Synthesis of orchestrators from service choreographies

With service interaction modelling, it is customary to distinguish between two types of models: choreographies and orchestrations. A choreography describes interactions within a collection of services from a global perspective, where no service plays a privileged role. Instead, services interact in a peer-to-peer manner. In contrast, an orchestration describes the interactions between one particular service, the orchestrator, and a number of partner services. The main proposition of this work is an approach to bridge these two modelling viewpoints by synthesising orchestrators from choreographies. To start with, choreographies are defined using a simple behaviour description language based on communicating finite state machines. From such a model, orchestrators are initially synthesised in the form of state machines. It turns out that state machines are not suitable for orchestration modelling, because orchestrators generally need to engage in concurrent interactions. To address this issue, a technique is proposed to transform state machines into process models in the Business Process Modelling Notation (BPMN). Orchestrations represented in BPMN can then be augmented with additional business logic to achieve value-adding mediation. In addition, techniques exist for refining BPMN models into executable process definitions. The transformation from state machines to BPMN relies on Petri nets as an intermediary representation and leverages techniques from theory of regions to identify concurrency in the initial Petri net. Once concurrency has been identified, the resulting Petri net is transformed into a BPMN model. The original contributions of this work are: an algorithm to synthesise orchestrators from choreographies and a rules-based transformation from Petri nets into BPMN.

A thermal expulsion approach to homogeneous large-volume methacrylate monolith preparation; enabling large-scale rapid purification of biomolecules

Numerous efforts have been dedicated to the synthesis of large-volume methacrylate monoliths for large-scale biomolecules purification but most were obstructed by the enormous release of exotherms during preparation, thereby introducing structural heterogeneity in the monolith pore system. A significant radial temperature gradient develops along the monolith thickness, reaching a terminal temperature that supersedes the maximum temperature required for structurally homogenous monoliths preparation. The enormous heat build-up is perceived to encompass the heat associated with initiator decomposition and the heat released from free radical-monomer and monomer-monomer interactions. The heat resulting from the initiator decomposition was expelled along with some gaseous fumes before commencing polymerization in a gradual addition fashion. Characteristics of 80 mL monolith prepared using this technique was compared with that of a similar monolith synthesized in a bulk polymerization mode. An extra similarity in the radial temperature profiles was observed for the monolith synthesized via the heat expulsion technique. A maximum radial temperature gradient of only 4.3°C was recorded at the center and 2.1°C at the monolith peripheral for the combined heat expulsion and gradual addition technique. The comparable radial temperature distributions obtained birthed identical pore size distributions at different radial points along the monolith thickness.

One-step digit-set-restricted modified signed-digit adder using an incoherent correlator based on a shared content-addressable memory

An efficient one-step digit-set-restricted modified signed-digit (MSD) adder based on symbolic substitution is presented. In this technique, carry propagation is avoided by introducing reference digits to restrict the intermediate carry and sum digits to {1,0} and {0,1}, respectively. The proposed technique requires significantly fewer minterms and simplifies system complexity compared to the reported one-step MSD addition techniques. An incoherent correlator based on an optoelectronic shared content-addressable memory processor is suggested to perform the addition operation. In this technique, only one set of minterms needs to be stored, independent of the operand length. (C) 2002 society or Photo-Optical Instrumentation Engineers.

Modelagem de estruturas inteligentes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Functionalization of hyperbranched polycarbosilanes - block copolymers and electrochemistry

