M
Micaela Verucchi
Researcher at University of Modena and Reggio Emilia
Publications - 15
Citations - 113
Micaela Verucchi is an academic researcher from University of Modena and Reggio Emilia. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 3, co-authored 9 publications receiving 17 citations.
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Proceedings ArticleDOI
A Systematic Assessment of Embedded Neural Networks for Object Detection
Micaela Verucchi,Gianluca Brilli,Davide Sapienza,Mattia Verasani,Marco Arena,Francesco Gatti,Alessandro Capotondi,Roberto Cavicchioli,Marko Bertogna,Marco Solieri +9 more
TL;DR: A comprehensive and fair comparison of the best-in-class Convolution Neural Networks (CNNs) for real-time embedded systems, detailing the effort made to achieve an unbiased characterization on cutting-edge system-on-chips.
Proceedings ArticleDOI
Latency-Aware Generation of Single-Rate DAGs from Multi-Rate Task Sets
TL;DR: A method is proposed to convert a multi-rate DAG task-set with timing constraints into a single- rate DAG that optimizes schedulability, age and reaction latency, by inserting suitable synchronization constructs.
Proceedings ArticleDOI
Real-Time clustering and LiDAR-camera fusion on embedded platforms for self-driving cars
TL;DR: Li et al. as mentioned in this paper presented a new approach for LiDAR and camera fusion, that can be suitable to execute within the tight timing requirements of an autonomous driving system, based on a new clustering algorithm developed for the Lidar point cloud, a new technique for the alignment of the sensors, and an optimization of the Yolo-v3 neural network.
Proceedings ArticleDOI
Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles
TL;DR: In this article, the authors introduce the Risk Ranked Recall (R^3$ ) metrics for object detection systems, which categorize objects within three ranks and assign them based on an objective cyber-physical model for the risk of collision.
Proceedings ArticleDOI
Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles.
TL;DR: In this article, the authors introduce the Risk Ranked Recall (R^3$) metrics for object detection systems, which categorize objects within three ranks and assign them based on an objective cyber-physical model for the risk of collision.