Experimental Noise Research Articles

Exploring Magnetic and Electrical Brain Stimulation in Parkinsonian Dyskinetic Monkeys

ABSTRACT: Background: Parkinson’s disease (PD) chronic L-Dopa treatment often triggers motor complications, such as L-Dopa-induced dyskinesias (LID). LID are reported to be associated with abnormal glutamatergic activity between the striatum and primary motor cortex (M1), resulting in M1 hyperactivation. Beneficial noninvasive brain stimulation (NIBS) paradigms were reported to normalize glutamatergic activity. The objective of the present study was thus to set up a NIBS paradigm in parkinsonian monkeys to investigate motor behavior under basal conditions and with L-Dopa treatment-inducing dyskinesias. Methods: Motor behavior was investigated in five 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) dyskinetic female Macaca fascicularis monkey models of PD, allowing us to monitor the administration of NIBS and drugs. NIBS used were inhibitory protocols, that is, cathodal transcranial direct current stimulation (c-tDCS) and continuous theta-burst stimulation (cTBS). A procedure of three weeks was developed to progressively acclimate animals to the experimental conditions, equipment and noise of c-tDCS and cTBS before stimulating them with either vehicle or L-Dopa. Results: One session of c-tDCS with L-Dopa yielded no effect, whereas five sessions briefly reduced LID but decreased the duration of L-Dopa anti-PD effects. cTBS alone improved (decreased) parkinsonian scores as compared to sham stimulation or vehicle alone. Two sessions of cTBS with L-Dopa decreased LID without affecting L-Dopa anti-PD effects. Conclusion: This is the first study testing c-tDCS and cTBS on the motor behavior of MPTP dyskinetic monkeys. As compared to medicated patients, MPTP monkeys offer the opportunity to evaluate NIBS after-effects in drug-free and LID conditions, which are critical in the search for new PD treatment.

Development of an Experimental Acoustic Noise Characterization Setup for Electric Motor Drive Applications

This paper presents the development of an experimental setup for acoustic noise characterization of electric motors. It describes the sound measurement microphones utilized in the setup and discusses the application of octave bands and A-weighting in noise measurement. Various methods for acoustic noise measurement and sound power calculation, including those based on sound pressure and sound intensity, are also covered. Given the relatively noisy test environment and restricted access around the test setup, discrete point sound intensity measurement is selected for sound power calculation. Initially, a stationary probe-holding fixture is designed and fabricated for sound intensity measurements. To enhance the fixture’s flexibility and the accuracy of the measurements, a transportable fixture is subsequently designed and fabricated. The necessary hardware and software settings for acoustic noise characterization are then developed. Finally, the setup is used to conduct acoustic noise characterization of an IPM motor, validating the application of the transportable probe-holding fixture.

Research on noise reduction method of centrifugal pump coupling fault signal based on LMD-FE-IWT

Abstract Aiming at the problem that the coupling fault signal of centrifugal pump presents the complexity of mutual coupling due to the mutual influence of various fault components, and it is easy to be affected by the surrounding environmental noise, which leads to the difficulty of noise reduction, the LMD-FE-IWT noise reduction method is proposed. This method first utilizes local mean decomposition (LMD) to adaptively decompose the vibration signal, and calculates the fuzzy entropy of each component. According to fuzzy entropy, the components are divided into two categories: disordered and ordered. After the improved wavelet threshold denoising of the disordered component, it is reconstructed with the ordered component to achieve the denoising effect. By analying the simulation signal and experimental data, the results show that, the proposed method outperforms other methods, with improvements in signal-to-noise ratio by 55.95%, 48.86%, and 31.71% for the simulation signal noise reduction. The effect is significant, enabling the effective extraction of characteristic frequencies associated with different faults for the experimental signal noise reduction.

