Noise Conditions Research Articles

A new QRS detector stress test combining temporal jitter and F-score (JF) reveals significant performance differences amongst popular detectors

QRS detection within an electrocardiogram (ECG) is the basis of virtually any further processing and any error caused by this detection will propagate to further processing stages. However, standard benchmarking procedures of QRS detectors are seriously flawed because they report almost always close to 100% accuracy for any QRS detection algorithm. This is due to the use of large temporal error margins and noise-free ECG databases which grossly overestimate their performance. The use of a large fixed error margin masks temporal jitter between detection and ground truth measurements. Here, we present a new performance measure (JF) which combines temporal jitter with the F-score, and also an ECG database with decreasing levels of signal to noise ratios based on noise generated from different tasks. Our new performance measure JF fully encompasses all the types of errors that can occur, equally weights them and provides a percentage value which allows direct comparison between QRS detection algorithms. In combination with the new noisy ECG database, the JF performance measure now varies between 50% and 100% for different detectors and signal to noise conditions thereby making it possible to find the best detector for an application.

Robust Biometric Verification Using Phonocardiogram Fingerprinting and a Multilayer-Perceptron-Based Classifier

Recently, a new set of biometric traits, called medical biometrics, have been explored for human identity verification. This study introduces a novel framework for recognizing human identity through heart sound signals, commonly referred to as phonocardiograms (PCGs). The framework is built on extracting and suitably processing Mel-Frequency Cepstral Coefficients (MFCCs) from PCGs and on a classifier based on a Multilayer Perceptron (MLP) network. A large dataset containing heart sounds acquired from 206 people has been used to perform the experiments. The classifier was tuned to obtain the same false positive and false negative misclassification rates (equal error rate: EER = FPR = FNR) on chunks of audio lasting 2 s. This target has been reached, splitting the dataset into 70% and 30% training and testing non-overlapped subsets, respectively. A recurrence filter has been applied to also improve the performance of the system in the presence of noisy recordings. After the application of the filter on chunks of audio signal lasting from 2 to 22 s, the performance of the system has been evaluated in terms of recall, specificity, precision, negative predictive value, accuracy, and F1-score. All the performance metrics are higher than 97.86% with the recurrence filter applied on a window lasting 22 s and in different noise conditions.

Impact of Adverse Weather and Image Distortions on Vision-Based UAV Detection: A Performance Evaluation of Deep Learning Models

Unmanned aerial vehicle (UAV) detection in real-time is a challenging task despite the advances in computer vision and deep learning techniques. The increasing use of UAVs in numerous applications has generated worries about possible risks and misuse. Although vision-based UAV detection methods have been proposed in recent years, a standing open challenge and overlooked issue is that of adverse weather. This work is the first, to the best of our knowledge, to investigate the impact of adverse weather conditions and image distortions on vision-based UAV detection methods. To achieve this, a custom training dataset was curated with images containing a variety of UAVs in diverse complex backgrounds. In addition, this work develops a first-of-its-kind dataset, to the best of our knowledge, with UAV-containing images affected by adverse conditions. Based on the proposed datasets, a comprehensive benchmarking study is conducted to evaluate the impact of adverse weather and image distortions on the performance of popular object detection methods such as YOLOv5, YOLOv8, Faster-RCNN, RetinaNet, and YOLO-NAS. The experimental results reveal the weaknesses of the studied models and the performance degradation due to adverse weather, highlighting avenues for future improvement. The results show that even the best UAV detection model’s performance degrades in mean average precision (mAP) by −50.62 points in torrential rain conditions, by −52.40 points in high noise conditions, and by −77.0 points in high motion blur conditions. To increase the selected models’ resilience, we propose and evaluate a strategy to enhance the training of the selected models by introducing weather effects in the training images. For example, the YOLOv5 model with the proposed enhancement strategy gained +35.4, +39.3, and +44.9 points higher mAP in severe rain, noise, and motion blur conditions respectively. The findings presented in this work highlight the advantages of considering adverse weather conditions during model training and underscore the significance of data enrichment for improving model generalization. The work also accentuates the need for further research into advanced techniques and architectures to ensure more reliable UAV detection under extreme weather conditions and image distortions.

