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Books on the topic 'Sparse signal'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

A wavelet tour of signal processing: The sparse way. 3rd ed. Amsterdam: Elsevier/Academic Press, 2009.

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2

Mächler, Patrick. VLSI architectures for compressive sensing and sparse signal recovery. Konstanz: Hartung-Gorre Verlag, 2013.

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3

Starck, J. L. Sparse image and signal processing: Wavelets, curvelets, morphological diversity. New York: Cambridge University Press, 2010.

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4

Starck, J. L. Sparse image and signal processing: Wavelets, curvelets, morphological diversity. Cambridge: Cambridge University Press, 2010.

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5

Elad, M. Sparse and redundant representations: From theory to applications in signal and image processing. New York: Springer, 2010.

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6

Xin hao xi shu biao shi li lun ji qi ying yong. Beijing: Ke xue chu ban she, 2013.

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7

Hlawatsch, Franz. Time-Frequency Analysis and Synthesis of Linear Signal Spaces. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2815-6.

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8

Brown, Susan. Police signal boxes in Glasgow: A survey of surroundings, structure and spare parts. [Glasgow]: [Glasgow Building Preservation Trust], 1993.

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9

Time-frequency analysis and synthesis of linear signal spaces: Time-frequency filters, signal detection and estimation, and range-Doppler estimation. Boston: Kluwer Academic Publishers, 1998.

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10

Hlawatsch, F. Time-Frequency Analysis and Synthesis of Linear Signal Spaces: Time-Frequency Filters, Signal Detection and Estimation, and Range-Doppler Estimation. Boston, MA: Springer US, 1998.

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11

Veatch, Donald W. Analog signal conditioning for flight-test instrumentation. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1987.

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12

Veatch, Donald W. Analog signal conditioning for flight-test instrumentation. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1987.

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13

Veatch, Donald W. Analog signal conditioning for flight-test instrumentation. Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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14

Desmond, Ball. China's Signals Intelligence (SIGINT): Satellite programs. Canberra: Strategic and Defence Studies Centre, 2003.

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15

Lane, Helen W., Philip C. Stepaniak, and Jeffrey R. Davis. Loss of signal: Aeromedical lessons learned from the STS-107 Columbia Space Shuttle mishap. Edited by United States. National Aeronautics and Space Administration. Washington, District of Columbia: National Aeronautics and Space Administration, 2014.

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16

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

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17

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

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18

service), SpringerLink (Online, ed. Mathematical SETI: Statistics, Signal Processing, Space Missions. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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19

1951-, Lukin Konstantin A., ed. Interstellar propagation of electromagnetic signals. New York: Kluwer Academic/Plenum Publishers, 2000.

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20

Ghassemlooy, Zabih. Optical wireless communications: System and channel modelling with MATLAB. Boca Raton, FL: Taylor & Francis, 2012.

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21

Culp, Robert D. Polarization signals of several common spacecraft materials. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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22

Pine Gap: Australia and the US geostationary signals intelligence satellite program. Sydney: Allen & Unwin, 1988.

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23

W, Popoola, and Rajbhandari S, eds. Optical wireless communications: System and channel modelling with MATLAB. Boca Raton, FL: Taylor & Francis, 2012.

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24

An introduction to frames and Riesz bases. Boston, MA: Birkhuser, 2003.

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25

G, Chen, and Chui C. K, eds. Discrete H [infinity] optimization: With applications in signal processing and control systems. 2nd ed. Berlin: Springer, 1997.

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26

Frames and bases: An introductory course. Boston: Birkhäuser, 2008.

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27

Hickey, Andrew T. Cities of signs: Learning the logic of urban spaces. New York, NY: Peter Lang, 2012.

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28

Altimira, Gemma Barberà. The meaning of space in sign language: Reference, specificity and structure in Catalan sign language discourse. Boston: De Gruyter Mouton, 2015.

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29

Public space, public relations: Grossformatige Werbung als ein Beispiel des Umgangs mit öffentlichen Räumen. Frankfurt am Main: Societätsverlag, 2008.

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30

Simonnet, Michel. Measures and probabilities. New York: Springer, 1996.

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31

Schmauks, Dagmar. Orientierung im Raum: Zeichen für die Fortbewegung. Tübingen: Stauffenburg, 2002.

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32

Temporary Services (Group of artists). Public phenomena. Chicago, Ill: Half Letter Press LLC, 2008.

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33

Kongress, Deutsche Gesellschaft für Semiotik Internationaler. Signs & time: An international conference on the semiotics of time in Tübingen = Zeit & Zeichen. Tübingen: G. Narr, 1998.

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34

Helwani, Karim. Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations. Springer, 2016.

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35

Helwani, Karim. Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations. Springer, 2014.

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36

Murtagh, Fionn, Jean-Luc Starck, and Jalal M. Fadili. Sparse Image and Signal Processing: Wavelets and Related Geometric Multiscale Analysis. Cambridge University Press, 2015.

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37

Elad, Michael. Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer, 2014.

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38

Giron-Sierra, Jose Maria. Digital Signal Processing with Matlab Examples, Volume 3: Model-Based Actions and Sparse Representation. Springer, 2018.

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39

Cheng, Hong. Sparse Representation, Modeling and Learning in Visual Recognition: Theory, Algorithms and Applications. Springer, 2015.

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40

Cheng, Hong. Sparse Representation, Modeling and Learning in Visual Recognition: Theory, Algorithms and Applications. Springer, 2016.

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41

Stegeman, Dick F., and Michel J. A. M. Van Putten. Recording of neural signals, neural activation, and signal processing. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199688395.003.0005.

