DSP From Ground Up™ on ARM Processors [UPDATED] from Udemy

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 200+ 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 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.

What's inside

Syllabus

Setting Up

Downloading CubeIDE

Installing CubeIDE

Getting the required documentation

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers practical DSP algorithm development, which is essential for embedded systems engineers working with signal processing applications on ARM processors

Explores signal statistics, quantization, sampling theorem, and filter design, which are fundamental concepts in signal processing

Teaches bare-metal ADC driver development, which is crucial for interfacing with sensors and acquiring real-world signals in embedded systems

Includes filter design using Matlab, which is a common tool in signal processing for designing and analyzing filter characteristics

Involves implementing algorithms using the CMSIS-DSP library, which is optimized for ARM Cortex-M processors and enhances performance

Requires learners to download and install CubeIDE, which may pose a barrier to entry for learners unfamiliar with this specific IDE

Reviews summary

Practical dsp on arm processors

According to learners, this updated course provides a solid foundation in Digital Signal Processing (DSP) with a strong focus on practical implementation on ARM processors. Many students found the approach of explaining DSP concepts without heavy mathematical derivations to be particularly helpful. The course is described as having excellent hands-on coding exercises and useful demonstrations, especially regarding the use of the CMSIS-DSP library and bare-metal programming for signal acquisition. While largely positive, some students noted that a basic understanding of C programming and embedded systems is beneficial or even necessary to keep up with the material, suggesting it might not be truly for absolute beginners in those areas.

Some background in C/embedded systems helps.

"While it says 'From Ground Up', having some prior experience with C and embedded development is definitely an advantage."

"Might be challenging for complete beginners to embedded systems; a basic understanding of microcontrollers is assumed."

"Some parts move quickly if you're not already familiar with the ARM development environment like CubeIDE."

"I found the bare-metal driver sections easier because I had prior embedded experience."

Positive impact from recent updates.

"The updated content seems to have fixed some issues mentioned in older reviews."

"Noticed improvements in the clarity of explanations in the newer sections."

"It's good to see the course is actively maintained and improved based on feedback."

"The updated project setup instructions were very helpful."

Good integration of CMSIS-DSP functions.

"Integration of the CMSIS-DSP Library and comparison with custom implementations was very insightful."

"Learned how to leverage optimized CMSIS-DSP functions for performance."

"Working with CMSIS-DSP float32_t examples was a key takeaway."

"Using CMSIS-DSP for convolution and filtering made the concepts concrete."

Includes FIR/IIR, DFT, sampling, and filtering.

"Covers essential DSP topics like FIR/IIR filters, DFT, and the sampling theorem thoroughly."

"The sections on digital filter design and implementation were particularly strong."

"Learned about signal statistics, convolution, and working with different filter types."

"The syllabus covers a good range of foundational and practical DSP techniques."

Explains DSP concepts without complex math.

"Understanding the foundations of signal processing without the hassle of complex mathematical derivations is true."

"The explanation here is simple and to the point. Keeps the viewer's interest captured and avoids unnecessary details."

"I finally understood concepts like Convolution and DFT without getting lost in equations."

"The course successfully demystifies DSP theory for practical application."

Course excels in hands-on ARM/embedded DSP.

"The practical exercises on the ARM Cortex M4 were exactly what I needed to learn for a new assignment."

"Really appreciated the hands-on approach and implementation details for embedded systems."

"Getting to work directly with bare-metal ADC drivers and CMSIS-DSP library was invaluable."

"Applying DSP algorithms to live signals on the microcontroller was a great practical experience."

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 DSP From Ground Up™ on ARM Processors [UPDATED] with these activities:

Review Linear Systems Theory

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Reinforce your understanding of linear systems, superposition, and impulse responses, which are fundamental concepts for understanding signal processing.

Browse courses on Linear Systems

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Review definitions of linearity, time-invariance, causality, and stability.
Work through examples of determining if a system is linear or time-invariant.
Practice problems involving superposition and impulse response calculations.

Brush up on C programming

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Strengthen your C programming skills, as the course involves bare-metal programming and driver development.

Browse courses on C Programming

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Review pointers, memory allocation, and data structures in C.
Practice writing simple C programs for embedded systems.
Familiarize yourself with bitwise operations and register manipulation.

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

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Supplement your learning with a comprehensive DSP textbook that provides a broader and deeper understanding of the concepts covered in the course.

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

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Read chapters related to filter design and implementation.
Work through examples and exercises in the book.
Compare the book's approach to the course material.