The Impact of Soundscapes on Healthcare Teams: A Literature Review

Healthcare teams often work in complex systems where soundscapes include numerous noise sources and levels which could impact teamwork. While studies have analyzed the impact of sound on patients, articles describing clinician and clinical team experience are relatively sparse. The purpose of this literature review was to determine what is currently known about soundscapes and their impact on teamwork and team performance in hospital-based healthcare settings. Eleven studies met the inclusion criteria. The Input-Process-Output framework and SEIPS 2.0 model guided our review and categorization of the included studies. The work environments studied included the operating room (OR), perioperative area, and a simulation. Noise input was grouped by existing noise, music or controlled sound conditions, and communication wearables during clinical or simulated tasks. Nine of the 11 articles studied the impact sound environment on team communication and three articles measured other team processes. The findings from this review assessed both existing and experimental noise conditions discovering associations with team performance processes and outcomes. Overall, previous studies have found that noise can impact individuals and elements of teamwork, especially communication. Future research is needed to develop best practices and innovative solutions supporting a positive soundscape for teamwork.

From Simulations to Inference: Using Machine Learning to Tune Patient-Specific Finite-Element Models of the Middle Ear Towards Objective Diagnosis.

Computational models, particularly finite-element (FE) models, are essential for interpreting experimental data and predicting system behavior, especially when direct measurements are limited. A major challenge in tuning these models is the large number of parameters involved. Traditional methods, such as one-by-one sensitivity analyses, are time-consuming, subjective, and often return only a single set of parameter values, focusing on reproducing averaged data rather than capturing the full variability of experimental measurements. In this study, we applied simulation-based inference (SBI) using neural posterior estimation (NPE) to tune an FE model of the human middle ear. The training dataset consisted of 10,000 FE simulations of stapes velocity, ear-canal (EC) input impedance, and absorbance, paired with seven FE parameter values randomly sampled within plausible ranges. The neural network learned the association between parameters and simulation outcomes, returning the probability distribution of parameter values that can reproduce experimental data. Our approach successfully identified parameter sets that reproduced three experimental datasets simultaneously. By accounting for experimental noise and variability during training, the method provided a probability distribution of parameters, representing all valid combinations that could fit the data, rather than tuning to averaged values. The network demonstrated robustness to noise and exhibited an efficient learning curve due to the large training dataset. SBI offers an objective alternative to laborious sensitivity analyses, providing probability distributions for each parameter and uncovering interactions between them. This method can be applied to any biological FE model, and we demonstrated its effectiveness using a middle-ear model. Importantly, it holds promise for objective differential diagnosis of conductive hearing loss by providing insight into the mechanical properties of the middle ear.

Experimental and statistical methods for microrheological characterization of heterogeneity in human respiratory mucus mimics of health and disease progression

Human respiratory mucus (HRM) is extremely soft, compelling passive microrheology for linear viscoelastic characterization. We focus this study on the use of passive microrheology to characterize HRM heterogeneity, a phenomenon in normal HRM that becomes extreme during cystic fibrosis (CF) disease. Specifically, a fraction of the mucin polymers comprising HRM phase-separate into insoluble structures, called flakes, dispersed in mucin-depleted solution. We first reconstitute HRM samples to the MUC5B:MUC5AC mucin ratios consistent with normal and CF clinical samples, which we show recapitulate progressive flake formation and heterogeneity. We then employ passive particle tracking with 200 nm and 1 μm diameter beads in each reconstituted sample. To robustly analyze the tracking data, we introduce statistical denoising methods for low signal-to-noise tracking data within flakes, tested and verified using model-generated synthetic data. These statistical methods provide a fractional Brownian motion classifier of all successfully denoised, tracked beads in flakes and the dilute solution. From the ensemble of classifier data, per bead diameter and mucus sample, we then employ clustering methods to learn and infer multiple levels of heterogeneity: (i) tracked bead data within vs. outside flakes and (ii) within-flake data buried within or distinguishable from the experimental noise floor. Simulated data consistent with experimental data (within and outside flakes) are used to explore form(s) of the generalized Stokes–Einstein relation (GSER) that recover the dynamic moduli of homogeneous and heterogeneous truth sets of purely flakelike, dilute solution, and mixture samples. The appropriate form of GSER is applied to experimental data to show (i) flakes are heterogeneous with gel and sol domains; (ii) dilute solutions are heterogeneous with only sol domains; and (iii) flake and dilute solution properties vary with probe diameter.

