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.

Statistical and Information-Theoretic Methods for Data-Analysis

In this Thesis, we develop theory and methods for computational data analysis. The problems in data analysis are approached from three perspectives: statistical learning theory, the Bayesian framework, and the information-theoretic minimum description length (MDL) principle. Contributions in statistical learning theory address the possibility of generalization to unseen cases, and regression analysis with partially observed data with an application to mobile device positioning. In the second part of the Thesis, we discuss so called Bayesian network classifiers, and show that they are closely related to logistic regression models. In the final part, we apply the MDL principle to tracing the history of old manuscripts, and to noise reduction in digital signals.

Geant4 Hadronic Cascade Models and CMS Data Analysis : Computational Challenges in the LHC era

This work belongs to the field of computational high-energy physics (HEP). The key methods used in this thesis work to meet the challenges raised by the Large Hadron Collider (LHC) era experiments are object-orientation with software engineering, Monte Carlo simulation, the computer technology of clusters, and artificial neural networks. The first aspect discussed is the development of hadronic cascade models, used for the accurate simulation of medium-energy hadron-nucleus reactions, up to 10 GeV. These models are typically needed in hadronic calorimeter studies and in the estimation of radiation backgrounds. Various applications outside HEP include the medical field (such as hadron treatment simulations), space science (satellite shielding), and nuclear physics (spallation studies). Validation results are presented for several significant improvements released in Geant4 simulation tool, and the significance of the new models for computing in the Large Hadron Collider era is estimated. In particular, we estimate the ability of the Bertini cascade to simulate Compact Muon Solenoid (CMS) hadron calorimeter HCAL. LHC test beam activity has a tightly coupled cycle of simulation-to-data analysis. Typically, a Geant4 computer experiment is used to understand test beam measurements. Thus an another aspect of this thesis is a description of studies related to developing new CMS H2 test beam data analysis tools and performing data analysis on the basis of CMS Monte Carlo events. These events have been simulated in detail using Geant4 physics models, full CMS detector description, and event reconstruction. Using the ROOT data analysis framework we have developed an offline ANN-based approach to tag b-jets associated with heavy neutral Higgs particles, and we show that this kind of NN methodology can be successfully used to separate the Higgs signal from the background in the CMS experiment.

Accelerator Mass Spectrometry and Bayesian Data Analysis

Accelerator mass spectrometry (AMS) is an ultrasensitive technique for measuring the concentration of a single isotope. The electric and magnetic fields of an electrostatic accelerator system are used to filter out other isotopes from the ion beam. The high velocity means that molecules can be destroyed and removed from the measurement background. As a result, concentrations down to one atom in 10^16 atoms are measurable. This thesis describes the construction of the new AMS system in the Accelerator Laboratory of the University of Helsinki. The system is described in detail along with the relevant ion optics. System performance and some of the 14C measurements done with the system are described. In a second part of the thesis, a novel statistical model for the analysis of AMS data is presented. Bayesian methods are used in order to make the best use of the available information. In the new model, instrumental drift is modelled with a continuous first-order autoregressive process. This enables rigorous normalization to standards measured at different times. The Poisson statistical nature of a 14C measurement is also taken into account properly, so that uncertainty estimates are much more stable. It is shown that, overall, the new model improves both the accuracy and the precision of AMS measurements. In particular, the results can be improved for samples with very low 14C concentrations or measured only a few times.

Residual noise covariance for Planck low-resolution data analysis

Aims: Develop and validate tools to estimate residual noise covariance in Planck frequency maps. Quantify signal error effects and compare different techniques to produce low-resolution maps. Methods: We derive analytical estimates of covariance of the residual noise contained in low-resolution maps produced using a number of map-making approaches. We test these analytical predictions using Monte Carlo simulations and their impact on angular power spectrum estimation. We use simulations to quantify the level of signal errors incurred in different resolution downgrading schemes considered in this work. Results: We find an excellent agreement between the optimal residual noise covariance matrices and Monte Carlo noise maps. For destriping map-makers, the extent of agreement is dictated by the knee frequency of the correlated noise component and the chosen baseline offset length. The significance of signal striping is shown to be insignificant when properly dealt with. In map resolution downgrading, we find that a carefully selected window function is required to reduce aliasing to the sub-percent level at multipoles, ell > 2Nside, where Nside is the HEALPix resolution parameter. We show that sufficient characterization of the residual noise is unavoidable if one is to draw reliable contraints on large scale anisotropy. Conclusions: We have described how to compute the low-resolution maps, with a controlled sky signal level, and a reliable estimate of covariance of the residual noise. We have also presented a method to smooth the residual noise covariance matrices to describe the noise correlations in smoothed, bandwidth limited maps.

