Mario Molinara

Mario Molinara
Università degli studi di Cassino e del Lazio Meridionale | UNICAS · Department of Electrical and Information Engineering

Professor

About

87
Publications
6,463
Reads
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792
Citations
Introduction
Mario Molinara received a “Laurea” degree with honors in Computer Science in 1999 from University of Sannio, Italy. In 2003 he received a Ph.D Degree in “Computer Science and Telecommunication” from University of Salerno, Italy. In 2004 he joined the Department of Electrical and Information Engineering (DIEI), where is now a Researcher in Computer Science at University of Cassino and Southern Lazio. Molinara has authored over 80 research papers in International Journals and Conference Proc.
Additional affiliations
December 2004 - January 2016
Università degli studi di Cassino e del Lazio Meridionale
Position
  • Research Assistant

Publications

Publications (87)
Conference Paper
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
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...
Article
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...
Conference Paper
Full-text available
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...
Article
Full-text available
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...
Article
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...
Chapter
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...
Article
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...
Conference Paper
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...
Article
Full-text available
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...
Article
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...
Article
Full-text available
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...
Article
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...
Chapter
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...
Chapter
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...
Chapter
Full-text available
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...
Article
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...
Article
Full-text available
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...
Article
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...
Article
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...
Chapter
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...
Chapter
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...
Chapter
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...
Chapter
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,...
Chapter
Full-text available
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...
Chapter
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...
Conference Paper
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...
Article
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,...
Article
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...
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
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...
Conference Paper
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...
Conference Paper
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...
Conference Paper
Full-text available
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...
Conference Paper
Full-text available
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...
Conference Paper
Quantum noise is a signal-dependent, Poisson-distributed noise and the dominant noise source in digital mammography. Quantum noise removal or equalization has been shown to be an important step in the automatic detection of microcalcifications. However, it is often limited by the difficulty of robustly estimating the noise parameters on the images....
Conference Paper
Recent advances in Computer-Aided Detection (CADe) for the automatic detection of clustered microcalcifications on mammograms show that cascade classifiers can compete with high-end commercial systems. In this paper, we introduce a deep cascade detector where the learning algorithm of each binary pixel classifier has been redesigned in the early st...
Article
To distinguish objects from non-objects in images under computational constraints, a suitable solution is to employ a cascade detector that consists of a sequence of node classifiers with increasing discriminative power. However, among the millions of image patches generated from an input image, only very few contain the searched object. When train...
Article
Full-text available
Numerical models are instrumental to more effective flood forecasting and management services though they suffer from numerous uncertainty sources. An effective model calibration is hence essential. In this research work, a methodology of optimal sampling design has been investigated and developed for water drainage networks. Optimal hydrometer sen...
Conference Paper
The Electrical Impedance Spectroscopy (EIS) measurements, in addition to being used for research purposes, are increasingly spreading in clinical setting. Thanks to the improvement of electronic and information technology, the necessary equipment is becoming increasingly simple and small, suitable for example for home care applications. In this wor...
Conference Paper
In this paper we present an algorithm for finding an accurate estimate of the contour of masses in mammograms. We assume that a rough estimate of the region containing the mass is known: in particular it is available the location of an area inside the mass (core) and a closed curve beyond which the mass does not extend. The proposed method employs...
Conference Paper
Full-text available
When mammograms are analyzed through a Computer Aided Diagnosis (CAD) system the presence of the pectoral muscle can affect the results of the automatic detection of breast lesions. This problem is particularly evident in mediolateral oblique (MLO) view where the pectoral muscle appears as a high intensity region across the margin of the mammogram....
Conference Paper
