SavedAdd your journal

Journals / Politeknik Dergisi / 2025 / Cilt: 28 Sayı: 4

The Application of Diffraction Analysis in Microcontrollers

Kırınım Analizinin Mikro Denetleyicilerde Uygulanması

Ömer Faruk Acar(KARABÜK ÜNİVERSİTESİ, LİSANSÜSTÜ EĞİTİM ENSTİTÜSÜ, BİLGİSAYAR MÜHENDİSLİĞİ (DR))0000-0003-1508-8108
Burhan Selçuk(KARABÜK ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ, BİLGİSAYAR MÜHENDİSLİĞİ PR.)0000-0002-5141-5148
Okan Erkaymaz(MİLLİ SAVUNMA ÜNİVERSİTESİ, DENİZ HARP OKULU, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-1996-8623
Pages
1195–1203
Publication Date
2025-07-06
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study is anticipated to promote the broader integration of neural networks into edge devices.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Although the use of artificial neural networks in computer systems has become widespread in many fields, limitations arise in small-scale computers.
📊
Key Findings & Data:In resource-constrained small computers, large-scale systems are required for model development and training.
💡
Conclusion & Impact:This study is anticipated to promote the broader integration of neural networks into edge devices.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Kırınım Analizi · Yapay Sinir Ağı · Mikro Denetleyici

Abstract

Although the use of artificial neural networks in computer systems has become widespread in many fields, limitations arise in small-scale computers. In resource-constrained small computers, large-scale systems are required for model development and training. In this study, the Diffraction Analysis algorithm has been adapted for small devices, demonstrating the successful designing of an artificial neural network. Real-time diffraction analysis has been conducted using the IRIS, wine and diabetes datasets. This study is anticipated to promote the broader integration of neural networks into edge devices.

Özet

Yapay sinir ağlarının bilgisayarlı sistemlerde kullanımı birçok alanda yaygınlaşsa da küçük bilgisayarlarda sınırlamalara takılmaktadır. Kaynakları kısıtlı olan küçük bilgisayarlarda modelin oluşturulması ve eğitimi için büyük ölçekli sistemlere ihtiyaç duyulmaktadır. Bu çalışmada Kırınım Analizi algoritmasını küçük cihazlara uyarlanarak yapay sinir ağının başarılı bir şekilde oluşturulduğu gösterilmektedir. IRIS, wine ve diyabet veri setleri kullanılarak gerçek zamanlı kırınım analizi yapılmıştır. Çalışmanın sinir ağlarının uç cihazlarda yaygınlaşmasına katkı sunacağı beklenmektedir.

🔖

Cite This Article

Acar, Ö. F., Selçuk, B., Erkaymaz, O., *, Ö. F. A. (2025). The Application of Diffraction Analysis in Microcontrollers. Politeknik Dergisi, Cilt: 28 Sayı: 4, 1195–1203. https://doi.org/10.2339/politeknik.1560537

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (35)

