As an example, Figure 3 shows an acquisition carried out in the Masquefa station turnout of the Igualada-Martorell line of Ferrocarrils de la Generalitat de Catalunya (FGC) in December 2007 compared to a second acquisition of the same turnout carried out in December 2011. Different gyroscope models, acquisition hardware and processing software, low pass filters, etc. where used; however the gyroscope yaw rates are very similar, including small systematic variations superimposed to the basic oscillation pattern.Figure 3.Yaw rates of a turnout in the Masquefa station measured in two experimental campaigns with different hardware and software. (a) December 2007. (b) December 2011. Small differences are due to gyroscope noise and slightly different train velocities and .
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?The Binary Track DetectionThe detection of a turnout deriving a train to a parallel track siding from the main track can be obtained by processing the turn rate signal provided by a MEMS gyroscope. The turnout detection is a binary decision, similar to a single bit detection in telecommunications or target detection in radar [8,9]. Assuming a single track line, at a certain Kilometric Point (KP) where the track splits into a main track and a siding parallel track, two hypotheses are possible: H1 means the train has been diverted to the siding track, and H0 means that Brefeldin_A the train remains on the main track.
A turnout detector after processing the gyro signal will take a decision between two possible outcomes: D1 corresponds to a positive detection of the turnout, locating the train on the siding, whereas D0 means that the detector after processing the gyro, data has decided that the train head has followed the main track.
For every hypothesis, corre
Small, cheap, highly mobile robots can be used to solve a wide variety of problems [1]. The availability of off-the-shelf components for such micro air vehicles (MAVs) has made them an active topic of current research and even commercial development (e.g., [2�C4]). In particular, MAVs are well suited for exploration of unknown or difficult to access indoor environments, such as buildings and caves.
They can be used to map out a space of interest, or search within for items of interest. MAVs can also be used to deploy a stationary wireless sensor network (WSN) for long-term persistent sensing of that environment.Though there are many details differentiating Batimastat specific mission scenarios, at the most basic they all involve gathering data throughout an unknown environment and passing it to a remote base station. Only in a very narrow range of specifications can such a mission be accomplished without any communication at all (e.g.