Thematische Bibliographien / Communication modeling

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Auswahl der wissenschaftlichen Literatur zum Thema „Communication modeling“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 1. Februar 2022

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Zeitschriftenartikel zum Thema "Communication modeling"

Davis, Peter. „Modeling communication dynamics“. Nonlinear Theory and Its Applications, IEICE 5, Nr. 2 (2014): 113–26. http://dx.doi.org/10.1587/nolta.5.113.

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Szuromi, P. „Modeling Chemical Communication“. Science Signaling 2, Nr. 56 (03.02.2009): ec43-ec43. http://dx.doi.org/10.1126/scisignal.256ec43.

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Ponzio, Augusto. „Modeling, Communication, and Dialogism“. American Journal of Semiotics 20, Nr. 1 (2004): 157–78. http://dx.doi.org/10.5840/ajs2004201/42.

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van der Rijst, Nardo B. J. „Communication oriented organizational modeling“. ACM SIGGROUP Bulletin 18, Nr. 2 (August 1997): 48–52. http://dx.doi.org/10.1145/265665.265679.

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Wang, Randolph Y., Arvind Krishnamurthy, Richard P. Martin, Thomas E. Anderson und David E. Culler. „Modeling communication pipeline latency“. ACM SIGMETRICS Performance Evaluation Review 26, Nr. 1 (Juni 1998): 22–32. http://dx.doi.org/10.1145/277858.277867.

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KRYSSANOV, V., K. KAKUSHO, E. KULESHOV und M. MINOH. „Modeling hypermedia-based communication“. Information Sciences 174, Nr. 1-2 (28.06.2005): 37–53. http://dx.doi.org/10.1016/j.ins.2004.08.006.

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Jinguo Quan, Jinguo Quan, Bo Bai Bo Bai, Shuang Jin Shuang Jin und Yan Zhang Yan Zhang. „Indoor positioning modeling by visible light communication and imaging“. Chinese Optics Letters 12, Nr. 5 (2014): 052201–52204. http://dx.doi.org/10.3788/col201412.052201.

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Mortezaee, Mojtaba. „EARNING MANAGEMENT MODELING BASED ON FINANCIAL COMMUNICATION“. International Journal of Research -GRANTHAALAYAH 6, Nr. 10 (31.10.2018): 274–79. http://dx.doi.org/10.29121/granthaalayah.v6.i10.2018.1194.

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The present research is focused on financial communication area and aim to investigate the relationship between company’s web-based financial communications, information asymmetry and earning management. The research is aim to examine whether financial communication besides its usefulness could be act as contributory means for management in order to earning management. In other words, aim to challenges management incentives toward web-based financial information disclosure.

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Hiji, Masahiro, Masatoshi Miyazaki und Hiroshi Nunokawa. „Communication computation model for modeling dynamic human communication“. Systems and Computers in Japan 27, Nr. 12 (1996): 63–72. http://dx.doi.org/10.1002/scj.4690271206.

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Решетников und Sergey Reshetnikov. „Modeling in communication: general trends“. Modern Communication Studies 2, Nr. 3 (27.06.2013): 14–20. http://dx.doi.org/10.12737/592.

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The author overviews the main factors concerning human conscience: both individual and collective conscience of the national group concerning the communicative function. Particular reference is being made to priority of national pictures of the world in the process of intercultural communication and need to consider this phenomenon in order to raise the efficiency of international relations on all levels.

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Dissertationen zum Thema "Communication modeling"

Spampinato, Daniele. „Modeling Communication on Multi-GPU Systems“. Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9068.

