Heterogeneous System-on-Chip-Based Lattice-Boltzmann Visual Simulation System

Abstract

Cerebral aneurysm is a cerebrovascular disorder caused by a weakness in the wall of an artery or vein, that causes a localised dilation or ballooning of the blood vessel. It is life-threatening, hence an early and accurate diagnosis would be a great aid to medical professionals in making the correct choice of treatment. HemeLB is a massively parallel lattice-Boltzmann simulation software which is designed to provide the radiologist with estimates of flow rates, pressures and shear stresses throughout the relevant vascular structures, intended to eventually permit greater precision in the choice of therapeutic intervention. However, in order to allow surgeries and doctors to view and visualise the results in real-time at medical environments, a cost-efficient, practical platform is needed. In this paper, we have developed and evaluated a version of HemeLB on various heterogeneous system-on-chip platforms, allowing users to run HemeLB on a low cost embedded platform and to visualise the simulation results in real-time. A comprehensive evaluation of implementation on the Zynq SoC and Jetson TX1 embedded graphic processing unit platforms are reported. The achieved results show that the proposed Jetson TX1 implementation outperforms the Zynq implementation by a factor of 19 in terms of site updates per second.

Agent-based model simulation for ground penetration radar based on Netlogo platform

“…where A = 2ϵ0ϵr−σ∆t 2ϵ0ϵr+σ∆t and B = 2∆t 2ϵ0ϵr+σ∆t . The (15) is found by replacing the value of the conductivity with σ = 0 in (18).…”

“…This approach can be used in medical science to model and simulate blood flow in brain aneurysm by by hybridization of the multi-agent model and Laticce boltzman method [16]. The researches use Lattice Boltzman method to simulate the blood flow in cerbral aneurysm by using different architecture to accelerate the aforementioned method [17], [18].…”

A fast direct locator for radiation source based on composite convolution neural network

“…In [9], multilayer perceptron (MLP) neural network was adopted to realize mapping between objective positions and positioning parameters in two-step method for indoor WiFi-based localization. However, deep neural networks often require a large number of fully connected relationships, which puts great pressure on real-time computation and pretraining of nodes, making it difficult to achieve the real-time superiority of advanced optimization methods as described in [10][11][12]. The scheme based on LSTM network is introduced in the context of ultra wide band (UWB) technology, whose relative positioning error can reach 1.4% [13], but the disadvantage is also high complexity.…”