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Optical twin-single sideband (Twin-SSB) modulation, due to the left sideband (LSB) and right sideband (RSB) signal carrying individual data, has become an attractive technique in fiber transmission because it satisfies the demand of the explosive increase in data traffic. This paper focuses on reducing the complexity of Twin-SSB system and further enhancing the spectral efficiency by proposing a polarization division multiplexing (PDM) Twin-SSB modulation scheme. LSB and RSB signals are extracted using de-mapping algorithm instead of optical bandpass filters (OBPFs) to reduce system complexity. To further improve spectral efficiency, PDM is employed to meet the polarization multiplexing transmission and achieve a higher transmission capacity. Based on the PDM Twin-SSB system, the LSB is 3-arr phase-shift-keying (3PSK) modulated, while RSB is quadrature phase-shift keying (QPSK) modulated. We simulated that the bit error ratio (BER) performance of LSB and RSB of X-polarization (X-Pol) and Y-polarization (Y-Pol) at 8-Gbaud, 10-Gbaud, 12-Gbaud, 14-Gbaud, and 16-Gbaud in the case of back-to-back (BTB) and 2 km standard single-mode fiber (SSMF) transmission. The simulation results verify the effectiveness and practical feasibility of the proposed PDM Twin-SSB scheme for future short-distance transmission owing to low cost, simplified structure, low algorithm complexity, and high data transmission capacity. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) has a wide range for applications in mobile robotics. Lightweight and inexpensive vision sensors have been widely used for localization in GPS-denied or weak GPS environments. Mobile robots not only estimate their pose, but also correct their position according to the environment, so a proper mathematical model is required to obtain the state of robots in their circumstances. Usually, filter-based SLAM/VO regards the model as a Gaussian distribution in the mapping thread, which deals with the complicated relationship between mean and covariance. The covariance in SLAM or VO represents the uncertainty of map points. Therefore, the methods, such as probability theory and information theory play a significant role in estimating the uncertainty. In this paper, we combine information theory with classical visual odometry (SVO) and take Jensen-Shannon divergence (JS divergence) instead of Kullback-Leibler divergence (KL divergence) to estimate the uncertainty of depth. A more suitable methodology for SVO is that explores to improve the accuracy and robustness of mobile devices in unknown environments. Meanwhile, this paper aims to efficiently utilize small portability for location and provide a priori knowledge of the latter application scenario. Therefore, combined with SVO, JS divergence is implemented, which has been realized. It not only has the property of accurate distinction of outliers, but also converges the inliers quickly. Simultaneously, the results show, under the same computational simulation, that SVO combined with JS divergence can more accurately locate its state in the environment than the combination with KL divergence. [ABSTRACT FROM AUTHOR]