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An Empirical Evaluation of the Received Signal Strength Indicator for fixed outdoor 802.11 links
(2015)
For the evaluation of the received signal strength indication (RSSI) a different methodology compared to previous publications is introduced in this paper by exploiting a spectral scan feature of recent Qualcomm Atheros WiFi NICs. This method is compared to driver reports and to an industrial grade spectrum analyzer. During the conducted outdoor experiments a decreased scattering of the RSSI compared to previous publications is observed. By applying well-known mathematical tests for normality it is possible to show that the RSSI does not follow a normal distribution in a line-of-sight outdoor environment. The evaluated spectral scan features offers additional possibilities to develop interference classifiers which is an important step for frequency allocation in long-distance 802.11 networks.
Annual Report 2013 - 2014
(2015)
This paper proposes an Artificial Plasmodium Algorithm (APA) mimicked a contraction wave of a plasmodium of physarum polucephalum. Plasmodia can live using the contracion wave in their body to communicate to others and transport a nutriments. In the APA, each plasmodium has two information as the wave information: the direction and food index. We apply APA to a maze solving and route planning of road map.
This book chapter describes application examples of gas chromatography/mass spectrometry and pyrolysis – gas chromatography/mass spectrometry in failure analysis for the identification of chemical materials like mineral oils and nitrile rubber gaskets. Furthermore, failure cases demanding identification of polymers/copolymers in fouling on the compressor wall of a car air conditioner and identification of fouling on the surface of a bearing race from the automotive industry are demonstrated. The obtained analytical results were then used for troubleshooting and remedial action of the technological process.
This paper proposes a new artificial neural network-based maximum power point tracker for photovoltaic application. This tracker significantly improves efficiency of the photovoltaic system with series-connection of photovoltaic modules in non-uniform irradiance on photovoltaic array surfaces. The artificial neural network uses irradiance and temperature sensors to generate the maximum power point reference voltage and employ a classical perturb and observe searching algorithm. The structure of the artificial neural network was obtained by numerical modelling using Matlab/Simulink. The artificial neural network was trained using Bayesian regularisation back-propagation algorithms and demonstrated a good prediction of the maximum power point. Relative number of Vmpp prediction errors in range of ±0.2V is 0.05% based on validation data.