Abstract: Methods, systems, and apparatus for controlling smart devices are described. In one aspect a method includes capturing, by a camera on a user device, a plurality of successive images for display in an application environment of an application executing on the user device, performing an object recognition process on the images, the object recognition process including determining that a plurality of images, each depicting a particular object, are required to perform object recognition on the particular object, and in response to the determination, generating a user interface element that indicates a camera operation to be performed, the camera option capturing two or more images, determining that a user, in response to the user interface element, has caused the indicated camera operation to be performed to capture the two or more images, and in response, determining whether a particular object is positively identified from the plurality of images.

Abstract: Systems and methods for continuous monitoring of the behavior of animals, such as small rodents, are provided. Monitoring can include video, audio, and other sensor modalities. In one embodiment, the system can include cameras, arena design, environmental sensors, and ultrasonic sensors. The system uniquely provides a continuous long-term monitoring system suitable for mouse behavioral study. Further provided is a neural network based tracker configured for use with video data acquired by the monitoring system. 3 different neural network architectures have been tested to determine their performance on genetically diverse mice under varying environmental conditions. It has been observed that that an encoder-decoder segmentation neural network achieves high accuracy and speed with minimal training data.

Abstract: A learning apparatus includes a hardware processor. The hardware processor obtains a first medical image. The hardware processor generates a second medical image in which a feature according to feature information related to a portion other than a recognition target of the first medical image is made close to the feature according to standard feature information which is to be a predetermined standard. The second medical image is used to train a machine learning model.

Abstract: Embodiments described herein include systems for implementing annotation-efficient deep learning in computer-aided diagnosis.

Abstract: An electronic device according to an embodiment disclosed in the disclosure may include an ultra-wideband (UWB) communication module, a communication module, and a processor operatively connected with the UWB communication module and the communication module. The processor may determine a position of the electronic device based on a UWB, may determine an area where the electronic device is positioned, may play a multimedia content corresponding to the area, may acquire interest information regarding a plurality of multimedia contents including the multimedia content, may select at least one multimedia content from the plurality of multimedia contents based on the interest information, may generate a user multimedia content by using the at least one selected multimedia content, and may transmit the user multimedia content to a server through the communication module.

Abstract: Disclosed are a method and device for analyzing human tissue on the basis of a medical image. A tissue analysis device generates training data including a two-dimensional medical image and volume information of tissue by using a three-dimensional medical image, and trains, by using the training data, an artificial intelligence model that obtains a three-dimensional size, volume, or weight of tissue by dividing at least one or more normal or diseased tissues from a two-dimensional medical image in which a plurality of tissues are displayed overlapping on the same plane. In addition, the tissue analysis device obtains a three-dimensional size, volume, or weight of normal or diseased tissue from an X-ray medical image by using the artificial intelligence model.

Abstract: The method of labeling an image of cells of a patient, in particular an immunocytochemistry image comprises the following steps. First, a digital image of a stained immunocytochemistry biological sample of the patient is received. Following by the step that a computerized classification of cells in the digital image based on color, shape or texture in the digital image, the digital image is labeled by application of a trained neural network on at least one portion of the digital image which comprises a digital image of one cell classified under a first category during the computerized classification.

Abstract: Disclosed are systems and methods for extracting high resolution oculometric parameters. A video stream having a video of a face of a user is processed to obtain a set of oculometric parameters, such as eyelid data, iris data (e.g., iris translation, iris radius and iris rotation), and pupil data (e.g., pupil center and pupil radius) at a first resolution. A deconvolution process is performed on the video stream to improve accuracy or resolution of the oculometric parameters, based on stimulus information of a video stimulus displayed on a client device associated with the user, environment data of an environment in which the user is located, device data of the client device, etc. The oculometric parameters are then processed using a prediction model that is trained based on high resolution oculometric parameters obtained using eye tracking devices to predict oculometric parameters at a resolution greater than the first resolution.

Abstract: The present invention relates to a method and system for the transformation of raw mammograms to normalised presentation and where the pixel values are independent of imaging conditions. The performed method includes: contrast enhancement, for improved visibility of the breast tissue composition, whereby a region of the breast is segmented and a contrast-stretching algorithm applied to the segmented region to preferably create an enhanced raw image or mammogram; local ‘maximum’ transform, whereby a 2-dimensional first filter is designed to extract the maximum pixel value from a region of interest (ROI) to preferably create a local maximum image or map; ratio map derivation, whereby the pixel value of the ratio map measures a relative response of the said pixel to its local maximum thus capturing the difference between breast composition regardless of mammogram variations.

Abstract: An information processing apparatus comprising, at least one first processor configured to carry out a first process on data input from at least one sensor to produce first processed data, a selector configured to select, according to a first predetermined condition, at least one of a plurality of second processes, and at least one second processor configured to receive the first processed data from the at least one first processor and to carry out the selected at least one of the plurality of second processes on the first processed data to produce second processed data, each of the plurality of second processes having a lower processing load than the first process.

