利用本征脸方法减少冷原子云吸收成像照片中的干涉条纹

本征脸方法是广泛应用于人脸识别的一种图像处理方法，本文将其引入到原子芯片上囚禁的冷原子云吸收成像照片的图像处理中，以减少其中的干涉条纹，增加信噪比。本文首先介绍了吸收成像照片的标准处理方法以及干涉条纹的产生原因，由于参考照片和吸收成像照片中的干涉条纹会发生随机的相对变化，处理后干涉条纹难以消除。和标准的处理方法相比，本征脸方法不是使用1张而是50张参考照片，利用这些照片重构出一张新的参考照片，这张照片比那50张中的任何一张都更近似于吸收成像照片，因此和只使用1张参考照片相比，处理之后的干涉条纹对比度明显降

Morphological ordered gray-scale erosion for optical image processing

An ordered gray-scale erosion is suggested according to the definition of hit-miss transform. Instead of using three operations, two images, and two structuring elements, the developed operation requires only one operation and one structuring element, but with three gray-scale levels. Therefore, a union of the ordered gray-scale erosions with different structuring elements can constitute a simple image algebra to program any combined image processing function. An optical parallel ordered gray-scale erosion processor is developed based on the incoherent correlation in a single channel. Experimental results are also given for an edge detection and a pattern recognition. (C) 1998 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(98)00306-7].

判别分析系统应运于生物标本鉴定的初步研究

计算机技术和数学方法、手段应用于生物标本鉴定的研究工作在国外开展已有多年，但在国内却并没有许多人涉猎，也没有受到足够的重视。 鉴于这一领域的重要性及其实际意义，本文综合、扩充、改进了多元统计判别分析和模糊模式识别中的多种定量化判别方法，初步在计算机上实现了一个可用于生物标本鉴定或其它与判别、识别有关方面的系统，并将其用于桔梗科沙参属三个种：泡沙参、多歧沙参和裂叶沙参及菖蒲科中两个种的标本鉴定上面，获得了比较满意的判别效果。同时，为了弥补数量化标本鉴定的不足，本文作者还设计开发了一个描述性的基于检索表的人机交互式的标本鉴定模块。另外，本系统还包括模糊系统聚类和典型相关分析等模块，可供生物分类及其它定量分析运算时选用。 对于以上各种判别、识别方法的差异及优劣，文中根据实例做出了综合分析和比较，并认为在所有方法当中，逐步判别法和模糊协方差识别法最适宜于生物标本鉴定之用。最后，作者展望了未来计算机用于生物标本鉴定的前景。

Origin and evolution of the RIG-I like RNA helicase gene family

Background: The DExD/H domain containing RNA helicases such as retinoic acid-inducible gene I (RIG-I) and melanoma differentiation-associated gene 5 (MDA5) are key cytosolic pattern recognition receptors (PRRs) for detecting nucleotide pathogen associated molecular patterns (PAMPs) of invading viruses. The RIG-I and MDA5 proteins differentially recognise conserved PAMPs in double stranded or single stranded viral RNA molecules, leading to activation of the interferon system in vertebrates. They share three core protein domains including a RNA helicase domain near the C terminus (HELICc), one or more caspase activation and recruitment domains (CARDs) and an ATP dependent DExD/H domain. The RIG-I/MDA5 directed interferon response is negatively regulated by laboratory of genetics and physiology 2 (LGP2) and is believed to be controlled by the mitochondria antiviral signalling protein (MAVS), a CARD containing protein associated with mitochondria. Results: The DExD/H containing RNA helicases including RIG-I, MDA5 and LGP2 were analysed in silico in a wide spectrum of invertebrate and vertebrate genomes. The gene synteny of MDA5 and LGP2 is well conserved among vertebrates whilst conservation of the gene synteny of RIG-I is less apparent. Invertebrate homologues had a closer phylogenetic relationship with the vertebrate RIG-Is than the MDA5/LGP2 molecules, suggesting the RIG-I homologues may have emerged earlier in evolution, possibly prior to the appearance of vertebrates. Our data suggest that the RIG-I like helicases possibly originated from three distinct genes coding for the core domains including the HELICc, CARD and ATP dependent DExD/H domains through gene fusion and gene/domain duplication. Furthermore, presence of domains similar to a prokaryotic DNA restriction enzyme III domain (Res III), and a zinc finger domain of transcription factor (TF) IIS have been detected by bioinformatic analysis. Conclusion: The RIG-I/MDA5 viral surveillance system is conserved in vertebrates. The RIG-I like helicase family appears to have evolved from a common ancestor that originated from genes encoding different core functional domains. Diversification of core functional domains might be fundamental to their functional divergence in terms of recognition of different viral PAMPs.

Wavelet denoising and feature extraction of seismic signal for footstep detection

Seismic sensors are widely used to detect moving target in ground sensor networks. Footstep detection is very important for security surveillance and other applications. Because of non-stationary characteristic of seismic signal and complex environment conditions, footstep detection is a very challenging problem. A novel wavelet denoising method based on singular value decomposition is used to solve these problems. The signal-to-noise ratio (SNR) of raw footstep signal is greatly improved using this strategy. The feature extraction method is also discussed after denosing procedure. Comparing, with kurtosis statistic feature, the wavelet energy feature is more promising for seismic footstep detection, especially in a long distance surveillance.

The structure and function of neurons with variable nonlinear transfer function

One novel neuron with variable nonlinear transfer function is firstly proposed, It could also be called as subsection transfer function neuron. With different transfer function components, by virtue of multi-thresholded, the variable transfer function neuron switch on among different nonlinear excitated state. And the comparison of output's transfer characteristics between it and single-thresholded neuron will be illustrated, with some practical application experiments on Bi-level logic operation, at last the simple comparison with conventional BP, RBF, and even DBF NN is taken to expect the development foreground on the variable neuron.. The novel nonlinear transfer function neuron could implement the random nonlinear mapping relationship between input layer and output layer, which could make variable transfer function neuron have one much wider applications on lots of reseach realm such as function approximation pattern recognition data compress and so on.

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．

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.