@article {, title = {Machine Learning in Capital Markets: Decision Support System for Outcome Analysis}, journal = {IEEE Access}, volume = {8}, year = {2020}, pages = {109080-109091}, doi = {10.1109/ACCESS.2020.3001455}, author = {R. Rosati and L. Romeo and C. A. Goday and T. Menga and E. Frontoni} } @article {, title = {Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0}, journal = {Expert Systems with Applications}, volume = {140}, year = {2020}, pages = {112869}, author = {Romeo, Luca and Loncarski, Jelena and Paolanti, Marina and Bocchini, Gianluca and Mancini, Adriano and Frontoni, Emanuele} } @article {, title = {Measurement of Users{\textquoteright} Well-Being Through Domotic Sensors and Machine Learning Algorithms}, journal = {IEEE Sensors Journal}, volume = {20}, year = {2020}, pages = {8029-8038}, doi = {10.1109/JSEN.2020.2981209}, author = {S. Casaccia and L. Romeo and A. Calvaresi and N. Morresi and A. Monteri{\`u} and E. Frontoni and L. Scalise and G. M. Revel} } @article {, title = {Multiple instance learning for emotion recognition using physiological signals}, journal = {IEEE Transactions on Affective Computing}, year = {2019}, author = {Romeo, Luca and Cavallo, Andrea and Pepa, Lucia and Berthouze, Nadia and Pontil, Massimiliano} } @article {paolanti2017modelling, title = {Modelling and Forecasting Customer Navigation in Intelligent Retail Environments}, journal = {Journal of Intelligent \& Robotic Systems}, year = {2017}, month = {10}, pages = {1{\textendash}16}, abstract = {

Understanding shopper behaviour is one of the keys to success for retailers. In particular, it is necessary that managers know which retail attributes are important to which shoppers and their main goal is to improve the consumer shopping experience. In this work, we present sCREEN (Consumer REtail ExperieNce), an intelligent mechatronic system for indoor navigation assistance in retail environments that minimizes the need for active tagging and does not require metrics maps. The tracking system is based on Ultra-wideband technology. The digital devices are installed in the shopping carts and baskets and sCREEN allows modelling and forecasting customer navigation in retail environments. This paper contributes the design of an intelligent mechatronic system with the use of a novel Hidden Markov Models (HMMs) for the representation of shoppers{\textquoteright} shelf/category attraction and usual retail scenarios such as product out of stock or changes on store layout. Observations are viewed as a perceived intelligent system performance. By forecasting consumers next shelf/category attraction, the system can present the item location information to the consumer, including a walking route map to a location of the product in the retail store, and/or the number of an aisle in which the product is located. Effective and efficient design processes for mechatronic systems are a prerequisite for competitiveness in an intelligent retail environment. Experiments are performed in a real retail environment that is a German supermarket, during business hours. A dataset, with consumers trajectories, timestamps and the corresponding ground truth for training as well as evaluating the HMM, have been built and made publicly available. The results in terms of Precision, Recall and F1-score demonstrate the effectiveness and suitability of our approach, with a precision value that exceeds the 76\% in all test cases.

}, issn = {0921-0296}, doi = {10.1007/s10846-017-0674-7}, url = {https://doi.org/10.1007/s10846-017-0674-7}, author = {Marina Paolanti and Daniele Liciotti and R. Pietrini and Adriano Mancini and Emanuele Frontoni} } @conference {Cenci:2017:MAP:3109761.3109773, title = {Movements Analysis of Preterm Infants by Using Depth Sensor}, booktitle = {Proceedings of the 1st International Conference on Internet of Things and Machine Learning}, series = {IML {\textquoteright}17}, year = {2017}, month = {10-2017}, pages = {12:1{\textendash}12:9}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {

Qualitative assessment of general movements in preterm infants is widely used in clinical practice. It can enable early detection of neurological dysfunctions and consequent neuromotor impairments in high risk infants. However, the outcome of these assessments is not standardized and it is influenced by examiner{\textquoteright}s subjective interpretation. For this reason, there is an increasing interest in the use of automated movement recognition technologies being applied in this field. In this work, we use a video-based system for preterm infant{\textquoteright}s movements assessment to provide a 3D\ motion analysis method able to extract some important indicators from the sequence of depth images collected by using an RGB-D sensor placed over the infant lying on the crib. The advantage of the proposed method is that it is objective, contactless, non-invasive, easy to install, affordable and suitable to be used in an indoor environment with poor lighting, as might be rooms in the Neonatal Intensive Care Unit, where these infants are taken into care. Experimental results show that the proposed method is able to derive from statistical analysis of depth data some key performance indicators, each of which describes different characteristics of the infant{\textquoteright}s spontaneous movements. Preliminary tests are conducted in the experimental phase on a preterm infant hospitalized in a women{\textquoteright}s and children{\textquoteright}s hospital. The project can be used to investigate the relationship between the characteristics of spontaneous movements and the presence of pathologies as cerebral palsy or other minor neurological dysfunctions.

