基于VxWorks系统的二维硬件加速显示驱动技术

针对当前VxWorks系统图形显示驱动已无法满足用户需求的问题,提出了一种基于二维(2D)硬件加速引擎的图形显示驱动技术,实现了UGL的二维硬件加速、硬件光标、硬件双缓冲、Overlay覆盖等功能,提高了VxWorks系统的图形显示性能,满足了实时系统的应用需求.目前该设计已在多型海军装备中得到应用验证.     

溶胶—凝胶法制备掺钛SnO2薄膜的气敏光学特性研究

采用溶胶—凝胶(sol—gel)法制备掺钛SnO2薄膜,通过制备过程中变化不同质量分数的钛掺杂来制备3种不同质量分数(5%,10%,15%)的掺钛SnO2薄膜。在常温下对膜片在丙酮、甲烷气氛中进行气敏光学特性测试。结果表明:掺钛SnO2薄膜在常温下对不同的测试气体具有不同的反应,并且薄膜的灵敏度与掺杂量有关;质量分数为5%钛掺杂SnO2薄膜在丙酮气氛下的灵敏度在1300～2500 nm波长范围保持在13%左右,而质量分数为15%钛掺杂SnO2薄膜在甲烷气氛下的灵敏度在1700～2300 nm范围内保持在7%左右。     

聚3—己基噻吩及其掺杂TiO2材料对NO2的气敏特性

制作了一种工作于室温下的NO2气体传感器。该传感器以有机聚合物聚3—己基噻吩(P3HT)为敏感材料,以聚对苯二甲酸乙二醇酯(PET)薄膜为柔性衬底。对敏感膜进行了SEM表征。室温下,对不同体积分数的NO2进行了测试,对器件尺寸的影响进行了分析,并对响应规律进行了总结。经实验,对NO2测试下限可达到0.2×10-6。为提高敏感性、减小恢复时间,尝试对敏感膜掺杂TiO2,并同未掺杂的敏感膜进行实验比较,发现对体积分数1.5×10-6的NO2,掺杂TiO2可将敏感性提高8%,恢复性也有明显改善,对周期为1060 s的循环测试表明:掺杂TiO2下阻值基本能完全恢复。     

CeO_2基极限电流氧传感器的研究

采用Gd掺杂的CeO2(Ce0.8Gd0.2O1.9,CGO)粉体,干压成型后于空气气氛下1450℃烧结4 h。通过物理特性分析,发现在该烧结制度下烧结成型的样品基本满足氧传感器对敏感材料的要求。制备了一种新型结构的致密扩散障型极限电流氧传感器。经测试,发现该传感器在较高氧体积分数范围内有较好的极限电流特性,并为此建立了相关的理论模型,较好地解释了此类传感器极限电流和氧体积分数的关系。     

Exploration of artificial neural network to predict morphology of TiO2 nanotube

Artificial neural network (ANN) was developed to predict the morphology of TiO₂ nanotube prepared by anodization. The collected experimental data was simplified in an innovative approach and used as training and validation data, and the morphology of TiO₂ nanotube was considered as three parameters including the degree of order, diameter and length. Applying radial basis function neural network to predict TiO₂ nanotube degree of order and back propagation artificial neural network to predict the nanotube diameter and length were emphasized in this paper. Some important problems such as the selection of training data, the structure and parameters of the networks were discussed in detail. It was proved in this paper that ANN technique was effective in the prediction work of TiO₂nanotube fabrication process.     

Recurrent type-2 fuzzy neural network using Haar wavelet energy and entropy features for speech detection in noisy environments

This paper proposes a new method to detect the boundary of speech in noisy environments. This detection method uses Haar wavelet energy and entropy (HWEE) as detection features. The Haar wavelet energy (HWE) is derived by using the robust band that shows the most significant difference between speech and nonspeech segments at different noise levels. Similarly, the wavelet energy entropy (WEE) is computed by selecting the two wavelet energy bands whose entropy shows the most significant speech/nonspeech difference. The HWEE features are fed as inputs to a recurrent self-evolving interval type-2 fuzzy neural network (RSEIT2FNN) for classification. The RSEIT2FNN is used because it uses type-2 fuzzy sets, which are more robust to noise than type-1 fuzzy sets. The recurrent structure in the RSEIT2FNN helps to remember the context information of a test frame. The RSEIT2FNN outputs are compared with a parameter threshold to determine whether it is a speech or nonspeech period. The HWEE-based RSEIT2FNN detection was applied to speech detection in different noisy environments with different noise levels. Comparisons with different detection methods verified the advantage of the proposed method of using HWEE.     

Constructing 3D human model from front and side images

Constructing 3D human model from 2D images provides a cost-effective approach to visualize digital human in virtual environment. This paper presents a systematic approach for constructing 3D human model using the front and side images of a person. The silhouettes of human body are first detected and the feature points on the silhouettes are subsequently identified. The feature points are further used to obtain the body dimensions that are necessary for identifying a template 3D human model. The shape of the template human model can be modified by the free-form deformation method. Moreover, the proposed approach has been applied for constructing the 3D human models of 30 subjects. The comparisons between the constructed 3D models and the 3D scanning models of the 30 subjects indicate that the proposed system is very effective and robust.     

一种基于帮助节点的可扩展P2P VoD服务

提出一种借助于帮助节点提升点播系统服务容量的策略来改善系统的可扩展性.对有帮助节点参与的点播系统进行建模分析,建立了系统对帮助节点的需求与各关键参数间的关系;在此基础上提出一个可行的帮助节点管理策略,该策略基于对系统运行中各关键参数的周期性观测完成对帮助节点的管理维护;并提出一个基于净收益最大化的数据调度策略与帮助节点管理策略配合共同实现在高效利用帮助节点的基础上降低媒体服务器负载,提升可扩展性的目标.     

不同入射角下的雷达后向散射系数图像模拟

选择了新疆渭库三角洲(库车县、新和县、沙雅县)为实验区,利用Ulaby和Chapp的后向散射模型分别模拟了HV极化和HH极化的入射角为20°、24°、30°、33°、37°和40°的雷达后向散射系数图像。分析了不同入射角下图像各点后向散射系数值的散点图以及统计特征,得出由于入射角度的变化,两种极化的图像后向散射系数值变化较大,其变化趋势均符合余弦函数曲线分布,且模拟SAR图像很好的保留了地面散射信息。     

基于改进2DPCA的快速交通标志识别法

为了提高交通标志的识别速度,提出了一种样本类中间值结合均值的的2DPCA.该方法采用每类训练样本中间值的均值代替训练样本的平均值,以此重建散布矩阵从而得到最优投影矩阵.在已经建立的两个交通标志图像集上用该方法进行了实验,结果表明新方法识别速度得到了大幅提升并且取得了较高的识别率.