Design and application of discrete wavelet packet transform based multiresolution controller for liquid level system

A new controller based on discrete wavelet packet transform (DWPT) for liquid level system (LLS) has been presented here. This controller generates control signal using node coefficients of the error signal which interprets many implicit phenomena such as process dynamics, measurement noise and effect of external disturbances. Through simulation results on LLS problem, this controller is shown to perform faster than both the discrete wavelet transform based controller and conventional proportional integral controller. Also, it is more efficient in terms of its ability to provide better noise rejection. To overcome the wind up phenomenon by considering the saturation due to presence of actuator, anti-wind up technique is applied to the conventional PI controller and compared to the wavelet packet transform based controller. In this case also, packet controller is found better than the other ones. This similar work has been extended for analogous first order RC plant as well as second order plant also.     

一种基于小波变换的分形图象编码方法

小波和分形相结合的图象编码方法,其基本思想是先利用小波变换实现图象的多分辨率分解,然后在小波域内采用分形理论对图象进行编码。本文提出了一种新的分形编码方法-基于固定区域定义域块的值域块编码。这种方法的优点是分形码不包含定义域块-值域块匹配对的坐标,并且值域块的尺寸小到2×2。图象仍能保证较好的压缩率和质量。仿真实验证明了该方法的有效性。     

A palmprint based biometric authentication system using dual tree complex wavelet transform

Palmprint identification has emerged as one of the most popular and promising biometric modalities for personal identity verification due to its ease of acquisition, non-invasive procedure, high user acceptance and reliability. This paper proposes the development of a new method for palmprint based biometric authentication which utilizes the textural information available on the palmprint by employing the Dual Tree Complex Wavelet Transform (DTCWT). The method proposes to construct a region of interest (ROI) for the scanned color images of the palm, and then determine a histogram of the two dimensional image. This enables to utilize a feature extraction module, implemented using the one-dimensional (1D) Dual Tree Complex Wavelet Transform (DTCWT) on the histogram signal. The DTCWT is an improvement over the discrete wavelet transform (DWT) as it provides nearly shift invariant performance, reduced aliasing and directional wavelets in higher dimensions. Backpropagation neural-network (BPNN) based binary classifiers are developed for authentication utilizing the features extracted. The system is developed on the basis of several scanned color images of palms of individuals in real-life, in our laboratory. The experimental results obtained from the data have demonstrated the utility of the proposed system, by exhibiting an overall mean accuracy as high as 98.35%.     

基于FFT和小波变换的谐波分析策略

有源电力滤波器是一种用于动态抑制谐波、补偿无功的新型电力电子装置,它能够对大小和频率都变化的谐波以及变化的无功进行补偿。该文提出了FFT变换与小波变换相结合的检测电路,小波变换分析将信号分成了稳态部分及非稳态部分,稳态部分采用傅里叶变换,非稳态部分采取模极大值分析。利用Matlab对其仿真很好的验证了该方法是可行的,可有效滤除谐波。     

Study of small size wavelet transform processor and wavelet inverse-transform processor using SAW devices

The objective of this research was to investigate the possibility of reducing the sizes of dyadic wavelet transform processor and dyadic wavelet inverse-transform processor using surface acoustic wave (SAW) devices. The motivation for this work was prompted by these processors which are of large sizes. Although these processors are being used, many fields need small size processors, so this work proposes two novel methods of reducing the sizes of these processors: firstly, the architecture which is that various interdigital transducers (IDTs) stand in a line is used to reduce the sizes of these processors. In this architecture, when the electrode-pairs numbers of various IDTs are larger than 20, the bulk acoustic wave (BAW) is eliminated, so this architecture cannot only reduce the sizes of these processors, but also eliminate BAW; secondly, as long as the electrode-overlap envelopes of IDTs will weaken fast with time t, the sizes of these processors are also reduced.     

