• Documents
  • Authors
  • Tables

CiteSeerX logo

Advanced Search Include Citations

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 21 - 27 of 27

RICE UNIVERSITY Regime Change: Sampling Rate vs. Bit-Depth in Compressive Sensing

by Jason Noah Laska , 2011
"... The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demon-strated that structured signals can be acquired with just a small number of linear measurements, on the order of t ..."
Abstract - Add to MetaCart
The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demon-strated that structured signals can be acquired with just a small number of linear measurements, on the order

JEAN-LUC GENNISSON

by Universite Paris, Vi Pierre, Et Marie Curie, Espci Paristech, Habilitation A Diriger Des, Dr. Damien, Pr. Michel, Dr. Mickaël, Tanter Examinateur, Jean-luc Gennisson, H. D. R. Université, Paris Vi
"... RECHERCHES Elastographie par ondes de cisaillement ..."
Abstract - Add to MetaCart
RECHERCHES Elastographie par ondes de cisaillement

JOURNAL OF DISPLAY TECHNOLOGY 1 Digital Holography at Shot Noise Level

by Frédéric Verpillat, Fadwa Joud, Michael Atlan, Michel Gross
"... rs io ..."
Abstract - Add to MetaCart
Abstract not found

1 Minimum Variance Estimation of a Sparse Vector within the Linear Gaussian Model:

by Alexander Junga (corresponding, Zvika Ben-haimc, Yonina C. Eldard
"... ar ..."
Abstract - Add to MetaCart
Abstract not found

Minimum Variance Estimation of a Sparse Vector Within the Linear Gaussian Model: An

by Zvika Ben-haim, Yonina C. Eldar
"... Abstract — We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian noise. Our analysis is based on the theory of reproducing kernel Hilbert spaces (RKHS). After a characterizat ..."
Abstract - Add to MetaCart
Abstract — We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian noise. Our analysis is based on the theory of reproducing kernel Hilbert spaces (RKHS). After a characterization of the RKHS associated with the SLGM, we derive a lower bound on the minimum variance achievable by estimators with a prescribed bias function, including the important special case of unbiased estimation. This bound is obtained via an orthogonal projection of the prescribed mean function onto a subspace of the RKHS associated with the SLGM. It provides an approximation to the minimum achievable variance (Barankin bound) that is tighter than any known bound. Our bound holds for an arbitrary system matrix, including the overdetermined and underdetermined cases. We specialize

Active Mask Framework for Segmentation of Fluorescence Microscope Images

by Gowri Srinivasa, Advisor Prof, Prof Matthew, C. Fickus, Prof Adam, D. Linstedt, Prof Robert, F. Murphy
"... m]]l]]s¶D]]¿÷mB]iv]b]oD]m¶¨]iv]§]iv]r]j]t¿rv]]irj]]t]]m] / | ap]]r¿]ÎNy]s¶D]]mb¶r]ix} Û]Ix]]rd]mb]} p—N]t]o%ism] in]ty]m] / || Û]Is]¡uÎc]rN]]riv]nd]p]*N]m]st¶ I always bow to Śri ̄ Śāradāmbā, the limitless ocean of the nectar of compassion, who bears a rosary, a vessel of nectar, the symbol of ..."
Abstract - Add to MetaCart
m]]l]]s¶D]]¿÷mB]iv]b]oD]m¶¨]iv]§]iv]r]j]t¿rv]]irj]]t]]m] / | ap]]r¿]ÎNy]s¶D]]mb¶r]ix} Û]Ix]]rd]mb]} p—N]t]o%ism] in]ty]m] / || Û]Is]¡uÎc]rN]]riv]nd]p]*N]m]st¶ I always bow to Śri ̄ Śāradāmbā, the limitless ocean of the nectar of compassion, who bears a rosary, a vessel of nectar, the symbol of knowledge and a book in Her lotus hands. Dedicated to the Lotus Feet of the revered Sadguru. This thesis presents a new active mask (AM) framework and an algorithm for segmenta-tion of digital images, particularly those of punctate patterns from fluorescence microscopy. Fluorescence microscopy has greatly facilitated the task of understanding complex sys-tems at cellular and molecular levels in recent years. Segmentation, an important yet dif-ficult problem, is often the first processing step following acquisition. Our team previously demonstrated that a stochastic active contour based algorithm together with the concept of

Scenario Generation and Reduction for Long-term and Short-term Power System Generation Planning under Uncertainties

by Yonghan Feng, James D. Mccalley, William Q. Meeker, Jo Min, Lizhi Wang
"... ii ..."
Abstract - Add to MetaCart
Abstract not found
Results 21 - 27 of 27
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University