Yazar "0000-0002-4597-0954" için listeleme
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Biometric identification using fingertip electrocardiogram signals
Güven, Gökhan; Gürkan, Hakan; Güz, Ümit (Springer London Ltd, 2018-07)In this research work, we present a newly fingertip electrocardiogram (ECG) data acquisition device capable of recording the lead-1 ECG signal through the right- and left-hand thumb fingers. The proposed device is ... -
Bürünsel, sözcüksel ve biçimbilgisel bilgiyi kullanan co-training ile Türkçe konuşma dilinin otomatik cümle bölütlemesi
Güz, Ümit; Gürkan, Hakan (Tübitak, 2015-04)Co-training, web sayfası sınıflandırması, kelime anlam açıklaştırma ve adlandırılmış varlık tanıma gibi pek çok sınıflandırma işlevinde başarı ile kullanılan oldukça etkili bir makine öğrenme algoritmasıdır. Co-training, ... -
Cascaded model adaptation for dialog act segmentation and tagging
Güz, Ümit; Tür, Gökhan; Hakkani Tür, Dilek; Cuendet, Sebastien (Elsevier Ltd, 2010-04)There are many speech and language processing problems which require cascaded classification tasks. While model adaptation has been shown to be useful in isolated speech and language processing tasks, it is not clear what ... -
Compression of the biomedical images using quadtree-based partitioned universally classified energy and pattern blocks
Gezer, Murat; Gargari, Sepideh Nahavandi; Güz, Ümit; Gürkan, Hakan (Springer London, 2019-03-15)In this work, an efficient low bit rate image coding/compression method based on the quadtree-based partitioned universally classified energy and pattern building blocks (QB-UCEPB) is introduced. The proposed method combines ... -
Compression of the CT images using classified energy and pattern blocks
Gökbay, İnci Zaim; Gezer, Murat; Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık Binboğa (IEEE, 2013)In this work, a new biomedical image compression method is proposed based on the classified energy and pattern blocks (CEPB). CEPB based compression method is specifically applied on the Computed Tomography (CT) images and ... -
EEG signal compression based on classified signature and envelope vector sets
Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık Binboğa (Wiley, 2009-03)In this paper, a novel method to compress electroencephalogram (EEG) signal is proposed. The proposed method is based on the generation process of the classified signature and envelope vector sets (CSEVS), which employs ... -
EEG signal compression based on classified signature and envelope vector sets
Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık Binboğa (IEEE Computer Society, 2007)In this paper, a novel method to compress ElectroEncephaloGram (EEG) Signal is proposed. The proposed method is based on the generation Classified Signature and Envelope Vector Sets (CSEVS) by using an effective k-means ... -
Effective semi-supervised learning strategies for automatic sentence segmentation
Dalva, Doğan; Güz, Ümit; Gürkan, Hakan (Elsevier Science BV, 2018-04-01)The primary objective of sentence segmentation process is to determine the sentence boundaries of a stream of words output by the automatic speech recognizers. Statistical methods developed for sentence segmentation requires ... -
An efficient ECG data compression technique based on predefined signature and envelope vector banks
Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık Binboğa (IEEE, 2005)In this paper, a new method to compress ElectroCardioGram (ECG) Signal by means of "Predefined Signature and Envelope Vector Banks-PSEVB" is presented. In this work, on a frame basis, any ECG signal is modeled by multiplying ... -
Elektroensefalogram (EEG) işaretlerinin sıkıştırılmasında özgün bir yaklaşım
Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık Binboğa (IEEE, 2008)Bu çalışmada, Elektroensefalogram(EEG) işaretlerinin yeniden oluşturulmasına yönelik olarak yeni bir yöntem sunulmaktadır. Sunulan yöntem, etkin bir k-ortalamalı sınıflandırma algoritması kullanılarak Sınıflandırılmış Temel ... -
Extension of conventional co-training learning strategies to three-view and committee-based learning strategies for effective automatic sentence segmentation
Dalva, Doğan; Güz, Ümit; Gürkan, Hakan (IEEE, 2018)The objective of this work is to develop effective multi-view semi-supervised machine learning strategies for sentence boundary classification problem when only small sets of sentence boundary labeled data are available. ... -
Extraction and comparison of various prosodic feature sets on sentence segmentation task for Turkish broadcast news data
Dalva, Doğan; Revidi, İzel D.; Güz, Ümit; Gürkan, Hakan (IEEE, 2014)In this work, prosodic features of the Turkish Broadcast News (BN) data are extracted using an open source prosodic feature extraction tool based on Praat. The profiles and effectiveness of these features are also investigated ... -
Generative and discriminative methods using morphological information for sentence segmentation of Turkish
Güz, Ümit; Favre, Benoit; Hakkani Tür, Dilek; Tür, Gökhan (IEEE-INST Electrical Electronics Engineers Inc, 2009-07)This paper presents novel methods for generative, discriminative, and hybrid sequence classification for segmentation of Turkish word sequences into sentences. In the literature, this task is generally solved using statistical ... -
Model adaptation for dialog act tagging
Tür, Gökhan; Güz, Ümit; Hakkani Tür, Dilek (IEEE, 2006)In this paper, we analyze the effect of model adaptation for dialog act tagging. The goal of adaptation is to improve the performance of the tagger using out-of-domain data or models. Dialog act tagging aims to provide a ... -
Modeling of electrocardiogram signals using predefined signature and envelope vector sets
Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık Binboğa (Hindawi Publishing Corporation, 2007)A novel method is proposed to model ECG signals by means of "predefined signature and envelope vector sets (PSEVS)." On a frame basis, an ECG signal is reconstructed by multiplying three model parameters, namely, predefined ... -
Multi-view semi-supervised learning for dialog act segmentation of speech
Güz, Ümit; Cuendet, Sebastien; Hakkani Tür, Dilek; Tür, Gökhan (IEEE-INST Electrical Electronics Engineers Inc, 2010-02)Sentence segmentation of speech aims at determining sentence boundaries in a stream of words as output by the speech recognizer. Typically, statistical methods are used for sentence segmentation. However, they require ... -
A new algorithm for high speed speech and audio coding
Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık Binboğa (IEEE, 2007)In this work, a new mathematical modeling approach is proposed for the representation of the speech and audio signals. This approach is based on the generation of the so called Predefined Signature Sequence (PSS) and ... -
A new coding method for speech and audio signals
Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık Binboğa (IEEE, 2005)In this paper a new representation or modeling method of speech signals is introduced. The proposed method is based on the generation of the so-called Predefined Signature S={S R } and Envelope vector E={E K } Sets (PSEVS). ... -
A new method to represent speech signals via predefined signature and envelope sequences
Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık Binboğa (Hindawi Publishing Corporation, 2007)A novel systematic procedure referred to as "SYMPES" to model speech signals is introduced. The structure of SYMPES is based on the creation of the so-called predefined "signature S = {S(R)(n)} and envelope E = {E(K) (n)}" ... -
A New speech coding algorithm using zero cross and phoneme based SYMPES
Şişman, Burak; Güz, Ümit; Gürkan, Hakan; Yarman, Bekir Sıddık Binboğa (IEEE, 2013-07-11)In this work, a new low bit rate hybrid speech coding approach which combines the benefits of the SYMPES (Systematic Procedure for Predefined Envelope and Signature Sequences) and zero cross and phoneme based segmentation ...