Noise-to-signal Ratio Research Articles

PLATO’s signal and noise budget

ESA’s PLATO mission aims the detection and characterization of terrestrial planets around solar-type stars as well as the study of host star properties. The noise-to-signal ratio (NSR) is the main performance parameter of the PLATO instrument, which consists of 24 Normal Cameras and 2 Fast Cameras. In order to justify, verify and breakdown NSR-relevant requirements the software simulator PINE was developed. PINE models the signal pathway from a target star to the digital output of a camera based on physical models and considers the major noise contributors. In this paper, the simulator’s coarse mode is introduced which allows fast performance analyses on instrument level. The added value of PINE is illustrated by exemplary applications.

Carbon ion radiography with a composite ionization chamber detector

Range uncertainty in carbon ion therapy can diminish treatment efficacy because it may cause deviation from the planned dose distribution. The precise and accurate determination of relative stopping power (RSP) maps of carbon ions in the patient is a direct solution to this problem. To obtain RSP maps in patients undergoing carbon ion radiography, our team developed a preliminary prototype of a composite ionization chamber detection (CICD) system. The CICD prototype employs synchronously gated integral electronics with the ability to measure the depth-to-dose curve and the beam profile simultaneously. Carbon ion radiography experiments were performed on hemispherical, sloped, and stepped phantoms using the Heavy Ion Medical Machine (HIMM) beam. The beam energy was 190.19 MeV/μ and the beam spot full width at half maximum (FWHM) was 7.42 mm. The radiographic image of the sloped phantom, the thickness prediction accuracy of each pixel (2 mm) is 88.25%, its absolute mean error (AME) is 1.07 mm, and the maximum absolute deviation (MAD) is 2.64 mm. The prediction accuracy of the CICD prototype is mainly affected by electronic noise, with a noise-to-signal ratio (NSR) of about 14.36 dB. Carbon ion radiography simulations were performed in this study using Geant4 software to eliminate the effect of the electronic noise. The thickness prediction accuracy is 98.54%, 98.62%, and 99.07% per pixel for hemispherical, sloped and stepped phantoms, respectively, with AME of 0.09 mm, 0.27 mm, and 0.48 mm. Carbon ion radiography utilizing the CICD prototype scheme has the ability to refine the accuracy and resolution of radiographic images, consequently establishing a scientific foundation for diminishing the effects of range uncertainty and fully exploiting the advantages of precision particle therapy.

Analyses of BER-referred noise-to-signal ratio in online measurements of high-baud-rate coherent receivers.

Digital coherent transmission features a very large transmission bandwidth and has played a main role in core optical transmission networks. With the progress of semiconductor technologies, practical coherent transceivers with rates over 100 Gbaud are becoming feasible. With such advances, the transceiver components must have lower power consumption and lower costs, and it becomes important to know how each component contributes to the overall transmission performance. Here, to decompose the effects of noise factors in high-baud-rate DP-16QAM transmissions, we used the theoretical relationship between the bit error rate (BER) and noise-to-signal ratio (NSR) and performed linear analyses. The NSR could be decomposed into individual noise contributions according to dependences on the inverse signal and local photocurrents. The obtained parameters were shown to be useful for predicting required optical signal-to-noise ratio (ROSNR) characteristics.

Comparison of On-Line Partial Discharge Detection Techniques for High Voltage Power Cable Joints and Terminations

