Firmware, Edge Computing and Connected Devices

Embedded Systems & IoT

An advanced embedded systems programme where students design, program, test, and optimize connected hardware systems using microcontrollers, sensors, wireless communication, edge computing, and TinyML.

Age12 - 16

Duration8 months

FormatOne 3-Hour Session Per Week

Pathway StageLevel 03: Specialisation

Focus Areas

ESP32 & Wireless Communication

STM32 & Precision Control

Raspberry Pi & Edge Computing

Edge AI & TinyML

System Integration & Engineering Capstone

Learning Outcomes

Coding & Automation

Architect and program advanced ESP32 and STM32-based embedded systems.

Design power-efficient wireless

Design power-efficient wireless IoT nodes using communication protocols such as MQTT, LoRa, Zigbee, and HTTP.

Implement deterministic control

Implement deterministic control systems using GPIO, interrupts, timers, PWM, ADC, DAC, and PID control.

Prototype Building

Build hybrid embedded systems that combine microcontrollers, Raspberry Pi, sensors, cameras, dashboards, and edge computing workflows.

Prototype Building

Deploy AI models on microcontrollers and edge devices using TinyML and Edge AI workflows.

Prototype Building

Integrate, test, optimize, document, and present a full embedded system prototype using engineering standards.

Tools Used

ESP32

PlatformIO

STM32

STM32CubeIDE

Raspberry Pi

Linux

Python

Edge Impulse

NodeRED

MQTT Broker

Git

Logic Analyzer

Oscilloscope

Prerequisites

Prior ESP32, Arduino, or microcontroller programming experience is required

Students should have strong MCU coding ability and be comfortable writing, testing, and debugging code independently

Basic electronics knowledge is required, including voltage, current, sensors, circuits, and safe wiring practices

Completion of Robotics and IoT or equivalent embedded/electronics experience is recommended

Students without prior Meu Labs experience can request an entry test to assess their readiness

Course Structure

Advanced hands-on lab sessions where students learn through firmware development, circuit integration, debugging, testing, benchmarking, and system optimization

Continuous progress tracking through completed engineering builds, instructor feedback, peer reviews, technical documentation, system performance, and debugging ability

Portfolio and certification outcomes with student work documented through firmware projects, system diagrams, sensor integrations, IoT prototypes, TinyML models, capstone builds, showcases, and a course completion certificate

Engineering-first learning with production-style documentation, real debugging scenarios, system architecture thinking, and performance benchmarking