降雨衰耗对LMDS正交极化的影响*

研究了降雨对视距传输的LMDS正交极化产生的影响。该LMDS系统的工作频率为10GHz~40GHz当中的四个频率,LMDS系统发射水平极化和垂直极化信号,并在两个高密度雨区持续降雨时测量。研究结果表明,降雨衰耗产生的去极化影响随着工作频率的提高而增加,在10GHz附近去极化影响最低。     

LMDS系统安全认证协议的形式分析与改进

利用形式化分析密码协议的方法来检测密码协议中潜在的安全漏洞,已成为密码学中一个新的研究方向。文章用BAN逻辑形式分析方法,对基于802.16的LMDS系统的安全认证协议进行了分析,针对协议中存在的不足提出了更为安全、合理的解决方案。     

Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors

Tropical forests are an important component of the global carbon balance, yet there is considerable uncertainty in estimates of their carbon stocks and fluxes, which are typically estimated through analysis of aboveground biomass in field plots. Remote sensing technology is critical for assessing fine-scale spatial variability of tropical forest biomass over broad spatial extents. The goal of our study was to evaluate relatively new technology, small-footprint, discrete-return lidar and hyperspectral sensors, for the estimation of aboveground biomass in a Costa Rican tropical rain forest landscape. We derived a suite of predictive metrics for field plots: lidar metrics were calculated from plot vertical height profiles and hyperspectral metrics included fraction of spectral mixing endmembers and narrowband indices that respond to photosynthetic vegetation, structure, senescence, health and water and lignin content. We used single- and two-variable linear regression analyses to relate lidar and hyperspectral metrics to aboveground biomass of plantation, managed parkland and old-growth forest plots. The best model using all 83 biomass plots included two lidar metrics, plot-level mean height and maximum height, with an r² of 0.90 and root-mean-square error (RMSE) of 38.3 Mg/ha. When the analysis was constrained to plantation plots, which had the most accurate field data, the r² of the model increased to 0.96, with RMSE of 10.8 Mg/ha (n = 32). Hyperspectral metrics provided lower accuracy in estimating biomass than lidar metrics, and models with a single lidar and hyperspectral metric were no better than the best model using two lidar metrics. These results should be viewed as an initial assessment of using these combined sensors to estimate tropical forest biomass; hyperspectral data were reduced to nine indices and three spectral mixture fractions, lidar data were limited to first-return canopy height, sensors were flown only once at different seasons, and we explored only linear regression for modeling. However, this study does support conclusions from studies at this and other climate zones that lidar is a premier instrument for mapping biomass (i.e., carbon stocks) across broad spatial scales.     

GRNN在翻斗式雨量计中的应用

针对当前翻斗式雨量计传感器存在的计量误差,提出了有效的应对措施,并使用3类曲线拟合算法对北京市7.21暴雨实际观测资料的雨量计数据进行预测和比对.实验结果表明:广义回归神经网络(GRNN)算法预测效果最好.针对气象部门对雨量计测量数据的使用方式局限性,据此结合预测数据给出了一种智能信息处理的方案,能够将雨量信息较为准确、直观和全面地展现出来,增强了气象部门对应急事件的处理能力.     

无线电引信实时数据采集发送模块设计

利用Altera公司的开发软件Quartus Ⅱ 8.1进行VHDL编程,给出了一种利用CPLD进行曼彻斯特编码以实现无线电引信实时数据采集及传输的技术方案.整个系统分为衰减网络、调理电路、A/D转换电路、A/D控制及编码和电光转换电路五部分,最终将电信号转化为光信号,实现电信号安全可靠输出.     

基于GPU粒子系统的大规模雨雪场景实时模拟

基于粒子系统的雨雪模拟大幅提高了三维场景的真实感,但传统的基于中央处理(CPU)的粒子系统的渲染效率难以达到在大规模场景中进行雨雪渲染的要求.为此,提出了一种基于GPU的粒子系统来渲染雨雪场景的算法.该算法在视点前的一个固定区域内产生和绘制粒子,在顶点着色器中进行粒子属性的更新,在几何着色器中将粒子从点扩展为矩形,并对每一帧中的粒子的属性进行缓存处理,保证了粒子属性更新的连续性.此外,采用多幅雪花纹理与粒子随机组合,使雪花效果符合多样性和随机性.实验结果表明,该算法能在大规模场景中进行雨雪效果的实时渲染,并有较高的真实感.     

Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape

Meso-scale digital terrain models (DTMs) and canopy-height estimates, or digital canopy models (DCMs), are two lidar products that have immense potential for research in tropical rain forest (TRF) ecology and management. In this study, we used a small-footprint lidar sensor (airborne laser scanner, ALS) to estimate sub-canopy elevation and canopy height in an evergreen tropical rain forest. A fully automated, local-minima algorithm was developed to separate lidar ground returns from overlying vegetation returns. We then assessed inverse distance weighted (IDW) and ordinary kriging (OK) geostatistical techniques for the interpolation of a sub-canopy DTM. OK was determined to be a superior interpolation scheme because it smoothed fine-scale variance created by spurious understory heights in the ground-point dataset. The final DTM had a linear correlation of 1.00 and a root-mean-square error (RMSE) of 2.29 m when compared against 3859 well-distributed ground-survey points. In old-growth forests, RMS error on steep slopes was 0.67 m greater than on flat slopes. On flatter slopes, variation in vegetation complexity associated with land use caused highly significant differences in DTM error distribution across the landscape. The highest DTM accuracy observed in this study was 0.58-m RMSE, under flat, open-canopy areas with relatively smooth surfaces. Lidar ground retrieval was complicated by dense, multi-layered evergreen canopy in old-growth forests, causing DTM overestimation that increased RMS error to 1.95 m.A DCM was calculated from the original lidar surface and the interpolated DTM. Individual and plot-scale heights were estimated from DCM metrics and compared to field data measured using similar spatial supports and metrics. For old-growth forest emergent trees and isolated pasture trees greater than 20 m tall, individual tree heights were underestimated and had 3.67- and 2.33-m mean absolute error (MAE), respectively. Linear-regression models explained 51% (4.15-m RMSE) and 95% (2.41-m RMSE) of the variance, respectively. It was determined that improved elevation and field-height estimation in pastures explained why individual pasture trees could be estimated more accurately than old-growth trees. Mean height of tree stems in 32 young agroforestry plantation plots (0.38 to 18.53 m tall) was estimated with a mean absolute error of 0.90 m (r²=0.97; 1.08-m model RMSE) using the mean of lidar returns in the plot. As in other small-footprint lidar studies, plot mean height was underestimated; however, our plot-scale results have stronger linear models for tropical, leaf-on hardwood trees than has been previously reported for temperate-zone conifer and deciduous hardwoods.     

机载雷达功能仿真中目标回波分析

从机载雷达功能仿真的目标探测模型出发,以信噪比的求解为基准,考虑机载雷达目标探测过程中受到的大气衰减和地表镜面反射等背景因素的影响和制约,分析了典型大气状况和地表类型条件下目标回波功率的模型.大气衰减系数和地表镜面反射系数经过优化建模,以达到定量计算的要求.利用此模型提出机载雷达目标探测的效能指标.在效能仿真的基础上,运用典型值采样的方法,重点分析了镜面反射对机载雷达回波能量的定量影响.仿真结果表明了该模型的正确性和有效性.     

Remote sensing of water clarity in Tampa Bay

We examined the spatial and temporal variability of the Secchi Disk Depth (SDD) within Tampa Bay, Florida, using the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) satellite imagery collected from September 1997 to December 2005. SDD was computed using a two-step process, first estimating the diffuse light attenuation coefficient at 490 nm, K_d(490), using a semi-analytical algorithm and then SDD using an empirical relationship with K_d(490). The empirical SDD algorithm (SDD = 1.04 × K_d(490)^− 0.82, 0.9 < SDD < 8.0 m, r² = 0.67, n = 80) is based on historical SDD observations collected by the Environmental Protection Commission of Hillsborough County (EPCHC) in Tampa Bay. SeaWiFS derived SDD showed distinctive seasonal variability, attributed primarily to chlorophyll concentrations and color in the rainy season and to turbidity in the dry season, which are in turn controlled by river runoff and winds or wind-induced sediment resuspension, respectively. The Bay also experienced strong interannual variability, mainly related to river runoff variability. As compared to in situ single measurements, the SeaWiFS data provide improved estimates of the “mean” water clarity conditions in this estuary because of the robust, frequent, and synoptic coverage. Therefore we recommend incorporation of this technique for routine monitoring of water quality in coastal and large estuarine waters like Tampa Bay.     

New models for region of interest reader classification analysis in chest radiographs

In several computer-aided diagnosis (CAD) applications of image processing, there is no sufficiently sensitive and specific method for determining what constitutes a normal versus an abnormal classification of a chest radiograph. In the case of lung nodule detection or in classifying the perfusion of pneumoconiosis, multiple radiograph readers (radiologists) are asked to examine and score specific regions of interest (ROIs). The readers provide size, shape and perfusion grades for the presence of opacities in each region and then use all the ROI grades to classify the lung as normal or abnormal. The combined grades from all readers are then used to arrive at a consensus normal or abnormal classification. In this paper, using area under the ROC curve, we evaluate new mathematical models that are based on mathematical statistics, logic functions, and several statistical classifiers to analyze reader performance in grading chest radiographs for pneumoconiosis as the first step toward applying this technique to early detection of nodules found in lung cancer. In pneumoconiosis, rounded opacities are on the order of 1-10 mm in size, while lung nodules are often not diagnosed until they reach a size on the order of 1 cm.