Noise Reduction Techniques Research Articles

Analysis of Audio Filtering Algorithms for Noise Cancellation

Real-time signal noise reduction poses a significant challenge in communication systems. To combat intrusive background noise during portable device conversations, an active noise cancellation (ANC) is employed. ANC generates anti-noise signals using techniques such as Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) algorithms, Recursive Least Squares (RLS) and modified Recursive Least Squares (M-RLS) for effective reduction of unwanted sound and enhancing the output signal quality. The study includes various real-time noise signals to create anti-noise responses, successfully negating corresponding noise in input signals, which include sine waves, live voices, and music. Through a dedicated micro-controller device and electronic network, an "Anti-noise" wave is introduced to achieve active noise control (ANC). This research identifies ANC module specifications and effectively transmits them through the ANC library, demonstrating the efficiency of the proposed noise reduction technique.

A practical subcritical rod worth measurement technique based on the improved neutron source multiplication method

The control rod worth is a key safety parameter required to be measured in commercial pressurized water reactors (PWRs). Conventionally, the control rod worth is measured after reaching the critical state, which occupies the considerable time in the zero-power physics test. In this study, an efficient control-rod worth measurement technique has been proposed based on the improved neutron-source multiplication method, which can be implemented with the source-range detector count rates in the subcritical states. Moreover, the noise reduction technique has been adopted to smooth the large fluctuation existing in the original signals. In order to verify the engineering performance of the proposed measurement technique, the measured source-range detector count rates during the rod withdrawal process before reaching critical state in a CNP1000 reactor have been employed. It demonstrated that almost all estimated results of control rod worth satisfy the engineering acceptance criteria, except one control rod with the relative difference over 10 %, which indicates the capability of the proposed method in estimating control rod worth.

New Approach for Multimodal Medical Image Fusion using PDE-based Technique

Accurate diagnosis of diseases is essential for effective medical treatment, and medical image fusion plays a crucial role in achieving this goal. This article proposes a novel technique for multimodal medical image fusion based on fourth order PDE-based techniques. The proposed technique uses inputted CT and brain MRI images, and applies a PDE-based technique for noise reduction and edge preservation. Subsequently, a computed fusion rule using principal component analysis is applied to generate a fused image of the brain that presents clear and essential components for medical diagnosis and analysis. The article provides a comparative study of the proposed technique with other existing techniques, demonstrating its optimal performance. The proposed technique can significantly enhance medical image diagnosis and analysis for accurate disease detection and treatment.

Efficient denoising of cold atom images using the optimized eigenface recognition algorithm.

Absorption imaging is a widely employed technique for detecting cold atom clouds and Bose-Einstein condensates (BECs). There are situations where such images may suffer from unwanted interference fringes, resulting in uncertainties in determining crucial parameters such as the atom number, temperatures, or even dynamics in small timescales. Reducing the acoustic vibrations and recording image frames synchronized with the source of such vibrations can largely reduce these fringes; however, some residual fringes still need to be taken care of for precision measurements. In this study, we propose an efficient image post-processing technique for noise reduction that effectively mitigates such interference patterns. Our approach makes use of the well-known eigenface recognition algorithm, combined with an optimized masking strategy applied to the image of the atomic cloud using a small number of basis sets. The use of a limited basis set ensures minimal computational time, allowing this method to be readily incorporated into every experimental run. Through the application of our technique, we successfully reduce interference fringes and improve the accuracy of parameter estimation by 50% in the absorption imaging of cold atoms. The temperature uncertainties of cold 87 R b atoms are reduced by more than 50% after the algorithm is applied. This approach holds significant promise for enhancing the reliability and precision of experimental measurements in diverse research fields where absorption imaging is employed.

Simulation-guided development of an optical calorimeter for high dose rate dosimetry.

