Measurement Noise Research Articles

Traffic noise prediction model using GIS and ensemble machine learning: a case study at Universiti Teknologi Malaysia (UTM) Campus.

This study represents a pioneering effort to integrate geographic information systems (GIS) and ensemble machine learning methods to predict noise levels on a university campus. Three ensemble models including random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) were developed to predict traffic noise based on data collected over a 4-week period at the Universiti Teknologi Malaysia (UTM) campus. Noise measurements were obtained during peak morning hours (7:30 to 9:30 a.m.) on weekdays within the UTM campus in Johor. Additional predictor variables, including data from the digital elevation model (DEM) and land use, were incorporated to capture the complex nonlinear relationships influencing noise levels. The models were optimized through hyperparameter tuning, resulting in high precision, as evidenced by performance metrics such as the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). The XGB model emerged as the most accurate, with R2 = 0.96, MAE = 0.9, and MSE = 0.3. Noise maps generated using the inverse distance weighting (IDW) interpolation technique highlighted the spatial distribution of noise levels, classified into five classes considering WHO standards. The findings identified distance from roads, the number of light vehicles, and proximity to green areas as the most significant predictors. However, challenges remain in accurately predicting noise levels associated with other predictors. The outcomes of the study indicate the superior performance of the XGB model compared to the GB and RF models. The study recommends several measures to manage and control noise pollution on the UTM campus, including raising awareness, regulating and enforcing vehicle speed limits, reevaluating land use, installing sound insulation systems, and planting trees and vegetation buffer zones around and within educational buildings.

A Robust Trajectory Multi-Bernoulli Filter for Superpositional Sensors

This paper proposes a trajectory multi-Bernoulli filter applied to the superpositional sensor model for multi-target tracking in the presence of unknown measurement noise. This filter can provide a Multi-Bernoulli approximation of the posterior density on a set of alive trajectories at the current time step. We also provide a Gaussian mixture (GM) implementation of this filter, employing a mixture of Gaussian and inverse Wishart distributions to represent the combined state of measurement noise and target information. Subsequently, the variational Bayesian (VB) method is employed to approximate the posterior distribution, ensuring its form remains consistent with the prior distribution. This method is capable of directly generating trajectory estimates and can jointly estimate both multi-object tracking and measurement noise covariance. The performance of this algorithm is verified through simulation. Finally, a computationally more efficient L-scan approximation is provided. The simulation results indicate that the filter can achieve robust tracking performance, adapting to unknown measurement noise.

Temperature dependence of the low-frequency noise in AlGaN/GaN fin field effect transistors

Low-frequency (LF) noise measurements are compared for Schottky-gate AlGaN/GaN heterostructure planar and fin field-effect transistors (FinFETs) as functions of gate voltage and measuring temperature. The noise of each device type is consistent with a carrier number fluctuation model. Similar effective defect-energy Eo distributions are derived for each of the two device architectures from measurements of excess drain-voltage noise-power spectral density vs temperature from 80 to 380 K. Defect- and/or impurity-related peaks are observed in the inferred energy distributions for Eo < 0.2 eV, Eo ≈ 0.45 eV, and Eo > 0.6 eV. Significant contributions to the LF noise are inferred for nitrogen vacancies and ON and FeGa impurity complexes. Ga dangling bonds at fin interfaces with gate metal are likely candidates for enhanced noise observed in FinFETs, relative to planar devices. Reducing the concentrations of these defects and impurity complexes should reduce the LF noise and enhance the performance, reliability, and radiation tolerance of GaN-based high electron mobility transistors.

A Spatial-Frequency Approach to Point-Wise Frequency Response Function Estimation with Digital Image Correlation

The use of digital image correlation for modal analysis is becoming an appealing option thanks to its non-contact and full-field measurement process. However, frequency response function (FRF) estimation can be challenging due to the limited number of time domain data and heavy measurement noise. Thereby, the present work aims to propose a method which improves the estimation accuracy of point-wise FRFs. Firstly, a Gaussian-process-based spatial-frequency model is proposed, which makes use of the intrinsic properties of the FRF and the local spatial information of field measurements. Then, a Bayesian solution is developed, which is enforced by a stable and efficient numerical procedure. Finally, the effectiveness of the proposed method is verified by making a comparison with the spectral estimator through the use of simulated data, and it is further validated based on an experimental application.

Assessing tumor microstructure with time-dependent diffusion imaging: Considerations and feasibility on clinical MRI and MRI-Linac.

