Nagrinėjama skirtingais medicinos informatikos standartais ar jų skirtingomis versijomis sąveikaujančių asmens sveikatos priežiūros įstaigos informacinių sistemių integracija. Pagal Kauno Dainavos poliklinikos informacinių sistemų integracijos uždavinius pasirinktas artimiausias teorinis integracijos modelis. Jo pagrindu parengti tarpinio (integracinio) IS sluoksnio/modulio funkciniai reikalavimai, užtikrinantys vienareikšmius paciento sąryšius su jo sveikatos duomenimis, esančiais skirtingose sistemose. Panaudojant medicinos informatikos standarto HL7 bibliotekas 2-ai ir 3-iai versijoms, sukurtas ir sėkmingai išbandytas tarpinio (integracinio) IS sluoksnio/modulio prototipas, leidžiantis automatiškai formuoti procedūrų užsakymo identifikatorius ir, jų pagrindu, nuorodas į duomenų archyvą. Integralumas tarp 2-os ir 3-os versijos sutampančių komponentų užtikrinamas trigeriais: įterpiant, keičiant bei trinant įrašus iš vietos versijos komponento, jis bus atitinkamai modifikuotas ir kitoje versijoje. Toks duomenų sluoksnio realizavimas leidžia sumažinti verslo logikos sluoksnio sudėtingumą, kai naudojama trijų lygių architektūra, bei pačių duomenų transformacijų skaičių. / The research is devoted to an integration of health information systems, when the interoperability should be utilised using different health informatics standards or their versions. To meet the integration goals, the theoretical model, closest to a particular situation of the Dainava outpatient clinic in Kaunas, had been chosen. Based on the model, the functional requirements for the middleware (integration) layer had been defined in order to ensure unambiguous links between patient data, stored in different systems. The middleware software prototype had been developed using messaging libraries for the HL7 versions v2 and v3. The middleware was successfully tested for automatic generation of orders for procedures and links to the procedure result data in the archive. The integration of the HL7 v2 and v3 coincidental components is achieved using trigger events for synchronous data manipulations. Such rules, implemented at the data layer, leads to less complex logics at business process layer and to lower number of data transformations. After automatic orders generation from referrals model implementation, we had to produce Worklist management system prototype to ensure full control of coming patient examinations. In this scenario DICOM based data models and terms had been chosen to use.

Nesta tese estudamos o fluxo dirigido, a primeira componente na expansão de Fourier da distribuição azimutal das partículas emitidas. Diferente do fluxo elíptico, que é muito bem estudado e descrito na literatura, a física que gera este observável ainda não é muito conhecida. Mostramos que este observável é altamente sensível a condições iniciais, comparando vários resultados numéricos para diferentes condições iniciais, entre analíticas e numéricas. Propomos que o fluxo dirigido também é sensível à aceleração longitudinal e formulamos um modelo analítico baseado nesta hipótese. Este modelo sugere quais ingredientes são relevantes para o fluxo dirigido. Ele é confrontado com sucesso com cálculos numéricos, resultados experimentais e cálculos que não incluem a aceleração longitudinal. / In this thesis we study the directed flow, the first component in the Fourier\'s expansion of the azimuthal distribution of emitted particles. Unlike the elliptic flow, which is well studied and described in the literature, the physics that generates this observable is not yet well described. We show that this observable is highly sensible to the initial conditions, comparing several numeric results with different initial conditions, between analytic and numeric ones. We propose that the directed flow is also sensitive to the longitudinal acceleration and we formulate an analytic model based in this hypothesis. This model suggests which ingredients are relevant to the directed flow. It is confronted with success against numeric calculus, experimental results, and calculus that do not include the longitudinal acceleration.

