Modular AI for Real-Time Video Analytics on Edge Devices

The objective is to develop a single, modular AI model for edge devices that can perform multiple real-time video analytics tasks, including customer flow analysis, incident detection, security monitoring, and compliance tracking, while being optimized for edge hardware and ensuring GDPR compliance. Tasks and Activities: Model Development : Build a shared backbone AI model with task-specific outputs for modular functionalities. Optimize the model for edge devices by implementing quantization and pruning techniques. Edge Integration : Develop containerized modules for dynamic task activation on Balena OS. Implement real-time processing for video analytics tasks like object detection, tracking, and incident alerts. GDPR Compliance : Integrate face-blurring and anonymization features into the model for privacy protection. Performance Testing and Optimization : Test and optimize the model across various edge hardware scenarios (e.g., single or multi-camera setups). Ensure the system supports OTA updates for easy deployment and maintenance. Deliverables: A fully integrated, modular AI model capable of performing multiple tasks on edge devices. A containerized system for easy deployment, management, and updates via Balena OS. A GDPR-compliant, real-time video processing system with dynamic task activation and resource allocation.

EdgeApp Hub: Decentralized App Store for Seamless Offline & Online Access

To develop a decentralized app store platform, EdgeApp Hub , that enables users to download, update, and manage Android applications both offline and online through distributed edge nodes. The platform will leverage P2P communication, AI-driven app recommendations, and secure infrastructure for a scalable and user-friendly solution. Tasks and Activities System Architecture Design Define the technical architecture, including edge nodes, P2P protocols, distributed app repositories, and AI modules. Design data flows for app downloads, updates, and caching. Backend Development Build the backend system for managing app metadata, versioning, and P2P communication. Implement distributed databases or ledgers for app indexing and metadata storage. AI Integration Develop AI models for app recommendations, user behavior analysis, and predictive caching. Optimize models for resource-constrained edge devices. Frontend Development Create a user-friendly web/mobile app store interface with offline and online functionalities. Include features like app search, recommendations, and update notifications. Security Implementation Ensure app validation with cryptographic signatures and integrity checks. Implement end-to-end encryption and secure user authentication. Testing and Optimization Conduct functional and performance testing on edge nodes and P2P networks. Optimize the platform for low-bandwidth environments and seamless offline access. Deliverables Technical Design Document Comprehensive documentation of the architecture, system workflows, and protocols. Functional Prototypes A working prototype of the app store interface with core features. Operational P2P communication between edge nodes. AI Recommendation System A trained and deployed AI module for app recommendations and caching. Deployment Package Fully containerized application stack ready for deployment on edge nodes. User manual and installation guides. Testing Report Detailed report on testing results, including performance, security, and user feedback. This project will provide learners with hands-on experience in edge computing, decentralized systems, and AI integration, preparing them for real-world challenges in tech innovation.

Edge-Optimized Front-End Development for Distributed File Management and Collaboration

ARED Group Inc. is seeking to develop a responsive front-end interface that seamlessly integrates with their distributed edge infrastructure, utilizing Ceph for storage. The primary goal is to create a user-friendly interface that allows for efficient file management, real-time collaboration, and secure data transfer between edge nodes and end-user devices. This project will enable users to interact with the distributed storage system effortlessly, ensuring that data is accessible and manageable from any device. The interface must be responsive, ensuring optimal performance across various screen sizes and devices. The project will provide learners with an opportunity to apply their knowledge of front-end development, distributed systems, and secure data transfer protocols.

Voice-Activated AI Assistant for Enhanced Visitor Experience

Project Goal The main objective of SmartArena is to enhance the visitor experience at BK Arena by integrating a voice-activated AI assistant into the arena's web application. This AI assistant will provide real-time, personalized support to visitors, helping them navigate the arena, access information about events, amenities, and services, make reservations and orders, and receive personalized recommendations. Problem Statement Visitors to large venues like BK Arena often face challenges such as finding information about events, locating amenities, and making reservations or orders. Traditional methods of accessing this information can be cumbersome and time-consuming, leading to a suboptimal visitor experience. Expected Outcome By the end of this project, learners are expected to achieve the following outcomes: Enhanced Visitor Engagement : Visitors will have an interactive, voice-activated AI assistant that can provide instant responses to their queries, improving their overall experience. Improved Accessibility : The AI assistant will support multiple languages and voice interaction, making it accessible to a diverse audience. Increased Operational Efficiency : The AI assistant will streamline processes such as information dissemination, navigation assistance, and order management, reducing the burden on arena staff. Personalized Visitor Experience : By leveraging user data, the AI assistant will offer personalized recommendations and services, enhancing visitor satisfaction. Seamless Integration : The AI assistant will be seamlessly integrated into the existing web application and edge infrastructure, ensuring reliable and scalable performance.