Cable joints and terminations play a vital role in providing dependable electrical connection, mechanical support and physical safeguard. These provide electrical stress control to shielded power cables. Despite their usefulness, they suffer from partial discharges (PD) because of enhanced voltage stress, moisture ingress and poor workmanship (during installation). Therefore, it is necessary to undertake on-line PD detection to determine their state. Some of these techniques are capacitive coupler (CC); acoustic emission (AE); and high frequency current transducer (HFCT). This article presents a literature review of these techniques based on the cost, availability, and applicability. The comparative analysis is also provided on location of their sensors, quantification and detection ability. The CC technique involves the quantification of the coupler sensor input measured in mV/pC by realizing the time of flight between two sensors for which the results are used to estimate location of the PD. Meanwhile, the AE technique has an advantage of high immunity to electrical noise, with a caveat that acoustic signals are highly attenuated within the cable joints. Additionally, the combination of the acoustic sensors and PD electrical couplers can be used to discriminate PDs from electrical noise. The HFCT has two methods – with demonising (HFCT-WiD) and without demonising (HFCT-WoD). The HFCT-WiD technique can significantly reduce the sensor’s detection sensitivity due to its high value of noise to signal ratio (NSR). Comparatively, the HFCT has the best results of quantification and detection ability for PDs among all three techniques investigated. However, in places where electrical noise is severe, PD activities may be detected effectively with AE technique. Further work is needed to statistically map these methods and establish their correlation with experimental data.

Perturbation theory for cross data matrix-based PCA

Principal component analysis (PCA) has long been a useful and important tool for dimension reduction. However, this method must be used with care under certain circumstances such as high dimension and small sample size. In general, low dimension with large sample size or large signal to noise ratio is vital to guarantee the consistency of the leading eigenvalues and eigenvectors obtained by PCA. Cross data matrix (CDM)-based PCA is another way to estimate PCA components, through splitting data into two subsets and calculating singular value decomposition for the cross product of the corresponding covariance matrices. It has been shown that CDM-based PCA has a broader region of consistency than ordinary PCA for leading eigenvalues and eigenvectors. Although the difference in regions of consistency is well studied, an interesting practical as well as theoretical question is how they differ in eigenvalues and eigenvectors estimation, especially for the case where both fall in a common region of consistency. In this article, we derive the finite sample approximation results as well as the asymptotic behavior for CDM-based PCA via matrix perturbation. Furthermore, we also derive a comparison measure for CDM-based PCA vs. ordinary PCA. This measure only depends on the data dimension, noise correlations and the noise-to-signal ratio (NSR). Using this measure, we develop an algorithm, which selects good partitions and integrates results from these good partitions to form a final estimate for CDM-based PCA. Numerical and real data examples are presented for illustration.

Speckle Noise Detection and Removal for Laser Speech Measurement Systems

Laser speech measurement is a new sound capture technology based on Laser Doppler Vibrometry (LDV). It avoids the need for contact, is easily concealed and is ideal for remote speech acquisition, which has led to its wide-scale adoption for military and security applications. However, lasers are easily affected by complex detection environments. Thus, speckle noise often appears in the measured speech, seriously affecting its quality and intelligibility. This paper examines all of the characteristics of impulsive noise in laser measured speech and proposes a novel automatic impulsive noise detection and removal method. This method first foregrounds noise using decorrelation based on a linear prediction (LP) model that improves the noise-to-signal ratio (NSR) of the measured signal. This makes it possible to detect the position of noise through a combination of the average short-time energy and kurtosis. The method not only precisely locates small clicks (with a duration of just a few samples), but also finds the location of longer bursts and scratches (with a duration of up to a hundred samples). The located samples can then be replaced by more appropriate samples whose coding is based on the LP model. This strategy avoids unnecessary processing and obviates the need to compromise the quality of the relatively large fraction of samples that are unaffected by speckle noise. Experimental results show that the proposed automatic speckle noise detection and removal method outperforms other related methods across a wide range of degraded audio signals.

Seismic Interference Noise Attenuation by Convolutional Neural Network Based on Training Data Generation

External source interference noise (ESIN) is a common kind of noise in marine seismic data acquisition. According to the noise-to-signal ratio (NSR), a shot gather can be divided into a low NSR part and a high NSR part. The existing ESIN attenuation methods work well in high NSR parts of shot gathers. However, because the signals in low NSR parts are much stronger than ESINs, these methods cannot suppress the ESINs in low NSR parts, and they usually damage the signals. In this letter, we propose a deep-learning method to suppress the ESINs in low NSR parts based on a convolutional neural network (CNN). The end-to-end fully convolutional network needs labeled training samples; however, the real data are unlabeled, i.e., the ESINs in low NSR parts are unknown. To obtain the labeled training data, we propose a sample generation method based on real data. The ESINs in high NSR parts extracted by the traditional methods and the signals of the clean shot gathers are added together to synthesize training samples. We then use the synthesized data and its ESINs to train the network. The experiments prove that the proposed method can suppress the ESINs in low NSR parts properly and protect the signals as well.

