KEYWORDS: Modulation, Digital signal processing, Optical communications, Telecommunications, Tolerancing, Signal to noise ratio, Fiber lasers, Chromium, Optical networks, Optical amplifiers
A modulation format identification (MFI) method based on the nonlinear power transformation via logistics regression is adopted for coherent optical receives systems. The amplitude variance, fourth power transformation and fast Fourier transform of input signals are utilized for special features extraction in our work. Five typical optical modulation formats (i.e.,16/32/64QAM and Q/8PSK) with the transmission rate of 28 GBaud are numerically simulated to demonstrate the feasibility. The simulation results show that our method has great performance even under low optical signal noise ratio (OSNR). Compared with the MFI algorithm based on Stokes space and asynchronous delay tapped sampling, our MFI algorithm requires less time to achieve similar performance of optical receive systems. Especially, this method exhibits tolerances to the laser linewidth and nonlinearity.
An improved CenterNet is proposed for signal recognition with time-frequency image input. The signal is transformed into time-frequency image by short-time Fourier transform, hence, the signal recognition is transformed into investigating the object detection problem in the field of image detection. Then, the advanced achievements of image detection are adopted to enhance the performance of signal recognition. Here, an improved CenterNet-based object detection network, which demonstrates great advantages in detection speed, is proposed. The results show that the proposed method identifies the signal modulation format with high speed. After training and testing on the self-collected data set with 6 types and 7800 samples, the mean average precision achieves 98.38% and the frame per second reaches 21.4. Compared with the original CenterNet, the detection speed increases more than 4 times while only reducing recognition accuracy by 0.3%, where this algorithm gives a promising way for applications of real-time signal recognition.
The detection of rail surface defects is of great significance for railway safety. To detect the rail surface defect, the laserinduced ultrasonic rail propagation model is established by the finite element method. The intrinsic relationship between the defect depth, of the defect on rail surface and the acoustic surface wave is investigated by discussing the variation of the reflected wave and the transmitted wave both in the time and frequency domain, respectively. Quantitative evaluation of defect depth is given based on the energy of the reflected and transmitted wave, which providing a promising theoretical way for the estimation of the rail surface defect feature.
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