Abstract: A system and method for non-destructive optical coherence tomography (OCT) is provided. The system includes: an input interface for receiving OCT data including at least a C-scan; a processing unit executable to detect a feature on a surface or subsurface of the object, trained using a training set and configured to: separate the C-scan into A-scans; using a neural network, successively analyze each A-scan to detect the presence of an A-scan feature associated with the object; separate the C-scan into B-scans; segment each of the B-scans to determine thresholds associated with the object; using a neural network, successively analyze each segmented B-scan to detect the presence of an B-scan feature associated with the object; convert the C-scan to one or more two-dimensional representations; and using a neural network, detect the presence of an C-scan feature associated with the object.

Abstract: A method and system for increasing data quality of a dataset for semi-supervised machine learning analysis. The method includes: receiving known class label information for a portion of the data in the dataset; receiving clustering parameters from a user; determining a data cleanliness factor, and where the data cleanliness factor is below a predetermined cleanliness threshold: assigning data without class label information as a data point to a cluster using the clustering parameters, each cluster having a cluster class label associated with such cluster; and determining a measure of assignment, and where the measure of assignment for each data point is below a predetermined assignment threshold, receiving a class label for such data points, otherwise, assigning the respective cluster class label to each data point with the respective measure of assignment below the predetermined assignment threshold; and otherwise, outputting the dataset with associated class labels for machine learning analysis.

Abstract: Systems and methods for a computer-implemented data trust are provided. A system for providing a data trust for a data asset includes a data trust domain. The data trust domain includes a parent node associated with a trustee. The trustee administers the data trust. The data trust domain also includes a plurality of data partner nodes. The data partner nodes include at least one data producer node associated with a data producer and at least one data consumer node associated with a data consumer. The nodes in the data trust domain are communicatively connected to each other via a network. The data trust is administered according to a set of governance rules. The set of governance rules is defined in a smart contract.

Abstract: Embodiments described herein relate to systems and methods for specular surface inspection, and particularly to systems and methods for surface inspection comprising inverse synthetic aperture imaging (“ISAI”) and specular surface geometry imaging (“SSGI”). Embodiments may allow an object under inspection, to be observed, imaged and processed while continuing to be in motion. Further, multiple optical input sources may be provided, such that the object does not have to be in full view of all optical sensors at once. Further, multi-stage surface inspection may be provided, wherein an object under inspection may be inspected at multiple stages of an inspection system, such as, for an automotive painting process, inspection at primer, inspection at paint, inspection at final assembly. SSGI imaging modules are also described for carrying out micro-deflectometry.

Abstract: Systems and methods for sharing data assets via a computer-implemented data trust are provided herein. The method includes creating, in response to a user input, a data trust domain. Creating the domain includes instantiating a private network. The network includes a plurality of domain nodes. The domain nodes include a data producer node and a data consumer node. The data asset is provided by the data producer node. The method also includes defining access rights for the data asset as between the data consumer node and the data producer node. The method also includes creating a data pathway object. The data pathway object specifies the access rights for the data asset. The flow of data within the data trust domain is controlled according to the data pathway object.

Abstract: A system and method for non-destructive optical coherence tomography (OCT) is provided. The system includes: an input interface for receiving OCT data including at least a C-scan; a processing unit executable to detect a feature on a surface or subsurface of the object, trained using a training set and configured to: separate the C-scan into A-scans; using a neural network, successively analyze each A-scan to detect the presence of an A-scan feature associated with the object; separate the C-scan into B-scans; segment each of the B-scans to determine thresholds associated with the object; using a neural network, successively analyze each segmented B-scan to detect the presence of an B-scan feature associated with the object; convert the C-scan to one or more two-dimensional representations; and using a neural network, detect the presence of an C-scan feature associated with the object.

