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背景噪声

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In the experiment design, two methods are put forward to change the background radiation.

结果表明,随着背景辐射的减小,器件的测量噪声亦减小,利用非平衡载流子和器件有效寿命理论对器件的产生复合噪声进行了计算,计算结果与实验结果在随背景辐射变化的趋势上相似。

The cross-high-order-cumulant such as the cross-fourth-order cumulant is firstly proposed to measure the signal and for the first time prove that the CHOC can control the non-relevant noises and Gaussian noises. And based on the cross-high-order spectral Yule-Walker equations, the sinusoidal signal matrix is decomposed by the way of singular value decomposition method.

摘 要:本文以互四阶累积量为依据,首次证明了互高阶累积量可以有效地抑制非相关噪声和高斯噪声;并在建立互高阶累积量的Yule-Walker方程的基础上,通过该矩阵的奇异值分解,建立了信号矢量空间与噪声矢量空间;首次提出了混合噪声背景下正弦参数估计的互高阶谱Pisarenko方法。

This text mainly is to panorama video frequency picture, the usage partitions new calculate way automatically according to the video frequency object of timespace consociation at preparing processing stage's ash to video frequency sequence degree diagram to carry on a partial contrast degree to strengthen a processing, increasing foreground object and the contrast degree of background, the filter which passes 3 ×s 3 templates filters in addition to contrast degree strengthen after of lead go into of a little amount 噪 voice;Then aim at the circumstance of complicated background, the calculate way in the text puts forward the covariance characteristic backlog which makes use of immediately signal to get the background that the calculate way needs, overcoming the irregularity that the handicraft obtains background, carrying out a background information to obtain automatically;The standpoint put forward a background backlog finally to categorize a discussion in the process, solved object stop sport time is longer result in the partition throw the problem for losing.

本文主要是对全景视频图像,运用基于时空联合的视频对象自动分割新算法,在预处理阶段对视频序列的灰度图进行局部对比度增强处理,增加前景物体与背景的对比度,通过3×3模板的滤波器滤除对比度增强后的引入的少量噪声;然后针对复杂背景的情况,文中的算法提出利用随即信号的统计特性积累得到算法所需的背景,克服手工获取背景的弊端,实现背景信息的自动获取;最后提出背景积累过程中分类讨论的观点,解决了物体停止运动时间较长时造成分割丢失的问题。

The motionless backgroup situation usually happens at the video camera parked to opposite quiescent condition , the first wipe off yawp by using pretreatment method,the second using the method of difference Statistic in two frame to analyse and compute background , Above this base to utilize the resumeed background and present frame message detect movement target and it"s movement track ; In dynamic backgroup movement image,it is very difficult to exactly compute ground since the background is varied complexly , the article research the optical flow model、 background model、 moving object detection based on entropy,and depart target from background in movement picture , detect moving target and plain it"s track.

静止背景的情况通常发生在摄像机相对静止状态,首先用预处理的方法去除噪声,其次采用帧间差分统计的方法进行分析计算背景,在此基础之上利用恢复的背景及当前帧的信息检测运动目标及其运动轨迹;对于动态背景的运动图像,由于背景是不断变化且呈现出复杂的特征,因此很难准确求出静止背景,本文在光流矢量场和配准方法的基础上探讨研究了光流模型、背景模型和基于熵的方法,在动态图像上分离不同的目标与背景,直接检测出运动目标并画出相应的轨迹。针对运动图像的复杂性,本文提出了构建模型知识库的设想。

Then, Otus's threshold method is used to segment the image and locate the sea-sky-line, through which the target potential area can be determinated.

采用维纳自适应滤波,抑制随机噪声和高斯噪声;利用Otus阈值法分割图像,确定海天线和目标潜在区;利用Top Hat算子进行形态滤波处理,抑制平缓变化背景并保留高亮度区的目标和强噪声;选择结构元素进行形态开运算,去掉细小的背景杂波干扰;针对远距离舰艇小目标总是出现在海天线附近以及红外目标灰度高于其邻域背景的特点,确定阈值即可分离出真正的目标。

This method firstly filters complicated background noise with a band pass filter then macks prediction in timedomainand affirm alarm in frequencydomain .

该方法首先将背景噪声带通滤波,然后对信号时域预报警及频域报警确认。

With the recent appearance of broadband OBS, the background noise in the extended frequency range is even more significant.

现在广泛使用的宽频带洋底地震计在扩展频段内记录到的背景噪声更强。

It can be used in measuring the dynamic information of the high rotational speed gear transmission system.

采用红外线作为传输媒介,克服了在高速旋转,且环境背景噪声大的齿轮传动动态信息传输失真的问题。

At last we obtained obvious resonance Raman spectrum of carotenoid in human skin after the fluorescence background has been fitted and subtracted from the original spectra.

利用拟合差分算法有效去除了荧光背景噪声,获得了明显的人体皮肤中类胡萝卜素的拉曼光谱。

The demodulated resonance technique combined with spectrum analysis and fuzzy-closest-principle were developed and applied to the fault diagnosis of bearings in drilling pump.

在钻井生产现场,由于工作条件恶劣及振源多,背景噪声强烈,因而用理论计算所得到的故障特征频率与实测信号经一般频谱分析后得到的响应信息缺乏一一对应关系。

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