用于千兆以太网基带铜缆接收器均衡的甚高频自适应 连续时间Gm-C 二阶带通滤波器的研究和设计

本文设计了用于千兆以太网基带铜缆接收器均衡的甚高频自适应连续时间Gm-C二阶带通滤波器。基于最陡梯度下降算法，带通滤波器的零点在57－340MHz的频率范围内可以自适应地调节，中心频率为1.278GHz。通过外接电阻伺服环路，滤波器中跨导转换器的跨导值不受工艺偏差和温度变化的影响，采用CSMC－HJ0.6μm CMOS工艺器件模型，用Cadence Spectres仿真器仿真了设计的自适应滤波器电路，仿真结果验证了设计原理和设计的电路。系统的最长学习时间为880个参考时钟周期。     

珠钢滤波-无功补偿优化调整方案

本文主要论述了珠钢滤波-无功补偿优化调整方案在生产中的应用。     

通信局房供电系统的谐波治理

本文在理论分析谐波产生及危害的基础上,针对某一通信局房,确定最终谐波治理方案并取得较好的治理效果,供同行在设计时参考。     

生物除臭滤池在污水厂污泥处理段的除臭效能

对采用天然有机滤料和合成无机滤料的生物除臭滤池在加拿大多伦多市Ash-bridge’s Bay污水厂污泥处理站的干化和转运段的应用效果进行了分析。详细介绍了污泥脱水工艺、臭气的产生及其组分，进而对软木树皮滤料和矿物合成永久性滤料的除臭效果进行了比较。还讨论了一些具体的运行经验，并采用动态模型对不同条件下的运行数据进行了分析。     

新型自洁式熔体过滤器

自洁式熔体过滤器由多只并联滤芯和1只旋转阀组成,并联的滤芯可同时工作,还可根据压力降大小依次进行自动切换和反冲洗。1只滤芯反冲洗时并不影响其余滤芯继续工作。滤芯的反冲洗无需使用外来清洁液,而是利用了来用其余几只滤芯的少量过滤液。自洁式熔体过滤器能够进行全自动连续过滤和间歇式反冲洗,用于高温、高压聚合物熔体的过滤,特别适用于废丝纺等过滤器需频繁拆卸、更换及清洗的场合。     

Structural transformation of FePt nanocomposite films during annealing and its effects on magnetic properties

Fe100-xPtx(x=30at.%-60at.% ) nanocomposite films were deposited on natural-oxidized Si(100) substrates by magnetron sputtering. The as-deposited films were annealed between 373 and 1073 K. In situ X-ray diffraction shows that the FePt nanocomposite films undergo a phase transformation from a disordered FCC phase to an ordered L10 phase between 673 and 773 K. The coercivity is 306 kA·m-1 whiles the average grain sizes is about 10 nm in the optimized FePt alloy film sample annealed at 673K. The adjustable coercivity and fine grain size suggest that this FePt nanocomposites system is suitable as recording media at extremely high areal density.     

Optimal and self‐tuning fusion Kalman filters for discrete‐time stochastic singular systems

Based on the optimal fusion estimation algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion Kalman filter weighted by scalars is presented for discrete‐time stochastic singular systems with multiple sensors and correlated noises. A cross‐covariance matrix of filtering errors between any two sensors is derived. When the noise statistical information is unknown, a distributed identification approach is presented based on correlation functions and the weighted average method. Further, a distributed self‐tuning fusion filter is given, which includes two stage fusions where the first‐stage fusion is used to identify the noise covariance and the second‐stage fusion is used to obtain the fusion state filter. A simulation verifies the effectiveness of the proposed algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

TFM调制及其改进技术的研究

通过对TFM调制技术的研究,修正了其中预调制滤波器冲击响应波形g(f)在时间轴上的位置及其频谱G(w)的表达式.然后将TFM基带信号进行近似,即对TFM的相位路径进行进一步的控制,得到一个改进的TFM调制技术.改进后的TFM调制技术和原TFM调制技术相比,频带压缩能力提高了一倍,误码性能不变,但会出现3 dB的寄生调幅,需要提高发射机的发射功率.     

A novel distributed approach to robust fault detection and identification

In this paper, we propose a novel distributed robust fault detection and identification (RFDI) scheme for a class of nonlinear systems. Firstly, a detection and identification estimator—robust fault tracking approximator (RFTA) is designed for online health monitoring. A novel feature of the RFTA is that it can simultaneously detect and accurately identify the shape and magnitude of the fault and disturbance. Moreover, it takes less online training time compared with the traditional neural network based fault diagnosis schemes. For some distributed systems, a network of distributed estimators is constructed where the RFTA is embedded into each estimator. Then we use consensus filter to filter the outputs of each estimator. One of the most important merits of the consensus filter is that its outputs can dramatically improve the accuracy of fault detection and identification. Next, the stability of the distributed RFDI scheme is rigorously investigated. Finally, two numerical examples are given to illustrate the feasibility and effectiveness of the proposed approach.     

Kernel Spectral Matched Filter for Hyperspectral Imagery

In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral matched filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the matched filter is derived by expressing the spectral matched filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral matched filter is equivalent to a nonlinear matched filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral matched filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral matched filter outperforms the conventional linear matched filter.