Presence Of Strong Noise Research Articles

The detection of magnetic gradient aided by correlated random walk

We propose a novel method for sensing the gradient direction of a weak magnetic field overwhelmed by a noisy background through studying the correlated random walk of Brown particles that carry with magnetic moment. Using numerical simulation, we find that the snapshot of the distribution of Brown particles that touched the top surface of a cubic container provides useful information, i.e., the center of the distribution pattern will deflect towards the direction of magnetic gradient. We also find that the impact of magnetic noise can be effectively suppressed by the presence of correlation when the concentration of Brown particles is in the region of 0.2ρ0<ρ<0.7ρ0. Our findings may motivate novel scheme to identify the direction of magnetic gradient in the presence of strong noise, which is undoubtedly important for practical applications.

Compression of metrological quantum information in the presence of noise

In quantum metrology, information about unknown parameters θ=(θ1,...,θM) is accessed by measuring probe states ρ̂θ. In experimental settings where copies of ρ̂θ can be produced rapidly (e.g., in optics), the information-extraction bottleneck can stem from high postprocessing costs or detector saturation. In these regimes, it is desirable to compress the information encoded in ρ̂θ⊗n into m&lt;n copies of a postselected state: ρ̂θps⊗m. Remarkably, recent works have shown that, in the absence of noise, compression can be lossless, for m/n arbitrarily small. Here, we fully characterize the family of filters that enable lossless compression. Further, we study the effect of noise on quantum-metrological information amplification. Motivated by experiments, we consider a popular family of filters, which we show is optimal for qubit probes. Further, we show that, for the optimal filter in this family, compression is still lossless if noise acts after the filter. However, in the presence of depolarizing noise before filtering, compression is lossy. In both cases, information extraction can be implemented significantly better than simply discarding a constant fraction of the states, even in the presence of strong noise. Published by the American Physical Society 2024

The Self-Powered Thermoelectric System Driving an Electric Field for Raman-Spectrum Glucose Quantitative Analysis

For a long time, blood glucose monitoring has been a challenging issue in the field of medicine. Although it is possible to visit a hospital for regular blood tests to track blood sugar concentration, the physiological reactions triggered by blood collection might lead to feeling weak, dizzy, or even vomiting. Therefore, especially for diabetic patients, they need to frequently monitor their blood sugar concentration in their daily lives to ensure their health. Currently, the primary methods for blood glucose testing still rely on invasive techniques, such as using fingertip glucose meters and other methods which require skin puncture to obtain blood samples. Although these methods provide a quick assessment of current blood glucose concentration, they come with the pain and discomfort of blood collection process and the potential risk of infection. As a result, in recent years, many researchers have been dedicated to developing non-invasive blood glucose monitoring methods. However, accurately measuring glucose concentration inside the human body remains a challenging task. One of the primary issues is the presence of strong noise and background noise. To avoid this challenge, we have developed an innovative detection platform that is combines surface-enhanced Raman spectroscopy (SERS) and external electric field sensing technology. With the electromagnetic effect, we add external electric field in our platform to excite more the surface plasmon resonance. By using this method, we can achieve signal amplification to detect lower concentrations of glucose. Now, we can detect down to 0.001mM of glucose. We also exhibit self-powered electric field system, using the flexible themoelctric device to combine our platform. By the property of self-powered, we success to create a self-powered electric field system. We focus on driving themoelectric through the temperature different between body temperature and environment. The approach is aimed at generating power from the human body, providing a self-powered thermoelectric system for Raman-spectrum glucose sensors. This innovative approach holds promise for advancing non-invasive blood glucose monitoring and benefiting a larger population of diabetes patients in the future.

EcoDetect-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Domestic Waste Exposure in Intricate Environmental Landscapes.

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model's efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model's capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO's adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development.