}, keywords = {3D tracking, clustering, modelling, preterm infant{\textquoteright}s movement analysis}, isbn = {978-1-4503-5243-7}, doi = {10.1145/3109761.3109773}, url = {http://doi.acm.org/10.1145/3109761.3109773}, author = {Annalisa Cenci and Daniele Liciotti and Emanuele Frontoni and Primo Zingaretti and Virgilio Paolo Carnielli} } @conference {mancini2016multi, title = {A multi/hyper-spectral imaging system for land use/land cover using unmanned aerial systems}, booktitle = {Unmanned Aircraft Systems (ICUAS), 2016 International Conference on}, year = {2016}, pages = {1148{\textendash}1155}, publisher = {IEEE}, organization = {IEEE}, author = {Adriano Mancini and Emanuele Frontoni and Primo Zingaretti} } @article {Fulvio2016273, title = {Multi-Point Stereovision System for Contactless Dimensional Measurements}, journal = {Journal of Intelligent and Robotic Systems: Theory and Applications}, volume = {81}, number = {2}, year = {2016}, note = {cited By 0}, pages = {273-284}, abstract = {

The paper focuses on the design of a fast and reliable multi-point vision-based measurement system able to be flexible, low cost and accurate for quality control in industrial robotics applications. The paper discusses a new method for integrating visual quality control in highly dynamic manufacturing lines, where products are added or removed from production. The structure of the vision-based measurement system is described. In particular, the stereo system is created by the movement of a single camera mounted on a six-axis manipulator. The visual software is structured in three phases: single camera and stereo calibration, addition of the visual inspection tasks for the object to be measured, stereo measurement. The feasibility of the proposed solution has been tested in a real industrial application with strong requirements on robot speed. The comparison of experimental results on a target with a simple and well-defined shape has shown that the proposed solution provides better results, in terms of accuracy and measurement speed, when compared to commercial libraries and RGBD vision systems. Besides, the robot motion policy adopted by the solution proposed here guarantees a continuous movement without the need for stop\&go phases. {\textcopyright} 2015, Springer Science+Business Media Dordrecht.

