Feature-based matching of underwater images

This work investigates performance of recent feature-based matching techniques when applied to registration of underwater images. Matching methods are tested versus different contrast enhancing pre-processing of images. As a result of the performed experiments for various dominating in images underwater artifacts and present deformation, the outperforming preprocessing, detection and description methods are proposed

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.

A hybrid feature selection approach for the early diagnosis of Alzheimer's disease

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5). Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

The relevance of theobromine for the beneficial effects of cocoa consumption.

Cocoa consumption began in America and in the mid sixteenth Century it quickly spread to Europe. Beyond being considered a pleasant habit due to its rich sweet lingering taste, chocolate was considered a good nutrient and even a medicine. Traditionally, health benefits of cocoa have been related with the high content of antioxidants of Theobroma cocoa beans. However, the direct psychoactive effect due to methylxanthines in cocoa is notable. Theobromine and caffeine, in the proportions found in cocoa, are responsible for the liking of the food/beverage. These compounds influence in a positive way our moods and our state of alertness. Theobromine, which is found in higher amounts than caffeine, seems to be behind several effects attributed to cocoa intake. The main mechanisms of action are inhibition of phosphodiesterases and blockade of adenosine receptors. Further mechanisms are being explored to better understand the health benefits associated to theobromine consumption. Unlike what happens in other mammals -pets- included, theobromine is safe for humans and has fewer unwanted effects than caffeine. Therefore, theobromine deserves attention as one of the most attractive molecules in cocoa.

Comparison of Local Feature Detectors and Descriptors for Visual Object Categorization

Local features are used in many computer vision tasks including visual object categorization, content-based image retrieval and object recognition to mention a few. Local features are points, blobs or regions in images that are extracted using a local feature detector. To make use of extracted local features the localized interest points are described using a local feature descriptor. A descriptor histogram vector is a compact representation of an image and can be used for searching and matching images in databases. In this thesis the performance of local feature detectors and descriptors is evaluated for object class detection task. Features are extracted from image samples belonging to several object classes. Matching features are then searched using random image pairs of a same class. The goal of this thesis is to find out what are the best detector and descriptor methods for such task in terms of detector repeatability and descriptor matching rate.

Development of simple and efficient synthetic strategies for production of fine chemicals of pharmaceutical relevance : metal-mediated allylation combined with applied catalysis

Den snart 200 år gamla vetenskapsgrenen organisk synteskemi har starkt bidragit till moderna samhällens välfärd. Ett av flaggskeppen för den organiska synteskemin är utvecklingen och produktionen av nya läkemedel och speciellt de aktiva substanserna däri. Därmed är det viktigt att utveckla nya syntesmetoder, som kan tillämpas vid framställningen av farmaceutiskt relevanta målstrukturer. I detta sammanhang är den ultimata målsättningen dock inte endast en lyckad syntes av målmolekylen, utan det är allt viktigare att utveckla syntesrutter som uppfyller kriterierna för den hållbara utvecklingen. Ett av de centralaste verktygen som en organisk kemist har till förfogande i detta sammanhang är katalys, eller mera specifikt möjligheten att tillämpa olika katalytiska reaktioner vid framställning av komplexa målstrukturer. De motsvarande industriella processerna karakteriseras av hög effektivitet och minimerad avfallsproduktion, vilket naturligtvis gynnar den kemiska industrin samtidigt som de negativa miljöeffekterna minskas avsevärt. I denna doktorsavhandling har nya syntesrutter för produktion av finkemikalier med farmaceutisk relevans utvecklats genom att kombinera förhållandevis enkla transformationer till nya reaktionssekvenser. Alla reaktionssekvenser som diskuteras i denna avhandling påbörjades med en metallförmedlad allylering av utvalda aldehyder eller aldiminer. De erhållna produkterna innehållende en kol-koldubbelbindning med en närliggande hydroxyl- eller aminogrupp modifierades sedan vidare genom att tillämpa välkända katalytiska reaktioner. Alla syntetiserade molekyler som presenteras i denna avhandling karakteriseras som finkemikalier med hög potential vid farmaceutiska tillämpningar. Utöver detta tillämpades en mängd olika katalytiska reaktioner framgångsrikt vid syntes av dessa molekyler, vilket i sin tur förstärker betydelsen för de katalytiska verktygen i organiska kemins verktygslåda.

Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task

The Design and Implementation of a Power Management Feature to a Configuration Management System

Green IT is a term that covers various tasks and concepts that are related to reducing the environmental impact of IT. At enterprise level, Green IT has significant potential to generate sustainable cost savings: the total amount of devices is growing and electricity prices are rising. The lifecycle of a computer can be made more environmentally sustainable using Green IT, e.g. by using energy efficient components and by implementing device power management. The challenge using power management at enterprise level is how to measure and follow-up the impact of power management policies? During the thesis a power management feature was developed to a configuration management system. The feature can be used to automatically power down and power on PCs using a pre-defined schedule and to estimate the total power usage of devices. Measurements indicate that using the feature the device power consumption can be monitored quite precisely and the power consumption can be reduced, which generates electricity cost savings and reduces the environmental impact of IT.

Papillary neoplasias of the biliary tract

The authors conducted a revisional study of intraepithelial papillary lesions of the bile ducts, characterized by being a kind of rare, intraductal growing cholangiocarcinoma. Articles published in the last 10 years were reviewed. The authors considered that the adenoma-carcinoma development is an important feature to warrant prophylactic measures through excisions. The histological type and biomolecular behavior may have relevance in the postoperative course of such lesions, which have a better prognosis when compared with other histological types.

Scaling up stepwise feature introduction to construction of large software systems

Developing software is a difficult and error-prone activity. Furthermore, the complexity of modern computer applications is significant. Hence,an organised approach to software construction is crucial. Stepwise Feature Introduction – created by R.-J. Back – is a development paradigm, in which software is constructed by adding functionality in small increments. The resulting code has an organised, layered structure and can be easily reused. Moreover, the interaction with the users of the software and the correctness concerns are essential elements of the development process, contributing to high quality and functionality of the final product. The paradigm of Stepwise Feature Introduction has been successfully applied in an academic environment, to a number of small-scale developments. The thesis examines the paradigm and its suitability to construction of large and complex software systems by focusing on the development of two software systems of significant complexity. Throughout the thesis we propose a number of improvements and modifications that should be applied to the paradigm when developing or reengineering large and complex software systems. The discussion in the thesis covers various aspects of software development that relate to Stepwise Feature Introduction. More specifically, we evaluate the paradigm based on the common practices of object-oriented programming and design and agile development methodologies. We also outline the strategy to testing systems built with the paradigm of Stepwise Feature Introduction.

A feature-based object-oriented expert system to model and support product design

In this paper a computer program to model and support product design is presented. The product is represented through a hierarchical structure that allows the user to navigate across the product’s components, and it aims at facilitating each step of the detail design process. A graphical interface was also developed, which shows visually to the user the contents of the product structure. Features are used as building blocks for the parts that compose the product, and object-oriented methodology was used as a means to implement the product structure. Finally, an expert system was also implemented, whose knowledge base rules help the user design a product that meets design and manufacturing requirements.