The conducted research aims to create a unified framework for interpreting visual data captured by UAV platforms, moving from pixel-level processing to complex semantic and structural representations. Three-level perception architecture The system is hierarchically organized to transform raw data into informed decisions: Synthetic dataset ClaraVid To train models capable of handling…

Advances in Natural Language and Speech Processing. Recent research within the Romanian AI Hub focuses on improving the efficiency of recommendation systems, the security of language models, and advancing speech synthesis technologies for the Romanian language. Knowledge Graph-based Recommender Systems (TRL 3). A recipe recommendation system is proposed, using a hybrid architecture…

The research focuses on identifying AI-based solutions and Digital Twin concepts to address complex forecasting challenges in distributed energy networks (Smart Grids). Forecasting challenges and Digital Twin architecture. Representation-augmented Temporal Fusion Transformer (TFT) architecture. This hybrid approach learns stabilized temporal representations conditioned on structural information, providing a...

Monitoring outer space is becoming increasingly important in the context of the growing number of satellites and the risks associated with space debris. An AI-based approach is proposed for detecting and analyzing space objects using optical instruments. The main goal of this approach is the development of efficient methods for: The system is designed for…

The research focuses on developing innovative solutions for minimally invasive surgery and robotic implantology, eliminating the need for complex hardware sensors through the use of advanced computer vision techniques. Automatic localization and positioning of the ATHENA robot. Sensorless force estimation in minimally invasive surgery. Markerless perception for navigation in dental implantology.

Medical Image Captioning: ImageHRM and ImageTRM Architectures The research focuses on developing advanced models for generating accurate descriptions of medical images, using hierarchical and recursive structures. ImageHRM (Hierarchical Reasoning Model) ImageTRM (Tiny Recursive Model) Transferable Knowledge Base Augmented Language Models (KBLaM) The portability of knowledge base-augmented language models across…

Artificial intelligence is used in agricultural environment monitoring through the integration of mobile robots and aerial imagery. The main goal is to develop automated solutions for crop analysis, with a focus on early disease detection and understanding plant structure. Modern agriculture requires efficient and scalable methods for monitoring crop health. The use of drones and mobile robots enables…

Agentic AI: Video Segmentation for Polyp Detection (Gastro Clinic) The implementation aims to optimize polyp detection through real-time video segmentation, using a colonoscopic agent based on SOTA (State-of-the-Art) architectures. Architecture and Methodology Performance and Validation Automatic Invoice Management & Resident Support System The system integrates administrative document processing and academic support through neuro-symbolic methods and ontologies…

Recent HRIA research, conducted in partnership with medical universities, aims to improve diagnostic accuracy through neural architectures optimized for various types of scans (CT, MRI, X-ray, OCT). Vascular Network Segmentation and Radiographic Analysis. Structural improvements are introduced to standard models to increase sensitivity in detecting fine anatomical structures: Angio-OCT (Retinal Networks): The…

Processing satellite imagery using artificial intelligence offers new opportunities for environmental analysis and monitoring, especially in agriculture. This approach focuses on developing automated methods for soil classification and extracting relevant information from satellite data. Creation and expansion of datasets. A first essential step is the construction and improvement of…