scholarly journals Blind Separation of Cyclostationary Sources Sharing Common Cyclic Frequencies Using Joint Diagonalization Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Amine Brahmi ◽  
Hicham Ghennioui ◽  
Christophe Corbier ◽  
François Guillet ◽  
M’hammed Lahbabi
Keyword(s):  
State Of The Art ◽  
Bfgs Method ◽  
Blind Separation ◽  
Joint Diagonalization ◽  
Mixing Matrix ◽  
Update Strategy ◽  
Quasi Newton ◽  
Source Signals ◽  
New Criteria ◽  
Diagonalization Algorithm

We propose a new method for blind source separation of cyclostationary sources, whose cyclic frequencies are unknown and may share one or more common cyclic frequencies. The suggested method exploits the cyclic correlation function of observation signals to compose a set of matrices which has a particular algebraic structure. The aforesaid matrices are automatically selected by proposing two new criteria. Then, they are jointly diagonalized so as to estimate the mixing matrix and retrieve the source signals as a consequence. The nonunitary joint diagonalization (NU-JD) is ensured by Broyden-Fletcher-Goldfarb-Shanno (BFGS) method which is the most commonly used update strategy for implementing a quasi-Newton technique. The efficiency of the method is illustrated by numerical simulations in digital communications context, which show good performances comparing to other state-of-the-art methods.

Blind Separation of a Mixture of Uniformly Distributed Source Signals: A Novel Approach

1999 ◽  
Vol 11 (4) ◽  
pp. 1011-1034 ◽  
Author(s):  
Jayanta Basak ◽  
Shun-ichi Amari
Keyword(s):  
Output Signal ◽  
Learning Rule ◽  
Blind Separation ◽  
Distributed Source ◽  
Novel Approach ◽  
Noisy Signals ◽  
Mixing Matrix ◽  
Unit Hypercube ◽  
Source Signals ◽  
Convergent Algorithm

A new, efficient algorithm for blind separation of uniformly distributed sources is proposed. The mixing matrix is assumed to be orthogonal by prewhitening the observed signals. The learning rule adaptively estimates the mixing matrix by conceptually rotating a unit hypercube so that all output signal components are contained within or on the hypercube. Under some ideal constraints, it has been theoretically shown that the algorithm is very similar to an ideal [Formula: see text] convergent algorithm, which is much faster than the existing [Formula: see text] convergent algorithms. The algorithm has been generalized to take care of the noisy signals by adaptively dilating the hypercube in conjunction with its rotation.

A New Approximate Joint Diagonalization Algorithm Based on the Fused Complex-Valued Cost Function

2011 ◽  
Vol 135-136 ◽  
pp. 76-79
Author(s):  
Xian Feng Xu ◽  
Chen Dong Duan
Keyword(s):  
Numerical Simulation ◽  
Cost Function ◽  
Iterative Scheme ◽  
Fast Convergence ◽  
Joint Diagonalization ◽  
Mixing Matrix ◽  
Diagonally Dominant ◽  
Complex Valued ◽  
Diagonalization Algorithm

A new algorithm to solve the fused complex-valued cost function for approximate joint diagonalization (AJD), named CVAJD (Complex-Valued Approximate Joint Diagonalization), is presented. The CVAJD algorithm adopts an iterative scheme to update the demixing matrix through the strictly diagonally-dominant residual mixing matrix obtained in each of iterations. Due to the relaxation of several constraints on the target matrices, it has more general utilizations. Besides, it is also easy to implement. A numerical simulation illustrates fast convergence and good performance of the CVAJD.

