WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 74Citation - Scopus: 89QERP: Quality-Of (QoS) Aware Evolutionary Routing Protocol for Underwater Wireless Sensor Networks(IEEE-Inst Electrical Electronics Engineers Inc, 2018-09) Faheem, Muhammad; Tuna, Gurkan; Gungor, Vehbi CagriQuality-of-service (QoS) aware reliable data delivery is a challenging issue in underwater wireless sensor networks (UWSNs). This is clue to impairments of the acoustic transmission caused by excessive noise, extremely long propagation delays, high bit error rate, low bandwidth capacity, multipath effects, and interference. To address these challenges, meet the commonly used UWSN performance indicators, and overcome the inefficiencies of the existing clustering-based routing schemes, a novel QoS aware evolutionary cluster based routing protocol (QERP) has been proposed for UWSN-based applications. The proposed protocol improves packet delivery ratio, and reduces average end-to-end delay and overall network energy consumption. Our comparative performance evaluations demonstrate that QERP is successful in achieving low network delay, high packet delivery ratio, and low energy consumption.Article Citation - WoS: 5Citation - Scopus: 4Investigating Strain Rate Effects on Damage Mechanisms in Hybrid Laminated Composites Using Acoustic Emission(Elsevier Sci Ltd, 2025-12) Gulsen, Abdulkadir; Kolukisa, Burak; Etcil, Mustafa; Caliskan, Umut; Zafar, Hafiz Muhammad Numan; Demirbas, Munise Didem; Bakir-Gungor, BurcuHybrid composites, which combine distinct fiber types such as carbon, basalt, and aramid, provide a synergistic balance of strength, stiffness, impact resistance, and energy dissipation, making them appealing for critical applications in aerospace, automotive, and other high-performance industries. Monitoring damage progression in these composites is vital for ensuring structural integrity and preventing catastrophic failures. Acoustic emission (AE) serves as a powerful, noninvasive technique for real-time structural health monitoring, capturing the transient stress waves generated when damage events occur. This study utilizes AE to examine the influence of strain rate on damage modes in carbon/basalt/aramid hybrid composites under three-point bending. An unsupervised feature selection based on Laplacian scores is employed to identify the most relevant AE features with damage modes, while SHapley Additive Explanations (SHAP) are used to evaluate the correlation between AE features and strain rates. The correlation analysis results indicate that peak frequency (PF) serves as a key indicator, demonstrating significant shifts at higher strain rates. Gaussian Mixture Model (GMM) clustering is used to analyze hybrid composites by examining clustered AE signals based on selected features identified through Laplacian scores, with Silhouette scores employed to determine the optimal number of clusters. This study highlights the role of AE in understanding fiber interactions and damage evolution, offering valuable insights into the mechanical performance and optimization of carbon/basalt/aramid hybrid composite structures.Article Citation - WoS: 64Citation - Scopus: 76EDHRP: Energy Efficient Event Driven Hybrid Routing Protocol for Densely Deployed Wireless Sensor Networks(Academic Press Ltd- Elsevier Science Ltd, 2015-12) Faheem, Muhammad; Abbas, Muhammad Zahid; Tuna, Gurkan; Gungor, Vehbi CagriEfficient management of energy resources is a challenging research area in Wireless Sensor Networks (WSNs). Recent studies have revealed that clustering is an efficient topology control approach for organizing a network into a connected hierarchy which balances the traffic load of the sensor nodes and improves the overall scalability and the lifetime of WSNs. Inspired by the advantages of clustering techniques, we have three main contributions in this paper. First, we propose an energy efficient cluster formation algorithm called Active Node Cluster Formation (ANCF). The core aim to propose ANCF algorithm is to distribute heavy data traffic and high energy consumption load evenly in the network by offering unequal size of clusters in the network. The developed scheme appoints each cluster head (CH) near to the sink and sensing event while the remaining set of the cluster heads (CHs) are appointed in the middle of each cluster to achieve the highest level of energy efficiency in dense deployment. Second, we propose a lightweight sensing mechanism called Active Node Sensing Algorithm (ANSA). The key aim to propose the ANSA algorithm is to avoid high sensing overlapping data redundancy by appointing a set of active nodes in each cluster with satisfy coverage near to the event. Third, we propose an Active Node Routing Algorithm (ANRA) to address complex inter and intra cluster routing issues in highly dense deployment based on the node dominating values. Extensive experimental studies conducted through network simulator NCTUNs 6.0 reveal that our proposed scheme outperforms existing routing techniques in terms of energy efficiency, end-to-end delay and data redundancy, congestion management and setup robustness. (C) 2015 Elsevier Ltd. All rights reserved.Article Citation - WoS: 12Citation - Scopus: 19An Approximate Spectral Clustering Ensemble for High Spatial Resolution Remote-Sensing Images(IEEE-Inst Electrical Electronics Engineers Inc, 2015-05) Tasdemir, Kadim; Moazzen, Yaser; Yildirim, IsaUnsupervised clustering of high spatial resolution remote-sensing images plays a significant role in detailed land-cover identification, especially for agricultural and environmental monitoring. A recently promising method is approximate spectral clustering (SC) which enables spectral partitioning for large datasets to extract clusters with distinct characteristics without a parametric model. It also facilitates the use of various information types via advanced similarity criteria. However, it requires an empirical selection of a similarity criterion optimal for the corresponding application. To address this challenge, we propose an approximate SC ensemble (ASCE2) which fuses partitionings obtained by different similarity representations. Contrary to existing spectral ensembles for remote-sensing applications, the proposed ASCE2 employs neural gas quantization instead of random sampling, advanced similarity criteria instead of traditional distance-based Gaussian kernel with different decay parameters, and a two-level ensemble. We evaluate the proposed ASCE2 with three measures (accuracy, adjusted Rand index, and normalized mutual information) using five remote-sensing images, two of which are commonly available. We apply the ASCE2 in two applications for agricultural monitoring: 1) land-cover identification to determine orchard fields using a WorldView-2 image (0.5-m spatial resolution) and 2) finding lands in good agricultural condition using multitemporal RapidEye images (5-m spatial resolution). Experimental results indicate a significant betterment of the resulting partitionings obtained by the proposed ensemble, with respect to the evaluation measures in these applications.