Mario Molinara

University of Cassino and Southern Lazio | UNICAS · Department of Electrical and Information Engineering

Covid-19 infection influenced the screening test rate of breast cancer worldwide due to the quarantine measures, routine procedures reduction, and delay of early diagnosis, causing high mortality risk and severity of the disease. X-ray mammography is the gold standard for diagnosing early signs of breast cancer, and Artificial Intelligence enables...

A significant number of women are diagnosed with breast cancer each year. Early detection of breast masses is crucial in improving patient prognosis and survival rates. In recent years, deep learning techniques, particularly object detection models, have shown remarkable success in medical imaging, providing promising tools for the early detection...

Papyrology is the field of study dedicated to ancient texts written on papyri. One significant challenge faced by papyrologists and paleographers is the identification of writers, also referred to as scribes, who penned the texts preserved on papyri. Traditionally, paleographers relied on qualitative assessments to differentiate between writers. Ho...

This paper presents an innovative approach for detecting illegal microdumps using very high-resolution optical satellite imagery, addressing a significant environmental monitoring challenge in Campania, Italy. Due to the regional vulnerability to illegal dumping, exacerbated by the waste management crisis, there is a pressing need for enhanced surv...

Gait analysis (GAn) is crucial for assessing human biomechanics, aiding rehabilitation, and informing health diagnostics. Traditional optoelectronic systems represent the gold standard for determining joint kinematics, but they present challenges due to high costs and technical demands in offering precise 3D marker-based GAn. Inertial measurement u...

In the field of cybersecurity, the ability to gather detailed information about target systems is a critical component of the reconnaissance phase of cyber attacks. This phase, known as cybersecurity reconnaissance, involves techniques that adversaries use to collect information vital for the success of subsequent attack stages. Traditionally, reco...

Asbestos, a hazardous material associated with severe health issues, requires accurate identification for safe management and removal. This study presents a novel end-to-end deep learning approach using a transformer-based YOLOv5 network for detecting asbestos roofs in high-resolution orthophotos, filling a gap in the scientific literature where en...

The paper proposes a new experimental measurement method for State of Charge (SoC) estimation able to optimize between measurement time and target Accuracy adoptable in Battery Management System (BMS) design where both these parameters are key parameters for the overall management performance. The method is applicable when the SoC is estimated in c...

Early screening for breast cancer is an effective tool to detect tumors and decrease mortality among women. However, COVID restrictions made screening difficult in recent years due to a decrease in screening tests, reduction of routine procedures, and their delay. This preliminary study aimed to investigate mass detection in a large-scale OMI-DB da...

Objective analysis of gait abilities (Gait Analysis, GAn) in clinic is an essential motor assessment to improve clinical decision-making and provide precision rehabilitation approaches to recover gait functions. GAn is usually based on wearable motion sensors or camera-based systems, which generate an extensive set of data which are challenging to...

This work compares two behavioral modeling approaches for predicting AC power loss in Ferrite-Core Power Inductors (FCPIs), normally used in Switch-Mode Power Supply (SMPS) applications. The first modeling approach relies on a genetic programming algorithm and a multi-objective optimization technique. The resulting AC power loss model uses the volt...

In the last decade, Convolutional Neural Networks (CNNs) have been the de facto approach for automated medical image detection. Recently, Vision Transformers have emerged in computer vision as an alternative to CNNs. Specifically, the Shifted Window (Swin) Transformer is a general-purpose backbone that learns attention-based hierarchical features a...

The technological step towards sensors’ miniaturization, low-cost platforms, and evolved communication paradigms is rapidly moving the monitoring and computation tasks to the edge, causing the joint use of the Internet of Things (IoT) and machine learning (ML) to be massively employed. Edge devices are often composed of sensors and actuators, and t...

The problem of detecting illegal pollutants in wastewater is of fundamental importance for public health and security. The availability of distributed, low–cost and low–power monitoring systems, particularly enforced by IoT communication mechanisms and low-complexity machine learning algorithms, would make it feasible and easy to manage in a widesp...

The detection of contaminants in several environments (e.g., air, water, sewage systems) is of paramount importance to protect people and predict possible dangerous circumstances. Most works do this using classical Machine Learning tools that act on the acquired measurement data. This paper introduces two main elements: a low-cost platform to acqui...

In the context of energy digitisation, Load Profiling can be a useful tool for making decisions about the use and the health of an electrical load and could be adopted for strategic services related to the energy efficiency, characterisation, prediction, optimisation, and diagnosis of monitored systems. To accomplish for this task it is important...

This paper proposes a deep leaning technique for accurate detection and reliable classification of organic pollutants in water. The pollutants are detected by means of cyclic voltammetry characterizations made by using low-cost disposable screen-printed electrodes. The paper demonstrates the possibility of strongly improving the detection of such p...

