MultiDiscriminator Distributed Generative Model for MultiLayer RF Metasurface Discovery Metasurfacebased antenna beam control is defining a new engineering paradigm in radiofrequency applications such as communications, radar, and analog spatial signal processing. Metasurfaces are composite electromagnetic material surfaces that are made of subwavelength scattering particles, or metaatoms, with negligible thickness and optimized to control electromagnetic waves in unprecedented fashions through modified boundary conditions. Conventional metasurface design is a tedious process that requires iteratively solving Maxwellu0027s equations. This becomes increasingly challenging as stateoftheart metasurfaces require complex bianisotropic responses over multiple layers of meta atoms and several frequency bands. In this paper, to reduce design time and optimization overhead, we employ a multidiscriminator distributed generative adversarial network for inverse design of multi layer metasurfaces. Unlike conventional design approaches, our proposed approach is able to jointly design multiple layers, discover new meta atom patterns, and avoid solving Maxwellxe2x80x99s equations numerically or analytically. Results show that generated triplelayer metaatoms can achieve frequency resonances within 7% of the input values.

3DMaskGAN:Unsupervised SingleView 3D Object Reconstruction 3D object reconstruction has always been a hot topic in computer vision. Especially in recent years, many methods of learning volumetric predictions achieve robust 3D reconstruction using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones. However, the majority of methods employ strong shape priors and exist computationally waste in predicting 3D shapes. In this paper, we propose 3DMaskGAN, a novel framework to efficiently accomplish the task of unsupervised singleview 3D object reconstruction. We use 3D Generative Adversarial Networks (GAN) to predict the 3D shape from the singleview image and improve reconstruction accuracy by applying 2D projection masks instead of 3D priors simultaneously. The key idea is to insert a projector (a buildin camera system to approximate the true rendering pipeline) into the framework of 3D GAN to synthesize novel masks for optimization. We learn singleclass and multiclass objects to evaluate our network. Experimental results show that our framework achieves impressive performance with fewer training iterations in terms of unsupervised shape predictions.