Relevant bibliographies by topics / Underwater acoustic research

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Underwater acoustic research'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Underwater acoustic research"

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Viola, Salvatore, and Giorgio Riccobene. "15 years of acoustic detection studies at INFN." EPJ Web of Conferences 216 (2019): 01002. http://dx.doi.org/10.1051/epjconf/201921601002.

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Since the early 2000s, thanks to national, regional and European projects, a number of abyssal detectors equipped with acoustic sensors have been installed and operated by INFN in two cabled submarine research infrastructures off Eastern Sicily. The INFN’s interest in underwater acoustics arises from the need to develop an acoustic positioning system for the KN3NeT telescope and to study the possibility of neutrino acoustic detection. Theuse of innovative technologies for data acquisition and transmission systems have enabled the first long-term studies on the deep marine environment of the Ionian Sea with a variety of lines of research. Data acquired through the INFN underwater infrastructures have allowed the continuous monitoring of the underwater acoustic noise and several studies on cetacean species present in the area and on seismic sources . An overview of the main activities of INFN on detection and localization of underwater acoustic sources will be presented.
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Akyildiz, Ian F., Dario Pompili, and Tommaso Melodia. "Underwater acoustic sensor networks: research challenges." Ad Hoc Networks 3, no. 3 (May 2005): 257–79. http://dx.doi.org/10.1016/j.adhoc.2005.01.004.

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Li, Fulong, Xiaohong Shen, Ling Wang, and Haiyan Wang. "Research of Mobile Underwater Acoustic Communication of M-Ary Combining FDM and Piecewise-LFM." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 4 (August 2019): 704–13. http://dx.doi.org/10.1051/jnwpu/20193740704.

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Aiming at the countering doppler shift, a mobile underwater acoustic communication scheme of M-ary via FDM-PLFM is proposed. The cross-correlation between piecewise linear frequency modulation(PLFM) and traditional LFM signal is analyzed based on the cross-correlation function. Combining with frequency division multiplexing(FDM), the spectrum utilization of traditional underwater acoustic LFM communication scheme is improved. With the underwater acoustic LFM communication scheme, the new scheme does not require the doppler frequency shift compensation at the low doppler shift. The simulation results show that the present scheme has lower bit error rate than that for the traditional underwater acoustic M-ary LFM communication method under higher data rate conditions.
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Liu, Tong Qing, Guang Jie Han, Chuan Zhu, and Chen Yu Zhang. "Application Research on Aqua-Sim for Underwater Acoustic Sensor Networks." Advanced Materials Research 605-607 (December 2012): 1046–49. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.1046.

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In this paper, we present a simulator based on NS2 for Underwater Acoustic Sensor Networks (UASNs), named Aqua-Sim, which simulates the attenuation of underwater acoustic channels and the collision behaviors effectively. In addition, Aqua-Sim supports 3D network deployment and provides some MAC and routing protocols. Through communication between two nodes and localization of unknown nodes, the application examples of Aqua-Sim are shown. We also present the graphical user interface of Aqua-3D using an example application.
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Wang, Yuan, Zhou Mo Zeng, Yi Bo Li, Wen Zhang, and Hao Feng. "Research on Doppler and Channel Estimation for Multicarrier Spread Spectrum Underwater Acoustic Communication System." Advanced Materials Research 1079-1080 (December 2014): 752–56. http://dx.doi.org/10.4028/www.scientific.net/amr.1079-1080.752.

