Relevant bibliographies by topics / Underwater acoustic signals

Academic literature on the topic 'Underwater acoustic signals'

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Published: 23 February 2024

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Journal articles on the topic "Underwater acoustic signals"

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Brown, David A., Paul J. Gendron, and John R. Buck. "Graduate education in acoustic engineering, transduction, and signal processing University of Massachusetts Dartmouth." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A123. http://dx.doi.org/10.1121/10.0015756.

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The University of Massachusetts Dartmouth has an established graduate program of study with a concentration in Applied Acoustics leading to the M.S. and Ph.D. degree in Electrical Engineering. The program offers courses and research opportunities in the area of electroacoustic transduction, underwater acoustics, and signal processing. Courses include the Fundamentals of Acoustics, Random Signals, Underwater Acoustics, Introduction to Transducers, Electroacoustic Transduction, Medical Ultrasonics, Digital Signal Processing, Detection Theory, and Estimation Theory. The ECE department established the university’s indoor underwater acoustic test and calibration facility which is one of the largest academic facilities supporting undergraduate and graduate thesis and sponsored research. The department has collaborations with many marine acoustic related companies including nearby Naval Undersea Warfare Center in Newport, RI and Woods Hole Oceanographic Institute in Cape Cod, MA. The presentation will highlight recent theses and dissertations, course offerings, and industry and government collaborations that support acoustical engineering, transduction, and signal processing.
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Yu, Miao, Yutong He, and Qian Kong. "Research on Pattern Extraction Method of Underwater Acoustic Signal Based on Linear Array." Mathematical Problems in Engineering 2022 (April 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/1819423.

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Underwater acoustic signal is an important reference data for marine information research. The research and application of underwater acoustic signal have been widely concerned and valued by countries and enterprises. With the needs of modern military development and the development of marine industry, the research and application of underwater acoustic signal will develop faster and faster. In order to better understand marine information, it is necessary to collect seawater acoustic signal data. Aiming at the purpose of recording underwater acoustic signals placed in the ocean for a long time, this study innovates the calibration and recording of large dynamic range of long-time underwater acoustic signals, improves the circuit setting methods such as receiving, amplification, and sampling, designs a large dynamic series of long-time underwater acoustic signal recording device, and adopts the linear array extraction method, so that it can monitor the underwater acoustic biological sound under the condition of low power. It can also monitor the blasting sound of offshore engineering. Hardware circuit design mainly includes main control chip selection, amplification circuit design, filter circuit design, analog-to-digital conversion circuit design, storage circuit design, and some auxiliary circuit design. The fourth chapter introduces the software development process of large dynamic range underwater acoustic signal recorder and mainly introduces the system development tool, system clock working method, real-time clock module working method, underwater acoustic signal acquisition method, data storage scheme design, and the use of FatFs file system. The underwater acoustic signal data is stored on a MicroSD in the form of TXT file; linear array extraction method is used for feature extraction. Compared to other methods, the transformer will suppress DC and low-frequency interference signals, thus achieving high-pass filtering characteristics. Finally, the performance and experimental results of the whole underwater acoustic signal recording device are analyzed. After testing, the underwater acoustic signal recording device designed in this paper works stably and can record underwater acoustic signals with large dynamic range for a long time in low-power mode.
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Gaudette, Jason E., and James A. Simmons. "Linear time-invariant (LTI) modeling for aerial and underwater acoustics." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A95. http://dx.doi.org/10.1121/10.0018285.

