Unwanted Noise Research Articles

Experimental evaluation of feedforward active noise control system with optical laser microphone and proportional-integral-differential filter

In this paper, we propose a feedforward active noise control (ANC) system with an optical laser microphone utilizing a proportional-integral-differential (PID) filter. The feedforward ANC system with the optical laser microphone has been proposed to relax the causality constraint. This system adopts a first-order differentiator to modify the velocity picked up by the optical laser microphone. This modification achieves the improvement of the coherence between the velocity as the reference signal and the unwanted noise as the sound pressure. However, the frequency response of the first-order differentiator is similar to the high-pass filter and attenuates the reference signal at low frequencies. This causes the degradation of the noise reduction performance of the conventional ANC system. To solve this problem, the proposed ANC system adopts the PID filter instead of the first-order differentiator. By adjusting the coefficients of the PID filter, the proposed system avoids the attenuation of the reference signal at low frequencies. Then, the noise reduction performance can be improved. Experimental results show that the proposed ANC system reduces the unwanted noise compared to the conventional ANC system and relaxes the causality constraint compared to the basic ANC system in the real-time operation.

3D vibration modes estimation of complex-shaped structure by Phase-based motion magnification and DIC method

Knowing the 3D mode shapes of a structure is important in solving unwanted vibration and noise problems in mechanical systems. Accordingly, various analysis methods for estimating the vibration modes of complex-shaped structures have been proposed. However, it is difficult to attach accelerometers to a complex-shaped structure, and to cover a large area, many accelerometers must be attached. So experimental verification of the mode shapes of an actual structure is insufficient. Therefore, the purpose of this study is to experimentally estimate the 3D vibration modes of a complex structure using two cameras by Phase-based image magnification and DIC method. In addition, by using non-contact sensor such as a camera, it is possible to estimate the modes without affecting the mass or stiffness of the structure.

Indonesian users' active sound design preference for armored electric SUV interior

This paper explores the potential of Active Sound Design (ASD) in the Indonesian electric vehicle (EV) market, where there are a projected 2 million EV users by 2030. While ASD is a crucial aspect in addressing the lack of engine sound in EV, it is still developing and mainly focused on certain regions. The ASD approach includes active noise control (ANC) to reduce unwanted noise inside the cabin and engine sound enhancement by inserting additional engine sound through the speakers. The study evaluates the original EV stimulus, ANC stimulus, and two ASD stimuli (ICE and futuristic) of an armored electric SUV using an audiovisual car cabin soundscape simulator. The results show that the ANC technology (improved EV with noise filter) is the most preferred stimulus with the highest comfort value perceived by the participants. These insights can help EV manufacturers optimize their sound design solutions for future EV productions in Indonesia.

AdaRes: A deep learning-based model for ultrasound image denoising: Results of image quality metrics, radiomics, artificial intelligence, and clinical studies.

The quality of ultrasound images is degraded by speckle and Gaussian noises. This study aims to develop a deep-learning (DL)-based filter for ultrasound image denoising. A novel DL-based filter using adaptive residual (AdaRes) learning was proposed. Five image quality metrics (IQMs) and 27 radiomics features were used to evaluate denoising results. The effect of our proposed filter, AdaRes, on four pre-trained convolutional neural network (CNN) classification models and three radiologists was assessed. AdaRes filter was tested on both natural and ultrasound image databases. IQMs results indicate that AdaRes could remove noises in three different noise levels with the highest performances. In addition, a radiomics study proved that AdaRes did not distort tissue textures and it could preserve most radiomics features. AdaRes could also improve the performance classification using CNNs in different settings. Finally, AdaRes also improved the mean overall performance (AUC) of three radiologists from 0.494 to 0.702 in the classification of benign and malignant lesions. AdaRes filtered out noises on ultrasound images more effectively and can be used as an auxiliary preprocessing step in computer-aided diagnosis systems. Radiologists may use it to remove unwanted noises and improve the ultrasound image quality before the interpretation.

