Abstract-Fiber-induced intra-and inter-channel nonlinearities are experimentally tackled using blind nonlinear equalization (NLE) by unsupervised machine learning based clustering (MLC) in ~46 Gb/s single-channel and ~20 Gb/s (middle-channel) multi-channel coherent multi-carrier signals (OFDM-based). To that end we introduce, for the first time, Hierarchical and FuzzyLogic C-means (FLC) based clustering in optical communications. It is shown that among the two proposed MLC algorithms, FLC reveals the highest performance at optimum launched optical powers (LOPs), while at very high LOPs Hierarchical can compensate more effectively nonlinearities only for low-level modulation formats. When employing BPSK and QPSK, FLC outperforms K-means, Fast-Newton support vector machines, supervised artificial neural networks and NLE with deterministic Volterra analysis. In particular, for the middle channel of a QPSK WDM coherent optical OFDM system at optimum -5 dBm of LOP and 3200 km of transmission, FLC outperforms Volterra-NLE by 2.5 dB in Q-factor. However, for a 16-QAM single-channel system at 2000 km, the performance benefit of FLC over IVSTF reduces to ~0.4 dB at a LOP of 2 dBm (optimum). Even when using novel sophisticated clustering designs in 16 clusters, no more than additional ~0.3 dB Q-factor enhancement is observed. Finally, in contrast to the deterministic Volterra-NLE, MLC algorithms can partially tackle the stochastic parametric noise amplification.
We develop a novel compressive coded rotating mirror (CCRM) camera to capture events at high frame rates in passive mode with a compact instrument design at a fraction of the cost compared to other high-speed imaging cameras. Operation of the CCRM camera is based on amplitude optical encoding (grey scale) and a continuous frame sweep across a low-cost detector using a motorized rotating mirror system which can achieve single pixel shift between adjacent frames. Amplitude encoding and continuous frame overlapping enable the CCRM camera to achieve a high number of captured frames and high temporal resolution without making sacrifices in the spatial resolution. Two sets of dynamic scenes have been captured at up to a 120 Kfps frame rate in both monochrome and colored scales in the experimental demonstrations. The obtained heavily compressed data from the experiment are reconstructed using the optimization algorithm under the compressive sensing (CS) paradigm and the highest sequence depth of 1400 captured frames in a single exposure has been achieved with the highest compression ratio of 368 compared to other CS-based high-speed imaging technologies. Under similar conditions the CCRM camera is 700× faster than conventional rotating mirror based imaging devices and could reach a frame rate of up to 20 Gfps.
Compressive coded rotating mirror (CCRM) camera is a novel high-speed imaging system that operates under amplitude optical encoding and frame sweeping modalities in a passive imaging mode that is capable of reconstructing 1400 frames from a single shot image acquisition and achieves the highest compression ratio of 368 compared to the other compressive sensing (CS) based single-shot imaging modalities. The integrated optical encoding and compression adds a strong layer of encryption on the observed data and facilitates the integration of the CCRM camera with the imaging applications that require highly efficient data encryption and compression due to capturing highly sensitive data or limited transmission and storage capacities. CCRM uses amplitude encoding that significantly extends the key space where the probability of having the exact encoder pattern is estimated as $$P\left( A\right) \ =\ 1/{10}^{122,500}$$ P A = 1 / 10 122 , 500 , hence drastically reducing the possibility of data recovery in a brute force manner. Data reconstruction is achieved under CS based algorithms where the obtained amplitude-based pattern from optical encoder operates as the key in the recovery process. Reconstruction on the experimental as well as the synthetic data at various compression ratios demonstrate that the estimated key with less than 95$$\%$$ % matching elements were unable to recover the data where the achieved averaged structural similarity (SSIM) of 0.25 before 95$$\%$$ % encoder similarity and 0.85 SSIM at 100$$\%$$ % encoder similarity demonstrates the high-sensitivity of the proposed optical encryption technique.
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