Noise Estimation Research Articles

A low noise 3×3 coupler demodulation scheme based on spectral subtraction with multi-window minimum statistical noise estimation

A low noise 3×3 coupler demodulation scheme based on spectral subtraction with multi-window minimum statistical noise estimation

Study on the Robust Filter Method of SINS/DVL Integrated Navigation Systems in a Complex Underwater Environment

This paper proposes an improved adaptive filtering algorithm based on the Sage–Husa adaptive Kalman filtering algorithm to address the issue of measurement noise characteristics impacting the navigation accuracy in strapdown inertial navigation system (SINS)/Doppler Velocity Log (DVL) integrated navigation systems. Addressing the non-positive definite matrix problem prevalent in traditional adaptive filtering algorithms and aiming to enhance measurement noise estimation accuracy, this method incorporates upper and lower thresholds determined by a discrimination factor. In the presence of abnormal measurement data, these thresholds are utilized to adjust the covariance of the innovation, subsequently re-estimating the system’s measurement noise through a decision factor based on the innovation. Simulation and experiment results demonstrate that the proposed improved adaptive filtering algorithm outperforms the classical Kalman filter (KF) in terms of navigation accuracy and stability. Furthermore, the filtering performance surpasses that of the Sage–Husa algorithm. The simulation results in this paper show that the relative position positioning error of the improved method is reduced by 49.44% compared with the Sage–Husa filtering method.

Automatic estimation of the wind turbine noise with recurrent neural networks

There is growing interest in the development of renewable energies, particularly wind power. However, wind turbines generate noise that can affect the sound environment of nearby residents. This study focuses on the isolation of wind farm noise (WFN) from the surrounding ambient noise. Our method is based on a Recurrent Neural Network (RNN) Architecture that captures temporal dependencies in the acoustic signal. This proposal is compared to Non-Negative Matrix Factorization (NMF) that has shown first promising results on a previous study on simulated sound scenes. Our approach relies on Long Short Term Memory (LSTM) RNN, conducted using an end-to-end trained model and a Gated Recurrent Unit (GRU). The training and testing dataset is constructed by superimposing measured background noise with synthesized wind turbine noise, effectively simulating realistic environmental conditions. This study aims to reduce the acoustic impact of wind turbines on communities and attempt to control turbine operation using advanced machine learning techniques.

Estimation of bridge noise during the passage of trains with worn wheel treads from rail vibration in a different section

Bridge noise may increase when a train with worn wheel treads passes. This study attempted to estimate bridge noise when a train with worn wheels passed based on rail vibration in a different section, i.e., using accelerometers on the rail near a station and a vehicle maintenance depot. The rail vibration in a different section was converted into that in a section where bridge noise was required to be estimated. In the estimation, the vibration properties of the wheel, track, and contact spring in both sections were considered, and it was assumed that wheel roughness exceeded rail roughness at spatial frequencies where the influence of the wear was observed. The bridge noise generated by one wheel was estimated from the bridge vibration and converted from the rail vibration with transfer functions by considering the radiation area of the viaduct. The overall bridge noise was obtained by summing the noise generated by all the wheels in a train. The estimated results were consistent with the measured values at frequencies where the wear had a strong influence.

Effect of background noise and diffraction on localization of sound sources using distributed acoustic measurement equipment

This study is focused on the development of a system designed to pinpoint the positions of sound sources within a plane through the analysis of cross-correlated signals obtained from distributed microphones. Our investigation delved into assessing the impact of physical barriers and background noise on the accuracy of the proposed method for localizing sound sources. Prior research has demonstrated that measurements conducted within an anechoic chamber yield estimations accurate to within ±20 mm. When the signal-to-noise ratio of the target sound source is reduced by the background noise, the estimation is performed at the point of background noise generation. Furthermore, the introduction of physical barriers between the sound source and the microphones resulted in sound diffraction, thereby leading to estimation errors attributable to the varying lengths of diffraction paths. These findings underscore the importance of considering environmental factors in sound source localization endeavors. The estimation at Mic, where the sound-receiving level drops below -10 dB due to physical barriers, confirmed that the background noise source point is estimated as well as the background noise estimation error.

