A Graphical Pattern Editor for Java Frameworks

Ohjelmistojen uudelleenkäyttö on hyvin tärkeä käsite ohjelmistotekniikan alueella.Ohjelmistojen uudelleenkäyttötekniikat parantavat ohjelmistokehitysprosessin laatua. Yleisiä ratkaisuja sekä ohjelmiston suunnittelun että arkkitehtuurin uudelleenkäyttöön ovat olio-ohjelmointi ja sovelluskehykset. Tähän asti ei ole ollut olemassa yleisiä tapoja sovelluskehysten erikoistamiseen. Monet nykyääntunnetuista sovelluskehyksistä ovat hyvin suuria ja mutkikkaita. Tällaisten sovelluskehyksien käyttö on monimutkaista myös kokeneille ohjelmoijille. Hyvin dokumentoidut uudelleenkäytettävät sovelluskehyksen rajapinnat parantavat kehyksen käytettävyyttä ja tehostavat myös erikoistamisprosessiakin sovelluskehyksen käyttäjille. Sovelluskehyseditori (framework editor, JavaFrames) on prototyyppityökalu, jota voidaan käyttää yksinkertaistamaan sovelluskehyksen käyttöä. Perusajatus JavaFrames lähestymistavassa ovat erikoistamismallit, joita käytetään kuvamaan sovelluskehyksen uudelleenkäytettäviä rajapintoja. Näihin malleihin perustuen JavaFrames tarjoaa automaattisen lähdekoodi generaattorin, dokumentoinninja arkkitehtuurisääntöjen tarkistuksen. Tämä opinnäyte koskee graafisen mallieditorin kehittämistä JavaFrames ympäristöön. Työssä on laadittu työkalu,jonka avulla voidaan esittää graafisesti erikoistamismalli. Editori sallii uusien mallien luomisen, vanhojen käyttämättä olevien poistamisen, kuten myös yhteyksien lisäämisen mallien välille. Tällainen graafinen tuki JavaFrames ympäristöönvoi huomattavasti yksinkertaistaa sen käyttöä ja tehdä sovellusten kehittämisprosessista joustavamman.

Evaluation of Bubble Formation and Break Up in Suppression Pools by Using Pattern Recognition Methods

During a possible loss of coolant accident in BWRs, a large amount of steam will be released from the reactor pressure vessel to the suppression pool. Steam will be condensed into the suppression pool causing dynamic and structural loads to the pool. The formation and break up of bubbles can be measured by visual observation using a suitable pattern recognition algorithm. The aim of this study was to improve the preliminary pattern recognition algorithm, developed by Vesa Tanskanen in his doctoral dissertation, by using MATLAB. Video material from the PPOOLEX test facility, recorded during thermal stratification and mixing experiments, was used as a reference in the development of the algorithm. The developed algorithm consists of two parts: the pattern recognition of the bubbles and the analysis of recognized bubble images. The bubble recognition works well, but some errors will appear due to the complex structure of the pool. The results of the image analysis were reasonable. The volume and the surface area of the bubbles were not evaluated. Chugging frequencies calculated by using FFT fitted well into the results of oscillation frequencies measured in the experiments. The pattern recognition algorithm works in the conditions it is designed for. If the measurement configuration will be changed, some modifications have to be done. Numerous improvements are proposed for the future 3D equipment.

Model-View-Controller architectural pattern and its evolution in graphical user interface frameworks

Model-View-Controller (MVC) is an architectural pattern used in software development for graphical user interfaces. It was one of the first proposed solutions in the late 1970s to the Smart UI anti-pattern, which refers to the act of writing all domain logic into a user interface. The original MVC pattern has since evolved in multiple directions, with various names and may confuse many. The goal of this thesis is to present the origin of the MVC pattern and how it has changed over time. Software architecture in general and the MVC’s evolution within web applications are not the primary focus. Fundamen- tal designs are abstracted, and then used to examine the more recent versions. Prob- lems with the subject and its terminology are also presented.

Epidemiology, management and outcome of facial injuries

Dental injuries are common and the incidence of maxillofacial injuries has increased over the recent decades in Finland. Accidental injuries are the global leading cause of death among children over the age of one year and among adults under the age of 40 globally. Significant resources and costs are needed for the treatment of these patients. The prevention is the most economical way to reduce trauma rates and costs. For the prevention it is crucial to know the prevalences, incidences and risk factors related to injuries. To improve the quality of treatment, it is essential to explore the causes, trauma mechanisms and management of trauma. The above mentioned was the aim of this thesis. With a large epidemiological cohort study (5737 participants) it was possible to estimate lifetime prevalence of and risk factors for dental trauma in general population (Study I). The prevalence of dental fractures was 43% and the prevalence of dental luxations and avulsions was 14%. Male gender, a history of previous non-dental injuries, mental distress, overweight and high alcohol consumption were positively associated with the occurrence of dental injuries Study II was conducted to explore the differences in type and multiplicity of mandibular fractures in three different countries (Canada, Finland and Kuwait). This retrospective study showed that the differences in mandibular fracture multiplicity and location are based on different etiologies and demographic patterns. This data can be exploited for planning of measures to prevent traumatic facial fractures. The etiology, management and outcome of 63 pediatric skull base fracture (Study III) and 20 pediatric frontobasal fracture patients (Study IV) were explored. These retrospective studies showed that, both skull base fracture and frontobasa fracture are rare injuries in childhood and although intracranial injuries and morbidity are frequent, permanent neurological or neuropsychological deficits are infrequent. A systematic algorithm (Study V) for computer tomography (CT) image review was aimed at clinicians and radiologists to improve the assessment of patients with complex upper midface and cranial base trauma. The cohort study was cross sectional and data was collected in the Turku and Oulu University Hospitals. A novel image-reviewing algorithm was created to enhance the specificity of CT for the diagnosis of frontobasal fractures. The study showed that an image-viewing algorithm standardizes the frontobasal trauma detection procedure and leads to better control and assessment. The purpose of the retrospective subcranial craniotomy study (VI) was to review the types of frontobasal fractures and their management, complications and outcome when the fracture is approached subcranially. The subcranial approach appears to be successful and have a reasonably low complication rate. It may be recommended as the technique of choice in multiple and the most complicated frontal base fractures where the endoscopic endonasal approach is not feasible.

Deep generative models for facial keypoints detection

A new area of machine learning research called deep learning, has moved machine learning closer to one of its original goals: artificial intelligence and general learning algorithm. The key idea is to pretrain models in completely unsupervised way and finally they can be fine-tuned for the task at hand using supervised learning. In this thesis, a general introduction to deep learning models and algorithms are given and these methods are applied to facial keypoints detection. The task is to predict the positions of 15 keypoints on grayscale face images. Each predicted keypoint is specified by an (x,y) real-valued pair in the space of pixel indices. In experiments, we pretrained deep belief networks (DBN) and finally performed a discriminative fine-tuning. We varied the depth and size of an architecture. We tested both deterministic and sampled hidden activations and the effect of additional unlabeled data on pretraining. The experimental results show that our model provides better results than publicly available benchmarks for the dataset.