Present Noise Models Research Articles

Assessment of an Open-Source Aircraft Noise Prediction Model Using Approach Phase Measurements

An open-source simulation model for aircraft noise prediction is presented and validated using backpropagated noise measurements for a state-of-the-art engine and aircraft. The validation is focused on approach procedures and was performed using ground-based noise measurements that were taken at 17 recording stations for a total of 18 consecutive flights carried out during the morning of 8 April 2021. The flights were performed using two A321neo aircraft with LEAP-1A engines. It is demonstrated that the presented noise model provides a satisfactory estimation of the source noise for varying approach configurations and flight conditions. Configurations using a greater number of high-lift devices are particularly well predicted in the mid- and high-frequency regions, whereas the lower configuration settings show greater spectral deviations, which are partly attributed to measurement uncertainties caused by the increased aircraft–microphone distance. The model can predict the overall mean total sound intensity level within a 2 dB accuracy for all configurations, while the average predicted level at each microphone differs by less than 3 dB from the measurement average, for all cases except one. Variation in aircraft speed showed to have a strong impact on the predicted total noise, which matches the well-recognized sixth-power Mach number far-field sound intensity scaling law for airframe noise models, while the measurements indicated a less significant dependency. This is mainly due to installation effects and noise reduction measures that are not included in the models. Nevertheless, the variations in the spectra of the predicted and measured noise showed similar patterns.

Noise Model of Large-Format Readout Integrated Circuit for Infrared Focal Plane Array

With the upscaling of the pixel format and the downscaling of the pixel pitch of infrared focal plane arrays (FPAs), the demand for ultra-low-noise design is increasing drastically. Existing methods of noise modelling lack transistor-level analysis and are unable to provide directly effective guidance for circuit design because of extremely complicated mathematical expressions. In this work, a comprehensive noise model of large-format readout integrated circuit (ROIC) is presented, for the first time elucidating the noise mechanism with a full research framework, including transistor-, circuit-, block-, and chip-level analysis. By utilizing a novel approximation method, simplified analytical formulas with high accuracy are obtained for flicker noise and other noise. These simplified formulas clear up the confusions of how the various functional circuit blocks influence noise performance. Moreover, based on the presented noise model, the impacts of crucial parameters on FPA noise are analyzed in detail, and strategies regarding the trade-off between the signal-to-noise ratio (SNR) and other aspects of performance are proposed for low-noise design. Taking an astronomical application with a very long integration time as an example, the calculated results show that an ultralow noise of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${5}{\,e^{-}}$ </tex-math></inline-formula> can be achieved by leveraging the above approaches.

Identifying and mitigating noise sources in precision pulsar timing data sets

ABSTRACT Pulsar timing array projects measure the pulse arrival times of millisecond pulsars for the primary purpose of detecting nanohertz-frequency gravitational waves. The measurements include contributions from a number of astrophysical and instrumental processes, which can either be deterministic or stochastic. It is necessary to develop robust statistical and physical models for these noise processes because incorrect models diminish sensitivity and may cause a spurious gravitational wave detection. Here we characterize noise processes for the 26 pulsars in the second data release of the Parkes Pulsar Timing Array using Bayesian inference. In addition to well-studied noise sources found previously in pulsar timing array data sets such as achromatic timing noise and dispersion measure variations, we identify new noise sources including time-correlated chromatic noise that we attribute to variations in pulse scattering. We also identify ‘exponential dip’ events in four pulsars, which we attribute to magnetospheric effects as evidenced by pulse profile shape changes observed for three of the pulsars. This includes an event in PSR J1713+0747, which had previously been attributed to interstellar propagation. We present noise models to be used in searches for gravitational waves. We outline a robust methodology to evaluate the performance of noise models and identify unknown signals in the data. The detection of variations in pulse profiles highlights the need to develop efficient profile domain timing methods.

Statistic Model of Homodyne Acousto-Optic Spectrum Analyzer

Introduction. Acousto-optic spectrum analyzers interferometric schemes have been developed to increase dynamic range. It was assumed that dynamic range, expressed in dB, would double. An expected increase was not achieved yet.Aim. To analyze the homodyne acousto-optic spectrum analyzer noise characteristics, to estimate the signal-tonoise ratio and the dynamic range.Materials and methods.A mathematical model was compiled which took into account the need to form quadrature components to obtain an amplitude spectrum of an input signal, shot noise and readout noise.Results. An interferometric scheme did not allow to achieve dynamic range doubling compared to an acoustooptical power spectrum analyzer. The dynamic range increase was less than 1.35 dB. Constant illumination led to a significant increase of the spectrum analyzer self-noise due to shot noise, compared to which thermal noise and readout noise became insignificant. The spurious-free dynamic range estimation expression was obtained. It was prior determined by acousto-optic interaction nonlinearity. With typical analyzer blocks parameters the spurious-free dynamic range covered a single-signal dynamic range. Signal-to-noise ratio estimation expression was presented.Conclusion. The homodyne acousto-optic spectrum analyzer single-signal dynamic range is determined primarily by the photosensor saturation charge. One needs to optimize their relation by taking into account light source power, acousto-optical modulator diffraction efficiency and photosensor saturation charge. Presented noise model gives more accurate estimation of the dynamic range with an error of 1 dB.

