47,739 results on '"noise reduction"'
Search Results
552. Reduction of Noise Emission of Hydraulic Power Units
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Fiebig, Wiesław, Rosikowski, Piotr, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Stryczek, Jarosław, editor, and Warzyńska, Urszula, editor
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- 2021
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553. Computed Tomography Image Reconstruction Using Fuzzy Complex Diffusion Regularization
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Devi, Manju, Singh, Sukhdip, Tiwari, Shailendra, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Manoj Kumar, editor, Dhaka, Vijaypal Singh, editor, Perumal, Thinagaran, editor, Dey, Nilanjan, editor, and Tavares, João Manuel R. S., editor
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- 2021
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554. Mobile Robot Platform for Studying Sensor Fusion Localization Algorithms
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Negirla, Paul-Onut, Nagy, Mariana, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Balas, Valentina Emilia, editor, Jain, Lakhmi C., editor, Balas, Marius Mircea, editor, and Shahbazova, Shahnaz N., editor
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- 2021
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555. Noise Reduction with Detail Preservation in Low-Dose Dental CT Images by Morphological Operators and BM3D
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Marconato Stringhini, Romulo, Welfer, Daniel, Tello Gamarra, Daniel Fernando, Nogara Dotto, Gustavo, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Siarry, Patrick, editor, Ma, Kun, editor, and Kaklauskas, Arturas, editor
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- 2021
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556. Investigation on Noise Reduction During Cutting of High-Strength Materials Based on Machine Acoustic Simulation
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Friesen, D., Krimm, R., Fries, S., Brunotte, K., Behrens, B.-A., Behrens, Bernd-Arno, editor, Brosius, Alexander, editor, Hintze, Wolfgang, editor, Ihlenfeldt, Steffen, editor, and Wulfsberg, Jens Peter, editor
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- 2021
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557. Identifying Ecosystem Service Hotspots to Support Urban Planning in Trento
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Geneletti, Davide, Cortinovis, Chiara, Newman, Peter, Series Editor, Desha, Cheryl, Series Editor, Sanches-Pereira, Alessandro, Series Editor, Arcidiacono, Andrea, editor, and Ronchi, Silvia, editor
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- 2021
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558. Unsupervised Noise Reduction for Nanochannel Measurement Using Noise2Noise Deep Learning
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Takaai, Takayuki, Tsutsui, Makusu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gupta, Manish, editor, and Ramakrishnan, Ganesh, editor
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- 2021
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559. Reducing Negative Impact of Noise in Boolean Matrix Factorization with Association Rules
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Krajča, Petr, Trnecka, Martin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Abreu, Pedro Henriques, editor, Rodrigues, Pedro Pereira, editor, Fernández, Alberto, editor, and Gama, João, editor
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- 2021
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560. Fine-Grained Entity Typing via Label Noise Reduction and Data Augmentation
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Li, Haoyang, Lin, Xueling, Chen, Lei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jensen, Christian S., editor, Lim, Ee-Peng, editor, Yang, De-Nian, editor, Lee, Wang-Chien, editor, Tseng, Vincent S., editor, Kalogeraki, Vana, editor, Huang, Jen-Wei, editor, and Shen, Chih-Ya, editor
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- 2021
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561. Spectral Analysis for Automatic Speech Recognition and Enhancement
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Oruh, Jane, Viriri, Serestina, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Renault, Éric, editor, Boumerdassi, Selma, editor, and Mühlethaler, Paul, editor
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- 2021
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562. Performance Optimization
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Gao, Shuo, Yan, Shuo, Zhao, Hang, Nathan, Arokia, Gao, Shuo, Yan, Shuo, Zhao, Hang, and Nathan, Arokia
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- 2021
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563. Implementation of an FPGA Real-Time Configurable System for Enhancement of Lung and Heart Images
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Sowmya, K. B., Rakshak Udupa, T. S., Holla, Shashank K., Khelassi, Abdeldjalil, editor, and Estrela, Vania Vieira, editor
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- 2021
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564. Simulation Study on Noise Reduction Effect of Substation Noise Barrier
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Cai, Xuan, Zhan, Xuelei, Cai, Yong, Wang, Li, Oberst, Sebastian, editor, Halkon, Benjamin, editor, Ji, Jinchen, editor, and Brown, Terry, editor
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- 2021
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565. Vortex shedding-induced noise reduction using (DBD) plasma actuator
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Al-Sadawi, Laith Ayad, Chong, T. P., and Wissink, J.
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629.1 ,DBD plasma actuator ,Plasma ,Vortex shredding ,Flow control ,Noise reduction - Abstract
The Dielectric Barrier Discharge (DBD) plasma actuators have received a significant attention of many researchers in the last few decades. The main focus of these studies has been on the flow control areas such as turbulent boundary layer separation and turbulent skin friction reduction. Little attention has been paid on the effect of the DBD plasma actuators on the aerodynamic noise reduction. In this regard, the aim of the current work is to investigate the effect of the DBD plasma actuator driven at relatively low voltages on vortex-induced noise. The first part of the current work includes an extensive assessment of the effect of the DBD plasma actuator on the narrowband tonal noise radiated from a flat plate with blunt trailing edge and an airfoil (NACA 0012) with blunt and cut-in type serrated trailing edge. The measurements were carried out at Reynolds numbers between 0.75 x 10 to the power of 5 and 4 x 10 to the power of 5. It is found that the DBD plasma actuator effectiveness depends on the direction of the generated electric wind. For example, a high reduction in the narrowband tonal noise level is achieved when a direct streamwise electric wind is injected into the wake region. However, using a plasma actuator, which can induce streamwise vortices into the wake region, shows more superior noise reduction capability at lower voltages. Flow measurement results revealed that the mechanism responsible for the narrowband tonal noise reduction when the electric wind is directly injected into the wake is not due the momentum injection into the wake deficit. Rather, the streamwise jet isolates the two separated shear layers and prevents the interaction between them. On the other hand, it is found that the break-up of the spanwise coherence of the vortex shedding is responsible for the significant reduction in the tonal noise level when the spanwise actuation is used. The second part of the current work comprises the effect of the DBD plasma actuator on both the narrowband tonal noise and interaction broadband noise radiated from both single and tandem cylinder, respectively. The experiments were conducted at subcritical Reynolds number ReD = 1.1 x 10 to the power of 4. The actuators were positioned at different azimuthal angles 27° ≤ θj ≤ 153°. For the single cylinder case, the acoustic results show the DBD plasma actuator that is positioned at θj = 133° leads to a more reduction in the narrowband tonal noise level when compared to the other angles. It is found that the streamwise jet produced by the plasma actuators plays an important role in prevention of the interaction between the shear layers that separates from the cylinder. For the tandem cylinders case, the acoustic results show that the simultaneous actuation of both the upstream and the downstream cylinders leads to more reduction in both the narrowband tonal noise and the interaction broadband noise level compared with the case where only the upstream or the downstream cylinder is actuated. The mechanism responsible for this noise reduction is found to be mainly due to the streamwise jet induced by the upstream cylinder activation, which delays the vortex shedding formation and reduces the turbulence intensity in the near wake region. On the other hand, the plasma induced jet against the main-flow direction works as a virtual fluidic barrier which displaces the wake produced by the upstream cylinder away from the downstream cylinder.
