Abstract
Sampling theory has traditionally drawn tools from functional and complex analysis. Past successes, such as the Shannon-Nyquist theorem and recent advances in frame theory, have relied heavily on the application of geometry and analysis. The reliance on geometry and analysis means that these results are dependent on the symmetries of the space of samples. There is a subtle interplay between the topology of the domain of the functions being sampled, and the class of functions themselves. Bandlimited functions are somewhat limiting; often one wishes to sample from other classes of functions. The correct topological tool for modeling all of these situations is the sheaf; a tool which allows local structure and consistency to derive global inferences. This chapter develops a general sampling theory for sheaves using the language of exact sequences, recovering the Shannon-Nyquist theorem as a special case. It presents sheaf-theoretic approach by solving several different sampling problems involving non-bandlimited functions. The solution to these problems show that the topology of the domain has a varying level of importance depending on the class of functions and the specific sampling question being studied.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
An open cover of a topological space X is a collection of open sets whose union is X.
- 2.
N must be at least 3 to use an abstract simplicial complex model of the circle. If N is 1 or 2, one must instead use a CW complex. This does not change the analysis presented here.
- 3.
The interested reader should consider [12] for a practical discussion of several of these conditions in dimensions 1 and 2.
- 4.
|A| represents the cardinality of a set A.
References
M. Baker, X. Faber, Metrized graphs, Laplacian operators, and electrical networks, in Quantum Graphs and Their Applications (American Mathematical Society, Providence, 2006), pp. 15–34
M. Baker, R. Rumely, Harmonic analysis on metrized graphs. Can. J. Math. 59(2), 225–275 (2007)
J. Benedetto, W. Heller, Irregular sampling and the theory of frames: I. Note Math. 10(1), 103–125 (1990)
G. Bredon, Sheaf Theory (Springer, New York, 1997)
J. Brüning, M. Lesch, Hilbert complexes. J. Funct. Anal. 108, 88–132 (1992)
F. Chazal, D. Cohen-Steiner, A. Lieutier, A sampling theory for compact sets in euclidean space. Discret. Comput. Geom. 41, 461–479 (2009)
J. Curry, Sheaves, cosheaves and applications (2013) [arxiv:1303.3255]
J. Curry, R. Ghrist, M. Robinson, Euler calculus and its applications to signals and sensing, in Proceedings of Symposia in Applied Mathematics: Advances in Applied and Computational Topology, ed. by A. Zomorodian (American Mathematical Society, Providence, 2012)
P. Dragotti, M. Vetterli, T. Blue, Sampling moments and reconstructing signals of finite rate of innovation: Shannon meets Strang–Fix. IEEE Trans. Signal Process 55(5), (2007)
M. Ebata, M. Eguchi, S. Koizumi, K. Kumahara, Analogues of sampling theorems for some homogeneous spaces. Hiroshima Math. J. 36, 125–140 (2006)
L. Ehrenpreis, Sheaves and differential equations. Proc. Am. Math. Soc. 7(6), 131–1138 (1956)
G. Farin, Curves and Surfaces for CAGD (Elsevier, Amsterdam, 1985)
H.G. Feichtinger, K. Gröchenig, Theory and practice of irregular sampling. Wavelets: Mathematics and Applications (CRC Press, Boca Raton, 1994), pp. 305–363
H. Feichtinger, I. Pesenson, A reconstruction method for band-limited signals on the hyperbolic plane. Sampling Theory Signal Image Process. 4(2), 107–119 (2005)
R. Ghrist, Y. Hiraoka, Applications of sheaf cohomology and exact sequences to network coding, in Proc. NOLTA (2011)
R. Godement, Topologie algebrique et théorie des faisceaux (Herman, Paris, 1958)
K. Gröchening, Reconstruction algorithms in irregular sampling. Math. Comput. 59(199), 181–194 (1992)
A. Hatcher, Algebraic Topology (Cambridge University Press, Cambridge, 2002)
J.H. Hubbard, Teichmüller Theory, vol. 1 (Matrix Editions, Ithaca, 2006)