An Improved Noise Model for SiGe HBT With an Inductive Breakdown Network in the Avalanche Region

In this paper, an improved radio-frequency (RF) noise model is extended for silicon germanium (SiGe) heterojunction bipolar transistors (HBTs) by applying RF avalanche delay mechanism to investigate noise performance in the breakdown region where reliability is a concern. The impact of inductive feedback due to base–collector (BC) junction avalanche breakdown on noise performance is analyzed and explained by utilizing this improved noise model incorporating an inductive network. The avalanche effects on noise sources are verified and explained physically by the dead-space theory. The contributions of electrons and holes to the avalanche noise are taken into account by including an electron- and hole-breakdown network in the presented noise model. The presented method is compared with the conventional approach. When the avalanche noise is absorbed by base and collector noise sources as in the conventional RF noise model without a breakdown network, simulated noise parameters deviate from experimental data at breakdown. Instead, good agreement between measured and simulated noise parameters and $S$ -parameters from the presented RF noise model is achieved in the inductive breakdown and active regions. The presented noise model and the obtained parameters can be applied to SiGe HBT circuits for reliability investigation.

Investigation of quantum and classical correlations in a quantum dot system under decoherence

In this paper, we investigate quantitatively the thermal classical and quantum correlations in an isolated quantum dot system (QDS) including the effects of different parameters. We show that the quantum discord (QD) is more resistant against the temperature effect and might be finite even for higher temperatures in the asymptotic limit. Decoherence in a QDS caused by interaction with its environment is another interesting issue in the quantum information field. Assuming Markovian dynamics for the time evolution, we present noise models for the QDS by using Kraus operators for several noisy channels; in particular bit flip, bit-phase flip, phase flip, and depolarizing channels. By analytical and numerical analyses, we investigate the dynamics of different kinds of correlations, namely, the mutual information, the classical correlation, the entanglement of formation (EOF), and the QD in different channels. The sudden change in behavior in the decay rates of correlations and their immunity against certain decoherences are shown. We explore a symmetry among these channels and provide the decoherence areas for which both classical and quantum correlations remain affected in the QDS.

Realistic camera noise modeling with application to improved HDR synthesis

Due to the ongoing miniaturization of digital camera sensors and the steady increase of the “number of megapixels”, individual sensor elements of the camera become more sensitive to noise, even deteriorating the final image quality. To go around this problem, sophisticated processing algorithms in the devices, can help to maximally exploit the knowledge on the sensor characteristics (e.g., in terms of noise), and offer a better image reconstruction. Although a lot of research focuses on rather simplistic noise models, such as stationary additive white Gaussian noise, only limited attention has gone to more realistic digital camera noise models. In this article, we first present a digital camera noise model that takes several processing steps in the camera into account, such as sensor signal amplification, clipping, post-processing,.. We then apply this noise model to the reconstruction problem of high dynamic range (HDR) images from a small set of low dynamic range (LDR) exposures of a static scene. In literature, HDR reconstruction is mostly performed by computing a weighted average, in which the weights are directly related to the observer pixel intensities of the LDR image. In this work, we derive a Bayesian probabilistic formulation of a weighting function that is near-optimal in the MSE sense (or SNR sense) of the reconstructed HDR image, by assuming exponentially distributed irradiance values. We define the weighting function as the probability that the observed pixel intensity is approximately unbiased. The weighting function can be directly computed based on the noise model parameters, which gives rise to different symmetric and asymmetric shapes when electronic noise or photon noise is dominant. We also explain how to deal with the case that some of the noise model parameters are unknown and explain how the camera response function can be estimated using the presented noise model. Finally, experimental results are provided to support our findings.

Comprehensive study of noise processes in electrode electrolyte interfaces

A general circuit model is derived for the electrical noise of electrode–electrolyte systems, with emphasis on its implications for electrochemical sensors. The noise power spectral densities associated with all noise sources introduced in the model are also analytically calculated. Current and voltage fluctuations in typical electrode–electrolyte systems are demonstrated to originate from either thermal equilibrium noise created by conductors, or nonequilibrium excess noise caused by charge transfer processes produced by electrochemical interactions. The power spectral density of the thermal equilibrium noise is predicted using the fluctuation-dissipation theorem of thermodynamics, while the excess noise is assessed in view of charge transfer kinetics, along with mass transfer processes in the electrode proximity. The presented noise model not only explains previously reported noise spectral densities such as thermal noise in sensing electrodes, shot noise in electrochemical batteries, and 1/f noise in corrosive interfaces, it also provides design-oriented insight into the fabrication of low-noise micro- and nanoelectrochemical sensors.