Gradient-index Solar Sail and its Optimal Orbital Control

Solar sails with the capability of generating a tangential radiation pressure at the sun-pointing attitude, such as refractive sails can provide more efficient methods for attitude and orbital control of sailcraft. This paper presents the concept of gradient-index sail as an advanced class of refractive sail, which operates by guiding the solar radiation through a structure made of graded refractive index material. The design of the sail's refractive index distribution is performed by transformation optics, and the resultant index realized by the effective refractive index of non-resonant bulk metamaterials made of silica. The performance of the sail was evaluated by using ray tracing for a broad spectrum of solar radiation under the normal incidence angle, which showed an efficiency of 90.5% for generation of a tangential radiation pressure. We also studied the orbital control of the tangential-radiation-pressure-generating sails, and showed that the full orbital control, including the modification of orbital axes, eccentricity, and inclination can be applied by changing the attitude of the sail merely around the sun-sail axis, while the sail keeps the sun-pointing attitude at every point of the orbit

Deep Networks for Compressed Image Sensing

The compressed sensing (CS) theory has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. Several CS reconstruction models have been recently proposed and obtained superior performance. However, there still exist two important challenges within the CS theory. The first one is how to design a sampling mechanism to achieve an optimal sampling efficiency, and the second one is how to perform the reconstruction to get the highest quality to achieve an optimal signal recovery. In this paper, we try to deal with these two problems with a deep network. First of all, we train a sampling matrix via the network training instead of using a traditional manually designed one, which is much appropriate for our deep network based reconstruct process. Then, we propose a deep network to recover the image, which imitates traditional compressed sensing reconstruction processes. Experimental results demonstrate that our deep networks based CS reconstruction method offers a very significant quality improvement compared against state of the art ones.Comment: This paper has been accepted by the IEEE International Conference on Multimedia and Expo (ICME) 201