Nanopowder management and plasma parameters in nanofabrication of low-dimensional quantum structures in reactive silane-based plasmas

Management of nanopowder and reactive plasma parameters in a low-pressure RF glow discharge in silane is studied. It is shown that the discharge control parameters and reactor volume can be adjusted to ensure lower abundance of nanopowders, which is one of the requirements of the plasma-assisted fabrication of low-dimensional quantum nanostructures. The results are relevant to micro- and nanomanufacturing technologies employing low-pressure glow discharge plasmas of silane-based gas mixtures.

Ge/Si quantum dot formation from non-uniform cluster fluxes

The controlled growth of ultra-small Ge/Si quantum dot (QD) nuclei (≈1 nm) suitable for the synthesis of uniform nanopatterns with high surface coverage, is simulated using atom-only and size non-uniform cluster fluxes. It is found that seed nuclei of more uniform sizes are formed when clusters of non-uniform size are deposited. This counter-intuitive result is explained via adatom-nanocluster interactions on Si(100) surfaces. Our results are supported by experimental data on the geometric characteristics of QD patterns synthesized by nanocluster deposition. This is followed by a description of the role of plasmas as non-uniform cluster sources and the impact on surface dynamics. The technique challenges conventional growth modes and is promising for deterministic synthesis of nanodot arrays.

Part 2. MOO methods for multidisciplinary design using parallel evolutionary algorithms, game theory and hierarchical topology. (Part 2.) Numerical aspects and implementation of model test cases

These lecture notes describe the use and implementation of a framework in which mathematical as well as engineering optimisation problems can be analysed. The foundations of the framework and algorithms described -Hierarchical Asynchronous Parallel Evolutionary Algorithms (HAPEAs) - lie upon traditional evolution strategies and incorporate the concepts of a multi-objective optimisation, hierarchical topology, asynchronous evaluation of candidate solutions , parallel computing and game strategies. In a step by step approach, the numerical implementation of EAs and HAPEAs for solving multi criteria optimisation problems is conducted providing the reader with the knowledge to reproduce these hand on training in his – her- academic or industrial environment.

Part 3. MOO methods for multidisciplinary design using parallel evolutionary algorithms, game theory and hierarchical topology. (Part 3.) Practical application to the design and optimisation of UAV systems

These lecture notes highlight some of the recent applications of multi-objective and multidisciplinary design optimisation in aeronautical design using the framework and methodology described in References 8, 23, 24 and in Part 1 and 2 of the notes. A summary of the methodology is described and the treatment of uncertainties in flight conditions parameters by the HAPEAs software and game strategies is introduced. Several test cases dealing with detailed design and computed with the software are presented and results discussed in section 4 of these notes.

Numerical simulation of self-organized nano-islands in plasma-based assembly of quantum dot arrays

This work presents the details of the numerical model used in simulation of self-organization of nano-islands on solid surfaces in plasma-assisted assembly of quantum dot structures. The model includes the near-substrate non-neutral layer (plasma sheath) and a nanostructured solid deposition surface and accounts for the incoming flux of and energy of ions from the plasma, surface temperature-controlled adatom migration about the surface, adatom collisions with other adatoms and nano-islands, adatom inflow to the growing nano-islands from the plasma and from the two-dimensional vapour on the surface, and particle evaporation to the ambient space and the two-dimensional vapour. The differences in surface concentrations of adatoms in different areas within the quantum dot pattern significantly affect the self-organization of the nano-islands. The model allows one to formulate the conditions when certain islands grow, and certain ones shrink or even dissolve and relate them to the process control parameters. Surface coverage by selforganized quantum dots obtained from numerical simulation appears to be in reasonable agreement with the available experimental results.

Controlling electronic and adiabatic isolation of quantum dots from the substrate : an ionization-energy theoretic study

Recent controversy on the quantum dots dephasing mechanisms (between pure and inelastic) is re-examined by isolating the quantum dots from their substrate by using the appropriate limits of the ionization energy theory and the quantum adiabatic theorem. When the phonons in the quantum dots are isolated adiabatically from the phonons in the substrate, the elastic or pure dephasing becomes the dominant mechanism. On the other hand, for the case where the phonons from the substrate are non-adiabatically coupled to the quantum dots, the inelastic dephasing process takes over. This switch-over is due to different elemental composition in quantum dots as compared to its substrate. We also provide unambiguous analysis as to understand why GaAs/AlGaAs quantum dots may only have pure dephasing while InAs/GaAs quantum dots give rise to the inelastic dephasing as the dominant mechanism. It is shown that the elemental composition plays an important role (of both quantum dots and substrate) in evaluating the dephasing mechanisms of quantum dots.