Statistical noise in PD-(L)1 inhibitor trials: Unravelling the durable-responder effect

BackgroundProgrammed-death-1/ligand-1 inhibitors (PD-1/L1i’s) have emerged as pivotal treatments for many cancers. A notable feature of this class of medicines is the dichotomous response pattern: A small (but clinically-relevant) percentage of patients (5% - 20%) benefit from deep and durable responses resembling functional cures (durable responders), while most patients experience only a modest or negligible response. Accurately predicting durable responders remains elusive due to the lack of a reliable biomarker. Another notable feature of these medicines is that different PD-1/L1’s have obtained statistically significant results, leading to marketing approval, for some cancer indications, but not for others, with no discernible pattern. These puzzling inconsistencies have generated extensive discussions among oncologists. Proposed (but not entirely convincing) explanations include true underlying differences in efficacy for some types of cancer, but not others; or subtle differences in trial design. ObjectiveTo investigate a less-explored hypothesis—the durable-responder effect: An initially unidentified group of durable responders generates more statistical noise than anticipated, leading to low-powered randomised controlled trials (RCTs) that report randomly variable results. Study designEmploying simulation, this investigation divides participants in PD-(L)1i RCTs into two groups: durable responders and patients with a more modest response. Drawing on published data for melanoma, lung and urothelial cancers, multiple pre-specified scenarios are replicated 50,000 times, systematically varying the durable-responder percentage from 5% to 20% and the modest-response hazard ratio for overall survival [HR(OS)] from 0.8 to 1.0. This allowed evaluation of the effect of durable responders on power, point estimates of the treatment effect for OS, and the probability of a misleading signal for harm. ResultsWhen the treatment effect for the modest responders is similar to the comparator arm, statistical power remains below 80%, limiting the ability to reliably detect durable responders. Conversely, there is a material probability of obtaining a statistically significant result that exaggerates the treatment effect by chance. For instance, with an average HR(OS) of 0.93 (corresponding to 5% durable responders), statistically significant trials (7.2%) show an average HR(OS) of 0.77. Additionally, when 5% are durable responders, there is a 20% probability that the HR(OS) will exceed 1.0—suggesting potential harm, when none exists. ConclusionThis paper adds to the possible explanations for the puzzlingly inconsistent results from PD-(L)1i RCTs. Initially unidentified durable responders introduce features typical of imprecise, low-powered studies: a propensity for false-negative results; estimates of benefit that might not replicate; and misleading signals for harm.

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.

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.

Simulation study on magneto-acoustic concentration tomography of magnetic nanoparticles based on vectorial acoustic source and BICGSTAB method

Abstract To overcome the vulnerability to noise of the reconstructed image and simplify the cumbersome iteration process of algorithm in the magneto-acoustic concentration tomography with magnetic induction (MACT-MI) for magnetic nanoparticles (MNPs), we established the matrix relationship between the concentration of MNPs and the first-order derivative of sound pressure based on the reconstruction method of vectorial acoustic source, and proposed the application of BICGSTAB method in solving the concentration distribution. Firstly, a simulation model was established in COMSOL Multiphysics. Secondly, the obtained data were substituted into the derived formula for imaging reconstruction. Finally, the quality of the reconstructed image was analyzed. The effects of MNP radius, shape, asymptotic concentration, and SNR on the reconstruction results were studied. Simulation results show that under the same noise condition, compared with the reconstruction method based on the LSQR-trapezoidal method, the average correlation coefficient increased by 32.9%, the average relative error decreased by 48.5%, the average structural similarity increased by 48.2%, and the average iterations decreased by 58.5%. The proposed method shows superior imaging quality and noise immunity. The research provides a theoretical basis for subsequent experimental research.

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.

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.

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.

Low-dose radiographic inspection of welding by a novel aperiodic reverse stochastic resonance method

Abstract Low-dose radiographic inspection is a growing trend in industry to minimize radiation risks to humans and the environment. However, reduction in radiation dose often introduces significant noise, which affects image quality and hinders accurate identification of subtle defects. This study addresses this issue by introducing a novel phenomenon called aperiodic reverse stochastic resonance (ARSR), observed in nonlinear systems excited by aperiodic binary signals. ARSR enables simultaneous amplitude amplification and reversal of signals under specific noise conditions. Leveraging ARSR, we propose an image denoising framework for low-dose radiographic inspections. First, a set of projection data is obtained by using Radon transform to reduce the dimensionality of X-ray images from different angles. Then, the projection data is modulated based on the ARSR system. Finally, the image is reconstructed based on the inverse Radon transform. Simulations and experimental comparison results in welding applications validate the effectiveness of the framework, demonstrating significant improvements in image quality for low-dose radiographic defect detection. Unlike advanced methods such as Gaussian filtering, BM3D, and DnCNN, which operate at the pixel level, ARSR performs denoising at the projection data stage, reducing noise impact, preserving original information, and focusing on physical data processing during imaging. This approach enhances the detection of subtle defects, highlighting the potential of stochastic resonance in image processing.