}, doi = {10.1007/s10846-015-0249-4}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84955089994\&partnerID=40\&md5=9c4abfb9d88e7dff178bbd704c850bbb}, author = {Fulvio, G.D. and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti} } @article {Pierdicca201525, title = {Making visible the invisible. Augmented reality visualization for 3D reconstructions of archaeological sites}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {9254}, year = {2015}, note = {cited By 0}, pages = {25-37}, doi = {10.1007/978-3-319-22888-4_3}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84944688602\&partnerID=40\&md5=5988b5e6786fdbcd931f64ac7f8483f5}, author = {Roberto Pierdicca and Emanuele Frontoni and Primo Zingaretti and Eva Savina Malinverni and F. Colosi and R. Orazi} } @conference {ercoli2015measurement, title = {A measurement procedure for the assessment of thermoregulatory activitity in premature babies}, booktitle = {Medical Measurements and Applications (MeMeA), 2015 IEEE International Symposium on}, year = {2015}, pages = {229{\textendash}233}, publisher = {IEEE}, organization = {IEEE}, author = {Ercoli, Ilaria and Scalise, Lorenzo and Annalisa Cenci and Marchionni, Paolo and Enrico Primo Tomasini and Virgilio Paolo Carnielli} } @article {Benini2012507, title = {A modular framework for fast prototyping of cooperative unmanned aerial vehicle}, journal = {Journal of Intelligent and Robotic Systems: Theory and Applications}, volume = {65}, number = {1-4}, year = {2012}, note = {cited By 4}, pages = {507-520}, abstract = {Unmanned Aerial Vehicles (UAVs) are gaining increasing interest thanks to their flexibility and versatility. However, these systems are very complex and a good simulation platform is needed. In this paper, a new Framework for simulation and fast prototyping of UAV control laws is presented. The Framework exploits the high realism of the simulations carried out in a three-dimensional virtual environment with the easiness of use of development systems such as Matlab{\textregistered} for fast prototyping of control systems. Then a novel method that exploits the benefits of Model Predictive Control (MPC) for cooperative scenarios is introduced. The obtained results show good performances of MPC in solving the formation problem of unmanned aerial vehicles; finding an optimal solution and taking into account different constraints. The developed framework allows also to easily change from simulated agent to real one. {\textcopyright} 2011 Springer Science+Business Media B.V.}, doi = {10.1007/s10846-011-9577-1}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84855437595\&partnerID=40\&md5=e426704537bd52bc6397b9152f01e978}, author = {Benini, A. and Adriano Mancini and Minutolo, R. and Sauro Longhi and Montanari, M.b} } @conference {Dragoni2010421, title = {Multiple Neural Networks and Bayesian belief revision for a never-ending unsupervised learning}, booktitle = {Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA{\textquoteright}10}, year = {2010}, note = {cited By 0}, pages = {421-426}, abstract = {A system of Multiple Neural Networks has been proposed to solve the face recognition problem. Our idea is that a set of expert networks specialized to recognize specific parts of face are better than a single network. This is because a single network could no longer be able to correctly recognize the subject when some characteristics partially change. For this purpose we assume that each network has a reliability factor defined as the probability that the network is giving the desired output. In case of conflicts between the outputs of the networks the reliability factor can be dynamically re-evaluated on the base of the Bayes Rrule. The new reliabilities will be used to establish who is the subject. Moreover the network disagreed with the group and specialized to recognize the changed characteristic of the subject will be retrained and then forced to correctly recognize the subject. Then the system is subjected to continuous learning. {\textcopyright} 2010 IEEE.}, doi = {10.1109/ISDA.2010.5687229}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79851492270\&partnerID=40\&md5=51abe7b48e1259d0bbdbd2e8c498e0b4}, author = {Dragoni, A.F. and Vallesi, G. and Paola Baldassarri} } @conference {Dragoni2009164, title = {Multiple neural networks system for dynamic environments}, booktitle = {ISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications}, year = {2009}, note = {cited By 0}, pages = {164-168}, abstract = {We propose a "Multiple Neural Networks" system for dynamic environments, where one or more neural nets may no longer be able to properly operate, due to sensible partial changes in the characteristics of the individuals. We assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net{\textquoteright}s "degree of reliability" is defined as "the probability that the net is giving the desired output", in case of conflicts between the outputs of the various nets the re-evaluation of their "degrees of reliability" can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used for making the final choice, by applying the "Inclusion based" algorithm over all the maximally consistent subsets of the global outcome. Finally, the nets recognized as responsible for the conflicts will be automatically forced to learn about the changes in the individuals{\textquoteright} characteristics and avoid to make the same error in the immediate future. {\textcopyright} 2009 IEEE.}, doi = {10.1109/ISDA.2009.85}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77949498622\&partnerID=40\&md5=1b4b7bdf4786549e90ad4ae17f59a011}, author = {Dragoni, A.F. and Paola Baldassarri and Vallesi, G. and Mazzieri, M.} } @conference {Tascini199518, title = {Model attraction in medical image object recognition}, booktitle = {Proceedings of SPIE - The International Society for Optical Engineering}, volume = {2436}, year = {1995}, note = {cited By 0}, pages = {18-29}, abstract = {This paper presents as new approach to image recognition based on a general attraction principle. A cognitive recognition is governed by a {\textquoteright}focus on attention{\textquoteright} process that concentrates on the visual data subset of task- relevant type only. Our model-based approach combines it with another process, focus on attraction, which concentrates on the transformations of visual data having relevance for the matching. The recognition process is characterized by an intentional evolution of the visual data. This chain of image transformations is viewed as driven by an attraction field that attempts to reduce the distance between the image-point and the model-point in the feature space. The field sources are determined during a learning phase, by supplying the system with a training set. The paper describes a medical interpretation case in the feature space, concerning human skin lesions. The samples of the training set, supplied by the dermatologists, allow the system to learn models of lesions in terms of features such as hue factor, asymmetry factor, and asperity factor. The comparison of the visual data with the model derives the trend of image transformations, allowing a better definition of the given image and its classification. The algorithms are implemented in C language on a PC equipped with Matrox Image Series IM-1280 acquisition and processing boards. The work is now in progress.}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-0029452584\&partnerID=40\&md5=181cb1d8753867d28066421ab8229a59}, author = {Guido Tascini and Primo Zingaretti} }