噪聲閾 的英文怎麼說

中文拼音 [zàoshēngyù]
噪聲閾 英文
noise threshold
  • 噪 : 動詞1. （蟲或鳥叫） chirp 2. （大聲叫嚷） make noise; make an uproar; clamour
  • 閾 : 名詞1. [書面語] （門坎兒） threshold; doorsill2. （界限; 范圍） threshold
  1. This paper adopts an adaptive learning algorithm based on hierarchy clustering to update user profile, which continuously abstract the cancroids of one class of optimum information from the feedback flow of system, which effectively shield the learning process from plenty of feedback noises produced by distorted threshold and sparseness of initial information, which also can imitate artificial feedback approximately to perfect the intelligence of adaptive learning mechanism

    摘要本文採用一種基於層次聚類的自適應學習策略，從系統反饋的信息流中，動態提取一類最優信息的質心更新用戶模型，有效屏蔽了閾值失真和初始信息稀疏造成的大量反饋噪聲，並且能夠近似模仿人工反饋，完善自適應學習機制的智能性。
  2. Partial wavelet coefficients ca n ' t be set zero when the threshold is undersize so parts of noises are retained and some useful signals will be taked off when the threshold is iusto major so parts of useful signals are lost. these cases result in dissatisfactory denoising

    如果閾值選取過小，那麼有一部分的噪聲小波系數將不能被置零，從而在去噪后的信號中保留了部分噪聲信息，使去噪的效果變差。
  3. After scanning the object image and labeling the connected component, the background and foreground noise could be removed easily by threshold value area expunction

    該方法僅需對圖像作一次全掃描即可標記物體所有連通部分，統計標識號，根據閾值面積消除法即可快速去除圖像中所有前景和背景噪聲。
  4. Combining the basic laws of psychophysics and signal detection theory, we use two - alternative forced - choice ( 2afc ) to study the threshold change of human electric sense under different noise levels

    在心理物理實驗中，結合心理物理學和信號檢測論的基本原理，採用了二間隔的強迫選擇法（ 2afc ）作為實驗方法，研究不同噪聲量級情況下人體電感覺閾值的變化。
  5. Firstly, the complex characteristics of the seal images caused in the process of producing conditions are analyzed. to solve these problems respectively, the circularity clusters and the ostu method are firstly used to realize the shape classification and threshold processing of different seal images. then the image denoise is performed well by scanning beam seed filling and labeling algorithm

    論文中首先分析了印鑒圖像由於蓋印條件造成的圖像本身的一些復雜特點，提出了運用圓形度聚類和最大方差比演算法對圖像進行形狀分類和閾值處理，隨后利用掃描線種子填充演算法和貼刪標簽演算法進行噪聲的去除等預處理。
  6. When the input noise is the single - peak gaussian noise, the noise can improve the signal correlation only for the sub - threshold signal, i. e. subthreshold stochastic resonance ( sr ) exists

    當輸入噪聲為單峰高斯噪聲時，輸入信號在閾下時噪聲才能改善信號的相關性，即隨機諧振現象存在。
  7. However, when the input noise is the double - peak gaussian mixture noise, the noise can improve the signal correlation for the subthreshold signal and the suprathreshold signal, i. e., both sr and suprathreshold stochastic resonance ( ssr ) often occurs

    而當輸入噪聲為雙峰高斯混合噪聲時，不僅輸入信號在閾下時隨機諧振現象有時存在，而且輸入信號在閾上時噪聲往往也能改善信號的相關性，即閾上隨機諧振現象存在。
  8. According to the different characteristics between signal and noise on wavelet transform domain, also considering the voiced and unvoiced speech has different features, a modified method of speech denoising which is using a changing threshold at different scales is proposed

    摘要分析了信號和噪聲在小波域的不同特徵表現，並根據語音中濁音和清音的特點，提出了一種改進的多尺度多閾值的小波域語音去噪方法。
  9. That is, using a soft thresholding to remove noise components from the wavelet coefficients of the voiced and unvoiced speech in noisy speech respectively in a different way, which is not only removing noise but also is preventing the quality degradation of the unvoiced sounds and enhancing the signal - noise ratio

