Martins Ezuma

Works (8)

Updated: July 5th, 2023 15:31

2022 journal article

<p>Wavelet transform analytics for RF-based UAV detection and identification system using machine learning & nbsp;</p>

Wavelet transform analytics for RF-based UAV detection and identification system using machine learning & nbsp;

PERVASIVE AND MOBILE COMPUTING, 82.

By: O. Medaiyese *, M. Ezuma n, A. Lauf * & I. Guvenc n 

author keywords: Interference; RF fingerprinting; Scattergram; Scalogram; SqueezeNet; UAVs; Wavelet transform
TL;DR: This work performed a thorough comparative analysis on a radio frequency (RF) based drone detection and identification system (DDI) under wireless interference by using machine learning algorithms, and a pre-trained convolutional neural network-based algorithm called SqueezeNet, as classifiers. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: March 4, 2022

2022 journal article

Comparative Analysis of Radar-Cross-Section-Based UAV Recognition Techniques

IEEE SENSORS JOURNAL, 22(18), 17932–17949.

By: M. Ezuma n, C. Anjinappa n, V. Semkin * & I. Guvenc n 

author keywords: Deep learning (DL); machine learning (ML); radar cross section (RCS); statistical learning (SL); target identification and recognition; unmanned aerial vehicles (UAVs)
TL;DR: The study shows that, while the average accuracy of all the algorithms increases with the signal-to-noise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 4, 2022

2022 journal article

Hierarchical Learning Framework for UAV Detection and Identification

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION, 6, 176–188.

By: O. Medaiyese *, M. Ezuma n, A. Lauf * & A. Adeniran *

author keywords: Autonomous aerial vehicles; Feature extraction; Wireless fidelity; Radio frequency; Bluetooth; Sensors; Signal to noise ratio; Autoencoder; Hilbert Huang transform; RF fingerprinting; unmanned aerial system; wavelet packet transform
TL;DR: A radio frequency (RF) based UAV detection and identification system by exploiting signals emanating from both the UAV and its flight controller, respectively and utilizes a semi-supervised learning approach for the detection of UAV or UAV’s control signals in the presence of other wireless signals such as Bluetooth and WiFi. (via Semantic Scholar)
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Source: Web Of Science
Added: April 18, 2022

2021 article

FPGA prototyping of synchronized chaotic map for UAV secure communication

2021 IEEE AEROSPACE CONFERENCE (AEROCONF 2021).

By: C. Nwachioma *, M. Ezuma n & O. Medaiyese *

TL;DR: A digital design of the secure communication system involving the transmission of bitstreams between the ABS and GBS, and a prototype of the communication system on field-programmable gate arrays (FPGAs) is realized. (via Semantic Scholar)
Source: Web Of Science
Added: September 13, 2021

2021 journal article

Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 58(1), 27–46.

By: M. Ezuma n, C. Anjinappa n, M. Funderburk n & I. Guvenc n 

author keywords: Radar cross-sections; Radar; Radar imaging; Target recognition; Signal to noise ratio; Drones; Propellers; Akaike information criterion (AIC); automatic target recognition (ATR); Bayesian information criterion (BIC); classification; compact-range chamber; detection; radar cross-section (RCS); unmanned aerial vehicle (UAV)
TL;DR: From the model selection analysis, it is observed that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively well and the best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: February 10, 2022

2021 article

Semi-supervised Learning Framework for UAV Detection

2021 IEEE 32ND ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC).

By: O. Medaiyese *, M. Ezuma n, A. Lauf * & I. Guvenc n 

author keywords: local outlier factor; wavelet packet transform; UAV; RF fingerprinting; detection
TL;DR: A local outlier factor model is developed as the UAV detection algorithm using the coefficient variances of the wavelet packets from WiFi and Bluetooth signals and can be extended to the detection of rogue RF devices in an environment. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: May 23, 2022

2019 journal article

A New Chaotic Oscillator-Properties, Analog Implementation, and Secure Communication Application

IEEE ACCESS, 7, 7510–7521.

By: C. Nwachioma *, J. Humberto Perez-Cruz *, A. Jimenez *, M. Ezuma n & R. Rivera-Blas *

author keywords: Chaotic system; Lyapunov spectrum; bifurcation analysis; analog circuit; secure communication; self-synchronization
TL;DR: This paper reports a new 3-dimensional autonomous chaotic system with four nonlinearities, studied with respect to its numerical solutions in phase space, including sensitive dependence on initial conditions, equilibrium points, bifurcation, and maximal Lyapunov exponent. (via Semantic Scholar)
Source: Web Of Science
Added: February 11, 2019

2019 journal article

Detection and Classification of UAVs Using RF Fingerprints in the Presence of Wi-Fi and Bluetooth Interference

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, 1, 60–76.

By: M. Ezuma n, F. Erden n , C. Anjinappa n, O. Ozdemir n & I. Guvenc n 

author keywords: Interference; machine learning; Markov models; RF fingerprinting; unmanned aerial vehicles (UAVs); UAV detection and classification
TL;DR: This paper investigates the problem of detection and classification of unmanned aerial vehicles (UAVs) in the presence of wireless interference signals using a passive radio frequency (RF) surveillance system and investigates the performance of the NCA and five different ML classifiers for 15 different types of UAV controllers. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: January 2, 2020

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