Academic literature on the topic 'Oceanic mixing – Data processing'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Oceanic mixing – Data processing.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Oceanic mixing – Data processing"
Kurekin, Andrey A., Peter E. Land, and Peter I. Miller. "Internal Waves at the UK Continental Shelf: Automatic Mapping Using the ENVISAT ASAR Sensor." Remote Sensing 12, no. 15 (August 2, 2020): 2476. http://dx.doi.org/10.3390/rs12152476.
Full textDerstroff, Bettina, Imke Hüser, Efstratios Bourtsoukidis, John N. Crowley, Horst Fischer, Sergey Gromov, Hartwig Harder, et al. "Volatile organic compounds (VOCs) in photochemically aged air from the eastern and western Mediterranean." Atmospheric Chemistry and Physics 17, no. 15 (August 9, 2017): 9547–66. http://dx.doi.org/10.5194/acp-17-9547-2017.
Full textParedi, Davide, Tommaso Lucchini, Gianluca D’Errico, Angelo Onorati, Lyle Pickett, and Joshua Lacey. "Validation of a comprehensive computational fluid dynamics methodology to predict the direct injection process of gasoline sprays using Spray G experimental data." International Journal of Engine Research 21, no. 1 (August 22, 2019): 199–216. http://dx.doi.org/10.1177/1468087419868020.
Full textDing, Yongsheng, Hua Han, and Fengming Liu. "Intelligent integrated data processing model for oceanic warning system." Knowledge-Based Systems 23, no. 1 (February 2010): 61–69. http://dx.doi.org/10.1016/j.knosys.2009.07.003.
Full textFu, Hongli, Jinkun Yang, Wei Li, Xinrong Wu, Guijun Han, Yuanfu Xie, Shaoqing Zhang, Xuefeng Zhang, Yingzhi Cao, and Xiaoshuang Zhang. "A Potential Density Gradient Dependent Analysis Scheme for Ocean Multiscale Data Assimilation." Advances in Meteorology 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/9315601.
Full textTziperman, Eli. "Calculating the Time-Mean Oceanic General Circulation and Mixing Coefficients from Hydrographic Data." Journal of Physical Oceanography 18, no. 3 (March 1988): 519–25. http://dx.doi.org/10.1175/1520-0485(1988)018<0519:cttmog>2.0.co;2.
Full textSubramanian, A. C., A. J. Miller, B. D. Cornuelle, E. Di Lorenzo, R. A. Weller, and F. Straneo. "A data assimilative perspective of oceanic mesoscale eddy evolution during VOCALS-REx." Atmospheric Chemistry and Physics Discussions 12, no. 8 (August 20, 2012): 20901–30. http://dx.doi.org/10.5194/acpd-12-20901-2012.
Full textKantha, Lakshmi, and Hubert Luce. "Mixing Coefficient in Stably Stratified Flows." Journal of Physical Oceanography 48, no. 11 (November 2018): 2649–65. http://dx.doi.org/10.1175/jpo-d-18-0139.1.
Full textBlacic, T. M., and W. S. Holbrook. "First images and orientation of internal waves from a 3-D seismic oceanography data set." Ocean Science Discussions 6, no. 3 (October 20, 2009): 2341–56. http://dx.doi.org/10.5194/osd-6-2341-2009.
Full textBarth, M. F., R. B. Chadwick, and D. W. van de Kamp. "Data processing algorithms used by NOAA's wind profiler demonstration network." Annales Geophysicae 12, no. 6 (May 31, 1994): 518–28. http://dx.doi.org/10.1007/s00585-994-0518-1.
Full textDissertations / Theses on the topic "Oceanic mixing – Data processing"
Yates, James William. "Mixing Staged Data Flow and Stream Computing Techniques in Modern Telemetry Data Acquisition/Processing Architectures." International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/608707.
Full textToday’s flight test processing systems must handle many more complex data formats than just the PCM and analog FM data streams of yesterday. Many flight test programs, and their respective test facilities, are looking to leverage their computing assets across multiple customers and programs. Typically, these complex programs require the ability to handle video, packet, and avionics bus data in real time, in addition to handling the more traditional PCM format. Current and future telemetry processing systems must have an architecture that will support the acquisition and processing of these varied data streams. This paper describes various architectural designs of both staged data flow and stream computing architectures, including current and future implementations. Processor types, bus design, and the effects of varying data types, including PCM, video, and packet telemetry, will be discussed.