基于IIR小波滤波器的涡街流量计数字信号处理系统

涡街流量计被广泛应用于过程工业,但是,由于易受由管道振动和流场扰动等因素干扰,现场测量精度无法保证,测量小流量困难。本文将小波滤波器组应用于涡街流量计信号处理中,研究基于小波滤波的涡街信号处理方法,分析了小波滤波器的幅频特性对信号分析的影响,构造了幅频特性好的IIR型小波滤波器组。本文研制了基于ADSP2181的涡街流量计数字信号处理系统,并进行了气体流量和水流量标定实验,实验结果表明,基于小波滤波的涡街流量计数字信号处理系统有良好的适配性能,性能指标优于常规的涡街流量计处理系统。     

抗电磁干扰的心电信号监测系统

为了实现电磁干扰环境下的心电信号监测,提出了一种抗电磁干扰的心电信号监测系统。论文给出了输出光电流的计算公式,分析了半波电压及插入损耗对系统灵敏度的影响。利用铌酸锂电光晶体搭建实验系统,测试了5位健康志愿者的心电信号。应用提出的系统和电学心电信号采集系统分别测试了正常环境及电磁环境下志愿者的心电信号。测试结果表明:在正常环境下,本系统能获得与电学心电信号采集系统同样清晰的心电信号波形;在电磁环境下,本系统获得的心电信号优于电学心电信号采集系统。最后定量计算了两者的信噪比,计算结果表明:在电磁干扰下,本系统的信噪比变化量为0.54dB(V2)/0.49dB(V4),而电学心电信号采集系统的信噪比变化量为24.07dB(V2)/16.75dB(V4)。     

小波变换在电网谐波分析中的应用

随着电力电子技术的广泛应用,谐波对电力系统的污染越来越严重,检测、分析和抑制谐波已经成为电力系统环境治理的重要课题.利用小波变换及传统的傅立叶变换对电网中的谐波进行了检测、分析,并通过MATLAB进行了仿真.仿真结果表明,利用小波变换可以将信号中不同频率的谐波有效地提取出来,并进行有针对性的分析,具有更高的分析精度.     

基于FPGA的9/7小波变换算法实现

提出了一种基于现场可编程门阵列(FPGA)的高速9/7二维离散小波变换(2-D DWT)设计。在实现一维离散小波变换(1-D DWT)时采用多级流水线技术,并使用了改进的提升算法。同时,使用正则符号编码(CSD编码)和优化的移位加法操作实现乘法器,使其便于通过硬件实现,且加快了处理速度。在进行二维离散小波变换时采用改进的基于行的结构,只需完成3行行变换即可开始列变换,减少了系统资源的占用。设计通过MATLAB与ModelSim联合仿真,可以稳定运行在60MHz时钟频率下,完全能够满足高速图像实时处理的要求。     

基于小波变换的离心风机故障诊断方法

异常振动是离心风机故障的主要表现形式,严重时影响生产运行。引起风机振动的原因很多,本文运用小波变换对离心风机振动故障进行分析,总结出引起离心风机振动超标的主要原因,对故障点进行检修。     

利用小波变换提高数字全息信噪比

光学和数字全息需要解决的基本问题是在图像重构过程中出现的散斑噪声,因为散斑降低了信噪比（SNR）。目前很多方法都可以减小散斑噪声,以提高数字全息信噪比。现利用小波滤波的方法来提高数字全息重构过程中的信噪比。提供了实验结果,实验结果证明该方法是可行的。     

基于Red-Black小波变换的多光谱图像融合方法

针对张量积小波在多光谱图像融合中的不足,提出了一种基于Red-Black小波变换的多光谱图像和高空间分辨率全色图像融合方法.首先对多光谱图像进行IHS变换,将得到的亮度分量和高空间分辨率全色图像进行直方图匹配,并分别作多尺度Red-Black小波分解,然后对低频部分采用加权平均、高频部分采用替代的融合算法对分解子图像进行融合,最后对融合后的各级子图像进行Red-Black重构和IHS逆变换得到融合结果图像,采用客观性能指标对融合结果图像进行了评价.实验结果表明,该方法有较好的融合效果,其保持光谱质量和空间分辨率信息的能力比基于IHS变换融合方法、基于DWT的融合方法和基于IHS-DWT的融合方法都强.     

基于冗余小波和分水岭变换的肺结节识别

利用CT影像检测肺结节,已成为目前诊断和预防早期肺癌的主要方法.对肺结节的有效识别,是实现肺癌计算机辅助诊断的关键.本文根据医学影像的特点,提出一种基于冗余小波变换和分水岭变换的肺结节识别方法,该方法通过保持平移不变的冗余小波变换得到细节信息增强的影像,然后用基于有向欧式距离变换的分水岭算法实现对肺结节的分割.实验表明,该方法对CT序列影像进行肺结节识别可以得到良好的分割结果,从而为医生对肺结节的诊断提供更加准确的客观依据.     

