Present day email is provided by centralized services running in the cloud. The services transparently connect users behind middleboxes and provide backup, redundancy, and high availability at the expense of user privacy. In present day mobile environments, users can access and modify email from multiple devices with updates reconciled on the central server. Prioritizing updates is difficult and may be undesirable. Moreover, legacy email protocols do not provide optimal email synchronization and access. Recent phenomena of the Internet of Things (IoT) will see the number of interconnected devices grow to 27 billion by 2021. In the first part of my dissertation I am proposing a decentralized email architecture which takes advantage of user's a IoT devices to maintain a complete email history. This addresses the email reconciliation issue and places data under user control. I replace legacy email protocols with a synchronization protocol to achieve eventual consistency of email and optimize bandwidth and energy usage. The architecture is evaluated on a Raspberry Pi computer. There is an extensive body of research on Social Network Analysis (SNA) based on email archives. Typically, the analyzed network reflects either communication between users or a relationship between the email and the information found in the email's header and the body. This approach discards either all or some email attachments that cannot be converted to text; for instance, images. Yet attachments may use up to 90% of an email archive size. In the second part of my dissertation I suggest extracting the network from email attachments shared between users. I hypothesize that the network extracted from shared email attachments might provide more insight into the social structure of the email archive. I evaluate communication and shared email attachments networks by analyzing common centrality measures and classication and clustering algorithms. I further demonstrate how the analysis of the shared attachments network can be used to optimize the proposed decentralized email architecture.

Concept description är en data mining-uppgift som strävar efter en begripligbeskrivning av koncept och klasser, inte exakta prediceringar. Syftet medstudien är att visa hur prototypbaserad klustring kan skapa förståelse för endatamängds underliggande domän, enligt concept description. Experimenthar utförts med data från pokerdomänen. Datamängden samlades in från ettlow-limit, shorthanded bord, hos en av de stora Internetsiterna för onlinepokerspel. De två experimenten utfördes med samma datamängd, men däruppsättningen attribut skiljde sig åt. Klustringen utfördes med denprototypbaserade klustringsalgoritmen K-means. För att data mining–uppgiften skulle lösas på tillfredställande vis, presenterades experimentensresultat i diagram och tabeller som var möjliga att analysera. Klustren somexperimenten resulterade i visar sig vara väl separerade. Den doldainformationen, som lyftes fram av klustringsexperimenten, kunde verifierasav domänens teori. Analysen av resultatet visade att klustring med denprototypbaserade klustringsalgoritmen K-means är en metod som går bra attanvända för att skapa förståelse i en datamängds underliggande domän. / Uppsatsnivå: D

In the 21st century, the proliferation of steer-by-wire systems has become a central issue in the automobile industry. With such systems there is often an objective to minimise vibrations on the steering wheel to increase driver comfort. Nevertheless, steering wheel vibration is also recognised as an important medium that assists drivers in judging the vehicle's subsystems dynamics as well as to indicate important information such as the presence of danger. This has led to studies of the possible role of vibrational stimuli towards informing drivers of environment conditions such as road surface types. Numerous prior studies were done to identify how characteristics of steering wheel vibrational stimuli might influence driver road surface detection which suggested that there is no single, optimal, acceleration gain that could improve the detection of all road surface types. There is currently a lack of studies on the characteristics of transient vibrations of steering wheel as appear to be an important source of information to the driver road surface detection. Therefore, this study is design to identify the similarity characteristics of transient vibrations for answering the main research question: "What are the time-domain features of transient vibrations that can optimise driver road surface detection?" This study starts by critically reviewing the existing principles of transient vibrations detection to ensure that the identified transient vibrations from original steering wheel vibrations satisfy with the definition of transient vibrations. The study continues by performing the experimental activities to identify the optimal measurement signal for both identification process of transient vibrations and driver road surface detection without taking for granted the basic measurement of signal processing. The studies then identify the similarity of transient vibrations according to their time-domain features. The studies done by performing the high-dimensional reduction techniques associated with clustering methods. Result suggests that the time-domain features of transient vibrations that can optimise driver road surface detection were found to consist of duration (Δt), amplitude (m/s2), energy (r.m.s) and Kurtosis.

The explosion of internet traffic, advent of social media sites such as Facebook and Twitter, and increased availability of digital cameras has saturated life with images and videos. Never before has it been so important to sift quickly through large amounts of digital information. Salient Object Detection (SOD) is a computer vision topic that finds methods to locate important objects in pictures. SOD has proven to be helpful in numerous applications such as image forgery detection and traffic sign recognition. In this thesis, I outline a novel SOD technique to automatically isolate important objects from the background in images.

