R&D Performance Analysis: Case Studies on the Challenges and Promotion of the Evaluation and Measurement of R&D

Due to the intense international competition, demanding, and sophisticated customers, and diverse transforming technological change, organizations need to renew their products and services by allocating resources on research and development (R&D). Managing R&D is complex, but vital for many organizations to survive in the dynamic, turbulent environment. Thus, the increased interest among decision-makers towards finding the right performance measures for R&D is understandable. The measures or evaluation methods of R&D performance can be utilized for multiple purposes; for strategic control, for justifying the existence of R&D, for providing information and improving activities, as well as for the purposes of motivating and benchmarking. The earlier research in the field of R&D performance analysis has generally focused on either the activities and considerable factors and dimensions - e.g. strategic perspectives, purposes of measurement, levels of analysis, types of R&D or phases of R&D process - prior to the selection of R&Dperformance measures, or on proposed principles or actual implementation of theselection or design processes of R&D performance measures or measurement systems. This study aims at integrating the consideration of essential factors anddimensions of R&D performance analysis to developed selection processes of R&D measures, which have been applied in real-world organizations. The earlier models for corporate performance measurement that can be found in the literature, are to some extent adaptable also to the development of measurement systemsand selecting the measures in R&D activities. However, it is necessary to emphasize the special aspects related to the measurement of R&D performance in a way that make the development of new approaches for especially R&D performance measure selection necessary: First, the special characteristics of R&D - such as the long time lag between the inputs and outcomes, as well as the overall complexity and difficult coordination of activities - influence the R&D performance analysis problems, such as the need for more systematic, objective, balanced and multi-dimensional approaches for R&D measure selection, as well as the incompatibility of R&D measurement systems to other corporate measurement systems and vice versa. Secondly, the above-mentioned characteristics and challenges bring forth the significance of the influencing factors and dimensions that need to be recognized in order to derive the selection criteria for measures and choose the right R&D metrics, which is the most crucial step in the measurement system development process. The main purpose of this study is to support the management and control of the research and development activities of organizations by increasing the understanding of R&D performance analysis, clarifying the main factors related to the selection of R&D measures and by providing novel types of approaches and methods for systematizing the whole strategy- and business-based selection and development process of R&D indicators.The final aim of the research is to support the management in their decision making of R&D with suitable, systematically chosen measures or evaluation methods of R&D performance. Thus, the emphasis in most sub-areas of the present research has been on the promotion of the selection and development process of R&D indicators with the help of the different tools and decision support systems, i.e. the research has normative features through providing guidelines by novel types of approaches. The gathering of data and conducting case studies in metal and electronic industry companies, in the information and communications technology (ICT) sector, and in non-profit organizations helped us to formulate a comprehensive picture of the main challenges of R&D performance analysis in different organizations, which is essential, as recognition of the most importantproblem areas is a very crucial element in the constructive research approach utilized in this study. Multiple practical benefits regarding the defined problemareas could be found in the various constructed approaches presented in this dissertation: 1) the selection of R&D measures became more systematic when compared to the empirical analysis, as it was common that there were no systematic approaches utilized in the studied organizations earlier; 2) the evaluation methods or measures of R&D chosen with the help of the developed approaches can be more directly utilized in the decision-making, because of the thorough consideration of the purpose of measurement, as well as other dimensions of measurement; 3) more balance to the set of R&D measures was desired and gained throughthe holistic approaches to the selection processes; and 4) more objectivity wasgained through organizing the selection processes, as the earlier systems were considered subjective in many organizations. Scientifically, this dissertation aims to make a contribution to the present body of knowledge of R&D performance analysis by facilitating dealing with the versatility and challenges of R&D performance analysis, as well as the factors and dimensions influencing the selection of R&D performance measures, and by integrating these aspects to the developed novel types of approaches, methods and tools in the selection processes of R&D measures, applied in real-world organizations. In the whole research, facilitation of dealing with the versatility and challenges in R&D performance analysis, as well as the factors and dimensions influencing the R&D performance measure selection are strongly integrated with the constructed approaches. Thus, the research meets the above-mentioned purposes and objectives of the dissertation from the scientific as well as from the practical point of view.

