A CMOS Duty-Cycled Coherent RF Front-End IC for IR-UWB Systems

Ouvry, Laurent; Masson, Gilles; Hameau, Frédéric; Gaillard, B. Gerbert; Caillat, B.

doi:10.1109/icuwb.2015.7324396

Cited by 5 publications

(1 citation statement)

References 10 publications

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“…This mobility model is supplemented by a hybrid mobility model called Chaotic Ant Colony Optimization for Coverage (CACOC), which combines the ACO with the system's three ordinary differential equations. The mobility model is data-centric in a multi-level swarm perception networked on the multi-layer FANET architecture [35], [36]. Also, a collision avoidance technique was incorporated into a predictive mobility model, based on the assumption that all UAVs were flown at different altitudes to avoid collisions [37].…”

Section: Related Workmentioning

confidence: 99%

Chaos Detection and Mitigation in Swarm of Drones Using Machine Learning Techniques and Chaotic Attractors

NEBE,

SANNI,

ADETONA

et al. 2022

IJACSA

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Most existing identification and tackling of chaos in swarm drone missions focus on single drone scenarios. There is a need to assess the status of a system with multiple drones, hence, this research presents an on-the-fly chaotic behavior detection model for large numbers of flying drones using machine learning techniques. A succession of three Artificial Intelligence knowledge discovery procedures, Logistic Regression (LR), Convolutional Neural Network (CNN), Gaussian Mixture Models (GMMs) and Expectation-Maximization (EM) were employed to reduce the dimension of the actual data of the swarm of drone's flight and classify it as non-chaotic and chaotic. A onedimensional, multi-layer perceptive, deep neural network-based classification system was also used to collect the related characteristics and distinguish between chaotic and non-chaotic conditions. The Rössler system was then employed to deal with such chaotic conditions. Validation of the proposed chaotic detection and mitigation technique was performed using realworld flight test data, demonstrating its viability for real-time implementation. The results demonstrated that swarm mobility horizon-based monitoring is a viable solution for real-time monitoring of a system's chaos with a significantly reduced commotion effect. The proposed technique has been tested to improve the performance of fully autonomous drone swarm flights.

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Section: Related Workmentioning

confidence: 99%

Chaos Detection and Mitigation in Swarm of Drones Using Machine Learning Techniques and Chaotic Attractors

NEBE,

SANNI,

ADETONA

et al. 2022

IJACSA

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An Intermittent Frequency Synthesizer With Accurate Frequency Detection for Fast Duty-Cycled Receivers

et al. 2020

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An intermittent frequency synthesizer for fast duty-cycled receivers is presented in this paper. Different from state-of-art techniques which try to eliminate the initial phase error that degrades the intermittent frequency detection, a new frequency detector is proposed to maintain an accurate frequency detection regardless of the initial phase error. Moreover, an averaged fraction division scheme (AFDS) is integrated in the synthesizer to improve the frequency resolution. The frequency synthesizer is implemented and verified by using discrete devices and an FPGA. The measurement results show that the frequency detector provides a frequency detection accuracy of 0.88%, and the synthesizer achieves a frequency resolution of 0.625 MHz when controlled by a duty-cycled signal with a 6-µs period and a 2-µs time duration. The frequency synthesizer can perform an intermittent frequency hopping from 160MHz to 140MHz while requiring a period of 430 µs without disturbing the VCO's oscillation.

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Hardware platform of Analog-to-Information converter using Non Uniform Wavelet Bandpass Sampling for RF signal activity detection

Pelissier

Masson

Ouvry

et al. 2018

2018 IEEE International Symposium on Circuits and Systems (ISCAS)

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A CMOS Duty-Cycled Coherent RF Front-End IC for IR-UWB Systems

Cited by 5 publications

References 10 publications

Chaos Detection and Mitigation in Swarm of Drones Using Machine Learning Techniques and Chaotic Attractors

Chaos Detection and Mitigation in Swarm of Drones Using Machine Learning Techniques and Chaotic Attractors

An Intermittent Frequency Synthesizer With Accurate Frequency Detection for Fast Duty-Cycled Receivers

Hardware platform of Analog-to-Information converter using Non Uniform Wavelet Bandpass Sampling for RF signal activity detection

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