The ZED camera is binocular vision system that can be used to provide a 3D perception of the world. It can be applied in autonomous robot navigation, virtual reality, tracking, motion analysis and so on. This paper proposes a mathematical error model for depth data estimated by the ZED camera with its several resolutions of operation. For doing that, the ZED is attached to a Nvidia Jetson TK1 board providing an embedded system that is used for processing raw data acquired by ZED from a 3D checkerboard. Corners are extracted from the checkerboard using RGB data, and a 3D reconstruction is done for these points using disparity data calculated from the ZED camera, coming up with a partially ordered, and regularly distributed (in 3D space) point cloud of corners with given coordinates, which are computed by the device software. These corners also have their ideal world (3D) positions known with respect to the coordinate frame origin that is empirically set in the pattern. Both given (computed)  coordinates from the camera’s data and known (ideal) coordinates of a corner can, thus, be compared for estimating the error between the given and ideal point locations of the detected corner cloud. Subsequently, using a curve fitting technique, we obtain the equations that model the RMS (Root Mean Square) error. This procedure is repeated for several resolutions of the ZED sensor, and at several distances. Results showed its best effectiveness with a maximum distance of approximately sixteen meters, in real time, which allows its use in robotic or other online applications.

Background. Epidemiological figures of Covid-19 epidemic in Italy are worse than those observed in China. Methods. We modeled the Covid-19 outbreak in Italian Regions vs. Lombardy to assess the epidemics progression and predict peaks of new daily infections and total cases by learning from the entire Chinese epidemiological dynamics. We trained an artificial neural network model, a modified autoencoder with Covid-19 Chinese data, to forecast epidemic curve of the different Italian regions, and use the susceptible-exposed-infected-removed (SEIR) compartment model to predict the spreading and peaks. We have estimated the basic reproduction number (R 0 ) -which represents the average number of people that can be infected by a person who has already acquired the infection -both by fitting the exponential growth rate of the infection across a 1-month period, and also by using a day by day assessment, based on single observations. Results. The expected peak of SEIR model for new daily cases was at the end of March at national level. The peak of overall positive cases is expected by April 11 th in Southern Italian Regions, a couple of days after that of Lombardy and Northern regions. According to our model, total confirmed cases in all Italy regions could reach 160,000 cases by April 30 th and stabilize at a plateau. Conclusions. Training neural networks on Chinese data and use the knowledge to forecast Italian spreading of Covid-19 has resulted in a good fit, measured with the mean average precision between official Italian data and the forecast.

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Abstract-This paper introduces the use of a flexible and affordable educational robot specifically developed for the practical experimentation inherent to technological disciplines. The robot has been designed to be reconfigurable and extendible, serving as an experimental platform across several undergraduate courses. As most students have a mobile cell phone, this was used as the main control computer for the so-called CellBot, thus avoiding any need to deal with the details of microcontrollers or other embedded computing devices. Assessment results are also presented, based on a pre-and post-survey of student opinion administered to 204 science and engineering students from several universities. Among the conclusions are that 83% of the students prefer to use these low-cost robots as tools to improve their learning of the theory in several disciplines, and 71% of the students stated that they prefer to have their own robot to experiment with, instead of using a didactic kit loaned to them by the university.

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Problems related to quality (and quantity) of water in natural resources or in artificial reservoirs are frequently arising and are at the center of attention of authorities and governments around the world. Many times the monitoring is not performed in an efficient time frame and a precise manner, whereas the adoption of fast and punctual solutions would undoubtedly improve the water quality and consequently enhance the life of people. To minimize or diminish such kinds of problems, we propose an architecture for sensors installed in a robotic platform, an autonomous sail boat, able to acquire raw data relative to water quality, to process and make them available to people that might be interested in such information. The main contributions are the sensors architecture itself, which uses low cost sensors, with practical experimentation done with a prototype. Results show data collected for points in lakes and rivers in the northeast of Brazil. This embedded system is fixed in the sailboat robot with the intention to facilitate the study of water quality for long endurance missions. This robot can help monitoring water bodies in a more consistent manner. Nonetheless the system can also be used with fixed vases or buoys in strategic points.