Intensity Noise Research Articles

Identification of blood at simulated crime scenes using silver nanoparticles with SERS.

The analysis of substances and samples obtained from a crime scene is very important in solving forensic cases. To determine the variables involved in a crime and to expedite the investigation process, the rapid analysis of body fluids in small quantities and within environments containing diverse components is particularly necessary. For this reason, it is of great importance to analyze biological fluids with rapid, noncontaminating, nondestructive, low-cost, and accurate techniques. In recent years, with advancements in laser technology, spectroscopic methods have been introduced as analytical techniques in forensic medicine and chemical studies. This study focuses on surface-enhanced Raman spectroscopy (SERS) to demonstrate the detection of blood samples in simulated crime scenes. To minimize the background signal from fluorescent biomolecules in blood, dilution was performed with two different components and Raman analysis was performed for four different concentrations of blood. In general, a decrease in noise in the spectra was observed as the blood was diluted. Crime scenes consisting of pure blood, blood diluted with ethanol and distilled water (1:2, 1:4, and 1:8), a blood-mineral water mixture, a blood-cherry juice mixture, and silver nanoparticle-added mixtures were simulated, and their spectra were examined. Chemometric analyses of the data were performed. Despite high noise and low peak intensities, blood-identifying signals were detected when examining different blood concentrations. It was observed that silver nanoparticles provided high enhancement of blood peaks thanks to their strong plasmonic properties.

A wavelength-division-multiplex depolarized Sagnac-based interferometer for noise suppression

The Sagnac-based interferometer (SI) has been developed over several decades and is widely recognized for its sensitivity and stability. Most SIs utilize polarization-maintaining (PM) fiber and polarizers to ensure polarization reciprocity, or depolarizers to mitigate the effects of polarization non-reciprocity. The depolarizing method was considered more promising from a marketability standpoint. Additionally, broadband optical sources were typically used in SIs to eliminate the Kerr effect and backscatter. However, broadband light source SIs are hindered by high excess relative intensity noise (excess RIN) generated by the self-interference of different spectral components. To enhance the performance of SIs, it is crucial to suppress the excess RIN. In this study, we demonstrate a wavelength-division-multiplex depolarized Sagnac-based interferometer and suppress the excess RIN by utilizing the overlap of two interfering lights, which shifts the primary frequency of noise outside the detection range. Experimental results show that the noise equivalent displacement at a light intensity of 42 μW achieves 170.18 fm/√Hz, which encompasses both shot noise and non-optical noise, indicating successful suppression of excess RIN.

A pyramid-style neural network model with alterable input for reconstruction of physics field on turbine blade surface from various sparse measurements

Data from turbine cascade experiments typically exhibits low spatial–temporal resolution, along with inevitable noise and local data missing. This paper aims to establish a super-resolution model to reconstruct the complete pressure and temperature fields on the blade surface from limited observations, using the deep learning method. Depending on the three-dimensional geometric complexity of the blade, the required cross-sectional data varies, resulting in input data of different sizes. Conventional surrogate models typically confine themselves to a single input format, leading to limited compatibility with diverse inputs. A pyramid-style model with four optional input ports is proposed, designed to accommodate data with different numbers of span-wise sections. Three training strategies have been evaluated, where distributed training has been proven to be more time-effective and flexible while maintaining high prediction precision, compared to holistic and conventional training. Generally, the average relative error over the whole dataset falls below 0.18%, the average peak signal-to-noise ratio exceeds 52 dB, and the average structural similarity index measurement surpasses 0.999. As the number of span-wise sections and the amount of information in the input increase, the overall performance shows a consistent upward trend. Robustness analyses are conducted by applying artificial noises of varying intensities. The results indicate that the model can tolerate noise intensities of up to 5% with a satisfactory reconstruction accuracy. In addition, the model is quite sensitive to the measurement data noises in complex flow regions such as expansion waves, suggesting that more intensive measurements should be targeted to those regions when conducting cascade experiments. The proposed method satisfies the intended objectives and provides an idea for future applications in digital twin platforms.