An Urban Acoustic Rainfall Estimation Technique Using a CNN Inversion Approach for Potential Smart City Applications

The need for robust rainfall estimation has increased with more frequent and intense floods due to human-induced land use and climate change, especially in urban areas. Besides the existing rainfall measurement systems, citizen science can offer unconventional methods to provide complementary rainfall data for enhancing spatial and temporal data coverage. This demand for accurate rainfall data is particularly crucial in the context of smart city innovations, where real-time weather information is essential for effective urban planning, flood management, and environmental sustainability. Therefore, this study provides proof-of-concept for a novel method of estimating rainfall intensity using its recorded audio in an urban area, which can be incorporated into a smart city as part of its real-time weather forecasting system. This study proposes a convolutional neural network (CNN) inversion model for acoustic rainfall intensity estimation. The developed CNN rainfall sensing model showed a significant improvement in performance over the traditional approach, which relies on the loudness feature as an input, especially for simulating rainfall intensities above 60 mm/h. Also, a CNN-based denoising framework was developed to attenuate unwanted noises in rainfall recordings, which achieved up to 98% accuracy on the validation and testing datasets. This study and its promising results are a step towards developing an acoustic rainfall sensing tool for citizen-science applications in smart cities. However, further investigation is necessary to upgrade this proof-of-concept for practical applications.

A rail corrugation index to characterize noise impacts and grinding effectiveness on rail transit systems

Despite general agreement that rail corrugation generates unwanted noise, there is a need to quantify the relationship between rail corrugation and noise and to leverage this relationship within preventive rail grinding programs. This paper develops a novel rail corrugation index (RCI) and demonstrates the suitability of that index to characterize the relationship between corrugation and noise, to assess grind effectiveness, and to predict noise as a function of rail corrugation. Using a time-series data set collected at a North American rail transit property, the proposed RCI illustrated corrugation growth as a function of accumulated tonnage and an expected reduction of corrugation after grinding. The RCI also correlated well with corresponding wayside noise observations. The evident response behavior and the relationship between rail corrugation and wayside noise gave rise to the assessment of grind effectiveness using the RCI. Further, a modelling effort demonstrated that noise can be predicted using the RCI calculated from only one rail (left or right) on a tangent section. This result suggests that there may also be an opportunity to predict corrugation using noise data, thus limiting the need for track downtime required to measure corrugation.

An advanced median filter for improving the signal-to-noise ratio of seismological datasets

Strong noise usually disturbs the recorded seismic waves, resulting in low-quality seismic data sometimes with an extremely low signal-to-noise ratio (S/N), which negatively impacts the subsequent seismological processes, e.g., imaging, inversion, and interpretation. Suppressing undesirable noise is a meaningful procedure to increase the S/N of seismological datasets. This issue can be partially addressed using the recently proposed structure-oriented space-varying median filter (SOSVMF) by filtering out the unwanted noise from noise-corrupted signals. However, the SOSVMF approach has limitations when the raw data is corrupted by various sources of noise which is more common in practice. To conquer the difficulties in improving the S/N in the face of diverse types of strong noise, we introduce MATamf, an open-source MATLAB code package based on a novel advanced median filter (AMF). The proposed AMF workflow takes advantage of multiple denoising operators, i.e., the bandpass (BP) filter, SOSVMF, dip filter in the frequency–wavenumber (FK) domain, Curvelet, and local orthogonalization (LO), all in the same framework to simultaneously attenuate all types of noise and improve S/N. To demonstrate the usefulness of the MATamf package and its advantage over conventional denoising methods, we introduce the principles behind the proposed AMF framework and present results from a variety of seismological problems, including seismic reflection, distributed acoustic sensing (DAS), as well as global seismological tasks of receiver function and SS-precursors imaging. All results show that the proposed AMF workflow can be conveniently utilized to improve the S/N of a wide spectrum of seismological applications.

Design of an integrated circuit with blanking technique for neural signal recording based on CMOS

Neural signals play a crucial role in unraveling the mysteries of the nervous system. However, due to their minute amplitudes in the range of microamperes and microvolts, recording these signals poses significant challenges, largely stemming from their vulnerability to noise interference. Thus, the development of neural signal acquisition circuits holds paramount importance. This paper presents a successful endeavor in designing an integrated circuit using 0.18um CMOS technology, which exhibits exceptional accuracy in recording neural signals. The circuit employs a combination of amplifiers and filters, while incorporating the innovative blanking technology to effectively eliminate unwanted noise. Remarkably, this design accomplishes an impressive gain of 88.437dB. Consequently, the circuit enables the accurate capture of nerve signals, thereby positioning itself as an ideal candidate for implantable neural signal detection devices. With its ability to overcome the hurdles associated with signal sensitivity and noise interference, this integrated circuit represents a significant advancement in the field of neural signal acquisition, holding immense potential for further exploration and application in neuroscientific research and medical interventions.

