3 citations · 6 across the 9 of their papers we have counts for
10 papers
Few-Shot Adaptation for Anomalous Sound Detection at Low False-Positive Rate
Simone Lugani, Christof Pichler, Hannes Wegleiter +3
Reliable fault detection is crucial for industrial machinery in large-scale facilities, where unexpected failures can cause costly downtime and production interruptions. Although a…
RepTC: Representation-Aware Optimization for Efficient Traffic Classification on Edge IoT Devices
Adel Chehade, Edoardo Ragusa, Paolo Gastaldo +1
Traffic classification (TC) is crucial to secure Internet of Things (IoT) networks, whose edge nodes often operate under privacy, bandwidth, and energy constraints. Yet, encrypted…
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,…
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras
Edoardo Ragusa, Giovanni Paolo Canuti, Simone Lugani +2
While depth sensors have the potential to complement RGB data for affordance segmentation in wearable robots, their usage seems to remain underexplored. The paper proposes two appr…
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…
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…
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…
Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers
Adel Chehade, Edoardo Ragusa, Paolo Gastaldo +1
In this paper, we present a practical deep learning (DL) approach for energy-efficient traffic classification (TC) on resource-limited microcontrollers, which are widely used in Io…
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…
Tiny Neural Networks for Session-Level Traffic Classification
Adel Chehade, Edoardo Ragusa, Paolo Gastaldo +1
This paper presents a system for session-level traffic classification on endpoint devices, developed using a Hardware-aware Neural Architecture Search (HW-NAS) framework. HW-NAS op…