企业设备点检管理系统初探

应用.NET技术及无线射频识别技术,研究开发基于Web的设备点检管理系统,实现企业重要设备的快速点检。从技术层面、系统架构、实现功能等角度对建立设备点检管理系统平台进行了初步探讨。     

C语言程序教学的问卷调查分析与教学改革

C语言程序是学习和掌握计算机技术的基础课程,如何改革该课程教学,提高教学质量,一直是人们所关注与探讨的问题。本文从对学生的问卷调查结果的分析入手,总结、剖析了我院的C语言程序课堂教学中存在的问题,并结合实际提出的一些建议,以期对教学改革有所意义。     

网络安全产品的协同应用——构建人性化校园网安全问题辅助决策平台

目前随着局域网规模的扩大和各种网络安全问题的出现,大家对网络安全问题越来越重视。针对不同的安全问题,各种各样的网络安全产品不断的涌现,往往一个单位购买或自己开发了很多安全产品。但是大部分的安全产品都独立运行,且功能上会有重复。如果通过一个中间平台能让它们之间协同操作,发挥各自优势,及时发现网络中的问题主机及主机使用者的详细信息并做进一步沟通、处理,构成一个主动服务式管理的网络安全系统。这样就大大提高设备的利用率和使用价值,对网络中出现的安全问题能做到及时发现及时处理,将其消灭在萌芽状态,避免发生重大损失。笔者结合自己单位的实际情况,描述了一个安全问题辅助决策平台,并分析了其构成模块和处理流程。     

基于智能Agent和数据挖掘技术的蜜罐系统的研究

传统的网络安全技术被动防护的特点,很难满足现在网络的需要。文章利用Agent的智能化和分布式协同处理功能,在传统蜜罐的基础上,提出一种基于智能Agent的蜜罐系统结构,并对获取的非法数据进行关联、挖掘,根据已有的入侵方法找出未来可能发生的入侵方式。系统利用智能Agent的分布式处理能力特点完成对各个服务器和主机的保护;利用数据挖掘算法在大量数据中提出有用信息不断更新知识库;利用专家系统实现对非法访问到蜜罐系统的漂移;利用蜜罐系统作为非法数据容器和分析工厂。文章首先介绍传统网络安全技术的不足;然后针对智能Agent、蜜罐技术的研究现状进行阐述;接着提出基于Agent和数据挖掘技术的蜜罐系统的模型;最后指出其发展前景和面临的挑战。     

中文语音合成模块XF-S4240在导航终端中的应用

介绍XF-S4240的功能特点、硬件接口及通信方式,并给出该模块在车载导航终端中的应用实例。实验结果表明,XF-S4240模块的语音效果清晰自然,并且能灵活更改语音内容,因此可以很好地满足车载语音导航的需要。     

A generalized least absolute deviation method for parameter estimation of autoregressive signals

This paper proposes a generalized least absolute deviation (GLAD) method for parameter estimation of autoregressive (AR) signals under non-Gaussian noise environments. The proposed GLAD method can improve the accuracy of the estimation of the conventional least absolute deviation (LAD) method by minimizing a new cost function with parameter variables and noise error variables. Compared with second- and high-order statistical methods, the proposed GLAD method can obtain robustly an optimal AR parameter estimation without requiring the measurement noise to be Gaussian. Moreover, the proposed GLAD method can be implemented by a cooperative neural network (NN) which is shown to converge globally to the optimal AR parameter estimation within a finite time. Simulation results show that the proposed GLAD method can obtain more accurate estimates than several well-known estimation methods in the presence of different noise distributions.     

A forward-constrained regression algorithm for sparse kernel density estimation.

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward-constrained regression (FCR) manner. The proposed algorithm selects significant kernels one at a time, while the leave-one-out (LOO) test score is minimized subject to a simple positivity constraint in each forward stage. The model parameter estimation in each forward stage is simply the solution of jackknife parameter estimator for a single parameter, subject to the same positivity constraint check. For each selected kernels, the associated kernel width is updated via the Gauss-Newton method with the model parameter estimate fixed. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate the efficacy of the proposed approach.     

Independent Component Analysis for Time-dependent Processes Using AR Source Model

Independent component analysis is a fundamental and important task in unsupervised learning, that was studied mainly in the domain of Hebbian learning. In this paper, the temporal dependencies are explained by assuming that each source is an autoregressive (AR) process and innovations are independently and identically distributed (i.i.d). First, the likelihood of the model is derived, which takes into account both spatial and temporal information of the sources. Next, batch and on-line blind source separation algorithms are developed by maximizing likelihood function, and their local stability analysis are introduced simultaneously. Finally, computer simulations show that the algorithms achieve better separation of the mixed signals and mixed nature images which are difficult to be separated by the basic independent component analysis algorithms.     

A minimum approximate-BER beamforming approach for PSK modulated wireless systems

A bearnforming algorithm is introduced based on the general objective function that approximates the bit error rate for the wireless systems with binary phase shift keying and quadrature phase shift keying modulation schemes. The proposed minimum approximate bit error rate （ABER） beamforming approach does not rely on the Gaussian assumption of the channel noise. Therefore, this approach is also applicable when the channel noise is non-Gaussian. The simulation results show that the proposed minimum ABER solution improves the standard minimum mean squares error beamforming solution, in terms of a smaller achievable system＇s bit error rate.