Week 2

Developed with ChatGPT

This code was developed collaboratively with ChatGPT. View development transcript

Key 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;
}

ChatGPTMario2D Arduino Code

Developed with ChatGPT

This code was developed collaboratively with ChatGPT. View development transcript

Key Features

Game Mechanics

Touch-based controls (P3=LEFT, P4=RIGHT, P0=JUMP)
Adaptive difficulty thresholds
Collision detection and physics
Coin collection and enemy interaction
Coyote time and jump buffering

Technical Implementation

OLED Display (SSD1306) rendering
Adafruit FreeTouch library integration
State machine architecture
Offscreen watchdog protection
Robust collision detection system

Code Preview

// Touch control mapping and adaptive thresholds
#define N_TOUCH   6
#define THRESHOLD 500
uint8_t touch_pins[N_TOUCH] = {2,3,4,5,6,7};  // P0..P5 -> pins (2,3,4,5,6,7)

// Game state management
enum GameState { MENU, PLAYING, PAUSED, GAME_OVER };
GameState currentState = MENU;

// Player physics and collision detection
struct Player {
  float x, y, vx, vy;
  bool onGround, canJump;
  int lives, coins;
};

// Adaptive difficulty system
#define USE_ADAPTIVE_THRESH 1
int adaptive_thresholds[N_TOUCH] = {500, 500, 500, 500, 500, 500};

void updateGame() {
  // Physics update
  player.vy += GRAVITY;
  player.y += player.vy;
  
  // Collision detection with platforms
  for (int i = 0; i < N_PLATFORMS; i++) {
    if (checkCollision(player, platforms[i])) {
      player.onGround = true;
      player.vy = 0;
    }
  }
}

CircleSquareShapeSongSwingalong Arduino Code

Developed with ChatGPT

This code was developed collaboratively with ChatGPT. View development transcript

Key Features

Multiplayer Communication

ESP-NOW wireless protocol
MAC address-based player identification
Real-time state synchronization
Ready state coordination
Dual ESP32-S3 device support

Educational Features

Shape recognition and interaction
Touch-based geometric learning
Visual feedback systems
Collaborative problem solving
Real-time synchronization

Code Preview

// ESP-NOW communication setup
#include 
#include 

// Player state structure for network synchronization
struct PlayerState {
  int x, y, vx, vy;
  bool onGround;
};

struct NetPacket {
  PlayerState st;
  bool ready;
};

// MAC address-based player identification
uint8_t macP1[] = {0xD8, 0x3B, 0xDA, 0x75, 0x05, 0xAC};
uint8_t macP2[] = {0xD8, 0x3B, 0xDA, 0x75, 0xE1, 0x9C};

// Touch controls for shape interaction
#define N_TOUCH 6
#define THRESHOLD 100000UL
int touch_pins[N_TOUCH] = {9, 8, 7, 2, 3, 1};
#define IDX_LEFT  1
#define IDX_RIGHT 0
#define IDX_JUMP  2

// ESP-NOW callback for receiving data
void OnDataRecv(const uint8_t * mac, const uint8_t *incomingData, int len) {
  memcpy(&otherState, incomingData, sizeof(otherState));
  otherReady = true;
}

QPAD21 Touch Monitor Arduino Code

A touch monitoring utility developed with ChatGPT assistance for debugging and calibrating touch sensor inputs on the QPAD21 development board. This tool provides real-time visualization of touch sensor values and helps optimize touch thresholds for embedded applications.

Developed with ChatGPT

This code was developed collaboratively with ChatGPT. View development transcript

Key Features

Touch Monitoring

Real-time touch sensor value display
Multi-channel touch input monitoring
Threshold calibration assistance
Serial output for data logging
Visual feedback on OLED display

Debugging Tools

Touch state visualization
Raw sensor value output
Threshold testing interface
Performance monitoring
Calibration guidance

Code Preview

// Touch monitoring and calibration utility
#include 
#include 
#include 

#define N_TOUCH 6
#define THRESHOLD 500
uint8_t touch_pins[N_TOUCH] = {2,3,4,5,6,7};
Adafruit_FreeTouch* touch_devices[N_TOUCH];

int touch_values[N_TOUCH] = {0,0,0,0,0,0};
bool pin_touched[N_TOUCH] = {false,false,false,false,false,false};

void updateTouchMonitor() {
  for (int i = 0; i < N_TOUCH; i++) {
    int value = touch_devices[i]->measure();
    touch_values[i] = value;
    pin_touched[i] = (value > THRESHOLD);
    
    // Serial output for debugging
    Serial.print("Touch ");
    Serial.print(i);
    Serial.print(": ");
    Serial.print(value);
    Serial.print(" ");
    Serial.println(pin_touched[i] ? "PRESSED" : "RELEASED");
  }
}

Get MAC Address Arduino Code

A utility developed with ChatGPT assistance for retrieving and displaying MAC addresses on ESP32 devices. This tool is essential for ESP-NOW communication setup, allowing developers to identify device MAC addresses for peer-to-peer networking configuration in multiplayer embedded applications.