Polycarbosilanes are a class of polymers at the interface between organic and inorganic chemistry. They are characterized by a high thermal and chemical inertness and high flexibility, especially pronounced for branched structures. Linear polycarbosilanes are well known as precursors for the preparation of SiCx ceramics. Additionally, more sophisticated architectures like dendrimers, hyperbranched polymers or block copolymers have been the subject of research for more than a decade. The scope of this work was to expand the properties and fields of application for polycarbosilane-containing structures. Thus, the work is divided in two major parts. The first part covers the synthesis and characterization of hyperbranched polycarbosilanes containing organometallic moieties. Hyperbranched poly-carbosilanes were synthesized using hydrosilylation of diallylmethylsilane and methyldiundecenylsilane. The degree of branching for polydiallymethylsilane was determined using standard 1H-NMR spectroscopy. The functional building blocks ferrocenyldimethylsilane and diferrocenylmethylsilane were synthesized which contain an isolated ferrocene unit or two ferrocenes bridged by silicon, respectively. Hyperbranched polycarbosilanes functionalized with ferrocenyl moieties were synthesized by modification of preformed polymers or by copolymerization of AB2 carbosilane monomers with AX-type ferrocenylsilanes. Polymers with Mn = 2500-9000g/mol and ferrocene contents of up to 67wt% were obtained. Electrochemical characterization by cyclic voltammetry revealed that polymers functionalized with isolated ferrocene units showed a single reversible oxidation wave, while voltammograms for polymers functionalized with diferrocenyl silane exhibited two well-separated reversible oxidation-reduction waves. This shows that the polymer bound ferrocenes bridged by silicon are electronically communicating and thus oxidation of the first ferrocene shifts the oxidation potential for the adjacent one. The polymers were utilized successfully for the preparation of modified electrodes with persistent and reproducible electrochemical response in organic solvents as well as in aqueous solution. The presented work has proven that ferrocenyl-functionalized hyperbranched polymers exhibit similar electrochemical properties as the analogous dendrimers. In a further approach it was shown that hyperbranched polymers containing organometallic moieties can be synthesized by polymerization of a new ferrocene-containing AB2 monomer - diallylferrocenylsilane. The second part of this work is dedicated to the preparation of core-functional hyperbranched polycarbosilanes. Low molecular weight ambifunctional molecules were synthesized that contain double bonds for the attachment of a polycarbosilane polymer as well as a second functionality available for further reaction and modification. Reactive vinyl groups in the core molecule allow an efficient attachment of hyperbranched polycarbosilane which was proven by MALDI-ToF and GPC. In combination with slow monomer addition techniques molecular weight and polydispersity of the polymers were controlled successfully. Core-functional polymers were characterized by NMR-spectroscopy, MALDI-ToF and GPC. Polymers with polydispersities <2 and molecular weights up to 5300g/mol were obtained. Transformation of the double bonds of the carbosilane was demonstrated with various silanes using hydrosilylation reaction or hydrogenation. Additionally, the core-functionality was varied resulting in polymers with bromo-, phthalimide-, amine- or azide moieties. Thus, a versatile synthetic strategy was developed that allows the synthesis of tailor-made polymers.A promising approach is the application of the polymer building blocks in copolymer synthesis. Bisglycidolization of amine-functional polycarbosilanes produces macro-initiators that are suitable for the multibranching-ring opening polymerization of glycidol. This experiments lead to the first example of hyperbranched-hyperbranched amphiphilic block copolymers, hb-PG-b-hb-PCS. Furthermore, the implementation of copper-catalyzed cycloaddition between azide-functional polycarbosilane and alkyne-functional poly(ethoxyethyl glycidylether) resulted in linear-hyperbranched block copolymers. The facile removal of acetal protecting groups provided convenient access to lin-PG-b-hb-PCS.

Revisiting Polya's summation techniques using a spreadsheet : from addition tables to Bernoulli polynomials

Recurrence relations in mathematics form a very powerful and compact way of looking at a wide range of relationships. Traditionally, the concept of recurrence has often been a difficult one for the secondary teacher to convey to students. Closely related to the powerful proof technique of mathematical induction, recurrences are able to capture many relationships in formulas much simpler than so-called direct or closed formulas. In computer science, recursive coding often has a similar compactness property, and, perhaps not surprisingly, suffers from similar problems in the classroom as recurrences: the students often find both the basic concepts and practicalities elusive. Using models designed to illuminate the relevant principles for the students, we offer a range of examples which use the modern spreadsheet environment to powerfully illustrate the great expressive and computational power of recurrences.