Advanced concept for noise monitoring in smart cities through wireless sensor units with AI classification technologies

The rapid urbanization in the cities has indicated the importance of sustainable development for creating smart cities with high quality of life. Environmental noise as one of the main concerns has to be addressed according to International Directives and Legislation. Traditional noise estimation methods are costly and time-consuming, but the emerging technologies like low-cost wireless sensor units (WSUs) offer more granular data collection and analysis. Requirements for more accurate assessment of the noise pollution by detecting the dominant noise sources impose the need to apply novel approaches in the technical systems for noise monitoring. To this aim, this paper investigates the advanced methods for noise monitoring through WSUs with AI classification technologies. The proposed concept of the device not only quantitively describes the noise pollution, but also tends to recognize the class of the disturbing sound events. The accuracy of the WSUs are configured by comparing the results with professional hand-held analyzer, showing powerful and affordable low-cost units able to publish results through cloud and fog computing based on the IoT and smart city technologies. The paper discusses the challenges for merging noise measurement technology and the AI algorithms to classify and quantify the different classes of sound events.

Impact of traffic simulation parameters on the estimation of noise exposure in an urban environment

Since road traffic is the most impactful noise source in the European cities, the evaluation of citizens' exposure is crucial. Although the noise map accuracy is affected by uncertainties in traffic volume, to perform extensive traffic monitoring is not practical and expensive, even in small cities. Traffic simulation software uses routing algorithms to suggest the fastest path to distribute vehicles from an origin through a network to a desired destination which interacts within the urban environment. Therefore, it could represent an important tool for noise mapping in the critical task to fill the data gaps in traffic volumes, also for implementing action plans using a suitable traffic management approach. However, there is a lack of evidence on how the choice of traffic simulation parameters influences noise estimation. In this work, we design a simulation framework to evaluate the impact of routing algorithms on the estimation of population noise exposure in an urban area. Using an open-source pipeline based on public databases and open-source software SUMO and NoiseModelling, we evaluated the implication on the traffic distribution and resulting noise exposure for a set of traffic model key parameters.

Cryogenic sapphire optical reference cavity with crystalline coatings at 1 × 10-16 fractional frequency instability.

The frequency stability of a laser locked to an optical reference cavity is fundamentally limited by thermal noise in the cavity length. These fluctuations are linked to material dissipation, which depends on both the temperature of the optical components and the material properties. Here, the design and experimental characterization of a sapphire optical cavity operated at 10K with crystalline coatings at 1069nm is presented. Theoretical estimates of the thermo-mechanical noise indicate a thermal noise floor below 4.5 × 10-18. Major technical noise contributions including vibrations, temperature fluctuations, and residual amplitude modulation are characterized in detail. The short-term performance is measured via a three-cornered hat analysis with two other cavity-stabilized lasers, yielding a noise floor of 1 × 10-16. The long-term performance is measured against an optical lattice clock, indicating cavity stability at the level of 2 × 10-15 for averaging times up to 10 000s.

Research on AIS algorithm based on factor graph inference in Doppler localization

Abstract In this paper, an adaptive incremental smoothing (AIS) algorithm based on factor graph inference is proposed to solve the problem of improving the Doppler positioning accuracy of low-Earth orbit communication satellites. Traditional methods such as iterative least squares (ILS) algorithm and Extended Kalman filter (EKF) have certain limitations in real-time Doppler localization. The AIS algorithm uses factor graph inference to identify interference factors, and adopts adaptive smoothing to reduce the impact of estimation errors and promote adaptive noise estimation. The ground station Starlink satellite constellation static positioning was taken as an example, and the simulation was carried out on the STK software simulation platform to verify the feasibility and effectiveness of the algorithm. The experimental results show that the positioning accuracy of the AIS algorithm based on factor graph inference is significantly improved compared with the ILS and EKF algorithms in real-time Doppler positioning solutions. The aim of this research is to improve the positioning accuracy of low-orbit satellites and provide a reference for practical applications.

Quantitative suppression of unsteady powerline noise in transient electromagnetic surveys: Adjustment of base-frequency and optimal choice of stacking-times.

