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Computing environments designed to process data and respond to external events within strictly defined time constraints.
Explore 4 awesome GitHub repositories matching software engineering & architecture · Real-Time Systems. Refine with filters or upvote what's useful.
This project is a community-driven educational repository that serves as a comprehensive directory of university-level computer science video lectures. It provides a structured learning path for students and professionals, aggregating high-quality academic resources to facilitate self-paced study across a wide range of technical disciplines. The repository distinguishes itself through a collaborative maintenance model, utilizing version control workflows to allow contributors to expand and update the collection. Content is organized within a single, version-controlled document that leverages
Highlights advanced coursework on designing systems that meet strict temporal performance requirements.
This project provides a framework for managing multi-agent systems, designed to automate complex software development, infrastructure, and business workflows. It functions as a multi-agent workflow orchestrator that routes tasks to domain-specific workers while maintaining state persistence and infrastructure automation. By leveraging large language models, the system decomposes high-level objectives into actionable plans, ensuring that complex operations are executed with consistency and reliability. The framework distinguishes itself through its hierarchical agent registry and policy-driven
Evaluates system demands to support the architecture of real-time communication environments.
OTP is a concurrent programming framework and distributed computing platform that serves as the Erlang runtime environment. It provides a fault-tolerant operating environment designed for building scalable, real-time systems that manage massive amounts of simultaneous tasks through asynchronous messaging. The environment is distinguished by its use of an actor-based concurrency model and hierarchical supervision trees that automatically restart failed processes. It supports hot code loading to allow system updates without downtime and utilizes a preemptive user-space scheduler to manage light
Designed as a computing environment that responds to events within predictable timeframes for real-time workloads.
This application is a real-time computer vision system designed to identify and label objects within live video feeds, recorded files, and static images. It functions as a comprehensive framework that integrates pre-trained machine learning models with video processing pipelines to perform multi-object localization and visual data tracking. The system distinguishes itself through a multithreaded architecture that decouples frame acquisition from detection logic, ensuring the interface remains responsive during continuous analysis. It provides specialized scripts for training and optimizing cu
Balances processing speed and detection accuracy through multithreading and parallel frame analysis in a real-time vision system.