Herrmann, Felix J., Deli Wang, Gilles Hennenfent, and Peyman P. Moghaddam. "Seismic data processing with curvelets: a multiscale and nonlinear approach." Society of Exploration Geophysicists, 2007. http://hdl.handle.net/2429/557.
Full textHorowitz, Michael (Michael Joshua) 1962. "Western South Atlantic holocene and glacial deepwater hydrography derived from benthic foraminiferal Cd/Ca and stable carbon isotope data." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/69183.
Full textIncludes bibliographical references (leaves 16-21).
Today, deep waters produced in the North Atlantic are exported through the western South Atlantic. Antarctic intermediate water (AAIW) also enters the Atlantic in this region. Circumpolar deep water (CDW) fills the depths below AAIW and above and below northern source waters. A depth transect of cores from 1567-3909 m water depth in the western South Atlantic are ideally located to monitor inter-ocean exchange of deep water, and variations in the relative strength of northern versus southern source water production. Last glacial maximum (LGM) Cd/Ca and 813C data indicate a nutrient-depleted intermediate-depth water mass. In the mid-depth western South Atlantic, a simple conversion of LGM 813C data suggests significantly less nutrient enrichment than LGM Cd/Ca ratios, but Cd/Ca and 613C data can be reconciled when plotted in CdW/ 13C space. Paired LGM Cd/Ca and S13C data from mid-depth cores suggest increasingly nutrient rich waters below 2000 m, but do not require an increase in Southern Ocean water contribution relative to today. Cd/Ca data suggest no glacial-interglacial change in the hydrography of the deepest waters of the region. To maintain relatively low Cd/Ca ratios (low nutrients) in the deepest western South Atlantic waters, and in CDW in general, during the LGM requires an increased supply of nutrient-depleted glacial North Atlantic intermediate water (GNAIW) and/or nutrient-depleted glacial Subantarctic surface waters to CDW to balance reduced NADW contribution to CDW. LGM Cd/Ca and 513C data suggest strong GNAIW influence in the western South Atlantic which in turn implies export of GNAIW from the Atlantic, and entrainment of GNAIW into the Antarctic Circumpolar current.
by Michael Horowitz.
S.M.
Siepka, Damian. "Development of multidimensional spectral data processing procedures for analysis of composition and mixing state of aerosol particles by Raman and FTIR spectroscopy." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10188/document.
Full textSufficiently adjusted, multivariate data processing methods and procedures can significantly improve the process for obtaining knowledge of a sample composition. Spectroscopic techniques have capabilities for fast analysis of various samples and were developed for research and industrial purposes. It creates a great possibility for advanced molecular analysis of complex samples, such as atmospheric aerosols. Airborne particles affect air quality, human health, ecosystem condition and play an important role in the Earth’s climate system. The purpose of this thesis is twofold. On an analytical level, the functional algorithm for evaluation of quantitative composition of atmospheric particles from measurements of individual particles by Raman microspectrocopy (RMS) was established. On a constructive level, the readily accessible analytical system for Raman and FTIR data processing was developed. A potential of a single particle analysis by RMS has been exploited by an application of the designed analytical algorithm based on a combination between a multicurve resolution and a multivariate data treatment for an efficient description of chemical mixing of aerosol particles. The algorithm was applied to the particles collected in a copper mine in Bolivia and provides a new way of a sample description. The new user-friendly software, which includes pre-treatment algorithms and several easy-to access, common multivariate data treatments, is equipped with a graphical interface. The created software was applied to some challenging aspects of a pattern recognition in the scope of Raman and FTIR spectroscopy for coal mine particles, biogenic particles and organic pigments
Nadarajah, Kumaravel. "Computers in science teaching: a reality or dream; The role of computers in effective science education: a case of using a computer to teach colour mixing; Career oriented science education for the next millennium." Thesis, Rhodes University, 2000. http://hdl.handle.net/10962/d1003341.
Full textMay, Glenn H. "MicroSoar : a high speed microstructure profiling system." Thesis, 1997. http://hdl.handle.net/1957/28796.
Full textGraduation date: 1998
"Development of a Cantonese-English code-mixing speech recognition system." Thesis, 2011. http://library.cuhk.edu.hk/record=b6075190.
Full textCode-mixing is a common phenomenon in bilingual societies. It refers to the intra-sentential switching of two languages in a spoken utterance. This thesis addresses the problem of the automatic recognition of Cantonese-English code-mixing speech, which is widely used in Hong Kong.