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- 2018
566. Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors
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Artem T. Turov, Fedor L. Barkov, Yuri A. Konstantinov, Dmitry A. Korobko, Cesar A. Lopez-Mercado, and Andrei A. Fotiadi
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distributed acoustic sensing ,DAS ,activation function ,noise reduction ,optical fiber sensors ,image processing ,Industrial engineering. Management engineering ,T55.4-60.8 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
This work studies the application of low-cost noise reduction algorithms for the data processing of distributed acoustic sensors (DAS). It presents an improvement of the previously described methodology using the activation function of neurons, which enhances the speed of data processing and the quality of event identification, as well as reducing spatial distortions. The possibility of using a cheaper radiation source in DAS setups is demonstrated. Optimal algorithms’ combinations are proposed for different types of the events recorded. The criterion for evaluating the effectiveness of algorithm performance was an increase in the signal-to-noise ratio (SNR). The finest effect achieved with a combination of algorithms provided an increase in SNR of 10.8 dB. The obtained results can significantly expand the application scope of DAS.
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- 2023
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567. Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network
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Trinh Thuc Lai, Tuan Phong Tran, Jaehyuk Cho, and Myungsik Yoo
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wireless sensor network ,noise reduction ,weather effect ,data reconstruction ,convolutional autoencoder ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
Maintaining data dependability within wireless sensor network (WSN) systems has significant importance. Nevertheless, the deployment of systems in unattended and hostile areas poses a major challenge in dealing with noise. Consequently, several investigations have been conducted to address the issue of noise-affected data recovery. Nevertheless, previous research has primarily focused on the internal noise of the system. Neglecting to include external factors that impact the WSN system in the study might lead to findings that are not true to reality. Hence, this research takes into account both internal and external noise factors, such as rain, fog, or snow conditions. Moreover, in order to maintain the temporal characteristics and intersensor relationships, the data from multiple sensor nodes are consolidated into a two-dimensional matrix format. The stacked convolutional autoencoder (SCAE) model is proposed, which has the capability to extract data features. The stack design of the SCAE enables it to effectively mitigate the issue of vanishing gradients. Moreover, the weight sharing approach used between the two subnetworks also enhances the efficiency of the weight initialization procedure. Thorough experiments are conducted using both simulated WSN systems and real-world sensing data. Experimental results demonstrate that the SCAE outperforms existing methods for reconstructing noisy data.
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- 2023
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568. Improving Software Defect Prediction in Noisy Imbalanced Datasets
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Haoxiang Shi, Jun Ai, Jingyu Liu, and Jiaxi Xu
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software defect prediction ,class imbalance ,undersampling ,propensity score matching ,oversampling ,noise reduction ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
Software defect prediction is a popular method for optimizing software testing and improving software quality and reliability. However, software defect datasets usually have quality problems, such as class imbalance and data noise. Oversampling by generating the minority class samples is one of the most well-known methods to improving the quality of datasets; however, it often introduces overfitting noise to datasets. To better improve the quality of these datasets, this paper proposes a method called US-PONR, which uses undersampling to remove duplicate samples from version iterations and then uses oversampling through propensity score matching to reduce class imbalance and noise samples in datasets. The effectiveness of this method was validated in a software prediction experiment that involved 24 versions of software data in 11 projects from PROMISE in noisy environments that varied from 0% to 30% noise level. The experiments showed a significant improvement in the quality of datasets pre-processed by US-PONR in noisy imbalanced datasets, especially the noisiest ones, compared with 12 other advanced dataset processing methods. The experiments also demonstrated that the US-PONR method can effectively identify the label noise samples and remove them.
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- 2023
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569. A Simple Denoising Algorithm for Real-World Noisy Camera Images
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Manfred Hartbauer
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night vision ,real-world camera pictures ,noise reduction ,multi-core denoising ,image enhancement ,image processing ,Photography ,TR1-1050 ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
The noise statistics of real-world camera images are challenging for any denoising algorithm. Here, I describe a modified version of a bionic algorithm that improves the quality of real-word noisy camera images from a publicly available image dataset. In the first step, an adaptive local averaging filter was executed for each pixel to remove moderate sensor noise while preserving fine image details and object contours. In the second step, image sharpness was enhanced by means of an unsharp mask filter to generate output images that are close to ground-truth images (multiple averages of static camera images). The performance of this denoising algorithm was compared with five popular denoising methods: bm3d, wavelet, non-local means (NL-means), total variation (TV) denoising and bilateral filter. Results show that the two-step filter had a performance that was similar to NL-means and TV filtering. Bm3d had the best denoising performance but sometimes led to blurry images. This novel two-step filter only depends on a single parameter that can be obtained from global image statistics. To reduce computation time, denoising was restricted to the Y channel of YUV-transformed images and four image segments were simultaneously processed in parallel on a multi-core processor.
