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Journal articles on the topic 'Adaptive acoustics'

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Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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1

Nixon, Elizabeth, Sue Holland-Crimmin, Brian Lupotsky, James Chan, Jon Curtis, Karen Dobbs, and Zoe Blaxill. "Applications of Adaptive Focused Acoustics to Compound Management." Journal of Biomolecular Screening 14, no. 5 (June 2009): 460–67. http://dx.doi.org/10.1177/1087057109335677.

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Since the introduction of lithotripsy kidney stone therapy, Focused Acoustics and its properties have been thoroughly utilized in medicine and exploration. More recently, Compound Management is exploring its applications and benefits to sample integrity. There are 2 forms of Focused Acoustics: Acoustic Droplet Ejection and Adaptive Focused Acoustics, which work by emitting high-powered acoustic waves through water toward a focused point. This focused power results in noncontact plate-to-plate sample transfer or sample dissolution, respectively. For the purposes of this article, only Adaptive Focused Acoustics will be addressed. Adaptive Focused Acoustics uses high-powered acoustic waves to mix, homogenize, dissolve, and thaw samples. It facilitates transferable samples through noncontact, closed-container, isothermal mixing. Experimental results show significantly reduced mixing times, limited degradation, and ideal use for heat-sensitive compounds. Upon implementation, acoustic dissolution has reduced the number of samples requiring longer mixing times as well as reducing the number impacted by incomplete compound dissolution. It has also helped in increasing the overall sample concentration from 6 to 8 mM to 8 to 10 mM by ensuring complete compound solubilization. The application of Adaptive Focused Acoustics, however, cannot be applied to all Compound Management processes, such as sample thawing and low-volume sample reconstitution. This article will go on to describe the areas where Adaptive Focused Acoustics adds value as well as areas in which it has shown no clear benefit.

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2

Bartram, J. "Adaptive methods in underwater acoustics." IEEE Journal of Oceanic Engineering 11, no. 4 (October 1986): 487. http://dx.doi.org/10.1109/joe.1986.1145213.

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3

Lacoume, J. L. "Adaptive methods for underwater acoustics." Signal Processing 13, no. 1 (July 1987): 103–5. http://dx.doi.org/10.1016/0165-1684(87)90118-6.

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4

Ogoli, David M. "Adaptive thermal comfort factors in classroom acoustics." Journal of the Acoustical Society of America 150, no. 4 (October 2021): A120. http://dx.doi.org/10.1121/10.0007833.

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5

Mansfield Adams, William. "Adaptive acoustical tomography." Journal of the Acoustical Society of America 79, S1 (May 1986): S16. http://dx.doi.org/10.1121/1.2023088.

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6

Ma, Kougen, and J. Melcher. "Adaptive Control of Structural Acoustics using Intelligent Structures with Embedded Piezoelectric Patches." Journal of Vibration and Control 9, no. 11 (November 2003): 1285–302. http://dx.doi.org/10.1177/1077546304030676.

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In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

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7

TOPA, M. D., I. MURESAN, B. S. KIREI, and I. HOMANA. "Digital Adaptive Echo-Canceller for Room Acoustics Improvement." Advances in Electrical and Computer Engineering 10, no. 1 (2010): 50–53. http://dx.doi.org/10.4316/aece.2010.01008.

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8

Zwislocki, Jozef J. "Hot topics in physiological acoustics: The adaptive cochlea." Journal of the Acoustical Society of America 93, no. 4 (April 1993): 2345. http://dx.doi.org/10.1121/1.406261.

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9

Cray, Benjamin, and Ivars Kirsteins. "A Comparison of Optimal SONAR Array Amplitude Shading Coefficients." Acoustics 1, no. 4 (October 2, 2019): 808–15. http://dx.doi.org/10.3390/acoustics1040047.

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This paper compares two different approaches to deriving shading coefficients (weights) for optimal first order and second order directional sensors (that is; sonobuoys, vectors and dyadic sensors). The first approach is an analytical or a physics-based derivation, involving computations with gradients and linearized momentum; the second is an adaptive minimum variance distortionless response (MVDR) derivation, which finds weights that minimize the cross spectral density (CSD) matrix. The two approaches are shown to be equivalent. In other words, the adaptive MVDR processing procedure does indeed converge to a physics-based solution, without any pre-existing physical knowledge of the behavior of the acoustic field. This suggests that adaptive algorithms innately seek physics-based solutions when these solutions are optimum. The intent of this short communication is not to advocate for one type of adaptive processing method over another. The observation that is presented here is important though, it confirms that at least in an idealized noise field, adaptive processing converges on an optimal set of shading coefficients, similarly derived based on well-established physical acoustics.

