A method for external fish parasite monitoring in aquaculture, comprising the steps of: —submerging a camera (52) in a sea pen containing fish, the camera having a field of view; —capturing images of the fish with the camera (52); and—identifying external fish parasite on the fish by analyzing the captured images, characterized in that a target region within the field of view of the camera (52) is illuminated from above and below with light of different intensities and/or spectral compositions.

The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. A communication environment analysis method according to the present invention comprises the steps of: receiving satellite information and image information of a certain area; identifying area information of an object that is not contained in the image information, on the basis of the satellite information; determining characteristic information of the object; and analyzing a communication environment of the certain area on the basis of the characteristic information, wherein the object is one that causes signal attenuation due to at least one of signal scattering and signal absorption.

A traffic information processing equipment, system and method. The traffic information processing equipment includes an image recognition and decision device and a warning device. The image recognition and decision device is configured to process a received traffic route image to identify a scene, and determine whether to perform a warning operation according to the scene to obtain a determination result. The warning device is configured to generate warning information according to the determination result for sending prompt information to vehicles in a traffic route.

Provided is a position estimation device capable of highly accurate position estimation. A position estimation device 1 of the present invention is the position estimation device 1 which estimates a current position of a moving object 100 equipped with an imaging device 12, estimates the current position of the moving object 100, create a plurality of virtual positions based on the current position, creates virtual images at the plurality of virtual positions, respectively, compares the plurality of virtual images with an actual image to calculate a comparison error, calculates a weight based on at least one of information acquired by the imaging device 12 and information of a current position error of the moving object, performs weighting on the comparison error using the weight, and corrects the current position based on the comparison error to be weighted.

A chip recognizing and learning system includes: a game recording device configured to record a state of chips piled up on a gaming table as an image by a camera; a chip determining device including an artificial intelligence device configured to analyze the recorded image of the state of the chips to determine the numbers and kinds of chips bet by a player; and a teaching device configured to input, in a case where it is determined that there is a doubt for an error in a determining result of the chip determining device, the image used for determination of the chip determining device and the correct numbers and correct kinds of chips for the error as teaching data to the artificial intelligence device to allow the artificial intelligence device to perform learning.

Disclosed is a large-scale crop phenology extraction method based on a shape model fitting method. The method comprises: acquiring a multi-year vegetation index time sequence curve in a localized geographic region; performing smooth fitting on the vegetation index time sequence curve by using a dual logistic function fitting means; establishing shape models by using reference curves and reference points of agrometeorological stations; performing shape model fitting by means of transformation; and obtaining a phenological period extraction value of the localized geographic region by means of calculation using the optimal scaling parameter. According to the present invention, macroscopic features of the curve are used, such that the influence of localized fluctuation and noise of the curve can be reduced, and a better extraction precision is obtained; and each phenological period of a crop can be extracted at the same time.

Disclosed is a large-scale crop phenology extraction method based on a shape model fitting method. The method comprises: acquiring a multi-year vegetation index time sequence curve in a localized geographic region; performing smooth fitting on the vegetation index time sequence curve by using a dual logistic function fitting means; establishing shape models by using reference curves and reference points of agrometeorological stations; performing shape model fitting by means of transformation; and obtaining a phenological period extraction value of the localized geographic region by means of calculation using the optimal scaling parameter. According to the present invention, macroscopic features of the curve are used, such that the influence of localized fluctuation and noise of the curve can be reduced, and a better extraction precision is obtained; and each phenological period of a crop can be extracted at the same time.

Neural stimulation is provided according to a closed loop algorithm to treat sleep disordered breathing (SOB), including obstructive sleep apnea (OSA). The closed loop algorithm is executed by a system comprising a processor (which can be within the neural stimulator). The closed loop algorithm includes monitoring physiological data (e.g., EMG data) recorded by a sensor implanted adjacent to an anterior lingual muscle; identifying a trigger within the physiological data, wherein the trigger is identified as a biomarker for a condition related to sleep (e.g., inspiration); and applying a rule-based classification (which can learn) to the trigger to determine whether one or more parameters of a stimulation should be altered based on the biomarker.

In a general aspect, a data management system for spatial phase imaging is described. A data management system for spatial phase imaging includes: a storage engine configured to receive and store input data in a record format, the input data including: pixel-level first-order primitives generated based on electromagnetic (EM) radiation received from an object located in a field-of-view of an image sensor device; and pixel-level second-order primitives generated based on the first-order primitives. The data management system further includes: an analytics engine configured to determine a plurality of features of the object based on the pixel-level first-order primitives and the pixel-level second-order primitives; and an access engine configured to provide a user access to the plurality of features of the object determined by the analytics engine and to the input data stored by the storage engine.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a temporal range of a fire. In some implementations, a server obtains a date when a fire occurred within a region. The server obtains satellite imagery of the region from before the date when the fire occurred. The server generates a first statistical distribution from the satellite imagery. The server determines a start date of the fire using the first statistical distribution. The server obtains second satellite imagery of the region from before and after the start date. The server selects a second set of imagery from the second satellite imagery from before the start date. The server generates a second statistical distribution from the second set of imagery. The server determines an end date of the fire using the second statistical distribution. The server provides the start date and the end date for output.