Optical Calorimetry (OC) is based on interferometry and provides a direct measurement of spatially resolved absorbed dose to water by measuring refractive index changes induced by radiation. The purpose of this work was to optimize and characterize in software an OC system tailored for ultra-high dose rate applications and to build and test a prototype in a clinical environment.A radiation dosimeter using the principles of OC was designed in optical modelling software. Traditional image quality instruments, fencepost and contrast phantoms, were utilized both in software and experimentally in a lab environment to investigate noise reduction techniques and to test the spatial and dose resolution of the system. Absolute dose uncertainty was assessed by measurements in a clinical 6 MV Flattening Filter Free (FFF) photon beam with dose rates in the range 0.2-6Gy/s achieved via changing the distance from the source.Design improvements included: equalizing the pathlengths of the interferometer, isolating the system from external vibrations and controlling the system's internal temperature as well as application of mathematical noise reduction techniques. Simulations showed that these improvements should increase the spatial resolution from 22 to 35 lp/mm and achieve a minimum detectable dose of 0.2Gy, which was confirmed experimentally. In the FFF beam, the absolute dose uncertainty was dose rate dependent and decreased from 2.5 ± 0.8 to 2.5 ± 0.2Gy for dose rates of 0.2 and 6Gy/s, respectively.A radiation dosimeter utilizing the principles of OC was developed and constructed. Optical modelling software and image quality phantoms allowed for iterative testing and refinement. The refined OC system proved capable of measuring absorbed dose to water in a linac generated photon beam. Reduced uncertainty at higher dose rates indicates the potential for OC as a dosimetry system for high dose rate techniques such as microbeam and ultra-high dose-rate radiotherapy.

A fusion of VGG-16 and ViT models for improving bone tumor classification in computed tomography

Background and ObjectiveBone tumors present significant challenges in orthopedic medicine due to variations in clinical treatment approaches for different tumor types, which includes benign, malignant, and intermediate cases. Convolutional Neural Networks (CNNs) have emerged as prominent models for tumor classification. However, their limited perception ability hinders the acquisition of global structural information, potentially affecting classification accuracy. To address this limitation, we propose an optimized deep learning algorithm for precise classification of diverse bone tumors. Materials and MethodsOur dataset comprises 786 computed tomography (CT) images of bone tumors, featuring sections from two distinct bone species, namely the tibia and femur. Sourced from The Second Affiliated Hospital of Fujian Medical University, the dataset was meticulously preprocessed with noise reduction techniques. We introduce a novel fusion model, VGG16-ViT, leveraging the advantages of the VGG-16 network and the Vision Transformer (ViT) model. Specifically, we select 27 features from the third layer of VGG-16 and input them into the Vision Transformer encoder for comprehensive training. Furthermore, we evaluate the impact of secondary migration using CT images from Xiangya Hospital for validation. ResultsThe proposed fusion model demonstrates notable improvements in classification performance. It effectively reduces the training time while achieving an impressive classification accuracy rate of 97.6%, marking a significant enhancement of 8% in sensitivity and specificity optimization. Furthermore, the investigation into secondary migration's effects on experimental outcomes across the three models reveals its potential to enhance system performance. ConclusionOur novel VGG-16 and Vision Transformer joint network exhibits robust classification performance on bone tumor datasets. The integration of these models enables precise and efficient classification, accommodating the diverse characteristics of different bone tumor types. This advancement holds great significance for the early detection and prognosis of bone tumor patients in the future.

Application of EMD combined with wavelet algorithm for filtering slag noise in steel cord conveyor belt

This study proposes a noise reduction technique that combines empirical mode decomposition (EMD) with wavelet thresholding to remove noise interference generated by metal slag in the damage signal of steel cord conveyor belts. The initial step involves decomposing the original signal into high-frequency and low-frequency intrinsic mode function (IMF) components using empirical mode decomposition (EMD), guided by the metal slag signal characteristics analysis. The high-frequency noise is filtered away while the low-frequency IMF components are kept intact through the wavelet threshold de-noising technique. The slag noise-containing IMF components are deleted, and the filtered signal and the low-frequency IMF components are utilized for signal reconstruction to provide the optimum noise reduction result. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

