Random Noise Model Research Articles

Statistical Analysis and Modeling of Random Telegraph Noise Based on Gate Delay Measurement

We propose a characterization methodology for random telegraph noise (RTN) based on gate delay measurement. To convert delay change to MOSFET threshold voltage fluctuation, $ {\Delta V_{T}}$ , a topology-reconfigurable ring oscillator is utilized. We discuss the issue of detecting RTN-induced discrete fluctuations in the delay and develop a kernel density-based method to detect the fluctuations. Characterization results of several RTN properties from a test chip fabricated in a 65 nm bulk process are presented. Particular focus is given on the suitable distribution to present RTN-induced overall $ {\Delta V_{T}}$ distribution and its gate area dependency. The results show that lognormal distribution is better at representing the total $ {\Delta V_{T}}$ distribution. RTN-induced delay fluctuation of 40% has been observed for a single gate under weak inversion operation. Local process variation and RTN amplitude are found to be uncorrelated. The proposed methodology is thus suitable for characterizing RTN of devices operating under switching condition.

Seismic Exploration Random Noise on Land: Modeling and Application to Noise Suppression

In seismic exploration, random noise suppression is one of the key problems in seismic data processing. For random noise attenuation, the most important thing is the understanding of seismic random noise generation and propagation. Seismic random noise is considered as temporal and spatial random processes, and it can be analyzed only qualitatively for now, due to its high variability. In this paper, we classify seismic random noise sources by their generation factors and simulate the random noise of the desert in West China. According to Green’s function, it can be assumed that seismic random noise sources are point-like sources that are distributed around geophones. A seismic random noise record is taken as the superimposed wave field exited by all the independent sources in a homogeneous isotropy half-infinite surface. Based on the wind vibration theory and preliminary study about ambient vibrations, the noise source functions are determined. We obtain the waveforms of different kinds of noise by solving the inhomogeneous wave equations and analyze the characteristics qualitatively and quantitatively. The seismic synthetic record with a 1.6-s time and 250-m distances is obtained, and the characteristics are compared between the simulated and the real noise record in time domain and space domain, respectively. The comparative results show the same characteristics of the simulated noise and the real noise, which demonstrates the feasibility of the proposed method. According to the noise modeling, it is known that the near-field cultural noise is the main component of the random noise in the desert, on the basis of which complex diffusion filtering is selected. The filtered results by complex diffusion filtering is compared with the results of time–frequency peak filtering, which is a popular filtering method of seismic random noise suppression in recent years. The comparative results show that complex diffusion filtering is more suitable for the noise of the desert in the Tarim Basin. This result proves that seismic random noise modeling can provide the guidance for noise attenuation. It lays a foundation for researching the propagation characteristics and better attenuation of seismic random noise in the future.

Ramp Noise Projection in CMOS Image Sensor Single-Slope ADCs

Noise analysis of the ramp reference voltage and its projection at the output of a conventional single-slope ramp analog-to-digital converter (ADC) is presented. This paper gives insight on the reference voltage noise origins during the continuous-time ramping phase of column-parallel CMOS image sensor ADCs, as well as its effect on the final ADC output noise. Theoretical modeling of an occurring random walk noise on continuous-time-based ramp references is presented first. Noise on the ramp does not directly add to the output noise of the ADC, but it is modulated and projected subject to given ramp rise time conditions. Test measurements show that the signal-dependent output noise is consistent with the rate of noise increase by the proposed random walk noise model.

Adaptive denoising for simplified signal-dependent random noise model in optoelectronic detector

Adaptive denoising for simplified signal-dependent random noise model in optoelectronic detector

Angle measurement uncertainty statistical distribution of pulsed laser quadrant photodetector

