Effect Of Additive Noise Research Articles

Effects of auditory noise intensity and color on the dynamics of upright stance

Previous work assessing the effect of additive noise on the postural control system has found a positive effect of additive white noise on postural dynamics. This study covers two separate experiments that were run sequentially to better understand how the structure of the additive noise signal affects postural dynamics, while also furthering our knowledge of how the intensity of auditory stimulation of noise may elicit this phenomenon. Across the two experiments, we introduced three auditory noise stimulations of varying structure (white, pink, and brown noise). Experiment 1 presented the stimuli at 35 dB while Experiment 2 was presented at 75 dB. Our findings demonstrate a decrease in variability of the postural control system regardless of the structure of the noise signal presented, but only for high intensity auditory stimulation.

Fuzzy Mandelbric Set and Its Perturbations by Dynamical Noises

In this paper, we introduce a membership function used to form the fuzzy Mandelbric set and investigate the structural effects of additive and multiplicative dynamic noises on it. The newly defined membership function of this fuzzy set and its perturbations is a generalization of the indicator function for the classical Mandelbric set. We present an algorithm for detecting each complex number’s fuzzy membership degree. Through the use of the membership degrees of each complex number and experimental mathematics based on the visualizations of a variety of versions by utilizing computer-aided design, we gain a deep foresight for the structure characteristics of the additive and multiplicative perturbed fuzzy Mandelbric sets. Our novel approach allows us to identify the symmetry states of the Mandelbric set and its perturbations by the membership degrees of complex numbers, unlike the existing methods described in the literature.

Transmission of information on a single carrier frequency in a full-duplex radio line

Telecommunication communication is usually provided in duplex mode, which requires the provision of two carrier frequencies and at least twice the bandwidth of the radio channel in a wireless version (subject to a priori equality of the capacities of information sources at both ends of the radio line). Throughout the development of wireless telecommunications technologies, the issue of the efficiency of using the frequency spectrum allocated for transmitting/receiving messages is acutely relevant, namely, reducing the bandwidth occupied by the message. This task has significant limitations, primarily based on minimizing the distortion of transmitted messages that may occur when artificially limiting the bandwidth. Information compression/compaction methods provide some savings in the width of the frequency spectrum, but a marginal reduction in the occupied frequency band can be achieved when the receiving and transmitting paths of both directions operate at the same carrier frequency. The article provides a brief overview of methods to improve the efficiency of using the frequency resource, provides a computer model and a study of the method of wireless information transmission that allows a full-duplex wireless communication channel to be implemented on a single carrier frequency. The above and investigated computer model of the method based on the use of a circulator made it possible to evaluate the effect of additive noise, the difference in amplitude and phase of the interference signal from the transmitter and a copy of the signal from the radio transmitter in "antiphase" on the number of errors (BER). According to the research results, it can be concluded that in order to reduce the number of errors when using a compensator, it is necessary to ensure an exact match of the interference signal from the transmitter and a copy of the signal from the radio transmitter in "antiphase" in amplitude and the phase difference between these signals is equal to 1800. Since an increase in the amplitude difference starting from 0.001 and the phase deviation from 0.10 from 1800 leads to a significant increase in the number of errors even with large values of the signal-to-noise ratio. In addition, when developing a device based on this method, it is necessary to take into account the difference in the amplitudes of the useful signal and "interference from the transmitter". This parameter specifies additional requirements for the device. The task of compensating for interference from the transmitter is simplified by the fact that the parameters of the transmitting signal and the characteristics of the device elements are known in advance.

