Evaluation of the long range magnetostatic field is the most time-consuming part in a micromagnetic simulation. In a magnetic system with N particles, the traditional direct pairwise summation method yields O(N2) asym...
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Evaluation of the long range magnetostatic field is the most time-consuming part in a micromagnetic simulation. In a magnetic system with N particles, the traditional direct pairwise summation method yields O(N2) asymptotic computation time. Here an adaptive fast algorithm fully implementing the multipole and local expansions of the field integral is shown to yield O(N) computation time. Fast Fourier transform techniques are generalized to entail finite size magnetic systems with nonperiodic boundary conditions, yielding O(N log2 N) computation time. Examples are given for calculating domain wall structures in Permalloy thin films. The efficiency or the fast Fourier transform makes it almost always the faster method for any large size system, while the multipole algorithm remains effective for more complex geometries and systems with highly irregular or nonuniform particle distributions.
In this paper, we consider the estimation of sparse nonlinear communication channels. Transmission over the channels is represented by sparse Volterra models that incorporate the effect of Power Amplifiers. Channel es...
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Model order selection problems are important for signal processing and its various applications in wireless communications, radar theory, navigation, control theory, and others. We described three model order selectio...
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Breast cancer is the most commonly diagnosed and the second leading cause of cancer death among women. In this paper we have proposed a multi stage system for detection of microcalcification using adaptive algorithms....
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An equivalent filter bank structure for multiple model adaptive estimation (MMAE) is developed that uses the residual and state estimates from a single Kalman filter and linear transforms to produce equivalent residua...
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An equivalent filter bank structure for multiple model adaptive estimation (MMAE) is developed that uses the residual and state estimates from a single Kalman filter and linear transforms to produce equivalent residuals of a complete Kalman filter bank. The linear transforms, which are a function of the differences bet tween the system models used by the various Kalman filters, are developed for modeling differences in the system input matrix, the output matrix, and the state transition matrix. The computational cost of this new structure is compared with the cost of the standard Kalman biter bank (SKFB) for each of these modeling differences. This structure is quite similar to the generalized likelihood ratio (GLR) structure, where the linear transforms can be used td compute the matched biters used in the GLR approach. This approach produces the best matched filters in the sense that they truly represent the time history of the residuals caused by a physically motivated failure model.
This paper presents two new, closely related adaptive algorithms for LS system identification. The starting point for the derivation of the algorithms is the inverse Cholesky factor of the;data correlation matrix, obt...
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This paper presents two new, closely related adaptive algorithms for LS system identification. The starting point for the derivation of the algorithms is the inverse Cholesky factor of the;data correlation matrix, obtained via a QR decomposition (QRD). Both algorithms are of O(p) computational complexity, with p being the order of the system. The first algorithm is a fixed order QRD scheme with enhanced-parallelism. The second is an order recursive lattice type algorithm based exclusively on orthogonal Givens rotations, with lower complexity compared to previously derived ones. Both algorithms are derived following a new approach, which exploits efficient time and order updates of a specific state vector quantity.
In this paper we consider the problem of scheduling a given task graph on a dynamic network, where processors may become available or unavailable during the lifetime of the computation. We show that known list schedul...
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Issues relating to the choice of adaptive algorithms for use in single user Minimum Mean Square Error (MMSE) Direct Sequence (DS) Code Division Multiple Access (CDMA) receivers are discussed. Such receivers have the p...
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Issues relating to the choice of adaptive algorithms for use in single user Minimum Mean Square Error (MMSE) Direct Sequence (DS) Code Division Multiple Access (CDMA) receivers are discussed. Such receivers have the potential to dramatically increase capacity of CDMA systems compared to systems with conventional correlator receivers, without the high complexity associated with many proposed multi-user receivers. To be practical for many applications, the convergence rate of the adaptive algorithms must be improved, while maintaining low residual mean-squared error (MSE), robustness to noise and realisable complexity. The slow convergence of the Least Mean Squares (LMS) algorithm is highlighted for this application. Recursive Least Squares (RLS) algorithms are shown to give much quicker convergence, but have much higher sensitivity to thermal noise. algorithms normally termed 'Fast-RLS', are shown to more complex than the conventional RLS algorithm when used in a CDMA receiver.
We describe a fault-tolerant distributed storage system for local area networks. Our system implements Persistent, Associative, Shared Object (PASO) memory. A PASO memory stores a set of data objects that can be acces...
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Rotorcraft vibration suppression has traditionally relied on passive devices, such as vibration isolation systems and dynamic absorbers that are effective at a single rotor speed. However, modern rotorcraft designs ar...
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