Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 6Citation - Scopus: 10Using Chemosensory-Induced Eeg Signals To Identify Patients With <i>de Novo</I> Parkinson's Disease(Elsevier Sci Ltd, 2024) Olcay, Orkan; Onay, Fatih; Ozturk, Guliz Akin; Oniz, Adile; Ozgoren, Murat; Hummel, Thomas; Guducu, CagdasObjective: Parkinson's disease (PD) patients generally exhibit an olfactory loss. Hence, psychophysical or electrophysiological tests are used for diagnosis. However, these tests are susceptible to the subjects' behavioral response bias and require advanced techniques for an accurate analysis. Proposed Approach: Using well-known feature extraction methods, we characterized chemosensory-induced EEG responses of the participants to classify whether they have PD. The classification was performed for different time intervals after chemosensory stimulation to see which temporal segment better separates healthy controls and subjects with de novo PD. Results: The performances show that entropy and connectivity features discriminate effectively PD and HC participants when olfactory-induced EEG signals were used. For these methods, discrimination is over 80% for segments 100-700 and 200-800 milliseconds after stimulus onset. Comparison with Existing Methods: We compared the performance of our framework with linear predictive coding, bispectrum, wavelet entropy-based methods, and TDI score-based classification. While the entropy- and connectivity-based methods elicited the highest classification performances for olfactory stimuli, the linear predictive coding-based method elicited slightly higher performance than our framework when the trigeminal stimuli were used. Conclusion: This is one of the first studies that use chemosensory-induced EEG signals along with different feature extraction methods to classify healthy subjects and subjects with de novo PD. Our results show that entropy and functional connectivity methods unravel the chemosensory-induced neural dynamics encapsulating critical information about the subjects' olfactory performance. Furthermore, time- and frequency-resolved feature analysis is beneficial for capturing disease-affected neural patterns.Correction Corrections To “massive Mimo-Noma Based Mec in Task Offloading for Delay Minimization”(IEEE, 2023) Yılmaz, Saadet Simay; Özbek, Berna[No abstract available]Article Citation - WoS: 2Citation - Scopus: 2A Non-Resonant Approach for Dielectric Constant Reconstructions Via Newton Iterations(Elsevier, 2023) Özkal, Ceren; Yaman, FatihIn this study, a Newton–Raphson-based iterative method has been proposed to obtain dielectric constants accurately from measurements. The originalities of the approach lie in its applicability at non-resonant frequencies, which brings a significant experimental simplicity by avoiding critical coupling, expansion of available frequencies in different bands with the same cost-efficient low-Q (?60) cavity. The direct problem involves either measuring power values inside a cavity (14.6 × 5 × 20.6) cm via a spectrum analyzer or simulating the complete setup via CST-MWS software at one of the non-resonant modes, 1.5 GHz. The solution to the inverse problem provides fastly converging results with an error rate of 1% for the unknown permittivities. The experiments were carried out using five different liquid samples even though the proposed technique does not have a limitation on solid materials. Applicability and the effectiveness of the introduced method is illustrated in detail and comparisons with the perturbation method is provided. © 2023 Elsevier GmbHArticle Citation - WoS: 6Citation - Scopus: 7Time-Resolved Eeg Signal Analysis for Motor Imagery Activity Recognition(Elsevier, 2023) Olcay, Bilal Orkan; Karaçalı, BilgeAccurately characterizing brain activity requires detailed feature analysis in the temporal, spatial, and spectral domains. While previous research has proposed various spatial and spectral feature extraction methods to distinguish between different cognitive tasks, temporal feature analysis for each separate brain region and frequency band has been largely overlooked. This study introduces two novel approaches for recognizing cognitive activity: temporal entropic profiling and time-aligned common spatio-spectral patterns analysis. These approaches capture and use discriminative short-lived signal segments for motor imagery activity recognition. In Approach-1, we evaluated nine different measures to determine timing parameters that showed altered behavior associated with maximal inter-activity differences, which we then used in a machine-learning framework. In Approach-2, we used the best-performing signal characteristic measures from Approach-1 to determine the optimum latency of each channel at each frequency band for a CSP-based activity recognition strategy. We evaluated both approaches on two online available motor imagery EEG datasets and achieved average recognition accuracy levels of 86%. We compared our methods with four established BCI methods. The performance results show that our approaches exceeded the benchmark methods' performances, with notable improvements in the proposed time-aligned common spatio-spectral patterns approach. This study demonstrates that motor imagery recognition performance is improved when a temporal analysis is adopted alongside spatio-spectral neural feature analysis and that timing parameters associated with the maximal entropic difference of EEG segments to the cognitive tasks varied between different brain regions and subjects. © 2023 Elsevier LtdArticle Citation - WoS: 15Citation - Scopus: 17Massive Mimo-Noma Based Mec in Task Offloading for Delay Minimization(IEEE, 2023) Yilmaz, Saadet Simay; Özbek, BernaMobile edge computing (MEC) has been considered a promising technology to reduce task offloading and computing delay by enabling mobile devices to offload their computation-intensive tasks. Non-orthogonal multiple access (NOMA) is regarded as a promising method of increasing spectrum efficiency, while Massive multiple-input multiple-output (MIMO) can support a larger number of users for simultaneous offloading. These two technologies can effectively facilitate offloading and further improve the performance of MEC systems. In this work, we propose a NOMA and Massive MIMO assisted MEC system for delay-sensitive applications. Our objective is to minimize the overall computing and transmission delay under users' transmit power and MEC computing capability. Through the pairing scheme for Massive MIMO-NOMA, the users with the higher channel gain can offload all their data, while the users with the lower channel gain can offload a portion of their data to the MEC. Performance