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Books on the topic 'Signals’ analysis methods'

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

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1

Signals and systems: Analysis using transform methods and MATLAB. Boston: McGraw-Hill, Higher Education, 2004.

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2

Signals and systems: Analysis using transform methods and MATLAB. 2nd ed. New York: McGraw-Hill, 2012.

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3

Leonowicz, Zbigniew. Parametric methods for time-frequency analysis of electric signals. Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej, 2006.

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4

An introduction to the digital analysis of stationary signals. Bristol, England: A. Hilger, 1989.

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5

Li, Jian, Ph. D., 1965- and Stoica Petre, eds. Spectral analysis of signals: The missing data case. [San Rafael, Calif.]: Morgan & Claypool Publishers, 2005.

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6

Signals and systems analysis in biomedical engineering. 2nd ed. Boca Raton: Taylor & Francis Group, 2010.

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7

1968-, Ling Tonghua, and Zhang Yiping 1970-, eds. Bao po zhen dong xin hao fen xi li lun yu ji shu: Analysis of blast vibration singals-theories and methods. Beijing: Ke xue chu ban she, 2009.

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8

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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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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9

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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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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10

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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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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11

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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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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12

Yamin, Dang, and Zhang Chuanyin, eds. Fei xian xing da di ce liang xin hao xiao bo fen xi li lun yu fang fa: Wavelet analysis theories and methods for non-linear geodetic signals. Beijing Shi: Ce hui chu ban she, 2011.

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13

Theis, Fabian J. Biomedical signal analysis: Methods and applications. Cambridge, MA: MIT Press, 2009.

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14

Robinson, Enders A. Geophysical signal analysis. Tulsa, Okla: Society of Exploration Geophysicists, 2000.

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15

Cooper, George R. Probabilistic methods of signal and system analysis. 2nd ed. Fort Worth: Saunders, 1986.

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16

Cooper, George R. Probabilistic methods of signal and system analysis. 2nd ed. New York: CBS Publishing Japan Ltd, 1986.

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17

D, McGillem Clare, ed. Probabilistic methods of signal and system analysis. 2nd ed. New York: Oxford University Press, 1996.

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18

D, McGillem Clare, ed. Probabilistic methods of signal and system analysis. 3rd ed. New York: Oxford University Press, 1999.

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19

Cooper, George R. Probabilistic methods of signal and system analysis. 2nd ed. New York: Holt, Rinehart, and Winston, 1986.

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20

Shiavi, Richard. Introduction to applied statistical signal analysis. 2nd ed. San Diego: Academic Press, 1999.

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21

Introduction to applied statistical signal analysis. Homewood, IL: Aksen Associates, 1991.

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22

Jokinen, Heikki. A survey of signal preprocessing methods. Tampere: Tampere University of Technology, Dept. of Electrical Engineering, Measurement Technology, 1989.

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23

A first course in statistics for signal analysis. Boston, MA: Birkhäuser, 2005.

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24

Cerutti, Sergio, and Carlo Marchesi. Advanced methods of biomedical signal processing. Hoboken, N.J: Wiley, 2011.

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25

Felinger, Attila. Data analysis and signal processing in chromatography. Amsterdam [Netherlands]: Elsevier, 1998.

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26

Remco, Duits, Jongbloed Geurt, Lieshout Marie-Colette, Davies Laurie, and SpringerLink (Online service), eds. Mathematical Methods for Signal and Image Analysis and Representation. London: Springer London, 2012.

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27

Dahlhaus, Rainer, Jürgen Kurths, Peter Maass, and Jens Timmer, eds. Mathematical Methods in Signal Processing and Digital Image Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75632-3.

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28

Florack, Luc, Remco Duits, Geurt Jongbloed, Marie-Colette van Lieshout, and Laurie Davies, eds. Mathematical Methods for Signal and Image Analysis and Representation. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2353-8.

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29

Cédric, Demeure, ed. Statistical signal processing: Detection, estimation, and time series analysis. Reading, Mass: Addison-Wesley Pub. Co., 1991.

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30

Qian, Shie. Joint time-frequency analysis: Methods and applications. Upper Saddle River, N.J: PTR Prentice Hall, 1996.

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31

P, Petropulu Athina, ed. Higher-order spectra analysis: A nonlinear signal processing framework. Englewood Cliffs, N.J: PTR Prentice Hall, 1993.

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32

Nikias, Chrysostomos L. Higher-order spectral analysis: A nonlinear signal processing framework. Englewood Cliffs, N.J: Prentice Hall, 1993.

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33

Evidence-based technical analysis: Applying the scientific method and statistical inference to trading signals. Hoboken, N.J: John Wiley & Sons, 2007.

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34

Introduction to applied statistical signal analysis: Guide to biomedical and electrical engineering applications. 3rd ed. Oxford: Academic, 2006.

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35

Fei·, Yuan·. Computer methods for analysis of mixed-mode switching circuits. Boston· MA: Kluwer Academic·, 2003.

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36

A first course in statistics for signal analysis. 2nd ed. New York: Birkhäuser, 2011.

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37

(Jaroslaw), Zygierewicz J., ed. Practical biomedical signal analysis using MATLAB. Boca Raton: Taylor & Francis, 2012.

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38

Higgins, J. R. Sampling theory in Fourier and signal analysis: Foundations. Oxford: Clarendon Press, 1996.

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39

Ignatʹev, V. A. Thin-walled cellular structures: Methods for their analysis. Rotterdam: Balkema, 1999.

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40

Digital spectral analysis: Parametric, non-parametric, and advanced methods. London: ISTE, 2011.

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41

McGillem, Clare D. Continuous and discrete signal and system analysis. 3rd ed. Philadelphia: Saunders College Pub., 1991.

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42

McGillem, Clare D. Continuous and discrete signal and system analysis. 3rd ed. New York: Oxford University Press, 1991.

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43

Massopust, Peter Robert, Ole Christensen, and Brigitte Forster. Four short courses on harmonic analysis: Wavelets, frames, time-frequency methods, and applications to signal and image analysis. New York: Birkhauser Boston, 2010.

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44

Dilsavor, Ronald Louis. Analysis of modified SMI method for adaptive array weight control. Washington, DC: National Aeronautics and Space Administration, 1989.

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45

Mehrmann, V. L. (Volker Ludwig), 1955-, Olshevsky Vadim 1961-, Tyrtyshnikov, E. E. (Evgeniĭ Evgenʹevich), Barel Marc van 1960-, and SpringerLink (Online service), eds. Numerical Methods for Structured Matrices and Applications: The Georg Heinig Memorial Volume. Basel: Birkhäuser Basel, 2010.

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46

Boashash, Boualem. Time-Frequency Signals Analysis: Methods and Applications. Halsted Pr, 1992.

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47

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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48

Signals and Systems: Analysis Using Transform Methods and MATLAB. McGraw-Hill Education, 2017.

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49

Gevins, A. S. Methods of Analysis of Brain Electrical and Magnetic Signals. Elsevier Publishing Company, 1987.

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50

S, Gevins A., and Rémond Antoine, eds. Methods of analysis of brain electrical and magnetic signals. Amsterdam: Elsevier, 1987.

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