Mujib Ur Rahman@mujib2660

Reduced Food Costs and Dependency: By empowering individuals to grow their own food, governments can indirectly reduce the strain on food supply chains, potentially leading to lower food prices and reduced reliance on external sources. Improved Public Health: Homegrown, organic produce can contribute to healthier communities, reducing healthcare costs associated with diet-related illnesses. Community Resilience: Urban gardening can foster a sense of community, strengthen social bonds, and create more resilient neighborhoods, which can indirectly reduce the burden on government services. New Social Programs may be created to promote the planting, harvesting and communal exchange of urban food, providing access to seeds and other resources. Thus, our GRUF App shall play a very important role, leveraging the power of xAI Grok to help us advance towards a much healthier and stronger economy.

The Daily Network Outage Forecaster: Empowering Underserved Communities During Climate Challenges Introduction Network connectivity has become an essential lifeline for education, communication, and emergency response. However, in underserved areas, where infrastructure is fragile, weather-induced network outages can have severe consequences. These disruptions exacerbate existing inequalities and significantly impact vulnerable communities, especially during emergencies. Events like the Los Angeles wildfires have shown how sudden environmental changes can sever critical communication lifelines, leaving entire communities isolated when connectivity is needed most. To address these challenges, the Daily Network Outage Forecaster offers a proactive solution. This AI-powered tool predicts potential network disruptions by analyzing real-time weather data, historical outage records, and regional infrastructure vulnerabilities. It enables schools, community centers, and local authorities to prepare effectively, ensuring systems dependent on connectivity remain operational during critical times.

The Autonomous Space Health Guardian (ASHG) is an AI-driven system designed to monitor and optimize astronaut health during space missions. It leverages machine learning models to analyze biometric data, predict potential health risks, and provide real-time insights for preventive care. The system integrates a Flask-based API for AI-powered predictions and a Streamlit web app for an intuitive user interface. Using a combination of deep learning models and real-time monitoring, ASHG ensures astronauts receive proactive health recommendations, reducing the risk of medical emergencies in space. The AI agent can detect anomalies in vital signs, predict potential illnesses, and suggest personalized countermeasures. Additionally, it can adapt to environmental factors such as microgravity and radiation exposure, enhancing astronaut well-being. Designed for NASA, SpaceX, and deep-space exploration, this innovative system can revolutionize space health management, enabling safer and more efficient long-duration missions

- Photogrammetry Processing: Automatically processes drone images to create orthomosaic maps. - Modern Image Alignment: Uses edge detection, feature matching, and static point analysis for precise image placement. - Interactive Map Visualization: Displays processed images as overlays on interactive maps using Folium. - Multi-Resolution Tiling: Supports efficient map rendering at multiple zoom levels. - Performance Optimizations:Utilizes parallel processing and optimized image handling for large datasets. - Customizable Overlays: Enhanced overlays with popups, markers, and custom CSS for improved user experience. ## Usage - Upload drone images via the web interface. - Start photogrammetry processing from the dashboard. - View the generated orthomosaic and overlays on the interactive map. Key Technologies - Python, Django for backend and web framework - OpenCV, NumPy, Pillow for image processing - Folium** for map visualization - Concurrent Futures for parallel processing Advanced Techniques - Edge-based and Feature-based Alignment: Ensures accurate image stitching using Canny edge detection and ORB/SIFT feature matching. - Static Point Analysis: Fine-tunes image placement by analyzing overlapping features, similar to panorama stitching. - Multi-Resolution Tiles: Generates smaller, optimized PNG overlays for efficient map loading.