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. .

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.

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.

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.

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.

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.

Seismic acceleration signal analysis and application

Although various types of geophones are applied in seismic exploration,there are only three common types of signals produced by geophones:displacement,velocity,and acceleration signals.Currently,our understanding of the signal characteristics,such as the generation mechanism,the geophysical properties,and the significance of the corresponding rock physics,remains unclear,which makes it difficult to both scientifically evaluate and take full advantage of the different types of geophones.In this paper,the mechanism by which seismic waves are generated is studied based on the spring–damped vibration theory.The physical characteristics of the three above-mentioned signal types and the relationships among the physical properties of the signals and medium are analyzed,as well as the signalto-noise ratio(SNR),resolution,and spectrum characteristics.Based on laboratory tests,field experiments,and applications,we obtained the following conclusions.The acceleration signal reflects the elastic characteristics of the medium and the change rules,and the signal strength is positively correlated with physical property changes.The acceleration signal has favorable attributes,such as small distortion,high fidelity,strong high-frequency amplitudes,and a wide frequency band.Therefore,the acceleration signal is more suitable for high-precision seismic exploration of complex media.In addition,the P-wave acceleration signal more accurately reflects the elastic Young modulus,shear modulus,and density changes than the velocity signal.However,the sensitivity decreases with increasing shear modulus and density.For the S-wave,the acceleration signal is more sensitive to the shear modulus and density than the velocity signal.

Novel non-coherent integration method using binary phase-coded radar signal

The m series with 511 bits is taken as an example being applied in non-coherent integra- tion algorithm. A method to choose the bi-phase code is presented, which is 15 kinds of codes are picked out of 511 kinds of m series to do non-coherent integration. It is indicated that the power in- creasing times of larger target sidelobe is less than the power increasing times of smaller target main- lobe because of the larger target＇ s pseudo-randomness. Smaller target is integrated from larger tar- get sidelobe, which strengthens the detection capability of radar for smaller targets. According to the sidelobes distributing characteristic, a method is presented in this paper to remove the estimated sidelobes mean value for signal detection after non-coherent integration. Simulation results present that the SNR of small target can be improved approximately 6. 5 dB by the proposed method.

Research and Simulation on Weak Signal Detection Based on Duffing Oscillator and Damping Ratio Perturbation

The chaotic system is sensitive to the initial value, and this can be applied in the weak signal detection. There are periodic, critical and chaotic states in a chaotic system. When the system is in the critical state, a small perturbation of system,n parameter may lead to a qualitative change of the system＇s state. This paper introduces a new method to detect weak signals by the way of disturbing the damping ratio. The authors choose the duffing equation, using MATLAB to carry on the simulation, to study the changes of the system when the signal to be measured is added to the damping ratio. By means of observing the phase loots chart and time damin chart, the weak signal will be detected.

Long term integration of radar signals with unknown Doppler shift for ubiquitous radar

Ubiquitous radar is a new radar system that provides continuous and uninterrupted multifunction capability within a coverage volume. Continuous coverage from close-in ＂pop-up＂ targets in clutter to long-range targets impacts selection of waveform parameters. The coherent processing interval （CPI） must be long enough to achieve a certain signal-to-noise ratio （SNR） that ensures the efficiency of detection. The condition of detection in the case of low SNR is analyzed, and three different cases that would occur during integration are discussed and a method to determine the CPI is presented. The simulation results show that targets detection with SNR as low as -26 dB in the experimental system can possibly determine the CPI.

Performance Analysis on WDMA Networks with Wavelength Routers and In-line Amplifiers under Non-ideal Signal Extinction Ratio

Signal extinction ratio (EXT) induced penalty in WDMA networks with wavelength routers and in line optical amplifiers is investigated. Theoretical results show that EXT can greatly influence network performance when signal related beat noise dominates. EXT should be higher than 10dB to prevent steep degradation of power penalty. Furthermore, a trade off between the number of wavelength routers and that of in line amplifiers within WDMA networks should be made.

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.

Signal Separation and Instantaneous Frequency Estimation Based on Multi-scale Chirplet Sparse Signal Decomposition

An approach based on multi-scale ehirplet sparse signal decomposition is proposed to separate the malti-component polynomial phase signals, and estimate their instantaneous frequencies. In this paper, we have generated a family of multi-scale chirplet functions which provide good local correlations of chirps over shorter time interval. At every decomposition stage, we build the so-called family of chirplets and our idea is to use a structured algorithm which exploits information in the family to chain chirplets together adaptively as to form the polyncmial phase signal component whose correlation with the current residue signal is largest. Simultaueously, the polynomial instantaneous frequency is estimated by connecting the linear frequency of the chirplet functions adopted in the current separation. Simulation experiment demonstrated that this method can separate the camponents of the multi-component polynamial phase signals effectively even in the low signal-to-noise ratio condition, and estimate its instantaneous frequency accurately.

PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

Data transmission through a wireless network has faced various signal problems in the past decades.The orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among others.The main problem faced by 5G wireless OFDM is distortion of transmission signals in the network.This transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various techniques.This study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless communication.Partial transmit sequence(PTS)helps in the fast transfer of data in wireless LTE.PTS is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G networks.Result indicates that the proposed system outperforms other existing techniques.Therefore,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm optimization.Hence,the specified design supports in improving the proposed PAPR reduction architecture.

GNSS线极化天线干涉信号反演土壤湿度算法测试

利用全球导航卫星系统干涉信号(GNSS-IR)测量土壤湿度已成为热门的研究课题。搭载低成本线性极化天线的智能手机可以方便快捷采集干涉信号信噪比(SNR)。分别仿真垂直和水平线性极化天线采集的GNSS干涉信号,给出2种极化方式下干涉信号SNR波形和反射率随卫星高度角变化的结果。对于垂直极化分量,电磁波会在入射角65°~85°左右时发生全透射,导致干涉信号振荡效果消失,而水平极化不存在该现象。同时,分别仿真右旋圆极化(RHCP)直射和左旋圆极化(LHCP)反射天线采集的GNSS信号,并计算直反射信号的幅值比。在仿真基础上分别利用不同极化天线进行实验,结果表明:采用线性极化天线采集的GNSS干涉信号振荡效果几乎不受卫星高度角的限制,可以为土壤湿度反演提供更多的有效数据,并且反演得到的土壤湿度与同位数据具有良好的一致性,两者的相关性达到0.95。使用搭载圆极化天线的双通道接收机采集北斗系统卫星数据进行对比,相关性达到0.91。对于不同的设备,智能手机采集的GNSS数据占用空间相对比于双通道接收机降低1%,且反演结果相关性接近,由于干涉信号提取直反射信号需要一定的振荡周期,故反演结果的时间分辨率要低于双通道接收机。