医学显微图像识别与分析

随着生物医学的发展,医学已经从组织、器官的研究进入到显微细胞阶段,因此医学显微图像技术已经十分成熟,在医学领域的应用也越来越广泛。目前医学中常使用的医学显微图像多为细胞显微图像,通过对病变细胞显微图像的识别和分析来诊断疾病,诊断率较高。而能够准确的识别显微图像,选择合理的分割方法至关重要。本文简要的为大家介绍医学显微图像常用的分割方法。     

自适应超混沌系统修正广义同步及其电路实现

运用自适应控制方法,实现超混沌Chen系统的同结构投影同步,并能辨识出响应系统的未知参数。首先通过采用MATLAB进行数字仿真,表明该自适应控制方法的可行性以及系统的参数辨识作用。其次利用EWB仿真设计模拟电路,结果与MATLAB的仿真结果相吻合,从而进一步证明了该自适应控制方法的有效性和系统参数辨识方法。     

Supervised and Reinforcement Evolutionary Learning for Wavelet-based Neuro-fuzzy Networks

This study presents a wavelet-based neuro-fuzzy network (WNFN). The proposed WNFN model combines the traditional Takagi–Sugeno–Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This study adopts the non-orthogonal and compactly supported functions as wavelet neural network bases. A novel supervised evolutionary learning, called WNFN-S, is proposed to tune the adjustable parameters of the WNFN model. The proposed WNFN-S learning scheme is based on dynamic symbiotic evolution (DSE). The proposed DSE uses the sequential-search-based dynamic evolutionary (SSDE) method. In some real-world applications, exact training data may be expensive or even impossible to obtain. To solve this problem, the reinforcement evolutionary learning, called WNFN-R, is proposed. Computer simulations have been conducted to illustrate the performance and applicability of the proposed WNFN-S and WNFN-R learning algorithms.     

基于.NET精简框架的嵌入式RFID服务组件

嵌入式射频识别(RFID)中间件技术发展缓慢,严重阻碍了RFID技术在企业级移动管理平台中的大规模应用。该文研究和开发了一种可应用于多种应用场景并支持多种平台调用的嵌入式RFID服务组件EmbedMid,提出了基于EmbedMid的移动管理平台架构,指出EmbedMid存在的不足和今后的研究方向。     

身份认证协议的新安全属性

总结了针对身份认证协议的一类新型攻击的特征。对于攻击的分析,引入了两个新的安全属性定义,其一定义在单个证明者的环境中,另一个则定义在多个证明者的环境中。并可证明这两个安全属性本质上是等价的。因此,只要满足了其中单证明者环境下的安全要求,身份认证协议也能安全地运行在多个证明者的环境下。     

使用RFID和车牌识别的奥运会停车场监控系统

2008年的奥运会已经圆满成功的举行,其中也离不开各种监控系统的帮助。该文对奥运会期间的停车场系统设计进行分析,其中主要使用了车牌识别和RFID识别的双重保险,使得奥运会停车场的监控系统的安全性有了更好的保证。     

基于S3C44B0X的RFID以太网读卡器设计

针对广泛应用的RFID技术,在嵌入式系统设计的原理图级设计实现了一种基于S3C44B0X的RFID以太网读卡器,包括主控模块、存储模块、以太网模块、RFID读模块等电路的电路原理图和关键部分的分析。在系统原理图上设计实现的以太网读卡器,目前系统测试运行稳定。     

基于模糊RBF神经网络的换热器建模

针对换热器的复杂非线性特征,利用一种模糊RBF神经网络结构,对其网络学习算法进行部分改进,并用于解决换热器的建模问题。采用模糊RBF神经网络不仅符合人的思维推理方式,也提高了神经网络的学习泛化能力,在改进的学习算法中通过学习率的值的不断变化和添加动量项,可以使学习速度加快,提高了辨识换热器模型的准确性。通过与传统的学习算法的仿真比较,验证了所提出的改进学习算法在辨识精度和稳定性方面具有更好的效果。     

New method for identifying finite degree Volterra series

In this paper, the identification of a class of nonlinear systems which admits input-output maps described by a finite degree Volterra series is considered. In actual fact, it appears that this class can model many important nonlinear multivariable processes not only in engineering, but also in biology, socio-economics, and ecology.To solve this identification problem, we propose a method based on local gradient search in a local parameterization of the state-space realization of finite degree Volterra series with infinite horizon. Using the local parameterization not only reduces the amount of the gradient calculations to the minimal value, but also overcomes the nonuniqueness problem of the optimal solution.Moreover, we propose a sequential projection method to provide an initial estimation of the parameters of finite degree Volterra series realization. This initial estimation is used to initialize the gradient search method.     

Real time identification of discrete event systems using Petri nets

The paper defines the identification problem for Discrete Event Systems (DES) as the problem of inferring a Petri Net () model using the observation of the events and the available output vectors, that correspond to the markings of the measurable places. Two cases are studied considering different levels of the system knowledge. In the first case the place and transition sets are assumed known. Hence, an integer linear programming problem is defined in order to determine a modelling the DES. In the second case the transition and place sets are assumed unknown and only an upper bound of the number of places is given. Hence, the identification problem is solved by an identification algorithm that observes in real time the occurred events and the corresponding output vectors. The integer linear programming problem is defined at each observation so that the can be recursively identified. Some results and examples characterize the identified systems and show the flexibility and simplicity of the proposed technique. Moreover, an application to the synthesis of supervisory control of systems via monitor places is proposed.