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Research on distributed task planning model for multi-autonomous underwater vehicle (MAUV). A scroll time domain quantum artificial bee colony (STDQABC) optimization algorithm is proposed to solve the multi-AUV optimal task planning scheme. In the uncertain marine environment, the rolling time domain control technique is used to realize a numerical optimization in a narrowed time range. Rolling time domain control is one of the better task planning techniques, which can greatly reduce the computational workload and realize the tradeoff between AUV dynamics, environment and cost. Finally, a simulation experiment was performed to evaluate the distributed task planning performance of the scroll time domain quantum bee colony optimization algorithm. The simulation results demonstrate that the STDQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The STDQABC algorithm can effectively improve MAUV distributed tasking planning performance, complete the task goal and get the approximate optimal solution.

In this paper, a novel mechanism is studied to improve the performance of the channel state information (CSI) feedback in massive multiple-input multiple-output (MIMO) systems. The proposed mechanism encompasses convolutional neural network (CNN)-based CSI compression and reconstruction structure. In this structure, the long-short term memory (LSTM) is adopted to learn temporal correlation of channels, and then, an attention mechanism is developed to perceive local information and automatically weight feature information. In addition, the CNN framework is further adjusted to reduce the number of training parameters and accelerate CSI recovery. The CNN structure with optimal training parameters can be achieved via offline iterative training and learning based on various training datasets. Comparative experimental studies demonstrate the effectiveness of the proposed approach that the trained CNN can obtain the higher feedback accuracy and better system performance in massive MIMO CSI online feedback reconstruction. Moreover, the proposed scheme in the less parameters-based neural network owns a higher performance with lower computational complexity compared to the conventional algorithms. INDEX TERMS Massive MIMO, frequency division duplex (FDD), CSI feedback, long short-term memory (LSTM), attention mechanism.

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We propose a space benchmark sensor with onboard SI (Système International) traceability by means of quantum optical radiometry. Correlated photon pairs generated by spontaneous parametric down-conversion (SPDC) in nonlinear crystals are used to calibrate the absolute responsivity of a solar observing radiometer. The calibration is systematic, insensitive to degradation and independent of external radiometric standards. Solar spectral irradiance at 380–2500 nm is traceable to the photon rate and Planck’s constant with an expected uncertainty of about 0.35%. The principle of SPDC calibration and a prototype design of the solar radiometer are introduced. The uncertainty budget is analyzed in consideration of errors arising from calibration and observation modes.

The knowledge of fault geometry plays an important role in reservoir modeling and characterization. Seismic attributes, such as volumetric dip, coherence, and curvature, provide an efficient and objective tool to extract the fault geometric attributes. Traditionally, we use the noise-attenuated full-bandwidth seismic data to compute coherence followed by smoothing, sharpening, and skeletonization. However, different stratigraphic reflectors with relatively similar waveforms and amplitudes juxtaposing across a fault will algorithmically appear to be continuous, with the resulting fault image being broken. This leads to pseudo fault breakpoints and challenges the accurate extraction of other fault geometric attributes. Because the phase of the similar reflections across the faults varies with different spectral components, such non-stratigraphic alignments occur for only a few spectral components such that a multispectral coherence algorithm produces more continuous fault images. We evaluate the influence of spectral voice selection and spectral decomposition algorithm on the quality of fault imaging in multispectral coherence images using a 3D seismic survey acquired in the Taranaki Basin, New Zealand. Of the algorithms evaluated, we find the high-resolution maximum entropy based multispectral coherence method provides better result than those based on other spectral decomposition algorithms, which especially improves the fault continuity. However, the lateral resolution of fault imaging in multispectral coherence is decreased compared to the full-bandwidth coherence, because the fault image is smeared when we combine the coherence volumes computed using different spectral voices. We perform a fault enhancement workflow on the maximum entropy based multispectral coherence volume to improve the lateral resolution of fault imaging, which helps delineate the minor faults.

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