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Yayın On the extraction of the channel allocation information in spectrum pooling systems(IEEE, 2007-04) Öner, Mustafa Mengüç; Jondral, Friedrich K.The spectrum pooling strategy allows a license owner to share a part of his licensed spectrum with a secondary wireless system (the rental system, RS) during its idle times. The coexistence of two mobile systems on the same frequency band poses many new challenges, one of which is the reliable extraction of the channel allocation information (CAI), i.e. the channel occupation of the licensed system (LS). This paper presents a strategy for the extraction of the CAI based on exploiting the distinct cyclostationary characteristics of the LS and RS signals and demonstrates, via simulations, its application on a specific spectrum pooling scenario, where the LS is a GSM network and the RS is an OFDM based WLAN system.Yayın Generation of optimum signature base sequences for speech signals(IEEE, 2000) Yarman, Bekir Sıddık Binboğa; Akdeniz, RafetIn our previous publications [1-6], we proposed a novel method to represent signals in terms of, so called, "Signature Base Functions-SBF' which were extracted from the physical features of the waveform under consideration. In [1-6], SBF were determined in ad-hoc manner, which requires tedious search process, and they were not orthogonal. Furthermore, optimality of SBF was in question. In this work however, we suggest a well-organised procedure to generate "Optimum Orthogonal Signature Base Functions-OSBF' for selected waveforms, which in turn provides excellent means for signal representations. II is shown that the new method of signal representation, which is based on OSBF, requires less computation time with substantial signal compression and results in efficient speaker dependent recognition.Yayın Generative and discriminative methods using morphological information for sentence segmentation of Turkish(IEEE-INST Electrical Electronics Engineers Inc, 2009-07) Güz, Ümit; Favre, Benoit; Hakkani Tür, Dilek; Tür, GökhanThis 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 models that take advantage of lexical information among others. However, Turkish has a productive morphology that generates a very large vocabulary, making the task much harder. In this paper, we introduce a new set of morphological features, extracted from words and their morphological analyses. We also extend the established method of hidden event language modeling (HELM) to factored hidden event language modeling (fHELM) to handle morphological information. In order to capture non-lexical information, we extract a set of prosodic features, which are mainly motivated from our previous work for other languages. We then employ discriminative classification techniques, boosting and conditional random fields (CRFs), combined with fHELM, for the task of Turkish sentence segmentation.Yayın Bir otomatik hedef tanıma algoritmasının geliştirilmesi(IEEE, 2013-04-24) Aldemir, Erdoğan; Yıldız, Nerhun; Tavşanoğlu, Ahmet VedatBu bildiri kapsamında bir Otomatik Hedef Tanıma (OHT) sistemi ele alınarak geliştirilmiş ve geliştirilen sistemin Matlab benzetimleri bildiride sunulmuştur. İkinci olarak OHT sistemlerinde kullanılan ve literatürde sıkça karşılaşılan klasik kenar belirleme algoritmalarının dışında yeni bir kenar belirleme algoritması önerilmiştir. Son olarak da Freeman zincir kodlamasının özellik çıkartma aşamasında kullanılabileceği gösterilmiştir. İlgili sistemin sınıflandırma ve karar verme aşaması hariç tamamı değişik test görüntüleri üzerinde denenmiş ve insan gözüne hitap edebilecek seviyede başarılı sonuçlar elde edilmiştir. İleride sınıflandırma aşamasının da gerçeklenmesi ile tasarlanan OHT sisteminin başarımının daha tarafsız bir ölçüt ile test edilmesi hedeflenmektedir. Ayrıca sistemin donanıma yönelik olarak optimizasyonu ile bir Field Programmable Gate Array (FPGA) gerçeklemesinin yapılması hedefler arasındadır.Yayın Biometric identification using fingertip electrocardiogram signals(Springer London Ltd, 2018-07) Güven, Gökhan; Gürkan, Hakan; Güz, ÜmitIn 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 high-sensitive, dry-contact, portable, user-friendly, inexpensive, and does not require using conventional components which are cumbersome and irritating such as wet adhesive Ag/AgCl electrodes. One of the other advantages of this device is to make it possible to record and use the lead-1 ECG signal easily in any condition and anywhere incorporating with any platform to use for advanced applications such as biometric recognition and clinical diagnostics. Furthermore, we proposed a biometric identification method based on combining autocorrelation and discrete cosine transform-based features, cepstral features, and QRS beat information. The proposed method was evaluated on three fingertip ECG signal databases recorded by utilizing the proposed device. The experimental results demonstrate that the proposed biometric identification method achieves person recognition rate values of 100% (30 out of 30), 100% (45 out of 45), and 98.33% (59 out of 60) for 30, 45, and 60 subjects, respectively.Yayın Extension of conventional co-training learning strategies to three-view and committee-based learning