This dissertation thesis is focused on the issue of further extension of computers into the human life and its stronger influencing of human being, in the foreign literature defined as ubiquitous computing, everyware or pervasive computing. The basic goals of the thesis are follows: - Compare different views on this issues and make their assessment - Analyze possible effect, especially for banking and insurance area - Propose some specific innovation in named areas The results of this thesis should: - Serve as a basis for further studies in the ubiquitous computing area - Be usable as a basis for further scientific research - Bring some practical application, with which can we met in practice in future years Whole work place emphasis on the innovation process, with which is topic very closely linked and which is constantly emphasizing both in technical as in commercial spheres. The goals of the thesis are fulfilled in the individual chapters in the following way: - Definition of the goals and introduction of the topic -- Chapter 1 - Analysis of current status of knowledge -- Chapter 2 - Brief characteristic of main UC concepts -- Chapter 3 - Practical experience and innovation from UC -- Chapter 4 - Final evaluation -- Chapter 5 The scientific benefits of this work have been awarded by presentation of main parts of the thesis on international conferences and publication in the scientific magazines.

Campus universitários são ambientes compostos de recursos e pessoas que utilizam os tais. Um dos principais recursos utilizados pela comunidade de um campus são os objetos de aprendizagem. Tais objetos existem de maneira abundante, espalhados no ambiente ou concentrados em um único local. Entretanto, a abundancia desses objetos faz com que uma pessoa sinta-se cognitivamente cansada ao ter que analisar vários objetos e selecionar apenas alguns. Esse cansaço cognitivo acaba levando a pessoa a escolher um conjunto de objetos de aprendizagem que não satisfarão suas necessidades e interesses da melhor maneira possível. A computação evoluiu de grandes mainframes a pequenos computadores espalhados em um ambiente. Hoje é possível a existência de ambientes pervasivos, onde os recursos computacionais estão sempre presentes e agindo de forma invisível ao usuário. Tais ambientes tornam possível o acompanhamento das atividades do usuário, provendo informações contextuais que podem ser utilizadas para ajudar a seleção dos melhores recursos (ex. objetos de aprendizagem, restaurantes, salas de aula) à determinada pessoa. A localização é uma informação contextual de grande importância na seleção de tais recursos. Tal informação pode ser facilmente obtida através do sinal de GPS do dispositivo móvel de um usuário e utilizada em conjunto com os interesses do usuário para recomendar os recursos próximos que melhor atenderão ao mesmo. Neste contexto este trabalho descreve uma abordagem para recomendar objetos de aprendizagem físicos ou virtuais que estejam relacionados aos prédios próximos a atual localização do usuário. Para executar tal tarefa é descrito um sistema de recomendação que utiliza a informação de localização, obtida através do dispositivo móvel do usuário, combinada à informações do perfil do usuário, dos objetos de aprendizagem relacionados aos prédios e informações tecnológicas do dispositivo para instanciar um modelo ontológico de contexto. Após instanciado o modelo são utilizadas regras semânticas, escritas em forma de antecedente e consequente, que fazem uma correspondência entre os interesses do usuário e o domínio de conhecimento do objeto de aprendizagem para filtrar os objetos próximos ao usuário. De posse desses objetos recomendados o sistema os apresenta em uma interface adaptativa que mostra a localização tanto dos objetos quanto do usuário. Para validar a abordagem apresentada é desenvolvido um estudo de caso onde as regras semânticas de recomendação são executadas sobre o modelo ontológico desenvolvido. O resultado gerado por tais regras é um conjunto de pares (usuário, objeto de aprendizagem recomendado) e prova a validade da abordagem. / University campus are environments composed of resources and people who use them. One of the main resources used by a campus community are learning objects. Such objects are abundantly even scattered in the environment or concentrated in one location. However the abundance of such objects makes a person feel cognitively tired when having to analyze various objects and select just a few of them. This cognitive fatigue eventually leads the person to choose a set of learning objects that do not meet their needs and interests in the best possible way. Computing has evolved from large mainframe to small computers scattered in an environment. Today it is possible the existence of pervasive environments where computational resources are always present and acting in a manner invisible to the user. Such environments make it possible to monitor user activities, providing contextual information that can be used to help select the best resources (e.g. learning objects, restaurants, classrooms) to a particular person. The location is a contextual information of great importance in the selection of such resources. Such information can be easily obtained through the GPS signal from a mobile device and used with the user’s interests to recommend the nearby resources that best attend his needs and interests. In this context, this work describes an approach to recommend physical or virtual learning objects that are related to buildings near the user’s current location. To accomplish such a task we described a recommender system that uses the location information, obtained through the user's mobile device, combined with information from the user’s profile, learning objects related to buildings and technological information from the device to instantiate an ontological context model. Once the model is instantiated we used semantic rules, written in the form of antecedent and consequent, to make a match between the user’s interests and the knowledge domain of the learning object in order filter the user’s nearby objects. With such recommended objects, the system presents them in an adaptive interface that shows both the object and the user location. To validate the presented approach we developed a case study where the recommendation semantic rules are executed on the developed ontological model. The income generated by such rules is a set of pairs (user, recommended learning object) and proves the validity of the approach.

