Dynamic Injection of Scribble Features into Graphical Diagram Editors

Almost everyone sketches. People use sketches day in and day out in many different and heterogeneous fields, to share their thoughts and clarify ambiguous interpretations, for example. The media used to sketch varies from analog tools like flipcharts to digital tools like smartboards. Whereas analog tools are usually affected by insufficient editing capabilities like cut/copy/paste, digital tools greatly support these scenarios. Digital tools can be grouped into informal and formal tools. Informal tools can be understood as simple drawing environments, whereas formal tools offer sophisticated support to create, optimize and validate diagrams of a certain application domain. Most digital formal tools force users to stick to a concrete syntax and editing workflow, limiting the user’s creativity. For that reason, a lot of people first sketch their ideas using the flexibility of analog or digital informal tools. Subsequently, the sketch is "portrayed" in an appropriate digital formal tool. This work presents Scribble, a highly configurable and extensible sketching framework which allows to dynamically inject sketching features into existing graphical diagram editors, based on Eclipse GEF. This allows to combine the flexibility of informal tools with the power of formal tools without any effort. No additional code is required to augment a GEF editor with sophisticated sketching features. Scribble recognizes drawn elements as well as handwritten text and automatically generates the corresponding domain elements. A local training data library is created dynamically by incrementally learning shapes, drawn by the user. Training data can be shared with others using the WebScribble web application which has been created as part of this work.

Transport magnetischer Partikel durch maßgeschneiderte magnetische Feldlandschaften zur Anwendung in mikrofluidischen Mischprozessen

Die Miniaturisierung von konventioneller Labor- und Analysetechnik nimmt eine zentrale Rolle im Bereich der allgemeinen Lebenswissenschaften und medizinischen Diagnostik ein. Neuartige und preiswerte Technologieplattformen wie Lab-on-a-Chip (LOC) oder Mikrototalanalysesysteme (µTAS) versprechen insbesondere im Bereich der Individualmedizin einen hohen gesellschaftlichen Nutzen zur frühzeitigen und nichtinvasiven Diagnose krankheitsspezifischer Indikatoren. Durch den patientennahen Einsatz preiswerter und verlässlicher Mikrochips auf Basis hoher Qualitätsstandards entfallen kostspielige und zeitintensive Zentrallaboranalysen, was gleichzeitig Chancen für den globalen Einsatz - speziell in Schwellen- und Entwicklungsländern - bietet. Die technischen Herausforderungen bei der Realisierung moderner LOC-Systeme sind in der kontrollierten und verlässlichen Handhabung kleinster Flüssigkeitsmengen sowie deren diagnostischem Nachweis begründet. In diesem Kontext wird der erfolgreichen Integration eines fernsteuerbaren Transports von biokompatiblen, magnetischen Mikro- und Nanopartikeln eine Schlüsselrolle zugesprochen. Die Ursache hierfür liegt in der vielfältigen Einsetzbarkeit, die durch die einzigartigen Materialeigenschaften begründet sind. Diese reichen von der beschleunigten, aktiven Durchmischung mikrofluidischer Substanzvolumina über die Steigerung der molekularen Interaktionsrate in Biosensoren bis hin zur Isolation und Aufreinigung von krankheitsspezifischen Indikatoren. In der Literatur beschriebene Ansätze basieren auf der dynamischen Transformation eines makroskopischen, zeitabhängigen externen Magnetfelds in eine mikroskopisch veränderliche potentielle Energielandschaft oberhalb magnetisch strukturierter Substrate, woraus eine gerichtete und fernsteuerbare Partikelbewegung resultiert. Zentrale Kriterien, wie die theoretische Modellierung und experimentelle Charakterisierung der magnetischen Feldlandschaft in räumlicher Nähe zur Oberfläche der strukturierten Substrate sowie die theoretische Beschreibung der Durchmischungseffekte, wurden jedoch bislang nicht näher beleuchtet, obwohl diese essentiell für ein detailliertes Verständnis der zu Grunde liegenden Mechanismen und folglich für einen Markteintritt zukünftiger Geräte sind. Im Rahmen der vorgestellten Arbeit wurde daher ein neuartiger Ansatz zur erfolgreichen Integration eines Konzepts zum fernsteuerbaren Transport magnetischer Partikel zur Anwendung in modernen LOC-Systemen unter Verwendung von magnetisch strukturierten Exchange-Bias (EB) Dünnschichtsystemen verfolgt. Die Ergebnisse zeigen, dass sich das Verfahren der ionenbe-schussinduzierten magnetischen Strukturierung (IBMP) von EB-Systemen zur Herstellung von maßgeschneiderten magnetischen Feldlandschaften (MFL) oberhalb der Substratoberfläche, deren Stärke und räumlicher Verlauf auf Nano- und Mikrometerlängenskalen gezielt über die Veränderung der Materialparameter des EB-Systems via IBMP eingestellt werden kann, eignet. Im Zuge dessen wurden erstmals moderne, experimentelle Verfahrenstechniken (Raster-Hall-Sonden-Mikroskopie und rastermagnetoresistive Mikroskopie) in Kombination mit einem eigens entwickelten theoretischen Modell eingesetzt, um eine Abbildung der MFL in unterschiedlichen Abstandsbereichen zur Substratoberfläche zu realisieren. Basierend auf der quantitativen Kenntnis der MFL wurde ein neuartiges Konzept zum fernsteuerbaren Transport magnetischer Partikel entwickelt, bei dem Partikelgeschwindigkeiten im Bereich von 100 µm/s unter Verwendung von externen Magnetfeldstärken im Bereich weniger Millitesla erzielt werden können, ohne den magnetischen Zustand des Substrats zu modifizieren. Wie aus den Untersuchungen hervorgeht, können zudem die Stärke des externen Magnetfelds, die Stärke und der Gradient der MFL, das magnetfeldinduzierte magnetische Moment der Partikel sowie die Größe und der künstlich veränderliche Abstand der Partikel zur Substratoberfläche als zentrale Einflussgrößen zur quantitativen Modifikation der Partikelgeschwindigkeit genutzt werden. Abschließend wurde erfolgreich ein numerisches Simulationsmodell entwickelt, das die quantitative Studie der aktiven Durchmischung auf Basis des vorgestellten Partikeltransportkonzepts von theoretischer Seite ermöglicht, um so gezielt die geometrischen Gegebenheiten der mikrofluidischen Kanalstrukturen auf einem LOC-System für spezifische Anwendungen anzupassen.

