基于TMS320VC5410的最小系统设计

TMS320VC5410是TI公司生产的一款定点数字信号处理(DSP)芯片。基于DSP的最小系统是以其简洁的硬件结构来实现DSP的一般功能,广泛应用于工业生产、教学、科研、电子设计等领域。主要介绍了基于TMS320VC5410的最小系统硬件电路设计方案,阐述了重要电路部分的抗干扰设计和软件调试过程中应注意的若干问题。最后提出了简单易行的软件调试方法以及部分程序代码。     

基于数据挖掘的OLAP分析技术及应用

数据仓库和数据挖掘技术是目前信息技术研究的热点问题之一,介绍了数据挖掘(data mining)、数据仓库(datawarehouse)及联机分析处理(OLAP)技术。并以零售业决策支持系统为例,介绍了该系统的体系结构、数据仓库的设计、系统实现关键技术及各子系统功能,着重论述了如何利用OLAP技术实现企业决策支持系统。     

RFCCU反再系统防结焦措施的应用及技术分析

介绍了济南分公司140万t／a RFCCU两个周期运行后反再系统的结焦情况，详细介绍了防治结焦所采取的措施，并进行了技术分析。两次停工后的设备检查，证明所采取的防治措施是十分有效的。     

Data-efficient blind OFDM channel estimation using receiver diversity

We investigate non data-aided channel estimation for cyclically prefixed orthogonal frequency division multiplexing (OFDM) systems. By exploiting channel diversity using only two receive antennas, a blind deterministic algorithm is proposed. Identifiability conditions are derived that guarantee the perfect channel retrieval in the absence of noise. In the presence of noise, the proposed method has the desired property of being data efficient-only a single OFDM block is needed to achieve good estimation performance for a wide range of SNR values. The algorithm is also robust to input symbols as it does not have any restriction on the input symbols with regard to their constellation or their statistical properties. In addition, this diversity-based algorithm is computationally efficient, and its performance compares favorably to most existing blind algorithms.     

数字签名技术在解决密钥泄露方面的发展

密钥泄露对数字签名的危害越来越成为人们关注的焦点，该问题很难找到完全彻底的解决办法，只能尽可能减少其危害。对此专家和学者提出了很多新的数字签名机制，这里详细介绍了其中的一些，并指出它们各自的优缺点。针对在SiBIR数字签名机制中由于网络拥塞或攻击者的恶意拦截使得更新信息丢失造成signer和home base不能保持同步的问题，我们提出了超时同步机制。     

分布式无线传感器网络通信协议研究

在归纳分布式无线传感器网络研究进展的基础上，给出了综合分布式无线传感器网络技术各个方面的协议体系结构，并在新的体系结构下，系统地阐述了分布式无线传感器网络通信协议研究的进展，介绍了典型的应用案例和国外相关计划的研究情况。最后，指明了这一处于发展初期的研究领域的未来发展方向。     

Phosphorus‐containing polyaryloxydiphenylsilanes with high flame retardance arising from a phosphorus–silicon synergistic effect

Polyaryloxydiphenylsilanes were prepared from phosphorus‐containing diols and diphenydichlorolsilane through solution polymerization. With a stoichiometric imbalance in feed monomers, the resulting polymers exhibited moderate melting points and good processing properties. The polymers prepared showed initial decomposition temperatures above 340 °C, excellent thermal stability, high char yields at 850 °C and very high limited oxygen index values of 56–59. The polymers' char yields and their (P + Si) contents showed linear relationships. © 2003 Society of Chemical Industry     

热作模具钢表面激光堆焊耐磨合金层的研究

本文主要通过在H13钢上用大功率CO_2激光束进行堆焊,阐述了激光堆焊工艺变化对堆焊组织和性能的影响,在本试验条件下,得到了高密度弥散金属化合物组织的激光堆焊层,平均硬度为HV800,与基体呈冶金结合,耐磨性比H13模具钢提高149%。     

A Neural Network Approach for Signal Detection in Digital Communications

In this paper, we propose a new approach for signal detection in wireless digital communications based on the neural network with transient chaos and time-varying gain (NNTCTG), and give a concrete model of the signal detector after appropriate transformations and mappings. It is well known that the problem of the maximum likelihood signal detection can be described as a complex optimization problem that has so many local optima that conventional Hopfield-type neural networks fail to solve. By refraining from the serious local optima problem of Hopfield-type neural networks, the NNTCTG makes use of the time-varying parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks only with point attractors, so that it can be expected to have much ability to search for globally optimal or near-optimal solutions. After going through a transiently inverse-bifurcation process, the NNTCTG can approach the global optimum or the neighborhood of global optimum of our problem. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for the signal detection in digital communications.     

EEG spectral abnormalities and psychosis as predictors of cognitive and functional decline in probable Alzheimer's disease

We examined whether either psychotic features (e.g., delusions and hallucinations) or EEG abnormalities are associated with more rapid progression of Alzheimer's disease (AD). AD patients with psychosis have exhibited more EEG abnormalities than those without psychosis, and both abnormal EEG and psychosis have been noted to be predictors of functional and cognitive decline in AD. Ninety-five probable AD patients participating in a longitudinal study of dementia had an EEG and a semistructured psychiatric interview at baseline. Using EEG spectral analysis, we classified records as normal/abnormal based on the parasagittal mean frequency. Patients with abnormal EEGs were more functionally (e.g., Blessed Rating Scale for activities of daily living) and cognitively (e.g., Mini-Mental State) impaired than patients with normal EEG. AD patients with psychosis were only more functionally impaired than patients without psychosis. A two-factor analysis showed no interaction between abnormal EEG and psychosis. In addition, using a Cox proportional hazard model adjusted for age and education, the presence of an abnormal EEG or psychotic symptom at study entry was associated with higher risk of reaching severe cognitive and functional impairment during follow-up. Neither abnormal EEG nor the presence of psychosis predicted death. These results indicate that both abnormal EEG and psychosis are independent predictors of disease progression but not of physical survival.