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Spreadspectrum orthogonal frequency-division multiplexing (SS-OFDM), which appliesspread spectrum technique into OFDM, performs robustly in severely multipathfading underwater channels. However, this technology is sensitive to thefrequency shift. Furthermore, communication in underwater acoustic (UWA)channels is more vulnerable to Doppler effect than other wireless channels dueto the low speed of acoustic signal propagation. It presents challenges forcommunication between underwater mobile nodes. Therefore, accurate Dopplerfrequency shift estimation and compensation is important. Aself-designed receiver structure for SS-OFDM system including a novel Dopplerscale estimation method and channel estimation via compressed sensing method isproposed. The simulation and experimental results offer strong proofs for ourscheme to be a viable option for acoustic communication between underwatermobile nodes and compressed sensing method outperforms the conventionalleast-squares (LS) method in channel estimation.
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Zhang, Minghong, and Xinwei Luo. "Underwater Acoustic Target Recognition Based on Generative Adversarial Network Data Augmentation." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 2 (August 1, 2021): 4558–64. http://dx.doi.org/10.3397/in-2021-2737.

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Underwater acoustic target recognition is an important aspect of underwater acoustic research. In recent years, machine learning has been developed continuously, which is widely and effectively applied in underwater acoustic target recognition. In order to acquire good recognition results and reduce the problem of overfitting, Adequate data sets are essential. However, underwater acoustic samples are relatively rare, which has a certain impact on recognition accuracy. In this paper, in addition of the traditional audio data augmentation method, a new method of data augmentation using generative adversarial network is proposed, which uses generator and discriminator to learn the characteristics of underwater acoustic samples, so as to generate reliable underwater acoustic signals to expand the training data set. The expanded data set is input into the deep neural network, and the transfer learning method is applied to further reduce the impact caused by small samples by fixing part of the pre-trained parameters. The experimental results show that the recognition result of this method is better than the general underwater acoustic recognition method, and the effectiveness of this method is verified.
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Kozaczka, Eugeniusz, Jacek Domagalski, and Ignacy Gloza. "Investigation of the underwater noise produced by ships by means of intensity method." Polish Maritime Research 17, no. 3 (January 1, 2010): 26–36. http://dx.doi.org/10.2478/v10012-010-0025-0.

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Investigation of the underwater noise produced by ships by means of intensity method This paper presents results of research on spectral structure of underwater noise acoustic field radiated into water by selected ships moving in shallow waters. Underwater acoustic field of ships in motion is associated with acoustic activity of ship mechanisms and equipment under work. Vibration energy radiated by the mechanisms and devices is transmitted by ship structural elements to surrounding water where it is propagated in the form of acoustic waves of a wide frequency band. In this paper are presented results of the research on propagation of energy of acoustic waves in the near fiel, obtained from acoustic pressure measurements by means of two sensors located close to each other.
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Zhao, Xinsa, Peng Yang, Rongrong Zhao, and Jianning Han. "Research on acoustic conduction mechanism of underwater acoustic channel based on metamaterials." AIP Advances 10, no. 11 (November 1, 2020): 115321. http://dx.doi.org/10.1063/5.0030198.

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Bruno, Michael, Alexander Sutin, Kil Woo Chung, Alexander Sedunov, Nikolay Sedunov, Hady Salloum, Hans Graber, and Paul Mallas. "Satellite Imaging and Passive Acoustics in Layered Approach for Small Boat Detection and Classification." Marine Technology Society Journal 45, no. 3 (May 1, 2011): 77–87. http://dx.doi.org/10.4031/mtsj.45.3.10.

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AbstractThe research being conducted in the Center for Secure and Resilient Maritime Commerce, a Department of Homeland Security National Center of Excellence for Port Security, examines some basic science issues and emerging technologies to improve the security of ports and inland waterways as well as coastal and offshore operations. This research follows a layered approach, utilizing above-water and underwater surveillance techniques. The investigated layers include satellite-based wide-area surveillance, high-frequency radar systems providing over-the-horizon monitoring, and nearshore and harbor passive acoustic surveillance. In this paper, we present a brief review of the Stevens research in passive acoustics aimed at achieving underwater and surface targets detection, classification, and tracking. The passive acoustic data were combined with satellite imagery provided by the University of Miami CSTARS facility’s electro-optical and synthetic aperture radar satellite imaging capabilities. Advantages of concurrent use of satellite imaging and passive acoustics for maritime domain awareness are analyzed.
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Zhang, Kai, De Shi Wang, Peng Wang, and Yi Qun Du. "Research on the Broadband Dual-Excited Underwater Acoustic Transducer." Advanced Engineering Forum 2-3 (December 2011): 144–47. http://dx.doi.org/10.4028/www.scientific.net/aef.2-3.144.

