Architecture for a Session Initiation Protocol Based Presence Client

Mitä on läsnäolo? Tämä työ määrittelee läsnäolon tietyn henkilön, laitteen tai palvelun halukkuudeksi kommunikoida. Nykyään on olemassa lukuisia läsnäolotietoa levittäviä sovelluksia, joista jokainen käyttää erilaista protokollaa tehtävän suorittamiseen. Vasta viime aikoina sovellusten kehittäjät ovat huomanneet tarpeen yhdelle sovellukselle, joka kykenee tukemaan lukuisia läsnäoloprotokollia. Session Initiation Protocol (SIP) voi levittää läsnäolotietoa muiden ominaisuuksiensa lisäksi. Kun muita protokollia käytetään vain reaaliaikaiseen viestintään ja läsnäolotiedon lähetykseen, SIP pystyy moniin muihinkin asioihin. Se on alunperin suunniteltu aloittamaan, muuttamaan ja lopettamaan osapuolien välisiä multimediaistuntoja. Arkkitehtuurin toteutus käyttää kahta Symbian –käyttöjärjestelmän perusominaisuutta: asiakas-palvelin rakennetta ja kontaktitietokantaa. Asiakaspalvelin rakenne erottaa asiakkaan protokollasta tarjoten perustan laajennettavalle usean protokollan arkkitehtuurille ja kontaktitietokanta toimii läsnäolotietojen varastona. Työn tuloksena on Symbianin käyttöjärjestelmässä toimiva läsnäoloasiakas.

Centralized Identity Management for Web Applications

Internet-palvelujen määrä kasvaa jatkuvasti. Henkilöllä on yleensä yksi sähköinen identiteetti jokaisessa käyttämässään palvelussa. Autentikointitunnusten turvallinen säilytys käy yhä vaikeammaksi, kun niitä kertyy yhdet jokaisesta uudesta palvelurekisteröitymisestä. Tämä diplomityö tarkastelee ongelmaa ja ratkaisuja sekä palvelulähtöisestä että teknisestä näkökulmasta. Palvelulähtöisen identiteetinhallinnan liiketoimintakonsepti ja toteutustekniikat – kuten single sign-on (SSO) ja Security Assertion Markup Language (SAML) – käydään läpi karkeiden esimerkkien avulla sekä tutustuen Nokia Account -hankkeessa tuotetun ratkaisun konseptiin ja teknisiin yksityiskohtiin. Nokia Account -palvelun ensimmäisen version toteutusta analysoidaan lopuksi identiteetinhallintapalveluiden suunnitteluperiaatteita ja vaatimuksia vasten.

Calculation analysis of energy saving tools for fan and pump applications

This thesis analyses the calculation of FanSave and PumpSave energy saving tools calculation. With these programs energy consumption of variable speed drive control for fans and pumps can be compared to other control methods. With FanSave centrifugal and axial fans can be examined and PumpSave deals with centrifugal pumps. By means of these programs also suitable frequency converter can be chosen from the ABB collection. Programs need as initial values information about the appliances like amount of flow and efficiencies. Operation time is important factor when calculating the annual energy consumption and information about it are the length and profile. Basic theory related to fans and pumps is introduced without more precise instructions for dimensioning. FanSave and PumpSave contain various methods for flow control. These control methods are introduced in the thesis based on their operational principles and suitability. Also squirrel cage motor and frequency converter are introduced because of their close involvement to fans and pumps. Second part of the thesis contains comparison between results of FanSave’s and PumpSave’s calculation and performance curve based calculation. Also laboratory tests were made with centrifugal and axial fan and also with centrifugal pump. With the results from this thesis the calculation of these programs can be adjusted to be more accurate and also some new features can be added.