Characterisation of soils with the use of instrumental techniques : a multivariate forensic study

The value of soil evidence in the forensic discipline is well known. However, it would be advantageous if an in-situ method was available that could record responses from tyre or shoe impressions in ground soil at the crime scene. The development of optical fibres and emerging portable NIR instruments has unveiled a potential methodology which could permit such a proposal. The NIR spectral region contains rich chemical information in the form of overtone and combination bands of the fundamental infrared absorptions and low-energy electronic transitions. This region has in the past, been perceived as being too complex for interpretation and consequently was scarcely utilized. The application of NIR in the forensic discipline is virtually non-existent creating a vacancy for research in this area. NIR spectroscopy has great potential in the forensic discipline as it is simple, nondestructive and capable of rapidly providing information relating to chemical composition. The objective of this study is to investigate the ability of NIR spectroscopy combined with Chemometrics to discriminate between individual soils. A further objective is to apply the NIR process to a simulated forensic scenario where soil transfer occurs. NIR spectra were recorded from twenty-seven soils sampled from the Logan region in South-East Queensland, Australia. A series of three high quartz soils were mixed with three different kaolinites in varying ratios and NIR spectra collected. Spectra were also collected from six soils as the temperature of the soils was ramped from room temperature up to 6000C. Finally, a forensic scenario was simulated where the transferral of ground soil to shoe soles was investigated. Chemometrics methods such as the commonly known Principal Component Analysis (PCA), the less well known fuzzy clustering (FC) and ranking by means of multicriteria decision making (MCDM) methodology were employed to interpret the spectral results. All soils were characterised using Inductively Coupled Plasma Optical Emission Spectroscopy and X-Ray Diffractometry. Results were promising revealing NIR combined with Chemometrics is capable of discriminating between the various soils. Peak assignments were established by comparing the spectra of known minerals with the spectra collected from the soil samples. The temperature dependent NIR analysis confirmed the assignments of the absorptions due to adsorbed and molecular bound water. The relative intensities of the identified NIR absorptions reflected the quantitative XRD and ICP characterisation results. PCA and FC analysis of the raw soils in the initial NIR investigation revealed that the soils were primarily distinguished on the basis of their relative quartz and kaolinte contents, and to a lesser extent on the horizon from which they originated. Furthermore, PCA could distinguish between the three kaolinites used in the study, suggesting that the NIR spectral region was sensitive enough to contain information describing variation within kaolinite itself. The forensic scenario simulation PCA successfully discriminated between the ‘Backyard Soil’ and ‘Melcann® Sand’, as well as the two sampling methods employed. Further PCA exploration revealed that it was possible to distinguish between the various shoes used in the simulation. In addition, it was possible to establish association between specific sampling sites on the shoe with the corresponding site remaining in the impression. The forensic application revealed some limitations of the process relating to moisture content and homogeneity of the soil. These limitations can both be overcome by simple sampling practices and maintaining the original integrity of the soil. The results from the forensic scenario simulation proved that the concept shows great promise in the forensic discipline.

The encapsulation of gold nanoparticles using RAFT, ATRP and miniemulsion polymerisation techniques