A Computer Aided Detection (CAD) system has frequently to deal with a significant skew between positive and negative class. For this reason we propose a solution based on an ensemble of classifiers structured as a “cascade” of dichotomizers where each node is robust to such skew since it is trained by a learning algorithm based on ranking instead o...
Conference Paper
In this paper we present a cascade-based framework to detect clusters of microcalcifications on mammograms. The algorithm is based on a sliding window technique where a detector is structured as a “cascade” of simple boosting classifiers with increasing complexity. Such a method couples the effectiveness of the cascade approach with the RankBoost a...
Conference Paper
Full-text available
The presence of clusters of microcalcifications in mammograms is particularly significant for early detection of breast cancer. In this paper a Computer Aided Detection system designed for this task is described. The detection of microcalcifications is performed by means of a segmentation based on a watershed transform and a further analysis based...
Conference Paper
In this paper we present a cascade-based framework for object detection in which the node classifiers are trained by a learning algorithm based on ranking instead of classification error. Such an approach is particularly suited for facing the asymmetry between positive and negative class, that is a huge problem in object detection applications. Oth...
Conference Paper
Full-text available
The conventional approach to the detection of microcalcifications on mammographies is to employ a sliding window technique. This consists in applying a classifier function to all the subwindows contained in an image and taking each local maximum of the classifier as a possible position of a microcalcification. Although effective such an approach su...
Conference Paper
Mammography is a noninvasive diagnostic technique widely used for early cancer detection in women breast. The automatic detection and classification of some abnormalities in mammograms is of challenging scientific and technical interest because of the associated research topics and the potential clinical applications. Therefore, the design of a med...
Conference Paper
In recent years, classifier combination has been of great interest for the pattern recognition community as a method to improve classification performance. Several combination rules have been proposed based on maximizing the accuracy and the Area under the ROC curve (AUC). Taking into account that there are several applications which focus only on...
Article
In this paper, we propose a method for the linear combination of several dichotomizers aimed at maximizing the area under the receiver operating characteristic (ROC) curve of the resulting classification system. This is particularly suited for real applications where it is difficult to exactly determine the key parameters such as costs and priors....
Article
Full-text available
The aim of this paper is to describe a novel system for computer-aided detection of clusters of microcalcifications on digital mammograms. Mammograms are first segmented by means of a tree-structured Markov random field algorithm that extracts the elementary homogeneous regions of interest. An analysis of such regions is then performed by means of...
Conference Paper
When dealing with two-class problems the combination of several dichotomizers is an established technique to improve the classification performance. In this context the margin is considered a central concept since several theoretical results show that improving the margin on the training set is beneficial for the generalization error of a classifie...
Conference Paper
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Article
Nondestructive testing techniques for the diagnosis of defects in solid materials can follow three steps, i.e., detection, location, and characterization. The solutions currently on the market allow for good detection and location of defects, but their characterization in terms of the exact determination of defect shape and dimensions is still an o...
Conference Paper
Mammography is a not invasive diagnostic technique widely used for early cancer detection in women breast. A particularly significant clue of such disease is the presence of clusters of microcalcifications. The automatic detection and classification of such clusters is a very difficult task because of the small size of the microcalcifications and o...
Conference Paper
Full-text available
Two class classification problems in real world are often characterized by imbalanced classes. This is a serious issue since a classifier trained on such a data distribution typically exhibits a prediction accuracy highly skewed towards the majority class. To improve the quality of the classifier, many approaches have been proposed till now for bui...
Conference Paper
Two class classifiers are used in many complex problems in which the classification results could have serious consequences. In such situations the cost for a wrong classification can be so high that can be convenient to avoid a decision and reject the sample. This paper presents a comparison between two different reject rules (the Chow’s and the R...
Conference Paper
The combination of classifiers is an established technique to improve the classification performance. The combination rules proposed up to now generally try to decrease the classification error rate, which is a performance measure not suitable in many real situations and particularly when dealing with two class problems. In this case, a good altern...
Article
The combination of classifiers is an established technique to improve the classification performance. The possible combination rules proposed up to now generally try to decrease the classification error rate, which is a performance measure not suitable in many real situations and particularly when dealing with two-class problems. In this case, a go...
Conference Paper
Nondestructive testing techniques for diagnosis in solid materials can be carried out in three steps: defect detection, location and characterization. The actual solutions allow defect detection and location, but the defect characterization in terms of exact determination of the shape and dimension is still an open question. The present paper propo...