Dans cette thèse, nous proposons un modèle de calcul parallèle, appelé « matrice cellulaire », pour apporter des réponses aux problématiques de calcul parallèle appliqué à la résolution de problèmes d’appariement de graphes euclidiens. Ces problèmes d’optimisation NP-difficiles font intervenir des données réparties dans le plan et des structures élastiques représentées par des graphes qui doivent s’apparier aux données. Ils recouvrent des problèmes connus sous des appellations diverses telles que geometric k-means, elastic net, topographic mapping, elastic image matching. Ils permettent de modéliser par exemple le problème du voyageur de commerce euclidien, le problème du cycle médian, ainsi que des problèmes de mise en correspondance d’images. La contribution présentée est divisée en trois parties. Dans la première partie, nous présentons le modèle de matrice cellulaire qui partitionne les données et définit le niveau de granularité du calcul parallèle. Nous présentons une boucle générique de calcul parallèle qui modélise le principe des projections de graphes et de leur appariement. Dans la deuxième partie, nous appliquons le modèle de calcul parallèle aux algorithmes de k-means avec topologie dans le plan. Les algorithmes proposés sont appliqués au voyageur de commerce, à la génération de maillage structuré et à la segmentation d'image suivant le concept de superpixel. L’approche est nommée superpixel adaptive segmentation map (SPASM). Dans la troisième partie, nous proposons un algorithme de recherche locale parallèle, appelé distributed local search (DLS). La solution du problème résulte des opérations locales sur les structures et les données réparties dans le plan, incluant des évaluations, des recherches de voisinage, et des mouvements structurés. L’algorithme est appliqué à des problèmes d’appariement de graphe tels que le stéréo-matching et le problème de flot optique. / In this thesis, we propose a parallel computing model, called cellular matrix, to provide answers to problematic issues of parallel computation when applied to Euclidean graph matching problems. These NP-hard optimization problems involve data distributed in the plane and elastic structures represented by graphs that must match the data. They include problems known under various names, such as geometric k-means, elastic net, topographic mapping, and elastic image matching. The Euclidean traveling salesman problem (TSP), the median cycle problem, and the image matching problem are also examples that can be modeled by graph matching. The contribution presented is divided into three parts. In the first part, we present the cellular matrix model that partitions data and defines the level of granularity of parallel computation. We present a generic loop for parallel computations, and this loop models the projection between graphs and their matching. In the second part, we apply the parallel computing model to k-means algorithms in the plane extended with topology. The proposed algorithms are applied to the TSP, structured mesh generation, and image segmentation following the concept of superpixel. The approach is called superpixel adaptive segmentation map (SPASM). In the third part, we propose a parallel local search algorithm, called distributed local search (DLS). The solution results from the many local operations, including local evaluation, neighborhood search, and structured move, performed on the distributed data in the plane. The algorithm is applied to Euclidean graph matching problems including stereo matching and optical flow.

This thesis proposes a new chroma keying method that can automatically detect background, foreground, and unknown regions. For background color detection, we use K-means clustering in color space to calculate the limited number of clusters of background colors. We use spatial information to clean the background regions and minimize the unknown regions. Our method only needs minimum inputs from user. For unknown regions, we implement the alpha matte based on Wang's robust matting algorithm, which is considered one of the best algorithms in the literature, if not the best. Wang's algorithm is based on modified random walk. We proposed a better color selection method, which improves matting results in the experiments. In the thesis, a detailed implementation of robust matting is provided. The experimental results demonstrate that our proposed method can handle images with one background color, images with gridded background, and images with difficult regions such as complex hair stripes and semi-transparent clothes.

Biological data is accumulated at a fast pace. However, raw data are generally difficult to understand and not useful unless we unlock the information hidden in the data. Knowledge/information can be extracted as the patterns or features buried within the data. Thus data mining, aims at uncovering underlying rules, relationships, and patterns in data, has emerged as one of the most exciting fields in computational science. In this dissertation, we develop efficient approaches to the structure pattern analysis of RNA and protein three dimensional structures. The major techniques used in this work include term rewriting and clustering algorithms. Firstly, a new approach is designed to study the interaction of RNA secondary structures motifs using the concept of term rewriting. Secondly, an improved K-means clustering algorithm is proposed to estimate the number of clusters in data. A new distance descriptor is introduced for the appropriate representation of three dimensional structure segments of RNA and protein three dimensional structures. The experimental results show the improvements in the determination of the number of clusters in data, evaluation of RNA structure similarity, RNA structure database search, and better understanding of the protein sequence-structure correspondence.

This thesis proposes a new chroma keying method that can automatically detect background, foreground, and unknown regions. For background color detection, we use K-means clustering in color space to calculate the limited number of clusters of background colors. We use spatial information to clean the background regions and minimize the unknown regions. Our method only needs minimum inputs from user. For unknown regions, we implement the alpha matte based on Wang's robust matting algorithm, which is considered one of the best algorithms in the literature, if not the best. Wang's algorithm is based on modified random walk. We proposed a better color selection method, which improves matting results in the experiments. In the thesis, a detailed implementation of robust matting is provided. The experimental results demonstrate that our proposed method can handle images with one background color, images with gridded background, and images with difficult regions such as complex hair stripes and semi-transparent clothes.

Cluster analysis is one of the typical tasks in Data Mining, and it groups data objects based only on information found in the data that describes the objects and their relationships. The purpose of this thesis is to verify a modified K-means algorithm in asymmetrical cases, which can be regarded as an extension to the research of Vladislav Valkovsky and Mikael Karlsson in Department of Informatics and Media. In this thesis an experiment is designed and implemented to identify clusters with the modified algorithm in asymmetrical cases. In the experiment the developed Java application is based on knowledge established from previous research. The development procedures are also described and input parameters are mentioned along with the analysis. This experiment consists of several test suites, each of which simulates the situation existing in real world, and test results are displayed graphically. The findings mainly emphasize the limitations of the algorithm, and future work for digging more essences of the algorithm is also suggested.

We study the asymptotic behavior of the quantization error for general information functions and prove results for distributions P with regular support. We characterize the information functions for which the uniform distribution on the set of prototypes converges weakly to P. (author's abstract) / Series: Forschungsberichte / Institut für Statistik