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The aim of this thesis is to employ and evaluate an evaluation board with the standard microprocessor freescale MPC5554EVB board for implementation of control algorithms which are created in Matlab/Simulink instead of using dSPACE prototyping hardware. The Simulink real-time model shall be compiled to the MPC5554EVB board. TargetLink is a powerful software tool which allows an automatic generation of efficient C code from Simulink and facilitates model-based control design. The goal of this thesis is to learn how to use TargetLink in a control design workflow from model to real code and what are the limitations of a microprocessor platform and to evaluate the capabilities of TargetLink to generate a working code for a generic microprocessor.

Due to the large influx of computers in our daily lives, human-computer interaction has become crucially important. For a long time, focusing on what users need has been critical for designing interaction methods. However, new perspective tends to extend this attitude to encompass how human desires, interests, and ambitions can be met and supported. This implies that the way we interact with computers should be revisited. Centralizing human values rather than user needs is of the utmost importance for providing new interaction techniques. These values drive our decisions and actions, and are essential to what makes us human. This motivated us to introduce new interaction methods that will support human values, particularly human well-being.

The aim of this thesis is to design new interaction methods that will empower human to have a healthy, intuitive, and pleasurable interaction with tomorrow’s digital world. In order to achieve this aim, this research is concerned with developing theories and techniques for exploring interaction methods beyond keyboard and mouse, utilizing human body. Therefore, this thesis addresses a very fundamental problem, human motion analysis.

Technical contributions of this thesis introduce computer vision-based, marker-less systems to estimate and analyze body motion. The main focus of this research work is on head and hand motion analysis due to the fact that they are the most frequently used body parts for interacting with computers. This thesis gives an insight into the technical challenges and provides new perspectives and robust techniques for solving the problem.

From an early age, people display the ability to quickly and effortlessly interpret the orientation and movement of human body parts, thereby allowing one to infer the intentions of others who are nearby and to comprehend an important nonverbal form of communication. The ease with which one accomplishes this task belies the difficulty of a problem that has challenged computational systems for decades, human motion analysis.

Technological developments over years have resulted into many systems for measuring body segment positions and angles between segments. In these systems human body is typically considered as a system of rigid links connected by joints. The motion is estimated by the use of measurements from mechanical, optical, magnetic, or inertial trackers. Among all kinds of sensors, optical sensing encompasses a large and varying collection of technologies.

In a computer vision context, human motion analysis is a topic that studies methods and applications in which two or more consecutive images from an image sequences, e.g. captured by a video camera, are processed to produce information based on the apparent human body motion in the images.

Many different disciplines employ motion analysis systems to capture movement and posture of human body for applications such as medical diagnostics, virtual reality, human-computer interaction etc.

This thesis gives an insight into the state of the art human motion analysissystems, and provides new methods for capturing human motion.

This paper presents a novel approach for performing intuitive gesture based interaction using depth data acquired by Kinect. The main challenge to enable immersive gestural interaction is dynamic gesture recognition. This problem can be formulated as a combination of two tasks; gesture recognition and gesture pose estimation. Incorporation of fast and robust pose estimation method would lessen the burden to a great extent. In this paper we propose a direct method for real-time hand pose estimation. Based on the range images, a new version of optical flow constraint equation is derived, which can be utilized to directly estimate 3D hand motion without any need of imposing other constraints. Extensive experiments illustrate that the proposed approach performs properly in real-time with high accuracy. As a proof of concept, we demonstrate the system performance in 3D object manipulation On two different setups; desktop computing, and mobile platform. This reveals the system capability to accommodate different interaction procedures. In addition, a user study is conducted to evaluate learnability, user experience and interaction quality in 3D gestural interaction in comparison to 2D touchscreen interaction.

A direct method for recovering three-dimensional (3D) head motion parameters from a sequence of range images acquired by Kinect sensors is presented. Based on the range images, a new version of the optical flow constraint equation is derived, which can be used to directly estimate 3D motion parameters without any need of imposing other constraints. Since all calculations with the new constraint equation are based on the range images, Z(x, y, t), the existing techniques and experiences developed and accumulated on the topic of motion from optical flow can be directly applied simply by treating the range images as normal intensity images I(x, y, t). In this reported work, it is demonstrated how to employ the new optical flow constraint equation to recover the 3D motion of a moving head from the sequences of range images, and furthermore, how to use an old trick to handle the case when the optical flow is large. It is shown, in the end, that the performance of the proposed approach is comparable with that of some of the state-of-the-art approaches that use range data to recover 3D motion parameters.

Currently, the most common way to control an electric wheelchair is to use joystick. However, there are some individuals unable to operate joystick-driven electric wheelchairs due to sever physical disabilities, like quadriplegia patients. This paper proposes a novel head pose estimation method to assist such patients. Head motion parameters are employed to control and drive an electric wheelchair. We introduce a direct method for estimating user head motion, based on a sequence of range images captured by Kinect. In this work, we derive new version of the optical flow constraint equation for range images. We show how the new equation can be used to estimate head motion directly. Experimental results reveal that the proposed system works with high accuracy in real-time. We also show simulation results for navigating the electric wheelchair by recovering user head motion.

This paper presents a novel approach for performing intuitive 3D gesture-based interaction using depth data acquired by Kinect. Unlike current depth-based systems that focus only on classical gesture recognition problem, we also consider 3D gesture pose estimation for creating immersive gestural interaction. In this paper, we formulate gesture-based interaction system as a combination of two separate problems, gesture recognition and gesture pose estimation. We focus on the second problem and propose a direct method for recovering hand motion parameters. Based on the range images, a new version of optical flow constraint equation is derived, which can be utilized to directly estimate 3D hand motion without any need of imposing other constraints. Our experiments illustrate that the proposed approach performs properly in real-time with high accuracy. As a proof of concept, we demonstrate the system performance in 3D object manipulation. This application is intended to explore the system capabilities in real-time biomedical applications. Eventually, system usability test is conducted to evaluate the learnability, user experience and interaction quality in 3D interaction in comparison to 2D touch-screen interaction.

