Noise Conditions Research Articles

Improved analytical solution with optimization constraints using TDOA and FDOA measurements for USV/AUV collaborative localization

The paper introduces an enhanced constrained two-step weighted least squares (WLS) analytic localization algorithm designed to determine the AUV’s position and velocity within the collaborative localization system, utilizing time difference of arrival and frequency difference of arrival measurements. This algorithm integrates optimization constraints between intermediate variables and target motion parameters, producing precise analytical solution based on WLS minimization criterion. The proposed process not only reduces computational burden by providing an analytical solution but also enhances estimation accuracy through the incorporation of optimization constraints. Analytical assessments and statistical simulations highlight the superior estimation accuracy of the proposed algorithm, and demonstrate that the position and velocity estimation accuracy align with the Cramér-Rao lower bound under appropriate measurement noise conditions.

Reducing statistical noise in frequency ratio measurement between Ca+ and Sr optical clocks with a frequency-synthesized local oscillator from a Sr optical clock

Optical frequency ratio measurement between optical atomic clocks is essential to precision measurement as well as the redefinition of the second. Currently, the statistical noise in frequency ratio measurement of most ion clocks is limited by the frequency instability of ion clocks. In this work, we reduce the statistical noise in the frequency ratio measurement between a transportable Ca+ optical clock and a Sr optical lattice clock down to 2.2×10−15/τ. The local oscillator of the Ca+ optical clock is frequency-synthesized from the Sr optical lattice clock, enabling a longer probe time for Ca+ clock transition. Compared to previous measurement using independent local oscillators, we achieve 10-fold reduction in comparison campaign duration.

Differential Analysis of Emergency Vehicle Detection in Urban Traffic : A Systematic Review

The differential analysis of emergency vehicle detection in urban traffic is crucial for improving response times and reducing traffic-related delays during emergencies. This systematic review aims to analyze various methods and technologies, such as acoustic, visual, and sensor-based systems, used for detecting emergency vehicles in complex urban environments. The motivation behind this study is the growing need for more efficient traffic management systems that prioritize emergency vehicles, minimizing delays caused by congestion. However, limitations include the inconsistent performance of detection systems in varying weather, lighting, and noise conditions, as well as integration challenges with existing infrastructure. The objective is to evaluate current detection methods, identify their limitations, and propose potential improvements to enhance the accuracy and reliability of emergency vehicle detection in urban traffic systems.

Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification.

Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher inherent cognition states. In this work, motivated by constructing the sparse causality brain networks under the strong physiological outlier noise conditions, we proposed a dual Laplacian Granger causality analysis (DLap-GCA) by imposing Laplacian distributions on both model parameters and residuals. In essence, the first Laplacian assumption on residuals will resist the influence of outliers in electroencephalogram (EEG) on causality inference, and the second Laplacian assumption on model parameters will sparsely characterize the intrinsic interactions among multiple brain regions. Through simulation study, we quantitatively verified its effectiveness in suppressing the influence of complex outliers, the stable capacity for model estimation, and sparse network inference. The application to motor-imagery (MI) EEG further reveals that our method can effectively capture the inherent hemispheric lateralization of MI tasks with sparse patterns even under strong noise conditions. The MI classification based on the network features derived from the proposed approach shows higher accuracy than other existing traditional approaches, which is attributed to the discriminative network structures being captured in a timely manner by DLap-GCA even under the single-trial online condition. Basically, these results consistently show its robustness to the influence of complex outliers and the capability of characterizing representative brain networks for cognition information processing, which has the potential to offer reliable network structures for both cognitive studies and future brain-computer interface (BCI) realization.

Multitask-Transfer-Learning Method for Random-Force Frequency Identification Considering Multisource Uncertainties

Accurate reconstruction of unknown external forces from measurable responses is critical for ensuring structural safety and minimizing maintenance costs of aircraft structures. This paper presents a novel multitask-transfer-learning method for random-force frequency identification that accounts for modeling and measurement uncertainties. A data-driven convolutional neural network (CNN) model is utilized to capture the relationship between the power spectral densities of external forces and measured responses, addressing the inherent ill-posedness of traditional model-driven force identification methods. To shorten the frequency-dependent training time in the full frequency domain, a transfer-learning strategy is implemented, fine-tuning hyperparameters from a CNN model trained at one source frequency to another target frequency. Furthermore, an iterative dimensionwise collocation method based on nonprobabilistic interval modeling is introduced to quantify the uncertain boundaries of external loads caused by multisource uncertainties. By incorporating a multitask-learning framework, the process of establishing CNN models for collocated samples is accelerated, reducing the computational effort for uncertainty quantification. The proposed method is validated through both numerical and experimental examples, demonstrating its accuracy, robustness, and computational efficiency for force identification in the full frequency domain, even under conditions of insufficient measurements, measurement noises, and material dispersions.

