Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 31Citation - Scopus: 35Transmitter Localization in Vessel-Like Diffusive Channels Using Ring-Shaped Molecular Receivers(IEEE-Inst Electrical Electronics Engineers Inc, 2018-12) Turan, Meric; Akdeniz, Bayram Cevdet; Kuran, Mehmet Comma Sukru; Yilmaz, H. Birkan; Demirkol, Ilker; Pusane, Ali E.; Tugcu, Tuna; Birkan Yilmaz, H.Molecular communication via diffusion in vessellike environment targets critical applications such as the detection of abnormal and unhealthy cells. In this letter, we derive the analytical formulation of the channel model for diffusion dominated movement, considering ring-shaped (i. e., patch) observing receivers, and Poiseuille flow with the aim of localization of the transmitter cell. Then, we derive formulations using this channel model for two different application scenarios. We assume that the emission start time is known in the first scenario and unknown in the second one. We successfully localize the transmitter cell using a single receiver for the first scenario, whereas two receivers are used to localize the transmitter cell in the second scenario. At last, the devised analytical framework is validated with simulations.Article Citation - WoS: 16Citation - Scopus: 23Review on Energy Application Using Blockchain Technology With an Introductions in the Pricing Infrastructure(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Al-Abri, Tariq; Onen, Ahmet; Al-Abri, Rashid; Hossen, Abdulnasir; Al-Hinai, Amer; Jung, Jaesung; Ustun, Taha SelimWith the rapid transformation of the energy sector towards modern power systems represented by smart grids (SGs), microgrids (MG), and distributed generation, blockchain (BC) technology has shown the capability for solving security, privacy, and reliability challenges that hinder progress. Currently, the energy structure is forming a decentralized system that prioritizes customer satisfaction. BC technology undertakes power network stockholders in a secure energy market, transparent transactions, and fair competition and offers promising energy solutions. This paper is a comprehensive review of energy applications using BC integration. Firstly, we introduce the drivers of BC leverage that make it a potentially important component of the power network. Following that, we provide background information on BC and its application in areas other than the energy sector. Subsequently, we discuss studies and sort potential energy applications from various recent papers and surveys that have already adopted BC technology in the energy sector. Then, we summarize the pricing infrastructure for applying BC in the energy sector and identify the requirements to build it. Finally, energy security and privacy challenges based on BC are highlighted, along with potential drawbacks and concerns related to the pricing infrastructure.Article Citation - WoS: 45Citation - Scopus: 52Peer-to-Peer Relative Localization of Aerial Robots With Ultrawideband Sensors(IEEE-Inst Electrical Electronics Engineers Inc, 2021-09) Guler, Samet; Abdelkader, Mohamed; Shamma, Jeff S.Robots in swarms take advantage of localization infrastructure, such as a motion capture system or global positioning system (GPS) sensors to obtain their global position, which can then be communicated to other robots for swarm coordination. However, the availability of localization infrastructure needs not to be guaranteed, e.g., in GPS-denied environments. Likewise, the communication overhead associated with broadcasting locations may be undesirable. For reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, we propose an onboard relative localization framework for multirobot systems. The setup consists of an anchor robot with three onboard ultrawideband (UWB) sensors and a tag robot with a single onboard UWB sensor. The anchor robot utilizes the three UWB sensors to estimate the tag robot's location by using its onboard sensing and computational capabilities solely, without explicit interrobot communication. Because the anchor UWB sensors lack the physical separation that is typical in fixed UWB localization systems, we introduce filtering methods to improve the estimation of the tag's location. In particular, we utilize a mixture Monte Carlo localization (MCL) approach to capture maneuvers of the tag robot with acceptable precision. We validate the effectiveness of our algorithm with simulations as well as indoor and outdoor field experiments on a two-drone setup. The proposed mixture MCL algorithm yields highly accurate estimates for various speed profiles of the tag robot and demonstrates superior performance over the standard particle filter and the extended Kalman filter.Article Citation - WoS: 115Citation - Scopus: 173Peer-to-Peer Energy Trading in Virtual Power Plant Based on Blockchain Smart Contracts(IEEE-Inst Electrical Electronics Engineers Inc, 2020) Seven, Serkan; Yao, Gang; Soran, Ahmet; Onen, Ahmet; Muyeen, S. M.A novel Peer-to-peer (P2P) energy trading scheme for a Virtual Power Plant (VPP) is proposed by using Smart Contracts on Ethereum Blockchain Platform. The P2P energy trading is the recent trend the power society is keen to adopt carrying out several trial projects as it eases to generate and share the renewable energy sources in a distributed manner inside local community. Blockchain and smart contracts are the up-and-coming phenomena in the scene of the information technology used to be considered as the cutting-edge research topics in power systems. Earlier works on P2P energy trading including and excluding blockchain technology were focused mainly on the optimization algorithm, Information and Communication Technology, and Internet of Things. Therefore, the financial aspects of P2P trading in a VPP framework is focused and in that regard a P2P energy trading mechanism and bidding platform are developed. The proposed