RESDEN: A Novel Deep Unified Model for Face Recognition System

The Face Recognition technology plays a significant role in the field of Computer Vision in contemporary times. The research article is centered on a Facial attendance system that utilizes a deep learning technique to recognize face photos. To execute face identification and classification via the use of deep learning processes, many Convolutional Neural Network (CNN) models are taken into account. Previous studies have mostly focused on either the ResNet or DenseNet-based convolutional neural network model. The present research utilizes the merging of ResNet and DenseNet to propose a hybrid model. The proposed work is expected to provide enhanced efficiency and accuracy. In the training and testing stages of the simulation, considerations are made for both binary and category classifications. The current research focuses on the use of the LFW dataset. The pictures undergo an initial step of the noise reduction process. The evaluation of picture quality is conducted by taking into account metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). After the proposed model has undergone training, it generates photographs of superior quality. Finally, the proposed system incorporates the RESDEN framework, which integrates DenseNet with a noise reduction technique, a segmentation mechanism, and a CNN based on ResNet. A comparative analysis has been conducted to evaluate the accuracy of several filtered picture sets across different convolutional neural network (CNN) models. The simulation results indicate that the suggested model exhibited a good level of performance and accuracy.

A position-adaptive noise-reduction method using a deep denoising filter bank for dedicated breast positron emission tomography images.

Dedicated breast positron emission tomography (db-PET) is more sensitive than whole-body positron emission tomography and is thus expected to detect early stage breast cancer and determine treatment efficacy. However, it is challenging to decrease the sensitivity of the chest wall side at the edge of the detector, resulting in a relative increase in noise and a decrease in detectability. Longer acquisition times and injection of larger amounts of tracer improve image quality but increase the burden on the patient. Therefore, this study aimed to improve image quality via reconstruction with shorter acquisition time data using deep learning, which has recently been widely used as a noise reduction technique. In our proposed method, a multi-adaptive denoising filter bank structure was introduced by training the training data separately for each detector area because the noise characteristics of db-PET images vary at different locations. Input and ideal images were reconstructed based on 1- and 7-min collection data, respectively, using list mode data. The deep learning model used residual learning with an encoder-decoder structure. The image quality of the proposed method was superior to that of existing noise reduction filters such as Gaussian filters and nonlocal mean filters. Furthermore, there was no significant difference between the maximum standardized uptake values before and after filtering using the proposed method. Taken together, the proposed method is useful as a noise reduction filter for db-PET images, as it can reduce the patient burden, scan time, and radiotracer amount in db-PET examinations.

A 0.4-to-0.8 V 0.1-to-5 MS/s 10 b two-step SAR ADC with TDC-based fine quantizer in 40-nm CMOS

This paper presents an energy efficient two-step hybrid-domain successive approximation register (SAR) analog-to-digital converter (ADC). Comparator offset calibration and kickback noise reduction techniques are proposed to maintain the high dynamic range and elevated residual signal quality of the coarse ADC. A feedback adjustment technology is proposed to reduce energy consumption of voltage-to-time converter (VTC). The VTC offset and LSB gain error between the coarse ADC and fine time-to-digital converter (TDC) are calibrated to ensure the gain accuracy. The proposed SAR ADC is designed in a 40-nm CMOS process and occupies 0.0363 mm2 silicon area. The post-simulation results show the proposed ADC achieved a spurious-free dynamic range (SFDR) of 74.8 dB and a signal-to-noise and distortion ratio (SNDR) of 56.42 dB at 0.4 V supply voltage and Nyquist input rate. The operating frequency scales from 100 kS/s to 5 MS/s. The converter has a power supply range of 0.4–0.8 V, and the figure of merit (FOM) is 4.85–10.6 fJ/conversion step.

Advancements in end-to-end isolated Kannada ASR system by combining robust noise elimination technique and TDNN