Cross-lingual speaker adaptation has also been investigated in the thesis. Speaker independent (SI) model mapping between Cantonese and English is established at different levels of acoustic units, viz phones, states, and Gaussian mixture components. A novel approach for cross-lingual speaker adaptation via Gaussian component mapping is proposed and has been proved to be effective in most speech recognition tasks.
This study starts with the investigation of the linguistic properties of Cantonese-English code-mixing, which is based on a large number of real code-mixing text corpora collected from the internet and other sources. The effects of language mixing for the automatic recognition of Cantonese-English codemixing utterances are analyzed in a systematic way. The problem of pronunciation dictionary, acoustic modeling and language modeling are investigated. Subsequently, a large-vocabulary code-mixing speech recognition system is developed and implemented.
While automatic speech recognition (ASR) of either Cantonese or English alone has achieved a great degree of success, recognition of Cantonese-English code-mixing speech is not as trivial. Unknown language boundary, accents in code-switched English words, phonetic and phonological differences between Cantonese and English, no regulated grammatical structure, and lack of speech and text data make the ASR of code-mixing utterances much more than a simple integration of two monolingual speech recognition systems. On the other hand, we have little understanding of this highly dynamic language phenomenon. Unlike in monolingual speech recognition research, there are very few linguistic studies that can be referred to.
Cao, Houwei.
Adviser: P.C. Ching.
Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: .
Thesis (Ph.D.)--Chinese University of Hong Kong, 2011.
Includes bibliographical references (leaves 129-140).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
Burton, Neil Lorraine. "The analysis of turbulent flows using a digital computer, with special reference to the plane mixing layer." Thesis, 2015. http://hdl.handle.net/10539/16475.
Full textLi, Guoqing. "Simulating interdecadal variation of the thermohaline circulation by assimilating time-dependent surface data into an ocean climate model /." 1994. http://collections.mun.ca/u?/theses,75354.
Full text"Automatic speech recognition of Cantonese-English code-mixing utterances." 2005. http://library.cuhk.edu.hk/record=b5892425.
Full textThesis (M.Phil.)--Chinese University of Hong Kong, 2005.
Includes bibliographical references.
Abstracts in English and Chinese.
Chapter Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Background --- p.1
Chapter 1.2 --- Previous Work on Code-switching Speech Recognition --- p.2
Chapter 1.2.1 --- Keyword Spotting Approach --- p.3
Chapter 1.2.2 --- Translation Approach --- p.4
Chapter 1.2.3 --- Language Boundary Detection --- p.6
Chapter 1.3 --- Motivations of Our Work --- p.7
Chapter 1.4 --- Methodology --- p.8
Chapter 1.5 --- Thesis Outline --- p.10
Chapter 1.6 --- References --- p.11
Chapter Chapter 2 --- Fundamentals of Large Vocabulary Continuous Speech Recognition for Cantonese and English --- p.14
Chapter 2.1 --- Basic Theory of Speech Recognition --- p.14
Chapter 2.1.1 --- Feature Extraction --- p.14
Chapter 2.1.2 --- Maximum a Posteriori (MAP) Probability --- p.15
Chapter 2.1.3 --- Hidden Markov Model (HMM) --- p.16
Chapter 2.1.4 --- Statistical Language Modeling --- p.17
Chapter 2.1.5 --- Search A lgorithm --- p.18
Chapter 2.2 --- Word Posterior Probability (WPP) --- p.19
Chapter 2.3 --- Generalized Word Posterior Probability (GWPP) --- p.23
Chapter 2.4 --- Characteristics of Cantonese --- p.24
Chapter 2.4.1 --- Cantonese Phonology --- p.24
Chapter 2.4.2 --- Variation and Change in Pronunciation --- p.27
Chapter 2.4.3 --- Syllables and Characters in Cantonese --- p.28
Chapter 2.4.4 --- Spoken Cantonese vs. Written Chinese --- p.28
Chapter 2.5 --- Characteristics of English --- p.30
Chapter 2.5.1 --- English Phonology --- p.30
Chapter 2.5.2 --- English with Cantonese Accents --- p.31
