基于径向基函数(RBF)映射理论的遥感影像分类模型研究

与传统统计方法的分类器相比较，人工神经网络（ＡＮＮ）方法应用于遥感影像分类，不需预先假设样本空间的参数化统计分布，具有复杂的映射能力。大多数ＡＮＮ分类器采用误差反向传播（ＢＰ）学习算法的多层感知器模型（ＢＰＮＮ），其主要缺陷是学习速度缓慢、容易陷入局部极小而导致难以收敛等。基于径向基函数（ＲＢＦ）映射理论的神经网络模型融合了参数化统计分布模型和非参经线性感知器映射模型的优点，在实现快速学习的同时，  

常见模糊蕴涵算子的模糊系统及其响应函数

详细讨论了较常见的21种模糊蕴涵算子构成的模糊控制器及其响应函数.主要结果是,异常蕴涵算子模糊控制算法都可归结为某种插值方法,它们相应的模糊控制器均具有函数逼近的泛性且彼此等效;而正常蕴涵算子(包括正规蕴涵算子的导出算子)模糊控制算法均非插值方法,其模糊控制器都不具有函数逼近的泛性,除Zadeh蕴涵算子外,这些正常蕴涵算子的模糊控制器均只具备阶跃输出功能且在一定意义下相互等效.  

芯片功耗与工艺参数变化:下一代集成电路设计的两大挑战

目前Intel、AMD、IBM等国际著名厂商均将其集成电路生产工艺全面转入65nm制程,对于以高性能为目标的高端芯片设计,受到了来自成本、设计与测试复杂性等方面的诸多挑战.文中主要论述了其中两个重要而具体的挑战:高功耗和日益显著的工艺参数变化(process variation).首先分析了纳米工艺下芯片功耗的组成、高功耗的诸多危害和目前主要的低功耗设计方法;然后分析了工艺参数变化的组成,较大工艺参数变化对电路设计的影响以及电路性能分析、功耗分析和低功耗设计的统计式算法;最后结合笔者的研究工作,简单地对下一代集成电路设计的相关研究热点进行预测.  

城市应急联动系统的研究与实现

分析了当前城市应急管理的现状和存在的问题，描述了城市应急联动系统的概念与作用，提出了以应急联动服务平台为核心的城市应急联动系统的体系结构和功能特点，并结合实例分析了该系统的业务模式、拓扑结构、技术路线及关键技术等。  

基于模型的山地遥感图象辐射订正方法

对太阳直射、天空满散射和邻近地形的反射辐射进行了较为详细的分析，提出了崎岖山地真实反射率的恢复模型及相应的快速算法，从而实现了消除山地卫星影像中的地形的影响，恢复阴影所包含的细节。利用ＴＭ影像的实验表明，此方法可以有效地实现对影像的订正，为遥感图象的后处理提供更多的信息。  

利用SLEUTH模型进行北京城市扩展模拟研究

元胞自动机模型具有较强的模拟空间复杂系统时空演变的能力,可以进行有效的城市增长和土地利用演化方面的模拟,现已成为城市地理学研究的重要工具。SLEUTH模型是元胞自动机模型的一种具体实现,它包含了四种城市增长方式和五个增长系数,再加上两个控制因素层一起作用于输入数据,能够较好地模拟城市的扩展。本文详细介绍了SLEUTH模型的校正和运行过程,并修改控制参数将其应用于北京城市扩展的模拟和预测中,通过分析模型的运行结果,深入地研究了北京城市发展的模式和成因,同时也指出了模型存在的问题。  

Time-domain analysis methodology for large-scale RLC circuits and its applications

With soaring work frequency and decreasing feature sizes, VLSI circuits with RLC parasitic components are more like analog circuits and should be carefully analyzed in physical design. However, the number of extracted RLC components is typically too large to be analyzed efficiently by using present analog circuit simulators like SPICE. In order to speedup the simulations without error penalty, this paper proposes a novel methodology to compress the time-descritized circuits resulted from numerical integration approximation at every time step. The main contribution of the methodology is the efficient structure-level compression of DC circuits containing many current sources, which is an important complement to present circuit analysis theory. The methodology consists of the following parts: 1) An approach is proposed to delete all intermediate nodes of RL branches. 2) An efficient approach is proposed to compress and back-solve parallel and serial branches so that it is error-free and of linear complexity to analyze circuits of tree topology. 3) The Y toπtransformation method is used to error-free reduce and back-solve the intermediate nodes of ladder circuits with the linear complexity. Thus, the whole simulation method is very accurate and of linear complexity to analyze circuits of chain topology. Based on the methodology, we propose several novel algorithms for efficiently solving RLC-model transient power/ground (P/G) networks. Among them, EQU-ADI algorithm of linear-complexity is proposed to solve RLC P/G networks with mesh-tree or mesh-chain topologies. Experimental results show that the proposed method is at least two orders of magnitude faster than SPICE while it can scale linearly in both time- and memory-complexity to solve very large P/G networks.  

具有部分监督的遥感影像模糊聚类方法研究及应用

遥感信息主要反映的是地球表层信息。由于地球表层系统的复杂性和开放性，地表信息是多维的、无限的，遥感信息传递过程中的局限性以及遥感信息之间的复杂相关性，决定了遥感信息其结果的不确定性和多解性。模糊分类是遥感影像分类研究的重要趋势。在Ｆｕｚｚｙ－ＩＳＯＤＡＴＡ 方法的基础上，探讨如何在样本数据集中融合部分知识和随机样本，通过聚类分析获得目标类别的模糊隶属度矩阵和特征空间的特征模式的方法。提出了基于该方法的遥感影像模糊分类模型  

An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions

Due to technical and budget limitations, remote sensing instruments trade spatial resolution and swath width. As a result not one sensor provides both high spatial resolution and high temporal resolution. However, the ability to monitor seasonal landscape changes at fine resolution is urgently needed for global change science. One approach is to “blend” the radiometry from daily, global data (e.g. MODIS, MERIS, SPOT-Vegetation) with data from high-resolution sensors with less frequent coverage (e.g. Landsat, CBERS, ResourceSat). Unfortunately, existing algorithms for blending multi-source data have some shortcomings, particularly in accurately predicting the surface reflectance of heterogeneous landscapes. This study has developed an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) based on the existing STARFM algorithm, and has tested it with both simulated and actual satellite data. Results show that ESTARFM improves the accuracy of predicted fine-resolution reflectance, especially for heterogeneous landscapes, and preserves spatial details. Taking the NIR band as an example, for homogeneous regions the prediction of the ESTARFM is slightly better than the STARFM (average absolute difference [AAD] 0.0106 vs. 0.0129 reflectance units). But for a complex, heterogeneous landscape, the prediction accuracy of ESTARFM is improved even more compared with STARFM (AAD 0.0135 vs. 0.0194). This improved fusion algorithm will support new investigations into how global landscapes are changing across both seasonal and interannual timescales.  

磁共振颅脑图像的脑组织自动获取方法

提出了一种针对MRI磁共振图像通过两次分割实现颅脑图像脑组织自动获取的方法.通过基于Catt扩散模型的各项异性滤波,实现了在保持图像细节的同时有效地消除图像的噪声.然后通过改进的基于相似性区域合并的分水岭算法解决了过分割问题,实现了脑组织区域的初次分割.由于颅脑图像不同组织之间边缘模糊且自身容易受到噪声的影响,导致区域合并过程中可能会误将非脑组织作为脑组织合并,因此,采用水平集方法将初次分割获得的脑组织轮廓作为初始轮廓曲线,实现了脑组织的自动分割.实验结果验证了算法的可行性和实用性.