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The performance of bandwidth and transmitting voltage response to the piezoelectric dual-excited underwater acoustic transducer are studied by FEM software ANSYS in the paper. At first, the mechanism of the dual-excited transducer is analyzed, once this is done, The FEM models with different axial dimension of the piezoelectric stack and the mid mass, Al and steel are specified for the material of mid mass respectively. the calculated curves are contrasted, the results were found that the appropriate proportion of the mid-mass to the stack will broaden the bandwidth and enhance the transmitting voltage response, the increase of the thickness of the mid-mass will reduce the transmitting voltage response, and the bandwidth can be expanded to 13kHz, the resonance frequency has shifted to a higher value, it ranges from 34kHz to 46kHz. The results show that one octave bandwidth could be achieved and provide support for the next production and experiment of the broadband underwater acoustic transducer.
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Dissertations / Theses on the topic "Underwater acoustic research"

1

Hurdle, Burton G. "Acoustic interference fields in the ocean." Thesis, Open University, 1988. http://oro.open.ac.uk/57051/.

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Two areas of underwater acoustics are investigated: ocean-bottom scattering and acoustic fields in geometrically dispersive sound channels. The purpose is to describe and provide an understanding of the physical mechanisms in these two areas by comparing analyzed results from ocean experiments with theoretical computations. Experiments using directive 19.5-kHz transducers illustrate temporal and spacial behavior of signals scattered from the ocean bottom. The signals fluctuate, as a function of acoustic geometry, in linear relation to source and receiver motion and to signal frequency. Spacial structure of the acoustic field depends on frequency and acoustic geometry and is independent of motion and bottom roughness. Data supporting these observations are included as well as data showing the effects of bottom type on the scattered returns, that is, the existence of subbottom returns in some data. Volume-scattering-strength profiles are also provided from data obtained in these experiments. Continuous-wave (CW) and impulsive sources covering frequencies 5 to 260 Hz were towed and deployed respectively over ranges up to 3000 km, with reception on fixed hydrophones. Analyses of measured propagation losses of these low-frequency acoustic signals in the dispersive channel provide insights into the nature of the propagation and the acoustic channel. Both the CW signals and the arrivals of the impulsive signals are analyzed in terms of transmission loss, convergence-zone structure, source-motion effects, interference structure, and channel characteristics. The systematic variation (internal tides) of the medium and its influence on the interference field are discussed. The state of modeling, both simple and complicated, is reviewed and compared with results of the ocean experiments. Relationships are provided between this work and the broader field of underwater acoustics. Suggested areas for future research are made.
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Lynam, Christopher Philip. "Ecological and acoustic investigations of jellyfish (Scyphozoa and Hydrozoa)." Thesis, St Andrews, 2006. https://research-repository.st-andrews.ac.uk/handle/10023/466.

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Chiu, Ching-Sang Denner Warren W. "Report on the Office of Naval Research USA-China Conference on Shallow Water Acoustics, December 18-21, 1995." Monterey, CA : Naval Postgraduate School, 1997. http://catalog.hathitrust.org/api/volumes/oclc/37486128.html.

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Boyle, John K. "Performance Metrics for Depth-based Signal Separation Using Deep Vertical Line Arrays." PDXScholar, 2015. https://pdxscholar.library.pdx.edu/open_access_etds/2198.

