汽车转弯自动照明控制系统数据采集滤波方法

为了减少汽车转弯自动照明控制系统数据滤波处理中的延时,在滑动中值滤波方法基础上,提出了一种带补偿的限幅滑动中值滤波算法,通过补偿的方式跟随信号的变化趋势。该算法通过2个步骤完成滤波,先对数据进行带补偿方式的限幅处理,再进行滑动中值滤波处理。该算法综合了滑动中值滤波法的实时性以及限幅滤波法对偶然性干扰滤除的有效性,实现更准确的滤波。仿真与实测结果表明,与滑动中值滤波方法相比较,改进后的算法滤波能够实时地滤除数据中的干扰,处理时间可减少62%,滤波效果较好,且速度信号的数据滞后时间较短,实时性较高,满足系统设计要求。     

Detecting EEG Sleep Spindles by Digital Filtering with Zero Phase Error

EEG signal is a kind of very complex non stationary signal. It is of important clinical values for using EEG most efficiently to distinguish all kinds of components in EEG or getting needed components to judge some pathology features effectively. The phase moving of the system can change the relative phase relation between the components in the inputed signal, so there can be a great change of the feature in time domain of inputed signal. The FRR and RRF digital filtering methods and their realization are discussed. By using these methods, the system can be of the feature of zero phase error, and the filtering of EEG signals with zero phase error can be realized. Based on the definition of sleep spindles, we can detect sleep spindles in time domain directly by using digital filtering with zero phase error.     

EEG Signal Denoising Based on Wavelet Transform

The article focuses on the method of noise cancellation for EEG signal. The method of notch filter is discussed. According to the frequency of noise and the principle of notch filter, the design result of the notch filter and the denoised signal are presented. Then, the analysis of EEG signal are proposed based on wavelet transform (WT) and noise cancellation using WT. Wavelet transform is a multi-resolution time-frequency analysis method. It can decompose mixed signal into signals at different frequency bands. The EEG signal is analyzed and denoised using WT, then the results are presented respectively. Comparing the experiment results shows that WT can detect and process noise in the EEG signal effectively.     

Dynamic Characteristic of Micromachined Arrayed Microphone

Several kinds of microphone developed via silicon Micro Electro Mechanical System (MEMS) processes in recent years are investigated in this paper. The analysis and research of the merits and demerits of them show that: these conventional microphones mainly function as acoustic electric converters; the filtering against noise is implemented by a special filter circuit or a digital filter. Therefore, a design method for silicon micro array filter microphone is presented, where the filtering is achieved while converting signals. The effects of damping, silicon reed number and sensor element pair on dynamic characteristic are discussed; it follows that: Increasing damping can reduce microphone's sensitivity, while increasing silicon reed number can improve the sensitivity curve, furthermore, the implementation of the filter function is simultaneous with acquiring of the noise signal.     

Real time monitoring for multivariate statistical process with on line multiscale filtering

By analyzing shortages of current MSPCA model, an on line multi variable statistical process monitoring method is proposed, which uses some concepts from online multi scale filtering and can be applied to sensor fault diagnosis. In the method, wavelet decomposition is employed to the signals using edge correction filter in a fixed length data window, and then wavelet denoising is conducted with wavelet threshold filtering. Next, an on line multi scale model is constructed for data combining wavelet transformation and adaptive PCA in the previous data window. This model avoids time waste in direct signal denoising and reduces time cost in multi scale data with conventional PCA, which eventually increases accuracy in fault diagnosis. Experiments on eight vibration signals of 6135D diesel engine under severe leak condition prove the practicability and feasibility of the proposed method.     

Application of Trap Filter of 50 Hz in Brainwave Data Acquisition System

It's always difficult in technology to eliminate power interference of 50 Hz in Brainwave Data Acquisition System .Though traditional ways to eliminate it can also gain some effects, they have drawbacks such as high cost and low currency at all time. The use of trap filter of 50 Hz may solve the question radically. Through the authors' repetitious experimentation, it is proved that trap filter of VCVS not only eliminate power interference of 50 Hz farthest but also have merits of simple circuit structure, convenient coupling and upper reliability. The authors offer collected brainwave in strong power interference environment. It may be seen that it is very clear and clean. Because power interference of (50 Hz) exists in almost all Data Acquisition System, this type of trap filter possesses better currency, it is expected to win extensive application in medical apparatus of measurement of other biological electricity signals and industrial observation and control system.     