Optimal Sphere-Sensor Ratio for Microsphere-Enhanced Photodetector Sensors in Cameras/Telescopes With and Without Central Obscuration

A microsphere lens on top of a photodetector sensor increases its sensitivity and reduces its Noise-to-Signal Ratio (NSR), as shown in our previous work. This paper discusses a methodology to determine the optimal sphere-sensor ratio, in terms of increased current sensitivity, for microsphere-lens-enhanced photodetectors, using computational analysis including a modeling, with an experimentally derived photo-detector sensor responsivity and simulation setup that emulates conditions generally found within cameras and telescopes. In particular, we study and analyze the effect that several variables have on such ratios. We focus on variables such as microsphere material type, photodetector hardware technology type, light source temperature, and in the case of reflective telescopes, central obscurations from a secondary mirror. Results of the effect of microsphere-sensor misalignment on such ratios are also presented. As we show in this paper, since the optimized ratio is dependent on a given wavenumber considered, therefore the proposed methodology uses an integral optimization over a given spectral band to derive the optimized ratio over that band. Some of our findings show, for the first time, that the optimal ratio is virtually independent of a given central obscuration. Finally, we demonstrate here the flexibility of the proposed methodology in showing performances of microsphere-enhanced photodetector sensors.

Effect of Amplitude and Number of Repetitions of the Perturbation on System Identification of Human Balance Control During Stance.

To unravel the underlying mechanisms of human balance control, system identification techniques are applied in combination with dedicated perturbations, like support surface translations. However, it remains unclear what the optimal amplitude and number of repetitions of the perturbation signal are. In this study we investigated the effect of the amplitude and number of repetitions on the identification of the neuromuscular controller (NMC). Healthy participants were asked to stand on a treadmill while small continuous support surface translations were applied in the form of a periodic multisine signal. The perturbation amplitude varied over seven conditions between 0.02 and 0.20 m peak-to-peak (ptp), where 6.5 repetitions of the multisine signal were applied for each amplitude, resulting in a trial length of 130 sec. For one of the conditions, 24 repetitions were recorded. The recorded external perturbation torque, body sway and ankle torque were used to calculate both the relative variability of the frequency response function (FRF) of the NMC, i.e., a measure for precision, depending on the noise-to-signal ratio (NSR) and the nonlinear distortions. Results showed that the perturbation amplitude should be minimally 0.05 m ptp, but higher perturbation amplitudes are preferred since they resulted in a higher precision, due to a lower noise-to-signal ratio (NSR). There is, however, no need to further increase the perturbation amplitude than 0.14 m ptp. Increasing the number of repetitions improves the precision, but the number of repetitions minimally required, depends on the perturbation amplitude and the preferred precision. Nonlinear contributions are low and, for the ankle torque, constant over perturbation amplitude.

Spectral noise-to-signal ratio priority method with application for visible and near-infrared analysis of whole blood viscosity.