Adaptive MCMC methods with applications in environmental and geophysical models

This work presents new, efficient Markov chain Monte Carlo (MCMC) simulation methods for statistical analysis in various modelling applications. When using MCMC methods, the model is simulated repeatedly to explore the probability distribution describing the uncertainties in model parameters and predictions. In adaptive MCMC methods based on the Metropolis-Hastings algorithm, the proposal distribution needed by the algorithm learns from the target distribution as the simulation proceeds. Adaptive MCMC methods have been subject of intensive research lately, as they open a way for essentially easier use of the methodology. The lack of user-friendly computer programs has been a main obstacle for wider acceptance of the methods. This work provides two new adaptive MCMC methods: DRAM and AARJ. The DRAM method has been built especially to work in high dimensional and non-linear problems. The AARJ method is an extension to DRAM for model selection problems, where the mathematical formulation of the model is uncertain and we want simultaneously to fit several different models to the same observations. The methods were developed while keeping in mind the needs of modelling applications typical in environmental sciences. The development work has been pursued while working with several application projects. The applications presented in this work are: a winter time oxygen concentration model for Lake Tuusulanjärvi and adaptive control of the aerator; a nutrition model for Lake Pyhäjärvi and lake management planning; validation of the algorithms of the GOMOS ozone remote sensing instrument on board the Envisat satellite of European Space Agency and the study of the effects of aerosol model selection on the GOMOS algorithm.

Optimizing Mobile Java Location-Based Services in Symbian OS

This thesis evaluates methods for obtaining high performance in applications running on the mobile Java platform. Based on the evaluated methods, an optimization was done to a Java extension API running on top the Symbian operating system. The API provides location-based services for mobile Java applications. As a part of this thesis, the JNI implementation in Symbian OS was also benchmarked. A benchmarking tool was implemented in the analysis phase in order to implement extensive performance test set. Based on the benchmark results, it was noted that the landmarks implementation of the API was performing very slowly with large amounts of data. The existing implementation proved to be very inconvenient for optimization because the early implementers did not take performance and design issues into consideration. A completely new architecture was implemented for the API in order to provide scalable landmark initialization and data extraction by using lazy initialization methods. Additionally, runtime memory consumption was also an important part of the optimization. The improvement proved to be very efficient based on the measurements after the optimization. Most of the common API use cases performed extremely well compared to the old implementation. Performance optimization is an important quality attribute of any piece of software especially in embedded mobile devices. Typically, projects get into trouble with performance because there are no clear performance targets and knowledge how to achieve them. Well-known guidelines and performance models help to achieve good overall performance in Java applications and programming interfaces.

Adaptive optimization schemes for mobile VoIP applications Battery life and call quality aspects

In this thesis programmatic, application-layer means for better energy-efficiency in the VoIP application domain are studied. The work presented concentrates on optimizations which are suitable for VoIP-implementations utilizing SIP and IEEE 802.11 technologies. Energy-saving optimizations can have an impact on perceived call quality, and thus energy-saving means are studied together with those factors affecting perceived call quality. In this thesis a general view on a topic is given. Based on theory, adaptive optimization schemes for dynamic controlling of application's operation are proposed. A runtime quality model, capable of being integrated into optimization schemes, is developed for VoIP call quality estimation. Based on proposed optimization schemes, some power consumption measurements are done to find out achievable advantages. Measurement results show that a reduction in power consumption is possible to achieve with the help of adaptive optimization schemes.

Older consumers adopting information and communication technology: Evaluating opportunities for health care applications