Background: The ALS Online Genetics Database (ALSoD) website holds mutation, geographical, and phenotype data on genes implicated in amyotrophic lateral sclerosis (ALS) and links to bioinformatics resources, publications, and tools for analysis. On average, there are 300 unique visits per day, suggesting a high demand from the research community. To enable wider access, we developed a mobile-friendly version of the website and a smartphone app. Objective: We sought to compare data traffic before and after implementation of a mobile version of the website to assess utility. Methods: We identified the most frequently viewed pages using Google Analytics and our in-house analytic monitoring. For these, we optimized the content layout of the screen, reduced image sizes, and summarized available information. We used the Microsoft. NET framework mobile detection property (HttpRequest. IsMobileDevice in the Request. Browser object in conjunction with HttpRequest. UserAgent), which returns a true value if the browser is a recognized mobile device. For app development, we used the Eclipse integrated development environment with Android plug-ins. We wrapped the mobile website version with the WebView object in Android. Simulators were downloaded to test and debug the applications. Results: The website automatically detects access from a mobile phone and redirects pages to fit the smaller screen. Because the amount of data stored on ALSoD is very large, the available information for display using smartphone access is deliberately restricted to improve usability. Visits to the website increased from 2231 to 2820, yielding a 26% increase from the pre-mobile to post-mobile period and an increase from 103 to 340 visits (230%) using mobile devices (including tablets). The smartphone app is currently available on BlackBerry and Android devices and will be available shortly on iOS as well. Conclusions: Further development of the ALSoD website has allowed access through smartphones and tablets, either through the website or directly through a mobile app, making genetic data stored on the database readily accessible to researchers and patients across multiple devices.

En tung industri har oftast utrustning installerad som kan bidra till övertoner på ett elnät. Samtidigt är oftast elnäten starka inom dessa industrier vilket ska bidra till att reducera spänningsövertonerna som går att finna på respektive fas.Studien har arbetat med de villkor och föreskrifter som gäller både för en ansluten elanläggning till Vattenfalls elnät, men även hur en elkvalitetsmätning ska genomföras och vilka krav som behöver uppfyllas.Åkers Sweden AB, där studien har ägt rum, har induktionsugnar för smältning av metaller. Det förekommer olika tekniska lösningar samt 3 olika fabrikat. Mätningar har genomförts på samtliga dessa fabrikat för att kontrollera om någon av dessa urskiljer sig gentemot de övriga.Genomförda mätningar indikerar att spänningsövertoner förekommer men inte i sådan utsträckning att man inte uppfyller gällande regelverk. Det finns dock anledning till att fortsätta arbetet och utöka mätningarna till övriga objekt inom företaget som inte har ingått i denna studie.Företaget har ett effektabonnemang och en kontroll av detta med avseende på nu gällande effekt och förbrukning under 2015 har genomförts för att kunna ge förslag på eventuella förändringar. Eftersom företaget har ungefär samma produktionsvolym 2016 som 2015 finns anledning till att se över nu gällande avtal för att kunna erhålla en kostnadsreducering.En enkät har skickats ut till samtliga anställda inom företaget med syfte att både upplysa de anställda om vilka kvalitetsproblem som kan förekomma inom ett elnät, men även få en återkoppling på hur de anställda upplever nuvarande situation. Enkäten fick låg svarsfrekvens men svaren som inkom indikerade att de anställda upplever att företaget har en god elkvalitet.

To develop a Real-Time system, which can capture and track the web user’s head motion. Based on the head motion information, the system should be able to identify whether the user is interested in the current homepage or not. A future system property would be to find out in which area of the homepage the user’s interest is moving.

The introduction of advanced metering infrastructure (AMI) smart meters has given rise to fine-grained electricity usage data at different levels of time granularity. AMI collects high-frequency daily energy consumption data that enables utility companies and data aggregators to perform a rich set of grid operations such as demand response, grid monitoring, load forecasting and many more. However, the privacy concerns associated with daily energy consumption data has been raised. Existing studies on data anonymization for smart grid data focused on the direct application of perturbation algorithms, such as microaggregation, to protect the privacy of consumers. In this paper, we empirically show that reliance on microaggregation alone is not sufficient to protect smart grid data. Therefore, we propose DFTMicroagg algorithm that provides a dual level of perturbation to improve privacy. The algorithm leverages the benefits of discrete Fourier transform (DFT) and microaggregation to provide additional layer of protection. We evaluated our algorithm on two publicly available smart grid datasets with millions of smart meters readings. Experimental results based on clustering analysis using k-Means, classification via k-nearest neighbor (kNN) algorithm and mean hourly energy consumption forecast using Seasonal Auto-Regressive Integrated Moving Average with eXogenous (SARIMAX) factors model further proved the applicability of the proposed method. Our approach provides utility companies with more flexibility to control the level of protection for their published energy data.

The development in smart meter technology has made grid operations more efficient based on fine-grained electricity usage data generated at different levels of time granularity. Consequently, machine learning algorithms have benefited from these data to produce useful models for important grid operations. Although machine learning algorithms need historical data to improve predictive performance, these data are not readily available for public utilization due to privacy issues. The existing smart grid data simulation frameworks generate grid data with implicit privacy concerns since the data are simulated from a few real energy consumptions that are publicly available. This paper addresses two issues in smart grid. First, it assesses the level of privacy violation with the individual household appliances based on synthetic household aggregate loads consumption. Second, based on the findings, it proposes two privacy-preserving mechanisms to reduce this risk. Three inference attacks are simulated and the results obtained confirm the efficacy of the proposed privacy-preserving mechanisms.

This report contains an investigation of fluid injection techniques used in the purpose of reducing deleterious flow effects occurring in the draft tube of Francis turbines when operating outside nominal load. There is a focus on implement ability at Jämtkrafts hydroelectric power plants and two power plants were investigated, located in series with each other named Lövhöjden and Ålviken. The only profitable scenario found with some degree of certainty was an increase in the operating range upwards to allow overload operation.

