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.     

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.     

A new algorithm for the detection of tool breakage in milling

In this paper a novel signal processing algorithm is presented for the detection of tool breakage using the displacement signal (RORPA). The algorithm extracts information from consecutive revolutions to decrease dependencies on runout conditions and idle noise, creating a much more robust signal for the automatic determination of tool breakage conditions. The algorithm is explained as well as the determination of dynamic thresholds. The thresholds are set using the information regarding the cutting conditions (mean displacement signal and its slope during transient milling). Examples with real data are also presented. A critique of the overall performance and future work incorporating information from x and y channels completes the discussion.     

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.     

Lower and Upper Bounds for Misclassification Probability Based on Renyi's Information

Fano's inequality has proven to be one important result in Shannon's information theory having found applications in numerous proofs of convergence. It also provides us with a lower bound on the symbol error probability in a communication channel, in terms of Shannon's definitions of entropy and mutual information. This result is also significant in that it suggests insights on how the classification performance is influenced by the amount of information transferred through the classifier. We have previously extended Fano's lower bound on the probability of error to a family of lower and upper bounds based on Renyi's definitions of entropy and mutual information. These new bounds however, despite their theoretical appeal, were practically incomputable. In this paper, we present some modifications to these bounds that will allow us to utilize them in practical situations. The significance of these new bounds is threefold: Illustrating a theoretical use of Renyi's definition of information, extending Fano's result to include an upper bound for probability of classification error, and providing insights on how the information transfer through a classifier affects its performance. The performance of the modified bounds is investigated in various numerical examples, including applications to digital communication channels that are designed to point out the major conclusions.