A method and system that efficiently selects sensors without requiring advanced expertise or extensive experience even in a case of new machines and unknown failures. An abnormality detection system includes a storage unit for storing a latent variable model and a joint probability model, an acquisition unit for acquiring sensor data that is output by a sensor, a measurement unit for measuring the probability of the sensor data acquired by the acquisition unit based on the latent variable model and the joint probability model stored by the storage unit, a determination unit for determining whether the sensor data is normal or abnormal based on the probability of the sensor data measured by the measurement unit, and a learning unit for learning the latent variable model and the joint probability model based on the sensor data output by the sensor.

The invention relates to an artificial intelligence cataract analysis system, including a pattern recognition module for recognizing a photo mode of an input eye image, wherein the photo mode is divided according to the slit width of the illuminating slit during photographing of the eye image and/or whether a mydriatic treatment is carried out; a preliminary analysis module used for selecting a corresponding deep learning model for eye different photo modes, analyzing the characteristics of lens in the eye image by using a deep learning model, and further performing classification in combination with cause and severity degree of a disease. The invention can perform cataract intelligent analysis on eye images with different photo modes by using deep learning models, so that the analysis accuracy is improved.

A system and process to determine whether a digital image has been manipulated involves determining expected locations of non-standard pixels in the digital image, and determining a feature for evaluating the non-standard pixels. The feature in pixels of the digital image that are located at the expected locations of non-standard pixels is then measured, and a statistical measure of the feature of pixels in the digital image that are located at the expected locations of non-standard pixels is evaluated. The digital image is assessed to determine a probability that the digital image includes a manipulated portion, based on the statistical measure. The system and process can also determine a make and model of an image sensing device via an examination of the non-standard pixels.

An example operation may include one or more of scanning, by a mobile node, a physical object to generate a scan data, extracting, by the mobile node, a set of features from the scan data, generating, by the mobile node, a feature vector based on the set of the features, applying, by the mobile node, a cryptographic hash function to the feature vector to produce a hash value, encrypting, by the mobile node, the set of the features with the hash value, and executing a smart contract to store the encrypted set of the features on a blockchain.

According to the first aspect, there is provided a method, by a server, for dynamically identifying a user of an account for posting images, comprising: determining, by the server, if images posted in the account of the user includes an image capturing device; extracting, by the server, a characteristic of the image of the image capturing device when it is determined that the images in the account includes the image capturing device; and identifying, by the server, an image capturing device of the user in response to the extraction of the characteristic of the image of the image capturing device.

There is disclosed a method of handling a sensor, comprising the steps of: defining a subset of sensor components of the sensor; challenging said subset under uniform conditions; receiving output signal values from said subset; for each component of the subset, determining the statistical moment of order i of the temporal distribution of the output signal value of said each sensor component; determining one or more outliers sensor components, said outliers sensor components being components whose i.sup.th order statistical moment has a difference with the mean value of the spatial distribution of the chosen moment over the subset superior in absolute value to a threshold, the i.sup.th order statistical moment of one sensor component being estimated on the temporal distribution associated to this sensor component. Developments describe in particular the use of imaging sensors, key generation, authentication, helper data files and the handling of videos.

A system and method to verify the authenticity of a physical object, based on the efficient acquisition and digital post-processing of a large amount of optical data. An optical system, comprised of an array of microscope-type micro-cameras and a patterned illumination source, acquires spatial, spectral and angular information about the physical object in the form of micro-camera images. The set of all acquired images comprise one object dataset, which a post-processing system then digitally transforms into a multi-gigabyte set of semi-random keys. Authentication takes place at a later date following a challenge-and-response protocol. The high resolution (<15 m) of the acquired data presents a significant challenge to attempted duplication of the physical object, and the large size (>1 Gigabyte) of the key set similarly prevents both physical and digital forgery attempts.

An autonomous cargo handling system having a sensor self-calibration system may comprise a sensing agent configured to monitor a sensing zone, and a system controller in electronic communication with the first sensing agent. The system controller may be configured to receive structural cargo deck data from the first sensing agent, generate a real-time cargo deck model, identify a cargo deck component in the real-time cargo deck model, and determine a position of the sensing agent relative to the cargo deck component.

An image forensics system estimates a camera response function (CRF) associated with a digital image, and compares the estimated CRF to a set of rules and compares the estimated CRF to a known CRF. The known CRF is associated with a make and a model of an image sensing device. The system applies a fusion analysis to results obtained from comparing the estimated CRF to a set of rules and from comparing the estimated CRF to the known CRF, and assesses the integrity of the digital image as a function of the fusion analysis.

Plant health estimation is required to be performed so as to detect any health issues in early stages, so as to take counter measures. Existing systems for the plant health estimation perform the health estimation by considering data obtained from satellite images of the plants being monitored. However this alone may not be much effective as the satellite images fail to provide information on many parameters which have direct or indirect impact on health of the plants. Disclosed herein are a method and a system for plant health estimation, wherein the system performs health estimation at a macro level and a micro level. The macro level health estimation is done using satellite images of the plants as inputs, whereas the micro level health estimation is done by collecting and processing sensor data with respect to various parameters that affect health of a plant.