In this paper, a bidirectional RF front-end based on the WIN Semiconductor’s 100 nm GaAs pHEMT process that can support the extension of E-band vector network analyzers (VNA) is proposed. For E-band VNA extensions, an amplifier multiplier chain (AMC) with high output power and high harmonic rejection characteristics is required to effectively drive the following bidirectional passive mixer. In this design, the AMC includes a 20–30 GHz frequency doubler, an E-band frequency tripler, and an E-band balanced power amplifier. The frequency doubler features a push-push architecture to effectively improve the rejection characteristics of odd harmonics. Following this stage is a frequency tripler based on an antiparallel diode pair (APDP) topology, which includes a pair of power enhancement T-lines to improve the output power of the AMC. The output of the AMC is a balanced power amplifier with source degeneration inductors to enhance the amplifier’s bandwidth and stability. To further expand the bandwidth of the AMC, driver amplifiers with an integrated RC negative feedback network are included. Additionally, a compact matching network with bandpass characteristics is integrated between the doubler and tripler to enhance harmonic rejection performance. Measurement results indicate that the peak output power of the E-band AMC is 20.9 dBm, and the harmonics rejection is better than 25 dBc. The intermediate frequency bandwidth of the bidirectional RF front-end covers 100 MHz–8 GHz, and the corresponding up-conversion gain and down-conversion gain are better than -8.2dB and -8 dB, respectively.

Outliers are usually unavoidable in measurement processes due to various factors and often compromise the robustness of data reconstruction. To address this issue, a novel moving domain Chebyshev basis function (MDCBF) method is developed. This method introduces the concept of the influence domain to build the moving models and eliminate the local abnormal points using Student’s t-regression and Jarvis–Patrick (JP) clustering. First, related points are incorporated into the local domains through the influence domain, transforming the entire model into multiple local models. Within each influence domain, iterative Student’s t-regression is employed to generate a series of preliminary estimation models. The absolute residual of each point can be calculated relative to the estimation model. JP clustering is then employed to remove the points associated with the clusters exhibiting larger absolute residuals, while the preserved points are used in the next regression iteration. Finally, a weighted Chebyshev basis function is employed to determine the local estimated value from the preserved points. By moving the influence domain across the dataset, global reconstruction can be achieved. Additionally, an enhanced MDCBF strategy is proposed to address the extreme case of continuous outliers. In both simulation and experimental datasets, the MDCBF method demonstrates superior robustness and enhanced reconstruction accuracy under varying levels of contamination.

In order to solve group decision making (GDM) problems where the preference relations are provided by decision makers with incomplete fuzzy and interval numbers, this paper develops a GDM approach on the basis of multiple objective optimization and goal programming methods. The proposed approach first analyzes the two types of preference relations and then builds their respective optimization models. Subsequently, an integrated programming model combining the two preference relations is developed to minimize the inconsistency among the decision makers' opinions. By solving the programming model, the ranking of alternatives or selection of the most desirable alternative can be obtained using the intermediate priority vector. Two numerical examples including incomplete fuzzy and interval preference relations are examined to illustrate and show the applicability of the proposed approach.