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Coupling commodity CPUs and modern GPUs give you heterogeneous systems that are cheap, high-performance with incredible FLOPS counts. Recent evolution of GPGPU models and technologies make these systems even more appealing as compute devices for a range of HPC applications including image processing, seismic processing and other physical modeling, as well as linear programming applications. In fact, graphics vendor such as NVIDIA and AMD are now targeting HPC with some of their products. Due to the power and frequency walls, the trend is now to use multiple GPUs on a given system, much like you will find multiple cores on CPU-based systems. However, increasing the hierarchy of resource wides the spectrum of factors that may impact on the performance of the system. The lack of good models for GPU-based, heterogeneous systems also makes it harder to understand which factors impact performance the most. The goal of this thesis is to analyze such factors by investigating and benchmarking NVIDIA's multi-GPU solution, their recent NVIDIA Tesla S1070 Computing System. This system combines four T10 GPUs making available up to 4 TFLOPS of computational power. Based on a comparative study of fundamental parallel computing models and on the specific heterogeneous features exposed by the system, we define a test space for performance analysis. As a case study, we develop a red-black, SOR PDE solver for Laplace equations with Dirichlet boundaries, well known for requiring constant communication in order to exchange neighboring data. To aid both design and analysis, we propose a model for multi-GPU systems targeting communication between the several GPUs. The main variables exposed by the benchmark application are: domain size and shape, kind of data partitioning, number of GPUs, width of the borders to exchange, kernels to use, and kind of synchronization between the GPU contexts. Among other results, the framework is able to point out the most critical bounds of the S1070 system when dealing with applications like the one in our case study. We show that the multi-GPU system greatly benefits from using all its four GPUs on very large data volumes. Our results show the four GPUs almost four times faster than a single GPU, and twice as fast as two. Our analysis outcomes also allow us to refine our static communication model, enriching it with regression-based predictions.

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Dani, Janak. „Transmission distortion modeling for wireless video communication“. Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/5845.

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Thesis (M.S.)--University of Missouri-Columbia, 2005.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (January 22, 2007) Includes bibliographical references.

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Liu, Tuo. „Analytical modeling of HSUPA-enabled UMTS networks for capacity planning“. Connect to full text, 2008. http://ses.library.usyd.edu.au/handle/2123/4055.

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Thesis (Ph. D.)--University of Sydney, 2009.
Title from title screen (viewed February 20, 2009). Includes graphs and tables. Includes list of publications co-authored with others. Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Information Technologies, Faculty of Engineering and Information Technologies. Degree awarded 2009; thesis submitted 2008. Includes bibliographical references. Also available in print form.

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Leong, Sang-Yick. „Channel modeling, estimation and equalization in wireless communication“. Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4183.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2005.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 25, 2006) Vita. Includes bibliographical references.

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Malafaia, Frederico Rafael Teixeira. „Modeling high bitrate communication interfaces with MatLab®“. Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/14543.

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Mestrado em Engenharia Electrónica e Telecomunicações
Now-a-days, high-speed digital data transmission is under continuous development. The constant increasing on the bitrates has been lead to the need of more sophisticated and complex receivers, systems that provide the recovering of the transmitted data over a dispersive channel that degrades the transmitted signal quality. Therefore, the receiver shall compensate the distortion introduced by the channel as well as synchronize the received signal that in addition to distortion, is also affected by jitter. The distortion derived from the channel is attenuated by means of equalization circuits that offset the channel frequency response at the transmission rate, making it as flat as possible for the desired frequency. On the other hand, the synchronization of the received signal is achieved by means of clock and data recovery circuits that usually recover the clock signal through the data transitions for sampling the received data. The main focus of this thesis concerns the modeling of a data receiver for a high-speed interface. The simulation of the data receiver block implies the modeling of a transmission channel depending on its characteristics. The proposed transmission system, from the transmitter to the output of the data recovery block, includes equalization filters for signal conditioning, of which several distinct architectures are studied. It’s proposed two architectures for the clock and data recovery circuit. The first one is a 2x oversampling clock and data recovery circuit based on a Phase Tracking architecture. The second one, is a 3x oversampling clock and data recovery based on a Blind Sampling architecture. By modeling both of the architectures of the clock and data recovery circuit, it’s intended to analyze the respective jitter tolerance results. It is crucial to know the amount of jitter that can be tolerated by these circuits in order to recover the data with a satisfying bit error ratio. The obtained results show a very close match to the theoretical values, where the 2x and 3x oversampling architecture presents a jitter tolerance of, approximately, 12UI and 23UI respectively for low jitter frequencies.
Hoje em dia, a transmissão de dados digital de alto débito binário encontra-se em constante evolução. O contínuo aumento das taxas de transmissão tem vindo a exigir sistemas de receção cada vez mais sofisticados e complexos, que facultem a recuperação dos dados transmitidos ao longo de um canal dispersivo que degrada a qualidade do sinal transmitido. Consequentemente, cabe ao recetor compensar a distorção introduzida pelo canal bem como a sincronização do sinal recebido que, para além de sofrer distorção, vem também afetado por jitter. A distorção introduzida pelo canal é atenuada através de circuitos de igualização, que compensam a resposta em frequência do canal à frequência de transmissão, de maneira a tornar a mesma o mais plana possível para a frequência desejada. Por sua vez, a sincronização do sinal recebido é conseguida através de circuitos de recuperação de dados e relógio, que, geralmente, geram um sinal de relógio a partir das transições do sinal de dados que é posteriormente utilizado para fazer a amostragem dos dados recebidos. O principal foco desta tese incide na modelação de um sistema de receção de dados de uma interface de alta velocidade. A simulação do bloco de receção de dados implica a modelação de um canal de transmissão em função das características do mesmo. O sistema de transmissão proposto, desde o transmissor até à saída do bloco de recuperação de dados, inclui filtros de igualização para acondicionamento de sinal, dos quais várias arquiteturas distintas são estudadas. São propostas duas arquiteturas para o circuito de recuperação de dados e relógio. A primeira trata-se de um circuito de recuperação de dados e relógio com sobre-amostragem 2x, baseado numa arquitetura de Phase Tracking. A segunda arquitetura trata-se de um circuito de recuperação de dados e relógio com sobre-amostragem 3x, baseado num arquitetura Blind Sampling. A análise de resultados da modelação de ambas as arquiteturas do circuito de recuperação de dados e relógio é realizada através da aquisição das respetivas curvas de tolerância de jitter. É fundamental conhecer a quantidade de jitter tolerado por estes circuitos a fim de recuperar os dados com uma probabilidade de erro de bit satisfatória. Os resultados obtidos mostram uma correspondência bastante próxima dos valores teóricos, onde a arquitetura com sobre-amostragem 2x apresenta uma tolerância de jitter de, aproximadamente, 12UI e a arquitetura com sobre-amostragem 3x apresenta uma tolerância de, aproximadamente, 23UI para baixas frequências de jitter.

