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Publication Open Access A cross-layer design for QoS support in cognitive radio sensor networks for smart grid applications(Institute of Electrical and Electronics Engineers (IEEE), 2012) Güngör, Vehbi C.; Shah, Ghalib Asadullah; Akan, Özgür Barış; Faculty Member; College of EngineeringIn this paper, we propose a cross-layer design to meet the QoS requirements for smart grids employing the cognitive radio sensor networks for their control and monitoring operations. Existing routing protocols pertaining to QoS support are not able to simultaneously handle traffic of different characteristics present in smart grids. Therefore, considering the traffic heterogeneity of smart grid applications exhibiting diverse QoS requirements, a set of priority classes is defined in order to differentiate the traffic for the respective service. Specifically, the problem is formulated as a weighted network utility maximization (WNUM) whose objective is to maximize the weighted sum of flows service. A cross-layer heuristic solution is provided to solve the utility optimization problem by performing joint routing, dynamic spectrum allocation and medium access. Performance of the proposed protocol is evaluated using ns-2, which shows that the number of flows belonging to each class are served according to their weight fraction with their respective data rate, latency and reliability requirement.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 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 Automatic detection of road types from the third military mapping survey of Austria-Hungary historical map series with deep convolutional neural networks(Institute of Electrical and Electronics Engineers (IEEE), 2021) Department of History; Kabadayı, Mustafa Erdem; Can, Yekta Said; Gerrits, Petrus Johannes; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267; N/A; N/AWith the increased amount of digitized historical documents, information extraction from them gains pace. Historical maps contain valuable information about historical, geographical and economic aspects of an era. Retrieving information from historical maps is more challenging than processing modern maps due to lower image quality, degradation of documents and the massive amount of non-annotated digital map archives. Convolutional Neural Networks (CNN) solved many image processing challenges with great success, but they require a vast amount of annotated data. For historical maps, this means an unprecedented scale of manual data entry and annotation. In this study, we first manually annotated the Third Military Mapping Survey of Austria-Hungary historical map series conducted between 1884 and 1918 and made them publicly accessible. We recognized different road types and their pixel-wise positions automatically by using a CNN architecture and achieved promising results.
Publication Metadata only Automatic detection of road types from the third military mapping survey of Austria-Hungary historical map series with deep convolutional neural networks(IEEE-inst Electrical Electronics Engineers inc, 2021) N/A; N/A; Department of History; Can, Yekta Said; Gerrits, Petrus Johannes; Kabadayı, Mustafa Erdem; Resercher; Master Student; Faculty Member; Department of History; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; N/A; 33267With the increased amount of digitized historical documents, information extraction from them gains pace. Historical maps contain valuable information about historical, geographical and economic aspects of an era. Retrieving information from historical maps is more challenging than processing modern maps due to lower image quality, degradation of documents and the massive amount of non-annotated digital map archives. Convolutional Neural Networks (CNN) solved many image processing challenges with great success, but they require a vast amount of annotated data. for historical maps, this means an unprecedented scale of manual data entry and annotation. in this study, we first manually annotated the Third Military Mapping Survey of austria-Hungary historical map series conducted between 1884 and 1918 and made them publicly accessible. We recognized different road types and their pixel-wise positions automatically by using a CNN architecture and achieved promising results.Publication Open Access Channel sensing in molecular communications with single type of ligand receptors(Institute of Electrical and Electronics Engineers (IEEE), 2019) Kuşcu, Murat; Department of Electrical and Electronics Engineering; Akan, Özgür Barış; Faculty Member; Department of Electrical and Electronics Engineering; College of EngineeringMolecular communication (MC) uses molecules as information carriers between nanomachines. MC channel in practice can be crowded with different types of molecules, i.e., ligands, which can have similar binding properties causing severe cross-talk on ligand receptors. Simultaneous sensing of multiple ligand types provides opportunities for eliminating interference of external molecular sources and multi-user interference, and developing new multiple access techniques for MC nanonetworks. In this paper, we investigate channel sensing methods that use only a single type of receptors and exploit the amount of time receptors stay bound and unbound during ligand-receptor binding reaction to concurrently estimate the concentration of multiple types of ligands. We derive the Cramer-Rao Lower Bound for multi-ligand estimation, and propose practical and low-complexity suboptimal estimators for channel sensing. We analyze the performance of the proposed methods in terms of normalized mean squared error (NMSE), and show that they can efficiently estimate the concentration of ligands up to 10 different types with an average NMSE far below 10(-2). Lastly, we propose a synthetic receptor design based on modified kinetic proofreading scheme to sample the unbound and bound time durations, and a chemical reaction network to perform the required computations in synthetic cells.Publication Open Access Cyclic-prefixed single-carrier transmission with reconfigurable intelligent surfaces(Institute of Electrical and Electronics Engineers (IEEE), 2022) Li, Q.; Wen, M.; Alexandropoulos, G.C.; Kim, K.J.; Poor