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- 2023
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570. Research on Identification and Detection of Transmission Line Insulator Defects Based on a Lightweight YOLOv5 Network
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Zhilong Yu, Yanqiao Lei, Feng Shen, Shuai Zhou, and Yue Yuan
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insulators ,defect detection ,YOLOv5 ,noise reduction ,RepVGG ,Science - Abstract
Transmission line fault detection using drones provides real-time assessment of the operational status of transmission equipment, and therefore it has immense importance in ensuring stable functioning of the transmission lines. Currently, identification of transmission line equipment relies predominantly on manual inspections that are susceptible to the influence of natural surroundings, resulting in sluggishness and a high rate of false detections. In view of this, in this study, we propose an insulator defect recognition algorithm based on a YOLOv5 model with a new lightweight network as the backbone network, combining noise reduction and target detection. First, we propose a new noise reduction algorithm, i.e., the adaptive neighborhood-weighted median filtering (NW-AMF) algorithm. This algorithm employs a weighted summation technique to determine the median value of the pixel point’s neighborhood, effectively filtering out noise from the captured aerial images. Consequently, this approach significantly mitigates the adverse effects of varying noise levels on target detection. Subsequently, the RepVGG lightweight network structure is improved to the newly proposed lightweight structure called RcpVGG-YOLOv5. This structure facilitates single-branch inference, multi-branch training, and branch normalization, thereby improving the quantization performance while simultaneously striking a balance between target detection accuracy and speed. Furthermore, we propose a new loss function, i.e., Focal EIOU, to replace the original CIOU loss function. This optimization incorporates a penalty on the edge length of the target frame, which improves the contribution of the high-quality target gradient. This modification effectively addresses the issue of imbalanced positive and negative samples for small targets, suppresses background positive samples, and ultimately enhances the accuracy of detection. Finally, to align more closely with real-world engineering applications, the dataset utilized in this study consists of machine patrol images captured by the Unmanned Aerial Systems (UAS) of the Yunnan Power Supply Bureau Company. The experimental findings demonstrate that the proposed algorithm yields notable improvements in accuracy and inference speed compared to YOLOv5s, YOLOv7, and YOLOv8. Specifically, the improved algorithm achieves a 3.7% increase in accuracy and a 48.2% enhancement in inference speed compared to those of YOLOv5s. Similarly, it achieves a 2.7% accuracy improvement and a 33.5% increase in inference speed compared to those of YOLOv7, as well as a 1.5% accuracy enhancement and a 13.1% improvement in inference speed compared to those of YOLOv8. These results validate the effectiveness of the proposed algorithm through ablation experiments. Consequently, the method presented in this paper exhibits practical applicability in the detection of aerial images of transmission lines within complex environments. In future research endeavors, it is recommended to continue collecting aerial images for continuous iterative training, to optimize the model further, and to conduct in-depth investigations into the challenges associated with detecting small targets. Such endeavors hold significant importance for the advancement of transmission line detection.
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- 2023
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571. Investigating Perampanel Antiepileptic Drug by DFT Calculations and SERS with Custom Spinning Cell
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Nicolò Simone Villa, Chiara Picarelli, Federica Iacoe, Chiara Giuseppina Zanchi, Paolo M. Ossi, Andrea Lucotti, and Matteo Tommasini
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quantitative SERS ,spinning cell ,therapeutic drug monitoring ,noise reduction ,Organic chemistry ,QD241-441 - Abstract
SERS, a clinical practice where medical doctors can monitor the drug concentration in biological fluids, has been proposed as a viable approach to therapeutic drug monitoring (TDM) of the antiepileptic drug Perampanel. The adoption of an acidic environment during the SERS experiments was found to be effective in enhancing the spectroscopic signal. In this work, we combine SERS experiments, conducted with a custom spinning cell in controlled acidic conditions, with DFT calculations aimed at investigating the possible protonated forms of Perampanel. The DFT-simulated Raman spectra of protonated Perampanel accounts for most of the observed SERS signals, thus explaining the effective role of protonation of the analyte. Our results suggest protonation as a viable approach to fostering SERS of alkaline drugs.
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- 2023
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572. Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging
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Monika Wolfmayr
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noise reduction ,nonlinear optimization ,low-rank modeling ,hyperspectral imaging ,signal-to-noise ratio improvement ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
An approach to parameter optimization for the low-rank matrix recovery method in hyperspectral imaging is discussed. We formulate an optimization problem with respect to the initial parameters of the low-rank matrix recovery method. The performance for different parameter settings is compared in terms of computational times and memory. The results are evaluated by computing the peak signal-to-noise ratio as a quantitative measure. The potential improvement in the performance of the noise reduction method is discussed when optimizing the choice of the initial values. The optimization method is tested on standard and openly available hyperspectral data sets, including Indian Pines, Pavia Centre, and Pavia University.
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- 2023
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573. Nonlinear Nonlocal Algorithm for Video Filtering
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Ahmed Fouad El Ouafdi and Hassan El Houari
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video filter ,noise reduction ,nonlocal filter ,bayesian filtering ,nonlinear filter ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Video sequences are frequently contaminated by noise throughout the acquisition process, resulting in considerable degradation of video display quality. In this paper, we present a novel method of video filtering. The proposed filter is developed from an optimization problem in which a Bayesian term and a noisy video sequence prior distribution are combined. The method begins by segmenting the video sequence into space-time blocks and then substituting each noisy block by a weighted average of non-local neighbor blocks. Gradient-based weights are used to dynamically adjust the edge preservation and smoothness of the reference block. The obtained formulation enables nonlinear filtering and, hence, preserving key features such as edges and corners while using the intrinsic Bayesian filtering framework. Experiments on different video sequences with varying degrees of noise show that the proposed method performs better than state-of-the-art video filtering approaches.
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- 2021
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574. A low‐noise current‐reused CMOS active inductor by exploiting Gm‐boosting technique
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Abdollah Sabbaghi and Emad Ebrahimi
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active inductor ,Gm‐boosting ,low noise ,low power ,noise reduction ,Telecommunication ,TK5101-6720 ,Electricity and magnetism ,QC501-766 - Abstract
Abstract This work introduces a new low‐noise current‐reused CMOS active inductor (AI) based on Gyrator‐C configuration. In the proposed AI, a simple common‐source amplifier is utilised for boosting effective Gm and improving noise performance of the AI. While the Gm‐boosting technique improves the noise performance as well as the quality factor of the inductor, a resistor is also added to the feedback path for more quality factor improvement. Rigorous analysis of the proposed circuit shows a significant noise performance and quality factor enhancement. In order to verify the concept and confirm the mathematical analysis, the AI is designed and simulated in a commercial 0.18 μm RF‐CMOS technology. The simulation results show that the proposed inductor operates in 1 to 7.2 GHz frequency range, has a 9 nH inductance at 2.864 GHz frequency and 650 μW total power dissipation at 1.8‐V supply voltage. Maximum quality factor of 90 is achieved at 2.864 GHz frequency and a quality factor greater than 40 is obtained from 2.4 to 3.3 GHz. The input‐referred current noise of the proposed inductor is as low as 22 pA/√Hz showing 30.5% improvement compared to the conventional AI. The proposed AI is also tunable and sweeping the tuning voltage results in changing extracted inductance from 4.35 to 15.2 nH with only a chip area of 0.003 mm2. Post‐layout and different Monte Carlo simulation results also confirm the robust operation of the proposed AI against different process non‐idealities.
- Published
- 2021
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575. Applications of Machine Learning in Ambulatory ECG
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Joel Xue and Long Yu
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ambulatory ECG ,machine learning ,deep learning ,pattern recognition ,noise reduction ,Holter ECG ,Medicine - Abstract
The ambulatory ECG (AECG) is an important diagnostic tool for many heart electrophysiology-related cases. AECG covers a wide spectrum of devices and applications. At the core of these devices and applications are the algorithms responsible for signal conditioning, ECG beat detection and classification, and event detections. Over the years, there has been huge progress for algorithm development and implementation thanks to great efforts by researchers, engineers, and physicians, alongside the rapid development of electronics and signal processing, especially machine learning (ML). The current efforts and progress in machine learning fields are unprecedented, and many of these ML algorithms have also been successfully applied to AECG applications. This review covers some key AECG applications of ML algorithms. However, instead of doing a general review of ML algorithms, we are focusing on the central tasks of AECG and discussing what ML can bring to solve the key challenges AECG is facing. The center tasks of AECG signal processing listed in the review include signal preprocessing, beat detection and classification, event detection, and event prediction. Each AECG device/system might have different portions and forms of those signal components depending on its application and the target, but these are the topics most relevant and of greatest concern to the people working in this area.
