Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

A performance lower bound for quadratic timing recovery accounting for the symbol transition density

The symbol transition density in a digitally modulated signal affects the performance of practical synchronization schemes designed for timing recovery. This paper focuses on the derivation of simple performance limits for the estimation of the time delay of a noisy linearly modulated signal in the presence of various degrees of symbol correlation produced by the varioustransition densities in the symbol streams. The paper develops high- and low-signal-to-noise ratio (SNR) approximations of the so-called (Gaussian) unconditional Cramér–Rao bound (UCRB),as well as general expressions that are applicable in all ranges of SNR. The derived bounds are valid only for the class of quadratic, non-data-aided (NDA) timing recovery schemes. To illustrate the validity of the derived bounds, they are compared with the actual performance achieved by some well-known quadratic NDA timing recovery schemes. The impact of the symbol transitiondensity on the classical threshold effect present in NDA timing recovery schemes is also analyzed. Previous work on performancebounds for timing recovery from various authors is generalized and unified in this contribution.

ML approaches to channel estimation for pilot-aided multi-rate DS/CDMA systems

This paper analyzes the asymptotic performance of maximum likelihood (ML) channel estimation algorithms in wideband code division multiple access (WCDMA) scenarios. We concentrate on systems with periodic spreading sequences (period larger than or equal to the symbol span) where the transmitted signal contains a code division multiplexed pilot for channel estimation purposes. First, the asymptotic covariances of the training-only, semi-blind conditional maximum likelihood (CML) and semi-blind Gaussian maximum likelihood (GML) channelestimators are derived. Then, these formulas are further simplified assuming randomized spreading and training sequences under the approximation of high spreading factors and high number of codes. The results provide a useful tool to describe the performance of the channel estimators as a function of basicsystem parameters such as number of codes, spreading factors, or traffic to training power ratio.

Motion estimation using higher-order statistics

The objective of this paper is to introduce a fourth-order cost function of the displaced frame difference (DFD) capable of estimatingmotion even for small regions or blocks. Using higher than second-orderstatistics is appropriate in case the image sequence is severely corruptedby additive Gaussian noise. Some results are presented and compared to those obtained from the mean kurtosis and the mean square error of the DFD.

System identification using a linear combination of cumulant slices

In this paper we develop a new linear approach to identify the parameters of a moving average (MA) model from the statistics of the output. First, we show that, under some constraints, the impulse response of the system can be expressed as a linear combination of cumulant slices. Then, thisresult is used to obtain a new well-conditioned linear methodto estimate the MA parameters of a non-Gaussian process. Theproposed method presents several important differences withexisting linear approaches. The linear combination of slices usedto compute the MA parameters can be constructed from dif-ferent sets of cumulants of different orders, providing a generalframework where all the statistics can be combined. Further-more, it is not necessary to use second-order statistics (the autocorrelation slice), and therefore the proposed algorithm stillprovides consistent estimates in the presence of colored Gaussian noise. Another advantage of the method is that while mostlinear methods developed so far give totally erroneous estimates if the order is overestimated, the proposed approach doesnot require a previous estimation of the filter order. The simulation results confirm the good numerical conditioning of thealgorithm and the improvement in performance with respect to existing methods.

ML estimator and hybrid beamformer for multipath and interference mitigation in GNSS receivers

This paper addresses the estimation of the code-phase(pseudorange) and the carrier-phase of the direct signal received from a direct-sequence spread-spectrum satellite transmitter. Thesignal is received by an antenna array in a scenario with interferenceand multipath propagation. These two effects are generallythe limiting error sources in most high-precision positioning applications.A new estimator of the code- and carrier-phases is derivedby using a simplified signal model and the maximum likelihood(ML) principle. The simplified model consists essentially ofgathering all signals, except for the direct one, in a component withunknown spatial correlation. The estimator exploits the knowledgeof the direction-of-arrival of the direct signal and is much simplerthan other estimators derived under more detailed signal models.Moreover, we present an iterative algorithm, that is adequate for apractical implementation and explores an interesting link betweenthe ML estimator and a hybrid beamformer. The mean squarederror and bias of the new estimator are computed for a numberof scenarios and compared with those of other methods. The presentedestimator and the hybrid beamforming outperform the existingtechniques of comparable complexity and attains, in manysituations, the Cramér–Rao lower bound of the problem at hand.

