Wolfgang Rosenstiel

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This paper presents an approach for cycle-accurate simulation of embedded software by integration in an abstract SystemC model. Compared to existing simulation-based approaches, we present a hybrid method that resolves performance issues by combining the advantages of simulation-based and analytical approaches. In a first step, cycle-accurate static(More)
In this paper, we present a novel fully adaptive and fault-tolerant routing algorithm for Network-on-Chips (NoCs) called Force-Directed Wormhole Routing (FDWR). The proposed routing algorithm is implemented in the switches of a TLM (Transaction Level Model) packet switching NoC using SystemC. Based on these switches, mesh, torus, and hypercube topologies(More)
We present a rigorous but transparent semantics definition of SystemC that covers method, thread, and clocked thread behavior as well as their interaction with the simulation kernel process. The semantics includes watching statements, signal assignment, and wait statements as they are introduced in SystemC V1.0. We present our definition in form of(More)
The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and(More)
Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed(More)
During the last ten years there has been growing interest in the development of Brain Computer Interfaces (BCIs). The field has mainly been driven by the needs of completely paralyzed patients to communicate. With a few exceptions, most human BCIs are based on extracranial electroencephalography (EEG). However, reported bit rates are still low. One reason(More)
We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one(More)
OBJECTIVE The purpose of this study is to show the effectiveness of EEG alpha spindles, defined by short narrowband bursts in the alpha band, as an objective measure for assessing driver fatigue under real driving conditions. METHODS An algorithm for the identification of alpha spindles is described. The performance of the algorithm is tested based on(More)