Informatik und Kommunikation
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Intelligenzexplosion
(2016)
Virtuelle Hochschulen
(2002)
Virtuelle Fachhochschule
(2004)
Auf der Basis eines Wettbewerbs des BMBF von 1997 hat das Konsortium Virtuelle Fachhochschule (VFH) 43 Mio. DM erhalten und 1999 damit begonnen Online-Studiengänge zu entwickeln. Weitere Themenfelder dieses Bundesleitprojektes sind: Struktur und Organisation einer virtuellen Hochschule, Lehr- und Lernformen in Online-Studiengängen, etc. Beteiligt sind 10 Fachhochschulen, 2 Universitäten, sowie diverse Organisationen und Wirtschaftsunternehmen. Im Jahre 2001 startete der Bachelor-Online-Studiengang Medieninformatik (www.oncampus.de). Das Projekt läuft noch bis ins Jahr 2004. Der Autor ist Vizegesamtprojektleiter, Mitglied im Teilvorhaben Struktur und Organisation sowie Entwickler von 3 Lernmodulen zur Physik für das Medieninformatikstudium.
Am Beispiel dieses Großprojektes werden einige Besonderheiten und Erfahrungen zu den Themen: Organisationsformen, Betreuung, Lehrdeputat, Workload, Ergonomie, Evaluation, Akkreditierung und die Entwicklung der Physik-Online-Lernmodule dargestellt.
Professor Dr. Dieter Hannemann ist Physiker und Ingenieur und hat sich nach seinen Studien der Technischen Informatik zugewandt. An der Fachhochschule Bochum, Abt. Gelsenkirchen, gründete er 1979 den Bereich Mikrocomputertechnik, dessen Aktivitäten vor allem im Bereich des Technologietransfers liegen. Zum 1.8.1992 wurde die Fachhochschulabteilung Keimzelle der neuen FH Gelsenkirchen, an deren Aufbau Prof. Hannemann als Prorektor und Gründungsdekan des Fachbereichs Informatik maßgeblich beteiligt war und ist.
Der Studiengangsführer des neu gegründeten Fachbereichs Ingenieurinformatik an der FH Gelsenkirchen beschreibt den deutschlandweit einmaligen Diplom-Studiengang Mikroinformatik mit seinen zwei Ausprägungen ‚Technische- und Angewandte Mikroinformatik‘: Die Aufgabenbereiche, das Praxissemester, die Einschreibungsvoraussetzungen, die Berufsfelder, den Studienverlauf und die Fächerinhalte.
Dieser Artikel bietet einen Bericht über Entwicklungstendenzen und erste Erfahrungen virtueller Lernräume im Internet. Der Autor geht auf die Kommunikation im Internet, Lernräume im WWW, die Rollenverteilung im Lernraum ein, mit den Anforderungen aus Sicht der Lernenden, Lehrenden und der Verwaltung.
The article highlights gender codes in design, particularly in web design, by means of current examples. Different aspects of gender-specific design are looked at in detail and their inherent problems discussed: on the one hand the development of a special solution (gender-specific for women), on the other hand, web design with reduced functionality and simplification of information (i.e. image representation) which sometimes even leads to a negation of technology. The article illustrates that gender codes and stereotypical role models can be embodied on different design levels of web design (use and artefact): in structure/navigation, in creative elements by the use of shape, colour and imagery and on a textual level. These design decisions have an impact on the power of users to act, their individual gender identity and the structural gender identity/social perception of gender. The article demonstrates that gender codes in current web design are very present and aims to sensitize the topic.
Problem: A group of robots, called a swarm, is placed in an unknown environment and is supposed to explore it independently. The goal of the exploration is the creation of a common map.
Implementation
- Equipping six Kobuki robots with appropriate sensor technology, a large battery, a router and the Jetson board
- Setup of the Jetson-Boards with self-made ROS2 nodes and the set up mesh network
- Writing of launch files for the common start of all functions
- Reinforcement learning is used to train an AI that controls the swarm by selecting points for the robots to approach and navigating to them and navigating them there.
- Setting up a responsive website using Angular and the Bootstrap
Framework.
Problem
- How to effectively use aerial robots to support rescue forces?
- How to achieve good flight characteristics and long flight times?
- How to enable simple and intuitive control?
- How to efficiently record image data of the environment?
- How to generate flight and image data for rescue forces?
Implementation:
The flying robot was designed in Autodesk Fusion360. In order to achieve high stability as well as low weight, the frame was milled from carbon. Mounts such as for GPS and 360° camera were 3D printed. A special feature is that the flying robot is not visible in the panoramic view of the 360° camera. The flight controller of the robot was set up using Ardupilot. The communication with the robot is done via MAVLink (UDP).To support different platforms, a software was realized as a web application. The front end was created using HTML, CSS and Javascript.
The back end is based on Flask-Socket-IO (Python). For the intelligent recognition of motor vehicles a micro controller with an integrated camera is used. For the post-processing of flight and video data a pipeline was implemented for automation.
The video shows a very high resolution 3D point cloud !!! of the outdoor area of the German Rescue Robotics Center. For the recording, a 25-second POI flight was performed with a Mavic 3. From the 4K video footage captured during this flight, 77 images were cropped and localized within 4 minutes using colmap and processed using Neural Radiance Fields (NeRF). The nerfacto model of Nerfstudio was trained on an Nvidia RTX 4090 for 8 minutes. In summary, a top 3D model is available to task forces after about 13 minutes. The calculation is performed locally on site by the RobLW of the DRZ. The video shown here shows a free camera path rendered at 60 hz (Full HD).
Nerf(acto) for the 3D modeling of the Computer Science building of Westfälische Hochschule GE
(2023)
The video shows a very high resolution 3D point cloud !!! of the computer science building of the University of Applied Science Gelsenkirchen. For the recording a 3 minute flight with a M30T was performed. The 105 images taken by the wide-angle camera during this flight were localized within 3 minutes using colmap and processed using Neural Radiance Fields (NeRF). The nerfacto model of Nerfstudio was trained on an Nvidia RTX 4090 for 8 minutes. Thus, a top 3D model is available after about 15 minutes.
The video shown here shows a free camera path rendered at 60 hz (Full HD).
From the 360° images of the former video (
• German rescue robotic center captured... ) we now generate the 3D point cloud. The UAV needs 3 minutes to capture the outdoor scenario and the hall from inside and outside. The 3D point cloud generation is 5x slower than the video. It uses a VSLAM algorithm to localize the k-frames (green) and with 3 k-frames it use a 360° PatchMatch algorithm implemented at a NVIDIA graphic card (CUDA) to calculated the dense point clouds.The hall ist about 70 x 20 meters.
The video shows the first test of a small spherical UAV (35 cm) with 4 rotors for missions in complex environments such as buildings, caves or tunnels. The spherical design protects the vehicle's internal components and allows the UAV to roll over the ground when the environment allows. The drone can land and take off in any position and come into contact with objects without endangering the propellers and can restart even after crashes.