Communications
This pdf file contains supplementary information to a published article, "Early Warning of mmWave Signal Blockage and AoA Transition Using sub-6 GHz Observations," by Ziad Ali, Alexandra Duel-Hallen and Hans Hallen, published in IEEE Communications Letters, 2019.
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This work introduces an E-plane hollow waveguide antenna array feeding network to feed 4 units of 2x2 sub-arrays as aperture elements. The feeding network and the sub-arrays are built as one object and fabricated using 3D-printing technology. The material used in 3D-printing is polymer and the cavity of the antenna array with the feeding network were metalized using sufficient layer of conductive silver epoxy. The introduced design exhibits an 18% bandwidth at center frequency of 14 GHz with a gain of 16 dBi.
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These are the optimized excitation currents obtained by different algorithms.
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This data set provides experimental results for the analysis of extreme values of trilateration localization error in wireless communication systems. The analysis is based upon the analytical model of trilateration localization error described and discussed in the manuscript titled "An Analytical Model of Trilateration Localization Error".
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In order to increase the diversity in signal datasets, we create a new dataset called HisarMod, which includes 26 classes and 5 different modulation families passing through 5 different wireless communication channel. During the generation of the dataset, MATLAB 2017a is employed for creating random bit sequences, symbols, and wireless fading channels.
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These files are used for reivision
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Smart Grids (SG) are a novel paradigm introduced for optimizing the management of the power generation, transmission, distribution and consumption. A SG system can efficiently work only if all the components are connected through a communication network able to satisfy the SG applications requirements. Wireless communications are the most appropriate candidates for handling SG requirements due to their flexibility.
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FIWARE Innovation Arena ideas submitted, including text and images
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This is the proof of NP-hard for DCB multi-objective optimization
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We present two synthetic datasets on classification of Morse code symbols for supervised machine learning problems, in particular, neural networks. The linked Github page has algorithms for generating a family of such datasets of varying difficulty. The datasets are spatially one-dimensional and have a small number of input features, leading to high density of input information content. This makes them particularly challenging when implementing network complexity reduction methods.
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