基于包络延拓和本征波匹配的时变DoS攻击频谱检测

DoS攻击信号具有非平稳时变特性,湮没在色噪声背景的复杂网络环境中,对之难以有效检测.传统方法中采用基于非平稳时变信号处理的Hough变换单谱脉冲响应检测算法,由于二次型时频分布的边缘效应会引起较大包络衰减,检测性能不好.因此提出一种基于包络延拓和本征波匹配的时变DoS攻击信号频谱检测算法来对DoS攻击检测信号进行双曲调频分解,构建信号数学演化模型,得到信号包络和本征波特征提取结果.采用双线性Hough变换法分析频谱特征畸变,进行瞬时频率估计,得到信号的单谱脉冲响幅频响应,在包络时频特征空间优化搜索路径实现包络延拓,基于最小均方误差准则设计本征波匹配滤波器,控制DoS频谱偏移,实现信号频谱检测.仿真结果表明,本算法能在强色噪声背景干扰下提高检测性能,检测概率高于传统算法,且能准确估计参量信息,提高对DoS攻击信号的主动防御能力.     

基于DCT的实值离散Gabor变换最优窗宽选择

文中针对Gabor变换中最优窗函数宽度选择的问题,提出了以提高Gabor表示的聚集性和时频分辨率为目的的基于DCT核的实值离散Gabor变换最优窗函数宽度选择算法。对香农熵的取值范围进行了研究,使其更适合度量时频分布的聚集性,进而根据熵度量实现了与信号非平稳性相适应的最优窗函数宽度的选择。实验结果表明,该算法对单分量及多分量信号都能有效地选择最优窗函数宽度,同时能够获得聚集性好、时频分辨率高的Gabor表示,并且具有很好的抗噪能力。     

A time–frequency approach for noise reduction

This paper proposes a technique for reducing noise from a signal's time series using a time–frequency distribution. The technique is based on the SVD of the matrix associated with the time–frequency representation of the signal. In this approach the time–frequency representation of the signal is initially divided into signal subspace and noise subspace using singular values of the time–frequency matrix as a criterion for space division. Since singular vectors are the span bases of the matrix, reducing the effect of noise from the singular vectors and using them in reproducing the matrix enhances the information embedded in the time–frequency representation of the signal. The proposed approach utilizes the Savitzky–Golay low-pass filter for noise attenuation from the singular vectors. The results of applying the proposed method on both synthetic signals and newborn EEGs indicate superiority of the proposed technique over the existing one in reducing noise from signals.     

Time–frequency and time–time filtering with the S-transform and TT-transform

The limitations of frequency-domain filtering methods have motivated the development of alternative techniques, in which a filter is applied to a time–frequency distribution instead of the Fourier spectrum. One such distribution is the S-transform, a modified short-time Fourier transform whose window scales with frequency, as in wavelets. Recently it has been shown that the S-transform's local spectra have time-domain equivalents. Since each of these is associated with a particular window position on the time axis, collectively they give a time–time distribution. This distribution, called the TT-transform, exhibits differential concentration of different frequency components, with higher frequencies being more strongly concentrated around the localization position than lower frequencies. This leads to the idea of filtering on the time–time plane, in addition to the time–frequency plane. Examples of time–frequency filtering and time–time filtering are presented.     

改进的多尺度域变步长自适应算法在FOCT中的应用研究

为了更有效的提高光纤电流互感器FOCT(Fiber-Optical Current Transformer)的信噪比,在分析FOCT输出信号特性的基础上,结合变步长自适应算法和小波变换理论,提出一种针对处理FOCT输出信号的改进多尺度域变步长自适应滤波算法,并设计了一种基于ActiveX技术的变步长自适应滤波系统,通过该系统将改进算法与现有的变步长自适应算法进行了对比,结果表明此算法的收敛速度和稳态精度都得到了很大的改善.然后将此算法在FOCT中进行了应用测试,测试结果反映了该算法能有效提高FOCT的检测信噪比和抗噪声干扰能力.     

