An application of fractal image-set coding in facial recognition

Faces are complex patterns that often differ in only subtle ways. Face recognition algorithms have difficulty in coping with differences in lighting, cameras, pose, expression, etc. We propose a novel approach for facial recognition based on a new feature extraction method called fractal image-set encoding. This feature extraction method is a specialized fractal image coding technique that makes fractal codes more suitable for object and face recognition. A fractal code of a gray-scale image can be divided in two parts – geometrical parameters and luminance parameters. We show that fractal codes for an image are not unique and that we can change the set of fractal parameters without significant change in the quality of the reconstructed image. Fractal image-set coding keeps geometrical parameters the same for all images in the database. Differences between images are captured in the non-geometrical or luminance parameters – which are faster to compute. Results on a subset of the XM2VTS database are presented.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Fast searching algorithm for candidate satellite-node set in NLMG

The Node-based Local Mesh Generation (NLMG) algorithm, which is free of mesh inconsistency, is one of core algorithms in the Node-based Local Finite Element Method (NLFEM) to achieve the seamless link between mesh generation and stiffness matrix calculation, and the seamless link helps to improve the parallel efficiency of FEM. Furthermore, the key to ensure the efficiency and reliability of NLMG is to determine the candidate satellite-node set of a central node quickly and accurately. This paper develops a Fast Local Search Method based on Uniform Bucket (FLSMUB) and a Fast Local Search Method based on Multilayer Bucket (FLSMMB), and applies them successfully to the decisive problems, i.e. presenting the candidate satellite-node set of any central node in NLMG algorithm. Using FLSMUB or FLSMMB, the NLMG algorithm becomes a practical tool to reduce the parallel computation cost of FEM. Parallel numerical experiments validate that either FLSMUB or FLSMMB is fast, reliable and efficient for their suitable problems and that they are especially effective for computing the large-scale parallel problems.

Determining a set of surrogate parameters to evaluate urban stormwater quality

This thesis details methodology to estimate urban stormwater quality based on a set of easy to measure physico-chemical parameters. These parameters can be used as surrogate parameters to estimate other key water quality parameters. The key pollutants considered in this study are nitrogen compounds, phosphorus compounds and solids. The use of surrogate parameter relationships to evaluate urban stormwater quality will reduce the cost of monitoring and so that scientists will have added capability to generate a large amount of data for more rigorous analysis of key urban stormwater quality processes, namely, pollutant build-up and wash-off. This in turn will assist in the development of more stringent stormwater quality mitigation strategies. The research methodology was based on a series of field investigations, laboratory testing and data analysis. Field investigations were conducted to collect pollutant build-up and wash-off samples from residential roads and roof surfaces. Past research has identified that these impervious surfaces are the primary pollutant sources to urban stormwater runoff. A specially designed vacuum system and rainfall simulator were used in the collection of pollutant build-up and wash-off samples. The collected samples were tested for a range of physico-chemical parameters. Data analysis was conducted using both univariate and multivariate data analysis techniques. Analysis of build-up samples showed that pollutant loads accumulated on road surfaces are higher compared to the pollutant loads on roof surfaces. Furthermore, it was found that the fraction of solids smaller than 150 ìm is the most polluted particle size fraction in solids build-up on both roads and roof surfaces. The analysis of wash-off data confirmed that the simulated wash-off process adopted for this research agrees well with the general understanding of the wash-off process on urban impervious surfaces. The observed pollutant concentrations in wash-off from road surfaces were different to pollutant concentrations in wash-off from roof surfaces. Therefore, firstly, the identification of surrogate parameters was undertaken separately for roads and roof surfaces. Secondly, a common set of surrogate parameter relationships were identified for both surfaces together to evaluate urban stormwater quality. Surrogate parameters were identified for nitrogen, phosphorus and solids separately. Electrical conductivity (EC), total organic carbon (TOC), dissolved organic carbon (DOC), total suspended solids (TSS), total dissolved solids (TDS), total solids (TS) and turbidity (TTU) were selected as the relatively easy to measure parameters. Consequently, surrogate parameters for nitrogen and phosphorus were identified from the set of easy to measure parameters for both road surfaces and roof surfaces. Additionally, surrogate parameters for TSS, TDS and TS which are key indicators of solids were obtained from EC and TTU which can be direct field measurements. The regression relationships which were developed for surrogate parameters and key parameter of interest were of a similar format for road and roof surfaces, namely it was in the form of simple linear regression equations. The identified relationships for road surfaces were DTN-TDS:DOC, TP-TS:TOC, TSS-TTU, TDS-EC and TSTTU: EC. The identified relationships for roof surfaces were DTN-TDS and TSTTU: EC. Some of the relationships developed had a higher confidence interval whilst others had a relatively low confidence interval. The relationships obtained for DTN-TDS, DTN-DOC, TP-TS and TS-EC for road surfaces demonstrated good near site portability potential. Currently, best management practices are focussed on providing treatment measures for stormwater runoff at catchment outlets where separation of road and roof runoff is not found. In this context, it is important to find a common set of surrogate parameter relationships for road surfaces and roof surfaces to evaluate urban stormwater quality. Consequently DTN-TDS, TS-EC and TS-TTU relationships were identified as the common relationships which are capable of providing measurements of DTN and TS irrespective of the surface type.

