Mean first-passage time of an asymmetric bistable system driven by colour-correlated noise

In this paper, the effect of every parameter （including p, q, r, λ, τ） on the mean first-passage time （MFPT） is investigated in an asymmetric bistable system driven by colour-correlated noise. The expression of MFPT has been obtained by applying the steepest-descent approximation. Numerical results show that （1） the intensity of multiplicative noise p and the intensity of additive noise q play different roles in the MFPT of the system, （2） suppression appears on the curve of the MFPT with small λ （e.g. λ 〈 0.5） but there is a peak on the curve of the MFPT when λ is big （e.g. λ 〉 0.5）, and （3） with different values of r （e.g. r = 0.1, 0.5, 1.5）, the effort of τ on the MFPT is diverse.

Effect of spatially correlated noise on stochastic synchronization in globally coupled FitzHugh–Nagumo neuron systems

The phenomenon of stochastic synchronization in globally coupled FitzHugh–Nagumo（FHN） neuron system subjected to spatially correlated Gaussian noise is investigated based on dynamical mean-field approximation（DMA） and direct simulation（DS）. Results from DMA are in good quantitative or qualitative agreement with those from DS for weak noise intensity and larger system size. Whether the consisting single FHN neuron is staying at the resting state, subthreshold oscillatory regime, or the spiking state, our investigation shows that the synchronization ratio of the globally coupled system becomes higher as the noise correlation coefficient increases, and thus we conclude that spatial correlation has an active effect on stochastic synchronization, and the neurons can achieve complete synchronization in the sense of statistics when the noise correlation coefficient tends to one. Our investigation also discloses that the noise spatial correlation plays the same beneficial role as the global coupling strength in enhancing stochastic synchronization in the ensemble. The result might be useful in understanding the information coding mechanism in neural systems.

The mean first passage time of a three-level atomic optical bistable system subjected to noise

The transient properties of a three-level atomic optical bistable system in the presence of multiplicative and additive noises are investigated. The explicit expressions of the mean first-passage time （MFPT） of the transition from the high intracavity intensity state to the low one are obtained by numerical computations. The impacts of the intensities of the multiplicative noise DM and the additive noise DA, the intensity of correlation between two noises λ, and the intensity of the incident light y on the MFPT are discussed, respectively. Our results show： （i） for the case of no correlation between two noises （2, = 0.0）, the increase in DM and DA can lead to an increase in the probability of the transition to the low intracavity intensity state, while the increase in y can lead to a retardation of the transition; and （ii） for the case of correlation between two noises （λ≠ 0.0）, the increase in λ can cause an increase in the probability of the transition, and the increase in DA can cause a retardation of the transition firstly and then an increase in the probability of the transition, i.e., the noise-enhanced stability is observed for the case of correlation between two noises.

The statistical fluctuation of a single-mode laser system driven by coloured pump noise with signal modulation and the quantum noise with cross-correlation between its real and imaginary parts

Using the linear approximation method, this paper studies the statistical property of a single-mode laser driven by both coloured pump noise with signal modulation and the quantum noise with cross-correlation between its real and imaginary parts, and calculates the steady-state mean normalized intensity fluctuation and intensity correlation time. It analyses the influences of the modulation signal, the net gain coefficient, the noise and its correlation form on the statistical fluctuation of the laser system respectively. It is found that the coloured pump noise modulated by the signal has a great suppressing action on the statistical fluctuation of the laser system; the pump noise self-correlation time and the specific frequency of modulation signal have the result that the statistical fluctuation tends to zero. Furthermore, the ＇colour＇ correlation of pump noise has much influences on the statistical fluctuation of the laser system. Increasing the intensity of pump noise will augment the statistical fluctuation of the laser system, but the intensity of quantum noise and the coefficient of cross-correlation between its real and imaginary parts have less influence on the statistical fluctuation of the laser system. Therefore, from the conclusions of this paper the statistical property can be known and a theoretical basis for steady operation and output of the laser system can be provided.

Time delay and cross-correlated Gaussian noises-induced stochastic stability and regime shift between steady states for an insect outbreak system

In this paper, we focus on investigating the stochastic stability and the regime transition between the endangered state and the boom state for a time-delayed insect growth system driven by correlated external and internal noises. By use of the Fokker Planck equation, the method of small time delay approximation and the fast descent method, we explore in detail the joint action of noise terms and time delay on the mean reproduction and depression time for the insect population. Our investigations indicate that the pseudo-resonance phenomenon of the mean first-passage time (MFPT) occurs because of the impact of different noises and time delay. Through the numerical calculation, it is discovered that multiplicative noise can speed up the shift of the insect population from the boom state to the endangered one, while the noise correlation and time delay can propel the insect system to evolve from the endangered state to the boom state and improve the biological stability. In addition, the impact of the additive noise on the stability of the biological system depends on the positive and negative situation of the noise correlation. On the other hand, during the process of suppressing the insect explosion, it is beneficial to the pest control to amplify the association noise strength and weaken the intensities of the multiplicative, additive noises and time delay. However, during the process of eliminating the pests, it can produce nice effect on the disinsection to increase time delay, the intensities of multiplicative and additive noises and weaken the strength of noise correlation.

