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    A fuzzy and wavelet-based image compression algorithm : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand
    (Massey University, 2006) Huang, Li
    Nowadays, the Internet and digital image widespread are used in the industry, commerce, military, traffic and all walks of life. However, this kind of general use resulted in required for less transmission time and storage space. Image compression can address the problem of reducing the amount of data to represent a digital image. The image will satisfy the transmission and the preserved request after the compression. With the increasing use some technologies in the image processing, image compression also requires new technology to get the high compression ratio and more better image quality. Therefore, a new standard has been developed by Joint Photographic Experts Group (JPEG). Apart from JPEG, there are other algorithms developed for image compression, Normally, EZW, SHIPT and VQ algorithms. However, they all deal with the calculation of coefficients with too much complexity; as a consequence, compressing still image takes too much time. In the light of these problems, this thesis introduces a new method for dealing with the requirements of the coefficients while retaining the important detail in the image, by employing a Fuzzy Logic technique reduce the number of the coefficients, and then utilizes the Huffman or LZW algorithm to complete the image compression. The algorithm developed in this research, called IWF algorithm, is based on four key techniques: 1) a wavelet transform for decomposition. This technique allows the combination of lossless and lossy compression with extremely high compression rate and image quality. 2) Quantization, this technique generally works by compressing a range of value to a single quantum value. By reducing the number of discrete symbols in a given stream, the stream becomes more compressible. This step in the IWF process is a lossy transformation. 3) Adaptation of Fuzzy logic techniques. This step uses the Fuzzy Logic techniques to handle the wavelet coefficients, enable the wavelet coefficients to have the same value in the high subbands. 4) Adaptation of Lossless data compression techniques. Keywords: Image Compression, Fuzzy Logic, Wavelet transforms, Decomposition, Haar Wavelet transform.
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    Synthesized cooperative strategies for intelligent multi-robots in a real-time distributed environment : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand
    (Massey University, 2009) Lin, Caoyun
    In the robot soccer domain, real-time response usually curtails the development of more complex Al-based game strategies, path-planning and team cooperation between intelligent agents. In light of this problem, distributing computationally intensive algorithms between several machines to control, coordinate and dynamically assign roles to a team of robots, and allowing them to communicate via a network gives rise to real-time cooperation in a multi-robotic team. This research presents a myriad of algorithms tested on a distributed system platform that allows for cooperating multi- agents in a dynamic environment. The test bed is an extension of a popular robot simulation system in the public domain developed at Carnegie Mellon University, known as TeamBots. A low-level real-time network game protocol using TCP/IP and UDP were incorporated to allow for a conglomeration of multi-agent to communicate and work cohesively as a team. Intelligent agents were defined to take on roles such as game coach agent, vision agent, and soccer player agents. Further, team cooperation is demonstrated by integrating a real-time fuzzy logic-based ball-passing algorithm and a fuzzy logic algorithm for path planning. Keywords Artificial Intelligence, Ball Passing, the coaching system, Collaborative, Distributed Multi-Agent, Fuzzy Logic, Role Assignment
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    Genetic network programming with fuzzy reinforcement learning nodes for multi-behaviour robot control : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Science in Computer Science, Massey University, Albany campus, New Zealand
    (Massey University, 2014) Wang, Wenhan
    This research explores a new approach for building a complex intelligent robot multi-behaviour comprising of a variety of intelligent subsystems that are fused together into one hybrid system. The work mainly focuses on integrating reinforcement learning and fuzzy logic with genetic network programming, examining the different architectures, and aims to achieve multi-objective behaviours and alleviate the problem of learning and calibration by repeated interaction with the environment. Different components of the learning algorithm are studied separately and also in combination. They are developed systematically using an increasing level of complexity for robot behaviours. As a test bed, the work investigates how to achieve ball pursuit and wall avoidance behavioiurs simultaneously, in the realm of the robot soccer game. The training procedure and test environment is designed, as well as a variety of fitness functions are experimented for the multi-behaviour objectives [sic]. Furthermore, the novel evolutionary architecture is combined with hill-climbing to accelerate the search for the best individual.
