Abstract: A method includes obtaining a two-dimensional image, obtaining a two-dimensional image annotation that indicates presence of an object in the two-dimensional image, obtaining three-dimensional sensor information, generating a top-down representation of the three-dimensional sensor information, and obtaining a top-down annotation that indicates presence of the object in the top-down representation. The method also includes determining a bottom surface of a three-dimensional cuboid based on map information, determining a position, a length, a width, and a yaw rotation of the three-dimensional cuboid based on the top-down annotation, and determining a height of the three-dimensional cuboid based on a two-dimensional image annotation, and the position, the length, the width, and the yaw rotation of the three-dimensional cuboid.

Abstract: At a centralized model trainer, one or more neural network based models are trained using an input data set. At least a first set of parameters of a model is transmitted to a model deployment destination. Using a second input data set, one or more adaptive parameters for the model are determined at the model deployment destination. Using the adaptive parameters, one or more inferences are generated at the model deployment destination.

Abstract: A Ring-Arrayed Forward-viewing (RAF) ultrasound imaging and administration device combines an ultrasonic (US) US imager including a plurality of single element transducers arranged in a circular frame to define a ring array, and an instrument posture tracking circuit coupled to the ring array for performing RF (radio frequency) data acquisition with the plurality of ring-arrayed transducers. A needle holster is concentrically disposed in the ring array and is adapted to receive and direct an insertion instrument such as a needle, probe or extraction tool along an axis defined by a center of the ring array directed by the concentric needle holster. The tracking circuit includes a processor having instructions for instrument posture tracking and US imaging.

Abstract: An ultrasonic imaging configuration rendering an image based on a reflected beam aligned with a needle insertion axis provides enhanced images and guidance. A percutaneous insertion guidance device for ultrasonic (US) imaging includes an US transducer and a reflector for redirecting imaging signals between the transducer and an imaged region. An enclosure or bracket is adapted to support the reflector at a predetermined angle based on a surgical target in the imaged region such that a forward beam direction from the transducer reflects in a direction of the needle insertion as the needle passes through a discontinuity or slot in the reflector. The resulting return signal is based on an axial alignment of the US signals with the needle travel towards the surgical target.

Abstract: The present invention discloses a trusted alliance block chain digital calibration certificate system and an operation method therefor. The system comprises: a distributed ledger system (10) of a distributed storage system based on a hyperledger fabric; a CA system (11) for management of all entity identity certificates and registration of identities in the system; a UTC time calibration server (12) for providing a UTC time calibration service for all block chain node servers of the system; and a digital calibration certificate subsystem (13) for generating, by means of alliance management, instrument organization management, and calibration institution management, a digital calibration certificate from calibration certificate data by a digital signature technology, extracting related entity information, and forming original information of multiple data blocks.

Abstract: Sensor data captured by one or more sensors may be received at an analysis system. A neural network may be used to detect an object in the sensor data. A plurality of polygons surrounding the object may be generated in one or more subsets of the sensor data. A prediction of a future position of the object may be generated based at least in part on the polygons. One or more commands may be provided to a control system based on the prediction of the future position.

Abstract: Sensor data captured by one or more sensors may be received at an analysis system. A neural network may be used to detect an object in the sensor data. A plurality of polygons surrounding the object may be generated in one or more subsets of the sensor data. A prediction of a future position of the object may be generated based at least in part on the polygons. One or more commands may be provided to a control system based on the prediction of the future position.

Abstract: A method includes obtaining a two-dimensional image, obtaining a two-dimensional image annotation that indicates presence of an object in the two-dimensional image, determining a location proposal based on the two-dimensional image annotation, determining a classification for the object, determining an estimated size for the object based on the classification for the object, and defining a three-dimensional cuboid for the object based on the location proposal and the estimated size.

Abstract: A system includes a state-dependent action policy and a state-dependent transition policy. The state-dependent action policy determines an action based on environment states and a current agent state selected from a predetermined group of agent states. The state-dependent transition policy is implemented using one or more machine learning models and is configured to control transitions between agent states from the predetermined group of agent states.

Abstract: A Ring-Arrayed Forward-viewing (RAF) ultrasound imaging and administration device combines an ultrasonic (US) US imager including a plurality of single element transducers arranged in a circular frame to define a ring array, and an instrument posture tracking circuit coupled to the ring array for performing RF (radio frequency) data acquisition with the plurality of ring-arrayed transducers. A needle holster is concentrically disposed in the ring array and is adapted to receive and direct an insertion instrument such as a needle, probe or extraction tool along an axis defined by a center of the ring array directed by the concentric needle holster. The tracking circuit includes a processor having instructions for instrument posture tracking and US imaging.

