Abstract: An ambient monitoring device includes a housing, contactless sensors coupled to the housing, and a processing device disposed in the housing. The processing device is coupled to the contactless sensors. The processing device is to receive, from one or more contactless sensors of the plurality of contactless sensors, historical sensor data. The processing device is further to determine baseline vital signs based on the historical sensor data. The processing device is further to receive, from the one or more contactless sensors, current sensor data associated with a user that is within a threshold distance of the ambient monitoring device without contacting the ambient monitoring device. The processing device is further to determine, based on the current sensor data, current vital signs of the user. Responsive to determining the current vital signs do not meet the baseline vital signs, the processing device is to provide an alert.

Abstract: Bride combiners with resonators for radio-frequency applications. A bridge combiner can be implemented as a coupling circuit that includes a common node and configured to couple the common node to a first group of filters through a first path and to couple the common node to a second group of one or more filters through a second path. The coupling circuit can include a resonator such that an impedance provided by each filter of the first group for a signal in each band of the second group results in the signal being sufficiently excluded from the first path, and such that an impedance provided by each filter of the second group for a signal in each band of the first group results in the signal being sufficiently excluded from the second path.

Abstract: The present disclosure relates to an endoscopic surgery system, an image processing apparatus, and an image processing method that enable better endoscopic observation. Motion components at the time of imaging are estimated on the basis of an image captured by an endoscope, a degree of reliability indicating a degree that the motion components are motion components for the entire image is set, and a correction amount used when correction processing of correcting a blur is performed on the image is controlled according to the degree of reliability. For example, in a case where the motion components are single motion components, the degree of reliability is set to 1.0, and in a case where the motion components are a plurality of motion components, the degree of reliability is set to 0.0. The present technology can be applied to, for example, an endoscopic surgery system used in surgery using an endoscope.

Abstract: A radio frequency (RF) switch includes a signal terminal, a reference voltage terminal and a shunt switch path. The shunt switch path includes a first sub-shunt circuit and a second sub-shunt circuit. The second sub-shunt circuit includes a first transistor and a second transistor coupled in parallel. When switched to a first state, the RF switch has first impedance; when switched to a second state, the RF switch has second impedance; and when switched to a third state, the RF switch has third impedance. The first impedance, the second impedance, and the third impedance are different.

Abstract: An estimating device includes processing circuitry configured to acquire image data lacking part of a body of a person, restore a defective part of the body of the person in the image data by using the image data acquired as an input, and using a whole body restoration model for restoring the defective part of the body of the person in the image data, and estimate skeleton data by using the image data restored as an input, and using a skeleton estimation model for estimating the skeleton data related to a skeleton of the person.

Abstract: The present invention concern systems and methods for maintaining diversity in AI and ML environments through the cooperation of various AI and ML systems such that they optimize for social and cultural diversity. The examination of behavior, infrastructure, and governance, mimicking of genetic biodiversity, and application of the foregoing to machine reasoning mitigates the tendency of systems to find optimized or single best solutions. AI and ML environments may thus derive multiple diverse solutions that contribute to richer ecosystems in which human beings may function and thrive.

Abstract: A medical information processing apparatus according to an embodiment includes a processing circuitry. The processing circuitry is configured to acquires one or more first images that are images of parts obtained by classifying a subject appearing in a medical image into each part, generates, from the acquired one or more first images, a second image that is an image of the subject spatially continuous according to an anatomical criterion, and synthesizes the generated second image with a background to generate a third image.

Abstract: Volumetric quantification can be performed for various parameters of an object represented in volumetric data. Multiple views of the object can be generated, and those views provided to a set of neural networks that can generate inferences in parallel. The inferences from the different networks can be used to generate pseudo-labels for the data, for comparison purposes, which enables a co-training loss to be determined for the unlabeled data. The co-training loss can then be used to update the relevant network parameters for the overall data analysis network. If supervised data is also available then the network parameters can further be updated using the supervised loss.

Abstract: Example test methods and apparatus are described. One example method includes receiving an uplink radio frequency signal by a test device from a terminal device, where the uplink radio frequency signal is generated by superimposing at least two test signals, and each of the at least two test signal corresponds to one communication protocol. The test device extracts the at least two test signals from the uplink radio frequency signal. The test device separately tests the at least two test signals, and obtains an uplink test result of the terminal device.

Abstract: A control system includes a controller, a transmission unit, an application executing unit, and a management unit. The controller executes a control program controlling a control target, and manages pieces of process data referred to or updated in the control program. The transmission unit transmits data sets including values of the pieces of process data managed by the controller, each of the data sets including one or more process data values. The application execution unit executes one or more applications using the value of the process data included in the data set transmitted by the transmission unit. The management unit determines a data set validating transmission in the data sets transmittable by the transmission unit according to the application executed by the application execution unit.

