Intensity Noise Research Articles

Dynamical analysis and fault detection application of a time-delayed multi-stable stochastic resonance system driven by white correlated noises.

Bearing fault diagnosis is a crucial means to ensure the normal operation of machinery, reduce maintenance costs, enhance equipments safety and extend the service life of bearings. However, the noise interference in input signals often affects the effective extraction of bearing fault signals. In this paper, a time-delay multi-stable stochastic resonance (SR) system driven by white correlated noises is proposed. Firstly, the expressions of the steady-state probability density (SPD) function and the mean first passage time (MFPT) is derived. Simultaneously, we delve into the influences of various system parameters, including multiplicative noise intensity, additive noise intensity, noise correlated strength, time-delay, and time-delay feedback intensity, on the dynamical behavior exhibited by particles within the system. Then, according to the signal-to-noise ratio (SNR) formula, the paper investigates the impact of system parameters on the SNR. It is found that the ease of SR occurrence is directly related to the system parameters. Finally, the experimental results demonstrate that the proposed method exhibits superior performance in detecting faults compared with the classical bistable SR system and the quad-stable SR system.

Event Camera Denoising Using Asynchronous Spatio-Temporal Event Denoising Neural Network

Abstract. Compared to conventional cameras, event cameras represent a noteworthy advancement in neuromorphic imaging technology, garnering considerable attention from researchers due to their distinct advantages. However, event cameras are susceptible to significant levels of measurement noise, which can detrimentally affect the performance of algorithms reliant on event stream for tasks such as perception and navigation. In this study, we introduce a novel method for denoising event stream, aiming to filter out events that do not accurately reflect genuine logarithmic intensity changes within the observed scene. Our approach focuses on the asynchronous nature and spatiotemporal properties of events, culminating in the development of a novel Asynchronous Spatio- Temporal Event Denoising neural Network(ASTEDNet). This network operates directly on event streams, circumventing the need to convert event stream into denser formats like image frames, thereby preserving their inherent asynchronous nature. Drawing upon principles from graph encoding and temporal convolutional networks, we incorporate spatiotemporal feature attention mechanisms to capture the temporal and spatial correlations between events. This enables the classification of each active event pixel in the original stream as either representing a genuine intensity change or noise. Comparative evaluations conducted on multiple datasets against state-of-the-art methods demonstrate the remarkable efficacy and robustness of our proposed algorithm in noise removal while retaining meaningful event information within the scene.

Stochastic Analysis for the Dual Virus Parallel Transmission Model with Immunity Delay.

In this article, the qualitative properties of a stochastic dual virus parallel transmission model with immunity delay are analyzed. First, we use Lyapunov theory to study the existence and uniqueness of the global positive solution of the proposed model. Second, the threshold values of the persistence and extinction of two viruses were obtained. Finally, the numerical simulation verifies the theoretical results. The results show that the immunity delay and the intensity of noise have important effects on the two diseases spreading in parallel.

Measuring the impact of laser relative intensity noise on heterodyne interferometers using differential wavefront sensing

Laser-intensity fluctuations cause undesired phase noise even in balanced heterodyne interferometers. However, in space missions in particular, such as LISA Pathfinder or LISA, direct measurements of these fluctuations at the relevant frequencies are often not available. Hence, it can be challenging to estimate their impact on the interference phase. To address this, we propose a new method for characterizing laser relative intensity noise (RIN) using differential wavefront sensing (DWS), with the latter being a well-established technique typically used for angular sensing and control. Unlike other methods, this approach does not require an additional reference interferometer and instead takes advantage of the inherent phase subtraction of DWS. This allows us to estimate the RIN value at the heterodyne frequency and its harmonic, relative to the sensor noise floor of the total measurement system. Moreover, it provides a strategy to identify the ideal set point for minimizing RIN couplings in DWS. Published by the American Physical Society 2024

High robust eddy current tuned tandem mass dampers-inerters for structures under the ground acceleration

In this work, a novel eddy current tuned tandem mass dampers-inerters, referred to as EC-TTMDI is proposed, a theoretical analysis model of the vibration control mechanism is established, and parameterized analysis and design are conducted using the intelligent optimization algorithm. This model is the single-degree-of-freedom (SDOF) structure with EC-TTMDI subjected to Gaussian white noise base excitation. Specifically, the expression for the displacement variance of the SDOF structure-EC-TTMDI system was firstly obtained with resorting to both the equivalent linearization of eddy current damper (ECD) and residue theorem. The optimization objective function was then defined as minimization of the displacement variance, which is product of the white noise intensity and the square of H2 norm of the model transfer function. Employing the pattern-search algorithm, the variation trends of the objective function and the optimum design parameters were subsequently scrutinized. The vibration damping mechanism of EC-TTMDI is then revealed in terms of control effectiveness, robustness, structural frequency response, suppression bandwidth, and stroke. Finally, the time history response analysis of the SDOF structure-EC-TTMDI system subjected to the near-field impulse, near-field non-impulse, and far-field earthquakes was conducted to further confirm the performance superiority of EC-TTMDI. Results demonstrates that EC-TTMDI offers higher robustness and better control effectiveness compared to TTMDI and TMDI. Furthermore, EC-TTMDI features the damping force limiting characteristic, which enhances the integrity and reliability of TTMDI.