Abstract: Embodiments described herein relate to systems and methods for specular surface inspection, and particularly to systems and methods for surface inspection comprising inverse synthetic aperture imaging (“ISAI”) and specular surface geometry imaging (“SSGI”). Embodiments may allow an object under inspection, to be observed, imaged and processed while continuing to be in motion. Further, multiple optical input sources may be provided, such that the object does not have to be in full view of all optical sensors at once. Further, multi-stage surface inspection may be provided, wherein an object under inspection may be inspected at multiple stages of an inspection system, such as, for an automotive painting process, inspection at primer, inspection at paint, inspection at final assembly. SSGI imaging modules are also described for carrying out micro-deflectometry.

Abstract: An application provisioning system and method. A server provides an application provisioning service. A user of a client provides a schema defining an application. The application interacts with peripherals coupled to the client and receives input from sensors coupled to the peripherals. The sensor data is provided to the server for processing, including by neural networks. The application includes a workflow defining a finite state machine that traverses states at least partially based on the response to sensor data. The server may provide dynamic reallocation of compute resources to resolve demand for classifier training job requests; use of jurisdictional certificates to define data usage and sharing; and data fusion. Applications include manufacturing verification, medical diagnosis and treatment, genomics and viral detection.

Abstract: A system and method for identifying an analyte based on the presence of at least one volatile organic compound (“VOC”) in the analyte. The method includes: receiving image data from a sensor array, the sensor array having been exposed to the analyte, the sensor array including at least one sensor configured to respond to the presence of the at least one VOC in the analyte; processing the image data to derive one or more input image features; and using a trained machine learning classification technique, detecting the at least one VOC and classifying the analyte based on the one or more input image features, the machine learning classification technique trained using one or more reference images of known analytes.

Abstract: A method and system for analysis of interferometric domain optical coherence tomography (OCT) data of an object. The method includes: receiving the OCT data comprising one or more A-scans; successively analyzing each of the one or more A-scans, using a trained feed-forward neural network, to detect one or more features associated with the object by associating A-scan raw data with a descriptor for each of the one or more features, the feed-forward neural network trained using previous A-scans with one or more known features; generating location data associated with the one or more features for localizing the one or more features in the one or more A-scans; and outputting the feature detection and the location data.

Abstract: A method and system for analysis of an object of interest in a scene using 3D reconstruction. The method includes: receiving image data comprising a plurality of images captured of the scene, the image data comprising multiple perspectives of the scene; generating at least one reconstructed image by determining three-dimensional structures of the object from the imaging data using a reconstruction technique, the three-dimensional structures comprising depth information of the object; identifying the object from each of the reconstructed images, using a trained machine learning model, by segmenting the object in the reconstructed image, segmentation comprises isolating patterns in the reconstructed image that are classifiable as the object, the machine learning model trained using previous reconstructed multiple perspective images with identified objects; and outputting the analysis of the reconstructed images.

Abstract: A method and system for increasing data quality of a dataset for semi-supervised machine learning analysis. The method includes: receiving known class label information for a portion of the data in the dataset; receiving clustering parameters from a user; determining a data cleanliness factor, and where the data cleanliness factor is below a predetermined cleanliness threshold: assigning data without class label information as a data point to a cluster using the clustering parameters, each cluster having a cluster class label associated with such cluster; and determining a measure of assignment, and where the measure of assignment for each data point is below a predetermined assignment threshold, receiving a class label for such data points, otherwise, assigning the respective cluster class label to each data point with the respective measure of assignment below the predetermined assignment threshold; and otherwise, outputting the dataset with associated class labels for machine learning analysis.

Abstract: Embodiments described herein relate to systems and methods for specular surface inspection, and particularly to systems and methods for surface inspection comprising inverse synthetic aperture imaging (“ISAI”) and specular surface geometry imaging (“SSGI”). Embodiments may allow an object under inspection, to be observed, imaged and processed while continuing to be in motion. Further, multiple optical input sources may be provided, such that the object does not have to be in full view of all optical sensors at once. Further, multi-stage surface inspection may be provided, wherein an object under inspection may be inspected at multiple stages of an inspection system, such as, for an automotive painting process, inspection at primer, inspection at paint, inspection at final assembly. SSGI imaging modules are also described for carrying out micro-deflectometry.