The purpose of this dissertation is to analyse older consumers' adoption of information and communication technology innovations, assess the effect of aging related characteristic, and evaluate older consumers' willingness to apply these technologies in health care services. This topic is considered important, because the population in Finland (as in other welfare states) is aging and thus offers a possibility for marketers, but on the other hand threatens society with increasing costs for healthcare. Innovation adoption has been under research from several aspects in both organizational and consumer research. In the consumer behaviour, several theories have been developed to predict consumer responses to innovation. The present dissertation carefully reviews previous research and takes a closer look at the theory of planned behaviour, technology acceptance model and diffusion of innovations perspective. It is here suggested that there is a possibility that these theories can be combined and complemented to predict the adoption of ICT innovations among aging consumers, taking the aging related personal characteristics into account. In fact, there are very few studies that have concentrated on aging consumers in the innovation research, and thus there was a clear indent for the present research. ICT in the health care context has been studied mainly from the organizational point of view. If the technology is thus applied for the communication between the individual end-user and service provider, the end-user cannot be shrugged off. The present dissertation uses empirical evidence from a survey targeted to 55-79 year old people from one city in Southern-Carelia. The empirical analysis of the research model was mainly based on structural equation modelling that has been found very useful on estimating causal relationships. The tested models were targeted to predict the adoption stage of personal computers and mobile phones, and the adoption intention of future health services that apply these devices for communication. The present dissertation succeeded in modelling the adoption behaviour of mobile phones and PCs as well as adoption intentions of future services. Perceived health status and three components behind it (depression, functional ability, and cognitive ability) were found to influence perception of technology anxiety. Better health leads to less anxiety. The effect of age was assessed as a control variable, in order to evaluate its effect compared to health characteristics. Age influenced technology perceptions, but to lesser extent compared to health. The analyses suggest that the major determinant for current technology adoption is perceived behavioural control, and additionally technology anxiety that indirectly inhibit adoption through perceived control. When focusing on future service intentions, the key issue is perceived usefulness that needs to be highlighted when new services are launched. Besides usefulness, the perception of online service reliability is important and affects the intentions indirectly. To conclude older consumers' adoption behaviour is influenced by health status and age, but also by the perceptions of anxiety and behavioural control. On the other hand, launching new types of health services for aging consumers is possible after the service is perceived reliable and useful.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Molecular Characteristics of Neuroblastoma with Special Reference to Novel Prognostic Factors and Diagnostic Applications

Molecular Characteristics of Neuroblastoma with Special Reference to Novel Prognostic Factors and Diagnostic Applications Department of Medical Biochemistry and Genetics Annales Universitatis Turkuensis, Medica-Odontologica, 2009, Turku, Finland Painosalama Oy, Turku, Finland 2009 Background: Neuroblastoma, which is the most common and extensively studied childhood solid cancer, shows a great clinical and biological heterogeneity. Most of the neuroblastoma patients older than one year have poor prognosis despite intensive therapies. The hallmark of neuroblastoma, biological heterogeneity, has hindered the discovery of prognostic tumour markers. At present, few molecular markers, such as MYCN oncogene status, have been adopted into clinical practice. Aims: The aim of the study was to improve the current prognostic methodology of neuroblastoma, especially by taking cognizance of the biological heterogeneity of neuroblastoma. Furthermore, unravelling novel molecular characteristics which associate with neuroblastoma tumour progression and cell differentiation was an additional objective. Results: A new strictly defined selection of neuroblastoma tumour spots of highest proliferation activity, hotspots, appeared to be representative and reliable in an analysis of MYCN amplification status using a chromogenic in situ hybridization technique (CISH). Based on the hotspot tumour tissue microarray immunohistochemistry and high-resolution oligo-array-based comparative genomic hybridization, which was integrated with gene expression and in silico analysis of existing transcriptomics, a polysialylated neural cell adhesion molecule (NCAM) and poorly characterized amplicon at 12q24.31 were discovered to associate with outcome. In addition, we found that a previously considered new neuroblastoma treatment target, the mutated c-kit receptor, was not mutated in neuroblastoma samples. Conclusions: Our studies indicate polysialylated NCAM and 12q24.31 amplicon to be new molecular markers with important value in prognostic evaluation of neuroblastoma. Moreover, the presented hotspot tumour tissue microarray method together with the CISH technique of the MYCN oncogene copy number is directly applicable to clinical use. Key words: neuroblastoma, polysialic acid, neural cell adhesion molecule, MYCN, c-kit, chromogenic in situ hybridization, hotspot

