基于专利合作申请数据分析的加权网络研究

基于电子信息类部分企业专利合作申请的数据构建了加权合作网络,针对加权合作网络中边权值分布和点强度分布呈现的厚尾效应和幂率特征的问题,通过对网络的动态演化结构参数和统计特征进行分析构建了一种新的加权合作网络模型。从理论分析和数值仿真实验两方面对该模型进行了分析和研究,证明了该模型的科学性,实验结果出现了与现实网络一样的厚尾和幂律特征。结合现实网络给出了电子信息类的部分企业合作现状及其原因分析,为该行业以后的发展提供一些参考。     

基于M2M平台的无线传感网的研究

M2M技术的先进理念引来新一轮“无线革命”,而近期高速率和低速率的无线个域网（WPAN）技术因为巨大的市场需求成为一个新的研究热点。高速率的WPAN技术主要用在数字多媒体设备互连中,而低速率的WPAN技术主要应用在传感器网络、自动化控制中。简单介绍了M2M的概念,讨论了M2M技术应用于无线传感网的前景,最后给出了一个基于M2M平台的无线传感网的主要用于解决软硬件兼容设计的实现方案。     

基于压缩式双共轭梯度算法对大规模电源/地线网络的快速分析

采用压缩式双共轭梯度算法分析大规模电源/地线网络.首先以稀疏存储结构对大规模的系数矩阵进行压缩处理,然后采用双共轭梯度算法对网络进行模拟.双共轭梯度算法采用2组共轭向量组作为搜索方向,收敛速度快.实验数据表明:在保证精度的情况下,该算法在加快电路网络分析求解效率的同时,大幅度地节省了计算所占用的内存,它适用于分析超大规模的电源/地线网络.     

可见光-近红外HSV图像融合的场景类字典稀疏识别方法

针对典型自然场景智能观测的需求,为提高稀疏分类器在小样本数据库上的识别精度,提出一种可见光和近红外（NIR）HSV图像融合的场景类字典稀疏识别方法。首先,利用一直应用在计算机视觉显示领域中的图像HSV伪彩色处理技术将近红外图像与可见光图像融合;然后,对融合图像进行通用搜索树（GiST）特征和分层梯度方向直方图（PHOG）特征的提取与融合;最后,结合提出的类字典稀疏识别方法得到场景分类结果。所提方法在RGB-NIR数据库上的实验识别精度达到了74.75%。实验结果表明,融合近红外信息的场景图像的识别精度高于未融合时的识别精度,所提方法能够有效增加稀疏识别框架下场景目标的信息表征质量。     

基于名义尺度的空间自相关指数测度方法研究*

从空间自相关的基本构成出发,提出了一种测度名义尺度的空间自相关指数——自邻接指数,给出了基于名义尺度下全局空间自相关和局部空间自相关的测度方法,并在ArcView平台下通过Avenue二次编程进行了算法实现。最后通过例子展示了自邻接指数在土地利用格局分析中发挥的重要作用。     

基于改进的BP神经网络入侵检测方法研究

针对遗传算法局部搜索能力弱和收敛速度慢,在选择操作之后加上了禁忌搜索算法,并对交叉操作进行改进,最后用禁忌搜索作为变异操作,从而加快算法的收敛速度,并用此改进的遗传算法来优化BP神经网络的权值。实验证明,采用该方法优化BP神经网络权值,能克服BP神经网络收敛速度慢、局部极小问题。     

多功能Pt-Ce6纳米平台作为过氧化氢纳米酶和NIR-Ⅱ光热剂用于增强PDT/PTT肿瘤治疗

实体肿瘤的缺氧严重影响着基于氧气的光动力疗法(PDT)的效果.另外,单一治疗模式通常难以达到满意的治疗效果.为此,我们设计合成了一种多功能纳米复合材料Pt-Ce6用于克服肿瘤乏氧,实现PDT/PTT协同治疗.在该体系中,我们使用多孔Pt纳米粒子作为过氧化氢纳米酶、近红外二区(NIR-Ⅱ)光热转换剂和光敏剂二氢卟吩e6(...     

基于DSP的嵌入式系统的程序引导设计

作对以DSP为核心的嵌入式系统的程序引导功能进行了深入的研究及分析,并以TI公司的TMS320C33芯片为实例,归纳了其实现流程及设计方案,从硬件和软件两个方面阐述了实现该功能的关键点和值得注意的地方。该设计已被应用于一个配电网环网柜智能终端装置。运行实践表明,是一种简便、可靠的方案。     

Matisse: A System-on-Chip Design Methodology Emphasizing Dynamic Memory Management

MATISSE is a design environment intended for developing systems characterized by a tight interaction between control and data-flow behavior, intensive data storage and transfer, and stringent real-time requirements. Matisse bridges the gap from a system specification, using a concurrent object-oriented language, to an optimized embedded single-chip hardware/software implementation. Matisse supports stepwise exploration and refinement of dynamic memory management, memory architecture exploration, and gradual incorporation of timing constraints before going to traditional tools for hardware synthesis, software compilation, and inter-processor communication synthesis. With this approach, specifications of embedded systems can be written in a high-level programming language using data abstraction. Application of MATISSE on telecom protocol processing systems in the ATM area shows significant improvements in area usage and power consumption.     

Single-Step Fast Tissue Clearing of Thick Mouse Brain Tissue for Multi-Dimensional High-Resolution Imaging

Recent advances in optical clearing techniques have dramatically improved deep tissue imaging by reducing the obscuring effects of light scattering and absorption. However, these optical clearing methods require specialized equipment or a lengthy undertaking with complex protocols that can lead to sample volume changes and distortion. In addition, the imaging of cleared tissues has limitations, such as fluorescence bleaching, harmful and foul-smelling solutions, and the difficulty of handling samples in high-viscosity refractive index (RI) matching solutions. To address the various limitations of thick tissue imaging, we developed an Aqueous high refractive Index matching and tissue Clearing solution for Imaging (termed AICI) with a one-step tissue clearing protocol that was easily made at a reasonable price in our own laboratory without any equipment. AICI can rapidly clear a 1 mm thick brain slice within 90 min with simultaneous RI matching, low viscosity, and a high refractive index (RI = 1.466), allowing the imaging of the sample without additional processing. We compared AICI with commercially available RI matching solutions, including optical clear agents (OCAs), for tissue clearing. The viscosity of AICI is closer to that of water compared with other RI matching solutions, and there was a less than 2.3% expansion in the tissue linear morphology during 24 h exposure to AICI. Moreover, AICI remained fluid over 30 days of air exposure, and the EGFP fluorescence signal was only reduced to ~65% after 10 days. AICI showed a limited clearing of brain tissue >3 mm thick. However, fine neuronal structures, such as dendritic spines and axonal boutons, could still be imaged in thick brain slices treated with AICI. Therefore, AICI is useful not only for the three-dimensional (3D) high-resolution identification of neuronal structures, but also for the examination of multiple structural imaging by neuronal distribution, projection, and gene expression in deep brain tissue. AICI is applicable beyond the imaging of fluorescent antibodies and dyes, and can clear a variety of tissue types, making it broadly useful to researchers for optical imaging applications.