Embedded System Programme Training Course

This instructor-led, live training in New Zealand (online or onsite) is designed for engineers who wish to learn how to use embedded C to program a variety of microcontrollers based on different processor architectures, including 8051, ARM Cortex M-3, and ARM9.

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.

This instructor-led, live training in New Zealand (available online or on-site) is designed for engineers and computer scientists who wish to apply the fundamentals of circuits and electronics to design devices and systems that leverage the properties of electrical components for developing hardware functionalities.

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

• Set up and configure the necessary tools and programs for circuits and circuit board development.
• Understand the basic principles underpinning circuits and electronics engineering.
• Utilise primary electronic components to construct efficient computer hardware technologies.
• Optimise electronic devices by implementing circuit analysis methods.
• Apply the fundamentals of electronics and circuits to the development of enterprise applications.

This instructor-led, live training in New Zealand (online or on-site) is designed for engineers and scientists who wish to learn and apply DSP implementations to efficiently manage various signal types and gain better control over multi-channel electronic systems.

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

• Set up and configure the necessary software platforms and tools for Digital Signal Processing.
• Understand the concepts and principles that form the foundation of DSP and its applications.
• Familiarise themselves with DSP components and apply them in electronic systems.
• Develop algorithms and operational functions using results derived from DSP.
• Utilise the basic features of DSP software platforms and design signal filters.
• Synthesise DSP simulations and implement various types of filters for DSP.

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 (available online or on-site) is designed for intermediate-level automotive engineers and technicians seeking hands-on experience in testing, simulating, and diagnosing ECUs using Vector tools such as CANoe and CANape.

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

• Understand the role and function of ECUs within automotive systems.
• Set up and configure Vector tools including CANoe and CANape.
• Simulate and test ECU communication across CAN and LIN networks.
• Analyse data and carry out diagnostics on ECUs.
• Create test cases and automate testing workflows.
• Calibrate and optimise ECUs using practical, real-world approaches.

This instructor-led, live training in New Zealand (online or onsite) is aimed at intermediate-level automotive engineers and embedded systems developers who wish to understand the theoretical aspects of ECUs, with a focus on Vector-based tools and methodologies used in automotive design and development.

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

• Understand the architecture and functions of ECUs in modern vehicles.
• Analyse communication protocols used in ECU development.
• Explore Vector-based tools and their theoretical applications.
• Apply model-based development principles to ECU design.

In this instructor-led, live training in New Zealand, participants will learn how to code using FreeRTOS as they progress through the development of a simple RTOS project using a microcontroller.

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

• Grasp the fundamental concepts of real-time operating systems.
• Familiarise themselves with the FreeRTOS environment.
• Learn how to code using FreeRTOS.
• Interface FreeRTOS applications with hardware peripherals.

This instructor-led, live training in New Zealand (available online or on-site) is designed for FPGA developers who wish to use Vivado to design, debug, and implement hardware solutions.

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

• Develop HDL systems using C code and Vivado tools.
• Generate and implement soft processors within Vivado.
• Test and simulate C code using Vivado.

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.