Local and Global Feature Extraction for Invariant Object Recognition

Perceiving the world visually is a basic act for humans, but for computers it is still an unsolved problem. The variability present innatural environments is an obstacle for effective computer vision. The goal of invariant object recognition is to recognise objects in a digital image despite variations in, for example, pose, lighting or occlusion. In this study, invariant object recognition is considered from the viewpoint of feature extraction. Thedifferences between local and global features are studied with emphasis on Hough transform and Gabor filtering based feature extraction. The methods are examined with respect to four capabilities: generality, invariance, stability, and efficiency. Invariant features are presented using both Hough transform and Gabor filtering. A modified Hough transform technique is also presented where the distortion tolerance is increased by incorporating local information. In addition, methods for decreasing the computational costs of the Hough transform employing parallel processing and local information are introduced.

Product placement in integrated marketing communications strategy

Perinteisten markkinointiviestintäkanavien menettäessä jatkuvasti tehoaan mediakentän ja kohderyhmien sirpaloituessa yhä pienempiin yksiköihin markkinointiorganisaatiot etsivät vaihtoehtoisia tapoja tavoittaakseen kohdeyleisönsä. Yksi vaihtoehtoinen markkinointiviestintäkeino on tuotesijoittelu (product placement), jossa (merkki)tuotteita sijoitetaan erilaisten viihdetuotantojen, kuten elokuvien, televisio-ohjelmien ja tietokonepelien, tarinan yhteyteen, jotta yhä medialukutaitoisempi kohdeyleisö ei pystyisi välttämään kaupallista viestiä esimerkiksi vaihtamalla televisiokanavaa tai kääntämällä lehden sivua. Koska tuote on sijoitettu kerrottavan tarinan sisään, markkinointiviestin — eli tuotteen havaitsemisen — välttäminen on huomattavasti vaikeampaa kuin perinteisten markkinointiviestintämenetelmien kohdalla. Lisäksi, sijoitellut tuotteet ovat tavallisesti kiinteässä yhteydessä tarinan juonen ja henkilöhahmojen kanssa siten, että tuote saa näistä yhteyksistä positiivista vahvistusta imagolleen. Pro Gradu-tutkielman tarkoituksena oli selvittää tuotesijoittelun käyttökelpoisuutta markkinointiviestinnässä sekä miten kulutushyödykemarkkinoijat voivat hyödyntää menetelmää markkinointiviestintästrategioissaan. Tuotesijoittelun poikkeava luonne markkinointiviestintävälineenä tuotti kysymyksen miten tuotesijoittelua voitaisiin hyödyntää yhteistyössä muiden markkinointiviestintäkeinojen kanssa. Tätä varten tutkimuksessa tuotesijoittelu yhdistettiin integroidun markkinointiviestinnän (IMC) viitekehykseen. IMC-konsepti syntyi markkinointiviestinnässä vastaamaan samaan tarpeeseen kuin tuotesijoittelukin: pirstaloitunut mediakenttä ja yksittäiset kohderyhmät vaativat kehittyneempää ja yhtenäisempää markkinointiviestinnän suunnittelua ja toteutusta. Tutkimuksen johtopäätöksenä tuotesijoittelu todettiin käyttökelpoiseksi markkinointiviestintäkeinoksi mikäli viestinnän tavoitteena on muu kuin tuotteen myyntiin suorasti vaikuttaminen. Tuotesijoittelu on sen sijaan erittäin tehokas tuotetietoisuuden lisäämisessä, erityisesti tunnistamisen kohdalla. Tuotesijoittelu voi myös tuottaa suoran ostotarpeen mutta tällöin viestin vastaanottajalla täytyy olla vallitseva tarve kyseisen tuoteryhmän osalta ennen altistumista ko. markkinointiviestille. Tuotesijoittelu voidaan sisällyttää IMC-suunnitteluprosessiin markkinointiviestintästrategian kiinteänä osana. Integraatio markkinointiviestinnässä siten, että tuotesijoittelua tuettaisiin muilla viestintäkeinoilla yhtenäisen kampanjan kehittämiseksi on kuitenkin paljon ennakoitua harvinaisempaa, johtuen ehkä eniten tuotesijoittelun poikkeuksellisesta luonteesta ja kyseisen viestintämuodon vaikeasta hallittavuudesta markkinoijan taholta. Tutkimus toteutettiin normatiivisena case-tutkimuksena pääasiassa sekundäärisiä tietolähteitä hyödyntäen. Case-tutkimuksia varten kerättiin primääristä tietoa kyselylomakkeella kahdesta tuotesijoittelua käyttävästä kansainvälisestä yhtiöstä, jonka lisäksi myös sekundäärisiä tietolähteitä hyödynnettiin case-osan tiedonkeruussa.