Abstract: Embodiments described herein relate to systems and methods for specular surface inspection, and particularly to systems and methods for surface inspection comprising inverse synthetic aperture imaging (“ISAI”) and specular surface geometry imaging (“SSGI”). Embodiments may allow an object under inspection, to be observed, imaged and processed while continuing to be in motion. Further, multiple optical input sources may be provided, such that the object does not have to be in full view of all optical sensors at once. Further, multi-stage surface inspection may be provided, wherein an object under inspection may be inspected at multiple stages of an inspection system, such as, for an automotive painting process, inspection at primer, inspection at paint, inspection at final assembly. SSGI imaging modules are also described for carrying out micro-deflectometry.

Abstract: For use in MR imaging of a patient a plurality of surface electrodes such as ECG or defibrillator are provided for obtaining electrical signals for determining electrical activity within the body of the patient and remain in place during the MR imaging. The surface electrodes include a quick disconnect wire for carrying the signals to a signal processing system to be removed during the MR imaging to prevent heating. Each electrode comprises a conductive layer divided by slits into separate side by side sections to reduce eddy currents which are induced in the surface electrodes when they are exposed to variations in the magnetic field. The sections all are connected through the layer to the conductive location to allow the signal therefrom to be connected to the communication conductor.

Abstract: In MR imaging in a suite of rooms including a diagnostic room and a second surgical room, the magnet includes a diagnostic table separable from a docking station of the magnet and the second room includes a second surgical table. An imaging control computer controls operation of the imaging system and a movement control computer controls operation of the magnet moving system and the patient support table. During imaging in the surgical room, the diagnostic table is separated and docked at a secondary docking station while an emulating computer system cooperate with the imaging control computer system by emulating outputs from the disconnected diagnostic table for controlling operation of the imaging system.

Abstract: An application provisioning system and method. A server provides an application provisioning service. A user of a client provides a schema defining an application. The application interacts with peripherals coupled to the client and receives input from sensors coupled to the peripherals. The sensor data is provided to the server for processing, including by neural networks. The application includes a workflow defining a finite state machine that traverses states at least partially based on the response to sensor data. The server may provide dynamic reallocation of compute resources to resolve demand for classifier training job requests; use of jurisdictional certificates to define data usage and sharing; and data fusion. Applications include manufacturing verification, medical diagnosis and treatment, genomics and viral detection.

Abstract: In an MR guided surgical system which is carried out in the bore of an MR magnet and uses fluorescence to detect tumor cells, there is provided a microscope system for viewing the required part of a patient which includes stereoscopic viewing components arranged for use in generating 2D and 3D images displayed to the surgeon. The optical assembly is adjustable to change the view and the visual images are overlaid by the MR images. The visual image can be adjusted in response to movement of the surgical tool and the MR image displayed and/or the image obtained can be modified in response to change in the visual image and/or movement of the tool. The components in the bore are made compatible with the MR environment. A fluorescence delivery system is operated to automatically activate the delivery system in response to detection of the level of fluorescence.

Abstract: In a MRI system housed within a room there is provided a movable magnet and additional components for other procedures on the patient, a control system is provided for the relative movement of the magnet and components. This includes a plurality of magnetic field sensors mounted on the components for measuring the magnetic field at the location of the component and an optional camera positioning system so that the control system can estimate relative positions of the components relative to the magnet from the sensed field strengths from the set of sensors to avoid collisions during the movements.

Abstract: In MR imaging in a suite of rooms including a diagnostic room and a second surgical room, the magnet includes a diagnostic table separable from a docking station of the magnet and the second room includes a second surgical table. An imaging control computer controls operation of the imaging system and a movement control computer controls operation of the magnet moving system and the patient support table. During imaging in the surgical room, the diagnostic table is separated and docked at a secondary docking station while an emulating computer system cooperate with the imaging control computer system by emulating outputs from the disconnected diagnostic table for controlling operation of the imaging system.