Abstract: The invention relates to a method (100) for supervised training of an artificial neural network for medical image analysis. The method comprises acquiring (SI) first and second sets of training samples, wherein the training samples comprise feature vectors and associated predetermined labels, the feature vectors being indicative of medical images and the labels pertaining to anatomy detection, to semantic segmentation of medical images, to classification of medical images, to computer-aided diagnosis, to detection and/or localization of biomarkers or to quality assessment of medical images. The accuracy of predetermined labels may be better for the second set of training samples than for the first set of training samples. The neural network is trained (S3) by reducing a cost function, which comprises a first and a second part.

Abstract: An estimating device includes processing circuitry configured to acquire image data lacking part of a body of a person, restore a defective part of the body of the person in the image data by using the image data acquired as an input, and using a whole body restoration model for restoring the defective part of the body of the person in the image data, and estimate skeleton data by using the image data restored as an input, and using a skeleton estimation model for estimating the skeleton data related to a skeleton of the person.

Abstract: Embodiments of this application disclose a backscatter communication method and a related apparatus. The method includes: An excitation device determines a first sequence, generates a first signal, and sends the first signal, where the first signal carries the first sequence; after receiving the first signal, a backscatter device modulates backscatter device data onto the received first signal to obtain a second signal, and backscatters the second signal, to implement first scrambling on the backscatter device data by using the first sequence; and a receiving device determines the first sequence, receives the second signal from the backscatter device, and demodulates the received second signal based on the first sequence, to obtain the backscatter device data carried on the second signal.

Abstract: Provided is a dynamic imaging quality control device that performs quality control concerning dynamic imaging in which dynamics of a subject is imaged by sequential radiation emissions to the subject. The dynamic imaging quality control device includes a hardware processor. The hardware processor presents information on the quality control. The information on the quality control includes at least one of information on a framerate, information on a system sensitivity, and information on an image region.

Abstract: Embodiments described herein provide a method for generating training data for Al based atlas mapping slice localization, and a system and method for using the training data to train a deep learning network. Training data development maps each slice of an input medical image to a position in a full body reference atlas along the longitudinal body axis. The method constructs a landmarking table of 2D slices indicating known anatomic landmarks of a reference subject, and interpolated slices. A final step for obtaining training data uses regression analysis techniques to create a vector of longitudinal axis coordinates of all slices from the input image. The training data is used to train a deep learning model to create an AI-based atlas mapping slice localizer model. The trained AI-based atlas mapping slice localizer model can be applied to generate mapping inputs to autosegmentation models to improve efficiency and reliability of contouring.

Abstract: Aspects of the subject disclosure may include, for example, obtaining pre-treatment images; analyzing the pre-treatment images according to an imaging model that includes a machine learning model; predicting, according to the analyzing the pre-treatment images, one or more clinical variables; obtaining on-treatment images; analyzing the on-treatment images according to the imaging model; predicting, based on the analyzing the on-treatment images, the one or more clinical variables for the on-treatment images; and presenting event estimation information in a graphical user interface. Other embodiments are disclosed.

Abstract: A system and method for automatically detecting, classifying, and processing objects captured in an image. The system receives an image from the image source and detects one or more objects in the image. The system then performs a high-level classification of each of the one or more objects in the image and extracts each of the one or more objects from the image. The system then performs a specific classification of each of the one or more objects and determines a price of each of the one or more objects. Finally, the system generates a pricing report comprising a price of each of the one or more objects.

Abstract: An apparatus, method and computer program is described including initialising trainable parameters of a receiver of a transmission system, wherein the receiver includes a demodulation module for demodulating received symbols, a quantization module for generating quantized versions of the demodulated symbols and a decoder for generating a decoded output derived from the quantized versions of the demodulated symbols, wherein the demodulation module has at least some trainable weights and the quantization module has at least some trainable weights; receiving a first training sequence of messages at the receiver; obtaining or generating a loss function; and updating at least some of the trainable parameters of receiver based on the loss function, wherein updating at least some of the trainable parameters of receiver includes updating at least some of the trainable weights of the demodulation module and updating at least some of the trainable weights of the quantization module.

Abstract: According to an embodiment of the present disclosure, disclosed is a method to read a chest image. The method includes: determining whether or not to identify presence of cardiomegaly for a chest image; detecting a lung region and a heart region respectively which are included in the chest image, by using a neural network model, when it is determined to identify presence of cardiomegaly of the chest image; and calculating a cardiothoracic ratio of the chest image using the detected lung region and the detected heart region.

Abstract: A radio frequency module includes a power amplification element, a transmission filter, and a transmission matching element, which are transmission-only components for processing only a transmission signal, a low noise amplification element, a reception filter, and a reception matching element, which are reception-only components for processing only a reception signal, an antenna switch, which is a transmission-reception dual-use component for processing both a transmission signal and a reception signal, and a first module board and a second module board, which are arranged to face each other. The transmission-only components are mounted on a main surface of the first module board, and the reception-only components and the transmission-reception dual-use component are mounted on respective main surfaces of the second module board.