Prof. Dharma Agrawal
School of Computing Sciences and Informatics
819D Old Chemistry,
University of Cincinnati,
Cincinnati, OH 45221-0008
Selecting LTE and Wireless Mesh Networks for Indoor/Outdoor Applications
ABSTRACT: The smart phone usage and multimedia devices have been increasing yearly and predictions indicate drastic increase in the upcoming years. Recently, various wireless technologies have been introduced to add flexibility to these gadgets. As data plans offered by the network service providers are expensive, users are inclined to utilize freely accessible and commonly available Wi-Fi networks indoors. LTE (Long Term Evolution) has been a topic of discussion in providing high data rates outdoors and various service providers are planning to roll out LTE networks all over the world. The objective of this presentation is to compare usefulness of these two leading wireless schemes based on LTE and Wireless Mesh Networks (WMN) and bring forward their advantages for indoor and outdoor environments. We also investigate to see if a hybrid LTE-WMN network may be feasible. Both these networks are heterogeneous in nature, employ cognitive approach and support multi hop communication. The main motivation behind this work is to utilize similarities in these networks, explore their capability of offering high data rates and generally have large coverage areas. In this work, we compare both these networks in terms of their data rates, range, cost, throughput, and power consumption. We also compare 802.11n based WMN with Femto cell in an indoor coverage scenario, while for outdoors; 802.16 based WMN is compared with LTE. The main objective is to help users select a network that could provide enhanced performance in a cost effective manner.
Institute for Economic Geography and GIScience,
Vienna University of Economics and Business,
1090 Vienna, Austria
Neoclassical growth theory, regions and spatial externalities
ABSTRACT: The presentation considers the standard neoclassical growth model in a Mankiw-Romer-Weil world with externalities across regions. The reduced form of this theoretical model and its associated empirical model lead to a spatial Durbin model, and this model provides very rich own- and cross-partial derivatives that quantify the magnitude of direct and indirect (spillover or externalities) effects that arise from changes in region’s characteristics (human and physical capital investment or population growth rates) at the outset in the theoretical model. A logical consequence of the simple dependence on a small number of nearby regions in the initial theoretical specification leads to a final-form model outcome where changes in a single region can potentially impact all other regions. This is perhaps surprising, but of course we must temper this result by noting that there is a decay of influence as we move to more distant or less connected regions. Using the scalar summary impact measures introduced by LeSage and Pace (2009) we can quantify and summarize the complicated set of non-linear impacts that fall on all regions as a result of changes in the physical and human capital in any region. We can decompose these impacts into direct and indirect (or externality) effects. Data for a system of 198 regions across 22 European countries over the period 1995 to 2004 are used to test the predictions of the model and to draw inferences regarding the magnitude of regional output responses to changes in physical and human capital endowments. The results reveal that technological interdependence among regions works through physical capital externalities crossing regional borders.
Advanced Methods for Computational Collective Intelligence
ABSTRACT: Is the intelligence of a collective larger than the intelligence of its members? How does one determine the knowledge of a collective on the basis of the knowledge of its members? Many examples show that the knowledge of a collective is not a usual union of the knowledge of its members. If we assume that the members of a collective possess their knowledge states about some common real world, and these states reflect to some degree the proper (real) state of the knowledge about the real world, then a question arises: How does one determine the knowledge of the collective, and what is its quality? This talk will present an approach to answer these questions. There will also be the reference of computational collective intelligence methods to big data.