The investigation into the encapsulation of gold nanoparticles (AuNPs) by poly(methyl methacrylate) (PMMA) was undertaken. This was performed by three polymerisation techniques including: grafting PMMA synthesised by reversible addition-fragmentation chain transfer (RAFT) polymerisation to AuNPs, grafting PMMA synthesised by atom transfer radical polymerisation (ATRP) from the surface of functionalised AuNPs and by encapsulation of AuNPs within PMMA latexes produced through photo-initiated oil-in-water (o/w) miniemulsion polymerisation. The grafting of RAFT PMMA to AuNPs was performed by the addition of the RAFT functionalised PMMA to citrate stabilised AuNPs. This was conducted with a range of PMMA of varying molecular weight distribution (MWD) as either the dithioester or thiol end-group functionalities. The RAFT PMMA polymers were characterised by gel permeation chromatography (GPC), ultraviolet-visible (UV-vis), Fourier transform infrared-attenuated total reflectance (FTIR-ATR), Fourier transform Raman (FT-Raman) and proton nuclear magnetic resonance (1H NMR) spectroscopies. The attachment of PMMA to AuNPs showed a tendency for AuNPs to associate with the PMMA structures formed, though significant aggregation occurred. Interestingly, thiol functionalised end-group PMMA showed very little aggregation of AuNPs. The spherical polymer-AuNP structures did not vary in size with variations in PMMA MWD. The PMMA-AuNP structures were characterised using scanning electron microscopy (SEM), transition electron microscopy (TEM), energy dispersive X-ray analysis (EDAX) and UV-vis spectroscopy. The surface confined ATRP grafting of PMMA from initiator functionalised AuNPs was polymerised in both homogeneous and heterogeneous media. 11,11’- dithiobis[1-(2-bromo-2-methylpropionyloxy)undecane] (DSBr) was used as the surface-confined initiator and was synthesised in a three step procedure from mercaptoundecanol (MUD). All compounds were characterised by 1H NMR, FTIR-ATR and Raman spectroscopies. The grafting in homogeneous media resulted in amorphous PMMA with significant AuNP aggregation. Individually grafted AuNPs were difficult to separate and characterise, though SEM, TEM, EDAX and UV-vis spectroscopy was used. The heterogeneous polymerisation did not produce grafted AuNPs as characterised by SEM and EDAX. The encapsulation of AuNPs within PMMA latexes through the process of photoinitiated miniemulsion polymerisation was successfully achieved. Initially, photoinitiated miniemulsion polymerisation was conducted as a viable low temperature method of miniemulsion initiation. This proved successful producing a stable PMMA with good conversion efficiency and narrow particle size distribution (PSD). This is the first report of such a system. The photo-initiated technique was further optimised and AuNPs were included into the miniemulsion. AuNP encapsulation was very effective, producing reproducible AuNP encapsulated PMMA latexes. Again, this is the first reported case of this. The latexes were characterised by TEM, SEM, GPC, gravimetric analysis and dynamic light scattering (DLS).

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Reliability analysis of transform-based stereo matching techniques, and a new matching constraint

Stereo vision is a method of depth perception, in which depth information is inferred from two (or more) images of a scene, taken from different perspectives. Practical applications for stereo vision include aerial photogrammetry, autonomous vehicle guidance, robotics and industrial automation. The initial motivation behind this work was to produce a stereo vision sensor for mining automation applications. For such applications, the input stereo images would consist of close range scenes of rocks. A fundamental problem faced by matching algorithms is the matching or correspondence problem. This problem involves locating corresponding points or features in two images. For this application, speed, reliability, and the ability to produce a dense depth map are of foremost importance. This work implemented a number of areabased matching algorithms to assess their suitability for this application. Area-based techniques were investigated because of their potential to yield dense depth maps, their amenability to fast hardware implementation, and their suitability to textured scenes such as rocks. In addition, two non-parametric transforms, the rank and census, were also compared. Both the rank and the census transforms were found to result in improved reliability of matching in the presence of radiometric distortion - significant since radiometric distortion is a problem which commonly arises in practice. In addition, they have low computational complexity, making them amenable to fast hardware implementation. Therefore, it was decided that matching algorithms using these transforms would be the subject of the remainder of the thesis. An analytic expression for the process of matching using the rank transform was derived from first principles. This work resulted in a number of important contributions. Firstly, the derivation process resulted in one constraint which must be satisfied for a correct match. This was termed the rank constraint. The theoretical derivation of this constraint is in contrast to the existing matching constraints which have little theoretical basis. Experimental work with actual and contrived stereo pairs has shown that the new constraint is capable of resolving ambiguous matches, thereby improving match reliability. Secondly, a novel matching algorithm incorporating the rank constraint has been proposed. This algorithm was tested using a number of stereo pairs. In all cases, the modified algorithm consistently resulted in an increased proportion of correct matches. Finally, the rank constraint was used to devise a new method for identifying regions of an image where the rank transform, and hence matching, are more susceptible to noise. The rank constraint was also incorporated into a new hybrid matching algorithm, where it was combined a number of other ideas. These included the use of an image pyramid for match prediction, and a method of edge localisation to improve match accuracy in the vicinity of edges. Experimental results obtained from the new algorithm showed that the algorithm is able to remove a large proportion of invalid matches, and improve match accuracy.

