Boundary estimation in sensor networks: Theory and methods

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2634, Issue , 2003, Pages 80-95

  Nowak, Robert ^a   Mitra, Urbashi ^b  

^a Rice University   (United States)

^b University of Southern California ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ENERGY UTILIZATION; MEAN SQUARE ERROR; SENSOR NODES;

ACHIEVABLE PERFORMANCE; BOUNDARY ESTIMATION; CONTAMINATION LEVELS; DISTRIBUTED PHENOMENA; MEAN SQUARED ERROR; REGULARITY ASSUMPTION; THEORETICAL BOUNDS; THEORY AND METHODS;

SENSOR NETWORKS;

EID: 34547278772     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36978-3_6     Document Type: Article

References (10)

1. L. Breiman, J. Friedman, R. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, Belmont, CA, 1983.
2. K. Chintalapudi and R. Govindan. Localized edge detection in sensor fields. University of Southern California, Computer Science Department, Technical Report, 02-773, 2002. available at http://www.cs.usc.edu/tech-reports/technical-reports.html.
3. D. Donoho. Wedgelets: Nearly minimax estimation of edges. Ann. Statist., 27:859-897, 1999.
4. D. Ganesan, D. Estrin, and J. Heideman. DIMENSIONS: Why do we need a new data handling architecture for sensor networks? In Proceedings of IEEE/ACM HotNets-I, Princeton, NJ, October 2002.
5. E. Kolaczyk and R. Nowak. Multiscale likelihood analysis and complexity penalized estimation. Annals of Statistics (tentatively accepted for publication). Also available at www.ece.rice.edu/~nowak/pubs.html, 2002.
6. A. P. Korostelev and A. B. Tsybakov. Minimax theory of image reconstruction. Springer-Verlag, New York, 1993.
7. B. Laurent and P. Massart. Adaptive estimation of a quadratic functional by model selection. The Annals of Statistics, (5), October 2000.
8. Q. Li and A. Barron. Mixture density estimation. In S.A. Solla, T.K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12. MIT Press, 2000.
9. C. Scott and R. Nowak. Dyadic classification trees via structural risk minimization. In Proc. Neural Information Processing Systems (NIPS), Vancouver, CA, Dec. 2002.
10. R. Willett and R. Nowak. Platelets: A multiscale approach to recovering edges and surfaces in photon-limited imaging. IEEE Trans. Med. Imaging, to appear in the Special Issue on Wavelets in Medical Imaging, 2003.