In this paper we propose a Downlink Angle of Departure (DL-AOD) positioning algorithm for UE coordinates estimation using channel measurements. The channel estimation is performed with a multi-element transmit antenna array at the base station (gNB) and a single or multi-element receive antenna array at the user device (UE). The algorithm performs total receive channel power maximization with respect to the multi-dimensional space-time filter coefficients to derive an optimal DL-AOD solution. It is shown that the DL-AOD measurements may suffer from the Non-Line of Sight (NLOS) propagation phenomenon, specifically in indoor environments, that causes an angular bias relative to the true Line of Sight (LOS) direction. To reduce the impact of NLOS links on the resulting positioning accuracy the reliability function is introduced for correct weighting of the DL-AOD measurements in the positioning equations. The performance evaluation of the proposed algorithm is provided for the Industrial Internet of Things (I-IoT) use case.

The Hogweed of Sosnowskyi (lat. Heracleum sosnówskyi) is poisonous for humans, dangerous for farming crops, and local ecosystems. This plant is fast-growing and has already spread all over Eurasia: from Germany to the Siberian part of Russia, and its distribution expands year-by-year. In-situ detection of this harmful plant is a tremendous challenge for many countries. Meanwhile, there are no automatic systems for detection and localization of hogweed. In this article, we report on an approach for fast and accurate detection of hogweed. The approach includes the Unmanned Aerial Vehicle (UAV) with an embedded system on board running various Fully Convolutional Neural Networks (FCNN). We propose the optimal architecture of FCNN for the embedded system relying on the trade-off between the detection quality and frame rate. We propose a model that achieves ROC AUC 0.96 in the hogweed segmentation task, which can process 4K frames at 0.46 FPS on NVIDIA Jetson Nano. The developed system can recognize the hogweed on the scale of individual plants and leaves. This system opens up a wide vista for obtaining comprehensive and relevant data about the spreading of harmful plants allowing for the elimination of their expansion.