Random noise stimulation in the treatment of patients with neurological disorders

Random noise stimulation technique involves applying any form of energy(for instance,light,mechanical,electrical,sound)with unpredictable intensities through time to the brain or sensory receptors to enhance sensory,motor,or cognitive functions.Random noise stimulation initially employed mechanical noise in auditory and cutaneous stimuli,but electrical energies applied to the brain or the skin are becoming more frequent,with a series of clinical applications.Indeed,recent evidence shows that transcranial random noise stimulation can increase corticospinal excitability,improve cognitive/motor performance,and produce beneficial aftereffects at the behavioral and psychological levels.Here,we present a narrative review about the potential uses of random noise stimulation to treat neurological disorders,including attention deficit hyperactivity disorder,schizophrenia,amblyopia,myopia,tinnitus,multiple sclerosis,post-stroke,vestibular-postural disorders,and sensitivity loss.Many of the reviewed studies reveal that the optimal way to deliver random noise stimulation-based therapies is with the concomitant use of neurological and neuropsychological assessments to validate the beneficial aftereffects.In addition,we highlight the requirement of more randomized controlled trials and more physiological studies of random noise stimulation to discover another optimal way to perform the random noise stimulation interventions.

Estimation of random errors for lidar based on noise scale factor

Estimation of random errors, which are due to shot noise of photomultiplier tube(PMT) or avalanche photodiode(APD) detectors, is very necessary in lidar observation. Due to the Poisson distribution of incident electrons, there still exists a proportional relationship between standard deviation and square root of its mean value. Based on this relationship,noise scale factor(NSF) is introduced into the estimation, which only needs a single data sample. This method overcomes the distractions of atmospheric fluctuations during calculation of random errors. The results show that this method is feasible and reliable.

Modeling random telegraph signal noise in CMOS image sensor under low light based on binomial distribution

The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.

Random telegraph noise on the threshold voltage of multi-level flash memory

We investigate the impact of random telegraph noise（RTN） on the threshold voltage of multi-level NOR flash memory.It is found that the threshold voltage variation（？Vth） and the distribution due to RTN increase with the programmed level（Vth） of flash cells. The gate voltage dependence of RTN amplitude and the variability of RTN time constants suggest that the large RTN amplitude and distribution at the high program level is attributed to the charge trapping in the tunneling oxide layer induced by the high programming voltages. A three-dimensional TCAD simulation based on a percolation path model further reveals the contribution of those trapped charges to the threshold voltage variation and distribution in flash memory.

Dynamics of entropic uncertainty for three types of three-level atomic systems under the random telegraph noise

We study the dynamics of the entropic uncertainty for three types of three-level atomic systems coupled to an environment modeled by random matrices. The results show that the entropic uncertainty in the Ξ-type atomic system is lower than that in the V-type atomic system which is exactly the same as that in the Λ-type atomic system. In addition, the effect of relative coupling strength on entropic uncertainty is opposite in Markov region and non-Markov region, and the influence of a common environment and independent environments in Markov region and non-Markov region is also opposite. One can reduce the entropic uncertainty by decreasing relative coupling strength or placing the system in two separate environments in the Markov case. In the non-Markov case, the entropic uncertainty can be reduced by increasing the relative coupling strength or by placing the system in a common environment.

Efficient Global Threshold Vector Outlyingness Ratio Filter for the Removal of Random Valued Impulse Noise

This research paper proposes a filter to remove Random Valued Impulse Noise (RVIN) based on Global Threshold Vector Outlyingness Ratio (GTVOR) that is applicable for real time image processing. This filter works with the algorithm that breaks the images into various decomposition levels using Discrete Wavelet Transform (DWT) and searches for the noisy pixels using the outlyingness of the pixel. This algorithm has the capability of differentiating high frequency pixels and the “noisy pixel” using the threshold as well as window adjustments. The damage and the loss of information are prevented by means of interior mining. This global threshold based algorithm uses different thresholds for different quadrants of DWT and thus helps in recovery of noisy image even if it is 90% affected. Experimental results exhibit that this method outperforms other existing methods for accurate noise detection and removal, at the same time chain of connectivity is not lost.

A Noise-Resistant Superpixel Segmentation Algorithm for Hyperspectral Images

The superpixel segmentation has been widely applied in many computer vision and image process applications.In recent years,amount of superpixel segmentation algorithms have been proposed.However,most of the current algorithms are designed for natural images with little noise corrupted.In order to apply the superpixel algorithms to hyperspectral images which are always seriously polluted by noise,we propose a noiseresistant superpixel segmentation(NRSS)algorithm in this paper.In the proposed NRSS,the spectral signatures are first transformed into frequency domain to enhance the noise robustness;then the two widely spectral similarity measures-spectral angle mapper(SAM)and spectral information divergence(SID)are combined to enhance the discriminability of the spectral similarity;finally,the superpixels are generated with the proposed frequency-based spectral similarity.Both qualitative and quantitative experimental results demonstrate the effectiveness of the proposed superpixel segmentation algorithm when dealing with hyperspectral images with various noise levels.Moreover,the proposed NRSS is compared with the most widely used superpixel segmentation algorithm-simple linear iterative clustering(SLIC),where the comparison results prove the superiority of the proposed superpixel segmentation algorithm.

