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[File Format] 3 DL : 0: Abstract-The mobile radio channel can be simulated as a complex-valued random process with a time-varying variance. This paper describes an implementation of that process using a real-time Digital Signal Processing (DSP) technique. The simulator design permits the user to select the simulation parameters, including: vehicular speed, carrier frequency, ratio between the line-of-sight component and the multipath component, and the variance of average power. The design is based on variable sampling rate DSP techniques, and offers a novel solution to suppress the aliasing terms at intermediate stages where access to analog filters is cumbersome and costly.
Date : 2025-12-25 Size : 474kb User : 章清
[File Format] 115 DL : 0: 本文针对基于经验模态分解ＥＭＤ的时空滤波器存在的固有模态函数分量中频率混叠交叉导致有用信号与噪声一起被滤除的问题结合小波在时间尺度两域表征信号局部特征的特性提出了一种基于能量估计实现ＥＭＤ分解层数确定-In this paper, based on empirical mode decomposition EMD temporal filter mode functions inherent component of cross-frequency aliasing and noise together lead to useful signal is filtered in the time scale of the problem combined with wavelet domain characterization of two local features of the signal characteristics of proposed achieve energy estimation based on EMD decomposition level determined
Date : 2025-12-25 Size : 562kb User : 张力
[File Format] Study-on-compound-fault-diagnosis DL : 0: 针对滚动轴承复合故障信号特征难以分离的问题，提出将双树复小波变换和独立分量分析( ICA) 结合的故障诊断方法该方法首先将非平稳的故障信号通过双树复小波变换分解为若干不同频带的分量由于各个分量存在一定的频率混叠，对故障信号特征提取有很大的干扰，进而引入 ICA 对各个分量所组成的混合信号进行盲源分离，从而尽可能消除频率混叠最后对从混合信号中分离出来的独立分量信号进行希尔伯特包络解调，即可实现对复合故障特征信息的分离和故障识别-Aiming at the difficulty of separating the fault feature from compound rolling bearing fault signal，a new fault diagnosis method is proposed based on dual-tree complex wavelet transform ( DT-CWT) and independent com- ponent analysis ( ICA) ． Firstly，DT-CWT is used to decompose the non-stationary fault vibration signal into several components with different frequency bands． Because frequency aliasing exists in the components，this problem dis- turbs the feature extraction of the fault signal． Then，ICA is introduced to perform blind source separation on the mixed signal consisting of various components to eliminate the frequency aliasing as far as possible． Finally，Hilbert envelope decomposition is performed on the independent signal components separated from the mixed signal． Thus the compound fault feature information can be separated，and the fault identification is achieved．
Date : 2025-12-25 Size : 906kb User : 侯蒙蒙