Using Pearson’s System of Curves to Approximate the Distributions of the Difference between Two Correlated Estimates of Signal-to-Noise Ratios: The Cases of Bivariate Normal and Bivariate Lognormal Distributions

Background: The signal-to-noise ratio (SNR) is recognized as an index of measurements reproducibility. We derive the maximum likelihood estimators of SNR and discuss confidence interval construction on the difference between two correlated SNRs when the readings are from bivariate normal and bivariate lognormal distribution. We use the Pearsons system of curves to approximate the difference between the two estimates and use the bootstrap methods to validate the approximate distributions of the statistic of interest. Methods: The paper uses the delta method to find the first four central moments, and hence the skewness and kurtosis which are important in the determination of the parameters of the Pearsons distribution. Results: The approach is illustrated in two examples;one from veterinary microbiology and food safety data and the other on data from clinical medicine. We derived the four central moments of the target statistics, together with the bootstrap method to evaluate the parameters of Pearsons distribution. The fitted Pearsons curves of Types I and II were recommended based on the available data. The R-codes are also provided to be readily used by the readers.

Acquisition Practice of High Signal-to-Noise Ratio in YTB Block of Sichuan Basin

YTB block in Sichuan basin is a favorable area to exploit oil and gas in shallow tight rock. 3D seismic project of this zone has two characteristics. Firstly, it has high requirements for the tolerance rate of the construction process and the acquisition of high signal-to-noise ratio seismic data;Second, there are widely obstacles and noises that lead to difficult acquisition construction organization. In acquisition practice, high signal-to-noise ratio seismic data was obtained by reasonable design of construction scheme, optimization of excitation parameters, improvement of receiving conditions and optimization of obstacle crossing observation system. .

Adaptive Bistable Stochastic Resonance Based Weak Signal Reception in Additive Laplacian Noise

Weak signal reception is a very important and challenging problem for communication systems especially in the presence of non-Gaussian noise,and in which case the performance of optimal linear correlated receiver degrades dramatically.Aiming at this,a novel uncorrelated reception scheme based on adaptive bistable stochastic resonance(ABSR)for a weak signal in additive Laplacian noise is investigated.By analyzing the key issue that the quantitative cooperative resonance matching relationship between the characteristics of the noisy signal and the nonlinear bistable system,an analytical expression of the bistable system parameters is derived.On this basis,by means of bistable system parameters self-adaptive adjustment,the counterintuitive stochastic resonance(SR)phenomenon can be easily generated at which the random noise is changed into a benefit to assist signal transmission.Finally,it is demonstrated that approximately 8dB bit error ratio(BER)performance improvement for the ABSR-based uncorrelated receiver when compared with the traditional uncorrelated receiver at low signal to noise ratio(SNR)conditions varying from-30dB to-5dB.

An Analytical Theory of the Signal-to-Noise Ratio of Hall Plates with Four Contacts and a Single Mirror Symmetry

This work gives an analytical theory of the signal-to-thermal-noise ratio (SNR) of classical Hall plates with four contacts at small magnetic field. In contrast to previous works, the symmetry of the Hall plates is reduced to only a single mirror axis, whereby the average of potentials of the two output contacts off this mirror axis differs from the average of potentials at the two supply contacts on the mirror axis, i.e. the output common mode differs from 50%. Surprisingly, at fixed power dissipated in the Hall plate, the maximum achievable SNR is only 9% smaller for output common modes of 30% and 70% when compared to the overall optimum at output common modes of 50%. The theory is applied to Vertical Hall effect devices with three contacts on the top surface and one contact being the buried layer in a silicon BiCMOS process. Geometries are found with large contacts and only a moderate loss in SNR.

Noises and Signal-to-Noise Ratio of Nanosize EIS and ISFET Biosensors

The results of comparative theoretical analyzes of the behavior of internal low-frequency noises, signal-to-noise ratio and sensitivity to DNA molecules for EIS and ISFET based nanosize biosensors are presented. It is shown that EIS biosensor is more sensitive to the presence of DNA molecules in aqueous solution than ISFET sensor. Internal electrical noises level decreases with the increase of concentration of DNA molecules in aqueous solution. In the frequency range 10−3 - 103 Hz noises level for EIS sensor about in three orders is higher than for ISFET sensor. In the other hand, signal-to-noise ratio for capacitive EIS biosensor is much higher than for ISFET sensor.

