Research on fiber optic gyro signal de-noising based on wavelet packet soft-threshold

Gyro＇s drift is not only the main drift error which influences gyro＇s precision but also the primary factor that affects gyro＇s reliability. Reducing zero drift and random drift is a key problem to the output of a gyro signal. A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro （FOG）. The coefficients are obtained from the three-layer wavelet packet decomposition. By setting the high frequency part which is greater than wavelet packet threshold as zero, then reconstructing the nodes which have been filtered out noise and interruption, the soft threshold function is constructed by the coefficients of the third nodes. Compared wavelet packet de-noise with forced de-noising method, the proposed method is more effective. Simulation results show that the random drift compensation is enhanced by 13.1%, and reduces zero drift by 0.052 6°/h.

TRANSLATION-INVARIANT BASED ADAPTIVE THRESHOLD DENOISING FOR IMPACT SIGNAL

A translation-invariant based adaptive threshold denoising method formechanical impact signal is proposed. Compared with traditional wavelet denoising methods, itsuppresses pseudo-Gibbs phenomena in the neighborhood of signal discontinuities. To remedy thedrawbacks of conventional threshold functions, a new improved threshold function is introduced. Itpossesses more advantages than others. Moreover, based on utilizing characteristics of signal, aadaptive threshold selection procedure for impact signal is proposed. It is data-driven andlevel-dependent, therefore, it is more rational than other threshold estimation methods. Theproposed method is compared to alternative existing methods, and its superiority is revealed bysimulation and real data examples.

Wavelet threshold method of resolving noise interference in periodic short-impulse signals chaotic detection

The chaotic oscillator has already been considered as a powerful method to detect weak signals, even weak signals accompanied with noises. However, many examples, analyses and simulations indicate that chaotic oscillator detection system cannot guarantee the immunity to noises （even white noise）. In fact the randomness of noises has a serious or even a destructive effect on the detection results in many cases. To solve this problem, we present a new detecting method based on wavelet threshold processing that can detect the chaotic weak signal accompanied with noise. All theoretical analyses and simulation experiments indicate that the new method reduces the noise interferences to detection significantly, thereby making the corresponding chaotic oscillator that detects the weak signals accompanied with noises more stable and reliable.

A Squared-Chebyshev wavelet thresholding based 1D signal compression

In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression;but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches.

New Wavelet Thresholding Algorithm in Dropping Ambient Noise from Underwater Acoustic Signals

Underwater Wireless Communication, largely dependent on the acoustic communication between the machines, is largely affected by various types of noise in the shallow and deep water. However ambient noise which is due to multiple sources (e.g. shipping, wind) and no one source dominates. Ambient noise masks the acoustic signal to a large extent. Hence today it has drawn the attention of the experts to reduce its effect on the received signal. This paper discusses ambient noise problem and devises a new wavelet thresholding method to reduce its effect. Afterwards a comparative study on statistical parameters is shown to prove the efficiency of the devised method.

腰椎间盘突出症模型大鼠疼痛的针刺干预

背景:针灸是缓解腰椎间盘突出症腰痛的有效方法,但其机制目前尚未明确。JAK2/STAT3信号通路相关因子可调节机体炎症反应,参与神经病理性疼痛的过程。目的:基于JAK2/STAT3信号通路研究针刺对腰椎间盘突出症模型大鼠的作用机制。方法:采用随机数字表法将40只SD大鼠分为假手术组、模型组、针刺组与针刺+激动剂组,每组10只。模型组、针刺组及针刺+激动剂组采用自体髓核移植法构建L5腰椎间盘突出症模型,造模3 d后,针刺组开始进行针刺治疗(作用于阳陵泉、肾俞、环跳、大肠俞等穴位),针刺+激动剂组造模后第6,12,18天针刺治疗前向L4/L5椎间隙鞘内注射JAK2激动剂香豆霉素A1,针刺治疗1次/d,20 min/次,连续治疗15 d。造模前及造模后3,6,9,12,15,18 d,检测大鼠机械缩足阈值;造模后18 d,检测血清炎症因子水平,苏木精-伊红染色观察L5-L6组织形态,RT-PCR检测L5-L6组织JAK2、STAT3 mRNA表达,Western Blot检测L5-L6组织JAK2、STAT3、p-JAK2及p-STAT3蛋白表达。结果与结论:①模型组大鼠造模后不同时间点的机械缩足阈值均低于假手术组(P<0.05),针刺组大鼠造模后9,12,15,18 d的机械缩足阈值均高于模型组(P<0.05),针刺+激动剂组大鼠造模后9,12,15,18 d的机械缩足阈值均低于针刺组(P<0.05);②与假手术组比较,模型组白细胞介素6、肿瘤坏死因子α、神经递质P物质、脑部神经肽Y水平均升高(P<0.05);与模型组比较,针刺组4种炎症因子水平均降低(P<0.05);与针刺组比较,针刺+激动剂组4种炎症因子水平均升高(P<0.05);③苏木精-伊红染色显示模型组大鼠腰椎退行性变化明显,针刺组与针刺+激动剂组大鼠腰椎退行性变化减轻,针刺组减轻更明显;④与假手术组比较,模型组JAK2与STAT3 mRNA表达、p-JAK2与p-STAT3蛋白表达均升高(P<0.05);与模型组比较,针刺组JAK2与STAT3 mRNA表达、p-JAK2与p-STAT3蛋白表达均降低(P<0.05);与针刺组比较,针刺+激动剂组JAK2与STAT3 mRNA表达、p-JAK2与p-STAT3蛋白表达均升高(P<0.05);⑤结果表明,针刺干预可通过降低腰椎间盘突出症模型大鼠的炎症反应来缓解疼痛,其作用机制可能与抑制JAK2/STAT3信号通路有关。

