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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 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 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 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.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 Tree Ensembles on the induced discrete space(Institute of Electrical and Electronics Engineers Inc., 2016-05) Yıldız, Olcay TanerDecision trees are widely used predictive models in machine learning. Recently, K-tree is proposed, where the original discrete feature space is expanded by generating all orderings of values of k discrete attributes and these orderings are used as the new attributes in decision tree induction. Although K-tree performs significantly better than the proper one, their exponential time complexity can prohibit their use. In this brief, we propose K-forest, an extension of random forest, where a subset of features is selected randomly from the induced discrete space. Simulation results on 17 data sets show that the novel ensemble classifier has significantly lower error rate compared with the random forest based on the original feature space.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 similarity based unsupervised technique for training convolutional filters(IEEE, 2023-05-17) Erkoç, Tuğba; Eskil, Mustata TanerAchieving satisfactory results with Convolutional Neural Networks (CNNs) depends on how effectively the filters are trained. Conventionally, an appropriate number of filters is carefully selected, the filters are initialized with a proper initialization method and trained with backpropagation over several epochs. This training scheme requires a large labeled dataset, which is costly and time-consuming to obtain. In this study, we propose an unsupervised approach that extracts convolutional filters from a given dataset in a self-organized manner by processing the training set only once without using backpropagation training. The proposed method allows for the extraction of filters from a given dataset in the absence of labels. In contrast to previous studies, we no longer need to select the best number of filters and a suitable filter weight initialization scheme. Applying this method to the MNIST, EMNIST-Digits, Kuzushiji-MNIST, and Fashion-MNIST datasets yields high test performances of 99.19%, 99.39%, 95.03%, and 90.11%, respectively, without applying backpropagation training or using any preprocessed and augmented data.Yayın Hierarchical quantization indexing for wavelet and wavelet packet image coding(Elsevier Science BV, 2010-02) Ateş, Hasan Fehmi; Tamer, EnginIn this paper, we introduce the quantization index hierarchy, which is used for efficient coding of quantized wavelet and wavelet packet coefficients. A hierarchical classification map is defined in each wavelet subband, which describes the quantized data through a series of index classes. Going from bottom to the top of the tree, neighboring coefficients are combined to form classes that represent some statistics of the quantization indices of these coefficients. Higher levels of the tree are constructed iteratively by repeating this class assignment to partition the coefficients into larger Subsets. The class assignments are optimized using a rate-distortion cost analysis. The optimized tree is coded hierarchically from top to bottom by coding the class membership information at each level of the tree. Context-adaptive arithmetic coding is used to improve coding efficiency. The developed algorithm produces PSNR results that are better than the state-of-art wavelet-based and wavelet packet-based coders in literature.
