5,065 research outputs found
Statistical Analysis to Extract Effective Parameters on Overall Energy Consumption of Wireless Sensor Network (WSN)
In this paper, we use statistical tools to analysis dependency between
Wireless Sensor Network (WSN) parameters and overall Energy Consumption (EC).
Our approach has two main phases: profiling, and effective parameter
extraction. In former, a sensor network simulator is re-run 800 times with
different values for eight WSN parameters to profile consumed energy in nodes;
then in latter, three statistical analyses (p-value, linear and non-linear
correlation) are applied to the outcome of profiling phase to extract the most
effective parameters on WSN overall energy consumption.Comment: 5-pages. This paper has been accepted in PDCAT-2012 conference
(http://www.pdcat2012.org/
An Energy Driven Architecture for Wireless Sensor Networks
Most wireless sensor networks operate with very limited energy sources-their
batteries, and hence their usefulness in real life applications is severely
constrained. The challenging issues are how to optimize the use of their energy
or to harvest their own energy in order to lengthen their lives for wider
classes of application. Tackling these important issues requires a robust
architecture that takes into account the energy consumption level of functional
constituents and their interdependency. Without such architecture, it would be
difficult to formulate and optimize the overall energy consumption of a
wireless sensor network. Unlike most current researches that focus on a single
energy constituent of WSNs independent from and regardless of other
constituents, this paper presents an Energy Driven Architecture (EDA) as a new
architecture and indicates a novel approach for minimising the total energy
consumption of a WS
Forecasting and Conditional Projection Using Realistic Prior Distributions
This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied to ten macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variables responses are damped by the prior, considerable interaction among the variables is shown to be captured by the estimates.We provide unconditional forecasts as of 1982:12 and 1983:3.We also describe how a model such as this can be used to make conditional projections and to analyze policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12.While no automatic causal interpretations arise from models like ours, they provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables, which may help inevaluating causal hypotheses, without containing any such hypotheses themselves.
Active data-centric framework for data protection in cloud environment
Cloud computing is an emerging evolutionary computing model that provides highly scalable services over highspeed Internet on a pay-as-usage model. However, cloud-based solutions still have not been widely deployed in some sensitive areas, such as banking and healthcare. The lack of widespread development is related to users’ concern that their confidential data or privacy would leak out in the cloud’s outsourced environment. To address this problem, we propose a novel active data-centric framework to ultimately improve the transparency and accountability of actual usage of the users’ data in cloud. Our data-centric framework emphasizes “active” feature which packages the raw data with active properties that enforce data usage with active defending and protection capability. To achieve the active scheme, we devise the Triggerable Data File Structure (TDFS). Moreover, we employ the zero-knowledge proof scheme to verify the request’s identification without revealing any vital information. Our experimental outcomes demonstrate the efficiency, dependability, and scalability of our framework.<br /
Delayed hepatic uptake of multi-phosphonic acid poly(ethylene glycol) coated iron oxide measured by real-time Magnetic Resonance Imaging
We report on the synthesis, characterization, stability and pharmacokinetics
of novel iron based contrast agents for magnetic resonance imaging (MRI).
Statistical copolymers combining multiple phosphonic acid groups and
poly(ethylene glycol) (PEG) were synthesized and used as coating agents for 10
nm iron oxide nanocrystals. In vitro, protein corona and stability assays show
that phosphonic acid PEG copolymers outperform all other coating types
examined, including low molecular weight anionic ligands and polymers. In vivo,
the particle pharmacokinetics is investigated by monitoring the MRI signal
intensity from mouse liver, spleen and arteries as a function of the time,
between one minute and seven days after injection. Iron oxide particles coated
with multi-phosphonic acid PEG polymers are shown to have a blood circulation
lifetime of 250 minutes, i.e. 10 to 50 times greater than that of recently
published PEGylated probes and benchmarks. The clearance from the liver takes
in average 2 to 3 days and is independent of the core size, coating and
particle stability. By comparing identical core particles with different
coatings, we are able to determine the optimum conditions for stealth MRI
probes.Comment: 19 pages 8 figures, RSC Advances, 201
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