Abstract: There is provided a wireless communication device, comprising: a plurality of radio units including a first radio unit and a second radio unit; and a control unit configured to control the plurality of radio units to form a multi-cell on the ground, wherein the control unit has a removal processing performing unit configured to perform, based on a first reception radio wave in which the first radio unit received a first transmission radio wave which includes a first signal transmitted by a first user terminal, and a second reception radio wave in which the second radio unit received a second transmission radio wave which includes a second signal transmitted by a second user terminal, removal processing to remove a component of the first transmission radio wave contained as an interference wave in the second reception radio wave.

Abstract: An antenna and an electronic device including an antenna are provided. The electronic device includes a housing including a conductive member; a wireless communication circuit; a circuit board; a first conductive path arranged on at least one part of the circuit board and electrically connected to the wireless communication circuit; a capacitor arranged on the first conductive path; a second conductive path electrically connected to the first conductive path between the wireless communication circuit and the capacitor and arranged on at least one part of the circuit board; a first conductive pad electrically connected to the first conductive path; a second conductive pad electrically connected to the second conductive path and coupled with the first conductive pad; and a conductive connection member for electrically connecting the first conductive pad and the conductive member.

Abstract: In various embodiments, a computer-implemented method of training a neural network for relighting an image is described. A first training set that includes source images and a target illumination embedding is generated, the source images having respective illuminated subjects. A second training set that includes augmented images and the target illumination embedding is generated, where the augmented images corresponding to the source images. A first autoencoder is trained using the first training set to generate a first output set that includes estimated source illumination embeddings and first reconstructed images that correspond to the source images, the reconstructed images having respective subjects that are i) from the corresponding source image, and ii) illuminated based on the target illumination embedding.

Abstract: A method for reconstructing target cardiac images is provided. The method may include: obtaining a plurality of projection data corresponding to a plurality of cardiac motion phases; determining a plurality of cardiac motion parameters corresponding to at least a portion of the plurality of cardiac motion phases based on the plurality of projection data; determining a phase of interest based on the plurality of cardiac motion parameters; and/or reconstructing the one or more target cardiac images of the phase of interest.

Abstract: In various examples, a two-dimensional (2D) and three-dimensional (3D) deep neural network (DNN) is implemented to fuse 2D and 3D object detection results for classifying objects. For example, regions of interest (ROIs) and/or bounding shapes corresponding thereto may be determined using one or more region proposal networks (RPNs)—such as an image-based RPN and/or a depth-based RPN. Each ROI may be extended into a frustum in 3D world-space, and a point cloud may be filtered to include only points from within the frustum. The remaining points may be voxelated to generate a volume in 3D world space, and the volume may be applied to a 3D DNN to generate one or more vectors. The one or more vectors, in addition to one or more additional vectors generated using a 2D DNN processing image data, may be applied to a classifier network to generate a classification for an object.

Abstract: In one aspect, a method comprises: initializing a front end circuit of a wireless device into a first mode in which a radio frequency (RF) signal processing path comprises a low noise amplifier (LNA) having an output coupled to an RF filter; and in response to an RF signal received in the front end circuit having a level greater than a first threshold, reconfiguring the front end circuit into a second mode in which the RF filter is coupled to an input of the LNA.

Abstract: Disclosed herein are methods of utilizing machine learning methods to analyze microscope images of populations of cells.

Abstract: A vehicle safety system operating on a driver's cellphone and comprising selective blocking apparatus, controlled by a processor, for blocking specified functionalities of the cellphone only when the cellphone is in a moving vehicle; and communication functionality operative to receive from a remote server and feed to said processor, updates which determine which specified functionalities to block.

Abstract: A frequency converter up-converts a transmission signal in an intermediate frequency hand into a signal in a wireless frequency hand as a USB or an LSR of a local oscillation signal using the local oscillation signal. When PMhigh?1/2×IFwid?RFtx?PMhigh?1/2×CHwid, the frequency converter is configured to satisfy ?LO=PMhigh?1/2×Fwid?IFcent and IFtx=RFtx??LO. Alternatively, when PMlow+1/2×IFwid?RFtx?PMlow+1/2×CHwid, the frequency converter is configured to satisfy ?LO=PMlow+1/2×IFwid+IFcent and IFtx=?LO?RFtx.

Abstract: Methods for segmenting medical images from different modalities include integrating a plurality of types of quantitative image descriptors with a deep 3D convolutional neural network. The descriptors include: (i) a Gibbs energy for a prelearned 7th-order Markov-Gibbs random field (MGRF) model of visual appearance, (ii) an adaptive shape prior model, and (iii) a first-order appearance model of the original volume to be segmented. The neural network fuses the computed descriptors to obtain the final voxel-wise probabilities of the goal regions.