Breast cancer segmentation using a hybrid AttendSeg architecture combined with a gravitational clustering optimization algorithm using mathematical modelling

Abstract Breast cancer diagnosis relies on breast ultrasound (BUS) and the early breast cancer screening saves lives. Computer-aided design (CAD) tools diagnose tumours via BUS tumour segmentation. Thus, breast cancer analysis automation may aid radiologists. Early detection of breast cancer might help the patients to survive and in context with this many approaches have been demonstrated by different researches, however, some of the works are weak in the segmentation of breast cancer images. to tackle these issues, this study propose a novel Hybrid Attendseg based gravitational clustering optimization (HA-GC) method which is utilized to segment breast cancer as normal malignant, and benign. For this we have taken the dataset known as breast ultrasound (BUS) images. This method constructively segments the breast cancer images. Prior to the segmentation, pre-processing is carried out which can be used to normalize the images incorporated with the removal of unwanted noises and format the images Optimization selects the best qualities. An experiment is conducted and compared the results with the parameters such as Dice coefficient, Jacquard, Precision, and Recall and attained over 90% and ensures the usage of present work in the segmentation of breast cancer images.

Modeling of silencer by using various porous materials in a trifurcated waveguide structure

Researchers in applied mathematics, physics, and engineering are investigating how to reduce unwanted or ravage noise to an acceptable level. This article describes a trifurcated waveguide structure that has soft upper boundary surfaces and rigid lower boundary surfaces with different porosity effects. There is an asymmetrical arrangement of ducts around the x-axis. There is a major problem in determining how scattered fields behave in different duct areas that are subject to a variety of material attributes for walled ducts. In standard practice, the mode-matching method can be used to address governing system arising from the Sturm–Liouville eigenvalue problem. The scattering configuration for A-glass, Basalt Wool, E-glass, and Steel Wool types of porous materials is examined for different settings of the underlying structure in order to analyze noise effects. It is pertinent to predict the bulk acoustic properties at frequencies below the range in which standard measurement methods are capable of measuring in many acoustic modeling applications. A semi-analytical method for porous media is presented here that provides physically feasible predictions of bulk properties at arbitrarily low frequencies, as well as reasonable predictions of bulk properties at higher frequencies. A close relationship exists between silencer geometries and underlying structures from a practical perspective.

A - 175 EEG Phase Can be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and SNR.

Electroencephalogram (EEG) phase can capture neural oscillation dynamics. Accordingly, EEG phase is a potential target for neurofeedback and brain-computer interfaces (BCIs). However, rigorous tests of the generalizability of real-time EEG phase are needed, requiring accurate phase estimates. We examined how cognitive states affect EEG phase prediction accuracy in the parieto-occipital alpha band. We identified datasets through public repositories. After preprocessing, we used the Educated Temporal Prediction Algorithm (ETP) to predict future peaks. We compared these predictions to the original signal, and defined accuracy from 0 to 100%. Our independent variable was cognitive state (eyes-open rest, eyes-closed rest, task), dependent variable was accuracy, and covariates were EEG instantaneous power and signal-to-noise ratio (SNR). We used a linear mixed-effects model, with random effects for dataset and individual. Across the 11 datasets, we had 543 participants and 1,641,074 predictions. There were no significant accuracy differences among the conditions (p < 1e-5), with a baseline accuracy of 59.07%. Power had an effect on accuracy, with a unit increase leading to a 13.1% increase in accuracy (p < 1e-5). SNR had an effect on accuracy, with an effect size of 0.46% (p < 1e-5), with a significant negative interaction with power with an effect size of -0.51% (p < 1e-5). Our results indicated that we could predict EEG phase accurately across cognitive conditions and datasets, with higher accuracy for high instantaneous band-power and SNR. Accordingly, real-time EEG phase experiments, closed-loop technologies, and BCIs should minimize external unwanted noise while targeting periods of high power, as opposed to manipulating experimental and cognitive conditions.

Organalysis: Multifunctional Image Preprocessing and Analysis Software for Cardiac Organoid Studies.

Due to a growing need in visualizing human pluripotent stem cell-derived organoids from recent advancements in the field, an efficient bulk-processing application is necessary to provide preprocessing and image analysis services. In this study, we developed Organalysis, a high-accuracy, multifunctional, and accessible application that meets these needs by providing the functionality of image manipulation and enhancement, organoid area and intensity calculation, fractal analysis, noise removal, and feature importance computation. The image manipulation feature includes brightness and contrast adjustment. The area and intensity calculation computes six values for each image: organoid area, total image area, percentage of the image covered by organoid, the total intensity of organoid, the total intensity of organoid-by-organoid area, and total intensity of organoid by total image area. The fractal analysis function computes the fractal dimension value for each image. The noise removal function removes superfluous marks from the input images, such as bubbles and other unwanted noise. The feature importance function trains a lasso-regularized linear regression machine learning algorithm to identify cardiac growth factors that are the strongest determinants for cell differentiation. The batch processing of this application further builds on existing services like ImageJ to provide a more convenient way to process multiple images. Collectively, the versatility and preciseness of Organalysis demonstrate novelty, since no other current imaging software combines the capability of batch processing and the breadth of feature analysis. Therefore, Organalysis provides unique functions in cardiac organoid research and proves to be invaluable in regenerative medicine.

