无FM功能的电视卡也能收FM广播

我们知道广播电台广播信号的输出主要采用两种调制方式,一是调幅(AM),一是调频(FM)。调频制无线电广播多用超短波(甚高频)无线电波传送信号, 使用频率约为87MHz-108MHz,主要靠空间波传送信号。由于调频信号失真度比较小,所以也被用于电视伴音信号的传输。由于收FM广播的收音机和收电视伴音的电子线路原理上是一样的,所以许多FM收音机是     

基于FPGA的多通道高速波形发生器设计

为了产生多通道的高速信号,波形发生器以FPGA为核心,结合高速高精度数模转换器和高速运算放大器,采用DDS技术来实现高速信号的产生。波形发生器采用PCI总线与上位机进行通信,上位机通过发送控制命令改变波形发生器输出信号的种类、频率、相位。波形发生器还可以进行AM调制、FM调制、ASK调制、PSK调制和FSK调制等。     

一种多功能正弦信号发生器的设计

介绍了一种以FPGA和单片机为控制核心,基于调制原理配合使用DDS专用芯片AD9851,实现了一种多功能正弦信号发生器。实现了在30Hz～12MHz频率范围内正弦信号的无失真输出,且在输出端接50Ω功率电阻的条件下,输出电压峰峰值在5.8V～6V范围内。系统还具有AM、FM、ASK、FSK、PSK调制的功能,整体工作稳定,界面友好,操作简单。     

AM/FM/GIS应用系统建设中若干问题的探讨

在进行多项AM／FM／GIS应用系统建设实践和对AM／FM／GIS的深入研究的基础上,阐述了AM／FM／GIS的特点,指出了当前AM／FM／GIS建设过程中存在的一系列问题,并从AM／FM／GIS标准,平台建设,模型集成,知识与规则的应用,多层体系结构,分布式计算等几个方面对AM／FM／GIS进行了较为详细的探讨,提出了解决相关问题的思路。     

基于软件无线电的解调算法研究

主要对软件无线电结构下的解调算法进行研究,并在此基础上用matalab对AM、FM、2FSK信号的解调算法进行了仿真,从而证明了算法的正确性和可行性。     

数控振荡器应用及其在FPGA中的实现

本文首先描述NCO的基本工作原理,然后介绍利用NCO产生调频信号(FM)、频移键控信号(FSK)、相移键控信号(PSK)、调幅信号(AM)和幅度键控信号(ASK)等多种调制信号的方法,最后以调幅信号(AM)为例介绍调制信号在FPGA中的实现。     

AM/FM/GIS领域模型研究

通过分析当前AM／FM／GIS在国内外日趋广泛的现状，作者指出AM／FM／GIS软件系统建设过程中存在的主要问题——软件可重用性差，指出其存在潜在的规律和本质的特征，通过找到这些规律和特征将能明显改善AM／FM／GIS系统建设的重用性。作者利用FODA的领域分析方法，对AM／FM／GIS系统进行了深入的研究，通过符合标准规范的表示方法，得到了领域特征模型、领域信息模型和领域操作模型，从不同的侧面精确描述了AM／FM／GIS的领域模型。     

AM/FM/GIS数据模型在信息基础设施中的应用研究

AM/FM/GIS系统建设中,基于几何空间与基于逻辑空间的对象进行统一管理是核心问题。提出空间对象模型的概念,并给出严格的形式化定义。通过对AM/FM/GIS数据模型的分析,运用面向对象的AM/FM/GIS数据建模方法,实现了信息基础设施中的数据存储与管理,提高了AM/FM/GIS系统的运行效率。     

基于动态载波AM算法参量阵的DSP设计与实现

为了解决普通AM参量阵产生的解调信号质量差、效率低等问题,设计了一个动态载波AM算法。以此算法为基础设计了一个DSP处理平台,并在此平台上实现了参量阵系统。经过系统硬件和软件两方面的优化设计,保证了算法运行的实时性和稳定性。最后,使用500Hz、800Hz和1100Hz的合成波作为测试信号,测试了系统输出的波形和参量阵前方5米处的频谱。对比测得的波形和理论波形,验证了此系统能够可靠地运行。对比普通AM和动态载波AM参量阵输出信号的频谱图,验证了动态载波AM参量阵能输出更好信噪比和能量更高的解调信号。     