Noise without noise? Art interventions in a Southeast Asian city

The experimental music community Jogja Noise Bombing based in the Indonesian city of Yogyakarta has for more than a decade experimented with pop-up performances of harsh noise played on DIY instruments in urban spaces. These urban spaces, however, tend themselves to be very "noisy". Since 2018 we have followed these performances online and through fieldwork in Indonesia with an ongoing curiosity that stays with the trouble of noise. We are attracted to the context of these performances because they challenge us to answer a series of paradoxical questions: what is noise aesthetics in a place where everything is "noisy" and where this noisiness, furthermore, does not seem to be experienced as a problem or provocation? Can one even speak of "noise" if it is not unwanted? The social aesthetics of Indonesian harsh noise music in its specific attunement to the soundscapes of the Indonesian city challenges Western conventional ontologies of noise and ideas of the relationship between "objective" noise and aesthetic performances of noise such as noise music in several ways.

Development and distribution of an in situ experimental wind turbine noise database

The PIBE project is the first French collaborative research project on wind turbine noise. One of its objectives is to quantify the experimental dispersion observed in situ on acoustic quantities/metrics and influencing parameters, and to compare it with the numerical dispersion estimated using acoustic propagation models. A long-term experimental campaign was carried out over consecutive 410 days around a wind farm comprising eight 80-metre-high and 90-metre diameter turbines. Acoustic sensors (five Class I sound level meters) were deployed on site, recording third-octave spectra and overall A- and Z-weighted values of the Leq indicator and fractile indices (L10, L50 and L90), at distances from 325 m to 1400 m from the wind turbines row. In addition, three 3D ultrasonic anemometers and a Lidar system were used to measure meteorological variables influencing long-range sound propagation (e.g. wind and temperature verical profiles). Besides, train and aircraft passing close to the site were inventoried to identify periods of high background noise. All experimental data have been processed into an ElasticSearch database. An interactive web application has been developed in R Shiny to facilitate processing, visualization and analysis of the experimental database.

Predicting the impact of wind turbine noise: general overview and results of the PIBE project

The PIBE project aims to improve wind turbine noise prediction methods and to explore new solutions for noise reduction. This five years project brought together French experts in aeroacoustics, sound propagation, experimental noise characterization and wind engineering, and was structured around three work packages. The first one aimed to study amplitude modulation phenomena and focused particularly the characterization of dynamic stall noise. Specific aerodynamic and acoustic measurements were carried out in a wind tunnel, showing the influence of several stall regimes on noise production. The second work package focused on quantifying the uncertainties of noise prediction methods. It developed an open-access online application (WindTUNE) that quantifies uncertainties on noise prediction of a wind farm, and a parametric and uncertainties calculation tool for the engineering application Code-TYMPAN. This axe also produced an open-access database of a 400 days campaign of meteorological and acoustical measurements around a wind farm. The last work package investigated new noise reduction devices using blades with modified leading and/or trailing edges. The efficiency of the solutions were characterized in a wind tunnel, both acoustically and aerodynamically. The paper presents the main results obtained at the end of the project.