Beyond EA Frameworks: Towards an Understanding of the Adoption of Enterprise Architecture Management

Enterprise architectures (EA) are considered promising approaches to reduce the complexities of growing information technology (IT) environments while keeping pace with an ever-changing business environment. However, the implementation of enterprise architecture management (EAM) has proven difficult in practice. Many EAM initiatives face severe challenges, as demonstrated by the low usage level of enterprise architecture documentation and enterprise architects' lack of authority regarding enforcing EAM standards and principles. These challenges motivate our research. Based on three field studies, we first analyze EAM implementation issues that arise when EAM is started as a dedicated and isolated initiative. Following a design-oriented paradigm, we then suggest a design theory for architecture-driven IT management (ADRIMA) that may guide organizations to successfully implement EAM. This theory summarizes prescriptive knowledge related to embedding EAM practices, artefacts and roles in the existing IT management processes and organization.

Semivariogram models for estimating fig fly population density throughout the year

The objective of this work was to select semivariogram models to estimate the population density of fig fly (Zaprionus indianus; Diptera: Drosophilidae) throughout the year, using ordinary kriging. Nineteen monitoring sites were demarcated in an area of 8,200 m2, cropped with six fruit tree species: persimmon, citrus, fig, guava, apple, and peach. During a 24 month period, 106 weekly evaluations were done in these sites. The average number of adult fig flies captured weekly per trap, during each month, was subjected to the circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, hole effect, K-Bessel, J-Bessel, and stable semivariogram models, using ordinary kriging interpolation. The models with the best fit were selected by cross-validation. Each data set (months) has a particular spatial dependence structure, which makes it necessary to define specific models of semivariograms in order to enhance the adjustment to the experimental semivariogram. Therefore, it was not possible to determine a standard semivariogram model; instead, six theoretical models were selected: circular, Gaussian, hole effect, K-Bessel, J-Bessel, and stable.

25-Hydroxyvitamin D in African-origin populations at varying latitudes challenges the construct of a physiologic norm.

BACKGROUND: The vitamin D-endocrine system is thought to play a role in physiologic processes that range from mineral metabolism to immune function. Serum 25-hydroxyvitamin D [25(OH)D] is the accepted biomarker for vitamin D status. Skin color is a key determinant of circulating 25(OH)D concentrations, and genes responsible for melanin content have been shown to be under strong evolutionary selection in populations living in temperate zones. Little is known about the effect of latitude on mean concentrations of 25(OH)D in dark-skinned populations. OBJECTIVE: The objective was to describe the distribution of 25(OH)D and its subcomponents in 5 population samples of African origin from the United States, Jamaica, Ghana, South Africa, and the Seychelles. DESIGN: Participants were drawn from the Modeling of the Epidemiologic Transition Study, a cross-sectional observational study in 2500 adults, ages 25-45 y, enrolled between January 2010 and December 2011. Five hundred participants, ∼50% of whom were female, were enrolled in each of 5 study sites: Chicago, IL (latitude: 41°N); Kingston, Jamaica (17°N); Kumasi, Ghana (6°N); Victoria, Seychelles (4°S); and Cape Town, South Africa (34°S). All participants had an ancestry primarily of African origin; participants from the Seychelles trace their history to East Africa. RESULTS: A negative correlation between 25(OH)D and distance from the equator was observed across population samples. The frequency distribution of 25(OH)D in Ghana was almost perfectly normal (Gaussian), with progressively lower means and increasing skewness observed at higher latitudes. CONCLUSIONS: It is widely assumed that lighter skin color in populations outside the tropics resulted from positive selection, driven in part by the relation between sun exposure, skin melanin content, and 25(OH)D production. Our findings show that robust compensatory mechanisms exist that create tolerance for wide variation in circulating concentrations of 25(OH)D across populations, suggesting a more complex evolutionary relation between skin color and the vitamin D pathway. This trial was registered at clinicaltrials.gov as NCT02111902.

On the differential evolution algorithm and its appliation to training radial basis function networks

The parameter setting of a differential evolution algorithm must meet several requirements: efficiency, effectiveness, and reliability. Problems vary. The solution of a particular problem can be represented in different ways. An algorithm most efficient in dealing with a particular representation may be less efficient in dealing with other representations. The development of differential evolution-based methods contributes substantially to research on evolutionary computing and global optimization in general. The objective of this study is to investigatethe differential evolution algorithm, the intelligent adjustment of its controlparameters, and its application. In the thesis, the differential evolution algorithm is first examined using different parameter settings and test functions. Fuzzy control is then employed to make control parameters adaptive based on an optimization process and expert knowledge. The developed algorithms are applied to training radial basis function networks for function approximation with possible variables including centers, widths, and weights of basis functions and both having control parameters kept fixed and adjusted by fuzzy controller. After the influence of control variables on the performance of the differential evolution algorithm was explored, an adaptive version of the differential evolution algorithm was developed and the differential evolution-based radial basis function network training approaches were proposed. Experimental results showed that the performance of the differential evolution algorithm is sensitive to parameter setting, and the best setting was found to be problem dependent. The fuzzy adaptive differential evolution algorithm releases the user load of parameter setting and performs better than those using all fixedparameters. Differential evolution-based approaches are effective for training Gaussian radial basis function networks.

