Baykaş, Tunçer
Loading...
Profile URL
Name Variants
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
Website
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
2600
h-index
22
Documents
0
Citations
0
Scholarly Output
35
Articles
17
Views / Downloads
270/710
Supervised MSc Theses
1
Supervised PhD Theses
1
WoS Citation Count
216
Scopus Citation Count
293
WoS h-index
6
Scopus h-index
7
Patents
0
Projects
0
WoS Citations per Publication
6.17
Scopus Citations per Publication
8.37
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 |
Current Page: 1 / 6
Scopus Quartile Distribution
Competency Cloud
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 A 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.Article Citation - WoS: 1Citation - Scopus: 1An Integrated Molecular Communication System Based on Acoustic Tweezers(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Zeshan, Arooba; Pusane, Ali Emre; Baykas, TuncerIn this work, a molecular communication link integrated with a micro-electro mechanical system (MEMS) based environment has been designed and simulated. The motivation behind this approach is to explore the possibility of merging acoustic tweezing technique with a molecular communication system to increase the accuracy and reliability of the overall communication link. The proposed design is simulated using finite element methods that mimic the actual environment for an accurate solution. We derive symbol error rate as a performance metric and further show that the proposed system outperforms the diffusion-based modulation techniques and facilitates a reliable communication in the presence of fluid flow and while being insusceptible to external factors.Article Citation - WoS: 11Citation - Scopus: 15Regression of Large-Scale Path Loss Parameters Using Deep Neural Networks(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Bal, Mustafa; Marey, Ahmed; Ates, Hasan F.; Baykas, Tuncer; Gunturk, Bahadir K.Path loss exponent and shadowing factor are among important wireless channel parameters. These parameters can be estimated using field measurements or ray-tracing simulations, which are costly and time-consuming. In this letter, we take a deep neural network-based approach, which takes either satellite image or height map of a target region as input, and estimates the desired channel parameters. We use the well-known VGG-16 architecture, pretrained on the ImageNet dataset, as the backbone to extract image features, modify it as a regression network to produce channel parameters, and retrain it on our dataset, which consists of satellite image or height map as input and channel parameters as target values. We demonstrate that deep networks can be successfully utilized in estimating path loss exponent and shadowing factor of a region, simply from the region's satellite image or height map. The trained models and test codes are publicly available on a Github page.Conference Object Citation - WoS: 2Predicting 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.Article A Comparative Analysis of Diversity Combining Techniques for Repetitive Transmissions in Time Spreading Scma Systems(John Wiley & Sons Ltd, 2024) Ulgen, Oguz; Tufekci, Tolga Kagan; Sadi, Yalcin; Erkucuk, Serhat; Anpalagan, Alagan; Baykas, TuncerSparse Code Multiple Access (SCMA) is a recently introduced wireless communication network technology. There are various techniques in SCMA systems to increase the system's efficiency, and one of these techniques is time spreading. By adding repetitive transmission and time spreading into SCMA, it is shown in previous works that the Bit-Error-Rate (BER) results are improved convincingly. However, in the previous works, other diversity combining techniques have not been considered. This paper introduces a new approach to further improve the performance of repetitive transmission in SCMA systems with time spreading by adding imperialist competitive algorithm in diversity combining. Alongside, four different combining techniques; equal gain combining, maximal ratio combining, selection combining, and genetic algorithm are considered to bring comparative analysis to show the significance of the new technique. Results show that the proposed method offers up to 2.3 dB gain in terms of BER, under certain conditions.