Four other activities

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Implement basic DSP algorithms in C

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Solidify your understanding of DSP algorithms by implementing them from scratch in C, focusing on performance and efficiency.

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Implement convolution, DFT, and FIR filters in C.
Optimize the code for ARM processors using CMSIS-DSP library.
Compare the performance of your implementation with CMSIS-DSP functions.

Develop a simple audio processing application

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Apply your knowledge by building a practical audio processing application on an ARM processor, such as a simple equalizer or noise reduction system.

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Acquire audio data using the ADC driver developed in the course.
Implement a digital filter to process the audio signal.
Output the processed audio signal using a DAC or speaker.
Test and refine the application on an ARM development board.

Write a blog post on filter design

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Deepen your understanding of filter design by explaining the different types of filters and their applications in a blog post.

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Research different types of digital filters (FIR, IIR, Moving Average).
Explain the design process for each filter type.
Provide examples of how these filters are used in real-world applications.
Publish the blog post on a personal website or online platform.

Contribute to a DSP-related open-source project

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Enhance your skills by contributing to an open-source project related to DSP on ARM processors, such as improving existing algorithms or adding new features.

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Find an open-source DSP project on GitHub or GitLab.
Identify an area where you can contribute (e.g., bug fix, new feature).
Submit a pull request with your changes.
Respond to feedback from the project maintainers.

Career center

Learners who complete DSP From Ground Up™ on ARM Processors [UPDATED] will develop knowledge and skills that may be useful to these careers:

Signal Processing Engineer

A Signal Processing Engineer analyzes, designs, and develops algorithms for processing signals, such as audio, image, or sensor data. This digital signal processing course helps understand the fundamentals of signal processing and implement various algorithms from scratch. The course covers essential topics like signal statistics, filter design, and Fourier transforms, all of which are crucial for a Signal Processing Engineer. Furthermore, this course provides a practical approach by implementing these algorithms on ARM processors, offering hands-on experience.

See salaries and explore the career path for Signal Processing Engineer

Embedded Systems Engineer

An Embedded Systems Engineer designs, develops, and tests software and hardware for embedded systems, often involving real-time signal processing. This DSP course, with its focus on ARM processors, helps build a foundation in implementing digital signal processing algorithms on embedded platforms. You will understand how to acquire signals through ADC drivers and manage data using structures like FIFO, which are crucial for embedded systems. This course may be useful to learn how to design and implement digital filters, a core skill for any Embedded Systems Engineer.

See salaries and explore the career path for Embedded Systems Engineer

Algorithm Developer

An Algorithm Developer designs and implements algorithms for various applications, often involving signal processing, image processing, or machine learning. This course helps develop a solid understanding of digital signal processing algorithms and their practical implementation on ARM processors. The course covers essential algorithms like convolution, Fourier transforms, and digital filters. The hands-on experience gained from this course is invaluable for an Algorithm Developer. In addition, this course helps gain a practical understanding of how to optimize algorithms for embedded systems.

See salaries and explore the career path for Algorithm Developer

Internet of Things Engineer

An Internet of Things Engineer designs and develops connected devices, often involving signal processing for sensor data and communication. This course helps build a foundation in signal processing, covering essential topics like signal statistics, filter design, and Fourier transforms. Furthermore, this course helps learn how to acquire and process live signals using ARM processors, which are commonly used in IoT devices. The experience gained from implementing digital filters and signal processing algorithms makes one a more effective Internet of Things Engineer.

See salaries and explore the career path for Internet of Things Engineer

Firmware Engineer

A Firmware Engineer develops low-level software that controls hardware devices. This course provides skills in developing drivers and data structures for signal acquisition, which are directly applicable to firmware development. The course's focus on ARM processors is particularly relevant, as many embedded systems use ARM-based microcontrollers. The practical DSP application on live signals helps the Firmware Engineer to integrate signal processing capabilities into the firmware to control devices. Learning about signal processing and algorithm implementation helps become a better Firmware Engineer.

See salaries and explore the career path for Firmware Engineer

Robotics Engineer

A Robotics Engineer designs, builds, and programs robots, often requiring signal processing for sensor data analysis and control systems. This course helps in developing the skills needed to process signals acquired from sensors using ADC drivers and data structures like FIFO. The course's focus on ARM processors is beneficial, as many robots use ARM-based microcontrollers for their embedded systems. You find the digital filter design and live signal processing aspects of the course to be extremely valuable, particularly when trying to filter noise acquired from sensors.

