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Книги з теми "Signals classification"

Автор: Grafiati
Опубліковано: 7 вересня 2022
Оновлено: 18 вересня 2022

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1

Workman, Michael J. Automatic classification of road signals. Birmingham: University of Birmingham, 1991.

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2

Kiasaleh, Kamran. Biological Signals Classification and Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54879-6.

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3

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

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4

Duzenli, Ozhan. Classification of underwater signals using wavelet-based decompositions. Monterey, Calif: Naval Postgraduate School, 1998.

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5

Bennett, Richard Campbell. Classification of underwater signals using a back-propagation neural network. Monterey, Calif: Naval Postgraduate School, 1997.

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6

Flowers, Nicholas. Remote classification of sea bed material using backscattered acoustic signals. Birmingham: University of Birmingham, 1987.

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7

Paszkiel, Szczepan. Analysis and Classification of EEG Signals for Brain–Computer Interfaces. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30581-9.

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8

Moukadem, Ali, Djaffar Ould Abdeslam, and Alain Dieterlen. Time-Frequency Domain for Segmentation and Classification of Non-Stationary Signals. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118908686.

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9

Manfredi, Claudia, ed. Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy. Florence: Firenze University Press, 2007. http://dx.doi.org/10.36253/978-88-5518-027-6.

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Анотація:
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference.
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10

Siuly, Siuly, Yan Li, and Yanchun Zhang. EEG Signal Analysis and Classification. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47653-7.

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11

Gacek, Adam, and Witold Pedrycz, eds. ECG Signal Processing, Classification and Interpretation. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-868-3.

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12

Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: Firenze University Press, 2013. http://dx.doi.org/10.36253/978-88-6655-470-7.

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Анотація:
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies.
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13

Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: Firenze University Press, 2009. http://dx.doi.org/10.36253/978-88-6453-096-3.

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Анотація:
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies.
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14

Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: Firenze University Press, 2011. http://dx.doi.org/10.36253/978-88-6655-011-2.

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Анотація:
The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies.
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15

Tang, Xiaoou. Transform texture classification. [Woods Hole, Mass: Massachusetts Institute of Technology, Woods Hole Oceanographic Institution, Joint Program in Oceanography/Applied Ocean Science and Engineering], 1996.

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16

Wichert, Terry S. Feature based neural network acoustic transient signal classification. Monterey, Calif: Naval Postgraduate School, 1993.

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17

Watling, Keith J. The Sigma-RBI handbook of receptor classification and signal transduction. 4th ed. Natick, MA: Sigma-RBI, 2001.

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18

Poncet, Andreas. Design of adaptive models for system identification, signal prediction, and pattern classification. Konstanz: Hartung-Gorre, 1997.

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19

Gini, Fulvio. Knowledge based radar detection, tracking, and classification. Hoboken, NJ: Wiley, 2008.

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20

Healey, Anthony J. Sonar signal acquisition and processing for identification and classification of ship hull fouling. Monterey, Calif: Naval Postgraduate School, 1993.

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21

Dougherty, Geoff. Pattern Recognition and Classification: An Introduction. New York, NY: Springer New York, 2013.

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22

Baram, Yoram. Estimation and classification by sigmoids based on mutual information. [Washington, D.C: National Aeronautics and Space Administration, 1994.

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23

Gini, Fulvio. Knowledge based radar detection, tracking, and classification. Hoboken, NJ: Wiley, 2008.

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24

service), SpringerLink (Online, ed. Soft Computing Approach to Pattern Classification and Object Recognition: A Unified Concept. New York, NY: Springer New York, 2012.

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25

Martin, Nadine, and Christian Doncarli. Décision dans le plan temps-fréquence. Paris: Hermès Science publications, 2004.

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26

Maes, Dominiek, Marina Sibila, and Maria Pieters, eds. Mycoplasmas in swine. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789249941.0000.

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Анотація:
Abstract This book contains 14 chapters that discuss the genetics, epidemiology, prevalence, pathogenesis, clinical signs, diagnosis, treatment, prevention and control of Mycoplasma infections in pigs. Chapter 1 discusses the phylogenetics and classification of Mycoplasma species in pigs; Chapter 2 describes the genomic diversity and antigenic variation of Mycoplasma hyopneumoniae strains; Chapter 3 discusses the pathogenesis, virulence factor and pathogenicity of Mycoplasma hyopneumoniae; Chapter 4 discusses the molecular epidemiology, risk factors, transmission and prevalence of Mycoplasma hyopneumoniae, Chapter 5 discusses the clinical signs and gross lesions of Mycoplasma hyopneumoniae infection; Chapter 6 discusses immune responses against Mycoplasma infections; Chapter 7 describes the interactions of Mycoplasma hyopneumoniae with other pathogens and their economic impact; Chapter 8 discusses the diagnosis of Mycoplasma hyopneumoniae infection and its associated diseases; Chapter 9 describes the general control measures against Mycoplasma hyopneumoniae infections; Chapter 10 describes the selection and efficacy of antimicrobials against Mycoplasma hyopneumoniae infections; Chapter 11 discusses the development and efficacy of vaccines against Mycoplasma hyopneumoniae; Chapter 12 describes the eradication of Mycoplasma hyopneumoniae in pig herds; Chapter 13 describes the epidemiology, prevalence, pathogenesis, clinical signs, diagnosis, treatment, prevention and control of Mycoplasma hyorhinis and Mycoplasma hyosynoviae in pig herds and Chapter 14 discusses the epidemiology, prevalence, transmission, pathogenesis, clinical signs, diagnosis, treatment, prevention, control and economic impact of Mycoplasma suis infection in pigs.
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27

Kiasaleh, Kamran. Biological Signals Classification and Analysis. Springer, 2015.

