The utilization of interactive technologies will affect learning in science classrooms of the future. And although these technologies have improved in form and function, their effective employment in university science classrooms has lagged behind the rapid development of new constructivist pedagogies and means of instruction. This dissertation examines the enlistment of instructional technologies, in particular tablet PCs and DyKnow Interactive Software, in a technologically enhanced, university-level, introductory physics course. Results of this qualitative case study of three university students indicate that (1) the use of interactive technology positively affects both student learning within force and motion and self-reported beliefs about physics, (2) ad hoc use of instructional technologies may not sufficient for effective learning in introductory physics, (3) student learners dictate the leveraging of technology in any classroom, and (4) that purposeful teacher structuring of classroom activities with technologies are essential for student construction of knowledge. This includes designing activities to elicit attention and make knowledge visible for low-level content, while augmenting student interactions and modelling procedural steps for higher-level content.

<p>This Master’s thesis describes an evaluation of the stream-based knowledge pro-cessing middleware framework DyKnow in multi-UAV traffic monitoring applica-tions performed at Saab Aerosystems. The purpose of DyKnow is “to providegeneric and well-structured software support for the processes involved in gen-erating state, object, and event abstractions about the environments of complexsystems." It does this by providing the concepts of streams, sources, computa-tional units (CUs), entity frames and chronicles.</p><p>This evaluation is divided into three parts: A general quality evaluation ofDyKnow using the ISO 9126-1 quality model, a discussion of a series of questionsregarding the specific use and functionality of DyKnow and last, a performanceevaluation. To perform parts of this evaluation, a test application implementinga traffic monitoring scenario was developed using DyKnow and the Java AgentDEvelopment Framework (JADE).</p><p>The quality evaluation shows that while DyKnow suffers on the usability side,the suitability, accuracy and interoperability were all given high marks.</p><p>The results of the performance evaluation high-lights the factors that affect thememory and CPU requirements of DyKnow. It is shown that the most significantfactor in the demand placed on the CPU is the number of CUs and streams. Italso shows that DyKnow may suffer dataloss and severe slowdown if the CPU istoo heavily utilized. However, a reasonably sized DyKnow application, such as thescenario implemented in this report, should run without problems on systems atleast half as fast as the one used in the tests.</p>

This Master’s thesis describes an evaluation of the stream-based knowledge pro-cessing middleware framework DyKnow in multi-UAV traffic monitoring applica-tions performed at Saab Aerosystems. The purpose of DyKnow is “to providegeneric and well-structured software support for the processes involved in gen-erating state, object, and event abstractions about the environments of complexsystems." It does this by providing the concepts of streams, sources, computa-tional units (CUs), entity frames and chronicles. This evaluation is divided into three parts: A general quality evaluation ofDyKnow using the ISO 9126-1 quality model, a discussion of a series of questionsregarding the specific use and functionality of DyKnow and last, a performanceevaluation. To perform parts of this evaluation, a test application implementinga traffic monitoring scenario was developed using DyKnow and the Java AgentDEvelopment Framework (JADE). The quality evaluation shows that while DyKnow suffers on the usability side,the suitability, accuracy and interoperability were all given high marks. The results of the performance evaluation high-lights the factors that affect thememory and CPU requirements of DyKnow. It is shown that the most significantfactor in the demand placed on the CPU is the number of CUs and streams. Italso shows that DyKnow may suffer dataloss and severe slowdown if the CPU istoo heavily utilized. However, a reasonably sized DyKnow application, such as thescenario implemented in this report, should run without problems on systems atleast half as fast as the one used in the tests.