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This chapter discusses recording of electrophysiological signals in the context of clinical neurophysiology. We first discuss the interpretation of signals and differences between signals in terms of their underlying (electro)physiology. As a most prominent aspect of applied electrophysiology, the biophysics of volume conduction in extracellular space is discussed. We also present some basics of advanced procedures to analyse neurophysiological data. Aspects of electrical stimulation are treated too, including recent developments in diagnostic and therapeutic constant current stimulation. We finally discuss the background of hazardous electric currents and the safety of bioelectric equipment. Aspects that are relevant in the digitization and post-processing of data are briefly reviewed.

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42

Signal and Image Representation in Combined Spaces. Elsevier, 1998. http://dx.doi.org/10.1016/s1874-608x(98)x8001-5.

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43

Y, Zeevi Y., and Coifman Ronald R, eds. Signal and image representation in combined spaces. San Diego: Academic Press, 1998.

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44

Marks II, Robert J. Handbook of Fourier Analysis & Its Applications. Oxford University Press, 2009. http://dx.doi.org/10.1093/oso/9780195335927.001.0001.

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Fourier analysis has many scientific applications - in physics, number theory, combinatorics, signal processing, probability theory, statistics, option pricing, cryptography, acoustics, oceanography, optics and diffraction, geometry, and other areas. In signal processing and related fields, Fourier analysis is typically thought of as decomposing a signal into its component frequencies and their amplitudes. This practical, applications-based professional handbook comprehensively covers the theory and applications of Fourier Analysis, spanning topics from engineering mathematics, signal processing and related multidimensional transform theory, and quantum physics to elementary deterministic finance and even the foundations of western music theory. As a definitive text on Fourier Analysis, Handbook of Fourier Analysis and Its Applications is meant to replace several less comprehensive volumes on the subject, such as Processing of Multifimensional Signals by Alexandre Smirnov, Modern Sampling Theory by John J. Benedetto and Paulo J.S.G. Ferreira, Vector Space Projections by Henry Stark and Yongyi Yang and Fourier Analysis and Imaging by Ronald N. Bracewell. In addition to being primarily used as a professional handbook, it includes sample problems and their solutions at the end of each section and thus serves as a textbook for advanced undergraduate students and beginning graduate students in courses such as: Multidimensional Signals and Systems, Signal Analysis, Introduction to Shannon Sampling and Interpolation Theory, Random Variables and Stochastic Processes, and Signals and Linear Systems.

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45

Wright, A. G. Signal-induced background. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199565092.003.0011.

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Signal-induced background has a time dependence that distinguishes it from the sources discussed in Chapter 6. These events refer to a progression in which a signal generates a subsequent one, correlated in time to the initial detection. The timescale for correlated background ranges from nanoseconds to days. The earliest signal is a prepulse generated by a photon incident on d1. Late pulses relate to the k-to-d1, and k-to-anode transit time. The next category, the afterpulses, spans ~100 ns to 10 μ‎s, with a peaked time distribution. There is a long-lived source of photons, extending to days and caused by exposure of a photomultiplier to bright light or to nuclear radiation. Afterpulses contribute to the slope of a photon-counting plateau characteristic, distort fluorescent decay, and pulse shape discrimination measurements. They also affect resolution, and processes of a statistical nature.

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46

Analog signal conditioning for flight-test instrumentation. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1987.

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47

Ashby, F. Gregory, and Fabian A. Soto. Multidimensional Signal Detection Theory. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.2.

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Multidimensional signal detection theory is a multivariate extension of signal detection theory that makes two fundamental assumptions, namely that every mental state is noisy and that every action requires a decision. The most widely studied version is known as general recognition theory (GRT). General recognition theory assumes that the percept on each trial can be modeled as a random sample from a multivariate probability distribution defined over the perceptual space. Decision bounds divide this space into regions that are each associated with a response alternative. General recognition theory rigorously defines and tests a number of important perceptual and cognitive conditions, including perceptual and decisional separability and perceptual independence. General recognition theory has been used to analyze data from identification experiments in two ways: (1) fitting and comparing models that make different assumptions about perceptual and decisional processing, and (2) testing assumptions by computing summary statistics and checking whether these satisfy certain conditions. Much has been learned recently about the neural networks that mediate the perceptual and decisional processing modeled by GRT, and this knowledge can be used to improve the design of experiments where a GRT analysis is anticipated.

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48

Popoff, Andrey. Fundamentals of Signal Processing in Metric Spaces with Lattice Properties. CRC Press, 2017. http://dx.doi.org/10.1201/9781315104119.

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49

Hilbert Space Methods in Signal Processing. Cambridge University Press, 2013.

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50

Innes, Martin, and Helen Innes. Signal Crimes, Social Reactions, and the Future of Environmental Criminology. Edited by Gerben J. N. Bruinsma and Shane D. Johnson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190279707.013.11.

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This chapter examines the precepts associated with the signal crimes perspective (SCP). It begins by setting out that a signal is something that transmits messages to an audience. Thinking in terms of signals and “signaling” opens up new ways of seeing crime, disorder, and social control. In particular, it keys into an event-based unit of analysis, as opposed to measuring impacts in an aggregated form. Having laid out the conceptual apparatus of the SCP, the discussion proceeds on to briefly consider how SCP compares with more established criminological frameworks for studying reactions to and consequences of crime. The latter sections of the chapter focus on the ways that changes to the information environment, associated with an era of “big data” and social media, are altering the incidents that signal and how their impacts travel across space and time.

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