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10

Sharples, Steve D., Matt Clark, and Mike G. Somekh. "All-optical adaptive scanning acoustic microscope." Ultrasonics 41, no. 4 (June 2003): 295–99. http://dx.doi.org/10.1016/s0041-624x(02)00461-4.

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11

Green, David J., Edwin A. Rudd, and James A. Laugharn. "Adaptive Focused Acoustics (AFA) Improves the Performance of Microtiter Plate ELISAs." Journal of Biomolecular Screening 19, no. 7 (February 19, 2014): 1124–30. http://dx.doi.org/10.1177/1087057114523650.

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We investigated the use of Adaptive Focused Acoustics (AFA) technology to improve the performance of microtiter plate enzyme-linked immunosorbent assays (ELISAs). Experiments were performed with commercially available AFA instrumentation and off-the-shelf 96-well microtiter plate sandwich ELISAs. AFA was applied over a range of acoustic energies, temperatures, and durations to the antigen/antibody binding step of an ELISA for measuring HIV-1 p24 in tissue culture samples. AFA-mediated antigen/antibody binding was enhanced up to 2-fold over passive binding at comparable temperatures and was superior or comparable at low temperature (8–10 °C) to passive binding at 37 °C. Lower nonspecific binding (NSB), lower inter- and intra-assay coefficients of variation (CVs), higher Z′ factors, and lower limits of detection (LODs) were measured in AFA-mediated assays compared with conventional passive binding. In a more limited study, AFA enhancement of antigen/antibody binding and lower NSB was measured in an ELISA for measuring IGFBP-3 in human plasma. We conclude from this study that application of AFA to antigen/antibody binding steps in microtiter plate ELISAs can enhance key assay performance parameters, particularly Z′ factors and LODs. These features render AFA-mediated binding assays potentially more useful in applications such as high-throughput screening and in vitro diagnostics than assays processed with conventional passive antigen/antibody binding steps.

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12

Yu, Kai, and Mark J. F. Gales. "Bayesian Adaptive Inference and Adaptive Training." IEEE Transactions on Audio, Speech and Language Processing 15, no. 6 (August 2007): 1932–43. http://dx.doi.org/10.1109/tasl.2007.901300.

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13

Kuo, Sen M., and Zhibing Pan. "Adaptive acoustic echo cancellation microphone." Journal of the Acoustical Society of America 93, no. 3 (March 1993): 1629–36. http://dx.doi.org/10.1121/1.406822.

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14

Gabard, G., H. Bériot, A. G. Prinn, and K. Kucukcoskun. "Adaptive, High-Order Finite-Element Method for Convected Acoustics." AIAA Journal 56, no. 8 (August 2018): 3179–91. http://dx.doi.org/10.2514/1.j057054.

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15

Zhang, Shi Ping, Guo Qing Shen, and Lian Suo An. "Application of Improved LMS Adaptive Algorithm in Acoustics Pyrometry." Applied Mechanics and Materials 672-674 (October 2014): 2025–28. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.2025.

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Acoustic pyrometry is a comparatively advanced method of temperature measurement developed in recent years, which possesses the essential characteristics of traditional temperature measurement approach. Considering the interferences, like strong background noise, reverberation and so on, in boiler furnace, the LMS (least mean square) adaptive filter algorithm should be improved to meet certain environment above. In order to make the LMS algorithm have the characteristic of fast convergence and small steady state error, an improved, power-normalized and variable step-size discrete cosine transform LMS algorithm is proposed, which combines the power-normalized discrete cosine transform LMS algorithm with the variable step size LMS algorithm that uses the sliding forgetting-weighted window. The time delay estimation simulation in the strong-noise environment verifies the improved DCT-MVSS LMS algorithm can achieve good performance.

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16

Campo, Nicola, Paolo Rissone, and Marco Toderi. "Adaptive pyramid tracing: a new technique for room acoustics." Applied Acoustics 61, no. 2 (October 2000): 199–221. http://dx.doi.org/10.1016/s0003-682x(99)00072-9.

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17

Lam, Yiu W., and Ian Drumm. "An adaptive beam tracing method for room acoustics prediction." Journal of the Acoustical Society of America 105, no. 2 (February 1999): 990. http://dx.doi.org/10.1121/1.425387.