See salaries and explore the career path for Robotics Engineer

Biomedical Engineer

Biomedical Engineers apply engineering principles to solve medical and healthcare-related problems, often using signal processing for analyzing biological signals. This course helps develop a solid understanding of digital signal processing algorithms and their practical implementation. The course covers topics like signal statistics, filter design, and Fourier transforms, which are essential for analyzing biomedical signals. With a graduate degree, you will be in a strong place to develop new and advanced biomedical solutions.

See salaries and explore the career path for Biomedical Engineer

Applications Engineer

Applications Engineers provide technical support to customers and develop applications using a company's products. This course helps gain a practical understanding of digital signal processing algorithms and their implementation on ARM processors, which is valuable for supporting customers using ARM-based products. The course's focus on ADC drivers, data structures, and digital filter design is highly relevant for developing customer applications and providing technical assistance. Overall, this course is useful to become a more effective Applications Engineer.

See salaries and explore the career path for Applications Engineer

Machine Learning Engineer

A Machine Learning Engineer develops and implements machine learning models for various applications. Signal processing techniques are often used for feature extraction and preprocessing of data before it is fed into machine learning models. This course may be useful in learning the fundamentals of signal processing, including topics like Fourier transforms and filter design. With a solid understanding of signal processing, a Machine Learning Engineer can improve the accuracy and performance of machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists analyze large datasets to extract meaningful insights and build predictive models. While not immediately obvious, a Data Scientist may find a background in signal processing useful when dealing with time-series data or sensor data. This course introduces key concepts like Fourier transforms and digital filter design. This course may be useful to understand and preprocess data effectively. You may find the practical implementations of algorithms to be a valuable asset to your career.

See salaries and explore the career path for Data Scientist

Test Engineer

A Test Engineer designs and implements tests to ensure the quality and reliability of products, often involving signal processing for analyzing test data. This course helps gain a foundation in digital signal processing, covering essential topics like signal statistics, filter design, and Fourier transforms. The practical implementation of algorithms on ARM processors provides valuable hands-on experience. This course may be useful to develop automated tests and analyze data to ensure product quality.

See salaries and explore the career path for Test Engineer

Audio Engineer

An Audio Engineer works with sound, whether it's recording, mixing, or mastering audio signals. This course, with its comprehensive look at signal processing, may be useful to understand the fundamentals needed to manipulate audio signals effectively. The course covers topics like filter design and Fourier transforms, which are essential for audio processing tasks. While the course focuses on ARM processors, the underlying principles are applicable to audio processing on various platforms. It would be useful to experiment with generating and filtering audio signals.

See salaries and explore the career path for Audio Engineer

Aerospace Engineer

Aerospace Engineers design aircraft, spacecraft, and related systems. Signal processing is crucial in many aspects of aerospace engineering, such as radar systems, navigation, and control. This course may be useful in developing a foundation in digital signal processing, covering essential concepts like filter design, convolution, and Fourier transforms. The focus on ARM processors is helpful, as many aerospace applications use embedded systems for real-time signal processing. One can leverage this knowledge to analyze sensor data and control systems in aerospace applications.

See salaries and explore the career path for Aerospace Engineer

Research Scientist

A Research Scientist conducts research in various scientific fields, often requiring advanced knowledge of signal processing for data analysis and modeling. This course may be useful in developing a strong foundation in digital signal processing, covering essential algorithms and their implementation on ARM processors. With a graduate degree, you will have a more complete foundation to publish novel research and write technical reports.

See salaries and explore the career path for Research Scientist

System Architect

System Architects design and oversee the implementation of complex systems, often requiring a broad understanding of signal processing for various components. The digital signal processing course may be useful to gain a high-level understanding of signal processing concepts and their applications. Although System Architects typically have a graduate degree, this course provides valuable insights into the design and implementation of signal processing systems. Exposure to essential algorithms further helps when communicating with signal processing engineers on how to build comprehensive system architectures.

See salaries and explore the career path for System Architect

Reading list

We've selected one books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in DSP From Ground Up™ on ARM Processors [UPDATED].

Digital Signal Processing: A Practical Guide for...

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Provides a practical and intuitive approach to understanding DSP concepts. It covers a wide range of topics, including signal processing fundamentals, filter design, and spectral analysis. It is particularly useful for students who want to gain a deeper understanding of the underlying principles of DSP without getting bogged down in complex mathematical derivations. This book valuable reference for both beginners and experienced practitioners in the field.

Digital Signal Processing: A Practical Guide for...

Paperback

$$$

Digital Signal Processing: A Practical Guide for...

Kindle Edition

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DSP From Ground Up™ on ARM Processors [UPDATED]

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