Powerline noise is a severe interference source in urban or mine transient electromagnetic (TEM) surveys. TEM systems generally adopt the synchronous detection scheme to suppress the powerline noise. However, when the powerline frequency fluctuates differing from its nominal value (50/60Hz), the considerable powerline noise residue still remains even after synchronous detection. To overcome this problem, this paper proposes the quantitative suppression method for powerline noise taking into account the instability of powerline frequency. The method is based on the adjustment of base-frequency and optimal choice of stacking-times. We represent mathematically the sufficient condition for powerline noise suppression by synchronous detection. It consists of two equations, one with respect to base-frequency and other with respect to stacking-times. The base-frequency is adjusted according to powerline frequency estimate. We first derive the mathematical relationship between frequency estimation accuracy, residual noise amplitude, and stacking-times. Based on it, we develop an efficient algorithm to determine the optimal stacking-times. The algorithm takes as an input the powerline noise estimates and the noise tolerance limit. The tolerance limit is set to a certain value desired by the user, such as 10, 4, and 1 µV. By adjusting base-frequency and determining optimal stacking-times, the powerline noise residue after synchronous detection is reduced to below the desired tolerance limit. We verify the effectiveness of the proposed method for both simulated and actual noise. Experimental results show that the method achieves quantitative suppression of unsteady powerline noise without any damage of effective signal and prevents the measurement time loss due to excessive stacking.

Iterative Immunostaining and NEDD Denoising for Improved Signal-To-Noise Ratio in ExM-LSCM.

Expansion microscopy (ExM) has significantly reformed the field of super-resolution imaging, emerging as a powerful tool for visualizing complex cellular structures with nanoscale precision. Despite its capabilities, the epitope accessibility, labeling density, and precision of individual molecule detection pose challenges. We recently developed an iterative indirect immunofluorescence (IT-IF) method to improve the epitope labeling density, improving the signal and total intensity. In our protocol, we iteratively apply immunostaining steps before the expansion and exploit signal processing through noise estimation, denoising, and deblurring (NEDD) to aid in quantitative image analyses. Herein, we describe the steps of the iterative staining procedure and provide instructions on how to perform NEDD-based signal processing. Overall, IT-IF in ExM-laser scanning confocal microscopy (LSCM) represents a significant advancement in the field of cellular imaging, offering researchers a versatile tool for unraveling the structural complexity of biological systems at the molecular level with an increased signal-to-noise ratio and fluorescence intensity. Key features • Builds upon the method developed by Mäntylä et al. [1] and introduces the IT-IF method and signal-processing platform for several nanoscopy imaging applications. • Retains signal-to-noise ratio and significantly enhances the fluorescence intensity of ExM-LSCM data. • Automatic estimation of noise, signal reconstruction, denoising, and deblurring for increased reliability in image quantifications. • Requires at least seven days to complete.

Periodicity search in the timing of the 25 millisecond pulsars from the second data release of the European pulsar timing array

Abstract In this work, we investigated the presence of strictly periodic, as well as quasi-periodic signals, in the timing of the 25 millisecond pulsars from the EPTA DR2 dataset. This is especially interesting in the context of the recent hints of a gravitational wave background in these data, and the necessary further study of red-noise timing processes, which are known to behave quasi-periodically in some normal pulsars. We used Bayesian timing models developed through the run_enterprise pipeline: a strict periodicity was modelled as the influence of a planetary companion on the pulsar, while a quasi-periodicity was represented as a Fourier-domain Gaussian process. We found that neither model would clearly improve the timing models of the 25 millisecond pulsars in this dataset. This implies that noise and parameter estimates are unlikely to be biased by the presence of a (quasi-)periodicity in the timing data. Nevertheless, the results for PSRs J1744−1134 and J1012+5307 suggest that the standard noise models for these pulsars may not be sufficient. We also measure upper limits for the projected masses of planetary companions around each of the 25 pulsars. The data of PSR J1909−3744 yielded the best mass limits, such that we constrained the 95-percentile to ∼2 × 10−4 M⊕ (roughly the mass of the dwarf planet Ceres) for orbital periods between 5 d–17 yr. These are the best pulsar planet mass limits to date.

