Michele Scarpiniti


Titolo Pubblicato in Anno
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
CoVal-SGAN: A Complex-Valued Spectral GAN architecture for the effective audio data augmentation in construction sites Proceedings of 2022 International Joint Conference on Neural Networks (IJCNN) 2022
A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS. SYSTEMS 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
A multimodal deep network for the reconstruction of T2W MR images Progresses in Artificial Intelligence and Neural Systems 2021
Efficient data augmentation using graph imputation neural networks Progresses in Artificial Intelligence and Neural Systems 2021
Quaternion widely linear forecasting of air quality Progresses in Artificial Intelligence and Neural Systems 2021
An accuracy vs. complexity comparison of deep learning architectures for the detection of covid-19 disease COMPUTATION 2021
Deep recurrent neural networks for audio classification in construction sites European Signal Processing Conference 2021
A wide multimodal dense U-net for fast magnetic resonance imaging European Signal Processing Conference 2021
MATLAB® per l'audio 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
Deep Belief Network based audio classification for construction sites monitoring EXPERT SYSTEMS WITH APPLICATIONS 2021
Introduzione all'audio real-time: Basi teoriche e prime applicazioni 2021
A histogram-based low-complexity approach for the effective detection of COVID-19 disease from CT and X-ray images APPLIED SCIENCES 2021
Music genre classification using stacked auto-encoders Smart Innovation, Systems and Technologies 2020


  • PE6_11
  • PE7_7


  • Big data & computing

Interessi di ricerca

Gli attuali interessi di ricerca sono nel campo dell'elaborazione non lineare del segnale, dei circuiti e degli algoritmi per l'elaborazione del segnale audio, array processing e la separazione di sorgenti. Inoltre, è attivo su tematiche di filtraggio adattativo non lineare, con particolare enfasi all'identificazione di sistemi non lineari. Altri interessi di ricerca riguardano la cancellazione dell'eco monofonico/stereofonico in ambiente avverso e in presenza di forti distorsioni non lineari e la localizzazione acustica di sorgenti all’interno di ambienti riverberanti. Si interessa anche di machine learning e di reti neurali per l'elaborazione del segnale.


adaptive signal processing
complex nonlinear filters
energy aware machine learning
fog computing (FC)

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