Robust Blind Beamforming Algorithm Using Joint Multiple Matrix Diagonalization

The objective of the blind beamforming is to restore the unknown source signals simply based on the observations, without a priori knowledge of the source signals and the mixing matrix. In this paper, we propose a new joint multiple matrix diagonalization (JMMD) algorithm for the robust blind beamforming. This new JMMD algorithm is based on the iterative eigen decomposition of the fourth-order cumulant matrices. Therefore, it can avoid the problems of the stability and the misadjustment, which arise from the conventional steepest-descent approaches for the constant-modulus or cumulant optimization. Our Monte Carlo simulations show that our proposed algorithm significantly outperforms the ubiquitous joint approximate diagonalization of eigen-matrices algorithm, relying on the Givens rotations for the phase-shift keying source signals in terms of signal-to-interference-and-noise ratio for a wide variety of signal-to-noise ratios     

Some Equivalences between Kernel Methods and Information Theoretic Methods

In this paper, we discuss some equivalences between two recently introduced statistical learning schemes, namely Mercer kernel methods and information theoretic methods. We show that Parzen window-based estimators for some information theoretic cost functions are also cost functions in a corresponding Mercer kernel space. The Mercer kernel is directly related to the Parzen window. Furthermore, we analyze a classification rule based on an information theoretic criterion, and show that this corresponds to a linear classifier in the kernel space. By introducing a weighted Parzen window density estimator, we also formulate the support vector machine in this information theoretic perspective.

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Time Series Segmentation Using a Novel Adaptive Eigendecomposition Algorithm

In this paper we present a new technique for time series segmentation built around a fast principal component analysis (PCA) algorithm that is on-line and stable. The traditional Generalized Likelihood Ratio Test (GLRT) has been used to solve the segmentation problem, but this has enormous limitations in terms of complexity and speed. Newer methods use gated experts and mixture models to detect transitions in time series. These techniques perform better than GLRT, but most of them require extensive training of relatively large neural networks. The segmentation method discussed in this paper is based on a novel idea that involves solving the generalized eigendecomposition of two consecutive windowed time series and can be formulated as a two-step PCA. Thus, the performance of our segmentation technique mainly depends on the efficiency of the PCA algorithm. Most of the existing techniques for PCA are based on gradient search procedures that are slow and they also suffer from convergence problems. The PCA algorithm presented in this paper is both online, and is proven to converge faster than the current methods.     

The dissipative structure of variational multiscale methods for incompressible flows

In this paper, we present a precise definition of the numerical dissipation for the orthogonal projection version of the variational multiscale method for incompressible flows. We show that, only if the space of subscales is taken orthogonal to the finite element space, this definition is physically reasonable as the coarse and fine scales are properly separated. Then we compare the diffusion introduced by the numerical discretization of the problem with the diffusion introduced by a large eddy simulation model. Results for the flow around a surface-mounted obstacle problem show that numerical dissipation is of the same order as the subgrid dissipation introduced by the Smagorinsky model. Finally, when transient subscales are considered, the model is able to predict backscatter, something that is only possible when dynamic LES closures are used. Numerical evidence supporting this point is also presented.     

The relationship among patient contemplation, early alliance, and continuation in psychotherapy.

The present study examined the relationship among contemplation stage of readiness to change, formation of an early therapeutic alliance, and psychological distress following the first session of psychotherapy. Significant correlations between the contemplation scores and the therapeutic alliance were found for patients in the contemplation stage. Although contemplation scores were not a factor in return for a second session of psychotherapy, the bond subscale of the alliance inventory did significantly contribute to whether patients returned for therapy. Patient psychological distress was not a significant factor in predicting the early alliance. Results indicate a need for further focus on contemplation with its inherent ambivalence, its relationship to alliance, and continuation in early psychotherapy. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Vascular endothelial growth factor-C (VEGF-C/VEGF-2) promotes angiogenesis in the setting of tissue ischemia