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Vertical line arrays (VLAs) deployed below the critical depth in the deep ocean can exploit reliable acoustic path (RAP) propagation, which provides low transmission loss (TL) for targets at moderate ranges, and increased TL for distant interferers. However, sound from nearby surface interferers also undergoes RAP propagation, and without horizontal aperture, a VLA cannot separate these interferers from submerged targets. A recent publication by McCargar and Zurk (2013) addressed this issue, presenting a transform-based method for passive, depth-based separation of signals received on deep VLAs based on the depth-dependent modulation caused by the interference between the direct and surface-reflected acoustic arrivals. This thesis expands on that work by quantifying the transform-based depth estimation method performance in terms of the resolution and ambiguity in the depth estimate. Then, the depth discrimination performance is quantified in terms of the number of VLA elements.
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林爭賢. "Research of Genetic Algorithm Applied on Underwater Acoustic Signal Recognition." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/79951622139649402835.

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碩士
中原大學
資訊工程學系
87
The recognition of underwater acoustic signal from ocean ambient noise is an important job of underwater signal processing. The propagation of underwater sound is affect due to multi-path, reverberation, and inhomogeneity. Besides, the random process and time vary characteristics of the signal cause it difficult to use an effective mathematics model to simulate the system. Hence, there are two emphases about this research. The one is underwater acoustic signal feature parameter extracted by using digital signal processing. The other is optimizing the fuzzy logic recognition system by applying genetic algorithm. Finally, the two parts are integrated to perform signal recognition task and prove that our research can be realized. During the feature parameter extraction stage, signal characteristic analysis and feature selection is implemented. Upon careful analysis the feature parameters of the signal and practical data are obtained from calculation, the result shows the feature parameters obtaining from the Spectrum can distinguish ship signature effectively. During the fuzzy logic recognition system modeling, each ship's feature parameters are utilized on to construct the preliminary fuzzy logic recognition system. In the systems, genetic algorithm is further used to fine-tune the membership functions of the if-then inference rules. Hence, the condition parts of the rules can be automatically adjusted to increase the recognition rate. Moreover, to make the recognition system intensively compact, applying genetic algorithm to filter out the insignificant or redundant rules. The pruned compact system not only needs less reasoning time, but also makes the reasoning process simpler and more explainable. By using object-orient language, a modular recognition modulate system architecture was developed which shorten program develop time and improve system execute performance. An actually realized underwater acoustic signal recognition system was established, the performance of system is simple in operation and fast in response.
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Jiang, Yan-Yau, and 江妍瑤. "The Research of the De-nosing for Underwater Acoustic Signal." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/ej8t67.

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碩士
中原大學
資訊工程研究所
91
During propagation of the underwater acoustic signal is affected by ocean interference and ambient noise disturbance, it adds random process and time vary characteristics to the signal. Therefore, in order to distinguish the weaken signal caused by long distance propagation loss the received signal must be processed properly. This research takes wavelet-based with choosing thresholding value by genetic algorithms for de-noising. It can be divided into three stages: (1) Wavelet transform of the underwater acoustic signals (2) Thresholding of wavelet coefficients (3) Inverse wavelet transform to reconstruct modified signals. And in second stage, this research makes use of Genetic Algorithms to obtain the optimal threshold value for shrinking to wavelet coefficients. In experiments, this research demonstrates two different types of noisy signals on the de-noising underwater acoustic signals system. First type is basic test signal, such as Blocks and Chirp so on. Second type is the actual underwater acoustic signals. Then, mean-square-error and signal-to-noise ratio are used to estimate this system and the other two traditional wavelet transform for de-noising methods. According to the outcomes of experiments, the proposed approach can achieve better performance on de-noising.
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Cheng, Yu-Ming, and 程瑜銘. "The Research of Hidden Markov Model Applied on Underwater Acoustic Signal Recognition." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/60215439808966973252.

