Abstract: A method for reconstructing target cardiac images is provided. The method may include: obtaining projection data, the projection data including a plurality of sub-sets of projection data, each sub-set of projection data corresponding to a cardiac motion phase; obtaining a plurality of sampled cardiac motion phases; generating a plurality of cardiac images of the plurality of sampled cardiac motion phases by reconstructing, based on the one or more sub-sets of projection data corresponding to the each sampled cardiac motion phase, one or more cardiac images of the each sampled cardiac motion phase; determining a plurality of cardiac motion parameters corresponding to the plurality of sampled cardiac motion phases based on the plurality of cardiac images; determining a mean phase based on the plurality of cardiac motion parameters corresponding to the plurality of sampled cardiac motion phases; and reconstructing the one or more target cardiac images of the mean phase.

Abstract: Apparatuses, methods, and systems for control of antenna steering of a wireless device, are disclosed. One method includes selecting beamforming direction settings of a multiple antenna array of a radio, retrieving spatial relationships between each of different beamforming directions, receiving, by the radio, a wireless signal from a base station and measuring signal metrics of the wireless signal, sensing motion of the wireless device, identifying a wireless connection direction for establishing a wireless connection with the base station based on the signal metrics, the plurality of beamforming direction settings used while receiving the wireless signal, and an orientation of the wireless device determined based on the sensed motion of the wireless device, and selecting a one of the plurality of beamforming direction settings for communicating with the base station based on the wireless connection direction, a beamforming setting that corresponds with the wireless connection.

Abstract: An apparatus includes an amplifier circuit and a protection circuit. The amplifier circuit may be configured to generate an output signal by amplifying an input signal received at an input port. The input signal may be a radio-frequency signal. The protection circuit may be configured to (i) generate a detection signal by detecting when a level of the input signal exceeds a corresponding threshold, where the level is a power level, a voltage level or both, (ii) route the input signal away from the input port of the amplifier circuit and disable the amplifier circuit both in response to the detection signal being continuously active at least a first time duration and (iii) route the input signal to the input port of the amplifier circuit and enable the amplifier circuit both in response to the detection signal being continuously inactive at least a second time duration.

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: The invention allows enhancing radio signal coverage in an elevator environment. An elevator antenna pair comprises a primary antenna that is configured to transmit and receive radio frequency signals with an external wireless communication network. The elevator antenna pair further comprises a secondary antenna that is communicatively connected to the primary antenna and configured to transmit and receive radio frequency signals with at least one radio frequency transceiver in an elevator car. The primary antenna is arranged within an elevator landing signalization unit, and the secondary antenna is arranged inside an elevator hoistway.

Abstract: Systems and methods for hierarchical segmentation and classification may include receiving an input object having a hierarchical data structure. The input object may be segmented at each hierarchical level in a descending manner. The components of each hierarchical level may be classified in an ascending manner. Classification at higher levels may depend on the results from classification at the lower levels. After classification, a hierarchically-classified object may be outputted.

Abstract: The present disclosure describes a method, an apparatus, and storage medium for recognizing medical image. The method includes obtaining, by a device, a medical image. The device includes a memory storing instructions and a processor in communication with the memory. The method further includes determining, by the device, the medical image through a first recognition model to generate a lesion recognition result used for indicating whether the medical image comprises a lesion; and in response to the lesion recognition result indicating that the medical image comprises a lesion, recognizing, by the device, the medical image through a second recognition model to generate a lesion degree recognition result of the medical image used for indicating a degree of the lesion. Manual analysis and customization of a feature extraction solution are not required, so that the efficiency and accuracy of medical image recognition are improved.

Abstract: A system includes a memory and a processor configured to execute computer instructions stored in the memory that when executed cause the system to perform operations. The computer instructions include a learning component that includes one or more trained models to learn pathogenic features of a given fungal species based on learning from a plurality of stored fungal species images. A fungal identifier component employs the trained models to determine pathogenic parameters of an unidentified fungal species image based on the learned pathogenic features. The fungal identifier component generates an output file that classifies the unidentified fungal species image according to the determined pathogenic parameters from the unidentified fungal species image.

Abstract: Methods and systems are provided for accurately filling holes, regions, and/or portions of images using iterative image inpainting. In particular, iterative inpainting utilize a confidence analysis of predicted pixels determined during the iterations of inpainting. For instance, a confidence analysis can provide information that can be used as feedback to progressively fill undefined pixels that comprise the holes, regions, and/or portions of an image where information for those respective pixels is not known. To allow for accurate image inpainting, one or more neural networks can be used. For instance, a coarse result neural network (e.g., a GAN comprised of a generator and a discriminator) and a fine result neural network (e.g., a GAN comprised of a generator and two discriminators).

Abstract: Systems and methods for detecting and classifying clinical features in medical images are disclosed. Natural language processes are applied to speech received from a dictation system to determine clinical and anatomical information for a medical image being viewed. In some examples, gaze location information identifying an eye position is received, as well as an image position for the medical image being viewed. Features of the medical image are detected and classified based on machine learning models. Anatomical associations are generated based on one or more of the classifications, the anatomical information, the gaze information, and the image position. The machine learning models can be trained based on the anatomical associations. In some examples, reports are generated based on the anatomical associations.