Direct measurement and analysis of total ionizing dose effect on 130 nm PD SOI SRAM cell static noise margin

In this work, the total ionizing dose（TID） effect on 130 nm partially depleted（PD） silicon-on-insulator（SOI） static random access memory（SRAM） cell stability is measured. The SRAM cell test structure allowing direct measurement of the static noise margin（SNM） is specifically designed and irradiated by gamma-ray. Both data sides＇ SNM of 130 nm PD SOI SRAM cell are decreased by TID, which is different from the conclusion obtained in old generation devices that one data side＇s SNM is decreased and the other data side＇s SNM is increased. Moreover, measurement of SNM under different supply voltages（Vdd） reveals that SNM is more sensitive to TID under lower Vdd. The impact of TID on SNM under data retention Vddshould be tested, because Vddof SRAM cell under data retention mode is lower than normal Vdd.The mechanism under the above results is analyzed by measurement of I–V characteristics of SRAM cell transistors.

Denoising of seismic data via multi-scale ridgelet transform

Noise has traditionally been suppressed or eliminated in seismic data sets by the use of Fourier filters and, to a lesser degree, nonlinear statistical filters. Although these methods are quite useful under specific conditions, they may produce undesirable effects for the low signal to noise ratio data. In this paper, a new method, multi-scale ridgelet transform, is used in the light of the theory of ridgelet transform. We employ wavelet transform to do sub-band decomposition for the signals and then use non-linear thresholding in ridgelet domain for every block. In other words, it is based on the idea of partition, at sufficiently fine scale, a curving singularity looks straight, and so ridgelet transform can work well in such cases. Applications on both synthetic data and actual seismic data from Sichuan basin, South China, show that the new method eliminates the noise portion of the signal more efficiently and retains a greater amount of geologic data than other methods, the quality and consecutiveness of seismic event are improved obviously as well as the quality of section is improved.

A voltage-controlled chaotic oscillator based on carbon nanotube field-effect transistor for low-power embedded systems

This paper presents a compact and low-power-based discrete-time chaotic oscillator based on a carbon nanotube field-effect transistor implemented using Wong and Deng＇s well-known model. The chaotic circuit is composed of a nonlinear circuit that creates an adjustable chaos map, two sample and hold cells for capture and delay functions, and a voltage shifter that works as a buffer and adjusts the output voltage for feedback. The operation of the chaotic circuit is verified with the SPICE software package, which uses a supply voltage of 0.9 V at a frequency of 20 kHz. The time series, frequency spectra, transitions in phase space, sensitivity with the initial condition diagrams, and bifurcation phenomena are presented. The main advantage of this circuit is that its chaotic signal can be generated while dissipating approximately 7.8 μW of power, making it suitable for embedded systems where many chaos-signal generators are required on a single chip.

Seismic data denoising based on data-driven tight frame dictionary learning method

Because of various complicated factors in seismic data collection,the random noise of seismic data is too difficult to avoid.This random noise reduces the quality of seismic data and increases the difficulty of seismic data processing and interpretation.Improving the denoising technology is significant.In order to improve seismic data denoising result,a novel method named data-driven tight frame(DDTF)is introduced in this paper.First,we get the sparse coefficients of seismic data with noise by DDTF.Then we remove the smaller sparse coefficient by using the hard threshold function.Finally,we get the denoised seismic data by inverse transform.Furthermore,the DDTF is compared with curvelet transform in the stimulation and practical seismic data experiments to validate its performance.DDTF can raise the signal-to-noise ratio of seismic data denoising and protect the effective signal well.

Estimation of Parameters of Boundary Value Problems for Linear Ordinary Differential Equations with Uncertain Data

In this paper we construct optimal, in certain sense, estimates of values of linear functionals on solutions to two-point boundary value problems (BVPs) for systems of linear first-order ordinary differential equations from observations which are linear transformations of the same solutions perturbed by additive random noises. It is assumed here that right-hand sides of equations and boundary data as well as statistical characteristics of random noises in observations are not known and belong to certain given sets in corresponding functional spaces. This leads to the necessity of introducing minimax statement of an estimation problem when optimal estimates are defined as linear, with respect to observations, estimates for which the maximum of mean square error of estimation taken over the above-mentioned sets attains minimal value. Such estimates are called minimax mean square or guaranteed estimates. We establish that the minimax mean square estimates are expressed via solutions of some systems of differential equations of special type and determine estimation errors.

Silicon-film-related random telegraph noise in UTBOX silicon-on-insulator nMOSFETs

This paper studies the amplitude of random telegraph noise （RTN） caused by a single trap in the sili- con film of ultra-thin buried oxide （UTBOX） silicon-on-insulator （SOl） devices. The film-defect-related RTN was identified and analyzed by low frequency noise measurement and time domain measurement. Emphasis is on the relative amplitude AID/ID, which is studied in the function of the front-gate, the back-gate and the drain-to-source biases. Interesting asymmetric or symmetric VDS dependence of switched source and drain are observed and sup- ported by calibrated Sentaurus simulations. It is believed the asymmetry of the VDs dependence of the switched source and drain is related to the lateral trap position along the source and drain.

