Estimation of brainstem auditory evoked potentials using a nonlinear adaptive filtering algorithm

In this study, we introduce a novel nonlinear system not only for tracking of both the latency and amplitude variations in brainstem auditory evoked potential (BAEP), but also for reduction of single-trial numbers in BAEP pattern extraction process. Trial-to-trial variations in auditory evoked potential (AEP) are very important in quantifying dynamical properties of the nervous system and in specifying the group-specific effects in clinical applications. Due to the nonlinear dynamics of the AEP, a nonlinear adaptive filtering technique is considered as a powerful tool for tracking such variations. Therefore, we have designed a wavelet network-based nonlinear adaptive filter (WaNe-NAF) satisfying asymptotic stability in the sense of Lyapunov. The simulation results are verified that the proposed WaNe-NAF can effectively track the trial-to-trial variations. We have also compared the WaNe-NAF with the most widely used ensemble averaging technique using real measured human BAEP data. The WaNe-NAF shows promise for requiring less number of ensembles than conventional ensemble averaging method to attain adequate signal quality. As a result, the proposed filtering system is suggested as a powerful tool in AEP acquisition and processing systems.     

An investigation of speech enhancement using wavelet filtering method

This paper investigates the utilization of wavelet filters via multistage convolution by Reverse Biorthogonal Wavelets (RBW) in high and low pass band frequency parts of speech signal. Speech signal is decomposed into two pass bands of frequency; high and low, and then the noise is removed in each band individually in different stages via wavelet filters. This approach provides better outcomes because it does not cut the speech information, which occurs when utilizing conventional thresholding. We tested the proposed method via several noise probability distribution functions. Subjective evaluation is engaged in conjunction with objective evaluation to accomplish optimal investigation method. The method is simple but has surprise high quality results. The method shows superiority over Donoho and Johnstone thresholding method and Birge-Massart thresholding strategy method.     

利用小波域同态滤波的伪造图像检测方法

针对伪造图像中常用的模糊操作,提出一种伪造图像的检测方法,该方法首先对伪造图像进行小波域同态滤波,增强处于高频段的人为模糊边缘,然后利用数学形态方法腐蚀掉自然边缘,保留增强的模糊边缘,最后对腐蚀后的边缘图像进行区域标定,从而定位出伪造区域。实验证明该算法相对基于传统同态滤波伪造检测方法,能够较准确定位伪造区域,降低误检率。     

Deblurring visual evoked potentials using commercially available software

Visual evoked potentials are useful clinical tools to study visual pathways of the brain. Although the temporal resolution is unsurpassed by other brain imaging technologies, the spatial resolution is diminished or blurred by the low conductance of the electrical signals through the skull. Methods have been proposed to improve the spatial resolution by downwardly projecting the electrical signals measured on the scalp to the surface of the cerebral cortex through the inverse solution of the equations governing static current flow. We describe the adaptation and combination of commercially available engineering software programs to solve this inverse problem and report the results of a sample run of the system. Before deblurring, the visual evoked potentials appeared to be diffusely localized over the posterior scalp. After deblurring, the visual evoked potentials were only found at the electrodes closest to the visual cortex, as would be predicted by our current knowledge of neuroanatomy.     

基于修正维纳滤波的小波包变换图像去噪

图像去噪是图像处理中一个非常重要的环节。为了改善降质图像质量,根据Donoho提出的小波阈值去噪算法,分析了维纳滤波原理,提出了一种基于修正维纳滤波的小波包变换图像去噪方法。利用修正维纳滤波对噪声图像进行处理,用处理后的图像计算噪声的标准方差,以此作为小波包的阈值。利用小波包对维纳滤波后的图像进行分解,实现对图像的低频和高频部分分别进行分解,用计算出的阈值对小波包树系数进行软阈值处理。利用小波包逆变换来获取去噪后的图像。结果表明：在噪声方差为0.01时,经该算法去噪后图像的PSNR比小波包自适应阈值去噪后的PSNR高出8.8 dB。该算法不仅能有效地去除加性高斯白噪声,而且能很好地保留边缘信息,极大地改善了图像的视觉质量。     

Identification of control chart patterns using wavelet filtering and robust fuzzy clustering

This paper proposes a hybrid framework composed of filtering module and clustering module to identify six common types of control chart patterns, including natural pattern, cyclic pattern, upward shift, downward shift, upward trend, and downward trend. In particular, a multi-scale wavelet filter is designed for denoising and its performance is compared to single-scale filters, including mean filter and exponentially weighted moving average (EWMA) filter. Moreover, three fuzzy clustering algorithms, based on fuzzy c means (FCM), entropy fuzzy c means (EFCM) and kernel fuzzy c means (KFCM), are adopted to compare their performance of pattern classification. Experimental results demonstrate that the excellent performance of EFCM and KFCM against outliers, especially in the case of high noise level embedded in the input data. Therefore, a hybrid framework combining wavelet filter with robust fuzzy clustering is suggested and proposed in this paper. Compared to neural network based approaches, the proposed method provides a promising way for the on-line recognition of control chart patterns because of its efficient computation and robustness against outliers.     

