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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6
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Publication Open Access Evolutionary multiobjective feature selection for sentiment analysis(Institute of Electrical and Electronics Engineers (IEEE), 2021) Pelin Angın; Deniz, Ayça; Department of International Relations; Angın, Merih; Faculty Member; Department of International Relations; College of Administrative Sciences and Economics; 308500Sentiment analysis is one of the prominent research areas in data mining and knowledge discovery, which has proven to be an effective technique for monitoring public opinion. The big data era with a high volume of data generated by a variety of sources has provided enhanced opportunities for utilizing sentiment analysis in various domains. In order to take best advantage of the high volume of data for accurate sentiment analysis, it is essential to clean the data before the analysis, as irrelevant or redundant data will hinder extracting valuable information. In this paper, we propose a hybrid feature selection algorithm to improve the performance of sentiment analysis tasks. Our proposed sentiment analysis approach builds a binary classification model based on two feature selection techniques: an entropy-based metric and an evolutionary algorithm. We have performed comprehensive experiments in two different domains using a benchmark dataset, Stanford Sentiment Treebank, and a real-world dataset we have created based on World Health Organization (WHO) public speeches regarding COVID-19. The proposed feature selection model is shown to achieve significant performance improvements in both datasets, increasing classification accuracy for all utilized machine learning and text representation technique combinations. Moreover, it achieves over 70% reduction in feature size, which provides efficiency in computation time and space.Publication Open Access An information theoretical analysis of broadcast networks and channel routing for FRET-based nanoscale communications(Institute of Electrical and Electronics Engineers (IEEE), 2012) Kuşcu, Murat; Malak, Derya; Akan, Özgür Barış; Faculty Member; College of EngineeringNanoscale communication based on Forster Resonance Energy Transfer (FRET) enables nanomachines to communicate with each other using the excited state of the fluorescent molecules as the information conveyer. In this study, FRET-based nanoscale communication is further extended to realize FRET-based nanoscale broadcast communication with one transmitter and many receiver nanomachines, and the performance of the broadcast channel is analyzed information theoretically. Furthermore, an electrically controllable routing mechanism is proposed exploiting the Quantum Confined Stark Effect (QCSE) observed in quantum dots. It is shown that by appropriately selecting the employed molecules on the communicating nanomachines, it is possible to control the route of the information flow by externally applying electric field in FRET-based nanonetworks.Publication Open Access Low complexity adaptation for reconfigurable intelligent surface-based MIMO systems(Institute of Electrical and Electronics Engineers (IEEE), 2020) Yiğit, Zehra; Altunbaş, İbrahim; Department of Electrical and Electronics Engineering; Başar, Ertuğrul; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 149116Reconfigurable intelligent surface (RIS)-based transmission technology offers a promising solution to enhance wireless communication performance cost-effectively through properly adjusting the parameters of a large number of passive reflecting elements. This letter proposes a cosine similarity theorem-based low-complexity algorithm for adapting the phase shifts of an RIS that assists a multiple-input multiple-output (MIMO) transmission system. A semi-analytical probabilistic approach is developed to derive the theoretical average bit error probability (ABEP) of the system. Furthermore, the validity of the theoretical analysis is supported through extensive computer simulations.Publication Open Access Throughput maximization in discrete rate based full duplex wireless powered communication networks(Wiley, 2020) Şadi, Yalçın; Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Iqbal, Muhammad Shahid; Faculty Member; PhD Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 7211; N/AIn this study, we consider a discrete rate full-duplex wireless powered communication network. We characterize a novel optimization framework for sum throughput maximization to determine the rate adaptation and transmission schedule subject to energy causality and user transmit power. We first formulate the problem as a mixed integer nonlinear programming problem, which is hard to solve for a global optimum in polynomial-time. Then, we investigate the characteristics of the solution and propose a polynomial time heuristic algorithm for rate adaptation and scheduling problem. Through numerical analysis, we illustrate that the proposed scheduling algorithm outperforms the conventional schemes such as equal time allocation half-duplex and on-off transmission schemes for different initial battery levels, hybrid access point transmit power and network densities.Publication Open Access A hybrid architecture for federated and centralized learning(Institute of Electrical and Electronics Engineers (IEEE), 2022) Elbir, Ahmet M.; Papazafeiropoulos, Anastasios K.; Kourtessis, Pandelis; Chatzinotas, Symeon; Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 7211Many of the machine learning tasks rely on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) entailing huge communication overhead. To overcome this, federated learning (FL) has been suggested as a promising tool, wherein the clients send only the model updates to the PS instead of the whole dataset. However, FL demands powerful computational resources from the clients. In practice, not all the clients have sufficient computational resources to participate in training. To address this common scenario, we propose a more efficient approach called hybrid federated and centralized learning (HFCL), wherein only the clients with sufficient resources employ FL, while the remaining ones send their datasets to the PS, which computes the model on behalf of them. Then, the model parameters are aggregated at the PS. To improve the efficiency of dataset transmission, we propose two different techniques: i) increased computation-per-client and ii) sequential data transmission. Notably, the HFCL frameworks outperform FL with up to 20% improvement in the learning accuracy when only half of the