Baykaş, Tunçer
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B.,Tuncer
Baykaş, Tunçer
Tunçer Baykaş
Baykaş T.
Baykaş, T.
T. Baykaş
B., Tuncer
Baykas, Tuncer
B., Tunçer
BAYKAŞ, Tunçer
Baykas,T.
Baykaş, TUNÇER
Baykas T.
Baykaş,T.
TUNÇER BAYKAŞ
Tunçer BAYKAŞ
BAYKAŞ, TUNÇER
Tuncer, Baykas
Baykas,Tuncer
Baykaş, Tunçer
Baykaş, Tunçer
Tunçer Baykaş
Baykaş T.
Baykaş, T.
T. Baykaş
B., Tuncer
Baykas, Tuncer
B., Tunçer
BAYKAŞ, Tunçer
Baykas,T.
Baykaş, TUNÇER
Baykas T.
Baykaş,T.
TUNÇER BAYKAŞ
Tunçer BAYKAŞ
BAYKAŞ, TUNÇER
Tuncer, Baykas
Baykas,Tuncer
Baykaş, Tunçer
Job Title
Doç. Dr.
Email Address
Main Affiliation
Electrical-Electronics Engineering
Status
Current Staff
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ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
SDG data is not available
Documents
130
Citations
2608
h-index
22
Documents
0
Citations
0
Scholarly Output
35
Articles
17
Views / Downloads
43/0
Supervised MSc Theses
1
Supervised PhD Theses
1
WoS Citation Count
220
Scopus Citation Count
301
WoS h-index
6
Scopus h-index
7
Patents
0
Projects
0
WoS Citations per Publication
6.29
Scopus Citations per Publication
8.60
Open Access Source
5
Supervised Theses
2
| Journal | Count |
|---|---|
| 2021 Ieee International Black Sea Conference on Communications and Networking (Ieee Blackseacom) | 2 |
| IEEE Communications Standards Magazine | 2 |
| 2018 26th Signal Processing and Communications Applications Conference (SIU) | 1 |
| 2018 IEEE Conference on Standards for Communications and Networking (CSCN) | 1 |
| 2019 27th Signal Processing and Communications Applications Conference (SIU) | 1 |
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35 results
Scholarly Output Search Results
Now showing 1 - 10 of 35
Article Citation - WoS: 2Citation - Scopus: 2Decoding Rhythmic Complexity: a Nonlinear Dynamics Approach via Visibility Graphs for Classifying Asymmetrical Rhythmic Frameworks of Turkish Classical Music(Elsevier Science inc, 2025) Mirza, Fuat Kaan; Baykas, Tuncer; Hekimoglu, Mustafa; Pekcan, Onder; Tuncay, Gonul PacaciThe non-isochronous, hierarchical rhythmic cycles (usuls) of Turkish Classical Music (TCM) exhibit emergent temporal structures that challenge conventional rhythm analysis based on metrical regularity. To address this challenge, this study presents a complexity-oriented framework for usul classification, grounded in nonlinear time series analysis and network-based representations. Rhythmic signals are processed through energy envelope extraction, diffusion entropy analysis, and spectral transformations to capture multiscale temporal dynamics. Visibility graphs (VGs) are constructed from these representations to encode underlying structural complexity and temporal dependencies. Features derived from VG adjacency matrices serve as complexity-sensitive descriptors and enable high-accuracy classification (0.99) across 40 usul classes and 628 compositions. Energy envelope-derived graphs provide the most discriminative information, highlighting the importance of amplitude modulation in encoding rhythmic structure. Beyond classification, the analysis reveals self-organizing patterns and signatures of complexity, such as quasi-periodicity, scale-dependent variability, and entropy saturation, suggesting that usuls function as adaptive, nonlinear systems rather than metrically constrained patterns. The topological features extracted from the resulting graphs align with theoretical constructs from complexity science, such as modularity and long-range temporal correlations. This positions usul as an exemplary case for studying structured temporal complexity in cultural artifacts through the lens of dynamical systems. These findings contribute to computational rhythm analysis by demonstrating the efficacy of complexity measures in characterizing culturally specific rhythmic systems.Article Citation - Scopus: 1A Novel Multiscale Graph Signal Processing and Network Dynamics Approach to Vibration Analysis for Stone Size Discrimination via Nonlinear Manifold Embeddings and a Convolutional Self-Attention Model(Springer Wien, 2025) Mirza, Fuat Kaan; Oz, Usame; Hekimoglu, Mustafa; Aydemir, Mehmet Timur; Pural, Yusuf Enes; Baykas, Tuncer; Pekcan, OnderUnderstanding nonlinear dynamics is critical for analyzing the hidden complexities of vibrational behavior in real-world systems. This study introduces a graph-theoretic