一种改进的小波域去噪算法

在Donoho D L和Johnston IM提出的多分辨分析小波阈值去噪方法的基础上,提出了一种新的双变量阈值函数。采用新的阈值函数的去噪效果无论在视觉效果,还是在信噪比增益和最小均方意义上均优于传统的硬阈值和软阈值,克服了采用硬阈值法去噪效果不佳和软阈值法过度光滑使信号失真的缺点。通过仿真实验结果,表明该方法的有效性和优越性。     

基于WTPCA-MSVMs过程监控方法

提出了基于小波变换主元分析和多支持向量机(wavelet transform PCA-Multiple support vector machines,WTPCA-MSVMs)的过程监控方法,该方法首先利用小波变换(wavelet transform,WT)对采样数据进行预处理,以有效抑制过程数据中所含的噪声和干扰信号;然后利用主元分析(principal component analysis,PCA)对预处理后的数据建立主元监控模型;考虑到实际工业过程故障数据的数量较少,而支持向量机(support vector Machine,SVM)在小样本学习方面具有良好的泛化能力的特性,最后提出了基于多支持向量机(multiple support vector machines,MSVMs)的故障诊断方法。对TE(tennessee eastman,TE)过程的监控应用表明了所提出方法的有效性。     

Gear fault diagnosis using active noise cancellation and adaptive wavelet transform

Acoustic signal from a gear mesh with faulty gears is in general non-stationary and noisy in nature. Present work demonstrates improvement of Signal to Noise Ratio (SNR) by using an active noise cancellation (ANC) method for removing the noise. The active noise cancellation technique is designed with the help of a Finite Impulse Response (FIR) based Least Mean Square (LMS) adaptive filter. The acoustic signal from the healthy gear mesh has been used as the reference signal in the adaptive filter. Inadequacy of the continuous wavelet transform to provide good time–frequency information to identify and localize the defect has been removed by processing the denoised signal using an adaptive wavelet technique. The adaptive wavelet is designed from the signal pattern and used as mother wavelet in the continuous wavelet transform (CWT). The CWT coefficients so generated are compared with the standard wavelet based scalograms and are shown to be apposite in analyzing the acoustic signal. A synthetic signal is simulated to conceptualize and evaluate the effectiveness of the proposed method. Synthetic signal analysis also offers vital clues about the suitability of the ANC as a denoising tool, where the error signal is the denoised signal. The experimental validation of the proposed method is presented using a customized gear drive test setup by introducing gears with seeded defects in one or more of their teeth. Measurement of the angles between two or more damaged teeth with a high level of accuracy is shown to be possible using the proposed algorithm. Experiments reveal that acoustic signal analysis can be used as a suitable contactless alternative for precise gear defect identification and gear health monitoring.     

基于改进经验小波变换的行星齿轮箱故障诊断

行星齿轮箱振动信号具有复杂多分量和调幅-调频的特点。幅值解调和频率解调方法能够避免传统Fourier频谱中的复杂边带分析,有效识别故障特征频率。经验小波变换通过对信号Fourier频谱的分割构造一组正交滤波器组,能提取具有紧支撑Fourier频谱的单分量成分,再对单分量成分运用Hilbert变换即可实现信号的解调分析。经验小波变换能够有效分离出调幅-调频成分,不存在模态混叠现象,具有完备的理论基础,自适应性好、算法简单、计算速度快。将改进的经验小波变换应用于行星齿轮箱振动信号的解调分析;提出了一种单分量个数的估算方法,解决了经验小波变换中的Fourier频谱划分问题;给出了对故障敏感的信号分量的选取方法,提高了分析的针对性。将改进方法应用于行星齿轮箱振动仿真信号和实验信号分析,验证了该方法的有效性。     

Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal

Rolling element bearing is one of the important components in rotary machines. Although a significant quantum of work has been done on bearing defect monitoring, estimation of defect size in bearing elements is still a challenge. In this paper, a technique based on decomposition using Symlet wavelet is proposed for measuring outer race defect width of taper roller bearing. Experiments were carried out on a customized test setup. Seeded defects of different size were introduced separately in the form of an axial groove on the outer race of taper roller bearings using laser engraving technique. It is not only difficult but ambiguous to detect the entry point in the groove defect by making use of the signal. The ambiguity gets reduced by using Symlet5 wavelet due to its linear phase nature which maintains sharpness in the signal even when there is a sudden change in signal. The proposed technique has been successfully implemented for measuring defect width over a range of 0.5776–1.9614 mm. The resulting measure of defect width has been also verified using image analysis. Maximum deviation in result has been found to be 4.06% for the defect width of 1.1820 mm at no load which was found further reduced to 1.02% with increase in load.