works on

From the 1 of 8 linked papers with an AI index.

most citedLightweight Neural Networks for Affordance Segmentation: Enhancement of the Decoder Module

3 citations · 4 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CV20263 cited

Lightweight Neural Networks for Affordance Segmentation: Enhancement of the Decoder Module

Simone Lugani, Edoardo Ragusa, Rodolfo Zunino +1

The deployment of deep neural networks for visual affordance segmentation on wearable robots poses may prove critical, due to some conflicting aspects of the problem. On one hand,…

cs.CV20261 cited

Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras

Edoardo Ragusa, Giovanni Paolo Canuti, Simone Lugani +2

The paper proposes hardware‑aware neural architecture search and a fine‑tuning pipeline to integrate depth data into compact RGB‑D networks for affordance segmentation on embedded…

cs.LG2026

BearingNAS: Obtaining In-Sensor Intelligent Fault Diagnosis Systems for Bearings Using a Laptop

Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo +2

This paper introduces BearingNAS, a Hardware-Aware Neural Architecture Search (HW-NAS) framework designed to shift the intelligence directly onto the sensor die via in-sensor proce…

cs.NI2026

Hardware-Aware Neural Architecture Search for Encrypted Traffic Classification on Resource-Constrained Devices

Adel Chehade, Edoardo Ragusa, Paolo Gastaldo +1

This paper presents a hardware-efficient deep neural network (DNN), optimized through hardware-aware neural architecture search (HW-NAS); the DNN supports the classification of ses…

cs.NI2025

Adversarial Robustness of Traffic Classification under Resource Constraints: Input Structure Matters

Adel Chehade, Edoardo Ragusa, Paolo Gastaldo +1

Traffic classification (TC) plays a critical role in cybersecurity, particularly in IoT and embedded contexts, where inspection must often occur locally under tight hardware constr…

cs.NI2025

Intrusion Detection on Resource-Constrained IoT Devices with Hardware-Aware ML and DL

Ali Diab, Adel Chehade, Edoardo Ragusa +4

This paper proposes a hardware-aware intrusion detection system (IDS) for Internet of Things (IoT) and Industrial IoT (IIoT) networks; it targets scenarios where classification is…