General Noise Research Articles

Feasibility of loop-gain tuning for general measurement systems inspired by quantum locking for DECIGO

Abstract A series of quantum locking theories have been proposed to enhance the quantum-noise-limited target sensitivity of the DECi-hertz Interferometer Gravitational wave Observatory. The quantum locking that uses a square completion optimizes the sensitivity across all frequencies. However, a substantial amount of data-series must be post-processed since the square completion is a form of signal processing technique. This paper approaches the optimal sensitivity across all frequencies from an alternative perspective: by optimizing the frequency dependence of a servo gain in a feedback loop. The optimal servo gain is formulated by comparing the alternative method with the square completion method for the same optical setup. This will be shown in general noise issues extending the framework of the quantum locking. We find that the optimal servo gain forms a non-feasible filter but has certain characteristics. We also find that the noise of the measurement signal deteriorates proportionally to the noise measured in the feedback loop when the servo gain is slightly imperfect.

Model-Based Causal Feature Selection for General Response Types

Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters, Bühlmann, and Meinshausen) is a method for causal feature selection which requires data from heterogeneous settings and exploits that causal models are invariant. ICP has been extended to general additive noise models and to nonparametric settings using conditional independence tests. However, the latter often suffer from low power (or poor Type I error control) and additive noise models are not suitable for applications in which the response is not measured on a continuous scale, but reflects categories or counts. Here, we develop transformation-model (tram) based ICP, allowing for continuous, categorical, count-type, and uninformatively censored responses (these model classes, generally, do not allow for identifiability when there is no exogenous heterogeneity). As an invariance test, we propose tram-GCM based on the expected conditional covariance between environments and score residuals with uniform asymptotic level guarantees. For the special case of linear shift trams, we also consider tram-Wald, which tests invariance based on the Wald statistic. We provide an open-source R package tramicp and evaluate our approach on simulated data and in a case study investigating causal features of survival in critically ill patients. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Study of noise perception and health effects on population through a questionnaire in Spanish households during the COVID-19 lockdown

During the first wave of COVID-19, an online questionnaire was conducted to gather the Spanish population's perception of noise from households and its relationship with health effects. This research, which was conducted with 582 participants, examined several sources of noise (general noise, noise produced by family members inside the home, noise from neighbors, noise from common areas, facilities, and exterior noise) and various self-declared health effects by the participants such as stress, lack of concentration, sleep disturbances, anxiety, or irritability. A descriptive and statistical analysis was established between variables to observe relational trends for two periods (before and during confinement). The study demonstrates interesting relational trends that allow for both associating different noise sources with potential health effects and contextualizing them against other causes that remained consistent in both periods.

Dynamics of stochastic differential equations with memory driven by colored noise.

In this paper, we will show two approaches to analyze the dynamics of a stochastic partial differential equation (PDE) with long time memory, which does not generate a random dynamical system and, consequently, the general theory of random attractors is not applicable. On the one hand, we first approximate the stochastic PDEs by a random one via replacing the white noise by a colored one. The resulting random equation does generate a random dynamical system which possesses a random attractor depending on the covariance parameter of the colored noise. On the other hand, we define a mean random dynamical system via the solution operator and prove the existence and uniqueness of weak pullback mean random attractors when the problem is driven by a more general white noise.

Wenn die Ruhe zum Problem wird

When silence becomes a problem In recent years, ambient and working noise in offices has virtually disappeared. Office buildings are now well protected against noise from outside, from adjoining rooms, from floors above or below due to legal regulations, and the general ambient noise is low. Office equipment has also become quieter by law. What has remained are people speaking, who are often well understood anywhere in the room due to the lack of ambient noise. The silence caused by the sinking of the surrounding and working equipment has become a problem.