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.

Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques, including thresholding, edge-based methods, region-based approaches, clustering, and graph-based segmentation. While these methods are computationally efficient and interpretable, they often face significant challenges when applied to complex, noisy, or variable medical images. The central focus of this review is the transformative impact of deep learning on medical image segmentation. We delve into prominent deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, Recurrent Neural Networks (RNNs), Adversarial Networks (GANs), and Autoencoders (AEs). Each architecture is analyzed in terms of its structural foundation and specific application to medical image segmentation, illustrating how these models have enhanced segmentation accuracy across various clinical contexts. Finally, the review examines the integration of deep learning with traditional segmentation methods, addressing the limitations of both approaches. These hybrid strategies offer improved segmentation performance, particularly in challenging scenarios involving weak edges, noise, or inconsistent intensities. By synthesizing recent advancements, this review provides a detailed resource for researchers and practitioners, offering valuable insights into the current landscape and future directions of medical image segmentation.

Efficient single-frequency Tm:YAG crystal-derived silica fiber laser at 1.7 µm.

A single-frequency distributed Bragg reflector (DBR) fiber laser operating at 1720 nm has been demonstrated for the first time using a Tm:YAG crystal-derived silica fiber (TCDSF), to the best of our knowledge. A single-frequency laser with an over 220 mW output power was achieved from a 1.5-cm-long TCDSF when being in-band pumped by a homemade 1610 nm fiber laser. The slope efficiency reached 25.30% for the absorbed pump power. The optical signal-to-noise ratio (OSNR) of the laser was ∼69.78 dB, and the laser linewidth was ∼84.5 kHz. Additionally, the measured relative intensity noise (RIN) remained at -130 dB/Hz at frequencies above 9 MHz. The experimental results indicate that the TCDSF is a promising gain medium for single-frequency lasers at 1.7 µm.

Reduction in Optical Feedback Noise of Atomic Force Microscopy by High-Frequency Current Modulation

Introduction: With the advancement of technology, nanotechnology has emerged as a prominent research field. The Atomic Force Microscope (AFM) serves as a vital tool in nanoscience and technology, playing an indispensable role across various domains. Method: However, in the AFM photoelectric sensing system, the optical feedback noise from the beam deflection method can lead to inaccuracies in signal identification and analysis, impacting the accuracy and reliability of AFM measurements. To mitigate this interference, this study proposes a highfrequency current superposition system aimed at reducing optical feedback noise. By superimposing high-frequency currents, the semiconductor laser transitions from a single-mode to a multi-mode operational state, thereby altering its mode of operation and consequently reducing optical feedback noise during sensing. Initially, mathematical modeling and simulation analysis were conducted on the high-frequency current superposition noise reduction system to examine the impact of high-frequency current on the intensity noise of the semiconductor laser. Subsequently, the design of the high-frequency current superposition noise reduction system was outlined, encompassing the development of a constant current drive circuit, a voltage-controlled oscillator circuit, and a biasing circuit. Finally, the high-frequency current superposition noise reduction system underwent testing. Results: During the high-frequency current superposition noise reduction test, the system's Signal-to-Noise Ratio (SNR) increased from 15.57 dB to 17.81 dB, and the system's noise peak-to-peak value decreased from 8 mV to 6 mV. Analysis of the superposition frequency and noise reduction effect determined the optimal superposition frequency of the AFM photoelectric sensor system to be 400 MHz. Characterization experiments of the high-frequency superposition noise reduction system compared the clarity of Escherichia coli images before and after noise reduction. Conclusion: The aforementioned experimental results demonstrated that high-frequency current superposition is an effective noise reduction method capable of mitigating optical noise in the AFM photoelectric sensing system.

Watt-level single-frequency optical parametric oscillator around 2 µm.