Quantitative imaging biomarkers (QIBs) can characterize tumor heterogeneity and provide information for biological guidance in radiotherapy (RT). Time-dependent diffusion MRI (TDD-MRI) derived parameters are promising QIBs, as they describe tissue microstructure with more specificity than traditional diffusion-weighted MRI (DW-MRI). Specifically, TDD-MRI can provide information about both restricted diffusion and diffusional exchange, which are the two time-dependent effects affecting diffusion in tissue, and relevant in tumors. However, exhaustive modeling of both effects can require long acquisitions and complex model fitting. Furthermore, several introduced TDD-MRI measurements can require high gradient strengths and/or complex gradient waveforms that are possibly not available in RT settings. In this study, we investigated the feasibility of a simple analysis framework for the detection of restricted diffusion and diffusional exchange effects in the TDD-MRI signal. To promote the clinical applicability, we use standard gradient waveforms on a conventional 1.5 T MRI system with moderate gradient strength (Gmax=45 mT/m), and on a hybrid 1.5 T MRI-Linac system with low gradient strength (Gmax=15 mT/m). Restricted diffusion and diffusional exchange were simulated in geometries mimicking tumor microstructure to investigate the DW-MRI signal behavior and to determine optimal experimental parameters. TDD-MRI was implemented using pulsed field gradient spin echo with the optimized parameters on a conventional MRI system and a MRI-Linac. Experiments in green asparagus and 10 patients with brain lesions were performed to evaluate the time-dependent diffusion (TDD) contrast in the source DW-images. Simulations demonstrated how the TDD contrast was able to differentiate only dominating diffusional exchange in smaller cells from dominating restricted diffusion in larger cells. The maximal TDD contrast in simulations with typical cancer cell sizes and in asparagus measurements exceeded 5% on the conventional MRI but remained below 5% on the MRI-Linac. In particular, the simulated TDD contrast in typical cancer cell sizes (r=5-10µm) remained below or around 2% with the MRI-Linac gradient strength. In patients measured with the conventional MRI, we found sub-regions reflecting either dominating restricted diffusion or dominating diffusional exchange in and around brain lesions compared to the noisy appearing white matter. On the conventional MRI system, the TDD contrast maps showed consistent tumor sub-regions indicating different dominating TDD effects, potentially providing information on the spatial tumor heterogeneity. On the MRI-Linac, the available TDD contrast measured in asparagus showed the same trends as with the conventional MRI but remained close to typical measurement noise levels when simulated in common cancer cell sizes. On conventional MRI systems with moderate gradient strengths, the TDD contrast could potentially be used as a tool to identify which time-dependent effects to include when choosing a biophysical model for more specific tumor characterization.

Robust deadbeat predictive current control for unipolar sinusoidal excited SRM with multi-parameter mismatch compensation

In the control of unipolar sinusoidal excited switched reluctance motors (SRMs), deadbeat predictive current controllers (DPCCs) have gained attention for their enhanced dynamic performance. However, periodic disturbances caused by mismatches between the predictive model’s nominal and actual system parameters degrade the control performance of SRMs. To address this issue, a robust DPCC with multi-parameter compensation is proposed to improve dq0-axes current control performance. By analyzing the impact of parameter mismatches, a Kalman filter (KF) is developed to compensate for inductance coefficient mismatches, mitigating periodic disturbances. Additionally, a disturbance estimator with measurement noise suppression is integrated into the DPCC for both state and disturbance estimation to handle residual uncertainties, including winding resistance mismatches, magnetic saturation, and unmodeled dynamics. Compared simulation and experimental results validate the effectiveness of the proposed robust DPCC.

Parameter estimation from an Ornstein-Uhlenbeck process with measurement noise

Parameter estimation from an Ornstein-Uhlenbeck process with measurement noise

Multi-bandwidth observer-based adaptive robust pressure control for electro-hydraulic brake system with uncertainties and measurement noise

