4,311 research outputs found
Performance of voice and video conferencing over ATM and gigabit ethernet backbone networks
Gigabit Ethernet and ATM network technologies have been modeled as campus network
backbones for the simulation-based comparison of their performance. Real-time voice and
video conferencing traffic is used to compare the performance of both backbone
technologies in terms of response times and packet end-to-end delays. Simulation results
show that Gigabit Ethernet has been able to perform the same and in some cases better than
ATM as a backbone network for video and voice conferencing providing network designers
with a cheaper solution to meet the growing needs of bandwidth-hungry applications in a
campus environment
Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality
The growing field of studies of opinion formation using physical formalisms and computer simulation based tools suffers from relative lack of connection to the 'real world' societal behaviour. Such sociophysics research should aim at explaining observations or at proposing new ones. Unfortunately, this is not always the case, as many works concentrate more on the models themselves than on the social phenomena. Moreover, the simplifications proposed in simulations often sacrifice realism on the altar of computability. There are several ways to improve the value of the research, the most important by promoting truly multidisciplinary cooperation between physicists aiming to describe social phenomena and sociologists studying the phenomena in the field. In the specific case of modelling of opinion formation there are a few technical ideas which might bring the computer models much closer to reality, and therefore to improve the predictive value of the sociophysics approach.Methodology, Agent Based Social Simulation, Qualitative Analysis; Evidence; Conditions of Application
Exploring associations between micro-level models of innovation diffusion and emerging macro-level adoption patterns
A micro-level agent-based model of innovation diffusion was developed that
explicitly combines (a) an individual's perception of the advantages or
relative utility derived from adoption, and (b) social influence from members
of the individual's social network. The micro-model was used to simulate
macro-level diffusion patterns emerging from different configurations of
micro-model parameters. Micro-level simulation results matched very closely the
adoption patterns predicted by the widely-used Bass macro-level model (Bass,
1969). For a portion of the domain, results from micro-simulations were
consistent with aggregate-level adoption patterns reported in the literature.
Induced Bass macro-level parameters and responded to changes in
micro-parameters: (1) increased with the number of innovators and with the rate
at which innovators are introduced; (2) increased with the probability of
rewiring in small-world networks, as the characteristic path length decreases;
and (3) an increase in the overall perceived utility of an innovation caused a
corresponding increase in induced and values. Understanding micro to macro
linkages can inform the design and assessment of marketing interventions on
micro-variables - or processes related to them - to enhance adoption of future
products or technologies.Comment: 20 pages, 4 figures and a table of supplementary data. Accepted for
publicatio
Performance Comparison of the RPL and LOADng Routing Protocols in a Home Automation Scenario
RPL, the routing protocol proposed by IETF for IPv6/6LoWPAN Low Power and
Lossy Networks has significant complexity. Another protocol called LOADng, a
lightweight variant of AODV, emerges as an alternative solution. In this paper,
we compare the performance of the two protocols in a Home Automation scenario
with heterogenous traffic patterns including a mix of multipoint-to-point and
point-to-multipoint routes in realistic dense non-uniform network topologies.
We use Contiki OS and Cooja simulator to evaluate the behavior of the
ContikiRPL implementation and a basic non-optimized implementation of LOADng.
Unlike previous studies, our results show that RPL provides shorter delays,
less control overhead, and requires less memory than LOADng. Nevertheless,
enhancing LOADng with more efficient flooding and a better route storage
algorithm may improve its performance
Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems
Majority of Artificial Neural Network (ANN) implementations in autonomous
systems use a fixed/user-prescribed network topology, leading to sub-optimal
performance and low portability. The existing neuro-evolution of augmenting
topology or NEAT paradigm offers a powerful alternative by allowing the network
topology and the connection weights to be simultaneously optimized through an
evolutionary process. However, most NEAT implementations allow the
consideration of only a single objective. There also persists the question of
how to tractably introduce topological diversification that mitigates
overfitting to training scenarios. To address these gaps, this paper develops a
multi-objective neuro-evolution algorithm. While adopting the basic elements of
NEAT, important modifications are made to the selection, speciation, and
mutation processes. With the backdrop of small-robot path-planning
applications, an experience-gain criterion is derived to encapsulate the amount
of diverse local environment encountered by the system. This criterion
facilitates the evolution of genes that support exploration, thereby seeking to
generalize from a smaller set of mission scenarios than possible with
performance maximization alone. The effectiveness of the single-objective
(optimizing performance) and the multi-objective (optimizing performance and
experience-gain) neuro-evolution approaches are evaluated on two different
small-robot cases, with ANNs obtained by the multi-objective optimization
observed to provide superior performance in unseen scenarios
Survey on wireless technology trade-offs for the industrial internet of things
Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment
Inferring causal relations from multivariate time series : a fast method for large-scale gene expression data
Various multivariate time series analysis techniques have been developed with the aim of inferring causal relations between time series. Previously, these techniques have proved their effectiveness on economic and neurophysiological data, which normally consist of hundreds of samples. However, in their applications to gene regulatory inference, the small sample size of gene expression time series poses an obstacle. In this paper, we describe some of the most commonly used multivariate inference techniques and show the potential challenge related to gene expression analysis. In response, we propose a directed partial correlation (DPC) algorithm as an efficient and effective solution to causal/regulatory relations inference on small sample gene expression data. Comparative evaluations on the existing techniques and the proposed method are presented. To draw reliable conclusions, a comprehensive benchmarking on data sets of various setups is essential. Three experiments are designed to assess these methods in a coherent manner. Detailed analysis of experimental results not only reveals good accuracy of the proposed DPC method in large-scale prediction, but also gives much insight into all methods under evaluation
A solid state Marx generator with a novel configuration
The new configuration proposed in this paper for Marx Generator (MG.) aims to generate high voltage for pulsed power applications through reduced number of semiconductor components with a more efficient load supplying process. The main idea is to charge two groups of capacitors in parallel through an inductor and take the advantage of resonant phenomenon in charging each capacitor up to a double input voltage level. In each resonant half a cycle, one of those capacitor groups are charged, and eventually the charged capacitors will be connected in series and the summation of the capacitor voltages can be appeared at the output of the topology. This topology can be considered as a modified Marx generator which works based on the resonant concept. Simulated models of this converter have been investigated in Matlab/SIMULINK platform and the acquired results fully satisfy the anticipations in proper operation of the converter
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