Gbati
Some of you may have taken some of my embedded systems courses from other online platforms. 
This is my private channel. 

I have been writing embedded firmware for years, I have built embedded devices like consumer products and robotic arms.
Till date I have 
trained over 75,000 students in embedded
 firmware development online till date
...including third year undergraduate university students in-person.

If you have taken any of my courses before you will know I start from the absolute basics, I do not assume that the student has any prior knowledge of the topic under discussion. You will also know that by the end of the course you understand the functions of every register used in developing the particular firmware or driver.

This method is the same for all of my published embedded systems courses. 

Our courses have been reviewed by 1000+ students
Here are some of the reviews

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