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Most newcomers to acoustic signal processing understand that linear time-invariant (LTI) filters can remove out-of-band noise from time series signals. What many acoustics researchers may not realize is that LTI models can be applied much more broadly, including to non-linear and time-variant systems. This presentation covers an overview of the autoregressive (AR), moving-average (MA), and autoregressive moving-average (ARMA) family of LTI models and their many useful applications in acoustics. Examples include analytic time-frequency processing of multi-component echolocation signals, fractional-delay filtering for acoustic time series simulations, broadband acoustic array beamforming, adaptive filtering for noise cancelation, and system identification for acoustic equalizers (i.e., flattening the frequency response of a source-receiver pair). This talk serves as a brief tutorial and inspiration for researchers who want to expand their use of signal processing, especially those in the fields of animal bioacoustics, aerial acoustics, and underwater acoustics.
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Taroudakis, Michael, Costas Smaragdakis, and N. Ross Chapman. "Denoising Underwater Acoustic Signals for Applications in Acoustical Oceanography." Journal of Computational Acoustics 25, no. 02 (January 25, 2017): 1750015. http://dx.doi.org/10.1142/s0218396x17500151.

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A method for denoising underwater acoustic signals used in applications of acoustical oceanography is presented. The method has been introduced for imaging denoising and has been modified to be applied with acoustic signals. The method keeps the energy significant part of the raw signal and reduces the effects of noise by comparing overlapping signal windows and keeping components which resemble true signal energy. It is shown by means of characteristic experiments in connection with a statistical signal characterization scheme based on wavelet transform, that using the statistical features of the wavelet sub-band coefficients of the denoised signal, tomography or geoacoustic inversions lead to a reliable estimation of the parameters of a marine environment.
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Ju, Yang, Zhengxian Wei, Li Huangfu, and Feng Xiao. "A New Low SNR Underwater Acoustic Signal Classification Method Based on Intrinsic Modal Features Maintaining Dimensionality Reduction." Polish Maritime Research 27, no. 2 (June 1, 2020): 187–98. http://dx.doi.org/10.2478/pomr-2020-0040.

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AbstractThe classification of low signal-to-noise ratio (SNR) underwater acoustic signals in complex acoustic environments and increasingly small target radiation noise is a hot research topic.. This paper proposes a new method for signal processing—low SNR underwater acoustic signal classification method (LSUASC)—based on intrinsic modal features maintaining dimensionality reduction. Using the LSUASC method, the underwater acoustic signal was first transformed with the Hilbert-Huang Transform (HHT) and the intrinsic mode was extracted. the intrinsic mode was then transformed into a corresponding Mel-frequency cepstrum coefficient (MFCC) to form a multidimensional feature vector of the low SNR acoustic signal. Next, a semi-supervised fuzzy rough Laplacian Eigenmap (SSFRLE) method was proposed to perform manifold dimension reduction (local sparse and discrete features of underwater acoustic signals can be maintained in the dimension reduction process) and principal component analysis (PCA) was adopted in the process of dimension reduction to define the reduced dimension adaptively. Finally, Fuzzy C-Means (FCMs), which are able to classify data with weak features was adopted to cluster the signal features after dimensionality reduction. The experimental results presented here show that the LSUASC method is able to classify low SNR underwater acoustic signals with high accuracy.
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Yan, Huichao, and Linmei Zhang. "Denoising of MEMS Vector Hydrophone Signal Based on Empirical Model Wavelet Method." Proceedings 15, no. 1 (July 8, 2019): 11. http://dx.doi.org/10.3390/proceedings2019015011.

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Underwater acoustic technology is a major method in current ocean research and exploration, which support the detection of seabed environment and marine life. However, the detection accuracy is directly affected by the quality of underwater acoustic signals collected by hydrophones. Hydrophones are efficient and important tools for collecting underwater acoustic signals. The collected signals of hydrophone often contain lots kinds of noise as the work environment is unknown and complex. Traditional signal denoising methods, such as wavelet analysis and empirical mode decomposition, product unsatisfied results of denoising. In this paper, a denoising method combining wavelet threshold processing and empirical mode decomposition is proposed, and correlation analysis is added in the signal reconstruction process. Finally, the experiment proves that the proposed denoising method has a better denoising performance. With the employment of the proposed method, the underwater acoustic signals turn smoothly and the signal drift of the collected hydroacoustic signal is improved. Comparing the signal spectrums of other methods, the spectral energy of the proposed denoising method is more concentrated, and almost no energy attenuation occurred.
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Li, Yuxing, Xiao Chen, Jing Yu, and Xiaohui Yang. "A Fusion Frequency Feature Extraction Method for Underwater Acoustic Signal Based on Variational Mode Decomposition, Duffing Chaotic Oscillator and a Kind of Permutation Entropy." Electronics 8, no. 1 (January 5, 2019): 61. http://dx.doi.org/10.3390/electronics8010061.