Chapter 2.6 --- References --- p.32
Chapter Chapter 3 --- Code-mixing and Code-switching Speech Recognition --- p.35
Chapter 3.1 --- Introduction --- p.35
Chapter 3.2 --- Definition --- p.35
Chapter 3.2.1 --- Monolingual Speech Recognition --- p.35
Chapter 3.2.2 --- Multilingual Speech Recognition --- p.35
Chapter 3.2.3 --- Code-mixing and Code-switching --- p.36
Chapter 3.3 --- Conversation in Hong Kong --- p.38
Chapter 3.3.1 --- Language Choice of Hong Kong People --- p.38
Chapter 3.3.2 --- Reasons for Code-mixing in Hong Kong --- p.40
Chapter 3.3.3 --- How Does Code-mixing Occur? --- p.41
Chapter 3.4 --- Difficulties for Code-mixing - Specific to Cantonese-English --- p.44
Chapter 3.4.1 --- Phonetic Differences --- p.45
Chapter 3.4.2 --- Phonology difference --- p.48
Chapter 3.4.3 --- Accent and Borrowing --- p.49
Chapter 3.4.4 --- Lexicon and Grammar --- p.49
Chapter 3.4.5 --- Lack of Appropriate Speech Corpus --- p.50
Chapter 3.5 --- References --- p.50
Chapter Chapter 4 --- Data Collection --- p.53
Chapter 4.1 --- Data Collection --- p.53
Chapter 4.1.1 --- Corpus Design --- p.53
Chapter 4.1.2 --- Recording Setup --- p.59
Chapter 4.1.3 --- Post-processing of Speech Data --- p.60
Chapter 4.2 --- A Baseline Database --- p.61
Chapter 4.2.1 --- Monolingual Spoken Cantonese Speech Data (CUMIX) --- p.61
Chapter 4.3 --- References --- p.61
Chapter Chapter 5 --- System Design and Experimental Setup --- p.63
Chapter 5.1 --- Overview of the Code-mixing Speech Recognizer --- p.63
Chapter 5.1.1 --- Bilingual Syllable / Word-based Speech Recognizer --- p.63
Chapter 5.1.2 --- Language Boundary Detection --- p.64
Chapter 5.1.3 --- Generalized Word Posterior Probability (GWPP) --- p.65
Chapter 5.2 --- Acoustic Modeling --- p.66
Chapter 5.2.1 --- Speech Corpus for Training of Acoustic Models --- p.67
Chapter 5.2.2 --- Features Extraction --- p.69
Chapter 5.2.3 --- Variability in the Speech Signal --- p.69
Chapter 5.2.4 --- Language Dependency of the Acoustic Models --- p.71
Chapter 5.2.5 --- Pronunciation Dictionary --- p.80
Chapter 5.2.6 --- The Training Process of Acoustic Models --- p.83
Chapter 5.2.7 --- Decoding and Evaluation --- p.88
Chapter 5.3 --- Language Modeling --- p.90
Chapter 5.3.1 --- N-gram Language Model --- p.91
Chapter 5.3.2 --- Difficulties in Data Collection --- p.91
Chapter 5.3.3 --- Text Data for Training Language Model --- p.92
Chapter 5.3.4 --- Training Tools --- p.95
Chapter 5.3.5 --- Training Procedure --- p.95
Chapter 5.3.6 --- Evaluation of the Language Models --- p.98
Chapter 5.4 --- Language Boundary Detection --- p.99
Chapter 5.4.1 --- Phone-based LBD --- p.100
Chapter 5.4.2 --- Syllable-based LBD --- p.104
Chapter 5.4.3 --- LBD Based on Syllable Lattice --- p.106
Chapter 5.5 --- "Integration of the Acoustic Model Scores, Language Model Scores and Language Boundary Information" --- p.107
Chapter 5.5.1 --- Integration of Acoustic Model Scores and Language Boundary Information. --- p.107
Chapter 5.5.2 --- Integration of Modified Acoustic Model Scores and Language Model Scores --- p.109
Chapter 5.5.3 --- Evaluation Criterion --- p.111
Chapter 5.6 --- References --- p.112
Chapter Chapter 6 --- Results and Analysis --- p.118
Chapter 6.1 --- Speech Data for Development and Evaluation --- p.118
Chapter 6.1.1 --- Development Data --- p.118
Chapter 6.1.2 --- Testing Data --- p.118
Chapter 6.2 --- Performance of Different Acoustic Units --- p.119
Chapter 6.2.1 --- Analysis of Results --- p.120
Chapter 6.3 --- Language Boundary Detection --- p.122
Chapter 6.3.1 --- Phone-based Language Boundary Detection --- p.123
Chapter 6.3.2 --- Syllable-based Language Boundary Detection (SYL LB) --- p.127
Chapter 6.3.3 --- Language Boundary Detection Based on Syllable Lattice (BILINGUAL LBD) --- p.129
Chapter 6.3.4 --- Observations --- p.129
Chapter 6.4 --- Evaluation of the Language Models --- p.130
Chapter 6.4.1 --- Character Perplexity --- p.130
Chapter 6.4.2 --- Phonetic-to-text Conversion Rate --- p.131
Chapter 6.4.3 --- Observations --- p.131
Chapter 6.5 --- Character Error Rate --- p.132
