Genom att använda scheman till textfiler som ska matas in ett system, kan textfilerna direkt översättas till systemets interna datastrukturer. Scheman kan också användas för att kontrollera att inte felaktiga värden matas in i systemet. För enklare textfiler som YAML och JSON är det ovanligt med format för att definiera scheman. Det vanligaste schema-formatet för YAML i nuläget är Kwalify, men det finns inte för Microsofts .NET-ramverk. Lösningen är att implementera Kwalify på .NET. Implementationen benämns Nkwalify. För verifiering av implementeringen används systemdatabasen i Lantmäteriets nya miljö för kartproduktion. Implementeringen jämförs med Xmlserializer, som har delvis samma användningsområde. Xmlserializer ingår som standard i .NET. Prestandatester visar att vid mycket stora datamängder är Xmlserializer upp till fem gånger snabbare på att ladda in data. Nkwalify stödjer dock fler datatyper och mer komplicerade datastrukturer än Xmlserializer samt matar ut data minst fem gånger snabbare. Nkwalify är lämplig för snabb och enkel överföring av komplexa data och för kontroll av inmatade värden. Det behövs ytterligare utveckling och tester för att Nkwalify ska vara produktionsklart.

In this thesis the problem of providing good recommendations to assist users to make the best choice out of numerous options is studied. To overcome the common problem of sparsity of the data, from which recommendations are inferred, additional label information assigned to items is considered. Based on a literature survey approaches that have proven to perform well were identified and combined into a single Framework. The proposed framework is based on the third-order Restricted Boltzmann machine which enables to incorporate label information as well as traditional rating information into a single model. The framework also implements the global-approach of collaborative filtering, where the user- and item-based approaches are both considered to improve the performance of the model. The proposed framework is implemented and evaluated using an experiment measuring the prediction error on test samples. The results obtained from the conducted experiments did not confirm the assumptions made about the improve of the models accuracy, when incorporating the additional label information. Reasons for this are identified and discussed.

Fully autonomous robotic systems can fulfill their functionality, without human interaction, but their efficiency is way lower, than a robotic system, which is teleoperated by a specialist. The teleoperation of robotic systems requires  continuously high attention from the operator, if this attention is taken away or reduced, the efficiency drops heavily. The combination of Adjustable Autonomy and Shared Control represent a promising approach, of how great efficiency could be maintained in a robotic system, with a minimum of human interaction.   The goal of this project is the re-implementation of the utilitarian voting scheme for navigation for usage with modern robotic platforms, as proposed in the publication "Experiments in Adjustable Autonomy" by Jacob W. Crandall and Michael A. Goodrich. This voting scheme combines a proposed direction, which is given by a human operator, with environmental sensor data to determine the best direction for a robots next movement.   The implemented prototype in this project was developed with ROS on TurtleBot and is processing the sensor data and calculating the best direction for the robot's movement in the same way, as the original prototype. Since the original setup consists of a Nomad SuperScout robot with sixteen sonar range finders, adjustments needed to be made, to run the same algorithm on a different setup. The  correct processing of the input data and estimation of the best direction was verified by pen and paper calculations. Finally, further ideas for improving the implemented prototype and usage in other scenarios were presented.

As robotic systems become more and more advanced the need to integrate existing deliberative functionalities such as chronicle recognition, motion planning, task planning, and execution monitoring increases. To integrate such functionalities into a coherent system it is necessary to reconcile the different formalisms used by the functionalities to represent information and knowledge about the world. To construct and integrate these representations and maintain a correlation between them and the environment it is necessary to extract information and knowledge from data collected by sensors. However, deliberative functionalities tend to assume symbolic and crisp knowledge about the current state of the world while the information extracted from sensors often is noisy and incomplete quantitative data on a much lower level of abstraction. There is a wide gap between the information about the world normally acquired through sensing and the information that is assumed to be available for reasoning about the world. As physical autonomous systems grow in scope and complexity, bridging the gap in an ad-hoc manner becomes impractical and inefficient. Instead a principled and systematic approach to closing the sensereasoning gap is needed. At the same time, a systematic solution has to be sufficiently flexible to accommodate a wide range of components with highly varying demands. We therefore introduce the concept of knowledge processing middleware for a principled and systematic software framework for bridging the gap between sensing and reasoning in a physical agent. A set of requirements that all such middleware should satisfy is also described. A stream-based knowledge processing middleware framework called DyKnow is then presented. Due to the need for incremental refinement of information at different levels of abstraction, computations and processes within the stream-based knowledge processing framework are modeled as active and sustained knowledge processes working on and producing streams. DyKnow supports the generation of partial and context dependent stream-based representations of past, current, and potential future states at many levels of abstraction in a timely manner. To show the versatility and utility of DyKnow two symbolic reasoning engines are integrated into Dy-Know. The first reasoning engine is a metric temporal logical progression engine. Its integration is made possible by extending DyKnow with a state generation mechanism to generate state sequences over which temporal logical formulas can be progressed. The second reasoning engine is a chronicle recognition engine for recognizing complex events such as traffic situations. The integration is facilitated by extending DyKnow with support for anchoring symbolic object identifiers to sensor data in order to collect information about physical objects using the available sensors. By integrating these reasoning engines into DyKnow, they can be used by any knowledge processing application. Each integration therefore extends the capability of DyKnow and increases its applicability. To show that DyKnow also has a potential for multi-agent knowledge processing, an extension is presented which allows agents to federate parts of their local DyKnow instances to share information and knowledge. Finally, it is shown how DyKnow provides support for the functionalities on the different levels in the JDL Data Fusion Model, which is the de facto standard functional model for fusion applications. The focus is not on individual fusion techniques, but rather on an infrastructure that permits the use of many different fusion techniques in a unified framework. The main conclusion of this thesis is that the DyKnow knowledge processing middleware framework provides appropriate support for bridging the sense-reasoning gap in a physical agent. This conclusion is drawn from the fact that DyKnow has successfully been used to integrate different reasoning engines into complex unmanned aerial vehicle (UAV) applications and that it satisfies all the stated requirements for knowledge processing middleware to a significant degree.