The demonstration of the latest enhancements in the end-to-end (E2E) isolated Kannada automatic speech recognition (ASR) system, achieved by combining a robust background noise elimination technique and a time delay neural network (TDNN), is presented in this work. An E2E Kannada ASR system consists of an interactive voice response system (IVRS), ASR models, and databases containing weather and agricultural commodity prices information. In the earlier spoken query system (SQS), the presence of babble, street noise, and other background noises led to a decrease in both offline and online speech recognition accuracies. To properly train the models, we increase the size of the database by collecting the Kannada speech data from an additional 500 farmers under real-time conditions. Moreover, the proposed noise elimination technique is employed to enhance the degraded speech data. Additionally, the efficacy of the TDNN is explored to improve the recognition accuracy of ASR models and the SQS system.The outcomes of the proposed speech enhancement algorithm demonstrate the absence of audible musical noise and other types of background noises in enhanced NOIZEUS and isolated Kannada speech databases. Leveraging Kannada language resources and the amalgamation of the proposed noise reduction technique and TDNN, a significant 1.1% enhancement in speech recognition accuracy is achieved in comparison with the previously developed deep neural network-hidden Markov model (DNN-HMM) based SQS. The enhanced isolated E2E ASR SQS system undergoes testing by 500 farmers, enabling them to access real-time agricultural commodity prices and weather forecasting information in their native Kannada language/dialects. This practical validation highlights the applicability and effectiveness of our advancements in real-world scenarios. The source code of proposed noise elimination technique, experimental results of ASR models and demo conversation of SQS are made publicly available at: https://sites.google.com/view/thimmarajayadavag/downloads

Unifying low and high-frequency noise mitigation: A novel dual noise barrier approach

Road highways and urban areas often employ noise barriers to shield residential areas from excessive traffic noise. Despite continuous efforts, developing materials for noise barriers that can effectively mitigate noise across a wide frequency range is still a challenge. Specifically, creating a single material capable of attenuating both low and high-frequency noise has proven to be a daunting task. Current practices incorporate porous materials for absorbing low-frequency noise at specific locations. To address this limitation and offer a technique for traffic noise reduction, this research proposes a novel approach utilizing two hindrances: pores for low-frequency and bristles for high-frequency sound waves. An ANSYS Harmonic Acoustic module simulation was conducted to assess the proposed technique's effectiveness. The simulation encompassed geometry definition, material selection, boundary condition constraints, meshing, and analysis. The results indicate a significant reduction in noise at high frequencies (16 000 Hz) of approximately 30 dB, decreasing from 101.4 to 68.1 dB. This demonstration showcases the potential of a staggered arrangement of bristles and pores to handle a wide range of frequency content and effectively mitigate both low and high-frequency traffic noise. The novel dual noise barrier approach offers a promising solution to combat diverse traffic noise challenges, providing a more peaceful soundscape for urban residents.

AE-BEGAN based Synthetic Data Augmentation for Sample-Limited High-Dimensional Problems with Application to NIR Spectral Data

Synthetic data augmentation holds substantial research and application value in scenarios characterized by limited samples and high dimensions. It enhances the analytical ability and efficiency of spectral analysis models. This paper proposes Autoencoder-Combined Boundary Equilibrium Generative Adversarial Networks (AE-BEGAN) as a new method for augmenting synthetic data in scenarios with limited samples and high dimensions, with a specific emphasis on near-infrared (NIR) spectral data. The spectral data first undergoes preprocessing procedures that encompass advanced noise reduction algorithms and techniques for removing abnormal samples, guaranteeing elimination of unwanted disturbances and outliers. Then, the pre-processed data is utilized to train the AE-BEGAN model, which generates augmented synthetic samples. Finally, real NIR spectral data obtained from lubricant samples exhibiting different water contents were employed to validate and test the performance of the model. The experimental results demonstrate that the AE-BEGAN model outperforms other GANs in generating synthetic data of high quality and diversity, as quantified by two evaluation metrics, α-Precision and β-Recall with scores of approximately 0.86 and 0.28, respectively. The application case study confirms that the AE-BEGAN model exhibits the capability to generate derived NIR spectra and expand the number of spectra in scenarios with limited samples and high dimensions.