Findings show that both air and water can be introduced in various locations to improve hydraulic efficiency around the turbine parts as well as reduce pressure pulsations in harmful operating regions. Investments in such systems have proven useful and profitable at several facilities with poorly adapted operating conditions. But due to losses in efficiency when operating injection systems, it turns out unprofitable in situations where it does not improve the operating range in a way that is resulting in increased annual or peak production.

With the number of users of social media and web platforms increasing day-by-day in recent years, cyberbullying has become a ubiquitous problem on the internet. Controlling and moderating these social media platforms manually for online abuse and cyberbullying has become a very challenging task. This paper proposes a Recurrent Neural Network (RNN) based approach for the identification and classification of cyberbullying posts. In highly imbalanced input data, a Tomek Links approach does under-sampling to reduce the data imbalance and remove ambiguities in class labelling. Further, the proposed classification model uses Max-Pooling in combination with Bi-directional Long Short-Term Memory (LSTM) network and attention layers. The proposed model is evaluated using Wikipedia datasets to establish the effectiveness of identifying and classifying cyberbullying posts. The extensive experimental results show that our approach performs well in comparison to competing approaches in terms of precision, recall, with F1 score as 0.89, 0.86 and 0.88, respectively.

The problem of generating oscillations of the inertia wheel pendulum is considered. We combine exact feedback linearization with two-relay controller, tuned using frequency-domain tools, such as computing the locus of a perturbed relay system. Explicit expressions for the parameters of the controller in terms of the desired frequency and amplitude are derived. Sufficient conditions for orbital asymptotic stability of the closed-loop system are obtained with the help of the Poincare map. Performance is validated via experiments. The approach can be easily applied for a minimum phase system, provided the behavior of the states of the zero dynamics is of no concern. Copyright (C) 2011 John Wiley & Sons, Ltd.

The purpose of the paper is to analyze the performance of several global position regulators for robot manipulators with Coulomb friction. All the controllers include a proportional-differential part and a switched part whereas the difference between the controllers is in the way of compensation of the gravitational forces. Stability analysis is also revisited within the nonsmooth Lyapunov function framework for the controllers with and without gravity pre-compensation. Performance issues of the proposed controllers are evaluated in an experimental study of a five degrees-of-freedom robot manipulator. In the experiments, we choose two criteria for performance analysis. In the first set of experiments, we set the same gains to all the controllers. In the second set of experiments, the gains of the controller were chosen such that the work done by the manipulator is similar.

OWL and SHACL are two prominent W3C standards for managing RDF graphs, the data model of the Web. They are used for different purposes and make different assumptions about the completeness of data: SHACL is used for expressing integrity constraints on complete data, while OWL allows inferring implicit facts from incomplete data; SHACL reasoners perform validation, while OWL reasoners do logical inference. Integrating these two tasks into one uniform approach is a relevant but challenging problem. The SHACL standard envisions graph validation in combination with OWL entailment, but it does not provide technical guidance on how to realize this. To address this problem, we propose a new intuitive semantics for validating SHACL constraints with OWL 2 QL ontologies based on a suitable notion of the chase. We propose an algorithm that rewrites a set of recursive SHACL constraints (with stratified negation) and an OWL 2 QL ontology into a stand-alone set of SHACL constraints that preserves validation for every input graph, which can in turn be evaluated using an off-the-shelf SHACL validator. We show that validation in this setting is EXPTIME-complete in combined complexity, but only PTIME-complete in data complexity, i.e., if the constraints and the ontology are fixed.

Today's ultra-connected world is generating a huge amount of data stored in databases and cloud environment especially in the era of transportation. These databases need to be processed and analyzed to extract useful information and present it as a valid element for transportation managers for further use, such as road safety, shipping delays, and shipping optimization. The potential of data mining algorithms is largely untapped, this paper shows large-scale techniques such as associations rule analysis, multiple criteria analysis, and time series to improve road safety by identifying hot-spots in advance and giving chance to drivers to avoid the dangers. Indeed, we proposed a framework DM-MCDA based on association rules mining as a preliminary task to extract relationships between variables related to a road accident, and then integrate multiple criteria analysis to help decision-makers to make their choice of the most relevant rules. The developed system is flexible and allows intuitive creation and execution of different algorithms for an extensive range of road traffic topics. DM-MCDA can be expanded with new topics on demand, rendering knowledge extraction more robust and provide meaningful information that could help in developing suitable policies for decision-makers.

I denna rapport introduceras de olika mobilnätsteknologierna, 1G till 5G. Fokus ligger på hur det framtida 5G-mobilnätet kommer att fungera och vilka möjligheter som kommer att finnas med det. Andra saker som rapporten tar med är de basstationer som kommer att användas i det framtida mobilnätet samt en kort beskrivning utav terminalen i 5G-nätet och de tjänster som kommer att finnas tillgängliga i den. Protokollstacken i 5G-nätet tas också upp. Dessutom presenteras Wi-Fi 802.11u standarden som kommer att vara en del av det framtida mobil-nätet. Vi får också läsa om hur 5G-nätet kommer att lösa vissa problem som är relaterade till dagens mobilnätsteknologier. Avslutningsvis beskrivs de olika åtkomstteknikerna som används inom telekommunikationssystemen. 

I denna rapport beskrivs tillverkningen av en aktivitetsmonitor. Denna aktivitetsmonitor kommer att användas för en cyklotron som producerar Fluor-18. Detektorerna placeras i var sin blylåda som har ledningar för Fluor-18. Med hjälp av denna aktivitetsmonitor kan man förutse möjliga problem med ledningarna som Fluor-18 transporteras igenom. I rapporten diskuteras det om olika metoder att detektera aktivitet samt hur konstruktion av aktivitetsmonitor sker. Detektorerna ska kunna överföra den insamlade data av aktivitet till en databas med hjälp av wifi och MySQL. 