clients perform FL while having 50% less communication overhead than CL since all the clients collaborate on the learning process with their datasets.Publication Open Access Minimum energy coding for wireless nanosensor networks(Institute of Electrical and Electronics Engineers (IEEE), 2012) Kocaoğlu, Murat; Akan, Özgür Barış; Faculty Member; College of EngineeringWireless nanosensor networks (WNSNs), which are collections of nanosensors with communication units, can be used for sensing and data collection with extremely high resolution and low power consumption for various applications. In order to realize WNSNs, it is essential to develop energy-efficient communication techniques, since nanonodes are severely energy-constrained. In this paper, a novel minimum energy coding scheme (MEC) is proposed to achieve energy-efficiency in WNSNs. Unlike the existing minimum energy codes, MEC maintains the desired Hamming distance, while minimizing energy, in order to provide reliability. It is analytically shown that, with MEC, codewords can be decoded perfectly for large code distance, if source set cardinality, M is less than inverse of symbol error probability, 1/ps. Performance analysis shows that MEC outperforms popular codes such as Hamming, Reed-Solomon and Golay in average energy per codeword sense.Publication Open Access Uplink achievable rate maximization for reconfigurable intelligent surface aided millimeter wave systems with resolution-adaptive ADCs(Institute of Electrical and Electronics Engineers (IEEE), 2021) Xiu, Yue; Zhao, Jun; Di Renzo, Marco; Sun, Wei; Gui, Guan; Wei, Ning; Department of Electrical and Electronics Engineering; Başar, Ertuğrul; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 149116In this letter, we investigate the uplink of a reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multi-user system. In the considered system, however, problems with hardware cost and power consumption arise when massive antenna arrays coupled with power-demanding analog-todigital converters (ADCs) are employed. To account for practical hardware complexity, we consider that the access point (AP) is equipped with resolution-adaptive analog-to-digital converters (RADCs). We maximize the achievable rate under hardware constraints by jointly optimizing the ADC quantization bits, the RIS phase shifts, and the beam selection matrix. The formulated problem is non-convex. To efficiently tackle this problem, a block coordinated descent (BCD)-based algorithm is proposed. Simulations demonstrate that an RIS can mitigate the hardware loss due to the use of RADCs, and that the proposed BCD-based algorithm outperforms state-of-the-art algorithms.Publication Open Access STAR-RIS-NOMA networks: an error performance perspective(Institute of Electrical and Electronics Engineers (IEEE), 2022) Aldababsa, Mahmoud; Department of Electrical and Electronics Engineering; Başar, Ertuğrul; Khaleel, Aymen; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 149116; N/AThis letter investigates the bit error rate (BER) performance of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) in non-orthogonal multiple access (NOMA) networks. In the investigated network, a STAR-RIS serves multiple non-orthogonal users located on either side of the surface by utilizing the mode switching protocol. We derive the closed-form BER expressions in perfect and imperfect successive interference cancellation cases. Furthermore, asymptotic analyses are also conducted to provide further insights into the BER behavior in the high signal-to-noise ratio region. Finally, the accuracy of our theoretical analysis is validated through Monte Carlo simulations. The obtained results reveal that the BER performance of STAR-RIS-NOMA outperforms that of the classical NOMA system, and STAR-RIS might be a promising NOMA 2.0 solution.Publication Open Access A queueing-theoretical delay analysis for intra-body nervous nanonetwork(Elsevier, 2015) Department of Electrical and Electronics Engineering; Abbasi, Naveed Ahmed; Akan, Özgür Barış; Faculty Member; Department of Electrical and Electronics Engineering; College of EngineeringNanonetworks is an emerging field of study where nanomachines communicate to work beyond their individual limited processing capabilities and perform complicated tasks. The human body is an example of a very large nanoscale communication network, where individual constituents communicate by means of molecular nanonetworks. Amongst the various intra-body networks, the nervous system forms the largest and the most complex network. In this paper, we introduce a queueing theory based delay analysis model for neuro-spike communication between two neurons. Using standard queueing model blocks such as servers, queues and fork-join networks, impulse reception and processing through the nervous system is modeled as arrival and service processes in queues. Simulations show that the response time characteristics of the model are comparable to those of the biological neurons.Publication Open Access Super-mode OFDM with index modulation(Institute of Electrical and Electronics Engineers (IEEE), 2020) Department of Electrical and Electronics Engineering; Doğukan, Ali Tuğberk; Başar, Ertuğrul; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 149116Orthogonal frequency division multiplexing (OFDM) with index modulation (OFDM-IM) appears as a promising multi-carrier waveform candidate for beyond 5G due to its attractive advantages such as operational flexibility and ease of implementation. However, OFDM-IM may not be a proper choice for 5G services such as enhanced mobile broadband (eMBB) since achieving high data rates is challenging because of its null subcarriers. One solution to enhance the spectral efficiency of OFDM-IM is the employment of multiple distinguishable constellations (modes) by also exploiting its null subcarriers for data transmission. This article proposes a novel IM technique called super-mode OFDM-IM (SuM-OFDM-IM), where mode activation patterns (MAPs) and subcarrier activation patterns (SAPs) are jointly selected and conventional data symbols are repetition coded over multiple subcarriers to achieve a diversity gain. For the proposed scheme, a low-complexity detector is designed, theoretical analyses are performed and a bit error rate (BER) upper bound is derived. The performance of the proposed system is also investigated through real-time experiments using a software-defined radio (SDR) based prototype. We show that SuM-OFDM-IM exhibits promising results in terms of spectral efficiency and error performance; thus, appears as a potential candidate for 5G and beyond communication systems.