基于最大熵估计Alpha谱缩放与平移量的温度与发射率分离算法

在热红外波段, 为了使温度与发射率分离过程不依赖数据库提供的经验信息, 并且实现更高的反演精度和更快的计算速度, 研究了一种新的温度与发射率分离算法. 首先, 在维恩近似原理的基础上, 求解了Alpha谱分布, 并利用Alpha谱描述光谱发射率的形状信息. 其次, 改进了最大熵温度与发射率分离算法: 应用最大熵估计模型对Alpha谱缩放与平移量进行估计, 减少了待估计参数的数量, 大幅简化了求解过程. 最后, 进行了算法的数值仿真实验: 求解了典型地物目标的温度与光谱发射率, 并且分析了算法对系统噪声的鲁棒性. 仿真数据表明: 发射率估计的最大RMSE为0.017, 温度估计的最大绝对误差的绝对值为0.62 K; 对系统添加测量信噪比为11的高斯白噪声, 发射率估计的相对RMSE为2.67%, 温度估计的相对误差为1.26%. 结果表明: 本文所述算法求解精度高, 计算速度快, 具备良好的鲁棒性.     

Multiband Spectrum Sensing Based on the Sample Entropy

Cognitive radios represent a real alternative to the scarcity of the radio spectrum. One of the primary tasks of these radios is the detection of possible gaps in a given bandwidth used by licensed users (called also primary users). This task, called spectrum sensing, requires high precision in determining these gaps, maximizing the probability of detection. The design of spectrum sensing algorithms also requires innovative hardware and software solutions for real-time implementations. In this work, a technique to determine possible primary users’ transmissions in a wide frequency interval (multiband spectrum sensing) from the perspective of cognitive radios is presented. The proposal is implemented in a real wireless communications environment using low-cost hardware considering the sample entropy as a decision rule. To validate its feasibility for real-time implementation, a simulated scenario was first tested. Simulation and real-time implementations results were compared with the Higuchi fractal dimension as a decision rule. The encouraging results show that sample entropy correctly detects noise or a possible primary user transmission, with a probability of success around 0.99, and the number of samples with errors at the start and end of frequency edges of transmissions is, on average, only 12 samples.     

A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction

The early fault diagnosis of rolling bearings has always been a difficult problem due to the interference of strong noise. This paper proposes a new method of early fault diagnosis for rolling bearings with entropy participation. First, a new signal decomposition method is proposed in this paper: intrinsic time-scale decomposition based on time-varying filtering. It is introduced into the framework of complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN). Compared with traditional intrinsic time-scale decomposition, intrinsic time-scale decomposition based on time-varying filtering can improve frequency-separation performance. It has strong robustness in the presence of noise interference. However, decomposition parameters (the bandwidth threshold and B-spline order) have significant impacts on the decomposition results of this method, and they need to be artificially set. Aiming to address this problem, this paper proposes rolling-bearing fault diagnosis optimization based on an improved coyote optimization algorithm (COA). First, the minimal generalized refined composite multiscale sample entropy parameter was used as the objective function. Through the improved COA algorithm, optimal intrinsic time-scale decomposition parameters based on time-varying filtering that match the input signal are obtained. By analyzing generalized refined composite multiscale sample entropy (GRCMSE), whether the mode component is dominated by the fault signal is determined. The signal is reconstructed and decomposed again. Finally, the mode component with the highest energy in the central frequency band is selected for envelope spectrum variation for fault diagnosis. Lastly, simulated and experimental signals were used to verify the effectiveness of the proposed method.     

模态信息非完备采样对水下声源检测的影响及改进方法

给出了海洋波导中位置未知简谐声源的广义似然比检测器及其理论检测性能.通过对检测器中模态相关矩阵进行特征值分解,将该检测器分解为与各阶特征值相对应的谱成分.推导了各阶谱成分的统计特性并以此获得了谱成分对输入信号的空间处理增益,该增益与谱成分对应的特征值成正比.当模态信息采样不完备时,模态相关矩阵存在部分接近于0的小特征值,相应的谱成分对输入信号的空间处理增益与其余谱成分相比非常小.通过舍去这部分谱成分,提出了有效谱检测器.该检测器在保持其输出中目标信号成分与广义似然比检测器相同的基础上,有效减少了其中的噪声成分,因此,具有比广义似然比检测器更好的检测性能.在典型的浅海环境下进行仿真实验,仿真结果验证了理论分析、推导以及有效谱检测器的有效性,并且表明非完备采样程度越严重,有效谱检测器相对于广义似然比检测器的检测性能提高越显著.此外,有效谱检测器具有比广义似然比检测器更好的数值计算稳健性.     

