Red color filter in an organo-soluble polyamide matrix for liquid crystal displays

A red color filter was laminated from a solution of red color pigment and an organo-soluble polyamide, based on 1,4-bis(3,4-dicarboxyphenoxy) benzene dianhydride (HQDPA) and 2,2'-dimethyl-4,4'-methylene dianiline (DMMDA). The red color filter in a polyamide matrix with negative birefringence plays an important role in twisted nematic liquid crystal displays (TN-LCDs). The red color filter, and also compensation films, extend the viewing angle of LCDs. (C) 1997 Elsevier Science S.A.

基于MIT规则的自适应Unscented卡尔曼滤波及其在旋翼飞行机器人容错控制的应用

提出一种新颖的基于MIT规则的自适应Unscented卡尔曼滤波(Unscented Kalman filter,UKF)算法,用来进行参数以及状态的联合估计。针对旋翼飞行机器人执行器提出一种执行器健康因子(Actuator health coefficients,AHCs)的故障模型结构,应用自适应UKF对AHCs参数进行在线估计,将联合估计的状态以及故障参数引入基于模型的反馈线性化控制结构,组成完整的容错控制系统。提出的自适应UKF算法以及容错控制结构经过中科院沈阳自动化研究所ServoHeli-20旋翼无人智能平台数学模型进行仿真试验验证,效果良好。

基于DSP旋翼飞行机器人增稳操纵杆设计

本文首先介绍了旋翼飞行机器人控制系统的功能与应用,着重介绍了其中基于tmx320f28335数字信号处理器的无线增稳操控系统工作原理、硬件构架以及软件流程,并对AD转换过程中的关键FIR滤波算法进行说明,详尽比较了不同滤波参数对滤波效果的影响,最后得到该方法可以应用于旋翼飞行机器人增稳控制系统的结论,并将应用该方法滤波后的控制信号应用于实际增稳飞行,以实际数飞行据验证上述结论。

一种低功耗微弱信号放大电路的优化设计与研究

针对远距离声源发射的水声信号微弱、水声接收设备电源能量有限的特点,提出一种功耗小、对无源元件误差灵敏度低、高增益放大的微弱水声信号通用放大电路。系统采用场效应管共源单调谐放大器为前置放大级,由四级级联低功耗运放构成带通滤波放大电路,省去传统的R、C低通网络,实现了对微弱水声信号的高增益放大和海洋背景噪声的归一化处理。通过计算电路网络传递函数极点证明了电路系统的稳定性。海上使用表明系统具有精度高、适应性强、电路稳定性好、功耗小等优点。

水声信号的窄带滤波研究

研究一种用于有色噪声背景下接收水声信号的窄带滤波方法。它用白化滤波器实现有色噪声的匹配滤波。该滤波电路具有优良的选频能力,抗干扰性强,功耗极低,较好地解决了水下信息传输中存在的通道之间频率间隔小、信号微弱、易受噪声干扰、用电池供电而要求工作时间长等问题。采用这种窄带滤波方法的水声接收机具有结构简单、性能稳定、对弱信号检测能力强等优点,增大了定位声纳的作用距离。