Den lämpligaste lösningen för detektion av aktivitet var med en fotodiod som skickar en puls då en foton från Fluor-18 träffar fotodioden, då går det att räkna antalet pulser över en sekund, detta ger ett mätvärde på pulser per sekund. En Raspberry Pi används för att räkna antalet pulser per sekund och skickar över insamlade data till en databas över wifi.

Projektet använder sig av konceptet “Design thinking” med en kontinuerlig utveckling under projekttiden. Projektet nämner även en del förbättringsförslag för att göra produkten i sin helhet mer hållbar, effektiv och smidig att hantera/bygga. Ett större problem i detta arbete är brus och störningar då detta system är känsligt när pulserna som detekteras inte överstiger 200 mV.

This paper is preoccupied with the following question: given a (possibly opaque) learning system, how can we understand whether its behaviour adheres to governance constraints? The answer can be quite simple: we just need to “ask” the system about it. We propose to construct an investigator agent to query a learning agent- the suspect agent- to investigate its adherence to a given ethical policy in the context of an information-seeking dialogue, modeled in formal argumentation settings. This formal dialogue framework is the main contribution of this paper. Through it, we break down compliance checking mechanisms into three modular components, each of which can be tailored to various needs in a vast amount of ways: an investigator agent, a suspect agent, and an acceptance protocol determining whether the responses of the suspect agent comply with the policy. This acceptance protocol presents a fundamentally different approach to aggregation: rather than using quantitative methods to deal with the non-determinism of a learning system, we leverage the use of argumentation semantics to investigate the notion of properties holding consistently. Overall, we argue that the introduced formal dialogue framework opens many avenues both in the area of compliance checking and in the analysis of properties of opaque systems.

The introduction of maker technology and personal fabrication has radically changed how we learn, design and innovate. In recent years, a growing number of people have begun to use a broad range of these creative technologies. A common challenge in the use of these electronic technologies, particularly for students, is the difficulties that arise during circuit wiring prototyping. Today, few studies have highlighted the factors that cause errors among student during their electronic circuit learning activities. This research investigates the causes of wiring problems and troubleshooting strategies during the prototyping of electronic circuits by students. We conducted an ethnographic study of undergraduate students at a university design school who were involved in prototyping electronic circuits with creative technologies. We performed a microanalysis of the students’ interactions and dialogues following the distributed cognition framework. Our results show the significance of meaningful representations of information on circuit wiring tools, in addition to the importance of common knowledge among the students for reasoning about electronic circuit wiring and the detection and solving of wiring errors. This study highlights the relations between the types of wiring errors and the students’ troubleshooting strategies. In addition to informing educational practitioners, our conclusions highlight the need for further studies and the demand for a redesign of electronic wiring prototyping tools.

Bilsäkerhetsföretaget Veoneer är utvecklare av avancerade kamerasystem inom förarassistans, men kunskapen om den absoluta noggrannheten i deras dynamiska kalibreringsalgoritmer som skattar fordonets orientering är begränsad.

I denna avhandling utvecklas och testas ett nytt mätsystem för att samla in referensdata av ett fordons orientering när det är i rörelse, mer specifikt dess pitchvinkel och rollvinkel. Fokus har legat på att skatta hur osäkerheten i mätsystemet påverkas av fel som introducerats vid dess konstruktion, samt att utreda dess potential när det kommer till att vara ett gångbart alternativ för att samla in referensdata för evaluering av prestandan hos algoritmerna.

Systemet bestod av tre laseravståndssensorer monterade på fordonets kaross. En rad mätförsök utfördes med olika störningar introducerade genom att köra längs en vägsträcka i Linköping med vikter lastade i fordonet. Det insamlade referensdatat jämfördes med data från kamerasystemet där bias hos de framräknade vinklarna skattades, samt att de dynamiska egenskaperna kamerasystemets algoritmer utvärderades. Resultaten från mätförsöken visade på att noggrannheten i mätsystemet översteg 0.1 grader för både pitchvinklarna och rollvinklarna, men några slutsatser kring eventuell bias hos algoritmerna kunde ej dras då systematiska fel uppstått i mätresultaten.

Directed Acyclic Graph (DAG)-based scheduling applications are critical to resource allocation in the Cloud, Edge, and Fog layers of cyber-physical systems (CPS). However, thermal anomalies in DVFS-enabled homogeneous multiprocessor systems (HMSS) may be exploited by malicious applications posing risks to the availability of the underlying CPS. This can negatively affect the trustworthiness of CPS. This paper proposes an algorithm to address the thermal risks in DVFS-enabled HMSS for periodic DAG-based applications. It also improves the current list scheduling-based Depth-First and Breadth-First techniques without violating the timing constraints of the system. We test the algorithm using standard benchmarks and synthetic applications in a simulation setup. The results show a reduction in the temperature peaks by up to 30%, average temperature by up to 22%, temperature variations up to 3 times, and temperature spatial gradients by up to 4 times as compared to the conventional Depth-First Scheduling algorithms.

