Enzo Baccarelli | Ricerc@Sapienza

Pubblicazioni

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

ERC

PE6_2
PE7_6
PE7_8

KET

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

Keywords

fog computing (FC)

ad-hoc distributed networks

analytical method optimization

edge machine learning

Internet of things (IoT)

• Networks → Online social networks

green networks

Progetti di Ricerca

Laboratori di ricerca

Laboratorio di Sistemi di Comunicazioni a Banda Larga