Micaela Verucchi

Orcid: 0000-0003-3898-8571

According to our database1, Micaela Verucchi authored at least 15 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
Model-Based Underwater 6D Pose Estimation From RGB.
IEEE Robotics Autom. Lett., November, 2023

A survey on real-time DAG scheduling, revisiting the Global-Partitioned Infinity War.
Real Time Syst., September, 2023

A benchmark analysis of data-driven and geometric approaches for robot ego-motion estimation.
J. Field Robotics, May, 2023

er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds.
CoRR, 2023

Uncovering the Background-Induced bias in RGB based 6-DoF Object Pose Estimation.
CoRR, 2023

Model-Based Underwater 6D Pose Estimation from RGB.
CoRR, 2023

2022
A Novel Real-Time Edge-Cloud Big Data Management and Analytics Framework for Smart Cities.
J. Univers. Comput. Sci., 2022

Biomedical Image Classification via Dynamically Early Stopped Artificial Neural Network.
Algorithms, 2022

Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds.
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2022

2021
Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles.
Proceedings of the 10th Mediterranean Conference on Embedded Computing, 2021

All You Can Embed: Natural Language Based Vehicle Retrieval With Spatio-Temporal Transformers.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020
Latency-Aware Generation of Single-Rate DAGs from Multi-Rate Task Sets.
Proceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium, 2020

Real-Time clustering and LiDAR-camera fusion on embedded platforms for self-driving cars.
Proceedings of the Fourth IEEE International Conference on Robotic Computing, 2020

A Systematic Assessment of Embedded Neural Networks for Object Detection.
Proceedings of the 25th IEEE International Conference on Emerging Technologies and Factory Automation, 2020

2019
Mise en abyme with Artificial Intelligence: How to Predict the Accuracy of NN, Applied to Hyper-parameter Tuning.
Proceedings of the Recent Advances in Big Data and Deep Learning, 2019


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