Abstract: The present invention includes a jig for installing a protection member to a portable electronic device and a guide jig device including the same. The jig includes a main body and retaining members formed to be elastically deformed and coupled or connected to the main body while surrounding a mounting space in which the portable electronic device formed in the main body is inserted. The guide jig device is advantageous in that it can be used for a portable electronic device that is manufactured and sold in a size relatively greater than the size of the portable electronic device disclosed before its release.

Abstract: Systems and methods for enhanced object detection for autonomous vehicles based on field of view. An example method includes obtaining an image from an image sensor of one or more image sensors positioned about a vehicle. A field of view for the image is determined, with the field of view being associated with a vanishing line. A crop portion corresponding to the field of view is generated from the image, with a remaining portion of the image being downsampled. Information associated with detected objects depicted in the image is outputted based on a convolutional neural network, with detecting objects being based on performing a forward pass through the convolutional neural network of the crop portion and the remaining portion.

Abstract: A method of selecting data for privacy preserving machine learning comprises: storing training data from a first party, storing a machine learning model, and storing criteria from the first party or from another party. The method comprises filtering the training data to select a first part of the training data to be used to train the machine learning model and select a second part of the training data. The selecting is done by computing a measure, using the criteria, of the contribution of the data to the performance of the machine learning model.

Abstract: A model building apparatus includes: a building unit that builds a generation model that outputs an adversarial example, which causes misclassification by a learned model, when a source sample is entered into the generation model; and a calculating unit that calculates a first evaluation value and a second evaluation value, wherein the first evaluation value is smaller as a difference is smaller between an actual visual feature of the adversarial example outputted from the generation model and a target visual feature of the adversarial example that are set to be different from a visual feature of the source sample, and the second evaluation value is smaller as there is a higher possibility that the learned model misclassifies the adversarial example outputted from the generation model. The building unit builds the generation model by updating the generation model such that an index value based on the first and second evaluation values is smaller.

Abstract: In example embodiments, techniques are provided for using machine learning to extract machine-readable labels for text boxes and symbols in P&IDs in image-only formats. A P&ID data extraction application uses an optical character recognition (OCR) algorithm to predict labels for text boxes in a P&ID. The P&ID data extraction application uses a first machine learning algorithm to detect symbols in the P&ID and return a predicted bounding box and predicted class of equipment for each symbol. One or more of the predicted bounding boxes may be decimate by non-maximum suppression to avoid overlapping detections. The P&ID data extraction application uses a second machine learning algorithm to infer properties for each detected symbol having a remaining predicted bounding box. The P&ID data extraction application stores the predicted bounding box and a label including the predicted class of equipment and inferred properties in a machine-readable format.

Abstract: An apparatus includes an integrated circuit that includes a microprocessor and a microcontroller unit circuit (MCU) coupled to the microprocessor. The MCU includes a central processing unit (CPU) core and a network processor that implements a wireless interface. The MCU is configured to execute a location application that facilitates a determination of a physical location of the apparatus. The MCU may also be configured to support one or more management functions. The microprocessor sends data to the MCU for wireless transmission by the MCU's wireless interface.

Abstract: A method for generating an object model includes: obtaining an initial morphable model; obtaining a plurality of initial images of an object, and depth images corresponding to the plurality of initial images; obtaining a plurality of target topological images by processing the plurality of initial images based on the depth images; obtaining a plurality of models to be synthesized by processing the initial morphable model based on the plurality of target topological images; and generating a target object model based on the plurality of models to be synthesized.

Abstract: The present disclosure provides a method and device for determining a resource occupation position, a user equipment and a storage medium, which relates to the field of communication technology. The method includes: receiving position configuration information including a configuration status of a base station regarding a candidate position for transmitting a synchronization signal block; determining valid configuration information in the position configuration information; and determining, according to the valid configuration information, the resource occupation position in all candidate positions in a transmission window of the synchronization signal block, the candidate positions being positions for transmitting the synchronization signal block.

Abstract: In some embodiments, systems and methods are provided to recognize retail products, comprising: a model training system configured to: identify a customer; access an associated customer profile; access and apply a set of filtering rules to a product database based on customer data; generate a listing of products specific to the customer; access and apply a model training set of rules to train a machine learning model based on the listing of products and corresponding image data for each of the products in the listing of products; and communicate the trained model to the portable user device associated with first customer.

Abstract: Methods for improving compliance with regulations pertaining to vehicle driving records are disclosed. One or more digital images from a camera mounted in a vehicle are received. Based on a determination that the vehicle has hours of service that have not been assigned to a driver, a subset of the one or more digital images corresponding to the hours of service are identified based on the timestamps. The subset of the one or more digital images are processed to identify a correspondence between a face of a person included in the one or more digital images and a face of a known person. Based on the correspondence transgressing a threshold level of correspondence, a user interface is generated for presentation on a device. The user interface includes an interactive user interface element for accepting a recommendation to assign the known person as the driver for the unassigned hours of service.

