2,720 research outputs found
Sensor Management for Tracking in Sensor Networks
We study the problem of tracking an object moving through a network of
wireless sensors. In order to conserve energy, the sensors may be put into a
sleep mode with a timer that determines their sleep duration. It is assumed
that an asleep sensor cannot be communicated with or woken up, and hence the
sleep duration needs to be determined at the time the sensor goes to sleep
based on all the information available to the sensor. Having sleeping sensors
in the network could result in degraded tracking performance, therefore, there
is a tradeoff between energy usage and tracking performance. We design sleeping
policies that attempt to optimize this tradeoff and characterize their
performance. As an extension to our previous work in this area [1], we consider
generalized models for object movement, object sensing, and tracking cost. For
discrete state spaces and continuous Gaussian observations, we derive a lower
bound on the optimal energy-tracking tradeoff. It is shown that in the low
tracking error regime, the generated policies approach the derived lower bound
Sensor Scheduling for Energy-Efficient Target Tracking in Sensor Networks
In this paper we study the problem of tracking an object moving randomly
through a network of wireless sensors. Our objective is to devise strategies
for scheduling the sensors to optimize the tradeoff between tracking
performance and energy consumption. We cast the scheduling problem as a
Partially Observable Markov Decision Process (POMDP), where the control actions
correspond to the set of sensors to activate at each time step. Using a
bottom-up approach, we consider different sensing, motion and cost models with
increasing levels of difficulty. At the first level, the sensing regions of the
different sensors do not overlap and the target is only observed within the
sensing range of an active sensor. Then, we consider sensors with overlapping
sensing range such that the tracking error, and hence the actions of the
different sensors, are tightly coupled. Finally, we consider scenarios wherein
the target locations and sensors' observations assume values on continuous
spaces. Exact solutions are generally intractable even for the simplest models
due to the dimensionality of the information and action spaces. Hence, we
devise approximate solution techniques, and in some cases derive lower bounds
on the optimal tradeoff curves. The generated scheduling policies, albeit
suboptimal, often provide close-to-optimal energy-tracking tradeoffs
The anomaly of contempt in the face of the court record
Categories and forms of contempt of court are not closed, whereby, judges have the discretion to use this power when they deem appropriate.However, there are a number of traditional categories
that have been created and used by the courts in Malaysia and the United Kingdom.Contempt in the face of the court record has not been a traditional category of contempt in either country, and, thus far, has only been recognised in Malaysia in one case.The aims of this paper are to consider what the scope of contempt in the face of the court record is, when it should apply and whether this category is clearly distinct from the other existing categories of contempt of court.It is suggested that it may not have been necessary to create the category of contempt in the face of the court record as there appears to be an overlap between this category and the other categories of
contempt of court
Controlling dielectric and magnetic properties of PVdF/Magnetite nanocomposite fibre webs.
The ability of filtration and separation media containing fibres to remove impurities from oil, water, and blood can be enhanced using magnetic fields. The ability to regulate the dielectric and magnetic behaviour of fibrous webs in terms of superparamagnetic or ferromagnetic properties by adjusting material composition is fundamental to meeting end-use requirements. Electrospun fibres were produced from PVdF (polyvinylidene fluoride) and nanomagnetite (Fe3O4 nanoparticles) from solutions of PVdF in dimethylacetamide containing Fe3O4 nanoparticle contents ranging from 3 to 10 wt%. Fibre dimensions, morphology, and nanoparticle agglomeration were characterised by environmental scanning electron microscopy (ESEM) and field emission gun transmission electron microscopy (FEGTEM). Dielectric behaviour of the fibre webs was influenced by web porosity and the Fe3O4 nanoparticle content. Impedance analysis of the webs indicated an increase in dielectric constant of ∼80% by the addition of 10 wt% Fe3O4 nanoparticles compared to 100 wt% PVdF. The dielectric constants of the webs were compared with those obtained from the theoretical mixing models of Maxwell and Lichtenecker. Vibrating sample magnetometer (VSM) magnetisation measurements indicated a blocking temperature above 300 K suggesting ferrimagnetic rather than superparamagnetic behaviour as a result of Fe3O4 nanoparticle agglomeration within fibres
Meson PVV Interactions are determined by Quark Loops
We show that all abnormal parity three-body meson interactions can be
adequately described by quark loops, evaluated at zero external momentum, with
couplings determined by symmetry. We focus primarily on radiative
meson decays which involve one pseudoscalar. The agreement with experiment for
non-rare decays is surprisingly good and requires very few parameters, namely
the coupling constants and and some mixing angles.
This agreement extends to some three-body decays that are dominated by pion
pairs in a P-wave state.Comment: 21 pages, Revtex, one figur
Pathway clusters of aging genes using data mining techniques
Exploring and identifying novel aging genes has been the current area of interest in Gerontology. A variety of techniques have been proposed to identify the genes that affect the centenarians and the focus is on the study of genes of interest affecting older population. However the study of aging related pathways using computational methods has not been discussed explicitly so far. In this paper, an attempt is made to cluster the aging genes into different biological pathways using data mining techniques. Text mining is used to identify the most relevant keywords from different pathway databases, which is used as one of the feature describing a gene. K-means clustering is done on the aging pathway dataset. The clusters formed are in good agreement with the background knowledge about the aging genes and their pathways. The quality of the K-means clustering is quite promising as it well separates the different aging genes into their respective pathways
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