Differential Amplify-and-Forward Relaying in Time-Varying Rayleigh Fading Channels

This paper considers the performance of differential amplify-and-forward (D-AF) relaying over time-varying Rayleigh fading channels. Using the auto-regressive time-series model to characterize the time-varying nature of the wireless channels, new weights for the maximum ratio combining (MRC) of the received signals at the destination are proposed. Expression for the pair-wise error probability (PEP) is provided and used to obtain an approximation of the total average bit error probability (BEP). The obtained BEP approximation clearly shows how the system performance depends on the auto-correlation of the direct and the cascaded channels and an irreducible error floor exists at high signal-to-noise ratio (SNR). Simulation results also demonstrate that, for fast-fading channels, the new MRC weights lead to a better performance when compared to the classical combining scheme. Our analysis is verified with simulation results in different fading scenarios

Scalar sextet in the 331 model with right-handed neutrinos

A Higgs sextet is introduced in order to generate Dirac and Majorana neutrino masses in the 331 model with right-handed neutrinos. As will be seen, the present sextet introduction leads to a rich neutrino mass structure. The smallness of neutrino masses can be achieved via, for example, a seesaw limit. The fact that the masses of the charged leptons are not effected by their new Yukawa couplings to the sextet is convenient for generating small neutrino masses.Comment: RevTeX4, 5 pages, no figure. To appear in Phys. Rev. D. Misprints removed (v.2

Trimers of MnO6 octahedra and ferrimagnetism of Ba4NbMn3O12

Ba4NbMn3O12 is reported, synthesized by a solid state method in air. The crystal structure, determined by performing refinements on room temperature powder X-ray diffraction data by the Rietveld method, consists of Mn3O12 trimers in the configuration of three face-sharing MnO6 octahedra, with the trimers arranged in triangular planes. An effective moment of 4.82 {\mu}B/f.u is observed and competing antiferromagnetic and ferromagnetic interactions between Mn ions are inferred from the Weiss temperature of -4 K and the ferrimagnetic ordering transition of approximately 42 K. Ba4NbMn3O12 is a semiconductor with a transport activation energy of 0.37 eV.Comment: 19 pages, 7 figure

Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data

Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.TU Berlin, Open-Access-Mittel - 201