Stochastic effects on solution landscapes for nematic liquid crystals

ABSTRACT We study the effects of additive and multiplicative noise on the solution landscape of nematic liquid crystals confined to a square domain within the Landau-de Gennes framework, as well as the impact of additive noise on the symmetric radial hedgehog solution for nematic droplets. The introduction of random noise can be used to capture material uncertainties and imperfections, which are always present in physical systems. We implement random noise in our framework by introducing a Q -Wiener stochastic process to the governing differential equations. On the square, the solution landscape for the deterministic problem is well understood, enabling us to compare and contrast the deterministic predictions and the stochastic predictions, while we demonstrate that the symmetry of the radial hedgehog solution can be violated by noise. This approach of introducing noise to deterministic equations can be used to test the robustness and validity of predictions from deterministic liquid crystal models, which essentially capture idealised situations.

Effect of Time-Varying Multiplicative Noise on DNN- k WTA Model.

Among many k -winners-take-all ( k WTA) models, the dual-neural network (DNN- k WTA) model is with significantly less number of connections. However, for analog realization, noise is inevitable and affects the operational correctness of the k WTA process. Most existing results focus on the effect of additive noise. This brief studies the effect of time-varying multiplicative input noise. Two scenarios are considered. The first one is the bounded noise case, in which only the noise range is known. Another one is for the general noise distribution case, in which we either know the noise distribution or have noise samples. For each scenario, we first prove the convergence property of the DNN- k WTA model under multiplicative input noise and then provide an efficient method to determine whether a noise-affected DNN- k WTA network performs the correct k WTA process for a given set of inputs. With the two methods, we can efficiently measure the probability of the network performing the correct k WTA process. In addition, for the case of the inputs being uniformly distributed, we derive two closed-form expressions, one for each scenario, for estimating the probability of the model having correct operation. Finally, we conduct simulations to verify our theoretical results.

A simulation study to evaluate the effect of 2D antiscatter grid primary transmission on flat panel detector based CBCT image quality

Abstact Purpose. Two-dimensional antiscatter grids’ (2D-ASGs) septal shadows and their impact on primary transmission play a critical role in cone-beam computed tomography (CBCT) image noise and artifact characteristics. Therefore, a numerical simulation platform was developed to evaluate the effect of 2D-ASG’s primary transmission on image quality, as a function of grid geometry and CBCT system properties. Methods. To study the effect of 2D-ASG’s septal shadows on primary transmission and CBCT image quality, two new methods were introduced; one to simulate projection signal gradients in septal shadows, and the other to simulate septal shadow variations due to gantry flex. Signal gradients in septal shadows were simulated by generating a system point spread function that was directly extracted from projection images of 2D-ASG prototypes in experiments. Variations in septal shadows due to gantry flex were simulated by generating oversampled shadow profiles extracted from experiments. Subsequently, the effect of 2D-ASG’s septal shadows on primary transmission and image quality was evaluated. Results. For an apparent septal thickness of 0.15 mm, the primary transmission of 2D-ASG varied between 72%–90% for grid pitches 1–3 mm. In low-contrast phantoms, the effect of 2D-ASG’s radiopaque footprint on information loss was subtle. At high spatial frequencies, information loss manifested itself as undersampling artifacts, however, its impact on image quality is subtle when compared to quantum noise. Effects of additive electronic noise and gantry flex induced ring artifacts on image quality varied as a function of grid pitch and septal thickness. Such artifacts were substantially less in lower resolution images. Conclusion. The proposed simulation platform allowed successful evaluation of CBCT image quality variations as a function of 2D-ASG primary transmission properties and CBCT system characteristics. This platform can be potentially used for optimizing 2D-ASG design properties based on the imaging task and properties of the CBCT system.

Additive Sensory Noise Effects on Operator Performance in a Lunar Landing Simulation.

