As datasets in general grow in size and complexity over time while the human cognitive ability to interpret said datasets essentially stays the same, it becomes important to enable intuitive visualization methods for analysis. Based on previous research in the field of information visualization and visual analytics, a dashboard visualization prototype handling real-time event based traffic was implemented and evaluated. The real-time data is collected by a script and sent to a self-implemented web server that opens up a websocket connection with the dashboard client where the data is then visualized. Said data consisted of transactions and related metadata of an ecommerce retail site applied to a real customer scenario. The dashboard was developed using an agile method, continuously involving the thesis supervisor in the design and functionality process. The final design also depended on the results of an interview with a representative from one of the two target groups. The two target groups consisted of 5 novice and 5 expert users to the field of information visualization and visual analytics. The intuitiveness of the dashboard visualization prototype was evaluated by conducting two user studies, one for each target group, where the test subjects were asked to interact with the dashboard visualization, answer some questions and lastly solving a predefined set of tasks. The time spent solving said tasks, the amount of serious misinterpretations and the number of wrong answers was recorded and evaluated. The results from the user study showed that the use of colors, icons, level on animation, the choice of visualization method and level of interaction were the most important aspects for carrying out an efficient analytical process according to the test subjects. The test subjects desired to zoom in on each component, to filter the contents of the dashboard and to get additional information about the components on-demand. The most important result produced from developing the dashboard was how to handle the scalability of the application. It is highly important that the websocket connection remain stable when scaling out to handle more concurrent HTTP requests. The research also conclude that the dashboard should handle visualization methods that are intuitive for all users, that the real-time data needs to be put into relation to historical data if one wishes to carry out a valid analytical process and that real-time data can be used to discover trends and patterns in an early-as-possible stage. Lastly, the research provides a set of guidelines for scalability, modularity, intuitiveness and relations between datasets.

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Visually extracting textual context from lips consists of pattern matching which results in a frequent use of machine learning approaches for the task of classification. Previous research has consisted of mostly audiovisual (multi modal) approaches and conventional cameras. This study isolates the visual medium and uses event-based cameras instead of conventional cameras. Classifying visual features is computationally expensive and the minimisation of excessive data can be of importance for performance which motivates the usage of event cameras. Event cameras are inspired by the biological vision and only capture changes in the scene while offering high temporal resolution (corresponding to frame rate for conventional cameras). This study investigates the importance of temporal resolution for the task of lip reading by modifying the ∆time used for collecting events. No correlation could be observed within the collected data set. The paper is not able to come to any conclusions regarding suitability of the chosen approach for the particular application. There are multiple other variables that could effect the results which makes it hard to dismiss the technology’s potential within the domain. / Visuell bedömning av vilka ord läppar talar består av mönstermatchning vilket resulterar i att maskininlärning ofta används för att klassificera data som text. Tidigare studier har i hög grad varit audiovisuella(multimodala) och konventionella kameror. Visuell analys är beräkningsmässigt dyrt vilket motiverar en minimering av överflödig data för att öka prestandan, vilket motiverar användningen av eventkameror. Eventkameror är inspirerade av biologisk syn och registrerar endast skillnaden i omgivningen, samtidigt som de har en hög tidsupplösning (motsvarande frame rate för konventionella kameror). Studien undersöker relevansen av tidsupplösning för maskinell läppläsning genom att modifiera ∆time som används för att samla events. Ingen korrelation mellan ∆time och träffsäkerheten kunde observeras med det dataset som användes. Studien kan inte avfärda potentialen för tekniken eftersom det finns många fler parametrar som kan påverka träffsäkerheten.

