2DPCA-SIFT:一种有效的局部特征描述方法

PCA-SIFT （Principal component analysis—scale invariant feature transform）方法通过对归一化梯度向量进行PCA降维,在保留特征不变性的同时,有效地降低了特征矢量的维数,从而提高了局部特征的匹配速度. 但PCA-SIFT中对本征向量空间的求解非常耗时,极大地限制了PCA-SIFT的灵活性与应用范围. 本文提出采用2DPCA对梯度向量块进行降维的特征描述方法. 该方法相比于PCA-SIFT,可以快速地求解本征空间. 实验结果表明:2DPCA-SIFT在多种图像变换匹配和图像检索实验中可以实现与PCA-SIFT相当的性能,并且从计算效率上看,2DPCA-SIFT具有更好的扩展性.     

石英晶体微天平的研究进展综述

石英晶体微天平(QCM)能够感知到纳克级的质量变化,在分析化学、电化学、有机化学、生物医学等方面得到了广泛的应用。根据与QCM电极表面相接触的研究介质即应用对象的不同,分别阐述了刚性薄层、牛顿流体、粘弹性膜层的基础方程、分析方法及应用,并探讨了其存在的问题与发展趋势。     

二氧化氯催化氧化处理活性艳红染料废水催化剂的制备与应用

研究了二氧化氯催化氧化用催化剂的制备条件与催化剂性能。实验结果表明，在工业催化剂载体上负载Cu^2+活性组分的催化剂制备优化条件为：w(Cu²⁺)=6%，焙烧温度500 ℃，焙烧时间4 h。用此催化剂进行二氧化氯催化氧化处理COD为2 700 mg·L^-1的活性艳红染料配制废水时，COD去除率达75%；催化剂寿命与再生实验表明，该方法在技术与经济上都具有较强实用性。     

一种交互式的岩心图像修补方法

传统的岩心图像修补方法往往无法对不同宽度的待修补域达到理想效果,并且难以对待修补区域做出选择,这在实际应用中很难满足需要。针对这些问题,提出基于联合一阶与二阶全变差（Combined TV-TV2）的交互式岩心修补方法。该方法首先进行均值漂移对图像进行预分割,然后使用基于区域合并的交互式最大相似度算法确定待修补的区域,最后以联合TV-TV2修补模型为基础,应用分裂Bregman迭代进行求解。实验结果表明,待修补的区域能够被确定,而对于不同宽度的修补区域,该算法能够获得满意的修补效果。     

Synthesis of polystyrene end-capped with pyrene via reversible addition-fragmentation chain transfer polymerization

A novel benzodithioate compound with a pyrene structure in the R group, pyrenylmethyl benzodithioate (PMB) was synthesized. Using PMB as the chain transfer agent (CTA), the RAFT polymerizations of styrene with AIBN as an initiator were carried out in different reaction conditions. The results indicated that PMB was an effective CTA for the RAFT polymerizations of styrene with the “living”/controlled characteristics. The structures of the obtained polymers were characterized by ¹H NMR. The results showed that majority of the polymer chains contained the pyrene moiety in the chain end. The enhanced fluorescence property in CHCl₃ solution was observed. The chain-extension experiments of the obtained polystyrene (PS) with the monomers of styrene and methyl acrylate were successfully carried out.     

Linear reconstruction measure steered nearest neighbor classification framework

The linear reconstruction measure (LRM), which determines the nearest neighbors of the query sample in all known training samples by sorting the minimum L₂-norm error linear reconstruction coefficients, is introduced in this paper. The intuitive interpretation and mathematical proofs are presented to reveal the efficient working mechanism of LRM. Through analyzing the physical meaning of coefficients and regularization items, we find that LRM provides more useful information and advantages than the conventional similarity measure model which calculates the distance between two entities (i.e. conventional point-to-point, C-PtP). Inspired by the advantages of LRM, the linear reconstruction measure steered nearest neighbor classification framework (LRM-NNCF) is designed with eight classifiers according to different decision rules and models of LRM. Evaluation on several face databases and the experimental results demonstrate that these proposed classifiers can achieve greater performance than the C-PtP based 1-NNs and competitive recognition accuracy and robustness compared with the state-of-the-art classifiers.     

Graph regularized multiset canonical correlations with applications to joint feature extraction

Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms.     

甲基丙烯酰胺丙基二甲基胺的合成与应用述评

介绍了甲基丙烯酰胺丙基二甲基胺（DMAPMA）的合成工艺路线,重点阐述了该产品在水处理行业、日化行业、造纸加工业、医药行业等中的实际应用.