A single-frequency optical parametric oscillator (OPO) operating in the wavelength range around 2 µm is reported. The OPO comprises a periodically poled LiNbO3 (PPLN) crystal for quasi-phase matching and a four-mirror ring-cavity resonant to the signal. The OPO signal and idler are tuned from 1.89 to 2.05 µm and from 2.21 to 2.42 µm, respectively, by changing the crystal temperature. Output powers in excess of 2 W are demonstrated at a pump power of 9 W for both the signal and idler over all of the respective tuning ranges. A detailed characterization of the intensity and frequency noise is presented. The OPO exhibits an integrated intensity noise below 0.1% and a linewidth of ∼58 kHz for an observation time of 1 ms. The reported performance makes this source an ideal candidate for the generation of squeezed light around 2 µm via cascaded second-harmonic generation (SHG) and parametric downconversion (PDC).

Switchable Dual-Wavelength Fiber Laser with Narrow-Linewidth Output Based on Parity-Time Symmetry System and the Cascaded FBG

In this paper, a dual-wavelength narrow-linewidth fiber laser based on parity-time (PT) symmetry theory is proposed and experimentally demonstrated. The PT-symmetric filter system consists of two optical couplers (OCs), four polarization controllers (PCs), a polarization beam splitter (PBS), and cascaded fiber Bragg gratings (FBGs), enabling stable switchable dual-wavelength output and single longitudinal-mode (SLM) operation. The realization of single-frequency oscillation requires precise tuning of the PCs to match gain, loss, and coupling coefficients to ensure that the PT-broken phase occurs. During single-wavelength operation at 1548.71 nm (λ1) over a 60-min period, power and wavelength fluctuations were observed to be 0.94 dB and 0.01 nm, respectively, while for the other wavelength at 1550.91 nm (λ2), fluctuations were measured at 0.76 dB and 0.01 nm. The linewidths of each wavelength were 1.01 kHz and 0.89 kHz, with a relative intensity noise (RIN) lower than −117 dB/Hz. Under dual-wavelength operation, the maximum wavelength fluctuations for λ1 and λ2 were 0.03 nm and 0.01 nm, respectively, with maximum power fluctuations of 3.23 dB and 2.38 dB. The SLM laser source is suitable for applications in long-distance fiber-optic sensing and coherent LiDAR detection.

Ecological and epidemiological ramification of fear: Exploring deterministic and stochastic dynamics in a predator–prey system with predator switching and harvesting

In an ecosystem, harvesting infected prey can assist in managing and containing the spread of the illness within the prey species. On the other hand, the harvesting of predators can be beneficial as it regulates their numbers, preventing them from over-consuming prey and subsequently preserving existence of the prey population. This study introduces a predator–prey model that encompasses prey infection, predator–prey interactions influenced by fear, switching and harvesting. We derive an analytic expression for the basic reproduction number, a critical determinant of disease spread. We investigate the global stability of disease-free and endemic equilibria contingent on the basic reproduction number’s value, highlighting the potential for disease eradication by maintaining it below unity. In-depth analysis of the deterministic model is undertaken, with a focus on Hopf bifurcations that delineate thresholds for disease-free and endemic states. Furthermore, the deterministic model is extended to incorporate environmental stochasticity. We obtain the conditions under which population extinction occurs. Our findings elucidate how the intensity of environmental noise influences population dynamics, providing valuable insights into extinction risks under varying noise levels.

Exploring the influence of refuges and additional foods on predator–prey interactions amidst environmental stochasticity and water level fluctuations

By considering that the predators have some additional foods apart from the focal prey and the prey take refuge, a predator–prey model with the effect of seasonality in a fluctuating environment is investigated in this study. Gaussian white noises, created by random fluctuation of water level, are introduced on the prey’s growth rate and predator’s mortality rate. Both the deterministic and stochastic systems are analyzed mathematically as well as numerically. Our analytical findings show that the intensity of environmental noise, additional foods and prey refuge play significant roles in survival as well as extinction of both prey and predator populations in the aquatic system. Our numerical results show that the effectual level of additional foods, depending on water level, has positive as well as negative effects on the predator and prey populations. We find that both species strongly persist at certain water level and for the low intensity of noise. However, for an increased value of noise intensity, they persist weakly and then go to extinction. Overall, our findings show that the variations of water level together with additional food and white noise plays crucial roles in persistence and extinction of species in the ecosystem.