一种自适应的合成孔径雷达图像目标检测方法

目标检测是自动目标识别的一个重要步骤,论文提出了一种自适应的SAR图像目标检测方法,该方法采用基于Weibull分布模型的恒虚警率(CFAR)检测技术,将参考窗口分块,判断各子块类型,根据各子块类型不同,自适应选择参考样本确定阈值。在检测过程中,利用灰度和方差特征,预先排除明显不为目标的像素。对CFAR检测结果,利用目标基本形状特征排除虚警。实验证明,该方法在同质区和非同质区背景下都具有较好的检测性能。     

基于 Hough 变换的多频分量的 LFM 信号的检测

LFM信号是一种典型的非平稳信号,其Wigner-Ville时频分布表现出明显的线状分布特征。 Hough变换是一种直线积分投影变换,利用Hough变换可以对WV变换的结果进行线积分,从而实现抑制噪声检测信号的目的。计算机仿真试验表明Hough变换可以在一定信噪比情况下实现多分量的LFM 信号检测,对两分量的LFM信号,可以作到0dB高斯白噪声下的准确检测。     

基于奇异谱分析和维纳滤波的语音去噪算法

针对维纳滤波算法对非平稳语音信号去噪存在的信号失真、信噪比(SNR)不高的问题,提出了一种奇异谱分析(SSA)和维纳滤波(WF)相结合的语音去噪算法SSA-WF。通过奇异谱分析将非线性、非平稳的语音信号初步去噪,提高含噪语音的信噪比以获取尽可能平稳的语音,并将其作为维纳滤波的输入,以剔除其中仍存在的高频噪声,最终获取纯净的去噪语音。在不同强度的背景噪声下进行仿真实验,结果表明SSA-WF算法在SNR和均方根误差(RMSE)等方面都要优于传统的语音去噪算法,能够有效去除背景噪声,降低有用信号的失真,适用于非线性、非平稳语音信号的去噪。     

Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations

This article considers the application of variational Bayesian methods to joint recursive estimation of the dynamic state and the time-varying measurement noise parameters in linear state space models. The proposed adaptive Kalman filtering method is based on forming a separable variational approximation to the joint posterior distribution of states and noise parameters on each time step separately. The result is a recursive algorithm, where on each step the state is estimated with Kalman filter and the sufficient statistics of the noise variances are estimated with a fixed-point iteration. The performance of the algorithm is demonstrated with simulated data.      

改进的变步长LMS自适应滤波算法及其仿真

对变步长的(LMS)自适应算法进行了讨论,本文提出了一种新的变步长LMS自适应滤波算法,并用计算机进行了仿真,结果表明该算法在误差接近于零时步长具有缓慢的变化的特性,并且在低信噪比的环境下有更好的抗噪性能,滤波效果更好。     

A Generative Probabilistic Oriented Wavelet Model for Texture Segmentation

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.     

小波滤波在超声管外测压回波处理中的应用

超声测压过程中信号受到噪声的干扰,使得回波信号质量较差。为提取后续特征,需对回波信号进行处理。而小波具有"变焦距"特性,在时域和频域中具有良好的局部分析能力,适合于超声等非平稳信号处理。在超声反射回波的数学模型基础上,利用小波滤波方法对超声信号进行预处理,提取强噪声背景中的弱回波信号。仿真及实验表明,用小波滤波能够有效抑制噪声,提高信噪比,且该方法原理简单,易于实现,为进一步研究超声管外测压奠定了基础。     

基于小波变换的非平稳信号去噪

传统的信号去噪算法往往仅对平稳噪声或缓慢变化的噪声有效, 且残留的信号噪声较大。基于小波变换的去噪算法对传统的小波阈值法进行了改进,根据信号与噪声在小波域的分布特性以及信号和噪声小波变换的模极大值随尺度的变化大小不同,得到噪声在小波域中的位置以及小波系数大小。实验结果表明：该算法对平稳和非平稳的噪声都能进行较好地去噪。     

面向复杂城市交通场景的一种实时车道线检测方法

车道线检测是智能交通监控及自动驾驶的基础步骤,为提高其鲁棒性和实时性,针对复杂城市交通场景中自动驾驶需要检测车道线的需求,提出了一种实时车道线检测算法,首先运用改进灰度化变换突显车道线的特征,并通过改进的Gabor滤波算法增强车道线的边缘信息;最后采用多约束霍夫变换筛选得到平行车道线从而实现实时车道线检测。实验表明,该方法在三种不同真实的交通道路场景下,提高了车道线检测精度及处理速度,可应用于实时车道线检测系统。     

