Object Localization by Smart Floors


The abstract goes here. Smartfloor and intelligent space.


Here is some sample LaTeX notation. By associativity, if \(\zeta\) is combinatorially closed then \(\delta = \Psi\). Since \[{S^{(F)}} \left( 2, \dots,-\mathbf{{i}} \right) \to \frac{-\infty^{-6}}{\overline{\alpha}},\] \(l < \cos \left( \hat{\xi} \cup P \right)\). Thus every functor is Green and hyper-unconditionally stable. Obviously, every injective homeomorphism is embedded and Clifford. Because \(\mathcal{{A}} > S\), \(\tilde{i}\) is not dominated by \(b\). Thus \({T_{t}} > | A |\).

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Subsection text here. Let’s show some more LaTeX: Obviously, \({W_{\Xi}}\) is composite. Trivially, there exists an ultra-convex and arithmetic independent, multiply associative equation. So \(\infty^{1} > \overline{0}\). It is easy to see that if \({v^{(W)}}\) is not isomorphic to \(\mathfrak{{l}}\) then there exists a reversible and integral convex, bounded, hyper-Lobachevsky point. One can easily see that \(\hat{\mathscr{{Q}}} \le 0\). Now if \(\bar{\mathbf{{w}}} > h' ( \alpha )\) then \({z_{\sigma,T}} = \nu\). Clearly, if \(\| Q \| \sim \emptyset\) then every dependent graph is pseudo-compactly parabolic, complex, quasi-measurable and parabolic. This completes the proof. (Kämpke 2008)

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Subsubsection text here. This is how you can cite other articles. Just type aureplacedverbaa where DOI is a Digital Object Identifier. For example cite this article published in IEEE INFOCOM 2001 (Aad 2001)

\label{fig:fig1} An example of a floating figure using the graphicx package.

Concept of Smart Floor design

Indoor localization is one of the most challenging and important tasks of various systems, especially in the domain of assistive robotic systems, intelligent space and smart environments. Methods that utilize cameras and/or laser range finders require complex recognition algorithms, which can then in turn consume a lot of processing power. A smart floor alleviates this kind of problem.

There are several ways how to implement a smart floor. One way is to utilize pressure sensors. Examples of this approach are described e.g. in (Mori 2004) and (Orr 2000), where pressure sensors were used to detect and track users in the testing environment. Equiping an entire floor with pressure sensors can be time and resource consuming in and on itself as every tile needs to send its data to the system. So a question of data transfer must be solved additionally. Moreover, as pointed out in (Mori 2004) pressure sensors have troubles with tracking multiple people near each other and with differentiating between them.

This problem can be solved by using the RFID (Radio-Frequency IDentification) technology. RFID relies on the wireless transfer of data using electromagnetic fields. RFID tags contain stored information, which can then be accessed by an appropriate reader (interrogator) as shown in fig. \ref{fig:fig2}. There are two kinds of RFID tags – passive and active ones. A passive tag (transponder) is powered by electromagnetic waves of the reader, whose antenna is induced and so it can send information to the reader. Its main limitation is in a very short range of several tens of centimeters. An active tag has its own local power source in the form of a battery and hereby it may operate at hundreds of meters from the reader. Of course, active RFID tags are more expensive as passive ones and their bigger dimensions can cause other problems in some applications, too. The use of both types of RFID tags was explored e.g. in (Kämpke 2008), (Johansson 2009), (Ku 2011) and (Khaliq).