柴达木盆地地震勘探关键技术研究

The rugged surface topography determined the seismic data acquisition construction conditions and the seismic wave explosive and receiver quality in Qaidam Basin. This dissertation systematically researched the seismic acquisition, imaging process and the attribute analysis techniques of complicated oil and gas reservoir. The main research achievements and cognitions are as follows: 1. Through the stimulation effects research and analysis from the aspect of lithologic water-containing differences, it’s specific that stable hydrous sand layer can effectively enhance the stimulation effects combined with the corresponding field tests. The seismic data S/N ratio has been improved due to the combination explosive stimulation. Through the fold number and maximum offset analyses of target horizon, the complicated geometry has been optimized and the S/N ratio of seismic data has been improved, which made an important basis for improvement of 3D seismic data. 2. It has been proved that the first arrival refraction static correction method under the model constraint of fine surface survey is suitable to the Qaidam Basin of western areas by the real seismic data processing. Although the refraction horizon of near surface has some changes in a certain extent, it’s steady basically. The refraction horizon can be continuously traced in sections, so it’s qualified for the refraction static correction method on the whole. 3. The research is based on the curved-ray pre-stack time migration techniques of rough topography, and improved the imaging precision of complex areas. This techniques adopted the constant and variable velocity scanning mode and enhanced the velocity analysis precision. The 3D pre-stack time migration techniques reasonably solved the imaging and velocity multiple solutions problems of steep-dip faults and the intersections of horizontal layers. What’s more, fine velocity analysis and mute are very important to enhance the imaging precision of the seismic data in complicated Wunan areas. 4. The 3D seismic data edge-preserving processing methods have been realized due to the image process techniques. Because this method uses the large range filter, it can attenuate the noise maximally. The faults, break points, lithologic pinchout points and lithologic body of small scale such as river will not be influenced by blur because of the edge-preserving characterization of the method which is really an effective assistant technique of low S/N ratio seismic data attribute analysis. 5. The use of spectral decomposition technique can effectively identify the reservoirs. The special geology body which will not be identified (or without obvious characters) in the seismic profile may be found through the details changes of different frequencies in the amplitude profiles.

HHT在电磁信号处理中的应用

In modem signal Processing，non－linear，non－Gaussian and non－stable signals are usually the analyzed and Processed objects，especially non－stable signals. The convention always to analyze and Process non－stable signals are： short time Fourier transform，Wigner－Ville distribution，wavelet Transform and so on. But the above three algorithms are all based on Fourier Transform，so they all have the shortcoming of Fourier Analysis and cannot get rid of the localization of it. Hilbert－Huang Transform is a new non－stable signal processing technology，proposed by N. E. Huang in 1998. It is composed of Empirical Mode Decomposition (referred to as EMD) and Hilbert Spectral Analysis (referred to as HSA). After EMD Processing，any non－stable signal will be decomposed to a series of data sequences with different scales. Each sequence is called an Intrinsic Mode Function (referred to as IMF). And then the energy distribution plots of the original non－stable signal can be found by summing all the Hilbert spectrums of each IMF. In essence，this algorithm makes the non－stable signals become stable and decomposes the fluctuations and tendencies of different scales by degrees and at last describes the frequency components with instantaneous frequency and energy instead of the total frequency and energy in Fourier Spectral Analysis. In this case，the shortcoming of using many fake harmonic waves to describe non－linear and non－stable signals in Fourier Transform can be avoided. This Paper researches in the following parts： Firstly，This paper introduce the history and development of HHT，subsequently the characters and main issues of HHT. This paper briefly introduced the basic realization principles and algorithms of Hilbert－Huang transformation and confirms its validity by simulations. Secondly, This paper discuss on some shortcoming of HHT. By using FFT interpolation, we solve the problem of IMF instability and instantaneous frequency undulate which are caused by the insufficiency of sampling rate. As to the bound effect caused by the limitation of envelop algorithm of HHT, we use the wave characteristic matching method, and have good result. Thirdly, This paper do some deeply research on the application of HHT in electromagnetism signals processing. Based on the analysis of actual data examples, we discussed its application in electromagnetism signals processing and noise suppression. Using empirical mode decomposition method and multi-scale filter characteristics can effectively analyze the noise distribution of electromagnetism signal and suppress interference processing and information interpretability. It has been founded that selecting electromagnetism signal sessions using Hilbert time-frequency energy spectrum is helpful to improve signal quality and enhance the quality of data.

