基于MoCA标准的家庭网络研究

本文描述了一种基于MOCA标准的家庭网络,并对该网络及设备进行了基本性能的测试.本文首先介绍了家庭网络的发展现状,对目前的各种家庭网络标准进行了比较.在此基础上详细描述了基于MOCA标准的家庭网络,并对该网络的设计,实现、网络性能进行了详述.然后对该网络进行了测试并得出了相关的测试结论.最后指出该网络的不足及改进之处.     

爆轰试验用新型射频连接器的结构设计

针对目前爆轰试验中使用的机械密封射频连接器存在的问题.设计了基于玻璃封接技术的新型密封射频连接器结构,并进行了射频反射补偿计算和强度分析.本项研究为爆轰条件下新型密封射频连接器的研制提供了设计依据.     

施肥模型研究综述

对施肥模型和参数研究的历史、现状和趋势进行了综述,并指出今后定量施肥研究的关键问题。     

0.25μm CMOS千兆以太网发送器设计

分析了千兆以太网体系结构,给出了符合IEEE 802.3z标准中1000BASE-X规范的发送器电路结构,并采用TSMC 0.25 μm CMOS 混合信号工艺设计了符合该规范的高速复接电路和锁相环时钟倍频电路.芯片核心电路面积分别为(0.3×0.26)mm2和(0.22×0.12)mm2.工作电压2.5 V时,芯片核心电路功耗分别为120 mW和100 mW.时钟倍频电路的10倍频输出时钟信号频率为1.25 GHz,其偏离中心频率1MHz处的单边带相位噪声仅为-109.7 dBc/Hz.在驱动50 Ω输出负载的条件下,1.25 Gbit/s的高速输出数据信号摆幅可达到410 mV.     

基于嵌入式Linux的远程数据采集系统

以嵌入式微控制器为核心,将传感器网络收集来的数据通过无线电发射机发送到远程接收机,从而实现远程数据采集.系统的优势是充分利用了短波无线链路资源,适合于工作在无人区等场合.以ARM7为控制器,功耗小,性能稳定,维护方便;通信方式采用串口通信,连接线少,实现相对简单.系统设计主要应用嵌入式μCLinux系统设计、无线电发射、调制解调器等方面的知识,文中给出了调制解调器拨号协议、系统原理和软件流程等.     

大功率开关电源的设计与实现

介绍了开关电源设计中需要考虑的几个基本问题,包括频率的确定、最大占孔比Dm的确定、逆变变压器的设计和开关功率管的选择,并以一种1 kW的PWM大功率半桥式开关电源为例,详细介绍实际的开关电源设计方案,并给出了实验结果.     

Distributed Optical Fiber Sensor for Multi-point Temperature Measurement

The distributed optical fiber sensing technology is overviewed, which is based on Raman scattering light theory. Basic operation principle, structure, system characteristics and signal processing are discussed. This structure and method of the signal processing possess of certain spatial resolution, hence will ensure the practicability of system.     

A wavelet-based multisensor data fusion algorithm

This paper presents a wavelet transform-based data fusion algorithm for multisensor systems. With this algorithm, the optimum estimate of a measurand can be obtained in terms of minimum mean square error (MMSE). The variance of the optimum estimate is not only smaller than that of each observation sequence but also smaller than the arithmetic average estimate. To implement this algorithm, the variance of each observation sequence is estimated using the wavelet transform, and the optimum weighting factor to each observation is obtained accordingly. Since the variance of each observation sequence is estimated only from its most recent data of a predetermined length, the algorithm is self-adaptive. This algorithm is applicable to both static and dynamic systems including time-invariant and time-varying processes. The effectiveness of the algorithm is demonstrated using a piecewise-smooth signal and an actual time-varying flow signal.