INTRODUCTION: Adding noise to a system to improve a weak signal's detectability is known as stochastic resonance (SR). SR has been shown to improve sensory perception and cognitive performance in certain individuals, but it is unknown whether this performance improvement can translate to meaningful macrocognitive enhancements in performance for complex, operational tasks.OBJECTIVE: We investigated human operator performance in a lunar landing simulation while applying auditory white noise and/or noisy galvanic vestibular stimulation.METHODS: We measured performance (N = 16 subjects) while completing simulation trials in our Aerospace Research Simulator. Trials were completed with and without the influence of auditory white noise, noisy galvanic vestibular stimulation, and both simultaneously in a multimodal fashion. Performance was observed holistically and across subdimensions of the task, which included flight skill and perception. Subjective mental workload was collected after completing four trials in each treatment.RESULTS: We did not find broad operator improvement under the influence of noise, but a significant interaction was identified between subject and noise treatment, indicating that some subjects were impacted by additive noise. We also found significant interactions between subject and noise treatment in performance subdimensions of flight skill and perception. We found no significant main effects on mental workload.CONCLUSIONS: This study investigated the utility of using additive sensory noise to induce SR for complex tasks. While SR has been shown to improve aspects of performance, our results suggest additive noise does not yield operational performance changes for a broad population, but specific individuals may be affected.Sherman SO, Shen Y-Y, Gutierrez-Mendoza D, Schlittenhart M, Watson C, Clark TK, Anderson AP. Additive sensory noise effects on operator performance in a lunar landing simulation. Aerosp Med Hum Perform. 2023; 94(10):770-779.

On the Nonlinear Stochastic Dynamics of an Atomic Force Microscope Cantilever

AbstractAtomic force microscopy (AFM) serves characterization and actuation in nanoscale applications. We study the stochastic dynamics of an AFM cantilever under tip-sample interactions represented by the Lennard–Jones and Morse potential energy functions. In both cases, we also study the contrasting dynamic effects of additive (external) and multiplicative (internal) noise. Moreover, for multiplicative noise, we study the two sub-cases arising from the Itô and Stratonovich interpretations of stochastic integrals. In each case, we also investigate the stochastic stability of the system by tracing the time evolution of the maximal Lyapunov exponent. Additionally, we obtain stationary probability densities for the unforced dynamics using stochastic averaging.

A complex networks based approach to nonlinear aeroelasticity

The present study investigates the effect of low intensity additive noise on a subcritical pitch-plunge aeroelastic system through the lens of complex networks. In the deterministic system, the 0→ fixed point loses its stability at the flutter point, via subcritical Hopf bifurcation. The unstable LCO becomes a high-amplitude stable LCO at the turning point, posing danger to structural integrity. The system displays bi-stability, and an analysis of the basin stability has revealed it to be highly susceptible to external perturbations. The external perturbations, modelled here as a direct random forcing in the form of a Gaussian white noise, could drastically change the deterministic dynamics. It brought in new dynamical states like noise induced intermittency and an advancement in the onset of LCO. Phenomenological and Dynamical bifurcations (for the stochastic system) were tracked and found to capture only the qualitative changes. To quantify the changes in the dynamics, response time-series were converted into complex networks, based on recurrences in the phase-space. Various network theory based measures such as edge density, clustering coefficient etc., were successful in capturing the dynamical transitions well quantitatively and to potentially serve as precursors for the aeroelastic system. Average path length, global efficiency measures signified the average separation of states and the robustness of the attractor, respectively. The networks also displayed the famous “small-world” property, indicating that transition between states occurs through very few intermediate states. The results have shown that a careful consideration of the stochastic perturbations is needed in the design and operations of such systems, and that complex networks based methods are successful in providing novel perspectives on the topology of the phase-space.

Training augmentation using additive sensory noise in a lunar rover navigation task.

