For large-scale integrated electronic equipment, the complex operating mechanisms make fault detection very difficult. Therefore, it is important to accurately identify analog circuit faults in a timely manner. To overcome this problem, this paper proposes a novel fault diagnosis method based on the deep belief network (DBN) and restricted Boltzmann machine (RBM) optimized by the gray wolf optimization (GWO) algorithm. First, DBN is used to extract the deep features of the analog circuit output signal. Then, GWO is used to optimize the penalty factor c and kernel parameter g of support vector machine (SVM). Finally, GWO-SVM is used to diagnose the signal features extracted by the DBN. Fault diagnosis simulation was conducted for the Sallen–Key band-pass filter and a four-opamp biquad highpass filter. The experimental results show that compared with the existing algorithms, the algorithm proposed in this paper improves the accuracy of Sallen–Key bandpass filter circuit to 100% and shortens the fault diagnosis time by about 90%; for four-opamp biquad highpass filter, the accuracy rate has increased to 99.68%, and the fault diagnosis time has been shortened by approximately 75%, and reduce hundreds of iterations. Moreover, the experimental results reveal that the proposed fault diagnosis method greatly improves the accuracy of analog circuit fault diagnosis, which solves a major problem in analog circuitry and has great significance for the future development of relevant applications.

We propose a filterless full-duplex radio-over-fiber system based on polarization multiplexing and demonstrate the generation of an 80 GHz millimeter wave using two Mach–Zehnder modulators. By adjusting the polarization direction, we could generate an 80 GHz frequency millimeter-wave signal and restore the original pure light carrier, providing a light source for the uplink. The simulation results show that the 80 GHz millimeter-wave signal was obtained with a 23.48 dB radio-frequency sideband suppression ratio. Furthermore, we showed that the proposed scheme is relatively flexible and free from the limitation of filter fixed bandwidth in addition to being simple and economical.

In high-speed optical communication, the blind phase search (BPS) algorithm performs carrier phase estimation better but with higher computational complexity (CC), bringing a larger computational burden to the digital signal processing unit. In this paper, a new low-complexity CPE algorithm (DBPS) is proposed for M-ary quadrature amplitude modulation (M-QAM) formats. It uses the BPS algorithm to estimate the compensation phase interval, before using dichotomy to quickly and accurately determine the compensation phase value. Simulation results show the CC (multiplication / addition) of DBPS is reduced by 2.79 / 2.84 (16-QAM), 5.35 / 5.45 (64-QAM), and 2.98 / 3.01 (128-QAM) than that of BPS, and DBPS has a smaller phase tracking error variance. DBPS can relax the limitation of optical communication rate caused by high-speed data operations.

By using an integral operation, we can introduce two signals, B X and B Y , which are defined as shown in Eq. (17) and (18). Scientific RepoRtS |(2020) 10:6077 | https://doi.

No abstract