Assoc. Prof.TugbaOzge Onur | Signal processing | Best Researcher Award

Assoc. Prof . Zonguldak Bulent Ecevit University , Turkey

Dr. Tuğba Özge Onur is an accomplished academic in Electrical-Electronics Engineering at Zonguldak Bülent Ecevit University 🇹🇷. With a research focus on ultrasonic imaging, signal processing, and digital holography 🧠📡, she has significantly contributed to medical and acoustic imaging technologies. She earned her PhD in 2016 and has steadily climbed the academic ladder to the position of Associate Professor 🧑‍🏫. Dr. Onur is known for her innovative use of algorithms and AI in engineering solutions 🤖📊. Her dedication to scientific research is reflected in numerous national journal publications and collaborative studies across disciplines 🌐📚.

Professional Profile

GOOGLE SCHOLAR

Education & Experience

Dr. Onur earned her BSc (2005), MSc (2008), and PhD (2016) in Electrical-Electronics Engineering from Bülent Ecevit University 🎓⚙️. Her doctoral work focused on ultrasonic target detection using echo estimation methods in solid, liquid, and tissue environments 🧬🩻. She began her academic career as a research assistant in 2005 and transitioned to a full-time faculty member in 2018 👩‍🔬📘. She has served across various roles within the Devreler ve Sistemler Teorisi department, showcasing a consistent commitment to academic excellence and education 👩‍🏫💼.

Professional Development

Over the years, Dr. Onur has enhanced her academic profile through continuous research, advanced imaging techniques, and algorithm development 🧪📐. She applies deep learning, genetic algorithms, and holography in solving engineering problems 🧠🔬. Actively contributing to national journals and interdisciplinary projects, she emphasizes collaboration and innovation 🔄🤝. Her teaching and mentorship roles help shape future engineers while she advances her own research line 🚀📈. Proficient in English with a strong YDS score (76.25) 🇬🇧📊, she stays updated through conferences, workshops, and academic networks 📅🌐.

 Research Focus

Dr. Onur’s research lies at the intersection of signal processing, digital holography, and ultrasonic imaging 🔍🖼️. She specializes in using AI-driven methods like binary genetic algorithms and deep learning for image reconstruction and tissue-mimicking phantom analysis 🤖💡. Her work contributes to medical diagnostics, non-invasive testing, and advanced visualization techniques 🧬🧠. She actively investigates hyperparameter effects in classification models and promotes computational efficiency in bioengineering tasks 🧑‍🔬📊. This multidisciplinary research bridges electronics, medicine, and computer science 🌉🔬, and supports real-world innovations in diagnostic imaging 🏥📈.

Awards & Honors

Dr. Tuğba Özge Onur has been recognized for her contributions to Turkish engineering and academic research 🏅🇹🇷. Her appointment as Associate Professor in 2024 by the Interuniversity Council of Turkey is a testament to her scholarly impact 📜🎖️. She has collaborated internationally, including with experts like Johan Carlson and Erika Svanström, enhancing her academic visibility 🌍🤝. Her publications in national journals have been appreciated for their originality and application of emerging technologies 🧠🔬. She remains a respected figure in her department, mentoring students and contributing to academic excellence 🌟📚.

Publication Top Notes

1.Improved Image Denoising Using Wavelet Edge Detection Based on Otsu’s Thresholding

📌 Onur, T.Ö. (2022). Acta Polytechnica Hungarica.
🔗 PDF – acta.uni-obuda.hu
📈 Citations: 29
📝 Summary: This study presents an enhanced image denoising technique combining wavelet transform and Otsu’s thresholding for edge detection. The method effectively preserves edge features while reducing noise in digital images, improving visual quality and accuracy for further image processing applications.

2.Dynamic Viscosity Prediction of Nanofluids Using Artificial Neural Network (ANN) and Genetic Algorithm (GA)

📌 Topal, H.İ., Erdoğan, B., Koçar, O., Onur, T.Ö., et al. (2024). Journal of the Brazilian Society of Mechanical Sciences and Engineering.
🔗 PDF – Springer / ResearchGate
📈 Citations: 8
📝 Summary: This paper predicts the viscosity of nanofluids using hybrid artificial intelligence models. ANN and GA were used to model and optimize prediction performance. The results are useful for heat transfer applications in energy systems, where fluid behavior under various temperatures and compositions is critical.

3.Discarding Lifetime Investigation of a Rotation Resistant Rope Subjected to Bending Over Sheave Fatigue

📌 Onur, Y.A., İmrak, C.E., Onur, T.Ö. (2019). Measurement, Elsevier.
🔗 PDF – academia.edu
📈 Citations: 20
📝 Summary: This research investigates the fatigue lifetime of rotation-resistant ropes under repetitive bending conditions. Theoretical and experimental data reveal critical discarding criteria, improving safety in industrial and mechanical systems reliant on rope-based transport or lifting mechanisms.

4.The Effect of Hyper Parameters on the Classification of Lung Cancer Images Using Deep Learning Methods

📌 Narin, D., Onur, T.Ö. (2022). Erzincan University Journal of Science and Technology.
🔗 PDF – dergipark.org.tr
📈 Citations: 16
📝 Summary: This paper explores how different hyperparameter settings in deep learning architectures influence the classification performance of lung cancer images. The study guides optimal model configuration for improving diagnostic accuracy in medical imaging.

5.Genetic Algorithm-Based Image Reconstruction Applying the Digital Holography Process with the Discrete Orthonormal Stockwell Transform Technique for Diagnosis of COVID-19

📌 Kaya, G.U., Onur, T.Ö. (2022). Computers in Biology and Medicine, Elsevier.
🔗 PDF – nih.gov
📈 Citations: 8
📝 Summary: This work develops an advanced reconstruction method combining genetic algorithms and the Discrete Orthonormal Stockwell Transform for holographic imaging. It is applied to improve diagnostic imaging of COVID-19, offering real-time and accurate image enhancement.

6.An Application of Filtered Back Projection Method for Computed Tomography Images

📌 Onur, T.Ö. (2021). International Review of Applied Sciences and Engineering.
🔗 PDF – akjournals.com
📈 Citations: 10
📝 Summary: This article investigates the application of the Filtered Back Projection (FBP) method in computed tomography (CT). It compares FBP with other analytical and iterative methods to demonstrate its computational advantages in producing high-resolution diagnostic images.

Conclusion

Dr. Tuğba Özge Onur’s technical depth, innovation in methodology, and real-world relevance of research make her a strong candidate for Best Researcher Awards. Her pioneering work in digital medical imaging and algorithm-based diagnostics positions her at the forefront of engineering solutions for healthcare, contributing to both academia and industry. She blends scientific rigor with technological creativity, fulfilling the key qualities recognized by top research honors. 🥇📚🔬