Application of an Improved Laplacian-of-Gaussian Filter for Bearing Fault Signal Enhancement of Motors

The presence of strong noise and vibration interference in fault vibration signals poses challenges for extracting fault features from motor bearings. Therefore, appropriate pre-filtering procedures can effectively suppress the impact of the noise interference and further enhance fault-related signals. In this work, an improved Laplacian-of-Gaussian (ILoG) filter is proposed to enhance the fault-related signal. The proposed ILoG approach employs an enhanced Kurtosis-based indicator known as Correlated Kurtosis (CK). The CK capitalizes on the cyclostationarity of fault-related impulses and mitigates the random nature of impulse noise. Subsequently, an objective function, based on CK statistics, is suggested to iteratively update LoG coefficients by maximizing the CK value of the output signal. Therefore, the ILoG filter can better highlight the fault cyclic impulses associated with bearing faults. Furthermore, the ILoG filter is capable of attenuating impulsive noise, a feature that is absent in the original LoG filter. The simulation and experimental results demonstrate that the proposed ILoG method provides a remarkable capability to effectively enhance the fault-induced components, thereby improving the diagnostic accuracy. Consequently, the ILoG filter holds great potential for application in motor bearing fault diagnosis.

Passive seismic monitoring of an injection-production process in an oilfield using reverse time imaging

Abstract Monitoring underground fluid migration caused by injection/production processes is crucial for guiding petroleum exploitation in mature oilfields and ultimately enhancing petroleum production. In this paper, we propose a time-lapse reverse time imaging (RTI) to dynamically monitor the injection/production processes within oilfield. By using RTI to track microseimicities at different time periods, we can analyze the relationship between injection/production activities and the spatiotemporal changes in microseismic distribution. The inferred relationship enables the time-lapse RTI to infer fluid migration patterns within oil reservoirs. To assess the accuracy and spatiotemporal resolution of the time-lapse RTI, we conducted numerical experiments to evaluate the imaging quality under different microseismic distribution scenarios. In addition, we assessed the method's stability under low signal-to-noise ratio conditions. Numerical results indicate that the time-lapse RTI can effectively distinguish the spatiotemporal variations of seismic swarms at depths of 0.5 kilometers within the target layer, even in the presence of strong noise. Practical applications show a significant correlation between changes in swarm distribution surrounding reservoirs and fluctuations in oil production. Using time-lapse RTI enables real-time monitoring of oilfield injection/production processes, thereby offering valuable insights for optimizing oilfield development and fostering future increases in petroleum production.

Stochastic dynamics of coral reef system with stage-structure for crown-of-thorns starfish

The outbreak of crown-of-thorns starfish (CoTS) is one of the most critical biological disturbances in coral reef systems and contributes greatly to the degradation of coral. Although many studies have focused on coral-CoTS interactions, there is no effective method for suppressing CoTS outbreaks in the long term, in part because of incomplete knowledge of their life cycle. In this paper, a stochastic system is formulated to characterize the interactions among coral, immature CoTS, and mature CoTS, where environmental noise is introduced into the growth of coral. The existence and uniqueness of a global positive solution with any given initial value are investigated, sufficient conditions are established for the extinction of CoTS, and the existence and uniqueness of a stable stationary distribution are explored by applying the Markov semigroup theory. In particular, by solving the Fokker–Planck equation, the approximate expression of the probability density function of the distribution around its quasi positive equilibrium is expounded under certain parametric restrictions. By employing Milstein’s higher-order method, numerical simulations are presented to validate the theoretical results. This study reveals that coral are more vulnerable and are more likely to go extinct in the presence of strong noise, while weak noise is more conducive to the existence of a stable stationary distribution, which implies the long-term persistence of coral and CoTS.

Optimizing AUV Navigation Using Factor Graphs with Side-Scan Sonar Integration

For seabed mapping, the prevalence of autonomous underwater vehicles (AUVs) employing side-scan sonar (SSS) necessitates robust navigation solutions. However, the positioning errors of traditional strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) systems accumulated significantly, further exacerbated by DVL’s susceptibility to failure in complex underwater conditions. This research proposes an integrated navigation approach that utilizes factor graph optimization (FGO) along with an improved pre-integration technique integrating SSS-derived position measurements. Firstly, the reliability of SSS image registration in the presence of strong noise and feature-poor environments is improved by replacing the feature-based methods with a Fourier-based method. Moreover, the high-precision inertial measurement unit (IMU) pre-integration method could correct the heading errors of SINS significantly by considering the Earth’s rotation. Finally, the AUV’s marine experimental results demonstrated that the proposed integration method not only offers improved SSS image registration and corrects initial heading discrepancies but also delivers greater system stability, particularly in instances of DVL data loss.

