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峰值特性
相关语句
  peak feature
    Triple Correlation Function of m-Sequence and Its Peak Feature
    m-序列的三阶相关函数及其峰值特性
短句来源
  peak feature
    Triple Correlation Function of m-Sequence and Its Peak Feature
    m-序列的三阶相关函数及其峰值特性
短句来源
  peak feature
    Triple Correlation Function of m-Sequence and Its Peak Feature
    m-序列的三阶相关函数及其峰值特性
短句来源
  “峰值特性”译为未确定词的双语例句
    The paper alsodiscusses the necessary condition of the being of ZCZ sequence pair, peak valuecharacteristic, construction methods and the transform properties of theself-correlation function.
    讨论了ZCZ序列偶存在的必要条件、峰值特性、构造方法以及自相关函数的变换性质。
短句来源
查询“峰值特性”译词为用户自定义的双语例句

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  peak feature
For Ho(Rh1-x Irx)4B4 with 0.275≤x≤0.4, two different AFM transitions were detected in a double-peak feature inC.
      
As the cloud radius increases, the upper peak is enhanced by the vertical velocity and a single-peak feature becomes dominant.
      
The large experimental background intensity makes a direct comparison with the calculated peak feature around 1.8 eV difficult.
      
The peak feature variation can be explained with the small particle size of the pristine Ge.
      
The spectroscopic results show that there is no peak feature near EF because of the presence of the small Ag cluster.
      
  peak feature
For Ho(Rh1-x Irx)4B4 with 0.275≤x≤0.4, two different AFM transitions were detected in a double-peak feature inC.
      
As the cloud radius increases, the upper peak is enhanced by the vertical velocity and a single-peak feature becomes dominant.
      
The large experimental background intensity makes a direct comparison with the calculated peak feature around 1.8 eV difficult.
      
The peak feature variation can be explained with the small particle size of the pristine Ge.
      
The spectroscopic results show that there is no peak feature near EF because of the presence of the small Ag cluster.
      
  peak feature
For Ho(Rh1-x Irx)4B4 with 0.275≤x≤0.4, two different AFM transitions were detected in a double-peak feature inC.
      
As the cloud radius increases, the upper peak is enhanced by the vertical velocity and a single-peak feature becomes dominant.
      
The large experimental background intensity makes a direct comparison with the calculated peak feature around 1.8 eV difficult.
      
The peak feature variation can be explained with the small particle size of the pristine Ge.
      
The spectroscopic results show that there is no peak feature near EF because of the presence of the small Ag cluster.
      


Scene matching is very important for automated navigate vehicles.It is very difficult to reach the applicable precision if SAR(Synthetic Aperture Radar)and optical images are directly matched based on the intensity information because of the difference of imaging methods.Edge is one of the most fundamental character of a image.In this paper a matching method based on edge and contour information of SAR and optical scene is put forward.Based on the characteristics of SAR and optical images,the new method effectively...

Scene matching is very important for automated navigate vehicles.It is very difficult to reach the applicable precision if SAR(Synthetic Aperture Radar)and optical images are directly matched based on the intensity information because of the difference of imaging methods.Edge is one of the most fundamental character of a image.In this paper a matching method based on edge and contour information of SAR and optical scene is put forward.Based on the characteristics of SAR and optical images,the new method effectively uses wavelet transform and fuzzy weighted median filter for edge detection and analyzes the feature of the peak on the correlation surface for finding the real match position and uses the technlolgy of multiresolution gradational searching to reduce the complexity of computation.Experimental results show that the matching algorithm is of high precision and stability.