approach to analyze the complex nonlinear temporal patterns in vibrational signals, utilizing the Tri-Axial Vibro-Dynamic Stone Classification dataset. This dataset captures high-resolution acceleration signals from controlled stone-crushing experiments, providing a unique opportunity to investigate temporal dynamics associated with distinct stone sizes. A 12-level Maximal Overlap Discrete Wavelet Transform is employed to perform multiscale signal decomposition, enabling the construction of transition graphs that encode transient and stable structural characteristics. Conceptually, transition graphs are analyzed as dynamic networks to uncover the interactions and temporal patterns embedded within vibrational signals. These networks are studied using a comprehensive suite of complexity metrics derived from information theory, graph theory, network science, and dynamical systems analysis. Metrics such as Shannon and Von Neumann's entropy evaluate signal dynamics' stochasticity and information retention. At the same time, the spectral radius measures the network's stability and structural robustness. Lyapunov exponents and fractal dimensions, informed by chaos theory and fractal geometry, further capture the degree of nonlinearity and temporal complexity. Complementing these dynamic measures, static network metrics-including the clustering coefficient, modularity, and the static Kuramoto index-offer critical discernment into the network's community structures, synchronization phenomena, and connectivity efficiency. Manifold learning techniques address the high-dimensional feature space derived from complexity metrics, with UMAP outperforming ISOMAP, Spectral Embedding, and PCA in preserving critical data structures. The reduced features are input into a convolutional self-attention model, combining localized feature extraction with long-term sequence modeling, achieving 100% classification accuracy across stone-size categories. This study presents a comprehensive framework for vibrational signal analysis, integrating multiscale graph-based representations, nonlinear dynamics quantification, and UMAP-based dimensionality reduction with a convolutional self-attention classifier. The proposed approach supports accurate classification and contributes to the development of data-driven tools for automated diagnostics and predictive maintenance in industrial and engineering contexts.Conference Object Citation - Scopus: 2Performance Evaluation of Dtn Routing Protocols for Drone Swarms Using a Web-Based Simulator(IEEE, 2023) Beisenkhanov, Dauren; Kizilirmak, Refik Caglar; Ukaegbu, Ikechi; Baykas, TuncerThe growing interest in drone swarms and their applications using wireless communication has resulted in the necessity for innovative network architectures. Drone swarms are subject to highly dynamic network topology, and the low drone density may constrain connectivity and overall network performance. The Delay Tolerant Network (DTN) architecture, which is based on dynamic discovery of neighbouring nodes and relay of data via store-and-forward mechanism, overcomes these challenges. In this study, we develop a DTN simulator for drone swarms using web technologies that operate within the web browser. We employ two commonly used DTN routing algorithms, namely epidemic routing and spray-and-wait routing, for varying numbers of nodes, simulation durations, and maximum allowed copies in the network. We compare these protocols by evaluating performance metrics such as delivery probability, number of hops/copies, and delivery time, through a case study. Additionally, we investigate the influence of the outage probability of the air-to-air channel between nodes.Article Citation - WoS: 15Citation - Scopus: 17Measurement-Based Large Scale Statistical Modeling of Air-To Wireless Uav Channels Via Novel Time-Frequency Analysis(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Ede, Burak; Kaplan, Batuhan; Kahraman, Ibrahim; Kesir, Samed; Yarkan, Serhan; Ekti, Ali Riza; Baykas, Tuncer; Cirpan, Hakan AliAny operation scenario for unmanned aerial vehicles also known as drones in real world requires resilient wireless link to guarantee capacity and performance for users, which can only be achieved by obtaining detailed knowledge about the propagation channel. Thus, this study investigates the large-scale channel propagation statistics for the line