A system for epileptic seizure detection in electroencephalography (EEG) is described in this paper. One of the challenges is to distinguish rhythmic discharges from nonstationary patterns occurring during seizures. The proposed approach is based on an adaptive and localized time-frequency representation of EEG signals by means of rational functions. The corresponding rational discrete short-time Fourier transform (DSTFT) is a novel feature extraction technique for epileptic EEG data. A multilayer perceptron classifier is fed by the coefficients of the rational DSTFT in order to separate seizure epochs from seizure-free epochs. The effectiveness of the proposed method is compared with several state-of-art feature extraction algorithms used in offline epileptic seizure detection. The results of the comparative evaluations show that the proposed method outperforms competing techniques in terms of classification accuracy. In addition, it provides a compact representation of EEG time-series.

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As the performance gap between memory systems and processors has increased, virtual memory management plays an important role in system performance. Different caching policies have different effects on the system performance. This paper studies an adaptive replacement policy which has low overhead on system and is easy to implement. Simulations show that our algorithm performs better than Least-Recently-Used (LRU) and Least-Frequently-Used (LFU). In addition, it performs similarly to LRU in worst cases. Index Terms-Weighting replacement policy, Adaptive page replacement policy, Memory managementInternational Symposium on Computer Science and its Applications 978-0-7695-3428-2/08 $25.00