Noviisiopettajana peruskoulussa : Aloittelevien opettajien autenttisia kokemuksia ensimmäisestä opettajavuodesta

As a Novice Teacher at Comprehensive School: The authentic experiences of the beginning teachers during their first year of teaching The aim of this study is to explicate the novice year of teaching in the light of teachers´ authentic experiences. The subject of this investigation is the teachers´ subjective world of experience during their first academic year of teaching and the sharing of these experiences in collaborative consulting meetings. The themes discussed in the meetings were introduced into the collaborative group by the novice teachers themselves, and the progress of discussion was con-trolled by them. The research data was gathered in a consultative working group where the way a novice teacher starts to interpret, analyze and identify his/her own complex and dynamic teaching situations was observed. The research data gathered in this way illuminates novice teachers´ world of experience and mental picture as well as the unconscious sides of school life. In a theoretical frame of reference, the work of a teacher is identified, according to systemic scientific thought, as a dynamic triangle in which the basic elements are the personality of the teacher, the role of the teacher and the school as an organization. These basic elements form a whole within which the teacher works. The dynamics of this triangle in a teacher’s work are brought to light through the study of the phenomena of groups and group dynamics since a teacher works either as a member of a group (working community), as a leader of a group (teaching situations) or in a network (parent – teacher cooperation). Therefore, tension and force are always present in teaching work. The main research problem was to explain how a novice teacher experiences his/her first working year as a teacher. The participants (n=5) were teaching at five different comprehensive schools in the city of Helsinki. This was their first long-term post as a teacher. The research data consists of seven collaborative consulting meetings, as well as recordings and transcripts of the meetings. A classificatory framework was developed for data analysis which enabled a sys-tematic qualitative content analysis based on theory and material. In addition to the consulting meetings, the teachers were interviewed at the beginning and at the end of the process of collecting the research material. The interviews were used to interpret the meanings of the content analysis based on raw data. The findings show that there is a gap between teacher education and the reality of school life, which causes difficulties for a novice teacher during his/her first teaching year. The gap is rather a global educational problem than a national one, and therefore it is independent of cultural factors. Novice teachers desire a well-structured theory of teacher education and a clear programme where the themes and content delve deeper and deeper into the subject matter during the study years. According to the novice teachers, teacher education frequently consists of sporadic and unconnected study and class situations. An individual content weakness of teacher education is the lack of insufficient initiation into evaluation processes. The novice teachers suggest that a student must be provided good-quality and competent guidance during the study years and during his or her induction. There should be a well-organized, structured and systematic induction program for novice teachers. The induction program should be overseen by an organization so that the requirements of a qualified induction can be met. The findings show that the novice teachers find the first year of teaching at comprehensive school emotionally loaded. The teachers experienced teaching as difficult work and found the workload heavy. Nevertheless, they enjoyed their job because, as they said, there were more pleasant than unpleasant things in their school day. Their main feeling at school was the joy of success in teaching. The novice teachers felt satisfaction with their pupils. The teachers experienced the more serious feelings of anger and disgust when serious violence took place. The most difficult situations arose from teaching pupils who had mental health problems. The toughest thing in the teacher´s work was teaching groups that are too heterogeneous. The most awkward problems in group dynamics happened when new immigrants, who spoke only their own languages, were integrated into the groups in the middle of the school year. Teachers wanted to help children who needed special help with learning but restated at the same time that the groups being taught shouldn’t be too heterogeneous. The teachers wished for help from school assistants so that they could personally concentrate more on teaching. Not all the parents took care of their children according to the comprehensive school law. The teachers found it hard to build a confidential relationship between home and school. In this study, novice teachers found it hard to join the teaching staff at school. Some of the teachers on staff were very strong and impulsive, reacting loudly and emotionally. These teachers provoked disagreement, conflicts, power struggles and competition among the other teachers. Although the novice teachers of the study were all qualified teachers, three of them were not interested in a permanent teaching job. For these teachers teaching at a primary school was just a project, a short period in their working life. They will remain in the teaching profession as long as they are learning new things and enjoying their teaching job. This study is an independent part of the research project on Interplay – Connecting Academic Teacher Education and Work, undertaken by the Department of Applied Sciences of Education at the University of Helsinki. Key words: novice teacher, emotions, groups and group dynamics, authority, co-operation between home and school, teacher community, leadership at school, induction, consulting