Chapter 6.5.1 --- Without Language Boundary Information --- p.133
Chapter 6.5.2 --- With Language Boundary Detector SYL LBD --- p.134
Chapter 6.5.3 --- With Language Boundary Detector BILINGUAL-LBD --- p.136
Chapter 6.5.4 --- Observations --- p.138
Chapter 6.6 --- References --- p.141
Chapter Chapter 7 --- Conclusions and Suggestions for Future Work --- p.143
Chapter 7.1 --- Conclusion --- p.143
Chapter 7.1.1 --- Difficulties and Solutions --- p.144
Chapter 7.2 --- Suggestions for Future Work --- p.149
Chapter 7.2.1 --- Acoustic Modeling --- p.149
Chapter 7.2.2 --- Pronunciation Modeling --- p.149
Chapter 7.2.3 --- Language Modeling --- p.150
Chapter 7.2.4 --- Speech Data --- p.150
Chapter 7.2.5 --- Language Boundary Detection --- p.151
Chapter 7.3 --- References --- p.151
Appendix A Code-mixing Utterances in Training Set of CUMIX --- p.152
Appendix B Code-mixing Utterances in Testing Set of CUMIX --- p.175
Appendix C Usage of Speech Data in CUMIX --- p.202
Books on the topic "Oceanic mixing – Data processing"
Administration, United States National Oceanic and Atmospheric. Report to Congress on data and information management 2005. [Silver Spring, Md.]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, 2005.
Find full textNational Research Council (U.S.). Board on Atmospheric Sciences and Climate. and National Academies Press (U.S.), eds. Environmental data management at NOAA: Archiving, stewardship, and access. Washington, D.C: National Academies Press, 2007.
Find full textUnited States. National Oceanic and Atmospheric Administration. Coastal activities. Boulder, Colo: U.S. Dept. of Commerce, National Geophysical Data Center, 1995.
Find full textOffice, General Accounting. Satellite data archiving: U.S. and foreign activities and plans for environmental information : report to congressional requesters. Washington, D.C: The Office, 1988.
Find full textMartinez, D. Fred. Development of an analytical model to predict volumetric properties. Helena]: Montana Dept. of Transportation, 1997.
Find full textOffice, General Accounting. Weather forecasting: NWS has not demonstrated that new processing system will improve mission effectiveness : report to the Chairman, Committee on Science, House of Representatives. Washington, D.C: The Office, 1996.
Find full textUnited States. Congress. House. Committee on Transportation and Infrastructure. Subcommittee on Coast Guard and Maritime Transportation. Finding your way: The future of Federal aids to navigation : hearing before the Subcommittee on Coast Guard and Maritime Transportation of the Committee on Transportation and Infrastructure, House of Representatives, One Hundred Thirteenth Congress, second session, February 4, 2014. Washington: U.S. Government Printing Office, 2014.
Find full textOffice, General Accounting. Weather forecasting: Systems architecture needed for national weather service modernization : report to Congressional requesters. Washington, D.C: The Office, 1994.
Find full textOffice, General Accounting. Weather forecasting: Unmet needs and unknown costs warrant reassessment of observing system plans : report to Congressional requesters. Washington, D.C: The Office, 1995.
Find full textWillemssen, Joel C. Department of Commerce: National Weather Service modernization and NOAA fleet issues : statement of Joel C. Willemssen, Director, Civil Agencies Information Systems, Accounting and Information Management Division and L. Nye Stevens, Director, Federal Management and Workforce Issues, General Government Division, before the Subcommittee on Energy and Environment, Committee on Science, House of Representatives. Washington, D.C. (P.O. Box 37050, Washington, D.C. 20013): The Office, 1999.
Find full textBook chapters on the topic "Oceanic mixing – Data processing"
Hedlin, Michael A. H., Jon Berger, and Frank L. Vernon. "Surveying Infrasonic Noise on Oceanic Islands." In Monitoring the Comprehensive Nuclear-Test-Ban Treaty: Data Processing and Infrasound, 1127–52. Basel: Birkhäuser Basel, 2002. http://dx.doi.org/10.1007/978-3-0348-8144-9_11.