Whole blood viscosity (WBV) is a group of important clinical indicators of cardio-cerebral vascular diseases. Existing detection methods for WBV are complex, making them inconvenient for large population screening. Blood viscosity is closely related to the deformability and aggregation of erythrocytes, which are associated with haemoglobin. Haemoglobin has obvious near-infrared (NIR) spectral absorption. Scattering occurs when NIR light enters a viscous blood sample, and its scattering degree is correlated with blood viscosity. Based on repeated spectral measurements and spectral similarity, spectral noise-to-signal ratio (NSR) was proposed to quantify the spectral scattering effect in the blood sample. A novel selection method of piecewise-continuous wavelengths, named NSR priority-partial least squares (NSRP-PLS), was proposed and applied for visible-NIR quantitative analysis of WBV with high, medium and low shear rates [WBV(H), WBV(M), WBV(L)]. Modelling was separately performed by gender to allow for systematic gender differences in blood viscosity. For the NIR-predicted and clinically measured values of the three WBV indicators in independent validation, the root mean square errors for prediction (SEP) were 0.498, 0.222 and 0.193 (mPa·s), respectively. And the correlation coefficients (RP) were 0.927, 0.934 and 0.927, respectively. Compared with the three current well-performing methods (MW-PLS, CARS-PLS and SPA-PLS), the proposed NSRP-PLS method achieved better predictive accuracy. Results indicated that visible-NIR spectroscopy combined with the NSRP-PLS method can be used for the quantitative analysis of WBV. The proposed analytical method is rapid, reagent-free and is scientific and meaningful for cardio-cerebral vascular diseases screening in large populations.

Random Effects in a Nonlinear Vibration-Based Piezoelectric Energy Harvesting System

Vibration-based energy harvesting is of increasing importance and there is a current challenge to improve energy harvesting capacity exploiting nonlinear and random effects. This article investigates random effects in a nonlinear energy harvesting system. The system is represented by a magnetoelastic structure with two piezoceramic layers attached to the root of a cantilever beam, obtaining a bimorph generator. The energy harvesting system is subjected to three excitation conditions: pure harmonic, pure random and a combination of harmonic and random excitations. Noise-to-Signal Ratio (NSR) is employed to quantify different combinations of the forcing terms, establishing a procedure to evaluate the system performance. This approach is based on Power Spectral Density (PSD) of input and output signals. Numerical simulations are carried out, identifying the better combinations of harmonic and random excitations for energy harvesting purposes. Discussions about the influence of the kind of response are carried out evaluating the differences between periodic and chaotic motions. Conclusions show that both random and nonlinear effects can be tuned in order to enhance energy harvesting capacity.

A Spin Glass Model for Reconstructing Nonlinearly Encrypted Signals Corrupted by Noise

We define a (symmetric key) encryption of a signal mathbf{s}in {mathbb {R^N}} as a random mapping mathbf{s}mapsto mathbf y =(y_1,ldots ,y_M)^Tin mathbb {R}^M known both to the sender and a recipient. In general the recipients may have access only to images mathbf{y} corrupted by an additive noise of unknown strength. Given the encryption redundancy parameter (ERP) mu =M/Nge 1 and the signal strength parameter R=sqrt{sum _i {s_i^2/N}}, we consider the problem of reconstructing mathbf{s} from its corrupted image by a Least Square Scheme for a certain class of random Gaussian mappings. The problem is equivalent to finding the configuration of minimal energy in a certain version of spherical spin glass model, with squared Gaussian random interaction potential. We use the Parisi replica symmetry breaking scheme to evaluate the mean overlap p_{infty }in [0,1] between the original signal and its recovered image (known as ’estimate’) as Nrightarrow infty , for a given (’bare’) noise-to-signal ratio (NSR) gamma ge 0. Such an overlap is a measure of the quality of the signal reconstruction. We explicitly analyze the general case of linear-quadratic family of random mappings and discuss the full p_{infty } (gamma ) curve. When nonlinearity exceeds a certain threshold but redundancy is not yet too big, the replica symmetric solution is necessarily broken in some interval of NSR. We show that encryptions with a nonvanishing linear component permit reconstructions with p_{infty }>0 for any mu >1 and any gamma <infty , with p_{infty }sim gamma ^{-1/2} as gamma rightarrow infty . In contrast, for the case of purely quadratic nonlinearity, for any ERP mu >1 there exists a threshold NSR value gamma _c(mu ) such that p_{infty }=0 for gamma >gamma _c(mu ) making the reconstruction impossible. The behaviour close to the threshold is given by p_{infty }sim (gamma _c-gamma )^{3/4} and is controlled by the replica symmetry breaking mechanism.