Noise in a CMOS digital pixel sensor

Based on the study of noise performance in CMOS digital pixel sensor （DPS）, a mathematical model of noise is established with the pulse-width-modulation （PWM） principle. Compared with traditional CMOS image sensors, the integration time is different and A/D conversion is implemented in each PWM DPS pixel. Then, the quantitative calculating formula of system noise is derived. It is found that dark current shot noise is the dominant noise source in low light region while photodiode shot noise becomes significantly important in the bright region. In this model, photodiode shot noise does not vary with luminance, but dark current shot noise does. According to increasing photodiode capacitance and the comparator＇s reference voltage or optimizing the mismatch in the comparator, the total noise can be reduced. These results serve as a guideline for the design of PWM DPS.

Correlation between dark current RTS noise and defects for AlGaInP multiple-quantum-well laser diode

The correlation model between dark current RTS noise and defects for A1GalnP multiple-quantum-well laser diode is derived. Experimental results show that dark current RTS noise caused carrier number fluctuations at the interface of the heterojunction in the active region. According to this correlation model, the defect types are determined, and the defects＇ energy levels are quantitatively determined. The comer frequency of RTS noise power spectral density is analyzed. The experimental results are in good agreement with the theoretical. This result provided an effective method for estimating the deep-level traps in the active region of A1GaInP multiple quantum well laser diode.

STATIC REGIME IMAGING OF LOCATIONS OF CERTAIN 3D ELECTROMAGNETIC IMPERFECTIONS FROM A BOUNDARY PERTURBATION FORMULA

We are concerned, in a static regime, with a three-dimensional bounded domain of certain an imaging approach of the locations in electromagnetic imperfections. This approach is related to Electrical Impedance Tomography and makes use of a new perturbation formula in the electric fields. We present two localization procedures, from a Current Pro- jection method that deals with the single imperfection context and an inverse Fourier process that is devoted to multiple imperfections configurations. These procedures extend those that were described in our previous work, since operating for a broader class of settings. Namely, the localization is additionally performed for certain purely electric imperfections, as established from numerical simulations.

Estimation of Elastic Parameters Using Two-Term Fatti Elastic Impedance Inversion

Elastic impedance （EI） inversion has been widely used in industry to estimate kinds of elastic parameters to distinguish lithology or even fluid. However, it is found that conventional three-term elastic impedance formula is unstable even with slight random noise in seismic data, due to the m-conditioned co- efficient matrix of elastic parameters. We presented two-term Fatti elastic impedance inversion method, which is more robust and accurate than conventional three-term elastic impedance inversion. In our method, density is ignored to increase the robustness of inversion matrix. Besides, P-impedance and S-impedance, which are less sensitive to random noise, are inverted instead of Vp and Vs in conventional three-term elastic impedance. To make the inversion more stable, we defined the range of K value as a con- straint. Synthetic tests claim that this method can obtain promising results with low SNR （signal noise ratio） seismic data. With the application of the method in a 2D line data, we achieved λp, μp and Vp/Vs sections, which matched the drilled well perfectly, indicating the potential of the method in reservoir prediction.

Efficient GMM estimation with singular system of moment conditions

Standard generalised method of moments(GMM)estimation was developed for nonsingular system of moment conditions.However,many important economic models are characterised by singular system of moment conditions.This paper shows that efficient GMM estimation of such models can be achieved by using the reflexive generalised inverses,in particular the Moore–Penrose generalised inverse,of the variance matrix of the sample moment conditions as the weighting matrix.We provide a consistent estimator of the optimal weighting matrix and establish its consistency.Potential issues of using generalised inverse and some remedies are also discussed.

A New Stable Algorithm to Compute Hankel Transform Using Chebyshev Wavelets

A new stable numerical method,based on Chebyshev wavelets for numerical evaluation of Hankel transform,is proposed in this paper.The Chebyshev wavelets are used as a basis to expand a part of the integrand,r f(r),appearing in the Hankel transform integral.This transforms the Hankel transform integral into a Fourier-Bessel series.By truncating the series,an efficient and stable algorithm is obtained for the numerical evaluations of the Hankel transforms of order ν>−1.The method is quite accurate and stable,as illustrated by given numerical examples with varying degree of random noise terms εθ_(i) added to the data function f(r),where θ_(i) is a uniform random variable with values in[−1,1].Finally,an application of the proposed method is given for solving the heat equation in an infinite cylinder with a radiation condition.

ASYMPTOTIC BEHAVIOR FOR GENERALIZED GINZBURG-LANDAU POPULATION EQUATION WITH STOCHASTIC PERTURBATION

In this paper,we are devoted to the asymptotic behavior for a nonlinear parabolic type equation of higher order with additive white noise.We focus on the Ginzburg-Landau population equation perturbed with additive noise.Firstly,we show that the stochastic Ginzburg-Landau equation with additive noise can be recast as a random dynamical system.And then,it is proved that under some growth conditions on the nonlinear term,this stochastic equation has a compact random attractor,which has a finite Hausdorff dimension.