Towards combining interactive mobile TV and smart spaces : architectures, tools and application development

Technological developments in microprocessors and ICT landscape have made a shift to a new era where computing power is embedded in numerous small distributed objects and devices in our everyday lives. These small computing devices are ne-tuned to perform a particular task and are increasingly reaching our society at every level. For example, home appliances such as programmable washing machines, microwave ovens etc., employ several sensors to improve performance and convenience. Similarly, cars have on-board computers that use information from many di erent sensors to control things such as fuel injectors, spark plug etc., to perform their tasks e ciently. These individual devices make life easy by helping in taking decisions and removing the burden from their users. All these objects and devices obtain some piece of information about the physical environment. Each of these devices is an island with no proper connectivity and information sharing between each other. Sharing of information between these heterogeneous devices could enable a whole new universe of innovative and intelligent applications. The information sharing between the devices is a diffcult task due to the heterogeneity and interoperability of devices. Smart Space vision is to overcome these issues of heterogeneity and interoperability so that the devices can understand each other and utilize services of each other by information sharing. This enables innovative local mashup applications based on shared data between heterogeneous devices. Smart homes are one such example of Smart Spaces which facilitate to bring the health care system to the patient, by intelligent interconnection of resources and their collective behavior, as opposed to bringing the patient into the health system. In addition, the use of mobile handheld devices has risen at a tremendous rate during the last few years and they have become an essential part of everyday life. Mobile phones o er a wide range of different services to their users including text and multimedia messages, Internet, audio, video, email applications and most recently TV services. The interactive TV provides a variety of applications for the viewers. The combination of interactive TV and the Smart Spaces could give innovative applications that are personalized, context-aware, ubiquitous and intelligent by enabling heterogeneous systems to collaborate each other by sharing information between them. There are many challenges in designing the frameworks and application development tools for rapid and easy development of these applications. The research work presented in this thesis addresses these issues. The original publications presented in the second part of this thesis propose architectures and methodologies for interactive and context-aware applications, and tools for the development of these applications. We demonstrated the suitability of our ontology-driven application development tools and rule basedapproach for the development of dynamic, context-aware ubiquitous iTV applications.

Support vector machines in spectral data classification

Työssä käydään läpi tukivektorikoneiden teoreettista pohjaa sekä tutkitaan eri parametrien vaikutusta spektridatan luokitteluun.

The yellow fever 17D vaccine virus: molecular basis of viral attenuation and its use as an expression vector

The yellow fever (YF) virus is the prototype flavivirus. The use of molecular techniques has unraveled the basic mechanisms of viral genome structure and expression. Recent trends in flavivirus research include the use of infectious clone technology with which it is possible to recover virus from cloned cDNA. Using this technique, mutations can be introduced at any point of the viral genome and their resulting effect on virus phenotype can be assessed. This approach has opened new possibilities to study several biological viral features with special emphasis on the issue of virulence/attenuation of the YF virus. The feasibility of using YF virus 17D vaccine strain, for which infectious cDNA is available, as a vector for the expression of heterologous antigens is reviewed

The challenge of vector development in gene therapy

Gene therapy is the treatment of diseases based on the transfer of genetic information. Agents that carry or deliver DNA to target cells are called vectors (Latin vector: carrier, deliverer). Ideally, a vector should accommodate an unlimited amount of inserted DNA, lack the ability of autonomous replication of its own DNA, be easily manufactured, and be available in concentrated form. Secondly, it should have the ability to target specific cell types or to limit its gene expression to specific cell types, and to achieve sustained gene expression in the long term or in a controlled fashion. Finally, it should not be toxic or immunogenic. Such a vector does not exist and none of the DNA delivery systems so far available for in vivo gene transfer is perfect with respect to any of these points. Gene therapy and the means to promote it depend heavily on the development and improvement of new gene vector systems.

