Franco Fummi

• University of Verona

With the advent of Industrial 4.0 and the push towards Industry 5.0, the data generated by the industries have become surprisingly large. This abundance of data significantly boosts machine and deep learning models for Predictive Maintenance (PdM). The PdM plays a vital role in extending the lifespan of industrial equipment and machines while also...

The fast fashion industry suffers from significant environmental impacts due to overproduction and unsold inventory. Accurately predicting sales volumes for unreleased products could significantly improve efficiency and resource utilization. However, predicting performance for entirely new items is challenging due to the lack of historical data and...

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 demanding, requiring cloud architectures prone to latency and bandwidth issues. This work presents VARADE, a novel solution implementing a light autoregressive framework based on...

Industrial control systems need to be highly reliable and precise at all times to ensure operational efficiency and prevent costly downtime. Early fault detection is crucial and is partially realized by runtime monitoring components. A correct-by-construction approach based on contract specifications allows early intervention in case of potential f...

Patterns of human motion in outdoor and indoor environments are substantially different due to the scope of the environment and the typical intentions of people therein. While outdoor trajectory forecasting has received significant attention, indoor forecasting is still an underexplored research area. This paper proposes SITUATE, a novel approach t...

Industrial control systems need to be highly reliable and precise at all times to ensure operational efficiency and prevent costly downtime. Early fault detection is crucial and is partially realized by runtime monitoring components. A correct-by-construction approach based on contract specifications allows early intervention in case of potential f...

In the past decade, Deep Neural Networks (DNNs) achieved state-of-the-art performance in a broad range of problems, spanning from object classification and action recognition to smart building and healthcare. The flexibility that makes DNNs such a pervasive technology comes at a price: the computational requirements preclude their deployment on mos...

Split Computing (SC), where a Deep Neural Network (DNN) is intelligently split with a part of it deployed on an edge device and the rest on a remote server is emerging as a promising approach. It allows the power of DNNs to be leveraged for latency-sensitive applications that do not allow the entire DNN to be deployed remotely, while not having suf...

Defect detection is the task of identifying defects in production samples. Usually, defect detection classifiers are trained on ground-truth data formed by normal samples (negative data) and samples with defects (positive data), where the latter are consistently fewer than normal samples. State-of-the-art data augmentation procedures add synthetic...

In this study, we show that diffusion models can be used in industrial scenarios to improve the data augmentation procedure in the context of surface defect detection. In general, defect detection classifiers are trained on ground-truth data formed by normal samples (negative data) and samples with defects (positive data), where the latter are cons...

Efficiently managing resource utilization is critical in manufacturing systems to optimize production efficiency, especially in dynamic environments where jobs continually enter the system and machine breakdowns are potential occurrences. In fully automated environments, co-ordinating the transport system with other resources is paramount for smoot...

This article introduces the regionalized resource process dependence graphs (RRPDGs): a manufacturing processes representation inspired by the regionalized value state dependence graphs traditionally used in software compilers. An RRPDG is an ordered sequence of nodes, each characterized by stereotyped input and output parameters, encapsulating a t...

The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures. TinyML carries an essential role within the fourth and fifth industrial revolutions in helping societies, econo...

Modern production lines are often composed of machinery from different vendors that must be connected with each other to increase the overall interoperability and flexibility. A common problem that arises in such systems is the complexity of the configuration task: they usually require each component to be manually configured. Thus, machinery requi...

The revolutionary technologies behind Industry 4.0 have opened a new era for manufacturing: connected and autonomous machines, collaborative robotics, and monitoring techniques are spreading to increase productivity and sustainability. From the workers’ perspective, they bring new safety threats but also opportunities to solve old ones, while conce...

Conventional automatic doors cannot distinguish between people wishing to pass through the door and people passing by the door, so they often open unnecessarily. This leads to the need to adopt new systems in both commercial and non-commercial environments: smart doors. In particular, a smart door system predicts the intention of people near the do...

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. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anom...

Human-centered robotic applications are becoming pervasive in the context of robotics and smart manufacturing and such a pervasiveness is even more expected with the shift to Industry 5.0. The always increasing level of autonomy of modern robotic platforms requires the integration of software applications from different domains to implement artific...

