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across Scotland’s west coast. It will evaluate the practicality of different image capture techniques and the potential of different sensor types (e.g., RGB, multispectral) to generate beach litter images
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Max Planck Institute for Intelligent Systems, Tübingen site, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 8 days ago
Interactions” to develop datasets and algorithms to capture and analyze eye gaze. About the project We are supporting the development of highly realistic human and animal avatars for use in research, film
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wireless sensor networks as well as research and education within Life Science, smart electronic sensors and medical systems. The Department of Electrical Engineering is an international workplace with
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Primary Supervisor -Prof Michal Mackiewicz Scientific background Marine litter is a key threat to the oceans health and the livelihoods. Hence, new scalable automated methods to collect and analyse
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Disse), the Chair of Geoinformatics (Prof. Thomas H. Kolbe), and the Chair of Algorithmic Machine Learning & Explainable AI (Prof. Stefan Bauer). The project aims to develop an integrated urban flood
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this PhD project, you will investigate the co-design between event-based learning algorithms and neuronal hardware units with multi-scale time constants. The algorithmic methodology will exploit recent
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electrical power, enabling smart sensors to operate without batteries. You will explore novel capacitor-based rectifier architectures, adaptive impedance-matching algorithms, and on-chip protection mechanisms
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communication Autonomous driving algorithms and technologies (e.g. vehicle control, path planning, scheduling) and sensors (e.g. lidars, radars, cameras, and GNSS) High-level integration of autonomous driving
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on sufficient and sufficiently clean water. However, we often lack the data to fully understand the dynamics of contaminants throughout the urban water cycle. Existing sensors for water quality monitoring do not
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description Cities depend on sufficient and sufficiently clean water. However, we often lack the data to fully understand the dynamics of contaminants throughout the urban water cycle. Existing sensors