Noise Coupling Research Articles

Optimized design of a gravitational wave telescope system based on pupil aberration.

The telescope is vital for accurate gravitational wave detection in the TianQin project. It must meet criteria like a geometric tilt-to-length (TTL) coupling noise c o e f f i c i e n t≤0.02√2n m/µr a d and wavefront R M S≤λ/30. Analyzing the pupil aberration's impact on geometric TTL noise, we devised an optimization method using the chief ray spot diagram's standard deviation. Implementing this in Zemax with a ZPL macro, we designed an optical system meeting TianQin's requirements. The system has a maximum geometric TTL noise coefficient of 0.0250nm/µrad over the science FOV and a wavefront RMS of 0.0111λ, confirming the method's feasibility.

Noise Coupling and Shielding Structures for Glass Core Substrate

Noise Coupling and Shielding Structures for Glass Core Substrate

Coupling of Magnetic Field Noise in Multilayer Magnetic Shields for Atomic Magnetometer

Coupling of Magnetic Field Noise in Multilayer Magnetic Shields for Atomic Magnetometer

The influence of synaptic strength and noise on the robustness of central pattern generator

<abstract><p>In this paper, we explore the mechanisms of central pattern generators (CPGs), circuits that can generate rhythmic patterns of motor activity without external input. We study the half-center oscillator, a simple form of CPG circuit consisting of neurons connected by reciprocally inhibitory synapses. We examine the role of asymmetric coupling factors in shaping rhythm activity and how different network topologies contribute to network efficiency. We have discovered that neurons with lower synaptic strength are more susceptible to noise that affects rhythm changes. Our research highlights the importance of asymmetric coupling factors, noise, and other synaptic parameters in shaping the broad regimes of CPG rhythm. Finally, we compare three topology types' regular regimes and provide insights on how to locate the rhythm activity.</p></abstract>

Visualization of Noise Coupling Paths Based on the Reciprocity Theorem

Visualization of Noise Coupling Paths Based on the Reciprocity Theorem

Unsupervised Intense VSP Coupling Noise Suppression With Iterative Robust Deep Learning

Unsupervised Intense VSP Coupling Noise Suppression With Iterative Robust Deep Learning

Effect of the focusing system on measurements in gravitational wave detection telescope

Telescopes primarily transmit and receive laser beams over long distances as part of a gravitational wave interferometric measurement system. Due to factors such as optical design, fabrication, and alignment, the wavefront at the exit pupil of the telescope inevitably experiences distortion, resulting in wavefront aberrations that couple with pointing jitter to generate tilt-to-length (TTL) coupling noise. During the process of gravitational wave detection, the large distance between the primary and secondary mirrors and temperature fluctuations in space can cause significant axial misalignment between them. This results in a substantial displacement of the primary-secondary mirror system’s primary focus along the axial direction, further degrading the wavefront at the exit pupil of the telescope. The TTL coupling noise caused in this scenario will affect the detection of gravitational waves, thus requiring the adjustment of the position of the three-four mirror system through the focusing system to minimize TTL coupling noise. In this paper, the model for TTL coupling noise was established using the first 36 orders of Zernike polynomials. The misalignment model of the primary-secondary mirror system was derived using geometric optics theory. The study investigates the influence of the telescope focusing system before and after focusing on the wavefront aberrations and TTL coupling noise at the exit pupil of the telescope. The analysis indicates that with a misalignment of 7.56 μm in the axial distance between the primary and secondary mirrors, the addition of a focusing system reduces the wavefront error at the exit pupil of the telescope from 0.0328 λ to 0.0046 λ. Furthermore, the maximum coupling noise between wavefront distortion and pointing jitter is reduced from 4 pm Hz−1/2 to 0.4 pm Hz−1/2. This provides valuable insights for the design of gravitational wave detection telescopes and the study of focusing systems.

Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning

Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. However, its distinct characteristics, such as unknown ground coupling and high noise level, pose challenges to signal processing. Existing machine learning models optimized for conventional seismic data struggle with DAS data due to its ultra-dense spatial sampling and limited manual labels. We introduce a semi-supervised learning approach to address the phase-picking task of DAS data. We use the pre-trained PhaseNet model to generate noisy labels of P/S arrivals in DAS data and apply the Gaussian mixture model phase association (GaMMA) method to refine these noisy labels and build training datasets. We develop PhaseNet-DAS, a deep learning model designed to process 2D spatio-temporal DAS data to achieve accurate phase picking and efficient earthquake detection. Our study demonstrates a method to develop deep learning models for DAS data, unlocking the potential of integrating DAS in enhancing earthquake monitoring.

