EVD

(redirected from Eigenvalue decomposition)
AcronymDefinition
EVDEbola Virus Disease
EVDEvent Dispatcher
EVDExternal Visual Display
EVDEnhanced Versatile Disc
EVDEbola Virus Detection
EVDElectronic Voucher Distribution (software)
EVDEigenvalue Decomposition
EVDExternal Ventricular Drain
EVDElectrochemical Vapor Deposition
EVDExtended Voluntary Departure (US immigration)
EVDExtraits Végétaux et Dérivés (French: Plant Extracts and Derivatives)
EVDEmergency Vehicle Driver
EVDEnhanced Voice Directory (Genie)
EVDEspaces Verts du Dauphiné (French: Green Spaces of Dauphine; Dauphine, France)
EVDEnglish Version for the Deaf (bible translation)
EVDEngine Valve Driver
EVDEnhanced Vendor Delivery
EVDEarly Version Demonstrations
References in periodicals archive ?
From the theorem, the algorithm introduced in [4][5] can be perfected by doing eigenvalue decomposition once, and is not further mentioned.
2) GED algorithm: In [21], the BSS was formulated as a generalized eigenvalue decomposition (GED) problem, when the signals are non-gaussian, non stationary or non-white.
We remark that L+kI has the eigenvalue decomposition T([LAMBDA]+ kI)[T.sup.-1].
Smadi, "Low complexity and high accuracy angle of arrival estimation using eigenvalue decomposition with extension to 2D AOA and power estimation," EURASIP Journal on Wireless Communications and Networking, vol.
Meanwhile, the possibility of fusing these two attractive optimized methods is certain: rank revealing QR factorization with eigenvalue decomposition from Xu et al.'s algorithm [15] and orthogonal gradient descent approach from Tian et al.'s algorithm [19] to obtain a new optimized method to overcome the computational complexity.
Then MATLAB code is developed to realize the model integration and complex eigenvalue decomposition algorithm in this paper.
Without eigenvalue decomposition and matrix inversion, the computational load of TDCM is the lowest.
After the eigenvalue decomposition of S, N eigenvalues [[lambda].sub.0], [[lambda].sub.1],..., [[lambda].sub.n-1] and corresponding eigenvectors [v.sub.0], [v.sub.1], ..., [v.sub.N-1] can be calculated.
Similar to Section 3.1, 19) can be considered as a generalized eigenvalue decomposition problem.
The MPM algorithm requires neither spectral searching nor covariance matrix estimation and its eigenvalue decomposition, which can reduce the computational load.
Equation (3) is known as eigenvalue decomposition or matrix similarity transform [2, 14].
One of the main drawbacks of GLRAM is that one eigenvalue decomposition is required at each iteration step.