Exploration of the social dimension of corporate responsibility in a welfare state

This study focuses on corporate social responsibility (or CSR)as the latest dimension to emerge in the corporate responsibility and sustainability agenda, which in the recent past has rapidly risen to the top of the list of concerns for civil societies worldwide. Despite the continuing debates and discussions about the scope, benefits, and impacts of CSR to business and community in various sectors, levels, and types of society, many companies have moved forward to confront the opportunities and challenges of CSR. Thus, this study is about those proactive companies with a focus on the importance of CSR and its management inside and outside the company. It is an exploration and learning from the experience of Finnish companies, as well as other actors interested or involved in shaping the course of CSR, locally and globally. It also looks closely at how national culture affects the views, thinking, and management of CSR in a welfare state. This dissertation primarily draws on the analyses of information collected from a series of qualitative interviews and the existing literature in the area. This is complemented by an analysis of written and published documents on CSR from various sources. The results of the study give insightful information and detailed descriptions of a roadmap useful in learning and understanding CSR in Finnish companies. Despite the varying conceptual connotations, essential roadmap indicators point to the importance of framing CSR within the corporate responsibility concept, Finnish development and the welfare state system, globalization, stakeholders, and the pursuit of sustainable development as the main drivers of CSR, the remarkable progress of CSR in companies, and identification of key management areas and practices relevant to CSR. Similarly,the study reveals the importance of culture as essential in understanding and learning CSR. Finnish culture has a positive influence on the views, thinking, and management practices of CSR issues. Such a positive influence of culture, therefore, makes it easy for business people to discuss and understand CSR, because those CSR issues are already considered common and taken-for-granted by Finns and are implicit in the welfare state provisions. The experience of Finnish companies in implementing CSR policies in the supply chain is a concrete proactive step in advancing the message of CSR, that is, to bring companies and suppliers together to work on improving and strengthening relationships towards socially responsible practices worldwide. Such a forward step to deal with CSR issues in the supply chain reflects the companies' commitments and belief that CSR can be managed with the suppliers and gain positive benefits. Despite the problems and complexities, particularly in the global supply chain, managing CSR for Finnish companies presents new opportunities and challenges that are expected to intensify in the near future. The focus on CSR policy implementation inthe supply chain points to the importance of companies taking initiatives and forging cooperation with suppliers with the aim of addressing and improving CSR questions in the supply chains. The proactive stance of Finnish companies toward CSR is complemented by the active supporting role of important societalactors such as the government and NGOs. These actors carry out various promotional efforts and campaigns, thus bringing CSR into the mainstream of Finnish companies and strengthening the synergistic learning about CSR within the Finnish business and civil circles. The efforts of the government and NGOs to promote CSR are indicative of the importance of multipartite involvement and the emergence of better civil regulations. Likewise, their drive to learn from each other, exchange experiences, and contribute in CSR debates facilitated the evolution of CSRnetworks in the country. The results of this study add to the mounting evidence that CSR, in general, has created a new dimension in managing corporate sustainability. This study provides compelling empirical evidence and some direct quotations about CSR in the Finnish context. This information can be used to learn and gain new useful insights, approaches, and concepts for managing CSR.