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Noel, Adam. „Modeling and analysis of diffusive molecular communication systems“. Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/54906.

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Diffusive molecular communication (MC) is a promising strategy for the transfer of information in synthetic networks at the nanoscale. If such devices could communicate, then it would expand their cumulative capacity and potentially enable applications such as cooperative diagnostics in medicine, bottom-up fabrication in manufacturing, and sensitive environmental monitoring. Diffusion-based MC relies on the random motion of information molecules due to collisions with other molecules. This dissertation presents a novel system model for three-dimensional diffusive MC where molecules can also be carried by steady uniform flow or participate in chemical reactions. The expected channel impulse response due to a point source of molecules is derived and its statistics are studied. The mutual information between consecutive observations at the receiver is also derived. A simulation framework that accommodates the details of the system model is introduced. A joint estimation problem is formulated for the underlying system model parameters. The Cramer-Rao lower bound on the variance of estimation error is derived. Maximum likelihood estimation is considered and shown to be better than the Cramer-Rao lower bound when it is biased. Peak-based estimators are proposed for the low-complexity estimation of any single channel parameter. Optimal and suboptimal receiver design is considered for detecting the transmission of ON/OFF keying impulses. Optimal joint detection provides a bound on detector performance. The weighted sum detector is proposed as a suboptimal alternative that is more physically realizable. The performance of a weighted sum detector can become comparable to that of the optimal detector when the environment has a mechanism to reduce intersymbol interference. A model for noise sources that continuously release molecules is studied. The time-varying and asymptotic impact of such sources is derived. The model for asymptotic noise is used to approximate the impact of multiuser interference and also the impact of older bits of intersymbol interference.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate

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Zajic, Alenka. „Space-time channel modeling, simulation, and coding“. Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26569.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Stuber,Gordon L.; Committee Member: Durgin, Gregory D.; Committee Member: Kim, Hyesoon; Committee Member: Li, Ye (Geoffrey); Committee Member: McLaughlin, Steven W.; Committee Member: Riley, George F.. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Dong, Lu. „MIMO Selection and Modeling Evaluations for Indoor Wireless Environments“. Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19767.

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Array-to-array, or multiple-input multiple-output (MIMO), links are known to provide extremely high spectral efficiencies in rich multipath environments, such as indoor wireless environments. The selection of a subset of receiver array antennas for a MIMO wireless link has been studied by many as a way to reduce cost and complexity in a MIMO system while providing diversity gain. Combined with a switched multi-beam beamformer, it becomes the beam selection system that can gain high signal-to-interference ratio (SIR) improvement in an interference-imited environment. The objective of this research is to evaluate the performance of low-complexity antenna or beam subset selection methods for small MIMO networks. The types of networks include (1) point-to-point MIMO links with out-of-system interference, (2)multi-user networks with a single, but possibly spatially distributed access point. We evaluate various selection techniques on measured indoor channels, which has not been done before. We propose a new practical selection metric, the peak-to-trough ratio of orthogonal frequency division multiplexing (OFDM) training symbols. We also compare antenna and beam selection on measured indoor channels under more general conditions than has previously been done. Finally, we consider some channel modeling issues associated with beamformers. We investigate the validity of three types of statistical MIMO channel models. A new beamformer is designed based on the ideal of the ``Weichselberger model.'