Design Of aesthetic acoustic metamaterials window panel based on Sierpiński triangle for sound-silencing with free airflow

Design of high-efficiency, low frequency (&amp;lt;1000Hz) soundproof window or wall absorber which is transparent to airflow is presented. Due to the massive rise in human population and modernization, environmental noise has significantly risen globally. Prolonged noise exposure can cause severe physiological and psychological symptoms like nausea, headaches, fatigue, and insomnia. There has been continuous growth in building construction and infrastructure like offices, bus stops, and airports due to urban population. Generally, a ventilated window is used for getting fresh air into the room, but at the same time, unwanted noise comes along. Researchers used traditional approaches like noise barrier mats in front of the window or designed the entire window using sound-absorbing materials. However, this solution is not aesthetically pleasing, and at the same time, it is heavy and not adequate for low-frequency noise shielding. To address this challenge, we design a transparent hexagonal panel based on Sierpiński fractal triangle which is aesthetically pleasing, demonstrates normal incident sound absorption coefficient more than 0.96 around 700 Hz and transmission loss around 23 dB, while maintaining air circulation through triangular cutout. Next, we present a concept of fabrication of large acoustic panel for large-scale applications which lead to suppressing the urban noise pollution

Tackling the strange wind noise on a commercial building—Case study

A mysterious noise was reported in a commercial building. This commercial building consists of multiple levels of car parks with one single level of office space at the top. This top-level office area is home to Renault Australia, Mulgrave, Victoria. The office staff at the Renault reported a dreadful “howling” noise around the office. It sounded like a huge truck thundering past the office, especially on windy days. Specialist engineers, acoustic and wind engineers, were summoned to investigate the root cause of this noise. The initial investigation revealed that on windy days, the exterior aluminium Z Perlin profile cladding created air turbulences, which resulted in this unwanted noise. After conducting tests in the Monash University wind tunnel, various recommendations were suggested and trialled. For example, removing some purlins to reduce the air-flow turbulence, attaching fibre cement sheets to seal the purlins, vibration damping treatment of the Z purling cladding, and absorptive infill on the metal purlins. This case study presents the analysis of the noise, the results of the various tests and the successful practical resolution.

A hat-shape error microphone array for user-friendly spatial active noise control

Spatial active noise control (ANC) has been developed in the past decades to reduce unwanted acoustic noise over a desired region by generating an anti-noise field with secondary loudspeakers. Unlike conventional multi-channel ANC systems, the integration of spherical harmonic-based sound field analysis allows the ANC system to reduce noise over a continuous region surrounding the user's head rather than discrete points. However, the use of a spherical error microphone array on the boundary of the region for spherical harmonic analysis poses a limitation as it obstructs user access to the controlled area. In this work, we introduce a hat-shaped error microphone array design which can be placed above the listener's head. The proposed error microphone array can achieve spherical harmonic-based residual noise field analysis and is more user-friendly, allowing unrestricted user head movement. We construct the hat-shaped error microphone array and present the results of ANC experiments conducted using the array.

Simultaneous measurement of unsteady surface pressure and leading edge noise of a NACA0012 airfoil

Airfoil-turbulence interaction noise, which is created whenever turbulent flow encounters an airfoil, is a major source of unwanted noise emitted byaircraft and turbomachinery. Despite being a major research focus overthepast decades, the accurate prediction and understanding of airfoil–turbulence interaction noise remain an open question. This experimental investigation involved simultaneous measurements of unsteady pressure fluctuations at 61 locations across the surface of a NACA0012 airfoil and far field sound pressure at a phased 64-microphone array. Various square-mesh turbulence grids were placed upstream of the airfoil to vary the isotropic free stream turbulence. Additionally, the influence of the airfoil’s angle of attack as well as different mean flow speeds on the unsteady surface pressure and radiated far field noise was investigated. Ultimately, the links between the simultaneously acquired unsteady surface pressure on the airfoil and the microphone array sound pressure were studied. This provided insight into the mechanisms of leading edge noise source generation and their influence on the radiated far field noise.

