الكادر التدريسي

يونيو 11, 2026, 10:50 ص
فارس عصمت فضيل (دكتوراه)
مدرس
مدرس في معالجة الإشارات والأنظمة

الهندسة الحيوية
کلیة الهندسة
جامعة دهوك

  • دكتوراه في الهندسة (Dr.-Ing.)، هندسة علوم الحياة، جامعة تيجنيشه هوخشوله ميتلهسين (THM) – جامعة العلوم التطبيقية، ألمانيا، ٢٠٢١-٢٠٢٥.
  • ماجستير في هندسة الاتصالات الإلكترونية والحاسوب، جامعة نوتنغهام، المملكة المتحدة، ٢٠١٣-٢٠١٤.
  • بكالوريوس في الهندسة الكهربائية وهندسة الحاسوب، جامعة دهوك، العراق، ٢٠٠٧-٢٠١١.

ولد فارس عصمت في بغداد، العراق، عام 1988. حصل على شهادة البكالوريوس في الهندسة الكهربائية وهندسة الحاسوب من جامعة دهوك، إقليم كوردستان-العراق، عام 2011. وأكمل دراسة الماجستير في هندسة الاتصالات الإلكترونية والحاسوب من جامعة نوتنغهام في نوتنغهام، المملكة المتحدة. انضم إلى قسم هندسة الكهرباء والحاسوب بجامعة دهوك عام 2014 كمدرس مساعد، ثم انضم إلى قسم هندسة الطب الحياتي بجامعة دهوك عام 2019. أكمل دراسة الدكتوراه في هندسة الطب الحياتي في كلية هندسة علوم الحياة (LSE) بجامعة تيجنيشه هوخشوله ميتلهسين (THM) – جامعة العلوم التطبيقية في غيسن، ألمانيا، بمرتبة الشرف العليا (امتياز مع مرتبة الشرف الأولى / Summa Cum Laude). تتركز اهتماماته البحثية في مجالات هندسة الطب الحياتي، معالجة الإشارات، إزالة الضوضاء من الإشارات الحيوية باستخدام مشفرات الترشيح التلقائية (Denoising Autoencoders)، معالجة الإشارات الحيوية بواسطة الشبكات العصبية، والنمذجة المتناثرة (Sparse Modeling) للإشارات الطبية الحيوية وإزالة الضوضاء منها. وله تعاون نشط مع باحثين في عدة تخصصات أخرى ضمن الهندسة الكهربائية وهندسة الحاسوب.

  • الإشارات والأنظمة (مرحلة البكالوريوس) (Signal and System)
  • تقنيات القياسات الطبية الحيوية (مرحلة البكالوريوس) (Biomedical Measurement Techniques)
  • الدوائر الكهربائية (مرحلة البكالوريوس) (Electrical Circuit)

  • Participating in the 2nd International Summer School on Cyprus part of the master course of “Biomedical Engineering” of the module “Signal and Image processing in Medicine” (6 ECTS). (this program was Granted by DAAD).
  • Participating in the DAAD project “Bioniq- Bio/MedPhys” to visit university of applied science, THM, Giessen in the period 1st of October 2018 to the 31th of December 2018, working on my PhD proposal in the field of Biomedical Engineering.
  • Participate in the DAAD project “Sustainable Development of Biomedical Engineering in Northern of Iraq-SD-BIONIQ” to visit university of applied science, THM, Giessen in the period 1st of September 2019 to the 30th of November 2019, working on my PhD proposal in the field of Biomedical Engineering.
  • Developing Msc project “LOCALIZATION OF MONOPOLE AND DIPOLE SOURCE IN 3 DIMENSIONS” for the Msc students of university of applied science, THM, Giessen as a part of the DAAD project “Bioniq- Bio/MedPhys”.
  • Training on EEG recording in Azadi Hospital for three months since 27th of March 2019
  • Participating in Innovation Expo of Duhok Province Universities 2017 with project title “Adapted TV remote control using EOG”. This event was funded by European Union and implemented by UNDP.

