Nowadays with the rapid development of cities, understanding the human mobility patterns of subscribers is crucial for urban planning and for network infrastructure deployment. Today mobile phones are electronic devices used for analyzing the mobility patterns of the subscribers in the network, because humans in their daily activities they carry mobile phones for communication purpose. For effective utilization of network infrastructure (NI) there is a need to study on mobility patterns of subscribers.   The aim of the thesis is to simulate the geospatial Telenor mobility data (i.e. three different subscriber categorized segments) and provide a visual support in google maps using google maps API, which helps in decision making to the telecommunication operators for effective utilization of network infrastructure (NI).    In this thesis there are two major objectives. Firstly, categorize the given geospatial telenor mobility data using subscriber mobility algorithm. Secondly, providing a visual support for the obtained categorized geospatial telenor mobility data in google maps using a geovisualization simulation tool.    The algorithm used to categorize the given geospatial telenor mobility data is subscriber mobility algorithm. Where this subscriber mobility algorithm categorizes the subscribers into three different segments (i.e. infrastructure stressing, medium, friendly). For validation and confirmation purpose of subscriber mobility algorithm a tetris optimization model is used. To give visual support for each categorized segments a simulation tool is developed and it displays the visualization results in google maps using Google Maps API.   The result of this thesis are presented to the above formulated objectives. By using subscriber mobility algorithm and tetris optimization model to a geospatial data set of 33,045 subscribers only 1400 subscribers are found as infrastructure stressing subscribers. To look informative, a small region (i.e. boras region) is taken to visualize the subscribers from each of the categorized segments (i.e. infrastructure stressing, medium, friendly).    The conclusion of the thesis is that the functionality thus developed contributes to knowledge discovery from geospatial data and provides visual support for decision making to telecommunication operators. Nowadays with the rapid development of cities, understanding the human mobility patterns of subscribers is crucial for urban planning and for network infrastructure deployment. Today mobile phones are electronic devices used for analyzing the mobility patterns of the subscribers in the network, because humans in their daily activities they carry mobile phones for communication purpose. For effective utilization of network infrastructure (NI) there is a need to study on mobility patterns of subscribers.   The aim of the thesis is to simulate the geospatial Telenor mobility data (i.e. three different subscriber categorized segments) and provide a visual support in google maps using google maps API, which helps in decision making to the telecommunication operators for effective utilization of network infrastructure (NI).    In this thesis there are two major objectives. Firstly, categorize the given geospatial telenor mobility data using subscriber mobility algorithm. Secondly, providing a visual support for the obtained categorized geospatial telenor mobility data in google maps using a geovisualization simulation tool.    The algorithm used to categorize the given geospatial telenor mobility data is subscriber mobility algorithm. Where this subscriber mobility algorithm categorizes the subscribers into three different segments (i.e. infrastructure stressing, medium, friendly). For validation and confirmation purpose of subscriber mobility algorithm a tetris optimization model is used. To give visual support for each categorized segments a simulation tool is developed and it displays the visualization results in google maps using Google Maps API.   The result of this thesis are presented to the above formulated objectives. By using subscriber mobility algorithm and tetris optimization model to a geospatial data set of 33,045 subscribers only 1400 subscribers are found as infrastructure stressing subscribers. To look informative, a small region (i.e. boras region) is taken to visualize the subscribers from each of the categorized segments (i.e. infrastructure stressing, medium, friendly).    The conclusion of the thesis is that the functionality thus developed contributes to knowledge discovery from geospatial data and provides visual support for decision making to telecommunication operators.

Analysis and characterisation of biomolecules using mass spectrometry has advanced over the past decade due to improvements in instrument design and capability; relevant use of complementary techniques; and available experimental and in silico data for comparison with cutting-edge research. This thesis presents ion mobility data, collected on an in-house modified QToF mass spectrometer (the MoQTOF), for a number of protein systems. Two haemoproteins, cytochrome c and haemoglobin, have been characterised and rotationally-averaged collision cross-sections for a number of multimeric species are presented. Intact multiply-charged multimers of the form [xCyt c + nH]z+ where x = 1 (monomer), x = 2 (dimer) and x = 3 (trimer) for cytochrome c have been elucidated and for species with x ≥ 2, reported for the first time. Fragment ions possibly attributed to a novel fragmentation mechanism, native electron capture dissociation, are reported with a brief discussion into their possible production from the dissociation of the gas-phase dimer species. Haemoglobin monomer globin subunits, dimers and intact tetramer have been successfully transferred to the gas phase, and their cross-sections elucidated. Comparisons with in silico computational data have been made and a discussion of the biologically-active tetramer association/dissociation technique is presented. Three further proteins have been studied and their gas-phase collision cross-sections calculated. Two regions of the large Factor H (fH) complement glycoprotein, fH 10-15 and fH 19-20, have been characterised for the first time by ion mobility-mass spectrometry. Much work using nuclear magnetic resonance spectroscopy has previously been achieved to produce structural information of these protein regions, however further biophysical characterisation using mass spectrometry may aid in greater understanding of the interactions these two specific regions have with other biomolecules. The DNA-binding core domain of the tumour suppressor p53 has been characterised and cross-sections produced in the presence and absence of the zinc metal ion that may control the domain’s biological activity. Within this core domain, p53 inactivation mutations have been shown to occur in up to 50% of human cancers, therefore the potential exists to further cancer-fighting activity through research on this region. Anterior Gradient-2 (AGR2) protein facilitates downregulation of p53 in an as yet unclear mechanism. Recent work using peptide aptamers has demonstrated that this downregulation can be disrupted and levels of p53 restored. Collision cross-sections for six peptide aptamers have been calculated, as well as cross-sections for multimers of AGR2 protein. A complex between one aptamer with the protein has also been elucidated. Use of the commercially available Synapt HDMS ion mobility-mass spectrometer at Waters MS Technologies Centre (Manchester, UK) allowed data to be collected for both Factor H protein regions and for the DNA-binding core domain of p53. Data are compared in the appropriate chapters with data collected using the MoQTOF.