AbstractCloud computing is a computing model that allows better management, higher utiliza-tion and reduced operating costs for datacenters while providing on demand resourceprovisioning for different customers. Data centers are often enormous in size andcomplexity. In order to fully realize the cloud computing model, efficient cloud man-agement software systems that can deal with the datacenter size and complexity needto be designed and built.This thesis studies automated cloud elasticity management, one of the main andcrucial datacenter management capabilities. Elasticity can be defined as the abilityof cloud infrastructures to rapidly change the amount of resources allocated to anapplication in the cloud according to its demand. This work introduces algorithms,techniques and tools that a cloud provider can use to automate dynamic resource pro-visioning allowing the provider to better manage the datacenter resources. We designtwo automated elasticity algorithms for cloud infrastructures that predict the futureload for an application running on the cloud. It is assumed that a request is either ser-viced or dropped after one time unit, that all requests are homogeneous and that it takesone time unit to add or remove resources. We discuss the different design approachesfor elasticity controllers and evaluate our algorithms using real workload traces. Wecompare the performance of our algorithms with a state-of-the-art controller. We ex-tend on the design of the best performing controller out of our two controllers anddrop the assumptions made during the first design. The controller is evaluated with aset of different real workloads.All controllers are designed using certain assumptions on the underlying systemmodel and operating conditions. This limits a controller’s performance if the modelor operating conditions change. With this as a starting point, we design a workloadanalysis and classification tool that assigns a workload to its most suitable elasticitycontroller out of a set of implemented controllers. The tool has two main components,an analyzer and a classifier. The analyzer analyzes a workload and feeds the analysisresults to the classifier. The classifier assigns a workload to the most suitable elasticitycontroller based on the workload characteristics and a set of predefined business levelobjectives. The tool is evaluated with a set of collected real workloads and a set ofgenerated synthetic workloads. Our evaluation results shows that the tool can help acloud provider to improve the QoS provided to the customers.

Peer-assisted cloud storage systems use the unutilizedresources of the clients subscribed to a storage cloudto offload the servers of the cloud. The provider distributesdata replicas on the clients instead of replicating on the localinfrastructure. These replicas allow the provider to providea highly available, reliable and cheap service at a reducedcost. In this work we introduce NileStore, a protocol forreplication management in peer-assisted cloud storage. Theprotocol converts the replica placement problem into a lineartask assignment problem. We design five utility functionsto optimize placement taking into account the bandwidth,free storage and the size of data in need of replication oneach peer. The problem is solved using a suboptimal greedyoptimization algorithm. We show our simulation results usingthe different utilities under realistic network conditions. Ourresults show that using our approach offloads the cloud serversby about 90% compared to a random placement algorithmwhile consuming 98.5% less resources compared to a normalstorage cloud.

We introduce NileStore, a replica placement algorithm based on an economical model for use in Peer-assisted cloud storage. The algorithm uses storage and bandwidth resources of peers to offload the cloud provider’s resources. We formulate the placement problem as a linear task assignment problem where the aim is to minimize time needed for file replicas to reach a certain desired threshold. Using simulation, We reduce the probability of a file being served from the provider’s servers by more than 97.5% under realistic network conditions.

Video-on-Demand (VoD) and video sharing services accountfor a large percentage of the total downstream Internet traf-fic. In order to provide a better understanding of the loadon these services, we analyze and model a workload tracefrom a VoD service provided by a major Swedish TV broad-caster. The trace contains over half a million requests gener-ated by more than 20000 unique users. Among other things,we study the request arrival rate, the inter-arrival time, thespikes in the workload, the video popularity distribution, thestreaming bit-rate distribution and the video duration distri-bution. Our results show that the user and the session ar-rival rates for the TV4 workload does not follow a Poissonprocess. The arrival rate distribution is modeled using a log-normal distribution while the inter-arrival time distributionis modeled using a stretched exponential distribution. Weobserve the “impatient user” behavior where users abandonstreaming sessions after minutes or even seconds of startingthem. Both very popular videos and non-popular videos areparticularly affected by impatient users. We investigate ifthis behavior is an invariant for VoD workloads.

Accurate understanding of workloads is key to efficient cloud resource management as well as to the design of large-scale applications. We analyze and model the workload of Wikipedia, one of the world's largest web sites. With descriptive statistics, time-series analysis, and polynomial splines, we study the trend and seasonality of the workload, its evolution over the years, and also investigate patterns in page popularity. Our results indicate that the workload is highly predictable with a strong seasonality. Our short term prediction algorithm is able to predict the workload with a Mean Absolute Percentage Error of around 2%.

Workload burstiness and spikes are among the main reasons for service disruptions and decrease in the Quality-of-Service (QoS) of online services. They are hurdles that complicate autonomic resource management of datacenters. In this paper, we review the state-of-the-art in online identification of workload spikes and quantifying burstiness. The applicability of some of the proposed techniques is examined for Cloud systems where various workloads are co-hosted on the same platform. We discuss Sample Entropy (SampEn), a measure used in biomedical signal analysis, as a potential measure for burstiness. A modification to the original measure is introduced to make it more suitable for Cloud workloads.

Autoscaling algorithms for elastic cloud infrastructures dynami-cally change the amount of resources allocated to a service ac-cording to the current and predicted future load. Since there areno perfect predictors, no single elasticity algorithm is suitable foraccurate predictions of all workloads. To improve the quality ofworkload predictions and increase the Quality-of-Service (QoS)guarantees of a cloud service, multiple autoscalers suitable for dif-ferent workload classes need to be used. In this work, we intro-duce WAC, a Workload Analysis and Classification tool that as-signs workloads to the most suitable elasticity autoscaler out of aset of pre-deployed autoscalers. The workload assignment is basedon the workload characteristics and a set of user-defined Business-Level-Objectives (BLO). We describe the tool design and its maincomponents. We implement WAC and evaluate its precision us-ing various workloads, BLO combinations and state-of-the-art au-toscalers. Our experiments show that, when the classifier is tunedcarefully, WAC assigns between 87% and 98.3% of the workloadsto the most suitable elasticity autoscaler.

Elasticity algorithms for cloud infrastructures dynamically change the amount of resources allocated to a running service according to the current and predicted future load. Since there is no perfect predictor, and since different applications’ workloads have different characteristics, no single elasticity algorithm is suitable for future predictions for all workloads. In this work, we introduceWAC, aWorkload Analysis and Classification tool that analyzes workloads and assigns them to the most suitable elasticity controllers based on the workloads’ characteristics and a set of business level objectives.

