Visual inspection has traditionally been used for structural health monitoring. However, assessments conducted by trained inspectors or using contact sensors on structures for monitoring a...

Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging

A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. LiDAR data are combined with data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors to conduct environment mapping for point clouds. The proposed method facilitates LiDAR recordings in an experimental winter wheat field. Crop height estimates ranging from 0.35-0.58 m are correlated to the applied nitrogen treatments of 0-300 kg N ha . The LiDAR point clouds are recorded, mapped, and analysed using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL). Crop volume estimation is based on a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m. Two different flight patterns are evaluated at an altitude of 6 m to determine the impacts of the mapped LiDAR measurements on crop volume estimations.

Designing and Testing a UAV Mapping System for Agricultural Field Surveying

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Handbook Of Optical Fibre Sensing Technology

This paper analyzes the joint effects of in-phase and quadrature (I/Q) imbalance and power amplifier (PA) distortion for RF multiple input multiple output (MIMO) transmitters in the presence of cro ...

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Digital Predistortion for Joint Mitigation of I/Q Imbalance and MIMO Power Amplifier Distortion

Unmanned Aerial Vehicles (UAVs), as a recently emerging technology, enabled a new breed of unprecedented applications in different domains. This technology's ongoing trend is departing from large remotely-controlled drones to networks of small autonomous drones to collectively complete intricate tasks time and cost-effectively. An important challenge is developing efficient sensing, communication, and control algorithms that can accommodate the requirements of highly dynamic UAV networks with heterogeneous mobility levels. Recently, the use of Artificial Intelligence (AI) in learning-based networking has gained momentum to harness the learning power of cognizant nodes to make more intelligent networking decisions. An important example of this trend is developing learning-powered routing protocols, where machine learning methods are used to model and predict topology evolution, channel status, traffic mobility, and environmental factors for enhanced routing. 
This paper reviews AI-enabled routing protocols designed primarily for aerial networks, with an emphasis on accommodating highly-dynamic network topology. To this end, we review the basics of UAV technology, different approaches to swarm formation, and commonly-used mobility models, along with their impact on networking paradigms. We proceed with reviewing conventional and AI-enabled routing protocols, including topology-predictive and self-adaptive learning-based routing algorithms. We also discuss tools, simulation environments, remote experimentation platforms, and public datasets that can be used for developing and testing AI-enabled networking protocols for UAV networks. We conclude by presenting future trends, and the remaining challenges in AI-based UAV Networking, for different aspects of routing, connectivity, topology control, security and privacy, energy efficiency, and spectrum sharing.

A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook.

In this paper, we propose and experimentally investigate an optical sensor based on a novel combination of a long-period fiber grating (LPFG) with a permanent magnet to measure electrical current in unmanned aerial vehicles (UAVs) The proposed device uses a neodymium magnet attached to the grating structure, which suffers from an electromagnetic force produced when the current flows in the wire of the UAV engine Therefore, it causes deformation on the sensor and thus, different shifts occur in the resonant bands of the transmission spectrum of the LPFG Finally, the results show that it is possible to monitor electrical current throughout the entire operating range of the UAV engine from 0 A to 10 A in an effective and practical way with good linearity, reliability and response time, which are desirable characteristics in electrical current sensing

An Optical Fiber Sensor and Its Application in UAVs for Current Measurements.

Unmanned Aerial Vehicles (UAVs) have become a prominent research field due to their vast applicability and reduced size. An appealing aspect of the UAVs is the ability to accomplish autonomous flights in several contexts and purposes, and a variety of applications have been developed, from military to civilian fields. The system proposed in this work is a novel and simplified interaction between the user and the UAV for autonomous flight, where the necessary computation is performed in an embedded computer, decreasing response time and eliminating the necessity of long-distance communication links with base stations. Results are presented with both hardware in the loop simulations and a real UAV using Pixhawk, and Odroid and ROS as companion computer and software platform for code development.

Autonomous UAV Outdoor Flight Controlled by an Embedded System Using Odroid and ROS

This work introduces a new method for long-period fiber grating (LPG) sensors interrogation. This proposal uses a fiber Bragg grating (FBG) array to extract spectral information of the LPG sensor and an Artificial Neural Network to process this information. The information is processed to estimate the LPG resonant wavelength, without prior knowledge on the LPG spectrum. Therefore, the interrogator is LPG-insensitive, can be easily manufactured by optical fiber sensors laboratories at low-cost and is suitable for in-field applications. We demonstrated the filter array and Multilayer Perceptron (MLP) design, which are the proposed interrogator core. Furthermore, we analyzed the interrogation performance by the Mean Squared Error (MSE), the Mean Absolute Error (MAE), and the distribution of the residuals. The results showed our proposal can estimate the LPG resonant wavelength with 2.82 nm uncertainty, considering a 95% confidence interval, over 75 nm dynamic range for several LPGs, with different spectral characteristics. Moreover, our proposal can be easily tailored for different dynamic ranges and resolutions with proper adjustments on the FBG array and MLP.

LPG Interrogator Based on FBG Array and Artificial Neural Network

This article discusses several different implementations of Volterra series models, and performs a fair comparison between them using measured data and well known figures of merit. Among the implemented models, an approach that uses sub-sampling techniques together with Volterra series in a parallel cascade structure shows an improved performance compared to the other variations of Volterra series models. Based on the results, new trends in RF power amplifier behavioral modeling are suggested.

Analysis of Variations of Volterra Series Models for RF Power Amplifiers

We report a production technique of reducing the polarization-dependent loss (PDL) always present in arc-induced long-period fiber gratings (LPFGs). The proposed fabrication method is based upon the rotation of the optical fiber by an axial angle of 180 deg at each electric arc discharge, therefore, inducing opposite point-by-point asymmetric modulations over the entire length of the LPFG. PDL measurements of different arc-induced LPFGs fabricated by both the conventional and proposed method are presented. Finally, the obtained results show a significant decrease of the intrinsic PDL of arc-induced LPFGs with the proposed fabrication technique.

Alexandre Bessa dos Santos

Papers

An Optical Fiber Sensor and Its Application in UAVs for Current Measurements.

Autonomous UAV Outdoor Flight Controlled by an Embedded System Using Odroid and ROS

LPG Interrogator Based on FBG Array and Artificial Neural Network

Analysis of Variations of Volterra Series Models for RF Power Amplifiers

Reduction of intrinsic polarization dependence in arc-induced long-period fiber gratings