Single Step 2-Port Device De-Embedding Algorithm for Fixture-DUT-Fixture Network Assembly

The de-embedding of measurement fixtures is relevant for an accurate experimental characterization of radio frequency and digital electronic devices. The standard technique consists in removing the effects of the measurement fixtures by the calculation of the transfer scattering parameters (T-parameters) from the available measured (or simulated) global scattering parameters (S-parameters). The standard de-embedding is achieved by a multiple steps process, involving the S-to-T and subsequent T-to-S parameter conversion. In a typical measurement setup, two fixtures are usually placed before and after the device under test (DUT) allowing the connection of the device to the calibrated vector network analyzer coaxial ports. An alternative method is proposed in this paper: it is based on the newly developed multi-network cascading algorithm. The matrices involved in the fixture-DUT-fixture cascading gives rise to a non-linear set of equations that is in one step analytically solved in closed form, obtaining a unique solution. The method is shown to be effective and at least as accurate as the standard multi-step de-embedding one.

Data Conversion Model Using the Principles of Geometric Model Structure Proximity Comparison

Research has been carried out into possible data conversion losses and the integration of heterogeneous automated systems in enterprises. A model of data conversion in the framework of heterogeneous automated systems interaction on the example of geometrical model structures comparison of heterogeneous automated systems is proposed. The model can be used for loss estimation using the representation of geometric models as data structures and conversion metrics. The article deals with the problem at the current stage of information support for lifecycles processes is the lack of integration of multi-vendor automation systems in enterprises. Losses in one stage of the lifecycle can lead to technical and economic difficulties in other stages of the lifecycle and problems can also be encountered when integrating automation systems and data conversion between enterprises. There is a need to develop an advanced parameter conversion model and compare the proximity of GM structures between automation systems. It is required to evaluate the efficiency of data conversion between environments using different formats using metrics.

Parameter Conversion between Controlled Pass-By Method and Alternative Close Proximity Method

To shorten the measurement period and reduce experiment costs, we investigated the parameter conversion between the experiment results of the controlled pass-by (CPB) method and alternative close proximity (A-CPX) method for automotive applications. The CPB and A-CPX methods were used to experiment with tire noise. The correlation between the tire noises of the two experimental methods was analyzed. Then, the quantitative transformation relationship between the tire noises of the two methods was obtained using an acoustic radiation propagation experiment in the semi-free field. The results indicate a good linear correlation between the experimental results of the two experimental methods. In the case of ignoring the shielding effect of the car body, the average difference between the measured value of the CPB method and the predicted value of the experimental tire is about 1.1 dB. When considering the shielding effect of the car body, the average difference between the measured value of the CPB method and the predicted value of the experimental tire is about 2.7 dB.

ID Insertion and Data Tracking with Frequency Offset for Physical Wireless Parameter Conversion Sensor Networks

As the applications of the internet of things are becoming widely diversified, wireless sensor networks require real-time data reception, accommodation of access from several sensors, and low power consumption. In physical wireless parameter conversion sensor networks (PhyC-SN), all the sensors use frequency shift keying as the modulation scheme and then access the channel to the fusion center, simultaneously. As a result, the fusion center can recognize the statistical tendency of all the sensing results at a time from the frequency spectrum of the received signal. However, the information source, i.e., the sensor, cannot be specified from the received signal because no ID-indicating sensor is inserted to the signal. The data-tracking technique for tracing the time continuity of the sensing results is available for decomposing the sequence of the sensing results per sensor but the error tracking, which is a wrong recognition between the sensing results and the sensor, occurs owing to the similarity of the sensing results. This paper proposes the sensing result separation technique using a fractional carrier frequency offset (CFO) for PhyC-SN. In the proposed scheme, the particular fractional CFO is assigned to each user and it is useful for the ID specifying sensor. The fractional CFO causes inter-carrier interference (ICI). The ICI cancellation of the narrowband wireless communications is proposed. The two types of data-tracking techniques are proposed and are selectively used by the fusion center. Since the proposed data-tracking technique is multi-dimensional, high accuracy of data tracking is achieved even under the similar tendency of the sensing results. Based on computer simulation, we elucidate the advantage of the proposed sensing results separation.