Signal-to-noise ratio of lensless ghost interference with thermal incoherent light

Factors influencing the signal-to-noise ratio （SNR） of lensless ghost interference with thermal incoherent light are investigated. Our result shows that the SNR of lensless ghost interference is related to the transverse length of the object, the position of the object in the imaging system and the transverse size of the light source. Furthermore, the effects of these factors on the SNR are discussed in detail by numerical simulations.

Enhancement of signal-to-noise ratio of ultracold polar NaCs molecular spectra by phase locking detection

We report a method of high-sensitively detecting the weak signal in photoassociation （PA） spectra of ultracold NaCs molecules by phase sensitive-demodulated trap-loss spectra of Na atoms from a photomultiplier tube. We find that the signal-to-noise ratio （SNR） of the PA spectra is strongly dependent on the integration time and the sensitivity of the lock-in amplifier, and our results show reasonable agreement with the theoretical analyses of the SNR with the demodulation parameters. Meanwhile, we investigate the effect of the interaction time of the PA laser with the colliding Na-Cs atom pairs on the SNR of the PA spectra. The atom loss rate is dependent on both the PA-induced atom loss and the loading of the MOT. The high-sensitive detection of the excited ultracold NaCs molecules lays a solid foundation for further study of the formation and application of ultracold NaCs molecules.

Signal-to-noise ratio comparison of angular signal radiography and phase stepping method

Grating-based x-ray phase contrast imaging has the potential to be applied in future medical applications as it is compatible with both laboratory and synchrotron source. However, information retrieval methods are important because acquisition speed, scanning mode, image quality, and radiation dose depend on them. Phase-stepping （PS） is a widely used method to retrieve information, while angular signal radiography （ASR） is a newly established method. In this manuscript, signal-to-noise ratios （SNRs） of ASR are compared with that of PS. Numerical experiments are performed to validate theoretical results. SNRs comparison shows that for refraction and scattering images ASR has higher SNR than PS method, while for absorption image both methods have same SNR. Therefore, our conclusions would have guideline in future preclinical and clinical applications.

Local Correlated Noise Improvement of Signal-to-Noise Ratio Gain in an Ensemble of Noisy Neuron

We theoretically investigate the collective response of an ensemble of leaky integrate-and-fire neuron units to a noisy periodic signal by including local spatially correlated noise. By using the linear response theory, we obtained the analytic expression of signal-to-noise ratio (SNR). Numerical simulation results show that the rms amplitude of internal noise can be increased up to?an optimal value where the output SNR reaches a maximum value. Due to the existence of the local spatially correlated noise in the units of the ensemble, the SNR gain of the collective ensemble response can exceed unity and can be optimized when the nearest-neighborhood correlation is negative. This nonlinear collective phenomenon of SNR gain amplification in an ensemble of leaky integrate-and-fire neuron units can be related to the array stochastic resonance (SR) phenomenon. Furthermore, we also show that the SNR gain can also be optimized by tuning the number of neuron units, frequency and?amplitude of the weak periodic signal. The present study illustrates the potential to utilize the local spatially correlation noise and the number of ensemble units for optimizing the collective response of the neuron to inputs, as well as a guidance in the design of information processing devices to weak signal detection.

Study of signal-to-noise ratio driven by colored noise

This paper investigates the signal-to-noise ratio(SNR)driven by colored noise and weak input signals.Based on the Cauchy-Schwarz and Rayleigh quotients inequalities,an analytical expression of SNR is developed,and its upper band is closely related to the Fisher information of noise.For mimicking the colored noise,we adopt the first-order moving-average model and propose the optimal input signal waveform.The stochastic resonance effect in threshold systems is demonstrated for the Gaussian mixture colored noise.The obtained results will be interesting in the case of improving the nonlinear filter performance by adding noise to the weak signal corrupted by the colored noise.

Noise analysis of grating-based x-ray differential phase-contrast imaging with angular signal radiography

X-ray phase-contrast imaging is one of the novel techniques,and has potential to enhance image quality and provide the details of inner structures nondestructively.In this work,we investigate quantitatively signal-to-noise ratio（SNR） of grating-based x-ray phase contrast imaging（GBPCI） system by employing angular signal radiography（ASR）.Moreover,photon statistics and mechanical error that is a major source of noise are investigated in detail.Results show the dependence of SNR on the system parameters and the effects on the extracted absorption,refraction and scattering images.Our conclusions can be used to optimize the system design for upcoming practical applications in the areas such as material science and biomedical imaging.

