Week 2
Embedded Programming · HTMAA 2025
Week Highlights
Key achievements and insights from the embedded programming week, including successful microcontroller programming, sensor integration, and the development of the ReactionAge reaction time measurement system.
Hello World! Success
First successful embedded programming implementation with microcontroller and display
ReactionAge Demo
ReactionAge system in action - measuring reaction time and predicting age
Embedded Programming Design Trades
Comprehensive analysis of critical design decisions in embedded system development
💻 Design Files Available
Download Arduino code, view circuit diagrams, and access all embedded programming assets from this week's assignments
📄 View Design FilesTable of Contents
Course Content
Training & Documentation
Embedded Programming
This week focuses on embedded programming fundamentals, exploring how to program microcontrollers and create interactive electronic systems. We'll dive into the world of digital electronics, sensors, and real-time programming to build responsive devices that can sense, process, and react to their environment.
This Week's Goals
- Master Embedded Programming
Learn microcontroller programming, sensor integration, and real-time system design - Build Interactive Systems
Create responsive electronic devices that can sense and react to environmental inputs - Develop ReactionAge Project
Implement a reaction time measurement system based on research methodologies
Assignments
- Group Assignment
Collaborative embedded programming project focusing on sensor integration and real-time data processing - Individual Assignment: ReactionAge
Build a reaction time measurement system based on research methodologies from cognitive science literature
Tools & Materials
- Microcontrollers
Arduino, ESP32, or similar development boards - Development Environment
Arduino IDE, PlatformIO, or VS Code with embedded extensions - Sensors & Components
Buttons, LEDs, displays, accelerometers, and other input/output devices - Prototyping Tools
Breadboards, jumper wires, resistors, and basic electronic components
Training Documentation
Essential training materials and resources for embedded programming fundamentals, microcontroller setup, and sensor integration techniques.
Electronics & Soldering Training
Soldering Fundamentals
Comprehensive guide to soldering techniques for embedded electronics, covering both wire soldering and solder paste methods.
Wire Soldering
- Temperature: 720-800°F
- Melt pad onto electrode first
- Anchor arms for precision control
- Use appropriate tip size for part
- Work from low to high profile parts
Solder Paste
- Use gloves for protection
- Highest magnification possible
- Temperature: 270-330°C
- Heat all sides together
- Required for SMD components
Safety First:
Always wear safety glasses and use proper ventilation. Lead-free solder requires extra caution due to flux crystallization at high temperatures.
How to Solder (Almost) Anything
Step-by-step video demonstrations of essential soldering techniques for embedded electronics, covering both wire soldering and surface-mount component assembly.
Wire Soldering Technique
Learn the proper technique for soldering wires to components, starting with one edge to keep the part in place, then soldering the rest easily by keeping your wrist stable and the solder tip on the pad to heat it up with the part, and slowly bringing in solder.
Wire soldering demonstration showing proper technique and wrist stability
Solder Paste Assembly
Complete process for surface-mount component assembly: apply solder paste first, place the component, then heat up with a heat gun to create strong, reliable connections.
Applying solder paste to PCB pads
Placing microcontroller on prepared solder paste
Using heat gun to reflow solder paste and complete the connection
Pro Tips:
For detailed soldering guides and advanced techniques, refer to the Electronics & Soldering Training section above for comprehensive documentation and resources.
Helpful Documentation
Comprehensive documentation covering embedded programming fundamentals, electronics theory, and practical implementation guides.
HTMA Electronics Slides
Official course slides covering microcontroller architecture, I/O techniques, sensor integration, and real-time system design.
Key Topics Covered
- Microcontroller Programming
Architecture and fundamentals - Sensor Integration
Data processing methods - Real-time Systems
Design principles and optimization
Group Assignment: Embedded Programming Design Trades
Comprehensive analysis of design tradeoffs in embedded programming, covering language selection, development workflows, and microcontroller choices. This collaborative project examines the critical decisions that shape embedded system development and their impact on project success.
Design Tradeoffs Summary
Comprehensive comparison of microcontroller architectures and their tradeoffs in embedded programming
Language Tradeoff
Programming language tradeoff analysis showing performance vs development speed considerations
As illustrated in the language tradeoff diagram above, the choice of programming language involves critical tradeoffs between performance and development speed. The analysis shows how different languages balance factors like memory efficiency, execution speed, development time, and ecosystem support for embedded system development.