A review of modern computational algorithms for Bayesian optimal design

Bayesian experimental design is a fast growing area of research with many real-world applications. As computational power has increased over the years, so has the development of simulation-based design methods, which involve a number of algorithms, such as Markov chain Monte Carlo, sequential Monte Carlo and approximate Bayes methods, facilitating more complex design problems to be solved. The Bayesian framework provides a unified approach for incorporating prior information and/or uncertainties regarding the statistical model with a utility function which describes the experimental aims. In this paper, we provide a general overview on the concepts involved in Bayesian experimental design, and focus on describing some of the more commonly used Bayesian utility functions and methods for their estimation, as well as a number of algorithms that are used to search over the design space to find the Bayesian optimal design. We also discuss other computational strategies for further research in Bayesian optimal design.

Outbreak detection algorithms for seasonal disease data : a case study using Ross River virus disease

Background Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. Methods We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. Results The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. Conclusions This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.

Comparative behavior of CdS and CdSe quantum dots in poly(3-hexylthiophene) based nanocomposites

CdS and CdSe nanoparticles have been prepared using conducting poly(3-hexylthiophene) (P3HT) matrix with an objective to understand the effect of nanoparticles on the polymer matrix using electrochemical and spectroscopic techniques. The spectroscopic results reveal that the electronic structure of polymer is strongly influenced by the characteristics of embedded semiconducting nanoparticles. SEM and TEM images show the ordered morphology of the CdS and CdSe nanoparticles in presence of the polymer matrix. Cyclic voltammetry performed both in the presence and absence of light enables us to understand the redox changes in P3HT due to CdS and CdSe quantum dots such as the generation of free radical in the excited state and their electrochemical band gaps.

A Study of feature extraction algorithms for optical flow tracking

Sparse optical flow algorithms, such as the Lucas-Kanade approach, provide more robustness to noise than dense optical flow algorithms and are the preferred approach in many scenarios. Sparse optical flow algorithms estimate the displacement for a selected number of pixels in the image. These pixels can be chosen randomly. However, pixels in regions with more variance between the neighbours will produce more reliable displacement estimates. The selected pixel locations should therefore be chosen wisely. In this study, the suitability of Harris corners, Shi-Tomasi's “Good features to track", SIFT and SURF interest point extractors, Canny edges, and random pixel selection for the purpose of frame-by-frame tracking using a pyramidical Lucas-Kanade algorithm is investigated. The evaluation considers the important factors of processing time, feature count, and feature trackability in indoor and outdoor scenarios using ground vehicles and unmanned aerial vehicles, and for the purpose of visual odometry estimation.

Document clustering algorithms, representations and evaluation for information retrieval

This thesis presents new methods for classification and thematic grouping of billions of web pages, at scales previously not achievable. This process is also known as document clustering, where similar documents are automatically associated with clusters that represent various distinct topic. These automatically discovered topics are in turn used to improve search engine performance by only searching the topics that are deemed relevant to particular user queries.

Zinc oxide quantum dot nanostructures

Zinc oxide (ZnO) is one of the most intensely studied wide band gap semiconductors due to its many desirable properties. This project established new techniques for investigating the hydrodynamic properties of ZnO nanoparticles, their assembly into useful photonic structures, and their multiphoton absorption coefficients for excitation with visible or infrared light rather than ultraviolet light. The methods developed are also applicable to a wide range of nanoparticle samples.

Explanation of relevance judgement discrepancy with quantum interference

A key concept in many Information Retrieval (IR) tasks, e.g. document indexing, query language modelling, aspect and diversity retrieval, is the relevance measurement of topics, i.e. to what extent an information object (e.g. a document or a query) is about the topics. This paper investigates the interference of relevance measurement of a topic caused by another topic. For example, consider that two user groups are required to judge whether a topic q is relevant to a document d, and q is presented together with another topic (referred to as a companion topic). If different companion topics are used for different groups, interestingly different relevance probabilities of q given d can be reached. In this paper, we present empirical results showing that the relevance of a topic to a document is greatly affected by the companion topic’s relevance to the same document, and the extent of the impact differs with respect to different companion topics. We further analyse the phenomenon from classical and quantum-like interference perspectives, and connect the phenomenon to nonreality and contextuality in quantum mechanics. We demonstrate that quantum like model fits in the empirical data, could be potentially used for predicting the relevance when interference exists.

High efficiency quantum dot-sensitised solar cells by material science and device architecture

This thesis studied cadmium sulfide and cadmium selenide quantum dots and their performance as light absorbers in quantum dot-sensitised solar cells. This research has made contributions to the understanding of size dependent photodegradation, passivation and particle growth mechanism of cadmium sulfide quantum dots using SILAR method and the role of ZnSe shell coatings on solar cell performance improvement.