The Unified Modelling Language (UML) and object-oriented analysis and design (OOAD) have become essential topics in both academia and industry. UML is the accepted standard modelling language for describing object-oriented (OO) systems for analysis and design, and many UML CASE tools have been built and are used in academia and industry. OO technology and UML is an ongoing area of research, and many applications have been developed using OO technology. However, observations on current software development practices in some computer companies have shown that many OO software developers are not adopting recognised OOAD techniques. Both information technology (IT) students at higher educational institutions and professionals have in general found difficulty in grasping OO concepts, and the role that UML diagrams play in the design of the analysis and design solution. They particularly find difficulty in performing abstractions of real-world problems within the context of OOAD. They are unable to effectively build class diagrams from the problem domain because they essentially do not know ‘what’ to model. They therefore prefer to start coding software applications before building the analysis and design artifacts. Most of these students invariably prefer to focus on the implementation phase of a software development lifecycle and consider the preceding analysis and design phases superfluous. The overall goal of this thesis is to contribute to a significant improvement in the way students and software developers analyse and design their OO systems. We present a new approach by introducing concept mapping as a tool to help novices in OOAD produce more appropriate UML class and sequence diagrams. The class and sequence diagrams are selected because they represent the essential static and behavioural aspects of a problem domain. The former is fundamental to the OO modelling process and the latter is one of the most widely used dynamic diagrams in UML. Concept mapping is a popular tool used in education for facilitating learning, comprehension and the development of knowledge. Within the context of OOAD, we propose to use concept maps as a graphical representation of fundamental concepts, and their relationships and responsibilities within a problem domain. A static concept map derived from expanded use cases (use case narratives) can subsequently evolve into a class diagram containing information on classes, attributes, associations and generalisation-specialisation hierarchies. A dynamic concept map derived from an expanded use case can evolve into a sequence diagram containing information on the interaction of objects (and their messages) to fulfil the responsibilities of a particular scenario of the use case. In this thesis, a study is initially conducted to investigate in detail the difficulties undergraduate students have when producing UML class and sequence diagrams. The results of the study reveal and confirm some of the fundamental problems that students have with OO modelling. In order to address these problems, a concept-driven approach is developed to help novices produce more appropriate UML class and sequence diagrams. The effectiveness of this approach is evaluated by three different experiments. The data from these experiments is analysed and there is sufficient statistical evidence to support the claim that the participants produce more appropriate class and sequence diagrams after being taught concept mapping techniques. As a result of this positive outcome, a set of guidelines is developed for teaching OO modelling with concept maps. These guidelines could be integrated into existing OOAD courses to help software engineering educators resolve some of the difficulties they face when teaching OOAD.

This thesis investigates the use of visual-motion information sampled through optical flow for the task of indoor obstacle avoidance on autonomous mobile robots. The methods focus on the practical use of optical flow and visual motion information in performing the obstacle avoidance task in real indoor environments. The methods serve to identify visual-motion properties that must be used in synergy with visual-spatial properties toward the goal of a complete robust visual-only obstacle avoidance system, as is evidently seen within nature. A review of vision-based obstacle avoidance techniques shows that early research mainly focused on visual-spatial techniques, which heavily rely on various assumptions of their environments to function successfully. On the other hand, more current research that looks toward the use of visual-motion information (sampled through optical flow) tends to focus on using optical flow in a subsidiary manner, and does not completely take advantage of the information encoded within an optical flow field. In the light of the current research limitations, this thesis describes two different approaches and evaluates their use of optical flow to perform the obstacle avoidance task. The first approach begins with the construction of a conventional range map using optical flow that stems from the structure-from-motion domain and the theory that optical flow encodes 3D environmental information under certain conditions. The second approach investigates optical flow in a causal mechanistic manner using machine learning of motor responses directly from optical flow - motivated from physical and behavioural evidence observed in biological creatures. Specifically, the second approach is designed with three main objectives in mind: 1) to investigate whether optical flow can be learnt for obstacle avoidance; 2) to create a system capable of repeatable obstacle avoidance performance in real-life environments; and 3) to analyse the system to determine what optical flow properties are actually being used for the motor control task. The range-map reconstruction results have demonstrated some good distance estimations through the use of a feature-based optical flow algorithm. However, the number of flow points were too sparse to provide adequate obstacle detection. Results froma differential-based optical flow algorithm