of sight air-to-air (A2A) drone communications to estimate the path loss exponent (PLE). We conducted a measurement campaign at 5.8 GHz, using low cost and light weight software defined radio based channel sounder which is developed in this study and then further integrated on commercially available drones. To determine the PLE, frequency-based, time-based and time-frequency based methods are utilized. Accuracy of the proposed method is verified under ideal conditions in a well-isolated anechoic chamber before the actual measurement campaign to verify the performance in a free space path loss environment. The path loss exponent for A2A wireless drone channel is estimated with these verified methods.Conference Object Citation - Scopus: 1Busy Tone Based Power Control For Coordination Of Ieee 802.11af And 802.22 System [ıeee 802.11af ve 802.22 Sistemlerinin Uyumlu Çalışmaları için Meşgul Ton Tabanlı Güç Kontrolü](Institute of Electrical and Electronics Engineers Inc., 2017) Ulgen, Oguz; Erküçük, Serhat; Karatalay, Onur; Baykaş, TunçerIn this paper a new power control algorithm based on busy tone approach has been proposed for the coordination of IEEE 802.22 and IEEE 802.11af systems in TV white space. Different from the earlier studies in addition to both 802.11af access point and clients listening to the busy tone they also adjust their communication power according to the location information and use hopping for communication if needed. Acccordingly interference caused to 802.22 systems has been reduced while the 802.11af systems are still able to communicate. This study quantifies the 802.11af and 802.22 system performances in terms of interfering packet rate and succesful packet transmission rate for different scenarios considering the communication parameters and channel models adapted for the standards. © 2017 IEEE.Article Citation - WoS: 9Citation - Scopus: 12Location Aware Vertical Handover in a VLC/WLAN Hybrid Network(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Zeshan, Arooba; Baykas, TuncerVisible light communication (VLC) has emerged as a promising technology for wireless communication as it offers higher data rates and secure data transmission along with providing indoor illumination. However, VLC is restricted by the line of sight (LoS) nature of the optical channel that consequently results in light path blockages. Therefore, an effective solution would be to combine VLC with a radio frequency (RF) system to form a hybrid VLC/RF network that would take into account the preferences of an end-user with the practicality of implementation. In such networks, an efficient vertical handover (VHO) technique is the most critical element as it ensures a seamless transition between the two networks. In this work, we propose a vertical handover technique that utilizes the user's location information to make a handover decision. We found that the frequency of light path blockages increases with the increasing number of users in a confined space, resulting in significant performance deterioration. This additional information is then utilized so that the VHO algorithm effectively selects the most feasible network. The proposed algorithm has been tested against the immediate vertical handover algorithm (I-VHO) and the dwell vertical handover algorithm (D-VHO) with two different dwell times. The average number of handovers, quality of experience (QoE), and packet loss have been set as performance metrics. We show from several simulation scenarios that the proposed method results in a fewer number of handovers while maintaining higher QoE and lower packet loss.Conference Object Citation - WoS: 1Busy Tone Implementation for Coexistence of Ieee 802.22 and 802.11 Af Systems(IEEE, 2015) Karatalay, Onur; Erküçük, Serhat; Baykaş, TunçerIn this paper a new algorithm based on busy tone approach has been proposed for the coexistence of IEEE 802.22 and IEEE 802.11af systems in TV white space. Different from the earlier study in addition to 802.11af access points listening to the busy tone signal their clients also listen to the busy tone and let the access points know once they hear it. Accordingly interference caused to 802.22 systems has been reduced. This study quantifies the improved system performance in terms of interfering packet rate for different hearing regions considering the communication parameters and channel models