Abstract: Aspects of the subject disclosure may include, for example, obtaining user input identifying a first user-identified network feature of a training image of a geographical region. The training image and the user-identified feature are provided to a neural network adapted to train itself according to the user-identified features to obtain a first trained result that classifies objects within the image according to the user-identified feature. The training image and the first trained result are displayed, and user-initiated feedback is obtained to determine whether a training requirement has been satisfied. If not satisfied, the user-initiated feedback is provided to the neural network, which retrains itself according to the feedback to obtain a second trained result that identifies an updated machine-recognized feature of the training image. The process is repeated until a training requirement has been satisfied, after which a map is annotated according to the machine-recognized feature.

Abstract: The present disclosure provides a method for object detection and recognition based on a neural network. The method includes: adding a detection layer following three detection layers of an existing YOLOv5 network model, to construct a new YOLOv5 network model; then, training the new YOLOv5 network model by considering an overlapping area between a predicted box and a ground truth box, a center-to-center distance between the two boxes, and an aspect ratio of the two boxes; and finally, inputting a to-be-detected image into the trained new YOLOv5 network model, outputting a predicted box of an object and probability values corresponding to a class to which the object belongs, and setting a class corresponding to a maximum probability value as a predicted class of the object in the to-be-detected image. This method can quickly and effectively detect multiple classes of objects. Especially, a detection effect for small objects is more ideal.

Abstract: Apparatus and methods are disclosed related to managing characteristics of a mobile device based upon capacitive detection of materials proximate the mobile device, a capacitive gesture system that can allow the same gestures be used in arbitrary locations within range of a mobile device. One such method includes receiving a first capacitive sensor measurement with a first capacitive sensor of the mobile device. The method further includes determining a value indicative of a material adjacent to the mobile device based on a correspondence between the first capacitive sensor measurement and stored values corresponding to different materials. The method further includes sending instructions to adjust a characteristic of the mobile device based on the determined value indicative of the material adjacent to the mobile device. In certain examples, gesture sensing can be performed using capacitive measurements from the capacitive sensors.

Abstract: A method and device for determining whether an object includes a defect are provided. The method includes the following steps. A tested image of a tested object is obtained. Selected good product sample data corresponding to the tested object is obtained. A dissimilarity value between the tested image and the selected good product sample data is calculated by using a dissimilarity model, and whether the tested object is a good product or a defective product is determined according to the dissimilarity value.

Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing at least one program and a method for accessing a set of images depicting at least a portion of a face. A set of facial regions of the face is identified, each facial region of the set of facial regions intersecting another facial region with at least one common vertex that is a member of a set of facial vertices. For each facial region of the set of facial regions, a weight formed from a set of region coefficients is generated. Based on the set of facial regions and the weight of each facial region of the set of facial regions, the face is tracked across the set of images.

Abstract: A system for providing management of contact records includes a contact record that is associated with an initiator and is indexed by a phone number of the originator. To initiate delivery of some part of the contact record of the originator, the originator enters a phone number of a recipient into an application and some part of the fields of the contact record are transmitted to a device of the recipient where they are stored in the recipient's contact list. In some embodiments, the part of the fields sent is determined by a relationship between the initiator and the recipient such as family, co-worker, acquaintance, friend, stranger, etc. When changes are made to the contact record, the recipient device will receive updated information. Features are provided for automatic deletion of the subset of fields from the recipient device upon request or after a specified time period.

Abstract: A method for supporting activation/deactivation of serving cells by a base station (BS) in a wireless communication system provides decreased overhead and decreased power consumption for a user equipment (UE). The method includes configuring M supportable serving cells in the UE, configuring an indicator indicating activation/deactivation of each of the M serving cells, configuring a medium access control (MAC) message which includes a MAC control element (CE) and a logical channel identifier (LCID), the MAC CE including the indicator configured for each of the M serving cells and having a length corresponding to an integer multiple of 8 bits, the LCID indicating that the MAC CE includes the indicator indicating activation/deactivation of each serving cell, and transmitting the configured MAC message to the UE. Accordingly, a control channel or data channel regarding a component carrier is selectively received depending on whether the component carrier is activated.