Accurate pressure regulation of electro-hydraulic brake system (EHB) is essential for enhancing braking performance in automobiles. However, component wear and pressure measurement noise can cause model parameters and controlled states to drift, resulting in system chattering. This nonlinear and uncertain state can significantly degrade pressure control performance. Motivated by this, the article presents a controlled state reconstruction-based adaptive robust pressure control technique with noise suppression. Firstly, a reduced dynamics model is established for controller design, which effectively captures the fundamental characteristic of the EHB. Secondly, a multi-bandwidth extended state observer (MBESO) capable of noise suppression is designed to reconstruct controlled states, including unknown disturbances and unmeasured pressure change rates of the EHB. Thirdly, an MBESO-based adaptive robust pressure control technique is introduced, where the control gain and robust factor are updated based on control and observation errors. In addition, the overall performance of the proposed control strategy is analyzed in the frequency domain, and close-loop stability is demonstrated via the Lyapunov method. Finally, the pressure control precision and robustness of the proposed algorithm are validated in both dynamic and static scenarios through sinusoidal and step-response experiments on a hardware-in-loop test bench. The results indicate that the proposed algorithm reduces dynamic pressure-tracking error by up to 62% compared to traditional method under sinusoidal conditions, with steady-state error remaining within 1 bar under step-response conditions.

Fan noise radial mode detection based on [formula omitted] regularization method

Radial mode detection has been widely employed in fan noise research. Many radial mode detection algorithms by conducting the convex L1 norm optimization have been proposed by taking advantage of the fact that the fan noise sound field is usually dominated by a limited set of radial modes. Nevertheless, L1 norm optimization has been shown to be suboptimal and cannot enforce further sparsity. A fan noise radial mode detection method based on L1/2 regularization algorithm is proposed in this paper to improve its robustness and dynamic range. This method employs the L1/2 regularization to directly solve the in-duct sound propagation model. Synthesized simulations indicates that the L1/2 regularization method can detect the correct radial mode order and amplitude under low signal-to-noise ratio. Moreover, the new method is insensitive to regularization parameter. An experimental system of radial rake is implemented to validate the new method. It is shown that the L-curve method, which do not require any prior information of the acoustic mode distribution or contaminating measurement noise, is an appropriate choice of the regularization parameter determination method for the L1/2 regularizer. It is demonstrated that the new L1/2 regularization method can enhance the mode sparsity, especially under low signal-to-noise ratio. Moreover, the resolution and dynamic range can be improved by the L1/2 regularization method, which makes it an effective and robust fan noise radial mode detection technique in practical applications.

Current-driven mechanical motion of double stranded DNA results in structural instabilities and chiral-induced-spin-selectivity of electron transport.

Chiral-induced-spin-selectivity of electron transport and its interplay with DNA's mechanical motion are explored in a double stranded DNA helix with spin-orbit-coupling. The mechanical degree of freedom is treated as a stochastic classical variable experiencing fluctuations and dissipation induced by the environment as well as force exerted by nonequilibrium, current-carrying electrons. Electronic degrees of freedom are described quantum mechanically using nonequilibrium Green's functions. Nonequilibrium Green's functions are computed along the trajectory for the classical variable taking into account dynamical, velocity dependent corrections. This mixed quantum-classical approach enables calculations of time-dependent spin-resolved currents. We showed that the electronic force may significantly modify the classical potential, which, at sufficient voltage, creates a bistable potential with a considerable effect on electronic transport. The DNA's mechanical motion has a profound effect on spin transport; it results in chiral-induced spin selectivity, increasing spin polarization of the current by 9% and also resulting in temperature-dependent current voltage characteristics. We demonstrate that the current noise measurement provides an accessible experimental means to monitor the emergence of mechanical instability in DNA motion. The spin resolved current noise also provides important dynamical information about the interplay between vibrational and spin degrees of freedom in DNA.

Innovation Adaptive UKF Train Location Method Based on Kinematic Constraints

To address the issue of reduced positioning accuracy caused by satellite signal interruptions when trains pass through long tunnels, a novel train positioning method based on an innovative adaptive unscented Kalman filter (UKF) under kinematic constraints is proposed. This method aims to improve the accuracy of the location of trains during operation. By considering the dynamic characteristics of the train, a dynamic kinematic-constrained inertial navigation system (INS)/odometer (ODO) combination positioning system is established. This system utilizes kinematic constraints to correct the accumulated errors of the INS. Additionally, the algorithm incorporates real-time estimation of the measurement noise covariance using innovation sequences. The updated adaptive estimation algorithm is applied within the UKF framework for nonlinear filtering, forming the innovative adaptive UKF algorithm. At each time step, the difference between the ODO sensor data and the INS output is used as the measurement input for the innovative adaptive UKF algorithm, enabling global estimation. This process ultimately yields the actual positioning result for the train. Simulation results demonstrate that the innovative adaptive UKF train positioning method, incorporating kinematic constraints, effectively mitigates the impact of satellite signal interruptions. Compared with the traditional INS/ODO positioning method, the innovative adaptive UKF method reduces position errors by 34.35% and speed errors by 36.33%. Overall, this method enhances navigation accuracy, minimizes train positioning errors, and meets the requirements of modern train positioning systems.

