@article {Frontoni2013509,
	title = {Customers{\textquoteright} activity recognition in intelligent retail environments},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8158 LNCS},
	year = {2013},
	note = {cited By 6},
	pages = {509-516},
	abstract = {This paper aims to propose a novel idea of an embedded intelligent system where low cost embedded vision systems can analyze human behaviors to obtain interactivity and statistical data, mainly devoted to customer behavior analysis. In this project we addressed the need for new services into the shop, involving consumers more directly and instigating them to increase their satisfaction and, as a consequence, their purchases. To do this, technology is very important and allows making interactions between costumers and products and between customers and the environment of the shop a rich source of marketing analysis. We construct a novel system that uses vertical RGBD sensor for people counting and shelf interaction analysis, where the depth information is used to remove the affect of the appearance variation and to evaluate customers{\textquoteright} activities inside the store and in front of the shelf, with products. Also group interactions are monitored and analyzed with the main goal of having a better knowledge of the customers{\textquoteright} activities, using real data in real time. Even if preliminary, results are convincing and most of all the general architecture is affordable in this specific application, robust, easy to install and maintain and low cost. {\textcopyright} 2013 Springer-Verlag.},
	doi = {10.1007/978-3-642-41190-8_55},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84887121570\&partnerID=40\&md5=8a6e9b3442d8d4dbd47b404bd8248a7b},
	author = {Emanuele Frontoni and Raspa, P. and Adriano Mancini and Primo Zingaretti and Valerio Placidi}
}
@conference {Raspa2010,
	title = {Selection of Lithium cells for EV battery pack using self organizing maps},
	booktitle = {EVS 2010 - Sustainable Mobility Revolution: 25th World Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exhibition},
	year = {2010},
	note = {cited By 0},
	abstract = {A challenging problem in energy storage systems for electric vehicles is the efficiency use of lithium multicell batteries. Because of production tolerances, unbalanced cells can be overstressed during usage leading to the reduction of the available capacity and premature failure of the battery pack. In order to reduce this problem the Universit{\`a} Politecnica delle Marche and FAAM Group S.p.A. developed a method for the selection and classification of homogenous cells to form uniform battery pack using Self Organizing Maps neural networks. Experimental data are collected from a set of LiFePo4 cells tested in FAAM laboratories. The selection considers both experimental data and identified characteristics: Discharge Voltage, Open Circuit Voltage, Total Capacity and identified parameters from Randle{\textquoteright}s equivalent circuit modelling. The State of Charge variability within each selected group of cells has been chosen as the clustering criterion to find the method which gives the best results in terms of homogeneity of the battery. Simulation results consider an experimental EV load profile and show a great reduction of the SOC variability and, consequently, in the balance of the battery pack for all the methods presented compared to random selection. Capacity and discharge voltage based methods give the best results over all.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84907415017\&partnerID=40\&md5=42dc9ff2c6cfe464e796818859e805ce},
	author = {Raspa, P. and Frinconi, L. and Adriano Mancini and Cavalletti, M. and Sauro Longhi and L. Fulimeni and P. Bellesi and Isidori, R.}
}