Control over wireless sensor and actuator networks is of growing interest in process industry since it enables flexible design, deployment, operation, and maintenance. An important problem in industrial wireless control is how to limit the amount of information that needs to be exchanged over the network. In this thesis, network scheduling and remote control co-design is considered to address this problem. In the first part, we propose a design of an optimal network schedule for state estimation over a multi-hop wireless sensor network. We formulate an optimization problem, minimizing a linear combination of the averaged estimation error and transmission energy. A periodic network schedule is obtained, which specifies when and through which routes each sensor in the network should transmit its measurement, so that an optimal remote estimate under sensor energy consideration is achieved. We also propose some suboptimal schedules to reduce the computational load. The effectiveness of the suboptimal schedules is evaluated in numerical examples. In the second part, we propose a co-design framework for sensor scheduling, routing, and control over a multi-hop wireless sensor and actuator network. For a decoupled plant and LQG control performance, we formulate an optimization problem and show that the optimal schedule, routing, and control can be obtained locally for each control loop. In this part, we also introduce algorithms to reconfigure the schedules and routes when a link in the network is disconnected. The results are illustrated in a numerical example. In the third part, we consider event-based feedforward control from a wireless disturbance sensor. We derive stability conditions when the closed-loop system is subject to actuator saturation. Feedforward control with anti-windup compensation is introduced to reduce the effect of actuator saturation. The effectiveness of the approach is illustrated in some numerical examples. / <p>QC 20181029</p>

Den tidiga fasen av företags innovationsprocess har fått stor uppmärksamhet i modern akademisk forskning. Det diskuteras hur man strukturerar denna fas kallad “fuzzy front end” kopplat till innovation och även ett koncept som allmänt kallas “öppen innovation” har introducerats - involvering av tredje parts individer i utvecklingsprocessen. Detta examensarbete fokuserar huvudsakligen på öppen innovation genom olika event för att främja innovationsarbete såsom: workshops, hackathons, idea jams och andra typer av sammanhang som inkluderar både externa deltagare och experter samt anställda och partners inom organisationer. Genom att designa, vara värd för, övervaka samt utvärdera ett hackathon för innovation som heter LiveHacks, samlar detta examensarbete relevant information för att förstå motiven för företag såväl som individer att delta i ett sådant sammanhang. Slutligen bedöms i detta examensarbete hur man kan organisera framgångsrika öppen innovations-eventsåsom hackathons. Dessutom har en generaliserad mall som tredje part skall kunna ta efter utvecklats. / The front end of a corporate’s innovation process has caught much attention in contemporary academic research. Efforts discuss how to structure the “fuzzy frontend of innovation” and introduce a concept widely known as “open source” – the involvement of third party individuals to the development process. This Masters thesis specifically focuses on open innovation through the medium of event-based innovation facilitation; e.g through workshops, hackathons, idea jams and other events that include both external users and experts as well as employees or partners within organizations. By designing, hosting, monitoring and evaluating an innovation event called “LiveHacks”, this thesis collects relevant data to understand both, the motives of corporations as well as of individuals to participate in open innovation events. Finally, this thesis assesses how to host successful open innovation events and develops a generalized template for third party adoption.

Neural networks have become increasingly adopted in society over the last few years. As neural networks consume a lot of energy to train, reducing the energy consumption of these networks is desirable from an environmental perspective. Spiking neural network is a type of neural network inspired by the human brain which is significantly more energy efficient than traditional neural networks. However, there is little research about how the hyper parameters of these networks affect the relationship between accuracy and energy. The aim of this report is therefore to analyse this relationship. To do this, we measure the energy usage of training several different spiking network models. The results of this study shows that the choice of hyper-parameters in a neural network does affect the efficiency of the network. While correlation between any individual factors and energy consumption is inconclusive, this work could be used as a springboard for further research in this area. / Under de senaste åren har neuronnät blivit allt vanligare i samhället. Eftersom neuronnät förbrukar mycket energi för att träna dem är det önskvärt ur miljösynpunkt att minska energiförbrukningen för dessa nätverk. Spikande neuronnät är en typ av neuronnät inspirerade av den mänskliga hjärnan som är betydligt mer energieffektivt än traditionella neuronnät. Det finns dock lite forskning om hur hyperparametrarna i dessa nätverk påverkar sambandet mellan noggrannhet och energi. Syftet med denna rapport är därför att analysera detta samband. För att göra detta mäter vi energiförbrukningen vid träning av flera olika modeller av spikande neuronnät-modeller. Resultaten av denna studie visar att valet av hyperparametrar i ett neuronnät påverkar nätverkets effektivitet. Även om korrelationen mellan enskilda faktorer och energiförbrukning inte är entydig kan detta arbete användas som en startpunkt för ytterligare forskning inom detta område.