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In order to effectively extract the frequency characteristics of an underwater acoustic signal under sensor measurement, a fusion frequency feature extraction method for an underwater acoustic signal is presented based on variational mode decomposition (VMD), duffing chaotic oscillator (DCO) and a kind of permutation entropy (PE). Firstly, VMD decomposes the complex multi-component underwater acoustic signal into a set of intrinsic mode functions (IMFs), so as to extract the estimated center frequency of each IMF. Secondly, the frequency of the line spectrum can be obtained by using DCO and a kind of PE (KPE). DCO is used to detect the actual frequency of the line spectrum for each IMF and KPE can determine the accurate frequency when the phase space track is in the great periodic state. Finally, the frequency characteristic parameters acted as the input of the support vector machine (SVM) to distinguish different types of underwater acoustic signals. By comparing with the other three traditional methods for simulation signal and different kinds of underwater acoustic signals, the results show that the proposed method can accurately extract the frequency characteristics and effectively realize the classification and recognition for the underwater acoustic signal.
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Li, Yuxing, Yaan Li, Xiao Chen, Jing Yu, Hong Yang, and Long Wang. "A New Underwater Acoustic Signal Denoising Technique Based on CEEMDAN, Mutual Information, Permutation Entropy, and Wavelet Threshold Denoising." Entropy 20, no. 8 (July 28, 2018): 563. http://dx.doi.org/10.3390/e20080563.

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Owing to the complexity of the ocean background noise, underwater acoustic signal denoising is one of the hotspot problems in the field of underwater acoustic signal processing. In this paper, we propose a new technique for underwater acoustic signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), mutual information (MI), permutation entropy (PE), and wavelet threshold denoising. CEEMDAN is an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD). First, CEEMDAN is employed to decompose noisy signals into many intrinsic mode functions (IMFs). IMFs can be divided into three parts: noise IMFs, noise-dominant IMFs, and real IMFs. Then, the noise IMFs can be identified on the basis of MIs of adjacent IMFs; the other two parts of IMFs can be distinguished based on the values of PE. Finally, noise IMFs were removed, and wavelet threshold denoising is applied to noise-dominant IMFs; we can obtain the final denoised signal by combining real IMFs and denoised noise-dominant IMFs. Simulation experiments were conducted by using simulated data, chaotic signals, and real underwater acoustic signals; the proposed denoising technique performs better than other existing denoising techniques, which is beneficial to the feature extraction of underwater acoustic signal.
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Yang, Shuang, and Xiangyang Zeng. "Combination of gated recurrent unit and Network in Network for underwater acoustic target recognition." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 6 (August 1, 2021): 486–92. http://dx.doi.org/10.3397/in-2021-1490.

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Underwater acoustic target recognition is an important part of underwater acoustic signal processing and an important technical support for underwater acoustic information acquisition and underwater acoustic information confrontation. Taking into account that the gated recurrent unit (GRU) has an internal feedback mechanism that can reflect the temporal correlation of underwater acoustic target features, a model with gated recurrent unit and Network in Network (NIN) is proposed to recognize underwater acoustic targets in this paper. The proposed model introduces NIN to compress the hidden states of GRU while retaining the original timing characteristics of underwater acoustic target features. The higher recognition rate and faster calculation speed of the proposed model are demonstrated with experiments for raw underwater acoustic signals comparing with the multi-layer stacked GRU model.
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Zhang, Zengmeng, Xing Cheng, Dayong Ning, Jiaoyi Hou, and Yongjun Gong. "Underwater acoustic beacon signal extraction based on dislocation superimposed method." Advances in Mechanical Engineering 9, no. 2 (February 2017): 168781401769167. http://dx.doi.org/10.1177/1687814017691671.