Quantum-enhanced greedy combinatorial optimization solver.

Combinatorial optimization is a broadly attractive area for potential quantum advantage, but no quantum algorithm has yet made the leap. Noise in quantum hardware remains a challenge, and more sophisticated quantum-classical algorithms are required to bolster their performance. Here, we introduce an iterative quantum heuristic optimization algorithm to solve combinatorial optimization problems. The quantum algorithm reduces to a classical greedy algorithm in the presence of strong noise. We implement the quantum algorithm on a programmable superconducting quantum system using up to 72 qubits for solving paradigmatic Sherrington-Kirkpatrick Ising spin glass problems. We find the quantum algorithm systematically outperforms its classical greedy counterpart, signaling a quantum enhancement. Moreover, we observe an absolute performance comparable with a state-of-the-art semidefinite programming method. Classical simulations of the algorithm illustrate that a key challenge to reaching quantum advantage remains improving the quantum device characteristics.

Mono-trend mode decomposition for robust feature extraction from vibration signals of rotating Machinery

The analysis of vibration signals of rotating machinery plays an important role in equipment fault diagnosis and health monitoring. However, the decomposition of fault characteristic components often lacks robustness and accuracy under nonstationary operating conditions due to potential strong background noise and the complexity of existing components. In this paper, we address this issue by utilizing the relationships among components of vibration signals generated by rotating structures, described as the mono-trend modes (MTMs). Our proposed method, called the MTM decomposition (MTMD), enables robust feature extraction from vibration signals under nonstationary operating conditions. Initially, the instantaneous angular speed (IAS) of the input shaft is measured by tachometers or estimated by the framework of parameterized resampling time–frequency transform. The instantaneous frequencies (IFs) of the targeted components are then determined based on their orders relative to the IAS. Subsequently, the instantaneous amplitudes (IAs) of the targeted components are estimated by solving a joint optimization problem that includes a penalty term designed in consideration of the low variation characteristic of IAs. This penalty term enhances the efficient distribution of energy among close-spaced components. As a result, all targeted MTMs can be extracted simultaneously without any accumulated error. Various simulated experiments and practical experiments on a high-power planetary gearbox are conducted to analyze and validate the effectiveness of the proposed MTMD method in extracting robust features, especially in the presence of strong noise and close-spaced components.

A Novel Adversarial Learning Framework for Passive Bistatic Radar Signal Enhancement

Passive Bistatic Radar (PBR) has significant civilian and military applications due to its ability to detect low-altitude targets. However, the uncontrollable characteristics of the transmitter often lead to subpar target detection performance, primarily due to a low signal-to-noise ratio (SNR). Coherent accumulation typically has limited ability to improve SNR in the presence of strong noise and clutter. In this paper, we propose an adversarial learning-based radar signal enhancement method, called radar signal enhancement generative adversarial network (RSEGAN), to overcome this challenge. On one hand, an encoder-decoder structure is designed to map noisy signals to clean ones without intervention in the adversarial training stage. On the other hand, a hybrid loss constrained by L1 regularization, L2 regularization, and gradient penalty is proposed to ensure effective training of RSEGAN. Experimental results demonstrate that RSEGAN can reliably remove noise from target information, providing an SNR gain higher than 5 dB for the basic coherent integration method even under low SNR conditions.

Ecological Effects of Predator Harvesting and Environmental Noises on Oceanic Coral Reefs.

Coral reefs provide refuge for prey and are important for the preservation of an oceanic ecosystem. However, they have been experiencing severe destruction by environmental changes and human activities. In this paper, we propose and analyze a tri-trophic food chain model consisting of coral, Crown-of-thorns starfish (CoTS), and triton in deterministic and stochastic environments. We investigate the effects of harvesting in the deterministic system and environmental noises in the stochastic system, respectively. The existence of possible steady states along with their stability is rigorously discussed. From the economic perspective, we examine the existence of the bionomic equilibrium and establish the optimal harvesting policy. Subsequently, the deterministic system is extended to a stochastic system through nonlinear perturbation. The stochastic system admits a unique positive global solution initiating from the interior of the positive quadrant. The long-time behaviors of the stochastic system are explored. Numerical simulations are provided to validate and complement our theoretical results. We show that over-harvesting of triton is not beneficial to coral reefs and modest harvesting of CoTS may promote sustainable growth in coral reefs. In addition, the presence of strong noises can lead to population extinction.

