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.

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.

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.

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.

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.

Effect of signal-to-noise ratio on the automatic clustering of X-ray diffraction patterns from combinatorial libraries

Hierarchical clustering algorithm has been applied to identify the X-ray diffraction(XRD)patterns from a high-throughput characterization of the combinatorial materials chips.As data quality is usually correlated with acquisition time,it is important to study the hierarchical clustering performance as a function of data quality in order to optimize the efficiency of high-throughput experiments.This work investigated the effects of signal-to-noise ratio on the performance of hier-archical clustering using 29 distance metrics for the XRD patterns from Fe−Co−Ni ternary combinatorial materials chip.It is found that the clustering accuracies evaluated by the F1 score only fluctuate slightly with signal-to-noise ratio varying from 15.5 to 22.3(dB)under the experimental condition.This suggests that although it may take 40-50 s to collect a visually high-quality diffraction pattern,the measurement time could be significantly reduced to as low as 4 s without substantial loss in phase identification accuracy by hierarchical clustering.Among the 29 distance metrics,Pearsonχ^(2)shows the highest mean F1 score of 0.77 and lowest standard deviation of 0.008.It shows that the distance matrixes calculated by Pearsonχ^(2)are mainly controlled by the XRD peak shifting characteristics and visualized by the metric multidimensional data scaling.

Usefulness of white blood cell count to mean platelet volume ratio for predicting long-term prognosis after primary percutaneous coronary intervention in patients with acute coronary syndrome

Background and Objective The white blood cell count to mean platelet volume ratio(WMR)has recently been described as a predictor of cardiovascular events in patients who undergo percutaneous coronary intervention(PCI).The aim of this study was to investigate the usefulness of admission WMR in predicting outcomes in patients with acute coronary syndrome(ACS).

面板数据均值渐变变点的Ratio检验

为解决时间序列因观测时间较短而难以确定变点的问题,通过分析独立同分布序列面板数据,对其均值的渐变变点进行检验.假设面板数据中每条时间序列的均值在同一时刻发生变化,提出Ratio检验统计量,并分别在原假设与备择假设下给出检验统计量的极限性质.数值模拟结果表明,面板数据的检验效果优于单条时间序列,实例分析也验证了该方法的有效性与实用性.

Effect of Strain Ratio on Fatigue Model of Ultra-fine Grained Pure Titanium

The ultra-fine grained(UFG)pure titanium was prepared by equal channel angular pressing(ECAP)and rotary swaging(RS).The strain controlled low cycle fatigue(LCF)test was carried out at room temperature.The fatigue life prediction model and mean stress relaxation model under asymmetrical stress load were discussed.The results show that the strain ratio has a significant effect on the low cycle fatigue performance of the UFG pure titanium,and the traditional Manson-coffin model can not accurately predict the fatigue life under asymmetric stress load.Therefore,the SWT mean stress correction model and three-parameter power curve model are proposed,and the test results are verified.The final research shows that the threeparameter power surface model has better representation.By studying the mean stress relaxation phenomenon under the condition of R≠-1,it is revealed that the stress ratio and the strain amplitude are the factors that significantly afiect the mean stress relaxation rate,and the mean stress relaxation model with the two variables is calculated to describe the mean stress relaxation phenomenon of the UFG pure titanium under different strain ratios.The fracture morphology of the samples was observed by SEM,and it was concluded that the final fracture zone of the fatigue fracture of the UFG pure titanium was a mixture of ductile fracture and quasi cleavage fracture.The toughness of the material increases with the increase of strain ratio at the same strain amplitude.

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.

基于初始质心的K-Means算法优化

由于传统K-Means聚类算法对初始质心的赋值具有随机性,使得模拟结果有着极大的波动。针对这一问题,文章采用均分法,首先将样本数据清洗,将偏差值较大的数据筛除,然后把处理后的样本数据在二维平面内均匀划分成若干等分,进行排序归纳计算出最佳初始质心用于聚类算法的初次迭代,并调用SSE等度量指标对迭代的质心进行更新,最后将样本数据划分成有意义的簇。实验结果表明,文章针对K-Means算法的优化在一定程度上减少了质心的迭代次数,节省了时间同时提高了准确率,对初始质心赋值优化具有有效性和实用性的特点。

基于K-means-RF耦合模型的成矿远景区预测

在使用机器学习的方法对研究区进行成矿远景区预测时,负样本选取的原则与方法一直是关注的重点。基于此,以陕西省铁佛寺-汉阴地区作为研究区,分析研究区成矿模型确定证据图层,使用K-means聚类方法客观地圈定负样本选取的范围,提高负样本选取的合理性。确定选取范围后,依照不同的原则选取4组负样本点和完全随机选取的负样本点组成5组数据集,使用随机森林(RF)模型进行最终的成矿预测。使用AUC值对RF模型的精度进行评价,对于高AUC值的模型使用精度提高比(AIR)判断模型的稳定性,最终确定使用K-means聚类的第三组负样本数据进行成矿预测的效果为5组数据集中最优。可为铁佛寺-汉阴地区的矿产勘查工作提供一定的理论参考和技术指导。

急性冠状动脉综合征患者入院早期白细胞计数与平均血小板体积比值和血小板与淋巴细胞比值与冠状动脉狭窄程度及院内主要不良心血管事件的评估价值

目的 分析急性冠状动脉综合征(ACS)患者的白细胞计数与平均血小板体积比值(WMR)和血小板与淋巴细胞比值(PLR)与冠状动脉病变严重程度的相关性,并探究WMR、PLR对冠状动脉狭窄程度及院内主要不良心血管事件(MACE)的预测价值。方法 本研究为回顾性分析,收集了2020年12月至2022年11月在新疆医科大学第一附属医院急诊抢救室因胸痛、胸闷首次诊断为ACS并接受经皮冠状动脉介入(PCI)治疗的391例患者数据。根据临床分型,将患者分为不稳定型心绞痛(UA)组147例,非ST段抬高型心肌梗死(NSTEMI)组114例,及ST段抬高型心肌梗死(STEMI)组130例。进一步按照是否出现院内MACE分为MACE组106例和无MACE组285例。依据Gensini评分中位数将患者分为重度病变组(Gensini评分≥53分)196例和轻度病变组(Gensini评分<53分)195例。比较各组患者的基本资料及实验室检查指标,使用二元多因素logistic回归模型筛选冠状动脉狭窄程度及院内MACE的影响因素,分析WMR、PLR与冠状动脉狭窄程度的相关性,并通过ROC曲线分析WMR、PLR及其联合对冠状动脉狭窄程度及院内MACE的预测诊断价值。结果 WMR和PLR与ACS患者冠状动脉狭窄程度均呈正相关(r=0.336,P<0.001;r=0.374,P<0.001)。二元多因素logistic回归分析表明,WMR和PLR均为冠状动脉狭窄程度和院内MACE的独立危险因素。ROC曲线结果显示,WMR、PLR及两者联合预测院内MACE的AUC分别为0.748、0.722和0.792;预测冠状动脉狭窄程度的AUC分别为0.694、0.716和0.770。结论 ACS患者入院早期WMR和PLR与冠状动脉狭窄程度呈正相关,并且具有预测院内MACE及冠状动脉狭窄程度的临床价值,其联合使用在诊断效果上更为优越。