Lebesgue and Sobolev spaces with variable exponents

Kirjallisuusarvostelu

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

A high-copy T7 Escherichia coli expression vector for the production of recombinant proteins with a minimal N-terminal His-tagged fusion peptide

We report here the construction of a vector derived from pET3-His and pRSET plasmids for the expression and purification of recombinant proteins in Escherichia coli based on T7 phage RNA polymerase. The resulting pAE plasmid combined the advantages of both vectors: small size (pRSET), expression of a short 6XHis tag at N-terminus (pET3-His) and a high copy number of plasmid (pRSET). The small size of the vector (2.8 kb) and the high copy number/cell (200-250 copies) facilitate the subcloning and sequencing procedures when compared to the pET system (pET3-His, 4.6 kb and 40-50 copies) and also result in high level expression of recombinant proteins (20 mg purified protein/liter of culture). In addition, the vector pAE enables the expression of a fusion protein with a minimal amino-terminal hexa-histidine affinity tag (a tag of 9 amino acids using XhoI restriction enzyme for the 5'cloning site) as in the case of pET3-His plasmid and in contrast to proteins expressed by pRSET plasmids (a tag of 36 amino acids using BamHI restriction enzyme for the 5'cloning site). Thus, although proteins expressed by pRSET plasmids also have a hexa-histidine tag, the fusion peptide is much longer and may represent a problem for some recombinant proteins.

Modulation of the expression of the transcription factor Max in rat retinal ganglion cells by a recombinant adeno-associated viral vector

Exclusion of the transcription factor Max from the nucleus of retinal ganglion cells is an early, caspase-independent event of programmed cell death following damage to the optic axons. To test whether the loss of nuclear Max leads to a reduction in neuroprotection, we developed a procedure to overexpress Max protein in rat retinal tissue in vivo. A recombinant adeno-associated viral vector (rAAV) containing the max gene was constructed, and its efficiency was confirmed by transduction of HEK-293 cells. Retinal ganglion cells were accessed in vivo through intravitreal injections of the vector in rats. Overexpression of Max in ganglion cells was detected by immunohistochemistry at 2 weeks following rAAV injection. In retinal explants, the preparation of which causes damage to the optic axons, Max immunoreactivity was increased after 30 h in vitro, and correlated with the preservation of a healthy morphology in ganglion cells. The data show that the rAAV vector efficiently expresses Max in mammalian retinal ganglion cells, and support the hypothesis that the Max protein plays a protective role for retinal neurons.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.

Production of L1 protein from different types of HPV in Pichia pastoris using an integrative vector

Human papillomavirus (HPV) infection is the most common sexually transmitted disease in the world and is related to the etiology of cervical cancer. The most common high-risk HPV types are 16 and 18; however, the second most prevalent type in the Midwestern region of Brazil is HPV-33. New vaccine strategies against HPV have shown that virus-like particles (VLP) of the major capsid protein (L1) induce efficient production of antibodies, which confer protection against the same viral type. The methylotrophic yeast Pichia pastoris is an efficient and inexpensive expression system for the production of high levels of heterologous proteins stably using a wild-type gene in combination with an integrative vector. It was recently demonstrated that P. pastoris can produce the HPV-16 L1 protein by using an episomal vector associated with the optimized L1 gene. However, the use of an episomal vector is not appropriate for protein production on an industrial scale. In the present study, the vectors were integrated into the Pichia genome and the results were positive for L1 gene transcription and protein production, both intracellularly and in the extracellular environment. Despite the great potential for expression by the P. pastoris system, our results suggest a low yield of L1 recombinant protein, which, however, does not make this system unworkable. The achievement of stable clones containing the expression cassettes integrated in the genome may permit optimizations that could enable the establishment of a platform for the production of VLP-based vaccines.

Ordered probit regression analysis of the effect of brand name on beer acceptance by consumers

Ordered probit regression was used to analyze data of sensory acceptance tests designed to study the effect of brand name on the acceptability of beer samples. Eight different brands of Pilsen beer were evaluated by 101 consumers in two sessions of acceptance tests: blind evaluation and brand information test. Ordered probit regression, although a relatively sophisticated technique compared to others used to analyze sensory data, was chosen to enable the observation of consumers' behavior using graphical interpretations of estimated probabilities plotted against hedonic scales. It can be concluded that brands B, C, and D had a positive effect on the sensory acceptance of the product, whereas brands A, F, G, and H had a negative influence on consumers' evaluation of the samples. On the other hand, brand E had little influence on consumers' assessment.