Extraction of partial discharge signal in predominant VHF range in the presence of strong noise in power transformer

Extraction of partial discharge signal in predominant VHF range in the presence of strong noise in power transformer

Reducing near-surface artifacts from the crossline direction by full-waveform inversion of interferometric surface waves

Seismic incoherent noise and waves scattered from objects in the crossline directions can cause 2D elastic full-waveform inversion (FWI) to produce artifacts in the resulting 2D models. We develop a complete workflow that can determine subsurface S-wave velocity ([Formula: see text]) models inverted from 2D near-surface seismic data more stably. We make use of a combination of supervirtual interferometry and a matched filter to accurately retrieve dominant surface waves from the field data, whereas the incoherent noise and 3D scattering events are significantly suppressed. The subsurface structures obtained from inverting the retrieved data can be interpreted together with the sections resulting from FWI of the original data to mitigate the potential misinterpretation of artifacts. Our results demonstrate that it is possible to invert 2D near-surface seismic data even when the data quality is lowered by the presence of strong noise and 3D scattered events caused by objects located in the crossline direction.

Bayesian on-line anticipation of critical transitions

The design of reliable indicators to anticipate critical transitions in complex systems is an important task in order to detect imminent regime shifts and to intervene at an early stage to either prevent them or mitigate their consequences. We present a data-driven method based on the estimation of a parameterized nonlinear stochastic differential equation that allows for a robust anticipation of critical transitions even in the presence of strong noise which is a characteristic of many real world systems. Since the parameter estimation is done by a Markov chain Monte Carlo approach, we have access to credibility bands allowing for a better interpretation of the reliability of the results. We also show that the method can yield meaningful results under correlated noise. By introducing a Bayesian linear segment fit it is possible to give an estimate for the time horizon in which the transition will probably occur based on the current state of information. This approach is also able to handle nonlinear time dependencies of the parameter that controls the transition. The method can be used as a tool for on-line analysis to detect changes in the resilience of the system and to provide information on the probability of the occurrence of critical transitions in future. Additionally, it can give valuable information about the possibility of noise induced transitions. The discussed methods are made easily accessible via a flexibly adaptable open source toolkit named ‘antiCPy’ which is implemented in the programming language Python.

Noise reduction and 3D image restoration of single photon counting LiDAR using adaptive gating

Single photon LiDAR is considered as one of the most important tools in acquiring target information with high accuracy under extreme imaging conditions, as it offers single photon sensitivity and picosecond timing resolution. However, such technique sense the scene with the photons reflected by the target, thus resulting in severe degradation of image in presence of strong noise. Range gating with high-speed electronics is an effective way to suppress the noise, unfortunately, such technique suffers from manually selecting the parameters and limited gating width. This paper presents a target information extracting and image restoration method under large observation window, which first obtain the depth distribution of the target and extract the information within the range by analyzing the model of signal and noise, then further improve the image quality by adopting advanced image restoration algorithm and henceforth shows better results than those denoising method that purely relying on hardware. In the experiment, photon-per-pixel (PPP) was as low as 3.020 and signal-to-background ratio (SBR) was as low as 0.106, the proposed method is able to improve SBR with a factor of 19.330. Compared to classical algorithm named cross correlation, the reconstruction signal to noise ratio (RSNR) increased 33.520dB by further cooperating with advanced image restoration algorithm, thus improved the ability of sensing accurate target information under extreme cases.