An improved wiener filter-based method for identifying stall inception of transonic compressor

The development of the modern aviation industry poses high demands on the design of aircraft engines recently. However, the stability of compressor flow is one of the key factors affecting further improvements in engine performance. The design of next-generation aircraft engines imposes higher requirements on compressor loading, which leads to the emergence of many new stall inceptions. As a result, the onset and evolution of flow instability become more complex. For accurately capturing stall inceptions of transonic compressors, the strong pressure disturbances caused by shock waves at the blade tip and the complex flow within blade passages result in significant challenges. To address this issue, this study draws inspiration from the design methods of Wiener filters in the field of speech recognition. Based on the characteristic signal mutations during rotating stall in compressors, a Wiener filter approach that uses time-delayed signals as noise estimates during filter training is developed. The method can be used for both offline and online analysis. It was applied to analyze the stall signals of a single rotor from a 1.5-stage transonic axial compressor under distorted inlet conditions at transonic rotational speeds and the entire stage under uniform inlet conditions at subsonic rotational speeds. The results indicate that, under inlet distortion, the compressor generates disturbance signals in the distorted sector before stall, and the earliest spike-inception disturbance occurs at the circumferential position of the rotor leaving the distorted sector. Under uniform inlet conditions, random disturbances could be detected throughout the circumference before stall onset, developing into spike waves at a circumferential location that subsequently triggered stall. Compared to conventional low-pass filters, discrete wavelet transforms, and empirical mode decomposition, the Wiener filter yielded more prominent spike wave structures in the filtered signals. Under distorted inlet conditions, the Wiener-filtered signals showed a 1 % decrease in autocorrelation coefficient and a 3.7 % increase in root mean square (RMS) upon the appearance of spike waves, more pronounced than the 0.5 % decrease in autocorrelation coefficient and 1.8 % increase in RMS achieved by conventional methods. Under uniform inlet conditions, the Wiener filter also detected a 9.7 % increase in RMS upon the appearance of spike waves, more pronounced than the 6.3 % increase observed with conventional methods.

When guided diffusion model meets zero-shot image super-resolution

Existing deep learning-based single-image super-resolution (SR) methods typically rely on vast quantities of paired data. As an essential solution, zero-shot SR methods require only a single image to handle image-specific degradation. However, these methods still struggle to recover fine-grained details due to the lack of supervised information. In this work, we propose a novel guided Diffusion model for Zero-shot image SR (ZeroDiff) to explicitly direct image quality enhancement. Specifically, we elaborate two key guidance strategies: (1) high-frequency guidance and (2) content-consistent guidance. The former concentrates on boosting fine-grained textures by embedding high-frequency information into the cross-attention mechanism of the noise estimator. The latter avoids the sampling deviating from the original image in terms of structure and low-frequency content. Specifically, the noisy images at each diffusion step are injected into the corresponding sampling step, encouraging the sampled image to be consistent with that of the corresponding diffusion step. Moreover, we design a progressive zoom-in paradigm by gradually enlarging the image size and enriching the image details to boost the sampling efficiency of diffusion models, while enabling high-quality image reconstruction. Extensive experiments reveal that our method achieves comparable results with other state-of-the-art methods in quantitative and qualitative evaluations on both face and natural images from synthetic and real-world datasets.

Semantic Segmentation-Driven Integration of Point Clouds from Mobile Scanning Platforms in Urban Environments

Precise and complete 3D representations of architectural structures or industrial sites are essential for various applications, including structural monitoring or cadastre. However, acquiring these datasets can be time-consuming, particularly for large objects. Mobile scanning systems offer a solution for such cases. In the case of complex scenes, multiple scanning systems are required to obtain point clouds that can be merged into a comprehensive representation of the object. Merging individual point clouds obtained from different sensors or at different times can be difficult due to discrepancies caused by moving objects or changes in the scene over time, such as seasonal variations in vegetation. In this study, we present the integration of point clouds obtained from two mobile scanning platforms within a built-up area. We utilized a combination of a quadruped robot and an unmanned aerial vehicle (UAV). The PointNet++ network was employed to conduct a semantic segmentation task, enabling the detection of non-ground objects. The experimental tests used the Toronto 3D dataset and DALES for network training. Based on the performance, the model trained on DALES was chosen for further research. The proposed integration algorithm involved semantic segmentation of both point clouds, dividing them into square subregions, and performing subregion selection by checking the emptiness or when both subregions contained points. Parameters such as local density, centroids, coverage, and Euclidean distance were evaluated. Point cloud merging and augmentation enhanced with semantic segmentation and clustering resulted in the exclusion of points associated with these movable objects from the point clouds. The comparative analysis of the method and simple merging was performed based on file size, number of points, mean roughness, and noise estimation. The proposed method provided adequate results with the improvement of point cloud quality indicators.

