Advanced Embedded Systems Development Training Course

In this instructor-led, live training in New Zealand, participants will learn how to program the Arduino for real-world applications, such as controlling lights, motors, and motion detection sensors. This course assumes the use of actual hardware components in a live lab environment (rather than software-simulated hardware).

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

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

A two-day course covering all design principles with code examples, integrated with recent industrial technology; highly valuable for automotive software developers

This instructor-led, live training (online or on-site) is designed for C developers seeking to master embedded C design principles.

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

• Understand the design considerations that ensure embedded C programs are reliable
• Define the functionality of an embedded system
• Establish program logic and structure to achieve desired outcomes
• Design robust, error-free embedded applications
• Extract optimal performance from target hardware

Course Format:

• Interactive lectures and discussions
• Exercises and practical sessions
• Hands-on implementation in a live lab environment

Course Customisation Options:

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

This instructor-led, live training in New Zealand (online or onsite) is designed for 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 advantages for edge AI applications.
• Set up a development environment for TinyML projects.
• Train, optimise, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
• Optimise AI models for power efficiency and memory constraints.

This instructor-led, live training in New Zealand (delivered either online or on-site) is designed for engineers seeking to master the design principles of microcontrollers.

Raspberry Pi is a compact, single-board computer.

In this instructor-led, live training, participants will learn how to set up and program the Raspberry Pi to function 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) to maximise development productivity
• Program the Raspberry Pi to control devices such as motion sensors, alarms, web servers and printers.
• Understand the Raspberry Pi architecture, including inputs and connectors for add-on devices.
• Explore the various options available in programming languages and operating systems
• Test, debug and deploy the Raspberry Pi to solve real-world problems

Audience

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

Course format

• A blend of lecture, discussion, exercises and extensive hands-on practice

Note

• Raspberry Pi supports a range of 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 New Zealand (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, and more.
• Add AI capabilities to hardware devices without relying on network connectivity.