Crosscorrelation migration of microseismic source locations with hybrid imaging condition

Locating microseismic sources is critical to monitoring the hydraulic fractures that occur during fluid extraction/injection in unconventional oil/gas exploration, geothermal operations, and CO2 sequestration. Waveform-based seismic location methods can reliably and automatically image weak microseismic source locations without phase picking. Among them, the crosscorrelation migration (CCM) method can avoid excitation time scanning by generating virtual gathers. We have adopted a CCM location method based on the hybrid imaging condition (HIC). There are four main steps in the implementation of this method: (1) selection of receivers with good azimuthal coverage, (2) generation of virtual gathers by correlating the reference receiver with the rest of the receivers, (3) summation of back projections in the virtual gathers, and (4) multiplication of all summations. The CCM-HIC method is tested on synthetic and field data sets, and the results are compared with those obtained by the conventional summation imaging condition and multiplication imaging condition. The comparison results determine that CCM-HIC is sufficiently robust to obtain a source image with better stability and higher spatial resolution, despite the presence of strong noise.

Noise-resistant quantum communications using hyperentanglement

Quantum information protocols are being deployed in increasingly practical scenarios, via optical fibers or free space, alongside classical communications channels. However, entanglement, the most critical resource to deploy to the communicating parties, is also the most fragile to the noise-induced degradations. Here we show that polarization-frequency hyperentanglement of photons can be effectively employed to enable noise-resistant distribution of polarization entanglement through noisy quantum channels. In particular, we demonstrate that our hyperentanglement-based scheme results in an orders-of-magnitude increase in the SNR for distribution of polarization-entangled qubit pairs, enabling quantum communications even in the presence of strong noise that would otherwise preclude quantum operations due to noise-induced entanglement sudden death. While recent years have witnessed tremendous interest and progress in long-distance quantum communications, previous attempts to deal with the noise have mostly been focused on passive noise suppression in quantum channels. Here, via the use of hyperentangled degrees of freedom, we pave the way toward a universally adoptable strategy to enable entanglement-based quantum communications via strongly noisy quantum channels.

Real-time chatter detection via iterative Vold-Kalman filter and energy entropy

Real-time chatter detection is important in improving the surface quality of workpieces in milling. Since the process from stable cutting to chatter is characterized by the progressive variation of the vibration energy distribution, entropy has been utilized to capture the decreasing randomness of vibration signals when chatter occurs. To make such an index more sensitive to transitions of the cutting state, the entropy can be computed based on signal components obtained through signal decomposition techniques. However, the classic empirical mode decomposition (EMD) is difficult to put into practice due to its weak robustness to noises. The up-to-date variational mode decomposition (VMD) has strict requirements on a priori information about the signal and thus is not applicable either. In this paper, a novel method named the iterative Vold-Kalman filter (I-VKF) is proposed under the framework of the greedy algorithm, where the Vold-Kalman filter (VKF), a classic order tracker for rotating machinery, is improved to recursively extract each signal component. In the meantime, a spectrum concentration index–based technique is developed for the estimation of the instantaneous chatter frequency to adaptively determine the filter parameter. Numerical examples demonstrate the superiority of the I-VKF over the original VKF, EMD, and VMD, especially in the presence of strong noises. Combined with the energy entropy of extracted components and an automatically calculated threshold, the proposed strategy greatly helps in timely chatter detection, which has been verified by dynamic simulation and experiments.

Sensing in the presence of strong noise by deep learning of dynamic multimode fiber interference

A new approach to optical fiber sensing is proposed and demonstrated that allows for specific measurement even in the presence of strong noise from undesired environmental perturbations. A deep neural network model is trained to statistically learn the relation of the complex optical interference output from a multimode optical fiber (MMF) with respect to a measurand of interest while discriminating the noise. This technique negates the need to carefully shield against, or compensate for, undesired perturbations, as is often the case for traditional optical fiber sensors. This is achieved entirely in software without any fiber postprocessing fabrication steps or specific packaging required, such as fiber Bragg gratings or specialized coatings. The technique is highly generalizable, whereby the model can be trained to identify any measurand of interest within any noisy environment provided the measurand affects the optical path length of the MMF’s guided modes. We demonstrate the approach using a sapphire crystal optical fiber for temperature sensing under strong noise induced by mechanical vibrations, showing the power of the technique not only to extract sensing information buried in strong noise but to also enable sensing using traditionally challenging exotic materials.