A device configured to emulate a quadrilateral lattice correlithm object generator includes multiple processing stages that operate together to output a quadrilateral lattice correlithm object. A quadrilateral lattice correlithm object has a quadrilateral geometric shape and is formed by at least four sub-lattice correlithm objects that are some number of bits away from each other in n-dimensional space.

The present disclosure provides for Non-Destructive Inspection of craft operating in high-atmosphere or outer space, by positioning a scintillating detector array leeward to a structural element of the craft relative to the Sun; collecting, by the detector array while the craft is in flight, solar radiation passing through the structural element; and outputting a radiographic image based on the solar radiation collected to an image analyzer. The image analyzer may composite several images taken over a period of time or decomposite images of intervening structural elements from the radiographic images. Automated alerts for non-conformances between the radiographic images and earlier-taken or architectural images are provided to users.

Embodiments disclosed herein may include a system including a server configured to receive from the mobile device the digital image capturing the object, execute an object recognition protocol to identify one or more image features of the digital image, determine an identification of the object based upon the one or more features of the digital image identified by the executed object recognition protocol, generate an object profile of the object based upon one or more data records of the object stored in the system databases where each respective record containing at least one data point corresponding to a valuation of the respective object, determine a value of the data point based upon the valuation of the respective object and a characteristic of a member, and transmit to the mobile device the object profile for the object captured in the digital image.

In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.

A method, system, and computer-readable medium for sharing measurement data with a remote device includes establishing, by a mobile computing device at an equipment site, a first communication connection with a measurement device that is configured to measure data with respect to equipment at the equipment site. The mobile computing device receives measurement data by way of the first communication connection. While remaining at the equipment site, the mobile computing device receives a request to share the measurement data with a selected contact via a remote device. In response, a second communication connection is established with the remote device, the measurement data to be shared is identified, and the identified measurement data is communicated to the remote device for review by the selected contact. Prior to establishing a communication connection with the remote device, permission to communicate the measurement data to the selected contact may be obtained from a gatekeeper.

This application provides a data stream identification method and apparatus and belongs to the field of Internet technologies. The method includes: obtaining packet transmission attribute information of N consecutive packets in a target data stream; generating feature images of the packet transmission attribute information of the N consecutive packets based on the packet transmission attribute information of the N consecutive packets; and inputting the feature images into a pre-trained image classification model, to obtain a target application identifier corresponding to the target data stream. According to this application, accuracy of identifying an application identifier corresponding to a data stream can be improved.

A computing platform that analyzes a captured video stream to identify a document depicted in the video stream, validates identification information corresponding to the document to display an information address associated with the document, and that initiates a transaction based on the validation of the identification information associated with the document.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

A method for image processing includes: acquiring features of multiple images of a target object and a standard feature of the target object; and determining trusted images of the target object from the multiple images of the target object according to similarities between the features of the multiple images of the target object and the standard feature thereof, wherein similarities between features of the trusted images of the target object and the standard feature of the target object meet a preset similarity requirement. The image processing method may be applied to application scenarios such as image comparison, identity recognition, target object search, and similar target object determination.

An retinal vessel image enhancement method comprises: constructing a blood vascular dictionary; applying Frangi-based filtering to retinal vessel images, deciding blood vessels in a second image sub-block belong to wide or thin vessels by directional filtering, and setting residual error weight and residual error threshold of a vascular region; calculating inner products between the second image sub-block and each first image sub-block, selecting a first image sub-block with maximum inner product, and calculating its corresponding sparse coefficient; calculating residual error image, and calculating the residual error of the vascular region according to the residual error weight of the vascular region, when the residual error is greater than the residual error threshold, the residual error image is set as a second image sub-block, repeating and calculating residual error; reconstructing the second image sub-block according to the sparse coefficient, then restructuring each reconstructed second image sub-block, obtaining enhanced retinal vessel images.