E-Learning-Webseite als facettenreiches Werkzeug in Forschung und Lehre

Im Rahmen des blended learning kann eine E-Learning-Webseite als Begleitmaterial einer Lehrveranstaltung eingesetzt werden oder Studierende zur aktiven Teilnahme an der Erstellung der Webseiteninhalte anregen. Darüber hinaus eignet sich eine solche Webseite als Plattform zur E-Learning-Forschung. Auch empirische Studien können dort eingebettet werden. Eine weitere wissenschaftliche Anwendung bietet die Analyse des Nutzerverhaltens, mit der sich aktuelle Forschungsergebnisse zum Lernen mit Hypermedien überprüfen lassen. Wir beschreiben eine solche, vielseitig einsetzbare Webseite, die eine Verknüpfung von universitärer Lehre und Forschung ermöglicht und als Anregung für ähnliche Projekte dienen kann. Erste Erfahrungen werden dabei berichtet und ausgewählte Empfehlungen für Dozierende und Forscher abgeleitet.

Report : enabling team collaboration with pervasive and mobile computing : a review of existing mobile computing technologies

The application of Information and Communication Technology (ICT) in construction industry has been recognised widely by some practitioners and researchers for the last several years. During the 1990s the international construction industry started using with the increasing confidence information and communication technology. The use of e-mail became usual and web-sites were established for marketing purposes. Intranets and extranets were also established to facilitate communication within companies and throughout their branches. One of the important applications of the ICT in construction industry was the use of mobile computing devices to achieve better communication and data transmission between construction sites and offices.

An example of the use of neural computing techniques in materials science - The modelling of fatigue thresholds in Ni-base superalloys

Two adaptive numerical modelling techniques have been applied to prediction of fatigue thresholds in Ni-base superalloys. A Bayesian neural network and a neurofuzzy network have been compared, both of which have the ability to automatically adjust the network's complexity to the current dataset. In both cases, despite inevitable data restrictions, threshold values have been modelled with some degree of success. However, it is argued in this paper that the neurofuzzy modelling approach offers real benefits over the use of a classical neural network as the mathematical complexity of the relationships can be restricted to allow for the paucity of data, and the linguistic fuzzy rules produced allow assessment of the model without extensive interrogation and examination using a hypothetical dataset. The additive neurofuzzy network structure means that redundant inputs can be excluded from the model and simple sub-networks produced which represent global output trends. Both of these aspects are important for final verification and validation of the information extracted from the numerical data. In some situations neurofuzzy networks may require less data to produce a stable solution, and may be easier to verify in the light of existing physical understanding because of the production of transparent linguistic rules. © 1999 Elsevier Science S.A.

A DSWN-based specific-purpose neural computing system

The Double Synapse Weighted Neuron (DSWN) is a kind of general-purpose neuron model, which with the ability of configuring Hyper-sausage neuron (HSN). After introducing the design method of hardware DSWN synapse, this paper proposed a DSWN-based specific purpose neural computing device-CASSANN-IIspr. As its application, a rigid body recognition system was developed on CASSANN-IIspr, which achieved better performance than RIBF-SVMs system.

Architecture research and hardware implementation on simplified neural computing system for face identification

This paper describes a special-purpose neural computing system for face identification. The system architecture and hardware implementation are introduced in detail. An algorithm based on biomimetic pattern recognition has been embedded. For the total 1200 tests for face identification, the false rejection rate is 3.7% and the false acceptance rate is 0.7%.

