44,097 research outputs found
A new look at the conditions for the synthesis of speed-independent circuits
This paper presents a set of sufficient conditions for the gate-level synthesis of speed-independent circuits when constrained to a given class of gate library. Existing synthesis methodologies are restricted to architectures that use simple AND-gates, and do not exploit the advantages offered by the existence of complex gates. The use of complex gates increases the speed and reduces the area of the circuits. These improvements are achieved because of (1) the elimination of the distributivity, signal persistency and unique minimal state requirements imposed by other techniques; (2) the reduction in the number of internal signals necessary to guarantee the synthesis; and finally (3) the utilization of optimization techniques to reduce the fan-in of the involved gates and the number of required memory elements.Peer ReviewedPostprint (published version
Solving for multi-class using orthogonal coding matrices
A common method of generalizing binary to multi-class classification is the
error correcting code (ECC). ECCs may be optimized in a number of ways, for
instance by making them orthogonal. Here we test two types of orthogonal ECCs
on seven different datasets using three types of binary classifier and compare
them with three other multi-class methods: 1 vs. 1, one-versus-the-rest and
random ECCs. The first type of orthogonal ECC, in which the codes contain no
zeros, admits a fast and simple method of solving for the probabilities.
Orthogonal ECCs are always more accurate than random ECCs as predicted by
recent literature. Improvments in uncertainty coefficient (U.C.) range between
0.4--17.5% (0.004--0.139, absolute), while improvements in Brier score between
0.7--10.7%. Unfortunately, orthogonal ECCs are rarely more accurate than 1 vs.
1. Disparities are worst when the methods are paired with logistic regression,
with orthogonal ECCs never beating 1 vs. 1. When the methods are paired with
SVM, the losses are less significant, peaking at 1.5%, relative, 0.011 absolute
in uncertainty coefficient and 6.5% in Brier scores. Orthogonal ECCs are always
the fastest of the five multi-class methods when paired with linear
classifiers. When paired with a piecewise linear classifier, whose
classification speed does not depend on the number of training samples,
classifications using orthogonal ECCs were always more accurate than the the
remaining three methods and also faster than 1 vs. 1. Losses against 1 vs. 1
here were higher, peaking at 1.9% (0.017, absolute), in U.C. and 39% in Brier
score. Gains in speed ranged between 1.1% and over 100%. Whether the speed
increase is worth the penalty in accuracy will depend on the application
Enforcement in Dynamic Spectrum Access Systems
The spectrum access rights granted by the Federal government to spectrum users come with the expectation of protection from harmful interference. As a consequence of the growth of wireless demand and services of all types, technical progress enabling smart agile radio networks, and on-going spectrum management reform, there is both a need and opportunity to use and share spectrum more intensively and dynamically. A key element of any framework for managing harmful interference is the mechanism for enforcement of those rights. Since the rights to use spectrum and to protection from harmful interference vary by band (licensed/unlicensed, legacy/newly reformed) and type of use/users (primary/secondary, overlay/underlay), it is reasonable to expect that the enforcement mechanisms may need to vary as well.\ud
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In this paper, we present a taxonomy for evaluating alternative mechanisms for enforcing interference protection for spectrum usage rights, with special attention to the potential changes that may be expected from wider deployment of Dynamic Spectrum Access (DSA) systems. Our exploration of how the design of the enforcement regime interacts with and influences the incentives of radio operators under different rights regimes and market scenarios is intended to assist in refining thinking about appropriate access rights regimes and how best to incentivize investment and growth in more efficient and valuable uses of the radio frequency spectrum
A novel prestack sparse azimuthal AVO inversion
In this paper we demonstrate a new algorithm for sparse prestack azimuthal
AVO inversion. A novel Euclidean prior model is developed to at once respect
sparseness in the layered earth and smoothness in the model of reflectivity.
Recognizing that methods of artificial intelligence and Bayesian computation
are finding an every increasing role in augmenting the process of
interpretation and analysis of geophysical data, we derive a generalized
matrix-variate model of reflectivity in terms of orthogonal basis functions,
subject to sparse constraints. This supports a direct application of machine
learning methods, in a way that can be mapped back onto the physical principles
known to govern reflection seismology. As a demonstration we present an
application of these methods to the Marcellus shale. Attributes extracted using
the azimuthal inversion are clustered using an unsupervised learning algorithm.
Interpretation of the clusters is performed in the context of the Ruger model
of azimuthal AVO
Quasi-concave density estimation
Maximum likelihood estimation of a log-concave probability density is
formulated as a convex optimization problem and shown to have an equivalent
dual formulation as a constrained maximum Shannon entropy problem. Closely
related maximum Renyi entropy estimators that impose weaker concavity
restrictions on the fitted density are also considered, notably a minimum
Hellinger discrepancy estimator that constrains the reciprocal of the
square-root of the density to be concave. A limiting form of these estimators
constrains solutions to the class of quasi-concave densities.Comment: Published in at http://dx.doi.org/10.1214/10-AOS814 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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