Adaptive instantaneous frequency estimation: Techniques and algorithms

This thesis deals with the problem of the instantaneous frequency (IF) estimation of sinusoidal signals. This topic plays significant role in signal processing and communications. Depending on the type of the signal, two major approaches are considered. For IF estimation of single-tone or digitally-modulated sinusoidal signals (like frequency shift keying signals) the approach of digital phase-locked loops (DPLLs) is considered, and this is Part-I of this thesis. For FM signals the approach of time-frequency analysis is considered, and this is Part-II of the thesis. In part-I we have utilized sinusoidal DPLLs with non-uniform sampling scheme as this type is widely used in communication systems. The digital tanlock loop (DTL) has introduced significant advantages over other existing DPLLs. In the last 10 years many efforts have been made to improve DTL performance. However, this loop and all of its modifications utilizes Hilbert transformer (HT) to produce a signal-independent 90-degree phase-shifted version of the input signal. Hilbert transformer can be realized approximately using a finite impulse response (FIR) digital filter. This realization introduces further complexity in the loop in addition to approximations and frequency limitations on the input signal. We have tried to avoid practical difficulties associated with the conventional tanlock scheme while keeping its advantages. A time-delay is utilized in the tanlock scheme of DTL to produce a signal-dependent phase shift. This gave rise to the time-delay digital tanlock loop (TDTL). Fixed point theorems are used to analyze the behavior of the new loop. As such TDTL combines the two major approaches in DPLLs: the non-linear approach of sinusoidal DPLL based on fixed point analysis, and the linear tanlock approach based on the arctan phase detection. TDTL preserves the main advantages of the DTL despite its reduced structure. An application of TDTL in FSK demodulation is also considered. This idea of replacing HT by a time-delay may be of interest in other signal processing systems. Hence we have analyzed and compared the behaviors of the HT and the time-delay in the presence of additive Gaussian noise. Based on the above analysis, the behavior of the first and second-order TDTLs has been analyzed in additive Gaussian noise. Since DPLLs need time for locking, they are normally not efficient in tracking the continuously changing frequencies of non-stationary signals, i.e. signals with time-varying spectra. Nonstationary signals are of importance in synthetic and real life applications. An example is the frequency-modulated (FM) signals widely used in communication systems. Part-II of this thesis is dedicated for the IF estimation of non-stationary signals. For such signals the classical spectral techniques break down, due to the time-varying nature of their spectra, and more advanced techniques should be utilized. For the purpose of instantaneous frequency estimation of non-stationary signals there are two major approaches: parametric and non-parametric. We chose the non-parametric approach which is based on time-frequency analysis. This approach is computationally less expensive and more effective in dealing with multicomponent signals, which are the main aim of this part of the thesis. A time-frequency distribution (TFD) of a signal is a two-dimensional transformation of the signal to the time-frequency domain. Multicomponent signals can be identified by multiple energy peaks in the time-frequency domain. Many real life and synthetic signals are of multicomponent nature and there is little in the literature concerning IF estimation of such signals. This is why we have concentrated on multicomponent signals in Part-H. An adaptive algorithm for IF estimation using the quadratic time-frequency distributions has been analyzed. A class of time-frequency distributions that are more suitable for this purpose has been proposed. The kernels of this class are time-only or one-dimensional, rather than the time-lag (two-dimensional) kernels. Hence this class has been named as the T -class. If the parameters of these TFDs are properly chosen, they are more efficient than the existing fixed-kernel TFDs in terms of resolution (energy concentration around the IF) and artifacts reduction. The T-distributions has been used in the IF adaptive algorithm and proved to be efficient in tracking rapidly changing frequencies. They also enables direct amplitude estimation for the components of a multicomponent

A review of rapid prototyping techniques for tissue engineering purposes

Rapid prototyping (RP) is a common name for several techniques, which read in data from computer-aided design (CAD) drawings and manufacture automatically threedimensional objects layer-by-layer according to the virtual design. The utilization of RP in tissue engineering enables the production of three-dimensional scaffolds with complex geometries and very fine structures. Adding micro- and nanometer details into the scaffolds improves the mechanical properties of the scaffold and ensures better cell adhesion to the scaffold surface. Thus, tissue engineering constructs can be customized according to the data acquired from the medical scans to match the each patient’s individual needs. In addition RP enables the control of the scaffold porosity making it possible to fabricate applications with desired structural integrity. Unfortunately, every RP process has its own unique disadvantages in building tissue engineering scaffolds. Hence, the future research should be focused into the development of RP machines designed specifically for fabrication of tissue engineering scaffolds, although RP methods already can serve as a link between tissue and engineering.