Two-stage unet with channel and temporal-frequency attention for multi-channel speech enhancement

In multi-channel speech enhancement, spectral masking and beamforming are two standard techniques. We propose a two-stage model combining the advantages of both approaches to enhance network performance. We propose the temporal-frequency self-attention block to effectively extract speech features in the temporal-frequency dimension with low complexity. We propose the residual efficient channel attention block, a lightweight module that learns channel attention through inter-channel interaction. Besides, We propose the real and imaginary beamforming as a replacement for traditional beamforming, estimating filter weights from the real and imaginary parts separately and fully utilizing the spatial information of the data. According to the experimental results, the performance of this model outperforms other models on both the L3DAS22 dataset and the spatial DNS dataset. Furthermore, our model demonstrates superior denoising and dereverberation under various noise and reverberation conditions.

Performance comparison of the Shack-Hartmann and pyramid wavefront sensors with a laser guide star for 40 m telescopes

Context. Upcoming giant segmented mirror telescopes will use laser guide stars (LGS) for their adaptive optics (AO) systems. Two options of wavefront sensors (WFSs) are the Shack-Hartmann wavefront sensor (SHWFS) and the pyramid wavefront sensor (PWFS). Aims. In this paper, we compare the noise performance of the PWFS and the SHWFS. We aim to identify which of the two is best to use in the context of a single or tomographic configuration. Methods. To compute the noise performance, we extended a noise model developed for the PWFS to be used with the SHWFS. To do this, we expressed the centroiding algorithm of the SHWFS as a matrix-vector multiplication, which allowed us to use the statistics of noise to compute its propagation through the AO loop. We validated the noise model with end-to-end simulations for telescopes of 8 and 16 m in diameter. Results. For an AO system with only one WFS, we found that given the same number of subapertures, the PWFS outperforms the SHWFS. For a 40 m telescope, the limiting magnitude of the PWFS is around one magnitude higher than the SHWFS. When using multiple WFS and a generalized least-squares estimator to combine the signal, our model predicts that in a tomographic system, the SHWFS performs better than the PWFS (with a limiting magnitude that is higher by a 0.3 magnitude. When using sub-electron RON detectors for the PWFS, the performance quality is almost identical for the two WFSs. Conclusions. We find that when using a single WFS with LGS, PWFS is a better alternative than the SH. For a tomographic system, both sensors would give roughly the same performance.

Modulation Interference from Speech-Modulated Noise in Bimodal Cochlear Implant Users

Purpose: This study examined modulation interference in sentence recognition for bimodal cochlear implant (CI) users.Methods: Thirteen bimodal users and thirteen normal-hearing (NH) listeners (reference) participated in this study. The adaptive Korean Matrix sentence-in-noise test measured the speech reception thresholds (SRTs) required for 50% speech-in-noise intelligibility. As background noise, we presented two background noises: stationary speech-shaped noise (SSN) and single-talker speech-modulated masker (International Collegium of Rehabilitative Audiology noise, ICRA). The amount of masking release due to fluctuations in the speech-modulated noise, called speech masking release (SMR), was calculated from the difference between SRTs with SSN and SRTs with ICRA noise. The SRTs were measured twice for bimodal CI users: CI-only and bimodal listening.Results: The mean SMR for NH listeners was approximately 12 dB. The mean SMR for bimodal CI users was about -5 dB, with either CI alone or bimodal listening. This means that bimodal CI users experienced modulation interference rather than masking release from the temporal fluctuations. We observed positive bimodal benefits, yet the interference from fluctuating maskers was similar between the CI-only and bimodal listening. The low-frequency aided hearing thresholds in the non-implanted ear were unrelated to the amount of SMR for bimodal users.Conclusion: NH listeners could take advantage of temporal fluctuations in the speech-modulated noise. However, bimodal CI listeners hardly glimpsed target speech in the dips of the speech-modulated masker, regardless of listening mode. Bimodal listening did not significantly reduce modulation interference compared to CI-only listening.