Supervised localimage feature detection

This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

Ion-exchange resins as stationary phase in reactive chromatography

Dynamic behavior of bothisothermal and non-isothermal single-column chromatographic reactors with an ion-exchange resin as the stationary phase was investigated. The reactor performance was interpreted by using results obtained when studying the effect of the resin properties on the equilibrium and kinetic phenomena occurring simultaneously in the reactor. Mathematical models were derived for each phenomenon and combined to simulate the chromatographic reactor. The phenomena studied includes phase equilibria in multicomponent liquid mixture¿ion-exchange resin systems, chemicalequilibrium in the presence of a resin catalyst, diffusion of liquids in gel-type and macroporous resins, and chemical reaction kinetics. Above all, attention was paid to the swelling behavior of the resins and how it affects the kinetic phenomena. Several poly(styrene-co-divinylbenzene) resins with different cross-link densities and internal porosities were used. Esterification of acetic acid with ethanol to produce ethyl acetate and water was used as a model reaction system. Choosing an ion-exchange resin with a low cross-link density is beneficial inthe case of the present reaction system: the amount of ethyl acetate as well the ethyl acetate to water mole ratio in the effluent stream increase with decreasing cross-link density. The enhanced performance of the reactor is mainly attributed to increasing reaction rate, which in turn originates from the phase equilibrium behavior of the system. Also mass transfer considerations favor the use ofresins with low cross-link density. The diffusion coefficients of liquids in the gel-type ion-exchange resins were found to fall rapidly when the extent of swelling became low. Glass transition of the polymer was not found to significantlyretard the diffusion in sulfonated PS¿DVB ion-exchange resins. It was also shown that non-isothermal operation of a chromatographic reactor could be used to significantly enhance the reactor performance. In the case of the exothermic modelreaction system and a near-adiabatic column, a positive thermal wave (higher temperature than in the initial state) was found to travel together with the reactive front. This further increased the conversion of the reactants. Diffusion-induced volume changes of the ion-exchange resins were studied in a flow-through cell. It was shown that describing the swelling and shrinking kinetics of the particles calls for a mass transfer model that explicitly includes the limited expansibility of the polymer network. A good description of the process was obtained by combining the generalized Maxwell-Stefan approach and an activity model that was derived from the thermodynamics of polymer solutions and gels. The swelling pressure in the resin phase was evaluated by using a non-Gaussian expression forthe polymer chain length distribution. Dimensional changes of the resin particles necessitate the use of non-standard mathematical tools for dynamic simulations. A transformed coordinate system, where the mass of the polymer was used as a spatial variable, was applied when simulating the chromatographic reactor columns as well as the swelling and shrinking kinetics of the resin particles. Shrinking of the particles in a column leads to formation of dead volume on top of the resin bed. In ordinary Eulerian coordinates, this results in a moving discontinuity that in turn causes numerical difficulties in the solution of the PDE system. The motion of the discontinuity was eliminated by spanning two calculation grids in the column that overlapped at the top of the resin bed. The reactive and non-reactive phase equilibrium data were correlated with a model derived from thethermodynamics of polymer solution and gels. The thermodynamic approach used inthis work is best suited at high degrees of swelling because the polymer matrixmay be in the glassy state when the extent of swelling is low.

Facing the broader dimensions of biological invasions

Invasive species are an excellent opportunity to think about the nature society desires, particularly in the face of global changes. Nature and human views of nature are rapidly evolving; our approach to bio- logical invasions through biosecurity institutions and land management policies must evolve in tandem with these changes. We review three dimensions that are insufficiently addressed. First, biological inva- sions are culturally shaped and interpreted. Humans play a major role in the movement and nurturing of alien life, and esthetics, perception, and emotion are deeply implicated in the management of invasive species. What people fear or regret with invasive species are not their effects on nature per se, but their effects on a particular desired nature, and policymaking must reflect this. Second, biological invasions are not restricted to negative impacts. Invasions take place in landscapes where many natural condi- tions have been altered, so policy tools must recognize that invasive species are a functional, structural, and compositional part of transformed ecosystems. In some cases, native species benefit from changes in resource availability caused by invasions or from protections provided by an invasive plant. Finally, invasive species can help ecosystems and people to adapt to global change by maintaining ecosystem processes such as productivity, carbon storage, and nutrient cycling in a context of climate change or land cover transformations. While recognition is growing among ecologists that novel, invaded ecosystems have value, and while the on-the-ground application of biosecurity policies has of necessity adjusted to local contexts and other agendas, invasion biology could aid policymaking by better addressing the three complexities inherent in the three dimensions highlighted above.

Modelling leaf development in Oxalis latifolia

This study was carried to develop functions that could explain the growth of Oxalis latifolia, in both early stages and throughout the season, contributing to the improvement of its cultural control. Bulbs of the Cornwall form of O. latifolia were buried at 1 and 8 cm in March 1999 and 2000. Samples were destructive at fixed times, and at each time the corresponding BBCH scale codes as well as the absolute number of growing and adult leaves were noted. Using the absolute number of adult leaves (transformed to percentages), a Gaussian curve of three parameters that explains the growth during the season (R2=0.9355) was developed. The BBCH scale permitted the fit of two regression lines that were accurately adjusted for each burial depth (R2=0.9969 and R2=0.9930 respectively for 1 and 8 cm). The best moment for an early defoliation in Northern Spain could be calculated with these regression lines, and was found to be the second week of May. In addition, it was observed that a burial depth of 8 cm does not affect the growing pattern of the weed, but it affects the number of leaves they produce, which decreases to less than a half of those produced at 1 cm.