Many recent pattern recognition applications rely on complex distributed architectures in which sensing and computational nodes interact together through a communication network. Deep neural networks (DNNs) play an important role in this scenario, furnishing powerful decision mechanisms, at the price of a high computational effort. Consequently, po...

BDDs are representations of a Boolean expression in the form of a directed acyclic graph. BDDs are widely used in several fields, particularly in model checking and hardware verification. There are several implementations for BDD manipulation, where each package differs depending on the application. This paper presents HermesBDD: a novel multi-core...

Since the last century, the exponential growth of the semiconductor industry has led to the creation of tiny and complex integrated circuits, e.g., sensors, actuators, and smart power. Innovative techniques are needed to ensure the correct functionality of analog devices that are ubiquitous in every smart system. The ISO 26262 standard for function...

Containerization and orchestration have become two key requirements in software development best practices. Containerization allows for better resource utilization, platform-independent development, and secure deployment of software. Orchestration automates the deployment, networking, scaling, and availability of containerized workloads and service...

Over the last decade, the industrial world has been involved in a massive revolution guided by the adoption of digital technologies. In this context, complex systems like cyber-physical systems play a fundamental role since they were designed and realized by composing heterogeneous components. The combined simulation of the behavioral models of the...

Historically, the concept of stuck-on/off defect did not originate from physical observations but rather to model the behavior of faults at the gate level. Digital stuck-at fault models where a transistor is considered frozen in on-state or off-state may not apply well on analog circuits because even a slight variation could create deviations of se...

In electronic design and testing, the simulation speed of analog components is crucial. Moreover, the simulation of heterogeneous components embedded in a Virtual Platforms (VP) needs to be fast and accurate. Often, the analog components are non-linear, and simulating them is not easy to ensure the model's convergence. In this context, techniques f...

Model development and simulation of biological networks is recognized as a key task in Systems Biology. Integrated with in vitro and in vivo experimental data, network simulation allows for the discovery of the dynamics that regulate biological systems. Stochastic Petri Nets (SPNs) have become a widespread and reference formalism to model metabolic...

This work presents a platform for the modelling, simulation and automatic parametrization of semi-quantitative metabolic networks. Starting from a network modelled through Petri Nets (PN) and represented in SBML, the platform converts the model into an internal representation implemented through an Electronic Design Automation (EDA) description lan...

This paper presents a methodology to formalize the behavior of the machinescomposing a production line, and to automatically generate their virtualprototypes for efficient and correct plant simulation. The approach exploitsassume-guarantee reasoning through contracts to model the interaction betweenthe different components of a production line. The...

Pervasive computing requires to build systems every day more complex and heterogeneous. Smart devices must be able to carry on sensing and actuation alongside with computation and communication. As such, many different technologies must be packed within the same object. Digital HW and SW coexist with analog components and Micro-Electro-Mechanical s...

Modeling Cyber-Physical Systems requires aggregating semantics and languages tailored to different specific domains, while simulating these systems requires integrating different tools and technologies. Academy and Industry are working to define standard interfaces allowing to facilitate such integration. The Functional Mockup Interface (FMI) stand...

This paper presents a methodology that relies on Assume-Guarantee Contracts to decompose the problem of synthesizing control software for a multi-robot system. Initially, each contract describes either a component (e.g., a robot) or an aspect of the system. Then, the design problem is decomposed into different synthesis and verification sub-problem...

Virtual platforms are a powerful support for the development and early validation of embedded SW. However, complex smart devices are built by aggregating heterogeneous components provided by different vendors, thus requiring the development of custom ad-hoc virtual platforms. Even worse, components of the underneath HW platform may belong to differ...

Smart systems are characterized by the integration in a single device of multi-domain subsystems of different technological domains, namely, analog, digital, discrete and power devices, MEMS, and power sources. Such challenges, emerging from the heterogeneous nature of the whole system, combined with the traditional challenges of digital design, di...

Each year automotive systems are becoming smarter thanks to their enhancement with sensing, actuation and computation features. The recent advancements in the field of autonomous driving have increased even more the complexity of the electronic components used to provide such services. ISO 26262 represents the natural response to the growing concer...

Verification of cyber-physical systems SW often requires simulation of accurate heterogeneous HW models. However, heterogeneous system simulators do not easily allow it and designers must connect multiple simulators in complex co-simulation environments. Furthermore, usually HW computing platforms are “approximated” by using abstracted models that...