基于SVMD与参数优化MCKD的轴承故障诊断

针对轴承故障信号存在噪声干扰,难以提取故障特征的问题,提出了一种将连续变分模态分解(SVMD)与改进的最大相关峭度反卷积(MCKD)相结合的轴承故障诊断方法。首先,为了表征轴承振动信号中的故障特征,将峭度与高斯核相结合,提出了比峭度指标更为突出的加权峭度指标;其次,利用SVMD方法对轴承信号进行了分解,获得了若干模态分量,并使用加权峭度指标从多个模态分量中筛选出了故障特征最丰富的模态分量;然后,以包络熵为标准,通过几何平均优化器(GMO)优化MCKD的滤波器长度和周期两个参数,获得了最佳的参数组合;最后,采用GMO-MCKD方法对轴承信号进行了降噪,对降噪后的信号进行了包络分析,提取了轴承特征频率;同时,采用粒子群优化(PSO)的变分模态分解(VMD)和粒子群优化的变分模态提取(VME),对轴承信号进行了对照分析。研究结果表明:采用SVMD-GMO-MCKD方法在辛辛那提数据集中诊断出轴承特征频率为234.4 Hz及其二倍频;在西储大学轴承数据集中诊断出轴承特征频率为108.96 Hz,二倍频为218.09 Hz。该方法可以增强滚动轴承的周期性冲击成分,在有干扰的背景下有效地提取出滚动轴承内圈和外圈的故障特征,且轴承故障特征提取效果优于PSO-VMD和PSO-VME方法。

一种改进权重的非局部均值图像去噪算法

提出了一种改进权重的非局部均值滤波方法。在高斯加权的欧氏距离基础上,结合相关系数来衡量图像邻域间的相似性,将其应用到图像邻域灰度矩阵间的相似性度量上,更好地利用了图像邻域间的相似性质。通过对添加不同噪声水平的噪声图像进行测试,实验结果表明,与传统的非局部均值滤波算法相比,所提出的算法在去噪性能上尤其是结构信息保持上均有显著提高。

基于相关权值的图像椒盐噪声自适应窗滤波

为有效滤除图像中椒盐噪声,提出一种基于相关权值的自适应窗滤波算法。算法基于极值检测判断噪声点并仅对噪声点滤波。引入灰度差刻画邻域像素与中心像素的相关性,以此为基础设置像素权值,对中心像素执行加权均值滤波。通过邻域窗口的自适应扩展适应噪声密度变化,并对邻域像素分区域设置权值,从而适应高椒盐噪声的滤除。仿真结果表明,本文算法能够有效滤除图像中的椒盐噪声,尤其在高椒盐噪声下性能表现更佳。

一种基于双通道自适应噪声对消的语音增强法

在自适应噪声对消(ANC)中,本文根据主辅通道噪声间的相关性,提出了一种自适应滤波语音增强算法。在低信噪比(-10～0dB)白噪声条件下,文中研究了辅助通道采集的噪声有无"串音"两种情况下的语音增强效果。研究表明:在无串音和有串音两种条件下,本文算法增强语音的信噪比分别比NLMS算法提高约14dB和5dB,PESQ_MOS得分分别比NLMS算法提高约1.13和0.83,同时增强语音的听觉质量得到了极大地改善。

分组相关快衰落信道下Turbo码译码算法研究

研究了分组相关快衰落信道的特性,推导出该信道下Turbo码的译码算法,并从推导结果确定:平均信噪比和信噪比弥散度是影响系统性能的两个重要参数。仿真结果表明,在分组相关快衰落信道下,采用该文提出的Turbo码译码算法,可以提高整个系统性能,在误比特率为10-3时,与信道编码采用卷积码的系统相比,信噪比改善2.9dB;在大平均信噪比和小信噪比弥散度条件下,系统性能较好,误比特率较低。

关联噪声驱动非对称双稳系统的MFTP

考虑了由关联噪声驱动的非对称双稳系统的平均首次穿越时间(MFPT)问题,运用最速下降法求得平均首次穿越时间的表达式,并讨论了各个参数(包括p,q,r,λ)对两个方向的平均首次穿越时间的影响。结果表明:1)噪声关联时间λ对平均首次穿越时间T+(x1→x2,λ,r)和T-(x2→x1,λ,r)的影响是不同的,T+随着λ的增加而增加,而T-却随着λ的增加而减小。2)平均首次穿越时间T+(x1→x2,λ,r)和T-(x2→x1,λ,r)都随加性噪声强度q的增加而减小;但乘性噪声P对T+和T-影响却是完全不同的,T-随着P的增加而减小,而T+随着P的增加曲线有一个极大值,即是一个共振峰。3)在不同的噪声关联强度的影响下,T+/T-的曲线随着P的增加呈现不同的发展趋势,λ=0.1时,T+/T-随着P的增加单调减小;而λ=0.7时,T+/T-随着P的增加先增加再减小,曲线出现了极值峰。

在关联噪声驱动下双稳系统的MFPT

讨论了在关联噪声驱动下双稳系统的瞬变性质 ,计算了该系统的平均第一通过时间 (MFPT) .数值计算表明 :在正关联的条件下 (λ>0 ) ,MFPT随噪声关联τ增大而减少 ;在负关联的条件下 (λ <0 ) ,MFPT随τ增大而增大 .同时还发现 ,在完全正关联的条件下 (λ=1 ) ,若加性噪声强度等于乘性噪声强度 (α =D) ,则MFPT趋于无穷大 ;若α/D≈ 0 .87,则平均第一通过时间等于零 .

