贝叶斯网络查询语义扩展的专家发现方法

专家发现是实体检索领域的一个研究热点，针对经典专家发现模型存在索引术语独立性假设与检索性能低的缺陷，提出一种基于贝叶斯网络模型的专家发现方法。该方法模型采用四层网络结构，能够实现图形化的概率推理，同时运用词向量技术能够实现查询术语的语义扩展。实验结果显示该模型在多个评价指标上均优于经典专家发现模型，能够有效实现查询术语语义扩展，提高专家检索性能。     

适用于无线体域网的动态口令认证协议

无线体域网中传输的是与生命高度相关的敏感数据，身份认证是信息安全保护的第一道防线。现有的基于人体生物信息的身份认证方案存在信息难提取、偶然性大和误差性大的问题，基于传统密码学的认证方案需较大计算资源和能量消耗，并不适用于无线体域网环境。为此，在动态口令和非对称加密机制基础上，提出一种适用于无线体域网的动态口令双向认证轻量协议，并对其进行形式化分析。通过理论证明、SVO逻辑推理及SPIN模型检测得出：该协议满足双向认证，且能够抵御重放攻击、伪装攻击、拒绝服务器攻击和口令离线攻击，具有较高安全性。     

面向噪声丢包感知的无线网络拥塞算法

针对无线网络链路干扰大、误码率高等特点，以及TCP Westwood算法（TCPW）存在估算带宽时过度依赖包的反馈，缺乏区分传输过程中丢包类型的缺点等问题，提出一种TCPW拥塞控制优化算法--TCPW-F。该算法利用发送速率等构建拥塞因子[F]作为判断丢包类型的依据，同时对判定发生噪声丢包时的拥塞窗口进一步调整，避免噪声丢包引起的窗口下降，提高该情况下窗口的发送效率。仿真结果表明，TCPW-F算法在时延性能方面表现更优，单位时间抖动趋于稳定的速度更快。在同一信道带宽下增大包生成速率，改进算法的实时吞吐量明显高于原算法，具备一定的噪声丢包感知能力，无线网络的TCP传输质量获得较大改善。     

基于条件信息卷积生成对抗网络的图像识别

传统的图像识别方法需要大量有标签样本进行训练，且模型训练难以达到稳定。针对这些问题，结合条件生成网络和信息最大化生成网络的结构优势建立了条件信息卷积生成网络（C-Info-DCGAN）。模型增加图像的类别信息和潜在信息作为输入数据，然后利用Q网络去更好地发挥类别信息和潜在信息对训练的引导作用，并且利用深度卷积网络来加强对图像特征的提取能力。实验结果表明，该方法能够加快模型训练收敛速度，并有效提高图像识别的准确率。     

结合边缘特征先验引导的深度卷积显著性检测

针对当前基于深度学习的显著性检测算法缺少利用先验特征和边缘信息，且在复杂场景中难以检测出鲁棒性强的显著性区域的问题，提出了一种结合边缘特征，利用先验信息引导的全卷积神经网络显著性检测算法。该算法利用三种被经常用到的先验知识结合边缘信息形成先验图，通过注意力机制将提取的先验特征与深度特征有效融合，最终通过提出的循环卷积反馈优化策略迭代地学习改进显著性区域，从而产生更可靠的最终显著图预测。经过实验定性定量分析，对比证明了算法的可靠性。     