The uncertain environments of future space missions means that astronauts will need to acquire new skills rapidly; thus, a non-invasive method to enhance learning of complex tasks is desirable. Stochastic resonance (SR) is a phenomenon where adding noise improves the throughput of a weak signal. SR has been shown to improve perception and cognitive performance in certain individuals. However, the learning of operational tasks and behavioral health effects of repeated noise exposure aimed to elicit SR are unknown. We evaluated the long-term impacts and acceptability of repeated auditory white noise (AWN) and/or noisy galvanic vestibular stimulation (nGVS) on operational learning and behavioral health. Subjects (n = 24) participated in a time longitudinal experiment to access learning and behavioral health. Subjects were assigned to one of our four treatments: sham, AWN (55 dB SPL), nGVS (0.5 mA), and their combination to create a multi-modal SR (MMSR) condition. To assess the effects of additive noise on learning, these treatments were administered continuously during a lunar rover simulation in virtual reality. To assess behavioral health, subjects completed daily, subjective questionnaires related to their mood, sleep, stress, and their perceived acceptance of noise stimulation. We found that subjects learned the lunar rover task over time, as shown by significantly lower power required for the rover to complete traverses (p < 0.005) and increased object identification accuracy in the environment (p = 0.05), but this was not influenced by additive SR noise (p = 0.58). We found no influence of noise on mood or stress following stimulation (p > 0.09). We found marginally significant longitudinal effects of noise on behavioral health (p = 0.06) as measured by strain and sleep. We found slight differences in stimulation acceptability between treatment groups, and notably nGVS was found to be more distracting than sham (p = 0.006). Our results suggest that repeatedly administering sensory noise does not improve long-term operational learning performance or affect behavioral health. We also find that repetitive noise administration is acceptable in this context. While additive noise does not improve performance in this paradigm, if it were used for other contexts, it appears acceptable without negative longitudinal effects.

Effects of additive sensory noise on cognition.

Adding noise to a system to improve a weak signal's throughput is known as stochastic resonance (SR). SR has been shown to improve sensory perception. Some limited research shows noise can also improve higher order processing, such as working memory, but it is unknown whether SR can broadly improve cognition. We investigated cognitive performance while applying auditory white noise (AWN) and/or noisy galvanic vestibular stimulation (nGVS). We measured cognitive performance (n = 13 subjects) while completing seven tasks in the cognition test battery (CTB). Cognition was assessed with and without the influence of AWN, nGVS, and both simultaneously. Performance in speed, accuracy, and efficiency was observed. A subjective questionnaire regarding preference for working in noisy environments was collected. We did not find broad cognitive performance improvement under the influence of noise (p > 0.1). However, a significant interaction was found between subject and noise condition for accuracy (p = 0.023), indicating that some subjects exhibited cognitive changes with the addition of noise. Across all metrics, noisy environment preference may trend to be a potential indicator of whether subjects will exhibit SR cognitive benefits with a significant predictor in efficiency (p = 0.048). This study investigated using additive sensory noise to induce SR in overall cognition. Our results suggest that using noise to improve cognition is not applicable for a broad population; however, the effect of noise differs across individuals. Further, subjective questionnaires may be a means to identify which individuals are sensitive to SR cognitive benefits, but further investigation is needed.

Demonstration of frequency stability limited by thermal fluctuation noise in silicon nitride nanomechanical resonators

The frequency stability of nanomechanical resonators (NMRs) dictates the performance level of many state-of-the-art sensors (e.g., mass, force, temperature, radiation) that relate an external physical perturbation to a resonance frequency shift. While this is obviously of fundamental importance, accurate models and understandings of sources of frequency instability are not always available. The contribution of thermomechanical noise to frequency stability has been well studied in recent years and is often the fundamental performance limitation. Frequency stability limited by thermal fluctuation noise has attracted less interest but is nevertheless of fundamental importance notably in temperature sensing applications. In particular, temperature-sensitive NMR have become promising candidates for replacing traditional bolometers in infrared radiation sensing. However, reaching the ultimate detectivity limit of thermal radiation sensors requires their noise to be dominated by fundamental thermal fluctuation, which has not been demonstrated to date. In this work, we first develop a theoretical model for computing the frequency stability of NMR by considering the effect of both additive phase noise (i.e., thermomechanical and experimental detection noise) and thermal fluctuation noise in a close-loop frequency tracking scheme. We thereafter validate this model experimentally and observe thermal fluctuation noise in SiN drum resonators of various sizes at room temperature. Our work shows that by using resonators of specific characteristics—such as high temperature sensitivity, high mechanical quality factors, and high mass-to-thermal-conductance ratio—one can minimize additive phase noise below thermal fluctuation noise. This paves the way for uncooled NMR-based radiation sensors that can reach the fundamental detectivity limit of thermal radiation sensing and outperform existing technologies.

