基于时窗熵的冲击波到时提取方法研究

针对冲击波超压场大区域、全过程重建的需求,开展冲击波到达时间的高精度提取方法研究;首先,分析了STA/LTA的冲击波到达时间提取模型,其次结合信息论理论提出了基于时窗熵的冲击波到达时间提取方法,再次通过仿真实验比较了不同噪声条件下本文的到达时间提取精度,对比了两种方法在同一噪声条件下的提取精度,分析结果表明本方法的提取精度在不同噪声条件下基本保持在0.77%,在低信噪比条件下,本方法的提取精度高于STA/LTA约6%,保证了低信噪比条件下冲击波到达时间的提取精度,解决了STA/LTA方法对于信号变化幅度小而发生漏拾的情况,避免了不必要的提取误差,实现了更高精度的冲击波到达时间的提取具有更高的提取精度,能够为大区域的冲击波超压场高精度重建提供有效的到达时间特征参数,在高价值弹药毁伤效能参数中具有一定的理论意义和工程使用价值。     

Clear P-wave arrival of weak events and automatic onset determination using wavelet filter banks

P-wave arrivals of many weak events cannot be precisely determined manually. Difference in power levels between noise and P-wave in wavelet detail of weak events enables us to determine P-wave arrival manually. Because of this power difference, automatic onset detection and picking algorithm is introduced using the same wavelet detail. Parameter settings are not needed as algorithm will work on data generated by either short or very broad band seismometers. Application of the proposed algorithm on data of three stations of Egyptian National Seismic Network (ENSN) in Cairo region shows a maximum standard deviation of 0.14 seconds of the corresponding manual picks.     

基于聚类和局部线性回归的初至波自动拾取算法

初至波拾取是地震数据处理中的关键步骤,会直接影响动校正、静校正和速度分析等的精度.目前,现有的算法受到背景噪声和复杂近地表条件的影响时拾取精度会降低.基于此,提出基于聚类和局部线性回归的初至波自动拾取算法(FPCL).该算法由预拾取和微调两个阶段来实现.预拾取阶段先基于k均值(k-means)技术找到初至波簇,再利用基...     

Automatic detection and picking of P-wave arrival in locally stationary noise using cross-correlation

Accurate picking of seismic wave arrivals plays a central role in many seismic studies. Nowadays, automatic-picking schemes are indispensable due to the large amount of digital data recorded by wide seismic networks and the need for rapid analysis. The increase of computer power allows the development of more sophisticated algorithms. Many of these algorithms are based on finding change in power, envelope, or statistical properties of the seismic signal in time or frequency domain. But, they have often ignored kind and characteristics of seismic background noise at each recording site. Such information may improve the detector and picker performance if it is taken into account. The aim of this study is to propose a method for picking the arrival of the P-wave in locally stationary seismic background noise. In fact, through analysis of background noise, we have found that it shows local regularity at seismically quiet sites. Therefore, if the background noise satisfies local stationarity, it is evident that occurrence of a seismic event will violate this stationarity. The transition from stationarity to non-stationarity is exploited to pick the P-arrival. To quantify the degree of signal stationarity, we use the normalized cross-correlation function. This method can detect and pick changes both in frequency and amplitude. Thus, it provides robust detection and picking of P-phase onsets even when the signal-to-noise ratio is low. Experimental results on real seismic data, consisting of local seismic events of different signal-to-noise ratios, and comparison with commonly used methods in practice demonstrate the reliable performance of the proposed method.     

基于数字图像处理的地震波初至时间检测

地震波初至时间的检测是地震信号处理中的一个重要环节.在地震波接收记录上,初至波振幅大于噪声振幅,初至时间位于纯噪声信号与地震有效信号和噪声之叠加信号之间的分界处.根据这些特征,本文把地震信号幅值数据处理成256级灰度图,再把灰度图二值化,然后通过检测二值图像的边缘,获得地震波的初至时刻.对于存在较多的连续异常记录道,或其它强干扰信号的地震波记录,提出了二次或多次用不同阈值对图像进行二值化的检测方法.对实际地震记录的处理表明,本文方法具有较高的精度和效率.     

基于谱减法的听觉模拟的语音增强

提出了一种适于低信噪比下的语音增强算法。该算法以传统的谱减法为基础,所用减参数是根据人耳听觉掩蔽效应提出的且是自适应的。对该算法进行了客观和主观测试,结果表明:相对于传统的谱减法,该算法能更好地抑制残留噪声和背景噪声,特别是对低信噪比的语音信号。     

语音清浊音分类及浊音谐波提取算法--三阶累积量基于正弦语音模型的应用

在低信噪比和非平稳噪声干扰下,语音信号的清浊音检测是语音信号处理中的一个重要研究问题。论文基于语音正弦模型,提出了一种清浊音分类和浊音谐波提取算法。该方法在分析了语音的三阶累积量谱后,用子谐波-谐波方法取得基音,并计算出谐波参数和高低频能量比值。它利用谱包络估计器得到谱包络及尖峰信号,结合最小均方估计准则下的迭代算法计算语音谐波的信噪比;通过对上面各计算结果的综合评价得出语音帧的浊音度,从而得到语音清浊音的分类和浊音谐波数。仿真结果表明,该算法在复杂噪声背景下,能有效进行语音分类,准确得到浊音度。同时该算法还具有实时性好、语音参数分析精度高的特点。     

