Digital Signal Processing(DSP) From Ground Up™ using Arduino from Udemy

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 Arduino board and programming language.

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

What's inside

Syllabus

Set up

Downloading Arduino

Installing Arduino

Capabilities of different arduino boards

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Uses the Arduino platform, which allows learners to implement DSP concepts in a hands-on, accessible environment, making it ideal for practical experimentation

Covers both Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters, providing a comprehensive understanding of digital filter design techniques

Explores the Fast Fourier Transform (FFT) algorithm, which is essential for efficient spectral analysis and signal processing in various applications

Requires the use of specific Arduino boards and the Arduino IDE, which may necessitate additional purchases for learners unfamiliar with the platform

Incorporates the CMSIS-DSP library, which is a standardized collection of DSP functions, potentially streamlining development and improving performance on ARM Cortex-M based microcontrollers

Focuses on developing algorithms from the ground up, which may require a significant time investment and a solid understanding of programming concepts

Reviews summary

Practical dsp implementation with arduino

According to learners, this course offers a largely practical approach to Digital Signal Processing, focusing on implementation using the Arduino platform. Reviewers appreciate that concepts are explained in plain language, successfully avoiding abstract mathematical theories, making it accessible. The course covers essential DSP algorithms like Convolution, DFT, and FFT, providing hands-on coding examples that help solidify understanding. This makes it a solid choice for those seeking applied DSP skills, particularly on microcontrollers, rather than deep theoretical knowledge.

Implements DSP on Arduino hardware.

"Implementing DSP algorithms directly on Arduino was the main draw for me."

"The course does a great job showing how to apply DSP concepts on a microcontroller like Arduino."

"Excellent for those wanting practical embedded DSP examples."

Teaches fundamental DSP algorithms.

"The sections on convolution, DFT, and FFT were clear and useful."

"Got a good handle on designing FIR and IIR filters through practical examples."

"Covers essential techniques needed for many DSP applications."

Explains concepts with minimal math.

"Appreciated the plain language explanations over complex math proofs."

"It makes DSP understandable without needing a strong math background."

"Concepts are presented in an easy-to-follow way for beginners."

Focuses on coding and implementation.

"I loved the focus on implementation with Arduino, not just theory."

"Putting DSP concepts into code right away was really helpful and practical."

"Finally, a DSP course that isn't buried in abstract math, making it very hands-on."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Digital Signal Processing(DSP) From Ground Up™ using Arduino with these activities:

Review Basic Circuit Theory

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Reinforce your understanding of fundamental circuit concepts like Ohm's Law and Kirchhoff's Laws. This will provide a solid foundation for understanding how signals are processed in electronic systems.

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Review notes and textbooks on basic circuit analysis.
Solve practice problems involving resistors, capacitors, and inductors.
Simulate simple circuits using online tools or software.

Read 'Understanding Digital Signal Processing' by Steven W. Smith

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Gain a deeper understanding of the theoretical underpinnings of DSP. This book will help you grasp the mathematical concepts behind the algorithms covered in the course.

View Digital Signal Processing: A Practical Guide... on Amazon

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Read the chapters relevant to the course syllabus.
Work through the examples and exercises in the book.
Take notes on key concepts and formulas.

Implement Convolution on Arduino

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Solidify your understanding of convolution by implementing it on the Arduino platform. This hands-on experience will help you appreciate the practical challenges of DSP.

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Write an Arduino sketch to perform convolution on sample signals.
Test the implementation with different input signals and kernels.
Optimize the code for performance on the Arduino.

Four other activities

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Create a DSP Algorithm Visualization

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Deepen your understanding of DSP algorithms by creating a visual representation of their operation. This will help you internalize the concepts and explain them to others.

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Choose a DSP algorithm, such as convolution or FFT.
Develop a visualization using a programming language or tool of your choice.
Create a video or interactive demo to explain the algorithm.

Build a Simple Spectrum Analyzer

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Apply your knowledge of DFT and FFT to build a spectrum analyzer on the Arduino. This project will integrate several concepts from the course and provide a tangible outcome.