Model-based study of a chemical-looping combustion process

Chemical-looping combustion (CLC) is a novel combustion technology with inherent separation of the greenhouse gas CO2. The technique typically employs a dual fluidized bed system where a metal oxide is used as a solid oxygen carrier that transfers the oxygen from combustion air to the fuel. The oxygen carrier is looping between the air reactor, where it is oxidized by the air, and the fuel reactor, where it is reduced by the fuel. Hence, air is not mixed with the fuel, and outgoing CO2 does not become diluted by the nitrogen, which gives a possibility to collect the CO2 from the flue gases after the water vapor is condensed. CLC is being proposed as a promising and energy efficient carbon capture technology, since it can achieve both an increase in power station efficiency simultaneously with low energy penalty from the carbon capture. The outcome of a comprehensive literature study concerning the current status of CLC development is presented in this thesis. Also, a steady state model of the CLC process, based on the conservation equations of mass and energy, was developed. The model was used to determine the process conditions and to calculate the reactor dimensions of a 100 MWth CLC system with bunsenite (NiO) as oxygen carrier and methane (CH4) as fuel. This study has been made in Oxygen Carriers and Their Industrial Applications research project (2008 – 2011), funded by the Tekes – Functional Material program. I would like to acknowledge Tekes and participating companies for funding and all project partners for good and comfortable cooperation.

Calculation of the carbon footprint of Finnish barley products - methodology and possible applications

The purpose of this thesis was to define how product carbon footprint analysis and its results can be used in company's internal development as well as in customer and interest group guidance, and how these factors are related to corporate social responsibility. From-cradle-to-gate carbon footprint was calculated for three products; Torino Whole grain barley, Torino Pearl barley, and Elovena Barley grit & oat bran, all of them made of Finnish barley. The carbon footprint of the Elovena product was used to determine carbon footprints for industrial kitchen cooked porridge portions. The basic calculation data was collected from several sources. Most of the data originated from Raisio Group's contractual farmers and Raisio Group's cultivation, processing and packaging specialists. Data from national and European literature and database sources was also used. The electricity consumption for porridge portions' carbon footprint calculations was determined with practical measurements. The carbon footprint calculations were conducted according to the ISO 14044 standard, and the PAS 2050 guide was also applied. A consequential functional unit was applied in porridge portions' carbon footprint calculations. Most of the emissions from barley products' life cycle originate from primary production. The nitrous oxide emissions from cultivated soil and the use and production of nitrogenous fertilisers contribute over 50% of products' carbon footprint. Torino Pearl barley has the highest carbon footprint due to the lowest processing output. The reductions in products' carbon footprint can be achieved with developments in cultivation and grain processing. The carbon footprint of porridge portion can be reduced by using domestically produced plant-based ingredients and by making the best possible use of the kettle. Carbon footprint calculation can be used to determine possible improvement points related to corporate environmental responsibility. Several improvement actions are related to economical and social responsibility through better raw material utilization and expense reductions.

Antenna Design for Contactless IC Card Applications

Contactless integrated circuit cards are one form of application of radio frequency identification. They are used in applications such as access control, identification, and payment in public transport. The contactless IC cards are passive which means that both the data and the energy are transferred to the card without contact using inductive coupling. Antenna design and optimization of the design for contactless IC cards defined by ISO/IEC14443 is studied. The basic operation principles of contactless system are presented and the structure of contactless IC card is illustrated. The structure was divided between the contactless chip and the antenna. The operation of the antenna was covered in depth and the parameters affecting to the performance of the antenna were presented. Also the different antenna technologies and connection technologies were provided. The antenna design process with the parameters and the design tools isillustrated and optimization of the design is studied. To make the design process more ideal a target of development was discovered, which was the implementation of test application. The optimization of the antenna design was presented based on the optimization criteria defined in this study. The solution for the implementation of these criteria and the effect of each criterion was found. For enhancing the performance of the antenna a focus for future study was proposed.