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28

Kiasaleh, Kamran. Biological Signals Classification and Analysis. Springer, 2016.

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29

Classification of Underwater Signals Using Wavelet-Based Decompositions. Storming Media, 1998.

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30

Brain Seizure Detection and Classification Using EEG Signals. Elsevier, 2022. http://dx.doi.org/10.1016/c2020-0-01737-8.

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31

Moukadem, Ali, Djaffar Ould Abdeslam, and Alain Dieterlen. Time-Frequency Domain for Segmentation and Classification of Non-stationary Signals: The Stockwell Transform Applied on Bio-signals and Electric Signals. Wiley-Interscience, 2014.

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32

Moukadem, Ali, Djaffar Ould Abdeslam, and Alain Dieterlen. Time-Frequency Domain for Segmentation and Classification of Non-Stationary Signals: The Stockwell Transform Applied on Bio-Signals and Electric Signals. Wiley & Sons, Incorporated, John, 2014.

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33

Moukadem, Ali, Djaffar Ould Abdeslam, and Alain Dieterlen. Time-Frequency Domain for Segmentation and Classification of Non-Stationary Signals: The Stockwell Transform Applied on Bio-Signals and Electric Signals. Wiley & Sons, Incorporated, John, 2014.

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34

Classification of Underwater Signals Using a Back-Propagation Neural Network. Storming Media, 1997.

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35

Paszkiel, Szczepan. Analysis and Classification of EEG Signals for Brain–Computer Interfaces. Springer, 2019.

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36

Classification and Analysis of Low Probability of Intercept Radar Signals Using Image Processing. Storming Media, 2003.

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37

Wendling, Fabrice, Marco Congendo, and Fernando H. Lopes da Silva. EEG Analysis. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.

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This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.
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38

Detection and Classification of Low Probability of Intercept Radar Signals Using Parallel Filter Arrays and Higher Order Statistics. Storming Media, 2002.

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39

Digital SIgnal Processing and Statistical Classification. Artech House Publishers, 2002.

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40

Jockers, Matthew L. Style. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037528.003.0006.

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This chapter shows how stylistic signals can be derived from high-frequency features and how the usage, or nonusage, of those features was susceptible to influences that are external to the so-called “authorial style,” external influences such as genre, time, and gender. These aspects of style were explored using a controlled corpus of 106 British novels where genre was a key point of analysis. The chapter first provides an overview of statistical or quantitative authorship attribution before discussing the author's project, in which he analyzed the degree to which novelistic genres express a distinguishable stylistic signal by focusing on the distribution of novels in a corpus based on their genres and decades of publication. Through a series of experiments, he demonstrates the use of the classification methodology as a way of measuring the extent to which factors beyond an individual author's personal style may play a role in determining the linguistic usage and style of the resulting text.
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41

Tranter, William H., Tamal Bose, Alireza Babaei, and Barathram Ramkumar. Blind Signal Classification Algorithms for Cognitive Radios. Springer, 2020.

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42

Tranter, William H., Tamal Bose, Alireza Babaei, Barathram Ramkumar, and Munawwar Mahmud Sohul. Blind Signal Classification Algorithms for Cognitive Radios. Springer London, Limited, 2020.

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43

Signal Classification Using The Mean Separator Neural Network. Storming Media, 2000.

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44

Zhang, Yanchun, Yan Li, and Siuly Siuly. EEG Signal Analysis and Classification: Techniques and Applications. Springer, 2018.

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45

Sherman, S. Murray, and W. Martin Usrey. Exploring Thalamocortical Interactions. Oxford University Press, 2021. http://dx.doi.org/10.1093/med/9780197503874.001.0001.

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Анотація:
The thalamus and cerebral cortex are active and necessary partners in the processing of signals essential for sensory, motor, and cognitive functions. This partnership is absolute, as neither the thalamus nor the cortex can be understood in any meaningful way in isolation from the other. This book provides readers with fundamental knowledge about the cells and circuits that mediate thalamocortical interactions and then explores new ideas that often challenge conventional understanding. Some of the major themes emphasized throughout the book include the need for a proper classification of thalamocortical and corticothalamic circuits, the role of spike timing for thalamocortical and corticothalamic communication and the mechanisms for modulating spike timing, the organization and function of corticothalamic feedback projections, the role of higher order thalamic nuclei in cortico-cortical communication and cortical functioning, attentional modulation of thalamocortical interactions, and a rethinking of efference copies and distinguishing neural signals as sensory versus motor. Importantly, to encourage readers to think beyond the material and views provided throughout the book, each chapter closes with a section on “Some Outstanding Questions” to stimulate creative approaches to increase our understanding of thalamocortical interactions.
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46

J, Watling Keith, ed. The RBI handbook of receptor classification and signal transduction. 3rd ed. Natick, MA: RBI, 1998.

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47

Leong, Wai Yie. EEG Signal Processing: Feature extraction, selection and classification methods. The Institution of Engineering and Technology, 2019.

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48

J, Watling Keith, Kebabian J. W, and Neumeyer John L, eds. The RBI handbook of receptor classification and signal transduction. Natick, MA: Research Biochemicals International, 1995.

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49

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection. Elsevier, 2019. http://dx.doi.org/10.1016/c2018-0-01888-5.

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50

Wai Yie Leong, ed. EEG Signal Processing: Feature extraction, selection and classification methods. Institution of Engineering and Technology, 2019. http://dx.doi.org/10.1049/pbhe016e.

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