Application-oriented research into advanced algorithms for multimodal sensor data processing for object detection, localization and classification, and for the extraction of object features
Provider: Univerzita Pardubice
Programme: Studentská grantová soutěž
Implementation period: 01.01.26 - 31.12.26
Workplace:
Fakulta elektrotechniky a informatiky - FEI
Investigator: Honc Daniel
Description:
In 2026, the project will strengthen the scientific and research activities of PhD students at the Faculty of Electrical Engineering and Informatics through applied research and experimental development in tasks aligned with the Faculty’s long-term priorities. The common objective is to develop and validate advanced methods for object detection, localization, identification and classification, and for extracting key object features from diverse data sources. The work is structured into three interconnected areas: (i) multimodal machine perception and robust processing of heterogeneous sensor data for applications in industry, materials research and biomedicine; (ii) radar and sensor systems with a focus on signal design, multichannel processing and adaptive control under changing conditions; and (iii) intelligent transport systems and data-driven decision-making, including risk detection and prediction on real-world data and scalable analysis of large datasets. The project will be carried out in close cooperation between PhD students and their supervisors and coordinated by the principal investigator, with emphasis on quality assurance, coherence across tasks and publication-ready results.
In 2026, the project will strengthen the scientific and research activities of PhD students at the Faculty of Electrical Engineering and Informatics through applied research and experimental development in tasks aligned with the Faculty’s long-term priorities. The common objective is to develop and validate advanced methods for object detection, localization, identification and classification, and for extracting key object features from diverse data sources. The work is structured into three interconnected areas: (i) multimodal machine perception and robust processing of heterogeneous sensor data for applications in industry, materials research and biomedicine; (ii) radar and sensor systems with a focus on signal design, multichannel processing and adaptive control under changing conditions; and (iii) intelligent transport systems and data-driven decision-making, including risk detection and prediction on real-world data and scalable analysis of large datasets. The project will be carried out in close cooperation between PhD students and their supervisors and coordinated by the principal investigator, with emphasis on quality assurance, coherence across tasks and publication-ready results.
