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LA O.G. LONGCOAT Project Proposal - Coggle Diagram
LA O.G. LONGCOAT Project Proposal
Home Facial Recognition System
Project Overview
Problem Statement
Purpose
Target Users
Expected Outcomes
System Scope
Key Features
Face Recognition Module
Face Detection Real-time Recognition Accuracy Issues
Authorized Error Handling
Notifications
Mobile App Alerts
SMS/Email Alerts
Unknown Visitor Alerts
Log Monitoring
Technologies
Hardware
Camera Module
Arduino
Smart Lock
Sensors
Smart Wi-Fi / Bluetooth
Software
OpenCV / Tensorflow
Mobile App Framework
Database
Cloud / Local Storage
API / MQTT
Methodology (Agile)
Plan
Define system requirements (IoT camera, facial recognition, alerts).
Estimate tasks (facial recognition, app dev, database setup).
Create user stories (e.g., alerts for unrecognized faces).
Develop
Build facial recognition (OpenCV or ML model).
Develop mobile app and backend APIs
Set up IoT environment and camera integration.
Test
Unit testing (camera, recognition, alerts).
Integration testing (camera → database → mobile alerts).
User testing (admin interface, app).
Design
Prototype deployment(small-scale test environtment
Monitor system functionality and performance
Review
Collect feedback from users
Address bugs and improve recognition
Sprint review and plan next steps
Deploy
Prototype deployment (small-scale test environment).
Monitor system functionality and performance.
ShelfSense
Project Overview
System Scope
Expected Outcomes
Target Users
Purpose
Problem Statement
Technologies
Hardware
Microcontroller
Load Cell
HX711 Amplifier Module
Power Supply
Platform / Stand
WiFi Router
Breadboard & Jumper Wires
Software
Arduino IDE
C / C++
Database (Firebase / MySQL)
Backend (Node.js / Python Flask)
Dashboard (ReactJS / HTML-CSS-JS, Chart.js / Recharts)
Communication Protocol
Key Features
Weight sensor to detect stock level
Automatic low-stock alerts
Basic Analytics Dashboard
Methodology (Agile)
Deploy:
Deploy in real-world setting (retail or warehouse).
Monitor system for real-time data accuracy.
Develop
Develop dashboard (real-time stock, trends).
Implement low-stock alert system
Set up IoT weight sensors and data transmission.
Design:
UI design for dashboard and alerts.
Data flow diagram.
High-level system design (sensors → cloud storage → dashboard).
Plan
Create user stories (e.g., low-stock alerts, data visualization).
Estimate tasks (sensor integration, dashboard dev).
Define system requirements (IoT sensors, dashboard, alerts).
Review:
Collect user feedback (usability, stock accuracy).
Fix bugs and enhance features (analytics, thresholds).
Sprint review and further improvements.
Test
Integration testing (sensors → dashboard → alerts).
Unit testing (sensor data, alerts, dashboard).
User testing (dashboard usability).