Dual-polarization carrier-assisted differential detection with asymmetric twin-SSB modulation and simplified receiver structure

Carrier-assisted differential detection (CADD) is a promising solution for high-capacity and cost-sensitive short-reach application scenarios, in which the optical field of a complex-valued double-sideband (CV-DSB) signal is reconstructed without using a local oscillator laser. In this work, we propose a polarization division multiplexed asymmetric twin single-sideband CADD (PDM-ATSSB CADD) scheme to realize the optical field recovery of the PDM CV-DSB signals. The polarization fading is solved by using a pair of optical bandpass filters (OBPFs) to suppress the unwanted other polarized offset carrier and signal, and the dual-polarization optical field is recovered by the CADD receiver. An asymmetric twin-SSB signal is used to relax the sharpness requirement of optical filter edges. We also propose a joint signal-signal beat interference (SSBI) iterative mitigation algorithm, which can effectively mitigate intra- and inter-polarization SSBI. The proposed PDM-ATSSB CADD scheme is validated for 30 Gbaud PDM asymmetric twin-SSB 16-ary quadrature amplitude modulation signals. We illustrate the parameter optimization process for PDM-ATSSB CADD, including optical delay and the number of iterations. The impacts of phase noise and relative intensity noise for laser and polarization impairment on PDM-ATSSB CADD are evaluated through numerical simulation. Compared with the PDM-symmetric-TSSB CADD (PDM-STSSB CADD), the required frequency gap to reach the 7% FEC threshold can be reduced by 1 GHz, and the OSNR sensitivity is improved by about 3 dB. Moreover, two simplified PDM-ATSSB CADD schemes are proposed and discussed, which could be considered hardware-efficient and integrative candidates for metro and inter-data center interconnects.

Impact of environmental stochastic fluctuations on the evolutionary stability of imitation dynamics.

To show the impact of environmental noise on imitation dynamics, the stochastic stability and stochastic evolutionary stability of a discrete-time imitation dynamics with random payoffs are studied in this paper. Based on the stochastic local stability of fixation states and constant interior equilibria in a two-phenotype model, we extend the concept of stochastic evolutionary stability to the stochastic imitation dynamics, which is defined as a strategy such that, if all the members of the population adopt it, then the probability for any mutant strategy to invade the population successfully under the influence of natural selection is arbitrarily low. Our main results show clearly that the stochastic evolutionary stability of the system depends only on the properties of the mean matrix of the random payoff matrix and is independent of the randomness of the random payoff matrix. Moreover, as two examples, we show also that under the framework of stochastic imitation dynamics, the noise intensity affects the evolution of cooperative behavior in a stochastic prisoner's dilemma game and the system's nonlinear dynamic behavior.

A Gradient-Based Optimizer Method for Improving the Feature Extraction Capability of Network Architecture and Its Application to Condition Monitoring of Rotating Machinery

Aiming at the problem of inaccurate feature extraction under the massive data of rotating machinery, this paper proposes a feature extraction capability enhancement method based on Gradient-Based Optimizer for trusted network architecture to monitor the conditions of bearings, which firstly transforms the original signal into a wavelet time-frequency image and visualizes the high-dimensional and difficult-to-visualize data in low dimensions. Then, different convolutional neural network architectures are tested by implanting different noise intensities to determine the most robust network architecture. Secondly, this paper adopts the grid method as well as the swarm optimization algorithm to guide the parameters of the model to achieve the best feature extraction effect, compares the optimization effect of several swarm optimization algorithms horizontally, and determines the most effective GBO algorithm among them. Finally, to improve the credibility of the best network model, this paper combines the grad-cam method with the trusted fusion method to improve the accuracy and reliability of the model. Validated by the bearing data of Case Western Reserve University, it shows that the proposed model in this paper has good credibility and accuracy.

Auditory Noise Facilitates Lower Visual Reaction Times in Humans.