- Published
- 2021
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576. A hybrid learning-based stochastic noise eliminating method with attention-Conv-LSTM network for low-cost MEMS gyroscope
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Yaohua Liu, Jinqiang Cui, and Wei Liang
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MEMS IMU ,deep learning ,noise reduction ,inertial navigation ,random noise ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Low-cost inertial measurement units (IMUs) based on microelectromechanical system (MEMS) have been widely used in self-localization for autonomous robots due to their small size and low power consumption. However, the low-cost MEMS IMUs often suffer from complex, non-linear, time-varying noise and errors. In order to improve the low-cost MEMS IMU gyroscope performance, a data-driven denoising method is proposed in this paper to reduce stochastic errors. Specifically, an attention-based learning architecture of convolutional neural network (CNN) and long short-term memory (LSTM) is employed to extract the local features and learn the temporal correlation from the MEMS IMU gyroscope raw signals. The attention mechanism is appropriately designed to distinguish the importance of the features at different times by automatically assigning different weights. Numerical real field, datasets and ablation experiments are performed to evaluate the effectiveness of the proposed algorithm. Compared to the raw gyroscope data, the experimental results demonstrate that the average errors of bias instability and angle random walk are reduced by 57.1 and 66.7%.
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- 2022
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577. Environmental Noise Reduction based on Deep Denoising Autoencoder
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A. Azmat, I. Ali, W. Ariyanti, M. G. L. Putra, and T. Nadeem
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DDAE ,limited data ,noise reduction ,autoencoders ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Technology (General) ,T1-995 ,Information technology ,T58.5-58.64 - Abstract
Speech enhancement plays an important role in Automatic Speech Recognition (ASR) even though this task remains challenging in real-world scenarios of human-level performance. To cope with this challenge, an explicit denoising framework called Deep Denoising Autoencoder (DDAE) is introduced in this paper. The parameters of DDAE encoder and decoder are optimized based on the backpropagation criterion, where all denoising autoencoders are stacked up instead of recurrent connections. For better speech estimation in real and noisy environments, we include matched and mismatched noisy and clean pairs of speech data to train the DDAE. The DDAE has the ability to achieve optimal results even for a limited amount of training data. Our experimental results show that the proposed DDAE outperformed the baseline algorithms. The DDAE shows superior performances based on three-evaluation metrics in noisy and clean pairs of speech data compared to three baseline algorithms.
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- 2022
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578. MSDenoiser: Muti-step adaptive denoising framework for super-resolution image from single molecule localization microscopy
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Qianghui Feng, Qihang Song, Meng Yan, Zhen Li Huang, and Zhengxia Wang
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noise reduction ,super-resolution image processing ,multi-step denoising framework ,adaptive parameter selection ,localization data ,Physics ,QC1-999 - Abstract
Recent developments in single-molecule localization microscopy (SMLM) enable researchers to study macromolecular structures at the nanometer scale. However, due to the complexity of imaging process, there are a variety of complex heterogeneous noises in SMLM data. The conventional denoising methods in SMLM can only remove a single type of noise. And, most of these denoising algorithms require manual parameter setting, which is difficult and unfriendly for biological researchers. To solve these problems, we propose a multi-step adaptive denoising framework called MSDenoiser, which incorporates multiple noise reduction algorithms and can gradually remove heterogeneous mixed noises in SMLM. In addition, this framework can adaptively learn algorithm parameters based on the localization data without manually intervention. We demonstrate the effectiveness of the proposed denoising framework on both simulated data and experimental data with different types of structures (microtubules, nuclear pore complexes and mitochondria). Experimental results show that the proposed method has better denoising effect and universality.
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- 2022
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579. A new conjugate gradient algorithm for noise reduction in signal processing and image restoration
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Pan Huang and Kaiping Liu
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signal processing ,image restoration ,weak convergence ,noise reduction ,conjugate gradient method ,Physics ,QC1-999 - Abstract
Noise-reduction methods are an area of intensive research in signal processing. In this article, a new conjugate gradient method is proposed for noise reduction in signal processing and image restoration. The superiority of this method lies in its employment of the ideas of accelerated conjugate gradient methods in conjunction with a new adaptive method for choosing the step size. In this work, using some assumptions, the weak convergence of the designed method was established. As example applications, we implemented our method to solve signal-processing and image-restoration problems. The results of our numerical simulations demonstrate the effectiveness and superiority of the new approach.
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- 2022
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580. A new approach to signal filtering method using K-means clustering and distance-based Kalman filtering
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M. Syauqi Hanif Ardani, Riyanarto Sarno, Malikhah Malikhah, Doni Putra Purbawa, Shoffi Izza Sabilla, Kelly Rossa Sungkono, Chastine Fatichah, Dwi Sunaryono, and Rahadian Indarto Susilo
- Subjects
Electronic nose ,K-means clustering ,Kalman filtering ,Noise reduction ,Signal processing ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
Human axillary odours taken by an electronic nose (e-nose) device that uses a Metal Oxide Semiconductor (MOS) sensor not only contains a gas signal from the pure source of the axillary odour but also has the potential to contain other substances such as perfume and deodorant. This situation requires noise reduction so that dirty data can be cleaned and produce better predictions without wasting a lot of data. The approach taken in this study is to detect data clusters and data centroids from each reference data. Dimensional reduction using Linear Discriminant Analysis (LDA) on the reference data is carried out, then look for the centroid of each data using K-Means Clustering and use it to be a good signal estimate and process using Kalman Filtering so that it can be used to process axillary odour data containing deodorant. The proposed method was tested by a stacked Deep Neural Network (DNN) approach and can increase accuracy by 18.95% and balanced accuracy by 11.865% compared to original invalid data before filtering. The proposed method is also tested by other classification methods and able to produce the highest accuracy with 79.29% in Support Vector Classifier (SVC) and Multi-Layer Perception (MLP), while other filtering methods only get the highest accuracy with 69.03% also in SVC and MLP. We also analysed the execution time of each tested methods.