参数未知的舰船辐射噪声检测方法

舰船辐射噪声的检测方法已有广泛研究,其中线谱特征是检测的重要依据。基于线谱特征的舰船辐射噪声检测方法常需要线谱数目、频率等参数作为检测的先验信息,这些信息不准确或无法确知时,检测方法的性能会受到显著影响。针对实际应用中对于未知舰船无法获得其参数信息的情况,研究基于连续谱谱峰特征的更为一般性的信号存在性检测方法。将窄带信号瞬时频率分析的思想扩展到舰船辐射噪声这类宽带信号,从信号频率与能量分布的角度推导得到宽带信号"瞬时频率"的近似表示式。构建宽带信号的瞬时频率检测器,并对其性能进行理论分析,结果表明该检测器的性能主要受舰船辐射噪声瞬时频率方差以及接收信噪比影响。通过计算机仿真和实际数据对算法的性能进行了验证。      

一种基于非完整二维相空间分量置换的混沌检测方法

由于混沌时间序列和随机过程具有很多类似的性质, 因而在实际中很难将两者区分开来. 混沌信号检测与识别是混沌时间序列分析中一个重要的课题. 混沌信号是由确定性的混沌映射或混沌系统产生的, 相比于高斯白噪声序列, 其在非完整的二维相空间中表现出更加丰富的结构特性. 本文通过研究混沌时间序列和高斯白噪声序列在非完整二维相空间中的分布特性, 利用混沌信号的非线性动力学特性, 提出了一种基于非完整二维相空间分量置换的混沌信号检测方法. 该方法首先由接收序列得到非完整的二维相空间, 基于第一维分量大小关系实现对第二维分量的置换与分组, 进一步求得F检验统计量. 然后利用混沌系统的局部特性, 获取非完整二维相空间的动力学结构信息, 实现对混沌序列的有效检测. 在高斯白噪声条件下对多种混沌信号进行了信号检测的数值仿真. 仿真结果表明: 相比置换熵检测, 本文所提算法所需数据量小、计算简单以及具有更低的时间复杂度, 同时对噪声具有更好的鲁棒性.     

乙醇含量拉曼光谱检测中噪声与背景同时消除方法研究

乙醇含量拉曼光谱检测中,拉曼光谱信号中的各种噪声及光谱荧光造成的基线漂移和样品池背景等,影响了校正模型的预测精度。利用总体平均经验模态分解,将光谱信号分解成若干无模态混叠的内在模式分量,根据排列熵的信号随机性检测判据判断出代表背景信息和噪声信息的内在模式分量,将其置零即可同时消除拉曼光谱中的噪声与背景。将总体平均经验模态分解与排列熵相结合的预处理方法应用于乙醇含量的拉曼光谱检测中,并与小波变换和平均平滑滤波做了对比。实验结果表明：应用总体平均经验模态分解与排列熵相结合的方法能够有效的同时消除乙醇含量拉曼光谱检测中的噪声和背景信息,提高校正模型的预测精度,且使用简便,无需参数设置,对乙醇含量拉曼光谱检测具有实用价值。     

等概率符号化样本熵应用于脑电分析

样本熵(或近似熵)以信息增长率刻画时间序列的复杂性,能应用于短时序列,因而在生理信号分析中被广泛采用.然而,一方面由于传统样本熵采用与标准差线性相关的容限,使得熵值易受非平稳突变干扰的影响,另一方面传统样本熵还受序列概率分布的影响,从而导致其并非单纯反映序列的信息增长率.针对上述两个问题,将符号动力学与样本熵结合,提出等概率符号化样本熵方法,并对其物理意义、数学推导及参数选取都做了详细阐述.通过对噪声数据的仿真计算,验证了该方法的正确性及其区分不同强度时间相关的有效性.此方法应用于脑电信号分析的结果表明,在不对信号做人工伪迹去除的前提下,只需要1.25 s的脑电信号即可有效地区分出注意力集中和注意力发散两种状态.这进一步证明了该方法可很好地抵御非平稳突变干扰,能快速获得短时序列的潜在动力学特性,对脑电生物反馈技术具有很大的应用价值.     