Adaptive Blind Separation with an Unknown Number of Sources

2004 ◽  
Vol 16 (8) ◽  
pp. 1641-1660 ◽  
Author(s):  
Ji-Min Ye ◽  
Xiao-Long Zhu ◽  
Xian-Da Zhang
Keyword(s):  
Mutual Information ◽  
Full Column Rank ◽  
Gradient Algorithm ◽  
Local Minima ◽  
Natural Gradient ◽  
Blind Separation ◽  
Unknown Number ◽  
Mixing Matrix ◽  
Source Signals ◽  
Source Number

The blind source separation (BSS) problem with an unknown number of sources is an important practical issue that is usually skipped by assuming that the source number n is known and equal to the number m of sensors. This letter studies the general BSS problem satisfying m ≥ n. First, it is shown that the mutual information of outputs of the separation network is a cost function for BSS, provided that the mixing matrix is of full column rank and the m×m separating matrix is nonsingular. The mutual information reaches its local minima at the separation points, where the m outputs consist of n desired source signals and m−n redundant signals. Second, it is proved that the natural gradient algorithm proposed primarily for complete BSS (m n) can be generalized to deal with the overdetermined BSS problem (m>n), but it would diverge inevitably due to lack of a stationary point. To overcome this shortcoming, we present a modified algorithm, which can perform BSS steadily and provide the desired source signals at specified channels if some matrix is designed properly. Finally, the validity of the proposed algorithm is confirmed by computer simulations on artificially synthesized data.

scholarly journals Correlation Tracking via Self-Adaptive Fusion of Multiple Features

Information ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 241 ◽  
Author(s):  
Zhi Chen ◽  
Peizhong Liu ◽  
Yongzhao Du ◽  
Yanmin Luo ◽  
Wancheng Zhang
Keyword(s):  
State Of The Art ◽  
Correlation Filter ◽  
Multiple Features ◽  
Multi Scale ◽  
Tracking Algorithms ◽  
Model Update ◽  
Adaptive Fusion ◽  
Tracking Model ◽  
Update Strategy ◽  
Self Adaptive

Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It will generate some erroneous information when the target objects undergo sophisticated scenario changes, such as background clutter, occlusion, out-of-view, and so forth. Long-term accumulation of erroneous model updating will cause tracking drift. In order to address problems that are mentioned above, in this paper, we propose a robust multi-scale correlation filter tracking algorithm via self-adaptive fusion of multiple features. First, we fuse powerful multiple features including histogram of oriented gradients (HOG), color name (CN), and histogram of local intensities (HI) in the response layer. The weights assigned according to the proportion of response scores that are generated by each feature, which achieve self-adaptive fusion of multiple features for preferable feature representation. In the meantime the efficient model update strategy is proposed, which is performed by exploiting a pre-defined response threshold as discriminative condition for updating tracking model. In addition, we introduce an accurate multi-scale estimation method integrate with the model update strategy, which further improves the scale variation adaptability. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracker performs superiorly against the state-of-the-art CF based methods.

scholarly journals Global Convergence of a Modified Two-Parameter Scaled BFGS Method with Yuan-Wei-Lu Line Search for Unconstrained Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Pengyuan Li ◽  
Zhan Wang ◽  
Dan Luo ◽  
Hongtruong Pham
Keyword(s):  
Unconstrained Optimization ◽  
Line Search ◽  
Optimization Problems ◽  
Medium Size ◽  
Bfgs Method ◽  
Nonconvex Functions ◽  
Bfgs Update ◽  
Unconstrained Optimization Problems ◽  
Quasi Newton ◽  
Two Parameter

The BFGS method is one of the most efficient quasi-Newton methods for solving small- and medium-size unconstrained optimization problems. For the sake of exploring its more interesting properties, a modified two-parameter scaled BFGS method is stated in this paper. The intention of the modified scaled BFGS method is to improve the eigenvalues structure of the BFGS update. In this method, the first two terms and the last term of the standard BFGS update formula are scaled with two different positive parameters, and the new value of yk is given. Meanwhile, Yuan-Wei-Lu line search is also proposed. Under the mentioned line search, the modified two-parameter scaled BFGS method is globally convergent for nonconvex functions. The extensive numerical experiments show that this form of the scaled BFGS method outperforms the standard BFGS method or some similar scaled methods.

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