Smart-phones, wearables and mobile devices in general are the sensors of our modern world. Their sensing capabilities offer the means to analyze and interpret our behaviour and surroundings. When it comes to human behaviour, perhaps the most informative feature is our location and mobility habits. Insights from human mobility are useful in a number of everyday practical applications, such as the improvement of transportation and road network infrastructure, ride-sharing services, activity recognition, mobile data pre-fetching, analysis of the social behaviour of humans, etc. In this dissertation, we develop algorithms for processing mobility data. The analysis of mobility data is a non trivial task as it involves managing large quantities of location information, usually spread out spatially and temporally across many tracking sensors. An additional challenge in processing mobility information is to publish the data and the results of its analysis without jeopardizing the privacy of the involved individuals or the quality of the data. We look into a series of problems on processing mobility data from individuals and from a population. Our mission is to design algorithms with provable properties that allow for the fast and reliable extraction of insights. We present efficient solutions - in terms of storage and computation requirements - , with a focus on distributed computation, online processing and privacy preservation.

Depuis quelques dizaines d’années, l’utilisation de téléphones contenant un capteur GPS a fortement augmenté. Cependant, tous ces usages ne sont pas sans menace pour la vie privée des utilisateurs. En effet, les données de mobilité qu’ils envoient à ces services peuvent être utilisées pour inférer des informations sensibles telles que leur domicile ou leur lieu de travail. C’est à ce moment qu’entrent en action les mécanismes de protection, visant à redonner aux utilisateurs le contrôle sur leur vie privée. Nous commençons par répertorier les mécanismes de protection existants et les métriques utilisées pour les évaluer. Cette première analyse met en avant une information particulièrement sensible : les points d’intérêt. Ces derniers représentent tous les lieux où les utilisateurs passent la majeure partie de leur temps. Cela nous conduit à proposer un nouveau mécanisme de protection, PROMESSE, dont le but principal est de cacher ces points d’intérêt. Les mécanismes de protection sont en général configurés par des paramètres, qui ont un grand impact sur leur efficacité. Nous proposons ALP, une solution destinée à aider les utilisateurs à configurer leurs mécanismes de protection à partir d’objectifs qu’ils ont spécifié. Enfin, nous présentons Accio, un logiciel regroupant la majeure partie du travail de cette thèse. Il permet de lancer facilement des expériences destinées à étudier des mécanismes de protection, tout en renforçant leur reproductibilité. / In the past decades, the usage of GPS-enabled smartphones has dramatically risen. However, all these usages do not come without privacy threats. Indeed, location data that users are sending to these services can be used to infer sensitive knowledge about them, such as where they live or where they work. This is were protection mechanisms come into play, whose goal is to put users back in control of their privacy. We start by surveying existing protection mechanisms and metrics used to evaluate them. This first analysis highlights a particularly sensitive information, namely the points of interest. These are all the places where users use to spend most of their time. This leads us towards building a new protection mechanism, PROMESSE, whose main goal is to hide these points of interest. Protection mechanisms tend to be configured by parameters, which highly impact their effectiveness in terms of privacy and utility. We propose ALP, a solution to help users to configure their protection mechanisms from a set of objectives they specified. Finally, we introduce Accio, which is a software encompassing most of our work. Its goal is to allow to easily launch location privacy experiments and enforce their reproducibility.

The idea behind this report is based on the fact that it is not only the number of users in the traffic network that is increasing, the number of connected devices such as probe vehicles and mobile sources has increased dramatically in the last decade. These connected devices provide large-scale mobility data and new opportunities to analyze the current traffic situation as they traverse through the network and continuously send out different types of information like Global Positioning System (GPS) data and Mobile Network Data (MND). Travel demand is often described in terms of an Origin Destination (OD) matrix which represents the number of trips from an origin zone to a destination zone in a geographic area. The aim of this master thesis is to develop and evaluate a data-driven method for estimation of dynamic OD matrices using unsupervised learning, sensor fusion and large-scale mobility data. Traditionally, OD matrices are estimated based on travel surveys and link counts. The problem is that these sources of information do not provide the quality required for online control of the traffic network. A method consisting of an offline process and an online process has therefore been developed. The offline process utilizes historical large-scale mobility data to improve an inaccurate prior OD matrix. The online process utilizes the results and tuning parameters from the offline estimation in combination with real-time observations to describe the current traffic situation. A simulation study on a toy network with synthetic data was used to evaluate the data-driven estimation method. Observations based on GPS data, MND and link counts were simulated via a traffic simulation tool. The results showed that the sensor fusion algorithms Kalman filter and Kalman filter smoothing can be used when estimating dynamic OD matrices. The results also showed that the quality of the data sources used for the estimation is of high importance. Aggregating large-scale mobility data as GPS data and MND by using the unsupervised learning method Principal Component Analysis (PCA) improves the quality of the large-scale mobility data and so the estimation results. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>