Control-enhanced non-Markovian quantum metrology

Quantum metrology promises unprecedented precision of parameter estimation, but it is often vulnerable to noise. While significant efforts have been devoted to improving the metrology performance in Markovian environments, practical control schemes specifically designed for non-Markovian noises are much less investigated. Here, we propose two control-enhanced quantum metrology schemes that are suitable for tackling general non-Markovian noises described by noise channels or noise spectra. We conduct experiments to verify the efficacy of these schemes on a nuclear magnetic resonance system. The experimental results involving multiqubit probes show that the parameter estimation precision can be greatly improved, significantly surpassing the standard quantum limit, with our schemes. At present, non-Markovian noises are widely encountered on diverse quantum devices, the proposed schemes are relevant for realistic metrology applications on these platforms.

Containment-Based Distributed Secondary Control for AC Shipboard Microgrids under General Noise

This paper investigates the secondary control problem of shipboard microgrids (SMGs) with a high percentage of new energy sources under general noise. Firstly, a polymorphic SMG model is constructed, which enables the software-defined functionality of the control strategy and allows heterogeneous distributed generators (DGs) in AC SMGs to exchange packets of different types. Secondly, due to the presence of highly dynamic and high-power loads in the SMGs, a containment-based distributed secondary control strategy is proposed to improve the flexibility of the DG voltage regulation. Then, considering the complexity and diversity of disturbances during ship navigation, general noise is introduced instead of white noise to describe various disturbances. Furthermore, based on the random differential equations (RDEs), the NOS stability of the proposed strategy is proved using Lyapunov theory, which proves the effectiveness of the containment-based distributed secondary control strategy under general noise. And, the containment error is obtained to prove that the voltage and frequency of the system converge to the convex hull spanned by the virtual leaders, ensuring the high quality of the power supply. Finally, the validity of the proposed containment-based strategy is verified by an AC SMG model with four DGs in three cases.

Regularization of an inverse source problem for fractional diffusion-wave equations under a general noise assumption

Regularization of an inverse source problem for fractional diffusion-wave equations under a general noise assumption

Channel assisted noise propagation in a two-step cascade.

Signal propagation in biochemical networks is characterized by the inherent randomness in gene expression and fluctuations of the environmental components, commonly known as intrinsic and extrinsic noise, respectively. We present a theoretical framework for noise propagation in a generic two-step cascade (S→X→Y) regarding intrinsic and extrinsic noise. We identify different channels of noise transmission that regulate the individual and the overall noise properties of each component. Our analysis shows that the intrinsic noise of S alleviates the general noise and information transmission capacity along the cascade. On the other hand, the intrinsic noise of X and Y acts as a bottleneck of information transmission. We also show a hierarchical relationship among the intrinsic noise levels of S, X, and Y, with S exhibiting the highest level of intrinsic noise, followed by X and then Y. This hierarchy is preserved within the two-step cascade, facilitating the highest information transmission from S to Y via X.

Fast Generation of Low PAPR and Low Sidelobe Noise Waveform

Noise radar waveform, as an important waveform in the field of low-probability-of-intercept radars, has been widely studied. However, the general noise waveform has a high peak average power ratio (PAPR), which leads to a reduction of signal-to-noise ratio. Thus, it is necessary to control the noise waveform PAPR to a suitable range. In this paper, the PAPR value reduction problem of low sidelobe noise waveform is studied. First, the proposed new algorithm is given a general overview. Then, the Combined Spectral Shaping and Peak-to-Average Power Ratio Reduction (COSPAR) algorithm is adjusted according to the alternate projection method and phase retrieval algorithm, and an improved COSPAR (ICOSPAR) algorithm with a faster execution speed and higher waveform generation efficiency is derived. The specific method performs waveform projection between the spectral constraint domain and the PAPR constraint domain and then evaluates the computational load of the ICOSPAR algorithm. Simulation results show that the ICOSPAR algorithm is reliable and efficient in generating low PAPRs and low sidelobe noise waveforms.

