Grants and Contributions

The Technology & Business feasibility study for AI-based Wireless Spectrum Assessment will investigate the emerging trends in Industrial IoT, 5G deployments and critical Infrastructure and how the AI / Machine Learning technology can solve new and unforeseen risks in the new and complex wireless environments.

The proposed project targets the quantification of nutrients in soil using newly available mobile near infrared (NIR) sensors coupled with cloud-based support in the form of data processing, machine learning and spectral matching algorithms/libraries.

While the current Sampols solution has been deployed in a few European cities and industrial sites, several new features for intelligent noise analysis based on machine learning will be developed which can allow decision makers to take corrective actions on noise pollution and opens new applications at a large scale with potential distribution to the Canadian market.

This project seeks to overcome those limitations by using machine learning models to identify and amplify weak signals, resulting in clearer and more detailed seismic images.

The Firm's proposal outlines the development of an AI-driven Financial Services Broker Platform that will transform broker-client interactions, data analysis, and value delivery. Using AI, machine learning, and natural language processing, the platform provides brokers with real-time insights, streamlines operations, and enhances client satisfaction.

Finally, to demonstrate that an AI algorithm can learn to generate or evaluate patterns from a limited set of rules. These steps are fundamental to laying the foundations for an intelligent, reliable and adaptable machine capable of supporting robust, accurate and scalable automated palletizing solutions.

EUREKA Cluster – CELTIC-NEXT SUSTAINET guarDian – C2024/3-1 led by the German Research Center for Artificial Intelligence (DFKI)

Integration of reconfigurable intelligence surface technology with Open Radio Access Network (O-RAN) compliant infrastructure to improve current 5G and future 6G wireless telecommunications, RF signal propagation and energy efficiency. Machine learning, heuristics and software-defined radio technologies will be used increase data throughput, reduce latency and optimize power consumption in dynamic multi-user scenarios for emerging applications like telehealth, immersive VR/AR, smart manufacturing and smart buildings.

Qube provides oil and gas producers with an end–to-end continuous monitoring solution that detects methane emissions with advanced sensors and powerful machine learning algorithms capable of quantifying emissions and localizing their source.

Power Grid Innovations (PGI) focuses on using IoT, smart sensors, artificial intelligence, machine learning, optimization, and big data analytics in the clean energy, Microgrid, and power systems industries.

The Augmented Reality Capture and Visualization Engine project will be undertaken by Mobovivo, ePlay Digital, other partners and potential clients to support Augmented Reality production and end-user experiences in sports and entertainment through Artificial Intelligence and Machine Learning. The proposed project would result in mobile applications, underlying technology, new Intellectual Property (IP), new databases, advanced algorithms to detect objects, motion, and human features, advanced visualizations techniques utilizing augmented reality, and business client demos to support the sales and export of the AR Capture and Visualization Engine (AR CAVE).