小波域地震资料的处理方法（WSSP）研究

The modeling formula based on seismic wavelet can well simulate zero - phase wavelet and hybrid-phase wavelet, and approximate maximal - phase and minimal - phase wavelet in a certain sense. The modeling wavelet can be used as wavelet function after suitable modification item added to meet some conditions. On the basis of the modified Morlet wavelet, the derivative wavelet function has been derived. As a basic wavelet, it can be sued for high resolution frequency - division processing and instantaneous feature extraction, in acoordance with the signal expanding characters in time and scale domains by each wavelet structured. Finally, an application example proves the effectiveness and reasonability of the method. Based on the analysis of SVD (Singular Value Decomposition) filter, by taking wavelet as basic wavelet and combining SVD filter and wavelet transform, a new de - noising method, which is Based on multi - dimension and multi-space de - noising method, is proposed. The implementation of this method is discussed the detail. Theoretical analysis and modeling show that the method has strong capacity of de - noising and keeping attributes of effective wave. It is a good tool for de - noising when the S/N ratio is poor. To give prominence to high frequency information of reflection event of important layer and to take account of other frequency information under processing seismic data, it is difficult for deconvolution filter to realize this goal. A filter from Fourier Transform has some problems for realizing the goal. In this paper, a new method is put forward, that is a method of processing seismic data in frequency division from wavelet transform and reconstruction. In ordinary seismic processing methods for resolution improvement, deconvolution operator has poor part characteristics, thus influencing the operator frequency. In wavelet transform, wavelet function has very good part characteristics. Frequency - division data processing in wavelet transform also brings quite good high resolution data, but it needs more time than deconvolution method does. On the basis of frequency - division processing method in wavelet domain, a new technique is put forward, which involves 1) designing filter operators equivalent to deconvolution operator in time and frequency domains in wavelet transform, 2) obtaining derivative wavelet function that is suitable to high - resolution seismic data processing, and 3) processing high resolution seismic data by deconvolution method in time domain. In the method of producing some instantaneous characteristic signals by using Hilbert transform, Hilbert transform is very sensitive to high - frequency random noise. As a result, even though there exist weak high - frequency noises in seismic signals, the obtained instantaneous characteristics of seismic signals may be still submerged by the noises. One method for having instantaneous characteristics of seismic signals in wavelet domain is put forward, which obtains directly the instantaneous characteristics of seismic signals by taking the characteristics of both the real part (real signals, namely seismic signals) and the imaginary part (the Hilbert transfom of real signals) of wavelet transform. The method has the functions of frequency division and noise removal. What is more, the weak wave whose frequency is lower than that of high - frequency random noise is retained in the obtained instantaneous characteristics of seismic signals, and the weak wave may be seen in instantaneous characteristic sections (such as instantaneous frequency, instantaneous phase and instantaneous amplitude). Impedance inversion is one of tools in the description of oil reservoir. one of methods in impedance inversion is Generalized Linear Inversion. This method has higher precision of inversion. But, this method is sensitive to noise of seismic data, so that error results are got. The description of oil reservoir in researching important geological layer, in order to give prominence to geological characteristics of the important layer, not only high frequency impedance to research thin sand layer, but other frequency impedance are needed. It is difficult for some impedance inversion method to realize the goal. Wavelet transform is very good in denoising and processing in frequency division. Therefore, in the paper, a method of impedance inversion is put forward based on wavelet transform, that is impedance inversion in frequency division from wavelet transform and reconstruction. in this paper, based on wavelet transform, methods of time - frequency analysis is given. Fanally, methods above are in application on real oil field - Sansan oil field.

