Comprehensive smart city technology Tools for Every Need

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smart city technology

  • Co-Sight is an open-source AI framework offering real-time video analytics for object detection, tracking, and distributed inference.
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    What is Co-Sight?
    Co-Sight is an open-source AI framework that simplifies development and deployment of real-time video analytics solutions. It provides modules for video data ingestion, preprocessing, model training, and distributed inference on edge and cloud. With built-in support for object detection, classification, tracking, and pipeline orchestration, Co-Sight ensures low-latency processing and high throughput. Its modular design integrates with popular deep learning libraries and scales seamlessly using Kubernetes. Developers can define pipelines via YAML, deploy with Docker, and monitor performance through a web dashboard. Co-Sight empowers users to build advanced vision applications for smart city surveillance, intelligent transportation, and industrial quality inspection, reducing development time and operational complexity.
    Co-Sight Core Features
    • Video data ingestion and preprocessing
    • Pipeline orchestration via YAML definitions
    • Multi-model fusion for detection, tracking, classification
    • Distributed inference on edge and cloud
    • Scalable deployment with Docker and Kubernetes
    • Built-in monitoring dashboard
  • A Java-based platform enabling development, simulation, and deployment of intelligent multi-agent systems with communication, negotiation, and learning capabilities.
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    What is IntelligentMASPlatform?
    The IntelligentMASPlatform is built to accelerate development and deployment of multi-agent systems by offering a modular architecture with distinct agent, environment, and service layers. Agents communicate using FIPA-compliant ACL messaging, enabling dynamic negotiation and coordination. The platform includes a versatile environment simulator allowing developers to model complex scenarios, schedule agent tasks, and visualize agent interactions in real-time through a built-in dashboard. For advanced behaviors, it integrates reinforcement learning modules and supports custom behavior plugins. Deployment tools allow packaging agents into standalone applications or distributed networks. Additionally, the platform's API facilitates integration with databases, IoT devices, or third-party AI services, making it suitable for research, industrial automation, and smart city use cases.
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