Πλοήγηση ανά Συγγραφέα "Hilas, C. S."
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Τεκμήριο A dynamic fuzzy model for processing lung sounds(2007-03) Mastorocostas, P. A.; Varsamis, D. N.; Mastorocostas, C. A.; Hilas, C. S.A dynamic fuzzy filter is proposed that performs the task of separation of lung sounds obtained from patients with pulmonary pathology. The consequent parts of the fuzzy rules are dynamic, consisting of block-diagonal recurrent neural networks. The lung sound category of coarse crackles is examined, and a comparative analysis with other fuzzy and neural filters is conducted.Τεκμήριο Optimising no acknowledgment policy on WLANs supporting voice over internet protocol(2014-01-02) Politis, A.; Hilas, C. S.; Papatsoris, A. D.The efficiency of the no acknowledgment policy, as defined by the IEEE 802.11e amendment, is analysed and an improvement modification to this mechanism is proposed for WLANs supporting voice over internet protocol.Τεκμήριο Pareto optimal design of dual-band base station antenna arrays using multi-objective particle swarm optimization with fitness sharing(2009-03) Goudos, S. K.; Zaharis, Z. D.; Kampitaki, D. G.; Rekanos, I. T.; Hilas, C. S.The design of dual-band base station antennas under constraints for mobile communications is addressed in this paper. Given the antenna geometry, the method of moments (MoM) is used to compute the antenna characteristics. Two distinct multi-objective evolutionary algorithms are applied in order to find the Pareto front of the feasible solutions that satisfy the design constraints. In the present work, the Multi-Objective Particle Swarm Optimization with fitness sharing (MOPSO-fs) is compared with the Nondominated Sorting Genetic Algorithm-II (NSGA-II) in order to optimize the antenna geometry. Two design cases are presented. The first case is a five-element array operating in GSM1800/UMTS frequency bands. The second base station antenna array consists of six elements operating in UMTS/WLAN (2.4 GHz) frequency bands.Τεκμήριο A telecommunications call volume forecasting system based on a recurrent fuzzy neural network(2013) Mastorocostas, P. A.; Hilas, C. S.; Varsamis, D. N.; Dova, S. C.The problem of telecommunications call volume forecasting is addressed to in this work. In particular, a foreacasting system is proposed, that is based on a dynamic fuzzy-neural model, where the consequent parts of the fuzzy rules are small Block-Diagonal Recurrent Neural Networks with internal feedback. The forecasting characteristics are highlighted and the prediction performance is evaluated by use of real-world telecommunications data. An extensive comparative analysis with a series of existing forecasters is conducting, including both traditional models as well as fuzzy and neurofuzzy approaches.Τεκμήριο A TSK-based fuzzy system for telecommunications time-series forecasting(2012) Mastorocostas, P. A.; Hilas, C. S.; Dova, S. C.; Varsamis, D. N.A two-stage model-building process for generating a Takagi-Sugeno-Kang fuzzy forecasting system is proposed in this paper. Particularly, the Subtractive Clustering (SC) method is first employed to partition the input space and determine the number of fuzzy rules and the premise parameters. In the sequel, an Orthogonal Least Squares (OLS) estimator determines the input terms which should be included in the consequent part of each fuzzy rule and calculate their parameters. A comparative analysis with well-established forecasting models is conducted on real world tele-communications data, in order to investigate the forecasting capabilities of the proposed scheme.