- Published
- 2022
- Full Text
- View/download PDF
581. Adaptive Image Reconstruction for Defense Against Adversarial Attacks.
- Author
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Yang, Yanan, Shih, Frank Y., and Chang, I-Cheng
- Subjects
- *
IMAGE reconstruction , *NOISE control , *FRAUD , *DECEPTION - Abstract
Adversarial attacks can fool convolutional networks and make the systems vulnerable to fraud and deception. How to defend against malicious attacks is a critical challenge in practice. Adversarial attacks are often conducted by adding tiny perturbations on images to cause network misclassification. Noise reduction can defend the attacks; however, it is not suited for all the cases. Considering that different models have different tolerance abilities on adversarial attacks, we develop a novel detecting module to remove noise by adaptive process and detect adversarial attacks without modifying the models. Experimental results show that by comparing the classification results on adversarial samples of MNIST and two subclasses of ImageNet datasets, our models can successfully remove most of the noise and obtain detection accuracies of 97.71% and 92.96%, respectively. Furthermore, our adaptive module can be assembled into different networks to achieve detection accuracies of 70.83% and 71.96%, respectively, on the white-box adversarial attacks of ResNet18 and SCD01MLP images. The best accuracy of 62.5% is obtained for both networks when dealing with the black-box attacks. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
582. A Novel Multi-User Carrier Index Differential Chaos Shift Keying Modulation Scheme.
- Author
-
Yang, Hua, Xu, Xiaofei, Jiang, Guoping, and Luo, Ronghua
- Subjects
- *
CODE division multiple access , *ADDITIVE white Gaussian noise , *HADAMARD codes , *RAYLEIGH fading channels , *BIT error rate , *IMAGE encryption - Abstract
In this paper, a novel multi-user carrier index differential chaos shift keying (MU CI-DCSK) modulation scheme is proposed. For a better utilization of spectrum resources, each user is allocated a private subcarrier for reference signal transmission, while the remaining subcarriers are public and shared by all users to transmit their own data-bearing signals. To avoid user interferences in this design, users are distinguished in a code division multiple access (CDMA) way based on Walsh codes. By exploiting the redundancies in the transmitted signals, repeated segments of received signals are averaged, leading to greatly reduced noises and a noticeable improvement in bit error rate (BER) performance. BER expressions of this new system are derived over the additive white Gaussian noise (AWGN) and multi-path Rayleigh fading channels. Simulation results and comparisons are performed to verify the feasibility and advantages of this new scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
583. Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement.
- Author
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Ribas, Dayana, Miguel, Antonio, Ortega, Alfonso, and Lleida, Eduardo
- Subjects
ARTIFICIAL neural networks ,SPEECH enhancement ,STATISTICAL learning ,DEEP learning ,SPEECH - Abstract
This paper proposes a Deep Learning (DL) based Wiener filter estimator for speech enhancement in the framework of the classical spectral-domain speech estimator algorithm. According to the characteristics of the intermediate steps of the speech enhancement algorithm, i.e., the SNR estimation and the gain function, there is determined the best usage of the network at learning a robust instance of the Wiener filter estimator. Experiments show that the use of data-driven learning of the SNR estimator provides robustness to the statistical-based speech estimator algorithm and achieves performance on the state-of-the-art. Several objective quality metrics show the performance of the speech enhancement and beyond them, there are examples of noisy vs. enhanced speech available for listening to demonstrate in practice the skills of the method in simulated and real audio. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
584. Bunch graph based dimensionality reduction using auto-encoder for character recognition.
- Author
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Bhadoria, Robin Singh, Samanta, Sovan, Pathak, Yadunath, Shukla, Piyush Kumar, Zubi, Ahmad Ali, and Kaur, Manjit
- Subjects
FEEDFORWARD neural networks ,PATTERN recognition systems ,CONVOLUTIONAL neural networks - Abstract
Sometimes a group of similar types of dimensions is also treated as nodes. However, these groups can be considered as bunch nodes which may contain several nodes. This paper also justifies the study on bunch graphs which introduced a concept of graphs, where bunch nodes are also allowed. The auto-encoder, a specific type of feedforward neural network generally applied for encoding data in an unsupervised learning methodology to achieve good performance and better-classified data. This kind of network is composed of an encoder and decoder. The encoder compresses the data to an extent or layer, and then from that central layer decoder starts reconstructing the original data. This paper also investigates the dimensionality reduction ability of auto-encoders for character recognition and manipulates the results to accomplish better handling side of auto-encoders. This paper also focuses on the abilities of auto-encoders to reduce noise in data along with dimensionality reduction, trying to interpret the difference between results generated using bunch graph cut techniques. The dataset associated with computing for implementation purposes has been taken from MNIST dataset. Mainly, the two-dimensional plots are used in this paper for comparing results generated associated with different parameters that help in recognizing the character as partial and non-partial separabilities. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
585. Noise Reduction for a High-Speed Jet of an SST Engine Based on Acoustic Test Results for a Rectangular Nozzle in the TsAGI AC-2 Anechoic Chamber.
- Author
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Belyaev, I. V., Gorbovskoy, V. S., Kazhan, A. V., and Faranosov, G. A.
- Subjects
- *
NOISE control , *JET plane noise , *NOZZLES , *ANECHOIC chambers , *JET engines , *COMMUNITIES - Abstract
It is understood that jet noise is an important noise source of future supersonic civil transport (SST) during takeoff and cutback regimes so that the development of SST jet-noise reduction methods is a necessary condition for compliance of the SST with the community noise regulations. Within the framework of investigation on the SST jet-noise reduction, the acoustic tests of a small-scale rectangular nozzle of the "mixer–ejector" type with a noise attenuation system are carried out for regimes corresponding to takeoff and cutback including the presence of coflow. The spectral characteristics and noise directivities in the far field are obtained for a wide range of azimuthal and polar angles. The results of these measurements are compared with two basic configurations: (i) one round nozzle with the same pressure and thrust as the rectangular nozzle; (ii) two identical round nozzles with the same pressure and total thrust as the rectangular nozzle. For equivalent jets corresponding to these two basic configurations, the noise in the far field is determined on the basis of the TsAGI semi-empirical method for calculating jet noise. The comparison with experimental data for the rectangular nozzle showed that there are ranges of observation angles and frequencies where noise reduction is obtained due to the use of the rectangular nozzle as compared to the equivalent round jets, and ranges where the rectangular nozzle is noisier. The recalculation of the spectra to the full scales and the assesment of SST noise in the EPNL metric show that the use of this rectangular nozzle can lead to the net effect of community noise reduction as compared to the round nozzles. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