Low-Frequency Discontinuous Noise Diagnosis and Reduction of Air Conditioner Outdoor Unit

Abnormal discontinuous low-frequency noise occurs in the outdoor unit when the air conditioner is operating at 86~96 Hz. This noise has the characteristics of low frequency, strong penetration, long transmission distance, and poor sound quality, which can cause serious noise pollution. In order to reduce the low-frequency noise of the outdoor unit of the air conditioner, the discontinuous noise frequency is first located by testing the time-domain and frequency-domain maps of the noise. It is then compared with the frequency of fan rotation noise and structural radiation noise. It is found that the discontinuous noise is caused by the coupling of fan rotation noise and structural radiation noise. It is found that low-frequency noise improvement is significant when the frequency difference between two noise peaks is greater than 8 Hz or when a single noise peak is less than 25 dB(A) by studying the methods of frequency modulation and amplitude modulation of noise peaks. By adjusting the fan speed from 930 rpm to 940 rpm, the problem of 94 Hz noise is solved. Then, through testing the natural frequencies of the pipe and shell, it is found that the natural frequencies of the pipe are close to the excitation frequencies. The structural radiation noise is mainly caused by the pipe. Modal analysis is conducted on the pipe. When the normal boundary with the condenser and the normal boundary with the valve of the pipe is constrained by springs, the simulated values are closer to the measured values. Finally, structural optimization design is carried out on the pipe. When the U-tube of the exhaust pipe is shortened by 30 mm, a 30 g mass block is added to the cold inlet pipe, and a 97 g mass block is added to the lower part of the valve pipe of the suction pipe. The discontinuous noise of the optimized prototype is significantly improved.

Effect of internal and external chaotic stimuli on synchronization of piezoelectric auditory neurons in coupled time-delay systems.

Hearing impairment is considered to be related to the damage of hair cells or synaptic terminals, which will cause varying degrees of hearing loss. Numerous studies have shown that cochlear implants can balance this damage. The human ear receives external acoustic signals mostly under complex conditions, and its biophysical mechanisms have important significance for reference in the design of cochlear implants. However, the relevant biophysical mechanisms have not yet been fully determined. Using the characteristics of special acoustoelectric conversion in piezoelectric ceramics, this paper integrates them into the traditional FitzHugh-Nagumo neuron circuit and proposes a comprehensive model with coupled auditory neurons. The model comprehensively considers the effects of synaptic coupling between neurons, information transmission delay, external noise stimulation, and internal chaotic current stimulation on the synchronization of membrane potential signals of two auditory neurons. The experimental results show that coupling strength, delay size, noise intensity, and chaotic current intensity all have a certain regulatory effect on synchronization stability. In particular, when auditory neurons are in a chaotic state, their impact on synchronization stability is sensitive. Numerical results provide a reference for exploring the biophysical mechanisms of auditory neurons. At the same time, we are committed to providing assistance in using sensors to monitor signals and repair hearing impairments.

Inductive and Capacitive Coupling Noise in Superconductive VLSI Circuits

Inductive and Capacitive Coupling Noise in Superconductive VLSI Circuits

DAS coupling noise suppression based on MCA–FK

DAS coupling noise suppression based on MCA–FK

The impact of telescope aberrations on the magnitude of tilt to length coupling noise in space based gravitational wave detectors