基于小波和维纳图像复原的像移补偿方法

提出一种基于小波变换和维纳滤波相结合的图像复原方法,有效地消除了航空成像系统的像移模糊.该算法先在小波变换的基础上对各个子频段的小波系数进行维纳滤波,以达到更好的消除模糊的目的.然后对维纳滤波后的图像进行小波逆变换,得到复原图像.经实验验证该算法对因前向像移造成的图像模糊有比较好的复原效果.     

Scalable video coding using motion-compensated temporal filtering and intra-band wavelet based compression

Scalable video compression is a crucial task that allows for high flexibility of video streams to different networks in various applications. Current video coding techniques exploit temporal correlation using motion-compensated predictive or filtering approaches. Particularly, motion-compensated temporal filtering (MCTF) is a useful framework for scalable video compression schemes. In this paper, we propose a new scalable video coding method that combines open-loop motion-compensated prediction with an embedded intra-band wavelet based compression. Our major objective is to provide a wavelet based video coding system that circumvents the drawbacks of conventional closed-loop prediction systems, without sacrificing compression performance. To improve the coding efficiency, we adaptively weight the target bitrate according to the temporal frame position in the temporal pyramid. Comparisons with state-of-the-art scalable video coding solutions confirm an overall coding efficiency gain of the proposed method specially at high bitrates.     

Extraction of melody from polyphonic music using modified morlet wavelet

In this paper, the extraction of melody for polyphonic music is discussed. The method proposes a simple but effective modification in the Morlet wavelet as a modified Morlet wavelet (MMW). The pitch detection concept is applied to the transformed wavelet music signal for primary melody estimation and extraction. The proposed method is then compared with three other methods reported in the literature (the method focused on Short Time Fourier Transform, multi-resolution Fast Fourier Transform, and Morlet Wavelet). The percentage gross pitch error (GPE) parameter is considered for purposes of comparison. In comparison, it is observed that the proposed approach has the lowest GPE. The simulation results also present a lower percentage error in the predominant pitch frequency estimation for the proposed method.     

An adaptive edge-preserving image denoising technique using patch-based weighted-SVD filtering in wavelet domain

Image denoising has always been one of the standard problems in image processing and computer vision. It is always recommendable for a denoising method to preserve important image features, such as edges, corners, etc., during its execution. Image denoising methods based on wavelet transforms have been shown their excellence in providing an efficient edge-preserving image denoising, because they provide a suitable basis for separating noisy signal from the image signal. This paper presents a novel edge-preserving image denoising technique based on wavelet transforms. The wavelet domain representation of the noisy image is obtained through its multi-level decomposition into wavelet coefficients by applying a discrete wavelet transform. A patch-based weighted-SVD filtering technique is used to effectively reduce noise while preserving important features of the original image. Experimental results, compared to other approaches, demonstrate that the proposed method achieves very impressive gain in denoising performance.     

Vector quantizer of medical image using wavelet transform and enhanced SOM algorithm

Vector quantizer takes care of special image features like edges, and it belongs to the class of quantizers known as the second-generation coders. This paper proposes a novel vector quantization method using the wavelet transform and the enhanced SOM algorithm for the medical image compression. We propose the enhanced self-organizing algorithm to resolve the defects of the conventional SOM algorithm. The enhanced SOM, at first, reflects the error between the winner node and the input vector to the weight adaptation by using the frequency of the selection of the winner node. Secondly, it adjusts the weight in proportion to the present weight change and the previous one as well. To reduce the blocking effect and the computation requirement, we construct training image vectors involving image features by using the wavelet transform and apply the enhanced SOM algorithm to them for generating a well-defined codebook. Our experimental results have shown that the proposed method energizes the compression ratio and the decompression quality.     

Computerized mapping system of cerebral evoked potentials

The introduction of computerized analysis systems in the study of bioelectrical signals is enhancing the understanding of the physiological mechanisms which underlie cerebral evoked potentials (EPs) in response to externally applied stimuli. In the present study, short latency (0-50-ms) and long latency (0-500-ms) somatosensory evoked potentials (SEPs) were recorded by 32 scalp electrodes from normal and pathological subjects during median nerve stimulation. An interpolation procedure for estimating data values between the neighboring electrodes allowed the mapping of cortical activity across the scalp. Time signals were also transformed by an FFT algorithm and frequency maps obtained following the same interpolation procedure. Temporal and frequency maps were graphically displayed using color and three-dimensional plots. The usefulness of computerized topographical analysis is discussed; the time and frequency computer maps obtained from the same subjects are compared and their relative advantages are evaluated.     