helped to increase the density of flow points, but highlighted the high sensitivity of the optical flow field to the rotational errors and outliers that plague the majority of frames under real-life robot situations. Final results demonstrated that current optical flow algorithms are ill-suited to estimate obstacle distances consistently, as range-estimation techniques require an extremely accurate optical flow field with adequate density and coverage for success. This is a difficult problem within the optical flow estimation domain itself. In the machine learning approach, an initial study to examine whether optical flow can be machine learnt for obstacle avoidance and control in a simple environment was successful. However,there were certain problems. Several critical issues which arise with the use of a machine learning approach were highlighted. These included sample set completeness, sample set biases, and control system instability. Consequently, an extended neural network was proposed that had several improvements made to overcome the initial problems. Designing an automated system for gathering training data helped to eliminate most of the sample set problems. Key changes in the neural network architecture, optical flow filters, and navigation technique vastly improved the control system stability. As a result, the extended neural network system was able to successfully perform multiple obstacle avoidance loops in both familiar and unfamiliar real-life environments without collisions. The lap times of the machine learning approach were comparable to those of the laser-based navigation technique. The the machine learning approach was 13% slower in the familiar and 25% slower in the unfamiliar environment. Furthermore, through analysis of the neural network approach, flow magnitudes were revealed to be learnt for range information in an absolute manner, while flow directions were used to detect the focus of expansion (FOE) in order to predict critical collision situations and improve control stability. In addition, the precision of the flow fields was highlighted as an important requirement, as opposed to the high accuracy of flow vectors. For robot control purposes, image-processing techniques such as region finding and object boundary detection were employed to detect changes between optical flow vectors in the image space.

The Unified Modelling Language (UML) and object-oriented analysis and design (OOAD) have become essential topics in both academia and industry. UML is the accepted standard modelling language for describing object-oriented (OO) systems for analysis and design, and many UML CASE tools have been built and are used in academia and industry. OO technology and UML is an ongoing area of research, and many applications have been developed using OO technology. However, observations on current software development practices in some computer companies have shown that many OO software developers are not adopting recognised OOAD techniques. Both information technology (IT) students at higher educational institutions and professionals have in general found difficulty in grasping OO concepts, and the role that UML diagrams play in the design of the analysis and design solution. They particularly find difficulty in performing abstractions of real-world problems within the context of OOAD. They are unable to effectively build class diagrams from the problem domain because they essentially do not know ‘what’ to model. They therefore prefer to start coding software applications before building the analysis and design artifacts. Most of these students invariably prefer to focus on the implementation phase of a software development lifecycle and consider the preceding analysis and design phases superfluous. The overall goal of this thesis is to contribute to a significant improvement in the way students and software developers analyse and design their OO systems. We present a new approach by introducing concept mapping as a tool to help novices in OOAD produce more appropriate UML class and sequence diagrams. The class and sequence diagrams are selected because they represent the essential static and behavioural aspects of a problem domain. The former is fundamental to the OO modelling process and the latter is one of the most widely used dynamic diagrams in UML. Concept mapping is a popular tool used in education for facilitating learning, comprehension and the development of knowledge. Within the context of OOAD, we propose to use concept maps as a graphical representation of fundamental concepts, and their relationships and responsibilities within a problem domain. A static concept map derived from expanded use cases (use case narratives) can subsequently evolve into a class diagram containing information on classes, attributes, associations and generalisation-specialisation hierarchies. A dynamic concept map derived from an expanded use case can evolve into a sequence diagram containing information on the interaction of objects (and their messages) to fulfil the responsibilities of a particular scenario of the use case. In this thesis, a study is initially conducted to investigate in detail the difficulties undergraduate students have when producing UML class and sequence diagrams. The results of the study reveal and confirm some of the fundamental problems that students have with OO modelling. In order to address these problems, a concept-driven approach is developed to help novices produce more appropriate UML class and sequence diagrams. The effectiveness of this approach is evaluated by three different experiments. The data from these experiments is analysed and there is sufficient statistical evidence to support the claim that the participants produce more appropriate class and sequence diagrams after being taught concept mapping techniques. As a result of this positive outcome, a set of guidelines is developed for teaching OO modelling with concept maps. These guidelines could be integrated into existing OOAD courses to help software engineering educators resolve some of the difficulties they face when teaching OOAD.

