{X,Y}-Cyclacene Graphs with Next Nearest Neighbor Interactions

Eigensolutions of {X( = C,B,N),Y( = C,B,N)}-cyclacene graphs with next nearest neighbor (nnn) interactions have been obtained in analytical forms by adapting n-fold rotational symmetry followed by two-fold rotational symmetry (or a plane of symmetry). Expressions of eigensolution indicate the subspectral relationship among such cyclacenes with an even number of hexagonal rings e.g., eigenvalues of {X,Y}-di-cyclacene are found in the eigenspectra of all such even cyclacenes. Total π-electron energies and highest occupied molecular orbital and lowest unoccupied molecular orbital (HOMO–LUMO) gaps are calculated using the analytical expressions obtained and are found to vary negligibly with the variation of nnn interactions in such cyclacenes. Total π-electron energy is found to increase due to increase in restriction intensity of nnn interactions, whereas the HOMO–LUMO gap of polyacenecs having the even number of hexagonal rings and with one electron at each site (atom) decreases with increase in the restriction intensity since such systems contain degenerate half-filled HOMO (bonding or nonbonding) that are much more vulnerable for perturbations imposed through nnn interactions.     

毛家河水电站库区岩溶发育特征及邻谷渗漏分析

毛家河水电站位于碳酸盐岩地区，库首距左岸邻谷可渡河14—23km、距右岸邻谷北盘江干流14．8km，水库两岸大型落水洞、溶洞及地下岩溶管道相对较为发育。为初步判断水库存在邻谷渗漏的可能性，对水库两岸可溶岩地层岩溶发育形态、特征及规律进行了勘察，并根据现场地质调查资料对3条可疑渗漏带进行了分析论证。     

Rectilinear steiner tree heuristics and minimum spanning tree algorithms using geographic nearest neighbors

We study the application of the geographic nearest neighbor approach to two problems. The first problem is the construction of an approximately minimum length rectilinear Steiner tree for a set ofn points in the plane. For this problem, we introduce a variation of a subgraph of sizeO(n) used by YaO [31] for constructing minimum spanning trees. Using this subgraph, we improve the running times of the heuristics discussed by Bern [6] fromO(n ² log n) toO(n log² n). The second problem is the construction of a rectilinear minimum spanning tree for a set ofn noncrossing line segments in the plane. We present an optimalO(n logn) algorithm for this problem. The rectilinear minimum spanning tree for a set of points can thus be computed optimally without using the Voronoi diagram. This algorithm can also be extended to obtain a rectilinear minimum spanning tree for a set of nonintersecting simple polygons.The results in this paper are a part of Y. C. Yee's Ph.D. thesis done at SUNY at Albany. He was supported in part by NSF Grants IRI-8703430 and CCR-8805782. S. S. Ravi was supported in part by NSF Grants DCI-86-03318 and CCR-89-05296.     

DKNNS:面向延迟敏感型应用的可扩展精确分布式K近邻搜索算法研究

为了降低用户访问延迟,延迟敏感型网络应用需要选择合适的邻近服务节点响应用户访问请求.分布式K近邻搜索通过可扩展的选择距任意用户节点邻近的K个服务节点,可以有效满足网络应用延迟优化的目的.已有工作在精确度以及可扩展性等方面存在不足.针对可扩展精确的K近邻搜索问题,文中提出了分布式K近邻搜索方法DKNNS(distributed K nearest neighbor search).DKNNS将大量的服务节点组织为邻近性感知的多级环,通过最远节点搜索机制选择优化的K近邻搜索初始化节点,然后基于回退方式快速的在目标节点邻近区域发现K个近邻.基于理论分析,模拟测试以及真实环境下的部署实验发现,在不同规模的节点集合下,DKNNS算法能够确定近似最优的K个服务节点.且DKNNS的查询延迟,查询开销均显著低于Meridian算法.最后,DKNNS的返回结果相对于Meridian具有较高的稳定性.     