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    Fuzzy motion controllers and hybrids : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand
    (Massey University, 2011) Gerdelan, Anton; Gerdelan, Anton
    This thesis describes implementations of motion control systems that are based on fuzzy logic; fuzzy motion controllers. The controllers are used by to drive a variety of simulated vehicles and computeranimated characters. The problem of heading towards a destination whilst simultaneously avoiding static and dynamic obstacles is addressed with fuzzy motion controllers. For situations where a level above reactive motion control is required, such as path-planning behaviour or traffic rule following, then hybrid algorithms are proposed; combining fuzzy motion controllers with other navigation algorithms. Consideration is given to behavioural level of detail models, with transition between behavioural models of different complexity based on the proximity, or visual importance of characters to the camera. Fuzzy controllers have a set of fuzzy rules, or a “rule base” that defines the inference of the controller. There is no assurance that hand-calibrated rule bases are optimal, and indeed that calibration based on fixed test environment will apply well to a dynamic environment. Special consideration is given to genetic-fuzzy systems, which use a genetic algorithm to automatically calibrate a rule base. Various architectures for genetic-fuzzy system are proposed and evaluated including dynamic systems, which have the ability to learn “on the fly”, rather than in fixed experiment scenarios. A relationship between genetic algorithm parameters and time-efficient fitness improvement is found.The time requirements of training more complex “cascading” fuzzy systems are discussed. Distributed and parallel training models are also considered. A new, modular agent middleware is proposed, which is the underpinning software that perceives the complex environment, feeds inputs into the fuzzy motion controllers, and effects output actions for each character and vehicle. The middleware model is successfully used to drive a range of vehicles and characters used in experiments. The problem of evaluating motion controllers within a scientific framework is discussed. Several candidate solutions are used, and a system for objectively evaluating mechanically simulated vehicle motion is defined and evaluated. A complete tool-chain for designing complex simulations and doing scientific experiments with them is is developed and discussed in detail, including simulation software design methods, libraries, visualisation tools, and useful algorithms, a well-defined mechanical simulation system, and practices for collecting data from simulations, and quantifying uncertainty.
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    A study of frequent pattern mining in transaction datasets : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand
    (Massey University, 2011) Xu, Luofeng
    Within data mining, the efficient discovery of frequent patterns—sets of items that occur together in a dataset—is an important task, particularly in transaction datasets. This thesis develops effective and efficient algorithms for frequent pattern mining, and considers the related problem of how to learn, and utilise, the characteristics of the particular datasets being investigated. The first problem considered is how to mine frequent closed patterns in dynamic datasets, where updates to the dataset are performed. The standard approach to this problem is to use a standard pattern mining algorithm and simply rerun it on the updated dataset. An alternative method is proposed in this thesis that is significantly more efficient provided that the size of the updates is relatively small. Following this is an investigation of the pattern support distribution of transaction datasets, which measures the numbers of times each pattern appears within the dataset. The evidence for the pattern support distribution of real retail datasets obeying a power law is investigated using qualitative appraisals and statistical goodness-of-fit tests, and the power law is found to be a good model. Based on this, the thesis demonstrates how to efficiently estimate the pattern support distribution based on sampling techniques, reducing the computational cost of finding this distribution. The last major contribution of the thesis is to consider novel ways to set the main user-specified parameters of frequent pattern mining, the minimum support, which defines how many times a pattern needs to be seen before it is ‘frequent’. This is a critical parameter, and very hard to set without a lot of knowledge of the dataset. A method to enable the user to specify rather looser requirements for what they require from the mining is proposed based on the assumption of a power-law-based pattern support distribution and fuzzy logic techniques.
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    Designing CBL systems for complex domains using problem transformation and fuzzy logic : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand
    (Massey University, 2007) Mohanarajah, Selvarajah
    Some disciplines are inherently complex and challenging to learn. This research attempts to design an instructional strategy for CBL systems to simplify learning certain complex domains. Firstly, problem transformation, a constructionist instructional technique, is used to promote active learning by encouraging students to construct more complex artefacts based on less complex ones. Scaffolding is used at the initial learning stages to alleviate the difficulty associated with complex transformation processes. The proposed instructional strategy brings various techniques together to enhance the learning experience. A functional prototype is implemented with Object-Z as the exemplar subject. Both objective and subjective evaluations using the prototype indicate that the proposed CBL system has a statistically significant impact on learning a complex domain. CBL systems include Learner models to provide adaptable support tailored to individual learners. Bayesian theory is used in general to manage uncertainty in Learner models. In this research, a fuzzy logic based locally intelligent Learner model is utilized. The fuzzy model is simple to design and implement, and easy to understand and explain, as well as efficient. Bayesian theory is used to complement the fuzzy model. Evaluation shows that the accuracy of the proposed Learner model is statistically significant. Further, opening Learner model reduces uncertainty, and the fuzzy rules are simple and resemble human reasoning processes. Therefore, it is argued that opening a fuzzy Learner model is both easy and effective. Scaffolding requires formative assessments. In this research, a confidence based multiple test marking scheme is proposed as traditional schemes are not suitable for measuring partial knowledge. Subjective evaluation confirms that the proposed schema is effective. Finally, a step-by-step methodology to transform simple UML class diagrams to Object-Z schemas is designed in order to implement problem transformation. This methodology could be extended to implement a semi-automated translation system for UML to Object Models.