Teacher Education, School Effectiveness and Improvement : A Study of Academic and Professional Qualification on Teachers' Job Effectiveness in Nigerian Secondary Schools

This academic work begins with a compact presentation of the general background to the study, which also includes an autobiography for the interest in this research. The presentation provides readers who know little of the topic of this research and of the structure of the educational system as well as of the value given to education in Nigeria. It further concentrates on the dynamic interplay of the effect of academic and professional qualification and teachers' job effectiveness in secondary schools in Nigeria in particular, and in Africa in general. The aim of this study is to produce a systematic analysis and rich theoretical and empirical description of teachers' teaching competencies. The theoretical part comprises a comprehensive literature review that focuses on research conducted in the areas of academic and professional qualification and teachers' job effectiveness, teaching competencies, and the role of teacher education with particular emphasis on school effectiveness and improvement. This research benefits greatly from the functionalist conception of education, which is built upon two emphases: the application of the scientific method to the objective social world, and the use of an analogy between the individual 'organism' and 'society'. To this end, it offers us an opportunity to define terms systematically and to view problems as always being interrelated with other components of society. The empirical part involves describing and interpreting what educational objectives can be achieved with the help of teachers' teaching competencies in close connection to educational planning, teacher training and development, and achieving them without waste. The data used in this study were collected between 2002 and 2003 from teachers, principals, supervisors of education from the Ministry of Education and Post Primary Schools Board in the Rivers State of Nigeria (N=300). The data were collected from interviews, documents, observation, and questionnaires and were analyzed using both qualitative and quantitative methods to strengthen the validity of the findings. The data collected were analyzed to answer the specific research questions and hypotheses posited in this study. The data analysis involved the use of multiple statistical procedures: Percentages Mean Point Value, T-test of Significance, One-Way Analysis of Variance (ANOVA), and Cross Tabulation. The results obtained from the data analysis show that teachers require professional knowledge and professional teaching skills, as well as a broad base of general knowledge (e.g., morality, service, cultural capital, institutional survey). Above all, in order to carry out instructional processes effectively, teachers should be both academically and professionally trained. This study revealed that teachers are not however expected to have an extraordinary memory, but rather looked upon as persons capable of thinking in the right direction. This study may provide a solution to the problem of teacher education and school effectiveness in Nigeria. For this reason, I offer this treatise to anyone seriously committed in improving schools in developing countries in general and in Nigeria in particular to improve the lives of all its citizens. In particular, I write this to encourage educational planners, education policy makers, curriculum developers, principals, teachers, and students of education interested in empirical information and methods to conceptualize the issue this study has raised and to provide them with useful suggestions to help them improve secondary schooling in Nigeria. Though, multiple audiences exist for any text. For this reason, I trust that the academic community will find this piece of work a useful addition to the existing literature on school effectiveness and school improvement. Through integrating concepts from a number of disciplines, I aim to describe as holistic a representation as space could allow of the components of school effectiveness and quality improvement. A new perspective on teachers' professional competencies, which not only take into consideration the unique characteristics of the variables used in this study, but also recommend their environmental and cultural derivation. In addition, researchers should focus their attention on the ways in which both professional and non-professional teachers construct and apply their methodological competencies, such as their grouping procedures and behaviors to the schooling of students. Keywords: Professional Training, Academic Training, Professionally Qualified, Academically Qualified, Professional Qualification, Academic Qualification, Job Effectiveness, Job Efficiency, Educational Planning, Teacher Training and Development, Nigeria.

