5 citations · 18 across the 12 of their papers we have counts for
12 papers
SAMpLE: A SystemC-AMS Machine LEarning-based Framework for Virtual Prototyping
Andrei Mihai Albu, Sara Vinco
Machine Learning (ML) is increasingly used in virtual prototypes of embedded systems to model behaviors that are difficult to capture analytically. However, integrating ML models i…
End-to-end Automated Deep Neural Network Optimization for PPG-based Blood Pressure Estimation on Wearables
Francesco Carlucci, Giovanni Pollo, Xiaying Wang +6
Photoplethysmography (PPG)-based blood pressure (BP) estimation is a challenging task, particularly on resource-constrained wearable devices. However, fully on-board processing is…
Late Breaking Results: CHESSY: Coupled Hybrid Emulation with SystemC-FPGA Synchronization
Lorenzo Ruotolo, Giovanni Pollo, Mohamed Amine Hamdi +7
The growing complexity of cyber-physical systems (CPSs) calls for early prototyping tools that combine accuracy, speed, and usability. Virtual Platforms (VPs) provide fast function…
Integrating SystemC TLM into FMI 3.0 Co-Simulations with an Open-Source Approach
Andrei Mihai Albu, Giovanni Pollo, Alessio Burrello +6
The growing complexity of cyber-physical systems, particularly in automotive applications, has increased the demand for efficient modeling and cross-domain co-simulation techniques…
Automatic integration of SystemC in the FMI standard for Software-defined Vehicle design
Giovanni Pollo, Andrei Mihai Albu, Alessio Burrello +6
The recent advancements of the automotive sector demand robust co-simulation methodologies that enable early validation and seamless integration across hardware and software domain…
MEbots: Integrating a RISC-V Virtual Platform with a Robotic Simulator for Energy-aware Design
Giovanni Pollo, Mohamed Amine Hamdi, Matteo Risso +9
Virtual Platforms (VPs) enable early software validation of autonomous systems' electronics, reducing costs and time-to-market. While many VPs support both functional and non-funct…
Coupling Neural Networks and Physics Equations For Li-Ion Battery State-of-Charge Prediction
Giovanni Pollo, Alessio Burrello, Enrico Macii +3
Estimating the evolution of the battery's State of Charge (SoC) in response to its usage is critical for implementing effective power management policies and for ultimately improvi…
VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge
Alessio Mascolini, Sebastiano Gaiardelli, Francesco Ponzio +5
Detecting complex anomalies on massive amounts of data is a crucial task in Industry 4.0, best addressed by deep learning. However, available solutions are computationally demandin…
Integrating SystemC-AMS Power Modeling with a RISC-V ISS for Virtual Prototyping of Battery-operated Embedded Devices
Mohamed Amine Hamdi, Giovanni Pollo, Matteo Risso +6
RISC-V cores have gained a lot of popularity over the last few years. However, being quite a recent and novel technology, there is still a gap in the availability of comprehensive…
Model-Driven Dataset Generation for Data-Driven Battery SOH Models
Khaled Sidahmed Sidahmed Alamin, Francesco Daghero, Giovanni Pollo +5
Estimating the State of Health (SOH) of batteries is crucial for ensuring the reliable operation of battery systems. Since there is no practical way to instantaneously measure it a…
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0
Luigi Capogrosso, Alessio Mascolini, Federico Girella +10
Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. A…
A Machine Learning-based Digital Twin for Electric Vehicle Battery Modeling
Khaled Sidahmed Sidahmed Alamin, Yukai Chen, Enrico Macii +2
The widespread adoption of Electric Vehicles (EVs) is limited by their reliance on batteries with presently low energy and power densities compared to liquid fuels and are subject…