WAC has two main components, the analyzer and the classifier. The analyzer analyzes workloads to extract some of the features used by the classifier, namely, workloads’ autocorrelations and sample entropies which measure the periodicity and the burstiness of the workloads respectively. These two features are used with the business level objectives by the clas-sifier as the features used to assign workloads to elasticity controllers. We start by analyzing 14 real workloads available from different applications. In addition, a set of 55 workloads is generated to test WAC on more workload configurations. We implement four state of the art elasticity algorithms. The controllers are the classes to which the classifier assigns workloads. We use a K nearest neighbors classifier and experiment with different workload combinations as training and test sets. Our experi-ments show that, when the classifier is tuned carefully, WAC correctly classifies between 92% and 98.3% of the workloads to the most suitable elasticity controller.

This thesis studies cloud capacity auto-scaling, or how to provision and release re-sources to a service running in the cloud based on its actual demand using an auto-matic controller. As the performance of server systems depends on the system design,the system implementation, and the workloads the system is subjected to, we focuson these aspects with respect to designing auto-scaling algorithms. Towards this goal,we design and implement two auto-scaling algorithms for cloud infrastructures. Thealgorithms predict the future load for an application running in the cloud. We discussthe different approaches to designing an auto-scaler combining reactive and proactivecontrol methods, and to be able to handle long running requests, e.g., tasks runningfor longer than the actuation interval, in a cloud. We compare the performance ofour algorithms with state-of-the-art auto-scalers and evaluate the controllers’ perfor-mance with a set of workloads. As any controller is designed with an assumptionon the operating conditions and system dynamics, the performance of an auto-scalervaries with different workloads.In order to better understand the workload dynamics and evolution, we analyze a6-years long workload trace of the sixth most popular Internet website. In addition,we analyze a workload from one of the largest Video-on-Demand streaming servicesin Sweden. We discuss the popularity of objects served by the two services, the spikesin the two workloads, and the invariants in the workloads. We also introduce, a mea-sure for the disorder in a workload, i.e., the amount of burstiness. The measure isbased on Sample Entropy, an empirical statistic used in biomedical signal processingto characterize biomedical signals. The introduced measure can be used to charac-terize the workloads based on their burstiness profiles. We compare our introducedmeasure with the literature on quantifying burstiness in a server workload, and showthe advantages of our introduced measure.To better understand the tradeoffs between using different auto-scalers with differ-ent workloads, we design a framework to compare auto-scalers and give probabilisticguarantees on the performance in worst-case scenarios. Using different evaluation cri-teria and more than 700 workload traces, we compare six state-of-the-art auto-scalersthat we believe represent the development of the field in the past 8 years. Knowingthat the auto-scalers’ performance depends on the workloads, we design a workloadanalysis and classification tool that assigns a workload to its most suitable elasticitycontroller out of a set of implemented controllers. The tool has two main components;an analyzer, and a classifier. The analyzer analyzes a workload and feeds the analysisresults to the classifier. The classifier assigns a workload to the most suitable elasticitycontroller based on the workload characteristics and a set of predefined business levelobjectives. The tool is evaluated with a set of collected real workloads, and a set ofgenerated synthetic workloads. Our evaluation results shows that the tool can help acloud provider to improve the QoS provided to the customers.

In-depth searching for specific content in medieval manuscripts requires labor-intensive, hence time-consuming manual manuscript screening. Using existing IT tools to carry out this task has not been possible, since state-of-the-art keyword spotting lacks the necessary metaknowledge or larger ontology that scholars intuitively apply in their investigations. This problem is being addressed in the “Research Südtirol/Alto Adige” 2019 project “MENS – Medieval Explorations in Neuro-Science (1050–1450): Ontology-Based Keyword Spotting in Manuscript Scans,” whose goal is to build a paradigmatic case study for compiling and subsequent screening of large collections of manuscript scans by using AI techniques for natural language processing and data management based on formal ontologies. We report here on the ongoing work and the results achieved so far in the MENS project.

Cytiva i Umeå producerar medicinskteknologisk utrustning och ett av dessa system som produceras är Biacore. Dessa system används inom läkemedelsutveckling och forskning inom proteinanalys. De temperaturgivare som används i Biacore ska kalibreras noggrant då det är viktigt att det inte blir några mätfel. Målet med detta examensarbete är att utveckla nuvarande kalibreringsstationen för temperaturgivare eftersom den har blivit omodern. Fokuset ligger mest på att utveckla en ny mjukvara, dock potentiell utveckling av hårdvaran är också av intresse. Mjukvaran ska beräkna kalibreringsfaktorer för de temperaturgivare som kalibreras på ett automatiserat och effektivt sätt. Utveckling av hårdvaran kan göras genom att byta ut nuvarande referenstermometern till en ny och integrera den i kalibreringsstationen. Ett program har utvecklats med ett användargränssnitt där användaren väljer vilken typ av temperaturgivare som kalibreras och dess identifikationsnummer. Baserat på denna information hittar programmet alla relevanta filer med mätvärdena, beräknar kalibreringsfaktorer för varje temperaturgivare och sparar resultatet. Detta automatiserar beräkning och sparande av data samt effektiviserar kalibreringsprocessen. Utvecklingen av mjukvaran gjordes med kodspråket Python och biblioteket Tkinter. Hårdvaruutvecklingen har påbörjats men inte uppnåtts på grund av tidsbrist.

Right from the very beginning, the text has vital importance in human life. As compared to the vision-based applications, preference is always given to the precise and productive information embodied in the text. Considering the importance of text, recognition, and detection of text is also equally important in human life. This paper presents a deep analysis of recent development on scene text and compare their performance and bring into light the real modern applications. Future potential directions of scene text detection and recognition are also discussed.

I detta examensarbete har ett nytt system f¨or f¨argm¨atning utvecklats och dess prestanda utv¨arderas.Det ¨ar en modul¨ar och spektralt avbildande goniofotometer som tar vinkeluppl¨osta spektral re-flektans och transmittans m¨atningar av material, som t.ex. textilier, papper, belagda materialoch flexibel elektronik. Det ¨ar ett mycket flexibelt system som kan, till skillnad fr˚an de flestakommersiella system, ta reflektansbilder av b˚ade h¨og spektral och spatial uppl¨osning ¨over ettbrett spektrum av m¨atningsgeometrier.