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.

Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy

Respiratory-induced tumor motion presents a critical challenge in lung cancer radiotherapy, potentially impacting treatment precision and efficacy. This study introduces an innovative, deep learning-based approach for real-time, markerless lung tumor tracking utilizing orthogonal X-ray projection images. It incorporates three key components: (1) a sophisticated data augmentation technique combining a hybrid deformable model with 3D thin-plate spline transformation, (2) a state-of-the-art Transformer-based segmentation network for precise tumor boundary delineation, and (3) a CNN regression network for accurate 3D tumor position estimation. We rigorously evaluated this approach using both patient data from The Cancer Imaging Archive and dynamic thorax phantom data, assessing performance across various noise levels and comparing it with current leading algorithms. For TCIA patient data, the average DSC and HD95 values were 0.9789 and 1.8423 mm, respectively, with an average centroid localization deviation of 0.5441 mm. On CIRS phantoms, DSCs were 0.9671 (large tumor) and 0.9438 (small tumor) with corresponding HD95 values of 1.8178 mm and 1.9679 mm. The 3D centroid localization accuracy was consistently below 0.33 mm. The processing time averaged 90 ms/frame. Even under high noise conditions (S2 = 25), errors for all data remained within 1 mm with tracking success rates mostly at 100%. In conclusion, the proposed markerless tracking method demonstrates superior accuracy, noise robustness, and real-time performance for lung tumor localization during radiotherapy. Its potential to enhance treatment precision, especially for small tumors, represents a significant step toward improving radiotherapy efficacy and personalizing cancer treatment.

Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline

We introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false discovery rate (FDR) control. This comprehensive method addresses common challenges in trend analysis, such as handling small, noisy datasets with outliers and issues related to spatial autocorrelation, cross-correlation, and multiple testing. We applied this RTA workflow to study tree cover trends in Los Alcornocales Natural Park (Southern Spain), Europe’s largest cork oak forest, analysing interannual changes in tree cover from 2000 to 2022 using Terra MODIS MOD44B data. Our results reveal that the TS estimator provides a robust measure of trend direction and magnitude, but its effectiveness is dramatically enhanced when combined with the CMK test. This combination highlights significant trends and effectively corrects for spatial autocorrelation and cross-correlation, ensuring that genuine environmental signals are distinguished from statistical noise. Unlike previous workflows, our approach incorporates the FDR control, which successfully filtered out 29.6% of false discoveries in the case study, resulting in a more stringent assessment of true environmental trends captured by multi-temporal remotely sensed data. In the case study, we found that approximately one-third of the area exhibits significant and statistically robust declines in tree cover, with these declines being geographically clustered. Importantly, these trends correspond with relevant changes in tree cover, emphasising the ability of RTA to detect relevant environmental changes. Overall, our findings underscore the crucial importance of combining these methods, as their synergy is essential for accurately identifying and confirming robust environmental trends. The proposed RTA framework has significant implications for environmental monitoring, modelling, and management.

Network mutual information measures for graph similarity

A wide range of tasks in network analysis, such as clustering network populations or identifying anomalies in temporal graph streams, require a measure of the similarity between two graphs. To provide a meaningful data summary for downstream scientific analyses, the graph similarity measures used for these tasks must be principled, interpretable, and capable of distinguishing meaningful overlapping network structure from statistical noise at different scales of interest. Here we derive a family of graph mutual information measures that satisfy these criteria and are constructed using only fundamental information theoretic principles. Our measures capture the information shared among networks according to different encodings of their structural information, with our mesoscale mutual information measure allowing for network comparison under any specified network coarse-graining. We test our measures in a range of applications on real and synthetic network data, finding that they effectively highlight intuitive aspects of network similarity across scales in a variety of systems.