Article
Acoustics
Foroogh Fathnia, Hossein Zamiri-Jafarian
Summary: This paper addresses an acoustic directed-self-assembly (DSA) problem by combining it with multi-frequency beamforming techniques. The proposed Dual-Frequency Beamforming (DFB) method optimizes the acoustic field by adjusting the excitation frequency, achieving better focusing precision when the two pressure traps are close to each other. Simulation results show the superiority of the DFB method over conventional techniques in terms of separability and focusing precision.
Article
Acoustics
Foroogh Fathnia, Hossein Zamiri-Jafarian
Summary: In this paper, the authors address the acoustic directed-self-assembly (DSA) problem by creating an appropriate acoustic field through fine adjustment of transducer operating parameters. The proposed method combines the DSA problem with multi-frequency beamforming techniques to enhance the spatial resolution of the pressure field. Simulation results demonstrate the superiority of the proposed Dual-Frequency Beamforming (DFB) method over conventional techniques in terms of separability and focusing precision.
Article
Geochemistry & Geophysics
Sven Peter Nasholm, Kamran Iranpour, Andreas Wuestefeld, Ben D. E. Dando, Alan F. Baird, Volker Oye
Summary: Distributed Acoustic Sensing (DAS) involves the transmission of laser pulses along a fiber-optic cable, measuring strain through backscattered pulses. DAS has differences from conventional sensors but has the potential for array processing algorithms.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2022)
Article
Acoustics
Long Chen, Yat-Sze Choy, Kai-Chung Tam, Cheng-Wei Fei
Summary: This study investigates a microphone array signal processing approach for faulty wheel identification and ground impedance estimation in a wheel/rail system. The study utilizes BW-MUSIC method for sound source location and LM-CN method for impedance estimation, achieving accurate results for faulty wheel identification. The technique overcomes challenges of contact-based methods and is effective for impulsive signals with broadband features.
Article
Environmental Sciences
Lulu Jiao, Xinghai Yang, Tianqi Quan, Jingjing Wang
Summary: This study proposes a high-precision DOA estimation model for underwater acoustic signals based on sparsity adaptation, consisting of a source sparsity adaptive model and a differential combination matching pursuit algorithm. Simulation results show that the proposed model achieves high prediction accuracy under different signal-to-noise ratios and array types, and has lower average absolute error compared to other algorithms.
FRONTIERS IN MARINE SCIENCE
(2022)
Article
Computer Science, Information Systems
Chao Liu, Jiaqi Zhen
Summary: This paper proposes a diagonal loading beamforming algorithm based on the Aquila optimizer to improve the beam performance reduction in non-ideal environments. The algorithm combines the identity matrix and the original covariance matrix linearly and uses the Aquila optimizer to optimize the diagonal loading process. The improved loading value is then combined with the classic sample matrix inversion algorithm to form the beam. Simulation results show that the proposed algorithm can improve the beam distortion caused by divergence of small eigenvalues and performs well in high SNR, low SNR, or small snapshots. It also exhibits better stability and adaptability compared to the classic sample matrix inversion algorithm and traditional diagonal loading algorithm.
Article
Engineering, Mechanical
Junfei Tai, Xiandong Liu, Xiaoran Wang, Yingchun Shan, Tian He
Summary: This paper proposes a novel localization method using frequency-domain notch-weighted beamforming to decouple two coupled acoustic emission (AE) waves for more accurate localization results. The method is validated through simulations and experiments, demonstrating its effectiveness in localizing simultaneous AE sources.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Electrical & Electronic
Augusto Aubry, Antonio De Maio, Stefano Marano, Massimo Rosamilia
Summary: This paper addresses the estimation of structured covariance matrix in radar signal processing applications in the presence of missing data. A general procedure using the expectation-maximization algorithm is developed for optimizing the observed-data likelihood function. The study contextualizes the estimation technique for two radar problems and introduces detection techniques based on classic information criteria.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Engineering, Electrical & Electronic
Yonghua Zhang, Xiaotong Tu, Saqlain Abbas, Hao Liang, Yue Huang, Xinghao Ding
Summary: This paper introduces the application of acoustic beamforming methods based on microphone arrays in sound source localization. Traditional methods suffer from poor spatial resolution. Researchers have attempted model-based and deep network-based methods to improve the resolution of the beamforming map. However, model-based methods have high computational complexity and rely on user-determined parameters, while deep network-based methods may struggle with generalization and generating the beamforming map directly. The paper proposes a new method to solve the inverse problem of sound imaging and further designs a deep neural network model that achieves real-time and high-resolution mapping of acoustic sources, demonstrating strong generalization capability.
Article
Acoustics
Yuji Liu, Huixiu Chen, Biao Wang
Summary: This paper proposes a method for estimating the arrival direction of underwater acoustic signals, using two-channel real and imaginary covariance matrices as input signals for a convolutional neural network. Compared to the traditional MUSIC algorithm, the CNN algorithm shows higher accuracy and shorter estimation time in low SNR environments.
