USB Device programming with STM32CubeIDE Training Course

This instructor-led, live training in Serbia (online or onsite) is designed for engineers eager to master embedded C programming for various microcontrollers based on diverse processor architectures, including 8051, ARM CORTEX M-3, and ARM9.

In this instructor-led, live training in Serbia, participants will learn how to program Arduino for practical applications, such as controlling lights, motors, and motion detection sensors. The course requires the use of real hardware components within a live lab environment, rather than software-simulated hardware.

Upon completing this training, participants will be able to:

• Program Arduino to control lights, motors, and other devices.
• Understand Arduino's architecture, including inputs and connectors for add-on devices.
• Integrate third-party components such as LCDs, accelerometers, gyroscopes, and GPS trackers to extend Arduino's functionality.
• Understand various programming language options, ranging from C to drag-and-drop interfaces.
• Test, debug, and deploy Arduino solutions to address real-world challenges.

This course explores modern C language dialects (C99, C11, C2x) as they apply to embedded programming, providing guidelines for efficient and error-resistant coding practices. All examples and exercises are implemented on microcontrollers from the STM32 family.

This instructor-led, live training in Serbia (online or on-site) is designed for intermediate-level embedded systems engineers and AI developers aiming to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be able to:

• Grasp the core principles of TinyML and its advantages for edge AI applications.
• Configure a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Leverage TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
• Optimize AI models to meet power efficiency and memory limitations.

This instructor-led, live training in Serbia (online or on-site) is designed for engineers who want to master the fundamental principles of microcontroller design.

The Raspberry Pi is a compact, single-board computer.

In this instructor-led live training, participants will learn how to configure and program the Raspberry Pi to function as an interactive and robust embedded system.

By the conclusion of this training, participants will be able to:

• Configure an Integrated Development Environment (IDE) to maximize development productivity.
• Program the Raspberry Pi to control devices such as motion sensors, alarms, web servers, and printers.
• Grasp the architecture of the Raspberry Pi, including its inputs and connectors for add-on devices.
• Understand the various options available in programming languages and operating systems.
• Test, debug, and deploy the Raspberry Pi to address real-world problems.

Audience

• Developers
• Hardware/software technicians
• Technical professionals across all industries
• Hobbyists

Format of the course

• A mix of lectures, discussions, exercises, and intensive hands-on practice.

Note

• The Raspberry Pi supports a wide range of operating systems and programming languages. This course will utilize Linux-based Raspbian as the operating system and Python as the programming language. For a specific setup, please contact us to arrange.
• Participants are responsible for purchasing the Raspberry Pi hardware and components themselves.

This instructor-led, live training in Serbia (online or onsite) targets engineers who wish to write, load, and run machine learning models on very small embedded devices.

By the end of this training, participants will be able to:

• Install TensorFlow Lite.
• Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
• Add AI to hardware devices without relying on network connectivity.

This course explores the common serial interfaces utilized in embedded systems, along with practical implementation details. The theoretical overview is supported by hands-on exercises employing STM32 microcontrollers.