  1. Wei Wei, “Automatic Design of Microcontroller System Simulation Based on Artificial Intelligence Technology and Data Intelligence Analysis”, Procedia Computer Science, 228: 966-973, (2023).
  2. Barchi F., Parisi E., Zanatta L., Bartolini A., Acquaviva A., “Energy efficient and low-latency spiking neural networks on embedded microcontrollers through spiking activity tuning”, Neural Computing & Applications, 36: 18897–18917 (2024).
  3. Orășan I. L., Seiculescu C., Căleanu C. D., “A Brief Review of Deep Neural Network Implementations for ARM Cortex-M Processor”, Electronics, 11(16): 2545 (2022).
  4. Ali Z., Jiao L., Baker T., Abbas G., Abbas Z. H., Khaf S., “A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing”, IEEE Access, 7:149623-149633, (2019).
  5. Xie Y. –L., Lin X. –R., Lee C. –Y., Lin C. –W., “Design and Implementation of an ARM-Based AI Module for Ectopic Beat Classification Using Custom and Structural Pruned Lightweight CNN”, IEEE Sensors Journal, 24(12):19834-19844, (2024).
  6. Wang X., Magno M., Cavigelli L., Benini L., “FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things”, Internet of Things Journal, 7(5): 4403-4417, (2020).
  7. Warden P., Situnayake D., “TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers”, O’Reilly Media, 978-1-492-05204-3, 1005 Gravenstein Highway North, Sebastopol, (2019).
  8. Banbury C., Reddi V.J., Lam M., Fu W., Holleman J., Huang X., Whatmough P., “Benchmarking tinyml systems: Challenges and direction”, arXiv preprint, arXiv:2003.04821, (2020).
  9. David R., Duke J., Jain M., Warden P., Shawahna A., Concolato C., “Tensor- flow lite micro: Embedded machine learning on tinyml systems”, Proceedings of Machine Learning and Systems, 3:800-811, (2021).
  10. Lopes A., Santos F. P., Oliveira D., Schiezaro M., Pedrini H., “Computer Vision Model Compression Techniques for Embedded Systems:A Survey”, Computer & Graphics, 123:104015, (2024).
  11. Yang K., Xing T., Liu Y., Li Z., Gong X., Chen X., “cDeepArch: A Compact Deep Neural Network Architecture for Mobile Sensing”, IEEE/ACM Transactions on Networking, 27(5):2043-2055, (2019).
  12. Li Y., Tong Z., “Model predictive control strategy using encoder-decoder recurrent neural networks for smart control of thermal environment”, Journal of Building Engineering, 42: 103017, (2021).
  13. Seyedolhosseini A., Masoumi N., Modarressi M., Karimian N., “Daylight adaptive smart indoor lighting control method using artificial neural networks”, Journal of Building Engineering, 29:101141, (2020).
  14. Santhoshi B. K., Mohanasundaram K., Kumar L. A., “ANN‑based dynamic control and energy management of inverter and battery in a grid‑tied hybrid renewable power system fed through switched Z‑source converter”, Electrical Engineering, 103:2285–2301, (2021).
  15. Çınar, Muhammet Ali and Kalyoncu, Mete and Şen, Muhammed Arif, “Üç Serbestlik Dereceli (3R) Bir Çizim Robotunun Tasarımı ve Arı Algoritması Kullanılarak Optimal Yörünge Kontrolü”, Politeknik Dergisi, 27(3): 873–885, (2024).
  16. Sarić R., Jokić D., Beganović N., Pokvić L. G., Badnjević A., “FPGA-based real-time epileptic seizure classification using Artificial Neural Network”, Biomedical Signal Processing and Control, 62:102106, (2020).
  17. Bruschi A. G., Drewiacki D., Bidinotto J. H., “Artificial neural networks for PIO events classification comparing different data collection procedures”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46:496, (2024).
  18. Sagu A., Gill N. S., Gulia P., “Artificial Neural Network for the Internet of Things Security”, International Journal of Engineering Trends and Technology, 68(11):137-144, (2020).
  19. Ali S. M., Elameer A. S., Jaber M. M., “IoT network security using autoencoder deep neural network and channel access algorithm”, Journal of Intelligent Systems, 31(1): 95-103, (2021).
  20. Calp, M. Hanefi and Bütüner, Resul, "Detecting the Cyber Attacks on IoT-Based Network Devices Using Machine Learning Algorithms", Politeknik Dergisi, 27(5): 1971–1989, (2024).
  21. Kumar J., Singh A. K., “Workload prediction in cloud using artificial neural network and adaptive differential evolution”, Future Generation Computer Systems, 81:41-52, (2018).
  22. Erkaymaz O., “Resilient back-propagation approach in small-world feed-forward neural network topology based on Newman–Watts algorithm”, Neural Computing & Applications, 32:16279–16289, (2020).
  23. Selçuk B., Tankül A. N. A., “Hamiltonian path, routing, broadcasting algorithms for connected square network graphs”, Engineering Science and Technology, an International Journal, 44:101454, (2023).
  24. Gao B., Woo W. L., Tian G., “Sensors, Signal, and Artificial Intelligent Processing”, Journal of Sensors, 2022:1-5, (2022).
  25. Kasap M., Yılmaz M., Çinar E., Yazıcı A., “Unsupervised dissimilarity-based fault detection method for autonomous mobile robots”, Autonomous Robot, 47:1503–1518, (2023).
  26. Kaycı, Barış and Demir, Batıkan Erdem and Demir, Funda, “Deep Learning Based Fault Detection and Diagnosis in Photovoltaic System Using Thermal Images Acquired by UAV”, Politeknik Dergisi, 27(1): 91–99, (2024).
  27. https://www.raspberrypi.org, “Raspberry Pi Foundation: Raspberry Pi”, (2024).
  28. https://datasheets.raspberrypi.org/pico/pico-datasheet.pdf, “Raspberry Pi Foundation: Raspberry Pi Pico Datasheet”, (2024).
  29. https://datasheets.raspberrypi.com/pico/raspberry-pi-pico-c-sdk.pdf, “Raspberry Pi Pico C/C++ SDK”, (2024).
  30. https://github.com/raspberrypi/debugprobe/releases/tag/debugprobe-v2.0.1, “Raspberry Pi Debug Probe v2.0.1”, (2024).
  31. https://www.raspberrypi.com/documentation/microcontrollers/raspberry-pi-pico.html#debugging-using-another-raspberry-pi-pico, “Debugging using another Raspberry Pi Pico”, (2024).
  32. https://datasheets.raspberrypi.com/pico/getting-started-with-pico.pdf, “Getting Started with Raspberry Pi Pico”, (2024).
  33. https://archive.ics.uci.edu/ml/datasets/iris, “UCI Machine Learning Repository: IRIS Data Set”, (2024).
  34. https://archive.ics.uci.edu/ml/datasets/Wine, “UCI Machine Learning Repository: Wine Data Set”, (2024).
  35. https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes, “UCI Machine Learning Repository: Pima Indians Diabetes Database”, (2024).