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Bölüm: Batman Üniversitesi Mühendislik - Mimarlık Fakültesi Bilgisayar Mühendisliği Bölümü × Konu: Aşırı Öğrenme Makineleri ×

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  • Küçük Resim
    Öğe
    İki kanal yüzey EMG işareti ile el aç/kapa ve el parmaklarının sınıflandırılması
    (IEEE, 2017-11-02) Sezgin, Necmettin; Ertuğrul, Ömer Faruk; Tekin, Ramazan; Tağluk, Mehmet Emin
    In this study, two-channel surface electromyogram (sEMG) signals were used to classify hand open/close with fingers. The bispectrum analysis of the sEMG signal recorded with surface electrodes near the region of the muscle bundles on the front and back of the forearm was classified by extreme learning machines (ELM) based on phase matches in the EMG signal. EMG signals belonging to 17 persons, 8 males and 9 females, with an average age of 24 were used in the study. The fingers were classified using ELM algorithm with 94.60% accuracy in average. From the information obtained through this study, it seems possible to control finger movements and hand opening/closing by using muscle activities of the forearm which we hope to lead to control of intelligent prosthesis hands with high degree of freedom.
  • Küçük Resim Yok
    Öğe
    Fingerprint recognition system based on law’s texture energy measures with extreme learning machines
    (INESEC, 2017) Çalışkan, Abidin; Acar, Emrullah; Budak, Cafer
    Fingerprint recognition systems are one of the most popular biometric systems used in many areas, including prisons, border controls, educational institutions and forensic medicine. This paper presents a new approach based on the texture features for fingerprint recognition system. The dataset which employed in this study is obtained from the Hong Kong Polytechnic University High-ResolutionFingerprint database. The proposed system was implemented in two basic stages. Firstly, the texture feature vectors were extracted from the images by using Law’s Texture Energy Measures (TEM) and totally 9 parameters were extracted for each image as a feature vector. Then, the obtained feature vectors were classified by using Extreme Learning Machines (ELM) model. Finally, the average performance of the proposed system was computed according to different tuning parameters and the highest accuracy rate was observed as 83.92 % among the all system architectures.