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. .

Effective DOA Estimation Under Low Signal-to-Noise Ratio Based on Multi-Source Information Meta Fusion

Efficiently performing high-resolution direction of arrival(DOA)estimation under low signal-to-noise ratio(SNR)conditions has always been a challenge task in the literatures.Obvi-ously,in order to address this problem,the key is how to mine or reveal as much DOA related in-formation as possible from the degraded array outputs.However,it is certain that there is no per-fect solution for low SNR DOA estimation designed in the way of winner-takes-all.Therefore,this paper proposes to explore in depth the complementary DOA related information that exists in spa-tial spectrums acquired by different basic DOA estimators.Specifically,these basic spatial spec-trums are employed as the input of multi-source information fusion model.And the multi-source in-formation fusion model is composed of three heterogeneous meta learning machines,namely neural networks(NN),support vector machine(SVM),and random forests(RF).The final meta-spec-trum can be obtained by performing a final decision-making method.Experimental results illus-trate that the proposed information fusion based DOA estimation method can really make full use of the complementary information in the spatial spectrums obtained by different basic DOA estim-ators.Even under low SNR conditions,promising DOA estimation performance can be achieved.

Influence of signal-to-noise ratio on accuracy of spectral analysis by near infrared spectroscopy

As one of the important indicators of spectrometer,signal-to-noise ratio(SNR)reflects the ability of spectrometer to detect weak signals.To investigate the influence of SNR on the prediction accuracy of spectral analysis,we first introduce the major factors affecting the spectral SNR.Taking green tea as an example,the influence of spectral SNR on the prediction accuracy of the origin identification model is analyzed by experiments.At the same time,the relationship between the spectral SNR and prediction accuracy of spectral analysis model is fitted.Based on this,the common methods for improving the spectral SNR are discussed.The results show that the accuracy of the prediction set model first decreases slowly,then decreases linearly,and finally tends to be flat as the spectral SNR decreases.Through calculation,in order to achieve the prediction accuracy of prediction model reaching 90%and 85%,the spectral SNR is required to be higher than 23.42 dB and 21.16 dB,respectively.The overall results provide certain parameters support for the development of new online analytical spectroscopic instruments,especially for the technical indicators of SNR.

An innovative detection method of high frequency BPSK signal with low signal-to-noise ratio

Based on chaotic oscillator system, this paper proposes a novel method on high frequency low signal- to-noise ratio BPSK（ Binary Phase Shift Keying） signal detection. Chaotic oscillator system is a typical non-lin- ear system which is sensitive to periodic signals and immune to noise at the same time. Those properties make it possible to detect low signal-to-noise ratio signals. The BPSK signal is a common signal type which is widely used in modern communication. Starting from the analysis of advantages of chaotic, os~.illator system and signal features of the BPSK signal, we put forward a unique method that can detect low signar-to-noise ratio BPSK sig- nals with high frequency. The simulation results show that the novel method can dclct.t low signal-to-noise ratio BPSK signals with frequency in an order of magnitude of l0s Hz, and the input Signal-to-Noise Ratio threshold can be -20 dB.

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.

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 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.

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 of Raman signal measured by multichannel detectors

Raman spectroscopy has been widely used to characterize the physical properties of two-dimensional materials(2DMs).The signal-to-noise ratio(SNR or S/N ratio)of Raman signal usually serves as an important indicator to evaluate the instrumental performance rather than Raman intensity itself.Multichannel detectors with outstanding sensitivity,rapid acquisition speed and low noise level have been widely equipped in Raman instruments for the measurement of Raman signal.In this mini-review,we first introduce the recent advances of Raman spectroscopy of 2DMs.Then we take the most commonly used CCD detector and IGA array detector as examples to overview the various noise sources in Raman measurements and analyze their potential influences on SNR of Raman signal in experiments.This overview can contribute to a better understanding on the SNR of Raman signal and the performance of multichannel detector for numerous researchers and instrumental design for industry,as well as offer practical strategies for improving spectral quality in routine measurement.