Noise is commonly seen as a disturbance but can influence any system it interacts with. This influence may not always be desirable, but sometimes it can improve the system's performance. For example, stochastic resonance is a phenomenon where adding the right amount of noise to a weak signal makes it easier to detect. This is known as sub-threshold detection. This sub-threshold detection's natural fingerprint is the fact that the threshold values follow an inverse U-shaped curve as the noise intensity increases. The minimum threshold value is the point of maximum sensitivity and represents the optimal point that divides the dynamics in two. Below that point, we can find the beneficial noise branch, where the noise can facilitate better detection. Above that point, the common detrimental noise concept can be found: adding noise hinders signal detection. The nervous system controls the movements and bodily functions in the human body. By reducing the sensory thresholds, we can improve the balance of these functions. Additionally, researchers have wondered if noise could be applied to different senses or motor mechanisms to enhance our abilities. In this work, noise is used to improve human reaction times. We tested the hypothesis that visual reaction times decrease significantly when the subject's perception is in the beneficial noise branch and closer to the optimal point than outside of this condition. Auditory noise was introduced in 101 human subjects using an interface capable of searching for the right amount of noise to place the subject in the beneficial noise branch close to the optimal point. When comparing the results, the reaction times decreased when the subjects were at the optimal point compared to when the subjects were outside of such conditions. These results reveal the possibility of using this approach to enhance human performance in tasks requiring faster reaction times, such as sports.

RDA-Net: A multi-cascade network for DAS background noise attenuation

The increasing attention to seismic data collection under complex geological conditions is attributed to the utilization of distributed acoustic sensing (DAS), which has gained significant prominence due to the rapid development of exploration techniques. However, the presence of background noise in DAS has adverse effects on the quality of DAS data and significantly affects the accuracy of the subsequent migration and imaging processing. In addition, these interferences have a different generation mechanism from random noise in geophone-acquired seismic data, resulting in the degradation of traditional denoising techniques. Recent advancements have introduced convolutional neural networks (CNNs) for DAS background noise attenuation, showcasing their superiority over conventional techniques in terms of denoising capability and processing accuracy. Unfortunately, most CNN-based methods only have a single-scale architecture and cannot precisely accomplish denoising tasks when confronted with weak reflection signals in DAS records. In this research, we introduce a residual dense-connection attention network called RDA-Net to further augment the denoising ability. More precisely, the multi-scale structure in the backbone of RDA-Net is employed to capture informative features from the DAS data. Simultaneously, we employ a self-enhanced spatial attention module to refine and strengthen the primary features, thereby positively impacting feature representation and denoising performance. Furthermore, dense connections are also leveraged to reinforce the feature-interaction capability and effective features. Compared with typical conventional methods and CNN-based frameworks, RDA-Net can improve the signal-to-noise ratio over 24 dB and show superiority in weak signal recovery and intense DAS noise attenuation, both for synthetic and field records.

Research on Vehicle Stability Control Based on a Union Disturbance Observer and Improved Adaptive Unscented Kalman Filter

This paper considers external disturbances imposed on vehicle systems. Based on a vehicle dynamics model of the vehicle with three degrees of freedom (3-DOFs), a union disturbance observer (UDO) composed of a nonlinear disturbance observer (NDO) and an extended state observer (ESO) was designed to obtain external disturbances and unmodeled items. Meanwhile, an improved adaptive unscented Kalman filter (iAUKF) with anti-disturbance and anti-noise properties is proposed, based on the UDO and the unscented Kalman filter (UKF) method, to evaluate the sideslip angle of vehicle systems. Finally, a vehicle yaw stability controller was designed based on UDO and the global fast terminal sliding mode control (GFTSMC) method. The results of co-simulation demonstrated that the proposed UDO was effectively able to observe external disturbances and unmodeled items. The proposed iAUKF, which considers external disturbances, not only achieves adaptive updating and adjustment of filtering parameters under different sensor noise intensities but can also resist external disturbances, improving the estimation accuracy and robustness of the UKF. In the anti-disturbance performance test, the maximum estimation error of the sideslip angle of the iAUKF under the three working conditions was less than 0.1°, 0.02°, and 0.5°, respectively. Based on the UDO and the GFTSMC, a vehicle yaw stability controller is described, which improves the accuracy of control and the robustness of the vehicle’s stability control system and greatly strengthens the driving safety of the vehicle.