f-x-y域去除随机噪音方法研究

The seismic survey is the most effective prospecting geophysical method during exploration and development of oil/gas. The structure and the lithology of the geological body become increasingly complex now. So it must assure that the seismic section own upper resolution if we need accurately describe the targets. High signal/noise ratio is the precondition of high-resolution. For the sake of improving signal/noise ratio, we put forward four methods for eliminating random noise on the basis of detailed analysis of the technique for noise elimination using prediction filtering in f-x-y domain. The four methods are put forward for settling different problems, which are in the technique for noise elimination using prediction filtering in f-x-y domain. For weak noise and large filters, the response of the noise to the filter is little. For strong noise and short filters, the response of the noise to the filter is important. For the response of the noise, the predicting operators are inaccurate. The inaccurate operators result in incorrect results. So we put forward the method using prediction filtering by inversion in f-x-y domain. The method makes the assumption that the seismic signal comprises predictable proportion and unpredictable proportion. The transcendental information about predicting operator is introduced in the function. The method eliminates the response of the noise to filtering operator, and assures that the filtering operators are accurate. The filtering results are effectively improved by the method. When the dip of the stratum is very complex, we generally divide the data into rectangular patches in order to obtain the predicting operators using prediction filtering in f-x-y domain. These patches usually need to have significant overlap in order to get a good result. The overlap causes that the data is repeatedly used. It effectively increases the size of the data. The computational cost increases with the size of the data. The computational efficiency is depressed. The predicting operators, which are obtained by general prediction filtering in f-x-y domain, can not describe the change of the dip when the dip of the stratum is very complex. It causes that the filtering results are aliased. And each patch is an independent problem. In order to settle these problems, we put forward the method for eliminating noise using space varying prediction filtering in f-x-y domain. The predicting operators accordingly change with space varying in this method. Therefore it eliminates the false event in the result. The transcendental information about predicting operator is introduced into the function. To obtain the predicting operators of each patch is no longer independent problem, but related problem. Thus it avoids that the data is repeatedly used, and improves computational efficiency. The random noise that is eliminated by prediction filtering in f-x-y domain is Gaussian noise. The general method can't effectively eliminate non-Gaussian noise. The prediction filtering method using lp norm (especially p=l) can effectively eliminate non-Gaussian noise in f-x-y domain. The method is described in this paper. Considering the dip of stratum can be accurately obtained, we put forward the method for eliminating noise using prediction filtering under the restriction of the dip in f-x-y domain. The method can effectively increase computational efficiency and improve the result. Through calculating in the theoretic model and applying it to the field data, it is proved that the four methods in this paper can effectively solve these different problems in the general method. Their practicability is very better. And the effect is very obvious.

利用分频与反演提高地震资料信噪比与分辨率方法研究

This thesis mainly talks about the wavelet transfrom and the frequency division method. It describes the frequency division processing on prestack or post-stack seismic data and application of inversion noise attenuation, frequency division residual static correction and high resolution data in reservoir inversion. This thesis not only describes the frequency division and inversion in theory, but also proves it by model calculation. All the methods are integrated together. The actual data processing demonstrates the applying results. This thesis analyzes the differences and limitation between t-x prediction filter and f-x prediction filter noise attenuation from wavelet transform theory. It considers that we can do the frequency division attenuation process of noise and signal by wavelet frequency division theory according to the differences of noise and signal in phase, amplitude and frequency. By comparison with the f-x coherence noise, removal method, it approves the effects and practicability of frequency division in coherence and random noise isolation. In order to solve the side effects in non-noise area, we: take the area constraint method and only apply the frequency division processing in the noise area. So it can solve the problem of low frequency loss in non-noise area. The residual moveout differences in seismic data processing have a great effect on stack image and resolutions. Different frequency components have different residual moveout differences. The frequency division residual static correction realizes the frequency division and the calculation of residual correction magnitude. It also solves the problems of different residual correction magnitude in different frequency and protects the high frequency information in data. By actual data processing, we can get good results in phase residual moveout differences elimination of pre-stack data, stack image quality and improvement of data resolution. This thesis analyses the characters of the random noises and its descriptions in time domain and frequency domain. Furthermore it gives the inversion prediction solution methods and realizes the frequency division inversion attenuation of the random noise. By the analysis of results of the actual data processing, we show that the noise removed by inversion has its own advantages. By analyzing parameter's about resolution and technology of high resolution data processing, this thesis describes the relations between frequency domain and resolution, parameters about resolution and methods to increase resolution. It also gives the processing flows of the high resolution data; the effect and influence of reservoir inversion caused by high resolution data. Finally it proves the accuracy and precision of the reservoir inversion results. The research results of this thesis reveal that frequency division noise attenuation, frequency residual correction and inversion noise attenuation are effective methods to increase the SNR and resolution of seismic data.