Novel methods for the control of blast noise from explosive depth hardening

DNV Spadeadam undertakes crucial major hazards testing to improve safety across a wide range of industries, including energy and infrastructure. One common form of testing is Explosive Depth Hardening (EDH) in which plastic explosives up to 10 kg TNT are used to harden pre-cast railway crossings in order to support national transport infrastructure. The operations produce extremely high intensity noise and are carried out up to 10 times per day. The source characteristics of the explosion are not well understood and vary significantly with operational demands. This has implications on occupational and environmental blast noise impacts. Quantification of the source characteristics are important for the exposures of site personnel with regards to providing adequate hearing protection. Moreover, for the purpose of managing environmental noise impacts on local communities, the uncertainties around source directivity and strength make long-range blast noise prediction difficult. This paper presents the current research on novel blast noise mitigation undertaken in the laboratory, such as experimental water curtains, water sprays, and metamaterial designs. It is proposed to implement novel methods developed in the laboratory for use during EDH shots at full-scale, and the potential improvements to occupational and environmental noise impacts will be discussed.

Sound of natural and artificial rain impact on ETFE membranes

Thin membranes are known to produce a drum-like effect when subjected to rainfall. Rain noise affects acoustic comfort and might disrupt communication, speech intelligibility and cognitive task performance. Therefore, assessment of the characteristics of noise produced by impact of rain on membranes in different architectural settings is important. Given inherent disparities between natural and artificial precipitation-primarily in terms of droplet size distribution and terminal velocities-underscores the importance of conducting evaluations using natural rain noise. The focus of this study is on the comparative assessment of the sound generated by natural rainfall versus the sound generated by the impact of individual droplet in a semi-anechoic room on single Ethylene tetrafluoroethylene (ETFE) membranes. The study assesses the extent of spectral variations of the rain sound produced in both approaches and the corresponding absolute value of noise intensity.

Inertial amplification as a performance enhancement method for snap-through vibration energy harvester

In recent years, there has been a lot of interest in exploiting the bistable behavior of snap-through systems to harvest energy from vibration sources. The efficient operation of any bistable VEH depends on its ability to exhibit large-amplitude interwell motion. Under weak ambient excitation, bistable VEH performs marginally because of the confinement of motion to a single well. Frequency up-conversion, multi-stability, and adaptive techniques are some of the performance enhancement strategies suggested for bistable-VEH. Considering the VEH's space constraints, the above designs are hard to implement in practical cases. This study introduces an inertial amplification mechanism (IAM) as a simple passive strategy to enhance the performance of a snap-through VEH, a concept not explored in previous studies. The addition of IAM increases the effective mass without increasing the physical mass and thereby enhances energy harvesting, especially from weak ambient excitation sources. The dynamics and performance of the enhanced snap-through VEH are investigated analytically and numerically under harmonic and random excitations. The harmonic balance method (HBM) derives the frequency-amplitude relationship, which shows a hardening behavior and an increase in bandwidth. The effective potential method provides a closed-form expression for the joint probability density function (Joint PDF), which is governed by the Fokker-Planck equation. The joint PDF shows a transition from bimodal to unimodal with an increase in the value of the geometrical parameter. The stochastic averaging method is employed to obtain the stationary probability density function, which defines the long-term dynamics of the VEH. The effects of noise intensity, mass ratio, and inertial amplifier angle on the dynamics are investigated. Finally, the performance of the proposed VEH is compared with a conventional snap-through VEH, an equivalent linear VEH, and a multistable VEH under harmonic and random excitation conditions. The findings suggest that the snap-through VEH with the IAM has advantages over the linear and multistable nonlinear VEH in terms of extracting energy from low-intensity harmonic and random excitation sources. This simple augmentation strategy preserves the original system's bistability, eliminating the need for the complex design of a multistable VEH.

Image quality of virtual monochromatic and material density iodine images for evaluation of head and neck neoplasms using deep learning-based CT image reconstruction – A retrospective observational study

PurposeTo compare the quality of deep learning image reconstructed (DLIR) virtual monochromatic images (VMI) and material density (MD) iodine images from dual-energy computed tomography (DECT) for the evaluation of head and neck neoplasms with CT scans from a conventional single-energy protocol. MethodA total of 294 head and neck CT scans (98 VMIs operated at 60 keV, 102 MD iodine images, and 94 images from a 120 kVp single-energy CT (SECT) protocol) were retrospectively evaluated. VMIs and MD iodine images were generated using the Gemstone Spectral Imaging (GSI) mode using DLIR and metal artifact reduction (MAR) algorithms. SECT images were generated using adaptive statistical iterative reconstruction (ASIR-V). Images were scored by two independent readers on a 6-point Likert-type scale for overall image quality, vessel contrast, soft tissue contrast, noise texture, noise intensity, artifact reduction, and sharpness. ResultsSubjective overall image quality was rated as superior or excellent in 98 % of DLIR-based MD iodine images and VMIs, but only in 55 % of ASIR-V-based SECT images. For each individual quality criterion, image quality of VMIs and MD iodine images was rated as better than that of SECT images (p < 0.001 in each case). Noise texture and intensity were rated better in MD iodine images than in VMIs. ConclusionDECT using both DLIR and MAR for the generation of VMIs and MD iodine images resulted in higher subjective quality of oncologic head and neck images than ASIR-V-based SECT. Noise reduction and noise texture were best achieved with DLIR-based MD iodine images.

