![]() The state-of-the-art deep learning-based object recognition YOLO algorithm and object tracking DeepSORT algorithm are combined to analyze digital images from fluid dynamic simulations of multi-core emulsions and soft flowing crystals and to track moving droplets within these complex flows. The fastest configuration of DropTrack runs inference at about 30 frames per second, well within the standards for real-time image analysis. DropTrack's performance is measured in terms of mean average precision (mAP), mean square error in counting the droplets, and inference speed. For our test case, the YOLO networks trained with 60% synthetic images show similar performance in droplet counting as with the one trained using 100% real images, meanwhile saving the image annotation work by 60%. We present an analysis of a double emulsion experiment as a case study to measure DropTrack's performance. This work partly resolves this problem by training object detector networks (YOLOv5) with hybrid datasets containing real and synthetic images. Training an object detector network for droplet recognition with manually annotated images is a labor-intensive task and a persistent bottleneck. DropTrack analyzes input videos, extracts droplets' trajectories, and infers other observables of interest, such as droplet numbers. Here, two deep learning-based cutting-edge algorithms for object detection (YOLO) and object tracking (DeepSORT) are combined into a single image analysis tool, DropTrack, to track droplets in microfluidic experiments. Sometimes the individual droplets in these dense clusters are hard to resolve, even for a human observer. Specifically, droplet tracking in dense emulsions is challenging as droplets move in tightly packed configurations. One fundamental analysis frequently desired in microfluidic experiments is counting and tracking the droplets. However, with Arduino, we can define the MAC address ourself.Deep neural networks are rapidly emerging as data analysis tools, often outperforming the conventional techniques used in complex microfluidic systems. ![]() Each piece of networking equipment has a unique serial number to identify itself over a network and this is normal hard-programmed into the equipment’s firmware. ![]() MAC address (media access control address) is a unique identifier assigned to each device participating in a physical network. For example, to assign the IP of Ethernet shield to 192.168.0.50, write the line: Specifying the IP address is done by writing the line:Īnd change it to match one own setup. IP address (Internet Protocol address) is a numerical label assigned to each device participating in a computer network that uses the Internet Protocol for communication. If DHCP is used, it may dynamically assign an IP to the shield. Validity of IP addresses depends on the configuration of one’s network. For older shields, a random one should work, but one should not use the same one for many boards. Current Ethernet shields come with a sticker indicating the MAC address. For a particular device, a MAC address is a globally unique identifier. The shield must be assigned a MAC and IP address using the Ethernet.begin() function. To control the Ethernet shield, you use the Ethernet.h library. The status of the switch will be sent to the Web server. When it is released, the output will be set to OFF. The Arduino will then set the status of the OUTPUT to ON. When the button is pushed, the Arduino will read a LOW value on this pin. Arduino's pin 8 is connected to the pushbutton and is configured as INPUT. Hardware RequiredĬonnect the components as shown above. To demonstrate how to use the Arduino as a Web server, we will read the state of a switch. ![]() Setup for Using an Arduino as a Web Server The setup is very simple: just plug the header pins of the shield into your Arduino, then connect an Ethernet cable to the shield. ![]() The Ethernet shield connects the Arduino to the Internet.
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