The engine in-cylinder pressure is a very important parameter for the optimization of internal combustion engines. This paper proposes an alternative recursive Kalman filter-based engine cylinder pressure reconstruction approach using sensor-fused engine speed. In the proposed approach, the fused engine speed is first obtained using the centralized sensor fusion technique, which synthesizes the information from the engine vibration sensor and engine flywheel angular speed sensor. Afterwards, with the fused speed, the engine cylinder pressure signal can be reconstructed by inverse filtering of the engine structural vibration signal. The cylinder pressure reconstruction results of the proposed approach are validated by two combustion indicators, which are pressure peak $P_{\max }$ and peak location $P_{\mathrm{loc}}$. Meanwhile, the reconstruction results are compared with the results obtained by the cylinder pressure reconstruction approach using the calculated engine speed. The results of sensor fusion can indicate that the fused speed is smoother when the vibration signal is trusted more. Furthermore, the cylinder pressure reconstruction results can display the relationship between the sensor-fused speed and the cylinder pressure reconstruction accuracy, and with more belief in the vibration signal, the reconstructed results will become better.

Line-of-sight (LOS) is often required in fixed broadband wireless access systems like LMDS or WiMAX. However, the percentage of customers in a cell which can be reached via a LOS link depends strongly on the environment and other scenario parameters. In this article the influence of these parameters on an introduced average LOS coverage ratio is analyzed using ray tracing calculations and approximately described by simple expressions. This makes it possible to rate, to compare and even to transfer coverage results to other scenarios, which allows a rough but easy estimation of the average LOS coverage for an arbitrary scenario based on a reference scenario.

Die Übertragungsfunktion eines mechanischen Systems ist eine der wichtigsten Messgrößen in der Schwingungsanalyse. Um die Übertragungsfunktion möglichst breitbandig bestimmen zu können, wird eine breitbandige Anregung benötigt, welche z.B. durch eine Plasmaanregung realisiert werden kann. Allerdings stellt die Bestimmung des Spektrums einer solchen Plasmaanregung, welche für die Ermittlung der Übertragungsfunktion notwendig ist, zurzeit noch eine Herausforderung in der Forschung dar. In diesem Beitrag bestimmen wir das Anregungsspektrum durch ein Simulationsmodell und vergleichen das Ergebnis mit dem Resultat einer bisher typischerweise eingesetzten experimentellen Schätzmethode. Der vorgestellte Ansatz benötigt einen deutlich geringeren Aufwand und stellt damit für praktische Anwendungen eine gute Alternative zum aktuellen Stand der Technik dar. The frequency response function (FRF) of a mechanical system is one of the most important measured characteristics in vibration analysis. To determine the FRF as broadband as possible, a broadband excitation, such as plasma excitation, is required. However, determination of the spectrum of the plasma excitation, which is necessary for the determination of FRF, is still a challenge to research. In this paper we determine the excitation spectrum by a simulation model and compare the result with the outcome of a typical experimental approach. The presented method requires significantly less effort and is therefore a good alternative to the state of the art for practical applications.

Input reconstruction problem is usually encountered in the areas such as virtual sensing, image restoration, sensor linearization, and communications. The input reconstruction problem can be seen as an inverse problem. Given a nominal system, two types of approaches are commonly used for solving the inverse problem. The first one is to directly invert the nominal system, and the second one is to derive an inversion of the nominal system in an indirect way. However, for the first type of approaches, system inversion cannot be directly conducted under some conditions such as there exist nonminimum-phase zeros in the nominal system, while for the second type of approaches, simultaneously guaranteeing stability and causality of the obtained inversion is still not solved well. In order to avoid the drawbacks existing in the two types of approaches, an alternative approach is proposed for input reconstruction. In this approach, the input signal is first modeled as the output of a state-space model, afterwards two Kalman filters for the model resulted by combining the input signal model and the nominal system model are implemented alternatively such that the input signal can be sequentially reconstructed in an infinite horizon. The proposed input reconstruction approach is applied in two examples, and simulation results can illustrate the effectiveness of the proposed approach.

The inverse system identification toolbox named INVSID 1.0 for MATLAB, which is used to identify the inversion of single-input single-output systems, is developed. The complete process from theoretical derivation to toolbox creation of developing the toolbox is demonstrated. Afterwards, numerical examples are illustrated to describe how the toolbox can be used to solve inverse identification problems. Simulation results demonstrate the effectiveness of the toolbox.

The inverse system identification toolbox named INVSID 1.0 for MATLAB, which is used to identify the inversion of single-input single-output systems, is developed. The complete process of developing the toolbox is demonstrated. Afterwards, a numerical example is illustrated to describe how the toolbox can be used to solve inverse identification problems. Simulation results demonstrate the effectiveness of the toolbox.