CLASSIFICATION OF COMPLEX TWO-DIMENSIONAL IMAGES IN A PARALLEL DISTRIBUTED PROCESSING ARCHITECTURE

Neural network analysis is proposed and evaluated as a method of analysis of marine biological data, specifically images of plankton specimens. The quantification of the various plankton species is of great scientific importance, from modelling global climatic change to predicting the economic effects of toxic red tides. A preliminary evaluation of the neural network technique is made by the development of a back-propagation system that successfully learns to distinguish between two co-occurring morphologically similar species from the North Atlantic Ocean, namely Ceratium arcticum and C. longipes. Various techniques are developed to handle the indeterminately labelled source data, pre-process the images and successfully train the networks. An analysis of the network solutions is made, and some consideration given to how the system might be extended.Plymouth Marine Laborator

Robust linear discriminant rules with coordinatewise and distance based approaches

Linear discriminant analysis (LDA) is one of the supervised classification techniques to deal with relationship between a categorical variable and a set of continuous variables. The main objective of LDA is to create a function to distinguish between groups and allocating future observations to previously defined groups. Under the assumptions of normality and homoscedasticity, the LDA yields optimal linear discriminant rule (LDR) between two or more groups. However, the optimality of LDA highly relies on the sample mean and sample covariance matrix which are known to be sensitive to outliers. To abate these conflicts, robust location and scale estimators via coordinatewise and distance based approaches have been applied in constructing new robust LDA. These robust estimators were used to replace the classical sample mean and sample covariance to form robust linear discriminant rules (RLDR). A total of six RLDR, namely four coordinatewise (RLDRM, RLDRMw, RLDRW, RLDRWw) and two distance based (RLDRV, RLDRT) approaches have been proposed and implemented in this study. Simulation and real data study were conducted to investigate on the performance of the proposed RLDR, measured in terms of misclassification error rates and computational time. Several data conditions such as non-normality, heteroscedasticity, balanced and unbalanced data set were manipulated in the simulation study to evaluate the performance of these proposed RLDR. In real data study, a set of diabetes data was used. This data set violated the assumptions of normality as well as homoscedasticity. The results showed that the novel RLDRV is the best proposed RLDR to solve classification problem since it provides as much as 91.03% accuracy in classification as shown in the real data study. The proposed RLDR are good alternatives to the classical LDR as well as existing RLDR since these RLDR perform well in classification problems even under contaminated data

Sampling designs for exploratory multivariate analysis.

This thesis is concerned with problems of variable selection, influence of sample size and related issues in the applications of various techniques of exploratory multivariate analysis (in particular, correspondence analysis, biplots and canonical correspondence analysis) to archaeology and ecology. Data sets (both published and new) are used to illustrate these methods and to highlight the problems that arise - these practical examples are returned to throughout as the various issues are discussed. Much of the motivation for the development of the methodology has been driven by the needs of the archaeologists providing the data, who were consulted extensively during the study. The first (introductory) chapter includes a detailed description of the data sets examined and the archaeological background to their collection. Chapters Two, Three and Four explain in detail the mathematical theory behind the three techniques. Their uses are illustrated on the various examples of interest, raising data-driven questions which become the focus of the later chapters. The main objectives are to investigate the influence of various design quantities on the inferences made from such multivariate techniques. Quantities such as the sample size (e.g. number of artefacts collected), the number of categories of classification (e.g. of sites, wares, contexts) and the number of variables measured compete for fixed resources in archaeological and ecological applications. Methods of variable selection and the assessment of the stability of the results are further issues of interest and are investigated using bootstrapping and procrustes analysis. Jack-knife methods are used to detect influential sites, wares, contexts, species and artefacts. Some existing methods of investigating issues such as those raised above are applied and extended to correspondence analysis in Chapters Five and Six. Adaptions of them are proposed for biplots in Chapters Seven and Eight and for canonical correspondence analysis in Chapter Nine. Chapter Ten concludes the thesis