基于图像熵的各向异性扩散相干斑噪声抑制

本文提出了一种基于图像熵的各向异性扩散滤波方法. 该方法使用图像熵作为边缘检测算子, 避免了由于均值和方差等统计量的估计带来的误差, 提高了边缘检测能力. 试验结果表明该方法能够获得比传统方法更好的相干斑噪声抑制效果. 关键词： 相干斑抑制 各向异性扩散 图像熵 合成孔径雷达     

基于主副瓣比加权的未知线谱目标检测方法研究

针对目标辐射线谱信号未知时的目标检测问题,依据水下目标辐射噪声含有高强度稳定线谱这一特征,该文提出了一种基于波束形成主副瓣比加权的宽带波束形成目标检测方法。该方法首先用二阶锥优化各频带波束形成,得到低旁瓣高增益波束形成;其次利用各频率单元波束形成主副瓣差异形成加权因子;然后利用加权因子对各频率单元波束形成进行加权统计,可以抑制背景噪声能量干扰,增强目标检测信噪比增益,克服传统线谱检测四维显示难点。理论分析和实验结果表明：该方法可以较好地增强目标线谱单元能量、抑制噪声、提高信噪比,改善能量累积检测法在远程目标检测方面的性能。     

水下运动声源的方位方差检测方法

为了提高对水下运动目标的检测能力,利用水下目标辐射信号与海洋环境噪声在空间分布特性上的差异,提出了一种方位方差检测方法。该方法采用时延估计法估计目标方位,对方位求方差以获得检验统计量。采用奈曼-皮尔逊准则,推导了该方法的检测概率关系式,得出了检测性能与信噪比关系曲线,并与宽带能量检测方法进行了比较,表明该方法的性能远优于宽带能量检测方法。理论分析和海上试验结果都验证了该方法的有效性。     

Time Dependence of Entropy Flux and Entropy Production of a Dissipative Dynamical System Driven by Non-Gaussian Noise

A stochastic dissipative dynamical system driven by non-Gaussian noise is investigated. A general approximate Fokker-Planck equation of the system is derived through a path-integral approach. Based on the definition of Shannon＇s information entropy, the exact time dependence of entropy flux and entropy production of the system is calculated both in the absence and in the presence of non-equilibrium constraint. The present calculation can be used to interpret the interplay of the dissipative constant and non-Gaussian noise on the entropy flux and entropy production.     

Study on the denoising method for the electromagnetic ultrasonic echoes from multiple interfaces

The electromagnetic ultrasound is used in the detection of interfaces of the adhesive multilayer structures to solve the unstable coupling problem in ultrasonic testing by traditional piezoelectric transducers. Based on the analysis of the transforming mechanism of electromag-netic ultrasound energy and the resultant dead zone from mutual inductance of the transducer, the wavelet filtering by soft-thresholding and adaptive noise canceling methods are used simul-taneously to the detected electromagnetic ultrasonic signals to overcome the drawbacks of the low signal to noise ratio （SNR） and the wide intrinsic dead zone of the transducer. Processed results in the interface detection of a three layered adhesive sample of steel and rubber materials demonstrate that the wavelet filtering enhances the SNR about 12dB while the adaptive noise canceling narrows the dead zone effectively.     

高灵敏度激光吸收光谱中的微弱信号处理

可调谐二极管激光频率调制吸收光谱（FM-TDLAS）技术是一种高灵敏度的痕量气体检测方法。为改善以CH₄为目标气体检测系统的信噪比,提高系统的检测灵敏度,以双平衡混频器（double balanced mixer）为核心,设计了一套用于提取被噪声淹没的微弱信号的调制解调高频相敏探测器。在对高频相敏探测器进行具体设计和分析的基础上, 结合实验,利用CH₄气体在1653nm附近的近红外吸收光谱,对整体电路的性能进行了测试,获得了具有较高信噪比的二次和一次谐波信号,检测灵敏度可达10×10^-6。     

一级近似下的单模激光增益模型随机共振

把周期信号输入单模激光,对激光增益模型中的乘法噪声项取一级近似通过计算相关函数得到功率谱,发现输出信号是洛伦兹型的.信噪比随抽运噪声强度和量子噪声强度的变化均出现随机共振,但共振峰对应的噪声强度比零级近似下的小.     