scheme is based on public blockchain network and auction is operated by smart contract addressing both cost and security concerns. The smart contract implementation and execution in a VPP framework including bidding, withdrawal, and control modules developments are the salient feature of this work. The proposed architecture is validated using realistic data with the Ethereum Virtual Machine (EVM) environment of Ropsten Test Network.Article Citation - WoS: 23Citation - Scopus: 26Optimal Location and Sizing of Electric Bus Battery Swapping Station in Microgrid Systems by Considering Revenue Maximization(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Kocer, Mustafa Cagatay; Onen, Ahmet; Jung, Jaesung; Gultekin, Hakan; Albayrak, SahinThe radical increase in the popularity of electric vehicles (EVs) has in turn increased the number of associated problems. Long waiting times at charging stations are a major barrier to the widespread adoption of EVs. Therefore, battery swapping stations (BSSs) are an efficient solution that considers short waiting times and healthy recharging cycles for battery systems. Moreover, swapping stations have emerged as a great opportunity not only for EVs, but also for power systems, with regulation services that can be provided to the grid particularly for small networks, such as microgrid (MG) systems. In this study, the optimum location and size that maximize the revenue of a swap station in an MG system are investigated. To the best of our knowledge, this study is first to solve the placing and sizing problem in the MG from the perspective of a BSS. The results indicate that bus 23 is the BSS's optimal location and is crucial for maximizing revenue and addressing issues like the provision of ancillary services in microgrid system. Finally, the swap demand profile of the station serving electric bus public transportation system was obtained using an analytical model based on public transportation data collected in Berlin, Germany.Article Citation - WoS: 3Citation - Scopus: 3Object Weight Perception in Motor Imagery Using Fourier-Based Synchrosqueezing Transform and Regularized Common Spatial Patterns(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Karakullukcu, Nedime; Altindis, Fatih; Yilmaz, BulentThis study addresses the challenge faced by individuals with upper-limb prostheses in regulating grip force and adapting movements to different object weights. Despite limited exploration, this research pioneers the use of EEG to estimate object weight perception in the context of upper-limb prostheses. Investigating neural correlates in this population provides valuable insights and aids the development of neurofeedback-based strategies for weight perception. Our objective is to identify EEG features predicting the weight perception of held objects. Employing Fourier-based synchrosqueezing transform (FSST) and regularized Common Spatial Patterns (CSP) features, we classify motor imagery waves representing three weight categories (light, medium, heavy). Subjects perform actual motor tasks before imagery sessions, and our approach integrates EEG features of both movements to train subject-specific machine learning models. Results reveal that FSST- singular value decomposition (SVD) features for medium and heavy objects are most distinctive. Achieving up to 90% accuracy, spatial features demonstrate effective classification of motor imagery for different weights. Unlike weight prediction studies, our focus is on visual perception and imagination of object weights, enhancing prosthetic hand system preconditioning. Binary classification surpasses 70% accuracy in predicting object weights, uniquely utilizing actual movement data for CSP algorithm regularization coefficient estimation.Article Citation - WoS: 26Citation - Scopus: 48Metabolic Imaging Based Sub-Classification of Lung Cancer(IEEE-Inst Electrical Electronics Engineers Inc, 2020) Bicakci, Mustafa; Ayyildiz, Oguzhan; Aydin, Zafer; Basturk, Alper; Karacavus, Seyhan; Yilmaz, BulentLung cancer is one of the deadliest cancer types whose 84% is non-small cell lung cancer (NSCLC). In this study, deep learning-based classification methods were investigated comprehensively to differentiate two subtypes of NSCLC, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). The study used 1457 F-18-FDG PET images/slices with tumor from 94 patients (88 men), 38 of which were ADC and the rest were SqCC. Three experiments were carried out to examine the contribution of peritumoral areas in PET images on subtype classification of tumors. We assessed multilayer perceptron (MLP) and three convolutional neural network (CNN) models such as SqueezeNet, VGG16 and VGG19 using three kinds of images in these experiments: 1) Whole slices without cropping or segmentation, 2) cropped image portions (square subimages) that include the tumor and 3) segmented image portions corresponding to tumors using random walk method. Several optimizers and regularization methods were used to optimize each model for the diagnostic classification. The classification models were trained and evaluated by performing stratified 10-fold cross validation, and F-score and area-under-curve (AUC) metrics were used to quantify the performance. According to our results, it is possible to say that inclusion of peritumoral regions/tissues both contributes to the success of models and makes segmentation effort unnecessary. To the best of our knowledge, deep learning-based models have not been applied to the subtype classification of NSCLC in PET imaging, therefore, this study is a significant cornerstone providing thorough comparisons and evaluations of several deep learning models on metabolic imaging for lung cancer. Even simpler deep learning models are found promising in this domain, indicating that any improvement in deep learning models in machine learning