USB Device programming with STM32CubeIDE Training Course

This instructor-led, live training in Uzbekistan (online or onsite) is designed for engineers who want to learn how to use embedded C to program various microcontrollers based on different processor architectures (8051, ARM CORTEX M-3, and ARM9).

In this instructor-led, live training in Uzbekistan, participants will learn how to program Arduino for real-world applications, such as controlling lights, motors, and motion detection sensors. This course focuses on using real hardware components in a live lab environment (not software-simulated hardware).

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

• Program Arduino to manage lights, motors, and other devices.
• Understand Arduino's architecture, including its inputs and connectors for add-on devices.
• Integrate third-party components like LCDs, accelerometers, gyroscopes, and GPS trackers to enhance Arduino's capabilities.
• Explore various programming language options, from C to drag-and-drop languages.
• Test, debug, and deploy Arduino to address real-world challenges.

This course explores the elements of contemporary C language standards (C99, C11, and C2x) that are relevant to embedded systems, along with best practices for writing efficient and robust code. All examples and exercises are conducted using microcontrollers from the STM32 family.

This instructor-led, live training session in Uzbekistan (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who aim to deploy machine learning models on microcontrollers utilizing TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be capable of:

• Grasping the core principles of TinyML and its advantages for edge AI solutions.
• Configuring a development environment suitable for TinyML initiatives.
• Training, optimizing, and deploying AI models on power-efficient microcontrollers.
• Leveraging TensorFlow Lite and Edge Impulse to create practical TinyML applications.
• Enhancing AI models for improved power efficiency and adherence to memory limitations.

This instructor-led, live training in Uzbekistan (online or onsite) is tailored for engineers aiming to learn the principles of microcontroller design.

The Raspberry Pi is an extremely compact, single-board computer.

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

Upon completion of this training, participants will be capable of:

• Configuring an IDE (integrated development environment) to optimize development productivity.
• Programming the Raspberry Pi to manage devices such as motion sensors, alarms, web servers, and printers.
• Gaining a comprehensive understanding of the Raspberry Pi's architecture, including input and connector options for add-on devices.
• Evaluating various programming language and operating system choices available for the platform.
• Testing, debugging, and deploying the Raspberry Pi to address real-world challenges.

Audience

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

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice.

Important Note

• The Raspberry Pi is compatible with various operating systems and programming languages. This course utilizes Linux-based Raspbian as the operating system and Python as the programming language. For alternative setups, please contact us to arrange.
• Participants are responsible for purchasing their own Raspberry Pi hardware and components.

This instructor-led, live training in Uzbekistan (online or onsite) is tailored for engineers who wish to develop, load, and operate machine learning models on compact embedded devices.

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

• Install TensorFlow Lite.
• Deploy machine learning models onto embedded devices to enable functionalities such as speech detection, image classification, and more.
• Integrate AI into hardware devices independently of network connectivity.

This course introduces the common serial interfaces utilized in embedded systems, along with practical implementation details. The theoretical lessons are reinforced by hands-on exercises using STM32 microcontrollers.