Computation Error Analysis of Block Floating Point Arithmetic Oriented Convolution Neural Network Accelerator Design

The heavy burdens of computation and off-chip traffic impede deploying the large scale convolution neural network on embedded platforms. As CNN is attributed to the strong endurance to computation errors, employing block floating point (BFP) arithmetics in CNN accelerators could save the hardware cost and data traffics efficiently, while maintaining the classification accuracy. In this paper, we verify the effects of word width definitions in BFP to the CNN performance without retraining. Several typical CNN models, including VGG16, ResNet-18, ResNet-50 and GoogLeNet, were tested in this paper. Experiments revealed that 8-bit mantissa, including sign bit, in BFP representation merely induced less than 0.3% accuracy loss. In addition, we investigate the computational errors in theory and develop the noise-to-signal ratio (NSR) upper bound, which provides the promising guidance for BFP based CNN engine design.

Performance Evaluation of Multivariate Adaptive Regression Splines (MARS) and Multiple Linear Regression (MLR) for Forward Conversion of Geodetic Coordinates (ϕ, λ, h) to Cartesian Coordinates (X, Y, Z)

In Ghana’s local Geodetic Reference Network, the standard forward transformation equation has played a major role in coordinate transformation between World Geodetic System 1984 (WGS84) and local geodetic datum. Thus, it is an initial step in forward conversion of geodetic coordinates (ϕ, λ, h) to Cartesian coordinates (X, Y, Z) in transformation from global to local datum and vice versa. Several studies in the recent decades have been conducted on converting Cartesian coordinates to geodetic coordinates (reverse procedure) through the utilisation of iterative, approximate, closed form, vector-based and computational intelligence algorithms. However, based on the existing literature covered pertaining to this present study, it was found that the existing knowledge do not fully adhere to the issue of evaluating alternative techniques in the case of the forward conversion. Hence, the aim of this present study was to explore the coordinate conversion performance of the Multivariate Adaptive Regression Splines (MARS) and Multiple Linear Regression (MLR). The performance of each model was assessed based on statistical indicators of Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE), Standard Deviation (SD), Noise to Signal Ratio (NSR), Correlation Coefficient (R), and Correlation of Determination (R2). The statistical findings revealed that the MARS and MLR offered satisfactory prediction of Cartesian coordinates. However, the MLR compared to MARS showed better stability and more accurate prediction results. From the results of this present study, the main conclusion drawn is that, MLR provides a promising alternative in the forward conversion of geodetic coordinates into Cartesian coordinates. Therefore, the capability of MLR as a powerful tool for solving majority of function approximation problems in mathematical geodesy has been demonstrated in this present study.

CVNS Synapse Multiplier for Robust Neurochips With On-Chip Learning

Designing low noise-to-signal-ratio (NSR) structures is one of the main concerns when implementing hardware-based neural networks. In this paper, a new continuous valued number system (CVNS) multiplication algorithm for low-resolution environment is proposed with accurate results. Using the proposed CVNS multiplication algorithm, VLSI implementation of a high-resolution mixed-signal CVNS synapse multiplier for neurochips with on-chip learning is realized. The proposed CVNS multiplication algorithm provides structures with lower NSR. Therefore, the proposed CVNS multiplication algorithm can be exploited to design robust CVNS Adaline for neurochips with on-chip learning.

A New Generic Method for Quantifying the Scale Predictability of the Fractal Atmosphere: Applications to Model Verification