Remote Observation of Radioactive Permit-Required Confined Spaces – Case: Posiva Oy’s Fuel Handling Cell

Posiva Oy’s final disposal facility’s encapsulation plant will start to operate in the 2020s. Once the operation starts, the facility is designed to run more than a hundred years. The encapsulation plant will be first of its kind in the world, being part of the solution to solve a global issue of final disposal of nuclear waste. In the encapsulation plant’s fuel handling cell the spent nuclear fuel will be processed to be deposited into the Finnish bedrock, into ONKALO. In the fuel handling cell, the environment is highly radioactive forming a permit-required enclosed space. Remote observation is needed in order to monitor the fuel handling process. The purpose of this thesis is to map (Part I) and compare (Part II) remote observation methods to observe Posiva Oy’s fuel handling cell’s process, and provide a possible theoretical solution for this case. Secondary purpose for this thesis is to provide resources for other remote observation cases, as well as to inform about possible future technology to enable readiness in the design of the encapsulation plant. The approach was to theoretically analyze the mapped remote observation methods. Firstly, the methods were filtered by three environmental challenges. These are the high levels of radiation, the permit-required confined space and the hundred year timespan. Secondly, the most promising methods were selected by the experts designing the facility. Thirdly, a customized feasibility analysis was created and performed on the selected methods to rank the methods with scores. The results are the mapped methods and the feasibility analysis scores. The three highest scoring methods were radiation tolerant camera, fiberscope and audio feed. A combination of these three methods was given as a possible theoretical solution for this case. As this case is first in the world, remote observation methods for it had not been thoroughly researched. The findings in this thesis will act as initial data for the design of the fuel handling cell’s remote observation systems and can potentially effect on the overall design of the facility by providing unique and case specific information. In addition, this thesis could provide resources for other remote observation cases.

Development of a bovine adenovirus type 2-based gene delivery vector /

ABSTRACT Recombinant adenoviruses are currently under intense investigation as potential gene delivery and gene expression vectors with applications in human and veterinary medicine. As part of our efforts to develop a bovine adenovirus type 2 (BAV2) based vector system, the nucleotide sequence of BAV2 was determined. Sixty-six open reading frames (ORFs) were found with the potential to encode polypeptides that were at least 50 amino acid (aa) residue long. Thirty-one of the BAV2 polypeptide sequences were found to share homology to already identified adenovirus proteins. The arrangement of the genes revealed that the BAV2 genomic organization closely resembles that of well-characterized human adenoviruses. In the course of this study, continuous propagation of BAV2 over many generations in cell culture resulted in the isolation of a BAV2 spontaneous mutant in which the E3 region was deleted. Restriction enzyme, sequencing and PCR analyses produced concordant results that precisely located the deletion and revealed that its size was exactly 1299 bp. The E3-deleted virus was plaque-purified and further propagated in cell culture. It appeared that the replication of such a virus lacking a portion of the E3 region was not affected, at least in cell culture. Attempts to rescue a recombinant BAV2 virus with the bacterial kanamycin resistance gene in the E3 region yielded a candidate as verified with extensive Southern blotting and PCR analyses. Attempts to purify the recombinant virus were not successful, suggesting that such recombinant BAV2 was helper-dependent. Ten clones containing full-length BAV2 genomes in a pWE15 cosmid vector were constructed. The infectivity of these constructs was tested by using different transfection methods. The BAV2 genomic clones did appear to be infectious only after extended incubation period. This may be due to limitations of various transfection methods tested, or biological differences between virus- and E. co//-derived BAV2 DNA.

For whom the cruiser rolls: a retrospective analysis of factors relating to recidivism in a sample of graduates from a court-ordered alcohol education and awareness program

This study investigated, retrospectively, whether recidivism in a sample of court-ordered'graduates of an alcohol education and awareness program could be predicted. This alcohol education program was based on adult education principles and was philosophically akin to the thoughts of Drs. Jack Mezirow, Stephen Brookfield, and Patricia Cranton. Data on the sample of 214 Halton IDEA (Impaired Driver Education and Awareness) graduates were entered into a spread sheet. Descriptive statistics were generated. Each of the 214 program graduates had taken several tests during the course of the IDEA program. These tests measured knowledge, attitude about impaired driving, and degree of alcohol involvement. Test scores were analyzed to determine whether those IDEA graduates who recidivated differed in any measurable way from those who had no further criminal convictions after a period of at least three years. Their criminal records were obtained from the Canadian Police Information Centre (CPIC). Those program graduates who reoffended were compared to the vast majority who did not reoffend. Results of the study indicated that there was no way to determine who would recidivate from the data that were collected. Further studies could use a qualitative model. Follow-up interviews could be used to determine what impact, if any, attendance at the IDEA program had on the life of the graduates.