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Signal processing and classification of biomedicals' signal in Sensors Network.

Sensor_Network1Signal processing methods have become increasingly important within the scope of biomedicine. Body-mounted sensor networks, henceforth referred to as „Body Sensor Networks“, consist of several sensor types and ensure a continuous measurement of the bodily functions. Among others, sensors for surface electromyography, electrocardiography as well as acceleration sensors or gyroscopes are used.

The data of the sensors in a Body Sensor Network are processed and combined so that the condition of a person can be evaluated with the help of classification methods. These results can be very important as far as injury prevention and assessment of the treatment process are concerned.

Induced by the further development of Body Sensor Networks, complicated measurement setups can be replaced in future. These Sensor Networks hold a lot of promise, because the collection of data can be done during everyday life independently from a test laboratory. Therefore the medical assessment of the general health condition can be done more precise and in the long term.

The goal of our studies is the development of tools for a skillful classification of biomedical output signals. In this context, the combination of the individual sensor signals in a Body Sensor Network plays an important role. In contrast to an isolated inspection of one signal, the efficiency of the results can be enhanced by involving more sensors.

Before regarding the interplay or classification of the signals, the data coming from the sensors must be processed first. However, the separation of useful information and artefacts is rather challenging. The disturbing signals are of different origins and have a serious influence on the sensor output signals. The varying sweat secretion during a measurement, which leads to a shift of the skin resistance, can be seen as an example of this. Therefore the aim is to eliminate these artefacts with signal processing techniques. In addition to this filtering procedure, the extraction of several features(e.g. standard deviation, mean value, median frequency, subspaces, etc.) is inevitable for the classification .

Finally the mapping of these extracted features to previously defined classes allows an assessment of the regarded system(human being).

Adaptive modeling and real-time identification of transcontinental energy transmission systems

ClusterElectrical transmission networks in Europe are operated closer to their allowable limits as a result of liberalization and fluctuating feed by renewable energies. To ensure the reliability and safety of the operation and to avoid large-scaled Blackouts, an interdisciplinary research group founded by the DFG is searching for profitable methods for the Evaluation of Network Status, Decision Logics on Countermeasures and Simulative Evaluation of Protection and Control Systems. Research focuses on the latest statistical and data-processing methods in context of transcontinental energy systems.

Static Clustering such as Spectral Clustering provides the opportunity to devide network models in topologically connected regions. Dynamic Clustering devide networks in similar sections based on Stability Indicators of synchronized measured values. For the stability analysis an ARMAX model is created for a node-specific eigenvalue analysis.The results can be used for the Decision Logic of Protective Functions considering Subnetworks and for creating Adaptive Models for the analysis of low-frequency oscillations.