As a positionsensitive detector, laser quadrant photodetector(QPD) is widely used in the areas such as laser guidance, laser radar and space optical communication. In the echoed laser pulse detection mode, the laser pulse signal arrived at the QPD photosensitive surface is changed in pulse amplitude, pulse width and pulse waveform due to the influences of target characteristic, atmospheric transmission and other complex factors. In addition, there are random noises in QPD itself and the signal processing circuit. These factors will have an uncertainty effect on the angle measurement accuracy of the QPD. However, the study on the statistical distribution of digital angle measurement of pulsed laser QPD has not been carried out so far. To investigate this angle measurement statistical distribution, the channel of laser angle measurement circuit and the echoed laser spot on QPD photosensitive surface should be modeled first. A measurable signal model in one quadrant of QPD processing circuit channel is established based on the type of random noise and the type of desired ideal signal. The random noise model is considered to be a Gaussian distribution, and the ideal laser pulse signal is considered to have the Gaussian or inverted parabolic distribution in the time domain. Taking into account the QPD symmetry, the statistical distributions of angle measurement value αy for five different spot centers are calculated by the Monte Carlo simulation method within the range θ0∈[0,π/4], under the conditions of different signal distribution types in the time domain, different total peak powers of the spots, different ideal signal widths at half maximum, and different standard deviations of equivalent noise voltage probability density. Simulation results show that the statistical distribution of the measured angle αy value is a normal distribution, and is influenced by the above-mentioned conditions, especially by the signaltonoise ratio in one quadrant. QPD possesses higher angular accuracy as the spot center is closer to the axis center. While the spot center is not closer to the axis center, the mean of statistical distribution of the QPD measurement angle αy is always less than the ideal angle measurement value. Therefore, in order to improve the angle measurement accuracy of the pulsed laser QPD for digital purpose, laser pulse transmit power should be increased, or the noise of each circuit channel of QPD should be reduced, or the laser pulse width should be increased by modulating appropriately.

New methods to estimate the observed noise variance for an ARMA model

Abstract For an ARMA model with an observed noise, the observed noise variance estimation is not only a part of the model identification, but also its estimation accuracy affects the following MA parameter estimation accuracy directly. However, it is difficult to improve the estimation accuracy of the observed noise variance, especially when the observed noise variance is small. In this paper, two new methods are proposed to estimate the observed noise variance accurately. In the first method, the lower lags of the auto-covariance function are used to estimate the observed noise variance with high estimation accuracy, but it is valid only when the AR order is greater than the MA order. In the second method, the ARMA model is approximated as a high-order AR model so that it is effective even though the AR order is equal to or less than the MA order. If the observed noise variance is too small, its estimation error may be too large to valid the estimate. An empirical criterion is proposed to judge the necessity of estimating the observed noise variance. The proposed methods are verified by simulations and applied to the random noise modeling for gyroscopes tentatively.

Sparse Representation Denoising Framework for 3-D Building Reconstruction From Airborne LiDAR Data

Random noise has a serious impact on the performance of three-dimensional (3-D) building reconstruction from airborne LiDAR data. In this paper, a sparse representation denoising framework for building roofs from airborne LiDAR is proposed. In the proposed framework, both the random noise and the local structural information are considered. At first, a systematic analysis for the random noise of the rasterized image of raw LiDAR data is presented in detail by taking the random noise of LiDAR data and its local structural information into consideration. With the proposed random noise model, the rasterized image can be adjusted into image with White Gaussian noise. Therefore, by adjusting the rasterized image with the proposed random noise model, sparse representation denoising framework designed for White Gaussian noise is employed in this paper. In order to realize the sparse representation-based denoising framework efficiently, multimanifolds structural dictionaries are learned from clean simulated data by employing K-SVD technique. Finally, three different implementations of the proposed denoising framework are proposed. Experimental results illustrate that the proposed denoising framework can efficiently restore the lost information caused by random noise of 3-D building roof data from airborne LiDAR with obvious improvement comparing with the classical K-SVD-based denoising method.

Hubness-aware kNN classification of high-dimensional data in presence of label noise

Learning with label noise is an important issue in classification, since it is not always possible to obtain reliable data labels. In this paper we explore and evaluate a new approach to learning with label noise in intrinsically high-dimensional data, based on using neighbor occurrence models for hubness-aware k-nearest neighbor classification. Hubness is an important aspect of the curse of dimensionality that has a negative effect on many types of similarity-based learning methods. As we will show, the emergence of hubs as centers of influence in high-dimensional data affects the learning process in the presence of label noise. We evaluate the potential impact of hub-centered noise by defining a hubness-proportional random label noise model that is shown to induce a significantly higher kNN misclassification rate than the uniform random label noise. Real-world examples are discussed where hubness-correlated noise arises either naturally or as a consequence of an adversarial attack. Our experimental evaluation reveals that hubness-based fuzzy k-nearest neighbor classification and Naive Hubness-Bayesian k-nearest neighbor classification might be suitable for learning under label noise in intrinsically high-dimensional data, as they exhibit robustness to high levels of random label noise and hubness-proportional random label noise. The results demonstrate promising performance across several data domains.

