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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 Incremental construction of rule ensembles using classifiers produced by different class orderings(IEEE, 2016) Yıldız, Olcay Taner; Ulaş, AydınIn this paper, we discuss a novel approach to incrementally construct a rule ensemble. The approach constructs an ensemble from a dynamically generated set of rule classifiers. Each classifier in this set is trained by using a different class ordering. We investigate criteria including accuracy, ensemble size, and the role of starting point in the search. Fusion is done by averaging. Using 22 data sets, floating search finds small, accurate ensembles in polynomial time.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 Automatic modulation classification for mimo systems using fourth-order cumulants(IEEE, 2012) Mühlhaus, Michael S.; Öner, Mustafa Mengüç; Dobre, Octavia Adina; Jkel, Holger U.; Jondral, Friedrich K.Automatic classification of the modulation type of an unknown communication signal is a challenging task, with applications in both commercial and military contexts, such as spectrum surveillance, cognitive radio, and electronic warfare systems. Most of the automatic modulation classification (AMC) algorithms found in the literature assume that the signal of interest has been transmitted using a single antenna. In this paper, a novel AMC algorithm for multiple input multiple output (MIMO) signals is proposed, which employs fourth-order cumulants as features for classification. First, perfect channel state information (CSI) is assumed. Subsequently, a case of more practical relevance is considered, where the channel matrix is unknown and has to be estimated blindly by employing independent component analysis (ICA). The performance of the proposed classification algorithm is investigated through simulations and compared with an average likelihood ratio test (ALRT) which can be considered as optimum in the Bayesian sense, but has a very high computational complexity.Yayın A haar classifier based call number detection and counting method for library books(IEEE, 2018-12-06) Kanburoğlu, Ali Buğra; Tek, Faik BorayCounting and organization of books in libraries is a routine and time-consuming task The task gets more complicated by misplaced books in shelves. In order to solve these problems, we propose an automated visual call number (book-id) detection and counting system in this paper. The method employs a Haar feature-based classifier from OpenCV library and cloud-based OCR system to decode characters from images. To develop and test the method, we have acquired and organized a dataset of 1000 book call numbers. The proposed method has been tested on 20 bookshelves images that contain 233 call numbers, which resulted in a true detection rate of 96% and false detection rate of 1.75 per image. For OCR step, the number of false recognized characters per call number was 0.76.Yayın Unsupervised textile defect detection using convolutional neural networks(Elsevier Ltd, 2021-12) Koulali, Imane; Eskil, Mustafa TanerIn this study, we propose a novel motif-based approach for unsupervised textile anomaly detection that combines the benefits of traditional convolutional neural networks with those of an unsupervised learning paradigm. It consists of five main steps: preprocessing, automatic pattern period extraction, patch extraction, features selection and anomaly detection. This proposed approach uses a new dynamic and heuristic method for feature selection which avoids the drawbacks of initialization of the number of filters (neurons) and their weights, and those of the backpropagation mechanism such as the vanishing gradients, which are common practice in the state-of-the-art methods. The design and training of the network are performed in a dynamic and input domain-based manner and, thus, no ad-hoc configurations are required. Before building the model, only the number of layers and the stride are defined. We do not initialize the weights randomly nor do we define the filter size or number of filters as conventionally done in CNN-based approaches. This reduces effort and time spent on hyper-parameter initialization and fine-tuning. Only one defect-free sample is required for training and no further labeled data is needed. The trained network is then used to detect anomalies on defective fabric samples. We demonstrate the effectiveness of our approach on the Patterned Fabrics benchmark dataset. Our algorithm yields reliable and competitive results (on recall, precision, accuracy and f1-measure) compared to state-of-the-art unsupervised approaches, in less time, with efficient training in a single epoch and a lower computational cost.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 A new approach for named entity recognition(IEEE, 2017) Ertopçu, Burak; Kanburoğlu, Ali Buğra; Topsakal, Ozan; Açıkgöz, Onur; Gürkan, Ali Tunca; Özenç, Berke; Çam, İlker; Avar, Begüm; Ercan, Gökhan; Yıldız, Olcay TanerMany sentences create certain impressions on people. These impressions help the reader to have an insight about the sentence via some entities. In NLP, this process corresponds to Named Entity Recognition (NER). NLP algorithms can trace a lot of entities in the sentence like person, location, date, time or money. One of the major problems in these operations are confusions about whether the word denotes the name of a person, a location or an organisation, or whether an integer stands for a date, time or money. In this study, we design a new model for NER algorithms. We train this model in our predefined dataset and compare the results with other models. In the end we get considerable outcomes in a dataset containing 1400 sentences.Yayın On the feature extraction in discrete space(Elsevier Sci Ltd, 2014-05) Yıldız, Olcay TanerIn many pattern recognition applications, feature space expansion is a key step for improving the performance of the classifier. In this paper, we (i) expand the discrete feature space by generating all orderings of values of k discrete attributes exhaustively, (ii) modify the well-known decision tree and rule induction classifiers (ID3, Quilan, 1986 [1] and Ripper, Cohen, 1995 [2]) using these orderings as the new attributes. Our simulation results on 15 datasets from UCI repository [3] show that the novel classifiers perform better than the proper ones in terms of error rate and complexity.
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