Developed with ChatGPT

This code was developed collaboratively with ChatGPT. View development transcript

Key Features

MAC Address Retrieval

WiFi STA MAC address extraction
Bluetooth MAC address retrieval
Serial output for easy copying
OLED display visualization
Multiple format output options

ESP-NOW Integration

Device identification for networking
Peer-to-peer communication setup
MAC address validation
Network configuration assistance
Multi-device coordination

Code Preview

// MAC address retrieval utility for ESP32
#include 
#include 

void setup() {
  Serial.begin(115200);
  
  // Get WiFi STA MAC address
  uint8_t mac[6];
  esp_read_mac(mac, ESP_MAC_WIFI_STA);
  
  Serial.print("WiFi STA MAC: ");
  for (int i = 0; i < 6; i++) {
    if (i > 0) Serial.print(":");
    if (mac[i] < 16) Serial.print("0");
    Serial.print(mac[i], HEX);
  }
  Serial.println();
  
  // Get Bluetooth MAC address
  esp_read_mac(mac, ESP_MAC_BT);
  Serial.print("Bluetooth MAC: ");
  for (int i = 0; i < 6; i++) {
    if (i > 0) Serial.print(":");
    if (mac[i] < 16) Serial.print("0");
    Serial.print(mac[i], HEX);
  }
  Serial.println();
}

Project Resources

Circuit Diagrams

Microcontroller connection schematics
Sensor integration diagrams
Power supply and signal conditioning circuits

Board Design Files

Complete KiCad project files for custom PCB design, including schematics, PCB layouts, and project configurations for embedded development boards.

File Type	Description	Download
KiCad Project	Complete KiCad project file containing all design data and settings	.kicad_pro
Schematic	Circuit schematic showing component connections and electrical design	.kicad_sch
PCB Layout	Physical PCB layout with component placement and trace routing	.kicad_pcb

Design Documentation:

For detailed board design principles, USB-C connector placement, and manufacturing considerations, refer to the Board Design Documentation section above.

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.

📄 View ChatGPT Transcript 📋 View Task Prompt

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.

📄 View Full Transcript

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.

📄 View ChatGPT Transcript 🔗 GitHub Repository 🔗 QPAD-xiao Documentation

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.

AI-Assisted Training Section Development and Documentation

Used Cursor AI for comprehensive development of the training section, including soldering techniques, QPAD programming guides, board design documentation, and troubleshooting procedures. The AI assisted with creating structured subsections, implementing video demonstrations, adding download links for design files, and developing comprehensive troubleshooting guides with multimeter testing procedures.

AI-Assisted Git Workflow Optimization

Used Cursor AI to analyze file sizes and break down large changes into smaller, manageable commits to avoid exceeding Git's 25MB commit size limit. The AI helped organize files by size, created a logical commit strategy, and automated the commit and push process for better version control practices.

AI-Assisted Game Development: ChatGPTMario2D

Used ChatGPT for collaborative development of the ChatGPTMario2D platformer game, including touch control implementation, adaptive difficulty systems, collision detection, and game physics. The AI provided guidance on embedded programming concepts, state machine architecture, and optimization for the QPAD21 development board.

AI-Assisted Multiplayer Game Development: CircleSquareShapeSongSwingalong

Used ChatGPT for collaborative development of the CircleSquareShapeSongSwingalong multiplayer game, including ESP-NOW wireless communication, MAC address-based player identification, synchronized game state management, and educational content integration. The AI provided guidance on embedded networking, real-time synchronization, and touch-based geometric learning systems.

AI-Assisted Touch Monitoring Utility Development

Used ChatGPT for collaborative development of the QPAD21 touch monitoring utility, including real-time sensor value display, threshold calibration assistance, debugging tools, and serial output for data logging. The AI provided guidance on touch sensor integration, calibration procedures, and embedded debugging techniques for the QPAD21 development board.

AI-Assisted MAC Address Utility Development

Used ChatGPT for collaborative development of the get_mac_address utility for ESP32 devices, including WiFi STA and Bluetooth MAC address retrieval, serial output formatting, and ESP-NOW integration support. The AI provided guidance on embedded networking concepts, MAC address extraction methods, and device identification techniques for multiplayer embedded applications.