Sampling linear inverse problems with noise

We study the effect of additive noise to the inversion of FIOs associated to a diffeomorphic canonical relation. We use the microlocal defect measures to measure the power spectrum of the noise in the phase space and analyze how that power spectrum is transformed under the inversion. In general, white noise, for example, is mapped to noise depending on the position and on the direction. In particular, we compute the standard deviation, locally, of the noise added to the inversion as a function of the standard deviation of the noise added to the data. As an example, we study the Radon transform in the plane in parallel and fan-beam coordinates, and present numerical examples.

Adaptive Zero-Padding with Impulsive Training Signal MMSE-SMI Adaptive Array Interference Suppression

In mobile communication systems, the channel state information (CSI) is severely affected by the noise effect of the receiver. The adaptive subcarrier grouping (ASG) for sample matrix inversion (SMI) based minimum mean square error (MMSE) adaptive array has been previously proposed. Although it can reduce the additive noise effect by increasing samples to derive the array weight for co-channel interference suppression, it needs to know the signal-to-noise ratio (SNR) in advance to set the threshold for subcarrier grouping. This paper newly proposes adaptive zero padding (AZP) in the time domain to improve the weight accuracy of the SMI matrix. This method does not need to estimate the SNR in advance, and even if the threshold is always constant, it can adaptively identify the position of zero-padding to eliminate the noise interference of the received signal. Simulation results reveal that the proposed method can achieve superior bit error rate (BER) performance under various Rician K factors.

Effects of multiplicative and additive colored noises on the stability of a simplified thermoacoustic combustor

Effects of multiplicative and additive colored noises on the stability of a simplified thermoacoustic combustor

Verification of Estimated Output Signal-to-Noise Ratios From a Phase Inversion Technique Using a Simulated Hearing Aid.

The signal-to-noise ratio (SNR) for speech presented in background noise may vary after being processed by digital hearing aids with nonlinear signal processing algorithms, such as wide dynamic range compression (WDRC). A phase inversion technique has been previously developed to assess the output SNR of hearing aids. However, systematic validations of this technique have not been conducted. This study aims to validate the phase inversion technique. A simulated hearing aid with multichannel WDRC was implemented, from which the output SNRs, computed via shadow filtering, for connected speech in background noise were directly computed. The agreement between the shadow filter output SNRs and those estimated using the phase inversion technique for the same stimuli was utilized to validate the phase inversion technique. The background noise was 2- or 20-talker babble noise, and the speech stimuli were presented at SNRs of -10 to +10 dB at the input of the simulated hearing aid. The simulated hearing aid was configured to provide amplification for four representative audiograms, and the WDRC was set to be fast or slow acting. To investigate the effects of additive noise, independent of the presented noise stimulus, on the phase inversion estimated output SNR, the same simulated hearing aid was implemented with an additive Gaussian noise at its input (45 and 60 dB SPL). Results showed that the phase inversion technique could either overestimate or underestimate output SNR, depending on the test condition; the estimation errors tended to coincide with temporal landmarks, such as natural pauses between consecutive sentences or fricatives; and increasing the simulated noise led to poorer estimates of output SNR. Results imply that the accuracy of the phase inversion technique is dependent on the test conditions. Thus, the phase inversion technique should be used with caution, and its validity should be evaluated further.