An innovative approach for enclosure design of power generators to meet noise regulation targets

The use of diesel-powered engine generators as a power source is very common for various industrial and residential applications. There are strict regulatory requirements and legislations to limit the noise levels from these generators below statutory limits. The purpose of the paper is to present an approach using statistical energy analysis (SEA) augmented by experimental inputs for the prediction of noise levels for the generators and propose strategies for optimized enclosure design that effectively mitigate noise to achieve regulatory requirements. The study begins by developing an SEA model that represents the acoustic behavior of the generator and enclosure. The airborne sources such as engine, alternator, radiator fan and exhaust are modelled explicitly using experimental noise source characterization. Based on the established SEA model and excitation sources, noise levels are predicted through the calculation of energy flow between subsystems. The predicted sound levels for an existing Diesel Generator with enclosure were validated with its test data to ensure correctness of SEA model. Parametric studies were carried out using simulations on key variables, including critical noise paths, panel thickness, and noise control treatments. These investigations helped identify optimal process parameters that reduce noise levels emitted by genset in adherence to noise regulations.

Rapid quantitative analysis of three elements (Al, Mg and Fe) in molten zinc based on laser-induced breakdown spectroscopy combined with machine learning algorithm

Hot-dip galvanizing represents one of the most cost-effective methods for the prevention of metal corrosion, and is therefore employed extensively across a range of fields. Carrying out the research and development of technology and devices for quantitative analysis of chemical elements in hot-dip galvanising process can provide theoretical basis and technical support for the efficiency of hot-dip galvanising process and reduction of energy consumption. A machine learning-assisted LIBS combined with a programmable logic controller (PLC) for simultaneous on-line/in-site monitoring of multiple elements in hot-dip galvanising solution (molten zinc) was developed in the current study. The LIBS spectral data of the on-site hot-dip galvanising solution was collected under optimised experimental conditions. In order to further reduce the influence of experimental noise on the analysis performance, the on-site LIBS spectral data were preprocessed and anomalous spectral data were screened based on normalisation and principal component analysis-mahalanobis distance (PCA-MD). On the basis of the optimised data, component prediction models for three key elements of on-site hot-dip galvanising solution were constructed. The storage and re-call of the model was achieved based on python combined with LabVIEW, thus real-time prediction of the on-site component content of hot-dip galvanising solution was achieved. The results show that the random forest model presents the best prediction results, in which the R2 is 0.9978 and the RMSE is 0.0013% for Al, the R2 is 0.9984 and the RMSE is 0.0011% for Mg, and the R2 is 0.9932 and the RMSE is 0.0001% for Fe. From the on-site analysis results of the constructed model, its MRE of Al, Mg and Fe is 0.0098, 0.0236, and 0.2102, respectively. In summary, the in-situ/on-line analysis system of hot-dip galvanising solution combined with machine learning constructed in this study shows excellent performance, which can satisfy the needs of hot-dip galvanising solution production site. This study is expected to provide theoretical basis and technical reference for quality control and process optimisation in other production sites in the metallurgical field.

Analysis of thermal instability for fiber backscatter phase and intensity.

In this Letter, we utilize the speckle model to measure the average random scattering rate of fiber backscatter and analyze its dependence on length, yielding a linear fitting coefficient of 0.23 ppm/m for a PM980-XP fiber. We incorporate the temperature coupling effect into the model and validate the model's accuracy by examining the distribution of the change rate of the backscattering rate relative to the temperature and the amplitude spectral density of the backscattered power. Our findings demonstrate that the typical shoulder-shaped noise in interferometer experiments is limited by stray light, and the dependence of shoulder-shaped noise on the fiber length and temperature noise level is analyzed.

FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data.

Machine learning (ML) for protein design requires large protein fitness datasets generated by high-throughput experiments for training, fine-tuning, and benchmarking models. However, most models do not account for experimental noise inherent in these datasets, harming model performance and changing model rankings in benchmarking studies. Here we develop FLIGHTED, a Bayesian method of accounting for uncertainty by generating probabilistic fitness landscapes from noisy high-throughput experiments. We demonstrate how FLIGHTED can improve model performance on two categories of experiments: single-step selection assays, such as phage display and SELEX, and a novel high-throughput assay called DHARMA that ties activity to base editing. We then compare the performance of standard machine-learning models on fitness landscapes generated with and without FLIGHTED. Accounting for noise significantly improves model performance, especially of CNN architectures, and changes relative rankings on numerous common benchmarks. Based on our new benchmarking with FLIGHTED, data size, not model scale, currently appears to be limiting the performance of protein fitness models, and the choice of top model architecture matters more than the protein language model embedding. Collectively, our results indicate that FLIGHTED can be applied to any high-throughput assay and any machine learning model, making it straightforward for protein designers to account for experimental noise when modeling protein fitness.

