一种大规模体数据压缩体绘制策略

针对基于GPU的大规模体数据直接体绘制过程中遇到的显存不足的问题，提出了一种大规模体数据的压缩绘制策略.该策略结合小波变换和分类矢量量化进行数据压缩，采用基于GPU的光线投射算法进行绘制，在绘制时，只解压变换当前绘制所需要的极少数数据，并结合多分辨率绘制，实现实时交互.基于CUDA的实验表明：该压缩绘制策略有效解决了显存不足问题，并且在保证交互绘制帧率的前提下获得了较好的成像质量.     

New Progression about Gravity Quantization in General Relativity Theory in Real Space-time

Professor Venzo de Sabbata,working at World Laboratory,Lausanne,Switzerland,and Department of Physics,University of Bologna and Ferrara,Italy,is a famous Italy Physicist in the world.He is engaged with a series of creating research works about gravity quantization of General Relativity Theory in the real space-time which attract the attentions of the world.This paper pays the attention to introduce the works of this field,containing mainly:1.quantizing the general relativity theory in the sense of Einstem-Cartan theory,i.e., general relativity adds the torsion,which means introducing spin into the theory of gravity.2. building a compact mathematical tool for research of gravity quantization,the geometric algebra in the real space-time--multivector algebra,i.e.,developing a spinor manifold in the real space-time.     

基于IPCC排放因子法估算碳足迹的京津冀生态补偿量化

构建合理的生态补偿量化标准关系着京津冀区域的健康发展。采用IPCC排放因子法计算京津冀地区2006-2015年的碳足迹,结果表明:10 a间京津冀地区碳足迹变化趋势分为2个阶段,2006到2013呈现快速增长趋势,年增长率约为8.5%,2013年之后基本保持不变;考虑森林、草地、农用地的固碳能力的前提下,测算了京津冀地区2006-2015年的碳承载力,结果表明:2009年京津冀地区的碳承载力有明显增高,之后基本保持平稳,略有提升;为了对比不同区域内(人口和区域面积)碳赤字对生态的影响,提出了碳赤字敏感度,进而利用碳赤字敏感度构建了生态补偿因子的概念,并据此确定京津冀三区生态补偿的量化标准,结果表明:河北和北京每年都应得到天津支付的一定额度的生态补偿,其中河北2012年应获得补偿最多(161亿元),北京2013年最多(61.5亿元)。研究结果对加快建立完善的京津冀横向生态补偿机制有参考意义。     

基于多重分形和LVQ神经网络的小麦病害智能识别

针对不同小麦病害有不同的形状特征,利用多重分形分析提取小麦病害图像的8个多重分形谱值作为小麦病害的形状特征参数,并利用这8个特征参数来索引图像数据库作为学习向量量化(LVQ)神经网络的输入,进行样本训练、分类识别.试验结果表明,该算法对小麦病害的识别率可达90.0％以上.     

Automatic acoustic detection of the red palm weevil

The red palm weevil (RPW) is a key pest of horticultural and ornamental palm species in Asia, the Middle East and the Mediterranean region, currently dispersing in Mediterranean European countries, endangering the landscape. The RPW larvae bore deep into palm crowns, trunks and offshoots, concealed from visual inspection until the palms are nearly dead. Traded palm trees are intensively transported between and within countries, spreading the pest worldwide. Consequently, an urgent need exists to identify and monitor concealed RPW larvae. Acoustic signals of boring RPW larvae can be recorded from the infested palms using off-the-shelf recording devices, but the resolution of the signals emitted by healthy palms is often difficult to discriminate. The purpose of this research was to develop a mathematical method to automatically detect acoustic activity of RPW in offshoots and implement it in a prototype setup. The methodology applied was similar to techniques used in the field of speech recognition, utilizing Vector quantization (VQ) or Gaussian mixture modeling (GMM). The algorithm successfully achieved detection ratios as high as 98.9%. The study shows that it is feasible to detect RPW sounds using the mathematical method of speech recognition and commercial recording devices, which could be utilized to monitor trade and transportation of offshoots.     

兴安落叶松林林下冻土中硫元素分布规律研究

通过对我国典型寒温带兴安落叶松林区的原始林、间伐林、原始湿地3种生态系统冻土中硫含量以及影响硫含量因素的研究。得出以下结论:①、3种生态系统冻土中硫含量范围0.0%~0.14%,有机质含量范围14 g/kg~240g/kg;通过统计分析,②、对冻土中硫含量的影响程度由大到小的因素为土壤层次、生态系统、坡位、坡向;③、硫含量表层最高,占4个层次硫总量的50%以上,向下含量逐渐减少;④、3种生态系统硫含量由高到低的顺序为原始湿地、原始林、间伐林,坡下坡中坡上硫含量差别不显著,阳坡硫含量稍高于阴坡,差别不显著。     

龙井茶下沉规律与感官品质相关性研究

在龙井茶三大产区制备和筛选了26个代表性茶样,分析其感官品质,观测其冲泡下沉规律并进行统计学分析,从龙井茶外形长度、宽度、厚度、质量等量化参数出发,对龙井茶的下沉规律与感官品质相关性进行对比研究。结果表明,茶叶外形是影响龙井茶下沉的重要因素,长宽比大的龙井茶下沉速率更快;茶叶等级与质量水平影响龙井茶下沉规律,但不能以下沉快慢作为判断茶叶感官品质高低的依据。     

Novel Image-adaptive Watermarking Algorithm

Digital watermarking is an effective method to provide copyright protection for digital media. A blind image- adaptive watermarking algorithm based on DCT domain is presented. The algorithm selects adaptively the step size of quantization to embed the watermark bits by modifying DC components via the human visual system model and local characteristics of image; at the same time some AC components of the blocks with less perceptual capacity are also modified to embed the same watermark bits by using quantization. Thus the relation between robustness and imperceptibility can be well balanced. The algorithm recovers the watermark without any reference to the original image. Compared with the similar algorithm reported in the literature, this method can give better image quality and is better robust against noise and commonly used image processing techniques.     

A floating threshold quantization algorithm to suppressmixing interferences in partial discharge

Due to the obvious difference of energy distribution frequencies from partial discharge (PD) signal and its mixing interferences (white noise and narrow brand), we uses the characteristic that node decomposition coefficients of wavelet packet transform can effectively show the energy change of signals to build up a floating threshold quantization algorithm (FTQA) varying with the noise energy of PD decomposition coefficients. It makes the node thresholds under the optimal base various with the noise strength in decomposition coefficients to self adaptively reality the choice of optimal threshold to finely partition PD decomposition coefficients. For simulated and real PD signals with mixing interferences, the conditional global threshold quantization algorithm (GTQA) and the proposed floating threshold quantization algorithm are employed to suppress the mixing interferences in PD signals and compared, and the results show that the proposed algorithm has the stronger suppression ability to mixing interference on PD signal and keeps perfect PD waveform via suppression.     

基于学习矢量量化神经网络的水稻白穗和正常穗的高光谱识别

水稻病虫害的发生会导致大量白穗的出现,对白穗和正常穗的区分是采取植保措施和灾害评估的基础。通过研究获取了由水稻二化螟和穗瘟造成的白穗和正常穗的室内光谱,选取红边斜率、红边面积、绿峰幅值和绿峰面积等4个高光谱变量作为输入向量,利用学习矢量量化（LVQ）神经网络对水稻白穗和正常穗进行分类。利用测试样本对网络进行测试,结果显示对白穗和正常稻穗的分类精度高达100%。研究表明,基于LVQ神经网络对水稻白穗和正常穗进行辨别的方法是切实可行的,可以补充和替代肉眼观测。