An algorithm of analysis tools on points distribution in high dimension space: The distance of a point and an infinite sub-space

This paper discusses the algorithm on the distance from a point and an infinite sub-space in high dimensional space With the development of Information Geometry([1]), the analysis tools of points distribution in high dimension space, as a measure of calculability, draw more attention of experts of pattern recognition. By the assistance of these tools, Geometrical properties of sets of samples in high-dimensional structures are studied, under guidance of the established properties and theorems in high-dimensional geometry.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

Continuous speech research based on HyperSausage Neuron

In this paper, we presents HyperSausage Neuron based on the High-Dimension Space(HDS), and proposes a new algorithm for speaker independent continuous digit speech recognition. At last, compared to HMM-based method, the recognition rate of HyperSausage Neuron method is higher than that of in HMM-based method.

A New Discrimination Method of Maize Seed Varieties Based on Near-Infrared Spectroscopy

A new discrimination method for the maize seed varieties based on the near-infrared spectroscopy was proposed. The reflectance spectra of maize seeds were obtained by a FT-NIR spectrometer (12 000-4 000 cm(-1)). The original spectra data were preprocessed by first derivative method. Then the principal component analysis (PCA) was used to compress the spectra data. The principal components with the cumulate reliabilities more than 80% were used to build the discrimination models. The model was established by Psi-3 neuron based on biomimetic pattern recognition (BPR). Especially, the parameter of the covering index was proposed to assist to discriminating the variety of a seed sample. The authors tested the discrimination capability of the model through four groups of experiments. There were 10, 18, 26 and 34 varieties training the discrimination models in these experiments, respectively. Additionally, another seven maize varieties and nine wheat varieties were used to test the capability of the models to reject the varieties not participating in training the models. Each group of the experiment was repeated three times by selecting different training samples at random. The correct classification rates of the models in the four-group experiments were above 91. 8%. The correct rejection rates for the varieties not participating in training the models all attained above 95%. Furthermore, the performance of the discrimination models did not change obviously when using the different training samples. The results showed that this discrimination method can not only effectively recognize the maize seed varieties, but also reject the varieties not participating in training the model. It may be practical in the discrimination of maize seed varieties.

OpenOffice.org对非BMP平面字符支持的实现

Unicode标准中的非BMP平面字符多用于古籍研究或者少数民族语言文字,由于这些字符的使用面特别窄,多数软件系统包括办公软件都不支持对这些字符的处理。本文以开源办公套件OpenOffice.org为基础,分析了它对非BMP平面支持的现状,然后着重探讨了实现对非BMP平面字符的全面支持所需要解决的一系列问题,并分别给出了合理的改进方案,最后以CJK和藏文为例展示了改进后的效果。

