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in underground facilities. The project aims to evaluate sensor technologies, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted
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to develop a data-efficient and uncertainty-aware framework for pigment mapping in Cultural Heritage. Heterogeneous spectral and imaging data from different sensors will be jointly exploited, including X-ray
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description The functioning of cities depends more than ever on urban infrastructures like transportation networks, power grids, water networks, Internet of Things sensors, and analytics platforms that gather
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the Department of Electrical Engineering at TU/e. The AIMS lab researches and develops AI models for systems equipped with sensors of multiple different modalities. We foster expertise in AI analysis of RGB
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rapidly evolving areas such as autonomous systems, data-driven modeling, learning-based control, optimization, complex networks, and sensor fusion. Research at the division is characterized by close
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. It will use signals from different sources—such as radio signals and internal sensors— to maintain robust and accurate PNT, even when satellite signals are weak or missing. A built-in intelligent
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multimodal sensor systems—both as standalone sensor solutions and as networked sensor systems. Implementation is carried out as embedded systems in the laboratory as well as for deployment in field experiments
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drone detection and localisation performance using radar systems. This can be achieved by improving the detection performance of individual sensors and by employing a cooperative network of sensors which
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information, they are computationally intensive, which prevents their systematic use in large parametric studies. Consequently, simplified or surrogate models (e.g., 1D network models, reduced-order models
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- Markovian Processes - Network Based Localisation / Radio based connectivity - Adaptive bandwidth - Mesh networking - Wireless Sensor Networks - Edge Computing - Time Delayed Systems - Adaptive Queuing