586. Unsupervised Image Restoration Using Partially Linear Denoisers.
- Author
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Ke, Rihuan and Schonlieb, Carola-Bibiane
- Subjects
- *
IMAGE reconstruction , *IMAGE denoising , *ARTIFICIAL neural networks , *SUPERVISED learning , *CONVOLUTIONAL neural networks - Abstract
Deep neural network based methods are the state of the art in various image restoration problems. Standard supervised learning frameworks require a set of noisy measurement and clean image pairs for which a distance between the output of the restoration model and the ground truth, clean images is minimized. The ground truth images, however, are often unavailable or very expensive to acquire in real-world applications. We circumvent this problem by proposing a class of structured denoisers that can be decomposed as the sum of a nonlinear image-dependent mapping, a linear noise-dependent term and a small residual term. We show that these denoisers can be trained with only noisy images under the condition that the noise has zero mean and known variance. The exact distribution of the noise, however, is not assumed to be known. We show the superiority of our approach for image denoising, and demonstrate its extension to solving other restoration problems such as image deblurring where the ground truth is not available. Our method outperforms some recent unsupervised and self-supervised deep denoising models that do not require clean images for their training. For deblurring problems, the method, using only one noisy and blurry observation per image, reaches a quality not far away from its fully supervised counterparts on a benchmark dataset. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
587. Low-frequency sound absorption of a tunable multilayer composite structure.
- Author
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Shao, Hanbo, He, Jincheng, Zhu, Jiang, Chen, Guoping, and He, Huan
- Subjects
- *
COMPOSITE structures , *ABSORPTION coefficients , *POROUS materials , *ACOUSTIC devices , *ABSORPTION of sound , *MULTILAYERED thin films , *ABSORPTION - Abstract
Our work investigates a tunable multilayer composite structure for applications in the area of low-frequency absorption. This acoustic device is comprised of three layers, Helmholtz cavity layer, microperforated panel layer, and the porous material layer. For the simulation and experiment in our research, the absorber can fulfill a twofold requirement: the acoustic absorption coefficient can reach near 0.8 in very low frequency (400 Hz) and the range of frequency is very wide (400–3000 Hz). In all its absorption frequency, the average of the acoustic absorption coefficient is over 0.9. Besides, the absorption coefficient can be tunable by the scalable cavity. The multilayer composite structure in our article solved the disadvantages in single material. For example, small absorption coefficient in low frequency in traditional material such as microperforated panel and porous material and narrow reduction frequency range in acoustic metamaterial such as Helmholtz cavity. The design of the composite structure in our article can have more wide application than single material. It can also give us a novel idea to produce new acoustic devices. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
588. RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing imbalanced data classification.
- Author
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Arafa, Ahmed, El-Fishawy, Nawal, Badawy, Mohammed, and Radad, Marwa
- Subjects
NOISE ,MACHINE learning ,CLASSIFICATION ,NOISE control ,OUTLIER detection - Abstract
Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot of the real-world data sets are naturally imbalanced. So, imbalanced classification is a serious problem in machine learning. The imbalanced class distribution misleads classifiers from correctly classifying the minor class. This paper introduces Reduced Noise-SMOTE (RN-SMOTE) for pre-processing imbalanced data. RN-SMOTE firstly, oversamples the training data using SMOTE which introduces noisy oversampled synthetic instances in the minority class. Then, applying DBSCAN to detect and remove noise. Next, the clean artificial instances are combined with the original data. Finally, RN-SMOTE applies SMOTE again to rebalance the dataset before introducing it to the underlying classifier. RN-SMOTE is evaluated using 9 different classifiers and 9 different imbalanced datasets with different imbalance ratios and five of them are used for outlier detection. The results proved that the performance of the classifiers has been improved with RN-SMOTE and outperformed the performance with original data and SMOTE with percentage based on the classifier, dataset and evaluation metric. Also, performance of RN-SMOTE has been compared to the performance of the current state of art and resulted in an increase up to 37.41%, 23.28%, 13.95% and 9.07% in terms of Recall, F1, Precision and Accuracy for RN-SMOTE. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
589. 超构材料波动功能调控研究进展.
- Author
-
李岩 and 金亚斌
- Subjects
CONSTRUCTION materials ,COMPOSITE materials ,CIVIL defense ,ENERGY harvesting ,NOISE control ,METAMATERIALS - Abstract
Copyright of Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2022
- Full Text
- View/download PDF
590. An E -Band High-Performance Variable Gain Low Noise Amplifier for Wireless Communications in 90-nm CMOS Process.
- Author
-
Wang, Yunshan, Chiu, Tzu-Yang, Chien, Chun-Chia, Tsai, Wei-Hsuan, and Wang, Huei
- Abstract
In this letter, a fully integrated variable gain low noise amplifier (VG-LNA) implemented in 90-nm CMOS process for ${E}$ -band millimeter-wave (MMW) backhaul communications is presented. The amplifier consists of two current-reused stages followed by a cascode stage and a current-steering cascode stage with $g_{m}$ -boosting and body-floating for higher gain. This work achieves a small-signal gain higher than 20 dB at 71.6–89.5 GHz and a 26.1-dB peak gain at 83 GHz, and 23-mW dc power dissipation. The gain control range is 8.9–25.7 dB at the center frequency. The measured noise figure (NF) is lower than 5.5 dB at 76–86 GHz with a minimum NF of 4.8 dB at 78 GHz. This VG-LNA shows competitive gain, NF, and low dc power consumption at ${E}$ -band among the low noise amplifiers (LNAs) in 90-nm CMOS technology, and comparable figure of merit to those MMW LNAs in better IC processes. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
591. An Artificial Intelligence Multiprocessing Scheme for the Diagnosis of Osteosarcoma MRI Images.
- Author
-
Wu, Jia, Xiao, Pei, Huang, Haojie, Gou, Fangfang, Zhou, Zhixun, and Dai, Zhehao
- Subjects
MAGNETIC resonance imaging ,ARTIFICIAL intelligence ,COMPUTER-aided diagnosis ,OSTEOSARCOMA ,NOISE control ,IMAGE segmentation - Abstract
Osteosarcoma is the most common malignant osteosarcoma, and most developing countries face great challenges in the diagnosis due to the lack of medical resources. Magnetic resonance imaging (MRI) has always been an important tool for the detection of osteosarcoma, but it is a time-consuming and labor-intensive task for doctors to manually identify MRI images. It is highly subjective and prone to misdiagnosis. Existing computer-aided diagnosis methods of osteosarcoma MRI images focus only on accuracy, ignoring the lack of computing resources in developing countries. In addition, the large amount of redundant and noisy data generated during imaging should also be considered. To alleviate the inefficiency of osteosarcoma diagnosis faced by developing countries, this paper proposed an artificial intelligence multiprocessing scheme for pre-screening, noise reduction, and segmentation of osteosarcoma MRI images. For pre-screening, we propose the Slide Block Filter to remove useless images. Next, we introduced a fast non-local means algorithm using integral images to denoise noisy images. We then segmented the filtered and denoised MRI images using a U-shaped network (ETUNet) embedded with a transformer layer, which enhances the functionality and robustness of the traditional U-shaped architecture. Finally, we further optimized the segmented tumor boundaries using conditional random fields. This paper conducted experiments on more than 70,000 MRI images of osteosarcoma from three hospitals in China. The experimental results show that our proposed methods have good results and better performance in pre-screening, noise reduction, and segmentation. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
592. Determination of Sanitary Protection Zones by the Noise Factor of Modern TPPS.
- Author
-
Tupov, V. B., Taratorin, A. A., Skvortsov, V. S., and Mukhametov, A. B.