基于PCA和希伯特包络分析的盲隐写分析算法

为克服传统算法采用离散小波变换（Discrete Wavelet Transform,DWT）最终引起的加性噪声隐写图像检测正确率较低而虚惊率较高的问题,提出了一种新的盲隐写分析算法。综合应用主成分分析（Principal Component Analysis,PCA）和希伯特包络分析的优点,提取隐写图像高频子带希伯特包络解析信息号的主成分特征统计量,计算主成分信息熵,构建信息熵敏感特征向量。采用非线性支持向量机（Support Vector Machine,SVM）分类器,基于Matlab7.1平台进行仿真研究,结果表明：该算法对于空域和DWT域隐写检测具有较高的检测正确率和较低的虚惊率。     

基于蚁群优化算法的旋转货架拣选路径规划

给出自动化立体仓库单拣选台分层水平旋转货架系统的数学模型,提出一种改进的蚁群优化算法,用于解决货物拣选路径规划问题。该算法能快速找到最优货物拣选路径,得到的解质量较高且计算时间短。仿真结果表明,该方法适用于求解中小规模货物拣选路径的规划问题,可以提高自动存储作业效率。     

A system on programmable chip design of a digitizer with improved trapezoidal filter validation

This paper presents a System on Programmable Chip (SoPC) design of a digitizer to determine particle features in nuclear physics covering arrival time and energy also for pileup events. The preamplified pulses from the radiation detector are digitized with a rate of 125 Ms/s. Pulse triggering and arrival time is measured by analysis of the pulse output after CR-RC filtering. Trapezoidal pulse shaping is applied for pulse-height energy measure and noise suppression. A new method is presented for trapezoidal flat top height analysis to ease calibration of the trapezoidal pulse shaping filter. The presented method also improves pulse analysis in terms of pileup identification and false pulse rejection. Experimental results obtain a repetitive pulse rate of 50 kHz. The digitizer is able to detect pileup events with a delay between pulses down to few micro seconds.     

Hierarchical abnormal event detection by real time and semi-real time multi-tasking video surveillance system

In this paper, we describe how to detect abnormal human activities taking place in an outdoor surveillance environment. Human tracks are provided in real time by the baseline video surveillance system. Given trajectory information, the event analysis module will attempt to determine whether or not a suspicious activity is currently being observed. However, due to real-time processing constrains, there might be false alarms generated by video image noise or non-human objects. It requires further intensive examination to filter out false event detections which can be processed in an off-line fashion. We propose a hierarchical abnormal event detection system that takes care of real time and semi-real time as multi-tasking. In low level task, a trajectory-based method processes trajectory data and detects abnormal events in real time. In high level task, an intensive video analysis algorithm checks whether the detected abnormal event is triggered by actual humans or not.     

基于自适应阈值的心音分段方法仿真研究

针对心音信号频率低、易受干扰并含有大量杂音的特点,提出了一种心音分段新方法。首先,采用dB6小波进行5层小波分解做心音信号降噪处理;然后,采用了一种提取降噪后心音信号连续平均能量包络的方法;最后,根据连续平均能量包络及自相关函数提出了一种自适应阈值心音分段方法。仿真结果表明,该方法所提取的心音信号包络特征更稳健,提出的分段算法与基于短时能熵比法和短时自相关函数法等心音分段算法相比,本文所提算法分段准确度更高。     

Automatic estimation and removal of noise from a single image

Image denoising algorithms often assume an additive white Gaussian noise (AWGN) process that is independent of the actual RGB values. Such approaches are not fully automatic and cannot effectively remove color noise produced by todays CCD digital camera. In this paper, we propose a unified framework for two tasks: automatic estimation and removal of color noise from a single image using piecewise smooth image models. We introduce the noise level function (NLF), which is a continuous function describing the noise level as a function of image brightness. We then estimate an upper bound of the real noise level function by fitting a lower envelope to the standard deviations of per-segment image variances. For denoising, the chrominance of color noise is significantly removed by projecting pixel values onto a line fit to the RGB values in each segment. Then, a Gaussian conditional random field (GCRF) is constructed to obtain the underlying clean image from the noisy input. Extensive experiments are conducted to test the proposed algorithm, which is shown to outperform state-of-the-art denoising algorithms.     

基于AIC准则和时窗能量比的电缆局部放电在线检测与定位

在利用多高频电流传感器进行电缆局部放电在线检测与定位时,针对局放信号初至时刻拾取精度不高影响定位精度的问题,提出一种基于AIC(Akaike''s Information Criterion)准则和时窗能量比的局放故障在线检测与精确定位方法.首先利用时窗能量比检测出局部放电发生的时窗,然后求取确定时窗的局部AIC特征曲线,并基于AIC准则精确拾取局放信号初至时刻.最后,运用到达时间法对局放源进行定位.仿真结果表明,该方法定位精度高,抗噪声干扰能力强,在-2 dB的噪声环境下可实现99.85％的定位准确率,具备工程实用价值.     