SAR(合成孔径雷达 )与可见光图像由于成像机理的不同 ,直接进行基于图像灰度信息的匹配定位很难达到实际应用的要求。边缘是图像最基本的特征之一 ,文中提出一种基于边缘信息的 SAR与可见光景象匹配算法。该算法针对 SAR与可见光图像的特点 ,利用小波变换结合模糊中值加权滤波 ,取得有效的边缘信息 ,利用图像的边缘特征结合相关峰值特性分析的后处理方法进行匹配 ,并采用多分辨率分级搜索技术减少计算量。实验证明本算法匹配精度高 ,稳定性好

m-sequence is one of the most wildly used codes in spread spectrum communications. The triple correlation function(TCF) of m-sequence and its peak feature are studied and described in this paper. The peak feature of the TCF is derived from the shift and add property and the dual-value autocorrelation property of m-sequence. The peak feature can be used to recognize m-sequence and work as the basis for recognizing the direct sequence spread spectrum signals. The dependence of peak number and peak position on...

m-sequence is one of the most wildly used codes in spread spectrum communications. The triple correlation function(TCF) of m-sequence and its peak feature are studied and described in this paper. The peak feature of the TCF is derived from the shift and add property and the dual-value autocorrelation property of m-sequence. The peak feature can be used to recognize m-sequence and work as the basis for recognizing the direct sequence spread spectrum signals. The dependence of peak number and peak position on the stage number and the feedback logic of the shift register has been shown by the simulation. Some important conclusions regarding the generation and detection of m-sequence have been achieved.① m-sequences may be generated if the number of the registers but the last register is odd . And it is certain that m-sequences will not be generated if the number of the registers but the last register is even. ② The TCF peak number of an m-sequence in one period is M-1 and the locations of TCF peak are symmetric. The location of TCF peak of m-sequences changes with the feedback logics of the linear feedback shift register(LFSRG).③ The correct detection probability of TCF peaks is higher than 98% for (snr=5 dB) and close to 100% for snr=10 dB with the threshold R_t=0.6 under additive white gaussian noise(AWGN) conditions.

针对m 序列的三阶相关函数(TCF)及其峰值特性进行了研究.由m 序列的平移相加性和二值自相关性可以推导出m 序列的三阶相关函数具有峰值特性.根据这一特性可以对m 序列进行识别,从而也为识别直接序列扩频(DS SS)信号提供理论依据.仿真实验说明了TCF峰值的个数及位置与移位寄存器级数及反馈逻辑的关系,得出了一些有关生成m 序列及其检测的重要结论:①除末级外,网络中其他级参加反馈的个数为奇数时,有可能产生m 序列,而参加反馈级数的个数为偶数时,肯定不会产生m 序列;②m 序列一个周期内的TCF峰值个数为M-1,峰值位置具有对称性;移位寄存器的反馈逻辑不同,产生的m 序列的TCF峰值位置也不相同;③考虑加性白色高斯噪声情况下,如果取阈值Rt=0.6,则当snr=5dB时,TCF峰值检测的正确率大于98%;而当snr=10dB时,检测的正确率为100%.

A new peak feature selection method for Mandarin digital speech recognition is introduced.This method isbased on the basis of different period features between Mandarin digital speech and noise and divided into two steps.The first step is to sample the frequency spectrum of speech by fundamental frequency to get its envelope andincrease its SNR. The second step is to select the peak feature of the envelope. The experimental results indicate thatthe peak feature of the envelope has an anti-noise character when...

A new peak feature selection method for Mandarin digital speech recognition is introduced.This method isbased on the basis of different period features between Mandarin digital speech and noise and divided into two steps.The first step is to sample the frequency spectrum of speech by fundamental frequency to get its envelope andincrease its SNR. The second step is to select the peak feature of the envelope. The experimental results indicate thatthe peak feature of the envelope has an anti-noise character when SNR > 5dB.

为了提高中小词汇量语音识别系统在噪声环境下的识别性能,以10个汉语数码语音为对象,利用汉语数码语音信号区别于噪声信号的准周期特性,提出了一种汉语数码语音频谱包络峰值特性的提取方法,首先用基频对语音频谱采样得到由谐波值构成的包络以提高信噪比,然后再对所得包络进行峰值提取得到汉语数码语音的峰值特征。实验结果表明,在信噪比大于5dB时,用该方法得到的峰值特征具有一定的抗噪性。

 
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