Abstract The authors revisit the issue regarding the predictability of a flow that possesses many scales of motion raised by Lorenz in 1969 and apply the general systems theory developed by Selvam in 1990 to error diagnostics and the predictability of the fractal atmosphere. They then introduce a new generic method to quantify the scale predictability of the fractal atmosphere following the assumptions of the intrinsic inverse power law and the upscale cascade of error. The eddies (of all scales) are extracted against the instant zonal mean, and the ratio of noise (i.e., the domain-averaged square of error amplitudes) to signal (i.e., the domain-averaged square of total eddy amplitudes), referred to as noise-to-signal ratio (NSR), is defined as a measure of forecast skill. The time limit of predictability for any wavenumber can be determined by the criterion or by the criterion , where is the golden ratio and m is a scale index. The NSR is flow adaptive, bias aware, and stable in variation (in a logarithm transformation), and it offers unique advantages for model verification, allowing evaluation of different model variables, regimes, and scales in a consistent manner. In particular, an important advantage of this NSR method over the widely used anomaly correlation coefficient (ACC) method is that it could detect the successive scale predictability of different wavenumbers without the need to explicitly perform scale decomposition. As a demonstration, this new NSR method is used to examine the scale predictability of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) 500-hPa geopotential height.

Area-efficient robust Madaline based on continuous valued number system

Achieving a low Noise-to-Signal Ratio (NSR) is one of the major concerns when implementing hardware-based neural networks. Continuous Valued Number System (CVNS) features have been exploited to improve the NSR. The efficiency of the network model in terms of area, power consumption, and NSR is measured using the product of the total number of neurons in the network multiplied by the network NSR, which indicates the number of neurons required for a specific NSR. The network proposed in this paper stores the weights in digital registers while the processing is done in the analog domain using CVNS arithmetic. The mathematical analysis and comparison between the proposed network and the previous structures prove that the proposed architecture improves upon in terms of both the NSR and the product of neuron multiplied by NSR.

디지털 영상에서 화질관리에 관한 노출지수(EI)의 유용성 연구

The aim of this study was to determine the correlation between exposure index (EI) and dose factors related to radiation dose optimization in digital radiography (DR) system. Two phantoms with built-in regional test object for quantitative assessment of images were used to produce image signals that acquired in chest radiography background. EI and entrane surface dose (ESD) increased proportionally with rise of radiation dose (kVp, mAs) in both DR and CR systems. Especially, DR detector was effective to form good contrast and hence, reached easily to improvement of image quality with minimal dose changes. It made operators possible to expect the accuracy of EI values deeply related to absorbed dose of the detector. The evaluation of images was obtained specially employed calculation of noise to signal ratio (NSR) and contrast to noise ratio (CNR). These measurements were performed for how exposure factors affect image quality. NSR was inversely proportional to kVp and mAs and low NSR represented high signal detection efficiency. Consequently, EI values was the measure of the amount of exposure received by the image receptor and it was proportional to exposure factors. Therefore the EI in a recommended range from manufacturer can offer optimal image quality. Also, continuous monitoring of EI values in the digital radiography can reduce the unnecessary patient dose and help the quality control of the system.

Resolution enhancement of ultrasonic B-mode images

The use of ultrasonic array imaging systems is now commonplace throughout the NDT industry, but interpretation of the images produced by such systems can be a time-consuming process, which is further complicated by the inherent suboptimal resolution of such images. This paper proposes a three-step processing chain: noise-to-signal ratio (NSR) deconvolution, beam model weighted synthetic aperture focusing technique (BMW-SAFT) and statistical background noise removal (SBNR) image noise removal, which can be used to significantly enhance the temporal and spatial resolution of these systems. A validated simulation model is used to generate B-scan image data. Conventional Wiener filter deconvolution and SAFT methods are compared to those proposed, with results indicating a significant improvement.

Radix-2/4 Streamlined Real Factor FFT Algorithms

In this paper three Real Factor FFT algorithms are presented. Two of them are based on Radix-2 and one on Radix-4. The computational complexity of Radix-2 and Radix-4 is shown as order ~ 41/2Nlog2N and ~ 4 1/8 Nlog2N respectively unlike their standard counterparts ~ 5Nlog2N and ~ 41/4 Nlog2N. Moreover, the proposed algorithms also require fewer multiplications than their standard FFTs. We then show that the fixed point implementation of 'real factor' FFT can be modified, with unique scaling procedure, so that its noise to signal ratio (NSR) is lower than the NSR of standard FFT. Finally, implementation issues are presented which verify the suitability of the proposed 'real factor' FFT's.