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Feng, Guangchao. „Indexing versus modeling intercoder reliability“. HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1452.

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Khalil, Hassan. „Stochastic Modeling for Wireless Communication Networks – Multiple Access Methods“. Thesis, Uppsala University, Department of Mathematics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-122031.

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Bücher zum Thema "Communication modeling"

Gburzyński, Paweł. Modeling Communication Networks and Protocols. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15391-5.

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Abboud, Khadige, und Weihua Zhuang. Mobility Modeling for Vehicular Communication Networks. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25507-1.

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Hoffmeyer, J. A. Wideband HF modeling and simulation. [Boulder, CO]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1987.

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Jiang, Hao, und Guan Gui. Channel Modeling in 5G Wireless Communication Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-32869-6.

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Wachsmuth, Ipke, und Günther Knoblich, Hrsg. Modeling Communication with Robots and Virtual Humans. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79037-2.

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Lahyane, Mustapha, und Edgar Martínez-Moro, Hrsg. Algebra for Secure and Reliable Communication Modeling. Providence, Rhode Island: American Mathematical Society, 2015. http://dx.doi.org/10.1090/conm/642.

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Mo, Jeonghoon. Performance modeling of communication networks with Markov chains. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2010.

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Bernhard, Gröne, und Tabeling Peter, Hrsg. Fundamental modeling concepts: Effective communication of IT systems. Chichester: J. Wiley & Sons, 2005.

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Giordano, Arthur A., und Allen H. Levesque. Modeling of Digital Communication Systems Using Simulink®. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781119009511.

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Smaini, Lydi. RF Analog Impairments Modeling for Communication Systems Simulation. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118438046.

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Buchteile zum Thema "Communication modeling"

Narandžić, M., A. Hong, W. Kotterman, R. S. Thomä, L. Reichardt, T. Fügen und T. Zwick. „Channel Modeling“. In Signals and Communication Technology, 15–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17496-4_2.

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Stüber, Gordon L. „Propagation Modeling“. In Principles of Mobile Communication, 33–145. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55615-4_2.

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Stüber, Gordon L. „Propagation Modeling“. In Principles of Mobile Communication, 43–163. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0364-7_2.

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Stüber, Gordon L. „Propagation Modeling“. In Principles of Mobile Communication, 35–113. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-6268-6_2.

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Liu, Jian-Qin, und Wuyi Yue. „Modeling Cell Communication by Communication Engineering“. In Modeling, Methodologies and Tools for Molecular and Nano-scale Communications, 257–71. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50688-3_11.

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Ölveczky, Peter Csaba. „Modeling Communication in Maude“. In Designing Reliable Distributed Systems, 183–98. London: Springer London, 2017. http://dx.doi.org/10.1007/978-1-4471-6687-0_11.

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Jeruchim, Michel C., Philip Balaban und K. Sam Shanmugan. „Modeling of Communication Systems“. In Simulation of Communication Systems, 303–462. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3298-9_4.

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Monroy, Idelfonso Tafur, und Eduward Tangdiongga. „Crosstalk Modeling“. In Crosstalk in WDM Communication Networks, 61–86. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3594-9_5.

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Ali, Syed Riffat. „Hardware Reliability Modeling“. In Signals and Communication Technology, 29–57. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01647-0_2.

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Gebali, Fayez. „Switch Modeling“. In Analysis of Computer and Communication Networks, 1–28. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74437-7_15.

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Konferenzberichte zum Thema "Communication modeling"

Al-Fedaghi, Sabah. „Modeling communication“. In the 26th annual ACM international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1456536.1456558.

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Beck, James E., und Christopher A. Lupini. „Modeling Automotive Intercontroller Communication“. In International Congress & Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1995. http://dx.doi.org/10.4271/950295.

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Hovem, Jens M., Shefeng Yan, Xueshan Bao und Hefeng Dong. „Modeling Underwater Communication Links“. In 2008 Second International Conference on Sensor Technologies and Applications (sensorcomm 2008). IEEE, 2008. http://dx.doi.org/10.1109/sensorcomm.2008.143.

