A survey on machine learning for data fusion
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TLDR
This paper offers a detailed introduction to the background of data fusion and machine learning in terms of definitions, applications, architectures, processes, and typical techniques, and proposes a number of requirements to review and evaluate the performance of existing fusion methods based on machine learning.About:
This article is published in Information Fusion.The article was published on 2020-05-01 and is currently open access. It has received 309 citations till now. The article focuses on the topics: Sensor fusion.read more
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Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision
Jorge Peña Queralta,Jussi Taipalmaa,Bilge Can Pullinen,Victor Kathan Sarker,Tuan Nguyen Gia,Hannu Tenhunen,Moncef Gabbouj,Jenni Raitoharju,Tomi Westerlund +8 more
TL;DR: The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area.
Journal ArticleDOI
Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques
Muhammad Saleem,Sagheer Abbas,Taher M. Ghazal,Muhammad Adnan Khan,Nizar M F Sahawneh,Munir Uddin Ahmad +5 more
TL;DR: In this paper , a fusion-based intelligent traffic congestion control system for VNs (FITCCS-VN) using ML techniques that collect traffic data and route traffic on available routes to alleviate traffic congestion in smart cities.
Journal ArticleDOI
Machine Learning in Agriculture: A Comprehensive Updated Review.
Lefteris Benos,Aristotelis C. Tagarakis,Georgios Dolias,Remigio Berruto,Dimitrios Kateris,Dionysis Bochtis +5 more
TL;DR: In this paper, a review of the recent literature on machine learning in agriculture is presented, where a plethora of machine learning algorithms are used, with those belonging to Artificial Neural Networks being more efficient.
Journal ArticleDOI
Multi-Branch Deep Residual Learning for Clustering and Beamforming in User-Centric Network
TL;DR: A deep residual learning framework is proposed, UcnBeamNet, to enhance the ability of approximating the iterative algorithm for sum rate maximization, where multi-branch subnets are connected in parallel to extract extra information.
Journal ArticleDOI
Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture
TL;DR: Artificial intelligence models and optimization algorithms can be considered a novel and reliable computational method in plant tissue culture.
References
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Tapas Kanungo,David M. Mount,Nathan S. Netanyahu,Christine D. Piatko,Ruth Silverman,Angela Y. Wu +5 more
TL;DR: This work presents a simple and efficient implementation of Lloyd's k-means clustering algorithm, which it calls the filtering algorithm, and establishes the practical efficiency of the algorithm's running time.
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A universal image quality index
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TL;DR: Although the new index is mathematically defined and no human visual system model is explicitly employed, experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error.
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An introduction to multisensor data fusion
David L. Hall,James Llinas +1 more
TL;DR: This paper provides a tutorial on data fusion, introducing data fusion applications, process models, and identification of applicable techniques.
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Multisensor data fusion: A review of the state-of-the-art
TL;DR: A comprehensive review of the data fusion state of the art is proposed, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies.
Proceedings ArticleDOI
Revisions to the JDL data fusion model
TL;DR: The Data Fusion Model maintained by the Joint Directors of Laboratories (JDL) Data Fusion Group is the most widely used method for categorizing data fusion-related functions as discussed by the authors, and the current effort to revise the expand this model to facilitate the cost-effective development, acquisition, integration and operation of multi-source/multi-source systems.