Assessment of noise levels by using noise prediction modeling

The third-most dangerous type of pollution, after air and water pollution, according to the World Health Organization, is noise pollution. Brief and prolonged exposure to noise pollution can have negative consequences on people, including psychological disorders, including anxiety and depression, hypertension, hormonal imbalances, and a rise in blood pressure that can result in cardiovascular disease. The WHO estimates that up to 40% of individuals in Europe are currently exposed to loud noises. This study makes an effort to predict noise levels in and around the School of Architecture and Planning (SAP) campus using data on traffic volume and flow, vehicle speed, and geometric mean of the road. Additionally, it does a comparison between the expected and actual noise levels and offers workable noise reduction techniques. A mathematical model that takes into consideration has been used to forecast the equivalent noise level. By comparing the expected and actual noise levels, it was found that all values are beyond the permitted limits. Five different locations within SAP were used to assess the amount of noise present. The Lobby recorded the highest and lowest noise levels, respectively, at 75.63 and 74.15 dB (A). There were 73.05, 71.01, 71.81, and 70.5 dB (A) accordingly as the strongest noises in the classroom and auditorium. The maximum noise levels in the library was 63.76 and 64.54 dB (A), respectively. A maximum noise level of 75.29 and 68.14 dB (A) was recorded for the studio.

Denoising task-related fMRI: Balancing noise reduction against signal loss.

Preprocessing fMRI data requires striking a fine balance between conserving signals of interest and removing noise. Typical steps of preprocessing include motion correction, slice timing correction, spatial smoothing, and high-pass filtering. However, these standard steps do not remove many sources of noise. Thus, noise-reduction techniques, for example, CompCor, FIX, and ICA-AROMA have been developed to further improve the ability to draw meaningful conclusions from the data. The ability of these techniques to minimize noise while conserving signals of interest has been tested almost exclusively in resting-state fMRI and, only rarely, in task-related fMRI. Application of noise-reduction techniques to task-related fMRI is particularly important given that such procedures have been shown to reduce false positive rates. Little remains known about the impact of these techniques on the retention of signal in tasks that may be associated with systemic physiological changes. In this paper, we compared two ICA-based, that is FIX and ICA-AROMA, two CompCor-based noise-reduction techniques, that is aCompCor, and tCompCor, and standard preprocessing using a large (n = 101) fMRI dataset including noxious heat and non-noxious auditory stimulation. Results show that preprocessing using FIX performs optimally for data obtained using noxious heat, conserving more signals than CompCor-based techniques and ICA-AROMA, while removing only slightly less noise. Similarly, for data obtained during non-noxious auditory stimulation, FIX noise-reduction technique before analysis with a covariate of interest outperforms the other techniques. These results indicate that FIX might be the most appropriate technique to achieve the balance between conserving signals of interest and removing noise during task-related fMRI.

A spatially variant high-order variational model for Rician noise removal.

Rician noise removal is an important problem in magnetic resonance (MR) imaging. Among the existing approaches, the variational method is an essential mathematical technique for Rician noise reduction. The previous variational methods mainly employ the total variation (TV) regularizer, which is a first-order term. Although the TV regularizer is able to remove noise while preserving object edges, it suffers the staircase effect. Besides, the adaptability has received little research attention. To this end, we propose a spatially variant high-order variational model (SVHOVM) for Rician noise reduction. We introduce a spatially variant TV regularizer, which can adjust the smoothing strength for each pixel depending on its characteristics. Furthermore, SVHOVM utilizes the bounded Hessian (BH) regularizer to diminish the staircase effect generated by the TV term. We develop a split Bregman algorithm to solve the proposed minimization problem. Extensive experiments are performed to demonstrate the superiority of SVHOVM over some existing variational models for Rician noise removal.

Enhanced urban growth modelling: Incorporating regional development heterogeneity and noise reduction in a cellular automata model - a case study of Zhengzhou, China

Rapid global urban expansion necessitates accurate simulations to comprehend future growth patterns and their implications. Cellular automata, such as the widely used patch-generating land use simulation (PLUS) model, ensure reliable accuracy in urban growth simulations. However, previous studies have overlooked the development heterogeneity amongst sub-regions by treating the study area as the homogenous. Based on the PLUS model, this study introduces a new model coupling regional development heterogeneity and noise reduction (RDHNR-PLUS), which is an enhanced model that incorporates relative development coefficients derived from nighttime light data to capture regional heterogeneity. It also employs noise reduction techniques inspired by image classification's small patch processing method. Results demonstrate that the relative development coefficients effectively reveal the potential of different regions, while the noise reduction process mitigates simulation noise. The combined approach enhances overall accuracy, figure of merit, and structural similarity by 4.56 %, 20.44 %, and 5.06 %, respectively. Additionally, we simulate and analyse the built-up area of Zhengzhou from 2000 to 2030. The findings indicate rapid past growth, particularly in new urban areas, with projected future expansion likely to slow down. However, built-up areas dominated by medium to high and high densities will occupy three-quarters of the region. These results underscore the sustainability challenges faced by Zhengzhou, including disaster risk, traffic congestion, and air and noise pollution.