MSAACNN for intense noise suppression in DAS-VSP records

Distributed optical fiber sensor (DAS) is an emerging acquisition technique and has begun to be widely applied in seismic exploration owing to its advantages in acquisition and deployment. Nonetheless, DAS record has a low signal-to-noise ratio (SNR) due to the intense background noise. How to suppress the DAS background noise and increase the SNR of the DAS records has gradually become one of the hot issues in the field of seismic data processing. To solve the challenging tasks in intense DAS noise suppression, a multiscale sparse asymmetric attention convolutional neural network (MSAACNN) is proposed. The network uses dilated convolutions to expand the receptive field and map more feature information. Moreover, asymmetric convolutions are introduced to form an asymmetric unit, aiming to strengthen the feature extraction ability and realize the interaction between feature information of different scales. Finally, a pyramid attention module is used to enhance the primary features and improve the network denoising performance. The experimental results show that MSAACNN can effectively suppress the complex background noise in DAS records, compared with the traditional denoising methods and typical convolutional neural network (CNN) architecture. Additionally, the recovered signal components in the processing results are clear and complete, with significantly improved SNR.

Impact of multiplexing noise on multilayer networks of bistable maps

We explore numerically the complex dynamics of multilayer networks (consisting of three and one hundred layers) of cubic maps in the presence of noise-modulated interlayer coupling (multiplexing noise). The coupling strength is defined by independent discrete-time sources of color Gaussian noise. Uncoupled layers can demonstrate different complex structures, such as double-well chimeras, coherent and spatially incoherent regimes. Regions of partial synchronization of these structures are identified in the presence of multiplexing noise. We elucidate how synchronization of a three-layer network depends on the initially observed structures in the layers and construct synchronization regions in the plane of multiplexing noise parameters “noise spectrum width – noise intensity”. It is shown that in a hundred-layer network, clusters of synchronized layers can be formed at certain optimal values of multiplexing noise parameters. The performed numerical studies confidently indicate that the spatial dynamics of multilayer networks can be controlled by varying multiplexing noise parameters.

Intensity noise and modulation dynamics of an epitaxial mid-infrared interband cascade laser on silicon

Interband cascade lasers typically have significantly lower threshold current and power consumption than quantum cascade lasers. They can also have advantages regarding costs and compactness with the photonic integration onto silicon substrates by epitaxial growth. This research introduces a novel examination of the relative intensity noise and the modulation dynamics of a silicon-based Fabry–Perot interband cascade laser emitting at 3.5 μm. The investigation delves into crucial parameters, such as relaxation oscillation frequency, differential gain, gain compression, and K-factor. The resonance patterns identified in relative intensity noise curves can provide essential insights for the thorough characterization of high-defect mid-infrared semiconductor structures intended for high-speed applications. Moreover, this study demonstrates the feasibility of reaching 10 Gbit/s free-space transmission using a silicon-based interband cascade laser in conjunction with an interband cascade infrared photodetector.

Improvement of optical noise in optical-injection-locked quantum dot lasers epitaxially grown on silicon by reducing external carrier noise

Abstract This study theoretically investigates the impact of external carrier noise from pumping sources on the optical noise of epitaxial quantum dot (QD) lasers on silicon. The findings indicate that the spectral linewidth and relative intensity noise (RIN) of silicon-based QD lasers using a quiet pump are significantly reduced. At 5.5 times the threshold current, the spectral linewidth decreases from 337.2 kHz to 213.2 kHz, and the RIN decreases from −141.3 dB Hz−1 to −168.8 dB Hz−1. This reduction is attributed to the lower external carrier noise level of the quiet pump, which also suppresses the spectral linewidth rebroadening effect at a high bias current. Moreover, when external optical injection locking is applied, the spectral linewidth further decreases to 24 kHz at an injection ratio of −70 dB and to 1.8 mHz at 0 dB. The RIN also slightly decreases to −172.4 dB Hz−1 with an injection ratio of 10. These results demonstrate that using a quiet pump is an effective and manageable strategy for significantly reducing both the spectral linewidth and RIN of QD lasers, thereby facilitating their application in next-generation photonic integrated circuits, continuous-variable quantum key distribution, quantum computing and ultra-precise quantum sensing.