Enzo Baccarelli


Titolo Pubblicato in Anno
Energy-minimizing 3D circular trajectory optimization of rotary-wing UAV under probabilistic path-loss in constrained hotspot environments VEHICULAR COMMUNICATIONS 2024
Multi-resolution twinned residual auto-encoders (MR-TRAE)—a novel DL model for image multi-resolution COGNITIVE COMPUTATION 2024
How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study THE JOURNAL OF SUPERCOMPUTING 2023
Twinned Residual Auto-Encoder (TRAE)-A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images EXPERT SYSTEMS WITH APPLICATIONS 2023
A novel unsupervised approach based on the hidden features of deep denoising autoencoders for COVID-19 disease detection EXPERT SYSTEMS WITH APPLICATIONS 2022
Exploiting probability density function of deep convolutional autoencoders’ latent space for reliable COVID-19 detection on CT scans THE JOURNAL OF SUPERCOMPUTING 2022
AFAFed—Asynchronous Fair Adaptive Federated learning for IoT stream applications COMPUTER COMMUNICATIONS 2022
Deepfogsim: A toolbox for execution and performance evaluation of the inference phase of conditional deep neural networks with early exits atop distributed fog platforms APPLIED SCIENCES 2021
An accuracy vs. complexity comparison of deep learning architectures for the detection of covid-19 disease COMPUTATION 2021
Learning-in-the-Fog (LiFo): Deep learning meets Fog Computing for the minimum-energy distributed early-exit of inference in delay-critical IoT realms IEEE ACCESS 2021
A histogram-based low-complexity approach for the effective detection of COVID-19 disease from CT and X-ray images APPLIED SCIENCES 2021
Optimized training and scalable implementation of Conditional Deep Neural Networks with early exits for Fog-supported IoT applications INFORMATION SCIENCES 2020
Differentiable branching in deep networks for fast inference ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 2020
Why should we add early exits to neural networks? COGNITIVE COMPUTATION 2020
Energy-efficient adaptive resource management for real-time vehicular cloud services IEEE TRANSACTIONS ON CLOUD COMPUTING 2019
VirtFogSim: A parallel toolbox for dynamic energy-delay performance testing and optimization of 5G Mobile-Fog-Cloud virtualized platforms APPLIED SCIENCES 2019
EcoMobiFog–Design and dynamic optimization of a 5G Mobile-Fog-Cloud Multi-Tier ecosystem for the real-time distributed execution of stream applications IEEE ACCESS 2019
SmartFog: Training the Fog for the energy-saving analytics of Smart-Meter data APPLIED SCIENCES 2019
Fairness-constrained optimized time-window controllers for secondary-users with primary-user reliability guarantees COMPUTER COMMUNICATIONS 2018
Energy performance of heuristics and meta-heuristics for real-time joint resource scaling and consolidation in virtualized networked data centers THE JOURNAL OF SUPERCOMPUTING 2018


  • PE6_2
  • PE7_6
  • PE7_8


  • Big data & computing

Interessi di ricerca

Distributed networked computing systems and architectures; Resource virtualization; Fog Computing; Drone-assisted 6G communication/computing systems; Internet of Things (IoT); Distributed communication systems for Social Networks; Distributed networked Machine Learning; Green networked computing platforms


fog computing (FC)
ad-hoc distributed networks
analytical method optimization
edge machine learning
Internet of things (IoT)
• Networks → Online social networks
green networks

Laboratori di ricerca

© Università degli Studi di Roma "La Sapienza" - Piazzale Aldo Moro 5, 00185 Roma