Quantum state estimation based on deep learning

Abstract We used deep learning techniques to construct various models for reconstructing quantum states from a given set of coincidence measurements. Through simulations, we demonstrated that our approach generates functionally equivalent reconstructed states for a wide range of pure and mixed input states. Compared to traditional methods, our system offers the advantage of faster speed. Additionally, by training our system with measurement results containing simulated noise sources, we could show a significant improvement in average fidelity compared to typical reconstruction methods. We also found that constraining the variational manifold to physical states, i.e., positive semi-definite density matrices, greatly enhances the quality of the reconstructed states in the presence of experimental imperfections and noise. Finally, we validated the correctness and superiority of our model by using data generated on IBMQ, a real quantum computer.

Chirality Detection in Scanning Tunneling Microscopy Data Using Artificial Intelligence.

Enantiospecific effects play an uprising role in chemistry and technical applications. Chiral molecular networks formed by self-assembly processes at surfaces can be imaged by scanning probe microscopy (SPM). Low contrast and high noise in the topography map often interfere with the automatic image analysis using classical methods. The long SPM image acquisition times restrain Artificial Intelligence-based methods requiring large training sets, leaving only tedious manual work, inducing human-dependent errors and biased labeling. By generating realistic looking synthetic images, the acquisition of real datasets is avoided. Two state-of-the-art object detection architectures are trained to localize and classify chiral unit-cells in a regular molecular chiral network formed by self-assembly of linear molecular bricks. The comparison of different architectures and datasets demonstrates that the training on purely synthetic data outperforms models trained using augmented datasets. A Faster R-CNN model trained solely on synthetic data achieved an excellent mean average precision of 99% on real data. Hence this approach and the transfer to real data show high success, also highlighting the high robustness against experimental noise and different zoom levels across the full experimentally reasonable parameter range. The generalizability of this idea is demonstrated by achieving equally high performance on a different structure,too.

New Fault Diagnosis Method for Rolling Bearings Based on Improved Residual Shrinkage Network Combined with Transfer Learning.

The fault diagnosis of rolling bearings is faced with the problem of a lack of fault data. Currently, fault diagnosis based on traditional convolutional neural networks decreases the diagnosis rate. In this paper, the developed adaptive residual shrinkage network model is combined with transfer learning to solve the above problems. The model is trained on the Case Western Reserve dataset, and then the trained model is migrated to a small-sample dataset with a scaled-down sample size and the Jiangnan University bearing dataset to conduct the experiments. The experimental results show that the proposed method can efficiently learn from small-sample datasets, improving the accuracy of the fault diagnosis of bearings under variable loads and variable speeds. The adaptive parameter-rectified linear unit is utilized to adapt the nonlinear transformation. When rolling bearings are in operation, noise production is inevitable. In this paper, soft thresholding and an attention mechanism are added to the model, which can effectively process vibration signals with strong noise. In this paper, the real noise is simulated by adding Gaussian white noise in migration task experiments on small-sample datasets. The experimental results show that the algorithm has noise resistance.

