mother wavelet 中文意思是什麼
mother wavelet
解釋
母小波-
By integrating conventional fourie transformation with the smooth average method of spectrum, the high frequency noise was not only removed, but also the signal was smoothed and the main frequency of the signal was found easily and then was extracted as the coarse features of the signal. on the basis, in order to analyze the detail features of the signal, the signal can be decomposed using the db6 mother wavelet function
在譜分析方法的傅立葉變換中引入平滑平均法對蜂窩結構材料和纖維增強材料聲-超聲檢測信號進行處理，不僅可以有效地去除信號的高頻噪聲，而且可以平滑信號，突出檢測信號的主頻，實現了對檢測信號「粗信息」特徵的提取；在此基礎上，合理選用db6小波基函數，對信號進行小波分解，對信號的特徵進行進一步的細微分析。 -
Wavelet discussed in this paper is a basis of l2 or other spaces created by a function ( mother wavelet ) expand and contract and shift
本文討論的小波是指由一個函數（母小波）經過伸縮與平移所產生的l ~ 2或其它空間的基底。 -
In this paper, it is expounded the relationship of two - channel perfect reconstruction filter banks to wavelet transform on the base of signal processing and engineering application ; avoiding complex path formula deduction, the selection of a desirable mother wavelet function becomes the design of two - channel perfect reconstruction filter banks
摘要從信號處理和工程應用的角度出發，避開了復雜的數學公式推導，闡述了雙通道濾波?組與小波變換的關系，並討論了母小波函數的選擇可歸結為正交鏡像濾波器的設計。 -
Where several mother wavelet functions were used to expand a function, it also can be seen as vector - valued wavelets that satisfy conditions in which matrics are involved. the main works of this paper are as follows : firstly, although haar function has bad vanish property in frequency domain, it is the only normal orthonormal basis with symmetry and real short - support property
首先，討論了對于尺度函數，相應的母小波構成空間的標準正交基的充要條件，提出了構造a尺度母小波的演算法，從理論上研究a尺度haar小波基的構造，提出了分解與重構公式，並對如何構造具有對稱性的a尺度haar小波基進行了探討。 -
In this paper, i studied the theory of wavelet in the field of sensor faults diagnose. then i discuss the effects that using various kinds of mother wavelet detect sensors faults in the concrete simulated example. at last, we studied the application of combining the wavelet and bp neural networks to sensors failure detection and simulated sensor faults diagnose by the computer
本文主要研究了小波分析在傳感器故障診斷中的應用，詳細地探討了各種小波基函數在傳感器故障診斷中的應用，並通過具體的模擬實例對各種小波基函數在傳感器故障檢測中的效果進行驗證，最後研究了結合小波分析和bp神經網路進行傳感器故障診斷的技術，同時做了計算機模擬診斷。 -
The two difficulty, one is which mother wavelet and decomposition scale can get best de - nosing effect in practical application, another important problem is threshold selection, which may be occurred in wavelet thresholding, is completely and deeply discussed
並針對閾值去噪法中會遇到的小波基及分解層數的選取和閾值的合理選取兩個難點，我們進行了系統深入的討論。 -
The writer analyzed mallat three criteria and introduced the optimized function based on these three criteria, at last found optimized mother wavelet for edge detection. at the end of this chapter single edge searching method based on local maximum is introduced and gained ideal edge image
分析mallat提出的評價邊緣檢測函數優劣的三個判別函數，利用這三個判別函數列出最優化方程，推導出邊緣檢測最優小波，最後創造性提出局部最大值單像素邊緣搜索法，提取出單像素寬度邊緣圖像，圖像效果達到了滿意的效果。 -
E there is one - to - one map between the mathematical formulation and circuit realization, and that the method is systematic and suited for designing the network function with zeros and high - order filter. at last, the limitation and applied fields of the methods for realizing one - dimension cwt based on state - space log - domain filter are pointed out, it is also proved by the theoretical analysis and calculation results that the methods are suited for synthesizing the wavelet functions with very small scale or the frequence - transloction version of the mother wavelet ( with much translocation in frequence - domain )
論文還給出了用狀態空間對數域濾波器實現一維連續小波函數的限制條件，並指出了這種實現方法的應用范圍，理論分析與計算結果表明該方法適合用於實現尺度很小的小波函數或由原基小波經頻譜搬移（搬移程度較大）后的小波函數。 -
The simulation analysis using the ideal spectrum signal prototype which is constructed based on the infrared ray spectrum of octane level measure indicates that this method gives better signal to noise ratios and performance index ( g ) than conventional wavelet thresholding method via different kinds of mother wavelets
模擬實驗結果顯示，對于不同的小波母函數，採用該方法去噪后的結果優于未經能量元轉換的原方法，而且對于含有較高頻信息的信號，該方法的結果比原方法明顯更優越。 -
My principal work is these : transforming image in wavelet at first, then encoding wavelet coefficients in sofm arithmetic methodology, lbg arithmetic methodology, dpcm encoding step by step, wavelet zero tree compression separately, reconstructing the image at last, so researching how to improve compression ratio, hi addition, the article puts forward a new arithmetic methodology, sl arithmetic methodology, on the base of comparing sofm arithmetic methodology with lbg arithmetic methodology what it needs emphasis is the variety of reconstructed image because of the diversity of mother wavelet
本人所做的工作主要圍繞著這樣的步驟：將圖像數據先進行小波變換，然後分別應用sofm演算法、 lbg演算法、 dpcm分步編碼、小波零樹壓縮等對圖像小波系數進行編碼，最後重構圖像。 akqnk44 wa從而研究如何能夠提高圖像壓縮比，並比較了各演算法在圖像壓縮中的特點。另外，根據sofm演算法、 lbg演算法在圖像壓縮中的各自特點，提出了一種新的算祛一七l演算法。 -
It also studies the influencing factors including sampling frequency, pd waveform, decline time, time intervals between pd pulses, mother wavelet, center frequency of interference, entropy threshold value and snr. in addition, it applies wavelet packet method （ wpm ） for non - stationary signal de - noising in on - line pd monitoring and analyzes the processing results under different pd waveform, sampling frequency and noise level. firstly, the curve of pd
基於小波包變換的非平穩信號消噪演算法（ wpm ）已較為成熟，本文將其應用於局部放電在線監測，通過研究其對不同局放波形、采樣率、噪聲水平下的處理效果，首次提出了理想白噪聲條件下提取局部放電信號的曲線，分析了各種因素對該演算法下信號失真的影響。 -
This paper is described wavelet transform theory, mother wavelet choice, the method to filter signal by wavelet transform and the result, prospered a way to extract feature originated from wavelet theory, which using wavelet packet analyzing method to subdivide signal both in low frequency and high frequency field, and consider energy of every layer as feature in frequency field, and in conjunction with the detailed analyzing character of wavelet packet in time - frequency plane, consider several minimum or maximum points in the lowest frequency band a s features in the time field
本文介紹了小波變換的理論、基小波的選擇和利用小波變換進行信號濾波的方法和濾波處理結果，並提出了一種基於小波理論的新的特徵值提取方法。即利用小波包分析方法將信號在低頻、高頻段作進一步的細分，以各層分解的能量作為信號的頻域特徵值，以最低頻帶的極值點作為時域特徵值，這樣的特徵值選取方法較全面的反映了信號的時-頻特徵，優于傳統的傅里葉分析方法。
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