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Acquire audio signals using a microphone connected to the Arduino.
Implement the FFT algorithm on the Arduino.
Display the frequency spectrum on an LCD screen or computer.
Add features like peak detection and frequency measurement.

Read 'The Scientist and Engineer's Guide to Digital Signal Processing'

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Expand your knowledge of practical DSP techniques and applications. This book will provide you with a broader perspective on the field.

View Digital Signal Processing: A Practical Guide... on Amazon

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Focus on chapters related to filter design and spectral analysis.
Implement some of the examples in the book on the Arduino.
Compare the results with the course materials.

Create a DSP Cheat Sheet

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Consolidate your knowledge by creating a cheat sheet of key DSP concepts, formulas, and algorithms. This will be a valuable reference for future projects.

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Review your notes, assignments, and quizzes from the course.
Identify the most important concepts and formulas.
Organize the information in a clear and concise format.

Career center

Learners who complete Digital Signal Processing(DSP) From Ground Up™ using Arduino will develop knowledge and skills that may be useful to these careers:

Signal Processing Engineer

A Signal Processing Engineer develops algorithms and systems for processing signals, such as audio, images, and sensor data. This course will be highly relevant to the Signal Processing Engineer by providing a solid foundation in the core concepts and techniques of digital signal processing. The course covers Discrete Fourier Transform, Fast Fourier Transform, and filter design (FIR and IIR), which are essential tools for signal analysis and manipulation. Because the Signal Processing Engineer utilizes these skills, the course is very important for the reader. The application of these techniques to the Arduino platform also provides practical experience in implementing signal processing algorithms.

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Acoustic Engineer

An Acoustic Engineer studies sound and vibration, and works to design solutions to noise and vibration problems. This course helps build a solid foundation by teaching the fundamentals of digital signal processing, which are essential for analyzing and manipulating audio signals. The course's coverage of Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) helps analyze the frequency components of sound, which is essential for tasks such as noise reduction and acoustic modeling. The Acoustic Engineer also utilizes filter design.

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Embedded Systems Engineer

An Embedded Systems Engineer designs, develops, and tests software and hardware for embedded systems, which are specialized computer systems within larger devices. This course helps build a foundation by providing hands-on experience with the Arduino platform, which is commonly used in embedded systems development. The course's focus on digital signal processing techniques, such as Convolution Kernel, Discrete Fourier Transform (DFT), and filter design (FIR and IIR), are highly relevant for processing sensor data in real-time embedded applications. Furthermore, the course covers the Fast Fourier Transform (FFT) algorithm which is helpful for the Embedded Systems Engineer.

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Instrumentation Engineer

An Instrumentation Engineer designs, develops, and maintains instruments and systems used for measurement and control in various industries. This course helps build a good foundation by covering signal processing techniques that are essential for processing data from sensors and other instruments. The course's focus on Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), and filter design (FIR and IIR) helps analyze and filter noisy signals, improving the accuracy and reliability of measurements. The Instrumentation Engineer will also implement signal processing algorithms on microcontrollers such as Arduino.

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Firmware Engineer

A Firmware Engineer develops low-level software that controls hardware devices. This course helps build a foundation by teaching how to implement digital signal processing algorithms on the Arduino platform, which is often used for prototyping and developing firmware for embedded systems. The course's focus on the Convolution Kernel algorithm, Discrete Fourier Transform (DFT), and filter design (FIR and IIR) are directly applicable to tasks such as signal processing, data acquisition, and motor control in firmware applications. The course helps build a foundation for the future Firmware Engineer.

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Research Scientist

A Research Scientist conducts research in a specific field, often requiring a Master's degree or PhD. The Research Scientist may discover new knowledge and develop new technologies. This course helps promote digital signal processing techniques that can be applied to various research areas, such as audio processing, image processing, and data analysis. The course's coverage of Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), and filter design (FIR and IIR) are valuable tools for analyzing and processing data in research projects. Furthermore, it helps them test the Convolution Kernel algorithm.