Abstract: A tracking system obtains first recognized object and its first information, detected in image(s) captured by camera(s). The first information includes the first recognized object's first features, first feature locations, first real-world dimensions, and a first time. A list of tracking objects is obtained, each including a second recognized object and its second information. The second information includes the second recognized object's second features, second feature locations, and second real-world dimensions. The system compares the first features with the second features stored in a given tracking object, the first feature locations with the second feature locations, and the first real-world dimensions with the second real-world dimensions. When they match, the first information comprising the first time is stored in the given tracking object.

Abstract: The present disclosure provides a method and a system for training a machine learning system. Multiple pieces of sample data are used for training the machine learning system. The method includes acquiring multiple sample sets, each sample set including sample data in a corresponding sampling time period; setting a sampling rate for each sample set according to the corresponding sampling time period; acquiring multiple sample sets sampled according to set sampling rates; determining importance values of the multiple sampled sample sets; correcting each piece of sample data in the multiple sampled sample sets by using a corresponding importance value to obtain corrected sample data; and inputting the corrected sample data into the machine learning system to train the machine learning system.

Abstract: Systems and techniques are described for gathering information on the health of individuals trapped in an accident to provide actionable information to a first responder system. In some implementations, a monitoring system monitors a property that includes sensors located at the property and generate first sensor data. A monitor control unit receives the first sensor data and generates an alarm event for the property based on the first sensor data. Based on generating the alarm event for the property, the monitor control unit dispatches an autonomous drone. The autonomous drone is configured to navigate the property. Using an onboard sensor, the autonomous drone generates second sensor data. Based on the second sensor data, the autonomous drone determines a location within the property where a person is likely located. The autonomous drone provides, for output, data indicating the location within the property where the person is likely located.

Abstract: Features from a style image are adapted to express a machine-readable code. For example, grains of rice depicted in a style image may be positioned to create a pattern mimicking that of a machine-readable code. The resulting output image can then be used as a graphical component in product packaging (e.g., as a background, border, or pattern fill), while also serving to convey a product identifier to a compliant reader device (e.g., a retail point-of-sale terminal). In some embodiments, a neural network is trained to apply a particular style image to machine readable codes. A great variety of other features and arrangements are also detailed.

Abstract: A neural network is trained using transfer learning to analyze medical image data, including 2D, 3D, and 4D images and models. Where the target medical image data is associated with a species or problem class for which there is not sufficient labeled data available for training, the system may create enhanced training datasets by selecting labeled data from other species, and/or labeled data from different problem classes. During training and analysis, image data is chunked into portions that are small enough to obfuscate the species source, while being large enough to preserve meaningful context related to the problem class (e.g., the image portion is small enough that it can't be determined whether it is from a human or canine, but abnormal liver tissues are still identifiable). A trained checkpoint may then be used to provide automated analysis and heat mapping of input images via a cloud platform or other application.

Abstract: The embodiments of the present disclosure provide a system for calculating a regional crowd movement and a method thereof. The system is applied to a target monitoring environment, a plurality of key travel nodes are planned in the target monitoring environment, and the system includes radio frequency labels deployed on pedestrians, calculation apparatuses deployed at the key travel nodes and a plurality of directional antennas. The directional antenna is configured to, when driven by the calculation apparatus, send a read-write signal to the radio frequency label in a coverage region; the directional antenna is further configured to forward a radio frequency signal simultaneously reflected by the radio frequency label at the time of obtaining the read-write signal to the calculation apparatus; the calculation apparatus is configured to, based on a tempo-spatial distribution circumstance of the radio frequency signals in the region.

