Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
14,452
Karhunen-Loeve expansion of stationary random signals with exponentially oscillating covariance function
"... Abstract. The Karhunen-Loeve expansion based on the calculation of the eigenvalues and eigenfunctions of the Karhunen-Loeve integral equation is known to have certain properties that make it optimal for many signal detection and filtering applications. We propose an analytical solution of the equat ..."
Abstract
- Add to MetaCart
Abstract. The Karhunen-Loeve expansion based on the calculation of the eigenvalues and eigenfunctions of the Karhunen-Loeve integral equation is known to have certain properties that make it optimal for many signal detection and filtering applications. We propose an analytical solution
ASYMPTOTIC ESTIMATES OF THE NORMS OF POSITIVE DEFINITE TÖPLITZ MATRICES AND DETECTION OF QUASI-PERIODIC COMPONENTS OF STATIONARY RANDOM SIGNALS
, 2005
"... Abstract. Asymptotic forms of the Hilbert-Scmidt and Hilbert norms of positive definite Töplitz matrices QN = (b(j − k)) N−1 j,k=0 as N → ∞ are determined. Here b(j) are consequent trigonometric moments of a generating non-negative mesure dσ(θ) on [−π, π]. It is proven that σ(θ) is continuous if an ..."
Abstract
- Add to MetaCart
if and only if any of those contributions is o(N). Analogous criteria are given for positive integral operators with difference kernels. Obtained results are applied to processing of stationary random signals, in particular, neutron signals emitted by boiling water nuclear reactors. 1.
Signal recovery from random measurements via Orthogonal Matching Pursuit
- IEEE TRANS. INFORM. THEORY
, 2007
"... This technical report demonstrates theoretically and empirically that a greedy algorithm called Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal. This is a massive improvement over previous ..."
Abstract
-
Cited by 802 (9 self)
- Add to MetaCart
This technical report demonstrates theoretically and empirically that a greedy algorithm called Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal. This is a massive improvement over
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
Abstract
-
Cited by 1513 (20 self)
- Add to MetaCart
Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear
Stable signal recovery from incomplete and inaccurate measurements,”
- Comm. Pure Appl. Math.,
, 2006
"... Abstract Suppose we wish to recover a vector x 0 ∈ R m (e.g., a digital signal or image) from incomplete and contaminated observations y = Ax 0 + e; A is an n × m matrix with far fewer rows than columns (n m) and e is an error term. Is it possible to recover x 0 accurately based on the data y? To r ..."
Abstract
-
Cited by 1397 (38 self)
- Add to MetaCart
Abstract Suppose we wish to recover a vector x 0 ∈ R m (e.g., a digital signal or image) from incomplete and contaminated observations y = Ax 0 + e; A is an n × m matrix with far fewer rows than columns (n m) and e is an error term. Is it possible to recover x 0 accurately based on the data y
Robust Uncertainty Principles: Exact Signal Reconstruction From Highly Incomplete Frequency Information
, 2006
"... This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal and a randomly chosen set of frequencies. Is it possible to reconstruct from the partial knowledge of its Fourier coefficients on the set? A typical result of this pa ..."
Abstract
-
Cited by 2632 (50 self)
- Add to MetaCart
This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal and a randomly chosen set of frequencies. Is it possible to reconstruct from the partial knowledge of its Fourier coefficients on the set? A typical result
Minimum energy mobile wireless networks
- IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
, 1999
"... We describe a distributed position-based network protocol optimized for minimum energy consumption in mobile wireless networks that support peer-to-peer communications. Given any number of randomly deployed nodes over an area, we illustrate that a simple local optimization scheme executed at each n ..."
Abstract
-
Cited by 749 (0 self)
- Add to MetaCart
We describe a distributed position-based network protocol optimized for minimum energy consumption in mobile wireless networks that support peer-to-peer communications. Given any number of randomly deployed nodes over an area, we illustrate that a simple local optimization scheme executed at each
ATOMIC DECOMPOSITION BY BASIS PURSUIT
, 1995
"... The Time-Frequency and Time-Scale communities have recently developed a large number of overcomplete waveform dictionaries -- stationary wavelets, wavelet packets, cosine packets, chirplets, and warplets, to name a few. Decomposition into overcomplete systems is not unique, and several methods for d ..."
Abstract
-
Cited by 2728 (61 self)
- Add to MetaCart
The Time-Frequency and Time-Scale communities have recently developed a large number of overcomplete waveform dictionaries -- stationary wavelets, wavelet packets, cosine packets, chirplets, and warplets, to name a few. Decomposition into overcomplete systems is not unique, and several methods
The capacity of wireless networks
- IEEE TRANSACTIONS ON INFORMATION THEORY
, 2000
"... When n identical randomly located nodes, each capable of transmitting at bits per second and using a fixed range, form a wireless network, the throughput @ A obtainable by each node for a randomly chosen destination is 2 bits per second under a noninterference protocol. If the nodes are optimally p ..."
Abstract
-
Cited by 3243 (42 self)
- Add to MetaCart
When n identical randomly located nodes, each capable of transmitting at bits per second and using a fixed range, form a wireless network, the throughput @ A obtainable by each node for a randomly chosen destination is 2 bits per second under a noninterference protocol. If the nodes are optimally
Factor Graphs and the Sum-Product Algorithm
- IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple c ..."
Abstract
-
Cited by 1791 (69 self)
- Add to MetaCart
A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple
Results 1 - 10
of
14,452
