Oscar Serrano Serrano

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This paper compares two methods to estimate the position of a mobile robot in an indoor environment using only odometric calculus and the WiFi energy received from the wireless communication infrastructure. In both cases we use a well-known probabilistic method based on the Bayes rule to accumulate localization probability as the robot moves on with an(More)
Across the world, organizations have teams gathering threat data to protect themselves from incoming cyber attacks and maintain a strong cyber security posture. Teams are also sharing information, because along with the data collected internally, organizations need external information to have a comprehensive view of the threat landscape. The information(More)
This paper describes a method to estimate the position of a mobile robot in an indoor scenario using the odometric calculus and the WiFi energy received from the wireless infrastructure. This energy will be measured by wireless network card on-board a mobile robot, and it will be used as another regular sensor to improve position estimation. The Bayes rule(More)
The need for more fluent information sharing has been recognized for years as a major requirement by the cyber security community. Information sharing at present is mostly a slow, inefficient, and manual process that in many cases uses non-structured data sources. It is true that several cyber security data sharing tools have emerged and are currently(More)
This paper describes how IEEE 802.11b wireless networks can be used to determine the position of a robot inside a building. For this purpose we carried on some experiments which have shown that localization using only a wireless network as the only sensorial information (without a motion model) is not possible without a preconstructed sensorial map. But(More)
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