yes / Coinciding with advances in whole-slide imaging scanners, it is become essential to automate the conventional image-processing techniques to assist pathologists with some tasks such as mitotic-cells detection. In histopathological images analysing, the mitotic-cells counting is a significant biomarker in the prognosis of the breast cancer grade and its aggressiveness. However, counting task of mitotic-cells is tiresome, tedious and time-consuming due to difficulty distinguishing between mitotic cells and normal cells. To tackle this challenge, several deep learning-based approaches of Computer-Aided Diagnosis (CAD) have been lately advanced to perform counting task of mitotic-cells in the histopathological images. Such CAD systems achieve outstanding performance, hence histopathologists can utilise them as a second-opinion system. However, improvement of CAD systems is an important with the progress of deep learning networks architectures. In this work, we investigate deep YOLO (You Only Look Once) v2 network for mitotic-cells detection on ICPR (International Conference on Pattern Recognition) 2012 dataset of breast cancer histopathology. The obtained results showed that proposed architecture achieves good result of 0.839 F1-measure.

The influenza A virus genome codes for up to 12 proteins. Segment 2 encodes three proteins, the polymerase subunit PB1, a small protein PB1-F2 and an N-terminally truncated version of PB1 called N40. Different functions have been reported for PB1-F2 such as induction of apoptosis, regulation of the viral polymerase activity, enhancement of secondary bacterial infections and modulation of the innate immune system. So far, no function has been ascribed to N40. To study PB1-F2 in more detail, its coding sequence was deleted from its original position and inserted downstream of the PB1 (segment 2), NA (segment 6) or M (segment 7) open reading frames (ORF) employing different strategies, including the use of an overlapping Stop-Start cassette, a duplicated promoter sequence and the self-cleaving 2A peptide derived from foot-and-mouth disease virus. Viruses with bicistronic segments were rescued and tested for their ability to express PB1-F2. Whereas no expression of PB1-F2 was detected from bicistronic segments 2 and 7, expression of PB1-F2 from segment 6 was observed in high levels. However, the phenotype of all these viruses was similar to that of viruses lacking PB1-F2 which made mutational analysis of PB1-F2 not worthwhile. Previously, the function of PB1-F2 was mainly studied using a virus deficient in PB1-F2 production but showing increased N40 expression. In the present study, recombinant WSN viruses lacking either PB1-F2 or N40, or both proteins were engineered and the effects of these mutations on the viral life cycle were examined. Viruses deficient for PB1-F2 that overexpressed N40 showed the most attenuated phenotype, whereas the loss of PB1-F2 alone did not obviously affect virus replication. Reduced viral polymerase activity was observed for viruses lacking N40, however attenuation in vivo was only seen in combination with the loss of PB1-F2. Neither the loss of PB1-F2 nor N40 alone had a great impact, but changes in the expression level of both proteins were disadvantageous for the virus. Increased levels of N40 shifted the polymerase activity towards replication, suggesting a new function for N40. Thus, it was shown that the segment 2 gene products and their expression level influence viral replication and pathogenicity, and a careful design of mutant recombinant viruses is vital for determining the experimental outcome.

No description available.

Ce travail porte sur le test fonctionnel, basé sur les spécifications et complètement automatisé des logiciels synchrones. Nous proposons une extension des techniques de test proposées par l'outil Lutess afin de prendre en compte des logiciels qui comportent des entrées/sorties numériques. La génération de données de test est abordée en s'appuyant sur les techniques de programmation par contraintes.<br /><br />Nous avons redéfini les méthodes de guidage de la génération afin de les adapter à ce nouveau contexte numérique. Ainsi, nous proposons, en plus de la génération aléatoire respectant les propriétés invariantes de l'environnement, le guidage du test basé sur des probabilités conditionnelles ou sur des propriétés de sûreté. Des connaissances partielles sur le logiciel, que nous appelons hypothèses de test, peuvent être intégrées dans le processus de génération et contribuer à l'amélioration du pouvoir de détection de fautes du guidage par propriétés de sûreté. Enfin, nous permettons l'utilisation conjointe de plusieurs techniques de guidage dans une même spécification.<br /><br />Une implémentation de ces méthodes de test a été réalisée dans une nouvelle version de l'outil, que nous appelons Lutess V2. L'applicabilité de ces méthodes dans un contexte plus réaliste a été évaluée sur une étude de cas significative d'un contrôleur de niveau d'eau dans une chaudière.