Noise shaping by interval correlations increases information transfer

The influence of intrinsic firing interspike interval correlations on the noise spectrum and information transfer is studied. This is done through the comparison of two simple firing models, one of which is a renewal process while the other displays interval correlations. These correlations are shown to shape the spike train power spectrum and, in particular, to decrease the noise power at low frequencies. Linear response theory and numerical simulations reveal how this shaping can increase the transmission of information about a time-varying signal. Our results are relevant to the analysis of nonrenewal point processes and signal detection in physics and biology.     

Spectrum Sensing Implemented with Improved Fluctuation-Based Dispersion Entropy and Machine Learning

Spectrum sensing is an important function in radio frequency spectrum management and cognitive radio networks. Spectrum sensing is used by one wireless system (e.g., a secondary user) to detect the presence of a wireless service with higher priority (e.g., a primary user) with which it has to coexist in the radio frequency spectrum. If the wireless signal is detected, the second user system releases the given frequency to maintain the principle of not interfering. This paper proposes a machine learning implementation of spectrum sensing using the entropy measure as a feature vector. In the training phase, the information about the activity of the wireless service with higher priority is gathered, and the model is formed. In the classification phase, the wireless system compares the current sensing report to the created model to calculate the posterior probability and classify the sensing report into either the presence or absence of wireless service with higher priority. This paper proposes the novel application of the Fluctuation Dispersion Entropy (FDE) measure recently introduced in the research community as a feature vector to build the model and implement the classification. An improved implementation of the FDE (IFDE) is used to enhance the robustness to noise. IFDE is further enhanced with an adaptive method (AIFDE) to automatically select the hyper-parameter introduced in IFDE. Then, this paper combines the machine learning approach with the entropy measure approach, which are both recent developments in spectrum sensing research. The approach is compared to similar approaches in literature and the classical energy detection method using a generated radar signal data set with different conditions of SNR(dB) and fading conditions. The results show that the proposed approach is able to outperform the approaches from literature based on other entropy measures or the Energy Detector (ED) in a consistent way across different levels of SNR and fading conditions.     

两种熵测度在量化射击运动员短时心率变异性信号复杂度上的一致性

利用两种基于熵的非线性复杂度测度：近似熵和样本熵,研究了专业射击运动员两种不同状态下(休息和练习赛)心率变异性信号的复杂度.计算结果表明：射击运动员休息时其心率变异性信号的熵值大于射击比赛时信号的熵值,这意味着运动员一旦进行射击比赛时,其心率变异性信号复杂度降低了,心跳变得更为规则了.为了更好地应用这两种基于熵的方法,进一步分析了算法中的两个重要影响因素：矢量匹配容差r和序列长度N对算法性能的影响.分析结果表明：只要参数选择在合适的范围内,近似熵和样本熵都能够正确地区分出两种不 关键词： 近似熵 样本熵 复杂度 射击     

Medical Image Authentication Method Based on the Wavelet Packet and Energy Entropy

The transmission of digital medical information is affected by data compression, noise, scaling, labeling, and other factors. At the same time, medical data may be illegally copied and maliciously tampered with without authorization. Therefore, the copyright protection and integrity authentication of medical information are worthy of attention. In this paper, based on the wavelet packet and energy entropy, a new method of medical image authentication is designed. The proposed method uses the sliding window to measure the energy of the detail information. In the time–frequency data distribution, the local details of the data are mined. The complexity of energy is quantitatively described to highlight the valuable information. Based on the energy weight, the local energy entropy is constructed and normalized. The adjusted entropy value is used as the feature vector of the authentication information. A series of experiments show that the authentication method has good robustness against shearing attacks, median filtering, contrast enhancement, brightness enhancement, salt-and-pepper noise, Gaussian noise, multiplicative noise, image rotation, scaling attacks, sharpening, JPEG compression, and other attacks.     

Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).