Abstract: Techniques are provided which may be implemented using various methods and/or apparatuses in a mobile device to crowd source mobile terrestrial transceiver identifiers associated with mobile terrestrial transports. In an embodiment, transceiver identifiers associated with mobile terrestrial transports may be identified and tracked through the detection and measurement of signals transmitted by mobile terrestrial transceivers received either by mobile devices located on the mobile terrestrial transport or through the detection and measurement of transient signals transmitted by passing mobile terrestrial transceivers received by stationary or near stationary mobile devices. The identity of detected mobile terrestrial transceivers and their locations and time observed may be uploaded to a server.

Abstract: The present disclosure provides a method for image capturing, a method for image capturing control and associated apparatuses. The method for image capturing includes: obtaining Region Of Interest (ROI) information of a target ROI corresponding to a current frame of image; buffering captured pixel data of the current frame of image; obtaining, when determining that the pixel data of the current frame of image has been buffered completely, pixel data associated with the target ROI from the buffered pixel data of the current frame of image based on the ROI information if the ROI information of the target ROI corresponding to the current frame of image can be obtained; and outputting the pixel data associated with the target ROI.

Abstract: Systems and methods for segmentation of report corpus using visual signatures are disclosed. According to one embodiment, a computer-implemented method comprises converting a document to a grayscale image and removing noise from the grayscale image by eroding isolated pixels. Connected regions in the grayscale image are determined and a region of the grayscale image having a square shape is identified. An area of the region is computed and if the area is larger than a threshold, determining that the document contains a form.

Abstract: The presently disclosed subject matter relates to a method for recognizing objects of a predefined type from among a set of types, within a set of digital images, including detecting an object of this predefined type within a digital image of the set, and determining a zone of the image encompassing the detected object, generating a signature by a convolutional neural network on the basis of this zone, allowing identification of the object in a one-to-one manner, determining on the basis of the signature of a set of attributes, storing in a database a record relating to the object associating the signature with the set of attributes, wherein the neural network is trained on a learning suite composed of a first set formed of objects associated with a set of attributes and of a second set formed of objects not associated with a set of attributes.

Abstract: The present disclosure provides a target detection method, target detection system and non-volatile storage medium. The target detection method includes: acquiring an image to be detected and anchor parameters of preset type number, wherein the anchor parameters are parameters of an anchor set on the image to be detected, and each type of anchor parameters include an anchor scale and an anchor aspect ratio; inputting the image to be detected and the anchor parameters into a target detection model; and carrying out target detection on the image to be detected on the basis of the anchor parameters by the target detection model to obtain a detection result, the detection result including a category and/or a position of a target object included in the image to be detected.

Abstract: A method, a system, and a computer program product may be provided for generating at least one route speed funnel for roadwork zone identification. The method may include generating a plurality of learned road signs from a plurality of road sign observations captured by a plurality of vehicles, determining a plurality of primary speed funnels, and generating at least one route speed funnel from the plurality of primary speed funnels based on a route validity condition. Each of the plurality of primary speed funnels comprises a different pair of learned road signs selected from the generated plurality of learned road signs. The method may further include determining a plurality of candidate speed funnels and temporally validating the plurality of candidate speed funnels to obtain the plurality of primary speed funnels.

Abstract: A network hub may be field programmable (e.g., upgradeable). A network hub may be operated with first settings for a configurable software defined radio (SDR) when the network hub receives an update from a server computer over a computer network. Upon installing the update, and rebooting the network hub, settings of the configurable SDR may be changed from the first settings to second, different settings, the second, different settings causing the network hub to operate using a second cellular-based radio technology that is different from a first cellular-based radio technology enabled by the first settings of the configurable SDR.

Abstract: Technologies disclosed herein are directed to enforcing blocking functionality on a mobile device (e.g., a mobile device within a vehicle). The mobile device receives a request to disable a blocking of a user interaction with one or more functions of the mobile device. The mobile device prompts a capture of an image by a camera of the mobile device. The mobile device determines whether the image is captured from a passenger space in the vehicle. Once so determined, the mobile device disables the blocking of the user interaction with the one or more functions of the mobile device.

Abstract: A computer-implemented method for data labeling is provided. The computer-implemented method assigns pseudo-labels to unlabeled examples of data using a similarity metric on an embedding space to produce pseudo-labeled examples. A curriculum learning model is trained using the pseudo-labeled examples. The curriculum learning model trained with the pseudo-labeled examples is employed in in a fine-tuning task to enhance classification accuracy of the data.

Abstract: An apparatus includes a feature extractor to generate image descriptors based on retail product tag images corresponding to a retailer category; a probability density function generator to generate a probability density function of probability values corresponding to visual features represented in the image descriptors; a sample selector to select ones of the probability values based on a sample selection algorithm that identifies positions in the probability density function of the ones of the probability values to be selected; a category signature generator to generate a category signature based on the selected ones of the probability values; and a processor to train a convolutional neural network (CNN) based on a feature descriptor and one of the retail product tag images, the feature descriptor including the category signature concatenated to one of the image descriptors, the training to cause the CNN to classify the one of the retail product tag images as a type of product tag.

