Low-Profile Fully-Printed Multifrequency Monopoles Loaded with Complementary Metamaterial Transmission Line

The design of a new class of multifrequency monopoles by loading a set of resonant-type complementary metamaterial transmission lines (CMTL) is firstly presented. Two types of CMTL elements are comprehensively explored: the former is the epsilon negative (ENG) one by loading complementary split ring resonators (CSRRs) with different configurations on the signal strip, whereas the latter is the double negative (DNG) one by incorporating the CSRRs and capacitive gaps. In both cases, the CMTLs are considered with different number of unit cells. By cautiously controlling the geometrical parameters of element structure, five antenna prototypes coving different communication standards (GSM, UMTS, DMB and WiMAX) are designed, fabricated and measured. Numerical and experimental results illustrate that the zeroth-order resonance frequencies of the ENG and DNG monopoles are in desirable consistency. Moreover, of all operating frequencies the antennas exhibit fairly good impedance matching performances better than -10dB and quasi-omnidirectional radiation patterns

Some properties of Ramsey numbers

AbstractIn this paper, some properties of Ramsey numbers are studied, and the following results are presented. 1.(1) For any positive integers k1, k2, …, km l1, l2, …, lm (m > 1), we have r ∏i=1m ki + 1, ∏i=1m li + 1 ≥ ∏i=1m [ r (ki + 1,li + 1) − 1] + 1.2.(2) For any positive integers k1, k2, …, km, l1, l2, …, ln , we have r ∑i=1m ki + 1, ∑j=1n lj + 1 ≥ ∑i=1m∑j=1n r (ki + 1,lj + 1) − mn + 1. Based on the known results of Ramsey numbers, some results of upper bounds and lower bounds of Ramsey numbers can be directly derived by those properties

On the clustering property of the random intersection graphs

A random intersection graph \mtl{\mcal{G}_{V,W,p}} is induced from a random bipartite graph \mtl{\mcal{G}^{*}_{V,W,p}} with vertices classes \mtl{V}, \mtl{W} and the edges incident between \mtl{v \in V} and \mtl{w \in W} with probability \mtl{p}. Two vertices in \mtl{V} are considered to be connected with each other if both of them connect with some common vertices in \mtl{W}. The clustering properties of the random intersection graph are investigated completely in this article. Suppose that the vertices number be \mtl{N = \mabs{V}} and \mtl{M=\mabs{W}} and \mtl{M = N^{\alpha},\ p=N^{-\beta}}, where \mtl{\alpha > 0,\, \beta > 0}, we derive the exact expressions of the clustering coefficient \mtl{C_{v}} of vertex \mtl{v} in \mtl{\mcal{G}_{V,W,p}}. The results show that if \mtl{\alpha < 2\beta} and \mtl{\alpha \neq \beta}, \mtl{C_{v}} decreases with the increasing of the graph size; if \mtl{\alpha = \beta} or \mtl{\alpha \geq 2\beta}, the graph has the constant clustering coefficients, in addition, if \mtl{\alpha > 2\beta}, the graph connecChangshui Zhangts almost completely. Therefore, we illustrate the phase transition for the clustering property in the random intersection graphs and give the condition that \mtl{\riG} being high clustering graph

High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles

<p>Abstract</p> <p>Background</p> <p>High content screening (HCS)-based image analysis is becoming an important and widely used research tool. Capitalizing this technology, ample cellular information can be extracted from the high content cellular images. In this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human H4 neuroglioma cells exposed to metal oxide nanoparticles. This system has been proved to be an essential tool in our study.</p> <p>Results</p> <p>The cellular images of H4 neuroglioma cells exposed to different concentrations of CuO nanoparticles were sampled using IN Cell Analyzer 1000. A fully automated cellular image analysis system has been developed to perform the image analysis for cell viability. A multiple adaptive thresholding method was used to classify the pixels of the nuclei image into three classes: bright nuclei, dark nuclei, and background. During the development of our image analysis methodology, we have achieved the followings: (1) The Gaussian filtering with proper scale has been applied to the cellular images for generation of a local intensity maximum inside each nucleus; (2) a novel local intensity maxima detection method based on the gradient vector field has been established; and (3) a statistical model based splitting method was proposed to overcome the under segmentation problem. Computational results indicate that 95.9% nuclei can be detected and segmented correctly by the proposed image analysis system.</p> <p>Conclusion</p> <p>The proposed automated image analysis system can effectively segment the images of human H4 neuroglioma cells exposed to CuO nanoparticles. The computational results confirmed our biological finding that human H4 neuroglioma cells had a dose-dependent toxic response to the insult of CuO nanoparticles.</p

Multi-Label Classification with Label Graph Superimposing

Images or videos always contain multiple objects or actions. Multi-label recognition has been witnessed to achieve pretty performance attribute to the rapid development of deep learning technologies. Recently, graph convolution network (GCN) is leveraged to boost the performance of multi-label recognition. However, what is the best way for label correlation modeling and how feature learning can be improved with label system awareness are still unclear. In this paper, we propose a label graph superimposing framework to improve the conventional GCN+CNN framework developed for multi-label recognition in the following two aspects. Firstly, we model the label correlations by superimposing label graph built from statistical co-occurrence information into the graph constructed from knowledge priors of labels, and then multi-layer graph convolutions are applied on the final superimposed graph for label embedding abstraction. Secondly, we propose to leverage embedding of the whole label system for better representation learning. In detail, lateral connections between GCN and CNN are added at shallow, middle and deep layers to inject information of label system into backbone CNN for label-awareness in the feature learning process. Extensive experiments are carried out on MS-COCO and Charades datasets, showing that our proposed solution can greatly improve the recognition performance and achieves new state-of-the-art recognition performance.Comment: AAAI 202