基于跨模态空间匹配的多模态肺部肿块分割网络

现有多模态分割方法通常先对图像进行配准,再对配准后的图像进行分割。对于成像特点差异较大的不同模态,两阶段的结构匹配与分割算法下的分割精度较低。针对该问题,该文提出一种基于跨模态空间匹配的多模态肺部肿块分割网络(MMSASegNet),其具有模型复杂度低和分割精度高的特点。该模型采用双路残差U型分割网络作为骨干分割网络,以充分提取不同模态输入特征,利用可学习的空间变换网络对其输出的多模态分割掩膜进行空间结构匹配;为实现空间匹配后的多模态特征图融合,形变掩膜和参考掩膜分别与各自模态相同分辨率的特征图进行矩阵相乘,并经特征融合模块,最终实现多模态肺部肿块分割。为提高端到端多模态分割网络的分割性能,采用深度监督学习策略,联合损失函数约束肿块分割、肿块空间匹配和特征融合模块,同时采用多阶段训练以提高不同功能模块的训练效率。实验数据采用T2权重(T2W)磁共振图像和扩散权重磁共振图像(DWI)肺部肿块分割数据集,该方法与其他多模态分割网络相比,DSC (Dice Similarity Coefficient)和HD (Hausdorff Distance)等评价指标均显著提高。     

艺术体操训练中虚拟现实技术的应用探讨

本课题构建了一种基于虚拟现实技术的艺术体操仿真系统，该仿真系统包括艺术体操人体运动跟踪模块、模型变形模块、虚拟现实场景仿真模块几部分构成。     

关于飞行模拟器列装的几点认识和启示

对近年来国外飞行模拟装备现状和发展趋势进行了较详细的分析，并从中得出了若干认识和启示。     

支持向量机训练算法及其应用

支持向量机是在统计学习理论基础上提出的一种新的机器学习方法。由于其出．色尊学习性能，该技术已成为机器学习界的研究热点，并成功地应用在模式识别、时间序列预测、苛线性系统辨识、控制及其它方面。本文简要介绍了支持向量机训练算法及其应用，并且讨论了未来的发展方向。     

Super-simple (ν, 5, 5) Designs

Super-simple designs are useful in constructing codes and designs such as superimposed codes and perfect hash families. In this article, we investigate the existence of a super-simple (ν, 5, 5) balanced incomplete block design and show that such a design exists if and only if ν ≡ 1 (mod 4) and ν ≥ 17 except possibly when ν = 21. Applications of the results to optical orthogonal codes are also mentioned. Research supported by NSERC grant 239135-01.     

Multilevel-in-width training for deep neural network regression

A common challenge in regression is that for many problems, the degrees of freedom required for a high-quality solution also allows for overfitting. Regularization is a class of strategies that seek to restrict the range of possible solutions so as to discourage overfitting while still enabling good solutions, and different regularization strategies impose different types of restrictions. In this paper, we present a multilevel regularization strategy that constructs and trains a hierarchy of neural networks, each of which has layers that are wider versions of the previous network's layers. We draw intuition and techniques from the field of Algebraic Multigrid (AMG), traditionally used for solving linear and nonlinear systems of equations, and specifically adapt the Full Approximation Scheme (FAS) for nonlinear systems of equations to the problem of deep learning. Training through V-cycles then encourage the neural networks to build a hierarchical understanding of the problem. We refer to this approach as multilevel-in-width to distinguish from prior multilevel works which hierarchically alter the depth of neural networks. The resulting approach is a highly flexible framework that can be applied to a variety of layer types, which we demonstrate with both fully connected and convolutional layers. We experimentally show with PDE regression problems that our multilevel training approach is an effective regularizer, improving the generalize performance of the neural networks studied.     

A real-time monitoring approach for bivariate event data

Early detection of changes in the frequency of events is an important task in many fields, such as disease surveillance, monitoring of high-quality processes, reliability monitoring, and public health. This article focuses on detecting changes in multivariate event data by monitoring the time-between-events (TBE). Existing multivariate TBE charts are limited because they only signal after an event occurred for each of the individual processes. This results in delays (i.e., long time-to-signal), especially when we are interested in detecting a change in one or a few processes with different rates. We propose a bivariate TBE chart, which can signal in real-time. We derive analytical expressions for the control limits and average time-to-signal performance, conduct a performance evaluation and compare our chart to an existing method. Our findings showed that our method is an effective approach for monitoring bivariate TBE data and has better detection ability than the existing method under transient shifts and is more generally applicable. A significant benefit of our method is that it signals in real-time and that the control limits are based on analytical expressions. The proposed method is implemented on two real-life datasets from reliability and health surveillance.     

Maneuver analysis methodology to predict vehicle impacts on training lands

Tactical mobility analysis techniques were merged with land management strategies to assess potential impacts of vehicle operations on training areas for rangeland planning and management. A vehicle mobility analysis was performed for a suite of vehicle types using the NATO Reference Mobility Model (NRMM II). Input parameters include terrain information (soil type, slope, vegetation, surface roughness, soil strength), terrain surface condition based on climate (terrain strength, freeze–thaw, moisture content, snow cover), and vehicle specifications (tire, power train, weight on each axle, ground clearance, dimensions, ride). The vehicle performance was spatially mapped over the terrain for different seasons of the year and used to calculate the maneuverable acreage, which was compared to acreage needed for training requirements. This can be related to land capability based on expected training impact (Maneuver Impact Miles, MIM) and Land Condition Curves which link training density to land condition. This methodology can be used to determine the suitability of training lands and the degree of land management or rehabilitation expected. The methodology was applied to the transformation of the Alaska training lands to support a new brigade unit called the Stryker Brigade Combat Team (SBCT3), but is equally useful for other training areas and military units. For summer use, Alaska training lands are capable of supporting four times the projected training requirements. For winter, when the ground is frozen, more than 10 times the area needed was available.     

一个二元叠加码M_q(t,k,d)生成的二元等重码

研究了二元叠加码Mq(t,k,d)也是一个二元等重码,给出了它成为最佳等重码的条件,研究了它的检错性.     

基于单片机的乒乓球训练系统设计

根据乒乓球训练辅助器材匮乏的实际需求出发,该课题设计了一种基于单片机的乒乓球训练系统,该系统主要由AT89C51单片机、数码管显示、压力传感器等组成。