Abstract: A foldable electronic device is provided. The foldable electronic device includes a first housing, a second housing, a third housing, a first folding unit configured to allow the first and second housings to be folded in a first folding manner, a second folding unit configured to allow the second and third housings to be folded in a second folding manner, a flexible display disposed across the first to third housings, and a metal plate disposed to face the flexible display to support the folding operation of the flexible display, and including a first folding area in which a first pattern is disposed and a second folding area in which a second pattern different from the first pattern is disposed.

Abstract: Systems and methods for generating a radio frequency (RF) signal by a digital-to-analog converter (DAC) with transmission frequency within a wide transmission frequency range are described. An output reactance of the DAC coupled (directly or indirectly) to one or more antennas corresponds to the transmission frequency of the RF signals. Multiple embodiments of the DAC are described to include circuitry for tuning the output reactance of the DAC, and therefore, shifting a center frequency to select a transmission frequency range (from multiple transmission frequency ranges) for providing the RF signals.

Abstract: An antenna calibration system for a phased array antenna in accordance with one embodiment of the present disclosure generally includes: a beamformer corresponding to a subset of antenna cells of a plurality of antenna cells, wherein the beamformer includes a calibration section for comparing a reference signal to a non-reference signal and a first operating antenna within the subset of antenna cells corresponding with the beamformer. In some implementations, the first operating antenna is configured to deliver a first reference signal (mTx) from the beamformer to be received by a second operating antenna for comparison with a first non-reference signal (Rx) in the beamformer, and/or the first operating antenna is configured to deliver a second non-reference signal (Tx) from a third operating antenna for comparison with a second reference signal (mRx) in the beamformer.

Abstract: A console for a radiography system includes at least one processor configured to execute display-related processing of displaying, at an imaging site, a radiographic image obtained by radiography, the display-related processing including reception processing of receiving the radiographic image from a radiographic image detection device and image processing of processing the received radiographic image to a radiographic image for display, computer aided diagnosis processing on the radiographic image after the image processing, and priority processing of giving priority to the display-related processing over the computer aided diagnosis processing in a case where the display-related processing and the computer aided diagnosis processing compete with each other.

Abstract: In various examples, systems and methods are disclosed that preserve rich spatial information from an input resolution of a machine learning model to regress on lines in an input image. The machine learning model may be trained to predict, in deployment, distances for each pixel of the input image at an input resolution to a line pixel determined to correspond to a line in the input image. The machine learning model may further be trained to predict angles and label classes of the line. An embedding algorithm may be used to train the machine learning model to predict clusters of line pixels that each correspond to a respective line in the input image. In deployment, the predictions of the machine learning model may be used as an aid for understanding the surrounding environment—e.g., for updating a world model—in a variety of autonomous machine applications.

Abstract: A passive radio frequency identification (RFID) reader is configured to dynamically vary the Q factor of its resonant antenna coil circuit in order to optimize its performance under different conditions. This RFID reader suitably provides optimal performance for both transponder reading and transponder writing operations, rather than being designed for optimal performance for only one operation or the other, or some fixed compromise between them.

Abstract: In general, devices, systems, and methods for fiducial identification and tracking are provided.

Abstract: A low voltage differential signaling (LVDS) receiver includes a receiver circuit including first and second inputs coupled to first and second conductive pads, respectively, and an output coupled to an input of a digital controller, and a dummy transmitter circuit including a first input coupled to receive a common mode voltage (VCM) tune signal, a second input coupled to a loopback input signal, a third input coupled to a loopback enable signal, a first output coupled to the first input of the receiver circuit, and a second output coupled to the second input of the receiver circuit. When a test mode of operation is enabled, the digital controller asserts the loopback enable signal, and the dummy transmitter circuit generates a pair of test differential signals based on the VCM tune signal, wherein the VCM tune signal varies to test the LVDS receiver over a range of common mode voltages.

Abstract: Device information may be obtained using LoRaWAN communications. A device information request may be generated and transmitted by a user device and include request parameters such as a device type for which information is desired, a type of information requested, a geographic range for the request, and the like and/or combinations thereof. Device information requests may also be propagated or repeated by intermediary devices to achieve the desired request range. Accordingly, a recipient device of the original request may propagate or repeat, using LoRaWAN, a request signal if it determines that the request range has not been met. If the request range has been met, the recipient device might not repeat the request.

Abstract: Provided are a machine learning system and method, an integration server, an information processing apparatus, a program, and an inference model creation method capable of solving a problem caused by non-uniform quality of learning data in federated learning and improving an inference accuracy of a model. Each of a plurality of client terminals classifies data stored in a medical institution based on a data acquisition condition and classifies learning data into each data group acquired under the same or a similar acquisition condition. Each client terminal executes machine learning of a learning model for each learning data group classified into each condition category and transmits each learning result and condition information to an integration server. The integration server integrates received learning results for each condition category to create a plurality of master model candidates and evaluates the inference accuracy of each master model candidate.