A method fuses DAS strain-rate data from traffic with vehicle trajectories to optimize fiber geometry estimates, achieving sub-meter accuracy in simulations and field tests.
Szeliski,Computer vision: algorithms and applications
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Benchmark study shows DCO methods for vector similarity search are not reliable silver bullets due to high sensitivity to data properties and hardware, making them unsuitable for production deployment.
A DRL controller for ASV floating waste capture, trained in simulation with a perception abstraction module, achieves centimeter-level accuracy in real-world field experiments across 14 disturbance regimes.
A hybrid CNN-ViT foundation model trained only on Dutch high-resolution imagery with temporal inputs achieves competitive results on global remote sensing benchmarks despite using fewer parameters and less pretraining data than larger state-of-the-art models.
citing papers explorer
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Buried Fiber-Optic Geolocalization with Distributed Acoustic Sensing
A method fuses DAS strain-rate data from traffic with vehicle trajectories to optimize fiber geometry estimates, achieving sub-meter accuracy in simulations and field tests.
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Distance Comparison Operations Are Not Silver Bullets in Vector Similarity Search: A Benchmark Study on Their Merits and Limits
Benchmark study shows DCO methods for vector similarity search are not reliable silver bullets due to high sensitivity to data properties and hardware, making them unsuitable for production deployment.
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Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture
A DRL controller for ASV floating waste capture, trained in simulation with a perception abstraction module, achieves centimeter-level accuracy in real-world field experiments across 14 disturbance regimes.
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Developing a foundation model for high-resolution remote sensing data of the Netherlands
A hybrid CNN-ViT foundation model trained only on Dutch high-resolution imagery with temporal inputs achieves competitive results on global remote sensing benchmarks despite using fewer parameters and less pretraining data than larger state-of-the-art models.