This paper describes approaches to retrieve important aerosol results from the strong lidar return signals that are received by the space-borne CALIPSO lidar system after reflecting off-ocean surfaces. Relations, from which the theoretically expected values of area under ocean surface returns can be computed, are presented. A detailed description of the lidar system response to the ocean surface returns and the processes of sampling and averaging of lidar return signals are provided. An effective technique that reconstructs the lidar response to surface returnsstarting from down-linked samplesand calculates the area under it, has been developed and described. The calculated area values are validated after comparing them to their theoretically predicted counterpart values. Methods to retrieve aerosol optical depths (AODs) from these calculated areas are described and retrieval results are presented, including retrieval comparison with independent AOD measurements made by an airborne High Spectral Resolution Lidar (HSRL) that yielded quite good agreement. Techniques and results are also presented on using the spectral ratios of the surface response areas to determine spectral ratios of aerosol round-trip transmission and AOD spectral difference, without need of a specific/accurate ocean-surface reflectance model.

Regression Wavelet Analysis (RWA) is a novel wavelet-based scheme for coding hyperspectral images that employs multiple regression analysis to exploit the relationships among spectral wavelet transformed components. The scheme is based on a pyramidal prediction, using different regression models, to increase the statistical independence in the wavelet domain For lossless coding, RWA has proven to be superior to other spectral transform like PCA and to the best and most recent coding standard in remote sensing, CCSDS-123.0. In this paper we show that RWA also allows progressive lossy-to-lossless (PLL) coding and that it attains a rate-distortion performance superior to those obtained with state-of-the-art schemes. To take into account the predictive significance of the spectral components, we propose a Prediction Weighting scheme for JPEG2000 that captures the contribution of each transformed component to the prediction process.

A high-speed four-state continuous-variable quantum key distribution (CV-QKD) system, enabled by wavelength-division multiplexing, polarization multiplexing, and orbital angular momentum (OAM) multiplexing, is studied in the presence of atmospheric turbulence. The atmospheric turbulence channel is emulated by two spatial light modulators (SLMs) on which two randomly generated azimuthal phase patterns yielding Andrews' spectrum are recorded. The phase noise is mitigated by the phase noise cancellation (PNC) stage, and channel transmittance can be monitored directly by the D.C. level in our PNC stage. After the system calibration, a total SKR of > 1.68 Gbit/s can be reached in the ideal system, featured with lossless channel and free of excess noise. In our experiment, based on commercial photodetectors, the minimum transmittances of 0.21 and 0.29 are required for OAM states of 2 (or -2) and 6 (or -6), respectively, to guarantee the secure transmission, while a total SKR of 120 Mbit/s can be obtained in case of mean transmittances. (C) 2016 Optical Society of America

Quantum key distribution (QKD) leverages underlying principles of quantum mechanics to realize distribution of keys with verifiable security. Despite appealing features of QKD, there are some fundamental and technical challenges that need to be solved prior to its widespread applications. First, QKD secret-key rate (SKR) is fundamentally limited by channel loss, as dictated by the rate-loss tradeoff. Quantum repeaters would be an ultimate solution to overcome this problem; however, they are well beyond the reach. The second challenge lies in the scalability and cost. Future's QKD systems must be suitable for mass production with low cost, reliable realignment-free operations, and small power consumption. To solve for these problems in a simultaneous manner, we propose to encode information in the orthogonal Slepian sequences' bases. Such an approach is highly robust against turbulence effects in free-space optical links and dispersion effects/fiber non-linearities in fiber-optics channels, thereby improving QKD distance. Moreover, exploiting multidimensional encoding space enables high spectral efficiency QKD so that the SKR can be significantly improved. Critically, generation, processing, and detection of Slepian states can be reliably implemented in an integrated quantum photonics platform, based on both reflective and transmissive waveguide Bragg gratings (WBGs). Proposed reflective/transmissive WBG-based Slepian states are applicable to both discrete variable and continuous variable QKD systems.