飞机噪声识别方法研究及FPGA固化实现

针对飞机噪声识别问题,提出了一套新的噪声识别方案,用1/3倍频程频谱分析法提取噪声特征,经相关系数分析法精简特征后再结合支持向量机进行分类。比较了改进的均方根法、BP神经网络和支持向量机的分类效果,结合QuartusII软件在现场可编程门阵列(FPGA)上实现了支持向量机的固化。实验结果表明,提出的飞机噪声识别方案准确率高,分类器的FPGA固化能够满足实时分类的要求。

基于自适应变步长最小均方的谐波检测算法的研究

从数字信号处理中的自适应噪声对消原理出发,介绍了一种改进的变步长最小均方(LMS)算法,该算法根据基波电流和谐波电流的不相关性,利用误差信号和参考输入的互相关估计来控制迭代步长,使得步长的更新不受谐波电流的影响。该算法原理简单,运算量小,易于实现,仿真结果表明了该谐波电流检测算法的有效性。

基于GPU并行计算的图像去噪研究

为更好保留经图像去噪后的特征信息,提高图像去噪计算速率,结合非局部均值算法的原理,提出了一种基于GPU并行计算的图像去噪方法。为了更好的保存去噪后的图像信息,采用欧氏距离与邻域相关系数结合方法,对邻域间相似性进行衡量;为提高算法执行速率,将图像复制到GPU内存,将计算结果复制到主机,从而节约主机开销。对添加不同噪声水平的噪声图像进行测试,结果表明,提出的算法在图像相干斑抑制和计算速率方面都有明显的优势。

噪声相关条件下Unscented卡尔曼滤波器设计

针对传统Unscented卡尔曼滤波器(UKF)在噪声相关条件下非线性滤波失效的问题,研究了一类带相关噪声的非线性离散系统UKF设计方法.文中基于最小均方误差估计准则,给出了系统噪声和量测噪声相关时UKF滤波递推公式,并采用Unscented变换(UT)来计算系统状态的后验均值和协方差.所设计的噪声相关条件下UKF有效克服了传统UKF必须假设系统噪声和量测噪声为互不相关高斯白噪声的局限性,拓展了UKF的应用范围.仿真实例验证了其可行性和有效性.

基于置信区间的自适应加权均值滤波算法

为了改善图像滤波的效果,提出1种基于置信区间的自适应加权均值滤波算法。根据高斯噪声的特点以及其对原图像的影响,仅对滤波窗口中处于置信区间的像素求加权均值。同时考虑了灰度相关性与距离相关性,将灰度测度因子和距离测度因子进行线性加权求和,得出加权系数。最后对加权均值滤波后的图像进行折中的灰度均衡化。实验结果证明,相对于标准均值滤波(SMF)算法和自适应均值滤波(AMF)算法,该文算法的滤波图像更加清晰,很好地恢复了原图像,同时保留了图像的边缘和细节;该文算法对应的归一化均方误差明显低于SMF算法和AMF算法。

均值量化语音信息隐藏算法研究

提出了一种新的均值量化的语音信息隐藏技术.采用对量化区间进行分段调整的方法来减少对载体语音DWT系数的修改量,提高信息隐藏的不可感知性;采用对落在抗攻击能力弱的边界区域的均值进行调整的方法来提高信息隐藏的抗攻击能力.实验结果表明,该算法在信噪比(signal noise ratio,SNR)和归一化相关系数(normalized cross-correlation,NC)等性能指标上较当前的均值量化算法有一定的提高.

线性离散随机系统最优和稳态Kalman平滑器

对于带非零均值相关噪声的线性离散随机系统,基于Kalman 滤波器和射影理论,提出了新的最优固定点、固定区间和固定滞后 Kalman 平滑器。它们避免了计算估计误差方差阵的逆矩阵,且具有通用性。推广和改进了带零均值的不相关噪声系统的经典结果。还提出了相应的稳态 Kalman 平滑器和极点配置 Kalman平滑器,并证明它们的局部或全局渐近稳定性。

基于相关性的JPEG高压缩图像峰值信噪比盲估计

量化噪声是JPEG图像压缩过程中产生的主要失真,在图像质量评估时需要对其大小进行估计。针对峰值信噪比(PSNR)估计方法在实际应用时终端用户无法获取原始图像的限制,基于图像空域相关性,引入JPEG压缩图像的斜率均方差和最大非零离散余弦变换系数的位置作为可测特征量,利用训练图像进行机器学习得到训练模型,将测试图像进行解析得到图像PSNR盲估计结果。实验结果表明,该PSNR估计结果优于目前流行的峰值信噪比盲估计方法。