Abstract: A computer-implemented method for segmenting an input image, the method comprises: generating a first segmentation of the input image using a first machine learning system, the first segmentation comprising multiple segments; receiving, from a user, at least one indication, wherein each indication corresponds to a particular segment from the multiple segments, and indicates one or more locations of the input image as belonging to that particular segment; constructing, for each segment of the multiple segments having at least one corresponding indication, a respective geodesic distance map, based on the input image and the user indications received for that segment; and generating a second segmentation using a second machine learning system based on the input image and the constructed geodesic distance maps.

Abstract: A medical image processing apparatus includes processing circuitry configured to apply a first trained model to input image data to obtain a first output based on the input data, where the input data includes clinical data. The processing circuitry is further configured to apply a second trained model to the input data to obtain a second output based on the input data, where the first trained model and the second trained model have been trained in dependence on a hierarchical relationship between the first output and the second output. The hierarchical relationship includes at least one of: a spatial hierarchy, a temporal hierarchy, an anatomical hierarchy, and a hierarchy of clinical conditions.

Abstract: A fingerprint identification apparatus and an electronic device, capable of reducing under-screen space occupied by the fingerprint identification apparatus and facilitating assembly of the fingerprint identification apparatus. The fingerprint identification apparatus is applicable to an electronic device having a display screen, and includes: at least one optical fingerprint sensor; and a flexible circuit board electrically connected to the at least one optical fingerprint sensor; where a first adhesive layer is disposed at a periphery of the flexible circuit board for connecting the flexible circuit board and the display screen, so that the at least one optical fingerprint sensor is located under the display screen, the at least one optical fingerprint sensor is configured to receive a fingerprint detecting signal returned by reflection or scattering via a human finger on the display screen, and the fingerprint detecting signal is used to detect fingerprint information of the finger.

Abstract: A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

Abstract: A section line recognition device is realized which can determine a line type of a section line in a road surface with high accuracy. In step 301, a feature amount is extracted from an image captured by each camera. This step corresponds to a process of a feature point extraction unit. In the next step 302, the extracted feature amount is transformed into bird's-eye view coordinates which is common coordinates. This step corresponds to a process of a coordinates transformation unit. Next, in step 303, a camera to be selected is determined. This step is a process of a camera determination unit. Next, in step 304, the state transition at the appearance position of the feature point on the bird's-eye view coordinates is determined using feature point coordinates of the camera selected in step 303. This step is a process of a state transition determination unit. Finally, in step 305, the line type is determined. This step is a process of a line type determination unit.

Abstract: Methods, systems, and devices for recovery techniques for subscriber identification module (SIM) detection error are described. A user equipment (UE) may determine an operating mode for the UE based on a SIM card switch occasion. The SIM card switch occasion may be a “hotswap” occasion of a SIM card while the UE is powered on. The UE may power a SIM card associated with the UE based on the operating mode for the UE and a previous status of a SIM slot associated with the UE. The UE may detect a presence of the SIM card in the SIM slot based on powering the SIM card. The UE may set a SIM sensing operation at the UE based on the detected presence of the SIM card.

Abstract: Presented are systems and methods for improving speed and quality of real-time per-pixel depth estimation of scene layouts from a single image by using an end-to-end Convolutional Spatial Propagation Network (CSPN). An efficient linear propagation model performs propagation using a recurrent convolutional operation. The affinity among neighboring pixels may be learned through a deep convolutional neural network (CNN). The CSPN may be applied to two depth estimation tasks, given a single image: (1) to refine the depth output of existing methods, and (2) to convert sparse depth samples to a dense depth map, e.g., by embedding the depth samples within the propagation procedure. The conversion ensures that the sparse input depth values are preserved in the final depth map and runs in real-time and is, thus, well suited for robotics and autonomous driving applications, where sparse but accurate depth measurements, e.g., from LiDAR, can be fused with image data.

Abstract: A method for providing an item of conversion information describing an allocation rule of at least one physical property value of a material in a voxel relating to an image value of the voxel in a three-dimensional image dataset recorded with an X-ray apparatus is provided. By scanning a phantom including at least one calibration material in the X-ray apparatus, a calibration database that is used for determining the allocation rule is determined. The image dataset is recorded with a receiving spectrum geared to an X-ray detector of the X-ray apparatus. The receiving spectrum is described by at least one spectral parameter. For determining the allocation rule dependent upon the spectral parameter, calibration data derived from the measured calibration dataset describing different receiving spectra is used.

Abstract: The apparatus relates to a pre-5th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4th-Generation (4G) communication system such as Long-Term Evolution (LTE). The disclosure relates to an apparatus including an electronic circuit for amplifying a signal. The apparatus includes a transceiver including an amplification circuit, and at least one processor coupled to the transceiver. The amplification circuit includes a first path to generate a first current corresponding to a voltage of an input signal, a second path to generate a second current corresponding to a voltage of the input signal, a separation unit to control each of the first current and the second current, a current mirror to generate a third current corresponding to the first current, and a folding unit to generate an output signal on the basis of the second current and the third current.

Abstract: Provided are an information processing apparatus, an information processing method, and a program capable of accurately extracting a target region from a medical image. An information processing apparatus includes an extraction unit that extracts information indicating a physique of a subject from an image acquired by imaging the subject, a specification unit that specifies a group into which the subject is classified by using the information indicating the physique of the subject extracted by the extraction unit, and a generation unit that generates a learned model for each group through machine learning using, as learning data, image data indicating a medical image acquired by imaging the subject for each group and information indicating a region extracted from the medical image.