البحث العلمي

1. Alfa, M., Samann, F., & Schanze, T. (2026). ML-CDAE: Multi-Lead Convolutional Denoising Autoencoder for Denoising 12-Lead ECG Signals. Signals, 7(1), 18. https://doi.org/10.3390/signals7010018
2. F. Samann and T. Schanze, “AE-DD: Autoencoder-Driven Dictionary with Matching Pursuit for Joint ECG Denoising, Compression, and Morphology Decomposition,” AI, vol. 6, no. 9, p. 234, Sep. 2025, doi: 10.3390/ai6090234. (Impact factor=5)
3. F. Samann, Towards Real-Time ECG Signal Denoising using Sparse and Shallow Running Denoising Autoencoder, Technische Hochschule Mittelhessen, 2025.
4. N. Busch, F. Samann, A. Neißner, M. Fiebich, and T. Schanze, “Denoising of low dose CT scans by means of Denoising Autoencoder,” Abstracts of the 58th Annual Meeting of the German Society of Biomedical Engineering, 2024.
5. A. Prächte, F. Samann, and T. Schanze, “Implementation of running denoising autoencoder (RunDAE) on Arduino for real-time denoising of ECG,” Abstracts of the 58th Annual Meeting of the German Society of Biomedical Engineering, 2024.
6. F. Samann, F. Hubich, T. Ott, and T. Schanze, “Automatisierungstechnik: Muscle fatigue detection based on sEMG signal using autocorrelation function and neural networks,” De Gruyter, 2024.
7. F. Samann and T. Schanze, “Denoising by spectral selections of SVD representations of Hankel matricificated data with application to PPG signals,” IFAC-PapersOnLine, vol. 58, no. 24, pp. 175–180, 2024.
8. F. Samann, F. Hubich, T. Ott, and T. Schanze, “Muscle fatigue detection based on sEMG signal using autocorrelation function and neural networks,” at - Automatisierungstechnik, vol. 72, no. 5, pp. 408–416, 2024.
9. F. Samann and T. Schanze, “RESEMBLING THE MORPHOLOGIES OF ECG SIGNALS USING REGULARIZED DENOISING AUTOENCODER,” Passer Journal of Basic and Applied Sciences, vol. 6 (Special Issue), pp. 341–351, 2024.
10. L. M. Meyer, F. Samann, and T. Schanze, “DualSort: online spike sorting with a running neural network,” Journal of Neural Engineering, vol. 20, no. 5, p. 056031, 2023. (Impact factor=4)
11. F. Samann and T. Schanze, “RunDAE model: Running denoising autoencoder models for denoising ECG signals,” Computers in Biology and Medicine, p. 107553, 2023. (Impact factor=7)
12. F. Samann, L. Meyer, and T. Schanze, “Removing noise and overlapping spikes from extracellular recordings using a regularized denoising autoencoder,” Current Directions in Biomedical Engineering, vol. 9, no. 1, pp. 279–282, 2023.
13. F. Samann and T. Schanze, “Multiple ECG segments denoising autoencoder model,” Biomedical Engineering/Biomedizinische Technik, vol. 68, no. 3, pp. 275–284, 2023. (Impact factor=0.9)
14. F. Samann and T. Schanze, “EMG based muscle fatigue detection using autocorrelation and k-means clustering,” Proceedings on Automation in Medical Engineering, vol. 2, no. 1, p. 739, 2023.
15. L. M. Meyer, T. Schanze, and F. Samann, “A single-hidden-layer neural network for the classification of spike-waveforms,” Proceedings on Automation in Medical Engineering, vol. 2, no. 1, p. 747, 2023.
16. B. Marwan, F. Samann, and T. Schanze, “Cleaning Noisy ECG based on the Signal Quality with Single and Multiple Hidden Layer Autoencoder,” 2022 2nd International Conference on Intelligent Cybernetics Technology, 2022.
17. B. Marwan, F. Samann, and T. Schanze, “Denoising of ECG with single and multiple hidden layer autoencoders,” Current Directions in Biomedical Engineering, vol. 8, no. 2, pp. 652–655, 2022.
18. F. Samann and T. Schanze, “Multiple parallel hidden layers autoencoder for denoising ECG signal,” Current Directions in Biomedical Engineering, vol. 8, no. 2, pp. 161–164, 2022.
19. F. Samann and T. Schanze, “Abstracts of the 2022 Joint Annual Conference of the Austrian (ÖGBMT), German (VDE DGBMT) and Swiss (SSBE) Societies for Biomedical Engineering,” Biomedical Engineering/Biomedizinische Technik, vol. 67, suppl. 1, pp. 1–580, 2022.