Comprehensive Two-Dimensional Gas Chromatography: Instrumental and Methodological Development

Comprehensive two-dimensional gas chromatography (GC×GC) offers enhanced separation efficiency, reliability in qualitative and quantitative analysis, capability to detect low quantities, and information on the whole sample and its components. These features are essential in the analysis of complex samples, in which the number of compounds may be large or the analytes of interest are present at trace level. This study involved the development of instrumentation, data analysis programs and methodologies for GC×GC and their application in studies on qualitative and quantitative aspects of GC×GC analysis. Environmental samples were used as model samples. Instrumental development comprised the construction of three versions of a semi-rotating cryogenic modulator in which modulation was based on two-step cryogenic trapping with continuously flowing carbon dioxide as coolant. Two-step trapping was achieved by rotating the nozzle spraying the carbon dioxide with a motor. The fastest rotation and highest modulation frequency were achieved with a permanent magnetic motor, and modulation was most accurate when the motor was controlled with a microcontroller containing a quartz crystal. Heated wire resistors were unnecessary for the desorption step when liquid carbon dioxide was used as coolant. With use of the modulators developed in this study, the narrowest peaks were 75 ms at base. Three data analysis programs were developed allowing basic, comparison and identification operations. Basic operations enabled the visualisation of two-dimensional plots and the determination of retention times, peak heights and volumes. The overlaying feature in the comparison program allowed easy comparison of 2D plots. An automated identification procedure based on mass spectra and retention parameters allowed the qualitative analysis of data obtained by GC×GC and time-of-flight mass spectrometry. In the methodological development, sample preparation (extraction and clean-up) and GC×GC methods were developed for the analysis of atmospheric aerosol and sediment samples. Dynamic sonication assisted extraction was well suited for atmospheric aerosols collected on a filter. A clean-up procedure utilising normal phase liquid chromatography with ultra violet detection worked well in the removal of aliphatic hydrocarbons from a sediment extract. GC×GC with flame ionisation detection or quadrupole mass spectrometry provided good reliability in the qualitative analysis of target analytes. However, GC×GC with time-of-flight mass spectrometry was needed in the analysis of unknowns. The automated identification procedure that was developed was efficient in the analysis of large data files, but manual search and analyst knowledge are invaluable as well. Quantitative analysis was examined in terms of calibration procedures and the effect of matrix compounds on GC×GC separation. In addition to calibration in GC×GC with summed peak areas or peak volumes, simplified area calibration based on normal GC signal can be used to quantify compounds in samples analysed by GC×GC so long as certain qualitative and quantitative prerequisites are met. In a study of the effect of matrix compounds on GC×GC separation, it was shown that quality of the separation of PAHs is not significantly disturbed by the amount of matrix and quantitativeness suffers only slightly in the presence of matrix and when the amount of target compounds is low. The benefits of GC×GC in the analysis of complex samples easily overcome some minor drawbacks of the technique. The developed instrumentation and methodologies performed well for environmental samples, but they could also be applied for other complex samples.

Data fusion and matching by maximizing statistical dependencies

The core aim of machine learning is to make a computer program learn from the experience. Learning from data is usually defined as a task of learning regularities or patterns in data in order to extract useful information, or to learn the underlying concept. An important sub-field of machine learning is called multi-view learning where the task is to learn from multiple data sets or views describing the same underlying concept. A typical example of such scenario would be to study a biological concept using several biological measurements like gene expression, protein expression and metabolic profiles, or to classify web pages based on their content and the contents of their hyperlinks. In this thesis, novel problem formulations and methods for multi-view learning are presented. The contributions include a linear data fusion approach during exploratory data analysis, a new measure to evaluate different kinds of representations for textual data, and an extension of multi-view learning for novel scenarios where the correspondence of samples in the different views or data sets is not known in advance. In order to infer the one-to-one correspondence of samples between two views, a novel concept of multi-view matching is proposed. The matching algorithm is completely data-driven and is demonstrated in several applications such as matching of metabolites between humans and mice, and matching of sentences between documents in two languages.