Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Improvement in Intrusion Detection With Advances in Sensor Fusion

Various intrusion detection systems (IDSs) reported in the literature have shown distinct preferences for detecting a certain class of attack with improved accuracy, while performing moderately on the other classes. In view of the enormous computing power available in the present-day processors, deploying multiple IDSs in the same network to obtain best-of-breed solutions has been attempted earlier. The paper presented here addresses the problem of optimizing the performance of IDSs using sensor fusion with multiple sensors. The trade-off between the detection rate and false alarms with multiple sensors is highlighted. It is illustrated that the performance of the detector is better when the fusion threshold is determined according to the Chebyshev inequality. In the proposed data-dependent decision ( DD) fusion method, the performance optimization of ndividual IDSs is first addressed. A neural network supervised learner has been designed to determine the weights of individual IDSs depending on their reliability in detecting a certain attack. The final stage of this DD fusion architecture is a sensor fusion unit which does the weighted aggregation in order to make an appropriate decision. This paper theoretically models the fusion of IDSs for the purpose of demonstrating the improvement in performance, supplemented with the empirical evaluation.

“Let’s see if you can see me”: Making connections with Google Earth™ in a preschool classroom

Bringing a social interaction approach to children’s geographies to investigate how children accomplish place in everyday lives, we draw on ethnomethodological and conversation analytic approaches that recognize children’s competence to manipulate their social and digital worlds. An investigation of preschool-aged children engaged with Google Earth™ shows how they both claimed and displayed technological understandings and practices such as maneuvering the mouse and screen, and referenced place through relationships with local landmarks and familiar settings such as their school. At times, the children’s competing agendas required orientation to each other’s ideas, and shared negotiation to come to resolution. A focus on children’s use of digital technologies as they make meaning of the world around them makes possible new understandings of place within the geographies of childhood and education.

Data distribution and task scheduling for distributed computing of all-to-all comparison problems

This research studied distributed computing of all-to-all comparison problems with big data sets. The thesis formalised the problem, and developed a high-performance and scalable computing framework with a programming model, data distribution strategies and task scheduling policies to solve the problem. The study considered storage usage, data locality and load balancing for performance improvement in solving the problem. The research outcomes can be applied in bioinformatics, biometrics and data mining and other domains in which all-to-all comparisons are a typical computing pattern.

Seamless journeys : enhancing accessibility to work through digital technologies for people with mobility-related impairments

Numerous disruptions and barriers are encountered by persons with mobility-related disabilities in their daily’s experience of going to work and the pressure these exert on gaining and maintaining their employment. The nature and extent of their difficulties to workforce participation entails a requirement for extensive planning and also strategies to address problems of being stranded (for example, when the bus they are waiting for is not accessible). This paper presents the conceptualisation and methods of understanding workforce participation as a journey, and a discussion on the role digital technologies play in helping people with mobility-related disabilities in their journeys to work and mitigating disruptions when these occur. This is presented through an initial case study that helped identify the sequence of supports needed to be in place to make the work journey possible. Importantly, the paper also highlights points of intervention for the use of digital technologies and where design can potentially help to enhance accessibility to work for people with mobility-related impairments by making journeys to work seamless.

Designing experiences with wearables : A case study exploring the blurring boundaries of art, design, technology, culture and distance

This paper details a workshop aimed at exploring opportunities for experience design through wearable art and design concepts. Specifically it explores the structure of the workshop with respect to facilitating learning through technology in the development of experiential wearable art and design. A case study titled Cloud Workshop: Wearables and Wellbeing; Enriching connections between citizens in the Asia-Pacific region was initiated through a cooperative partnership between Hong Kong Baptist University (HKBU), Queensland University of Technology (QUT) and Griffith University (GU). Digital technologies facilitated collaboration through an inter-disciplinary, inter-national and inter- cultural approach (Facer & Sandford, 2010) between Australia and Hong Kong. Students cooperated throughout a two-week period to develop innovative wearable concepts blending art, design and technology. An unpacking of the approach, pedagogical underpinning and final outcomes revealed distinct educational benefits as well as certain learning and technological challenges of the program. Qualitative feedback uncovered additional successes with respect to student engagement and enthusiasm, while uncovering shortcomings in the delivery and management of information and difficulties with cultural interactions. Potential future versions of the program aim to take advantage of the positives and overcome the limitations of the current pedagogical approach. It is hoped the case study will become a catalyst for future workshops that blur the boundaries of art, design and technology to uncover further benefits and potentials for new outcomes in experience design.

Designing self-service technologies for e-wellness

In the context of an international economic shift from manufacturing to services and the constant expansion of industries towards online services (Sheth and Sharma, 2008), this study is concerned with the design of self-service technologies (SSTs) for online environments. An industry heavily adopting SSTs across a variety of different services is Health and Wellness, where figures show an ever growing number of health and wellness apps being developed, downloaded and abandoned (Kelley, 2014). Little is known about how to enhance people’s engagement with online wellness SSTs to support self-health management and self-efficacy. This literature review argues that service design of wellness SSTs in online contexts can be improved by developing an enhanced understanding from a people perspective and customer experience point of view. Customer value, quality of service, usability, and self-efficacy all play an important role in understanding how to design SSTs for wellness and keep users engaged. There is a need for further study on how people interact and engage with online services in the context of wellness in order to design engaging wellness services.