    該方法採用軟限幅函數對濁音和清音信號的小波變換系數作不同的閾值處理，既抑制了噪聲，又減少了語音段信息的損失，提高了信噪比。
  10. In order to reduce the musical residual noise and the background noise, a speech enhancement method based on masking properties of the human auditory system is described. this method uses bark wavelet packet transform to simulate the frequency feature of human auditory model to get the threshold

    本文以最大限度減少殘留噪聲和背景噪聲為目的，採用bark子波分析的方法模擬人耳基底膜的頻率分析特性來進行語音增強，重點進行模擬人耳聽覺掩蔽效應來確定除噪閾值的研究。
  11. In the paper, we describe the whole configuration of the joint transform correlator ; introduce the correlative theories of joint fourier transform in detail ; in order to achieve better correlation result, the nonlinear processing of joint transform power spectrum ( jtps ) is put forward, the effect of jtps laplace sharpening and binary processing to the correlation image is given ; the method of the best binary threshold is determined ; we analyse the correlation image, present the way that removes liquid crystal diffraction spot, direct current spot and image noise, and binarize the correlation image finally

    本論文主要對提出的聯合變換相關的整體結構進行了描述；詳細介紹了聯合傅里葉變換的相關理論；為了得到更好的相關結果，本文提出對聯合功率譜進行非線性處理的方法，給出了聯合變換功率譜的拉普拉斯銳化和譜的二值化處理對相關峰圖像的影響及二值化最佳閾值的確定方法；對相關峰圖像信息進行了分析；提出了消除液晶衍射光斑、直流光斑和圖像噪聲的方法；最後對相關峰圖像進行了二值化處理。
  12. This thesis explains the necessity of the character recognition technology of the computer at first, describe the meaning in which the handwritten numeral discerns ; pretreatment technology of handwritten numeral recognition, including two value, line segmentation, word segmentation smooth, removing noising, standardization and thinning are discussed two value concretely discusses whole threshold value, some threshold value, dynamic threshold value and utilize space information to carry on threshold, which are several kinds of common method of choosing threshold value, especially utilize space information to carry on threshold value is describe in detail ; adopting to the foundation of thinning based on mathematics morphology, thinning algorithm of serials same and thinning algorithm of protecting shape are discussed ; afterwards, according to principle ' s diagram of the on - line character recognition, by analyzing the structure feature of the handwritten numeral, this thesis has proposed the online recognition te chnology of the free handwritten numeral based on the stroke feature and the online recognition technology of the free handwritten numeral based on the multistage classifying device. detail narrated noise removing, stroke characteristic definition and discernment, distance criterion of whole word match ; then under the foundation of handwritten numeral segmentation, off - line handwritten numeral recognition is researched. especially minimum distance classifying device, tree classifying device and adaptive resonance ( art ) network classifying device is discussed at the same time, believes degree analyses are introduced to integrate a lot of classifying devices ; at the end, the typical application of the handwritten numeral recognition was briefly narrated, its application in extensive data statistics, financial affairs, tax, finance and mail sorting have been explored

    二值化時對整體閾值二值化、局部閾值二值化、動態閾值二值化和利用空間信息進行閾值選取幾種常用的閾值選取方法進行討論，特別對利用空間信息進行閾值選取進行了詳細論述；在對通過對基於數學形態學的細化的基礎上，討論序貫同倫形態細化演算法和保形的快速形態細化演算法；然後依據聯機字元識別原理框圖，分析了手寫數字的結構特點，提出了基於筆劃特徵的任意手寫數字在線識別技術和基於多級分類器任意手寫數字在線識別技術，對其中涉及的筆劃識別前的噪聲處理、筆劃間特徵量的定義及識別、整字匹配的距離準則進行了詳細敘述；繼而在對手寫數字的分割的基礎下對脫機手寫數字識別進行了研究，對基於最小距離分類器字元識別、基於樹分類器的字元識別、基於自適應共振（ art ）網路的字元識別分別進行了詳細討論，並引入置信度分析將多個分類器進行了混合集成；最後簡單闡述了手寫數字識別的典型應用，對其在大規模數據統計、財務、稅務、金融及郵件分揀中的應用進行了探索。
  13. 2 ) set threshold in stationary wavelet domain to separate edge and noise, estimated wavelet coefficients in texture area to reconstruct the interferogram