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Jeong, Youngmin, Jo Woon Chong, Hyundong Shin und Moe Z. Win. „Modeling of intervehicle communication“. In GLOBECOM 2012 - 2012 IEEE Global Communications Conference. IEEE, 2012. http://dx.doi.org/10.1109/glocom.2012.6504003.

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Wang, Randolph Y., Arvind Krishnamurthy, Richard P. Martin, Thomas E. Anderson und David E. Culler. „Modeling communication pipeline latency“. In the 1998 ACM SIGMETRICS joint international conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/277851.277867.

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Sulik, Justin. „Modeling creativity and communication“. In The Evolution of Language. Proceedings of the 12th International Conference on the Evolution of Language (Evolang12). Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika, 2018. http://dx.doi.org/10.12775/3991-1.122.

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Bago, Marko, Sinisa Marijan und Nedjeljko Peric. „Modeling Controller Area Network Communication“. In 2007 5th IEEE International Conference on Industrial Informatics. IEEE, 2007. http://dx.doi.org/10.1109/indin.2007.4384805.

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Rui Ni, Xiaowei Qin, Wuyang Zhou und Guo Wei. „A novel communication service modeling“. In 2010 IEEE International Conference on Wireless Information Technology and Systems (ICWITS). IEEE, 2010. http://dx.doi.org/10.1109/icwits.2010.5611979.

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Shreesha S und Sudhir K. Routray. „Statistical modeling for communication networks“. In 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT). IEEE, 2016. http://dx.doi.org/10.1109/icatcct.2016.7912115.

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Matheson, Dan. „Modeling requirements: The customer communication“. In 2014 IEEE 5th International Workshop on Requirements Prioritization and Communication (RePriCo). IEEE, 2014. http://dx.doi.org/10.1109/reprico.2014.6895218.

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Berichte der Organisationen zum Thema "Communication modeling"

Eldridge, J. M. Modeling data throughput on communication networks. Office of Scientific and Technical Information (OSTI), November 1993. http://dx.doi.org/10.2172/10113401.

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Rose, Andrew M., Donna J. Mayo und Janice C. Redish. Modeling the Speech Communication Effect on Performance: Message Complexity. Fort Belvoir, VA: Defense Technical Information Center, Juni 1991. http://dx.doi.org/10.21236/ada254711.

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Rocke, David M., Garry Rodrigue, David L. Woodruff und Brian H. Kolner. Modeling Communication Losses and Interference in Fiber Optic Systems. Fort Belvoir, VA: Defense Technical Information Center, Dezember 2003. http://dx.doi.org/10.21236/ada422167.

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Grebogi, Celso. Active Chaotic Flows, Deterministic Modeling, and Communication with Chaos. Fort Belvoir, VA: Defense Technical Information Center, März 2001. http://dx.doi.org/10.21236/ada389714.

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Liang, George. Site Specific Propagation Prediction Software Tool For Communication Channel Modeling. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada357796.

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Blumenthal, Daniel J. (BMDO) Modeling and Simulation of Multiwavelength Conversion in Semiconductor Laser Optical Amplifiers for Logic, Switching, Communication. Fort Belvoir, VA: Defense Technical Information Center, Juni 1998. http://dx.doi.org/10.21236/ada413739.

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Agarwal, Vivek, Joseph Richardson und Yanliang Zhang. Wireless Sensor Node Power Profiling Based on IEEE 802.11 and IEEE 802.15.4 Communication Protocols. Modeling and Simulation. Office of Scientific and Technical Information (OSTI), Oktober 2015. http://dx.doi.org/10.2172/1245527.

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Author, Not Given. Appendix B of the Final Report of the Mid-Atlantic Marine Wildlife Surveys, Modeling, and Data. Workshop to Establish Coordination and Communication. Office of Scientific and Technical Information (OSTI), Juli 2013. http://dx.doi.org/10.2172/1220205.

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Author, Not Given. Appendix D of the Final Report of the Mid-Atlantic Marine Wildlife Surveys, Modeling, and Data. Workshop to Establish Coordination and Communication. Office of Scientific and Technical Information (OSTI), Juli 2013. http://dx.doi.org/10.2172/1220209.

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Barrios, Amalia E., Veena Gadwal und Richard Sprague. Modeling RF Digital Signals for Communications Applications. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada531221.

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