Abstract: Methods, systems and apparatus that provide for perceptual, cognitive, and motor behaviors in an integrated system implemented using neural architectures. Components of the system communicate using artificial neurons that implement neural networks. The connections between these networks form representations—referred to as semantic pointers—which model the various firing patterns of biological neural network connections. Semantic pointers can be thought of as elements of a neural vector space, and can implement a form of abstraction level filtering or compression, in which high-dimensional structures can be abstracted one or more times thereby reducing the number of dimensions needed to represent a particular structure.

Abstract: A system employs an inspection neural network (INN) to inspect data generated during an inference process of a primary neural network (PNN) to generate an indication of reliability for an output generated by the PNN. The system includes a sensor configured to capture sensor data. Sensor data captured by the sensor is provided to a data analyzer to generate an output using the PNN. An analyzer inspector is configured to capture inspection data associated with the generation of the output by the data analyzer, and use the INN to generate an indication of reliability for the PNN's output based on the inspection data. The INN is trained using a set of training data that is distinct from the training data used to train the PNN.

Abstract: Methods, systems and apparatus that provide for perceptual, cognitive, and motor behaviors in an integrated system implemented using neural architectures. Components of the system communicate using artificial neurons that implement neural networks. The connections between these networks form representations—referred to as semantic pointers—which model the various firing patterns of biological neural network connections. Semantic pointers can be thought of as elements of a neural vector space, and can implement a form of abstraction level filtering or compression, in which high-dimensional structures can be abstracted one or more times thereby reducing the number of dimensions needed to represent a particular structure.

Abstract: A system includes a neural network organized into layers corresponding to stages of inferences. The neural network includes a common portion, a first portion, and a second portion. The first portion includes a first set of layers dedicated to performing a first inference task on an input data. The second portion includes a second set of layers dedicated to performing a second inference task on the same input data. The common portion includes a third set of layers, which may include an input layer to the neural network, that are used in the performance of both the first and second inference tasks. The system may receive an input data and perform both inference tasks on the input data in a single pass. During training, a training sample with annotations for both inference tasks may be used to train the neural network in a single pass.

Abstract: A system employs an inspection neural network (INN) to inspect data generated during an inference process of a primary neural network (PNN) to generate an indication of reliability for an output generated by the PNN. The system includes a sensor configured to capture sensor data. Sensor data captured by the sensor is provided to a data analyzer to generate an output using the PNN. An analyzer inspector is configured to capture inspection data associated with the generation of the output by the data analyzer, and use the INN to generate an indication of reliability for the PNN's output based on the inspection data. The INN is trained using a set of training data that is distinct from the training data used to train the PNN.

Abstract: Methods, systems and apparatus that provide for perceptual, cognitive, and motor behaviors in an integrated system implemented using neural architectures. Components of the system communicate using artificial neurons that implement neural networks. The connections between these networks form representations—referred to as semantic pointers—which model the various firing patterns of biological neural network connections. Semantic pointers can be thought of as elements of a neural vector space, and can implement a form of abstraction level filtering or compression, in which high-dimensional structures can be abstracted one or more times thereby reducing the number of dimensions needed to represent a particular structure.

Abstract: Methods, systems and apparatus that provide for perceptual, cognitive, and motor behaviors in an integrated system implemented using neural architectures. Components of the system communicate using artificial neurons that implement neural networks. The connections between these networks form representations—referred to as semantic pointers—which model the various firing patterns of biological neural network connections. Semantic pointers can be thought of as elements of a neural vector space, and can implement a form of abstraction level filtering or compression, in which high-dimensional structures can be abstracted one or more times thereby reducing the number of dimensions needed to represent a particular structure.

Abstract: A method of identifying regions of different content in an image comprises dividing image data into a plurality of pixel blocks, extracting features of the pixel blocks and classifying the content of the pixel blocks based on extracted features. Extracting comprises, for each pixel block, convolving a magic square filter with the pixels of the pixel block and summing the results, and calculating the percentage of background pixels in the pixel block. The magic square filter is a 3×3 kernel that has a specific selectivity towards the statistical appearance and geometric alignment of a document based text of various fonts, sizes and styles. The complete sum of the magic square filter, as well as the sum of the rows, columns and diagonals of the magic square filter are zero.

Abstract: A method of identifying regions of different content in an image comprises dividing image data into a plurality of pixel blocks, extracting features of the pixel blocks and classifying the content of the pixel blocks based on extracted features.