‘Outbreak Gold Standard’Selection to Provide Optimized Threshold for Infectious Diseases Early-alert Based on China Infectious Disease Automated-alert and Response System

The China Infectious Disease Automated-alert and Response System（CIDARS） was successfully implemented and became operational nationwide in 2008. The CIDARS plays an important role in and has been integrated into the routine outbreak monitoring efforts of the Center for Disease Control（CDC） at all levels in China. In the CIDARS, thresholds are determined using the ？Mean＋2SD？ in the early stage which have limitations. This study compared the performance of optimized thresholds defined using the ？Mean ＋2SD？ method to the performance of 5 novel algorithms to select optimal ？Outbreak Gold Standard（OGS）？ and corresponding thresholds for outbreak detection. Data for infectious disease were organized by calendar week and year. The ？Mean＋2 SD？, C1, C2, moving average（MA）, seasonal model（SM）, and cumulative sum（CUSUM） algorithms were applied. Outbreak signals for the predicted value（Px） were calculated using a percentile-based moving window. When the outbreak signals generated by an algorithm were in line with a Px generated outbreak signal for each week, this Px was then defined as the optimized threshold for that algorithm. In this study, six infectious diseases were selected and classified into TYPE A（chickenpox and mumps）, TYPE B（influenza and rubella） and TYPE C [hand foot and mouth disease（HFMD） and scarlet fever]. Optimized thresholds for chickenpox（P_（55））, mumps（P_（50））, influenza（P_（40）, P_（55）, and P_（75））, rubella（P_（45） and P_（75））, HFMD（P_（65） and P_（70））, and scarlet fever（P_（75） and P_（80）） were identified. The C1, C2, CUSUM, SM, and MA algorithms were appropriate for TYPE A. All 6 algorithms were appropriate for TYPE B. C1 and CUSUM algorithms were appropriate for TYPE C. It is critical to incorporate more flexible algorithms as OGS into the CIDRAS and to identify the proper OGS and corresponding recommended optimized threshold by different infectious disease types.

Research on Anti-noise Processing Method of Production Signal Based on Ensemble Empirical Mode Decomposition(EEMD)

The grain production prediction is one of the most important links in precision agriculture. In the process of grain production prediction, mechanical noise caused by the factors of difference in field topography and mechanical vibration will be mixed in the original signal, which undoubtedly will affect the prediction accuracy. Therefore, in order to reduce the influence of vibration noise on the prediction accuracy, an adaptive Ensemble Empirical Mode Decomposition(EEMD) threshold filtering algorithm was applied to the original signal in this paper: the output signal was decomposed into a finite number of Intrinsic Mode Functions(IMF) from high frequency to low frequency by using the Empirical Mode Decomposition(EMD) algorithm which could effectively restrain the mode mixing phenomenon; then the demarcation point of high and low frequency IMF components were determined by Continuous Mean Square Error criterion(CMSE), the high frequency IMF components were denoised by wavelet threshold algorithm, and finally the signal was reconstructed. The algorithm was an improved algorithm based on the commonly used wavelet threshold. The two algorithms were used to denoise the original production signal respectively, the adaptive EEMD threshold filtering algorithm had significant advantages in three denoising performance indexes of signal denoising ratio, root mean square error and smoothness. The five field verification tests showed that the average error of field experiment was 1.994% and the maximum relative error was less than 3%. According to the test results, the relative error of the predicted yield per hectare was 2.97%, which was relative to the actual yield. The test results showed that the algorithm could effectively resist noise and improve the accuracy of prediction.

