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Becherini G.,Accademia Navale di Livorno | Di Fraia S.,Accademia Navale di Livorno | Genovesi G.,GSD S.r.l. EMC Laboratory | Petri A.,GSD S.r.l. EMC Laboratory | And 3 more authors.
IEEE Transactions on Plasma Science | Year: 2011

We present the results of an experimental analysis aimed at investigating the electromagnetic (EM) emission during rail launcher operation. In order to obtain such data, an experimental setup was assembled in a shielded semi-anechoic chamber, consisting of a pulsed power source-unit, a power coaxial cable and a rail launcher prototype. Several experiments were performed for different operating conditions and results were repeatable. Finally, we provide a qualitative modeling of some sources of electromagnetic transients and we discuss basic aspects driving the EM emission phenomena in railgun systems. © 2010 IEEE. Source


Becherini G.,Accademia Navale di Livorno | Di Fraia S.,Accademia Navale di Livorno | Tellini B.,University of Pisa
SPEEDAM 2010 - International Symposium on Power Electronics, Electrical Drives, Automation and Motion | Year: 2010

In this paper the authors describe an analytical procedure for preliminary design of Multistage Linear Induction Motor (MLIM) operating as heavy mass electromagnetic catapult. Through the use of the sheet current method we derive the thermal, mechanical and electrical modeling. The implemented methodology enables to individuate and optimize the main parameters of the system and in particular the number of barrel sections. The main steps of the procedure are clearly explained and discussed throughout the paper. Finally, we show the main quantities of interest calculated for a designed prototype. © 2010 IEEE. Source


Rossi A.,University of Pisa | Acito N.,Accademia Navale di Livorno | Diani M.,University of Pisa | Corsini G.,University of Pisa
Journal of Real-Time Image Processing | Year: 2014

In the field of hyperspectral image processing, anomaly detection (AD) is a deeply investigated task whose goal is to find objects in the image that are anomalous with respect to the background. In many operational scenarios, detection, classification and identification of anomalous spectral pixels have to be performed in real time to quickly furnish information for decision-making. In this framework, many studies concern the design of computationally efficient AD algorithms for hyperspectral images in order to assure real-time or nearly real-time processing. In this work, a sub-class of anomaly detection algorithms is considered, i.e., those algorithms aimed at detecting small rare objects that are anomalous with respect to their local background. Among such techniques, one of the most established is the Reed-Xiaoli (RX) algorithm, which is based on a local Gaussian assumption for background clutter and locally estimates its parameters by means of the pixels inside a window around the pixel under test (PUT). In the literature, the RX decision rule has been employed to develop computationally efficient algorithms tested in real-time systems. Initially, a recursive block-based parameter estimation procedure was adopted that makes the RX processing and the detection performance differ from those of the original RX. More recently, an update strategy has been proposed which relies on a line-by-line processing without altering the RX detection statistic. In this work, the above-mentioned RX real-time oriented techniques have been improved using a linear algebra-based strategy to efficiently update the inverse covariance matrix thus avoiding its computation and inversion for each pixel of the hyperspectral image. The proposed strategy has been deeply discussed pointing out the benefits introduced on the two analyzed architectures in terms of overall number of elementary operations required. The results show the benefits of the new strategy with respect to the original architectures. © 2012 Springer-Verlag Berlin Heidelberg. Source


Rossi A.,University of Pisa | Acito N.,Accademia Navale di Livorno | Diani M.,University of Pisa | Luison C.,Altran GmbH | And 2 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2013

Modern thermal cameras acquire IR images with a high dynamic range because they have to sense with high thermal resolution the great temperature changes of monitored scenarios in specific surveillance applications. Initially developed for visible light images and recently extended for display of IR images, high dynamic range compression (HDRC) techniques aim at furnishing plain images to human operators for a first intuitive comprehension of the sensed scenario without altering the features of IR images. In this context, the maritime scenario represents a challenging case to test and develop HDRC strategies since images collected for surveillance at sea are typically characterized by high thermal gradients among the background scene and classes of objects at different temperatures. In the development of a new IRST system, Selex ES assembled a demonstrator equipped with modern thermal cameras and planned a measurement campaign on a maritime scenario so as to collect IR sequences in different operating conditions. This has led to build up a case record of situations suitable to test HDRC techniques. In this work, a survey of HDRC approaches is introduced pointing out advantages and drawbacks with focus on strategies specifically designed to display IR images. A detailed analysis of the performance is discussed in order to address the task of visualization with reference to typical issues of IR maritime images, such as robustness to the horizon effect and displaying of very warm objects and flat areas. © 2013 SPIE. Source


Acito N.,Accademia Navale di Livorno | Rossi A.,University of Pisa | Diani M.,University of Pisa | Corsini G.,University of Pisa
Optical Engineering | Year: 2011

A well-established scheme for target detection in infrared (IR) surveillance systems consists of applying a suitable decision rule on the images with background clutter previously removed. Background removal is accomplished by subtracting, from the original image, the estimate of the spatially varying background signal obtained by a background estimation algorithm (BEA). The overall target detection performance is strongly influenced by the effectiveness of the employed BEA. Particularly, the BEA and its design parameters should be chosen so as to get an accurate estimate of the background signal and to avoid biases caused by the possible presence of targets (target leakage). In this work, we present a novel method for the choice and setting of the best performing background removal technique for the detection of dim point targets. The proposed procedure is based on the simulation of dim targets implanted on an acquired sample image representing the scenario of interest. The choice of the best performing BEA is made by exploring the performance of the detection scheme for several configurations of the characteristic parameters of the BEAs. The effectiveness of the BEA selection procedure is evaluated in two case studies where real image sequences acquired by IR cameras are employed. The results confirm the benefits introduced by the proposed technique. Indeed, the performance of the IR detection system with the BEA tuned according to the proposed selection criterion is improved in that the number of false alarms is reduced up to 2 orders of magnitude compared with BEAs in other common configurations. © 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). Source

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