Instantaneous frequency based spectral analysis of nuclear magnetic resonance spectroscopy data

Nuclear magnetic resonance spectroscopy signals are modelled as a sum of decaying complex exponentials in noise. The spectral analysis of these signals allowing for their decomposition and the estimation of the parameters of the components is crucial to the study of biochemical samples. This paper presents a novel Gabor filterbank/notch filtering instantaneous frequency (IF) estimator, that enables the extraction of weaker and shorter lived exponentials. This new approach is an iterative procedure where a Gabor filterbank is first employed to obtain a reliable estimate of the IF of the strongest component present. The estimated strongest component is then notch filtered, which un-masks weaker components, and the procedure repeated. The performance of this method was evaluated using an artificial signal and compared to the short time Fourier transform, reassigned STFT, and the original Gabor filterbank approach. The results clearly demonstrate its superiority in uncovering weaker signals and resolving components that are very close to one another in frequency. Furthermore, the new method is shown to be more robust than the ITCMP technique at low signal to noise ratios.     

基于小波极大模值的色谱噪声自适应滤除

利用小波模极大值理论提出了一种自适应滤除色谱噪声的算法。对带噪色谱信号小波分解获得不同分解层数上的模极大值点,利用信号与噪声的小波模极大值的衰减性显著差异,结合色谱谱图信号与非信号不同区域,根据同一序列点在不同层上的模值变化,区分出信号点与噪声点,再通过信号重组可得到滤噪后的色谱信号。实验结果表明,该算法不影响色谱峰峰高和峰位置,能有效地自适应降噪。     

基于谱减和LMS的自适应语音增强

针对宽带噪声背景下的语音增强问题,将短时语音视为非平稳或宽平稳信号,基于谱减法和自适应滤波的最小均方（LMS）算法,提出了一种FIR型自适应滤波算法（SSLMS）：用减谱法由短时噪声观测语音估计期望信号,作为滤波器输出信号的参考信号;用滤波器的输出与参考信号的差值为误差信号,用LMS算法求得滤波器权系数修正量,并修正滤波器。权系数最速下降调整中,采用了归一化LMS、符号LMS、块LMS技术,以简化保证权系数收敛的步长选择、减少权系数修正的运算量,从而提高自适应速度。对不同的语音在各种信噪比下仿真实验,并与改进的谱减法比较,结果表明,该法增强效果优于谱减法;在信噪比为3 dB时该法的增强效果仍然令人满意。     

DCT域维纳滤波语音增强

针对非平稳噪声和强背景噪声下声音信号难以提取的实际问题,提出了一种DCT域的维纳滤波方法。列出了DCT域清浊音分割步骤,给出了DCT域频谱信噪比迭代更新机制与具体实施方案,设计了DCT域的二维维纳滤波。实验仿真表明,该算法能有效地去噪滤波,改善可懂度,且在不同的噪声环境和信噪比条件下具有鲁棒性。该算法计算代价小,简单易实现。     

A method of time–time analysis: The TT-transform

A novel technique of analysis of an arbitrary primary time series into a set of secondary, time-limited, local, constituent time series, the TT-transform, is presented. The time–time representation is derived from the S-transform, a method of representation of a real time series as a set of complex, time-localized spectra. When integrated over time, the S-transform becomes the Fourier transform of the primary time series. Similarly, when summed over the primary time variable, the TT-transform reverts to the primary time series. The invertibility of the TT-transform points to the possibility of filtering and signal to noise improvements in the time domain, and some insight into the localized spectra of the S-transform.     

Mean frequency derived via Hilbert-Huang transform with application to fatigue EMG signal analysis

The mean frequency (MNF) of surface electromyography (EMG) signal is an important index of local muscle fatigue. The purpose of this study is to improve the mean frequency (MNF) estimation. Three methods to estimate the MNF of non-stationary EMG are compared. A novel approach based on Hilbert-Huang transform (HHT), which comprises the empirical mode decomposition (EMD) and Hilbert transform, is proposed to estimate the mean frequency of non-stationary signal. The performance of this method is compared with the two existing methods, i.e. autoregressive (AR) spectrum estimation and wavelet transform method. It is observed that our method shows low variability in terms of robustness to the length of the analysis window. The time-varying characteristic of the proposed approach also enables us to accommodate other non-stationary biomedical data analysis.