A system includes and maintains a machine learning algorithm. The machine learning algorithm is trained to identify non-targets in an environment. The system receives an image of the environment, and identifies the non-targets in the image using the trained machine learning algorithm. The system then generates a firing cut out map for overlaying on the image of the environment based on the identified non-targets in the image of the environment.

The disclosure provides a method for managing a cleaning route in a smart city, an Internet of Things system and storage medium. The method includes: obtaining target information of a target area within a preset time period based on an object platform; sending the target information to a management platform through a sensor network platform; determining a cleaning route of the target area by processing the target information of the target area based on the management platform, including: determining an estimated amount of fallen leaves of each section of road in the target area; determining an estimated falling range of fallen leaves of each section of road in the target area; determining a cleaning difficulty evaluation value of each section of road based on the estimated amount of fallen leaves and the estimated falling range; and determining a cleaning route based on the cleaning difficulty evaluation value.

The disclosure provides a method for managing a cleaning route in a smart city, an Internet of Things system and storage medium. The method includes: obtaining target information of a target area within a preset time period based on an object platform; sending the target information to a management platform through a sensor network platform; determining a cleaning route of the target area by processing the target information of the target area based on the management platform, including: determining an estimated amount of fallen leaves of each section of road in the target area; determining an estimated falling range of fallen leaves of each section of road in the target area; determining a cleaning difficulty evaluation value of each section of road based on the estimated amount of fallen leaves and the estimated falling range; and determining a cleaning route based on the cleaning difficulty evaluation value.

A verification system or method for detecting whether a CPA device is in a pre-lock position or in a full-lock position relative to the housing based on a scan system’s detection that a portion of a predetermined word or character on the CPA device and another portion of the predetermined word or character are separated from each other or joined together to form the predetermined word or character. The CPA device is fully engaged or in full-lock position or status with the housing when the scan system has recognized or determined that the portion of the predetermined word or character on the CPA device and the another portion of the predetermined word or character on the housing within a scan window are joined together to form the complete or full predetermined word or character.

A deep-ultraviolet microscopy system includes a light source for outputting a light beam for illuminating a biological sample, the light beam being inclusive of ultraviolet wavelengths; a reception space for reception of a biological sample for illumination by the light beam; an ultraviolet microscope objective for collecting and relaying light that interacts with the biological sample to an image capture device; and an ultraviolet sensitive image capture device for capturing images of the biological sample, with the microscopy system configured to capture multiple images of the biological sample at one or more ultraviolet wavelengths. A method of processing ultraviolet images of biological samples includes receiving a plurality of multi-spectral ultraviolet images of a biological sample; normalizing and scaling the images; and assigning each image to a channel in the RGB color-space based on wavelength.

A deep-ultraviolet microscopy system includes a light source for outputting a light beam for illuminating a biological sample, the light beam being inclusive of ultraviolet wavelengths; a reception space for reception of a biological sample for illumination by the light beam; an ultraviolet microscope objective for collecting and relaying light that interacts with the biological sample to an image capture device; and an ultraviolet sensitive image capture device for capturing images of the biological sample, with the microscopy system configured to capture multiple images of the biological sample at one or more ultraviolet wavelengths. A method of processing ultraviolet images of biological samples includes receiving a plurality of multi-spectral ultraviolet images of a biological sample; normalizing and scaling the images; and assigning each image to a channel in the RGB color-space based on wavelength.

An onboard cloud detection system comprising: a camera (1000) configured to acquire images of the Earth at predetermined acquisition intervals; and a data processing unit (2000) comprising: a cloud detection unit (2210) configured to use artificial intelligence, AI, algorithms to detect clouds; a packet utilization standard, PUS, application layer (2230) configured to issue telemetry and/or telecommands corresponding to a predetermined parameter of the output of the cloud detection unit (2210); and an interface configured to distribute the telemetry and/or telecommands to an external hardware and/or an external software terminal (3000, 4000).

An onboard cloud detection system comprising: a camera (1000) configured to acquire images of the Earth at predetermined acquisition intervals; and a data processing unit (2000) comprising: a cloud detection unit (2210) configured to use artificial intelligence, AI, algorithms to detect clouds; a packet utilization standard, PUS, application layer (2230) configured to issue telemetry and/or telecommands corresponding to a predetermined parameter of the output of the cloud detection unit (2210); and an interface configured to distribute the telemetry and/or telecommands to an external hardware and/or an external software terminal (3000, 4000).

This disclosure relates to the combining and interaction of multiple artificial intelligence (AI) models for medical image analysis. An example method includes obtaining AI models from model providers and organizing them to form associations. In response to a user request, base models are selected and provided. Additional models are further selected to combine with the base models, and medical image analysis results are presented based on applying a combination of the models to target medical image data.

An electronic device and method for generating an augmented image. The device includes an image sensor and an electronic processor. The electronic processor is configured to receive an image, retrieve a set of keywords, and identify a first set of features within the image corresponding to at least one keyword from the set of keywords, producing first metadata. The electronic processor is configured to compress the image, identify a second set of features within the compressed image, producing second metadata, and determine missing metadata between the first metadata and the second metadata. The electronic processor is configured to generate an augmented image by associating the missing metadata to the compressed image and perform at least one selected from the group consisting of transmitting the augmented image to another device and decompressing the augmented image to create a decompressed image and presenting the decompressed image including the missing metadata.