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Books on the topic 'Modeling of hydroacoustic signals'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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1

VanDerKamp, Martha M. Modeling and classification of biological signals. Monterey, Calif: Naval Postgraduate School, 1992.

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2

I︠A︡roshchuk, I. O. Metod statisticheskogo modelirovani︠i︡a v zadachakh gidroakustiki =: Statistical modeling method for hydroacoustic problems. Vladivostok: Dalʹnauka, 2002.

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3

Solano, Carlos Hernando Velasco. ARMA modeling of signals in the time domain. Monterey, Calif: Naval Postgraduate School, 1992.

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4

Devasahayam, Suresh R. Signals and Systems in Biomedical Engineering: Signal Processing and Physiological Systems Modeling. 2nd ed. Boston, MA: Springer US, 2013.

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5

Philippe, Müllhaupt, and SpringerLink (Online service), eds. Advances in the Theory of Control, Signals and Systems with Physical Modeling. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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6

Devasahayam, Suresh R. Signals and Systems in Biomedical Engineering: Physiological Systems Modeling and Signal Processing. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3531-0.

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7

Lévine, Jean, and Philippe Müllhaupt, eds. Advances in the Theory of Control, Signals and Systems with Physical Modeling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16135-3.

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8

Devasahayam, Suresh R. Signals and Systems in Biomedical Engineering: Signal Processing and Physiological Systems Modeling. Boston, MA: Springer US, 2000.

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9

Signals and systems in biomedical engineering: Signal processing and physiological systems modeling. New York: Kluwer Academic/Plenum Publishers, 2000.

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10

Feliu, Sebastián. Modelado e identificación de procesos de corrosión: Análisis de la respuesta a señales eléctricas = Modeling and identification of corrosion processes : response to electric signals. Madrid: Consejo Superior de Investigaciones Científicas, 1985.

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11

Signals and Systems with MATLAB Computing and Simulink Modeling. Orchard Publications, 2006.

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12

Jain, Lakhmi C., and Anna Esposito. Toward Robotic Socially Believable Behaving Systems - Volume II: Modeling Social Signals. Springer, 2016.

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13

Jain, Lakhmi C., and Anna Esposito. Toward Robotic Socially Believable Behaving Systems - Volume II: Modeling Social Signals. Springer, 2018.

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14

Signals and Systems with MATLAB Computing and Simulink Modeling, Fifth Edition. Orchard Publications, 2012.

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15

Devasahayam, Suresh R. Signals and Systems in Biomedical Engineering: Physiological Systems Modeling and Signal Processing. Springer, 2019.

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16

Devasahayam, Suresh R. Signals and Systems in Biomedical Engineering: Signal Processing and Physiological Systems Modeling. Springer, 2012.

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17

Levine, Jean, and Philippe Müllhaupt. Advances in the Theory of Control, Signals and Systems with Physical Modeling. Springer, 2010.

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18

Gustrau, Frank, and Dirk Manteuffel. EM Modeling of Antennas and RF Components for Wireless Communication Systems (Signals and Communication Technology). Springer, 2006.

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19

Gustrau, Frank, and Dirk Manteuffel. EM Modeling of Antennas and RF Components for Wireless Communication Systems (Signals and Communication Technology). Springer, 2006.

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20

Devasahayam, Suresh R. Signals and Systems in Biomedical Engineering: Signal Processing and Physiological Systems Modeling (Topics in Biomedical Engineering). Springer, 2000.

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21

Birney, Keith Andrew. Aspects of modeling subband decomposition signals and two-dimensional discrete cosine transform coefficients for image coding. 1991.

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22

Wendling, Fabrice, and Fernando H. Lopes da Silva. Dynamics of EEGs as Signals of Neuronal Populations. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0003.

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This chapter gives an overview of approaches used to understand the generation of electroencephalographic (EEG) signals using computational models. The basic concept is that appropriate modeling of neuronal networks, based on relevant anatomical and physiological data, allows researchers to test hypotheses about the nature of EEG signals. Here these models are considered at different levels of complexity. The first level is based on single cell biophysical properties anchored in classic Hodgkin-Huxley theory. The second level emphasizes on detailed neuronal networks and their role in generating different kinds of EEG oscillations. At the third level are models derived from the Wilson-Cowan approach, which constitutes the backbone of neural mass models. Another part of the chapter is dedicated to models of epileptiform activities. Finally, the themes of nonlinear dynamic systems and topological models in EEG generation are discussed.
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23

Jockers, Matthew L. Theme. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037528.003.0008.

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This chapter demonstrates how big data and computation can be used to identify and track recurrent themes as the products of external influence. It first considers the limitations of the Google Ngram Viewer as a tool for tracing thematic trends over time before turning to Douglas Biber's Corpus Linguistics: Investigating Language Structure and Use, a primer on various factors complicating word-focused text analysis and the subsequent conclusions one might draw regarding word meanings. It then discusses the results of the author's application of latent Dirichlet allocation (LDA) to a corpus of 3,346 nineteenth-century novels using the open-source MALLET (MAchine Learning for LanguagE Toolkit), a software package for topic modeling. It also explains the different types of analyses performed by the author, including text segmentation, word chunking, and author nationality, gender and time-themes relationship analyses. The thematic data from the LDA model reveal the degree to which author nationality, author gender, and date of publication could be predicted by the thematic signals expressed in the nineteenth-century novels corpus.
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24

Chimenti, Dale, Stanislav Rokhlin, and Peter Nagy. Physical Ultrasonics of Composites. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780195079609.001.0001.

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Physical Ultrasonics of Composites is a rigorous introduction to the characterization of composite materials by means of ultrasonic waves. Composites are treated here not simply as uniform media, but as inhomogeneous layered anisotropic media with internal structure characteristic of composite laminates. The objective here is to concentrate on exposing the singular behavior of ultrasonic waves as they interact with layered, anisotropic materials, materials which incorporate those structural elements typical of composite laminates. This book provides a synergistic description of both modeling and experimental methods in addressing wave propagation phenomena and composite property measurements. After a brief review of basic composite mechanics, a thorough treatment of ultrasonics in anisotropic media is presented, along with composite characterization methods. The interaction of ultrasonic waves at interfaces of anisotropic materials is discussed, as are guided waves in composite plates and rods. Waves in layered media are developed from the standpoint of the "Stiffness Matrix", a major advance over the conventional, potentially unstable Transfer Matrix approach. Laminated plates are treated both with the stiffness matrix and using Floquet analysis. The important influence on the received electronic signals in ultrasonic materials characterization from transducer geometry and placement are carefully exposed in a dedicated chapter. Ultrasonic wave interactions are especially susceptible to such influences because ultrasonic transducers are seldom more than a dozen or so wavelengths in diameter. The book ends with a chapter devoted to the emerging field of air-coupled ultrasonics. This new technology has come of age with the development of purpose-built transducers and electronics and is finding ever wider applications, particularly in the characterization of composite laminates.
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