Full textFouilloux, A., and A. Piacentini. "The PALM Project: MPMD Paradigm for an Oceanic Data Assimilation Software." In Euro-Par’99 Parallel Processing, 1423–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48311-x_200.
Full textBarillon, B., and P. H. Jézéquel. "Characterization of Convective Mixing in Industrial Precipitation Reactors by Real-time Processing of Trajectography Data." In 10th European Conference on Mixing, 329–36. Elsevier, 2000. http://dx.doi.org/10.1016/b978-044450476-0/50042-x.
Full textSengupta, Anirban, and Mahendra Rathor. "Structural transformation-based obfuscation using pseudo-operation mixing for securing data-intensive IP cores." In Secured Hardware Accelerators for DSP and Image Processing Applications, 339–56. Institution of Engineering and Technology, 2020. http://dx.doi.org/10.1049/pbcs076e_ch9.
Full textCamprubí, Lino, and Alexandra Hui. "Testing the Underwater Ear." In Testing Hearing, 301–26. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197511121.003.0012.
Full textNaik, Ganesh, and Dinesh Kant Kumar. "Semi Blind Source Separation for Application in Machine Learning." In Machine Learning Algorithms for Problem Solving in Computational Applications, 30–46. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1833-6.ch003.
Full textConference papers on the topic "Oceanic mixing – Data processing"
Piera, J., J. Catalan, and J. Norton. "Parameterization of turbulent mixing models derived from microstructure data processing. Applications to environmental research." In Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492). IEEE, 2003. http://dx.doi.org/10.1109/oceans.2003.178179.
Full textMiller, P., C. K. R. T. Jones, G. Haller, and L. Pratt. "Chaotic mixing across oceanic jets." In Chaotic, fractal, and nonlinear signal processing. AIP, 1996. http://dx.doi.org/10.1063/1.51055.
Full textvan Leeuwen, Peter Jan. "Efficient nonlinear data assimilation for oceanic models of intermediate complexity." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967700.
Full textCheng, Ligang, and Ying Zhang. "Retrieval of oceanic suspended sediment concentration with support vector regression." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815820.
Full textHAN, HUA, YONGSHENG DING, and FENGMING LIU. "A MAXIMUM ENTROPY APPROACH FOR COLLABORATIVE WARNING IN OCEANIC DATA PROCESSING." In Proceedings of the 8th International FLINS Conference. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812799470_0016.
Full textYang, Jiachen, Qiming Zhao, Chang Wang, Bin Jiang, Tianyuan Zhang, and Houbing Song. "Oceanic Data Processing System Based on Multi-sensor Interaction through Internet of Things." In 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC). IEEE, 2018. http://dx.doi.org/10.1109/pccc.2018.8711112.
Full textLi, Chang, Yong Ma, Yuan Gao, Zhongyuan Wang, and Jiayi Ma. "Sparse unmixing of hyperspectral data based on robust linear mixing model." In 2016 Visual Communications and Image Processing (VCIP). IEEE, 2016. http://dx.doi.org/10.1109/vcip.2016.7805498.
Full textZeng, Wenlong, Kai Tu, Daofu Han, and Haitao Yan. "A tunable self-mixing chaotic laser with ultra-high bandwidth." In Real-time Photonic Measurements, Data Management, and Processing V, edited by Bahram Jalali, Ming Li, and Mohammad Hossein Asghari. SPIE, 2020. http://dx.doi.org/10.1117/12.2575192.
Full textMattern, Christopher. "Combining Non-stationary Prediction, Optimization and Mixing for Data Compression." In 2011 First International Conference on Data Compression, Communications and Processing (CCP). IEEE, 2011. http://dx.doi.org/10.1109/ccp.2011.22.
Full textKeles, Cemal, Baris Baykant Alagoz, and Asim Kaygusuz. "Multi-source energy mixing for renewable energy microgrids by particle swarm optimization." In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. http://dx.doi.org/10.1109/idap.2017.8090163.
Full textReports on the topic "Oceanic mixing – Data processing"
Hall, Candice, and Robert Jensen. Utilizing data from the NOAA National Data Buoy Center. Engineer Research and Development Center (U.S.), March 2021. http://dx.doi.org/10.21079/11681/40059.
Full text