Refine
Year of publication
- 2010 (46) (remove)
Document Type
- Conference Proceeding (20)
- Contribution to a Periodical (10)
- Patent (5)
- Article (reviewed) (3)
- Article (unreviewed) (3)
- Book (2)
- Part of a Book (2)
- Master's Thesis (1)
Conference Type
- Konferenzartikel (11)
- Konferenz-Abstract (6)
- Konferenzband (2)
- Sonstiges (1)
Keywords
- Elektronische Pille (3)
- Brennstoffzelle (2)
- E-Learning (2)
- Elektrode (2)
- Glucose (2)
- Mikroelektronik (2)
- Schienenfahrzeug (2)
- 3D virtual reality (1)
- Agent <Künstliche Intelligenz> (1)
- Agent Fitness (1)
Institute
- Fakultät Elektrotechnik und Informationstechnik (E+I) (bis 03/2019) (46) (remove)
Open Access
- Open Access (29)
- Bronze (11)
- Closed Access (9)
- Closed (1)
- Grün (1)
The Institute of Applied Research Offenburg is working in the field of autonomous data loggers since many years. In collaboration with industry, a new RFID based active sensor data logger for continuous recording of temperature has been developed and is now manufactured in mass production. Compared to existing systems, an unusual large data memory is integrated, which can be used via a simplified file system in a flexible way. The system will be used to accompany and monitor temperature sensitive goods of high value. The transponder is the first member of a new class of logging devices, the smallest will be not larger than a 2 Euro-coin with a fully integrated ASIC frontend.
Luftbilder und Magnetfeldkarten – der Hochschul-Helikopter fotografiert aus der Vogelperspektive
(2010)
Der autonom und geregelt fliegende Helikopter der Hochschule Offenburg eignet sich als Träger für unterschiedliche Sensoren. Natürlich ist die naheliegendste Anwendung, mit einer Digitalkamera Luftaufnahmen zu machen. Abbildung 2.4-1 zeigt eines der Ergebnisse der ersten Luftbildflüge: Der Campus der Hochschule Offenburg von oben, aufgenommen mit einer digitalen Filmkamera mit entsprechend geringer Auflösung. Um bessere Ergebnisse zu erzielen, wurde inzwischen eine digitale Panoramakamera mit einem Leica-Objektiv und ca. 10 Mio. Pixel beschafft.
This Master's Thesis discusses intelligent sensor networks considering autonomous sensor placement strategies and system health management. Sensor networks for an intelligent system design process have been researched recently. These networks consist of a distributed collective of sensing units, each with the abilities of individual sensing and computation. Such systems can be capable of self-deployment and must be scalable, long-lived and robust. With distributed sensor networks, intelligent sensor placement for system design and online system health management are attractive areas of research. Distributed sensor networks also cause optimization problems, such as decentralized control, system robustness and maximization of coverage in a distributed system. This also includes the discovery and analysis of points of interest within an environment. The purpose of this study was to investigate a method to control sensor placement in a world with several sources and multiple types of information autonomously. This includes both controlling the movement of sensor units and filtering of the gathered information depending on individual properties to increase system performance, defined as a good coverage. Additionally, online system health management was examined in this study regarding the case of agent failures and autonomous policy reconfiguration if sensors are added to or removed from the system. Two different solution strategies were devised, one where the environment was fully observable, and one with only partial observability. Both strategies use evolutionary algorithms based on artificial neural networks for developing control policies. For performance measurement and policy evaluation, different multiagent objective functions were investigated. The results of the study show that in the case of a world with multiple types of information, individual control strategies performed best because of their abilities to control the movement of a sensor entity and to filter the sensed information. This also includes system robustness in case of sensor failures where other sensing units must recover system performance. Additionally, autonomous policy reconfiguration after adding or removing of sensor agents was successful. This highlights that intelligent sensor agents are able to adapt their individual control policies considering new circumstances.
This paper gives an overview of the implementation of an Active Noise Control system on the TMS320C6713 Digital Signal Processor from Texas Instruments in the Digital Signal Processing Lab at Hochschule Offenburg, Germany. This system is implemented considering some non-ideal environmental conditions on a real system instead of being limited to computer simulations. Changes over time on the physical acoustical path as well as reverberation and variation on the power of the reference signal can strongly degrade the performance of the system or even lead to instability. In order to try to minimize these effects, the Active Noise Control system was designed to support a fast and easy implementation and evaluation of different algorithms on the DSP in real-time. In Section 1 a brief introduction about active noise control system is given and in section 2 the basic algorithm is described. In section 3 the implementation of the system is described and in section 4 some final considerations are given.