High Performance Languages
- C/C++: Maximum control, minimal overhead
- Rust: Memory safety with performance
- Assembly: Direct hardware control
Rapid Development Languages
- Python: Fast prototyping, rich libraries
- JavaScript: Web integration capabilities
- MicroPython: Python for microcontrollers
Development Workflow Tradeoff
Development workflow tradeoff analysis comparing different methodologies and their impact on project outcomes
The development workflow tradeoff diagram above demonstrates how different methodologies balance project timeline, code quality, and team collaboration. This visual analysis compares traditional waterfall approaches with agile methodologies, showing the impact of various toolchains, testing strategies, and deployment processes on embedded system development success.
Traditional Approaches
- Waterfall: Sequential, documentation-heavy
- V-Model: Testing integrated with design
- Spiral: Risk-driven iterative approach
Modern Approaches
- Agile: Iterative, customer-focused
- DevOps: Continuous integration/deployment
- Test-Driven Development: Quality-first
Microcontroller Tradeoff
Microcontroller tradeoff analysis showing performance vs power consumption and cost considerations
The microcontroller tradeoff diagram above illustrates the critical balance between performance, power consumption, and cost in embedded system design. This visual analysis evaluates different architectures, processing capabilities, memory configurations, and peripheral options, providing a clear framework for selecting the optimal platform based on specific application requirements.
Low-Power Options
- ARM Cortex-M0+: Ultra-low power
- PIC: Simple, cost-effective
- MSP430: Energy-efficient design
High-Performance Options
- ARM Cortex-M4: DSP capabilities
- ESP32: WiFi/Bluetooth integrated
- STM32: Rich peripheral set
Use Case Analysis
Workflow examples showing different development approaches for ARM and RP2040 architectures
The example workflow table above provides specific recommendations for different embedded system applications, showing how the tradeoff analyses from the previous sections translate into practical implementation strategies. This comprehensive table demonstrates the relationship between project requirements and optimal technology choices.
Key Findings
As demonstrated in the workflow table above, ARM workflows emphasize professional-grade debugging with industry-standard tools, while RP2040 workflows emphasize accessibility and flexibility through drag-and-drop deployment and unique PIO capabilities. The comprehensive architecture comparison shows that optimal design choices depend heavily on project constraints, performance requirements, and development timeline. High-performance applications favor ARM Cortex-M with professional toolchains, while rapid prototyping benefits from RP2040's flexible development environment and built-in features.
Individual Assignment: ReactionAge
Build a reaction time measurement system based on research methodologies from cognitive science literature. This project combines embedded programming with scientific measurement principles to create a tool for studying human cognitive performance.
Core Features
- Stimulus Generation
Visual/auditory stimuli with precise timing - Response Detection
Accurate measurement using buttons/sensors - Data Analysis
Statistical processing and visualization
Research Foundation
Based on PLOS ONE study (2017) examining reaction time variability and cognitive decline.
Repository: github.com/saleemaldajani/reaction-timer-saleemaldajani
Reaction time model showing the relationship between age and cognitive performance, based on research from PLOS ONE (2017)
Related Research
Parts List / Bill of Materials
Download BOM| # | Image | Part | Description | Qty | Unit Price | Datasheet | Price |
|---|---|---|---|---|---|---|---|
| 1 |
|
ATmega32 Microcontroller | 8-bit AVR MCU, 32 KB Flash, 2 KB SRAM, ADC, timers, USART | 1 | $7.50 | $7.50 | |
| 2 |
|
0.96″ OLED Display Module (SSD1306) | 128×64 mono OLED, I²C/SPI interface | 1 | $2.50 | $2.50 | |
| 3 |
|
Male Header Pins (2.54 mm) | Breakaway straight male headers | 2 | $0.20 | $0.40 | |
| 4 |
|
Dual-Row Header (2×5, 2.54 mm) | 10-pin dual-row header, unshrouded | 1 | $0.50 | $0.50 | |
| 5 |
|
SMD MOSFET (AO3400A example) | N-channel MOSFET, SOT-23 | 2 | $0.15 | $0.30 | |
| 6 |
|
SMD Capacitor (MLCC 0603 example) | Ceramic capacitor, value TBD | 5 | $0.03 | $0.15 | |
| 7 |
|
SMD Resistors – 1 kΩ (code '1001') | Precision SMD resistors | 5 | $0.02 | $0.10 | |
| 8 |
|
SMD LED (0603, color TBD) | Indicator LED | 2 | $0.05 | $0.10 | |
| 9 |
|
USB-C Receptacle | Surface-mount USB Type‑C connector | 1 | $0.60 | $0.60 | |
| 10 |
|
Custom PCB (Capacitive-Touch Board) | Project-specific PCB | 1 | $10.00 | N/A | $10.00 |
| TOTAL | $22.15 | ||||||
Complete bill of materials for the ReactionAge embedded programming project. All prices are in USD and subject to supplier variations.
