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Integrated zoom and image stabilization system based on deformable mirrors (DMs) has advantages of miniaturization, rapid response and low energy consumption. Integrating the two capabilities on one DM-based imaging system poses considerable challenges. First, limited DM stroke will result in limited changeable aberration correction value for zoom and image stabilization. Second, the DM-based off-axis imaging system suffers a simple relative movement between optical axis and objects caused by carrier vibration may result in complicated optical system aberrations. To address these challenges, a reasonable assignment of the changeable aberration correction value for zoom and image stabilization is needed. Image stabilization and aberration correction in DM-based off-axis imaging system, should be considered when translation or rotation occurs. To troubleshoot the assignment issues of the correcting aberrations, we propose a graded optimization method which comprises two steps. To troubleshoot the image stabilization and aberration correction issues, we introduce vectorial ray tracing method and image point freezing principle to transform the aberration correction problem into an optimization problem of image coordinates. With the proposed method, we construct practical integrated freeform surfaces of DMs for a space camera with three mirrors. The sagittal heights of designed DM surface profiles are limited within their available stroke.

How to effectively organize drones to monitor pollutants from vessels is an important operational problem in port management. It is defined as the drone scheduling problem (DSP). The effectiveness of precise algorithms and heuristic algorithms in solving DSP has been reported in previous studies. In previous studies, the speed of the vessel was assumed to be constant. However, since the influence of sea waves and vessel power, such an assumption is difficult to satisfy in actual scenarios. The actual position of the vessel may deviate from the position information obtained through prior calculations. As the cumulative position deviation increases, it is possible to make the original feasible monitoring scheme infeasible. It is necessary to consider the emission monitoring dispatching of drones under vessel speed fluctuation in actual monitoring activities of the vessel. To deal with the problem, a dynamic dispatching strategy based on reinforcement learning (RL) is proposed. Considering the vessel speed fluctuation, the monitoring window is divided into multiple sub-time windows. The route information of the vessel in each sub-time window is updated according to the vessel speed fluctuations to reduce the accumulation of deviations between the prior position and the actual position. Then, a lightweight RL strategy is adopted to quickly (re)organize the monitoring scheme in each sub-time window. Numerical experiments illustrate the above division-conquer approach could effectively reduce the possibility of drone monitoring failure caused by vessel speed fluctuations. Also, the superiority of the RL-based dispatching strategy is illustrated by comparing it with multiple dispatching schemes. [ABSTRACT FROM AUTHOR]

This article proposes an optimized design for a wireless power transfer converter serving as an auxiliary power supply in a medium-voltage, high $\mathrm{d}v/\mathrm{d}t$ modular conversion system. A CLLC-CL circuit topology is implemented to generate a load-independent output voltage with a coupling-coefficient-independent resonant frequency. The output voltage of the circuit can be tuned by changing one pair of resonant LC parameters, which decouples the circuit gain from the coil design. Essential parameters of coil and magnet are extracted analytically or numerically to avoid time-consuming 3-D finite element analysis simulations for the subsequent optimization. After the design of the circuit and coil, a multiobjective optimization is carried out with objectives being efficiency, isolation capacitance, and insulation rating of the converter. Finally, experiments demonstrate a 48- to 48-V dc–dc converter with 100 W output power, 92.78% efficiency, 2.78 pF isolation capacitance, and 27 kV insulation rating to validate the optimization result. [ABSTRACT FROM AUTHOR]

Built on deep networks, end-to-end optimized image compression has made impressive progress in the past few years. Previous studies usually adopt a compressive auto-encoder, where the encoder part first converts image into latent features, and then quantizes the features before encoding them into bits. Both the conversion and the quantization incur information loss, resulting in a difficulty to optimally achieve arbitrary compression ratio. We propose iWave++ as a new end-to-end optimized image compression scheme, in which iWave, a trained wavelet-like transform, converts images into coefficients without any information loss. Then the coefficients are optionally quantized and encoded into bits. Different from the previous schemes, iWave++ is versatile: a single model supports both lossless and lossy compression, and also achieves arbitrary compression ratio by simply adjusting the quantization scale. iWave++ also features a carefully designed entropy coding engine to encode the coefficients progressively, and a de-quantization module for lossy compression. Experimental results show that lossy iWave++ achieves state-of-the-art compression efficiency compared with deep network-based methods; on the Kodak dataset, lossy iWave++ leads to 17.34 percent bits saving over BPG; lossless iWave++ achieves comparable or better performance than FLIF. Our code and models are available at https://github.com/mahaichuan/Versatile-Image-Compression. [ABSTRACT FROM AUTHOR]