乌桕梓油脂肪酸组成分析

乌柏是我国四大木本油料之一,种子含油量高,乌桕梓油是由种仁榨取所得的液体油脂,是轻工业、食品和国防等行业的重要油源。乌桕梓油甲酯化后,利用100 m×0.25 mm的GC毛细管柱,气相色谱法测定乌桕梓油的脂肪酸组成。测定结果表明：乌桕梓油中饱和脂肪酸占10.68%,以棕榈酸含量最高,占7.52%。不饱和脂肪酸占89.13%,单不饱和脂肪酸占18.63%,以油酸含量最高,占14.55%;多不饱和脂肪酸占70.50%;其中含量最高的为人体所必需的脂肪酸亚油酸和α-亚麻酸,分别占30.77%和39.30%。还含有相当数量天然植物中少有的奇数碳原子脂肪酸十一烷酸,占0.29%。其中月桂烯酸（占3.19%）和十一烷酸未见报道。     

Data Clustering Method Using a Modified Gaussian Kernel Metric and Kernel PCA

Most hyper‐ellipsoidal clustering (HEC) approaches use the Mahalanobis distance as a distance metric. It has been proven that HEC, under this condition, cannot be realized since the cost function of partitional clustering is a constant. We demonstrate that HEC with a modified Gaussian kernel metric can be interpreted as a problem of finding condensed ellipsoidal clusters (with respect to the volumes and densities of the clusters) and propose a practical HEC algorithm that is able to efficiently handle clusters that are ellipsoidal in shape and that are of different size and density. We then try to refine the HEC algorithm by utilizing ellipsoids defined on the kernel feature space to deal with more complex‐shaped clusters. The proposed methods lead to a significant improvement in the clustering results over K‐means algorithm, fuzzy C‐means algorithm, GMM‐EM algorithm, and HEC algorithm based on minimum‐volume ellipsoids using Mahalanobis distance.     

基于自适应最优核时频分布理论的ISAR成像方法

由于运动的复杂性, 传统的成像方法已无法满足机动目标成像的要求。在分析机动目标复杂运动引起目标回波多普勒相位非线性变化的基础上, 提出了一种基于自适应最优核时频分布(AOK TFR)理论的ISAR成像方法。通过计算回波数据不同距离单元的AOK TFR, 得到不同时刻目标的瞬态ISAR像, 且不受交叉项的影响;通过估计距离单元的瞬时多普勒频率并依此对成像时间段进行选择。仿真数据结果验证了该方法的有效性。     

TMS320C6X的SPLOOP技术

软件流水是一种实现循环迭代中指令级并行的指令调度技术。它可以克服多周期指令延迟对CPU处理性能的影响,保证循环核的运行效率最优。从C64X+开始,TMS320C6X系列DSP引入SPLOOP技术,软件上增加SPLOOP(D/W)、SPKERNEL等相关指令,硬件上增加软件流水缓存等专用模块,通过模调度软件流水模式,有效缩小了软件代码量,提升了执行代码效率。一般情况下,采用SPLOOP技术后机器编译输出的循环代码质量很高,编程人员无需再对代码进行进一步的手工优化。     

Multiple kernel-based multi-instance learning algorithm for image classification

In this paper, a novel multi-instance learning (MIL) algorithm based on multiple-kernels (MK) framework has been proposed for image classification. This newly developed algorithm defines each image as a bag, and the low-level visual features extracted from its segmented regions as instances. This algorithm is started from constructing a “word-space” from instances based on a collection of “visual-words” generated by affinity propagation (AP) clustering method. After calculating the distance between a “visual-word” and the bag (image), a nonlinear mapping mechanism is introduced for registering each bag as a coordinate point in the “word-space”. In this case, the MIL problem is transformed into a standard supervised learning problem, which allows multiple-kernels support vector machine (MKSVM) classifiers to be trained for the image categorization. Compared with many popular MIL algorithms, the proposed method, named as MKSVM-MIL, shows its satisfactorily experimental results on the COREL dataset, which highlights the robustness and effectiveness for image classification applications.     

基于多重核的稀疏表示分类

稀疏表示分类（SRC）及核方法在模式识别的很多问题中都得到了成功的运用.为了提高其分类精度,提出多重核稀疏表示及其分类（MKSRC）方法.提出一种快速求解稀疏系数的优化迭代方法并给出了其收敛到全局最优解的证明.对于多重核的权重给出了两种自动更新方式并进行了分析与比较.在不同的人脸图像库上的分类实验显示了所提出的多重核稀疏表示分类的优越性.     

基于Libnids库的Internet网络协议还原系统研究

近年来,互联网的规模呈现爆炸性增长,骨干网通信带宽达到了千兆甚至万兆,因此保护互联网的安全变得越来越重要。网络安全领域的各种产品如防火墙、入侵检测系统都是基于对网络数据的分析和预测而开发的,而截获和还原数据的协议还原技术成为设计这些网络安全产品的基石。文章研究并实现了基于Libnids库的Internet网络协议还原系统,该系统利用旁路数据链路帧的方式捕获数据包,借鉴Linux内核的实现方法进行IP分片组装和TCP流重组,有效实现了网络信息内容监控。该系统可以根据需要加入扩展模块,可根据需要还原多种应用层数据。     

反射式离轴数字全息再现像中的噪声抑制

为了抑制反射式离轴数字全息再现像中的零级像和散斑噪声对再现像的影响,利用高斯核函数在空域对数字全息图进行低通滤波,再用原数字全息图减去滤波后的低频信息来抑制零级像,采用非局部均值滤波来抑制散斑噪声。实验结果表明空域高斯核函数处理法能有效地抑制零级像,非局部均值滤波可以抑制散斑噪声但对部分细节的保持能力不强,改进后的非局部均值滤波在抑制散斑噪声的同时能很好地保护细节信息,提高了离轴数字全息再现像的质量。