Abstract: Disclosed is a method for wireless communication for determining the position of a wireless node. The method comprises receiving a set of reference signals from a set of at least four positioning devices, wherein the positioning devices are not arranged in a single plane. A signal propagation delay of each one of the set of reference signals is determined and a position of each one of the set of positioning devices is obtained. A convex localization problem is solved for the wireless node based at least in part on the determined signal propagation delay of each one of the set of reference signals and the position of each one of the set of positioning devices. The position of the wireless node is determined based at least in part on solving of the convex localization problem.

Abstract: A method for context based lane prediction, the method may include obtaining sensed information regarding an environment of the vehicle; providing the sensed information to a second trained machine learning process; and locating one or more lane boundaries by the second trained machine learning process. The second trained machine learning process is generated by: performing a self-supervised training process, using a first dataset, of a first machine learning process to provide a first trained machine learning process; wherein the first trained machine learning process comprises a first encoder portion and a first decoder portion; replacing the first decoder portion by a second decoder portion to provide a second machine learning process; and performing an additional training process, using a second dataset that is associated with lane boundary metadata, of the second machine learning process to provide a second trained machine learning process.

Abstract: In various examples, a neural network may be trained for use in vehicle re-identification tasks—e.g., matching appearances and classifications of vehicles across frames—in a camera network. The neural network may be trained to learn an embedding space such that embeddings corresponding to vehicles of the same identify are projected closer to one another within the embedding space, as compared to vehicles representing different identities. To accurately and efficiently learn the embedding space, the neural network may be trained using a contrastive loss function or a triplet loss function. In addition, to further improve accuracy and efficiency, a sampling technique—referred to herein as batch sample—may be used to identify embeddings, during training, that are most meaningful for updating parameters of the neural network.

Abstract: A system and method for using wireless location of mobile communication units for identifying and allocating resources for access by users of such mobile communication units via bringing the user and such a resource together. Such a user may be routed to such a resource even though the resource cannot be reserved for user access. A resource allocation priority between users mitigates a user not being able to access the user's allocated resource, e.g., due to another user accessing the resource. The resources can be, e.g., a parking space, public transportation (e.g., bus, train, taxi), seating at an open admission event, accessing a theme park ride, and the like. By wirelessly locating the user's mobile communication unit, the user may be navigated to such a user allocated resource.

Abstract: A method for processing positron emission tomography data is provided, this method includes: obtaining a first coordinate and a second coordinate respectively corresponding to two ends of a response line to be processed; determining corresponding dimensional coordinates of the response line to be processed in a sinogram based on the first coordinate and the second coordinate; and generating the sinogram corresponding to the response line to be processed based on the dimensional coordinates. According to this method, the amount of calculation of system matrix is reduced, the accuracy of position information of the generated response line is improved, and the accuracy of generated sinogram is improved accordingly.

Abstract: A technique for estimating a process condition without limiting forms (such as shapes) of an object is presented, and a process condition estimating apparatus for estimating a process condition in which an object is processed, and which includes an input unit configured to input measurement data acquired at a predetermined position of the object; and an estimation unit configured to estimate the process condition from the measurement data, based on a process-condition-estimating function for inputting the measurement data and outputting an estimation value of the process condition.

Abstract: The invention provides a technique capable of effectively and appropriately removing noise from various kinds of images including noise and artifacts and images in which a noise pattern changes due to a difference in imaging conditions. Based on a noise removal technique using AI, noise characteristics including artifacts are analyzed for each image, the image is classified based on an analysis result, an optimal neural network for a noise processing is applied for each classification, and the noise and the artifacts are reduced.

Abstract: A system may comprise a processor and a memory having computer readable instructions stored thereon. The computer readable instructions, when executed by the processor, cause the system to record position data for an instrument during an image capture period and determine an instrument position change from the recorded position data. The computer readable instructions, when executed by the processor, may also cause the system to compare the instrument position change to a position change threshold and based on the comparison, determine whether to use image data captured by an imaging system during the image capture period in a registration procedure.

Abstract: Systems, methods, and apparatus for segmenting internal structures depicted in an image. In one aspect, a method includes receiving data representing image data that depicts internal structures of a subject, providing an input data structure to a machine learning model, wherein the input data structure comprises fields structuring data that represents the received data representing the image data that depicts internal structures of the subject, wherein the machine learning model is a multi-stage deep convolutional network that has been trained to segment internal structures depicted by one or more images, receiving output data generated by the machine learning model based on the machine learning model's processing of the input data structure, and processing the output data to generate rendering data that, when rendered, a computer, causes the computer to output, for display, data that visually distinguishes between different internal structures depicted by the image data.

Abstract: This application relates to automated processes for determining item placement compliance within retail locations. For example, a computing device may obtain an image of a fixture within a store. The image may be captured by a camera with a field of view directed at the fixture. The computing device may apply a segmentation process to the image to determine a portion of the image. Further, the computing device may determine a correlation between the portion of the image and each of a plurality of item image templates. Each item image template may include an image of an item the retail location sells in the retail location. The computing device may determine, based on the correlations, one of the plurality of item image templates and its corresponding item. The computing device may then determine whether the item should be located at the fixture based on a planogram.