一种结合小波阈值滤波的自适应整体变分方法

在整体变分方法去噪原理的基础上,通过引入小波阈值滤波,用自适应正则项代替整体变分模型中的正则项,提出了一种依赖于信号的局部信息进行滤波的自适应整体变分方法,自适应地在整体变分正则化和各向同性光滑化之间调整滤波强度。为求解整体变分极小化问题,采用了滞后扩散定点迭代的方法。数值计算结果表明：提出的方法有效地减少了传统整体变分方法去噪后恢复信号中所出现的阶梯效应,很好地抑制了小波变换中固有的伪Gibbs现象,重构信号的边缘、不连续点位置十分精确,信噪比也得到明显改善。     

小波变换在传感器信号滤波中的应用

在流速测量过程中采集的加速度传感器信号是含有随机干扰的信号,为了改善滤波效果,尽可能排除随机信号的干扰,本文介绍了小波变换理论在加速度信息滤波中的应用,并进行了仿真实验。仿真结果表明,小波变换具有良好的时频特性,能有效检测出信号中所含各频率成分,减小测量误差,在传感器信号滤波方面有广阔的应用前景。     

小波域非Bayesian滤波方法研究

对近几年来小波域滤波方法的研究现状与新发展进行归纳总结。一方面从算法思想,原理和优缺点等角度对近年来所提出的较有代表性的小波滤波算法进行分析概括;另一方面选择一些典型的滤波算法和一些常用的信号,主要从信噪比（SNR）和均方误差（MSE）两个方面进行实验,并分别就同一种滤波算法,不同的信号以及同一个信号,不同的滤波算法的滤波情况进行对比分析。最后通过结合上述分析给出小波滤波的研究热点、难点、不足和有待解决的一些问题。     

A Bayesian approach to estimate evoked potentials

Several approaches, based on different assumptions and with various degree of theoretical sophistication and implementation complexity, have been developed for improving the measurement of evoked potentials (EP) performed by conventional averaging (CA). In many of these methods, one of the major challenges is the exploitation of a priori knowledge. In this paper, we present a new method where the 2nd-order statistical information on the background EEG and on the unknown EP, necessary for the optimal filtering of each sweep in a Bayesian estimation framework, is, respectively, estimated from pre-stimulus data and obtained through a multiple integration of a white noise process model. The latter model is flexible (i.e. it can be employed for a large class of EP) and simple enough to be easily identifiable from the post-stimulus data thanks to a smoothing criterion. The mean EP is determined as the weighted average of the filtered sweeps, where each weight is inversely proportional to the expected value of the norm of the correspondent filter error, a quantity determinable thanks to the employment of the Bayesian approach. The performance of the new approach is shown on both simulated and real auditory EP. A signal-to-noise ratio enhancement is obtained that can allow the (possibly automatic) identification of peak latencies and amplitudes with less sweeps than those required by CA. For cochlear EP, the method also allows the audiology investigator to gather new and clinically important information. The possibility of handling single-sweep analysis with further development of the method is also addressed.     

通道选择对诱发脑电单次提取精度影响的研究

采用“模拟自然阅读”诱发电位作为人脑和计算机之间的通信载体,用支持向量机从脑电中提取诱发电位。以被试4个通道记录到的脑电信号分别作为特征,信号时程固定为300ms,时段分别取100ms~400ms、200ms~500ms和300ms~600ms。三个被试的单通道最佳分类结果分别达到95.9%（被试M,通道Cz,300ms~600ms时段）,94.3%（被试H,通道Oz,100ms~400ms时段）和93.8%（被试T,通道Oz,200ms~500ms时段）。这一结果为简化脑—机接口设计打下了良好的基础。     

Diffusion filtering in image processing based on wavelet transform

Nonlinear diffusion filtering is a method for images or signals processing based on partial differential equations (PDEs). Its basic idea is to establish a suitable PDE model in the time-space domain and obtain a family of its solutions as the filtered ve…     

基于小波处理的高灵敏度GPS信号捕获方法

针对传统全球定位系统（GPS）弱信号高灵敏度捕获算法运算效率低的问题,提出了一种经过小波滤波预处理的同步数据块累加捕获方法。通过对采样的中频信号进行小波滤波预处理,根据有用信号与噪声在小波变换时所具有的不同特性来提高信噪比,同时降低基带处理的数据量;采用经过频率补偿后的同步数据块累加方法,减小多普勒频移搜索空间,提高运算效率,并能显著提高信噪比。仿真结果表明,与传统的高灵敏度捕获方法相比,该方法在捕获同样指标的微弱信号时,可以有效缩短捕获时间,改善微弱信号的捕获性能。     

基于小波变换的多分辨率分布式滤波

小波变换的多尺度特点非常适合多尺度信号的处理,可以用于多分辨率多传感器滤波.通过研究快速提升法小波变换的特性,提出一种可将估计误差方差最小化的动态分辨率分布式滤波算法,算法不需要把小波系数当成白噪声处理,并且能够有效地降低向量和矩阵维数,减少运算,有较好的滤波性能.同时在不同的分辨率级中,利用快速提升法小波变换作为一种连接信号的桥梁.这种算法也可用于动态多分辨率多传感器数据融合.