基于结构分析地震资料去噪方法研究

With the development of oil and gas exploration, the exploration of the continental oil and gas turns into the exploration of the subtle oil and gas reservoirs from the structural oil and gas reservoirs in China. The reserves of the found subtle oil and gas reservoirs account for more than 60 percent of the in the discovered oil and gas reserves. Exploration of the subtle oil and gas reservoirs is becoming more and more important and can be taken as the main orientation for the increase of the oil and gas reserves. The characteristics of the continental sedimentary facies determine the complexities of the lithological exploration. Most of the continental rift basins in East China have entered exploration stages of medium and high maturity. Although the quality of the seismic data is relatively good, this areas have the characteristics of the thin sand thickness, small faults, small range of the stratum. It requests that the seismic data have high resolution. It is a important task how to improve the signal/noise ratio of the high frequency of seismic data. In West China, there are the complex landforms, the deep embedding the targets of the prospecting, the complex geological constructs, many ruptures, small range of the traps, the low rock properties, many high pressure stratums and difficulties of boring well. Those represent low signal/noise ratio and complex kinds of noise in the seismic records. This needs to develop the method and technique of the noise attenuation in the data acquisition and processing. So that, oil and gas explorations need the high resolution technique of the geophysics in order to solve the implementation of the oil resources strategy for keep oil production and reserves stable in Ease China and developing the crude production and reserves in West China. High signal/noise ratio of seismic data is the basis. It is impossible to realize for the high resolution and high fidelity without the high signal/noise ratio. We play emphasis on many researches based on the structure analysis for improving signal/noise ratio of the complex areas. Several methods are put forward for noise attenuation to truly reflect the geological features. Those can reflect the geological structures, keep the edges of geological construction and improve the identifications of the oil and gas traps. The ideas of emphasize the foundation, give prominence to innovate, and pay attention to application runs through the paper. The dip-scanning method as the center of the scanned point inevitably blurs the edges of geological features, such as fault and fractures. We develop the new dip scanning method in the shap of end with two sides scanning to solve this problem. We bring forward the methods of signal estimation with the coherence, seismic wave characteristc with coherence, the most homogeneous dip-sanning for the noise attenuation using the new dip-scanning method. They can keep the geological characters, suppress the random noise and improve the s/n ratio and resolution. The rutine dip-scanning is in the time-space domain. Anew method of dip-scanning in the frequency-wavenumber domain for the noise attenuation is put forward. It use the quality of distinguishing between different dip events of the reflection in f-k domain. It can reduce the noise and gain the dip information. We describe a methodology for studying and developing filtering methods based on differential equations. It transforms the filtering equations in the frequency domain or the f-k domain into time or time-space domains, and uses a finite-difference algorithm to solve these equations. This method does not require that seismic data be stationary, so their parameters can vary at every temporal and spatial point. That enhances the adaptability of the filter. It is computationally efficient. We put forward a method of matching pursuits for the noise suppression. This method decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. It can extract the effective signal from the noisy signal and reduce the noise. We introduce the beamforming filtering method for the noise elimination. Real seismic data processing shows that it is effective in attenuating multiples and internal multiples. The s/n ratio and resolution are improved. The effective signals have the high fidelity. Through calculating in the theoretic model and applying it to the real seismic data processing, it is proved that the methods in this paper can effectively suppress the random noise, eliminate the cohence noise, and improve the resolution of the seismic data. Their practicability is very better. And the effect is very obvious.

An Optimal Scale for Edge Detection

Many problems in early vision are ill posed. Edge detection is a typical example. This paper applies regularization techniques to the problem of edge detection. We derive an optimal filter for edge detection with a size controlled by the regularization parameter $\\ lambda $ and compare it to the Gaussian filter. A formula relating the signal-to-noise ratio to the parameter $\\lambda $ is derived from regularization analysis for the case of small values of $\\lambda$. We also discuss the method of Generalized Cross Validation for obtaining the optimal filter scale. Finally, we use our framework to explain two perceptual phenomena: coarsely quantized images becoming recognizable by either blurring or adding noise.

Models of Noise and Robust Estimates

Given n noisy observations g; of the same quantity f, it is common use to give an estimate of f by minimizing the function Eni=1(gi-f)2. From a statistical point of view this corresponds to computing the Maximum likelihood estimate, under the assumption of Gaussian noise. However, it is well known that this choice leads to results that are very sensitive to the presence of outliers in the data. For this reason it has been proposed to minimize the functions of the form Eni=1V(gi-f), where V is a function that increases less rapidly than the square. Several choices for V have been proposed and successfully used to obtain "robust" estimates. In this paper we show that, for a class of functions V, using these robust estimators corresponds to assuming that data are corrupted by Gaussian noise whose variance fluctuates according to some given probability distribution, that uniquely determines the shape of V.