Multi-Agent Data Recognition System Based on Received Signal in Antenna on Board Telecom System
Volume 5, Issue 4, Page No 94-98, 2020
Author’s Name: Chafaa Hamrouni1,2,a)
View Affiliations
1Department of Computer Sciences, Khurma University College, Taif University, Khurma, 2935, Kingdom of Saudi Arabia
2Research Groups on Intelligent Machines Laboratory, National School of Engineering of Sfax (ENIS), Sfax University, Sfax, 3038, Tunisia
View Affiliations
1Department of Computer Sciences, Khurma University College, Taif University, Khurma, 2935, Kingdom of Saudi Arabia
2Research Groups on Intelligent Machines Laboratory, National School of Engineering of Sfax (ENIS), Sfax University, Sfax, 3038, Tunisia
a)Author to whom correspondence should be addressed. E-mail: chafa.hamrouni.tn@ieee.org
Adv. Sci. Technol. Eng. Syst. J. 5(4), 94-98 (2020); 
DOI: 10.25046/aj050412
Keywords: Antenna, Recognition System, Signal identification system, Switching System, Multi-Micro -strip Antennae Network, Fuzzy Control System
Export Citations
Information data recognition during traditional operating step in telecommunication system, as their interpretation, presents an important active research field. In this context, we propose as a solution multi-agent data identification system starting with received signal parameters in antenna network connected on board telecom system. Due to the Information Identification Data (IID) response variability that differ from one presented signal being to another, the IID remains difficult to detect and to recognize. In this paper, we presented a various problem related to IID recognition. We successfully developed a multimodal IID recognition based on two different modalities. We identify each hot moment relying on successful IID detected. Proposed solution is based on IID value caused by both information type and the power intensity value.
Received: 26 April 2020, Accepted: 21 June 2020, Published Online: 12 July 2020
- Q. Yu, L.S. Yan, S. Lee, Y. Xie, A. Willner, “Loop synchronous polarization scrambling technique for simulating polarization effects using recirculating fiber loops”, Journal of Lightwave Technology, 21, 1593-1600, Jul, 2003. DOI:10.1109/JLT.810103
- N.S. Murthy and P. Srikanth Gautham, “Adaptive Equalizers in Smart Antenna Systems”, IETE Journal of Education, 50, 111-117, 2009. DOI:10.1080/09747338.2009.10876058
- A. H. Levy and A. S. Willsky, “An efficient maximum entropy technique for 2-D isotropic random fields“ IEEE Journals & Magazines, 2, 741-744, 1988. DOI:10.1109/29.1589
- P. Seong Chuah, C. Zhenzhong and Y. C. Peng Tan, “Energy Minimization for Wireless Video Transmiss- -ions With Deadline and Reliability Constraints“, IEEE Transactions on Circuits and Systems for Video Technology, 23, 467-481, 2013. DOI:10.1109/ TCSVT.2210655
- M. Vesperini, F. Breon and D. Tanre,“Atmospheric water vapor content from space borne POLDER measurements“,IEEE Transactions on Geoscience and Remote Sensing, 37, 1613-1619, 1999.
- A.Verma, N.Srivastava and B. K. Kanaujia, “Analysis of Gunn Loaded Rectangular Microstrip Antenna”, IETE Journal of Education, 53, 80-87, 2012.
- K.L. Rao Member,“A Simplified Approach to the Antenna Problem”, IETE Journal of Education, 23, 7-8, 1982.
- C. Natalia, Estefania A., Vicente B.,“THOMAS: An agent platform for supporting normative multi agent systems”,Journal of Logic and Computation, 23(2), 309-333, 2013. DOI: 10.1093/logcom/exr025
- L. Frank, Lewis, B. Cui, Tiedong Ma, Yongduan Song, Chunhui Zhao, “Heterogeneous Multi-Agent Systems: Reduced-Order Synchronization and Geometry“, IEEE Transactions on Automatic Control, 61(5), 1391-1396, 2015. DOI: 10.1109/TAC.2015.2471716.
- Z. Guessoum, “Adaptive agents and multiagent systems “, IEEE Distributed Systems, 5, 7, 2004. DOI: 10.1109/MDSO.2004.10
- H. Zhang, Gang Feng, Huaicheng Yan, Qijun Chen, “Distributed self-triggered control for consensus of multi-agent systems“, IEEE/CAA Journal of Automatica Sinica, 1(1), 40-45, 2014. DOI: 10.1109/ JAS.2014.7004618.
- M. Guo, M.M. Zavlanos, D.V. Dimarogonas, “Controlling the Relative Agent Motion in Multi-Agent Formation Stabilization“, IEEE Transactions on Automatic Control, 59(3), 820-826, 2014. DOI: 10.1109/TAC.2013.2281480.
- P.K. Singhal, P.S. Tomar and N. Verma, “Topologies and Applications of Meander Line Antenna”, IETE Journal of Education, 48, 121-131, 2007.
- P. Gebhard, “A Layered Model of Affect”, In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS ’05, 29-36, New York, NY, USA, 2005.
- M. Dammak, M. Ben Ammar and A.M. Alimi. “Real-time analysis of non-verbal upper-body expressive gestures”, In Multimedia Computing and Systems (ICMCS), 2012 International Conference, 334–339, 2012.
- Q. Yu, L.S. Yan, S. Lee, Y. Xie and A. Khilla and I. Wolff, “Field Theory Treatment of H-Plane Waveguide Junction with Triangular Ferrite Post“, IEEE Transactions on Microwave Theory and Techniques, 41, 274-281, 1993.
- K. Ghamen and A. Caplier, “Estimation of facial expression intensity based on the belief theory”, In Proceedings Funchal, Portugal, 59-60, 2008.
- M. Dammak, A. Wali and A.M. Alimi, “Video summarization using viewer affective feedback”,In Hybrid Intelligent Systems (HIS), 13th International Conference, 279-284, 2013.
- M. Neji, M. Ben Ammar and A.M. Alimi. Towards an intelligent information research system based on the human behavior: recognitionof user emotional state. 12 th IEEE/ACIS International Conference onComputer and Information Science, 56-58, 2013.
- T. Xiong, Z. Pu, J. Yi, X. Tao, “Fixed-time observer based adaptive neural network time-varying formation tracking control for multi-agent systems via minimal learning parameter approach“, IET Control Theory & Applications journal, 14(9), 1147-1157, 2020. DOI: 10.1049/iet-cta.2019.0309
- M. Li , S. Chen, “ Adaptive Learning: A New Decentralized Reinforcement Learning Approach for Cooperative Multiagent Systems”, Jing CIEEE Control Systems Letters, 4(4), 898-903, 2020. DOI: 10.1109/LCSYS.2020.2995756hen
- X. Luo, X. Li, X. Li, J. Yan, X. Guan, “Globally Stable Formation Control of Nonholonomic Multiagent Systems With Bearing-Only Measurement“, IEEE Systems Journal, 14(2), 2901-2912, 2019. DOI: 10.1109/JSYST.2019.2935162.
- S. Zheng, H. Liu, “Improved Multi-Agent Deep Deterministic Policy Gradient for Path Planning-Based Crowd Simulation”, IEEE Access journal, 7, 147755-147770, 2019, Electronic, 2019. DOI: 10.1109/ACCESS.2019.2946659
- D. Xu, D. Chen; R. Zhang, “Performance and optimisation of agent compression and forwarding system for correlated sources »IET Communications, IET Control Theory & Applications journal, 13(16), 2523-2532, Date: October 2019. DOI: 10.1049/iet-com.2018.5704