Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Efficient Recovery of Linear Predicted Coefficients Based on Adaptive Steepest Descent Algorithm in Signal Compression for End-To Communications(John Wiley and Sons Ltd, 2025) Kamagara, A.; Kagudde, A.; Atakan, B.The efficiency of recovery and signal decoding efficacy at the receiver in end-to-end communications using linearly predicted coefficients are susceptible to errors, especially for highly compressed signals. In this paper, we propose a method to efficiently recover linearly predicted coefficients for high signal compression for end-to-end communications. Herein, the steepest descent algorithm is applied at the receiver to decode the affected linear predicted coefficients. This algorithm is used to estimate the unknown frequency, time, and phase. Subsequently, the algorithm facilitates down-conversion, time and carrier recovery, equalization, and correlation processes. To evaluate the feasibility of the proposed method, parameters such as multipath interference, additive white Gaussian noise, timing, and phase noise are modeled as channel errors in signal compression using the software-defined receiver. Our results show substantial recovery efficiency with noise variance between 0 and y × 10E − 3, where y lies between 0 and 10 using the modeled performance metrics of bit error rate, symbol error rate, and mean square error. This is promising for modeling software-defined networks using highly compressed signals in end-to-end communications. Copyright © 2025 Abel Kamagara et al. Journal of Electrical and Computer Engineering published by John Wiley & Sons Ltd.Article Citation - Scopus: 10Use of Magic Sandwich Echo and Fast Field Cycling Nmr Relaxometry on Honey Adulteration With Corn Syrup(John Wiley and Sons Ltd, 2022) Berk, B.; Cavdaroglu, C.; Grunin, L.; Ardelean, I.; Kruk, D.; Mazi, B.G.; Oztop, M.H.BACKGROUND: Adulteration is defined as the intentional addition of a material that is not a part of the nature. In this study, a non-conventional time domain nuclear magnetic resonance (TD-NMR) pulse sequence: magic sandwich echo (MSE) was used to detect the adulteration of honey by glucose syrup (GS) and high fructose corn syrup (HFCS) accompanied with T1 and T2 relaxation times. Also, fast field cycling NMR (FFC-NMR) relaxometry and multivariate analysis were performed to investigate the adulteration. RESULTS: Higher maltose in GS and changing glucose to water ratio of HFCS gave high correlation with the crystal content values. In HFCS adulteration, two separate populations of protons having different T2 values were detected and T1 times were also used to determine GS adulteration. Addition of GS increased T1 while addition of HFCS increased T2, significantly. CONCLUSION: The results showed that it is possible to differentiate the unadulterated and adulterated honey samples by using TD-NMR relaxation times and crystal content values obtained by the MSE sequence. By FFC-NMR relaxometry, not only GS addition but also the amount of GS was examined. The multivariate analysis technique of principal component analysis was able to distinguish the types of adulterants. © 2021 Society of Chemical Industry. © 2021 Society of Chemical Industry.Review Citation - WoS: 9Citation - Scopus: 12Readiness and Maturity Models for Industry 4.0: a Systematic Literature Review(John Wiley and Sons Ltd, 2023) Ünlü, H.; Demirörs, O.; Garousi, V.Industry 4.0 changes traditional manufacturing relationships from isolated optimized cells to fully integrated data and product flows across borders with its technological pillars. However, the transition to Industry 4.0 is not a straightforward journey in which organizations need assistance. A well-known approach that can be utilized during the early phases of the transition is to assess the capability of the organization. Maturity models are frequently used to improve capability. In this systematic literature review (SLR), we analyzed 22 maturity and readiness models based on 10 criteria: year, type, focus, structure, research methodology followed during the design of models, base frameworks, tool support, community support, objectivity, and extent of usage in practice. Our SLR provides a well-defined comparison for organizations to choose and apply available models. This SLR showed that (1) there is no widely accepted maturity/readiness model for Industry 4.0, as well as no international standard; (2) only a few models have received positive feedback from the industry, whereas most do not provide any practical usage information; and (3) the objectivity of the assessment method is controversial in most of the models. We have also identified a number of issues as open research areas for assessing readiness and maturity models for Industry 4.0. © 2023 John Wiley & Sons, Ltd.