Practical Trainable Temporal Postprocessor for Multistate Quantum Measurement

We develop and demonstrate a trainable temporal postprocessor (TPP) harnessing a simple but versatile machine learning algorithm to provide optimal processing of quantum measurement data subject to arbitrary noise processes for the readout of an arbitrary number of quantum states. We demonstrate the TPP on the essential task of qubit state readout, which has historically relied on temporal processing via matched filters in spite of their applicability for only specific noise conditions. Our results show that the TPP can reliably outperform standard filtering approaches under complex readout conditions, such as high-power readout. Using simulations of quantum measurement noise sources, we show that this advantage relies on the TPP’s ability to learn optimal linear filters that account for general quantum noise correlations in data, such as those due to quantum jumps, or correlated noise added by a phase-preserving quantum amplifier. Furthermore, we derive an exact analytic form for the optimal TPP weights: this positions the TPP as a linearly scaling generalization of matched filtering, valid for an arbitrary number of states under the most general readout noise conditions, all while preserving a training complexity that is essentially negligible in comparison with that of training neural networks for processing temporal quantum measurement data. The TPP can be autonomously and reliably trained on measurement data and requires only linear operations, making it ideal for field-programmable gate array implementations in circuit QED for real-time processing of measurement data from general quantum systems. Published by the American Physical Society 2024

Feynman–Kac Representation for Parabolic Anderson Equations with General Gaussian Noise

Feynman–Kac Representation for Parabolic Anderson Equations with General Gaussian Noise

Risks and benefits of air taxis as perceived in Germany

In a telephone survey on the acceptance of civilian drones in Germany conducted at the end of 2022 a certain part of questions was dedicated to air taxis explicitly. While the attitude towards civilian drones tended to be slightly more positive than in the preceding study, in the again nationwide representative study attitudes concerning air taxis were revealed to be relatively balanced, with a slight negative tendency. Inferential statistical analyses showed factors such as age, gender, active experience with drones, and interest in environmental protection to be significantly associated with the attitudes towards both, civilian drones and air taxis. According to prior findings on the relevance of noise concerns for drone acceptance in general, the current study asked for some noise related information, revealing subjective noise sensitivity and general noise annoyance being associated with the acceptance of civilian drones as well as air taxis. Evaluating information from answers to free format questions about air taxis, this contribution reveals reasons why in Germany—although the contributing factors are the same—the general attitude towards air taxis is less positive compared to civil drones, and what role noise considerations might play.

Robust Zero-Watermarking by Circular Features and 1-D NRDPWT Transformation

<p>This paper introduces a secure and robust zero-watermarking framework that leverages the advantages of zero-watermarking, ensuring non-destructive modification of original images and unlimited capacity. The proposed method enables robust watermark embedding while preserving the original image. It employs a novel feature extraction approach using circular areas based on image radius, enhancing feature resilience. Additionally, applying one-dimensional non-recursive discrete periodized wavelet transform (1-D NRDPWT) converts feature values into phi, contributing to enhanced stability and robustness. Enhanced security is achieved through the use of Shuffle and Pseudo-Random Number Generator (PRNG). Experimental results, evaluated using metrics such as Bit Error Rate (BER) and Normalized Correlation (NC), validate the exceptional performance of this watermarking technique. These findings underscore the framework’s robustness, security, reliability, and integrity against both general and geometric noise attacks, making it a secure and robust solution for modern digital image copyright protection. In summary, our method offers an effective defense against various noise attacks while ensuring the highest watermark quality without compromising the original image. It is a significant advancement in copyright protection applications.</p> <p> </p>

Hyperspectral Image Shadow Enhancement Using Three-Dimensional Dynamic Stochastic Resonance and Classification Based on ResNet

Classification is an important means of extracting rich information from hyperspectral images (HSIs). However, many HSIs contain shadowed areas, where noise severely affects the extraction of useful information. General noise removal may lead to loss of spatial correlation and spectral features. In contrast, dynamic stochastic resonance (DSR) converts noise into capability that enhances the signal in a way that better preserves the image’s original information. Nevertheless, current one-dimensional and 2D DSR methods fail to fully utilize the tensor properties of hyperspectral data and preserve the complete spectral features. Therefore, a hexa-directional differential format is derived in this paper to solve the system’s output, and the iterative equation for HSI shadow enhancement is obtained, enabling 3D parallel processing of HSI spatial–spectral information. Meanwhile, internal parameters are adjusted to achieve optimal resonance. Furthermore, the residual neural network 152 model embedded with the convolutional block attention module is proposed to diminish information redundancy and leverage data concealed within shadow areas. Experimental results on a real-world HSI demonstrate the potential performance of 3D DSR in enhancing weak signals in HSI shadow regions and the proposed approach’s effectiveness in improving classification.