Final report : enabling team collaboration with pervasive and mobile computing

This is the final report of research project 2002-057-C: Enabling Team Collaboration with Pervasive and Mobile Computing. The research project was carried out by the Australian Cooperative Research Centre for Construction Innovation and has two streams that consider the use of pervasive computing technologies in two different contexts. The first context was the on-site deployment of mobile computing devices, where as the second context was the use and development of intelligent rooms based on sensed environments and new human-computer interfaces (HCI) for collaboration in the design office. The two streams present a model of team collaboration that relies on continues communication to people and information to reduce information leakage. This report consists of five sections: (1) Introduction; (2) Research Project Background; (3) Project Implementation; (4) Case Studies and Outcomes; and (5) Conclusion and Recommendation. Introduction in Section 1 presents a brief description of the research project including general research objectives and structure. Section 2 introduces the background of the research and detailed information regarding project participants, objectives and significance, and also research methodology. Review of all research activities such as literature review and case studies are summarised in Project Implementation in Section 3. Following this, in Section 4 the report then focuses on analysing the case studies and presents their outcomes. Conclusion and recommendation of the research project are summarised in Section 5. Other information to support the content of the report such as research project schedule is provided in Appendices. The purpose of the final project report is to provide industry partners with detailed information on the project activities and methodology such as the implementation of pervasive computing technologies in the real contexts. The report summarises the outcomes of the case studies and provides necessary recommendation to industry partners of using new technologies to support better project collaboration.

Construction defect reporting using mobile and digital workbench technologies

Sending data between the construction site and an off-site design office is one of the more problematic areas in information technology for construction automation, particularly for construction defect management. The aim of this research is to investigate how mobile computing and new forms of human-computer interaction can be brought to bear on specific problems in construction management. The construction defect reporting system is one such application. Combining mobile and wireless computing technologies with a digital workbench, we have developed a system to facilitate remote telecollaboration between a construction site and an off-site engineering office. The application reported in this paper demonstrates how construction defect reporting can be streamlined by field collection of construction defect information using a mobile device and visualising the defect in a CAD model on a digital workbench in an engineering office. This paper reports on the design of the system and our tests of sending images from the construction site to the engineer’s office and positional accuracy of GPS for localization of the defect.

Neural network-based detection of virtual environment anomalies

The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.

Transdisciplinary approaches to urban computing

The research field of urban computing – defined as “the integration of computing, sensing, and actuation technologies into everyday urban settings and lifestyles” – considers the design and use of ubiquitous computing technology in public and shared urban environments. Its impact on cities, buildings, and spaces evokes innumerable kinds of change. Embedded into our everyday lived environments, urban computing technologies have the potential to alter the meaning of physical space, and affect the activities performed in those spaces. This paper starts a multi-themed discussion of various aspects that make up the, at times, messy and certainly transdisciplinary field of urban computing and urban informatics.

Single-electron tunneling depressing synapse for cellular neural networks

In this paper, a cellular neural network with depressing synapses for contrast-invariant pattern classification and synchrony detection is presented, starting from the impulse model of the single-electron tunneling junction. The results of the impulse model and the network are simulated using simulation program with integrated circuit emphasis (SPICE). It is demonstrated that depressing synapses should be an important candidate of robust systems since they exhibit a rapid depression of excitatory postsynaptic potentials for successive presynaptic spikes.

A reconfigurable neural environment on active networks

This paper proposes the deployment of a neural network computing environment on Active Networks. Active Networks are packet-switched computer networks in which packets can contain code fragments that are executed on the intermediate nodes. This feature allows the injection of small pieces of codes to deal with computer network problems directly into the network core, and the adoption of new computing techniques to solve networking problems. The goal of our project is the adoption of a distributed neural network for approaching tasks which are specific of the computer network environment. Dynamically reconfigurable neural networks are spread on an experimental wide area backbone of active nodes (ABone) to show the feasibility of the proposed approach.

Neural network control of a simple mobile robot

In recent years researchers in the Department of Cybernetics have been developing simple mobile robots capable of exploring their environment on the basis of the information obtained from a few simple sensors. These robots are used as the test bed for exploring various behaviours of single and multiple organisms: the work is inspired by considerations of natural systems. In this paper we concentrate on that part of the work which involves neural networks and related techniques. These neural networks are used both to process the sensor information and to develop the strategy used to control the robot. Here the robots, their sensors, and the neural networks used and all described. 1.