Advanced Embedded Systems Development Training Course

In this instructor-led, live training in Pakistan, participants will learn how to program the Arduino for real-world usage, such as to control lights, motors and motion detection sensors. This course assumes the use of 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 control lights, motors, and other devices.
• Understand Arduino's architecture, including inputs and connectors for add-on devices.
• Add third-party components such as LCDs, accelerometers, gyroscopes, and GPS trackers to extend Arduino's functionality.
• Understand the various options in programming languages, from C to drag-and-drop languages.
• Test, debug, and deploy the Arduino to solve real world problems.

A two day course covering all design principles with code examples coupled with recent industrial technology; very useful for automotive software developers

This instructor-led, live training (online or onsite) is aimed at C developers wishing to learn embedded C design principles.

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

• Understand the design considerations that make embedded C programs reliable
• Define the functionality of an embedded system
• Define the program logic and structure to obtain the desired result
• Design a reliable, error-free embedded application
• Obtain optimal performance from target hardware

Format of the Course:

• Interactive lecture and discussion
• Exercises and practice
• Hands-on implementation in a live-lab environment

Course Customization Options:

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Pakistan (online or onsite) is aimed at intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

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

• Understand the fundamentals of TinyML and its benefits for edge AI applications.
• Set up a development environment for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
• Optimize AI models for power efficiency and memory constraints.

This instructor-led, live training in Pakistan (online or onsite) is aimed at engineers who wish to learn the design principles of microcontroller design.

Raspberry Pi is a very small, single-board computer.

In this instructor-led, live training, participants will learn how to set up and program the Raspberry Pi to serve as an interactive and powerful embedded system.

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

• Set up an IDE (integrated development environment) for maximum development productivity
• Program Raspberry Pi to control devices such as motion sensor, alarms, web servers and printers.
• Understand Raspberry Pi's architecture, including inputs and connectors for add-on devices.
• Understand the various options in programming languages and operating systems
• Test, debug, and deploy the Raspberry Pi to solve real world problems

Audience

• Developers
• Hardware/software technicians
• Technical persons in all industries
• Hobbyists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• Raspberry Pi supports various operating systems and programming languages. This course will use  Linux-based Raspbian as the operating system and Python as the programming language. To request a specific setup, please contact us to arrange.
• Participants are responsible for purchasing the Raspberry Pi hardware and components.

This instructor-led, live training in Pakistan (online or onsite) is aimed at 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.