Multiresolution Wavelet Transform for Detection of EEG Abnormal Rhythms

The signal of brain activity is a non-stationary random signal including lots of physiology and disease information, which is of important action for doctors to judge pathological changes in brain. So the analysis and process of the EEG signals are always attended. In this paper, the authors take account of the time-frequency localization of wavelet transform and use multiresolution wavelet transform to detect EEG abnormal rhythms. The signals of different scales after EEG signals are transformed by multiresolution wavelet transform not only reflect the frequency information of the signals, namely the more great scale is the lower of the frequency of the signals,but also reflect the time information of the signals, namely EEG state at that time. The test results indicate that the abnormal rhythms of the EEG signals can be detected effectively if right wavelet basis is selected.     

Filtering Construct of Adaptive Filter Grou Pwith Multi-scale Decomposition

The principle and method of the adaptive filter and the filtering with wavelet transform were analyzed, and the model and method of adaptive filtering with wavelet transforms for the transient signal was established. The separated noise of signal by the multi-scale decomposition of wavelet transforms, was the input signal of adaptive filter, and accordingly the optimal filtering method of signal-noise decomposition was realized. By the adaptive filter grou Pbased on the wavelet transform, the optimal filtering to the multi-noise of signal is achieved at the same time, and the method presented in this paper has the excellent filtering capability. Examples of application demonstrate that this method presented is excellent to realize the optimal estimate to the valuable signal and noise of the transient signal in the same frequency segment.     

Frequency domain analysis and the application for extracting electroencephalogram signal characteristic waves

A new quantitative analysis method to describe the dynamic variation of electroencephalogram (EEG) signals was proposed. Based on the Fourier transformation, the method is called Fourier multi resolution analysis (FMRA). FMRA decomposes the frequency domain with a binary system and can resolve EEG signals into the basic rhythms of the four waves to study the dynamic characteristics of EEG signal rhythms. FMRA has clear physical meaning, and can obtain more information than wavelet multi resolution analysis does. FMRA can extract perfectly the rhythmic characteristics of EEG signals in the time and frequency domains.     

Signal Processing of Electroencephalogram and It''s Application

Electroencephalogram (EEG) signals are the electrical activities in the cortex or on the surface of scalp caused by the physiological activities of the brain which play a key role in the diagnosis of brain and the functional determination of brain. The authors discuss some methods for processing EEG signals from the view of the frequency domain and time domain, especially introduce some time-frequency analysis methods such as Wigner distribution, wavelet transform and matching pursuit etc. , ANN and non-linear analysis for EEG signals processing.     

Digital Filtering with Zero Phase Error

Aiming at the problem of phase error caused by normal filters in signal processing , summarizes the important meaning of digital filtering with zero phase error. Puts forward RRF Filtering which is a new way to realize digital filtering with zero phase error through reversing the time serials of the signal. Another way to realize digital filtering with zero phase error is suggested through reversing the time serials of normal filter. Testifies the way to realize digital filtering with zero phase error. Points out how to design the digital filter with zero phase frequency peculiarity. Solves the problem of phase error in DSP.     

The Application of the Theory of 40 Hz EEG in the Stady of Chaotic States of EEG Signals

In this paper,the theory of 40 Hz EEG bursts is firstly used to judge whether thesubjects cooperate with us or not,and to determine the segments which can be processed for the studyof chaotic states of EEG signals.The theory,the realization of detecting method and the practiceapplication of 40 Hz EEG bursts are studied.     

A Fast Median Filtering Algorithm

A fast median filtering algorithm basing on the coherence of data in adjacent windows is presented.During median filtering,only the first median value is found by conventional squencing algorithm,the other median values are obtained by bisecting search method and quickinserting new element in sequenced window.For convenience of moving filter window along signal data,a data structure is desinged,which can make the new input element to cover output element and greatly reduce the quantity of computation.A 2-dimensional fast median filtering method by moving filter window along snack path is also introduced.     

Noise-reduction of Digital Video Image Based on Time-recursive Filtering

With the analysis of the principle of time-recursive filter, the authors find that the nature of time-recursive filter is Weighted Mean Low-Pass filter and the relationship between filter coefficients and SNR and the artifact arising from the area where motion can not be tracked. One approach based on Automatic Control Method is proposed to solve the artifacts problem during the process of filtering and the authors designed the corresponding hardware based on FPGA and software respectively. The experimental results show that the time-recursive filtering is good at digital video image denoising and reduces the random noise of digital camera system.     