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Flight data are recorded in an acoustic beacon. A new signal extraction method led by random decrement technique is proposed to detect sound signals from thousands of meters under the sea. This method involves dislocation superimposed method and cross-correlation function to extract acoustic beacon signals with noise interference. First, the starting point is selected and the length of each segment is determined via two superposition ways. Second, the signal segment for linear superposition is intercepted to complete acoustic beacon signal extraction. Finally, the signals are subjected to cross-correlation and energy analyses to determine the accuracy of interception signals. During the experiment, the collected acoustic beacon signal is used as the test signal, and the signal is obtained as the simulation signal on the basis of the parameters of acoustic beacons. Results show that the correlation between the synthetic and concerned signals is more than 80% after a number of superposition are performed and the extraction effect is remarkable. Dislocation superimposed method is simple and easily operated, and the extracted signal waveform yields a high accuracy.
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Dissertations / Theses on the topic "Underwater acoustic signals"

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Barsanti, Robert J. "Denoising of ocean acoustic signals using wavelet-based techniques." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1996. http://handle.dtic.mil/100.2/ADA329379.

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Thesis (M.S. in Electrical Engineering and M.S. in Engineering Acoustics) Naval Postgraduate School, December 1996.
Thesis advisor(s): Monique P. Fargues and Ralph Hippenstiel. "December 1996." Includes bibliographical references (p. 99-101). Also available online.
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Yagci, Tayfun. "Target Classification And Recognition Using Underwater Acoustic Signals." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606373/index.pdf.

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Nowadays, fulfillment of the tactical operations in secrecy has great importance for especially subsurface and surface warfare platforms as a result of improvements in weapon technologies. Spreading out of the tactical operations to the larger areas has made discrimination of targets unavoidable. Due to enlargement of the weapon ranges and increasing subtle hostile threats as a result of improving technology, &ldquo
visual&rdquo
target detection methods left the stage to the computerized acoustic signature detection and evaluation methods. Despite this, the research projects have not sufficiently addressed in the field of acoustic signature evaluation. This thesis work mainly investigates classification and recognition techniques with TRN / LOFAR signals, which are emitted from surface and subsurface platforms and proposes possible adaptations of existing methods that may give better results if they are used with these signals. Also a detailed comparison has been made about the experimental results with underwater acoustic signals.
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Eldred, Randy Michael. "Doppler processing of phase encoded underwater acoustic signals." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA241283.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 1990.
Thesis Advisor(s): Miller, James H. Second Reader: Tummala, Murali. "September 1990." Description based on title screen as viewed on December 17, 2009. DTIC Identifier(s): Acoustic tomography, inverse problems, Fast Hadamard Transforms, theses. Author(s) subject terms: Acoustic tomography, Fast Hadamard Transform, maximal-length sequences, Doppler processing. Includes bibliographical references (p. 95-96). Also available in print.
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Bissinger, Brett Bose N. K. Culver R. Lee. "Minimum hellinger distance classification of underwater acoustic signals." [University Park, Pa.] : Pennsylvania State University, 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-4677/index.html.

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Jack, Susan Heather. "The investigation of underwater acoustic signals using Laser Doppler Anemometry." Thesis, University of Edinburgh, 2000. http://hdl.handle.net/1842/15088.