Robustness estimation for state-of-charge of a lithium-ion battery based on feature fusion

State of charge (SOC) of the lithium-ion battery stands as one of the crucial parameters of the battery management system. To enhance the robustness and accuracy of data-driven methods for estimating SOC, in this study, a novel framework based on feature selection and closed-loop estimation framework is proposed for estimating the SOC of a battery. Firstly, the maximum mutual information coefficient is used to correlate the data collected from the battery experiments. Secondly, an improved least squares support vector machine model (LSSVM) is proposed for SOC estimation using the sine cosine optimization algorithm for parameter optimization of LSSVM. Furthermore, based on the Sage-Husa adaptive Kalman filter, this study constructed a closed-loop estimation model. Finally, we conduct experiments on batteries with variable temperatures and working conditions, to verify its robustness and generalization. The results indicate that the proposed method has great SOC estimation accuracy compared to the control group. Even under simulated measurement noise conditions, the proposed method achieves the root mean square error 1.28 % and 1.12 % respectively, demonstrating strong robustness.

MWIR image deep denoising reconstruction based on single-pixel imaging

Single-pixel imaging enables cost-effective and highly sensitive mid-wave infrared imaging, with the added convenience facilitated by deep learning algorithms. However, most networks train separate models depending on compression rates, frequently neglecting real-world infrared noise, leading to inconveniences and suboptimal reconstruction performance. To address these issues, we propose to integrate intrinsic and external deep denoising priors for the reconstruction of compressive mid-wave infrared images from single-pixel measurements. The generalized alternating projection is unfolded into a designed network to harness the benefits of robustness and internal priors. Furthermore, to mitigate real-world noise in measurements, a Swin Transformer U-shaped network that embeds noise variance is introduced to learn external denoise prior. A comparative analysis of simulated and real infrared single-pixel imaging in experiments highlights the promising performance of the proposed method.

Three-dimensional spatiotemporal wind field reconstruction based on LiDAR and multi-scale PINN

In this numerical study, we use a multi-scale version of physics-informed neural network (PINN) for wind field reconstruction from LiDAR measurements. The reference velocity field is obtained from high-fidelity large-eddy simulation of neutral atmospheric boundary layer, and the LiDAR measurement strategy is restricted to that of a real LiDAR. The multi-scale PINN reconstructs the velocity field by minimizing a combination of the L2-error with respect to the LiDAR measurements and residuals of the governing equations. Our most significant finding is that adding a multi-scale layer to PINN enables us to: (i) capture wider range of scales, (ii) reconstruct the flow field outside the LiDAR scanning area, and (iii) reach faster convergence. We also find that for volumetric flow reconstruction and to deal with uncertainty in the wind direction, the reconstruction accuracy can be greatly improved by employing multiple LiDAR devices. Overall, our results show that the normalized errors in the reconstructed wind field are lower than 5% for all the experiments conducted in this study and that the errors do not increase even in the presence of measurement noise levels of typical LiDAR. These findings show the feasibility of three-dimensional dynamic wind field reconstruction across large wind farms using LiDAR devices.

Effectiveness of noise barriers in sensitive urban areas: a comprehensive study on noise reduction strategies and public health implications.

This study investigates the effectiveness of noise management strategies, focusing on installing barriers to reduce urban noise pollution, particularly around hospitals. The research in Sakarya, Turkey, employs comprehensive noise measurements and modeling techniques to evaluate barrier interventions. The study included 24-h noise averages and specific measures for daytime, evening, and nighttime noise levels, using SoundPlan software for modeling and mapping. Results reveal that noise levels frequently exceed international thresholds, especially in high-traffic areas. However, introducing noise barriers significantly reduces noise levels, particularly at specific measurement points, demonstrating their effectiveness. This underscores the crucial role of noise management strategies in sensitive urban areas and highlights the urgency for proactive interventions to manage noise pollution. The study identifies the need for future research, including long-term studies to assess noise barriers' durability and sustained effectiveness, and the integration of other noise reduction measures such as green infrastructure. Practical applications of this research include informing urban planners and policymakers about effective strategies for mitigating noise pollution and improving public health. In conclusion, this study provides valuable insights into urban noise management by empirically demonstrating the effectiveness of barrier interventions in reducing noise pollution. It underscores the importance of proactive measures to protect human health and preserve environmental balance in sensitive urban areas. The findings advocate for the broader implementation of noise barriers and other complementary strategies in urban planning to enhance the quality of urban life.

