17 citations · 43 across the 15 of their papers we have counts for
16 papers
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…
An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms
Andrea Mattia Garavagno, Edoardo Ragusa, Antonio Frisoli +1
Hardware-aware neural architecture search (HW-NAS) allows the integration of Convolutional Neural Networks (CNNs) in microcontrollers devices by automatically designing neural arch…
On-Device Neural Architecture Search
Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo +2
This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best…
Running hardware-aware neural architecture search on embedded devices under 512MB of RAM
Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo +1
This document proposes a novel approach to hardware-aware neural architecture search (HW NAS) that considers the resources available on the computing platform running it, enabling…
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…
Searching Neural Architectures for Sensor Nodes on IoT Gateways
Andrea Mattia Garavagno, Edoardo Ragusa, Antonio Frisoli +1
This paper presents an automatic method for the design of Neural Networks (NNs) at the edge, enabling Machine Learning (ML) access even in privacy-sensitive Internet of Things (IoT…
Segmenting Object Affordances: Reproducibility and Sensitivity to Scale
Tommaso Apicella, Alessio Xompero, Paolo Gastaldo +1
Visual affordance segmentation identifies image regions of an object an agent can interact with. Existing methods re-use and adapt learning-based architectures for semantic segment…
Affordance segmentation of hand-occluded containers from exocentric images
Tommaso Apicella, Alessio Xompero, Edoardo Ragusa +3
Visual affordance segmentation identifies the surfaces of an object an agent can interact with. Common challenges for the identification of affordances are the variety of the geome…
Container Localisation and Mass Estimation with an RGB-D Camera
Tommaso Apicella, Giulia Slavic, Edoardo Ragusa +2
In the research area of human-robot interactions, the automatic estimation of the mass of a container manipulated by a person leveraging only visual information is a challenging ta…