Selective Word Stress Effect for Speech Perception Enhancement in Hearing-Impaired Older Listeners

Purpose: Although it is known that communication problem of the older adults might be overcome by using slower speech or talking in a less noisy environment, there is still a lack of detailed skills. This study aimed to identify word stress benefit in speech perception for older listeners when different levels of background noise and speech rate were presented.Methods: Forty elderly (20 for hearing loss and 20 for normal hearing) were randomly recruited. Both groups conducted sentence tests at quiet and three signal-to-noise ratio (SNR) (+6, +3, 0 dB) and did them under four timealteration conditions (± 30% and ± 15%). Further, all sentences of both the noise and time alteration conditions had a 4, 6, or 8 dB up emphasized key word.Results: As expected, the hearing-impaired group had lower scores than the group with normal hearing in quiet and three SNR conditions. In quiet, both groups performed best at 6 dB emphasis. However, there was no significant difference in performance across 4, 6, and 8 dB up stress conditions when background noise was present. As time compression increased, speech perception scores decreased in both groups. Notably, at 30% expansion (slower speed), the normal hearing group scored the highest at 4 dB up emphasis, while the hearing-impaired group showed the best performance best at 8 dB up emphasis.Conclusion: Based on the current results, rather than simply speaking slowly, it would be more helpful for older adults with hearing loss to control their speech rate also emphasize key words during the conversation.

COMPARATIVE ANALYSIS OF THE EFFECTIVENESS OF NEURAL NETWORKS AT DIFFERENT VALUES OF THE SNR RATIO

This work is devoted to a comparative analysis of the effectiveness of neural networks, CNN and RNN, at different SNR ratios. The research conducted within the framework of this work showed that CNN convolutional neural networks demonstrate higher efficiency in speech signal recognition tasks, regardless of different levels of SNR ratio and language. Thus, the CNN neural network showed stable superiority over RNN under all conditions, especially at low SNR ratios. It was revealed that with an increase in the SNR ratio, the difference in accuracy between the CNN and RNN neural networks decreases, but the CNN continues to lead, which indicates its higher adaptability and ability to learn under conditions of different noise and interference levels. It is especially important to note that the advantage of CNN becomes noticeable at low SNR values, where the accuracy of the RNN decreases more significantly. As a result, with an SNR ratio of 3 dB, the recognition accuracy using CNN was 80% for the Kazakh language, whereas RNN showed a result in the region of 75%. With an increase in the SNR ratio to 21 dB, the difference in accuracy between CNN and RNN decreased, but CNN continued to lead, reaching 88% accuracy compared to 86% for RNN. In addition, the results showed that the effectiveness of the CNN and RNN depended on the language in which they were trained. Neural networks trained in Kazakh showed the best results in recognizing Kazakh speech but also successfully coped with recognizing the Russian language. This highlights the importance of considering language features when developing and training neural networks to improve their performance in multilingual environments.

DuINet: A dual-branch network with information exchange and perceptual loss for enhanced image denoising

Image denoising is a fundamental task in image processing and low-level computer vision, often necessitating a delicate balance between noise removal and the preservation of fine details. In recent years, deep learning approaches, particularly those utilizing various neural network architectures, have shown significant promise in addressing this challenge. In this study, we propose DuINet, a novel dual-branch network specifically designed to capture complementary aspects of image information. DuINet integrates an information exchange module that facilitates effective feature sharing between the branches, and it incorporates a perceptual loss function aimed at enhancing the visual quality of the denoised images. Extensive experimental results demonstrate that DuINet surpasses existing dual-branch models and several state-of-the-art convolutional neural network (CNN)-based methods, particularly under conditions of severe noise where preserving fine details and textures is critical. Moreover, DuINet maintains competitive performance in terms of the LPIPS index when compared to deeper or larger networks such as Restormer and MIRNet, underscoring its ability to deliver high visual quality in denoised images.