Complex software systems are usually developed as systems of systems (SoS’s) in which multiple constituent applications are composed and coordinated to fulfill desired system-level requirements. The constituent applications must be augmented with suitable coordination-specific interfaces, through which they can participate in coordinated interactions. Such interfaces as well as coordination rules have a crosscutting nature. Therefore, to increase the reusability of the applications and to increase the comprehensibility of SoS’s, suitable mechanisms are required to modularize the coordination rules and interfaces from the constituent applications. We introduce a new abstraction named as architectural event modules (AEMs), which facilitate defining constituent applications and desired coordination rules as modules of SoS’s. AEMs augment the constituent applications with event-based facades to let them participate in coordinated interactions. We introduce the EventArch language in which the concept of AEMs is implemented, and illustrate its suitability using a case study.

Pedestrian crowd simulation explores crowd behaviors in virtual environments. It is extensively studied in many areas, such as safety and civil engineering, transportation, social science, entertainment industry and so on. As a common phenomenon in pedestrian crowds, grouping can play important roles in crowd behaviors. To achieve more realistic simulations, it is important to support group modeling in crowd behaviors. Nevertheless, group modeling is still an open and challenging problem. The influence of groups on the dynamics of crowd movement has not been incorporated into most existing crowd models because of the complexity nature of social groups. This research develops a framework for group modeling in agent-based pedestrian crowd simulations. The framework includes multiple layers that support a systematic approach for modeling social groups in pedestrian crowd simulations. These layers include a simulation engine layer that provides efficient simulation engines to simulate the crowd model; a behavior-based agent modeling layers that supports developing agent models using the developed BehaviorSim simulation software; a group modeling layer that provides a well-defined way to model inter-group relationships and intra-group connections among pedestrian agents in a crowd; and finally a context modeling layer that allows users to incorporate various social and psychological models into the study of social groups in pedestrian crowd. Each layer utilizes the layer below it to fulfill its functionality, and together these layers provide an integrated framework for supporting group modeling in pedestrian crowd simulations. To our knowledge this work is the first one to focus on a systematic group modeling approach for pedestrian crowd simulations. This systematic modeling approach allows users to create social group simulation models in a well-defined way for studying the effect of social and psychological factors on crowd’s grouping behavior. To demonstrate the capability of the group modeling framework, we developed an application of dynamic grouping for pedestrian crowd simulations.

Event-based data are encountered daily in many disciplines and are used for various purposes. They are collections of ordered sequences of events where each event has a start time and a duration. Examples of such data include medical records, internet surfing records, transaction records, industrial process or system control records, and activity diary data. This thesis is concerned with the exploration of event-based data, and in particular the identification and analysis of sequences within them. Sequences are interesting in this context since they enable the understanding of the evolving character of event data records over time. They can reveal trends, relationships and similarities across the data, allow for comparisons to be made within and between the records, and can also help predict forthcoming events.The presented work has researched methods for identifying and exploring such event-sequences which are based on modern visualization, interaction and data mining techniques. An interactive visualization environment that facilitates analysis and exploration of event-based data has been designed and developed, which permits a user to freely explore different aspects of this data and visually identify interesting features and trends. Visual data mining methods have been developed within this environment, that facilitate the automatic identification and exploration of interesting sequences as patterns. The first method makes use of a sequence mining algorithm that identifies sequences of events as patterns, in an iterative fashion, according to certain user-defined constraints. The resulting patterns can then be displayed and interactively explored by the user.The second method has been inspired by web-mining algorithms and the use of graph similarity. A tree-inspired visual exploration environment has been developed that allows a user to systematically and interactively explore interesting event-sequences.Having identified interesting sequences as patterns it becomes interesting to further explore how these are incorporated across the data and classify the records based on the similarities in the way these sequences are manifested within them. In the final method developed in this work, a set of similarity metrics has been identified for characterizing event-sequences, which are then used within a clustering algorithm in order to find similarly behavinggroups. The resulting clusters, as well as attributes of the clusteringparameters and data records, are displayed in a set of linked views allowing the user to interactively explore relationships within these. The research has been focused on the exploration of activity diary data for the study of individuals' time-use and has resulted in a powerful research tool facilitating understanding and thorough analysis of the complexity of everyday life.