Modeling Robot Dynamic Performance for Endpoint Force Control

This research aims to understand the fundamental dynamic behavior of servo-controlled machinery in response to various types of sensory feedback. As an example of such a system, we study robot force control, a scheme which promises to greatly expand the capabilities of industrial robots by allowing manipulators to interact with uncertain and dynamic tasks. Dynamic models are developed which allow the effects of actuator dynamics, structural flexibility, and workpiece interaction to be explored in the frequency and time domains. The models are used first to explain the causes of robot force control instability, and then to find methods of improving this performance.

The Dynamic Structure of Everyday Life

Computational theories of action have generally understood the organized nature of human activity through the construction and execution of plans. By consigning the phenomena of contingency and improvisation to peripheral roles, this view has led to impractical technical proposals. As an alternative, I suggest that contingency is a central feature of everyday activity and that improvisation is the central kind of human activity. I also offer a computational model of certain aspects of everyday routine activity based on an account of improvised activity called running arguments and an account of representation for situated agents called deictic representation .

The Control of Human Arm Movement Models and Mechanical Constraints

A serial-link manipulator may form a mobile closed kinematic chain when interacting with the environment, if it is redundant with respect to the task degrees of freedom (DOFs) at the endpoint. If the mobile closed chain assumes a number of configurations, then loop consistency equations permit the manipulator and task kinematics to be calibrated simultaneously using only the joint angle readings; endpoint sensing is not required. Example tasks include a fixed endpoint (0 DOF task), the opening of a door (1 DOF task), and point contact (3 DOF task). Identifiability conditions are derived for these various tasks.

Safe Distributed Coordination of Heterogeneous Robots through Dynamic Simple Temporal Networks

Research on autonomous intelligent systems has focused on how robots can robustly carry out missions in uncertain and harsh environments with very little or no human intervention. Robotic execution languages such as RAPs, ESL, and TDL improve robustness by managing functionally redundant procedures for achieving goals. The model-based programming approach extends this by guaranteeing correctness of execution through pre-planning of non-deterministic timed threads of activities. Executing model-based programs effectively on distributed autonomous platforms requires distributing this pre-planning process. This thesis presents a distributed planner for modelbased programs whose planning and execution is distributed among agents with widely varying levels of processor power and memory resources. We make two key contributions. First, we reformulate a model-based program, which describes cooperative activities, into a hierarchical dynamic simple temporal network. This enables efficient distributed coordination of robots and supports deployment on heterogeneous robots. Second, we introduce a distributed temporal planner, called DTP, which solves hierarchical dynamic simple temporal networks with the assistance of the distributed Bellman-Ford shortest path algorithm. The implementation of DTP has been demonstrated successfully on a wide range of randomly generated examples and on a pursuer-evader challenge problem in simulation.

On the Convergence of Stochastic Iterative Dynamic Programming Algorithms

Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD(lambda) algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of convergence of these DP-based learning algorithms by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem. The theorem establishes a general class of convergent algorithms to which both TD(lambda) and Q-learning belong.

New Architectural Models for Visibly Controllable Computing: The Relevance of Dynamic Object Oriented Architectures and Plan Based Computing Models

Traditionally, we've focussed on the question of how to make a system easy to code the first time, or perhaps on how to ease the system's continued evolution. But if we look at life cycle costs, then we must conclude that the important question is how to make a system easy to operate. To do this we need to make it easy for the operators to see what's going on and to then manipulate the system so that it does what it is supposed to. This is a radically different criterion for success. What makes a computer system visible and controllable? This is a difficult question, but it's clear that today's modern operating systems with nearly 50 million source lines of code are neither. Strikingly, the MIT Lisp Machine and its commercial successors provided almost the same functionality as today's mainstream sytsems, but with only 1 Million lines of code. This paper is a retrospective examination of the features of the Lisp Machine hardware and software system. Our key claim is that by building the Object Abstraction into the lowest tiers of the system, great synergy and clarity were obtained. It is our hope that this is a lesson that can impact tomorrow's designs. We also speculate on how the spirit of the Lisp Machine could be extended to include a comprehensive access control model and how new layers of abstraction could further enrich this model.

Ameliorating the Overhead of Dynamic Optimization

Dynamic optimization has several key advantages. This includes the ability to work on binary code in the absence of sources and to perform optimization across module boundaries. However, it has a significant disadvantage viz-a-viz traditional static optimization: it has a significant runtime overhead. There can be performance gain only if the overhead can be amortized. In this paper, we will quantitatively analyze the runtime overhead introduced by a dynamic optimizer, DynamoRIO. We found that the major overhead does not come from the optimizer's operation. Instead, it comes from the extra code in the code cache added by DynamoRIO. After a detailed analysis, we will propose a method of trace construction that ameliorate the overhead introduced by the dynamic optimizer, thereby reducing the runtime overhead of DynamoRIO. We believe that the result of the study as well as the proposed solution is applicable to other scenarios such as dynamic code translation and managed execution that utilizes a framework similar to that of dynamic optimization.