- Abstract
The issues of noise reduction from power plants, especially those located in large cities, are an urgent problem. The main sources of constant noise of modern TPPs with advanced steam turbine (ST), combined cycle (CCGT) and gas turbine (GT) units are considered. At that, it was taken into account that modern gas turbine plants are equipped with effective silencers for air intakes. A formula for calculating the width of the sanitary protection zone, depending on the capacity of the STP, CCGTP, GTP units and their number, has been obtained. It is shown that the plant's sanitary protection zone depends both on traditional sources of noise (forced draft machines, transformers, natural draft counter flow cooling towers, gas-distributing plants) penetrating from the TPP premises, and on new sources, such as dry fan cooling towers. The noise characteristics of natural draft counter flow cooling towers and dry fan cooling towers are compared. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
593. EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms.
- Author
-
Soroush, Morteza Zangeneh, Tahvilian, Parisa, Nasirpour, Mohammad Hossein, Maghooli, Keivan, Sadeghniiat-Haghighi, Khosro, Harandi, Sepide Vahid, Abdollahi, Zeinab, Ghazizadeh, Ali, and Dabanloo, Nader Jafarnia
- Subjects
WAVELET transforms ,MACHINE learning ,BLIND source separation ,ELECTROENCEPHALOGRAPHY ,SIGNAL processing - Abstract
Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected using a mixture of well-known conventional classifiers and were removed employing stationary wavelet transform (SWT) to reserve neural information. The proposed method is a combination of signal processing techniques and machine learning algorithms, including multi-layer perceptron (MLP), K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) which have significant results while applying our proposed method to different scenarios. Simulated, semi-simulated, and real EEG signals are employed to evaluate the proposed method, and several evaluation criteria are calculated. We achieved acceptable results, for example, 98% average accuracy and 97% average sensitivity in artifactual EEG component detection or about 2% as mean square error in EEG reconstruction after artifact removal. Results showed that the proposed method is effective and can be used in future studies as we have considered different real-world scenarios to evaluate it. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
594. Speech to noise ratio improvement induces nonlinear parietal phase synchrony in hearing aid users.
- Author
-
Baboukani, Payam Shahsavari, Graversen, Carina, Alickovic, Emina, and Østergaard, Jan
- Subjects
HEARING aids ,SYNCHRONIC order ,SPEECH ,NOISE control ,SIGNAL-to-noise ratio - Abstract
Objectives: Comprehension of speech in adverse listening conditions is challenging for hearing-impaired (HI) individuals. Noise reduction (NR) schemes in hearing aids (HAs) have demonstrated the capability to help HI to overcome these challenges. The objective of this study was to investigate the effect of NR processing (inactive, where the NR feature was switched off, vs. active, where the NR feature was switched on) on correlates of listening effort across two different background noise levels [+3 dB signal-to-noise ratio (SNR) and +8 dB SNR] by using a phase synchrony analysis of electroencephalogram (EEG) signals. Design: The EEG was recorded while 22 HI participants fitted with HAs performed a continuous speech in noise (SiN) task in the presence of background noise and a competing talker. The phase synchrony within eight regions of interest (ROIs) and four conventional EEG bands was computed by using a multivariate phase synchrony measure. Results: The results demonstrated that the activation of NR in HAs affects the EEG phase synchrony in the parietal ROI at low SNR differently than that at high SNR. The relationship between conditions of the listening task and phase synchrony in the parietal ROI was nonlinear. Conclusion: We showed that the activation of NR schemes in HAs can non-linearly reduce correlates of listening effort as estimated by EEG-based phase synchrony. We contend that investigation of the phase synchrony within ROIs can reflect the effects of HAs in HI individuals in ecological listening conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
595. DNCNet: Deep Radar Signal Denoising and Recognition.
- Author
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Du, Mingyang, Zhong, Ping, Cai, Xiaohao, and Bi, Daping
- Subjects
- *
SIGNAL denoising , *ARTIFICIAL neural networks , *RADAR , *COMPUTER vision , *DEEP learning , *COGNITIVE radio - Abstract
Deep learning with its rapid development and advancement has achieved unparalleled performance in many areas like computer vision as well as cognitive radio and signal recognition. However, the performance of most deep neural networks would suffer from degradation in the data mismatch scenario, e.g., the test dataset has a related but nonidentical distribution with the training dataset. Considering the noise corruption, a classifier's accuracy might drop sharply when it is tested on a dataset with much lower signal-to-noise ratio compared to its training dataset. To address this dilemma, in this work, we propose an efficient denoising and classification network (DNCNet) for radar signals. The DNCNet consists of denoising and classification subnetworks. First, a radar signal detection and synthetic mechanism is designed to generate pairwise clean data and noisy data for the DNCNet to train its denoising subnetwork. Then, a two-phase training procedure is proposed to train the denoising subnetwork in the first phase and strengthen the mapping between the denoising results and perceptual representation in the second. Experiments on synthetic and benchmark datasets validate the excellent performance of the proposed DNCNet against state-of-the-art methods in terms of both signal restoration quality and classification accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
596. Deep Image Denoising With Adaptive Priors.
- Author
-
Jiang, Bo, Lu, Yao, Wang, Jiahuan, Lu, Guangming, and Zhang, David
- Subjects
- *
ARTIFICIAL neural networks , *IMAGE denoising , *IMAGE reconstruction , *NOISE measurement - Abstract
Image denoising methods using deep neural networks have achieved a great progress in the image restoration. However, the recovered images restored by these deep denoising methods usually suffer from severe over-smoothness, artifacts, and detail loss. To improve the quality of restored images, we first propose Supplemental Priors (SP) method to adaptively predict depth-directed and sample-directed prior information for the reconstruction (decoder) networks. Furthermore, the over-parameterized deep neural networks and too precise supplemental prior information may cause an over-fitting, restricting the performance promotion. To improve the generalization of denoising networks, we further propose Regularization Priors (RP) method to flexibly learn depth-directed and dataset-directed regularization noise for the retrieving (encoder) networks. By respectively integrating the encoder and decoder with these plug-and-play RP block and SP block, we propose the final Adaptive Prior Denoising Networks, called APD-Nets. APD-Nets is the first attempt to simultaneously regularize and supplement denoising networks from the adaptive priors’ view with drawing learning-based mechanism into producing adaptive regularization noise and supplemental information. Extensive experiment results demonstrate our method significantly improves the generalization of denoising networks and the quality of restored images with greatly outperforming the traditional deep denoising methods both quantitatively and visually. The code will be released at https://github.com/JiangBoCS/APD-Nets. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