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18

Stepanishen, P. R., Q. Huynh, and T. Greene. "Adaptive wavelet analysis of transient signals in structural acoustics." Journal of the Acoustical Society of America 94, no. 3 (September 1993): 1824. http://dx.doi.org/10.1121/1.407798.

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19

Lee, Kyeong-Hwan. "Adaptive Watermarking for MP3 Copyright Protections Using Psychological Acoustics." Journal of the Acoustical Society of Korea 32, no. 1 (January 31, 2013): 64–70. http://dx.doi.org/10.7776/ask.2013.32.1.064.

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20

Liao, Lei, and Andy W. H. Khong. "Adaptive Channel Equalization of Room Acoustics Exploiting Sparseness Constraint." IEEE Signal Processing Letters 18, no. 4 (April 2011): 275–78. http://dx.doi.org/10.1109/lsp.2011.2117418.

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21

Bismor, Dariusz. "Optimal and Adaptive Virtual Unidirectional Sound Source in Active Noise Control." Advances in Acoustics and Vibration 2008 (June 22, 2008): 1–12. http://dx.doi.org/10.1155/2008/647318.

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One of the problems concerned with active noise control is the existence of acoustical feedback between the control value (“active” loudspeaker output) and the reference signal. Various experiments show that such feedback can seriously decrease effects of attenuation or even make the whole ANC system unstable. This paper presents a detailed analysis of one of possible approaches allowing to deal with acoustical feedback, namely, virtual unidirectional sound source. With this method, two loudspeakers are used together with control algorithm assuring that the combined behaviour of the pair makes virtual propagation of sound only in one direction. Two different designs are presented for the application of active noise control in an acoustic duct: analytical (leading to fixed controller) and adaptive. The algorithm effectiveness in simulations and real experiments for both solutions is showed, discussed, and compared.

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22

Nied, Herman A. "Acoustic wave spot welder adaptive control." Journal of the Acoustical Society of America 77, no. 6 (June 1985): 2204. http://dx.doi.org/10.1121/1.391726.

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23

Carey, William M. "Adaptive Methods in Underwater Acoustics edited by Heinz G. Urban." Journal of the Acoustical Society of America 81, no. 6 (June 1987): 1999–2000. http://dx.doi.org/10.1121/1.394732.

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24

Barad, Michael F., Christoph Brehm, Cetin C. Kiris, and Rupak Biswas. "Parallel adaptive high-order CFD simulations characterising SOFIA cavity acoustics." International Journal of Computational Fluid Dynamics 30, no. 6 (July 2, 2016): 437–43. http://dx.doi.org/10.1080/10618562.2016.1222073.

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25

Choi, GiHun, Srikanth Kakumanu, Leah Schmitz, Gary LWG Robinson, Carl D. Beckett, and James A. Laugharn. "Continuous manufacturing of carboxyamidotriazoleencapsulated nanoemulsions using adaptive focused acoustics: Potential green technology for the pharmaceutical industry." Journal of Biomedical Engineering and Informatics 2, no. 2 (February 1, 2016): 70. http://dx.doi.org/10.5430/jbei.v2n2p70.

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Nanoemulsions containing hydrophobic drugs have a great potential in the pharmaceutical industries to improve thebioavailability of the drug. However, currently there is no cost-effective way of producing nanoemulsions in large scale.The need of subjecting emulsions to an extreme pressure of 50 MPa demands a large excess of energy for themanufacturing process, while low-energy method requires large amount of solvents. Here, nanoemulsions containing awell-characterized hydrophobic drug, carboxyamidotriazole (CAI), are produced in both batch and continuous modes todemonstrate the scalability of nanoemulsion production using Covaris’ Adaptive Focused Acoustics™ (AFA) technology.To move from batch scale to continuous flow, the acoustic and thermal energy inputs can be manipulated to adjust particlesize, while the composition and temperature of starting materials can be altered to achieve complete dissolution ofhydrophobic drugs, thus providing 100% encapsulation efficiency. Furthermore, using two AFA systems in series candrastically enhance the production flow rates, making AFA a competitive means for producing nanoemulsions in thepharmaceutical industry.

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26

Sankar, Sanjana, Asutosh Kar, Srikanth Burra, M. N. S. Swamy, and Vladimir Mladenovic. "Nonlinear acoustic echo cancellation with kernelized adaptive filters." Applied Acoustics 166 (September 2020): 107329. http://dx.doi.org/10.1016/j.apacoust.2020.107329.