Dynamics of a Dengue Transmission Model with Multiple Stages and Fluctuations

A vector–host model of dengue with multiple stages and independent fluctuations is investigated in this paper. Firstly, the existence and uniqueness of the positive solution are shown by contradiction. When the death rates of aquatic mosquitoes, adult mosquitoes, and human beings respectively control the intensities of white noises, and if R0s>1, then the persistence in the mean for both infective mosquitoes and infective human beings is derived. When R0s>1 is valid, the existence of stationary distribution is derived through constructing several appropriate Lyapunov functions. If the intensities of white noises are controlled and φ<0 is valid, then the extinction for both infective mosquitoes and infective human beings is obtained by applying the comparison theorem and ergodic theorem. Further, the main findings are verified through numerical simulations by using the positive preserving truncated Euler–Maruyama method (PPTEM). Moreover, several numerical simulations on the infection scale of dengue in Fuzhou City were conducted using surveillance data. The main results indicate that the decrease in the transfer proportion from aquatic mosquitoes to adult mosquitoes reduces the infection scale of infective human beings with dengue virus, and the death rates of aquatic mosquitoes and adult mosquitoes affect the value of the critical threshold R0s. Further, the controls of the death rates of mosquitoes are the effective routes by the decision-makers of the Chinese mainland against the spread of dengue.

Intensity noise reduction in quantum dot comb laser by lower external carrier fluctuations.

This work investigates the impact of carrier noise induced by an external current source on the linewidth enhancement factor (LEF) and relative intensity noise (RIN) of a 100 GHz quantum dot fourth-order colliding-pulse mode-locked laser (MLL), driven by a normal pump with Gaussian-distributed carrier sequences and a quiet pump with sub-Poissonian-distributed carrier sequences. The results indicate that under a normal pump, the LEFs are approximately zero for reverse saturable absorber (SA) bias voltages ranging from 0 to 2.5 V, and the laser achieves a RIN as low as -156 dB/Hz. When using a quiet pump, both the LEF and RIN are reduced across all SA bias conditions, particularly at low reverse SA bias voltages. Specifically, the LEF decreases by up to 0.58 at 0 V, and the average RIN spectrum is reduced by more than 3 dB at the same voltage. This work provides a straightforward approach for the development and optimization of multi-channel light sources for dense wavelength division multiplexing (DWDM) technologies with low optical noise.

Remote sensing of H2O/HDO in the atmospheric column based on a near-infrared laser heterodyne radiometer suppressing local oscillator relative intensity noise

Laser heterodyne radiometer (LHR) exploits the heterodyne signal generated by solar radiation beated with local oscillator (LO) light to extract information on atmospheric species in the atmospheric column. Most of currently reported LHRs are affected by LO-induced relative intensity noise (RIN), which poses a challenge for high-sensitivity detection. In this work, a near-infrared RIN-suppressed LHR is proposed for simultaneous detection of water vapor and HDO in the atmospheric column. The operability of a home-made sun tracker can be obtained from coarse tracking and fine tracking, which offers an excellent opportunity for unattended observation. A tunable distributed feedback (DFB) diode laser centered at 1552.9 nm is employed as LO, where LO-induced RIN is suppressed by a semiconductor optical amplifier (SOA) operating in the dynamic gain saturation regime. By locking LO power and suppressing LO-induced RIN, the SOA-assisted LHR is dominated by 1.27 times shot-noise and the signal-to-ratio (SNR) of heterodyne signal is increased by 5 times. Based on atmospheric transmission spectra measured with the RIN-suppressed LHR in Hefei, China, the total column abundances of water vapor and HDO are retrieved to be 2050 ppm and 0.6 ppm, respectively, with uncertainty of 2 %.

