Search

When starting an N -body simulation of an isolated galaxy, it is desirable to select particles from a distribution function to ensure that the model is in equilibrium. Random sampling from a DF is widely used, but results in a set of particles that differs by shot noise from that intended. This paper presents a method to reduce sampling noise that has been developed by the author in a many collaborations over a number of years. The technique has been partly described in past papers, though the ideas have not previously been gathered together, nor have its advantages been clearly demonstrated in past work. Of course, sampling errors can also be reduced by a brute force increase in the number of particles, but methods to achieve the same effect with fewer particles have obvious advantages. Here we not only describe the method, but also present three sets of simulations to illustrate the practical advantages of reducing sampling error. The improvements are not dramatic, but are clearly worth having. [ABSTRACT FROM AUTHOR]

A key goal of synthetic biology is to develop functional biochemical modules with network-independent properties. Antithetic integral feedback (AIF) is a recently developed control module in which two control species perfectly annihilate each other's biological activity. The AIF module confers robust perfect adaptation to the steady-state average level of a controlled intracellular component when subjected to sustained perturbations. Recent work has suggested that such robustness comes at the unavoidable price of increased stochastic fluctuations around average levels. We present theoretical results that support and quantify this trade-off for the commonly analyzed AIF variant in the idealized limit with perfect annihilation. However, we also show that this trade-off is a singular limit of the control module: Even minute deviations from perfect adaptation allow systems to achieve effective noise suppression as long as cells can pay the corresponding energetic cost. We further show that a variant of the AIF control module can achieve significant noise suppression even in the idealized limit with perfect adaptation. This atypical configuration may thus be preferable in synthetic biology applications. [ABSTRACT FROM AUTHOR]

This work evaluates the impact of the input voltage noise on a Hardware-In-the-Loop (HIL) system used in the emulation of power converters. A poor signal-to-noise ratio (SNR) can compromise the accuracy and precision of the model, and even make certain techniques for building mathematical models unfeasible. The case study presents the noise effects on a digitally controlled totem-pole converter emulated with a low-cost HIL system using an FPGA. The effects on the model outputs, and the cost and influence of different hardware implementations, are evaluated. The noise of the input signals may limit the benefits of increasing the resolution of the model. [ABSTRACT FROM AUTHOR]

Quantization of phase and/or amplitude has far-reaching effects on the radiation characteristics of the phased array (PA), including gain, minor lobe level, and point deviation. Traditionally, one common method to address such a nonlinear distortion is using random-phasing to interrupt the error periodicity. Here, we show that the distortion due to the quantization can be better remediated by spectrally shaping the error compared to the random-phasing (dithering) approaches. We adapted the method for phase-only and amplitude-phase synthesis of planar array designed based on analog beamforming (ABF). To do that, for the first time, 2-D real- and complex-coefficient minimum-phase digital finite impulse response (FIR) filters are designed based on the discrete Hilbert transform (DHT) method. In particular, the digital filter design for phase-only synthesis is comprehensively investigated, respecting the error spectra in the beamspace domain. It is shown that by pushing the error out of the so-called visible region, the decrease of antenna directivity due to the quantization can be compensated to some extent, which provides a quite advantage over the uniform distribution of error. For some cases, pushing the error out of the visible region might be impossible. For such cases, we proposed using the spaced-notches filter. It is also shown that the method is on maximum efficacy when both phase and amplitude of the excitation signal are controllable. Thus, complex-valued noise shaping (CV-NS) can be exploited for the phase-amplitude synthesis of the PA, showing quite promising performance. [ABSTRACT FROM AUTHOR]

Resonator-integrated optical gyroscopes have advantages such as all-solid-state, on-chip integration, miniaturized structure, and high precision. However, many factors deteriorate the performance and push it far from the shot-noise limited theoretical sensitivity. This paper reviews the mechanisms of various noises and their corresponding suppression methods in resonator-integrated optical gyroscopes, including the backscattering, the back-reflection, the polarization error, the Kerr effect, and the laser frequency noise. Several main noise suppression methods are comprehensively expounded through inductive comparison and reasonable collation. The new noise suppression technology and digital signal processing system are also addressed. [ABSTRACT FROM AUTHOR]

