Rüştü AKAY, Memduh SUVEREN, Muzaffer KANAAN

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

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.

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

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.

PDF

___

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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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), Zonguldak, Turkey, 16-19 May, 2016b.
Kanaan, M., Suveren, M., “In-Body Ranging with Ultra-Wideband Signals: Techniques and Modeling of the Ranging Error”, 2017, Wireless Communications and Mobile Computing, 2017, pp. 1-15.
Kanaan, M., Suveren, M., 2015, “A Novel Frequency-Dependent Path Loss Model for Ultra Wideband Implant Body Area Networks”, Measurement, Vol. 68, pp. 117-127.
Kennedy, J., Eberhart, R., “Particle Swarm Optimisation”, IEEE International Conference on Neural Networks, WA, Australia, pp. 1942-1948, 27-1 December 1995.
Khaleghi, A., Balasingham, I., 2009, “Improving in-Body Ultra Wideband Communication Using Near- Field Coupling of the Implanted Antenna”, Microwave and Optical Technology Letters, Vol. 51, pp. 585-589.
Lee, J. Y., Scholtz, R. A, 2002, “Ranging in a Dense Multipath Environment Using an UWB Radio Link”, IEEE Journal on Selected Areas in Communications, Vol. 20, No.9, pp.1677-1683, DOI:10.1109/JSAC.2002.805060.
Liu, C., Ouyang, C., Zhu, P., Tang, W., “An Adaptive Fuzzy Weight PSO Algorithm”, 2010 Fourth International Conference on Genetic and Evolutionary Computing, Shenzhen, China, pp. 8-10, 13-15 December 2010.
Pahlavan, K., Levesque, A. H., 2005, Wireless Information Networks, 2nd Edition, John Wiley & Sons, Inc., Hoboken, New Jersey, USA.
Shang, F., Champagne, B., Psaromiligkos, I., 2013, “Time of Arrival and Power Delay Profile Estimation for IR-UWB Systems”, Signal Processing, Vol. 93, pp.1317-1327, DOI: 10.1016/j.sigpro.2012.11.006.
Shi, Y., Eberhart, R., “Empirical Study of Particle Swarm Optimisation”, IEEE International Conference on Computational Intelligence, Washington, USA, 1945–1950, 6-9 July 1999.
Swain P., 2003, “Wireless capsule endoscopy”, Gut, Vol.52, p.iv48-iv50.
Talbi, E., 2009, Metaheuristics: from Design to Implementation, John Wiley & Sons, Hoboken.
Uy, N. Q., Hoai, N. X., Mckay, R., Tuan, P. M., “Initialising PSO with Randomised Lowdiscrepancy Sequences: The Comparative Results”, IEEE Congress on Evolutionary Computation, Singapore, pp. 1985–1992, 25-28 September 2007.
Vesterstrom, J., Thomsen, R., “A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems”, Congress on Evolutionary Computation, Porrtland, pp. 1980-1987, 19-23 June 2004.
Wang, J., Wang, Q., 2013, Body Area Communications: Channel Modeling, Communication Systems and EMC, John Wiley & Sons, Singapore.
Xin, J., Chen, G., Hai, Y., “A Particle Swarm Optimiser with Multi-Stage Linearly Decreasing Inertia Weight”, In IEEE International Conference on Computational Sciences and Optimisation, Sanya,China, pp. 505–508, 24-26 April 2009.
Yang, X. S., 2008, Nature-inspired Metaheuristic Algorithms, Luniver Press, UK.
Yuce, M. R., Keong, H. C, Chae, M. S, 2009, “Wideband Communication for Implantable and Wearable Systems”, IEEE Transactions on Microwave Theory and Techniques, Vol. 57, No. 10, pp.2597-2604.