Optical Character Recognition for Symbian OS

This Master's thesis addresses the design and implementation of the optical character recognition (OCR) system for a mobile device working on the Symbian operating system. The developed OCR system, named OCRCapriccio, emphasizes the modularity, effective extensibility and reuse. The system consists of two parts which are the graphical user interface and the OCR engine that was implemented as a plug-in. In fact, the plug-in includes two implementations of the OCR engine for enabling two types of recognition: the bitmap comparison based recognition and statistical recognition. The implementation results have shown that the approach based on bitmap comparison is more suitable for the Symbian environment because of its nature. Although the current implementation of bitmap comparison is lacking in accuracy, further development should be done in its direction. The biggest challenges of this work were related to developing an OCR scheme that would be suitable for Symbian OS Smartphones that have limited computational power and restricted resources.

Physical Realization of an Optical Automatic Target Recognition System

In the modern warfare there is an active development of a new trend connected with a robotic warfare. One of the critical elements of robotics warfare systems is an automatic target recognition system, allowing to recognize objects, based on the data received from sensors. This work considers aspects of optical realization of such a system by means of NIR target scanning at fixed wavelengths. An algorithm was designed, an experimental setup was built and samples of various modern gear and apparel materials were tested. For pattern testing the samples of actively arm engaged armies camouflages were chosen. Tests were performed both in clear atmosphere and in the artificial extremely humid and hot atmosphere to simulate field conditions.

Future trends of Finnish cement based construction

The construction material sector, as a capital intensive industry, is highly vulnerable to rapid fluctuations in the economic cycles. In Finland this was witnessed especially during the late 2000s, as in 2007 and 2008 the demand for several construction materials exceeded their supply and right after this, in 2009 the demand collapsed fast as a result of an international recession. These factors brought about the need to study the future trends of the market place of the commissioning company, Finnsementti Oy. As reliable short term market forecasts for the sector are difficult to compose, the study concentrates primarily in examining and identifying the trends that are likely to affect the Finnish cement industry, and as an extension, the concrete industry in a frame of 10 to 15 years. The study’s scope comprehends also the examination of the domestic construction sector, as it represents the end user industry of both cement and concrete. These motives for the study produce the research problem, which is to conduct a trend analysis for cement based building in the Finnish market area in the 2020s. The theoretical frame for composing a trend analysis in the case of this study is twofold. This is due to the fact that both, the macro and micro environments of the examined industries are studied. The main methods used are the PESTE-model (macro) and Porter’s five forces model (micro). The study applies a qualitative approach and the data is gathered by interviewing a group of experts from the cement, concrete and construction industries. The result of the paper is an overall trend analysis for the Finnish cement based building sector, which is based on ‘sub trend analyses’ concerning four identified sub-sectors of the Finnish construction industry. The results are a combination of findings from these sub-sectors and the analyzed data that deals with the studied sector’s macro and micro environment. The conclusions provide an overall picture of the examined sectors’ potential future as a whole and by defined sub-sectors of the construction industry. The recognition of future trends in different areas of the construction industry can be applied as a means for an industry actor’s decision making and in estimating the types of construction that are likely to grow or decline. Finally, based on the analyzed data and conclusions, the commissioning company is provided with a brief SWOT analysis, that provides additional tools for decision making and planning processes regarding the future.