    3 ）在靜態小波域設置濾波閾值以區分邊緣和噪聲，根據map準則來確定信號的小波系數，然後重建干涉相位圖。
  14. In order to study the effect of weak noise on the sound signal extraction of mouse ( mus musculus km ) inferior collicular ( ic ) neurons from environment, we examined the change in frequency tuning of neuron induced by a weak noise relative to 5 db below minimum threshold ( remt - 5 db ) of tone. 12 healthy adult mice ( 20 - 25 g, b. wt. )

    本實驗旨在探討弱噪聲對小鼠（ musmusculuskm ）中腦下丘（ inferiorcolliculusic ）神經元聲信號提取的影響及其神經機制，我們選用相當于純音閾下5db的白噪聲（ whitenoise ） ，比較加入噪聲前後神經元頻率調諧曲線的變化和注射-氨基丁酸（ - aminobutyricacid ， gaba ）能受體拮抗劑荷包牡丹堿（ bicuculline ， bic ）后對該變化的影響。
  15. We use a colorful noise to describe traffic flow and regard it as the uncontrolled input ( equaled to noise ). then we use the multi - rate sampling pi controller to eliminate the error caused by noise, and make the length of queue stabilize at the threshold. the availability of the strategies and stability of systems are proved by doing some analysis and simulation

    文中我們將一有色噪聲看作不可控輸入的擾動，採用基於pi的多采樣速率控制器以消除由此不可控輸入所造成的對系統輸出（交換節點緩沖區隊列長度）誤差的影響，使得系統輸出（隊列長度）穩定在閾值附近。
  16. At last the floating threshold algorithm based on wavelet packet transform is proposed to remove the noise from the signal of dc motor measurement in the dissertation, which is proved effectively by the experimental results

    提出了基於小波包變換的浮動閾值去噪演算法，並應用到直流電動機測試過程中的高頻噪聲去除過程中。
  17. The expected noise level calculated by this model confroms to the measured noise level. and we carried out noise annoyance investigation by sending out larges of questionnaires in different noise sensitive regions such as residential quarters and schools in order to know the effects of combined overhead road noise on subjective annoyance. by aulyztng the relationship of the annoyance effective factors ( noise effective factors and non - noise effective factors ) and noise annoyace, the mainly effective factors were maken out

    同時針對不同的敏感區域，進行了大量地高架復合道路噪聲主觀煩惱度調查研究，通過對噪聲因子及非噪聲因子與主觀煩惱度的相關關系分析，確定影響高架交通噪聲主觀煩惱的主要的非噪聲因子，並建立了噪聲煩惱度的預測模型，得出了不同敏感區域的高架交通噪聲的煩惱度閾值。
  18. With the distinguishing characteristics of character " s texture ( such as horizontal lines, vertical lines, or slant lines in a character ) that can be extracted directly, the character - regions are segmented from their background quickly, and the image - noises rising during the processing period can be removed by morphological filter

    首先提取字元的橫向、豎向紋理的方向信息，然後根據各自的閾值把候選字元區標識出來，用形態濾波的方法消除噪聲，最後用斜向紋理的平均斜向能量判斷是否是字元區。
  19. Firstly, a fourth - orders statistic hypothesis testing in inter - frame difference is used to automatically separate the moving areas from the background in a general video sequence. secondly, change areas are extracted as markers of the corresponding moving object. using the marker, region - growing algorithm can locate the precise boundary of moving object

    （ 3 ）基於輪廓跟蹤；首先介紹了提取初始模板方法?基於高次統計的判決演算法，通過選擇合適的背景噪聲閾值，最終得到較好的結果，然後對於後續幀中的邊界提取，提出將灰度圖像所使用的區域生長方法進行改進，即將它們運用到彩色圖像中，通過選擇合適的生長規則，得到較好邊界圖。
  20. Examples calculation indicated that establishing different annoyance membership function of different environmental more conformed the reality of expression for people ' s noise subjective response, and the annoyance probability and noise threshold calculated were more credible scientifically

    實例計算表明，對不同環境建立不同的煩惱隸屬函數，更符合人們對噪聲主觀反應所作表述的實際，計算得到的煩惱概率和噪聲閾值更科學可信。
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