Femtosecond laser plane-by-plane inscription of ultra-short DBR fiber lasers for sensing applications

We propose and demonstrate the inscription of ultra-short distributed Bragg reflector fiber lasers (DBR-FLs) in Er/Yb co-doped fiber (EYDF) using a femtosecond laser plane-by-plane (Pl-b-Pl) method. By integrating the spherical aberration (SA) with a laser 2D scanning process, a planar refractive index modification (RIM) region can be induced in the fiber core. Thanks to the Pl-b-Pl inscription, a high-quality fiber Bragg grating (FBG) in an EYDF is produced, exhibiting a grating strength exceeding 40 dB and an insertion loss of 0.1 dB. Subsequently, an ultra-short DBR-FL with an entire length of 7.3 mm is fabricated by the Pl-b-Pl inscription. The Pl-b-Pl ultra-short DBR-FL exhibits an improved slope efficiency of 0.7% compared with the DBR-FLs fabricated using another two direct-writing techniques, namely line-by-line (LbL) and point-by-point (PbP) methods. Furthermore, this ultra-short DBR-FL generates single-frequency and single-polarization radiation with a narrow linewidth (9.4 kHz) and a low relative intensity noise (-105.8 dB/Hz). Moreover, a wavelength-division-multiplexed array consisting of eight ultra-short DBR-FLs with distinct lasing wavelengths is successfully created. The Pl-b-Pl ultra-short DBR-FLs with excellent output performances offer significant potential for high-sensitivity sensing applications requiring high spatial resolution.

Modeling uncertainty: the impact of noise in T cell differentiation

BackgroundThe regulatory mechanisms guiding CD4 T cell differentiation are complex and are further influenced by intrinsic cell variability along with that of microenvironmental cues, such as cytokine and nutrient availability.ObjectiveThis study aims to expand our understanding of CD4 T cell differentiation by examining the influence of intrinsic noise on cell fate.MethodologyA model based on a complex regulatory network of early signaling events involved in CD4 T cell activation and differentiation was described in terms of a set of stochastic differential equation to assess the effect of noise intensity on differentiation efficiency to the Th1, Th2, Th17, Treg, and TFH effector phenotypes under defined cytokine and nutrient conditions.ResultsThe increase of noise intensity decreases differentiation efficiencies. In a microenvironment of Th1-inducing cytokines and optimal nutrient conditions, noise levels of 3%, 5% and 10% render Th1 differentiation efficiencies of 0.87, 0.76 and 0.62, respectively, underscoring the sensitivity of the network to random variations. Further increments of noise reveal that the network is relatively stable until noise levels of 20%, where the resulting cell phenotypes becomes heterogeneous. Notably, Treg differentiation showed the highest robustness to noise perturbations. A combined Th1-Th2 cytokine environment with optimal nutrient levels induces a dominant Th1 phenotype; however, removal of glutamine shifts the balance towards the Th2 phenotype at all noise levels, with an efficiency similar to that obtained under Th2-only cytokine conditions. Similarly, combinations of Th1/Treg and Treg/Th17-inducing cytokines along with the removal of either tryptophan or oxygen shift the dominant Th1 and Treg phenotypes towards Treg and Th17 respectively. Model results are consistent with differentiation efficiency patterns obtained under well-controlled experimental settings reported in the literature.ConclusionThe stochastic CD4 T cell mathematical model presented here demonstrates a noise-dependent modulation of T cell differentiation induced by cytokines and nutrient availability. Modeling results can be explained by the network topology, which assures that the system will arrive at stable states of cell functionality despite variable levels of biological intrinsic noise. Moreover, the model provides insights into the robustness of the T cell differentiation process.