基于DN值趋势面的成像光谱数据低信噪比波段修复研究

针对利用成像光谱数据进行反射光谱重建时，因连续多波段低信噪比图象的噪声造成的影响问题，提出了一种基于低信噪比波段DN值趋势面的图象修复方法．该方法在准确估计修复图象DN值均值趋势面的基础上，通过引入能量比约束条件，将高度相关的邻近波段方差信号引入修复图象，从而不仅较好地消除了噪声对反射光谱重建的影响，而且改善了低信噪比波段参与合成的图象质量．实验结果表明，该方法以一种新的低通滤波思想实现了修复病态波段、恢复光谱特征这一应用目标．     

基于依存分析的离合触发词合法分离形式判定

离合触发词的构词语素可能因插入、颠倒、省略而产生多种合法分离形式,这些分离形式与原形一样也能表征事件。为完整抽取事件,提出一种基于依存分析的离合触发词合法分离形式判定算法。该方法首先借助依存分析考察离合触发词合法分离形式在句中所受的依存约束,然后将这些约束转化为可计算的判定规则,最后利用判定规则对离合触发词的合法分离形式进行判定。实验结果显示,排除稀疏数据前,此方法的正确率、召回率、F值分别为82.2%、88.3%、85.1%;排除稀疏数据后,正确率、召回率、F值提升到82.4%、88.7%、85.4%。方法已基本具备应用潜质。     

基于自聚焦的变门限SAR/GMTI动目标检测方法

高分辨率合成孔径雷达（Synthetic aperture radar,SAR）合成孔径时间长导致动目标散焦严重、信杂噪比（Signal clutter noise ratio,SCNR）降低,增加了对动目标的检测难度。针对这种情况,本文提出了一种基于自聚焦的变门限SAR/(Ground moving target indication,GMTI)动目标检测算法。该算法利用距离相位误差和方位相位误差之间存在的固定关系实现对动目标的两维相位误差的估计与补偿,达到提高SCNR的目的。通过两级不同门限的恒虚警率（Constant false alarm rate,CFAR）检测分别实现动目标检测和排除虚警。实测数据结果表明,本文算法能有效地检测动目标,降低虚警概率。     

基于MVDR和ICA的语音识别方法研究

本文讨论了最小方差无失真响应建模方法,并与线性预测方法进行了比较,比较发现最小方差无失真响应滤波器能提供一个更好的原始语音包络。然后在研究ICA原理及FastICA快速算法的基础上,将MVDR参数提取方法与独立分量分析方法相结合,并与传统语音识别方法在有噪声和无噪声的情况下进行了比较,进而对识别率、计算时间等结果进行了分析。MVDR参数提取方法可以提高语音识别系统的识别率,但是会增加平均识别时间;而经过ICA特征变换后的语音识别系统具有较好的鲁棒性。     

A recurrent neural fuzzy network for word boundary detection invariable noise-level environments

This paper discusses the problem of automatic word boundary detection in the presence of variable-level background noise. Commonly used robust word boundary detection algorithms always assume that the background noise level is fixed. In fact, the background noise level may vary during the procedure of recording. This is the major reason that most robust word boundary detection algorithms cannot work well in the condition of variable background noise level. In order to solve this problem, we first propose a refined time-frequency (RTF) parameter for extracting both the time and frequency features of noisy speech signals. The RTF parameter extends the (time-frequency) TF parameter proposed by Junqua et al. from single band to multiband spectrum analysis, where the frequency bands help to make the distinction between speech signal and noise clear. The RTF parameter can extract useful frequency information. Based on this RTF parameter, we further propose a new word boundary detection algorithm by using a recurrent self-organizing neural fuzzy inference network (RSONFIN). Since RSONPIN can process the temporal relations, the proposed RTF-based RSONFIN algorithm can find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level. As compared to normal neural networks, the RSONFIN can always find itself an economic network size with high-learning speed. Due to the self-learning ability of RSONFIN, this RTF-based RSONFIN algorithm avoids the need for empirically determining ambiguous decision rules in normal word boundary detection algorithms. Experimental results show that this new algorithm achieves higher recognition rate than the TF-based algorithm which has been shown to outperform several commonly used word boundary detection algorithms by about 12% in variable background noise level condition, It also reduces the recognition error rate due to endpoint detection to about 23%, compared to an average of 47% obtained by the TF-based algorithm in the same condition.     

基于EMD-R/S分析的太赫兹光谱降噪

针对太赫兹时域光谱系统由于延时线的重合抖动、采样抖动等产生的噪声,提出使用经验模态分解-R/S分析方法对太赫兹光谱信号进行降噪.采集太赫兹时域光谱系统的时域信号,根据EMD算法将信号分解成本征模态函数(IMF);使用R/S分析法分别计算各个IMF的Hurst指数.根据设定的阈值判断是否各个IMF是否存在均值回复的情况....