A blind source separation algorithm based on negentropy and signal noise ratio

A novel blind source separation （BSS） algorithm based on the combination of negentropy and signal noise ratio （SNR） is presented to solve the deficiency of the traditional independent component analysis （ICA） algorithm after the introduction of the principle and algorithm of ICA. The main formulas in the novel algorithm are elaborated and the idiographic steps of the algorithm are given. Then the computer simulation is used to test the performance of this algorithm. Both the traditional FastlCA algorithm and the novel ICA algorithm are applied to separate mixed signal data. Experiment results show the novel method has a better performance in separating signals than the traditional FastlCA algorithm based on negentropy. The novel algorithm could estimate the source signals from the mixed signals more precisely.

基于ICEEMDAN和分布熵的SS-Y伸缩仪信号随机噪声压制方法

结合改进的自适应噪声完备集合经验模态分解(ICEEMDAN)与分布熵(DistEn),提出一种无需自定义算法参数、去噪效果较好的伸缩仪信号随机噪声压制方法。首先将伸缩仪信号进行ICEEMDAN处理,得到若干个本征模态函数(IMF);然后计算各IMF分量的分布熵值,根据不同分布熵值的大小和表征的分量信号混乱程度,有针对性地对各IMF进行取舍;最后进行线性重构。设计仿真信号去噪实验和SS-Y伸缩仪信号去噪实验,结果表明,基于ICEEMDAN-DistEn去噪模型的伸缩仪信号重构还原度较好,去噪效果显著,明显优于CEEMDAN-DistEn、小波去噪和卡尔曼滤波等去噪模型。

基于EM-KF算法的微地震信号去噪方法

针对微地震信号能量较弱,噪声较强,使微地震弱信号难以提取问题,提出了一种基于EM-KF(Expectation Maximization Kalman Filter)的微地震信号去噪方法。通过建立一个符合微地震信号规律的状态空间模型,并利用EM(Expectation Maximization)算法获取卡尔曼滤波的参数最优解,结合卡尔曼滤波,可以有效地提升微地震信号的信噪比,同时保留有效信号。通过合成和真实数据实验结果表明,与传统的小波滤波和卡尔曼滤波相比,该方法具有更高的效率和更好的精度。

磁共振AIR-魔毯线圈在胸椎结核扫描中的应用价值

目的探讨联合应用AIR-魔毯线圈磁共振成像(MRI)对胸椎结核扫描图像信噪比(signal to noise ratio,SNR)、对比噪声比(contrast to noise ratio,CNR)和脂肪抑制成像效果的价值。方法选取我院80例经手术病理证实为胸椎结核患者,按1:1随机分为两组,应用常规线圈(脊柱相控阵线圈,头颈联合线圈)、常规线圈联合AIR魔毯线圈对两组患者分别进行扫描。扫描序列包括胸椎矢状位T_(2)WI,T_(1)WI,T_(2)FLEX,进一步测量、比较SNR,CNR及脂肪抑制效果,分析MRI多序列诊断胸腰椎结核的准确率、特异度和灵敏度。结果常规线圈联合AIR-魔毯线圈扫描组,胸椎矢状位图像的SNR、CNR及压脂效果优于常规线圈组。结论联合应用AIR-魔毯线圈的图像SNR、CNR得到提高,脂肪抑制效果稳定良好。

基于三稳态随机共振的ZPW-2000移频信号检测方法研究

针对ZPW-2000移频信号谐波干扰检测技术在抑制噪声时,不可避免损害有用信号的问题,提出三稳态随机共振系统,将部分噪声能量向移频信号能量转化,减少移频信号能量损失。首先,将移频信号的中心频率从较高频段搬移到较低频段,着重研究移频信号的有用频段。选取尺度变化系数为1 000,保证输入信号在尺度变化的过程中满足数值计算的稳定性,使尺度变换后的小参数信号满足绝热近似理论。其次,使用混沌麻雀算法(chaos sparrow search optimization algorithm, CSSOA)优化三稳态随机共振系统,得到输出信噪比(signal-to-noise ratios, SNR)最大时的系统参数a、b、c以及γ。然后,将搬移至低频区段的移频信号输入至优化后的三稳态随机共振系统,实现信号降噪。最后,对降噪信号采用复调制快速傅里叶算法(zoom fast fourier transformation, ZFFT)实现低频信号的检测。仿真结果表明:对比三稳态随机共振系统处理前后移频信号频谱图,显示频域特征增强,在降噪的同时不损害移频信号的能量;以-5 dB为步长将噪声注入至移频信号时,输出信噪比至少可提升16.54 dB;降噪后的信号通过ZFFT算法解调能够完成ZPW-2000移频信号低频信号的检测。对比经验模态分解算法、小波阈值算法等算法处理不同信噪比的含噪移频信号,验证混沌麻雀算法优化的三稳态随机共振系统输出信噪比较大,降噪效果较好,具有良好的抗干扰能力。研究结果为实现在抑制噪声的同时减少移频信号能量损失提供参考。