THE EFFECT OF AN ADDITIVE NOISE ON SOME SLOW-FAST EQUATION NEAR A TRANSCRITICAL POINT

We consider the effect of small additive noise with intensity $\sigma$ on trajectories of a slow-fast system with small parameter $\varepsilon$ which admits bifurcation delay at a transcritical point. We estimate the probability that the perturbed stochastic paths stay in some tubular neighborhood of the deterministic path to show that small but not exponentially small noise destroys the bifurcation delay caused by transcritical point and obtain a noise intensity threshold value $N(\varepsilon)$ of order $\varepsilon^{\frac{3}{4}}$. When <i>e</i>^{-1/$\varepsilon$}《<i>σ</i>＜$N(\varepsilon)$, the paths are likely to leave the neighborhood of the corresponding determinate path before some time of order $\sqrt{\varepsilon|log\sigma|}$. When $\sigma>N(\varepsilon) $, the paths are likely to leave before some time of order $\sigma^{\frac{2}{3}}$.

Estimations and Control of Julia Sets of the SIS Model Perturbed by Noise.

The estimations and control of Julia sets of the SIS(susceptible-infectious-susceptible) model under noise perturbation are studied. At first, a discrete SIS model is introduced, and the effects of additive and multiplicative noises on the fractal characteristics of the SIS model are discussed. Then, estimations of the Julia sets of the SIS model under additive and multiplicative noise perturbations are given, respectively. At last, the feedback control method is used to set appropriate controllers to realize control of the Julia set, and the influence of noise on the Julia set of the SIS model is reduced. The reason why this method is effective is also explained.

Cardiac anomaly detection considering an additive noise and convolutional distortion model of heart sound recordings

Cardiac auscultation is an essential point-of-care method used for the early diagnosis of heart diseases. Automatic analysis of heart sounds for abnormality detection is faced with the challenges of additive noise and sensor-dependent degradation. This paper aims to develop methods to address the cardiac abnormality detection problem when both of these components are present in the cardiac auscultation sound. We first mathematically analyze the effect of additive noise and convolutional distortion on short-term mel-filterbank energy-based features and a Convolutional Neural Network (CNN) layer. Based on the analysis, we propose a combination of linear and logarithmic spectrogram-image features. These 2D features are provided as input to a residual CNN network (ResNet) for heart sound abnormality detection. Experimental validation is performed first on an open-access, multiclass heart sound dataset where we analyzed the effect of additive noise by mixing lung sound noise with the recordings. In noisy conditions, the proposed method outperforms one of the best-performing methods in the literature achieving an Macc (mean of sensitivity and specificity) of 89.55% and an average F-1 score of 82.96%, respectively, when averaged over all noise levels. Next, we perform heart sound abnormality detection (binary classification) experiments on the 2016 Physionet/CinC Challenge dataset that involves noisy recordings obtained from multiple stethoscope sensors. The proposed method achieves significantly improved results compared to the conventional approaches on this dataset, in the presence of both additive noise and channel distortion, with an area under the ROC (receiver operating characteristics) curve (AUC) of 91.36%, F-1 score of 84.09%, and Macc of 85.08%. We also show that the proposed method shows the best mean accuracy across different source domains, including stethoscope and noise variability, demonstrating its effectiveness in different recording conditions. The proposed combination of linear and logarithmic features along with the ResNet classifier effectively minimizes the impact of background noise and sensor variability for classifying phonocardiogram (PCG) signals. The method thus paves the way toward developing computer-aided cardiac auscultation systems in noisy environments using low-cost stethoscopes.

Numerical simulations of one- and two-dimensional stochastic neural field equations with delay.

Neural Field Equations (NFE) are intended to model the synaptic interactions between neurons in a continuous neural network, called a neural field. This kind of integro-differential equations proved to be a useful tool to describe the spatiotemporal neuronal activity from a macroscopic point of view, allowing the study of a wide variety of neurobiological phenomena, such as the sensory stimuli processing. The present article aims to study the effects of additive noise in one- and two-dimensional neural fields, while taking into account finite axonal velocity and an external stimulus. A Galerkin-type method is presented, which applies Fast Fourier Transforms to optimise the computational effort required to solve these equations. The explicit Euler-Maruyama scheme is implemented to obtain the stochastic numerical solution. An open-source numerical solver written in Julia was developed to simulate the neural fields in study.