Using Feedforward Neural Networks for Parameter Modeling of a 4G Link for Unmanned Aerial Vehicles

Author(s):  
Giancarlo Benincasa ◽  
Erich Leitgeb ◽  
Klaus Kainrath ◽  
Hristo Ivanov
2021 ◽  
Vol 103 (4) ◽  
Author(s):  
Mary L. Cummings ◽  
Hala Nassar ◽  
Vishwa Alaparthy

AbstractWhile increasingly popular, small unmanned aerial vehicles, aka drones, are often flown illegally over outdoor public gatherings or public facilities like prisons, threatening the safety of those nearby. There is a clear need to address interloping drones in public spaces from a sociotechnical perspective, including understanding the tradespace of variables. Through surveys, interviews, technology and infrastructure design, and experimentation, we developed a tradespace model of those variables that managers and designers of high-risk settings like public spaces and prisons need to consider in their development or renovation. These include cost considerations, both capital and infrastructure, as well as technology design elements of range and false alarm rates potentially exacerbated by convolutional neural networks (aka, deep learning). Results also highlight that environmental design elements can provide a possible low-tech solution in the design of obstructions that either eliminate or complicate a drone pilot’s line of sight. This effort demonstrates that managers and designers of high-risk settings like public spaces and prisons will have to balance sometimes competing objectives to obtain the best possible outcomes for public safety.


Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Moisés Lodeiro-Santiago ◽  
Pino Caballero-Gil ◽  
Ricardo Aguasca-Colomo ◽  
Cándido Caballero-Gil

This work presents a system to detect small boats (pateras) to help tackle the problem of this type of perilous immigration. The proposal makes extensive use of emerging technologies like Unmanned Aerial Vehicles (UAV) combined with a top-performing algorithm from the field of artificial intelligence known as Deep Learning through Convolutional Neural Networks. The use of this algorithm improves current detection systems based on image processing through the application of filters thanks to the fact that the network learns to distinguish the aforementioned objects through patterns without depending on where they are located. The main result of the proposal has been a classifier that works in real time, allowing the detection of pateras and people (who may need to be rescued), kilometres away from the coast. This could be very useful for Search and Rescue teams in order to plan a rescue before an emergency occurs. Given the high sensitivity of the managed information, the proposed system includes cryptographic protocols to protect the security of communications.


Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jianhua Zhang ◽  
Yang Li ◽  
Wenbo Fei

This brief addresses the position and attitude tracking fixed-time practical control for quadrotor unmanned aerial vehicles (UAVs) subject to nonlinear dynamics. First, by combining the radial basis function neural networks (NNs) with virtual parameter estimating algorithms, a NN adaptive control scheme is developed for UAVs. Then, a fixed-time adaptive law is proposed for neural networks to achieve fixed-time stability, and convergence time is dependent only on control gain parameters. Based on Lyapunov analyses and fixed-time stability theory, it is proved that the fixed-time adaptive neural network control is finite-time stable and convergence time is dependent with control parameters without initial conditions. The effectiveness of the NN fixed-time control is given through a simulation of the UAV system.


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