Pervasive computing sa zameriava odstránenie zložitostí pri interakcii s výpočtovou technikou a zvýšenie efektivity pri jej každodennom používaní. Ale i po viac ako 15 rokoch od sformulovania hlavných cieľov Pervasive computingu existujú aspekty interakcie ktoré stále nie sú súčasťou užívateľskej skúsenosti s dnešnou technológiou. Bezproblémová integrácia s prostredím vedúca k technologickej neviditeľnosti, alebo interakcia naprieč rôznymi zariadeniami predstavujú stále veľkú výzvu. Hlavným cieľom tejto práce je prispieť k tomu, aby sa ciele Pervasive computingu priblížili k realizovaniu tým, že predstavíme spôsob intuitívneho zdieľania informácií medzi osobným a verejne umiestneným zariadením. Predstavili sme tri interakčné techniky, ktoré podporujú intuitívnu výmenu obsahu medzi osobným zariadením a zdieľaným displejom. Tieto techniky sú založené na prenose videa, rozšírenej realite a analýze pohľadových dát. Okrem interakčných techník sme tiež predstavili mechanizmus pre získavanie, prenos a rekonštrukciu aplikačného stavu na cieľovom zariadení.

This investigation was concerned with determining the effectiveness of filial therapy as a method of intervention for families of children with pervasive developmental disorders.

With the proliferation of mobile applications and the abundance of wireless devices, it is increasingly common for devices to support multiple radios. When two devices are communicating they should choose the best available radio based on user preference and application requirements. This type of “radio switching” should happen automatically, so that the system optimizes performance dynamically. To achieve this objective, we design an Autonomous and Intelligent Radio Switching (AIRS) system to leverage the radio heterogeneity common in today's wireless devices. The AIRS system consists of three key components. First, we design a radio preference evaluation module to dynamically select the best radio according to users' preference, application's QoS requirements, and the device battery usage. Second, we propose a link quality measurement and prediction module to predict the radio quality under a variety of mobility and interference conditions. Third, we present a radio switching decision making module to switch to the preferred available radio intelligently, based on the preference and link quality evaluations. The AIRS system maintains connectivity, as well as improves link quality, via dynamic and intelligent radio switching, regardless of interference or collisions from the interfaces of other devices. The radio preference evaluation module is able to generate and adjust a preference list dynamically. Multiple users' requirements are satisfied in a mutually beneficial manner and the selected radio is Pareto optimal. The link prediction module is able to achieve an accuracy above 90% under a variety of mobility and interference conditions. The module can dynamically increase the link measurement interval and significantly reduce its power consumption, without sacrificing accuracy. The decision algorithm uses several parameters to avoid switching radios too frequently, and is able to provide dynamic, but stable radio switching, while balancing the competing objectives of high throughput and low power consumption. Overall, the AIRS system is able to achieve high goodput (application level throughput) and long battery life as applied to handoff management in a frequently changing mobile environment.

This study examined the costs of pervasive developmental disorder (PDD) treatment in a large healthcare organization. When compared to individual therapy and mixed therapy, family therapy had significantly fewer sessions, fewer episodes of care, and better cost-effectiveness. Individual therapy had significantly shorter treatment length than mixed therapy. There were no differences in treatment length or number of episodes by license, but dropout and cost-effectiveness were significantly different. Medical doctors had the highest dropout and best cost-effectiveness, while Marriage and Family Therapists had the lowest dropout and Masters of Social Work had the poorest cost-effectiveness. Children had significantly higher dropout than other age groups. An autism diagnosis was associated with fewer sessions but additional episodes of care when compared to PDD. Having a comorbid diagnosis is associated with longer treatment length but slightly fewer episodes of care. A regression model to predict number of episodes of care by intensity of treatment, provider type, and modality, intensity of treatment explained only 6% of the variance.

Yes / Human activity recognition from sensor readings have proved to be an effective approach in pervasive computing for smart healthcare. Recent approaches to ambient assisted living (AAL) within a home or community setting offers people the prospect of more individually-focused care and improved quality of living. However, most of the available AAL systems are often limited by computational cost. In this paper, a simple, novel non-wearable human activity classification framework using the multivariate Gaussian is proposed. The classification framework augments prior information from the passive RFID tags to obtain more detailed activity profiling. The proposed algorithm based on multivariate Gaussian via maximum likelihood estimation is used to learn the features of the human activity model. Twelve sequential and concurrent experimental evaluations are conducted in a mock apartment environment. The sampled activities are predicted using a new dataset of the same activity and high prediction accuracy is established. The proposed framework suits well for the single and multi-dwelling environment and offers pervasive sensing environment for both patients and carers. / Tertiary Education Trust Fund of Federal Government of Nigeria and by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement H2020-MSCA-ITN-2016 SECRET-722424

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