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In the context of cognitive radio or military applications, it is a crucial task to distinguish blindly various OFDM based systems (e.g., Wifi, Wimax, 3GPP/LTE, DVB-T) from each others. Existing OFDM based systems differ from their subcarrier spacing used in OFDM modulation. One can thus carry out recognition algorithms based on the value of the subcarrier(More)
We propose in this paper a detection algorithm based on a cost function that jointly tests the correlation induced by the cyclic prefix and the fact that this correlation is time-periodic. In the first part of the paper, the cost function is introduced and some analytical results are given. In particular, the noise and multipath channel impacts on its(More)
— An opportunistic radio is a radio able to detect the spectrum unused bands, and to adapt its transmission parameters in order to transmit within these free bands. An opportunistic terminal has also to be able to detect opportunistic access points and to recognize their used standards. As most standards are now based on OFDM modulation with distinct(More)
A graph based classifier is proposed to recognize the different time phases of the up & go test based on signals collected by an inertial sensor set on a person chest. This test being a sequential set of actions, a graph is used to model it and enforce the classification algorithm to estimate a solution with this constraint. The graph is described by a(More)
In this contribution, we propose a spread signal detector based on the averaged-power of its autocorrelation function. A cost function that tests this property is build and some results are given on its estimation. We also explain how to use it to detect signals in SISO and SIMO cases. We conclude this paper with some numerical estimation of the proposed(More)
In this contribution, we provide a simple condition on the statistics of the source signals ensuring that the Comon algorithm [2], originally designed for stationary data, achieves the separation of an instantaneous mixture of cyclostationary sources. The above condition is analyzed for digital communications signals and is (semi-analytically) proved to be(More)
In this paper, an algorithm able to detect epilepsy seizure based on 3D accelerometers and with patient adaptation is presented. This algorithm is based on a Bayesian approach using hidden Markov models for statistical modelling of moves signals. A particular focus is set on the learning procedure and in particular on its initialisation to ensure a good(More)
A system of epilepsy seizure detection in real life conditions and based on inertial sensors is presented in this paper with a focus on the signal processing to recognize seizure moves. This system is based on several models of signals, one corresponding to general movements, and two others describing seizures moves. The detection algorithm evaluates for a(More)