@conference {Gao2014,
	title = {Welcome message},
	booktitle = {MESA 2014 - 10th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, Conference Proceedings},
	year = {2014},
	note = {cited By 0},
	doi = {10.1109/MESA.2014.6935511},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84911971399\&partnerID=40\&md5=276da3fdd39f1fd66d31f47d0f38e04e},
	author = {Gao, Y. and Primo Zingaretti and Koo, J.C. and Emanuele Frontoni}
}
@article {Khoshelham2010123,
	title = {Performance evaluation of automated approaches to building detection in multi-source aerial data},
	journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
	volume = {65},
	number = {1},
	year = {2010},
	note = {cited By 25},
	pages = {123-133},
	abstract = {Automated approaches to building detection in multi-source aerial data are important in many applications, including map updating, city modeling, urban growth analysis and monitoring of informal settlements. This paper presents a comparative analysis of different methods for automated building detection in aerial images and laser data at different spatial resolutions. Five methods are tested in two study areas using features extracted at both pixel level and object level, but with the strong prerequisite of using the same training set for all methods. The evaluation of the methods is based on error measures obtained by superimposing the results on a manually generated reference map of each area. The results in both study areas show a better performance of the Dempster-Shafer and the AdaBoost methods, although these two methods also yield a number of unclassified pixels. The method of thresholding a normalized DSM performs well in terms of the detection rate and reliability in the less vegetated Mannheim study area, but also yields a high rate of false positive errors. The Bayesian methods perform better in the Memmingen study area where buildings have more or less the same heights. {\textcopyright} 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).},
	doi = {10.1016/j.isprsjprs.2009.09.005},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-72949110177\&partnerID=40\&md5=016e5e523686951f9fa75cccf7957c8d},
	author = {Khoshelham, K. and C. Nardinocchi and Emanuele Frontoni and Adriano Mancini and Primo Zingaretti}
}
@conference {Kidiamboko2008866,
	title = {A scalable telemedicine architecture for under developed countries. A case study: Democratic Republic of Congo},
	booktitle = {2008 Mediterranean Conference on Control and Automation - Conference Proceedings, MED{\textquoteright}08},
	year = {2008},
	note = {cited By 0},
	pages = {866-871},
	abstract = {Telemedicine, defined as the use of telecommunications technologies to provide medical information and services, is electronic transfer of medical data being able to include the sound and the images to practice remote medicine. In under developed countries, as the Democratic Republic of Congo, the capability to remote monitor patients that live in villages or small rural town, is a "Big step ahead" for the health system and citizens. In this paper we present a Scalable Telemedicine Architecture designated to be used in a real scenario as the Dem.Rep. of Congo. A prototype of low cost sensor, integrable into architecture, as a pulse oximeter called Ebloops (Experimental Blood Pressure Smart Sensor), is also presented. Pulse oximetry allows doctors to monitor vital parameters as oxygen levels in a human body, heart frequency and other ones. {\textcopyright} 2008 IEEE.},
	doi = {10.1109/MED.2008.4602255},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-52949139070\&partnerID=40\&md5=855f2f2f7ebeaf934c0f0718b306fcf0},
	author = {Kidiamboko, S. and Adriano Mancini and Sauro Longhi and Spalazzi, L.}
}
