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Electronics, Volume 7, Issue 6

June 2018 - 22 articles

Cover Story: The method uses object detection from 3D LiDAR point clouds fused with RGB camera detection from a convolutional neural network to provide a more robust fused detection. The CNN bounding-box data is processed in order to project it into the LiDAR space. The LiDAR detections centroids and their confidence values are then fused to provide a fused detection result. This method has been implemented in real-time and tested in an industrial setting on an industrial vehicle. View the paper here.

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Article

51 Citations

10,289 Views

19 Pages

Real-Time Ground Vehicle Detection in Aerial Infrared Imagery Based on Convolutional Neural Network

Xiaofei Liu,
Tao Yang and
Jing Li

Electronics2018, 7(6), 78;https://doi.org/10.3390/electronics7060078

23 May 2018

An infrared sensor is a commonly used imaging device. Unmanned aerial vehicles, the most promising moving platform, each play a vital role in their own field, respectively. However, the two devices are seldom combined in automatic ground vehicle dete...

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Article

5 Citations

4,937 Views

21 Pages

A Performance Study of Massive MIMO Heterogeneous Networks with Ricean/Rayleigh Fading

James Kweku Nkrumah Nyarko and
Christian Ango Mbom

Electronics2018, 7(6), 79;https://doi.org/10.3390/electronics7060079

23 May 2018

Massive multiple-input and multiple-out (massive MIMO) systems and heterogeneous networks (HetNet) are envisioned to meet the new performance objectives of evolving networks. Now, the main challenge of massive MIMO with HetNet in two-tier networks is...

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Electronics - ISSN 2079-9292

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