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How to create an efficient network between the Internet of Things and satellite constellations

A total of 4.6 billion people are connected to the internet through their mobile phones. For each of these people, there are more than three devices communicating with the internet. The Internet of Things (IoT) is composed .

Guillem boquetBorja martSpacex starlinkEutelsat onewebInternet interdisciplinary instituteWireless networksUniversitat oberta de catalunyaLow power satellite access time estimationThings services over nonterrestrial networksThings journal

Advancing Urban Safety with Distributed Acoustic Sensing

Advancing Urban Safety with Distributed Acoustic Sensing
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Nidhi dhullmayNidhi dhullYuria shutterstockResearch progress in distributed acoustic sensing techniquesUrban safetyDistributed acousticSmart cityRemote sensingThings journalResearch progressDistributed acoustic sensingMonitoring linear infrastructuresCurrent statusFiber optic acoustic sensingCreate data driven urban mobilitySmart fiber optic distributed acoustic sensing

Towards More Accurate 3D Object Detection for Robots and Self-Driving Cars

Towards More Accurate 3D Object Detection for Robots and Self-Driving Cars
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Hiroyuki tomiyamaCollege of sciencePixel feature alignment networkRitsumeikan universityRitsumeikan university researchKyushu universityProfessor hiroyuki tomiyamaPoint pixel feature alignmentThings journalMemory based point pixel fusionDeformable point pixel fusionSemantic alignment evaluatorDynamic point pixel feature alignmentUniversity research reportProject number

DGIST Achieves Simultaneous Performance Improvement and Energy Savings with Innovative Algorithm for 6G Vision Services!

DGIST Achieves Simultaneous Performance Improvement and Energy Savings with Innovative Algorithm for 6G Vision Services!
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Myeongjin kimPyeongjun choiJeongho kwakKunwoo leeDepartment of electrical engineeringInha universityProfessor jeongho kwakElectrical engineeringComputer sciencePresident kunwoo leeProfessor kwakOnline convex optimizationDongho hamProfessor yeongjin kimThings journalAuthore mail address

Network combines 3D LiDAR and 2D image data to enable more robust detection of small objects

Network combines 3D LiDAR and 2D image data to enable more robust detection of small objects
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Hiroyuki tomiyamaJuncheng wangRitsumeikan universityPixel feature alignment networkProfessor hiroyuki tomiyamaPoint pixel feature alignmentMemory based point pixel fusionDeformable point pixel fusionSemantic alignment evaluatorDynamic point pixel feature alignmentObject detectionThings journal