基于多重滤波的高分辨率影像中线性特征提取

在高分辨率遥感影像中,线性特征提取一直是个难题,难主要表现在线性地物的边缘信息和噪音是一对矛盾体,边缘信息越明显,噪音就越强。本文利用高通滤波和统计滤波器,构建一个多重滤波,对IKONOS影像进行处理,通过三种不同高通滤波窗口对IKONOS影像处理结果的比对,得到一个较好的高通滤波处理窗口,处理后的IKONOS影像边界特征突出、区域模糊程度减小,目标地物大致轮廓已经区分出来,在此基础上对影像进行统计滤波处理,噪音明显减少,灰度分布更加集中和平滑,对高通滤波和统计滤波处理后的图像进行边缘检测,经过统计滤波后的图像较只进行高通滤波后的图像检测结果较好。实验结果表明：滤波后的影像噪音明显降低,信噪比明显提高,统计滤波后的影像,能够提高检测精度,表明了从高分辨率卫星影像上自动提取矢量信息的可行性,并为边缘检测的细化与跟踪处理提供了可靠的数据来源。     

Wavelet Packet Decomposition Based on Entropy Threshold for White-noise Rejection in PD Signal

This paper analyzes the different characteristics of white-noise interference in the signals of partial discharge (PD) after wavelet transform. There is high value in lots of scales for PD and white-noise interference is to zero with increasing scale. The threshold is set for wavelet coefficient in all scales. If the coefficient of signal is higher than the threshold, it is PD signal. Otherwise it is noise interference. A threshold-based wavelet packet transform (WPT) algorithm is put forward to suppress white noise interference in PD signals. The results testifies that it has a favorable adaptability to extract PD signals using WPT.     

The Application of Narrowband Interference Suppression in Direct Sequence Spread Spectrum

In order to improve the system effect, it is necessary take the technique of digital signal process to suppress interference, because of the finite gain and the narrow-band interference in system of direct sequence spread spectrum. The authors design a new filter structure improving on tunable digital heterodyne adaptive notch filters. This filter can adaptively track the interference frequency and control the bandwidth and depth of notch, which has the excellent agility and utility. The experiment proves this method overcomes limitation of other interference suppressions, hardly weaken the system performances, enhances the stability of the filter system, and can quickly track the interference frequency.     

A Hybrid Filter Composed of Shunt Passive and Series Active Filter on DC Side in HVDC System

For requirements of suppressing harmonics on the dc side of HVDC system,a new hybrid filtering system a shunt passive and a small rated reactor transformer coupling series active filter is presented,and the harmonic compensation and control principles are discussed theoretically in this paper.Digital simulation results show excellent filtering characteristics of harmonic current and very high performance/cost by using a new hybrid filtering system proposed int this paper.     

The spatial distribution estimation of Interference temperature based on kriging method in cognitive radio

In order to resolve the problems of the traditional interference temperature estimation methods, such as unclear spatial characteristics, inaccurate estimation of spatial distribution, and the insufficient use of spectrum resources, this paper proposes an estimation method which utilizes the interference temperature sample values obtained by wireless sensors distribute in space to estimate the interference temperature in the total area. This method makes use of the geo-statistical interpolation method of Kriging to calculate the interference temperature data and obtain the 3-dimensional distribution of the interference temperature in the area. Kriging interpolation method is based on the variogram and structure analysis theory, makes optimal estimation of the variables in area. Simulation results demonstrate that the proposed method can estimate the spatial distribution of interference temperature in the interested area more accurately and the spatial spectrum utilization is improved.     

A blind adaptive reduced-rank receiver for ultra-wideband communication systems

A low-complexity blind adaptive receiver for ultra-wideband (UWB) systems in the presence of both multiple access interference (MAI) and inter symbol interference (ISI) is proposed, which is composed of two stages. In the first stage, a reduced-rank algorithm based on the multi-stage Wiener filter (MSWF) is considered to suppress the MAI and extract multi-path components. Channel estimation using the projection approximation subspace tracking with deflation (PASTd) algorithm and multi-path combining are then performed to further enhance the signal to interference plus noise ratio (SINR) of the desired user in the second stage. The simulation results show that the proposed receiver exhibits reasonably good bit error ratio (BER) performance compared with those of matched filter, conventional Rake receiver, decorrelating Rake receiver and adaptive receiver based on constant modulus algorithm (CMA). Moreover, it has faster convergence speed and less complexity.