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Yayın Linear expansions for frequency selective channels in OFDM(Elsevier GMBH, 2006) Şenol, Habib; Çırpan, Hakan Ali; Panayırcı, ErdalModeling the frequency selective fading channels as random processes, we employ a linear expansion based on the Karhumen-Loeve (KL) series representation involving a complete set of orthogonal deterministic vectors with a corresponding uncorrelated random coefficients. Focusing on OFDM transmissions through frequency selective fading, this paper pursues a computationally efficient, pilot-aided linear minimum mean square error (MMSE) uncorrelated KL series expansion coefficients estimation algorithm. Based on such an expansion, no matrix inversion is required in the proposed MMSE estimator. Moreover, truncation in the linear expansion of channel is achieved by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We first exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE. We also provide performance analysis results studying the influence of the effect of SNR and correlation mismatch on the estimator performance. Simulation results confirm our theoretical results and illustrate that the proposed algorithm is capable of tracking fast fading and improving performance.Yayın ISIKUN at the FinCausal 2020: Linguistically informed machine-learning approach for causality identification in financial documents(Association for Computational Linguistics (ACL), 2020) Özenir, Hüseyin Gökberk; Karadeniz, İlknurThis paper presents our participation to the FinCausal-2020 Shared Task whose ultimate aim is to extract cause-effect relations from a given financial text. Our participation includes two systems for the two sub-tasks of the FinCausal-2020 Shared Task. The first sub-task (Task-1) consists of the binary classification of the given sentences as causal meaningful (1) or causal meaningless (0). Our approach for the Task-1 includes applying linear support vector machines after transforming the input sentences into vector representations using term frequency-inverse document frequency scheme with 3-grams. The second sub-task (Task-2) consists of the identification of the cause-effect relations in the sentences, which are detected as causal meaningful. Our approach for the Task-2 is a CRF-based model which uses linguistically informed features. For the Task-1, the obtained results show that there is a small difference between the proposed approach based on linear support vector machines (F-score 94%), which requires less time compared to the BERT-based baseline (F-score 95%). For the Task-2, although a minor modifications such as the learning algorithm type and the feature representations are made in the conditional random fields based baseline (F-score 52%), we have obtained better results (F-score 60%). The source codes for the both tasks are available online (https://github.com/ozenirgokberk/FinCausal2020.git/).Yayın A robust localization framework to handle noisy measurements in wireless sensor networks(IEEE, 2009-09-14) Erten, Cesim; Karataş, ÖmerWe construct a robust localization framework to handle noisy measurements in wireless sensor networks. Traditionally many approaches employ the distance information gathered from ranging devices of the sensor nodes to achieve localization. However the measurements of these devices may contain noise both as hardware noise and as environmental noise due to the employment conditions of the network. It Is necessary to provide a general framework that handles such a noise in data and yet still be applicable within several localization algorithms. In order to handle noise in distance measurements, our framework utilizes convex constraints and confidence intervals of a random variable. At the end of the localization process nodes are assigned to a set of feasible regions with corresponding probabilities. The accuracy of the localization can be adjusted and the framework can easily be embedded to work within previously suggested localization algorithms.Yayın Joint channel tracking and symbol detection for OFDM systems with Kalman filtering(Urban & Fischer Verlag, 2003) Şen, Adnan; Çırpan, Hakan Ali; Panayırcı, ErdalThis paper proposes a new joint channel tracking and symbol detection scheme for pilot symbol-assisted OFDM systems in multipath fading. The proposed scheme uses Kalman filters for both channel tracking and subsequent equalization which are combined in the coupled estimator structure. Modelling the multipath fading channel as random processes to describe channel variations in a general AR framework lends itself to a state-space representation that enables the application of Kalman filtering for the tracking of channel variations. However, the proposed tracking algorithm requires knowledge of the transmitted symbols. This implies that an iterative method should be sought to obtain alternatively either channel or transmitted symbols. To compose the coupled estimator structure, a linear Kalman filter equalizer with the corresponding state-space model is therefore proposed for the detection of transmitted symbols. With the proposed Kalman filters, iterative structure is utilized to decode transmitted symbols and subsequently to track channel parameters. Finally, the performance of the proposed method is investigated through the experimental results.Yayın Chunking in Turkish with conditional random fields(Springer-Verlag, 2015-04-14) Yıldız, Olcay Taner; Solak, Ercan; Ehsani, Razieh; Görgün, OnurIn this paper, we report our work on chunking in Turkish. We used the data that we generated by manually translating a subset of the Penn Treebank. We exploited the already available tags in the trees to automatically identify and label chunks in their Turkish translations. We used conditional random fields (CRF) to train a model over the annotated data. We report our results on different levels of chunk resolution.Yayın All-words word sense disambiguation for Turkish(IEEE, 2017) Açıkgöz, Onur; Gürkan, Ali Tunca; Ertopçu, Burak; Topsakal, Ozan; Özenç, Berke; Kanburoğlu, Ali Buğra; Çam, İlker; Avar, Begüm; Ercan, Gökhan; Yıldız, Olcay TanerIdentifying the sense of a word within a context is a challenging problem and has many applications in natural language processing. This assignment problem is called word sense disambiguation(WSD). Many papers in the literature focus on English language and data. Our dataset consists of 1400 sentences translated to Turkish from the Penn Treebank Corpus. This paper seeks to address and discuss 6 different feature extraction methods and its classification performances using C4.5, Random Forests, Rocchio, Naive Bayes, KNN, Linear and multilayer Perceptron. This paper calls into question how the described features perform on a morphologically rich language (Turkish) with several classifiers.Yayın Adaptive Kalman receiver for OFDM systems with transmit diversity in mobile wireless channels(Walter De Gruyter GMBH, 2004-12) Şen, Adnan; Çırpan, Hakan Ali; Panayırcı, ErdalA new joint channel tracking and symbol detection scheme is proposed in this paper for pilot symbol assisted transmit diversity OFDM systems by exploiting the correlation of the adjacent subchannels. Modelling the channel frequency response of every subcarrier corresponding to each transmit antenna as random processes, we employ Kalman filters for both channel tracking and subsequent decoding with diversity gain. Among different stochastic models, the AR model is adopted herein for channel dynamics. Since the proposed adaptive receiver uses two Kalman filters to track the variations of the channel and subsequently to detect the information symbols, they are combined in the coupled receiver structure. Finally the performance of the proposed method is studied through experimental results.