Acoustic flow resonances in installed rectangular jets

Zonal Large Eddy Simulations (LES) of complex installed rectangular jet flows are performed for subsonic jet conditions of the Central Institute for Aviation Motors (CIAM) experiment, where a strong tone was reported. For far-field noise modelling, the zonal LES solutions are coupled with the permeable formulation of the Ffowcs Williams and Hawkings (FW-H) method. The obtained noise spectra predictions are compared with the acoustic measurements in the CIAM facility. To reduce statistical noise of the relatively short LES time series, contributions due to several dominant low frequency tones and the remaining broadband signal are computed separately, using a tailored Fourier transform technique for each signal type. For cross-validation, noise spectra measurements of an equivalent isolated round jet in the CIAM facility are compared with the empirical sJet model calibrated on the NASA Small Hot Jet Acoustic Rig (SHJAR) jet noise database. While the two datasets agree reasonably well for mid to high frequencies, larger discrepancies are observed for low frequencies, which are attributed to acoustic reflections in the non-anechoic CIAM facility. The differences in noise levels between the CIAM dataset and the sJet model representing the NASA jet noise data are used to determine the experimental uncertainty of the CIAM noise measurements for each frequency and observer angle. For the installed rectangular jet, it is shown that far-field noise spectra predictions of the zonal LES-FW-H method for both the fundamental tone, its sixth harmonic corresponding to the jet Strouhal number of around 1, and the broadband component are broadly within the experimental error bar for most observer angles. Some discrepancies between predictions of the zonal LES method and the CIAM measurements for the main low frequency tone for the 30° and 90°, where the experimental uncertainty at low frequencies is largest are also noted. After validating the acoustic solutions, spectral and conditional averaging analysis methods are applied to elucidate mechanisms of the acoustic-flow resonance in the installed rectangular jet flow. The numerical dispersion relation is obtained to analyse phase velocities of the upstream and downstream travelling pressure waves in the stream-wise direction. The conditional analysis of pressure fluctuations inside the jet reveals the emergence of internal reflection points corresponding to a rapid change of the acoustic wave phase as well as the saddle points interpreted as localised zones of vorticity shed from the side-wall edges. To independently investigate the effect of trailing edges and corner points of the jet embodiment geometry on closing the acoustic-flow resonance cycle, several modifications of the baseline rectangular nozzle are considered, such as introducing chevron-like lobes and cuts on the trailing edges of the side walls as well as smoothing the sharp corner points of the wall edges. Following the series of additional zonal LES runs with the modified installed nozzle geometries, an optimized tone-less modification of the original installed nozzle was obtained, in broad agreement with the conditional analysis results. The obtained tone-less modification of the installed rectangular jet is further analyzed to provide insights into its similarity and differences from the aeroacoustics of the reference isolated round jet in the same CIAM facility.

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.

Listening effort in children and adults in classroom noise

It is well known that hearing in noisy situations is more challenging than in quiet environments. This holds true for adults and especially for children. This study employed a child-appropriate dual-task paradigm to investigate listening effort in children aged six to ten years and young adults. The primary task involved word recognition, while the secondary task evaluated digit recall. Additionally, subjective perception of listening effort was assessed using a child-appropriate questionnaire. This study incorporated plausible sound reproduction and examined classroom scenarios including multi-talker babble noise with two signal-to-noise ratios (0 dB and −3 dB) in an anechoic and an acoustically simulated classroom environment. Forty-four primary school children aged six to ten (17 first- to second-graders and 18 third- to fourth-graders) and 25 young adults participated in this study. The results revealed differences in listening effort between the noise conditions in third- to fourth-graders and supported using the dual-task paradigm for that age group. For all three age groups, a greater subjective perception of listening effort in noise was found. Furthermore, a correlation between the subjective perception of listening effort and behavioural listening effort based on the experimental results was found for third- to fourth-graders and adults.

Research on improved dynamic load identification method based on Kalman filter under noise interference

As a mathematical approach for solving inverse problems, the dynamic load identification method is particularly useful when directly measuring the load acting on a structure is challenging. The Kalman filter (KF) algorithm is commonly employed for dynamic load identification. However, in practice, measurement noise can significantly affect the accuracy of the KF algorithm's results. In this study, we investigate the impact of response noise on the accuracy of load identification results using the KF algorithm. To eliminate the influence of measurement noise on identification accuracy, we propose an improved dynamic load identification method. This method incorporates adaptive techniques to assess varying levels of response noise and improve the load identification accuracy. Notably, this method is applicable in both physical and modal domains. We conduct numerical simulations using a 5-DOF system under different noise conditions. Compared to traditional dynamic load identification method based on KF algorithm, the improved method consistently achieves superior accuracy in load identification across various noise levels. Moreover, an experiment is conducted to further validate the feasibility of the proposed method. The results showed that the proposed method is effective and can achieve high-precision identification of unknown loads.

