In this work we introduce a triangular Delaunay mesh generator that can be trained using reinforcement learning to maximize a given mesh quality metric. Our mesh generator consists of a graph neural network that distributes and modifies vertices, and a standard Delaunay algorithm to triangulate the vertices. We explore various design choices and evaluate our mesh generator on diverse tasks including mesh generation, mesh improvement, and producing variable resolution meshes. The learned mesh generator outputs meshes that are comparable in quality to those produced by Triangle and DistMesh, two popular Delaunay-based mesh generators.

We present a learning based framework for mesh quality improvement on unstructured triangular and quadrilateral meshes. Our model learns to improve mesh quality according to a prescribed objective function purely via self-play reinforcement learning with no prior heuristics. The actions performed on the mesh are standard local and global element operations. The goal is to minimize the deviation of the node degrees from their ideal values, which in the case of interior vertices leads to a minimization of irregular nodes.

Abstract This research studies an image reconstruction technology that can help doctors observe endoscopic images obscured by blood in transurethral procedures. A new network structure is proposed, which applies encoder–decoder deep neural network to achieve the purpose of denoising and uses residual learning and cascade learning methods to relieve the problem of insufficient clarity and lack of detail in reconstruction image when restoring endoscopic images suffered from blood occlusion. The residual mechanism is used to estimate noise and compensate the images, instead of reconstructing overall image by an encoder and decoder deep network. It can potentially make the details of the image to be preserved and improve the resolution of the reconstructed image. This denoising model is embedded into a cascade network framework to further improve the quality of the restored image through the idea of repeated learning multiple times. On a customized endoscopic image sample set with real blood noise, for verifying the denoising ability, we designed two approaches of data augmentation to generate noised images obscured by different types and degree blood noises. In experiments, the new model has been compared to other approaches and the certain improved denoising effects can be observed.

In full-duplex systems, radio circuit's impairments is a significant factor that limits the cancellation of self-interference power. In this paper, we derive a closed-form expression of digital domain self- interference for a full-duplex orthogonal frequency division multiplexing (OFDM) system considering the RF impairments at the transmitter and the receiver. A digital-domain self-interference cancellation scheme (DSICS) that accounts for the power amplifier nonlinearity, IQ imbalance and phase noise effects at the transmitter and receiver is proposed. The DSICS can suppress the self-interference power effectively by compensating for the RF impairments associated with the self-interference signal.