Engineering of Business Critical Web Applications

Research focus of this thesis is to explore options for building systems for business critical web applications. Business criticality here includes requirements for data protection and system availability. The focus is on open source software. Goals are to identify robust technologies and engineering practices to implement such systems. Research methods include experiments made with sample systems built around chosen software packages that represent certain technologies. The main research focused on finding a good method for database data replication, a key functionality for high-availability, database-driven web applications. Research included also finding engineering best practices from books written by administrators of high traffic web applications. Experiment with database replication showed, that block level synchronous replication offered by DRBD replication software offered considerably more robust data protection and high-availability functionality compared to leading open source database product MySQL, and its built-in asynchronous replication. For master-master database setups, block level replication is more recommended way to build high-availability into the system. Based on thesis research, building high-availability web applications is possible using a combination of open source software and engineering best practices for data protection, availability planning and scaling.

Usability Centered Implementation of a Web Based Value Added Module

Customer specific functionalities are a challenging part of procurement and invoice automation environments. In Basware Enterprise Purchase to Payment product family the customer specific reports are supported only in a basic level without any seamless interface between all EPP products. Also other customer specific functionalities are not supported as there is no customizable interface between the applications and only the most common features are implemented to the products themselves. In this thesis foundations are created for a new web based value added module where it is possible to create seamless customer specific functionalities throughout the whole EPP product family. The work is implemented in a Proof of Concept type of piloting. The system is created in user centered way where the users are able to explain their requests and determine their needs. The result is an excellent foundation for a module that can be developed further.

Probiotic interventions in infancy: Benefit and Safety Assessment of Extended Applications

Immaturity of the gut barrier system in the newborn has been seen to underlie a number of chronic diseases originating in infancy and manifesting later in life. The gut microbiota and breast milk provide the most important maturing signals for the gut-related immune system and reinforcement of the gut mucosal barrier function. Recently, the composition of the gut microbiota has been proposed to be instrumental in control of host body weight and metabolism as well as the inflammatory state characterizing overweight and obesity. On this basis, inflammatory Western lifestyle diseases, including overweight development, may represent a potential target for probiotic interventions beyond the well documented clinical applications. The purpose of the present undertaking was to study the efficacy and safety of perinatal probiotic intervention. The material comprised two ongoing, prospective, double-blind NAMI (Nutrition, Allergy, Mucosal immunology and Intestinal microbiota) probiotic interventions. In the mother-infant nutrition and probiotic study altogether 256 women were randomized at their first trimester of pregnancy into a dietary intervention and a control group. The intervention group received intensive dietary counselling provided by a nutritionist, and were further randomized at baseline, double-blind, to receive probiotics (Lactobacillus rhamnosus GG and Bifidobacterium lactis) or placebo. The intervention period extended from the first trimester of pregnancy to the end of exclusive breastfeeding. In the allergy prevention study altogether 159 women were randomized, double-blind, to receive probiotics (Lactobacillus rhamnosus GG) or placebo 4 weeks before expected delivery, the intervention extending for 6 months postnatally. Additionally, patient data on all premature infants with very low birth weight (VLBW) treated in the Department of Paediatrics, Turku University Hospital, during the years 1997 - 2008 were utilized. The perinatal probiotic intervention reduced the risk of gestational diabetes mellitus (GDM) in the mothers and perinatal dietary counselling reduced that of fetal overgrowth in GDM-affected pregnancies. Early gut microbiota modulation with probiotics modified the growth pattern of the child by restraining excessive weight gain during the first years of life. The colostrum adiponectin concentration was demonstrated to be dependent on maternal diet and nutritional status during pregnancy. It was also higher in the colostrum received by normal-weight compared to overweight children at the age of 10 years. The early perinatal probiotic intervention and the postnatal probiotic intervention in VLBW infants were shown to be safe. To conclude, the findings in this study provided clinical evidence supporting the involvement of the initial microbial and nutritional environment in metabolic programming of the child. The manipulation of early gut microbial communities with probiotics might offer an applicable strategy to impact individual energy homeostasis and thus to prevent excessive body-weight gain. The results add weight to the hypothesis that interventions aiming to prevent obesity and its metabolic consequences later in life should be initiated as early as during the perinatal period.