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碩士
中原大學
資訊工程學系
88
During propagation of the underwater acoustic signal is affected by ocean interference and environmental noise disturbance, in order to distinguish the weaken signal caused by long distance propagation loss the received signal must be processed properly. There are two emphases about this research. The one is underwater acoustic signal feature parameter extraction by using wavelet packet decomposition. The other is the signal pattern recognition by using of Hidden Markov Model. Finally, combine the two procedures and establish a practical recognition system. During the feature parameter extraction stage, signal characteristic analysis and feature selection is discussed. It has been proved that using the wavelet packet decomposition method for feature parameter selective can obtain multi-resolution characteristics. Therefore, the feature parameters obtain by above maintain method can distinguish the different characteristics of ships. Besides, use vector quantization to clustering data, and find the characteristics of data gathering can get representative pattern feature parameters of each sample classification individually. During the recognition system modeling, use Hidden Markov Model theory to establish recognition system. The organization of the system includes two parts, in first part the stochastic probability process is used for statistical modeling of the underwater acoustic signal, in second part, the Viterbi algorithm is used to find the best recognition result.
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Hsiang-I, Chen, and 陳祥益. "Research of Fuzzy Recognition Applied on Read-Time Underwater Acoustic Signal Identification." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/99357859323774669943.

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Abstract:
碩士
中原大學
資訊工程研究所
86
There are two emphases about this research. The one is underwater acoustic signal feature parameter extraction by using digital signal processing and then getting the template feature parameter by training the self-organization map neural network. The other is the fuzzy logic recognition system and establishes the fuzzy logic rules to membership functions modification. Finally, the two parts are integrated to prove that our research can be realized. During the feature parameter extraction stage, signal characteristic analysis and feature selection are discussed. Upon careful analysis the feature parameters of the signal and practical data obtain from calculation, the result shows the feature parameters obtaining from the Spectrum can distinguish ship signature effectively. In order to get each ship''s template feature parameter, a self-organization map neural network was used in the training phase for clustering the input data and generate the center of data set. During the recognition system modeling, an algorithm which using template feature parameter to conform the fuzzy logic rule and fuzzy logic inference is developed. After getting the result of recognition from the fuzzy recognition algorithm, a modify process is execute which modifying the membership function of the fuzzy logic rules. According to the deviation between the recognition result and actual result, the modification process repeat until recognition rate is increasing. In order to achieve real-time recognition, we have to simplify the algorithms for the purpose of saving calculation time. By using object-orient language, and developing modular system architecture. We shorten program develop time and improve system performance. Finally, a realizable real-time acoustic signal recognition system was established.
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Lin, Liang-Ching, and 林良清. "Research of Wavelet Analysis Applied on Real-Time Underwater Acoustic Signal Identification." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/28569239773478415619.

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Abstract:
碩士
中原大學
資訊工程學系
87
In the process of spreading various underwater acoustic signals in the ocean, because of being affected by the ocean environment and all kinds of oceanic noises permeate through, it is necessary to have a appropriate signal procedure after receiving to identify the signals that energy is already decreased by long distance transmission and environment interruption. To study real-time underwater acoustic signal identification, it is divided into two important parts: the first research subject is wavelet analysis using in signal feature selection; the second is the identify application of fuzzy logic. Combine two parts and establish a reality, low cost, real-time identify system. When selecting feature parameter analysis, study signal character analysis and how to get the feature parameter individually. Being proved, in terms of the character of wavelet analysis multi-resolution and study different feature parameter selective method can get the feature parameter that realistic react different ships' character. Besides, use self-organization neural network training stage to clustering data, and find the character of data gathering center can get representative pattern feature parameter of each sample classification individually. Use fuzzy logic theory to establish identification system and make fuzzy logic rules by various classified pattern features. Get the identifiable result after using fuzzy logic recognition system. To the purpose of real-time identification, use multi-task and dual- buffer mode can let the signal collection and identification concurrent run which can improve the performance efficiency of the system, and establish a realistic real-time acoustic signal identify system. In addition, structure real-time client-server identify simulation system on the network can let the development of identify system more elastic.
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Cheng, Jie-Yeh, and 鄭傑爗. "The Research of Digital Signal Processing Chip Set Applied on Underwater Acoustic Signal Recognition." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/48676912429726616858.

