In this paper, a generalized three-dimensional(3D) scattering channel model for macrocellular land mobile environments is considered. This model simultaneously describes angular arrival of multi-path signals in the az...In this paper, a generalized three-dimensional(3D) scattering channel model for macrocellular land mobile environments is considered. This model simultaneously describes angular arrival of multi-path signals in the azimuth and elevation planes in an environment where uniformly distributed scatterers are assumed to be present in hemispheroids around the base station(BS) and mobile station(MS). Using this channel model, we first derive the closed-form expression for the joint and marginal probability density functions of the angle-of-arrival and time-of-arrival measured at the BS and the MS corresponding to the azimuth and elevation angles. Next, we derive an expression for the Doppler spectral distribution caused by motion of the MSs. Furthermore, we analyze the performance of multiple-input multiple-output antenna systems numerically. The results show that the proposed 3D scattering channel model performs better than previously proposed two-dimensional(2D) models for indoor and outdoor environments. We compare the results with previous scattering channel models and measurement results to validate the generalizability of our model.展开更多
肺癌是长期威胁人类健康的恶性疾病之一,针对传统方法在肺癌CT图像分类中的预处理过程复杂、工作量大的问题,本文提出了基于三维卷积神经网络(3D-CNN)模型的肺部CT图像分类方法。该模型以卷积神经网络模型为基础,并在训练的过程中使用...肺癌是长期威胁人类健康的恶性疾病之一,针对传统方法在肺癌CT图像分类中的预处理过程复杂、工作量大的问题,本文提出了基于三维卷积神经网络(3D-CNN)模型的肺部CT图像分类方法。该模型以卷积神经网络模型为基础,并在训练的过程中使用特定顺序输入策略,还在公开的Kaggle Data Science Bowl 2017数据集上进行了实验。实验表明,该方法对图像的分类准确率达到76%,比采用随机顺序的输入策略时有所提升,能够为肺部病理图像的分类研究提供有价值的参考。展开更多
基金supported by the National Nature Science Foundation of China (No.61471153)the Scientific and Technological Support Project (Industry) of Jiangsu Province (No. BE2011195)the Major Program of the Natural Science Foundation of Institution of Higher Education of Jiangsu Province (No. 14KJA510001)
文摘In this paper, a generalized three-dimensional(3D) scattering channel model for macrocellular land mobile environments is considered. This model simultaneously describes angular arrival of multi-path signals in the azimuth and elevation planes in an environment where uniformly distributed scatterers are assumed to be present in hemispheroids around the base station(BS) and mobile station(MS). Using this channel model, we first derive the closed-form expression for the joint and marginal probability density functions of the angle-of-arrival and time-of-arrival measured at the BS and the MS corresponding to the azimuth and elevation angles. Next, we derive an expression for the Doppler spectral distribution caused by motion of the MSs. Furthermore, we analyze the performance of multiple-input multiple-output antenna systems numerically. The results show that the proposed 3D scattering channel model performs better than previously proposed two-dimensional(2D) models for indoor and outdoor environments. We compare the results with previous scattering channel models and measurement results to validate the generalizability of our model.
文摘肺癌是长期威胁人类健康的恶性疾病之一,针对传统方法在肺癌CT图像分类中的预处理过程复杂、工作量大的问题,本文提出了基于三维卷积神经网络(3D-CNN)模型的肺部CT图像分类方法。该模型以卷积神经网络模型为基础,并在训练的过程中使用特定顺序输入策略,还在公开的Kaggle Data Science Bowl 2017数据集上进行了实验。实验表明,该方法对图像的分类准确率达到76%,比采用随机顺序的输入策略时有所提升,能够为肺部病理图像的分类研究提供有价值的参考。
