artificial intelligence for contemporary wireless and ... · raed shubair 30 regional director,...
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Artificial Intelligence for Contemporary Wireless and Healthcare Applications
Raed [email protected] & [email protected]
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Multidisciplinary Research
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Antennas & Propagation
Wireless Communication
Signal Processing
Nano Bioscience &
Nano Medicine
Machine Learning
Outlne
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Motivation RF Features Different Indoor Scenarios
Dataset from Real Measurements
Algorithms vs Features
Machine Learning Approach
Deep Learning Approach
Motivation
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New dimension for optimum communication
Intelligence of physical space Why Environment Classification:
efficient power consumption
appropriate modulation scheme
Multi-disciplinary Problem
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Propagation: type of signal transmission for each indoor scenario
Antennas: RF signal measurements
Communication: how critical is multipath
Signal Processing: manipulating RF feature for input to algorithms
Machine Learning Deep Learning
Indoor Scenarios
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Each Indoor Environment: Unique “Spatial” Signature!
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Each Indoor Environment: Unique “Spatial” Signature!
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Each Environment has its own Fingerprint!
Illustration of RF Dataset:Each Environment has its own Fingerprint!
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UMAP: Each Environment has its own Fingerprint!
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Machine Learning Algorithm: DT
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Machine Learning Algorithm: SVM
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Machine Learning Algorithm: k-NN
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Cascaded Approach:
Machine Learning & Hybrid Features
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Cascaded Approach: CNN & Learned Features
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Advantage of Environment Identification
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Classification Accuracy:Different Classifiers & RF Features
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Confusion Matrix:
an alternative representation of classification
accuracy
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New Concept: “Hybrid RF Features” Fingerprint
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Performance of Various Hybrid Features
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Performance of Various Hybrid Features
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Convolutional Neural Network for Feature Learning
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Deep Learning using CNN provides Perfect Classification!
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Deep Learning using CNN providesmost-accurate Indoor Localization!
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Confusion Matrix Using CNN-based
Deep Learning
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CNN with environment classification produced different features for different environments!
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Computational Compelxity
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References
• M. I. AlHajri, N. T. Ali and R. M. Shubair, "Classification of Indoor Environments for IoT Applications: A Machine Learning Approach," in IEEE Antennas and Wireless Propagation Letters, vol. 17, no. 12, pp. 2164-2168, Dec. 2018.
• M. I. AlHajri, N. T. Ali and R. M. Shubair, "A Machine Learning Approach for the Classification of Indoor Environments Using RF Signatures," 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA, 2018, pp. 1060-1062.
• M. I. AlHajri, N. T. Ali and R. M. Shubair, "Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach," in IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 11, pp. 2306-2310, Nov. 2019.
• M. I. AlHajri, N. T. Ali, and R. M. Shubair, “2.4 GHz indoor channel measurements,” IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/ggh1-6j32
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Acknowledgement
Mohamed Ibrahim AlHajriPhD Candidate & Graduate Research Assistant
Claude E. Shannon Communication and Network GroupResearch Laboratory of Electronics & EECS Department
Massachusetts Institute of Technology (MIT)
Raed Shubair
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Regional Director, IEEE Region 8 Middle East, IEEE Signal Processing SocietyChair, IEEE Educational Initiatives Program, IEEE Antennas and Propagation Society
Editor-in-Chief, Journal of Electromagnetics and Antennas Applications and TechnologiesEditor, IEEE Journal of Electromagnetics, RF, and Microwaves in Medicine and Biology
Founding Member: IEEE ComSoc, IEEE SPS, IEEE APS, IEEE MTTS, IEEE EMBSSenior Editor, IEEE Open Journal of Antennas and Propagation
Counselor, IEEE Student Branch, New York University Abu DhabiBoard Member, European School of Antennas
Co-Founder, MIT Scholars of the EmiratesFellow, MIT Electromagnetics Academy