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Laser Doppler Anemometry (LDA) has been used to study underwater acoustic signals both from emitting hydrophones and underwater explosions. A dual-beam LDA arrangement was used to capture Doppler signals arising from light scattered from particles suspended at the point of interest in the flow. These Doppler signals are analysed using either Hilbert transforms or wavelets, both of which allow instantaneous frequency information to be obtained. When an acoustic signal propagates through a medium it creates refractive index variations within the medium. The apparent motion of the scattering particles, as observed by the detector, which give rise to the Doppler signal, is therefore made up of two components. Firstly, the particles oscillate due to the sound field and secondly, the interference fringes oscillate due to the refractive index variations. This is termed the acousto-optic effect. A theory has been developed to investigate the effect of these refractive index variations on the analysed Doppler signals of an LDA system. Analysis of experimental Doppler signals using the Hilbert transform technique shows close agreement with the theoretical predictions. LDA has also been used to investigate the acoustic signal emitted by an oscillating explosion bubble. This is generated by an underwater spark which creates a similar situation to an underwater explosion in which a shock wave and an oscillating bubble are produced. Analysis of the Doppler signal using wavelets provides information on the bubble period, radius, energy and particle velocity. Explosive materials have traditionally been used for investigation of underwater explosions but they have the disadvantage of obscuring the area with explosion debris thus making optical investigation difficult. It is shown in this work that the use of LDA and analysis of Doppler signals using wavelets is an accurate technique for the investigation of acoustic signals from underwater explosions. This allows investigation of the area close to the explosion centre where measurements have been difficult to achieve with traditional techniques.
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Kendall, Elizabeth Ann Caughey Thomas Kirk. "Range dependent signals and maximum entropy methods for underwater acoustic tomography /." Diss., Pasadena, Calif. : California Institute of Technology, 1985. http://resolver.caltech.edu/CaltechETD:etd-04092008-080843.

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Sanderson, Josh. "Hierarchical Modulation Detection of Underwater Acoustic Communication Signals Through Maximum Likelihood Combining." Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1410872323.

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Heaney, Kevin Donn. "Inverting for source location and internal wave strength using long range ocean acoustic signals /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1997. http://wwwlib.umi.com/cr/ucsd/fullcit?p9737384.

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Evans, Benjamin Kerbin. "The effect of coded signals on the precision of autonomous underwater vehicle acoustic navigation." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/29044.

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Thesis (Ocean E.)--Joint program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Ocean Engineering and Woods Hole Oceanographic Institution), 1999.
Includes bibliographical references (p. 127-128).
Acoustic coded signaling offers potentially significant improvements over traditional "toneburst" methods in many underwater applications where error due to noise and multipath interference is a problem. In this thesis, the use of these spread spectrum techniques is analyzed for navigation of the REMUS autonomous underwater vehicle. The accuracy of the current system using Turyn and Barker sequences, as well as toneburst, is quantified, and the sources of the remaining error are examined.
by Benjamin Kerbin Evans.
Ocean E.
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Blount, Richard J. Jr. "Underwater acoustic model-based signal processing applied to the detection of signals from a planar array of point source elements." Thesis, New York : Kluwer Academic/Plenum Publishers, 1985. http://hdl.handle.net/10945/21597.

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Books on the topic "Underwater acoustic signals"

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Istepanian, Robert S. H. Underwater Acoustic Digital Signal Processing and Communication Systems. Boston, MA: Springer US, 2002.

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Underwater signal and data processing. Boca Raton, Fla: CRC Press, 1989.

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Abraham, Douglas A. Underwater Acoustic Signal Processing. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92983-5.

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Liu, Chaohui. Shui xia sheng xin hao chu li ji shu. 8th ed. Beijing Shi: Guo fang gong ye chu ban she, 2010.

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Otnes, Roald. Underwater Acoustic Networking Techniques. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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1937-, Urban Heinz G., and North Atlantic Treaty Organization. Scientific Affairs Division., eds. Adaptive methods in underwater acoustics. Dordrecht: D. Reidel Pub. Co., 1985.

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Eggen, Trym H. Underwater acoustic communication over Doppler spread channels. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1997.

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M, Bouvet, and Bienvenu G. 1941-, eds. High-resolution methods in underwater acoustics. Berlin: Springer-Verlag, 1991.

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Istepanian, Robert S. H., and Milica Stojanovic, eds. Underwater Acoustic Digital Signal Processing and Communication Systems. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3617-5.

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Tolstoy, Alexandra. Matched field processing for underwater acoustics. Singapore: World Scientific, 1993.