Burden of Influenza in Children

Background: Most children with influenza are treated as outpatients but, especially among young children, influenza-attributable illnesses often result in hospitalization. However, relatively scarce data exist on the clinical picture and the full disease burden of pediatric influenza. Prompt diagnosis of influenza could enable the institution of antiviral therapy and adequate cohorting of patients. Data are needed to help clinicians correctly suspect influenza at the time of hospital admission. Aims and methods: We conducted a prospective 2-year cohort study of respiratory infections in children aged ≤13 years to determine the incidence of influenza in outpatient children and to assess the clinical presentation of influenza in various age groups seen in primary care. We also determined the rates of different complications attributable to influenza and the absenteeism of the children and their parents due to the child’s influenza infection. We then conducted a further 16-year retrospective study of children ≤16 years of age, hospitalized with virologically confirmed influenza. We estimated the population-based rates of hospitalizations and determined the primary admission diagnoses of the hospitalized children in different age groups. Results: The average annual rate of influenza was highest (179 / 1000) among children <3 years old. In this age group, acute otitis media was diagnosed as a complication of influenza in 40% of children. High fever was the most prominent sign of influenza, and 20% of children <3 years of age had a fever ≥40oC. Most children had rhinitis already during the first days of the illness. The average annual incidence of influenzarelated hospitalization was highest (276 / 100,000) among infants <6 months of age, of whom 52% were primarily admitted due to sepsis-like illnesses. Respiratory symptoms accounted for 38% of the hospitalizations. Conclusions: Influenza causes a substantial burden of illness on outpatient children and their families. The clinical presentation of influenza is most severe in children <3 years of age. The high incidence of influenza-associated hospitalizations among infants aged <6 months calls for more effective ways to prevent influenza in this age group. The clinical manifestations of influenza vary widely in different age groups of children at the time of hospital admission. Awareness of this phenomenon is important for the early recognition of the illness and the potential initiation of effective antiviral treatment of these patients.

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.

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.

Automatic recognition of fish from video sequences

The problem of automatic recognition of the fish from the video sequences is discussed in this Master’s Thesis. This is a very urgent issue for many organizations engaged in fish farming in Finland and Russia because the process of automation control and counting of individual species is turning point in the industry. The difficulties and the specific features of the problem have been identified in order to find a solution and propose some recommendations for the components of the automated fish recognition system. Methods such as background subtraction, Kalman filtering and Viola-Jones method were implemented during this work for detection, tracking and estimation of fish parameters. Both the results of the experiments and the choice of the appropriate methods strongly depend on the quality and the type of a video which is used as an input data. Practical experiments have demonstrated that not all methods can produce good results for real data, whereas on synthetic data they operate satisfactorily.

Peeps into fairyland : a panorama picture book of fairy stories

Kansikuva, kolmiulotteinen pop-up-kirja

Recognition of nucleic acids by metal ion complexes

Metal-ion-mediated base-pairing of nucleic acids has attracted considerable attention during the past decade, since it offers means to expand the genetic code by artificial base-pairs, to create predesigned molecular architecture by metal-ion-mediated inter- or intra-strand cross-links, or to convert double stranded DNA to a nano-scale wire. Such applications largely depend on the presence of a modified nucleobase in both strands engaged in the duplex formation. Hybridization of metal-ion-binding oligonucleotide analogs with natural nucleic acid sequences has received much less attention in spite of obvious applications. While the natural oligonucleotides hybridize with high selectivity, their affinity for complementary sequences is inadequate for a number of applications. In the case of DNA, for example, more than 10 consecutive Watson-Crick base pairs are required for a stable duplex at room temperature, making targeting of sequences shorter than this challenging. For example, many types of cancer exhibit distinctive profiles of oncogenic miRNA, the diagnostics of which is, however, difficult owing to the presence of only short single stranded loop structures. Metallo-oligonucleotides, with their superior affinity towards their natural complements, would offer a way to overcome the low stability of short duplexes. In this study a number of metal-ion-binding surrogate nucleosides were prepared and their interaction with nucleoside 5´-monophosphates (NMPs) has been investigated by 1H NMR spectroscopy. To find metal ion complexes that could discriminate between natural nucleobases upon double helix formation, glycol nucleic acid (GNA) sequences carrying a PdII ion with vacant coordination sites at a predetermined position were synthesized and their affinity to complementary as well as mismatched counterparts quantified by UV-melting measurements.

Pretraining Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.