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The band-limited and power fading effects are two main limitations to intensity-modulation direct-detection (IM-DD) transmission systems due to the low-cost transceivers and interactions from fiber dispersion. Entropy loading (EL) has the ability to squeeze out the last bit by continuous adjustment of entropy. However, it is a passively process to adapt to the channel response. Moreover, the normalized generalized mutual information (NGMI) target based EL may be overestimated because the practical binary forward error correction (FEC) codes have a gap to the theoretic limit, which may result in unsatisfied transmission capacity or post-FEC performance. To optimize the NGMI, we analyze the NGMI upper and lower boundaries defined by the achievable information rate (AIR) and FEC overhead (OH). We propose to use the AIR as target and optimize the NGMI with the FEC OH as constraint. We conduct an experiment to target the beyond 200-Gb/s discrete multi-tone signal transmission over 2-km or 10-km single mode fiber using a 32-GHz electro-absorption modulated laser (EML). However, we find that the channel response and generalized mutual information (GMI) cannot support the target AIR even with NGMI optimization. We propose a joint EL and second-order Volterra filter (VF) to alleviate the power fading effects and maximize the transmission capacity. To achieve the AIR target and use below 20% SD-FEC, we find the optimized NGMI is 0.92 to simultaneously meet the 2-km and 10-km transmission requirement. We successfully transmit beyond 200-Gb/s DMT signal over 2-km or 10-km single mode fiber with BER below 2.6 × 10−2 in the EML-based direct-detection system. [ABSTRACT FROM AUTHOR]

Detecting the vertical profile of optical properties is an important task in the remote sensing of the upper ocean, especially for 3-D reconstruction. Ocean color remote sensing can only provide surface information, while the light detection and ranging (lidar) technique can provide depth-resolved data. Lidar can provide global-scale observations of the upper ocean for days and nights with minimal atmospheric correction errors. Unfortunately, due to the strong multiple scattering effects that occur when light propagates in seawater, the simple lidar equation may cause some deviations between the actual measurements and the simulation of the lidar signals. In this paper, we present a shipborne oceanic lidar, which was developed to detect the optical properties of seawater. For evaluating the performance of the lidar system, a Monte Carlo (MC) model was established to simulate lidar signals based on the simultaneous in situ inherent optical properties of seawater. The lidar measurements and the MC simulation can provide both the lidar signals and the retrieved lidar attenuation coefficient $\alpha $. The results of the comparison indicate that the lidar-measured signals correspond well with the MC-simulated signals at different experiment stations in the Yellow Sea and at various receiving fields of view (FOVs). We also observed strong correlations between the lidar-measured $\alpha $ and MC-simulated $\alpha $ at different stations ($r = 0.95$) and at various FOVs ($r = 0.96$). The results indicate the reliability of the developed lidar system. [ABSTRACT FROM AUTHOR]

The existing noise detection methods required the classifiers or distance measurements or data overall distribution, and 'curse of dimensionality' and other restrictions made them insufficiently effective in complex data, e.g., different attribute weights, high-dimensionality, containing feature noise, nonlinearity, etc. This is also the main reason that the existing noise filtering methods were not widely applied and formed an effective learning framework. To address this problem, we propose here a complete and efficient random forest method (CRF) specifically for the class noise detection by simulating the grid generation and expansion. The CRF is not based on distance measures or overall distribution or classifiers; besides, the voting mechanism makes it able to effectively process datasets containing feature noise. Furthermore, we introduce CRF based class noise filtering learning framework (CRF-NFL) and derive its mathematical model. The framework is then applied to many widely used classifiers including some state-of-the-art algorithms, e.g., k-means tree, GBDT, and XGBoost. Moreover, its parallelized is designed for large-scale data. The CRF-NFL show much better generalizability than the conventional classifiers and the relative density-based method, which is the most effective noise filtering method as far as we know. All research has formed an open source library, called CRF-NFL: http://www.cquptshuyinxia.com/CRF-NFL.html. [ABSTRACT FROM AUTHOR]

Because of the fact that complete seismic data can have a low rank in the frequency-space (f-x) domain, rank-reduction methods are classical techniques used for seismic data reconstruction. Models that employ nuclear-norm minimization signify convex relaxation methods in traditional rank minimization problems. However, the results obtained after solving the nuclear-norm minimization problem are usually suboptimal because the nuclear norm indicates a loose approximation of the rank function. To overcome the limitations of the nuclear norm, we propose a new method of seismic data reconstruction based on the log-sum function minimization, which is closer to the rank function than the convex nuclear norm. However, the problem based on the log-sum function is nonconvex. Consequently, a majorization–minimization framework has been adopted to solve the associated minimization problem. Numerical experiments performed using synthetic and real data demonstrate that the quality of reconstruction derived from our proposed algorithm is better than that of the singular value thresholding algorithm and the weighted nuclear norm method. [ABSTRACT FROM AUTHOR]