Det ¨ar viktigt att systemet kan producera geometriskt korrekta avbildningar av prover, samtf˚anga spektral information med h¨og noggrannhet. D.v.s. att systemet m˚aste vara b˚ade geometrisktoch fotometriskt kalibrerat. En spektral linje-kamera och ett stegbord med h¨og noggrannhetger 2D-skanningar av provet, d¨ar m¨atomr˚adet, provposition och m¨atgeometri styrs aven robotarm. Spektral och spatial uppl¨osning, karakterisering av ljusk¨allan och kamerans spektralanoggrannhet ¨ar n˚agra av de utv¨arderingar som utf¨orts. Den spektrala noggrannheten, ochd¨armed noggrannheten f¨or f¨argm¨atning, utv¨arderas med f¨argskillnadsformeln CIE ∆E^∗_ab. Underdessa utv¨arderingar har systemet m¨att f¨argskillnader p˚a ∆E^∗_ab < 14 och ∆E^∗_ab < 30 f¨or ett respektivematt och blankt prov. Dessa resultat visar stora f¨argskillnader som ¨ar mest sannolikt orsakasav det l˚aga signal-brusf¨orh˚allande f¨or korta v˚agl¨angder p˚a grund av ljusk¨allas position och spektralaf¨ordelning. Slutligen demonstreras systemets f¨orm˚aga att g¨ora vinkeluppl¨osta m¨atningarav goniokromatiska prover, vars f¨arg skiftar med betraktningsvinkeln. Systemet detekterarf¨argskiftningen framg˚angsrikt f¨or betraktningsvinklar 0 till 45 grader fr˚an ytnormalen.

A car with underinflated tires can lead to both safety and environmental issues. To combat this, markets have begun requiring new cars to feature a Tire Pressure Monitoring System. Systems without pressure sensors are referred to as indirect Tire Pressure Monitoring Systems, often utilizing wheel speed sensors in combination with other available sensor information to detect tire pressure losses. 

NIRA Dynamics is a company founded in Linköping, Sweden, most known for its indirect Tire Pressure Monitoring System called TPI. TPI needs to be verified in a large number of scenarios, which may be both difficult and expensive to realize in real vehicle tests. The purpose of this master thesis was to investigate and model what physical phenomena are associated with having a trailer connected to a car, relevant for TPI. The goal was to construct a hybrid simulation framework, making it possible to modify car-only data to reflect the effects of having a trailer connected. 

A car-trailer model was developed, showing close resemblance in simulations to real collected car-trailer sensor data. The model was then used to design a hybrid simulation framework, where car-only sensor signals were modified to mimic having different types of trailers attached. The hybrid simulation results show close resemblance to real collected trailer sensor data. By not requiring real trailer data for every scenario to evaluate software performance on, the proposed framework opens up the possibility to simulate data from a much larger number of trailer combinations than would otherwise have been feasible to test in real vehicle tests. 

En växande global befolkning och stor efterfrågan på grödor driver den teknologiska utvecklingen framåt inom det moderna lantbruket. I sin utveckling av nya styr- och reglerfunktioner för frontlastare behövde Ålö AB en pålitlig och effektiv plantmodell av deras frontlastare Quicke Q5M. Plantmodellen skulle inkludera det tillhörande mekaniska och hydrauliska systemet och kunna rendera simulering och 3D-animering i realtid. En automatisk nedställningsfunktion för material skulle också utvecklas för att automatisera den vanligt förekommande sysslan att ställa ned material från en upphöjd position. En plantmodell för Quicke Q5M med ett lastkännande hydraulsystem utvecklades i MATLAB Simulink med Simscape Multibody och Simscape Fluids. Prestandan av den simulerade plantmodellen utvärderades. Plantmodellen visade grundläggande överensstämmelse när den jämfördes med sensordata från en frontlastare i verkligheten. En reglerfunktion för automatisk nedställning av material utvecklades i Simulink. En algoritm för att detektera kollision med mark konstruerades genom att analysera sensordata från en frontlastare i verkligheten som ställt ned material på marken. För att detektera materialets kontakt med mark utfördes hastighets- och accelerationsestimeringar på sensordata från vinkelgivare och trycksensorer. Vid kontakt med mark avslutades nedställningsrörelsen automatiskt. Prestandan och noggrannheten av reglersystemet under olika scenarion analyserades och diskuterades. Trots att simuleringar av plantmodellen och den föreslagna nedställningsfunktionen såg realistiska ut var det svårt att kvantifiera överensstämmelsen mellan plantmodell och frontlastare då inga utförligare fysiska tester hanns med. Framtida utveckling av plantmodell och nedställningsfunktion rekommenderas därför att börja med ett implementeringstest av reglerfunktionen i en traktor i verkligheten. Det skulle ge ett mått på hur pass bra plantmodellen representerar frontlastaren samt möjliggöra utvärdering och vidare justering av reglerfunktionens parametrar i en verklig fysisk miljö.

Nowadays E-learning as a form of learning depending on networks and computer devices has been expanding by leaps and bounds. As a special kind of E-learning, M-learning (mobile learning) aims at the use of mobile devices anywhere at anytime by anybody. In this paper, the technologies to develop a M-learning system is deeply analyzed according to the layer model at first. Secondly, from the view of application, the standardization in M-learning is researched in order to accelerate the development and popularization of M-learning. Finally, quality estimation of M-learning system is discussed from the view of QoE (quality of experiences). The experience form end-user is the sole effective norm to judge the result of M-learning technology. It is no doubt that technologies, standardization and evaluation will play very important parts in the course of M-learning development.