6th International Scientific Conference on Hardwood Processing : PROCEEDINGS

City of Lahti, Finland, has the opportunity to host the 6th International Conference of Hardwood Processing (ISCHP 2017) during September 25 to 28, 2017. The main events of the conference are arranged in the spectacular Sibelius Hall, an internationally acknowledged congress and concert center with attractive wooden interiors and excellent acoustics. Lahti region constitutes also one of the main hardwood industry clusters in Finland with versatile manufacturing of hardwood products and processing machinery, high-quality birch resources, and long history of education in wood products sector. The conference exhibits a continuum to the 10-year old history of ISCHP, the previous events being in Canada (2007 and 2015), France (2009), USA (2011), and Italy (2013). The scientific collaborators in the ISCHP family, listed before in page 2, take in turn the responsibility of the conference organization, and now it is the Finnish turn. Natural Resources Institute Finland (Luke) is the responsible organizer of this conference with a substantial support from University of Eastern Finland (UEF) and Aalto University. We are especially happy of this unique task in 2017, while Republic of Finland celebrates its centennial history as an independent country. The main objective of this conference is to bring together the scientific and research communities working on hardwood, from the source to the customer, to share knowledge and ideas. Around 80 international experts, scientists, government employees, hardwood industry representatives, suppliers, and customers attend the conference to discuss recent progress and innovative work in this valuable area of wood-based economy. Topics covered by ISCHP 2017 1) Forest management, wood procurement, wood properties and quality and analysis of hardwoods 2) Markets, sustainability, and value chains of hardwood cluster 3) Hardwood product development and performance 4) Hardwood processing, optimization and technology development for solid and composite products 5) Hardwood biorefining and value-added chemical products This conference book contains abstracts of all presentations in the conference, descriptions of industry and field visits and practical information for the attendees. A total of 42 scientific oral and poster presentations from 131 authors coming from 22 countries, and four keynote presentations from invited academic and industry experts provide the basis for the scientific success of ISCHP 2017. Papers written on the presentations have undergone a scientific peer-review process. They are available for readers in an electronic format in the Conference Proceedings. On behalf of the organizing committee of the conference, I have the pleasure to wish the very best results and pleasure from the scientific and industry contents and networking with colleagues, not to talk about an enjoyable time in Lahti region. Erkki Verkasalo Chair of ISCHP 2017201

The post-viral fatigue syndrome

Post-viral fatigue syndrome (myalgic encephalomyelitis) is a physically debilitating disorder associated with chronic disabling fatigue. This thesis presents two studies which look at the impact of illness from a personal-psychological and from a family perspective. The first investigates the psychological features of the syndrome. The prevalence of psychiatric disorder in 20 patients with the PVFS was determined. Sixty percent satisfied criteria for a current psychiatric disorder. Diagnoses were of neurotic depression and other neuroses. Only 25 % of a comparatively disabled group of 20 arthritis sufferers received similar diagnoses. Diagnoses did not substantially differ in type from a group of 20 subjects with major depressive disorders, although selected differences in symptom profile and the role of previous life-time psychiatric episodes, suggest that the PVFS cannot be regarded as a variant form of depressive disorder. A logistic regression analysis achieved a satisfactory separation of the two disorders on the basis of psychiatric symptoms. The second study investigates 9 school-aged children with mothers suffering from the syndrome, and 9 children with healthy parents. The children in the PVFS group had been exposed to their mother's illness from between 18 months and 14 years. They were found to have significantly more problems in the school environment in comparison to controls, rated as more shy and anxious, less assertive and with more relationship problems with peers. General family orientation was less active with fewer out-of-home family pursuits. Family interactions were somewhat more negative. Child adjustment is discussed in terms of the linkages between family, school and peer-group in the lives of these children. Investigations into the adaptive potential of such linkages and the permeability of the boundaries between the spheres raise important questions for ameliorative work in the counselling of PVFS sufferers and their families

Multimedia Development of English Vocabulary Learning in Primary School

In this paper, we describe a prototype of web-based intelligent handwriting education system for autonomous learning of Bengali characters. Bengali language is used by more than 211 million people of India and Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. This research project was aimed to develop an intelligent Bengali handwriting education system. As an intelligent tutor, the system can automatically check the handwriting errors, such as stroke production errors, stroke sequence errors, stroke relationship errors and immediately provide a feedback to the students to correct themselves. Our proposed system can be accessed from smartphone or iPhone that allows students to do practice their Bengali handwriting at anytime and anywhere. Bengali is a multi-stroke input characters with extremely long cursive shaped where it has stroke order variability and stroke direction variability. Due to this structural limitation, recognition speed is a crucial issue to apply traditional online handwriting recognition algorithm for Bengali language learning. In this work, we have adopted hierarchical recognition approach to improve the recognition speed that makes our system adaptable for web-based language learning. We applied writing speed free recognition methodology together with hierarchical recognition algorithm. It ensured the learning of all aged population, especially for children and older national. The experimental results showed that our proposed hierarchical recognition algorithm can provide higher accuracy than traditional multi-stroke recognition algorithm with more writing variability