基于SiriusXM卫星广播服务的音频播放软件的设计

随着全球汽车工业的飞速发展,车载多媒体成为人们开车和娱乐的必要设备,而传统的FM和AM广播收音功能,由于接收信号的方向和传播距离的限制,其音质方面受到了一定的影响。采用卫星广播就能够提高音质,同时还可以提供更多的收音频道和功能。天狼星XM公司推出的SiriusXM卫星广播服务替代传统的FM和AM广播收音服务,从而改善车载系统的收音品质和用户体验。本文就是采用SiriusXM卫星广播服务对车载多媒体的音频播放软件进行了设计。     

A local normalized method to eliminate riding waves appearing in the empirical AM/FM decomposition

The intrinsic mode function (IMF) generated by empirical mode decomposition (EMD) is usually seen as an amplitude and frequency modulation (AM–FM) signal. To obtain the FM part of an IMF, an empirical AM/FM decomposition method has been proposed by Huang and his coworkers. However, riding waves may appear in the empirical decomposition, which makes the instantaneous frequency (IF) physically meaningless. To eliminate the riding waves, riding wave turnover-empirical AM/FM decomposition method has recently been developed by Yang et al. In this paper, a local normalized method is proposed to eliminate the riding waves. Different from the previous method by Yang et al., the proposed method does not need any more empirical AM/FM decomposition and is carried out locally, which can significantly reduce computational complexity.     

电力GIS中多层次智能故障诊断技术的研究

GIS因其强大的数据和空间分析功能，已被广泛应用于电力系统的各个方面。随着配网规模不断扩大，其运行方式变得更复杂，采用传统的、单一的故障诊断方法已不能满足实际系统的需要。本文提出一种分布式多层次智能故障诊断方法，它依据配电设备的类型进行故障诊断任务的分解，对分解后的子系统在多层智能推理策略的控制下按系统结构层次进行协同故障诊断，将最终的诊断结果在AM／FM／GIS中实时、动态地显示出来。该方法在大型复杂的配电系统中应用效果特别显著。     

Fast-varying AM–FM components extraction based on an adaptive STFT

A new method is proposed in this article for fast-varying AM–FM components extraction. There are two prominent characteristics in this method. Firstly, a new evaluation method for the instantaneous bandwidth is established, which is based on the instantaneous slope of the time-frequency curve with respect to the AM–FM component. Secondly, a new adaptive STFT algorithm is established, which adjusts the window width by adapting to the instantaneous bandwidth at each frequency position. In order to extract multiple AM–FM components from a signal, the width of the reconstruction area is required to be determined efficiently to avoid the interference caused by adjacent components. Simulations are given in the end, which show that the proposed method has good performance for fast-varying AM–FM components extraction from noisy signals.     

A rotating machinery fault diagnosis method based on local mean decomposition

Local mean decomposition (LMD) is a novel self-adaptive time–frequency analysis method, which is particularly suitable for the processing of multi-component amplitude-modulated and frequency-modulated (AM–FM) signals. By using LMD, any complicated signal can be decomposed into a number of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated signal from which physically meaningful instantaneous frequencies can be obtained. In fact, each PF is just a mono-component AM–FM signal. Therefore, the procedure of LMD may be regarded as the process of demodulation. While fault occurs in gear or roller bearing, the vibration signals picked up would exactly display AM–FM characteristics. So it is possible to diagnose gear and roller bearing fault by LMD. Targeting the modulation features of the gear or roller bearing fault vibration signal, a rotating machinery fault diagnosis method based on LMD is proposed. In this paper, firstly the LMD method is introduced; secondly, the LMD method is compared with another competing time–frequency analysis approach, namely, empirical mode decomposition (EMD) method and the results show the superiority of the LMD method; finally, the LMD method is applied to the gear and roller bearing fault diagnosis. The analysis results from the practical gearbox vibration signal demonstrate that the diagnosis approach based on LMD could identify gear and roller bearing work condition accurately and effectively.     

继电保护定值管理系统设计

基于工作流技术分析了定值单管理业务过程,利用UML活动图对定值单管理工作流程建模,在BEA Weblogic Integration支持下实现了定值单工作流系统,给出了与定值整定计算软件、配电AM/FM/GIS进行系统集成的方案.系统的实施实现了定值生产管理流程化、自动化和多部门协同.