基于跨层快速邻居感知的OLSR协议

提出了一种基于跨层快速邻居感知的OLSR协议——FS-OLSR,协议在采用hello消息进行链路感知和邻居探测的基础上,结合链路层反馈的信息对网络层邻居表进行更新,以增强节点的邻居感知能力;并将邻居节点的新鲜度作为MPR计算的依据,以提高拓扑消息中广播邻居节点的新鲜度,使之更加适合于快速移动场景。仿真结果表明,在快速移动场景中,FS-OLSR能够在增加少量路由开销的情况下,显著降低丢包率。     

基于K-shell分解与邻居节点度去噪的链路预测方法

链路预测是研究复杂网络结构和演化机制的重要工具,提高链路预测的精度具有重要价值。针对传统的基于网络拓扑结构相似性算法预测精度偏低的问题,从网络优化去噪的角度进行分析,提出了一种基于K-shell分解与邻居节点度（KSDNN）去噪的链路预测方法。该方法首先从全局的角度通过K-shell分解对复杂网络中所有节点进行重要性排序,然后从局部的角度结合节点邻居节点的度对节点重要性进行综合评判,最后对网络数据进行优化后进行链路预测。通过在四个不同的真实网络进行验证,实验结果表明,所提方法预测精度优于K-shell去噪的方法,且相较于传统算法预测精度平均提升了2%左右。     

Computer aided detection system for micro calcifications in digital mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Micro calcification clusters (MCCs) and masses are the two most important signs for the breast cancer, and their automated detection is very valuable for early breast cancer diagnosis. The main objective is to discuss the computer-aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three-steps procedure beginning with enhancement by using Histogram equalization (HE) and Morphological Enhancement, followed by segmentation based on Otsu's threshold the region of interest for the identification of micro calcifications and mass lesions, and at last classification stage, which classify between normal and micro calcifications ‘patterns and then classify between benign and malignant micro calcifications. In classification stage; three methods were used, the voting K-Nearest Neighbor classifier (K-NN) with prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction accuracy of 83%, and Artificial Neural Network classifier (ANN) with prediction accuracy of 77%.     

基于RKNN的选址算法及其应用研究

为了使各个设施的整体利用效果最佳, 结合空间位置影响力查询技术, 提出了基于RKNN的选址算法。针对空间对象对空间位置的贡献具有多重性, 建立了一种新的空间位置影响力的度量模型, 该模型更加符合实际应用情况; 然后, 利用该模型计算选址方案中各个设施的影响力, 并引入均衡系数评价选址方案的合理性, 均衡系数越小, 方案越合理。实际应用表明基于RKNN的选址算法使村镇基础设施的选址更加合理、有效。     

LDE融合光谱回归分类的光照变化人脸识别

针对光照变化人脸识别问题中传统的光谱回归算法不能很好地进行特征提取而严重影响识别性能的问题,提出了局部判别嵌入优化光谱回归分类的人脸识别算法。计算出训练样本的特征向量;借助于数据的近邻和分类关系,利用局部判别嵌入算法构建分类问题所需的嵌入,同时学习每种分类的子流形所需的嵌入;利用光谱回归分类算法计算投影矩阵,并利用最近邻分类器完成人脸的识别。在两大人脸数据库扩展YaleB及CMU PIE上的实验验证了该算法的有效性,实验结果表明,相比其他光谱回归算法,该算法取得了更高的识别率、更好的工作特性,并且降低了计算复杂度。     

An alignment-free fingerprint bio-cryptosystem based on modified Voronoi neighbor structures

Bio-cryptography is an emerging security technology which combines cryptography with biometrics. A good bio-cryptosystem is required to protect the privacy of the relevant biometric data as well as achieving high recognition accuracy. Fingerprints have been widely used in bio-cryptosystem design. However, fingerprint uncertainty caused by distortion and rotation during the image capturing process makes it difficult to achieve a high recognition rate in most bio-cryptographic systems. Moreover, most existing bio-cryptosystems rely on the accurate detection of singular points for fingerprint image pre-alignment, which is very hard to achieve, and the image rotation transformation during the alignment process can cause significant singular point deviation and minutiae changes. In this paper, by taking full advantage of local Voronoi neighbor structures (VNSs), e.g. local structural stability and distortion insensitivity, we propose an alignment-free bio-cryptosystem based on fixed-length bit-string representations extracted from modified VNSs, which are rotation- and translation-invariant and distortion robust. The proposed alignment-free bio-cryptosystem is able to provide strong security while achieving good recognition performance. Experimental results in comparison with most existing alignment-free bio-cryptosystems using the publicly-available databases show the validity of the proposed scheme.