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.Article Citation - WoS: 4Citation - Scopus: 9IEEE 802.11BB Reference Channel Models for Light Communications(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Miramirkhani, Farshad; Baykas, Tuncer; Elamassie, Mohammed; Uysal, MuratIncreasing industrial attention to visible light communications (VLC) technology led the IEEE 802.11 to establish the task group 802.11bb "Light Communications" (LC) for the development of a VLC standard. As a part of the standard development process, the development of realistic channel models according to possible use cases is of critical importance for physical layer system design. This article presents the reference channel models for the mandatory usage models adopted by IEEE 802.11bb for the evaluation of system proposals. The use cases include industrial, medical, enterprise, and residential scenarios. Channel impulse responses and corresponding frequency responses are obtained for each use case using a ray tracing approach based on realistic specifications for transmitters and receivers, and optical characterization of the environment.Master Thesis Scma Sistemlerinin Performans İncelemeleri(2023) Tüfekçi, Tolga Kağan; Baykaş, TunçerSürekli yükselen; veri hızı, düşük gecikme, spektral verimlilik ve veri hacmi ihtiyaçlarını karşılayabilmek adına yeni haberleşme yöntemleri geliştirilmektedir. Seyrek Kodlu Çoklu Erişim (Sparse Code Multiple Access - SCMA) bu tekniklerin arasında yer almaktadır. SCMA, dikgen olmayan çoklu erişim (Non-orthogonal Multiple Access - NOMA) yöntemidir. SCMA ile farklı kullanıcılar aynı frekans kaynaklarına atanmasından dolayı spektral verimlilik arttırılır. Her kullanıcıya; seyrek, çok boyutlu ve kompleks değerli kod kitapları atanır, bu sebeple SCMA sistemlerinin performansı da kod kitaplarının kalitesine bağlıdır. Bu tezde; SCMA sistemlerinin farklı senaryolar altındaki performansları incelenmiştir bunlar, 1) Hızlı Sönümlenen Rayleigh Kanallarının etkisi incelenmiş ve bu kanal tipinin SCMA performansındaki negatif etkileri azaltabilmek adına Hızlı Mesaj İletim Algoritması (High-Rate MPA) önerilmiştir. 2) Dikgen ve dikgen olmayan pilot sekansları ve bu sekanslardan yola çıkarak En Küçük Kareler Kestirimi (Least Squares Estimation) yapılmıştır. 3) Simgelerarası Karışmanın (Intersymbol Interference - ISI), SCMA sistemleri üzerindeki etkisi ve PN Sekanslarının üretimi ve kullanımı incelenmiştir. 4) Literatürde bulunan farklı kod kitaplarının SCMA sistemlerindeki performansı incelenmiş ve ayrıca Genetik Algoritma tabanlı bir kod kitabının tasarımı ve yine etkisi incelenmiştir.Article Citation - WoS: 1Citation - Scopus: 2IEEE 802.19.3 Coexistence Recommendations for IEEE 802.11 and IEEE 802.15.4 Based Systems Operating in Sub-1 GHz Frequency Bands(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Guo, Jianlin; Nagai, Yukimasa; Rolfe, Benjamin A.; Sumi, Takenori; Orlik, Philip; Robert, Joerg; Baykas, TuncerInternet of Things (IoT) applications are rapidly increasing. A broad range of low-power wide-area technologies have been developed in the Sub-1 GHz frequency bands to meet various application requirements. Massive IEEE 802.15.4g based systems have been deployed to provide low to moderate data rate capabilities. IEEE 802.11ah is designed to provide higher data rate capabilities than the data rates of IEEE 802.15.4g. In addition, other Sub-1 GHz band systems, including LoRa and SigFox, are also installed for applications with longer range communication need. There is considerable overlap in use cases targeted by these technologies. Due to the constrained spectrum allocation in the Sub-1 GHz frequency bands, these systems are likely to coexist. Therefore, the coexistence of heterogeneous Sub-1 GHz band wireless technologies becomes an issue to be addressed. Our measurements and simulations reveal significant interference among these systems. Previously the Sub-1 GHz band coexistence is not well addressed. Accordingly, IEEE New Standards Committee and Standard Board formed IEEE 802.19.3 Task Group in December 2018 to develop IEEE 802.19.3 standard for the coexistence of IEEE 802.11ah and IEEE 802.15.4g based systems to guide product deployment. IEEE 802.19.3 standard was published in April 2021. This article summarizes the Sub-1 GHz band systems, spectrum allocation, interference and noise measurements, coexistence issues, and coexistence recommendations presented in IEEE 802.19.3. It aims to introduce IEEE 802.19.3 standard to readers outside of IEEE 802 standard body and to application developers to raise awareness of potential coexistence issues and available coexistence techniques for the better system deployment. In addition, this article presents performance evaluation of the coexistence methods recommended in IEEE 802.19.3.