Nintendo Switch Controller Training for Toddlers
An interactive training module designed for young children, utilizing Nintendo Switch controller input to create an engaging letter-matching game that develops motor skills and cognitive recognition.
Game Mechanics
- Button Mapping
A, B, X, Y in inverted layout - Visual Stimulus
Flashing letters with bright colors - Progressive Difficulty
Adapts based on performance
Educational Benefits
- Motor Skills
Hand-eye coordination - Letter Recognition
Alphabet learning - Attention Building
Focus and concentration
Technical Features
- Controller Support
Pro Controller & Joy-Con - Display System
Large, colorful letters - Progress Tracking
Analytics & feedback
Design Files
Source code, circuit diagrams, and documentation files for embedded programming projects.
ReactionAge Arduino Code
Download .inoKey Features
Hardware Integration
- OLED Display (SSD1306)
- RGB LED indicators
- Capacitive touch sensors
- State machine architecture
Reaction Time Analysis
- Multiple prediction models
- Statistical data collection
- CSV export functionality
- Real-time age prediction
Code Preview
// Reaction time prediction models
const float A_POOLED = -35.392393f, B_POOLED = 0.1930446f;
const float A_MALE = -58.123019f, B_MALE = 0.2640664f;
const float A_FEMALE = -31.603679f, B_FEMALE = 0.176111015f;
float predictAgeFromRT(unsigned long rt_ms) {
float x = (float)rt_ms;
float a, b, c;
switch (currentModel) {
case SX_MALE: a=A_MALE; b=B_MALE; c=C_MALE; break;
case SX_FEMALE: a=A_FEMALE; b=B_FEMALE; c=C_FEMALE; break;
default: a=A_POOLED; b=B_POOLED; c=C_POOLED; break;
}
return a + b*x + c*x*x;
}Project Resources
Circuit Diagrams
- Microcontroller connection schematics
- Sensor integration diagrams
- Power supply and signal conditioning circuits
Reflections & Learnings
[Reflection notes placeholder.]
Contributions
Acknowledgments for collaborative support and technical assistance during the embedded programming project development.
Technical Support & Collaboration
Eghosa Ohenehn
Contributed valuable insights and technical discussions about microcontroller tradeoffs during the group assignment development. Provided expertise in evaluating different microcontroller architectures and their suitability for various embedded programming applications.
Anthony Pennes
Engaged in extensive discussions throughout the embedded programming design trades section, providing valuable technical insights and collaborative support during the group assignment development process.
Quentin Bolsée
Participated in numerous office hours discussions about embedded programming design trades, contributing significantly to the technical analysis and collaborative development of the group assignment content.
Special thanks to the collaborative learning environment that enabled knowledge sharing and technical problem-solving throughout the embedded programming week.
Ethical AI Use
Transparent documentation of AI assistance used in this week's work, following course guidelines for ethical AI usage.
AI-Assisted Template Generation and Task Planning
Used ChatGPT to generate the weekly template HTML structure and styling framework, and created structured task prompts for week 2 embedded programming content development.
AI-Assisted Content Development and Page Structure
Used Cursor AI for iterative content development, page structure optimization, and aesthetic improvements. The AI helped with HTML/CSS implementation, content organization, and maintaining consistent styling across the embedded programming documentation.
AI-Assisted BOM Table Implementation
Used Cursor AI to implement the comprehensive BOM table with clickable images, datasheet links, and full-width responsive design. The AI helped with HTML table structure, CSS styling, and integration of CSV data into an aesthetically pleasing table format.
AI-Assisted Image Recognition and BOM Creation
Used ChatGPT for image recognition of electronic components to identify parts and construct the bill of materials CSV file. The AI analyzed component images and provided detailed part specifications, descriptions, and pricing information for the embedded programming project.
AI-Assisted Project Content Updates and Documentation
Used Cursor AI for comprehensive project content updates, documentation improvements, and HTML structure optimization. The AI assisted with content organization, styling consistency, and integration of multimedia elements for the embedded programming documentation.
AI-Assisted Arduino Programming and Code Development
Used ChatGPT for Arduino programming assistance based on the ReactionAge project code from GitHub repository and Quentin Bolsée's QPAD documentation available on the course website embedded system assignment page. The AI provided guidance on embedded programming concepts, code optimization, and integration with the QPAD-xiao board design.
AI-Assisted Group Assignment Content Development and Table Implementation
Used Cursor AI for comprehensive development of the embedded programming design trades group assignment section. The AI assisted with creating structured subsections for language, development workflow, and microcontroller tradeoffs, implementing professional HTML tables with data extracted from PowerPoint presentations, and integrating download links for PDF and PPTX files.