An evaluation of three disclosure limitation models

In recent years, the random noise method has been gaining wider use in National Statistical Institutes (NSIs) to protect respondent data from disclosure. The random noise method takes a micro-data approach to disclosure limitation: a multiplier (noise factor) is applied to each unit prior to tabulation – thus, guaranteeing that different tabulations, from the lowest to the highest level, are consistent. In this paper we evaluate two different random noise models in an applied context. Our analysis suggests that both the Basic Noise Model (BNM) and the Alternate Noise Model (ANM) are unsatisfactory for protecting smaller units. To overcome this difficulty, we developed a (third) Mixed Perturbation Model (MPM) that combines the use of multiplicative noise to protect large units with the use of synthetic models to protect the smaller units. To accomplish this, we constructed a hybrid model (first logistic and then linear) to generate the synthetic data. Results indicate that the mixed approach performs better than either of the other two models, both in terms of reliability and disclosure limitation, although it too has weaknesses. Hence, areas for future study remain that our research suggests might be tackled next.

Automation of Model Parameter Estimation for Random Telegraph Noise

The modeling of random telegraph noise (RTN) of MOS transistors is becoming increasingly important. In this paper, a novel method is proposed for realizing automated estimation of two important RTN-model parameters: the number of interface-states and corresponding threshold voltage shift. The proposed method utilizes a Gaussian mixture model (GMM) to represent the voltage distributions, and estimates their parameters using the expectation-maximization (EM) algorithm. Using information criteria, the optimal estimation is automatically obtained while avoiding overfitting. In addition, we use a shared variance for all the Gaussian components in the GMM to deal with the noise in RTN signals. The proposed method improved estimation accuracy when the large measurement noise is observed.

Adiabatic quantum optimization with the wrong Hamiltonian

Analog models of quantum information processing, such as adiabatic quantum computation and analog quantum simulation, require the ability to subject a system to precisely specified Hamiltonians. Unfortunately, the hardware used to implement these Hamiltonians will be imperfect and limited in its precision. Even small perturbations and imprecisions can have profound effects on the nature of the ground state. Here we consider an imperfect implementation of adiabatic quantum optimization and show that, for a widely applicable random control noise model, quantum stabilizer encodings are able to reduce the effective noise magnitude and thus improve the likelihood of a successful computation or simulation. This reduction builds upon two design principles: summation of equivalent logical operators to increase the energy scale of the encoded optimization problem, and the inclusion of a penalty term comprising the sum of the code stabilizer elements. We illustrate our findings with an Ising ladder and show that classical repetition coding drastically increases the probability that the ground state of a perturbed model is decodable to that of the unperturbed model, while using only realistic two-body interaction. Finally, we note that the repetition encoding is a special case of quantum stabilizer encodings, and show that this in principle allows us to generalize our results to many types of analog quantum information processing, albeit at the expense of many-body interactions.

Statistical analysis of high density diffuse optical tomography

High density diffuse optical tomography (HD-DOT) is a noninvasive neuroimaging modality with moderate spatial resolution and localization accuracy. Due to portability and wear-ability advantages, HD-DOT has the potential to be used in populations that are not amenable to functional magnetic resonance imaging (fMRI), such as hospitalized patients and young children. However, whereas the use of event-related stimuli designs, general linear model (GLM) analysis, and imaging statistics are standardized and routine with fMRI, such tools are not yet common practice in HD-DOT. In this paper we adapt and optimize fundamental elements of fMRI analysis for application to HD-DOT. We show the use of event-related protocols and GLM de-convolution analysis in un-mixing multi-stimuli event-related HD-DOT data. Statistical parametric mapping (SPM) in the framework of a general linear model is developed considering the temporal and spatial characteristics of HD-DOT data. The statistical analysis utilizes a random field noise model that incorporates estimates of the local temporal and spatial correlations of the GLM residuals. The multiple-comparison problem is addressed using a cluster analysis based on non-stationary Gaussian random field theory. These analysis tools provide access to a wide range of experimental designs necessary for the study of the complex brain functions. In addition, they provide a foundation for understanding and interpreting HD-DOT results with quantitative estimates for the statistical significance of detected activation foci.