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27

Brooker, D. J., Kay L. Gemba, and Laurie T. Fialkowski. "Overcoming snapshot-deficient measurements with knowledge-aided approaches." JASA Express Letters 2, no. 5 (May 2022): 054804. http://dx.doi.org/10.1121/10.0010455.

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The use of knowledge-aided covariance is considered for processing underwater acoustic array data in snapshot-deficient scenarios. The knowledge-aided formalism is a technique that combines array data with a known covariance to produce an invertible estimate. For underwater acoustics, simulations of ambient noise provide the a priori covariance allowing degraded signals to be processed adaptively in situations where the sample covariance matrix is rank-deficient. The method is demonstrated for matched field processing using the 21 element array event S5 from the SWellEx-96 experiment. With five snapshots, the knowledge-aided approach significantly reduces localization ambiguity compared to the adaptive white noise gain constraint processor.

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28

Byun, Gihoon, and H. C. Song. "Adaptive array invariant." Journal of the Acoustical Society of America 148, no. 2 (August 2020): 925–33. http://dx.doi.org/10.1121/10.0001768.

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29

Chabries, Douglas M. "Adaptive noise suppressor." Journal of the Acoustical Society of America 87, no. 5 (May 1990): 2273. http://dx.doi.org/10.1121/1.399131.

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30

Chanaud, Robert, and Ronald Zuydervliet. "Adaptive sound masking." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3195. http://dx.doi.org/10.1121/1.2933335.

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31

Moulds, Clinton W. "Adaptive vibration canceller." Journal of the Acoustical Society of America 90, no. 1 (July 1991): 622. http://dx.doi.org/10.1121/1.401222.

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32

Gutta, Srinivas. "Adaptive alarm system." Journal of the Acoustical Society of America 118, no. 3 (2005): 1255. http://dx.doi.org/10.1121/1.2097077.

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33

Burkhardt, Russell C., and Leon H. Sibul. "Robust adaptive processing for underwater acoustic arrays." Journal of the Acoustical Society of America 91, no. 4 (April 1992): 2446. http://dx.doi.org/10.1121/1.403088.

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34

Stotts, Steven A., and Robert A. Koch. "Geo‐acoustic inversion with adaptive beamformed data." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3365. http://dx.doi.org/10.1121/1.2933969.

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35

Wage, Kathleen E., and Arthur B. Baggeroer. "Adaptive estimation of acoustic normal mode amplitudes." Journal of the Acoustical Society of America 95, no. 5 (May 1994): 2981. http://dx.doi.org/10.1121/1.408961.

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36

Orlov, Denis, Iosif Fiks, Galina Fiks, Pavel Korotin, and Victor Turchin. "Adaptive beamforming applied to underwater acoustic measurements." Journal of the Acoustical Society of America 123, no. 5 (May 2008): 3465. http://dx.doi.org/10.1121/1.2934326.

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37

Heaney, Kevin D. "Acoustic cost functions for autonomous adaptive sampling." Journal of the Acoustical Society of America 122, no. 5 (2007): 3004. http://dx.doi.org/10.1121/1.2942728.

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38

Wachinger, Christian, Tassilo Klein, and Nassir Navab. "Locally adaptive Nakagami-based ultrasound similarity measures." Ultrasonics 52, no. 4 (April 2012): 547–54. http://dx.doi.org/10.1016/j.ultras.2011.11.009.

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39

Kobayashi, Kazunori, Ken’ichi Furuya, and Akitoshi Kataoka. "An adaptive microphone array for howling cancellation." Acoustical Science and Technology 24, no. 1 (2003): 45–47. http://dx.doi.org/10.1250/ast.24.45.

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40

Yuan, Jing. "Adaptive Laguerre filters for active noise control." Applied Acoustics 68, no. 1 (January 2007): 86–96. http://dx.doi.org/10.1016/j.apacoust.2006.01.009.

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41

Asensio, C., M. Recuero, M. Ruiz, M. Ausejo, and I. Pavón. "Self-adaptive grids for noise mapping refinement." Applied Acoustics 72, no. 8 (July 2011): 599–610. http://dx.doi.org/10.1016/j.apacoust.2010.12.007.

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42

Mal’tsev, A. A. "Adaptive Active Noise and Vibration Control Systems." Acoustical Physics 51, no. 2 (2005): 195. http://dx.doi.org/10.1134/1.1884495.

