Browsing by Author "Gökçesu, K."
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Item Open Access Minimax optimal algorithms for adversarial bandit problem with multiple plays(IEEE, 2019) Vural, Nuri Mert; Gökçesu, Hakan; Gökçesu, K.; Kozat, Süleyman SerdarWe investigate the adversarial bandit problem with multiple plays under semi-bandit feedback. We introduce a highly efficient algorithm that asymptotically achieves the performance of the best switching m-arm strategy with minimax optimal regret bounds. To construct our algorithm, we introduce a new expert advice algorithm for the multiple-play setting. By using our expert advice algorithm, we additionally improve the best-known high-probability bound for the multi-play setting by O(√(m)). Our results are guaranteed to hold in an individual sequence manner since we have no statistical assumption on the bandit arm gains. Through an extensive set of experiments involving synthetic and real data, we demonstrate significant performance gains achieved by the proposed algorithm with respect to the state-of-the-art algorithms.Item Open Access An sEMG-based method to adaptively reject the effect of contraction on spectral analysis for fatigue tracking(ACM, 2018-10) Gökçesu, K.; Ergeneci, M.; Ertan, E.; Alkilani, Abdallah Zaid; Kosmas, P.Muscle fatigue detection and tracking has gained significant attention as the sports science and rehabilitation technologies developed. It is known that muscle fatigue can be evaluated through surface Electromyography (sEMG) sensors, which are portable, non-invasive and applicable for real-time systems. There are plenty of fatigue tracking algorithms, many of which uses frequency, time and time-frequency behaviors of sEMG signals. An example to most commonly used sEMG-based fatigue detection methods can be mean frequency (MNF), median frequency (MDF), zero-crossing rate (ZCR) and continuous wavelet transform (CWT). However, all of these muscle fatigue calculation methods are adversely affected by the dynamically changing sEMG contraction amplitude, since EMG spectrum also demonstrates a shift with the changing signal RMS; powerful contractions lead a shift to high frequency bounds and the opposite happens for the weak. To overcome that, we propose an adaptive algorithm, which learns the effect of contraction power on sEMG power spectral density (PSD) and subtracts that amount of frequency shift from the PSD. Copyright c 2018 ACM.