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Abstract:
碩士
中原大學
資訊工程研究所
91
This paper is using the low-cost hardware to simply the complexity of building an underwater acoustic signal recognition system and shorter the training time of characteristic. The system is based on TI TMS320C6711 DSK, using the AD535 chip to do A/D transformation. After that, the system uses the digital signal to make preprocessing and the Fast Fourier Transform to get sum of spectra or Welch Periodogram. The result is scaled and stands for a set of signal characteristic. These signal characteristics of ships are adjusted by the following method：K-Mean, average method, recursive concentration, the minimum distance method to get single characteristics center. As to several characteristic centers, the adaptive resonance theory and backprogation network have been traced, finally, the adaptive resonance theory shows a better result.
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Books on the topic "Underwater acoustic research"

1

Lynch, James F. Report on the Office of Naval Research Shallow-Water Acoustic Workshop 1-3 October 1996. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1997.

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NATO Advanced Study Institute on Acoustic Signal Processing for Ocean Exploration (1992 Funchal, Madeira Islands). Acoustic signal processing for ocean exploration. Dordrecht: Kluwer Academic Publishers, 1993.

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Donskoy, Dimitri. Soviet R&D in low-frequency underwater acoustics. Falls Church, VA: Delphic Associates, 1991.

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Beeman, John W., Noah S. Adams, and John H. Eiler. Telemetry techniques: A user guide for fisheries research. Bethesda, Md: American Fisheries Society, 2012.

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Frisk, George V. Report on the Office of Naval Research Shallow Water Acoustics Workshop: April 24-26, 1991. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1992.

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Chiu, Ching-Sang. Report of the Office of Naval Research Phase II International Workshop on Shallow-Water Acoustics, Seattle, June 27, 1998. Monterey, Calif: Naval Postgraduate School, 1998.

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Galaktionov, Mikhaïl. Aspects récents de l'acoustique sous-marine russe. Plouzané [France]: Editions de l'IFREMER, 1994.

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Mueller, Gordon. Monitoring impacts on inland fisheries using hydroacoustics. Denver, Colo: United States Dept. of the Interior, Bureau of Reclamation, 1993.

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Mueller, Gordon. Monitoring impacts on inland fisheries using hydroacoustics. Denver, Colo: U.S. Dept. of the Interior, Bureau of Reclamation, Denver Office, 1993.

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Acoustic Signal Processing for Ocean Exploration (NATO Science Series C: (closed)). Springer, 1993.

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Book chapters on the topic "Underwater acoustic research"

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Zhang, Bingsheng, Tianhe Xu, and Ruru Gao. "Research on Acoustic Velocity Correction Algorithm in Underwater Acoustic Positioning." In Lecture Notes in Electrical Engineering, 859–73. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0029-5_72.

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Sun, Xinyi, Desen Yang, Lianjin Hong, Shengguo Shi, and Hongkun Zhou. "Research of Axis Mismatches Between Pairs of Sensitive Elements of Underwater Acoustic Velocity Gradient Sensors." In Underwater Acoustics and Ocean Dynamics, 71–79. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2422-1_10.

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Li, Bo, Hong-juan Yang, Gong-liang Liu, and Xi-yuan Peng. "A Research on Underwater Acoustic Channel Modeling and Simulation of Shallow Sea." In Machine Learning and Intelligent Communications, 317–27. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52730-7_32.

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Voloshchenko, Alexander P., and Sergey P. Tarasov. "Experimental Research of Penetration of the Acoustic Inhomogeneous Plane Waves from Water into Air." In Exploration and Monitoring of the Continental Shelf Underwater Environment, 129–66. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119488309.ch5.