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43

Nan, Shanghan, and Guobing Qian. "Univariate kernel sums correntropy for adaptive filtering." Applied Acoustics 184 (December 2021): 108316. http://dx.doi.org/10.1016/j.apacoust.2021.108316.

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44

Shen, Katherine C., Srikanth Kakumanu, Carl D. Beckett, and James A. Laugharn. "Use of Adaptive Focused Acoustics™ ultrasound in controlling liposome formation." Ultrasonics Sonochemistry 27 (November 2015): 638–45. http://dx.doi.org/10.1016/j.ultsonch.2015.04.027.

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45

Mastryukov, A. F. "An inverse problem for the acoustics equations: A multilevel adaptive algorithm." Numerical Analysis and Applications 3, no. 3 (July 2010): 263–78. http://dx.doi.org/10.1134/s1995423910030067.

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46

Jansari, Chintan, Javier Videla, Sundararajan Natarajan, Stéphane P. A. Bordas, and Elena Atroshchenko. "Adaptive enriched geometry independent field approximation for 2D time-harmonic acoustics." Computers & Structures 263 (April 2022): 106728. http://dx.doi.org/10.1016/j.compstruc.2021.106728.

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47

Davidson, Grant A. "Adaptive‐block‐length, adaptive‐transform, and adaptive‐window transform coder, decoder, and encoder/decoder for high‐quality audio." Journal of the Acoustical Society of America 98, no. 4 (October 1995): 1832. http://dx.doi.org/10.1121/1.413398.

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48

Bartsch, Guido, and Christian Wulf. "Adaptive Multigrid for Helmholtz Problems." Journal of Computational Acoustics 11, no. 03 (September 2003): 341–50. http://dx.doi.org/10.1142/s0218396x03001997.

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Solving Helmholtz problems for low frequency sound fields by a truncated modal basis approach is very efficient. The most time-consuming process is the calculation of the undamped modes. Using traditional FE solvers, the user has to provide a mesh which has at least six nodes per wavelength in each spatial direction to achieve acceptable results. Because the mesh size increases with the 3rd power of the highest frequency of interest, this uniform dense mesh approach is a very expensive way of creating a modal space. However, the number of modes and the accuracy of the modal basis directly influences the solution quality. It is well known that the representation of sound fields by modal basis functions φi is optimal with respect to the L2 error norm. This means that having a modal basis Φ := {φi, i = 1⋯n}, the distance between true and approximated sound field takes its minimum in the mean square. So, it is necessary to have a FE basis which also minimizes the discretization error when computing the modal basis. One can reach this goal by applying adaptive mesh refinements. Additionally, this yields the opportunity of using fast multigrid methods to solve discrete eigenvalue problems. In context of this presentation we will discuss the results of our adaptive multigrid algorithms.

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49

Waldron, K. J. "Terrain Adaptive Vehicles." Journal of Vibration and Acoustics 117, B (June 1, 1995): 107–12. http://dx.doi.org/10.1115/1.2838649.

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Research on walking vehicles and variable configuration wheeled vehicles is reviewed. The central feature of the vehicles discussed is terrain adaptive capability. The principal elements of the technical problems of coordination and control are discussed for each vehicle type. Examples of each vehicle type are discussed and an extensive reference list is provided. Although the article is primarily a review article, it contains a new discussion of the coordination problem of robotic mechanisms.

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50

Aslan, Fauzi, and Roshun Paurobally. "Modelling and simulation of active noise control in a small room." Journal of Vibration and Control 24, no. 3 (May 6, 2016): 607–18. http://dx.doi.org/10.1177/1077546316647572.

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This paper presents the modeling and simulation results of active noise control (ANC) in a small room using the wave-based approach defined by particle velocities and sound pressure within the defined boundary conditions. The ANC system excitation is a single-frequency noise with an adaptive feedforward configuration. The Finite Difference Time Domain (FDTD) algorithm is used to model the room acoustics due to a boxed loudspeaker of single frequency. A control system based on the filtered-x least mean-squared (FxLMS) algorithm is utilized to synthesize a cancelling noise using a secondary loudspeaker. The single channel system is modified into a multichannel system and genetic algorithm (GA) is used to optimize the sensors and actuators placements simultaneously. Numerical results are plotted to demonstrate the performance of the control system. These show that the numerical modelling technique can be used to combine room acoustic simulation and FxLMS adaptive control. This provides a way for the optimum placement of the microphones and loudspeakers before being used in a practical complex enclosure.

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