Intelligent Sensing in Dynamic Environments Using Markov Decision Process

In a network of low-powered wireless sensors, it is essential to capture as many environmental events as possible while still preserving the battery life of the sensor node. This paper focuses on a real-time learning algorithm to extend the lifetime of a sensor node to sense and transmit environmental events. A common method that is generally adopted in ad-hoc sensor networks is to periodically put the sensor nodes to sleep. The purpose of the learning algorithm is to couple the sensor’s sleeping behavior to the natural statistics of the environment hence that it can be in optimal harmony with changes in the environment, the sensors can sleep when steady environment and stay awake when turbulent environment. This paper presents theoretical and experimental validation of a reward based learning algorithm that can be implemented on an embedded sensor. The key contribution of the proposed approach is the design and implementation of a reward function that satisfies a trade-off between the above two mutually contradicting objectives, and a linear critic function to approximate the discounted sum of future rewards in order to perform policy learning

Performance evaluation and enhancement of mobile and sensor networks

Deposited with permission of the author. © 2006 Dr. Malka Nishanthi HalgamugeThis thesis addresses the performance evaluation and enhancement of wireless networks. Part I investigates the problem of resource allocation in cellular networks, focusing on handoff, and Part II investigates resource allocation in sensor networks focusing on power management. (For complete abstract open document

Critical time delay of the pineal melatonin rhythm in humans due to weak electromagnetic exposure

259-265Electromagnetic fields (EMFs) can increase free radicals, activate the stress response and alter enzyme reactions. Intracellular signalling is mediated by free radicals and enzyme kinetics is affected by radical pair recombination rates. The magnetic field component of an external EMF can delay the "recombination rate" of free radical pairs. Magnetic fields thus increase radical life-times in biological systems. Although measured in nanoseconds, this extra time increases the potential to do more damage. Melatonin regulates the body's sleep-wake cycle or circadian rhythm. The World Health Organization (WHO) has confirmed that prolonged alterations in sleep patterns suppress the body's ability to make melatonin. Considerable cancer rates have been attributed to the reduction of melatonin production as a result of jet lag and night shift work. In this study, changes in circadian rhythm and melatonin concentration are observed due to the external perturbation of chemical reaction rates. We further analyze the pineal melatonin rhythm and investigate the critical time delay or maturation time of radical pair recombination rates, exploring the impact of the mRNA degradation rate on the critical time delay. The results show that significant melatonin interruption and changes to the circadian rhythm occur due to the perturbation of chemical reaction rates, as also reported in previous studies. The results also show the influence of the mRNA degradation rate on the circadian rhythm’s critical time delay or maturation time. The results support the hypothesis that exposure to weak EMFs via melatonin disruption can adversely affect human health

Plant Species.csv

A dataset of plant species in Glenelg Shire, Australia

OPTIMIZING HEATING EFFICIENCY OF HYPERTHERMIA: SPECIFIC LOSS POWER OF MAGNETIC SPHERE COMPOSED OF SUPERPARAMAGNETIC NANOPARTICLES

Magnetic nanoparticle (MNP) based thermal therapies have shown importance in clinical applications. However, it lacks a compromise between its robustness and limitations. We developed theoretical strategies to enhance the heating efficiency, which could be utilized in thermal therapies and calculated parameter dependence for superparamagnetic MNPs (approximative ellipsoid-shaped) within a sphere-shaped ball. Then we calculated specific loss power (SLP) for magnetic particles in a magnetic ball. The dependency of features of the nanoparticles (such as mean particle size, a number of particles, frequency and amplitude of the exposed field, relaxation time, and volume gap between particles and a sphere-shaped ball) on the SLP or the heating effect in superparamagnetic MNPs was analyzed. In this study, optimal parameter values were calculated using Kneedle Algorithm as the optimization technique to represent the accurate heating efficiency. The influence of a number of particles in a sphere-shaped ball shows that SLP of magnetic particles increases with the increasing number of particles (N); however, after N = 10 particles, the SLP increment is insignificant. The most remarkable result arising from this analysis is that when particles are closer together (less volume gap of a sphere-shaped ball), high SLP is found for the same number of particles. This model also predicts that the frequency dependency on the SLP is negligible when the frequency is higher than 10 kHz depending on the size of a sphere-shaped ball and nanoparticle parameters. This analysis has shown that the SLP of MNPs, in a sphere-shaped ball, strongly depends on magnetic parameters and properties of the particles. In brief, we have demonstrated, for the first time, impact on SLP of the accumulation of ellipsoid-shaped superparamagnetic nanoparticles into a sphere-shaped ball. This finding has essential suggestions for developing links between heating properties with loose aggregate and dense aggregate scenarios in the superparamagnetic condition