Constant False Alarm Rate Acquisition Algorithm for Compass B1C Signal

In order to improve the sensitivity of the Compass B1C signal acquisition for the receiver,the principle of constant false alarm rate(CFAR)is applied for the B1C pilot channel acquisition to realize the dynamic adjustment of the threshold of acquisition against the carrier to noise ratio.The non-coherent data/pilot combined acquisition algorithm for B1C signal is analyzed to make full use of the power of the B1C signal under the condition of low carrier to noise ratio.On this basis,to improve the acquisition sensitivity of the receiver,the principle of constant false alarm probability is applied for the non-coherent data/pilot combined acquisition algorithm.Theoretical analysis and simulations show that the non-coherent data/pilot combined acquisition algorithm with CFAR improves the B1C signal acquisition sensitivity of the receiver significantly,and achieves a better Receiver Operating Characteristic compared with the traditional acquisition algorithms.

Threshold Determination and Analysis of Laser Pulse Range Finder

Under different conditions, the highest detection probability should be acquired while receiving laser echo during laser pulse range finding. The threshold voltage of the signal detection can be set corresponding to different conditions by using resistor network. As a feedback loop, automatic noise threshold circuit could change the threshold voltage following the noise level. The threshold can track the noise closely, rapidly and accurately by adopting this combination. Therefore, the receiving capability of laser echo receiving system will be maximized, and it can detect weaker laser pulse from noise.

A New Image Denoising Scheme Using Soft-Thresholding

The VisuShrink is one of the important image denoising methods. It however does not provide good quality of image due to removing too many coefficients especially using soft-thresholding technique. This paper proposes a new image denoising scheme using wavelet transformation. In this paper, we modify the coefficients using soft-thresholding method to enhance the visual quality of noisy image. The experimental results show that our proposed scheme has better performance than the VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e., visual quality of the image.

A novel wavelet method for electric signals analysis in underwater arc welding

Electric signals are acquired and analyzed in order to monitor the underwater arc welding process. Voltage break point and magnitude are extracted by detecting arc voltage singularity through the modulus maximum wavelet （MMW） method. A novel threshold algorithm, which compromises the hard-threshold wavelet （HTW） and soft-threshold wavelet （STW） methods, is investigated to eliminate welding current noise. Finally, advantages over traditional wavelet methods are verified by both simulation and experimental results.

PMF+双门限Tong检测的北斗B1I信号捕获算法

针对传统Tong检测算法检测时间长、漏警概率大的问题,提出将PMF+双门限Tong检测算法应用于北斗B1I信号的捕获中。首先,将射频前端模块处理得到的中频信号下变频至基带,对基带信号进行降速等预处理;然后,利用部分匹配滤波(PMF)算法计算相关值,通过PMF算法将数据信号分成几个均等片段,对每一段信号都分别计算相关值,会出现三组具有同相位的相关结果;最后,利用双门限Tong检测算法对PMF算法计算得到的相关结果检测,达到捕获B1I信号的目的。仿真结果表明,利用该算法捕获北斗B1I信号,有效提高了捕获时的检测概率并缩短了检测时间,提高了捕获B1I信号的速度。

An Improved Image Denoising Method Based on Wavelet Thresholding

VisuShrink, ModineighShrink and NeighShrink are efficient image denoising algorithms based on the discrete wavelet transform (DWT). These methods have disadvantage of using a suboptimal universal threshold and identical neighbouring window size in all wavelet subbands. In this paper, an improved method is proposed, that determines a threshold as well as neighbouring window size for every subband using its lengths. Our experimental results illustrate that the proposed approach is better than the existing ones, i.e., NeighShrink, ModineighShrink and VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e. visual quality of the image.

QRS waves detection algorithm based on positive-negative adaptive threshold method

In order to accurately detect the occasional negative R waves in electrocardiography （ECG） signals, the positive-negative adaptive threshold method is adopted to determine the positive R waves and the negative R waves, according to difference characteristics of ECG signals. The Q and S waves can then be accurately positioned based on the basic characteristics of QRS waves. Finally, the algorithm simulation is made based on the signals from MIT-BIH database with MATLAB. The ex- perimental results show that the algorithm can improve the detection accuracy rate to 99. 91% and o- vercome the problem of larger computation load for wavelet transform and other methods, so the al- gorithm is suitable for real-time detection.

Implementation of GPR Signals De-Noising Based on DSP

An important issue of ground-penetrating radar （GPR） signals analysis is de-noising thai is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single processor （DSP） based on wavelet shrinkage algorithm. In order to realize real-time GPP, signals analysis, some key issues are discussed such as the realization of fast wavelet transformation, the selection of CPU chip and the optimization of data movement. Experimenial results show that the DSP based application not only basically meets the real-time requirement of GPP, signals analysis, but also assures the quality of the GPR signals analysis.