The method to determination of the utilization of thrust reversers after landing based on noise measurements and analysis of its results

The use of powerful thrust reversers after landing often results in a noise impact on the area of surrounding the airport. The use of thrust reversers, while not necessarily a prerequisite for landing maneuvers, is sometimes a significant means of ensuring more safety for the pilot. For this reason, the magnitude of the thrust reversers, and the segment of the runway in which the reversers are used, vary widely. We have established a method to determination of the utilization and the strength of reverse thrust from noise measurements taken at several stations along the runway side. This report also describes an analysis of the results of the operational conditions of thrust reversers at a multitude of Japanese airports that were examined using the methodology.

Correction for reverberation time in acoustic measurements from service and equipment - Comparison between different methods

The measured values of the sound pressure level in ordinary rooms in dwellings vary depending on the combined effect of the absorption and the scattering of the furniture, or to the presence of absorbent surfaces on walls and ceilings. Regarding the measurement of noise from building services, ISO 16032 and ISO 10052 methods allow to normalize the sound pressure levels to a reference reverberation time of 0,5s. Several European countries incorporate this reverberation time correction into their regulations. However, Spanish regulations, specifically RD 1367/2007, and the majority of regional decrees and ordinances concerning the evaluation of noise from activities or equipment within buildings, do not incorporate this correction, even when measurements are performed in two scenarios: before the building's occupation when rooms are empty, or when the building is fully furnished, as may occur in the case of complaints. For that reason, this study compares the values of the reverberation time index obtained in 180 residential rooms using various methods while also evaluating the benefits and drawbacks of their application.

Analysis of in-situ acoustical performance of a removable urban pavement with a porous top layer

A demonstrator of a new removable urban pavement with functionalized surface was implemented in the inner city of Nantes within the I-STREET CUD-SF project. Designed for easy maintenance operation, it is made of hexagonal cement concrete slabs with a dense body and a 4 cm thick porous top layer of grading 0/6 and porosity 25% for drainability. The noise performance of the pavement was assessed at urban speeds by simultaneous Coast-By and Close-Proximity noise measurements performed with a dedicated test vehicle after closure of the road to traffic. The results are compared with noise levels obtained with three reference surfaces, a DAC 0/10 and two VTAC of grading 0/4 and 0/6. The tested pavement gets ranked differently depending on the noise measurement method considered. The results are analyzed in the light of 3D texture measurements as well as sound absorption measurements performed in situ by the extended surface method and with Kundt's tube on core samples. The influence of texture irregularities at the junction between slabs and sand used for slabs stabilization is addressed. Finally, the Coast-By noise levels are compared with those obtained with the HyRoNE prediction model from 3D texture and sound absorption. The discrepancies observed are discussed.

Measuring drone noise for environmental reviews

In the United States, federal actions are covered by the National Environmental Policy Act which requires a review process to assess a proposed action's potential impact on the environment. One of the environmental impact categories that must be considered is noise. In order to assess the noise generated during a drone's typical operations, noise measurements of each portion of the drone's flight profile are needed. Because measurements required for type certification of aircraft are focused on source noise, they might not be sufficient to determine the noise resulting from a drone's entire operation from takeoff to landing. This paper discusses a range of feasible noise measurement approaches for assessing a drone operation's noise characteristics along with their associated fidelity. The paper presents a suggested practical measurement protocol that a drone manufacturer or operator could perform.

Identifying optimal feature sets for acoustic signal classification in environmental noise measurements

Whatever the sound source to be evaluated, spurious events or unwanted sounds will always be present in environmental noise measurements. Spurious events are not characteristic of standard residual noise and must be removed prior to subsequent analyses. Currently, the removal step is deferred solely to the objective evaluation of the sound pattern and/or spectrogram by an operator. This results in the loss of many man-hours. Machine learning can be used to develop a tool capable of recognizing and removing spurious events in noise measurements. The tool must be able to account for various sounds, whether human-made or animal, and must be applicable to any environmental scenario. This is not a straightforward task, in fact if humans can easily distinguish between two sounds, such as a birds' chirps and a car passing by, based on prior experience, a machine may not be able to do so without apprenticeship. Therefore, a learning methodology must be constructed for the machine by establishing recognizable patterns. The aim of this paper is to identify the feature sets which allow the algorithm to differentiate spurious sounds in the best way. These features will represent the semantic value of the signal.