A Deep Learning-Based Automatic Mosquito Sensing and Control System for Urban Mosquito Habitats.

Mosquito control is important as mosquitoes are extremely harmful pests that spread various infectious diseases. In this research, we present the preliminary results of an automated system that detects the presence of mosquitoes via image processing using multiple deep learning networks. The Fully Convolutional Network (FCN) and neural network-based regression demonstrated an accuracy of 84%.

Vision Marker-Based In Situ Examination of Bacterial Growth in Liquid Culture Media.

The detection of bacterial growth in liquid media is an essential process in determining antibiotic susceptibility or the level of bacterial presence for clinical or research purposes. We have developed a system, which enables simplified and automated detection using a camera and a striped pattern marker. The quantification of bacterial growth is possible as the bacterial growth in the culturing vessel blurs the marker image, which is placed on the back of the vessel, and the blurring results in a decrease in the high-frequency spectrum region of the marker image.

A Low Cost/Low Power Open Source Sensor System for Automated Tuberculosis Drug Susceptibility Testing.

In this research an open source, low power sensor node was developed to check the growth of mycobacteria in a culture bottle with a nitrate reductase assay method for a drug susceptibility test. The sensor system reports the temperature and color sensor output frequency change of the culture bottle when the device is triggered. After the culture process is finished, a nitrite ion detecting solution based on a commercial nitrite ion detection kit is injected into the culture bottle by a syringe pump to check bacterial growth by the formation of a pigment by the reaction between the solution and the color sensor.