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Journals / Selçuk Üniversitesi Mühendislik Bilim ve Teknoloji Dergisi / 2018 / Cilt: 6 - Sayı: 2

IN-BODY RANGING FOR ULTRA-WIDE BAND WIRELESS CAPSULE ENDOSCOPY USING NEURAL NETWORKS BASED ON PARTICLE SWARM OPTIMIZATION

Parçacık Sürü Optimizasyonuna Dayalı Sinir Ağları Kullanılarak Ultra Geniş Bant Kablosuz Kapsül Endoskopide Vücut İçi Mesafe Ölçümü

Rüştü AKAY
Memduh SUVEREN
Muzaffer KANAAN
Pages
207–217
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

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

Our results indicate that PSO-based techniques canoutperform traditional techniques by as much as 44%, depending on the activation functions used in theneural network.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:We consider the problem of accurate in-body ranging for localization of a wireless capsuleendoscope utilizing ultra-wide band (UWB) signaling.
📊
Key Findings & Data:First, we undertake asystematic performance analysis of the PSO technique for UWB-based in-body ranging and propose animproved version of the PSO algorithm.
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Conclusion & Impact:Our results indicate that PSO-based techniques canoutperform traditional techniques by as much as 44%, depending on the activation functions used in theneural network.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

We consider the problem of accurate in-body ranging for localization of a wireless capsuleendoscope utilizing ultra-wide band (UWB) signaling. In this context, we explore the joint use of neuralnetwork structures and learning algorithms based on metaheuristics, an example of which is particleswarm optimization (PSO). The contributions of this paper are three-fold. First, we undertake asystematic performance analysis of the PSO technique for UWB-based in-body ranging and propose animproved version of the PSO algorithm. Second, we quantitatively compare the performance of PSOtechniques against more traditional learning algorithms, such as Bayesian Regularization, Levenberg-Marquardt and Single Conjugate Gradient. Third, we quantify the impact of activation functions used todefine the neural network structure on performance. Our results indicate that PSO-based techniques canoutperform traditional techniques by as much as 44%, depending on the activation functions used in theneural network.

Özet

Bu makalede, ultra geniş bant (UWB) sinyalleri kullanan bir kablosuz kapsül endoskopunun lokalizasyonu için hassas vücut içi mesafe ölçümü problemi ele alınmaktadır. Bu bağlamda, yapay sinir ağları ve metaheuristik temelli öğrenme algoritmalarının (örnek olarak parçacık sürüsü optimizasyonu (PSO) ortak kullanımı irdelenmektedir. Makalenin literatüre katkıları şu şekilde özetlenebilir. İlk olarak, UWB tabanlı vücut içi mesafe ölçümü için PSO algoritmasının sistematik bir performans analizi yapılmış ve söz konusu problemin çözümü için PSO algoritmasının geliştirilmiş bir versiyonu önerilmiştir. İkinci olarak, önerilen PSO algoritmasının performansı Bayesian Regularization, Levenberg-Marquardt ve Single Conjugate Gradient gibi geleneksel öğrenme algoritmaları ile karşılaştırılmıştır. Son olarak, yapay sinir ağlarında kullanılan aktivasyon fonksiyonlarının performans üzerindeki etkileri incelenmiştir. Elde edilen sonuçlar, önerilen PSO algoritması vasıtası ile geleneksel tekniklere nazaran % 44’e varan performans artışları elde edilebileceğini göstermektedir.

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Cite This Article

AKAY, R., SUVEREN, M., KANAAN, M. (2022). IN-BODY RANGING FOR ULTRA-WIDE BAND WIRELESS CAPSULE ENDOSCOPY USING NEURAL NETWORKS BASED ON PARTICLE SWARM OPTIMIZATION. Selçuk Üniversitesi Mühendislik Bilim ve Teknoloji Dergisi, Cilt: 6 - Sayı: 2, 207–217. https://ajindex.com/articles/592124

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References (11)

  1. Alba, E., Marti, R., 2006, Metaheuristic Procedures for Training Neural Networks, Operations Research/Computer Science Interfaces Series, Springer, New York, NY, USA. Boussaïda, I., Lepagnot, J., Siarry, P., 2013, “A Survey on Optimization Metaheuristics”, Information Sciences, Vol. 237, pp. 82-117.
  2. Ch, S., Mathur S., 2012, “Particle Swarm Optimization Trained Neural Network for Aquifer Parameter Estimation”, KSCE Journal of Civil Engineering, Vol. 16, No. 3, pp. 298-307.
  3. Chavez-Santiago R., Balasingham I., “Computation of the Transmission Frequency Band for The Ultra Wideband Capsule Endoscope”, 7th International Symposium on Medical Information and Communication Technology (ISMICT), Tokyo, Japan, pp. 66-70, 6-8 March 2013.
  4. Chen, J., 2013, UWB Characteristics of RF Propagation for Body Mounted and Implanted Sensors, MSc Thesis, Worcester Polytechnic Institute, MA. Chen, J., Ye, Y., Pahlavan, K., “Comparison of UWB and NB RF Ranging Measurements in Homogenous Tissue for BAN Applications”, Wireless Telecommunications Symposium (WTS), Phoenix, Arizona, USA, pp.1-5, 17-19 April 2013.
  5. Dai, H., Ying, W. H., Xu, J., 2016, “Multi-layer Neural Network for Received Signal Strength-Based Indoor Localisation”, IET Communications, Vol. 10, No. 6, pp. 717-723.
  6. Fang, S. H., Lin, T. N., 2008, “Indoor Location System Based on Discriminant-Adaptive Neural Network in IEEE 802.11 Environments”, IEEE Transactions on Neural Networks, Vol. 19, No. 11, pp.1973- 1978.
  7. Garg, S., Patra, K., Pal, S. K., 2014, “Particle Swarm Optimization of a Neural Network Model in a Machining Process”, Sadhana, Vol. 39, No. 3, pp. 533-548.
  8. Garro, B. A., Vázquez, B. A., 2015, “Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms”, Computational Intelligence and Neuroscience, pp. 1-20, http://dx.doi.org/10.1155/2015/369298
  9. Jordehi, A. R., Jashi, J., 2013, “Parameter Selection in Particle Swarm Optimisation: a Survey”, Journal of Experimental & Theoretical Artificial Intelligence, Vol. 25, No. 4, pp.527–542.
  10. Kanaan, M., Suveren, M., “In-body Ranging for Ultra-Wide Band Wireless Capsule Endoscopy Using a Neural Network Architecture”, 10th International Symposium on Medical Information and Communication Technology (ISMICT), Worcester, USA, 1-5, 20-23 March 2016a.
  11. Kanaan, M., Suveren, M., “Ranging for In-Body Localization of Ultra Wide Band Wireless Endoscopy Capsules using Neural Networks”, 24th Signal Processing and Communication Application Conference, (SIU-2016), Zonguld