In this paper, we present an adaptive algorithm based on direct data domain which can compensate the mutual coupling with only one snapshot of the received signals. Without any auxiliary sensors and calibration sources, this algorithm can accurately estimate the coupling coefficients and recover the desired signal. Numerical simulation shows that the recovery of the desired signal is accurate in the presence of mutual coupling.
We measured in the frequency range from 300kHz to 6GHz of the transfer impedance of a commercially available calibration target newly developed for immunity testing against electrostatic discharges (ESDs) being prescribed by the International Electrotechnical Commission. The result showed that the transfer impedance has an almost flat frequency response up to 6GHz, while resonance phenomena were still observed at frequencies around 2.5 and 5GHz. With this result, the waveforms of injected currents onto the target were reconstructed from the observed output voltages for air discharges of an ESD generator with a charge voltage of 2kV.
A large-signal FET model for the simulation and design of switching-mode high-efficiency power amplifiers (PAs) is presented. The proposed nonlinear model is constructed by accurately characterizing the ON and OFF behaviors of the active FET device, along with its parameter extraction associated with the specific regions. The robustness of the model in predicting the switching-mode operation of on-wafer GaN-based HEMTs is demonstrated by experimental results. Moreover, the model has been employed for designing an inverse Class-F PA using a commercial high-power GaN HEMT. Good agreement between amplifier simulation and measurement proves the validity of the proposed large-signal model.
Many image hashing algorithms have been proposed to detect the malicious tampering for content authentication. However, their tampering localization performance degrades dramatically on images with content-preserving distortion, as these algorithms cannot distinguish the malicious tampering from content-preserving distortion. A novel framework for existing hashing algorithms to improve their performance on tampering localization in distorted images is proposed in this paper. High precision of tampering localization in distorted images is achieved by controlling the robustness of extracted features. By experimenting with classical image hashing algorithms, the correctness of the proposed framework is proved.