Calculation method of the signal-to-noise ratio attribute of seismic data based on structural orientation

At present,most signal-to-noise ratio(SNR)estimation methods can only calculate the global and not the local SNR of seismic data.This paper proposes a calculation method of a structure-oriented-based seismic SNR attribute.The purpose is to characterize the temporal and spatial variation of the seismic data SNR.First,the local slope parameters of the seismic events are calculated using a plane wave decomposition filter.Then,the singular value decomposition method is used to calculate the local seismic data SNR,thereby obtaining it in time and space.The proposed method overcomes the insufficiency of a conventional global SNR to characterize any local seismic data features and uses the SNR as an attribute of seismic data to more accurately describe the signal-noise energy distribution characteristics of seismic data in time and space.The results of a theoretical model test and real data processing show that the SNR attribute can be used not only for seismic data quality evaluation but also for analysis and evaluation of denoising methods.

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.

Speech Separation Methodology for Hearing Aid

In the design of hearing aids(HA),the real-time speech-enhancement is done.The digital hearing aids should provide high signal-to-noise ratio,gain improvement and should eliminate feedback.In generic hearing aids the perfor-mance towards different frequencies varies and non uniform.Existing noise can-cellation and speech separation methods drops the voice magnitude under the noise environment.The performance of the HA for frequency response is non uni-form.Existing noise suppression methods reduce the required signal strength also.So,the performance of uniform sub band analysis is poor when hearing aid is con-cern.In this paper,a speech separation method using Non-negative Matrix Fac-torization(NMF)algorithm is proposed for wavelet decomposition.The Proposed non-uniformfilter-bank was validated by parameters like band power,Signal-to-noise ratio(SNR),Mean Square Error(MSE),Signal to Noise and Dis-tortion Ratio(SINAD),Spurious-free dynamic range(SFDR),error and time.The speech recordings before and after separation was evaluated for quality using objective speech quality measures International Telecommunication Union-Telecommunication standard ITU-T P.862.

红外遥感相机成像参数优化研究

为了更好地优化红外遥感相机成像参数,文章首次提出信噪比-动态范围积相机性能综合评价指标。建立了红外遥感相机的噪声电压模型并深入分析噪声影响,当恒定噪声电压项远大于时变噪声电压项时噪声电压模型可以近似简化为线性模型。进而建模分析了积分电容和积分时间对信噪比和动态范围的影响,并首次提出信噪比-动态范围积作为相机性能评价的指标。信噪比-动态范围积随着积分时间同步增大,积分时间较小时增速较快,积分时间较大时增速迅速下降;信噪比-动态范围积随着积分电容的增大先增加后减小,存在极值。研究结果对红外遥感相机的参数优化设计具有重要作用。

自适应动态滤波网络地震随机噪声压制方法

由于地质及环境条件复杂,导致地震勘探采集资料信噪比相对较低,对后续的研究带来不利影响,因此地震勘探数据处理中对随机噪声的压制一直备受关注。现有算法无法较好实现对噪声的有效压制和对有效信号的极大保留,为此,将传统方法和深度学习相结合,提出了基于自适应动态滤波网络(Adaptive Dynamic Filtering Net,ADFNet)的方法压制地震资料中的随机噪声。该网络以编码器—解码器为架构,首先引入通道注意力机制(Attention Mechanism,AM)的思想,通过通道AM对空洞卷积多尺度数据特征集成,为网络提供了精准且丰富的特征表示;然后,引入动态卷积,以较低的计算复杂度实现对地震资料高频特征的学习,从而保留更丰富的细节信息。合成数据和实际数据的实验结果均表明,ADFNet可有效压制地震资料中的随机噪声,同时保留更丰富的地震数据细节,处理后的地震数据信噪比得到显著提升。