Tilt-to-length (TTL) coupling noise is one of the main noise sources that affect space gravitational wave detection, and the Tianqin project requires that the internal TTL coupling noise of the telescope used be less than 0.4 pm/Hz1/2 within the frequency band from 0.1 mHz to 1 Hz. In order to design a telescope that meets the requirements of TTL coupling noise and carry out preliminary error allocation research, it is necessary to analyze and calculate the TTL coupling noise, and then guide the design and optimization of the telescope system. This paper establishes a computational analysis model for the non-geometric TTL coupling noise inside the telescope using the first 36-order edge Zernike polynomials. A method was proposed to reduce the internal TTL coupling noise of the telescope by reducing the proportion of sensitive aberrations caused by non-geometric TTL coupling noise. Simulation results show that, with the RMS value of wavefront aberration at the telescope exit pupil unchanged, reducing the proportion of sensitive aberrations at the telescope exit pupil can effectively reduce the internal TTL coupling noise of the telescope. By optimizing the telescope optical system to reduce the proportion of noise-sensitive aberrations, the non-geometric TTL coupling noise inside the telescope has been reduced from 0.34 pm/Hz1/2 @ 0.1 mHz ∼ 1 Hz to 0.25 pm/Hz1/2. This result can provide some guidance for the design of telescope optics based on the suppression of internal TTL coupling noise in the telescope.

Noise-modulated nonlocal coupling: Chimera suppression and beyond

The spatio-temporal dynamics of networks of chaotic discrete-time systems is studied numerically in the presence of colored Gaussian noise-modulated nonlocal coupling between elements. As individual nodes of the networks, a modified Ricker map and a cubic map are used. Without noise, the networks demonstrate various types of chimera states, such as phase or double-well chimeras. The nonlocal coupling is modulated by either a common noise source or independent noise sources. The impact of noise-modulated coupling (coupling noise) on the dynamics of the networks is analyzed for a number of different random initial conditions and noise realizations. It is shown that the network dynamics and settled spatio-temporal structures can be controlled by varying the intensity and spectral characteristics of the coupling noise.

Signal encoding performance of astrocyte-dressed Morris Lecar neurons

There are more support cells called glia in the central nervous system than neurons. Although previous studies have shown that one of these glial cells, astrocytes, can communicate with neurons bidirectionally, giving a silent neuron the ability to fire or changing the neuron’s firing frequency, understanding the role of astrocytes in neuroscience is still in its infancy. With this motivation, in this study, the weak signal encoding performance of different types of astrocyte-dressed Morris Lecar neurons in the presence of noise naturally found in biological systems is systematically investigated. The results showed that astrocyte-dressed Morris Lecar neurons had a subthreshold signal frequency suitable for optimal encoding and that Morris Lecar neurons resonated at this frequency. In addition, the rate of inositol trisphosphate production in the astrocyte has been shown to affect signal detection at different noise intensities and coupling strengths. Finally, simulations performed by including the internal noise of the astrocytes originating from calcium ion channels in the model have shown how the signal transmission changes depending on the calcium channel number, noise intensity, and coupling strength. In this context, the presented study also contains important information in terms of examining stochastic astrocyte dynamics that affect the weak signal detection performance of different types of astrocyte-dressed Morris Lecar neurons.

A fast steering mirror with ultra-low geometric tilt-to-length coupling noise for space-borne gravitational wave detection

The misalignment between the geometric center and rotation center in fast steering mirrors (FSMs) poses a challenge for space-borne gravitational wave detection, as it results in geometric tilt-to-length (TTL) coupling noise. To address this issue, we have proposed a FSM structure that exhibits ultra-low geometric TTL coupling noise. The geometric TTL coupling noise is analyzed and evaluated strictly considering the FSM non-ideal tilt of the plane and the center shift characteristic of cartwheel flexure hinges under a small rotational angle. Compared to traditional FSM designs, our new design offers superior noise suppression capabilities and is capable of meeting the stringent piston effect noise requirements of 0.2 in the frequency band of under ideal conditions.

Fault diagnosis of gas turbine generator bearings using enhanced valuable sample strategy and convolutional neural network

