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 $U(N_f)$ 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 $g_{\pi qq}$ and $g_{\rho qq}$ 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