Destination branding : tracking brand equity for a competitive set of near-home destinations

In the emerging literature related to destination branding, little has been reported about performance metrics. The focus of most research reported to date has been concerned with the development of destination brand identities and the implementation of campaigns (see for example, Crockett & Wood 1999, Hall 1999, May 2001, Morgan et al 2002). One area requiring increased attention is that of tracking the performance of destination brands over time. This is an important gap in the tourism literature, given: i) the increasing level of investment by destination marketing organisations (DMO) in branding since the 1990s, ii) the complex political nature of DMO brand decision-making and increasing accountability to stakeholders (see Pike, 2005), and iii) the long-term nature of repositioning a destination’s image in the market place (see Gartner & Hunt, 1987). Indeed, a number of researchers in various parts of the world have pointed to a lack of market research monitoring destination marketing objectives, such as in Australia (see Prosser et. al 2000, Carson, Beattie and Gove 2003), North America (Sheehan & Ritchie 1997, Masberg 1999), and Europe (Dolnicar & Schoesser 2003)...

Destination brand positions of a competitive set of near-home destinations

Although the branding literature commenced during the 1940s, the first publications related to destination branding did not emerge until half a century later. A review of 74 destination branding publications by 102 authors from the first 10 years of destination branding literature (1998-2007) found at least nine potential research gaps warranting attention by researchers. In particular, there has been a lack of research examining the extent to which brand positioning campaigns have been successful in enhancing brand equity in the manner intended in the brand identity. The purpose of this paper is to report the results of an investigation of brand equity tracking for a competitive set of destinations in Queensland, Australia between 2003 and 2007. A hierarchy of consumer-based brand equity (CBBE) provided an effective means to monitor destination brand positions over time. A key implication of the results was the finding that there was no change in brand positions for any of the five destinations over the four year period. This leads to the proposition that destination position change within a competitive set will only occur slowly over a long period of time. The tabulation of 74 destination branding case studies, research papers, conceptual papers and web content analyses provides students and researchers with a useful resource on the current state of the field.

Determination of a set of surrogate parameters to assess urban stormwater quality

This paper presents the outcomes of a research project, which focused on developing a set of surrogate parameters to evaluate urban stormwater quality using simulated rainfall. Use of surrogate parameters has the potential to enhance the rapid generation of urban stormwater quality data based on on-site measurements and thereby reduce resource intensive laboratory analysis. The samples collected from rainfall simulations were tested for a range of physico-chemical parameters which are key indicators of nutrients, solids and organic matter. The analysis revealed that [total dissolved solids (TDS) and dissolved organic carbon (DOC)]; [total solids (TS) and total organic carbon (TOC)]; [turbidity (TTU)]; [electrical conductivity (EC)]; [TTU and EC] as appropriate surrogate parameters for dissolved total nitrogen (DTN), total phosphorus (TP), total suspended solids (TSS), TDS and TS respectively. Relationships obtained for DTN-TDS, DTN-DOC, and TP-TS demonstrated good portability potential. The portability of the relationship developed for TP and TOC was found to be unsatisfactory. The relationship developed for TDS-EC and TS-EC also demonstrated poor portability.

Journalism education dilemmas : career-focussed skill set, teamwork or critical thinking?

In Australian universities, journalism educators usually come to the academy from the journalism profession and consequently place a high priority on leading students to develop a career-focussed skill set. The changing nature of the technological, political and economic environments and the professional destinations of journalism graduates place demands on journalism curricula and educators alike. The profession is diverse, such that the better description is of many ‘journalisms’ rather than one ‘journalism’ with consequential pressures being placed on curricula to extend beyond the traditional skill set, where practical ‘writing’ and ‘editing’ skills dominate, to the incorporation of critical theory and the social construction of knowledge. A parallel set of challenges faces academic staff operating in a higher education environment where change is the only constant and research takes precedent over curriculum development. In this paper, three educators at separate universities report on their attempts to implement curriculum change to imbue graduates with better skills and attributes such as enhanced team work, problem solving and critical thinking, to operate in the divergent environment of 21st century journalism. The paper uses narrative case study to illustrate the different approaches. Data collected from formal university student evaluations inform the narratives along with rich but less formal qualitative data including anecdotal student comments and student reflective assessment presentations. Comparison of the three approaches illustrates the dilemmas academic staff face when teaching in disciplines that are impacted by rapid changes in technology requiring new pedagogical approaches. Recommendations for future directions are considered against the background or learning purpose.

Genotyping of enterococcus faecalis and enterococcus faecium isolates using a set of eight single nucleotide polymorphisms (SNPs)

A SNP genotyping method was developed for E. faecalis and E. faecium using the 'Minimum SNPs' program. SNP sets were interrogated using allele-specific real-time PCR. SNP-typing sub-divided clonal complexes 2 and 9 of E. faecalis and 17 of E. faecium, members of which cause the majority of nosocomial infections globally.