Parametric or Non-Parametric Estimation of Value-At-Risk

In the financial analyses the fact of predicting future states of the instruments subjected to investments is extremely important. It allows reducing risk and maximizing potential profits. That is why any ways which enable to predict the further negative results of taking decisions are very important and the knowledge about the measures and their efficiency are an additional advantage. Thus, the paper in which value at risk and an assessment of this measure are discussed seems to be of interest. Thus, in the paper Value at Risk (VaR) is presented as an instrument which reduces the risk and defines its scale. The analysis is made among twenty companies of the Warsaw Stock Exchange currently included in the WIG20. Taking into account the multitude of varieties of VaR calculation methods, some of them are proposed to carry out some kind of confrontation. Such an approach was designed to assess the effectiveness of simulation methods for more complex parametric methods. As a part of the resultant, which combines both of these groups, also a method which belongs to semi-parametric EKT (Note 1) group is proposed. At the same time the Monte Carlo and historical simulation as non-parametric methods are taken into consideration. The methods developed by the Risk Metrics with different approaches to the modeling of random noise (normal distribution, the Student's t distribution and GED (Note 2) are representatives of the second group). In order to evaluate the full effectiveness of these methods three levels of significance of 0,01, 0,05 and 0,10 are taken into account. The research horizon covers the period from 01.01.2010 to 31.12.2012. In the analyses 151 historical observations are used as the information necessary to estimate these models. In order to estimate the quantity there was used the dependence developed by Risk Metrics™, namely:

Multi-station superposition for magnetotelluric signal

Extending the observation time to increase the length of time series is the most effective but consuming way to improve the signal-to-noise ratio of magnetotelluric (MT) signal. Multi-station superposition technique at present proposed for magnetotelluric survey aims to attenuate random noises by multi-station synchronously observation at one site. The idea stems from seismic array stacking and is mathematically verified corresponding to a linear random noise model. The multi-station superposition can be done in time and frequency domains. A practical testing measurement was performed by setting three stations on vertexes of an equilateral triangle and a single station on centre of the triangle in desert area of the Tarim basin, western China. The results of the testing data demonstrate the effectiveness of the proposed scheme. The method has great potentials for long-period MT survey in dramatically reducing the observation time and suppressing the static shift caused by near-surface inhomogeneities.

Compact modeling and simulation of Random Telegraph Noise under non-stationary conditions in the presence of random dopants

A new methodology for circuit level transient simulation of Random Telegraph Noise (RTN) is proposed. The physically based methodology properly models the microscopic phenomena involved in RTN, including their stochastic nature. Using a modified BSIM code, the compact model is implemented in a SPICE simulator, accounting for non-stationary RTN effects under arbitrary bias. The probability of traps to capture or emit charge carriers is updated at each time step of the transient simulation according to the actual bias conditions of the device. Atomistic device simulations are performed in order to study the impact of trap position along the channel on the amplitude of the contribution of a trap to RTN. These device simulations take into account short-range Coulomb interactions and show the relevance of large local deviations of mobility values of carrier electrons, particularly for traps near the source end of the channel. As a case study, jitter in an oscillator is simulated. It is shown that the methodology properly addresses open issues in the literature, by properly accounting for bias-dependent, non-stationary statistic of RTN phenomena relevant to the design of integrated circuits.

Modeling of electron conduction in contact resistive random access memory devices as random telegraph noise

The intense development and study of resistive random access memory (RRAM) devices has opened a new era in semiconductor memory manufacturing. Resistive switching and carrier conduction inside RRAM films have become critical issues in recent years. Electron trapping/detrapping behavior is observed and investigated in the proposed contact resistive random access memory (CR-RAM) cell. Through the fitting of the space charge limiting current (SCLC) model, and analysis in terms of the random telegraph noise (RTN) model, the temperature-dependence of resistance levels and the high-temperature data retention behavior of the contact RRAM film are successfully and completely explained. Detail analyses of the electron capture and emission from the traps by forward and reverse read measurements provide further verifications for hopping conduction mechanism and current fluctuation discrepancies.

