832 resultados para ETS (Electronic computer system)


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It is our goal within this project to develop a powerful electronic system capable to claim, with high certainty, that a malicious software is running (or not) along with the workstations’ normal activity. The new product will be based on measurement of the supply current taken by a workstation from the grid. Unique technique is proposed within these proceedings that analyses the supply current to produce information about the state of the workstation and to generate information of the presence of malicious software running along with the rightful applications. The testing is based on comparison of the behavior of a fault-free workstation (established i advance) and the behavior of the potentially faulty device.

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The aim of this Master Thesis is the analysis, design and development of a robust and reliable Human-Computer Interaction interface, based on visual hand-gesture recognition. The implementation of the required functions is oriented to the simulation of a classical hardware interaction device: the mouse, by recognizing a specific hand-gesture vocabulary in color video sequences. For this purpose, a prototype of a hand-gesture recognition system has been designed and implemented, which is composed of three stages: detection, tracking and recognition. This system is based on machine learning methods and pattern recognition techniques, which have been integrated together with other image processing approaches to get a high recognition accuracy and a low computational cost. Regarding pattern recongition techniques, several algorithms and strategies have been designed and implemented, which are applicable to color images and video sequences. The design of these algorithms has the purpose of extracting spatial and spatio-temporal features from static and dynamic hand gestures, in order to identify them in a robust and reliable way. Finally, a visual database containing the necessary vocabulary of gestures for interacting with the computer has been created.

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The explosive growth of the traffic in computer systems has made it clear that traditional control techniques are not adequate to provide the system users fast access to network resources and prevent unfair uses. In this paper, we present a reconfigurable digital hardware implementation of a specific neural model for intrusion detection. It uses a specific vector of characterization of the network packages (intrusion vector) which is starting from information obtained during the access intent. This vector will be treated by the system. Our approach is adaptative and to detecting these intrusions by using a complex artificial intelligence method known as multilayer perceptron. The implementation have been developed and tested into a reconfigurable hardware (FPGA) for embedded systems. Finally, the Intrusion detection system was tested in a real-world simulation to gauge its effectiveness and real-time response.

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Transportation Department, Office of University Research, Washington, D.C.

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Indiana Department of Transportation, Indianapolis

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Indiana Department of Transportation, Indianapolis

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National Highway Traffic Safety Administration, Washington, D.C.