Towards testing the general bounce cosmology with the CMB B-mode auto-bispectrum

It has been shown that a three-point correlation function of tensor perturbations from a bounce model in general relativity with a minimally-coupled scalar field is highly suppressed, and the resultant three-point function of cosmic microwave background (CMB) B-mode polarizations is too small to be detected by CMB experiments. On the other hand, bounce models in a more general class with a non-minimal derivative coupling between a scalar field and gravity can predict the three-point correlation function of the tensor perturbations without any suppression, the amplitude of which is allowed to be much larger than that in general relativity. In this paper, we evaluate the three-point function of the B-mode polarizations from the general bounce cosmology with the non-minimal coupling and show that a signal-to-noise ratio of the B-mode auto-bispectrum in the general class can reach unity for ℓ max=100 in the full-sky case, with and without the lensing B-mode added to cosmic variance. Considering additionally the LiteBIRD experimental noise, we obtain a SNR smaller than unity.

Study of the birefringence noise in high-finesse ULE cavity

High-finesse ultra-low expansion (ULE) cavities are important components in many precision optical experiments, and the cavity length noise in ULE cavities has been intensively studied. However, some experiments, such as vacuum magnetic birefringence measurements, have focused on the phase difference between orthogonal polarizations. To determine the effect of high-reflectivity mirrors on birefringence noise, we investigated the birefringence noise of a ULE cavity with the finesse of 531000(1000) and obtained the noise spectrum of the intrinsic birefringence of the cavity. The single-pass phase difference due to the birefringence of the cavity mirror is (21.3±0.2)μrad and the noise for the birefringent cavity is 81.3prad/Hz at 0.1Hz and 2.6prad/Hz at 40Hz. These results may draw attention when considering noise in cavity-based optical experiments.

Low depth virtual distillation of quantum circuits by deterministic circuit decomposition

Virtual distillation using measurements of multiple copies of a quantum circuit have recently been proposed as a method of noise mitigation of expectation values. Circuit decompositions known as B gates were found only for 1-local Hamiltonians, however practical problems for chemistry require n-local Hamiltonians, which cannot be corrected with B gates. We discover low depth circuit decompositions for expectation values for n-local Pauli strings by combining multiple projections to recover the correct measurement statistics or expectation values. Our method adds linear entangling gates with the number of qubits, but it requires extra measurements. Furthermore, in applications to find ground states such as the variational quantum eigensolver (VQE) algorithm, the variational principle is required which states that the energy cannot go below the ground-state energy. We find that the variational principle is violated when using B gates and is preserved if we use our low depth decomposition on all expectation values. We perform a demonstration on real devices, and we show that our decomposition can mitigate real experimental noise in VQE for the H2 molecule with a two-qubit tapered mapping, H3 with three qubits, and H2 with four qubits. Our decomposition provides a way to perform duplicate circuit virtual distillation on real devices at significantly lower depth and for arbitrary observables. Published by the American Physical Society 2024

Vibration-based damage detection of beams using Hybrid Genetic Algorithm with combined l0 and l1 regularization

Vibration-based damage detection is an effective way of identifying beam damages before they become severe and potentially catastrophic. This paper introduces a novel and robust method for detecting multiple cracks in beams using a Hybrid Genetic Algorithm (HGA). The objective function of the proposed method, based on dynamic properties, can be employed with partial modal information and is enhanced by including the initial residuals between numerical and experimental models along with combined l0 and l1 regularization. This enhancement mitigates the impact of limited modal data, experimental noise, and modeling inaccuracies. Additionally, a method of selecting an appropriate regularization parameter by analyzing residual and solution norm curves is presented. The performance of the proposed method is evaluated and verified using both numerical simulations and experimental studies on a steel cantilever beam under four damage scenarios. In the experimental study, natural frequencies and mode shapes were determined under ambient vibration conditions using Enhanced Frequency Domain Decomposition (EFDD) and the Stochastic Subspace Identification (SSI) method. Finite element models of the beam were developed in ABAQUS software for numerical analysis, and a comparative study of numerical and experimental data was conducted. The proposed approach successfully identifies, locates and quantifies single and multiple damages in the beam, demonstrating its reliability and effectiveness, particularly in scenarios with limited modal data and significant experimental noise.