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。

基于FRFT的低信噪比LFM信号参数快速估计算法

基于分数阶傅里叶变换(Fractional Fourier Transform,FRFT)对线性调频(Linear Frequency Modulated,LFM)信号参数进行估计,问题关键是确定FRFT最佳阶数,根据误差迭代思想提出新的参数估计算法,该算法利用归一化带宽和旋转角的转化关系,由估计误差推算角度差值,有效降低了运算量,不需要调频斜率正负的先验信息,改进的对数搜索算法可以进一步提高参数估计结果的稳定性和可靠性。仿真结果表明,信噪比在-8 dB以上时该方法在高效率的前提下仍具有良好的参数估计性能,平均估计误差在1%以内,估计结果接近Cramer-Rao下限,满足工程实时处理需求。

简易烈度计记录的噪声水平评价:以泸定M_(S)6.8级地震为例

在我国地震预警和烈度速报系统中,烈度计被广泛使用。烈度计的频谱特征主要是通过检定实验室进行标定,但在破坏性地震中,烈度计响应特点缺少必要的研究分析。该文以2022年四川泸定M_(S)6.8级地震为例,对比分析了烈度计记录和强震仪记录在噪声水平、信噪比(signal-to-noise ratio,SNR)、高通滤波截止频率(f_(HP))取值以及有效周期范围(T_(max))方面的特点,综合评价了烈度计记录的频谱特征,分析了噪声影响。得到以下结论:在整个频域范围内,烈度计记录的SNR要比强震仪记录平均意义上低一个数量级,可以估计烈度计动态范围较强震仪大致低20 dB;烈度计记录的最佳f_(HP)取值相较于强震仪记录存在更大的不确定性,尤其是在竖直方向上;烈度计记录的最佳f_(HP)取值更大,反应谱T_(max)较传统强震仪记录在水平方向小1~2 s,在竖直方向小3 s左右。该研究结果有助于合理应用烈度计记录,服务地震学和地震工程学相关研究。

基于改进小波阈值的数据采集系统降噪研究

在数据采集过程中,噪声和干扰的存在严重影响了数据的准确性。为了获得质量较高的数据,减少噪声的影响,该文提出一种基于遗传算法的小波阈值函数去噪方法,并设计以FPGA为主控的数据采集系统进行了多次实验。实验结果表明,经改进算法处理过的数据信噪比达到了芯片手册中所规定的典型值大小。同时,相比传统的阈值函数去噪方法,将新算法应用于采集到的数据,其信噪比提高了7.0248 dB,均方根误差降低了0.1675,表现出了较好的去噪性能,具有较高的工程应用价值。

GEO卫星机会源近岸海面高度测量性能评估

基于同步地球轨道(Geostationary Earth Orbit,GEO)卫星的机会源技术已被证明其在近岸海面高度测量中的应用潜力,但目前研究主要围绕可行性论证展开,极少开展全方位的测量性能评估.为此,从仿真和试验两个角度评估了信号带宽、非相干累加次数、信噪比及波形峰值跟踪算法对GEO卫星机会源海面测高性能的影响.评估结果表明当非相干累加次数和信噪比大于最优非相干累加次数和最优信噪比时,测高性能将不再显著提升.因此,通过选择最优非相干累加次数,以及设计合理的接收天线增益、射频增益及接收机带宽使信噪比大于最优信噪比对GEO卫星机会源海面测高至关重要.通过对比插值和拟合两种波形跟踪算法发现拟合法能提供比插值法更高的测量精度.利用北斗B3I信号和ASTRA 19.2 E卫星信号开展GEO卫星海面测高的试验评估.试验结果表明:在非相干累加次数大于10000、信噪比大于7 dB时,北斗B3I信号和ASTRA 19.2 E卫星信号可分别提供精度约为0.20 m和0.10 m的海面高度.针对GEO卫星多频或多通道传输特点,提出了基于信号频谱合成提高海面测高精度的方法.当利用中卫1号卫星10个通道信号进行合成时,插值法和拟合法的仿真测距精度分别为3.50 cm和0.69 cm.