Virtual reality (VR) simulators have been created for various surgical specialties. The common theme is extensive use of graphics, confined spaces, limited functionality and limited tactile feedback. A development team at the University of Nottingham, UK, consisting of computer scientists, mechanical engineers, graphic designers and a neurosurgeon, set out to develop a haptic, e.g. tactile simulator for neurosurgery making use of boundary elements (BE). The relative homogeneity of the brain, allows boundary elements, e.g. 'surface only' rendering, to simulate the brain structure. A boundary element simplifies the computing equations saves computing time, by assuming the properties of the surface equal the properties of the body. A limited audit was done by neurosurgical users confirming the potential of the simulator as a training tool. This paper focuses on the application of the computational method and refers to the underlying mathematical structure. Full references are included regarding the mathematical methodology.

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Deep convolutional network (CNN) can make the pictures generated by GAN more reasonable, but limited by the local receptive field of CNN, there are still many unreasonable places in the authenticity and semantics of the multi-object images generated from text. Therefore, a GAN-based method that incorporates a non-local self-attention mechanism is proposed. By embedding a non-local self-attention structure in the network, the network obtains global semantic information and detailed features, and uses the obtained information to perform level-by-level encoding to generate the final relatively reasonable image. The amount of parameters and calculation of the entire model is also reduced a lot. The proposed method is verified on the public COCO-stuff dataset and uses multiple indicators such as Inception Score, FID and classification accuracy score to evaluate the authenticity and diversity of the generated images. Experimental results show that the quality of the generated images is superior to that of previously proposed methods.