Deep autoencoder with gated convolutional neural networks for improving speech quality in secured communications

In this study, we introduce a speech enhancement method to improve the quality of decrypted speech signals from hand-talk devices, which are highly susceptible to security attacks. Ensuring high-quality decrypted speech is essential because traditional speech enhancement methods struggle with artifacts only present during speech due to the encryption process applied selectively. This situation limits the effectiveness of traditional methods, which assume distortion is constant and can be estimated during silent periods. Our solution involves a deep-learning approach that employs a gated convolutional neural network (GCNN). Unlike typical convolutional neural networks (CNNs) that excel in processing spatial data but falter with temporal changes, our GCNN integrates a gating mechanism to enhance handling of temporal dynamics in speech data. This method directly maps distorted speech to its clean counterpart, bypassing the need for explicit noise estimation. Our experiments indicate that this deep-learning method significantly outperforms traditional speech enhancement techniques and conventional CNNs in several key evaluation metrics, offering a promising advancement in decrypted speech quality enhancement.

Machine-learning-based impairment-aware dynamic RMSCA in multi-core elastic optical networks

This paper presents a routing, modulation, spectrum, and core assignment (RMSCA) algorithm for space-division-multiplexing-based elastic optical networks (SDM-EONs) comprising multi-core links. A network state-dependent route and core selection method is proposed using a deep neural network (DNN) classifier. The DNN is trained using a metaheuristic optimization algorithm to predict lightpath suitability, considering the quality of transmission and resource availability. Physical layer impairments, including inter-core crosstalk, amplified spontaneous emission, and Kerr fiber nonlinearities, are considered, and a random forest (RF)-based link noise estimator is proposed. A feature importance selection analysis is provided for all the features considered for the DNN classifier and the RF link noise estimator. The proposed machine-learning-enabled RMSCA approach is evaluated on three network topologies, USNET, NSFNET, and COST-239 with 7-core and 12-core fiber links. It is shown to be superior in terms of blocking probability, bandwidth blocking probability, and acceptable computational speed compared to the standard and published benchmarks at different traffic loads.

Designing of Airspeed Measurement Method for UAVs Based on MEMS Pressure Sensors.

Airspeed measurement is crucial for UAV control. To achieve accurate airspeed measurements for UAVs, this paper calculates airspeed data by measuring changes in air pressure and temperature. Based on this, a data processing method based on mechanical filtering and the improved AR-SHAKF algorithm is proposed to indirectly measure airspeed with high precision. In particular, a mathematical model for an airspeed measurement system was established, and an installation method for the pressure sensor was designed to measure the total pressure, static pressure, and temperature. Secondly, the measurement principle of the sensor was analyzed, and a metal tube was installed to act as a mechanical filter, particularly in cases where the aircraft has a significant impact on the gas flow field. Furthermore, a time series model was used to establish the sensor state equation and the initial noise values. It also enhanced the Sage-Husa adaptive filter to analyze the unavoidable error impact of initial noise values. By constraining the range of measurement noise, it achieved adaptive noise estimation. To validate the superiority of the proposed method, a low-complexity airspeed measurement device based on MEMS pressure sensors was designed. The results demonstrate that the airspeed measurement device and the designed velocity measurement method can effectively calculate airspeed with high measurement accuracy and strong interference resistance.

Scaling laws for mitigated pile driving: Dependence of underwater noise on strike energy, pile diameter, ram weight, water depth, and mitigation systema).

Sound induced by impact pile driving is a possible risk to marine life. Therefore, it is common practice to use noise mitigation systems during piling to reduce the respective impact and to fulfill the prescribed noise limits. Scaling laws for the estimation of the underwater noise from unmitigated impact pile driving have been presented in von Pein, Lippert, Lippert, and von Estorff, "Scaling laws for unmitigated pile driving: Dependence of underwater noise on strike energy, pile diameter, ram weight, and water depth," Appl. Acoust. 198, 108986 (2022). This contribution shows how these scaling laws need to be changed if noise mitigation systems are considered. Scaling laws are developed for four different kinds of noise mitigation system setups. These include big bubble curtains, double big bubble curtain combinations, a fully absorbing system directly at the pile, and the combination of a system close to the pile and a double big bubble curtain. The derived scaling laws are verified and compared to publicly available measurement data.

Estimate of force noise from electrostatic patch potentials in LISA Pathfinder

Estimate of force noise from electrostatic patch potentials in LISA Pathfinder