adapted for the standards.Article Citation - Scopus: 3Stock Price Forecasting Through Symbolic Dynamics and State Transition Graphs With a Convolutional Recurrent Neural Network Architecture(Springer Science and Business Media Deutschland GmbH, 2025) Mirza, F.K.; Pekcan, Ö.; Hekimoğlu, M.; Baykaş, T.Accurate stock price forecasting remains a critical challenge in financial analytics due to volatile market conditions, non-stationary dynamics, and abrupt regime shifts that often defy traditional modeling techniques. This study proposes a comprehensive framework for stock price forecasting that integrates symbolic dynamics, graph-based state representations, and deep learning. By converting continuous-valued stock prices into discrete symbolic states representing amplitude and trend information, the method constructs transition matrices capturing probabilistic relationships within financial time series. These transition matrices are then processed by a convolutional recurrent neural network (CRNN), in which convolutional layers isolate local spatial dependencies in the symbolic-state domain, while recurrent LSTM layers capture multi-scale temporal dynamics extending across multiple time horizons. Experimental evaluations are conducted over prediction horizons of 1 day, 10 days, and 100 days, spanning pre-COVID, COVID, and post-COVID market regimes. The results indicate that while longer prediction horizons naturally incur greater forecasting uncertainty due to compounding variability, the integration of symbolic-state preprocessing with deep temporal modeling demonstrates significant robustness in handling non-stationary financial environments. During the stable pre-COVID period, the proposed methodology achieves reductions in mean squared error (MSE) of up to 98% relative to the volatile COVID phase, highlighting its capability to effectively leverage well-defined market patterns in stable economic conditions. Furthermore, the model consistently delivers competitive forecasting performance across all prediction horizons and market regimes. Collectively, these findings emphasize the potential of symbolic-state-based deep learning architectures as a viable pathway to address the complexity and volatility characteristic of modern financial markets. © The Author(s) 2025.Conference Object Citation - WoS: 2Citation - Scopus: 6Analysis of deep learning based path loss prediction from satellite images(IEEE, 2021) Alam, Muhammad Z.; Ates, Hasan F.; Baykas, Tuncer; Gunturk, Bahadir K.Determining the channel model parameters of a wireless communication system, either by measurements or by running electromagnetic propagation simulations, is a time-consuming process. Any rapid deployment of network demands faster determination of at least major channel parameters. In this paper, we investigate the idea of using deep convolutional neural networks and satellite images for channel parameters (i.e., path loss exponent n and shadowing factor sigma) prediction in a cellular network with aerial base stations. Specifically, we investigate the performance dependency of the method on three different factors: height of the transmitter antenna, quantization levels of the channel parameters and architectural design of CNN. The results presented in this paper show a high prediction accuracy of the channel parameters in real-time.Conference Object Citation - WoS: 2Citation - Scopus: 4Predicting Path Loss Distributions of a Wireless Communication System for Multiple Base Station Altitudes From Satellite Images(Ieee, 2022) Shoer, Ibrahim; Gunturk, Bahadir K.; Ates, Hasan F.; Baykas, TuncerIt is expected that unmanned aerial vehicles (UAVs) will play a vital role in future communication systems. Optimum positioning of UAVs, serving as base stations, can be done through extensive field measurements or ray tracing simulations when the 3D model of the region of interest is available. In this paper, we present an alternative approach to optimize UAV base station altitude for a region. The approach is based on deep learning; specifically, a 2D satellite image of the target region is input to a deep neural network to predict path loss distributions for different UAV altitudes. The neural network is designed and trained to produce multiple path loss distributions in a single inference; thus, it is not necessary to train a separate network for each altitude.
