Abstract: A method of analysis of a stream of screenshots in order to determine pool water quality and robot presence, wherein software analytics compares color or clarity of water in order to figure out if the pool has dirty pool water. The software analytics checks the acceptable boundary regarding water clarity, wherein dirty water will typically go cloudy and/or have a taint of green, and the software analytics determines that higher levels of green in the water indicates worse water quality. The software analytics utilizes machine learning, neural networks and artificial intelligence by being trained on high numbers of screenshots of pools in order to make an accurate determination as to water clarity, a taint of green in water, and cloudiness in water.

Abstract: A system for identifying microbial agents such as virus particles in a sample. The system includes at least one processing unit for identifying in an electron micrograph obtained from the sample a darker region and identifying virus particles within the darker region. The system can optionally include an electron microscope, a sample collector and sample treatment chamber.

Abstract: A detection and tracking system and method using a camera unit on a robot, or alternatively a camera mounted inside the pool overlooking the bottom of the pool, for safety monitoring for use in and around water-related environments. The robot is able to propel itself and move throughout the body of water, both on the surface and underwater, and the camera unit functions both on the surface and underwater. The robot optimizes the cleaning cycle of the body of water utilizing deep learning techniques. The robot has localization sensors and software that allow the robot to be aware of the robot's position in the pool. The camera is able to send its video feed live over the internet, the processing is performed in the cloud, and the robot sends and receives data from the cloud. The processing utilizes deep learning algorithms, including artificial neural networks, that perform video analytics.

Abstract: A detection and tracking system and method using a camera unit on a robot, or alternatively a camera mounted inside the pool overlooking the bottom of the pool, for safety monitoring for use in and around water-related environments. The robot is able to propel itself and move throughout the body of water, both on the surface and underwater, and the camera unit functions both on the surface and underwater. The robot optimizes the cleaning cycle of the body of water utilizing deep learning techniques. The robot has localization sensors and software that allow the robot to be aware of the robot's position in the pool. The camera is able to send its video feed live over the internet, the processing is performed in the cloud, and the robot sends and receives data from the cloud. The processing utilizes deep learning algorithms, including artificial neural networks, that perform video analytics. The robot acts as a game platform such that the camera unit on the robot facilitates augmented reality games.

Abstract: Systems and methods for the safety monitoring of a body of water. The system comprises one or more image capturing units mounted outside a body of water and overlooking the area of the body of water, as well as a processing unit enabling real-time detection and tracking of objects. The processing can be performed either on the actual image capturing unit, a network video recorder device or on a cloud device. The system utilizes deep learning algorithms, including artificial neural networks, that perform video analytics using a method comprising the following steps: a. the areas of interest around the body of water are defined upon initial set up of the system; in a man-made body of water, the area surrounding the pool would be defined as Area 1 and the pool itself would be defined as Area 2; in the context of the ocean, such interest areas are the beach area, ocean and any other pre-defined areas; b.