Recipe for success — Lessons learnt from using xAPI within the Connected Learning Analytics toolkit

An ongoing challenge for Learning Analytics research has been the scalable derivation of user interaction data from multiple technologies. The complexities associated with this challenge are increasing as educators embrace an ever growing number of social and content related technologies. The Experience API (xAPI) alongside the development of user specific record stores has been touted as a means to address this challenge, but a number of subtle considerations must be made when using xAPI in Learning Analytics. This paper provides a general overview to the complexities and challenges of using xAPI in a general systemic analytics solution - called the Connected Learning Analytics (CLA) toolkit. The importance of design is emphasised, as is the notion of common vocabularies and xAPI Recipes. Early decisions about vocabularies and structural relationships between statements can serve to either facilitate or handicap later analytics solutions. The CLA toolkit case study provides us with a way of examining both the strengths and the weaknesses of the current xAPI specification, and we conclude with a proposal for how xAPI might be improved by using JSON-LD to formalise Recipes in a machine readable form.

Ant colony optimization for reporting cell planning in mobile computing using selective paging strategy

Location management problem that arise in mobile computing networks is addressed. One method used in location management is to designate sonic of the cells in the network as "reporting cells". The other cells in the network are "non-reporting cells". Finding an optimal set of reporting cells (or reporting cell configuration) for a given network. is a difficult combinatorial optimization problem. In fact this is shown to be an NP-complete problem. in an earlier study. In this paper, we use the selective paging strategy and use an ant colony optimization method to obtain the best/optimal set of reporting cells for a given a network.

Inventing the Future: Intellectual Property and 3D Printing

A new technology – 3D printing – has the potential to make radical changes to aspects of the way in which we live. Put simply, it allows people to download designs and turn them into physical objects by laying down successive layers of material. Replacements or parts for household objects such as toys, utensils and gadgets could become available at the press of a button. With this innovation, however, comes the need to consider impacts on a wide range of forms of intellectual property, as Dr Matthew Rimmer explains. 3D Printing is the latest in a long line of disruptive technologies – including photocopiers, cassette recorders, MP3 players, personal computers, peer to peer networks, and wikis – which have challenged intellectual property laws, policies, practices, and norms. As The Economist has observed, ‘Tinkerers with machines that turn binary digits into molecules are pioneering a whole new way of making things—one that could well rewrite the rules of manufacturing in much the same way as the PC trashed the traditional world of computing.’

Computing the Stochastic Complexity of Simple Probabilistic Graphical Models

Minimum Description Length (MDL) is an information-theoretic principle that can be used for model selection and other statistical inference tasks. There are various ways to use the principle in practice. One theoretically valid way is to use the normalized maximum likelihood (NML) criterion. Due to computational difficulties, this approach has not been used very often. This thesis presents efficient floating-point algorithms that make it possible to compute the NML for multinomial, Naive Bayes and Bayesian forest models. None of the presented algorithms rely on asymptotic analysis and with the first two model classes we also discuss how to compute exact rational number solutions.

Exploring privacy for ubiquitous computing: Tools, methods and experiments

Ubiquitous computing is about making computers and computerized artefacts a pervasive part of our everyday lifes, bringing more and more activities into the realm of information. The computationalization, informationalization of everyday activities increases not only our reach, efficiency and capabilities but also the amount and kinds of data gathered about us and our activities. In this thesis, I explore how information systems can be constructed so that they handle this personal data in a reasonable manner. The thesis provides two kinds of results: on one hand, tools and methods for both the construction as well as the evaluation of ubiquitous and mobile systems---on the other hand an evaluation of the privacy aspects of a ubiquitous social awareness system. The work emphasises real-world experiments as the most important way to study privacy. Additionally, the state of current information systems as regards data protection is studied. The tools and methods in this thesis consist of three distinct contributions. An algorithm for locationing in cellular networks is proposed that does not require the location information to be revealed beyond the user's terminal. A prototyping platform for the creation of context-aware ubiquitous applications called ContextPhone is described and released as open source. Finally, a set of methodological findings for the use of smartphones in social scientific field research is reported. A central contribution of this thesis are the pragmatic tools that allow other researchers to carry out experiments. The evaluation of the ubiquitous social awareness application ContextContacts covers both the usage of the system in general as well as an analysis of privacy implications. The usage of the system is analyzed in the light of how users make inferences of others based on real-time contextual cues mediated by the system, based on several long-term field studies. The analysis of privacy implications draws together the social psychological theory of self-presentation and research in privacy for ubiquitous computing, deriving a set of design guidelines for such systems. The main findings from these studies can be summarized as follows: The fact that ubiquitous computing systems gather more data about users can be used to not only study the use of such systems in an effort to create better systems but in general to study phenomena previously unstudied, such as the dynamic change of social networks. Systems that let people create new ways of presenting themselves to others can be fun for the users---but the self-presentation requires several thoughtful design decisions that allow the manipulation of the image mediated by the system. Finally, the growing amount of computational resources available to the users can be used to allow them to use the data themselves, rather than just being passive subjects of data gathering.

Implementing technologies and strategies to maintain resistance to sunflower rust

This project proposes to implement resistance gene pyramiding strategies through close collaboration with Pacific Seeds. These strategies have been developed by Department of Primary Industries and Fisheries (DPI&F) researchers in two previous GRDC projects, DAQ356 and DAQ537. The gene pyramids will be incorporated into elite breeding material using techniques and technologies developed by DPI&F. These include the use of DNA markers. If successful, a range of elite lines/commercial hybrids containing strategic resistance gene pyramids will be available to growers. These lines will provide the industry with a directed strategy to manage the sunflower rust pathogen and reduce the risk of outbreaks of the disease.