Motion capture technology has been applied in many fields such as animation, medicine, military, etc. since it was first proposed in the 1970s. Based on the principles applied, motion capture technology is generally classified into six categories: 1) Optical; 2) Inertial; 3) Magnetic; 4) Mechanical; 5) Acoustic and 6) Markerless. Different from the other five kinds of motion capture technologies which try to track path of specific points with different equipment, markerless systems recognize human or non-human body's motion with vision-based technology which focuses on analyzing and processing the captured images for motion capture. The user doed not need to wear any equipment and is free to do any action in an extensible measurement area while a markerless motion capture system is working. Though this kind of system is considered as the preferred solution for motion capture, the difficulty for realizing an effective and high accuracy markerless system is much higher than the other technologies mentioned, which makes markerless motion capture development a popular research direction. Microsoft Kinect sensor has attracted lots of attention since the launch of its first version with its depth sensing feature which gives the sensor the ability to do motion capture without any extra devices. Recently, Microsoft released a new version of Kinect sensor with improved hardware and and targeted at the consumer market. However, to the best of our knowlege, the accuracy assessment of the sensor remains to be answered since it was released. In this thesis, we measure the depth accuracy of the newly released Kinect v2 depth sensor from different aspects and propose a trilateration method to improve the depth accuracy with multiple Kinects simultaneously. Based on the trilateration method, a low-cost, no wearable equipment requirement and easy setup human gait tracking system is realized.

No description available.

The thesis deals with the design and implementation of user interface for robotic manipulator control by means of gesture detection of the operator. The used manipulator is the Katana 6M180 with five degrees of freedom. Gestures are detected using an innovated sensor system which includes Microsoft Kinect version 2 and Leap Motion sensors. The first part provides an overview of existing solutions and used technologies. The second part describes the design of the user interface, its implementation and testing.

<div>Machine learning models are rising every day. Most of the Computer Vision oriented</div><div>machine learning models arise from Convolutional Neural Network’s(CNN) basic structure.</div><div>Machine learning developers use CNNs extensively in Image classification, Object Recognition,</div><div>and Image segmentation. Although CNN produces highly compatible models with</div><div>superior accuracy, they have their disadvantages. Estimating pose and transformation for</div><div>computer vision applications is a difficult task for CNN. The CNN’s functions are capable of</div><div>learning only shift-invariant features of an image. These limitations give machine learning</div><div>developers motivation towards generating more complex algorithms.</div><div>Search for new machine learning models led to Capsule Networks. This Capsule Network</div><div>was able to estimate objects’ pose in an image and recognize transformations to these</div><div>objects. Handwritten digit classification is the task for which capsule networks are to solve</div><div>at the initial stages. Capsule Networks outperforms all models for the MNIST dataset for</div><div>handwritten digits, but to use Capsule networks for image classification is not a straightforward</div><div>multiplication of parameters. By replacing the Capsule Network’s initial layer, a</div><div>simple Convolutional Layer, with complex architectures in CNNs, authors of Residual Capsule</div><div>Network achieved a tremendous change in capsule network applications without a high</div><div>number of parameters.</div><div>This thesis focuses on improving this recent Residual Capsule Network (RCN) to an</div><div>extent where accuracy and model size is optimal for the Image classification task with a</div><div>benchmark of the CIFAR-10 dataset. Our search for an exemplary capsule network led to</div><div>the invention of RCN2: Residual Capsule Network 2 and RCNX: Residual Capsule NeXt.</div><div>RCNX, as the next generation of RCN. They outperform existing architectures in the domain</div><div>of Capsule networks, focusing on image classification such as 3-level RCN, DCNet, DC</div><div>Net++, Capsule Network, and even outperforms compact CNNs like MobileNet V3.</div><div>RCN2 achieved an accuracy of 85.12% with 1.95 Million parameters, and RCNX achieved</div><div>89.31% accuracy with 1.58 Million parameters on the CIFAR-10 benchmark.</div>