H.V.; Department of Electrical and Electronics Engineering; Başar, Ertuğrul; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 149116In this paper, a cyclic-prefixed single-carrier (CPSC) transmission scheme with phase shift keying (PSK) signaling is presented for broadband wireless communications systems empowered by a reconfigurable intelligent surface (RIS). In the proposed CPSC-RIS, the RIS is configured according to the transmitted PSK symbols such that different cyclically delayed versions of the incident signal are created by the RIS to achieve cyclic delay diversity. A practical and efficient channel estimator is developed for CPSC-RIS and the mean square error of the channel estimation is expressed in closed-form. We analyze the bit error rate (BER) performance of CPSC-RIS over frequency-selective Nakagami-m fading channels. An upper bound on the BER is derived by assuming maximum-likelihood detection. Our simulation results in terms of BER corroborate the performance analysis and the superiority of CPSC-RIS over the conventional CPSC without an RIS and orthogonal frequency division multiplexing with an RIS.Publication Open Access Delay-sensitive and multimedia communication in cognitive radio sensor networks(Elsevier, 2012) Güngör, V. Çağrı; Biçen, Ahmet Ozan; Akan, Özgür Barış; Master Student; Faculty Member; College of EngineeringMultimedia and delay-sensitive data applications in cognitive radio sensor networks (CRSN) require efficient real-time communication and dynamic spectrum access (DSA) capabilities. This requirement poses emerging problems to be addressed in inherently resource-constrained sensor networks, and needs investigation of CRSN challenges with real-time communication requirements. In this paper, the main design challenges and principles for multimedia and delay-sensitive data transport in CRSN are introduced. The existing transport protocols and algorithms devised for cognitive radio ad hoc networks and wireless sensor networks (WSN) are explored from the perspective of CRSN paradigm. Specifically, the challenges for real-time transport in CRSN are investigated in different spectrum environments of smart grid, e.g., 500 kV substation, main power room and underground network transformer vaults. Open research issues for the realization of energy-efficient and real-time transport in CRSN are also presented. Overall, the performance evaluations provide valuable insights about real-time transport in CRSN and guide design decisions and trade-offs for CRSN applications in smart electric power grid.Publication Metadata only Engine compartment UWB channel model for intravehicular wireless sensor networks(IEEE-Inst Electrical Electronics Engineers Inc, 2014) Department of Computer Engineering; N/A; Department of Electrical and Electronics Engineering; Demir, Utku; Baş, Celalettin Ümit; Ergen, Sinem Çöleri; Undergraduate Student; Master Student; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 7211Intravehicular wireless sensor network (IVWSN) is a cutting edge research topic that delivers cost reduction, assembly, and maintenance efficiency by removing the wiring harnesses within the vehicle and enables the integration of new sensors into the locations inside a vehicle where cable connection is not possible. Providing energy efficiency through the low-duty-cycle operation and high reliability by exploiting the large bandwidth, ultrawideband (UWB) has been determined to be the most appropriate technology for IVWSNs. We investigate the UWB channel model for IVWSNs within the engine compartment of a vehicle by collecting an extensive amount of data for 19 x 19 links for different types and conditions of the vehicle. These include a Fiat Linea with engine off, Fiat Linea with engine on, and Peugeot Bipper with engine off. The path-loss exponent is estimated to be around 3.5 without exhibiting much variation when the engine is turned on and for different types of vehicles. The power variation around the expected path loss has lognormal distribution with zero mean and standard deviation in the range of [5.5, 6.3] dB for different types of vehicles with almost no variation when the engine of the same vehicle is turned on. The clustering phenomenon in the power delay profile (PDP) is well represented by a modified Saleh-Valenzuela (SV) model. The interarrival times of the clusters are modeled using a Weibull distribution. The cluster-amplitude and ray-amplitude decay functions are represented with a dual-slope linear model with breakpoint around 26.6 and 5.5 ns, respectively. The parameters of the Weibull distribution and these dual-slope linear models do not vary significantly for different types and conditions of the vehicle. The variations of the observed PDPs around the SV model is well modeled by independent normal random variables with zero mean and with a variance independent of the delay bin, and the type and condition of the vehicle. We propose a simulation model for the UWB channel within the engine compartment based on these findings and validate it by comparing the received energy and root mean square (RMS) delay spread of the generated and observed PDPs.Publication Open Access Federated learning for hybrid beamforming in mm-wave massive MIMO(Institute of Electrical and Electronics Engineers (IEEE), 2020) Elbir, Ahmet M.; Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 7211Machine learning for hybrid beamforming has been extensively studied by using centralized machine learning (CML) techniques, which require the training of a global model with a large dataset collected from the users. However, the transmission of the whole dataset between the users and the base station (BS) is computationally prohibitive due to limited communication bandwidth and privacy concerns. In this work, we introduce a federated learning (FL) based framework for hybrid beamforming, where the model training is performed at the BS by collecting only the gradients from the users. We design a convolutional neural network, in which the input is the channel data, yielding the analog beamformers at the output. Via numerical simulations, FL is demonstrated to be more tolerant to the imperfections and corruptions in the channel data as well as having less transmission overhead than CML.