Detect caterpillar, grasshopper, aphid and simulation program for neutralizing them by laser

The protection of crops from pests is relevant for any cultivated crop. But modern methods of pest control by pesticides carry many dangers for humans. Therefore, research into the development of safe and effective pest control methods is promising. This manuscript presents a new method of pest control. We used neural networks for pest detection and developed a powerful laser device (5 W) for their neutralization. In the manuscript methods of processing images with pests to extract the most useful feature are described in detail. Using the following pets as an example: aphids, grasshopper, cabbage caterpillar, we analyzed various neural network models and selected the optimal models and characteristics for each insect. In the paper the principle of operation of the developed laser device is described in detail. We created the program to search a pest in the video stream calculation of their coordinates and transmission data with coordinates to the device with the laser.

Progress in neural networks for EEG signal recognition in 2021

In recent years, neural networks showed unprecedented growth that ultimately influenced dozens of different industries, including signal processing for the electroencephalography (EEG) process. Electroencephalography, although it appeared in the first half of the 20th century, was not changed the physical principles of work to this day. But signal processing technology made significant progress in this area through the use of neural networks. But many different models of neural networks complicate the process of understanding the real situation in this area. This manuscript summarizes the current state of knowledge on this topic, summarizes and describes the most significant achievements in various fields of application of neural networks for processing EEG signals. We discussed in detail the results presented in recent research papers for various fields in which EEG signals have been involved. We also examined in detail the process of extracting features from EEG signals using neural networks. In conclusion, we have provided recommendations for the correct demonstration of research results in manuscripts on the subject of neural networks and EEG.