Recently, for modeling biological amplification processes, nonlinear amplifiers based on the Andronov–Hopf bifurcation have become a focus of attention. In this contribution, we analyze discrete-time implementations of this type of amplifiers. The effects of the time-discretization by explicit integration methods are discussed. A novel discrete-time system based on the Neimark–Sacker bifurcation is introduced, that outstandingly approximates the behavior of a particular Hopf-type amplifier.

This paper reports on an effective root-cause analysis method of memory effects in power amplifiers, as well as introduces compensation techniques on a circuit design level. Despite conventional memory-effect approaches, the discussed method uses a two-tone scan over a wide operation and modulation range. The approach is demonstrated on a 65-nm CMOS power amplifier with an OIP1 of 27 dBm and a PAE of over 30 % using WCDMA and LTE signals.

This work investigates the errors, numerically and analytically, which occur due to the discretization of surveillance space in a multi-sensor radar system. The result of these investigations help to imprve the efficiency of the well-known compressed sensing algorithms. The improvement is critical for the cases where the signal processing algorithms must deliver the results within very short time intervals, e.g. 5 milliseconds or less.

This paper presents a novel algorithm for traffic monitoring applications, where the powerful post-Doppler space-time adaptive processing (PD STAP) is combined with the OpenStreetMap database. The road map is applied after the PD STAP for recognizing and rejecting false detections, and for repositioning off-road vehicles. The algorithm was tested using real multi-channel data acquired by DLR’s airborne F-SAR. The experimental results are shown and discussed, and the novelties are highlighted.

This paper presents a comparative overview of models for lithium-ion batteries from an electrical Engineering Point of view. The comparison criteria and the different models are presented. After this comparison, a model is used to establish a new communication concept.

Currently, the electrification of the drive train of passenger cars takes place, and the task of obtaining precise knowledge about the condition of the on board batteries gains importance. Due to internal characteristics, several existing methods cannot be used. This work describes an impedance based approach using the Taylor Fourier transformation. The parameters extracted by the method can be used as features in machine learning algorithms.