To guarantee the quality of service (QoS) of a wireless network, a new packet scheduling algorithm using cross-layer design technique is proposed in this article. First, the demand of packet scheduling for multimedia transmission in wireless networks and the deficiency of the existing packet scheduling algorithms are analyzed. Then the model of the QoS-guaranteed packet scheduling (QPS) algorithm of high speed downlink packet access (HSDPA) and the cost function of packet transmission are designed. The calculation method of packet delay time for wireless channels is expounded in detail, and complete steps to realize the QPS algorithm are also given. The simulation results show that the QPS algorithm that provides the scheduling sequence of packets with calculated values can effectively improve the performance of delay and throughput.

Denna rapport beskriver mitt examensarbete utfört vid institutionen för Tillämpad Fysik och Elektronik vid Umeå Universitet. Uppdragsgivare var Digital Media Lab som har stöttat och assisterat mig under hela projektet.

Målet med projektet var att utveckla ett system som ska verifiera en persons identitet genom att använda handens geometri som en biometrisk identitet. Systemet skall tolerera några avvikelser i handplaceringar och kommer inte att använda stift eller pinnar. Systemet består även av olika bildbehandlingsmetoder programmerade i Matlab. Systemet är byggt för att fungera på en PC.

This work presents an extension of a design procedure for dynamic output feedback design for systems with nonlinearities satisfying quadratic constraints. In this work we used an axial gas compressor model described by the 3-state Moore-Greitzer compressor model (MG) that has some challenges for output feedback control design (Planovsky and Nikolaev 1990), (Rubanova 2013). The more general constraints for the investigation of the robustness with respect to parametric uncertainties and measurement noise are shown.

Forestry machines are heavy vehicles performing complex manipulation tasks in unstructured production forest environments. Together with the complex dynamics of the on-board hydraulically actuated cranes, the rough forest terrains have posed a particular challenge in forestry automation. In this study, the feasibility of applying reinforcement learning control to forestry crane manipulators is investigated in a simulated environment. Our results show that it is possible to learn successful actuator-space control policies for energy efficient log grasping by invoking a simple curriculum in a deep reinforcement learning setup. Given the pose of the selected logs, our best control policy reaches a grasping success rate of 97%. Including an energy-optimization goal in the reward function, the energy consumption is significantly reduced compared to control policies learned without incentive for energy optimization, while the increase in cycle time is marginal. The energy-optimization effects can be observed in the overall smoother motion and acceleration profiles during crane manipulation. 

1 SammanfattningCrossControl är ett företag som bland annat tillverkar integrerade datorlösningar. Datorerna drivs normalt med 18-30 VDC och förbrukar som mest 50W. Datorerna säljs till flertalet olika kunder som monterar dem i allt från skogsmaskiner till tåg. I de olika fordonen varierar spänningen i de befintliga elnäten. Detta skapar behovet av att omvandla spänningen till en nivå som datorerna klarar av. En sådan apparat kallas DC/DC-omvandlare. Spänningsomvandling kan utföras genom linjär reglering eller med hjälp av switchteknik. Målet för detta examensarbete är att konstruera en DC/DC-omvandlare som uppfyller de krav som utarbetats i samarbete med CrossControl. För att uppnå tillräckligt hög effektivitet, samt för att galvanisk isolation mellan in- och utgångsspänning är ett krav, baseras omvandlaren på en såkallad switchad flyback-lösning. Transformatorn är den enskilda komponent som har störst betydelse för funktionen hos en flybackomvandlare. Därför har en stor del av projektet handlat om att välja en passande transformator. Resultatet är en prototyp som klarar att reglera utspänningen till 24VDC för hela inspänningsområdet, och klarar en belastning på 50W. Effektiviteten slutade på 80% vilket är 5 procentenheter under målet. För att förbättra prototypen behövs dels övervakning och dels skydd mot att inspänningen går utanför det tänkta inspänningsområdet. För att uppnå 85% effek

Following an ongoing change towards an increasingly renewable power generation system Swedish grid operators are facing several challenges in coming years. As authorities plan for the decommissioning of nuclear power an increased reliance on de-centralised energy sources such as photo-voltaic distributed generation (PVDG) is expected. A technology observed in some cases to accompany local power quality issues severe enough to impose grid expansion measures from distribution system operators (DSOs). Considering a combination of an indicative utilisation inefficiency of classical grid expansion measures and a recent year maturing of various energy storage technologies, this report sets out to evaluate the possibility of utilising centralised energy storage systems (ESSs) for deferment of classical grid expansion measures.

For the purpose of identifying the most prominent problem scenarios of modern production- and consumption behaviours as well as the possible solutions offered by centralised ESSs, a literature review of journal articles and technical reports was conducted in combination with a case-study of an existing urban grid operated by Umeå Energi Elnät AB (UEEN). The work regarding ESSs is directed with specific focus towards evaluating which ESS services can potentially facilitate grid expansion deferment and what ESS placement is advisable for efficient utilisation. Assessing possible grid safety implications, potential for peak load shaving and the presently most suitable energy storage technology was also within the scope of the study.

The literature review reveals PVDG induced feeder line over-voltage and transformer overload the most likely and previously observed implications imposing grid expansion measures. The former more prominent in elongated, typically rural, grids and the latter in more densely populated urban grids. For deferment of over-voltage related grid expansion measures a centralised ESS can be utilised for voltage support provided placement is made close to the affected grid section, presumably far out the affected feeder line. This result is coherent throughout the reviewed literature and is supported by the results of the case-study. Distribution transformer overload and its imposed grid expansion measures can be deferred through load re-allocation and peak load shaving, two services proven achievable by centralised ESSs and the capacity for which increases if ESS placement is made closer the distribution transformer.

Provided present regulatory and standards are adhered to upon installation, significant negative impact of centralised ESSs on distribution grid safety can be avoided. Most energy storage technologies, including battery based energy storage technologies indicated from the literature review providing the most suitable characteristics for use in centralised distribution grid ESSs, utilise well established systems for grid connection hence no presently unsolvable grid safety implications are identified. Technical reports of real applications of centralised ESSs reinforce this argument as successful implementation without ESS caused grid safety implications have been achieved in the Swedish distribution grid in the past.