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Ad hoc positioning system (APS) using AoA
, 2003
"... AoA(Angle of Arrival) is a well known method used for positioning in providing services such as E911, and for other military and civil radiolocation applications, such as sonars and radars. Although devices such as GPS receivers and digital compasses provide good positioning and orientation outdoo ..."
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Cited by 484 (6 self)
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outdoors, there are many applications requiring the same facilities indoors, where line of sight access to satellites is unavailable, or earth magnetic readings are unreliable. We propose a method for all nodes to determine their orientation and position in an ad hoc network where only a fraction of nodes
Robust principal component analysis?
 Journal of the ACM,
, 2011
"... Abstract This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a lowrank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the lowrank and the ..."
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Cited by 569 (26 self)
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analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving
A Positive Fraction ErdősSzekeres Theorem
, 1998
"... We prove a fractional version of the Erdős–Szekeres theorem: for any k there is a constant ck> 0 such that any sufficiently large finite set X ⊂ R2 contains k subsets Y1,...,Yk, each of size ≥ ckX, such that every set {y1,...,yk}with yi ∈ Yi is in convex position. The main tool is a lemma stati ..."
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Cited by 24 (5 self)
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We prove a fractional version of the Erdős–Szekeres theorem: for any k there is a constant ck> 0 such that any sufficiently large finite set X ⊂ R2 contains k subsets Y1,...,Yk, each of size ≥ ckX, such that every set {y1,...,yk}with yi ∈ Yi is in convex position. The main tool is a lemma
On the optimality of the simple Bayesian classifier under zeroone loss
 MACHINE LEARNING
, 1997
"... The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containin ..."
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Cited by 818 (27 self)
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containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier’s probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero
Asymptotic stability of positive fractional 2D linear systems
"... Abstract. New necessary and sufficient conditions for the asymptotic stability of the positive fractional 2D systems are established. It is shown that the checking of the asymptotic stability of positive fractional 2D linear systems can be reduced to testing the stability of corresponding 1D positiv ..."
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Cited by 6 (5 self)
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Abstract. New necessary and sufficient conditions for the asymptotic stability of the positive fractional 2D systems are established. It is shown that the checking of the asymptotic stability of positive fractional 2D linear systems can be reduced to testing the stability of corresponding 1D
MINIMUM ENERGY CONTROL PROBLEM OF POSITIVE FRACTIONAL DISCRETETIME SYSTEMS
"... control, reachability. The minimum energy control problem of positive fractionaldiscrete time linear systems is addressed. Necessary and sufficient conditions for the reachability of the system are established. Sufficient conditions for the solvability of the minimum energy control of the positive ..."
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control, reachability. The minimum energy control problem of positive fractionaldiscrete time linear systems is addressed. Necessary and sufficient conditions for the reachability of the system are established. Sufficient conditions for the solvability of the minimum energy control of the positive
CE: Basic principles of ROC analysis
 Seminars in Nuclear Medicine
, 1978
"... The l imitations of diagnostic "accuracy " as a measure of decision performance require introduction of the concepts of the "sensit iv ity " and "specif ic i ty " of a diagnostic test, These measures and the related indices, "true positive fraction " and &quo ..."
Abstract

Cited by 376 (0 self)
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The l imitations of diagnostic "accuracy " as a measure of decision performance require introduction of the concepts of the "sensit iv ity " and "specif ic i ty " of a diagnostic test, These measures and the related indices, "true positive fraction "
Simple conditions for practical stability of positive fractional discretetime linear systems
 International Journal of Applied Mathematics and Computer Science 19(2): 263–269, DOI
, 2009
"... In the paper the problem of practical stability of linear positive discretetime systems of fractional order is addressed. New simple necessary and sufficient conditions for practical stability and for practical stability independent of the length of practical implementation are established. It is s ..."
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Cited by 11 (5 self)
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In the paper the problem of practical stability of linear positive discretetime systems of fractional order is addressed. New simple necessary and sufficient conditions for practical stability and for practical stability independent of the length of practical implementation are established
SingleInputSingleOutput Passive Macromodeling via Positive Fractions Vector Fitting
"... This paper introduces a constrained Vector Fitting algorithm which can directly identify a passive driving point function (impedance or admittance) from frequency domain data. The proposed Positive Fractions Vector Fitting (PFVF) algorithm formulates the residue identification step as a convex progr ..."
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Cited by 3 (3 self)
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This paper introduces a constrained Vector Fitting algorithm which can directly identify a passive driving point function (impedance or admittance) from frequency domain data. The proposed Positive Fractions Vector Fitting (PFVF) algorithm formulates the residue identification step as a convex
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