F. Terroso-Saenz - Academia.edu (original) (raw)
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Papers by F. Terroso-Saenz
International Conference on Fuzzy Systems, 2010
Information Fusion, 2012
ABSTRACT Nowadays, most people are used to driving their own vehicles to accomplish certain routi... more ABSTRACT Nowadays, most people are used to driving their own vehicles to accomplish certain routines like commuting, go shopping, and the like. Taking into account the increasing number of sensors vehicles are provided with, the present work states that it is possible to perceive the context of a vehicle by processing and fusioning the data of some of them. As a result, an on-board context-aware application that processes the usual itineraries of the Ego Vehicle as part of the vehicular context has been implemented. Particularly, the system follows a Complex Event Processing (CEP) approach, and it is able to detect the vehicular occupancy along with the meaningful points of the frequent itineraries whereby a density-based-cluster algorithm. Test results from simulations and real environments show the accuracy of the system when it comes to detect different types of itineraries.
IEEE Transactions on Intelligent Transportation Systems, 2000
Engineering Applications of Artificial Intelligence, 2013
Information Systems, 2015
ABSTRACT In this day and age, there exists an increasing need for systems and architectures able ... more ABSTRACT In this day and age, there exists an increasing need for systems and architectures able to process spatio-temporal data in a timely way. As a result, this paper presents CEP-traj, a novel middleware to ease the development of real-time trajectory-based services based on the Complex Event Processing (CEP) paradigm. By means of an event-based approach, the present middleware is able to detect a set of generic patterns along with meaningful changes of an entity׳s movement. In order to prove its suitability and feasibility, a vessel abnormal-behaviour detection system has been developed on the basis of the middleware׳s features. Finally, both synthetic and real datasets have been used to test the accuracy and performance of the middleware and the detection system implemented on top of the Esper engine.
International Conference on Fuzzy Systems, 2010
Information Fusion, 2012
ABSTRACT Nowadays, most people are used to driving their own vehicles to accomplish certain routi... more ABSTRACT Nowadays, most people are used to driving their own vehicles to accomplish certain routines like commuting, go shopping, and the like. Taking into account the increasing number of sensors vehicles are provided with, the present work states that it is possible to perceive the context of a vehicle by processing and fusioning the data of some of them. As a result, an on-board context-aware application that processes the usual itineraries of the Ego Vehicle as part of the vehicular context has been implemented. Particularly, the system follows a Complex Event Processing (CEP) approach, and it is able to detect the vehicular occupancy along with the meaningful points of the frequent itineraries whereby a density-based-cluster algorithm. Test results from simulations and real environments show the accuracy of the system when it comes to detect different types of itineraries.
IEEE Transactions on Intelligent Transportation Systems, 2000
Engineering Applications of Artificial Intelligence, 2013
Information Systems, 2015
ABSTRACT In this day and age, there exists an increasing need for systems and architectures able ... more ABSTRACT In this day and age, there exists an increasing need for systems and architectures able to process spatio-temporal data in a timely way. As a result, this paper presents CEP-traj, a novel middleware to ease the development of real-time trajectory-based services based on the Complex Event Processing (CEP) paradigm. By means of an event-based approach, the present middleware is able to detect a set of generic patterns along with meaningful changes of an entity׳s movement. In order to prove its suitability and feasibility, a vessel abnormal-behaviour detection system has been developed on the basis of the middleware׳s features. Finally, both synthetic and real datasets have been used to test the accuracy and performance of the middleware and the detection system implemented on top of the Esper engine.