Evaluation of channel congestion in dedicated short-range communication networks

This thesis presents a novel idea for an adaptive prioritized cross-layer design (APCLD) control algorithm to achieve comprehensive channel congestion control for vehicular safety communication based on DSRC technology. An appropriate evaluation metric and two control parameters have been established. Simulation studies have evaluated the DSRC network performance in different traffic scenario and under different channel conditions. The APCLD algorithm is derived from the results of the simulation analysis.

Wide-band spectral power fluctuations characterize the response of simulated cortical networks to increasing stimulus intensity

The hippocampus is an anatomically distinct region of the medial temporal lobe that plays a critical role in the formation of declarative memories. Here we show that a computer simulation of simple compartmental cells organized with basic hippocampal connectivity is capable of producing stimulus intensity sensitive wide-band fluctuations of spectral power similar to that seen in real EEG. While previous computational models have been designed to assess the viability of the putative mechanisms of memory storage and retrieval, they have generally been too abstract to allow comparison with empirical data. Furthermore, while the anatomical connectivity and organization of the hippocampus is well defined, many questions regarding the mechanisms that mediate large-scale synaptic integration remain unanswered. For this reason we focus less on the specifics of changing synaptic weights and more on the population dynamics. Spectral power in four distinct frequency bands were derived from simulated field potentials of the computational model and found to depend on the intensity of a random input. The majority of power occurred in the lowest frequency band (3-6 Hz) and was greatest to the lowest intensity stimulus condition (1% maximal stimulus). In contrast, higher frequency bands ranging from 7-45 Hz show an increase in power directly related with an increase in stimulus intensity. This trend continues up to a stimulus level of 15% to 20% of the maximal input, above which power falls dramatically. These results suggest that the relative power of intrinsic network oscillations are dependent upon the level of activation and that above threshold levels all frequencies are damped, perhaps due to over activation of inhibitory interneurons.

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Power quality improvements in low voltage distribution networks containing distributed energy resources

In this thesis various schemes using custom power devices for power quality improvement in low voltage distribution network are studied. Customer operated distributed generators makes a typical network non-radial and affect the power quality. A scheme considering different algorithm of DSTATCOM is proposed for power circulation and islanded operation of the system. To compensate reactive power overflow and facilitate unity power factor, a UPQC is introduced. Stochastic analysis is carried out for different scenarios to get a comprehensive idea about a real life distribution network. Combined operation of static compensator and voltage regulator is tested for the optimum quality and stability of the system.

Bayesian networks for information synthesis in complex systems

This thesis introduces a method of applying Bayesian Networks to combine information from a range of data sources for effective decision support systems. It develops a set of techniques in development, validation, visualisation, and application of Complex Systems models, with a working demonstration in an Australian airport environment. The methods presented here have provided a modelling approach that produces highly flexible, informative and applicable interpretations of a system's behaviour under uncertain conditions. These end-to-end techniques are applied to the development of model based dashboards to support operators and decision makers in the multi-stakeholder airport environment. They provide highly flexible and informative interpretations and confidence in these interpretations of a system's behaviour under uncertain conditions.

MIMO enhanced slotted cooperation in urban vehicular networks

The requirement of isolated relays is one of the prime obstacles in utilizing sequential slotted cooperative protocols for Vehicular Ad-hoc Networks (VANET). Significant research advancement has taken place to improve the diversity multiplexing trade-off (DMT) of cooperative protocols in conventional mobile networks without much attention on vehicular ad-hoc networks. We have extended the concept of sequential slotted amplify and forward (SAF) protocols in the context of urban vehicular ad-hoc networks. Multiple Input Multiple Output (MIMO) reception is used at relaying vehicular nodes to isolate the relays effectively. The proposed approach adds a pragmatic value to the sequential slotted cooperative protocols while achieving attractive performance gains in urban VANETs. We have analysed the DMT bounds and the outage probabilities of the proposed scheme. The results suggest that the proposed scheme can achieve an optimal DMT similar to the DMT upper bound of the sequential SAF. Furthermore, the outage performance of the proposed scheme outperforms the SAF protocol by 2.5 dB at a target outage probability of 10-4.