In transport information area, in order to improve control system, identifying vehicle plates is very important. For that reason we are focusing on recognizing the vehicle plate from an image. To distinguish one car from others, one should remove all area except the vehicle plate. Due to the extra difficulties, like limited light, distances, moving etc., it can be hard to detect the final vehicle plate. In this work, we have solved the location of vehicle plate at night time, by separating the objects from the background by using morphological processing and analyzing the characteristics of vehicle plates by using removal of noise and mathematical calculations. According to some characteristics of cars and vehicle plates we developed an algorithm to detect the number of final vehicle plate. This algorithm works fine, and the detected result is accurate.

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Detta examensarbete utfördes på IDA (Institutionen för datavetenskap) vid Linköpings universitet.  Syftet med det här examensarbetet var att utveckla ett program som skulle skapa förutsättningar för generativ konst med hjälp av MyPaint som är ett digitalt rit/målarverktyg. Metoden gick ut på att registrera vad användaren skapat för komponenter, dvs. musinteraktioner och kortkommandon, och därefter använda dem algoritmiskt. Examensarbetet resulterades i ett program (SharpArt), som fångar musinteraktioner samt simulerar tangentbordstryckningar (kortkommandon) från och till Mypaint, vilket i sin tur skapar komponenter som används algoritmiskt. Programmet kan även positionera objektet på canvasen enligt det önskade koordinatvärdet.

No description available.

Statistical gas distribution mapping has recently become a prominent research area in the robotics community. Gas distribution mapping using mobile robots aims for building map of gas dispersion in an unknown environment using the sampled gas concentrations accompanied by the corresponding atmospheric variables. In this context, wind is considered as one of the main driving forces and recently exploited as an environmental bias in the the modelling process. However, the existing approaches utilizing the wind data are based on very simple averaging window methods which do not take the specic spatio-temporal wind variations into account appropriately. In the current thesis work, under the heading of statistical wind modelling, the various aspects of the existing approaches to model both temporal and spatial wind variations are studied. Accordingly, in the undertaking of Mobile Robot Wind Mapping (MRWM) task, three individual methods for statistically wind speed modelling, wind direction modelling and spatial wind mapping are proposed and implemented. Particularly, wind speed is modelled in form of a Gaussian distribution where the valid averaging scale is dened using an online adaptive approach, namely Time-Dependent Memory Method (TDMM) . The wind direction is modelled by means of the mixturemodel of Von-Mises distribution and for the spatial mapping of modelled wind data, a recursive approach based on Linear Kalman lter is utilized. The proposed approaches for statistically wind speed and direction modelling are applied to and evaluated by real wind data, collected specically for this project. The wind mapping algorithm is implemented and tested using simulated data.

Den tekniska utvecklingen av allt mer komplexa filsystem samt ökad lagringskapacitet på medier försvårar arbetet med att finna samt återskapa förlorade filer. Detta medför att de verktyg som används inom området ständigt måste utvecklas för att möta upp denna komplexitet. När filer ”raderas” av användaren hanterar filsystemet denna process på olika vis beroende på vilken typ av radering som utförts. Möjligheterna att återskapa dessa filer underlättas genom en grundläggande kunskap om filsystemets arkitektur samt de osynliga processer som sker. Inom den profession, till exempel digital forensik, där det insamlande materialet har krav att klara rättspraxis används olika metoder för att bevara materialets integritet. De kommersiella verktyg som används är utvecklade för att bemöta de krav som ställs inom rättsliga processer. Denna undersökning anammade till viss del det förfarande som utövas inom den digitala forensiken. Huvudsyftet med denna studie var att undersöka möjligheterna att återskapa ”raderade” filer samt belysa eventuella differenser utifrån olika scenarier. Utifrån huvudsyftet tog undersökningen sikte på att vägleda datoranvändarna till att anamma de metoder som undersökts. Genom den litteraturöversikt som studerades samt de experiment som utfördes sammanfattades resultatet kvantitativt där återfunna samt återskapade filer sammanställdes. De resultat som denna undersökning kom fram till var att goda möjligheter finns för att återskapa de borttagna filerna. Vidare anses att datoranvändare mycket väl kan använda de metoder som ingick i denna undersökning för att återskapa sina ”raderade” filer.