基于空间变换技术的脉冲激光声信号检测

针对目前脉冲激光声信号检测方法极易受到环境噪声的影响,导致检测精确度不理想的问题。提出基于空间变换技术的脉冲激光声信号检测方法。首先,采用小波阈值法去除脉冲激光声信号中的噪声;其次,利用球傅里叶空间变换技术对声信号中的声源展开定位,获取声信号的准确位置;最后,分别提取声信号中的比值特征、比重特征、频率特征三种参数,在此基础上通过SVM算法对其展开分类识别,以此实现脉冲激光声信号的有效检测。实验结果表明,所提方法的声信号检测精度超过96%。

门槛阈值自匹配的车载呼救系统设计

为了提高车辆碰撞自动呼救系统(Automatic Crash Notification System,ACNS)的抗干扰能力和事故信息的准确性,提出了一种具有自适应门槛阈值的ACNS触发算法。基于门槛阈值与路面国际平整度指数之间的关联模型,设计ACNS触发算法,并实现ACNS门槛阈值随路面状况和车速的动态匹配;基于STM32F103ZET6微控制器,利用加速度信号采集模块、定位模块和通信模块等模块设计一种车辆事故自动呼救系统终端;利用道路实车试验和触发测试试验测试系统性能;结果表明:ACNS终端能够实现门槛阈值根据不同路面状况的动态匹配,并对相关信息进行实时处理及发送呼救信息。

New Modeling for Generation of Normal and Abnormal Heart Rate Variability Signals

This research is performed based on the modeling of biological signals. We can produce Heart Rate (HR) and Heart Rate Variability (HRV) signals synthetically using the mathematical relationships which are used as input for the Integral Pulse Frequency Modulation (IPFM) model. Previous researches were proposed same methods such as one model of ECG signal synthetically based on RBF neural network, a model based on IPFM with random threshold, method was based on the estimation of produced signals which are dependent on autonomic nervous system using IPFM model with fixed threshold, a new method based on the theory of vector space that based on time-varying uses of IPMF model (TVTIPMF) and special functions, and two different methods for producing HRV signals with controlled characteristics and structure of time-frequency (TF) for using non-stationary HRV analysis. In this paper, several chaotic maps such as Logistic Map, Henon Map, Lorenz and Tent Map have been used. Also, effects of sympathetic and parasympathetic nervous system and an internal input to the SA node and their effects in HRV signals were evaluated. In the proposed method, output amount of integrator in IPFM model was compared with chaotic threshold level. Then, final output of IPFM model was characterized as the HR and HRV signal. So, from HR and HRV signals obtaining from this model, linear features such as Mean, Median, Variance, Standard Deviation, Maximum Range, Minimum Range, Mode, Amplitude Range and frequency spectrum, and non-linear features such as Lyapunov Exponent, Shanon Entropy, log Entropy, Threshold Entropy, sure Entropy and mode Entropy were extracted from artificial HRV and compared them with characteristics as extracted from natural HRV signal. Also, in this paper two patients that called high sympathetic Balance and Cardiovascular Autonomy Neuropathy (CAN) which is detected and evaluated by HRV signals were simulated. These signals by changing the values of the some coefficients of the normal simulated signal and with extracted frequency feature from these signals were simulated. For final generation of these abnormal signals, frequency features such as energy of low frequency band (EL), energy of high frequency band (HL), ratio of energy in low frequency band to the energy in high frequency band (EL/EH), ratio of energy in low frequency band to the energy in all frequency band (EL/ET) and ratio of energy in high frequency band to the energy in all frequency band (EH/ET) from abnormal signals were extracted and compared with these extracted values from normal signals. The results were closely correlated with the real data which confirm the effectiveness of the proposed model. Various signals derived from the output of this model can be used for final analysis of the HRV signals, such as arrhythmia detection and classification of ECG and HRV signals. One of the applications of the proposed model is the easy evaluation of diagnostic ECG signal processing devices. Such a model can also be used in signal compression and telemedicine application.

基于小波变换的语音信号去噪算法优化

深入研究基于小波变换的语音信号去噪方法,并针对传统方法在复杂噪声环境下处理效果不佳的问题,提出一种基于自适应阈值的小波变换去噪优化方法。首先,分析小波变换去噪的基本原理。其次,深入研究自适应阈值技术的数学模型,并将其应用于小波变换,通过动态调整阈值来适应不同噪声环境的需求。最后,采用Aurora数据集进行实验验证。实验结果表明,该方法能够有效去除噪声。