Recently, vascular endothelial growth factor-C (VEGF-C or VEGF-2) was described as a specific ligand for the endothelial receptor tyrosine kinases VEGFR-2 and VEGFR-3. In vivo data, limited to constitutive overexpression in transgenic mice, have been interpreted as evidence that the growth-promoting effects of VEGF-C are restricted to development of the lymphatic vasculature. The current studies were designed to test the hypothesis that constitutive expression of VEGF-C in adult animals promotes angiogenesis. In vitro, VEGF-C exhibited a dose-dependent mitogenic and chemotactic effect on endothelial cells, particularly for microvascular endothelial cells (72% and 95% potency, respectively, compared with VEGF-A/VEGF-1). VEGF-C stimulated release of nitric oxide from endothelial cells and increased vascular permeability in the Miles assay; the latter effect was attenuated by pretreatment with the nitric oxide synthase inhibitor N(omega)-nitro-L-arginine methyl ester. Both VEGFR-2 and VEGFR-3 receptors were shown to be expressed in human saphenous vein and internal mammary artery. The potential for VEGF-C to promote angiogenesis in vivo was then tested in a rabbit ischemic hindlimb model. Ten days after ligation of the external iliac artery, VEGF-C was administered as naked plasmid DNA (pcVEGF-C; 500 microg) from the polymer coating of an angioplasty balloon (n = 8 each) or as recombinant human protein (rhVEGF-C; 500 microg) by direct intra-arterial infusion. Physiological and anatomical assessments of angiogenesis 30 days later showed evidence of therapeutic angiogenesis for both pcVEGF-C and rhVEGF-C. Hindlimb blood pressure ratio (ischemic/normal) after pcVEGF-C increased to 0.83 +/- 0.03 after pcVEGF-C versus 0.59 +/- 0.04 (P < 0.005) in pGSVLacZ controls and to 0.76 +/- 0.04 after rhVEGF-C versus 0.58 +/- 0.03 (P < 0.01) in control rabbits receiving rabbit serum albumin. Doppler-derived iliac flow reserve was 2.7 +/- 0.1 versus 2.0 +/- 0.2 (P < 0.05) for pcVEGF-C versus LacZ controls and 2.9 +/- 0.3 versus 2.1 +/- 0.2 (P < 0.05) for rhVEGF-C versus albumin controls. Neovascularity was documented by angiography in vivo (angiographic scores: 0.85 +/- 0.05 versus 0.51 +/- 0.02 (P < 0.001) for plasmid DNA and 0.74 +/- 0.08 versus 0.53 +/- 0.03 (P < 0.05) for protein), and capillary density (per mm2) was measured at necropsy (252 +/- 12 versus 183 +/- 10 (P < 0.005) for plasmid DNA and 229 +/- 20 versus 164 +/- 20 (P < 0.05) for protein). In contrast to the results of gene targeting experiments, constitutive expression of VEGF-C in adult animals promotes angiogenesis in the setting of limb ischemia. VEGF-C and its receptors thus constitute an apparently redundant pathway for postnatal angiogenesis and may represent an alternative to VEGF-A for strategies of therapeutic angiogenesis in patients with limb and/or myocardial ischemia.     

Sparse Least Logarithmic Absolute Difference Algorithm with Correntropy-Induced Metric Penalty

Sparse adaptive filtering algorithms are utilized to exploit system sparsity as well as to mitigate interferences in many applications such as channel estimation and system identification. In order to improve the robustness of the sparse adaptive filtering, a novel adaptive filter is developed in this work by incorporating a correntropy-induced metric (CIM) constraint into the least logarithmic absolute difference (LLAD) algorithm. The CIM as an \(l_{0}\)-norm approximation exerts a zero attraction, and hence, the LLAD algorithm performs well with robustness against impulsive noises. Numerical simulation results show that the proposed algorithm may achieve much better performance than other robust and sparse adaptive filtering algorithms such as the least mean p-power algorithm with \(l_{1}\)-norm or reweighted \(l_{1}\)-norm constraints.     

Fairness constrained diffusion adaptive power control for dense small cell network

Small cell is an emerging and promising technology for improving hotspots coverage and capacity, which tends to be densely deployed in populated areas. However, in a dense small cell network, the performances of users differ vastly due to the random deployments and the interferences. To guarantee fair performance among users in different cells, we propose a new distributed strategy for fairness constrained power control, referred to as the diffusion adaptive power control (DAPC). DAPC achieves overall network fairness in a distributed manner, in which each base station optimizes a local fairness with little information exchanged with neighboring cells. We study several adaptive algorithms to implement the proposed DAPC strategy. To improve the efficiency of the standard least mean square algorithm (LMS), we derive an adaptive step-size logarithm LMS algorithm, and discuss its convergence properties. Simulation results confirm the efficiency of the proposed methods.     

Beyond second-order statistics for learning: A pairwise interaction model for entropy estimation

Second order statistics have formed the basisof learning and adaptation due to its appealand analytical simplicity. On the other hand,in many realistic engineering problemsrequiring adaptive solutions, it is notsufficient to consider only the second orderstatistics of the underlying distributions. Entropy, being the average information contentof a distribution, is a better-suited criterionfor adaptation purposes, since it allows thedesigner to manipulate the information contentof the signals rather than merely their power. This paper introduces a nonparametric estimatorof Renyi's entropy, which can be utilized inany adaptation scenario where entropy plays arole. This nonparametric estimator leads to aninteresting analogy between learning andinteracting particles in a potential field. Itturns out that learning by second orderstatistics is a special case of thisinteraction model for learning. We investigatethe mathematical properties of thisnonparametric entropy estimator, provide batchand stochastic gradient expressions foroff-line and on-line adaptation, and illustratethe performance of the corresponding algorithmsin examples of supervised and unsupervisedtraining, including time-series prediction andICA.