Univ.-Prof. Dr.-Ing. Del Galdo, Giovanni

In this paper, we present cost-effective techniques to assess shielding performance of RFID Kanban racks equipped with UHF RFID readers. This includes a self-developed positioner, made from roof battens and stepper motors, which positions a tag in a vertical area around each side of the rack to sense the radio frequency (RF) distribution emitted from the reader. For the measurements on the shelves, we sticked 30 tags on a foam board in a 20 cm grid to detect leakages on the horizontal planes. The overall material cost is less than 50 € and we made the documentation and control software publicly available.

While traditional testing methods as conducted and open field tests have their limitations in terms of realism and repeatability of the scenario's conditions, Over-the-Air (OTA) testing enhances the way that GNSS and mobile communications systems with integrated antennas are tested, since it provides the freedom to accurately emulate an impinging wave with arbitrary polarization and direction radiated from any source/reflection such as GNSS satellites and terrestrial base stations. Therefore, real world scenarios can be precisely reproduced with total control of the environment's conditions. In this context, this paper describes the process to implement full polarimetric 3D Wave Field Synthesis (WFS) in an OTA testbed, from the principle to characterize the electromagnetic (EM) field in three dimensions to the system calibration and correspondent verification measurements inside the anechoic chamber, which will provide a deeper insight on the quality and reliability of the EM field for testing purposes.

We propose a compact hardware architecture for measuring sparse channel impulse responses (IR) by extending the M-Sequence ultra-wideband (UWB) measurement principle with the concept of compressed sensing. A channel is excited with a periodic M-sequence and its response signal is observed using a Random Demodulator (RD), which observes pseudo-random linear combinations of the response signal at a rate significantly lower than the measurement bandwidth. The excitation signal and the RD mixing signal are generated from compactly implementable Linear Feedback Shift registers (LFSR) and operated from a common clock. A linear model is derived that allows retrieving an IR from a set of observations using Sparse-Signal-Recovery (SSR). A Matrix-free model implementation is possible due to the choice of synchronous LFSRs as signal generators, resulting in low computational complexity. For validation, real measurement data of a time-variant channel containing multipath components is processed by simulation models of our proposed architecture and the classic M-Sequence method. We show successful IR recovery using our architecture and SSR, outperforming the classic method significantly in terms of IR measurement rate. Compared to the classic method, the proposed architecture allows faster measurements of sparse time-varying channels, resulting in higher Doppler tolerance without increasing hardware or data stream complexity.

Traditional black box clock skew models are either the power-law noise model or a more recent approach based on auto-regressive (AR) filters. Unfortunately, neither algorithm can accurately model short and long term skew due to limited degrees of freedom or stability constraints. We propose a novel model that employs the current AR algorithm recursively with appropriate pre- and post-processing to achieve numeric stability and accurate reproduction of short and long term effects. The model coefficients are derived from a measured skew signal, with the model output matching an exemplary original signal closely in terms of Allan variance and time-domain behavior.

Structure-borne acoustic emission (AE) measurement shows major advantages regarding quality assurance and process control in industrial applications. In this paper, laser beam welding of steel and aluminum was carried out under varying process parameters (welding speed, focal position) in order to provide data by means of structure-borne AE and simultaneously high-speed video recordings. The analysis is based on conventionally (e.g. filtering, autocorrelation, spectrograms) as well as machine learning methods (convolutional neural nets) and showed promising results with respect to the use of structure-borne AE for process monitoring using the example of spatter formation.