597. Deep Learning-Based Perceptual Video Quality Enhancement for 3D Synthesized View.
- Author
-
Zhang, Huan, Zhang, Yun, Zhu, Linwei, and Lin, Weisi
- Subjects
- *
DEEP learning , *CONVOLUTIONAL neural networks , *ARTIFICIAL neural networks , *IMAGE denoising , *PERCEPTUAL illusions - Abstract
Due to occlusion among views and temporal inconsistency in depth video, spatio-temporal distortion occurs in 3D synthesized video with depth image-based rendering. In this paper, we propose a deep Convolutional Neural Network (CNN)-based synthesized video denoising algorithm to reduce temporal flicker distortion and improve perceptual quality of 3D synthesized video. First, we analyze the spatio-temporal distortion, and model eliminating spatio-temporal distortion as a perceptual video denoising problem. Then, a deep learning-based synthesized video denoising network is proposed, in which a CNN-friendly spatio-temporal loss function is derived from a synthesized video quality metric and integrated with a single image denoising network architecture. Finally, specific schemes, i.e., specific Synthesized Video Denoising Networks (SynVD-Nets), and a general scheme, i.e., General SynVD-Net (GSynVD-Net), based on existing CNN-based denoising models, are developed to handle synthesized video with different distortion levels more effectively. Experimental results show that the proposed SynVD-Net and GSynVD-Net can outperform deep learning-based counterparts and conventional denoising methods, and significantly enhance perceptual quality of 3D synthesized video. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
598. Spatial Temporal Video Enhancement Using Alternating Exposures.
- Author
-
Shen, Wang, Cheng, Ming, Lu, Guo, Zhai, Guangtao, Chen, Li, Asif, M. Salman, and Gao, Zhiyong
- Subjects
- *
IMAGE enhancement (Imaging systems) , *IMAGE registration , *CAMERA movement , *IMAGE stabilization , *VIDEOS , *IMAGE reconstruction , *INTERPOLATION - Abstract
High-speed video acquisition under poor illumination conditions is a challenging task. Imaging using long exposure can ensure brightness and suppress noise. However, the captured images may be blurry due to fast object movements or camera shakes. Imaging with short exposure can record sharp textures, but the high camera gain may cause noticeable noise. To alleviate this dilemma, we design a camera system using alternating exposures, where frames expose cyclically in a short-long way. The system consists of restoration and interpolation modules to reconstruct sharp, noise-reduced, high-frame-rate frames from low-frame-rate alternate-exposed input images. We design an optical-flow-based alternate-complementary alignment architecture for spatial enhancement, which effectively aligns the short-exposed and long-exposed images in a two-stage progressive way. Moreover, it explores complementary information from short-exposed and long-exposed inputs to ensure consistency between outputs. We propose a flow-enhanced frame interpolation module for temporal enhancement, which refines the intermediate flows and reconstructs the intermediate images based on the restored images of the alignment network and warped input neighboring frames. The whole network with two modules is end-to-end jointly learnable. We first evaluate the algorithm on simulation data. To demonstrate practicality, we then test it on real data by setting up a prototype camera. We propose an effective spatial degradation regularization strategy to reduce the domain gap between simulation and real data. Besides, we extend our method by integrating multi-frame exposure fusion technology to reduce overexposure areas in real scenarios. Experimental results show that our method performs favorably against state-of-the-art methods on both synthetic data and real-world data. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
599. Numerical Demonstration of Unsupervised-Learning-Based Noise Reduction in Two-Dimensional Rayleigh Imaging.
- Author
-
Cai, Minnan, Jin, Hua, Lin, Beichen, Xu, Wenjiang, and You, Yancheng
- Subjects
- *
NOISE control , *SIGNAL-to-noise ratio , *MIE scattering , *IMAGE denoising , *IMAGE reconstruction , *RAYLEIGH-Benard convection - Abstract
The conventional denoising method in Rayleigh imaging in a general sense requires an additional hardware investment and the use of the underlying physics. This work demonstrates an alternative image denoising reconstruction model based on unsupervised learning that aims to remove Mie scattering and shot noise interference from two-dimensional (2D) Rayleigh images. The model has two generators and two discriminators whose parameters can be trained with either feature-paired or feature-unpaired data independently. The proposed network was extensively evaluated with a qualitative examination and quantitative metrics, such as PSNR, ER, and SSIM. The results demonstrate that the feature-paired training network exhibits a better performance compared with several other networks reported in the literature. Moreover, when the flame features are not paired, the feature-unpaired training network still yields a good agreement with ground truth data. Specific indicators of the quantitative evaluation show a promising denoising ability with a peak signal-to-noise ratio of ~37 dB, an overall reconstruction error of ~1%, and a structure similarity index of ~0.985. Additionally, the pre-trained unsupervised model based on unpaired training can be generalized to denoise Rayleigh images with extra noise or a different Reynolds number without updating the model parameters. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
600. A Novel Wireless Low-Cost Inclinometer Made from Combining the Measurements of Multiple MEMS Gyroscopes and Accelerometers.
- Author
-
Komarizadehasl, Seyedmilad, Komary, Mahyad, Alahmad, Ahmad, Lozano-Galant, José Antonio, Ramos, Gonzalo, and Turmo, Jose
- Subjects
- *
GYROSCOPES , *STRUCTURAL health monitoring , *INCLINOMETER , *ACCELEROMETERS , *NUMERICAL calculations , *FREEWARE (Computer software) - Abstract
Structural damage detection using inclinometers is getting wide attention from researchers. However, the high price of inclinometers limits this system to unique structures with a relatively high structural health monitoring (SHM) budget. This paper presents a novel low-cost inclinometer, the low-cost adaptable reliable angle-meter (LARA), which combines five gyroscopes and five accelerometers to measure inclination. LARA incorporates Internet of Things (IoT)-based microcontroller technology enabling wireless data streaming and free commercial software for data acquisition. This paper investigates the accuracy, resolution, Allan variance and standard deviation of LARA produced with a different number of combined circuits, including an accelerometer and a gyroscope. To validate the accuracy and resolution of the developed device, its results are compared with those obtained by numerical slope calculations and a commercial inclinometer (HI-INC) in laboratory conditions. The results of a load test experiment on a simple beam model show the high accuracy of LARA (0.003 degrees). The affordability and high accuracy of LARA make it applicable for structural damage detection on bridges using inclinometers. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
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