A computer vision approach to improve maintenance automation for thermal power plants lubrication systems

PurposeLubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually, resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper proposes a machine vision-based inspection system aimed at automating the oil leakage detection for improving the maintenance procedures.Design/methodology/approachThe approach aims at developing a novel modular-structured automatic inspection system. The image acquisition module collects digital images along a predefined inspection path using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the lubricating oil while suppressing unwanted background noise. The image processing module is designed to detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate the industrial suitability of the proposed inspection system.FindingsOn-site experimental results demonstrate the capabilities to complete the automatic inspection procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%.Practical implicationsThe proposed inspection system can be adopted in industrial context to detect lubricant leakage ensuring the equipment and the operators safety.Originality/valueThe proposed inspection system adopts a computer vision approach, which deploys the combination of two separate sources of light, to boost the detection capabilities, enabling the application for a variety of particularly hard-to-inspect industrial contexts.

Crossover smell agent optimized multilayer perceptron for precise brain tumor classification on MRI images

The Brain tumor is considered an unusual growth of cells in the nervous system that restricts the normal functionality of the brain. However, is generated in the skull and pressures the brain which affects the health of a person. So it is essential to detect and classify the brain tumor at an early stage before reaching the severity level. Meanwhile, brain tumor detection is performed based on MRI images which are considered an effective diagnosis system. But the detection and classification using MRI images is obtained as a complex task and cannot show the difference between normal and abnormal cells. So to overcome this issue the Crossover Smell Agent Optimized Multilayer Perception (CSA-MLP) is proposed to perform the exact detection and classification of tumor cells from MRI images. The images are collected from three datasets namely MR, Brain MRI, and Brain tumor datasets and they are preprocessed to remove the unwanted noise. After preprocessing the features of the images are extracted to perform the classification process. Moreover, the Convolutional Neural Network (CNN) classifier is used to classify healthy and unhealthy brain cells. The Multi-Layer Perceptron (MLP) is employed for the classification category that minimized the errors and enhanced the performance of the proposed method. The MLP is integrated with the CSA optimization algorithm to improve classification accuracy. The experimentation results revealed that the proposed method achieved a better accuracy of about 98.56% which enhanced the effectiveness compared to existing methods.

Detection of Epilepsy patients using coot optimization based feed forward multilayer neural network

ABSTRACT A familiar nervous system disorder characterised by seizures is called as Epilepsy. It is indeed hard to control the suitable type as an outcome of insufficient EEG information. In order to overcome these issues, a Multilayer Neural Network (MLNN)-based classifier is proposed to recognise if the patients are affected by epileptic disease or not. EEG signal is a contribution, and the input signal is preprocessed using antialiasing filter, finite impulse response, and band pass filter to eradicate unwanted noise present in the signal. After preprocessing, the features extracting process is done, and four extraction techniques are proposed in order to calculate the feature coefficient. The feature extraction outcome is fed into the MLNN classifier to predict the disease. MLNN performs with Coot-Optimization to reduce error and increase prediction accuracy. The future ideal applied in Matlab-software carried out numerous act metrics, and these parameters attained better performance such as accuracy of 96.5%, error of 0.03, precision of 98%, specificity is 97%, sensitivity is 95%, and so on. This displays the effectiveness of the future ideal than existing approaches such as ANN, SVM, KNN and NB. Based on this proposed classification, the epileptic disease prediction can be improved on this technique and can provide a living standard for patients.

Full-Reference Image Quality Assessment via Low-Level and High-Level Feature Fusion

We propose a full-reference image quality assessment (FR-IQA) method by incorporating low-level and high-level image features. First, in contrast to the preexisting deep IQA methods, which only use the features extracted by the deep network, we not only use the image gradient to replace the low-level features in the first two stages of the deep network, but also combine them with the middle-stage features of the deep network to construct the new low-level features. The deep features of shallow layers contain some unwanted noise and further result in a decline in IQA performance. Second, we combine the global features extracted by the self-attention-based model with the semantic features extracted by the convolutional neural network to form the high-level features. Instead of directly using the self-attention-based model trained on the classification task, we first train a no-reference (NR) IQA regression model on a larger dataset and then use the global features of this NR-IQA model. The self-attention-based model can capture the internal connections of the image and is more effective in extracting global information due to its larger perceptual field. In the final pooling stage, we combine the average pooling and the standard deviation pooling to obtain the dispersion and concentration of the similarity maps for a more comprehensive description of quality. Experiments show that our FR-IQA method is able to obtain competitive results on three standard IQA datasets.