Extraction of newly soliton wave structure of generalized stochastic NLSE with standard Brownian motion, quintuple power law of nonlinearity and nonlinear chromatic dispersion

Stochastic optical solitons are a fascinating phenomenon in nonlinear optics where soliton-like behavior emerges in systems affected by stochastic noise. This study investigates the influence of Brownian motion on wave propagation in optical fibers. The propagation is modeled using a stochastic nonlinear Schrödinger equation incorporating quintuple power-law nonlinearity and nonlinear chromatic dispersion. To explore this, the improved modified extended tanh (IMET) scheme, leveraging the extended Riccati equation, is employed. This technique facilitates the extraction of various stochastic solutions, including bright, dark, and singular solitons. Furthermore, solutions in the shapes of exponential, singular periodic, and Weierstrass elliptic forms are investigated. The study looks at how the strength of noise impacts various solutions, and Matlab software is used to create 2D and 3D graphs that show the results. It has been noted that when noise intensity rises, signal level falls and surface flattens.

Deep denoiser prior driven relaxed iterated Tikhonov method for low-count PET image restoration

Objective. Low-count positron emission tomography (PET) imaging is an efficient way to promote more widespread use of PET because of its short scan time and low injected activity. However, this often leads to low-quality PET images with clinical image reconstruction, due to high noise and blurring effects. Existing PET image restoration (IR) methods hinder their own restoration performance due to the semi-convergence property and the lack of suitable denoiser prior. Approach. To overcome these limitations, we propose a novel deep plug-and-play IR method called Deep denoiser Prior driven Relaxed Iterated Tikhonov method (DP-RI-Tikhonov). Specifically, we train a deep convolutional neural network denoiser to generate a flexible deep denoiser prior to handle high noise. Then, we plug the deep denoiser prior as a modular part into a novel iterative optimization algorithm to handle blurring effects and propose an adaptive parameter selection strategy for the iterative optimization algorithm. Main results. Simulation results show that the deep denoiser prior plays the role of reducing noise intensity, while the novel iterative optimization algorithm and adaptive parameter selection strategy can effectively eliminate the semi-convergence property. They enable DP-RI-Tikhonov to achieve an average quantitative result (normalized root mean square error, structural similarity) of (0.1364, 0.9574) at the stopping iteration, outperforming a conventional PET IR method with an average quantitative result of (0.1533, 0.9523) and a state-of-the-art deep plug-and-play IR method with an average quantitative result of (0.1404, 0.9554). Moreover, the advantage of DP-RI-Tikhonov becomes more obvious at the last iteration. Experiments on six clinical whole-body PET images further indicate that DP-RI-Tikhonov successfully reduces noise intensity and recovers fine details, recovering sharper and more uniform images than the comparison methods. Significance. DP-RI-Tikhonov’s ability to reduce noise intensity and effectively eliminate the semi-convergence property overcomes the limitations of existing methods. This advancement may have substantial implications for other medical IR.

The algorithm for denoising point clouds of annular forgings based on Grassmann manifold and density clustering

Abstract In the industrial sector, annular forgings serve as critical load-bearing components in mechanical equipment. During the production process, the precise measurement of the dimensional parameters of annular forgings is of paramount importance to ensure their quality and safety. However, owing to the influence of the measurement environment, the manufacturing process of annular forgings can introduce varying degrees of noise, resulting in inaccurate dimensional measurements. Therefore, researching methods for three-dimensional point cloud data to eliminate noise in annular forging point clouds is of significant importance for improving the accuracy of forging measurements. This paper presents a denoising approach for three-dimensional point cloud data of annular forgings based on Grassmann manifold and density clustering (GDAD). First, within the Grassmann manifold, the core points for density clustering are determined using density parameters. Second, density clustering is performed within the Grassmann manifold, with the Cauchy distance replacing the Euclidean distance to reduce the impact of noise and outliers on the analysis results. Finally, a search tree model was constructed to filter out incorrect point cloud clusters. The fusion of clustering results and the search tree model achieved denoising of point cloud data. Simulation experiments on annular forgings demonstrate that GDAD effectively eliminates edge noise in annular forgings and performs well in denoising point-cloud models with varying levels of noise intensity.

