Changing teachers´ practices in regular schools enrolling children with visual impairment

The number of persons with visual impairment in Tanzania is estimated to over 1.6 million. About half a million of these persons are children aged 7-13. Only about 1% of these children are enrolled in schools. The special schools and units are too few and in most cases they are far away from the children’s homes. More and more regular schools are enrolling children with visual impairment, but the schools lack financial resources, tactile teaching materials and trained special education teachers. Children with visual impairment enrolled in regular schools seldom get enough support and often fail in examinations. The general aim of this study was to contribute to increased knowledge and understanding about how teachers can change their teaching practices and thus facilitate the learning of children with visual impairment included in regular classrooms as they participate in an action research project. The project was conducted in a primary school in a poor rural region with a high frequency of blindness and visual impairment. The school was poorly resourced and the average number of pupils per class was 90. The teachers who participated in the collaborative action research project were the 14 teachers who taught blind or visually impaired pupils in grades 4 and 6, in total 6 pupils. The action research project was conducted during a period of 6 months and was carried out in five cycles. The teachers were actively involved in all the project activities; identifying challenges, planning solutions, producing teaching materials, reflecting on outcomes, collaborating and evaluating. Empirical data was collected with questionnaires, interviews, observations and focus group discussions. The findings of the study show that the teachers managed to change their teaching practices through systematic reflection, analysis and collaboration. The teachers produced a variety of tactile teaching materials, which facilitated the learning of the pupils with visual impairment. The pupils learned better and felt more included in the regular classes. The teachers gained new knowledge and skills. They grew professionally and started to collaborate with each other. The study contributes to new knowledge of how collaborative action research can be conducted in the area of special education in a Tanzanian school context. The study has also relevance to the planning of school-based professional development programs and teacher education programs in Tanzania and in other low-income countries. The results also point at strategies which can promote inclusion of children with disabilities in regular schools.

Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy

Diabetes is a rapidly increasing worldwide problem which is characterised by defective metabolism of glucose that causes long-term dysfunction and failure of various organs. The most common complication of diabetes is diabetic retinopathy (DR), which is one of the primary causes of blindness and visual impairment in adults. The rapid increase of diabetes pushes the limits of the current DR screening capabilities for which the digital imaging of the eye fundus (retinal imaging), and automatic or semi-automatic image analysis algorithms provide a potential solution. In this work, the use of colour in the detection of diabetic retinopathy is statistically studied using a supervised algorithm based on one-class classification and Gaussian mixture model estimation. The presented algorithm distinguishes a certain diabetic lesion type from all other possible objects in eye fundus images by only estimating the probability density function of that certain lesion type. For the training and ground truth estimation, the algorithm combines manual annotations of several experts for which the best practices were experimentally selected. By assessing the algorithm’s performance while conducting experiments with the colour space selection, both illuminance and colour correction, and background class information, the use of colour in the detection of diabetic retinopathy was quantitatively evaluated. Another contribution of this work is the benchmarking framework for eye fundus image analysis algorithms needed for the development of the automatic DR detection algorithms. The benchmarking framework provides guidelines on how to construct a benchmarking database that comprises true patient images, ground truth, and an evaluation protocol. The evaluation is based on the standard receiver operating characteristics analysis and it follows the medical practice in the decision making providing protocols for image- and pixel-based evaluations. During the work, two public medical image databases with ground truth were published: DIARETDB0 and DIARETDB1. The framework, DR databases and the final algorithm, are made public in the web to set the baseline results for automatic detection of diabetic retinopathy. Although deviating from the general context of the thesis, a simple and effective optic disc localisation method is presented. The optic disc localisation is discussed, since normal eye fundus structures are fundamental in the characterisation of DR.

Photosynthesis and Rubisco kinetics in spring wheat and meadow fescue under conditions of simulated climate change with elevated CO[sub 2] and increased temperatures

Selostus: Kevätvehnän ja nurminadan fotosynteesi ja Rubisco-kinetiikka simuloidun ilmastonmuutoksen eli kohotetun hiilidioksidipitoisuuden ja kohotetun lämpötilan oloissa

Coarse Global Visual Localization of a Mobile Robot

Localization, which is the ability of a mobile robot to estimate its position within its environment, is a key capability for autonomous operation of any mobile robot. This thesis presents a system for indoor coarse and global localization of a mobile robot based on visual information. The system is based on image matching and uses SIFT features as natural landmarks. Features extracted from training images arestored in a database for use in localization later. During localization an image of the scene is captured using the on-board camera of the robot, features are extracted from the image and the best match is searched from the database. Feature matching is done using the k-d tree algorithm. Experimental results showed that localization accuracy increases with the number of training features used in the training database, while, on the other hand, increasing number of features tended to have a negative impact on the computational time. For some parts of the environment the error rate was relatively high due to a strong correlation of features taken from those places across the environment.