strategies for effective automatic sentence segmentation(IEEE, 2018) Dalva, Doğan; Güz, Ümit; Gürkan, HakanThe 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. We propose three-view and committee-based learning strategies incorporating with co-training algorithms with agreement, disagreement, and self-combined learning strategies using prosodic, lexical and morphological information. We compare experimental results of proposed three-view and committee-based learning strategies to other semi-supervised learning strategies in the literature namely, self-training and co-training with agreement, disagreement, and self-combined strategies. The experiment results show that sentence segmentation performance can be highly improved using multi-view learning strategies that we propose since data sets can be represented by three redundantly sufficient and disjoint feature sets. We show that the proposed strategies substantially improve the average performance when only a small set of manually labeled data is available for Turkish and English spoken languages, respectively.Yayın Optimisation of pedestrian detection system using FPGA-CPU hybrid implementation for vehicle industry(Inderscience Enterprises Ltd., 2019) Özcan, Ahmet Remzi; Tavşanoǧlu, Ahmet VedatImproved image processing and developing technologies are rapidly expanding the application areas of image processing systems. In recent years, pedestrian detection systems have become one of the major safety technologies used in the automotive industry. This paper presents an optimised real-time pedestrian detection system using an FPGA-CPU based hybrid design. The histograms of oriented gradients (HOG) algorithm, which is extensively used for feature extraction in pedestrian detection applications, was implemented on a low-end FPGA. In the study, the original HOG descriptors are designed in low complexity without sacrificing performance. The obtained features were classified on a low-power single board computer with support vector machine (SVM). Tests with the INRIA pedestrian database show that the proposed model has high potential for use as a real-time low-cost pedestrian detection system in practice.Yayın Derin öznitelikler ile anlambilimsel görüntü bölütleme(Institute of Electrical and Electronics Engineers Inc., 2018-07-05) Sünetci, Sercan; Ateş, Hasan FehmiDerin evrişimsel sinir ağları (ESA) pek çok sınıflandırma probleminde olduğu gibi anlambilimsel görüntü bölütlemede de çok ciddi başarı göstermiştir. Fakat derin ağların eğitilmesi hem zaman alıcıdır hem de geniş bir eğitim veri kümesine ihtiyaç duymaktadır. Bir veri kümesinde eğitilen ağın başka bir görev ya da veri kümesine uygulanabilmesi için transfer öğrenme ile yeniden eğitilmesi gerekmektedir. Transfer öğrenmeye alternatif olarak ağ katmanlarından çıkarılan öznitelik vektörleri doğrudan sınıflandırma amaçlı kullanılabilir. Bu bildiride genel ESA mimarilerinden elde edilen özniteliklerin eğitim gerektirmeyen bir görüntü etiketleme yönteminde kullanılmasının sınıflandırma başarımına katkısı incelenmiştir. Derin ağlarda ‘öğrenilmiş’ öznitelikler ile SIFT gibi ‘el yapımı’ özniteliklerin birlikte kullanılmasının etiketleme doğruluğunu artırdığı gösterilmiştir. Varolan ön eğitimli ağların kullanılması sayesinde önerilen yaklaşım herhangi bir veri kümesinde yeniden eğitime gerek olmadan kolayca uygulanabilmektedir. Önerilen yöntem iki veri kümesinde test edilmiş ve etiketleme doğruluğu benzer yöntemlerle karşılaştırmalı olarak sunulmuştur.Yayın Feature extraction in shape recognition using segmentation of the boundary curve(Elsevier Science BV, 1997-10) Özuğur, Timuçin; Denizhan, Yağmur; Panayırcı, ErdalWe present a new method for feature extraction of two-dimensional shape information based on segmentation of the boundary curve. This approach partitions closed shapes into segments and finds their angular spans. The number of segments and the angular spans form the first two feature parameters of a given shape. Fourier coefficients of all segments constitute the final feature parameters. The algorithm renders the shapes independent of scale, rotation and translation, The main advantage of this method is to speed up substantially the recognition process of the shapes, mainly because it is possible to design the classification rule in a hierarchical way. It is therefore suitable for objects to be sorted in a factory environment where the silhouette boundary supplies sufficient information for identification.Yayın A novel biometric authentication approach using electrocardiogram signals(IEEE, 2013) Gürkan, Hakan; Güz, Ümit; Yarman, Bekir Sıddık BinboğaIn this work, we present a novel biometric authentication approach based on combination of AC/DCT features, MFCC features, and QRS beat information of the ECG signals. The proposed approach is tested on a subset of 30 subjects selected from the PTB database. This subset consists of 13 healthy and 17 non-healthy subjects who have two ECG records. The proposed biometric authentication approach achieves average frame recognition rate of %97.31 on the selected subset. Our experimental results imply that the frame recognition rate of the proposed authentication approach is better than that of ACDCT and MFCC based biometric authentication systems, individually.
