Detection of muramidase (lysozyme)-secreting leucocytes at the single-cell level by a protein A plaque assay

Using a modification of the protein A plaque assay, muramidase (lysozyme)-producing leucocytes were detected as plaque-forming cells. In the presence of anti-muramidase Ig and complement the secreted lysozyme resulted in lysis of protein-A-coated target erythrocytes. By the use of a monolayer technique individual plaque-forming cells could be identified by staining procedures. Granulocytes as well as monocytes were found to produce muramidase and thus to form plaques. This method could serve as a useful tool when studying lysozyme secretion. Furthermore, by the use of appropriate antisera, this method could be employed for the study of any cell type (any secretion), provided enough molecules are being secreted.     

基于DRM技术的IPTV版权保护模型研究

随着网络的迅猛发展,IPTV通过网际协议(IP)来提供包括数字电视节目在内的多种数字多媒体服务及增值业务。由于IPTV的核心是内容传播,所以内容的版权管理是IPTV的一个关键问题。本文介绍了数字版权管理的基本原理,分析了现有DRM系统的缺点,提出了一种IPTV系统的数字版权管理改进模型,详细阐述了其工作流程,并对其相关性能进行分析。     

Unsupervided pattern recognition for the classification of EMG signals

The shapes and firing rates of motor unit action potentials (MUAP's) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. In order to extract this information from EMG signals recorded at low to moderate force levels, it is required: i) to identify the MUAP's composing the EMG signal, ii) to classify MUAP's with similar shape, and iii) to decompose the superimposed MUAP waveforms into their constituent MUAP's. For the classification of MUAP's two different pattern recognition techniques are presented: i) an artificial neural network (ANN) technique based on unsupervised learning, using a modified version of the self-organizing feature maps (SOFM) algorithm and learning vector quantization (LVQ) and ii) a statistical pattern recognition technique based on the Euclidean distance. A total of 1213 MUAP's obtained from 12 normal subjects, 13 subjects suffering from myopathy, and 15 subjects suffering from motor neuron disease were analyzed. The success rate for the ANN technique was 97.6% and for the statistical technique 95.3%. For the decomposition of the superimposed waveforms, a technique using crosscorrelation for MUAP's alignment, and a combination of Euclidean distance and area measures in order to classify the decomposed waveforms is presented. The success rate for the decomposition procedure was 90%.     

逆向工程实体模型重建技术研究

针对CMM扫描线数据,进行了数据分层和数据排序,完成了数据拓扑关系的建立.研究了距离阈值法和向量角度法相结合的数据精简方法和基于分层维的特征提取技术.以基于分层维的数据处理技术研究为基础,提出了一种利用数据点特征的非均匀有理B样条(NURBS)曲面重构方法,在Solidworks中实现了实体模型的重建.     

基于高性能单片机的双CPU通用数控系统

讨论了基于高性能单片机的双CPU的通用数控系统的设计。系统采用两个高性能单片机为处理器，处理器间相互通信，协调工作。软件采用模块化设计。使用时针对具体数控系统功能要求，将通用系统略加增删，便能满足客户需求，体现了数控系统的通用性。通过在STAR-06TQ数控涂漆机上的实际应用，证明本系统能够满足预期设计要求。     

A simple model for runoff estimation

A simple model for runoff estimation is presented, with particular reference to Athens basin. The method is based on assumptions of a symmetrical exponential storm profile and a linear cumulative area‐time relation, while a power law is postulated for correlating the storm peak factor with rainfall duration. A general expression is derived for the peak flow and discussed in comparison with the rational formula. Further, the outflow hydrograph from subcatchments of uniform characteristics is approximated by a triangular form. The model parameters for the Athens area are estimated from local rainfall data. Specific applications in Athens basin indicate good agreement with results of established techniques or observations, implying that the present method may constitute a useful tool in design practice.     

Speaker identification for security systems usingreinforcement-trained pRAM neural network architectures

Speaker identification may be employed as part of a security system requiring user authentication. In this case, the claimed identity of the user is known from a magnetic card and PIN number, for example, and an utterance is requested to confirm the identity of the user. A fast response is necessary in the confirmation phase and a fast registration process for new users is desirable. The time encoded signal processing and recognition (TESPAR) digital language is used to preprocess the speech signal. A speaker cannot be identified directly from the single TESPAR vector since there is a highly nonlinear relationship between the vector's components such that vectors are not linearly separable. Therefore the vector and its characteristics suggest that classification using a neural network will provide an effective solution. Good classification performance has been achieved using a probabilistic RAM (pRAM) neuron. Four probabilistic pRAM neural network architectures are presented. A performance of approximately 97% correct classifications has been obtained, which is similar to results obtained elsewhere (M. Sharma and R.J. Mammone, 1996), and slightly better than a MLP network. No speech recognition stage was used in obtaining these results, so the performance relates only to identifying a speaker's voice and is therefore independent of the spoken phrase. This has been achieved in a hardware-realizable system which may be incorporated into a smart-card or similar application