Model of random telegraph noise in gate-induced drain leakage current of high-k gate dielectric metal-oxide-semiconductor field-effect transistors

Model of random telegraph noise (RTN) in gate-induced drain leakage (GIDL) current was proposed and explained together with that of gate edge tunneling (ET) current. A trap inside the gate dielectric between the drain and the gate of a metal-oxide-semiconductor field-effect transistor can affect theoretically both GIDL and gate ET currents at the same time. However, the RTNs for both currents were mostly uncorrelated and its cause was clarified. The RTN in GIDL current has a strong relation with a localized defect region in the drain overlapped by the gate. In the RTN of GIDL current, the shallower trap depth can show the larger amplitude of a RTN but sometimes show unexpectedly small amplitude if a trap is misaligned with the defect region.

Bootstrap LR Tests of Stationarity, Common Trends and Cointegration

The paper considers likelihood ratio (LR) tests of stationarity, common trends and cointegration for multivariate time series. As the distribution of these tests is not known, a bootstrap version is proposed via a state space representation. The bootstrap samples are obtained from the Kalman filter innovations under the null hypothesis. Monte Carlo simulations for the Gaussian univariate random walk plus noise model show that the bootstrap LR test achieves higher power for medium-sized deviations from the null hypothesis than a locally optimal and one-sided LM test, that has a known asymptotic distribution. The power gains of the bootstrap LR test are significantly larger for testing the hypothesis of common trends and cointegration in multivariate time series, as the alternative asymptotic procedure - obtained as an extension of the LM test of stationarity - does not possess properties of optimality. Finally, it is showed that the (pseudo) LR tests maintain good size and power properties also for non-Gaussian series. As an empirical illustration, we find evidence of two common stochastic trends in the volatility of the US dollar exchange rate against european and asian/pacific currencies.

Bootstrap LR tests of stationarity, common trends and cointegration

This paper considers the likelihood ratio (LR) tests of stationarity, common trends and cointegration for multivariate time series. As the distribution of these tests is not known, a bootstrap version is proposed via a state- space representation. The bootstrap samples are obtained from the Kalman filter innovations under the null hypothesis. Monte Carlo simulations for the Gaussian univariate random walk plus noise model show that the bootstrap LR test achieves higher power for medium-sized deviations from the null hypothesis than a locally optimal and one-sided Lagrange Multiplier (LM) test that has a known asymptotic distribution. The power gains of the bootstrap LR test are significantly larger for testing the hypothesis of common trends and cointegration in multivariate time series, as the alternative asymptotic procedure – obtained as an extension of the LM test of stationarity – does not possess properties of optimality. Finally, it is shown that the (pseudo-)LR tests maintain good size and power properties also for the non-Gaussian series. An empirical illustration is provided.

Reproducibility and clinical relevance of quantitative EEG parameters in cerebral ischemia: A basic approach

To investigate reproducibility and clinical relevance of quantitative EEG parameters in ischemic cerebrovascular disease. EEG was recorded in 31 patients suffering from subacute ischemic cerebrovascular disease. Ten age-matched control subjects were included as a reference group. Intra-recording, intrarater and interrater reproducibility was calculated for standard EEG measures and for a pair wise derived Brain Symmetry Index (pdBSI). Correlations between EEG parameters, clinical status and volume of ischemia on diffusion weighted imaging (DWI) were calculated. pdBSI was sensitive to asymmetry in the amplitude and frequency domain in a random white noise model and in a simulated sinusoidal model. Minimal Cronbach alpha for intra-recording, intra- and inter-rater reproducibility ranged between 0.95 and 0.99 for standard spectral parameters and between 0.96 and 0.99 for pdBSI. We found a significant difference in pdBSI between stroke patients and control or TIA (p=0.0003). pdBSI correlated significantly with NIH stroke scale at admission and DWI volume across different levels of stroke probability (Spearman's rho=0.64-0.70 and 0.79-0.84, respectively, p<0.00001 for both). pdBSI displays high multilevel reproducibility and reliably discriminates between stroke and TIA patients or control subjects, and correlates significantly with clinical and radiological status. Based on this methodological analysis, reliable EEG parameters can be evaluated in a general stroke population for clinically relevant state and outcome measures.