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Liu, Guangzhong, and Xueqin Chen. "A Positioning Research of Underwater Acoustic Sensor Networks Based on Support Vector Regression." In Future Computing, Communication, Control and Management, 9–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27326-1_2.

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Ning, Xiaoling, Zhong Liu, and Yasong Luo. "Research on Variable Step-Size Blind Equalization Algorithm Based on Normalized RBF Neural Network in Underwater Acoustic Communication." In Advances in Neural Networks – ISNN 2009, 1063–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01513-7_117.

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Guicking, Dieter. "Research on Underwater Acoustics in Göttingen." In Acoustics, Information, and Communication, 241–76. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05660-9_13.

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"Research Challenges and Clustering." In Underwater Acoustic Sensor Networks, 1. Auerbach Publications, 2010. http://dx.doi.org/10.1201/9781420067125-s1.

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Pompili, Dario, and Tommaso Melodia. "Research Challenges in Communication Protocol Design for Underwater Sensor Networks." In Underwater Acoustic Sensor Networks, 3–27. Auerbach Publications, 2010. http://dx.doi.org/10.1201/9781420067125-c1.

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"Research Challenges in Communication Protocol Design for Underwater Sensor Networks ............................................... DARio Pom Pil i AND To m m ASo m El o Di A." In Underwater Acoustic Sensor Networks, 19–44. Auerbach Publications, 2010. http://dx.doi.org/10.1201/9781420067125-6.

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Conference papers on the topic "Underwater acoustic research"

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Liang, Wei, Haibin Yu, Bangxiang Li, Hualiang Zhang, Jieyin Bai, and Jianying Zheng. "Experiment Research on Underwater Acoustic Sensor Network." In 2007 International Conference on Wireless Communications, Networking and Mobile Computing. IEEE, 2007. http://dx.doi.org/10.1109/wicom.2007.600.

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Chengbing He, Jianguo Huang, and Qunfei Zhang. "Research on bandwidth efficient underwater acoustic communications." In 2010 IEEE Region 10 Conference (TENCON 2010). IEEE, 2010. http://dx.doi.org/10.1109/tencon.2010.5686604.

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Miao Yanmin, Li Xia, and Fang Shiliang. "Congestion control research of underwater acoustic networks." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5691726.

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Du, Pengyu, Shengjun Xiong, and Chao Wang. "Research on mobile spread spectrum underwater acoustic communication." In 2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2019. http://dx.doi.org/10.1109/icspcc46631.2019.8960756.

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Zhao, Ruiqin, Yufei Hu, Xiaohong Shen, and Haiyan Wang. "Research on Underwater Acoustic Networks routing using simulations." In 2012 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2012. http://dx.doi.org/10.1109/icspcc.2012.6335666.

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Chen, Yun, Ping Cai, and Yilin Wang. "Research on FRFT-PPM underwater acoustic communication system." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5647908.

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Zheng, Lei, Bao-qin Wu, Jian Zhong, and Xiao-lei Sun. "Research on Underwater Communication Based on Acoustic Wave." In 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2019. http://dx.doi.org/10.1109/icicta49267.2019.00079.

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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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Lal, Chhagan, Roberto Petroccia, Mauro Conti, and Joao Alves. "Secure underwater acoustic networks: Current and future research directions." In 2016 IEEE Third Underwater Communications and Networking Conference (UComms). IEEE, 2016. http://dx.doi.org/10.1109/ucomms.2016.7583466.

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Wang, Yueyue, Yupeng Tai, Haibin Wang, Jun Wang, and Weiming Gan. "The research of MIMO-FBMC in underwater acoustic communication." In WUWNet'18: The 13th ACM International Conference on Underwater Networks & Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3291940.3291990.

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Reports on the topic "Underwater acoustic research"

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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, November 2015. http://dx.doi.org/10.21236/ada624104.

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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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Stein, Peter J., and Subramaniam D. Rajan. Using Navy Ranges for Basic Research in Underwater Acoustics. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada629354.

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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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