Comparison of Quality Assurance of Three Different CT Scanner Models: A Five-Year Study

This study focused on analysing the effectiveness of some CT scanner models; in this concern, this selected Siemens Medical CT Scanner, Philip CT Scanner, and GE Discovery STE. This study gathered primary data, which was the CT scan report of a patient, used by Philip CT Scanner. This report presented the patient’s scanning report for five years, which allowed a comparative study among these three machines. This study found that the Philip CT Scanner is less reliable due to its noise-reduction technique, while the Siemens Medical CT Scanner is most advantageous due to its PET/CT support to provide more efficient scanning.

Audio signal based danger detection using signal processing and deep learning

Since there have been more and more incidents of women being harassed in the recent past, girls need to think twice before going out of their houses. Sometimes, they are not even safe in their house or workplace. These circumstance does not change for children of all genders who stay alone because of their working parents or for other reasons. Also, there is no such organized procedure to ensure safety and take women and children out of such violence and harassment. To address this problem, the authors of this paper developed an Android-based automated system to detect danger for women and children using audio from the surroundings. As the Android phone is available to everyone nowadays, they focused on using this device rather than developing a system on some external hardware. Different signal processing methods with deep learning techniques are used for this work. This work also addresses noise from the environment for any chaos and nullifies them using different noise reduction techniques such as Reduce Energy Noise, Reduce Mel Frequency Cepstrum Coefficient (MFCC) up Noise, Reduce Median Noise, Reduce Centroid Noise, Audio DeNoise, Noisereduce by Sainburf et al. & Butterworth high pass filter. The Noisereduce by Sainburg et al. along with the InceptionV3 model architecture turns out to be the best to classify audio with 95.51% accuracy. A new model called AudioViT is introduced. It uses a Visual Transformer and Residual Network to identify the audio signal. The Android application also takes necessary action when any unfavorable situation is detected. Android device users can use this application without any cost, which will pave the way to ensure the safety of women and children.

Detection and evolution of disaster sources in high slopes of open-pit mines based on time-lapse high-density electricity method

High and steep slopes in open pit mines used for ore production require careful monitoring to ensure stability and safeguard lives, property, and social welfare. Understanding the evolutionary characteristics of hazard sources within these slopes is crucial for effective slope management. This article presents a novel approach to identify the evolution of hazard sources within slopes by utilizing the derivative of normalized inversion data versus time as a characteristic feature. To analyze the evolution of hazard sources, raw apparent resistivity data collected at different times are processed using smoothing and wavelet denoising algorithms to reduce noise. Geological constraints are then applied to the collected data through the parametric region method. The least squares method is employed for independent inversion, and the inversion results are standardized. Subsequently, the standardized inversion results are used to derive the derivative of resistivity versus time equation, which reveals the evolution of hazard sources within the slope. A case study is conducted on a large open pit rocky slope to analyze the effectiveness of the proposed approach. The results demonstrate the successful reduction of noise through the smoothing and wavelet threshold denoising algorithms, effectively suppressing interference caused by acquisition equipment and environmental electromagnetic noise. The application of the parametric region method with geological constraints highlights the spatial and temporal evolution characteristics of hazard sources within the slope. The findings indicate varying degrees of evolution of hazard sources within the slope, with significant changes observed mainly in the shallow part influenced by water erosion and blasting vibration. Areas with lithological stability show minimal changes, while original hazard source areas have evolved to different extents, distinguishable based on the size and sparsity of contour values. By combining inversion data analysis, noise reduction techniques, and geological constraints, the proposed approach provides valuable insights into the evolution of hazard sources within slopes. This information can aid in the development of effective slope management strategies in open pit mines, contributing to enhanced safety and protection of valuable assets.