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Book chapters on the topic "Underwater acoustic signals"

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Ziomek, Lawrence J. "Underwater Acoustic Communication Signals." In An Introduction to Sonar Systems Engineering, 639–90. 2nd ed. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003259640-14.

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Wasiljeff, Alexander, and Arthur Malunat. "Adaptive Processing of Broadband Acoustic Signals." In Underwater Acoustic Data Processing, 301–6. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2289-1_33.

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Nuttall, Albert H., Weita Chang, and Even B. Lunde. "Performance of Incoherent Pulse Compression of Costas Signals." In Underwater Acoustic Data Processing, 189–93. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2289-1_20.

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Kraus, D., and J. F. Böhme. "Parametric Methods for Estimation of Signals and Noise in Wavefields." In Underwater Acoustic Data Processing, 279–84. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2289-1_30.

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Mansour, Ali, Nabih Benchekroun, and Cedric Gervaise. "Blind Separation of Underwater Acoustic Signals." In Independent Component Analysis and Blind Signal Separation, 181–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11679363_23.

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Kumaresan, R. "Parameter Estimation of Signals Corrupted by Noise Using a Matrix of Divided Differences." In Underwater Acoustic Data Processing, 243–60. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2289-1_26.

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Bendig, H. "Practical Experience Gained During the Building of an Expert System for the Interpretation of Underwater Signals." In Underwater Acoustic Data Processing, 597–601. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2289-1_67.

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Chapman, N. R., J. M. Syck, and G. R. Carlow. "Vertical Directionality of Acoustic Signals Propagating Downslope to a Deep Ocean Receiver." In Progress in Underwater Acoustics, 573–79. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1871-2_67.

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Huynh, Quyen, Walter Greene, and John Impagliazzo. "Feature Extraction and Classification of Underwater Acoustic Signals." In Full Field Inversion Methods in Ocean and Seismo-Acoustics, 183–88. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8476-0_30.

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Zhou, Zhong, Hai Yan, Saleh Ibrahim, Jun-Hong Cui, Zhijie Shi, and Reda Ammar. "Enhancing Underwater Acoustic Sensor Networks Using Surface Radios: Issues, Challenges and Solutions." In Signals and Communication Technology, 283–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01341-6_11.

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Conference papers on the topic "Underwater acoustic signals"

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Aksuren, Ibrahim Gokhan, and Ali Koksal Hocaoglu. "Automatic Target Classification Using Underwater Acoustic Signals." In 2022 30th Signal Processing and Communications Applications Conference (SIU). IEEE, 2022. http://dx.doi.org/10.1109/siu55565.2022.9864771.

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Weiss, Lora G., and Teresa L. P. Dixon. "Wavelet-based signal recovery and denoising of underwater acoustic signals." In SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Andrew F. Laine and Michael A. Unser. SPIE, 1995. http://dx.doi.org/10.1117/12.217580.

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Feroze, Khizer, Sidra Sultan, Salman Shahid, and Faran Mahmood. "Classification of underwater acoustic signals using multi-classifiers." In 2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 2018. http://dx.doi.org/10.1109/ibcast.2018.8312302.

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Li, Tian-song, Tian-hua Zhou, Ning He, De-kun Zhang, and Yi-han Li. "Research on laser detection of underwater acoustic signals." In International Symposium on Photoelectronic Detection and Imaging: Technology and Applications 2007, edited by Liwei Zhou. SPIE, 2007. http://dx.doi.org/10.1117/12.790794.

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Cheng, Luoyu, Yanmiao Li, Yanyu Bai, Mengjia Li, and Feng-Xiang Ge. "Modulation Pattern Recognition of Underwater Acoustic Communication Signals." In 2021 CIE International Conference on Radar (Radar). IEEE, 2021. http://dx.doi.org/10.1109/radar53847.2021.10028645.

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"Session TP2a: MIMO underwater acoustic communications." In 2010 44th Asilomar Conference on Signals, Systems and Computers. IEEE, 2010. http://dx.doi.org/10.1109/acssc.2010.5757745.

