Liquid Galaxy project

Mentors: Student: Yasmina Ramadan Sevdanova

The objective of this project is to develop an application that connects to a Liquid Galaxy installation. The application will be developed using Dart/Flutter and will be structured into two main components: a backend and a frontend. The CHE project offers a visual and interactive experience that allows users to explore different historical sites in a city or a country. In my case, the project is focused on the city of Lleida. The CHE project is developed on the Liquid Galaxy platform, where Google Earth is displayed continuously and synchronously creating a unified panoramic view. On top of this visualization, KML files are integrated to display information about the different historical places in the city, allowing users to easily locate and explore them in a simple and intuitive way.

Mentors: Student: Mahmoud Mohammed

Liquid Galaxy is an open-source panoramic multi-display system used in museums, universities, and science centers worldwide. Its entire installation infrastructure install.sh and all supporting configuration files was written for Ubuntu 16.04 LTS, which reached End of Life in April 2021. As a result, Liquid Galaxy cannot be installed on any currently supported Ubuntu version, leaving existing deployments without security patches and blocking new ones entirely. This project is a systematic, layer-by-layer migration of Liquid Galaxy to Ubuntu 24.04 LTS (Noble Numbat). Rather than patching each broken line individually, I will introduce an OS abstraction layer a lib/ directory of functions that encapsulate every OS-dependent operation, from network detection and display manager configuration to package management and service control. This ensures the system can survive future Ubuntu LTS releases without requiring a full rewrite each time. Deliverables: 1) Updated install.sh working end-to-end on Ubuntu 24.04 2) lib/ OS abstraction layer with testable, version-aware functions 3) GitHub Actions CI pipeline 4) Post-install smoke test suite 5) MIGRATION.md, TROUBLESHOOTING.md, and updated README.md with complete architecture documentation.

Mentors: Student: Bhoomi Shivhare

EcoGrid Intelligence: AI-Driven Climate Resilience Analysis for Global Energy Infrastructure on Liquid Galaxy is an AI-driven geospatial analysis system designed to proactively identify global energy infrastructure at risk of operational failure due to severe climate events such as heatwaves, droughts, and wind anomalies. It addresses the problem of critical infrastructure and meteorological data being siloed, which prevents stakeholders from achieving a unified, spatial understanding of risk. The solution dynamically fuses live climate anomalies with global energy infrastructure maps to create an interactive analytical canvas on the Liquid Galaxy platform. The EcoGrid Intelligence system follows a layered architecture: Data Sources and Risk Assessment: Climate Data: Streams live and forecasted climate anomalies, including temperature deviations, precipitation extremes, and wind intensity, from the Open-Meteo API. Infrastructure Data: Geospatial coordinates, generation type (hydro, nuclear, solar), and operational capacity are sourced from the Global Power Plant Database. Risk Model: A deterministic model computes a Climate Vulnerability Score (CVS) by evaluating localized anomaly intensity against the plant type's operational sensitivities. AI Orchestration: Google’s Gemini models act as the system’s brain to autonomously determine region-relevant climate anomalies, trigger API calls, interpret the resulting CVS metrics, and structure the output for spatial rendering. Visualization: The results are converted into Keyhole Markup Language (KML), generating elements like Placemark, LookAt, and FlyTo for native, synchronized rendering and camera guidance on the Liquid Galaxy multi-screen rig. Outputs: Primary displays render immersive climate anomaly heatmaps and color-coded nodes indicating vulnerability levels, while secondary screens display dynamically generated HTML content and AI-driven insights.

Mentors: Student: Darpan Baviskar

The Liquid Galaxy ecosystem currently faces an unstructured and fragmented onboarding process. Crucial development information is scattered across various wikis and forums, leading to a steep learning curve and persistent architectural rule violations by new developers. Furthermore, the absence of an interactive sandbox makes it difficult for contributors to safely experiment with code and receive instant feedback, while advanced capabilities like 3D KML modeling remain heavily underutilized due to a lack of guided, hands-on tutorials. LG Interactive Onboarding radically overhauls contributor training by transforming the Liquid Galaxy rig itself into an immersive, interactive classroom. Operating on a "phone-as-driver, rig-as-canvas" philosophy, the system uses a smartphone application as a remote control to project educational content, live code exercises, and data visualizations directly onto the rig's 5-screen panoramic display. The platform integrates multi-modal learning—combining animated visual diagrams, auditory voice narration, and kinesthetic hands-on coding—with a privacy-conscious, on-device Gemini AI Mentor. This enforces core development best practices from day one and significantly reduces the manual onboarding workload for organization mentors.

Mentors: Student: Vinayak_Dhaka

This project proposes an interactive geospatial storytelling platform that visualizes rice agriculture across india using the liquid galaxy multi-screen visualization system. Rice is one of the most important crops in India, and understanding its geographical distribution, seasonal cycles, irrigation patterns, and production trends can help students, researchers, and the public better understand agricultural systems.