Abstract: A detection and response system includes a central server having at least one continuously trained neural network and at least one remote system connected to the central server. The at least one remote system includes a first sensor configured to provide an analyzable output corresponding to sensed information to an instanced copy of the continuously trained neural network, and a response system configured to generate a response to the instanced copy of the continuously trained neural network providing a positive detection. A training module is stored on the central server and is configured to update one of the continuously trained neural network and the instanced copy of the continuously trained neural network in response to receiving a data set including the positive detection.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing and decompressing data. In one aspect, a method comprises: processing data using an encoder neural network to generate a latent representation of the data; processing the latent representation of the data using a hyper-encoder neural network to generate a latent representation of an entropy model; generating an entropy encoded representation of the latent representation of the entropy model; generating an entropy encoded representation of the latent representation of the data using the latent representation of the entropy model; and determining a compressed representation of the data from the entropy encoded representations of: (i) the latent representation of the data and (ii) the latent representation of the entropy model used to entropy encode the latent representation of the data.

Abstract: A method and system is provided for detection of carbonate core features from core images. An input carbonate core image is separated into a plurality of first blocks, each of the plurality of first blocks having a first block size. An image of each of the separated plurality of first blocks is input into an artificial intelligence (AI) model. The AI model being trained to predict for each first block, one of a plurality of carbonate core features and a corresponding confidence value indicating a confidence of the predicted carbonate core feature being imaged in the first block. Any bounding boxes of a first set of bounding boxes are detected in the input core image based on the predicted one of the plurality of carbonate core features and the corresponding confidence values for each first block.

Abstract: An image processing apparatus and an image processing method for estimating a region of interest based on information of interest learned by a data recognition model satisfying a certain condition from among a plurality of data recognition models and for setting a focus are provided. The image processing apparatus includes estimating a region of interest by using a rule-based algorithm or an artificial intelligence (AI) algorithm. When estimating a region of interest by using an AI algorithm, the image processing apparatus may estimate a region of interest by using a machine learning algorithm, a neural network algorithm, or a deep learning algorithm.

Abstract: A method including: transmitting, by a control device, a first portion of content comprising a first portion of a signal corresponding to a multimedia presentation characteristic of a peripheral device; receiving, when an adjustment of the signal is below an adjustment threshold, a first instance of an input indicating a request to change the multimedia presentation characteristic; in response to receiving the first instance of the input, adjusting a second portion of the signal and transmitting a second portion of the content comprising the adjusted second portion of the signal; receiving, when the adjustment of the signal is at or above the adjustment threshold, a second instance of the input; and transmitting, in response to receiving the second instance of the input, a communication signal to the peripheral device to adjust a peripheral device control of an output of the multimedia presentation characteristic.

Abstract: A mobile station may comprise a receiver to receive, from a base station, a broadcast signal indicating a TDD configuration of uplink OFDM symbols and downlink OFDM symbols. The receiver may receive an indication of a symbol range and a subchannel range for use in transmission of CQI feedback. The mobile station may receive first downlink data and transmit first uplink data, using the TDD configuration, wherein the first uplink data comprises CQI feedback information transmitted in accordance with the symbol range and the subchannel range. The receiver may receive second downlink data, from the base station, on a different frequency as the first uplink data is transmitted, while the first uplink data is transmitted.

Abstract: In an approach to improving the image captioning performance of low-resource languages by leveraging multimodal inputs, one or more computer processors encode an image utilizing an image encoder, wherein the image is contained within a triplet comprising the image, one or more high-resource captions, and one or more low-resource captions. The one or more computer processors generate one or more high-resource captions utilizing the encoded image and the triplet inputted into a high-resource decoder. The one or more computer processors encode the one or more generated high-resource captions utilizing a high-resource encoder. The one or more computer processors add adaptive cycle consistency constraints on a set of calculated attention weights associated the triplet. The one or more computer processors generate one or more low-resource captions by simultaneously inputting the encoded image, the encoded high-resource caption, and the triplet into a trained low-resource decoder.