“Accent” is the pattern of speech pronunciation by which one can identify a person’s linguistic, social or cultural background. It is an important source of inter-speaker variability and a particular problem for automated speech recognition. The aim of the study was to investigate a new computational approach to accent classification which did not require phonemic segmentation or the identification of phonemes as input, and which could therefore be used as a simple, effective accent classifier. Through a series of structured experiments this study investigated the effectiveness of Support Vector Machines (SVMs) for speech accent classification using time-based units rather than linguistically-informed ones, and compared it to the accuracy of other machine learning methods, as well as the ability of humans to classify speech according to accent. A corpus of read-speech was collected in two accents of English (Arabic and “Indian”) and used as the main datasource for the experiments. Mel-frequency cepstral coefficients were extracted from the speech samples and combined into larger units of 10 to 150ms duration, which then formed the input data for the various machine learning systems. Support Vector Machines were found to classify the samples with up to 97.5% accuracy with very high precision and recall, using samples of between 1 and 4 seconds of speech. This compared favourably with a human listener study where subjects were able to distinguish between the two accent groups with an average of 92.5% accuracy in approximately 8 seconds. Repeating the SVM experiments on a different corpus resulted in a best classification accuracy of 84.6%. Experiments using a decision tree learner and a rule-based classifier on the original corpus gave a best accuracy of 95% but results over the range of conditions were much more variable than those using the SVM. Rule extraction was performed in order to help explain the results and better inform the design of the system. The new approach was therefore shown to be effective for accent classification, and a plan for its role within various other larger speech-related contexts was developed.

“Accent” is the pattern of speech pronunciation by which one can identify a person’s linguistic, social or cultural background. It is an important source of inter-speaker variability and a particular problem for automated speech recognition. The aim of the study was to investigate a new computational approach to accent classification which did not require phonemic segmentation or the identification of phonemes as input, and which could therefore be used as a simple, effective accent classifier. Through a series of structured experiments this study investigated the effectiveness of Support Vector Machines (SVMs) for speech accent classification using time-based units rather than linguistically-informed ones, and compared it to the accuracy of other machine learning methods, as well as the ability of humans to classify speech according to accent. A corpus of read-speech was collected in two accents of English (Arabic and “Indian”) and used as the main datasource for the experiments. Mel-frequency cepstral coefficients were extracted from the speech samples and combined into larger units of 10 to 150ms duration, which then formed the input data for the various machine learning systems. Support Vector Machines were found to classify the samples with up to 97.5% accuracy with very high precision and recall, using samples of between 1 and 4 seconds of speech. This compared favourably with a human listener study where subjects were able to distinguish between the two accent groups with an average of 92.5% accuracy in approximately 8 seconds. Repeating the SVM experiments on a different corpus resulted in a best classification accuracy of 84.6%. Experiments using a decision tree learner and a rule-based classifier on the original corpus gave a best accuracy of 95% but results over the range of conditions were much more variable than those using the SVM. Rule extraction was performed in order to help explain the results and better inform the design of the system. The new approach was therefore shown to be effective for accent classification, and a plan for its role within various other larger speech-related contexts was developed.