The problem of detecting pollutants in water with non-invasive and low-cost sensors is an open question. In this paper, we propose a system for the detection and classification of pollutants based on the improvement of a previous proposal, focused on geometric cones. The solution is based on a classifier suitable to be implemented aboard the so-cal...

Monitoring of aquatic ecosystems has been historically accomplished by intensive campaigns of direct measurements (by probes and other boat instruments) and indirect extensive methods such as aero-photogrammetry and satellite detection. These measurements characterized the research in the last century, with significant but limited improvements with...

Early diagnosis of neurodegenerative disorders, such as Alzheimer's Disease (AD), is very important to reduce their effects and to improve both quality and life expectancy of patients. In this context, it is generally agreed that handwriting is one of the first skills altered by the onset of such diseases. For this reason, the analysis of handwriti...

Water monitoring systems continuously working ensure real–time pollutant detection capabilities according to their sensitivity and specificity. It is necessary to balance such features because, although being able to sense several substances is a desired feature, the reduction of false positives is a primary goal a classification system should have...

To better asses the ageing and to reduce the hazards involved in the use of Lithium-Ion Batteries, multi-measurand monitoring units and strategies are urged. In this paper, a Cell Management Unit, based on the SENSIPLUS chip, a recently introduced multichannel, multi-mode sensor interface, is described. SENSIPLUS is a single System on a Chip combin...

It has been shown that digital games can help people, especially young people, get the most from cultural heritage. Successful usage of these games includes teaching and learning, as well as virtual reality tools used to simulate historical sites and events. They have also been shown to be a powerful and effective tool to make cultural heritage mor...

Nowadays, the problem of pollution in water is a very serious issue to be faced and it is really important to be able to monitoring it with non-invasive and low-cost solutions, like those offered by smart sensor technologies. In this paper, we propose an improvement of an our innovative classification system, based on geometrical cones, to detect a...

This paper is the editorial of the virtual special issue (VSI) “Artificial Intelligence for Distributed Smart Systems” (AI4DSS), of which the authors of this paper have been the guest editors. It aims to bring together the work of experts from the fields of artificial intelligence and that of smart sensing. Smart Sensing and, more generally, Smart...

In smart city framework, the water monitoring through an efficient, low-cost, low-power and IoT-oriented sensor technology is a crucial aspect to allow, with limited resources, the analysis of contaminants eventually affecting wastewater. In this sense, common interfering substances, as detergents, cannot be classified as dangerous contaminants and...

In the framework of palaeography, the availability of both effective image analysis algorithms, and high-quality digital images has favored the development of new applications for the study of ancient manuscripts and has provided new tools for decision-making support systems. The quality of the results provided by such applications, however, is str...

This paper is the editorial of the virtual special issue (VSI) ”Pattern Recognition and Artificial Intelligence Techniques for Cultural Heritage”, of which the authors of this paper have been the guest editors. It aims to bring together the work of experts from the fields of pattern recognition and artificial intelligence and that of cultural herit...

In the framework of indoor air monitoring, this paper proposes an Internet of Things ready solution to detect and classify contaminants. It is based on a compact and low–power integrated system including both sensing and processing capabilities. The sensing is composed of a sensor array on which electrical impedance measurements are performed throu...

Water pollution caused by human activities poses a serious global threat to human health. Sensor technologies enabling water monitoring are an important tool that can help facing this problem. In this work, we propose an embedded IoT-ready system based on a proprietary sensor technology for the detection and recognition of six water contaminants. T...

The challenge to detect contaminants inside water solutions is addressed in this paper, through the use of an integrated, low-cost, smart and IoT platform, namely SENSIPLUS. In particular, the complete process from the sensing phase to classification and results analysis is provided with further investigations about the limitations of the current p...

The work deals with a technique adopted to calibrate in laboratory chemiresistor gas sensing film based on graphene that work at room temperature installed on a micro sensor board for applications in open air and IOT scenario. From the study in controlled environment the beginning of poisoning due to chemisorption can be estimated for the sensing l...

The study presents a project aimed at the knowledge and enhancement of minor historical centers, based on a scientific survey integrated system, developed with typical gamification procedures. The implemented methodology, experimented in the small and ancient historical center of the Municipality of Atina in Lower Lazio, exploits the potential of a...

Internet of Things (IoT) is involving more and more fields where monitoring actions and fast and reliable data communication are simultaneously needed. Inside the general class of monitoring applications, those related to pollutant detection and classification are currently faced by many researchers and companies. Several approaches are being propo...

In this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial context and improving detection performance. The main innovation behind the proposed method is the use...

This paper presents an end-to-end system to identify writers in medieval manuscripts. The proposed system consists in a three-step model for detection and classification of lines in the manuscript and page writer identification. The first two steps are based on deep neural networks trained with transfer learning techniques and specialized to solve...