基于φ-OTDR的车辆振动信号检测方法

针对相位敏感光时域反射计(Phase Sensitive Optical Time Domain Reflectometer,φ-OTDR)系统在车辆振动信号检测中信号信噪比低且检测准确率受频率影响的问题,提出了一种引导边缘滤波检测(Guided Edge Filter,GEF)方法。GEF方法在减少噪声影响的同时保留并提取出有效的振动位置信息。使用PZT分别产生不同频率的振动信号,将GEF方法与直接振幅差分法进行实验对比。结果表明,使用GEF方法得到的平均信噪比相较于直接振幅差分法提高了3.26 dB。使用公路上行驶车辆的振动信号进行测试,结果表明,使用GEF方法得到的信噪比最高为3.98 dB,比直接振幅差分法提高了2.65 dB。

基于PE-CEEMD-SVD的Φ-OTDR信号降噪方法

为实现相位敏感光时域反射仪中相位信号的精确测量,提出了一种基于排列熵算法的互补集合经验模态分解联合奇异值分解的新型降噪方法(PE-CEEMD-SVD)。首先,对含有噪声的相位信号进行CEEMD分解,得到一系列频率不同的IMF分量;然后,将PE算法和相关系数机制相结合,保留较大相关的有用分量,对较小相关的噪声分量使用SVD算法进行二次降噪;最后将两次降噪后保留下来的有用分量进行重构。仿真和实验结果表明,相较于EMD、EEMD和CEEMD降噪方法,该方法可获得更高信噪比的信号,有利于相位信号的精确测量。

基于综合熵权-信噪比的球轴承外圈沟道ELID磨削加工参数优化

为提高球轴承外圈沟道表面的加工质量,实现对电解在线砂轮修整ELID磨削加工试验中摆动参数、磨削参数的精确控制,设计正交试验并基于试验数据建立表面质量与加工参数之间的二阶响应模型和指数回归模型;通过熵权理论确定表面粗糙度与轮廓度的综合权重,最终获得最优加工参数,并结合信噪比的基本特性分析加工参数对表面质量的影响程度。结果表明:指数回归模型对表面粗糙度预测较为精确,二阶响应模型对轮廓度预测较为精确,误差均在5%以下;表面粗糙度、轮廓度综合权重分别为0.4568,0.5432,最优加工参数为摆动幅度11°、摆动速度6°/s、砂轮转速19500 r/min、工件转速50 r/min;加工参数的影响程度由大到小依次为摆动幅度、摆动速度、工件转速、砂轮转速。

低信噪比下基于深度学习TCNN-MobileNet的调制识别

将深度学习算法应用于调制识别任务是近年来通信领域的一个研究热点,但现有方法存在网络复杂度高、硬件要求高、在低信噪比(SNR)下识别准确率不高等问题。结合离散小波变换方法,提出一种基于双路卷积神经网络级联可分离卷积网络(TCNN-MobileNet)的调制识别方法。首先通过小波变换对数据进行预处理,将信号作为输入传送到双路卷积神经网络中进行不同维度的特征提取;然后通过融合层进行特征融合并送入轻量级神经网络MobileNetV1中,进行调制识别模型训练;最后通过全连接层进行11种调制识别的分类输出。在公开数据集RML2016.10a上的实验结果表明,在-20dB的低SNR下TCNN-MobileNet的识别准确率可达88.71%,在18dB的高SNR下识别准确率可达96.66%,SNR在-20~18dB范围内时平均识别准确率为88.37%,相比于ResNet18、ResNet34等经典网络架构提升了约35%。TCNN-MobileNet识别方法在保证识别精度不变的情况下能够降低训练参数量以及网络训练时间,有效简化网络架构,降低对硬件设备的要求,对轻量级神经网络在调制识别中的应用具有借鉴意义。