Modal identification of wind turbine tower based on optimal fractional order statistical moments

AbstractIn vibration testing of civil engineering structures, the first two vibration modes are crucial in representing the global dynamic behavior of the structure measured. In the present study, a comprehensive method is proposed to identify the first two vibration modes of wind turbine towers, which is based on the analysis of fractional order statistical moments (FSM). This study offers novel contributions in two key aspects: (1) theoretical derivations of the relationship between FSM and vibration mode; and (2) successful use of 32/7‐order displacement statistical moment as the optimal FSM to identify wind turbine tower modes, by combining with noise resistance analysis, sensitivity analysis, and stability analysis, respectively. Using the proposed method, the FSM was first used to identify the modal vibration of wind turbine towers. By obtaining the response of the structure on the same vertical line, FSM was then calculated to estimate the corresponding structural modal vibration. Considering other influencing factors in the field test, the modal identification results of this index under different excitation forms and noise conditions were analyzed based on numerical simulation and verified with field wind tower test data. The results of the evaluation show that the proposed statistical moments of can accurately identify the first two vibration modes of wind turbine towers. This presents a new robust method for modal vibration identification, that is, simple and effective in its implementation.

Marine remote target signal extraction based on 128 line-array single photon LiDAR

LiDAR technology has garnered significant attention in recent years due to its superior directivity, high resolution, and precise 3D information acquisition capabilities, making it indispensable in navigation systems. Among its variants, single-photon LiDAR stands out for maritime applications, owing to its reduced power consumption and extended detection range. However, the high sensitivity of single-photon detectors often results in substantial noise, necessitating effective denoising before data can be utilized for identification, tracking, and other purposes. In this study, we present a novel 128-line, 1550 nm shipborne long-range single-photon LiDAR system, with data collected and analyzed in maritime environments. This system contends with challenges such as a large dynamic range, abundant noise photons, and the complexity of sea surface conditions. To address these issues, we propose an efficient and adaptive denoising algorithm based on the k-th nearest neighbor (KNN) methodology. By examining the distribution characteristics of signal and noise photons, our approach enables target extraction even under conditions of intense noise and sparse signals. Our method exhibits robust adaptability across various detection scenarios. Experimental evaluations demonstrate its efficacy, accurately identifying targets at distances of 3.2 km in clear weather and 1.6 km in foggy conditions.

Computer diffraction tomography: a comparative analysis of the use of controlled and wavelet filters for image processing

The paper provides digital processing of 2D X-ray projection images of a Coulomb-type point defect in a Si(111) crystal recorded by a detector against the background of statistical Gaussian noise. A managed filter and a wavelet filter with a 4th-order Daubechies function are used. The efficiency of filtering 2D images is determined by calculating the relative quadratic deviations of the intensities of filtered and reference (noiseless) 2D images averaged over all points. A comparison of the calculated values of the relative deviations of the intensities shows that the considered methods work quite well and both, in principle, can be effectively used in practice for noise processing of X-ray diffraction images used for 3D reconstruction of nanoscale defects of crystal structures.

Analyzing One- and Two-bit Data to Reduce Memory Requirements for F -statistic-based Gravitational Wave Searches

Searches for continuous-wave gravitational radiation in data collected by modern long-baseline interferometers, such as the Laser Interferometer Gravitational-wave Observatory (LIGO), the Virgo interferometer, and the Kamioka Gravitational Wave Detector, can be memory intensive. A digitization scheme is described that reduces the 64-bit interferometer output to a one- or two-bit data stream while minimizing distortion and achieving considerable reduction in storage and input/output cost. For the representative example of the coherent, maximum-likelihood matched filter known as the F -statistic, it is found using Monte Carlo simulations that the injected signal only needs to be ≈24% stronger (for one-bit data) and ≈6.4% stronger (for two-bit data with optimal thresholds) than a 64-bit signal in order to be detected with 90% probability in Gaussian noise. The foregoing percentages do not change significantly when the signal frequency decreases secularly, or when the noise statistics are not Gaussian, as verified with LIGO Science Run 6 data.

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

To reduce the noise floor in interferometric fiber-optic sensing systems based on 3 × 3 coupler demodulation, this paper proposes spectral subtraction based on multi-window minimum statistical noise estimation to process the two interference signals. This method estimates the noise spectrum of three types of windowed signals, averages these estimation results, and subtracts them from the signal, effectively removing noise from the interfering signal before demodulation, thereby minimizing noise aliasing during demodulation. Subsequently, the ellipse fitting algorithm and the arctangent algorithm are utilized to accomplish signal demodulation. The experimental results indicate that the system's noise floor is reduced by an average of 25 dB within the 0-5 kHz frequency range and achieves a maximum reduction of 30 dB to a level of 0.7 µrad/√Hz at 50 kHz. This scheme does not use additional optics devices, reducing the complexity and cost of the system, and thereby enhancing the feasibility of interferometric fiber optic sensing systems in various environments.