Abstract: Data processing systems (e.g. image processing systems) and methods are provided for processing a stream of data values (e.g. pixel values). In each of a plurality of iterations, a respective particular data value of the stream is processed by operating on a respective particular subset of data values of the stream. In each iteration: group indication data for at least one group is retrieved and used to define a set of groups into which data values within the particular subset can be grouped; each of the data values within the particular subset is grouped into one of the groups of the set of groups; the particular data value is processed using one or more of the data values of the particular subset in dependence on the classification of the data values into the groups; and group indication data is stored for a group, for use in a subsequent iteration.

Abstract: An information processing apparatus comprising: a holding unit configured to hold a plurality of learning models for estimating geometric information based on an input image captured by an image capturing apparatus; a selection unit configured to calculate, for each of the learning models, an evaluation value that indicates suitability of the learning model to a scene of the input image, and select a learning model from the plurality of learning models based on the evaluation values; and an estimation unit configured to estimate first geometric information using the input image and the selected learning model.

Abstract: A machine learning guided image segmentation process is performed by an electronic processor (10). Image segmentation (22) is performed to generate an initial segmented representation (50) of an anatomical structure in the medical image. Parameters of a geometric shape are fitted (52) to the anatomical structure in the medical image to produce initial fitted shape parameters (54). A classification is assigned for the anatomical structure in the medical image using at least one classifier (60) operating on the initial fitted shape parameters and the initial segmented representation of the anatomical structure. A final segmented representation (72) of the anatomical structure in the medical image is generated by operations including repeating (70) the image segmentation using the classification as prior knowledge. In illustrative embodiments, the anatomical structure is a heart and the geometric shape is an ellipsoid.

Abstract: A method is provided to operate a CN node to determine UP security activation. A UP session establishment request is obtained for a wireless device. An indication is obtained that the UP session establishment request is associated with an emergency session and/or that null ciphering and/or null integrity protection are applied to a CP associated with a CP session for the wireless device. It is determined that a UP should be configured for the UP session without activating integrity and/or confidentiality protection for the UP based on the indication. A UP security policy is provided to a RAN node associated with the wireless device, wherein the UP security policy indicates to configure the UP for the UP session without activating integrity and/or confidentiality protection based on determining that a UP should be configured for the UP session without activating integrity and/or confidentiality protection.

Abstract: A method for selecting one or more resources for use from among a set of resources comprises obtaining (201) a first sub-set of the resources. The resources belonging to the first sub-set are the ones which have, according to occupancy information gathered over a long period of time, highest probabilities of matching requirements related to estimated usage time and/or needed capacity. The method further comprises selecting (202) a second sub-set from among the first sub-set of the resources on the basis of second occupancy information gathered over a short period of time. The resources belonging to the second sub-set are the ones from among the first sub-set which, according to the second information, have highest probabilities of matching requirements related to estimated usage time and/or needed capacity. The subsequent use of the long and short term occupancy information increases the probability of optimal selection.

Abstract: Embodiments for intelligent temporary contact sharing between user equipment (UE) by a processor. A temporary exchange of contact data may be provided between a first UE and a second UE via a wireless communication network according to one or more policies, user preferences, historical data, a defined physical or virtual proximity between the first UE and the second UE, or a combination thereof. The contact data may be automatically deleted between the first UE and the second UE according to the one or more policies, the user preferences, the historical data, the defined physical or virtual proximity between the first UE and the second UE, or a combination thereof.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning to predict future handover success rates of cells of a wireless network and to determine updated network parameter values that may be used to improve the future handover success rates. Clustering algorithms may be applied to handover success rates associated with the cells to group the cells into different clusters. Machine learning (ML) models may be trained based on historical handover data associated with cells of the clusters to predict future handover success rates based on current handover success rates. The output of the ML models may be used to determine one or more updated network parameter values to be implemented by the wireless network to improve future handover success rates at one or more cells.

Abstract: A positioning system, includes a plurality of anchor stations each configured to transmit a radio frequency signal. A mobile station includes a radio frequency transceiver configured to transmit and receive a radio frequency signal from at least one of the plurality of anchor stations. A processing unit is configured to determine position information of the mobile unit based upon the transmitted and received radio frequency signal. Another positioning system uses a number of beacons ranging a number of mobile tags with ultra-wide band ranging, and communicating wirelessly with an application server.

Abstract: An antenna apparatus sited within a metal housing with improved frequency isolation between individual antennas includes the housing, a first ground portion, a first feed portion, and a second ground portion. A side frame of the housing defines at least one gap, the gap separates a first antenna and a second antenna. The first ground portion is formed by extending an end of the first antenna away from the gap. One end of the second ground portion is connected to the first feed portion, other end of the second ground portion is connected to ground. A first feed point feeds power and signal to the first antenna. A wireless communication device using the antenna apparatus is also provided.

Abstract: An electronic device may include scaling circuitry to scale input pixel data to a greater resolution. The directional scaling circuitry may include first interpolation circuitry to receive best mode data, including one or more angles corresponding to content of the image and interpolate first pixel values at first pixel positions diagonally offset from input pixel positions of the input pixel data based on the best mode data and input pixel values corresponding to the input pixel positions. The directional scaling circuitry may also include second interpolation circuitry to receive the best mode data and the input pixel values and interpolate second pixel values at second pixel positions horizontally or vertically offset from the input pixel positions based at least in part on the best mode data and the input pixel values.