The inventors of the present application developed novel retroreflective materials, and articles. One example of the present application is a retroreflective article, comprising a substrate and a machine-readable information disposed on the substrate, wherein the machine-readable information corresponds to a predetermined human-readable information.

A computerized method for authenticating documents having VIZ sections, the method comprising capturing an image of a document to be authenticated from a scanner and enhancing the captured image, and using a processor for identifying and cropping a VIZ section in the image.

A computerized method for authenticating documents having VIZ sections, the method comprising capturing an image of a document to be authenticated from a scanner and enhancing the captured image, and using a processor for identifying and cropping a VIZ section in the image.

A defect detection system includes one or more route optical sensors configured to generate image data depicting an underbody of a vehicle that is on a route. The defect detection system also includes a controller configured to input the image data generated by the one or more route optical sensors into a first machine learning algorithm that determines an area of interest in the image data. The area of interest contains equipment of interest. The controller is configured to input the image data that is within the area of interest into a second machine learning algorithm that detects a defect on the equipment of interest. The controller is configured to perform at least one responsive action based on a type of defect that is detected.

The invention comprises a method for a fast, accurate and automatic determination of reservoir and non-reservoir zones of rock cores. The method involves performing pre-processing on images taken under visible light, UV light and tomography of rock cores to extract metadata therefrom and prepare them to be analyzed by a trained artificial intelligence (AI). AI performs an analysis by pixel and by row to determine reservoir and non-reservoir zones. The method further comprises filtering the AI results to reclassify relatively small regions.

A food management method includes acquiring an image captured by imaging, using an imaging element, food or a storage container to which a food management tag including an identifier associated with the food is attached, and the storage container stores the food. The food management method includes recognizing, based on the image acquired, the food or the storage container to which the food management tag is attached. The food management method includes estimating the remaining amount of the food based on the size of the food management tag recognized. The food management method includes outputting remaining amount information related to the remaining amount of the food that has been estimated.

A fluid leak detection system includes one or more route optical sensors configured to generate image data depicting an underbody of a vehicle that is on the route. The fluid leak detection system also includes a controller capable of determining an area of interest in the image data, the area of interest containing a gear case. The controller is capable of detecting a fluid leak related to the gear case in the image data that is within the area of interest. The controller is capable of determining a location and severity of the fluid leak related to the gear case. The controller is capable of performing at least one responsive action based on a result of the detection and determination of the location and severity of the fluid leak.

A defect detection system includes one or more route optical sensors configured to generate image data depicting an underbody of a vehicle that is on a route. The defect detection system also includes a controller configured to input the image data generated by the one or more route optical sensors into a first machine learning algorithm that determines an area of interest in the image data. The area of interest contains equipment of interest. The controller is configured to input the image data that is within the area of interest into a second machine learning algorithm that detects a defect on the equipment of interest. The controller is configured to perform at least one responsive action based on a type of defect that is detected.

The present disclosure provides a multiplexed metasurface optical neural network device including a plurality of metasurface layers that modify amplitude and phase of incident light and a detector that captures output images. The metasurface layers generate diverse output images in response to random input light intensity profiles through spatial multiplexing, with the random input light intensity profiles being derived from a standard Gaussian distribution representing latent variables in a generative model. A method of operating the device as a generative model includes projecting random input light intensity profiles onto metasurface layers, modifying amplitude and phase through spatial multiplexing, transforming the profiles into output images containing predetermined information through light propagation, and capturing the output images using a detector.

The present disclosure provides a multiplexed metasurface optical neural network device including metasurface layers that modify amplitude and phase of incident light to perform distinct classification tasks, each task associated with a degree of freedom including wavelength or polarization, and a detector that captures output intensity information encoding classification weights. A method includes projecting light intensity profiles representing objects onto metasurface layers using different wavelengths or polarization states, and capturing output intensity information corresponding to classification into different sets of classes. The device enables handwritten digit recognition at one wavelength or polarization state and object classification at another. The device also operates as a generative model, with metasurface layers generating diverse output images in response to random input light intensity profiles through spatial multiplexing, where random profiles are derived from a standard Gaussian distribution representing latent variables.