B. Iverson, Cohomology of Sheaves. Aarhus universitet, Matematisk institut (1984)
P. Kuchment, Quantum graphs: an introduction and a brief survey, in Analysis on Graphs and Its Applications. (Isaac Newton Institute for Mathematical Sciences, Cambridge, 2007), pp. 291–312
J. Lilius, Sheaf semantics for Petri nets. Technical report, Helsinki University of Technology, Digital Systems Laboratory (1993)
P. Niyogi, S. Smale, S. Weinberger, Finding the homology of submanifolds with high confidence from random samples, in Twentieth Anniversary Volume, ed. by R. Pollack, J. Pach, J.E. Goodman (Springer, New York, 2009), pp. 1–23
I. Pesenson, Sampling of band-limited vectors. J. Fourier Anal. Appl. 7(1), 93–100 (2001)
I. Pesenson, An approach to spectral problems on riemannian manifolds. Pac. J. Math. 215(1), 183–199 (2004)
I. Pesenson, Band limited functions on quantum graphs. Proc. Am. Math. Soc. 133(12), 3647–3656 (2005)
I. Pesenson, Polynomial splines and eigenvalue approximations on quantum graphs. J. Approx. Theory 135(2), 203–220 (2005)
I. Pesenson, Analysis of band-limited functions on quantum graphs. Appl. Comput. Harmon. Anal. 21(2), 230–244 (2006)
I. Pesenson, Sampling in Paley-Wiener spaces on combinatorial graphs. Trans. Am. Math. Soc. 360(10), 5603 (2008)
I.Z. Pesenson, Removable sets and approximation of eigenvalues and eigenfunctions on combinatorial graphs. Appl. Comput. Harmon. Anal. 29, 123–133 (2010)
I.Z. Pesenson, M.Z. Pesenson, Sampling, filtering and sparse approximations on combinatorial graphs. J. Fourier Anal. Appl. 16(6), 921–942 (2010)
M. Robinson, Inverse problems in geometric graphs using internal measurements (2010) [arxiv:1008.2933]
M. Robinson, Asynchronous logic circuits and sheaf obstructions. Electron. Notes Theor. Comput. Sci. 283, 159–177 (2012)
M. Robinson, Understanding networks and their behaviors using sheaf theory, in GlobalSIP (2013)
M. Robinson, Topological Signal Processing (Springer, Heidelberg, 2014)
A. Schuster, D. Varolin, Interpolation and sampling for generalized Bergman spaces on finite Riemann surfaces. Rev. Mat. Iberoam.. 24(2), 499–530 (2008)
A. Shepard, A cellular description of the derived category of a stratified space. Ph.D. thesis, Brown University, 1980
S. Smale, D.X. Zhou, Shannon sampling and function reconstruction from point values. Bull. Am. Math. Soc. 41(3), 279–306 (2004)
M. Unser, Splines: a perfect fit for signal and image processing. IEEE Signal Process. Mag. 16(6), 22–38 (1999)
M. Unser, Sampling–50 years after Shannon. Proc. IEEE 88(4), 569–587 (2000)
M. Unser, J. Zerubia, A generalized sampling theory without band-limiting constraints. IEEE Trans. Circuits Syst. II Analog Digit. Signal Process. 45(8), 959–969 (1998)
R.G. Vaughan, N.L. Scott, D.R. White, The theory of bandpass sampling. IEEE Trans. Signal Process. 39(9), 1973–1984 (1991)
M. Vetterli, P. Marziliano, T. Blu, Sampling signals with finite rate of innovation. IEEE Trans. Signal Process. 50(6), 1417–1428 (2002)
S. Zhang, Admissible pairing on a curve. Invent. Math. 112(1), 171–193 (1993)
Acknowledgements
This work was partly supported under Federal Contract No. FA9550-09-1-0643. The author also wishes to thank the editor for the invitation to write this chapter. Portions of this chapter appeared in the proceedings of SampTA 2013, published by EURASIP. Finally, the author wishes to thank Isaac Pesenson for insightful comments that greatly improved the readability of this chapter.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Robinson, M. (2015). A Sheaf-Theoretic Perspective on Sampling. In: Pfander, G. (eds) Sampling Theory, a Renaissance. Applied and Numerical Harmonic Analysis. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-19749-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-19749-4_10
Publisher Name: Birkhäuser, Cham
Print ISBN: 978-3-319-19748-7
Online ISBN: 978-3-319-19749-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)