基于虚拟现实的复杂场景加载优化算法

目前大规模场景模型生成的需求量日益增加, 现提出了一种基于区域的自动 LOD (Levels Of Details)构建算法. 该算法基于动态网格简化算法, 在游戏设计制作的过程中, 游戏开发人员会经常需要对美工部门人员提供的模型进行优化. 为了可以使模型的面数得到简化, 并且不改变模型的外观. 当今流行的 LOD技术非常擅于处理这种情况, 判断模型与摄像机的距离如果超过一定范围之后, 自动调取不同层次的模型, 当模型距离摄像机很远的情况下使用面数低的模型替换高模, 这样可以提高帧率并且降低摄像机前的三角面以及顶点数从而减少渲染压力. 一般模型的简化分为静态和动态模型的简化.大部分情况下, 程序员会让美工部门提供几套不同面数的模型或者通过模型简化工具对高模进行减面并存成多个Mesh, 并在程序运行的时候, 根据模型与摄像机的距离远近动态的替换Mesh. 这是一种静态的方法.这里将尝试使用一种动态的网格简化和LOD技术相结合的方法. 这种新型算法的大大的简化了操作流程, 美术人员只需要提供一个模型, 程序员可以使用这种方法生成量级不同的低模, 根据摄像机与模型的远近自动的调取不同精度的模型.     

基于VR技术的全息化教学应用研究

目前,虚拟现实技术已经大量运用于教育领域,对于基于VR技术的全息化教学资源的建设与应用研究受到了广大学者的关注,如何运用好这一热点技术是当今教育领域的热门话题。首先阐述了全息化教学的资源应用现状和主要作用,然后介绍了全息化教学在典型专业教学中的应用,最后对全息化教学资源建设与开展提出了合理化建议。     

5G网络在智能交通中的应用发展趋势分析

Along with the advent of 5G era, China's transportation industry for intelligent development enters into the fast lane. Based on the integration of communication technology, Internet of things technology, big data technology, cloud computing technology, ar- tificial intelligence technology and so on, the whole process and all-round intelligent control are carried out in traffic construction, traffic management, road transportation, traffic service and so on, so as to ensure smooth and safe travel and to provide good servic- es to road, railway, civil aviation, water transportation and other transportation networks. It is the important feature of intelligent transportation in the 5G era. This paper analyses the trend of application and development of 5G network in intelligent transporta- tion, which is the research basis of perfecting transportation service and the scientific basis of promoting the intelligent and modern development of transportation in China.     

Differences between oculomotor and perceptual artifacts for temporally limited head mounted displays

We used perceptual and oculomotor measures to understand the negative impacts of low (phantom array) and high (motion blur) duty cycles with a high‐speed, AR‐likehead‐mounted display prototype. We observed large intersubject variability for the detection of phantom array artifacts but a highly consistent and systematic effect on saccadic eye movement targeting during low duty cycle presentations. This adverse effect on saccade endpoints was also related to an increased error rate in a perceptual discrimination task, showing a direct effect of display duty cycle on the perceptual quality. For high duty cycles, the probability of detecting motion blur increased during head movements, and this effect was elevated at lower refresh rates. We did not find an impact of the temporal display characteristics on compensatory eye movements during head motion (e.g., VOR). Together, our results allow us to quantify the tradeoff of different negative spatiotemporal impacts of user movements and make subsequent recommendations for optimized temporal HMD parameters.     

Computer‐aided design methodology for linearity enhancement of multiwatt GaN HEMT amplifiers using multiple parallel devices

This article presents a design methodology for linearizing GaN HEMT amplifiers based on splitting a large FET into multiple parallel FETs with same total gate periphery and by biasing them individually. By varying the biases, the magnitude and the phase of the IMD3 components at the output of FET changes. A detailed simulation methodology using commercial microwave CAD software is presented. Simulation results show that by biasing one device in Class AB and other(s) in deep Class AB mode, IMD3 components of parallel FETs can be made out of phase to each other leading to cancellation and improvement in linearity. Three prototype circuits were simulated using (a) a single 5 mm FET (1 × 5 mm), (b) two parallel 2.5 mm FETs (2 × 2.5 mm), and (c) four parallel 1.25 mm FETs (4 × 1.25 mm), for a total gate periphery of 5 mm, over the frequency range of 0.8 to 1.0 GHz. IMD3 improvement up to 20 dBc was achieved with the 4 × 1.25 mm circuit when the FET biases were optimized. Measurement results show improvement in linearity up to 20 dBc for 4 × 1.25 mm circuit. The proposed method improves linearity without a substantial penalty on the power consumption and is straightforward to implement.