天然免疫相关分子的进化

本论文结合功能研究和进化遗传学方法对动物天然免疫（innate immunity）相关分子的进化历程进行深入研究。受体对病原微生物的识别是天然免疫系统发挥功能的基础。作为模式识别受体（pattern recognition receptor, PRR），果蝇肽聚糖识别蛋白SD（PGRP-SD）在识别革兰氏阳性细菌的过程中发挥了重要作用。针对已有的黑腹果蝇（Drosophila melanogaster）群体数据，我们发现PGRP-SD在群体中存在2类高频的等位基因（分别为等位基因1和等位基因2）。以D. simulans为外群，我们追溯了黑腹果蝇2类等位基因上氨基酸的变化。这些氨基酸的结构特征和在蛋白质上所处的位置提示这2类等位基因在功能方面可能存在分化。通过功能研究的方法，我们发现在黑腹果蝇中该基因功能方面发生了显著的变化。等位基因2在有微生物时能激活天然免疫反应，但等位基因1的转基因果蝇成虫只要有外伤即便没有微生物的情况下即能激发天然免疫反应，而带有等位基因2果蝇成虫则不具有该功能。这一结果提示我们，发生在该等位基因上的氨基酸变化导致了其识别功能的变化。与推导的祖先基因相比，等位基因1发生了一个氨基酸的变化，因此导致其功能从识别细菌细胞壁组分肽聚糖转变为一未知的自身组分，即从病原相关分子模式（pathogen-associated molecular pattern，PAMP）识别受体转变为损伤相关识别模式（damage-associated molecular pattern, DAMP）识别受体。通过这一功能变化， 果蝇成虫可以通过仅识别自身损伤即可激活相应的免疫反应，对后续可能侵入的微生物进行杀伤。已有研究结果显示，微生物在进化过程中已经形成针对DAMP和PAMP规避策略。上述2类等位基因的同时存在能使黑腹果蝇同时具备两个机制，更加充分地抵抗病原微生物的入侵。结合功能研究和针对自然群体的群体遗传学分析，我们认为在黑腹果蝇群体中以高频共存的2类PGRP-SD等位基因可能可能受到了平衡选择（balancing selection）作用。上述工作主要研究了天然免疫系统识别受体的进化。而本论文的另一部分则主要针对天然免疫系统的效应分子（effector）进行了研究。作为重要的效应分子，抗菌肽在杀菌方面发挥着最为直接的作用。因此，研究抗菌肽的进化对于探索天然免疫系统的进化具有重要意义。本研究以两栖类动物大蹼铃蟾抗菌肽基因家族为例，通过对分别来自2个大蹼铃蟾个体的皮肤cDNA文库进行测序，我们鉴别出56个不同的抗菌肽cDNA序列。每一个cDNA均编码2个不同的抗菌肽，maximin 和maximin H。基于针对这些cDNA序列的分析，我们发现2类抗菌肽编码序列的非同义替代率均高于同义替代率，呈现高度分化的特征。但是，在信号肽和其它非抗菌肽编码区域并没有发现这种情况。这一结果提示抗菌肽可能受到超显性选择（overdominent selection, 即平衡选择）的影响。同时，我们分别从皮肤和肝脏克隆基因了7个抗菌肽的基因组编码序列并进行了测序。这些从不同组织获得的抗菌肽在各个编码序列中均存在序列的差异的同时呈现了相同的结构。这一结果提示不同抗菌肽间的差异不太可能来自于体细胞突变而是快速序列进化的结果。通过构建来自于同一个体的抗菌肽的不同编码区的基因树，我们发现结构域重排（domain shuffling）和/或基因转换（gene conversion）在这些抗菌肽的进化历程中发挥作用。

仿生模式识别在单镜头人脸识别中的应用

提出了一种基于仿生模式识别（Biomimatic Pattern Recognition）和多权值神经元网络(Multi-Weights.Neu-ral Network)的人脸识别新方法．对仿生模式识别理论在人脸识别中的应用模型作了讨论，并且介绍了一种新的人脸特征提取方法．本文通过实验对本文提出的基于仿生模式识别的方法和基于K近邻的方法做了对比，实验结果表明本文的方法克服了对未训练类型的人脸误识问题，提高了人脸识别系统的训练速度和正确识别率．

Architecture research and hardware implementation on simplified neural computing system for face identification

This paper describes a special-purpose neural computing system for face identification. The system architecture and hardware implementation are introduced in detail. An algorithm based on biomimetic pattern recognition has been embedded. For the total 1200 tests for face identification, the false rejection rate is 3.7% and the false acceptance rate is 0.7%.

Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles

Metabonomics, the study of metabolites and their roles in various disease states, is a novel methodology arising from the post-genomics era. This methodology has been applied in many fields, including work in cardiovascular research and drug toxicology. In this study, metabonomics method was employed to the diagnosis of Type 2 diabetes mellitus (DM2) based on serum lipid metabolites. The results suggested that serum fatty acid profiles determined by capillary gas chromatography combined with pattern recognition analysis of the data might provide an effective approach to the discrimination of Type 2 diabetic patients from healthy controls. And the applications of pattern recognition methods have improved the sensitivity and specificity greatly. (C) 2004 Elsevier B.V. All rights reserved.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.