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Image-to-Image Translation in PyTorch

This project optimizes the sensing accuracy of a UAV-based dual-baseline InSAR system using a swarm of three drones. Two independent digital elevation models (DEMs) are generated and fused to reduce height estimation errors. Real-time radar data is offloaded via an FDMA air-to-ground link for ground-based processing. The UAV formation and offloading power are jointly optimized to minimize the worst-case height error.

Vorlage für die Ausarbeitung einer Bachelor- oder Masterarbeit in LaTeX

Braindump der BFS Klausur vom Sommersemester 2019

neo ist ein einfacher und moderner Ersatz für das (optisch) bereits in die Jahre gekommene i4 Latex-Beamertheme.

This project investigates joint UAV formation and communication resource optimization for 3D bistatic InSAR-based DEM generation. The system uses two UAVs that coordinate SAR sensing and transmit radar data in real-time to a ground station. The optimization framework maximizes InSAR coverage under practical sensing and energy constraints using an alternating optimization approach combining classical, monotonic, and stochastic methods. Simulations show centimeter-scale elevation accuracy and highlight the role of UAV-GS communication in sensing performance.

Einfache kompakte Repovorlage für Präsentationen mit dem neo Theme (geeignet für Forks)

Braindump der BFS Klausur vom Sommersemester 2019

LaTeX template for elaborations at the i7.