The Muggy Weather Robotics Duo

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 The Muggy Weather Robotics Duo A C++ System That Thinks, Feels (Sensors!), and Acts Humidity is like the quiet character in the weather story that actually runs the show. On muggy days, everything feels heavier—breathing, drying laundry, running machines, even keeping a data center cool. For people, it’s about comfort and health; for machines, it’s about performance and reliability; for plants and buildings, it’s about moisture balance and mold risk. In robotics and automation, muggy weather isn’t just a nuisance—it’s a signal . It tells your systems when to ventilate, when to dehumidify, when to throttle physically demanding tasks, and when to take preventative maintenance actions. Today, we’ll build a two-program C++ system that “understands” muggy weather: Program A — sensor_hub.cpp A sensor-side program that generates (or ingests) a live stream of environmental data (temperature, relative humidity, pressure, CO₂, VOCs). Think of it as your robotic nose and skin , con...
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C++ Smart Document Summarizer and Analyzer

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 C++ Smart Document Summarizer and Analyzer

This project consists of two integrated C++ programs designed to process documents efficiently.

Program 1: Document Text Extractor and Formatter

This component extracts text from images (e.g., scanned PDFs, handwritten notes) and structures it into a readable format.

Features:

  • Uses OCR (Optical Character Recognition) to extract text.
  • Formats the extracted text into structured documents with headings, paragraphs, and bullet points.
  • Saves the processed text into a MySQL database for further use.

Program 2: AI-Powered Document Summarizer

This component analyzes and summarizes the extracted text using Natural Language Processing (NLP).

Features:

  • Categorizes documents into academic, legal, business, casual, etc.
  • Highlights key points and generates a concise summary.

Technology Stack

  • C++ (Core Logic & GUI) – Uses a lightweight GUI library like Qt.
  • Tesseract OCR – Extracts text from images.
  • MySQL – Stores extracted and summarized text.
  • Pre-trained NLP Model – Enhances text summarization.

How They Work Together

  1. The Text Extractor scans and extracts text from documents.
  2. The extracted text is stored in a database.
  3. The Summarizer retrieves the text, processes it using NLP, and generates a summary.

This integrated system provides an efficient way to extract, structure, and summarize documents using C++ and AI-powered techniques.

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