Quantum computing has the potential to revolutionize computing, but its significant sensitivity to noise requires sophisticated error correction and mitigation. Traditionally, noise on the quantum device is characterized directly through qubit and gate measurements, but this approach has drawbacks in that it does not adequately capture the effect of noise on realistic multi-qubit applications. In this paper, we simulate the relaxation of stationary quantum states on a quantum computer to obtain a unique spectroscopic fingerprint of the computer's noise. In contrast to traditional approaches, we obtain the frequency profile of the noise as it is experienced by the simulated stationary quantum states. Data from multiple superconducting-qubit IBM processors show that noise generates a bath within the simulation that exhibits both colored noise and non-Markovian behavior. Our results provide a direction for noise mitigation but also suggest how to use noise for quantum simulations of open systems. Quantifying, controlling, and correcting noise related errors is one of the current challenges in quantum computing. Here, the authors study the time dependence of the relaxation of a stationary state simulated on a quantum computer, and show that such spectroscopic signature is unique and can be used to characterize the noise on individual quantum computers. [ABSTRACT FROM AUTHOR]

This article analyzes the propagation of feedback noise in multisampled dc–dc power converters. The analytical calculations for noise attenuation, strictly valid for linear time-invariant systems, are found to offer good predictions for power converters, after suppressing the decimation effects of the digital pulsewidth modulator (DPWM). For control systems that employ a proportional-integral controller, without any digital low-pass filters, the nonlinearity caused by the DPWM decimation is found to saturate the noise attenuation properties, as the multisampling factor $N$ is increased. Strong noise attenuation is enabled using antialiasing digital filters, with a small impact on the dynamic response. For control systems that employ a proportional-integral-derivative controller, increase of the multisampling factor, without any digital filters, causes the amplification of the noise power. Therefore, a greater attenuation of high-frequency components is required to provide a significant noise reduction in the control bandwidth. Even with these antialiasing digital filters, the dynamic response of multisampled control systems is improved compared to double-update. This is proven by the analytical and experimental comparison of various multisampled control strategies in terms of dynamic response and noise attenuation capabilities. The experimental results, from a buck-type converter, match well with simulations and analytical calculations. [ABSTRACT FROM AUTHOR]

For convective clouds and precipitation, model uncertainty in cloud microphysics is considered one of the most significant sources of model error. In this study, samples for model microphysical uncertainty are obtained by calculating the differences between simulations equipped with two‐ and one‐moment schemes during a one‐month training period. The samples are then added to convective‐scale ensemble data assimilation as additive noise and combined with large‐scale additive noise based on samples from climatological atmospheric background error covariance. Two experiments, including the combination and large‐scale error only, are conducted for a one‐week convective period. The results reveal that the simulation with a two‐moment scheme triggers more convection and has larger ice‐phase precipitation particles, which produce a stronger signal in the melting layer. During data assimilation cycling, although more water is introduced to the model, it is shown that the combination performs better for both background and analysis and significantly improves short‐term ensemble forecasts of radar reflectivity and hourly precipitation. Plain Language Summary: One of the main difficulties hindering the improvements of weather forecasts is correct representation of uncertainties in clouds and precipitation in the numerical weather prediction models. The goal of this work is to improve the representation of this uncertainty when combining our prediction with observations. This way, we would obtain better initial condition for our model and better prediction of convection. Here, we obtain the samples for model error by computing the differences between simulations that use two cloud microphysical schemes in the model. Then such obtained samples for model microphysical uncertainty are combined with large‐scale error and incorporated into data assimilation. To evaluate the performance of this method, two experiments are carried out in a one‐week period over Germany. We find that the combination indeed achieves significant improvement of short‐term forecasts of radar reflectivity and hourly precipitation. Key Points: Model simulations with two microphysical schemes are used to represent uncertainty in clouds and precipitation during data assimilationIn data assimilation, including the combination of microphysical and large‐scale uncertainty improves the performanceThe improvement is also significant for short‐term ensemble forecasts of radar reflectivity and hourly precipitation [ABSTRACT FROM AUTHOR]