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DATAAIDED TIMING RECOVERY IN THE PRESENCE OF DATADEPENDENT NOISE
"... This paper presents a new dataaided timing recovery algorithm for channels with datadependent noise. Based on a datadependent GaussMarkov model of the noise, a maximumlikelihood timing recovery scheme is derived. The proposed timing recovery algorithm incorporates datadependent noise prediction ..."
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This paper presents a new dataaided timing recovery algorithm for channels with datadependent noise. Based on a datadependent GaussMarkov model of the noise, a maximumlikelihood timing recovery scheme is derived. The proposed timing recovery algorithm incorporates datadependent noise
DataAided Timing Recovery for Recording Channels With DataDependent Noise
"... In highdensity data storage systems, noise becomes highly correlated and data dependent as a result of media noise, channel nonlinearities, and frontend filters. In such environments, conventional timing recovery schemes will exhibit large residual timing jitter and, especially, datadependent tim ..."
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Cited by 1 (1 self)
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timing jitter. This paper presents a new dataaided timing recovery algorithm for data storage systems with datadependent noise. We derive a maximumlikelihood timing recovery scheme based on a datadependent Gauss–Markov model of the noise. The timing recovery algorithm incorporates datadependent
Reliable Communication in the Presence of Failures
 ACM Transactions on Computer Systems
, 1987
"... The design and correctness of a communication facility for a distributed computer system are reported on. The facility provides support for faulttolerant process groups in the form of a family of reliable multicast protocols that can be used in both local and widearea networks. These protocols at ..."
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Cited by 556 (20 self)
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attain high levels of concurrency, while respecting applicationspecific delivery ordering constraints, and have varying cost and performance that depend on the degree of ordering desired. In particular, a protocol that enforces causal delivery orderings is introduced and shown to be a valuable
Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise
, 2006
"... This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that ..."
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Cited by 496 (2 self)
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that has been contaminated with additive noise, the goal is to identify which elementary signals participated and to approximate their coefficients. Although many algorithms have been proposed, there is little theory which guarantees that these algorithms can accurately and efficiently solve the problem
Noise Trader Risk in Financial Markets
 Jolurnial of Political Economy
, 1990
"... We present a simple overlapping generations model of an asset market in which irrational noise traders with erroneous stochastic beliefs both affect prices and earn higher expected returns. The unpredictability of noise traders ’ beliefs creates a risk in the price of the asset that deters rational ..."
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Cited by 858 (23 self)
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We present a simple overlapping generations model of an asset market in which irrational noise traders with erroneous stochastic beliefs both affect prices and earn higher expected returns. The unpredictability of noise traders ’ beliefs creates a risk in the price of the asset that deters rational
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 ..."
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Cited by 1513 (20 self)
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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 measurements do we need to recover objects from this class to within accuracy ɛ? This paper shows that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal f ∈ F decay like a powerlaw (or if the coefficient sequence of f in a fixed basis decays like a powerlaw), then it is possible to reconstruct f to within very high accuracy from a small number of random measurements. typical result is as follows: we rearrange the entries of f (or its coefficients in a fixed basis) in decreasing order of magnitude f  (1) ≥ f  (2) ≥... ≥ f  (N), and define the weakℓp ball as the class F of those elements whose entries obey the power decay law f  (n) ≤ C · n −1/p. We take measurements 〈f, Xk〉, k = 1,..., K, where the Xk are Ndimensional Gaussian
Spacetime Interest Points
 IN ICCV
, 2003
"... Local image features or interest points provide compact and abstract representations of patterns in an image. In this paper, we propose to extend the notion of spatial interest points into the spatiotemporal domain and show how the resulting features often reflect interesting events that can be use ..."
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Cited by 791 (22 self)
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be used for a compact representation of video data as well as for its interpretation.. To detect
Pfinder: Realtime tracking of the human body
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... Pfinder is a realtime system for tracking people and interpreting their behavior. It runs at 10Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2D ..."
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Cited by 1464 (48 self)
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Pfinder is a realtime system for tracking people and interpreting their behavior. It runs at 10Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2
The NewReno Modification to TCP’s Fast Recovery Algorithm", RFC 3782
, 2004
"... This document specifies an Internet standards track protocol for the Internet community, and requests discussion and suggestions for improvements. Please refer to the current edition of the "Internet Official Protocol Standards " (STD 1) for the standardization state and status of this pro ..."
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Cited by 587 (10 self)
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of this protocol. Distribution of this memo is unlimited. Copyright Notice Copyright (C) The Internet Society (2004). All Rights Reserved. The purpose of this document is to advance NewReno TCP’s Fast Retransmit and Fast Recovery algorithms in RFC 2582 from Experimental to Standards Track status. The main change
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