Article
Environmental Sciences
Xiaoqiang Li, Jianfeng Chen, Jisheng Bai, Muhammad Saad Ayub, Dongzhe Zhang, Mou Wang, Qingli Yan
Summary: This paper proposes a CRNN-based method for underwater DOA estimation, which utilizes the phase component of the short-time Fourier transform of the array signals as input feature. The CRNN can extract high-level features and capture the temporal dependencies of the features. Experimental results demonstrate that the proposed method achieves high-accuracy DOA estimation at different SNR levels and has a relatively short processing time, thereby extending its applicability.
FRONTIERS IN MARINE SCIENCE
(2022)
Article
Chemistry, Analytical
Thomas Verellen, Florian Verbelen, Kurt Stockman, Jan Steckel
Summary: This study utilized non-invasive sensors to record the noise from a Machinery Fault Simulator, investigating how beamforming could improve the classification process in ultrasound condition monitoring applications. The findings will help enhance the implementation of predictive maintenance in industrial plants.
Article
Computer Science, Information Systems
Yumeng Sun, Jinguang Li, Linwei Wang, Junjie Xv, Yu Liu
Summary: This paper proposes a method that combines beamforming algorithm with Deep Learning neural network to achieve the detection of drone acoustic event using microphone array technology. The results demonstrate that the proposed method surpasses traditional methods in terms of detection range and accuracy.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Seungjong Lee, Taewook Kang, John Bell, Mohammad Haghighat, Alberto Martinez, Michael P. Flynn
Summary: The study combines DAS beamforming with CDB and frequency-domain feature extraction to improve the accuracy of automatic speech recognition with noise rejection. SDMs digitize eight microphone inputs, and the prototype device has a power consumption of only 3.95 mW.
IEEE JOURNAL OF SOLID-STATE CIRCUITS
(2022)
Article
Engineering, Electrical & Electronic
Sanket Vadhvana, Shekhar Kumar Yadav, Sankha Subhra Bhattacharjee, Nithin V. George
Summary: This paper presents an improved beamforming algorithm based on the nearest Kronecker product decomposition technique, which can better capture desired signals and suppress interfering signals in noisy environments through weight vector decomposition and update rule derivation.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Acoustics
Long Bai, Alexander Velichko, Bruce W. Drinkwater
IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL
(2015)
Article
Acoustics
Long Bai, Alexander Velichko, Bruce W. Drinkwater
IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL
(2015)
Article
Multidisciplinary Sciences
A. Velichko, L. Bai, B. W. Drinkwater
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2017)
Article
Acoustics
Long Bai, Alexander Velichko, Bruce W. Drinkwater
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
(2018)
Article
Materials Science, Characterization & Testing
Chaoyong Peng, Long Bai, Jie Zhang, Bruce W. Drinkwater
NDT & E INTERNATIONAL
(2018)
Article
Engineering, Aerospace
Xun Huang, Igor Vinogradov, Long Bai, Jianchao Ji
Article
Physics, Multidisciplinary
Xun Huang, Chi Xu, Long Bai
Article
Acoustics
Xun Huang, Long Bai, Igor Vinogradov, Edward Peers
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
(2012)
Article
Acoustics
Long Bai, Alexander Velichko, Bruce W. Drinkwater
IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL
(2019)
Article
Materials Science, Characterization & Testing
Long Bai, Alexander Velichko, Adam T. Clare, Paul Dryburgh, Don Pieris, Bruce W. Drinkwater
NDT & E INTERNATIONAL
(2020)
Article
Acoustics
Long Bai, Florian Le Bourdais, Roberto Miorelli, Pierre Calmon, Alexander Velichko, Bruce W. Drinkwater
Summary: Characterizing small surface-breaking notches can be achieved using two approaches: one based on a general coherent noise model within a Bayesian framework, and the other relying on supervised machine learning with a scattering matrix database. Convolutional neural networks (CNNs) show the best characterization accuracy among the machine learning approaches, and have similar characterization uncertainty to the Bayesian approach for favorably oriented notches. The performance of both approaches varies for unfavorably oriented notches, with the machine learning approach showing higher variance and lower biases.
IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL
(2021)
Article
Acoustics
Long Bai, Minkang Liu, Nanxin Liu, Xin Su, Fuyao Lai, Jianfeng Xu
Summary: This paper studies the dimensionality reduction problem of scattering matrix databases and proposes a supervised approach based on locality preserving projection (LPP) for accurate characterization of inclined defects. The LPP approach shows a significant improvement over PCA in both simulation and experiments, reducing the root-mean-square sizing error by 39.0% for ellipses and 11.1% for surface-breaking cracks.