One of the most important research topics in the field of palaeography is the identification of the different scribes who participated in the writing process of a medieval book. Using traditional palaeographic tools, a palaeographer spends a lot of time reading, measuring and comparing thousands of letters or graphic signs. The aim is to evaluate d...

Smart City is a modern concept, born in the last decade and spreading very quickly all over the world, aiming at improving the citizens’ life quality by largely adopting Information and Communication Technologies (ICT). It is addressed to optimize the government policy and people welfare by means of the analysis of data acquired with pervasive sens...

One main goal of paleographers is to identify the different writers who wrote a given manuscript. Recently, paleographers are starting to use digital tools which provide new and more objective ways to analyze ancient documents. On the other hand, in the last few years, deep learning techniques have been applied to many domains and to overcome its r...

Cognitive impairments affect skills such as communication, understanding or memory and they may be a short-term problem or a permanent condition. Among the diseases involving cognitive impairments, neurodegenerative ones are the most common and affect millions of people worldwide. Handwriting is one of the daily activities affected by these kinds o...

Palaeography aims to study ancient documents and the identification of the people who participated in the handwriting process of a given document is one of the most important problems. To this aim, expert paleographers typically analyze handwriting features such as letter heights and widths, distances between characters and angles of inclination. W...

Alzheimer’s disease (AD) is the most common neurodegenerative dementia of old age and the leading chronic disease contributor to disability and dependence among older people worldwide. Handwriting is among the motor activities compromised by AD, which is the result of a complex network of cognitive, kinaesthetic and perceptive-motor skills. Indeed,...

Breast cancer is the most frequent cancer among women, and also causes the greatest number of cancer-related deaths. One effective way to reduce breast-cancer related deaths is to use mammography as a screening strategy. In this framework, cluster of microcalcifications can be an important indicator of breast cancer. To help radiologists in their d...

In digital paleography, recent technology advancements are used to support paleographers in the study and analysis of ancient documents. One main goal of paleographers is to identify the different scribes (writers) who wrote a given manuscript. Deep learning has recently been applied to many domains. However, in order to overcome its requirement of...

In this digital era, one of the main challenge faced by cultural heritage is digitization. This challenge is particularly hard in countries like Italy, characterized by an extremely high number of Cultural goods. Data acquisition for many of these Cultural Heritage is extremely difficult, because of the complexity of surveys through traditional met...

Nowadays, an increasing concern about people and environmental health and safety is spreading all over the world, and technologies such as efficient monitoring systems are furthered. In the field of air quality, filtering systems, especially based on activated carbons (ACs), are commonly used. In most cases, their state of health is not monitored,...

In this work, we analyze how stabilizing the variance of intensity-dependent quantum noise in digital mammograms can significantly improve the computerized detection of microcalcifications (MCs). These lesions appear on mammograms as tiny deposits of calcium smaller than 20 pixels in diameter. At this scale, high frequency image noise is dominated...

Timely alerts provided to the communities at risk of landslides can prevent casualties and costly damages to people, buildings and infrastructures. The rainfalls are one of the primary triggering causes for landslides so that empirical approaches based on the correlation between landslides occurrence and rainfall characteristics, are considered eff...

The Italian territory is characterized by an extremely high number of Cultural goods. Knowledge and measurement of many of these Cultural Heritage is extremely difficult in relation to the complexity of surveys through traditional methodologies. The contribution proposes an original approach to the knowledge and measurement of Cultural Heritage bas...

Automated retinal blood vessel segmentation plays an important role in the diagnosis and treatment of various cardiovascular and ophthalmologic diseases. In this paper, an unsupervised algorithm based on denoising and mathematical morphology is proposed to extract blood vessels from color fundus images. Specifically, our method consists of the foll...

Microcalcifications are early indicators of breast cancer that appear on mammograms as small bright regions within the breast tissue. To assist screening radiologists in reading mammograms, supervised learning techniques have been found successful to detect microcalcifications automatically. Among them, Convolutional Neural Networks (CNNs) can auto...

This paper proposes the realization and preliminary characterization of a smart sensor platform for the online monitoring of the residual life of activated carbon air based filters. The platform performs the measurement to give reliable information about the filter maintenance or early warning for the presence of dangerous gases both in industrial...

The modern cities are addressing their innovation efforts for facing not just the common stresses cities accumulate daily, but also the sudden shocks can occur such as urban floods. Networked gauge stations are instrumental to robust floods alerts though they suffer from error and fault. For capturing the anomalous behavior of networked rain gauges...

Rainfall data collection gathered in continuous by a distributed rain gauge network is instrumental to more effective hydro-geological risk forecasting and management services though the input estimated rainfall fields suffer from prediction uncertainty. Optimal rain gauge networks can generate accurate estimated rainfall fields. In this research w...