A Novel Continuous-Variable Quantum Key Distribution Scheme Based on Multi-Dimensional Multiplexing Technology

Dual-polarization division multiplexing (DPDM) is considered to be a potential method to boost the secure key rate (SKR) of the continuous-variable quantum key distribution (CV-QKD) system. In this article, we propose a pilot alternately assisted local local oscillator (LLO) CV-QKD scheme based on multi-dimensional multiplexing, where time division multiplexing and frequency division multiplexing are combined with dual-polarization multiplexing techniques to dramatically isolate the quantum signal from the pilot tone. We establish a general excess noise model for the LLO CV-QKD system to analyze the influence mechanism of various disturbances (e.g., time-domain diffusion, frequency-domain modulation residual, and polarization perturbation) on the key parameters, such as the channel transmittance and excess noise. Specifically, the photon leakage noise from the reference path to the quantum path and that between quantum signals with two different polarization paths are simultaneously analyzed in the dual-polarization LLO CV-QKD scheme for the first time. Furthermore, a series of simulations are established to verify the performance of the proposed scheme. The results show that the maximal isolation degree achieves 84.0 dB~90.4 dB, and the crosstalk between pilot tones and quantum signals can be suppressed to a very small range. By optimizing the system parameters (e.g., modulation variance and repetition frequency), the SKR with 12.801 Mbps@25 km is achieved under the infinite polarization extinction ratio (PER) and 30 dB residual ratio of the frequency modulation in the nanosecond-level pulse width. Moreover, the performance of the proposed DPDM CV-QKD scheme under relatively harsh conditions is simulated; the results show that the SKR with 1.02 Mbps@25 km is achieved under a relatively low PER of 17 dB with the nanosecond-level pulse width and 20 dB residual ratio of the frequency modulation. Our work lays an important theoretical foundation for the practical DPDM LLO CV-QKD system.

Nonlinear Tikhonov regularization in Hilbert scales for inverse learning

In this paper, we study Tikhonov regularization scheme in Hilbert scales for a nonlinear statistical inverse problem with general noise. The regularizing norm in this scheme is stronger than the norm in the Hilbert space. We focus on developing a theoretical analysis for this scheme based on conditional stability estimates. We utilize the concept of the distance function to establish high probability estimates of the direct and reconstruction errors in the Reproducing Kernel Hilbert space setting. Furthermore, explicit rates of convergence in terms of sample size are established for the oversmoothing case and the regular case over the regularity class defined through an appropriate source condition. Our results improve upon and generalize previous results obtained in related settings.

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.

Feynman–Kac representation of parabolic Anderson equations with general Gaussian noise

UDC 519.21 We provide the Feynman–Kac representation for the parabolic Anderson equations driven by a general Gaussian noise. As a feature of the idea, we can mention the argument of subadditivity in establishing the required exponential integrability.

Noise Regulations in Japan

For noise regulations in Japan, environmental quality standards as noise-related administrative targets are provided under the Basic Environment Law and various measures are put in place under the Noise Regulation Law which was legislated as a law for the implementation of the Basic Environment Law. Environmental quality standards for general ambient noise are established by certain land-use types and time divisions, while separate environmental quality standards are specifically set for aircraft and Shinkansen Superexpress Railway noise. The Noise Regulation Law controls noise from factories and business that have specified facilities and sit in designated areas as well as noise from specified types of construction operations, with proper demarcation of regulation roles among national and local governments. Also, rationalization of Noise Regulation Low and Vibration Regulation Low were considered from 2021 to 2022. In recent years, new types of noise problems that are not well controlled by the Noise Regulation Law or the application of environmental quality standards, including complaints against noise from facilities not regulated under the Noise Regulation Law and against low-frequency noise, calling for responses to these problems.