community can be reflected well in this domain as well.Article Citation - WoS: 2Citation - Scopus: 2Machine-Generated Hierarchical Structure of Human Activities to Reveal How Machines Think(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Altin, Mahsun; Gursoy, Furkan; Xu, LinaDeep-learning based computer vision models have proved themselves to be ground-breaking approaches to human activity recognition (HAR). However, most existing works are dedicated to improve the prediction accuracy through either creating new model architectures, increasing model complexity, or refining model parameters by training on larger datasets. Here, we propose an alternative idea, differing from existing work, to increase model accuracy and also to shape model predictions to align with human understandings through automatically creating higher-level summarizing labels for similar groups of human activities. First, we argue the importance and feasibility of constructing a hierarchical labeling system for human activity recognition. Then, we utilize the predictions of a black box HAR model to identify similarities between different activities. Finally, we tailor hierarchical clustering methods to automatically generate hierarchical trees of activities and conduct experiments. In this system, the activity labels on the same level will have a designed magnitude of accuracy and reflect a specific amount of activity details. This strategy enables a trade-off between the extent of the details in the recognized activity and the user privacy by masking some sensitive predictions; and also provides possibilities for the use of formerly prohibited invasive models in privacy-concerned scenarios. Since the hierarchy is generated from the machine's perspective, the predictions at the upper levels provide better accuracy, which is especially useful when there are too detailed labels in the training set that are rather trivial to the final prediction goal. Moreover, the analysis of the structure of these trees can reveal the biases in the prediction model and guide future data collection strategies.Article Citation - WoS: 12Citation - Scopus: 14Insights Into Interface Treatments in P-Channel Organic Thin-Film Transistors Based on a Novel Molecular Semiconductor(IEEE-Inst Electrical Electronics Engineers Inc, 2017-05) Liguori, Rosalba; Usta, Hakan; Fusco, Sandra; Facchetti, Antonio; Licciardo, Gian Domenico; Di Benedetto, Luigi; Rubino, AlfredoOrganic thin-film transistors (OTFTs) were fabricated using a novel small molecule, C6-NTTN, as the semiconductor layer in several different architectures. The C6-NTTN layer was deposited via both vacuum evaporation at different substrate temperatures and via solution-processing, which yield maximum hole mobilities of 0.16 and 0.05 cm(2)/V . s, respectively. Surface treatments of the substrate, insulator, and metal contacts used for OTFT fabrication employing polymer films and different self-assembled monolayers were investigated. In particular, in bottom-gate devices, the insulator surface hydrophobicity was optimized by the deposition of poly(methyl methacrylate) or hexamethyldisilazane, while in the top-gate geometry, pentafluorobenzenethiol was efficiently used to modify the substrate surface energy and to change the contact work function. Atomic force microscopy analysis was exploited to understand the relationship between the semiconductor thin-film morphology and the device electrical performance. The results shown here indicate an inverse proportionality between the mobility and the interface trap density, with parameters depending especially on semiconductor-insulator interfacial properties, and a correlation between the threshold voltage and the characteristics of the semiconductor-metal interface.Article Impact of Input Sequence Types on Healthcare Intrusion Prediction Models(IEEE-Inst Electrical Electronics Engineers Inc, 2025) Yusof, Mohammad Hafiz Mohd; Balfaqih, Mohammed; Khan, Md Munir Hayet; Almohammedi, Akram A.; Balfagih, ZainPrediction models are vital for sensing zero-day and even n-day cyberattacks, particularly in healthcare infrastructure. Most existing research focuses on developing classifiers also known as IDS to enhance detection and accuracy. However, predictive intrusion models for healthcare remain underexplored, with limited studies investigating the comparative performance of univariate and multivariate inputs against single-step and multi-step outputs in time series models. This study aims to address these gaps by evaluating the accuracy and error performance of selected predictive models across various input and output configurations. The methodology involves transforming input data sequences into univariate l* n and multivariate m * n formats, establishing single-step and multi-step splitting functions, and evaluating these configurations using the benchmark CIRA-CIC-DoHBrw-2020 dataset. Algorithms including Bidirectional LSTM, Stacked LSTM, Vanilla LSTM, Transformer Encoder-Decoder, Vector Output LSTM (GRU core), and CNN were applied, with results visualized to assess performance. The findings reveal that the Multivariate LSTM model, when trained on a sequence of multivariate inputs, demonstrates superior predictive performance, achieving low MAE error rates of 0.4% for single-step predictions and 0.1% for multi-step predictions. Additionally, GRU and Transformer models exhibit heightened sensitivity to specific input sequence configurations. In conclusion, our study demonstrates that Transformer Encoder-Decoder based prediction models exhibit exceptional prediction performance. This effectiveness is attributed to their ability to capture contextual and critical information from input sequences. These findings provide valuable insights for designing advanced intrusion prediction models, paving the way for improved prediction capabilities in future systems.