Abstract: The present invention discloses methods and systems for communicating at a cellular router between a first wireless communication module and a first subscriber identity module (SIM). The cellular router receives a first request from a first wireless communication module and encapsulates the first request in a first modified request. The cellular router then sends the first modified request to a first SIM card in a first communication apparatus and waits for a first modified reply. While waiting for the first modified reply the cellular router sends at least one halt message to the first wireless communication module after a first time threshold. After receiving the first modified reply, the cellular router decapsulates the first modified reply to retrieve a first reply and sends the first reply to the first wireless communication module where the first modified reply is a reply to the first modified request.

Abstract: An image processing apparatus 10 includes a learning model construction unit 11 that generates a divided space(s) obtained by dividing a target space into one or more spaces and constructs a learning model for recognizing an object(s) included in the divided space, a learning model management unit 12 that manages the learning model and a region forming the divided space including the object recognized by the learning model in association with each other, a space estimation unit 13 that estimates a region forming a camera recognition space captured by a camera provided in a UI device; and a detection unit 14 that selects, from among the managed learning models, a specific learning model associated with the region forming the divided space including the estimated region forming the camera recognition space, and to detect the object included in a space displayed on the UI device using the selected specific learning model.

Abstract: Embodiments described herein provide a processing apparatus comprising compute logic to train a convolutional neural network (CNN) to perform autonomous re-localization for a service robot or mobile device. In one embodiment the apparatus comprises an image processor to process visual data received via a sensor and a general purpose graphics processing engine perform camera pose estimation for image data and generate a transformation matrix to transform positions of camera pose estimations to positions within a human readable map of the location. The images and transformed positions are uses to train the CNN to perform re-localization.

Abstract: A communication device includes a first antenna element, a second antenna element, a 180-degree hybrid circuit having a first port configured to generate a sum signal from multiple input signals and a second port configured to generate a difference signal from multiple input signals, a first phase shifter coupled with the 180-degree hybrid circuit to carry out a phase shift by a first phase value, and a second phase shifter coupled with the 180-degree hybrid circuit to carry out a phase shift by a second phase value. The first antenna element is connected to the first port of the 180-degree hybrid circuit while the second antenna element is connected to the second port of the 180-degree hybrid circuit.

Abstract: Embodiments of the invention provide apparatuses, systems, and methods for more accurate remote monitoring of a user's body. In some embodiments, a system for monitoring a user's body comprises a wearable device, a video sensor attached at a collar portion of the wearable device, a plurality of audio sensors spaced and attached at a body portion of a wearable device and a controller configured to determine a Jugular Venous Pressure (JVP) of the user, and determine audio characteristics of an output of the plurality of audio sensors to generate an audio heat map corresponding to at least one internal organ of the user.

Abstract: Systems and methods are provided for enhancing a medical image. An initial medical image having an initial field of view is received. An augmented medical image having an expanded field of view is generated using a trained machine learning model. The expanded field of view comprises the initial field of view and an augmentation region. The augmented medical image is output.

Abstract: Techniques for masking images based on a particular task are described. A system masks portions of an image that are not relevant to a particular task, thus, reducing the amount of data used by applications for image processing tasks. For example, images to be processed using a hair color classification model are masked so that only portions that show the person's hair are available for the model to analyze. The system configures different masker components to mask images for different tasks. A masker component can be implemented at a user device to mask images prior to sending to an application/task-specific model.

Abstract: The present invention enables a user to share his/her listening experience selectively with others without sharing headphones and without disturbing others who do not want to listen. In a preferred embodiment, a first listener can accomplish this by storing in a Portable Electronic Device or similar device a library of listening experiences, listening to one of the listening experiences, and while listening to that one listening experience streaming the one listening experience to at least one other Portable Electronic Device or similar device. A second listener at the other Portable Electronic Device can then listen to the same listening experience as the first listener at the same time. It is expected that the listening experiences will typically be songs or other music but the invention may be practiced with any type of audio content. The first listener may also create a playlist of the listening experiences in the library and make the playlist available to others.

Abstract: An apparatus and method for enhancing broadcast radio includes a DNN trained on data sets of audio created from a synthesized broadcasting process and original source audio. Broadcast radio signals are received at a radio module and processed through the DNN to produce enhanced audio.

Abstract: A method for identifying a misidentified study can utilize a set of photographs captured at substantially the same time as a corresponding set of medical images. The method can include determining similarities between the photographs through machine learning models and determining that a misidentified study exists when the similarity between the photographs fails to satisfy a threshold similarity.