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In this paper we describe traffic sign recognition with neural networks in the frequency domain. Traffic signs exist in all countries to regulate the traffic of vehicles and pedestrians. Each country has its own set of traffic signs that are more or less similar. They consist of a set of abstract forms, symbols, numbers and letters, which are combined into different signs. Automatic traffic sign recognition is important for driver assistance systems and for autonomous driving. Traffic sign recognition is a subtype of image recognition. The traffic signs are usually recorded by a camera and must be recognized in real time, i.e. assigned to a class. We use neural networks for traffic sign recognition. The special feature of our method is that the traffic sign recognition does not take place in the spatial domain but in the frequency domain. This has advantages because it is possible to significantly reduce the number of neurons and thus the computing effort of the neural network compared to a conventional neural network.
The use of nematic liquid crystal (LC) mixtures for microwave frequency applicationspresents a fundamental drawback: many of these mixtures have not been properly characterizedat these frequencies, and researchers do not have an a priori clear idea of which behavior they canexpect. This work is focused on developing a new procedure for the extraction of the main parametersof a nematic liquid crystal: dielectric permittivity and loss tangent at 11 GHz under differentpolarization voltages; splay elastic constantK11, which allows calculation of the threshold voltage(Vth); and rotational viscosityγ11, which allows calculating the response time of any arbitrary device.These properties will be calculated by using a resonator-based method, which is implementedwith a new topology of substrate integrated transmission line. The LC molecules should be rotated(polarized) by applying an electric field in order to extract the characteristic parameters; thus,the transmission line needs to have two conductors and low electric losses in order to preserve theintegrity of the measurements. This method was applied to a well-known liquid crystal mixture(GT3-23002 from MERCK) obtaining the permittivity and loss tangent versus bias voltage curves,the splay elastic constant, and the rotational viscosity of the mixture. The results validate the viabilityof the proposed method.