Application of Vis–NIR hyperspectral imaging in classification between fresh and frozen-thawed pork Longissimus Dorsi muscles

Fresh and frozen-thawed (F-T) pork meats were classified by Vis–NIR hyperspectral imaging. Eight optimal wavelengths (624, 673, 460, 588, 583, 448, 552 and 609 nm) were selected by successive projections algorithm (SPA). The first three principal components (PCs) obtained by principal component analysis (PCA) accounted for over 99.98% of variance. Gray-level-gradient co-occurrence matrix (GLGCM) was applied to extract 45 textural features from the PC images. The correct classification rate (CCR) was employed to evaluate the performance of the partial least squares-discriminate analysis (PLS-DA) models, by using (A) the reflected spectra at full wavelengths and (B) those at the optimal wavelengths, (C) the extracted textures based on the PC images, and (D) the fused variables combining spectra at the optimal wavelengths and textures. The results showed that the best CCR of 97.73% was achieved by applying (D), confirming the high potential of textures for fresh and F-T meat discrimination.     

Critical Configurations for 1-View in Projections from ℙ<Superscript>k</Superscript> → ℙ<Superscript>2</Superscript>

In this paper we describe, from a theoretical point of view, critical configurations for the projective reconstruction of a set of points, for a single view, i.e. for calibration of a camera, in the case of projections from ℙ^k to ℙ² for k ≥ 4. We give first a general result describing these critical loci in ℙ^k, which, if irreducible, are algebraic varieties of dimension k−2 and degree 3. If k=4 they can be either a smooth ruled surface or a cone and if k = 5 they can be a smooth three dimensional variety, ruled in planes, or a cone. If k≥ 6, the variety is always a cone, the vertex of which has dimension at least k − 6. The reducible cases are studied in Appendix A. These results are then applied to determine explicitly the critical loci for the projections from ℙ^k which arise from the dynamic scenes in ℙ³ considered in [13]. Marina Bertolini is currently Associate Professor of Geometry at the Department of Mathematics at the Università degli Studi di Milano, Italy. Her main field of research is Complex Projective Algebraic Geometry, with particular interest for the classification of projective varieties and for the geometry of Grassmann varieties. On these topics M. Bertolini has published more than twenty reviewed papers on national and international journals. She has been for some years now interested also in applications of Algebraic Geometry to Computer Vision problems. Cristina Turrini is Associate Professor of Geometry at the Department of Mathematics of Università degli Studi di Milano, Italy. Her main research interest is Complex Projective Algebraic Geometry: subvarieties of Grassmannians, special varieties, automorphisms, classification. In the last two years she has started to work on applications of Algebraic Geometry to problems of Computer Vision. She is author or co-author of about thirty reviewed papers. She is also involved in popularization of Mathematics, and on this subject she is co-editor of some books.     

用保形样条方法求解非定常对流扩散方程

在欧拉-拉格朗日分裂方法的基础上,本文发展了一种固定网格上的欧拉-拉格朗日分裂方法.保形样条方法(SPSM)被用来解决倒特征线插值问题.通过求解几个有精确解的例子,说明SPSM方法的解是单调无振荡的,并且数值耗散也是比较小的.     

K终端可靠度分解算法中多边形→链简化定理的完善

无向网络K终端可靠度的分解算法中,包括多边形→链简化在内的等可靠度简化和分解定理结合,可以降低算法的复杂度。本文完善了边随机无向网络和混合随机无向网络的4#,6#,7#型多边形→链简化定理。计算机编程验证了其正确性。     

一种改进的网格数据保持梯度滤波方法

 基于网格数据滤波的基本思想，针对杨高印提出的小子域滤波法存在子域划分重心不稳的问题，本文提出了新的子域划分方法，即通过考察滤波窗口内数据的数值分布或梯度变化情况，在充分考虑子域划分对称性的基础上，采用对称子域的均方根误差或平均梯度大小差值为判别指标确定数据变化的主梯度方向，并用此判别指标代替数据变化平缓系数判别指标，在滤波输出的取值上考虑数据变化的梯度方向上的两个相对称子域对输出的贡献大小进行加权平均，使滤波输出结果更稳定。通过数据试算，表明网格数据滤波法输出结果更加合理、稳健。     