A Taxonomy of image matching techniques for stereo vision

Stereo vision is a method of depth perception, in which depth information is inferred from two (or more) images of a scene, taken from different perspectives. Applications of stereo vision include aerial photogrammetry, autonomous vehicle guidance, robotics, industrial automation and stereomicroscopy. A key issue in stereo vision is that of image matching, or identifying corresponding points in a stereo pair. The difference in the positions of corresponding points in image coordinates is termed the parallax or disparity. When the orientation of the two cameras is known, corresponding points may be projected back to find the location of the original object point in world coordinates. Matching techniques are typically categorised according to the nature of the matching primitives they use and the matching strategy they employ. This report provides a detailed taxonomy of image matching techniques, including area based, transform based, feature based, phase based, hybrid, relaxation based, dynamic programming and object space methods. A number of area based matching metrics as well as the rank and census transforms were implemented, in order to investigate their suitability for a real-time stereo sensor for mining automation applications. The requirements of this sensor were speed, robustness, and the ability to produce a dense depth map. The Sum of Absolute Differences matching metric was the least computationally expensive; however, this metric was the most sensitive to radiometric distortion. Metrics such as the Zero Mean Sum of Absolute Differences and Normalised Cross Correlation were the most robust to this type of distortion but introduced additional computational complexity. The rank and census transforms were found to be robust to radiometric distortion, in addition to having low computational complexity. They are therefore prime candidates for a matching algorithm for a stereo sensor for real-time mining applications. A number of issues came to light during this investigation which may merit further work. These include devising a means to evaluate and compare disparity results of different matching algorithms, and finding a method of assigning a level of confidence to a match. Another issue of interest is the possibility of statistically combining the results of different matching algorithms, in order to improve robustness.

Machine learning techniques to improve software quality

A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are to develop. This research aims to make the testing process more efficient and effective by identifying those software modules most likely to contain faults, allowing testing efforts to be carefully targeted. This is done with the use of machine learning algorithms which use examples of fault prone and not fault prone modules to develop predictive models of quality. In order to learn the numerical mapping between module and classification, a module is represented in terms of software metrics. A difficulty in this sort of problem is sourcing software engineering data of adequate quality. In this work, data is obtained from two sources, the NASA Metrics Data Program, and the open source Eclipse project. Feature selection before learning is applied, and in this area a number of different feature selection methods are applied to find which work best. Two machine learning algorithms are applied to the data - Naive Bayes and the Support Vector Machine - and predictive results are compared to those of previous efforts and found to be superior on selected data sets and comparable on others. In addition, a new classification method is proposed, Rank Sum, in which a ranking abstraction is laid over bin densities for each class, and a classification is determined based on the sum of ranks over features. A novel extension of this method is also described based on an observed polarising of points by class when rank sum is applied to training data to convert it into 2D rank sum space. SVM is applied to this transformed data to produce models the parameters of which can be set according to trade-off curves to obtain a particular performance trade-off.

A review of optimization techniques used in the design of fibre composite structures for civil engineering applications

Fibre composite structures have become the most attractive candidate for civil engineering applications. Fibre reinforced plastic polymer (FRP) composite materials have been used in the rehabilitation and replacement of the old degrading traditional structures or build new structures. However, the lack of design standards for civil infrastructure limits their structural applications. The majority of the existing applications have been designed based on the research and guidelines provided by the fibre composite manufacturers or based on the designer’s experience. It has been a tendency that the final structure is generally over-designed. This paper provides a review on the available studies related to the design optimization of fibre composite structures used in civil engineering such as; plate, beam, box beam, sandwich panel, bridge girder, and bridge deck. Various optimization methods are presented and compared. In addition, the importance of using the appropriate optimization technique is discussed. An improved methodology, which considering experimental testing, numerical modelling, and design constrains, is proposed in the paper for design optimization of composite structures.