Hierarchical agglomerative clustering approach for direct and obstructed link classification in IsOWC systems

A novel. to the best of our knowledge, technique based on hierarchical agglomerative clustering (HAC) is proposed to classify the direct and obstructed links in intersatellite optical wireless communication (IsOWC) systems. Prior to the training phase, an exploratory data analysis and preprocessing technique are conducted on a dataset composing 250 instances with four unlabeled input features: relative intensity noise, propagation distance, pointing error, and insertion loss. These datasets extract from IsOWC systems utilizing on-off keying modulation. The HAC technique demonstrates exceptional performance, achieving a classification accuracy of 92.7%, surpassing other machine learning techniques. Additionally, the impact of input feature combinations is discussed in detail using dendrogram plots and various performance metrics. The results provide valuable insights for localizing satellite constellations in earth orbit and advancing global Internet accessibility.

Self-Supervised Image Denoising of Third Harmonic Generation Microscopic Images of Human Glioma Tissue by Transformer-Based Blind Spot (TBS) Network.

Third harmonic generation (THG) microscopy shows great potential for instant pathology of brain tumor tissue during surgery. However, due to the maximal permitted exposure of laser intensity and inherent noise of the imaging system, the noise level of THG images is relatively high, which affects subsequent feature extraction analysis. Denoising THG images is challenging for modern deep-learning based methods because of the rich morphologies contained and the difficulty in obtaining the noise-free counterparts. To address this, in this work, we propose an unsupervised deep-learning network for denoising of THG images which combines a self-supervised blind spot method and a U-shape Transformer using a dynamic sparse attention mechanism. The experimental results on THG images of human glioma tissue show that our approach exhibits superior denoising performance qualitatively and quantitatively compared with previous methods. Our model achieves an improvement of 2.47-9.50 dB in SNR and 0.37-7.40 dB in CNR, compared to six recent state-of-the-art unsupervised learning models including Neighbor2Neighbor, Blind2Unblind, Self2Self+, ZS-N2N, Noise2Info and SDAP. To achieve an objective evaluation of our model, we also validate our model on public datasets including natural and microscopic images, and our model shows a better denoising performance than several recent unsupervised models such as Neighbor2Neighbor, Blind2Unblind and ZS-N2N. In addition, our model is nearly instant in denoising a THG image, which has the potential for real-time applications of THG microscopy.

Analysis of the effect of cavitation on internal fluid excitation characteristics of centrifugal pumps

To explore the influence of cavitation on the internal fluid excitation characteristics of pumps, numerical simulations and performance testing evaluations were performed on the IS65-50-125 centrifugal pump. The prototype pump's exterior characteristic and cavitation performance curves, as well as its bubble volume distribution, were successfully replicated using numerical computations. The effect of cavitation on the internal pressure pulsation characteristics of the centrifugal pump under various operating situations was comprehensively investigated, indicating a relationship between the degree of cavitation and the root mean square values of pressure pulsation. Special emphasis was placed on the changes in features at intermediate and high frequencies, as well as the processes of rising bubble volume and vortex shedding at the impeller trailing edge on pressure pulsation. To validate the simulation results, a centrifugal pump vibration and noise testing platform was built, and studies on vibration intensity and internal sound field noise were conducted. The experimental results revealed that the vibration intensity and internal sound field sound pressure level of the centrifugal pump rose as cavitation conditions deteriorated, confirming the modeling results. This study's significant innovation is the precise identification of the pump's performance changes under different operating conditions by monitoring pressure pulsation changes at various frequencies, as well as an in-depth discussion of the impact mechanism of cavitation phenomena on the internal fluid excitation behavior of centrifugal pumps. The study demonstrates differences in pressure pulsation characteristics on the suction and pressure sides under various cavitation situations, as well as the process of vortex creation and shedding generated by bubbles in the impeller input channel during severe cavitation. This gives new theoretical basis for pump vibration and noise reduction, as well as significant improvements in centrifugal pump performance and stability.