Service robots are expected to be used in many household in the near future, provided that proper interfaces are developed for the human robot interaction. Gesture recognition has been recognized as a natural way for the communication especially for elder or impaired people. With the developments of new technologies and the large availability of inexpensive depth sensors, real time gesture recognition has been faced by using depth information and avoiding the limitations due to complex background and lighting situations. In this paper the Kinect Depth Camera, and the OpenNI framework have been used to obtain real time tracking of human skeleton. Then, robust and significant features have been selected to get rid of unrelated features and decrease the computational costs. These features are fed to a set of Neural Network Classifiers that recognize ten different gestures. Several experiments demonstrate that the proposed method works effectively. Real time tests prove the robustness of the method for realization of human robot interfaces.

Archaeological trace extraction in aerial or satellite data is a difficult issue for automatic algorithms due to the traces similarity to other image artifacts or to their poor boundary information, discontinuities and so on. We propose in this paper a modified region based active contour approach for archaeological trace identification that overcomes the limits of standard methods of region uniformity and different consistencies with respect to the background. The proposed approach introduces a directional energy model in the minimization of the conventional energy term used in the existing active contour approaches. The local trace direction is estimated automatically after an initial unconstrained evolution of the region. Then, an iterative block based directional procedure has been introduced to limit the application of the modified method to local and adjacent areas and to allow the processing of large images in which the traces may have complex intersections or follow a curved trajectory. Finally, in order to reduce the initialization dependance problem, we propose the use of one seed point for each trace as the initial curve. Tests on the extraction of archaeological traces such as centuriations and ancient roads, visible as crop marks, have demonstrated that the proposed method and the developed MATLAB-based Graphical User Interface (GUI) facilitate unskilled/semi-skilled users in their archae- ologic traces mapping operations and improve their detection precisions.

Radio frequency power amplifiers (PAs) play a key- role in transceivers for mobile communications and their linearity is a crucial aspect. In order to meet the linearity requirements dictated by the standard at a reasonable efficiency, the usage of a linearization technique is required. In this paper we propose a linearization by means of a new type of digital predistorter, defined directly in the I-Q domain. The architecture of the proposed predistorter can be understood as an enhancement of the memory polynomial model (MPM) by means of additional I-Q terms. The usage of the proposed predistorter allows a more robust linearization of the whole RF transmitter because the enhancement of the model with additional I-Q terms can guarantee a more versatile compensation which is beneficial when the distortion comes from the joint contribution of the PA and the quadrature modulator. The proof of concept is achieved by measurements on a commercial PA in GaN technology and the performance of the proposed predistorter is illustrated.

Smart Community is a geographical area that can cover neighborhood, urban, regional, national area whose residents, organizations, and governing institutions are using Information and Communications Technologies (ICT) to transform their region, life and work in significant and fundamental ways. Its aim is to facilitate the construction of a pervasive, high speed communications system and information services that will benefit all sectors of the community: education, healthcare, local government and business. Processing and receiving in real-time information by all the other community members, citizens themselves become distributed intelligent probe and actors on the Community area to make better decisions and enhance the quality of life and improve local economy producing enduring benefits. This work shows how Smart Communities and Mobile Wireless Sensor Networks (MWSN) can play an increasingly vital role in a healthcare system, improving citizens’ health, reducing mobility costs and user’s carbon footprint. The system architecture and the visual interface prototype of a Collaborative Health Navigation System, will be discussed.

Smart Community is a geographical area that can cover neighborhood, urban, regional, national area whose residents, organizations, and governing institutions are using Information and Communications Technologies (ICT) to transform their region, life and work in significant and fundamental ways. Its aim is to facilitate the construction of a pervasive, high speed communications system and information services that will benefit all sectors of the community: education, healthcare, local government and business. Processing and receiving in real-time information by all the other community members, citizens themselves become distributed intelligent probe and actors on the Community area to make better decisions and enhance the quality of life and improve local economy producing enduring benefits. This work shows how Smart Communities and Mobile Wireless Sensor Networks (MWSN) can play an increasingly vital role in a healthcare system, improving citizens’ health, reducing mobility costs and user’s carbon footprint. The system architecture and the visual interface prototype of a Collaborative Health Navigation System, will be discussed.

Accurate pollution monitoring in urban environment requires an extremely large number of measurement station. The very complex 3D structure of urban area and its fluid dynamic behavior cause to be necessary very dense sampling grid to evaluate quantitative pollution indexes that can be correlated to real observed health effects or to obtain accurate pollution trend analysis. In order to reduce the high building cost and the management complexity of these high density monitoring grids, some papers proposed mobile monitoring stations to piggyback on public buses obtaining more dense sampling grid with fewer stations. In this paper we propose an Health Navigation System application for smartphone, based on a network of low cost, high precision, miniaturized wireless mobile monitoring system that can be easily embedded on bike frame. The mobile network makes available an accurate pollution urban map to our Navigation System that bikers can use to determine the healthiest route.

This paper is concerned with modeling earthquake-induced ground accelerations and the simulation of the dynamic response of linear structures through the principles of stochastic dynamics. A fully evolutionary approach, with nonstationarity both in amplitude and in frequency content, is proposed in order to define the seismic action, based on seismological information in the form of a small number of input parameters commonly available in deterministic or probabilistic seismic design situations. The signal is obtained by filtering a Gaussian white-noise. The finite duration and time-varying amplitude properties are obtained by using a suitable envelope function. By utilizing a subset of the records from the PEER-NGA strong-motion database, and time-series analysis tools extended to nonstationary processes, the key transfer-function properties, in terms of circular frequency, damping ratio and spectral intensity factor, are identified. A regression analysis is conducted for practical and flexible application of this model, in order to empirically relate the identified time-varying parameters of the filter to the characteristics defining earthquake scenarios such as magnitude, rupture distance and soil type. A validation study and a parametric investigation using elastic response spectra is also included. Results show that the final seismic model can reproduce, with satisfactory accuracy, the characteristics of acceleration records in a region, over a broad range of response periods.

Technological innovations have produced remarkable results in the health care sector. In particular, computer-aided detection (CAD) systems are becoming very useful and helpful in supporting physicians for early detection and control of some diseases such as neoplastic pathologies. In this paper, two different CAD systems able to detect and to localize microcalcification clusters in mammographic images are implemented. The two methods utilize an artificial neural network and a support vector machine, respectively, as classifier. Adopting the MIAS database as procedure testing, the performance of the two implemented systems are compared in terms of sensitivity, specificity, accuracy, free-response operating characteristic curves, and Cohen's kappa coefficient. The obtained values for the previous parameters show the efficiency of both methods to operate as second opinion in microcalcification cluster detection, improving the screening process efficiency.