Gas turbine bearings operate continuously under complex and harsh conditions such as high temperatures, high pressures and high speeds. Bearing fault monitoring data often exhibits anomalies, noise, missing values, and strong coupling and non-linearity due to real-world random factors. In addition, the traditional convolutional neural network (CNN) is still limited by the scarcity of labeled samples in real-world conditions and cannot fully extract fault features. To address the complexities of strongly coupled fault data, inconsistent data quality, and the shortage of authentic labeled samples, this paper presents a novel method for bearing fault diagnosis. This method utilizes an enhanced valuable sample strategy in conjunction with CNNs, integrating data enhancement and active learning (AL) for preprocessing to create valuable training sets for the networks. At the outset, random dropout and scaling jitter operations are applied to the original data, effectively removing anomalies and gaps in the fault signals. This process simultaneously emulates variations in the frequency spectrum and amplitude of vibration signals under real operating conditions, thereby enhancing the quality of gas turbine bearing fault data. Subsequently, the method incorporates AL techniques, iteratively selecting unlabeled data samples with the highest information value, and annotating them using support vector machines. This strategy creates a valuable training dataset that further enhances the fault diagnosis capabilities of the model. In the subsequent phase, a three-dimensional CNN is employed to extract fault feature information efficiently from the fault data, culminating in precise fault classification. In order to validate the effectiveness and superiority of the algorithm, validation and comparison analyses were carried out using the Case Western Reserve University and XJTU open datasets. To evaluate the practical effectiveness, a gas turbine main bearing dataset collected in a real environment, named the BaiChuan dataset, is used for the engineering validation of the proposed method.

Self-Organization of Nonlinearly Coupled Neural Fluctuations Into Synergistic Population Codes.

Neural activity in the brain exhibits correlated fluctuations that may strongly influence the properties of neural population coding. However, how such correlated neural fluctuations may arise from the intrinsic neural circuit dynamics and subsequently affect the computational properties of neural population activity remains poorly understood. The main difficulty lies in resolving the nonlinear coupling between correlated fluctuations with the overall dynamics of the system. In this study, we investigate the emergence of synergistic neural population codes from the intrinsic dynamics of correlated neural fluctuations in a neural circuit model capturing realistic nonlinear noise coupling of spiking neurons. We show that a rich repertoire of spatial correlation patterns naturally emerges in a bump attractor network and further reveals the dynamical regime under which the interplay between differential and noise correlations leads to synergistic codes. Moreover, we find that negative correlations may induce stable bound states between two bumps, a phenomenon previously unobserved in firing rate models. These noise-induced effects of bump attractors lead to a number of computational advantages including enhanced working memory capacity and efficient spatiotemporal multiplexing and can account for a range of cognitive and behavioral phenomena related to working memory. This study offers a dynamical approach to investigating realistic correlated neural fluctuations and insights to their roles in cortical computations.

Spatiotemporal dedifferentiation of the global brain signal topography along the adult lifespan.

Age-related variations in many regions and/or networks of the human brain have been uncovered using resting-state functional magnetic resonance imaging. However, these findings did not account for the dynamical effect the brain's global activity (global signal [GS]) causes on local characteristics, which is measured by GS topography. To address this gap, we tested GS topography including its correlation with age using a large-scale cross-sectional adult lifespan dataset (n = 492). Both GS topography and its variation with age showed frequency-specific patterns, reflecting the spatiotemporal characteristics of the dynamic change of GS topography with age. A general trend toward dedifferentiation of GS topography with age was observed in both spatial (i.e., less differences of GS between different regions) and temporal (i.e., less differences of GS between different frequencies) dimensions. Further, methodological control analyses suggested that although most age-related dedifferentiation effects remained across different preprocessing strategies, some were triggered by neuro-vascular coupling and physiological noises. Together, these results provide the first evidence for age-related effects on global brain activity and its topographic-dynamic representation in terms of spatiotemporal dedifferentiation.

The role of multiplicative noise in critical dynamics

We study the role of multiplicative stochastic processes in the description of the dynamics of an order parameter near a critical point. We study equilibrium as well as out-of-equilibrium properties. By means of a functional formalism, we build the Dynamical Renormalization Group equations for a real scalar order parameter with Z2 symmetry, driven by a class of multiplicative stochastic processes with the same symmetry. We compute the flux diagram using a controlled ϵ-expansion, up to order ϵ2. We find that, for dimensions d=4−ϵ, the additive dynamic fixed point is unstable. The flux runs to a multiplicative fixed point driven by a diffusion function G(ϕ)=1+g∗ϕ2(x)/2, where ϕ is the order parameter and g∗=ϵ2/18 is the fixed point value of the multiplicative noise coupling constant. We show that, even though the position of the fixed point depends on the stochastic prescription, the critical exponents do not. Therefore, different dynamics driven by different stochastic prescriptions (such as Itô, Stratonovich, anti-Itô and so on) are in the same universality class.