Downlink Coverage Analysis in a Heterogeneous Cellular Network

In this paper, we consider the downlink signal-to-interference-plus-noise ratio (SINR) analysis in a heterogeneous cellular network with K tiers. Each tier is characterized by a base-station (BS) arrangement according to a homogeneous Poisson point process with certain BS density, transmission power, random shadow fading factors with arbitrary distribution, arbitrary path-loss exponent and a certain bias towards admitting the mobile-station (MS). The MS associates with the BS that has the maximum SINR under the open access cell association scheme. For such a general setting, we provide an analytical characterization of the coverage probability at the MS.Comment: 6 pages, 5 figures, submitted to IEEE Globecom 2012 - Wireless Communications Symposium on Apr 2, 201

Multi-tier Network Performance Analysis using a Shotgun Cellular System

This paper studies the carrier-to-interference ratio (CIR) and carrier-to-interference-plus-noise ratio (CINR) performance at the mobile station (MS) within a multi-tier network composed of M tiers of wireless networks, with each tier modeled as the homogeneous n-dimensional (n-D, n=1,2, and 3) shotgun cellular system, where the base station (BS) distribution is given by the homogeneous Poisson point process in n-D. The CIR and CINR at the MS in a single tier network are thoroughly analyzed to simplify the analysis of the multi-tier network. For the multi-tier network with given system parameters, the following are the main results of this paper: (1) semi-analytical expressions for the tail probabilities of CIR and CINR; (2) a closed form expression for the tail probability of CIR in the range [1,Infinity); (3) a closed form expression for the tail probability of an approximation to CIR in the entire range [0,Infinity); (4) a lookup table based approach for obtaining the tail probability of CINR, and (5) the study of the effect of shadow fading and BSs with ideal sectorized antennas on the CIR and CINR. Based on these results, it is shown that, in a practical cellular system, the installation of additional wireless networks (microcells, picocells and femtocells) with low power BSs over the already existing macrocell network will always improve the CINR performance at the MS.Comment: 6 pages, 3 figures, accepted at IEEE Globecom 201

Stochastic Ordering based Carrier-to-Interference Ratio Analysis for the Shotgun Cellular Systems

A simple analytical tool based on stochastic ordering is developed to compare the distributions of carrier-to-interference ratio at the mobile station of two cellular systems where the base stations are distributed randomly according to certain non-homogeneous Poisson point processes. The comparison is conveniently done by studying only the base station densities without having to solve for the distributions of the carrier-to-interference ratio, that are often hard to obtain.Comment: 10 pages, 0 figures, submitted for review to IEEE Wireless Communications Letters on October 11, 201

Evaluation of Antihyperglycemic Effect of Aqueous Extract of Leaves of Annona Squamosa in Streptozotocin Induced Diabetic Rats

AIM OF THE SUDY: The aim is to evaluate the antihyperglycemic effect of Annona squamosa leaf extract in streptozotocin induced diabetic rats by tail venepuncture method. METHODS: 24 adult male albino rats weighing 150-200g were selected from central animal house, Madurai Medical College, Madurai. Initially, 24 albino rats were divided into 4 groups of 6 animals each. Group I received normal feed. Group II received Tab. Glibenclamide1mg/kg orally. Group III and Group IV received Annona squamosa leaf extract 300mg/kg and 600mg/kg orally for 14days.The blood glucose level was monitored on day 1, 7 and 14 by tail vene puncture method. RESULTS: On day 7 and day 14, there was a significant fall in blood glucose level in the Annona treated groups, when compared with the control (p Test group 2 > Test group 1, when compared with the control group. CONCLUSION: It was observed that Annona leaf extract at 300 mg/kg and 600 mg/kg produce statistically significant reduction in blood glucose level in streptozotocin induced diabetic rats when compared with control group. Increased mRNA expression of GLUT4 in peripheral tissues, the insulin releasing property, free radical scavenging property, inhibition of intestinal absorption of glucose and inhibition of PTP1B, might be possible mechanisms for the antihyperglycemic activity of Annona squamosa

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support