This thesis presents a comparison between the synchronous and asynchronous model of computation in the area of knowledge processing. Focus lies on evaluating if a synchronous approach to knowledge processing is possible and practical. This has been done by implementing the reconfigurable fault diagnosis framework FlexDx using the synchronous programming language SIGNAL, a language designed to be used in embedded real-time systems. FlexDx have previously been implemented using the asynchronous knowledge processing middleware DyKnow, from which an example system with multiple failure scenarios consisting of input signals and results were available. Matlab code for many algorithms in FlexDx from the existing implementation could also be reused. The SIGNAL implementation was tested using one of the available scenarios and the results matched the expected results from the DyKnow implementation almost perfectly. The synchronous aspect of the new implementation was not a problem as the behavior of all parts of FlexDx that had to be reimplemented easily could be described synchronously. However, using SIGNAL for this purpose proved to be both complicated and cumbersome. This was partly because of the strict declarative coding style, but mostly because of limitations of SIGNAL and the POLYCHRONY compiler. Two such limitations caused most of the problems that were encountered. First, SIGNAL does not support dynamic arrays and all iteration constructs require that the number of iterations is determined at compile time. This could be overcome by using external types and processes, the method used in SIGNAL to import code written in other languages, to implement the needed functionality in C++ and Matlab. Second, the POLYCHRONY compiler provides very limited feedback that can be used to correct non-trivial coding errors, making the task of programming with SIGNAL far more complicated than necessary. While it is clear that a synchronous approach to knowledge processing works well, it is not practical to write a working implementation of FlexDx using only SIGNAL. Because of the limitations of SIGNAL a large part of the system had to be implemented using other languages.

Autonomous system needs to do a great deal of reasoning during execution in order to provide timely reactions to changes in their environment. Data needed for this reasoning process is often provided through a number of sensors. One approach for this kind of reasoning is evaluation of temporal logical formulas through progression. To evaluate these formulas it is necessary to provide relevant data for each symbol in a formula. Mapping relevant data to symbols in a formula could be done manually, however as systems become more complex it is harder for a designer to explicitly state and maintain thismapping. Therefore, automatic support for mapping data from sensors to symbols would make system more flexible and easier to maintain. DyKnow is a knowledge processing middleware which provides the support for processing data on different levels of abstractions. The output from the processing components in DyKnow is in the form of streams of information. In the case of DyKnow, reasoning over incrementally available data is done by progressing metric temporal logical formulas. A logical formula contains a number of symbols whose values over time must be collected and synchronized in order to determine the truth value of the formula. Mapping symbols in formula to relevant streams is done manually in DyKnow. The purpose of this matching is for each variable to find one or more streams whose content matches the intended meaning of the variable. This thesis analyses and provides a solution to the process of semantic matching. The analysis is mostly focused on how the existing semantic technologies such as ontologies can be used in this process. The thesis also analyses how this process can be used for matching symbols in a formula to content of streams on distributed and heterogeneous platforms. Finally, the thesis presents an implementation in the Robot Operating System (ROS). The implementation is tested in two case studies which cover a scenario where there is only a single platform in a system and a scenario where there are multiple distributed heterogeneous platforms in a system. The conclusions are that the semantic matching represents an important step towards fully automatized semantic-based stream reasoning. Our solution also shows that semantic technologies are suitable for establishing machine-readable domain models. The use of these technologies made the semantic matching domain and platform independent as all domain and platform specific knowledge is specified in ontologies. Moreover, semantic technologies provide support for integration of data from heterogeneous sources which makes it possible for platforms to use streams from distributed sources.

Autonomous systems require a lot of information about the environment in which they operate in order to perform different high-level tasks. The information is made available through various sources, such as remote and on-board sensors, databases, GIS, the Internet, etc. The sensory input especially is incrementally available to the systems and can be represented as streams. High-level tasks often require some sort of reasoning over the input data, however raw streaming input is often not suitable for the higher level representations needed for reasoning. DyKnow is a stream processing framework that provides functionalities to represent knowledge needed for reasoning from streaming inputs. DyKnow has been used within a platform for task planning and execution monitoring for UAVs. The execution monitoring is performed using formula progression with monitor rules specified as temporal logic formulas. In this thesis we present an analysis for providing spatio-temporal functionalities to the formula progressor and we extend the formula progression with spatial reasoning in RCC-8. The result implementation is capable of evaluating spatio-temporal logic formulas using progression over streaming data. In addition, a ROS implementation of the formula progressor is presented as a part of a spatio-temporal stream reasoning architecture in ROS. / Collaborative Unmanned Aircraft Systems (CUAS)