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Implementation issues in spectrum sensing for cognitive radios

by Danijela Cabric, Shridhar Mubaraq Mishra, Robert W. Brodersen - in Proc. the 38th. Asilomar Conference on Signals, Systems, and Computers , 2004
"... Abstract- There are new system implementation challenges involved in the design of cognitive radios, which have both the ability to sense the spectral environment and the flexibility to adapt transmission parameters to maximize system capacity while co-existing with legacy wireless networks. The cri ..."
Abstract - Cited by 440 (7 self) - Add to MetaCart
Abstract- There are new system implementation challenges involved in the design of cognitive radios, which have both the ability to sense the spectral environment and the flexibility to adapt transmission parameters to maximize system capacity while co-existing with legacy wireless networks

A Niched Pareto Genetic Algorithm for Multiobjective Optimization

by Jeffrey Horn, Nicholas Nafpliotis, David E. Goldberg - IN PROCEEDINGS OF THE FIRST IEEE CONFERENCE ON EVOLUTIONARY COMPUTATION, IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE , 1994
"... Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic a ..."
Abstract - Cited by 407 (6 self) - Add to MetaCart
Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic

Contour Detection and Hierarchical Image Segmentation

by Pablo Arbeláez, Michael Maire, Charless Fowlkes, Jitendra Malik - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2010
"... This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentati ..."
Abstract - Cited by 389 (24 self) - Add to MetaCart
This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our

Spectral grouping using the Nyström method

by Charless Fowlkes, Serge Belongie, Fan Chung, Jitendra Malik - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2004
"... Spectral graph theoretic methods have recently shown great promise for the problem of image segmentation. However, due to the computational demands of these approaches, applications to large problems such as spatiotemporal data and high resolution imagery have been slow to appear. The contribution ..."
Abstract - Cited by 316 (1 self) - Add to MetaCart
Spectral graph theoretic methods have recently shown great promise for the problem of image segmentation. However, due to the computational demands of these approaches, applications to large problems such as spatiotemporal data and high resolution imagery have been slow to appear. The contribution

Partitioning of Unstructured Problems for Parallel Processing

by Horst D. Simon , 1991
"... Many large scale computational problems are based on unstructured computational domains. Primary examples are unstructured grid calculations based on finite volume methods in computational fluid dynamics, or structural analysis problems based on finite element approximations. Here we will address th ..."
Abstract - Cited by 344 (16 self) - Add to MetaCart
a new decomposition algorithm will be discussed, which is based on the computation of an eigenvector of the Laplacian matrix associated with the graph. Numerical comparisons on large scale two and three dimensional problems demonstrate the superiority of the new spectral bisection algorithm.

Spectral hashing

by Yair Weiss, Antonio Torralba, Rob Fergus , 2009
"... Semantic hashing [1] seeks compact binary codes of data-points so that the Hamming distance between codewords correlates with semantic similarity. In this paper, we show that the problem of finding a best code for a given dataset is closely related to the problem of graph partitioning and can be sho ..."
Abstract - Cited by 284 (4 self) - Add to MetaCart
be shown to be NP hard. By relaxing the original problem, we obtain a spectral method whose solutions are simply a subset of thresholded eigenvectors of the graph Laplacian. By utilizing recent results on convergence of graph Laplacian eigenvectors to the Laplace-Beltrami eigenfunctions of manifolds, we

Probing the Pareto frontier for basis pursuit solutions

by Ewout van den Berg, Michael P. Friedlander , 2008
"... The basis pursuit problem seeks a minimum one-norm solution of an underdetermined least-squares problem. Basis pursuit denoise (BPDN) fits the least-squares problem only approximately, and a single parameter determines a curve that traces the optimal trade-off between the least-squares fit and the ..."
Abstract - Cited by 365 (5 self) - Add to MetaCart
on this curve; the algorithm is suitable for problems that are large scale and for those that are in the complex domain. At each iteration, a spectral gradient-projection method approximately minimizes a least-squares problem with an explicit one-norm constraint. Only matrix-vector operations are required

Numerical solution of saddle point problems

by Michele Benzi, Gene H. Golub, Jörg Liesen - ACTA NUMERICA , 2005
"... Large linear systems of saddle point type arise in a wide variety of applications throughout computational science and engineering. Due to their indefiniteness and often poor spectral properties, such linear systems represent a significant challenge for solver developers. In recent years there has b ..."
Abstract - Cited by 322 (25 self) - Add to MetaCart
Large linear systems of saddle point type arise in a wide variety of applications throughout computational science and engineering. Due to their indefiniteness and often poor spectral properties, such linear systems represent a significant challenge for solver developers. In recent years there has

New spectral methods for ratio cut partition and clustering

by Lars Hagen, Andrew B. Kahng - IEEE TRANS. ON COMPUTER-AIDED DESIGN , 1992
"... Partitioning of circuit netlists is important in many phases of VLSI design, ranging from layout to testing and hardware simulation. The ratio cut objective function [29] has received much attention since it naturally captures both min-cut and equipartition, the two traditional goals of partitionin ..."
Abstract - Cited by 296 (17 self) - Add to MetaCart
of partitioning. In this paper, we show that the second smallest eigenvalue of a matrix derived from the netlist gives a provably good approx-imation of the optimal ratio cut partition cost. We also dem-onstrate that fast Lanczos-type methods for the sparse sym-metric eigenvalue problem are a robust basis

Sampling signals with finite rate of innovation

by Martin Vetterli, Pina Marziliano, Thierry Blu - IEEE Transactions on Signal Processing , 2002
"... Abstract—Consider classes of signals that have a finite number of degrees of freedom per unit of time and call this number the rate of innovation. Examples of signals with a finite rate of innovation include streams of Diracs (e.g., the Poisson process), nonuniform splines, and piecewise polynomials ..."
Abstract - Cited by 350 (67 self) - Add to MetaCart
and reconstruction based on spline kernels. The key in all constructions is to identify the innovative part of a signal (e.g., time instants and weights of Diracs) using an annihilating or locator filter: a device well known in spectral analysis and error-correction coding. This leads to standard computational
Results 11 - 20 of 15,885