IEEE Organizations related to Drones

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No organizations are currently tagged "Drones"



Conferences related to Drones

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No conferences are currently tagged "Drones"


Periodicals related to Drones

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No periodicals are currently tagged "Drones"


Most published Xplore authors for Drones

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Xplore Articles related to Drones

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The Internet of Flying Things

Internet of Things A to Z: Technologies and Applications, None

Popularly known as drones, unmanned aerial vehicles (UAVs) have been applied in several fields, usually operating in cooperative and collaborative swarms to enable the execution of more dynamic missions. Thus, the newFlyingAdHocNetworks (FANETs) paradigm has emerged, a subset of mobile ad hoc networks with specific characteristics that arise from the aviation context. Recently, the ideas fromFANETshave started to be synthesized ...


TIMELINE

GPS, None

None


Expert drones

2017 Integrated Communications, Navigation and Surveillance Conference (ICNS), 2017

Summary form only given. The complete presentation was not made available for publication as part of the conference proceedings. This article consists only of a single slide from the author's conference presentation.


Back cover

Journal of Communications and Networks, 2018

None


Interactive workshop "How drones are changing the world we live in"

2016 Integrated Communications Navigation and Surveillance (ICNS), 2016

The interactive workshop includes panelists who are generating revenue by successfully integrating drones into their operations(surveying, cartography, entertainment). They will talk about how the drones have revolutionized their business, the roadblocks they had to overcome, and the challenges they forsee in the future.



Educational Resources on Drones

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IEEE-USA E-Books

  • The Internet of Flying Things

    Popularly known as drones, unmanned aerial vehicles (UAVs) have been applied in several fields, usually operating in cooperative and collaborative swarms to enable the execution of more dynamic missions. Thus, the newFlyingAdHocNetworks (FANETs) paradigm has emerged, a subset of mobile ad hoc networks with specific characteristics that arise from the aviation context. Recently, the ideas fromFANETshave started to be synthesized with those from the Internet of Things (IoT), originating theInternet ofFlyingThings (IoFT), a paradigm which enables an important new level of applications, solves known issues inUAVsandIoT, and expands the range of future applications. This chapter introduces the Internet of Flying Things, describing both the potential new degree of freedom provided to current and future applications and the new challenges for security and safety, before ending with an overview of open issues and new trends for future networks.

  • TIMELINE

    None

  • Expert drones

    Summary form only given. The complete presentation was not made available for publication as part of the conference proceedings. This article consists only of a single slide from the author's conference presentation.

  • Back cover

    None

  • Interactive workshop "How drones are changing the world we live in"

    The interactive workshop includes panelists who are generating revenue by successfully integrating drones into their operations(surveying, cartography, entertainment). They will talk about how the drones have revolutionized their business, the roadblocks they had to overcome, and the challenges they forsee in the future.

  • On the Zero-Forcing Receiver Performance for Massive MIMO Drone Communications

    We study the uplink ergodic rate performance of the zero-forcing (ZF) receiver in a Massive multiple-input and multiple-output (MIMO) enabled drone communication system. Considering a 3D geometric model for line-of-sight (LoS) propagation, approximate but accurate analyses of lower and upper bounds on the uplink ergodic rate with estimated channel state information (CSI) are provided.

  • Sorghum Yield Prediction using Machine Learning

    Estimation of a future agricultural production is an important challenge for farmers. In this paper, we propose a system based on machine learning algorithms to estimate farm yields. The experiments were conducted on a Sorghum field. We use TensorFlow with Convolutional Neural Networks and Linear Regression. These algorithms allow us 1) to detect the different ears of Sorghum on an image and 2) to estimate their weight. On our dataset, we obtain an average accuracy of 74,5% for the detection of sorghum and an average precision of 99% for the estimation of the weight.

  • Unmanned Aerial Vehicle-Based Non Destructive Diagnostics

    The paper proposes a cloud platform for analyzing the radiometric infrared videos uploaded by drones which patrol large photovoltaic plants. Thanks to artificial vision algorithms, it does not require any human support to select and associate the framed PV modules to the corresponding ones in the topology of the photovoltaic plant. The algorithm implements an innovative diagnostic protocol, which evaluates the thermal state of the photovoltaic module, whichever the environmental conditions are. The data automatically computed and collected in a multimedia database provide the O&M; technicians with significant information to monitor the ageing of each module of the photovoltaic plant. The proposed platform also integrates a cloud-based software, named DISS, which provides quantitative and deeper information about the thermal behavior of the photovoltaic modules.

  • Drones as collaborative sensors for image recognition

    Distributed sensor networks have the ability for more data collection and advanced tasks such as object recognition and tracking than a singular sensor. However, these sensor networks are often limited in their ability to coordinate either by their requirement to be connected to a central system or by their fixed position and static nature. Drones or unmanned aerial vehicles (UAVs) provide a unique opportunity in multi-sensor networks. Although drones are usually limited by their available power due to battery capacity, recent advances in technology and algorithms provide drones with more available computational power. In addition to computability augmentation in drone mobility, decentralized coordination enables drones to create the next generation of distributed multi-sensor networks. In this paper, we explore combination of the existing image processing and object recognition techniques in the perspective of collaborative drones, which can improve the robustness of image recognition tasks.

  • Multiscale reconstruction of natural and archaeological underwater landscape by optical and acoustic sensors

    The aim of this paper was to implement an integrated method for high- resolution surveys by using a robotics technology. In order to reconstruct underwater landscapes of high cultural value, geophysical and photogrammetric sensors were integrated on-board of an USV allowing a precise mapping of the seabed morphology as well as a detailed three-dimensional reconstruction of the archaeological remains.



Standards related to Drones

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No standards are currently tagged "Drones"