results are provided regarding to the sum data rate and overall system delay compared with the orthogonal multiple access (OMA)-MIMO based and Massive MIMO (M-MIMO) based MEC systems.Article Citation - WoS: 22Citation - Scopus: 26Adaptive Sign Algorithm for Graph Signal Processing(Elsevier, 2022) Yan, Yi; Kuruoğlu, Ercan Engin; Altınkaya, Mustafa AzizEfficient and robust online processing techniques for irregularly structured data are crucial in the current era of data abundance. In this paper, we propose a graph/network version of the classical adaptive Sign algorithm for online graph signal estimation under impulsive noise. The recently introduced graph adaptive least mean squares algorithm is unstable under non-Gaussian impulsive noise and has high computational complexity. The Graph-Sign algorithm proposed in this work is based on the minimum dispersion criterion and therefore impulsive noise does not hinder its estimation quality. Unlike the recently proposed graph adaptive least mean pth power algorithm, our Graph-Sign algorithm can operate without prior knowledge of the noise distribution. The proposed Graph-Sign algorithm has a faster run time because of its low computational complexity compared to the existing adaptive graph signal processing algorithms. Experimenting on steady-state and time-varying graph signals estimation utilizing spectral properties of bandlimitedness and sampling, the Graph-Sign algorithm demonstrates fast, stable, and robust graph signal estimation performance under impulsive noise modeled by alpha stable, Cauchy, Student's t, or Laplace distributions.Article Citation - WoS: 101Citation - Scopus: 129Scientific Applications of Distributed Acoustic Sensing: State-Of Review and Perspective(MDPI, 2022) Gorshkov, Boris G.; Yüksel, Kıvılcım; Fotiadi, Andrei A.; Wuilpart, Marc; Korobko, Dmitry A.; Zhirnov, Andrey A.; Lobach, Ivan A.This work presents a detailed review of the development of distributed acoustic sensors (DAS) and their newest scientific applications. It covers most areas of human activities, such as the engineering, material, and humanitarian sciences, geophysics, culture, biology, and applied mechanics. It also provides the theoretical basis for most well-known DAS techniques and unveils the features that characterize each particular group of applications. After providing a summary of research achievements, the paper develops an initial perspective of the future work and determines the most promising DAS technologies that should be improved.Article Citation - WoS: 3Citation - Scopus: 5Design and Implementation of Spatial Correlation-Based Clustering for Multiuser Miso-Noma Systems(Institute of Electrical and Electronics Engineers Inc., 2021) Göztepe, Caner; Özbek, Berna; Karabulut Kurt, GüneşIn this letter, we propose a novel user clustering algorithm for downlink multiuser multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) systems along with its implementation in a real-time testing environment. The proposed method selects the clusters by considering users' spatial channel properties against the generated orthogonal directions. This is then followed by power allocation and zero-forcing precoding steps to mitigate the interference between the selected clusters. Performance comparisons are provided in terms of both real-time tests and simulations. It is demonstrated that a notable improvement in capacity and reliability can be obtained through the proposed approach in multiuser MISO-NOMA systems with reduced complexity.Article Citation - WoS: 6Citation - Scopus: 6User Selection for Millimeter Wave Non-Uniform Full Dimensional Mimo(IEEE, 2020) Mumtaz, Rao; Gonzalez, Jonathan; Cumalı, İrem; Özbek, BernaThe millimeter wave (mmWave) based full-dimensional (FD) MIMO communication is one of the promising technology to fulfill the demand of high data rate for the sixth generation (6G) services including 6D hologram, haptic and multi-sensory communications. In order to satisfy the requirements of 6G applications, we investigate a non-uniform rectangular array (NURA) structure with FD-MIMO antenna systems for the multiuser mmWave communications. For the dense scenarios where the number of users to be served is high, we propose user selection algorithms for both digital and hybrid transceiver designs in FD-MIMO with NURA for the multiuser mmWave communications. For the digital transceivers, the users are selected based on their channel correlation considering FD-MIMO with NURA structures. For the hybrid transceivers, sequential user and beam selection is performed using the correlation between the beamspace channels in FD-MIMO with NURA case. The superiority of the NURA compared to uniform antenna structure is shown through the performance evaluations in the multiuser mmWave communications. Besides, the sum data rate results and complexity analysis denote the feasibility of the proposed algorithms compared to the joint user and beam selection schemes.Article Citation - WoS: 11Citation - Scopus: 12Expandable Polymer Assisted Wearable Personalized Medicinal Platform(Wiley, 2020) Babatain, Wedyan; Wicaksono, Irmandy; Buttner, Ulrich; El-atab, Nazek; Rehman, Mutee Ur; Hussain, Muhammad Mustafa; Gümüş, AbdurrahmanConventional healthcare, thoughts of treatment, and practice of medicine largely rely on the traditional concept of one size fits all. Personalized medicine is an emerging therapeutic approach that aims to develop a therapeutic technique that provides tailor-made therapy based on everyone's individual needs by delivering the right drug at the right time with the right amount of dosage. Advancement in technologies such as wearable biosensors, point-of-care diagnostics, microfluidics, and artificial intelligence can enable the realization of effective personalized therapy. However, currently, there is a lack of a personalized minimally invasive wearable closed-loop drug delivery system that is continuous, automated, conformal to the skin, and cost-effective. Here, design, fabrication, optimization, and application of a personalized medicinal platform augmented with flexible biosensors, heaters, expandable actuator and processing units powered by a lightweight battery are shown. The platform provides precise drug delivery and preparation with spatiotemporal control over the administered dose as a response to real-time physiological changes of the individual. The system is conformal to the skin, and the drug is transdermally administered through an integrated microneedle. The developed platform is fabricated using rapid, cost-effective techniques that are independent of advanced microfabrication facilities to expand its applications to low-resource environments.