In this paper, we propose a unified field-programmable gate array (FPGA) structure for a rate-adaptive forward error correction (FEC) scheme based on spatially coupled (SC) LDPC codes derived from quasi-cyclic (QC) LDPC codes. We described the unified decoder structure in detail and showed that the rate adaptation can be achieved by a controller on-the-fly. By FPGA based emulation, the results show that, with comparable complexity, the SC codes provide larger coding gain. The implemented unified structure can be employed for any template QC-LDPC code to achieve a spatially-coupling based code-rate adaptation scheme. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

A methodology is presented, by which atmospheric aerosol retrievals from a standard, elastic-scatter, lidar can be constrained by using information from coincident measurements from a high spectral resolution lidar (HSRL) or Raman lidar at a different wavelength. As high spectral resolution or inelastic-scattering lidars are now being incorporated coaxially into instruments with traditional, elastic-scatter channels at different wavelengths, a standard approach is needed to incorporate or fuse the diversity of spectral information so as to make maximal use of the aerosol measurements made from the elastic-scatter channel or channels. The approach is evaluated through simulation and with data from the NASA Langley Research Center Airborne HSRL instrument. The generality and extensibility of the method is also explored and discussed in the context of aerosol modeling. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

We experimentally demonstrate an eight-state continuous-variable quantum key distribution (CV-QKD) over atmospheric turbulence channels. The high secret key rate (SKR) is enabled by 4-D multiplexing of 96 channels, i.e., six-channel wavelength-division multiplexing, four-channel orbital angular momentum multiplexing, two-channel polarization multiplexing, and two-channel spatial-position multiplexing. The atmospheric turbulence channel is emulated by a spatial light modulator on which a series of azimuthal phase patterns yielding Andrews' spectrum are recorded. A commercial coherent receiver is implemented at Bob's side, followed by a phase noise cancellation stage, where channel transmittance can be monitored accurately and phase noise can be effectively eliminated. Compared to four-state CV-QKD, eight-state CV-QKD protocol potentially provides a better performance by offering higher SKR, better excess noise tolerance, and longer secure transmission distance. In our proposed CV-QKD system, the minimum transmittances of 0.24 and 0.26 are required for OAM states of 2 (or -2) and 6 (or -6), respectively, to guarantee the secure transmission. A maximum SKR of 3.744 Gb/s is experimentally achievable, while a total SKR of 960 Mb/s can be obtained in case of mean channel transmittances.

Purpose: A new reconstruction method for multi-contrast imaging and parameter mapping based on a union of local subspaces constraint is presented. Theory: Subspace constrained reconstructions use a predetermined subspace to explicitly constrain the relaxation signals. The choice of subspace size (K) impacts the approximation error vs noise-amplification tradeoff associated with these methods. A different approach is used in the model consistency constraint (MOCCO) framework to leverage the subspace model to enforce a softer penalty. Our proposed method, MOCCO-LS, augments the MOCCO model with a union of local subspaces (LS) approach. The union of local subspaces model is coupled with spatial support constraints and incorporated into the MOCCO framework to regularize the contrast signals in the scene. Methods: The performance of the MOCCO-LS method was evaluated in vivo on T-1 and T-2 mapping of the human brain and with Monte-Carlo simulations and compared against MOCCO and the explicit subspace constrained models. Results: The results demonstrate a clear improvement in the multi-contrast images and parameter maps. We sweep across the model order space (K) to compare the different reconstructions and demonstrate that the reconstructions have different preferential operating points. Experiments on T-2 mapping show that the proposed method yields substantial improvements in performance even when operating at very high acceleration rates. Conclusions: The use of a union of local subspace constraints coupled with a sparsity promoting penalty leads to improved reconstruction quality of multi-contrast images and parameter maps.

Post-processing schemes are crucial for object detection algorithms to improve the performance of detection in wide-area aerial imagery. We select appropriate parameters for three algorithms (variational minimax optimisation (Saha and Ray, 2009), feature density estimation (Gleason et al., 2011) and Zheng's scheme by morphological filtering (Zheng et al., 2013)) to achieve the highest average F-score on random sample frames, and then follow the same procedure to implement five post-processing schemes on each algorithm. Two low-resolution aerial videos are used as our datasets to compare automatic detection results with the ground truth objects on each frame. The performance analysis of post-processing schemes on each algorithm are presented under two sets of evaluation metrics.

Constellation shaping has been widely used in optical communication systems. We review recent advances in two-dimensional constellation shaping technologies for fiber-optic communications. The system architectures that are discussed include probabilistic shaping, geometric shaping, and hybrid probabilistic-geometric shaping solutions. The performances of the three shaping schemes are also evaluated for Gaussian-noise-limited channels.