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Robotics Engineer

A Robotics Engineer designs, builds, programs, and tests robots and robotic systems. This course may be useful by covering digital signal processing techniques that are essential for processing sensor data, controlling motors, and implementing feedback loops in robotic systems. The course’s coverage of filter design, including FIR and IIR filters, helps smooth sensor data and reduce noise, improving the performance of robot control algorithms. Furthermore, the techniques for developing and testing algorithms related to Convolution Kernel and Discrete Fourier Transform will be beneficial to the Robotics Engineer. The work of a Robotics Engineer often involves analyzing signals.

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Audio Engineer

An Audio Engineer records, edits, mixes, and masters audio for various applications, including music, film, and games. The course may be useful by providing a practical understanding of digital signal processing techniques that are used in audio processing. The course's coverage of Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) helps analyze audio signals in the frequency domain, which is essential for tasks such as equalization and audio effects design. In particular, the work of Audio Engineer requires understanding of filter design, and this course covers Finite Impulse Response and Infinite Impulse Response filters.

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Control Systems Engineer

A Control Systems Engineer designs and implements systems that automatically regulate and control dynamic systems, such as aircraft, robots, and industrial processes. The course may be useful by covering signal processing techniques that are used to analyze and filter sensor data in control systems. The course’s coverage of filter design, including FIR and IIR filters, helps smooth noisy sensor data and improve the performance of control algorithms. As a Control Systems Engineer, the reader will find the Convolution Kernel and Discrete Fourier Transform portions of the course highly important.

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Machine Learning Engineer

A Machine Learning Engineer develops and implements machine learning algorithms and systems. This course may be useful by providing a foundation in signal processing techniques that can be applied to feature extraction and data preprocessing in machine learning applications. The course's coverage of Discrete Fourier Transform and Fast Fourier Transform helps analyze the frequency components of data, which can be used to extract features for machine learning models. The Machine Learning Engineer also frequently uses filter design for processing data.

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Wireless Communications Engineer

A Wireless Communications Engineer designs and develops wireless communication systems, such as cellular networks and Wi-Fi. This course may be useful by providing a foundation in signal processing techniques that are used in wireless communication. The course's coverage of Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) helps analyze and process signals in the frequency domain, which is essential for tasks such as modulation, demodulation, and channel equalization. Further, the Wireless Communications Engineer will want to design Finite Impulse Response and Infinite Impulse Response filters.

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Biomedical Engineer

A Biomedical Engineer applies engineering principles to solve problems in medicine and biology. This course may be useful by covering signal processing techniques that can be applied to biomedical signal analysis, such as electrocardiography (ECG) and electroencephalography (EEG). The course's coverage of Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) helps analyze the frequency components of biomedical signals, which can reveal important diagnostic information. The Biomedical Engineer might also find filter design helpful.

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Data Analyst

A Data Analyst collects, cleans, and analyzes data to identify trends and insights. This course may be useful by covering signal processing techniques that can be applied to time series data analysis. The course's coverage of Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) helps analyze the frequency components of time series data, which can reveal patterns and trends that are not apparent in the time domain. Similarly, the Data Analyst applies filter design to smooth noisy data. The Data Scientist also benefits from knowledge of signal mean and standard deviation.

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Data Scientist

A Data Scientist analyzes large datasets to extract insights and build predictive models. This course may be useful by teaching digital signal processing techniques that can be applied to time series data analysis. The course's coverage of Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) helps analyze the frequency components of time series data, which can reveal patterns and trends that are not apparent in the time domain. Knowledge of signal mean and standard deviation is helpful. Similarly, the Data Scientist applies filter design to smooth noisy data, and the Fast Fourier Transform algorithm.

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Image Processing Engineer

An Image Processing Engineer develops algorithms and systems for processing and analyzing images. This course may be useful by covering signal processing techniques that can be applied to image processing. The course's coverage of Convolution Kernel may help implement image filtering and edge detection algorithms. Furthermore, the Image Processing Engineer may use Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) to analyze the frequency components of images. The potential Image Processing Engineer will use filter design to remove noise and enhance image quality.

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Digital Signal Processing(DSP) From Ground Up™ using Arduino

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Reviews summary

Practical dsp implementation with arduino

Activities

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