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Zheng, Kai, Yi Jiang, and Yongjun Li. "Passive Localization for Multi-AUVs by Using Acoustic Signals." In WUWNET'19: International Conference on Underwater Networks & Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3366486.3366507.

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Salin, Mikhail, and Alexander Ponomarenko. "Marine mammal calls detection in acoustic signals via gradient boosting model." In 6th Underwater Acoustics Conference and Exhibition. ASA, 2021. http://dx.doi.org/10.1121/2.0001476.

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Felis Enguix, Ivan, Rosa Martínez, Pablo Ruiz, and Hamid Er‐rachdi. "Compression techniques of underwater acoustic signals for real-time underwater noise monitoring ." In 6th International Electronic Conference on Sensors and Applications. Basel, Switzerland: MDPI, 2019. http://dx.doi.org/10.3390/ecsa-6-06581.

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Ou, Hui, John S. Allen, and Vassilis L. Syrmos. "Underwater Target Recognition Using Time-Frequency Analysis and Elliptical Fuzzy Clustering Classifications." In ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/omae2009-80211.

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A novel underwater target recognition approach has been developed based on the use of Wigner-type Time-Frequency (TF) analysis and the elliptical Gustafson-Kessel (GK) clustering algorithm. This method is implemented for the acoustic backscattered signals of the targets, and more precisely from the examination of echo formation mechanisms in the TF plane. For each of the training signals, we generate a clustering distribution which represents the signal’s TF characteristics by a small number of clusters. A feature template is created by combining the clustering distributions for the signals from the same training target. In the classification process, we calculate the clustering distribution of the test signal and compare it with the feature templates. The target is discriminated in terms of the best match of the clustering pattern. The advantages of GK clustering are that it allows elliptical-shaped clusters, and it automatically adjusts their shapes according to the distribution of the TF feature patterns. The recognition scheme has been applied to discriminate four spherical shell targets filled with different fluids. The data sets are the simulated acoustic responses from these targets, including the interferences caused by the seafloor interaction. [J. A. Fawcett, W. L. J. Fox, and A. Maguer, J. Acoust. Soc. Am. 104, 3296–3304 (1998)]. To evaluate the system robustness, white Gaussian noise is added to the acoustic responses. More than 95% of correct classification is obtained for high Signal-to-Noise Ratio (SNR), and it is maintained around 70% for very low SNRs.
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Reports on the topic "Underwater acoustic signals"

1

Fontes, N. R., and C. W. Therrien. Performance Analysis of the Wiener Filter with Applications to Underwater Acoustic Signals. Fort Belvoir, VA: Defense Technical Information Center, August 1997. http://dx.doi.org/10.21236/ada330083.

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Culver, Richard L., Leon H. Sibul, and David L. Bradley. Underwater Acoustic Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada460793.

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Vaccaro, Richard J. 1999 Underwater Acoustic Signal Processing Workshop. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada370148.

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Preisig, James. Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada611046.

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Preisig, James. Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems. Fort Belvoir, VA: Defense Technical Information Center, March 2015. http://dx.doi.org/10.21236/ada614150.

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Preisig, James. Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems. Fort Belvoir, VA: Defense Technical Information Center, August 2015. http://dx.doi.org/10.21236/ada621218.

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Preisig, James. Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems. Fort Belvoir, VA: Defense Technical Information Center, August 2015. http://dx.doi.org/10.21236/ada621219.

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Preisig, James. Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems. Fort Belvoir, VA: Defense Technical Information Center, November 2015. http://dx.doi.org/10.21236/ada624104.

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Ioup, George E., Juliette W. Ioup, and Grayson H. Rayborn. Application of Acoustic Signal Processing Techniques for Improved Underwater Source Detection and Localization. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada231834.

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D'Spain, Gerald L. Flying Wing Autonomous Underwater Glider for Basic Research in Ocean Acoustics, Signal/Array Processing, Underwater Autonomous Vehicle Technology, Oceanography, Geophysics, and Marine Biological Studies. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada496168.

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