Locality-preserving hash functions for general purpose parallel computation

Consider the problem of efficiently simulating the shared-memory parallel random access machine (PRAM) model on massively parallel architectures with physically distributed memory. To prevent network congestion and memory bank contention, it may be advantageous to hash the shared memory address space. The decision on whether or not to use hashing depends on (1) the communication latency in the network and (2) the locality of memory accesses in the algorithm.We relate this decision directly to algorithmic issues by studying the complexity of hashing in the Block PRAM model of Aggarwal, Chandra, and Snir, a shared-memory model of parallel computation which accounts for communication locality. For this model, we exhibit a universal family of hash functions having optimal locality. The complexity of applying these hash functions to the shared address space of the Block PRAM (i.e., by permuting data elements) is asymptotically equivalent to the complexity of performing a square matrix transpose, and this result is best possible for all pairwise independent universal hash families. These complexity bounds provide theoretical evidence that hashing and randomized routing need not destroy communication locality, addressing an open question of Valiant.This work was started when the author was a student at Oxford University, supported by a National Science Foundation Graduate Fellowship and a Rhodes Scholarship. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author and do not necessarily reflect the views of the National Science Foundation or the Rhodes Trust.     

保持几何特征的自适应弹性二次曲线模型

物体形状特征的准确提取往往受到图像获取、目标分割等过程中各种因素的影响,因而需要构造一种鲁棒的形状表现来保有其本质特征。揭示弹性二次曲线(EQW)模型中能量权重因子和样条重叠度因子与形状几何特性间的关系,提出一种自适应保持几何特征的EQW形状表现模型,并将该模型与交互式Live Wire算法相结合,应用至图像分割中,对于各种模态图像中的目标进行分割实验。直观上,本文方法能够在去除噪声同时较好地保持目标形状的显著特征;量化结果方面,交互式分割误差保持在较小稳定范围内,引入EQW模型带来的时间开销亦在可接受范围内。     

一种谱分解降维的模糊有监督局部保持投影策略

提出一种谱分解降维的模糊有监督局部保持投影策略。首先针对监督局部保持投影SLPP存在过学习和不能较好地保持图像空间的差异信息等问题,通过最小化局部离散度和最大化差异离散度准则提取投影方向,找到一种线性鉴别分析的等价形式。其次,通过采用模糊k近邻(FKNN)方法得到相应的样本分布隶属度信息,同时考虑到离群样本对整个分类结果的不利影响,提出一种模糊化方法,根据样本的隶属度对样本分布矩阵重定义所做的贡献,将每个样本的隶属度融入到SLPP特征抽取的过程中,从而得到完整有效的模糊样本特征向量集,有效解决了小样本问题的特征抽取问题。第三,提出一种谱分解的矩阵分析方法,在SLPP投影准则下,对散布矩阵实现降维。在ORL和NUST603人脸库上的实验结果验证了该方法的有效性。     

敏感信息的密码学保护方法

本文介绍了一种可以应用于敏感信息保护的密码学方法--保留格式加密。保留格式加密是一种全新的密码学技术,将一种特定格式的明文加密成相同格式的密文,因此密文的类型和长度与明文相同,不需要改动数据在数据库中的存储结构,降低数据库应用系统改造的成本,可以成为敏感信息保护的有效方法,具有较广的应用前景。     

正交拉普拉斯语种识别方法

提出了一种正交拉普拉斯语种识别方法,即在提取语音的i-vector后,采用正交局部保持投影进行子空间映射,将信号整体空间映射到语言信息加信道信息子空间,然后对映射后的矢量进行信道补偿处理,最后用支持向量机进行识别. 尽管i-vector最大限度地保留了语音的声学信息,但是并没有发现这些信息之间的内在结构. 利用正交局部保持投影在去除声学无关信息的基础上,进一步发现声学特征的内在结构,能够有效地提高特征的区分性. 在对NIST LRE 2003测试数据库实验后,发现新方法相较于基线系统来说,平均代价降低了28.91%.