20. F. Samann and T. Schanze, “Entrauschen von EKG-Signalen anhand von Autoencodern mit hybriden verborgenen Neuronenschichten,” DGMP 2022 – 53. Jahrestagung der Deutschen Gesellschaft für Medizinische Physik, 2022.
21. F. Samann and T. Schanze, “Denoising biomedical signals via adaptive low-rank matrix representation by singular value decomposition using wavelets,” 2021 4th Int. Conf. on Bio-Engineering for Smart Technologies, 2021.
22. F. Samann and T. Schanze, “Finding an optimal dictionary of different wavelet types using sparse modeling to denoise ECG signal,” Current Directions in Biomedical Engineering, vol. 7, no. 2, pp. 125–128, 2021.
23. F. Samann and T. Schanze, “Use of a trained denoising autoencoder to estimate the noise level in the ECG,” Current Directions in Biomedical Engineering, vol. 7, no. 2, pp. 562–565, 2021.
24. F. Samann, S. A. Bamerni, J. A. Khorsheed, and A. K. Al-sulaifanie, “Adaptive Real-Time Wavelet Denoising Architecture Based on Feedback Control Loop,” Journal of Engineering Research, vol. 9 (ICRIE Special Issue), pp. 1–18, 2021.
25. F. Samann and T. Schanze, “On estimating the optimal autoencoder model for denoising ECG using Akaike Information Criterion,” AUTOMED - Automation in Medical Engineering, 2021.
26. R. Bassam and F. Samann, “Smart Parking System based on Improved OCR Model,” IOP Conf. Ser.: Materials Science and Engineering, vol. 978, no. 1, p. 012007, 2020.
27. M. Schubert, F. Samann, and T. Schanze, “An improved simple experimental setup for superimposed PPG signal separation,” Innovative digitale Verarbeitung bioelektrischer und -magnetischer Signale, 2020.
28. M. Schubert, F. Samann, and T. Schanze, “QRS triggered averaging for superimposed PPG separation,” Proc. on Automation in Medical Engineering, vol. 1, no. 1, p. 014, 2020.
29. M. Schubert, F. Samann, and T. Schanze, “Towards non-invasive fetal blood oxygen level acquisition: ECG-triggered separation of superimposed PPG,” 54th Annual Conference of the German Society for Biomedical Engineering, vol. 1, pp. 1–2, 2020.
30. F. Samann, A. Rausch, and T. Schanze, “Electrical Dipole Source Localization using Hybrid Least Squares Method in combination with ICA,” Current Directions in Biomedical Engineering, vol. 5, no. 1, pp. 361–364, 2019.
31. F. Samann and T. Schanze, “An efficient ECG denoising method using discrete wavelet with Savitzky-Golay filter,” Current Directions in Biomedical Engineering, vol. 5, no. 1, pp. 385–387, 2019.
32. F. E. Samann, “Real-time Liquid Level and color Detection system using Image Processing,” Academic Journal of Nawroz University (AJNU), vol. 7, no. 4, pp. 223–227, 2018.
33. F. E. Samann and M. S. Hadi, “HUMAN TO TELEVISION INTERFACE FOR DISABLED PEOPLE BASED ON EOG,” Journal of University of Duhok, vol. 21, no. 1, pp. 54–64, 2018.
34. F. E. Samann, “SIMPLE AND ROBUST EYE MOVEMENTS DETECTION METHOD,” Journal of Duhok University, vol. 20, no. 1, pp. 152–163, 2017.
35. F. E. Samann, “INTERCHANNEL AND CROSS GAIN CROSSTALK EFFECTS IN WDM SYSTEMS WITH SOAs,” Ph.D. thesis, University of Nottingham, UK.

  • معالجة الإشارات الطبية الحيوية (Biomedical signal processing)
  • إزالة الضوضاء من تخطيط كهربائية القلب (ECG) (ECG denoising)
  • الشبكات العصبية (Neural networks)
  • التعلم الآلي في الرعاية الصحية (Machine learning in healthcare)

مشرف مشارك على أطروحات الماجستير في جامعة THM

  1. Neural Networks for Spike Sorting Applications
  2. Noisy ECG Cleaning Using Single- and Multi-layer Autoencoders Based on Signal Quality
  3. ML-CDAE: Multi-lead Convolutional Denoising Autoencoder for 12-lead ECG Signal Denoising
  4. Modeling and Localization of Electrical Signal Sources Using an L1-Regularized Multi-Monopole Approach