RaspberryPI for mosquito neutralization by power laser

• URL: http://arxiv.org/abs/2105.14190v1
• Date: Thu, 20 May 2021 01:38:45 GMT
• Title: RaspberryPI for mosquito neutralization by power laser
• Authors: R. Ildar
• Abstract summary: We developed a laser installation with Raspberry Pi that changing the direction of the laser with a galvanometer. A recommendation is given for the implementation of this device based on a microcontroller for subsequent use as part of an unmanned aerial vehicle.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this article for the first time, comprehensive studies of mosquito neutralization using machine vision and a 1 W power laser are considered. Developed laser installation with Raspberry Pi that changing the direction of the laser with a galvanometer. We developed a program for mosquito tracking in real. The possibility of using deep neural networks, Haar cascades, machine learning for mosquito recognition was considered. We considered in detail the classification problems of mosquitoes in images. A recommendation is given for the implementation of this device based on a microcontroller for subsequent use as part of an unmanned aerial vehicle. Any harmful insects in the fields can be used as objects for control.

Related papers

• Sensor Deprivation Attacks for Stealthy UAV Manipulation [51.9034385791934]
  Unmanned Aerial Vehicles autonomously perform tasks with the use of state-of-the-art control algorithms. In this work, we propose a multi-part. Sensor Deprivation Attacks (SDAs), aiming to stealthily impact. process control via sensor reconfiguration.
  arXiv  Detail & Related papers  (2024-10-14T23:03:58Z)
• Acoustic Identification of Ae. aegypti Mosquitoes using Smartphone Apps and Residual Convolutional Neural Networks [0.8808021343665321]
  We advocate for smartphone apps as low-cost, easy-to-deploy solution for raising awareness among the population on the proliferation of Aedes aegypti mosquitoes. devising such a smartphone app is challenging, for many reasons, including the required maturity level of techniques for identifying mosquitoes based on features that can be captured using smartphone resources.
  arXiv  Detail & Related papers  (2023-06-16T13:41:01Z)
• mAedesID: Android Application for Aedes Mosquito Species Identification using Convolutional Neural Network [0.0]
  It is important to control dengue disease by reducing the spread of Aedes mosquito vectors. Community awareness plays acrucial role to ensure Aedes control programmes and encourages the communities to involve active participation. Mobile application mAedesID is developed for identifying the Aedes mosquito species using a deep learning Convolutional Neural Network (CNN) algorithm.
  arXiv  Detail & Related papers  (2023-05-02T14:20:13Z)
• An Empirical Study of Adder Neural Networks for Object Detection [67.64041181937624]
  Adder neural networks (AdderNets) have shown impressive performance on image classification with only addition operations. We present an empirical study of AdderNets for object detection.
  arXiv  Detail & Related papers  (2021-12-27T11:03:13Z)
• CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification [53.56290185900837]
  We propose an algorithm for optimizing a neural network for object detection using preliminary binary frame classification. An autonomous mobile robot with 6 rolling-shutter cameras on the perimeter providing a 360-degree field of view was used as the experimental setup.
  arXiv  Detail & Related papers  (2021-10-22T15:05:37Z)
• Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks [55.41644538483948]
  Invertible neural networks are a recent technique in machine learning. We will be introducing invertible surrogate models that approximate complex forward simulation of the physics involved in laser plasma accelerators: iLWFA.
  arXiv  Detail & Related papers  (2021-06-01T12:26:10Z)
• Detect caterpillar, grasshopper, aphid and simulation program for neutralizing them by laser [0.0]
  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.
  arXiv  Detail & Related papers  (2021-04-22T05:02:27Z)
• Automatic glissade determination through a mathematical model in electrooculographic records [58.720142291102135]
  The glissadic overshoot is characterized by an unwanted type of movement known as glissades. In this paper we develop a procedure to determine if a specific saccade have a glissade appended to the end of it. A machine learning algorithm is trained, returning expected responses of the presence or not of this kind of ocular movement.
  arXiv  Detail & Related papers  (2021-04-19T17:56:55Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)
• Automatic Detection of Aedes aegypti Breeding Grounds Based on Deep Networks with Spatio-Temporal Consistency [2.4858193569899907]
  Aedes aegypti mosquito infects millions of people with diseases such as dengue zika, chikungunya, and urban yellow fever. Main form to combat these diseases is to avoid mosquito reproduction by searching for and eliminating the potential mosquito breeding grounds. In this work, we introduce a comprehensive dataset of aerial videos, acquired with an unmanned aerial vehicle, containing possible mosquito breeding sites.
  arXiv  Detail & Related papers  (2020-07-29T14:30:54Z)
• Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques [2.9822608774312327]
  Aedes aegypti and Anopheles stephensi mosquitoes (both of which are deadly vectors) are studied. CNN model based on Inception-ResNet V2 and Transfer Learning yielded an overall accuracy of 80% in classifying mosquitoes. In particular, the accuracy of our model in classifying Aedes aegypti and Anopheles stephensi mosquitoes is amongst the highest.
  arXiv  Detail & Related papers  (2020-05-25T15:58:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.